title
stringlengths 1
827
⌀ | uuid
stringlengths 36
36
| pmc_id
stringlengths 5
8
| search_term
stringclasses 44
values | text
stringlengths 8
8.58M
|
|---|---|---|---|---|
Effects of fluid shear stress on oral biofilm formation and composition and the transcriptional response of | f6274e3f-410c-43f4-9a43-1dc722d59d35 | 11912947 | Digestive System[mh] | INTRODUCTION The oral microbiome comprises over 600 bacterial species and varies by location, host age, environment, diet, and other factors ( ). Plaque biofilms are of particular interest due to their direct role in the development of dental caries and periodontitis, as well as other aspects of oral health, opportunistic infections, and cancer. The initiation of plaque formation relies on early colonizers such as Streptococcus gordonii and other streptococci ( ). Streptococcus gordonii is a gram-positive oral commensal that contributes to plaque formation by binding salivary proteins ( ) adsorbed onto the tooth surface and serving as a docking site to other members of the dental plaque community ( ). Streptococcus gordonii can also bind human endothelial cells ( ) and accumulate on damaged heart valves leading to infective endocarditis. In these scenarios, bacterial attachment and subsequent biofilm formation occur and contribute ultimately to the pathogenesis of caries, periodontitis, and infective endocarditis. While most biofilms are studied under static growth conditions, fluid flow is common to many microbial environments and has a particular impact on attachment and biofilm formation and maintenance. Fluid flow generates a shear force on the cells, resulting in numerous outcomes, depending on the microbe(s) and other environmental factors. In some cases, fluid flow can be beneficial, contributing to the distribution of public goods and structural organization of the biofilm community ( ; ). In other environments, fluid flow can increase microbial adhesion. Several studies have observed enhanced binding of bacterial cells under shear stress, termed shear-enhanced attachment, including Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, S. aureus , and Borrelia burgdorferi ( ; ; ; ; ). Some species use specialized catch-bond adhesins, adhesins whose interactions with specific receptors are strengthened upon mechanical stress ( ; ; ). In S. gordonii , Hsa/GspB and CshA are thought to function as catch-bond adhesins with Hsa specifically contributing to shear-enhanced attachment ( ; ; ). In all circumstances, microbes must respond and adapt to the mechanical influences of shear and turbulence. Recent studies suggest a wide range of genes are altered in bacteria exposed to fluid shear stress, and the transcriptional response is dependent on the amount of shear, type of model, and organism ( ; ; ; ; ). One of these recent studies suggests that rheosensing, a type of mechanosensing independent of bis-(3’−5’)-cyclic dimeric guanosine monophosphate (cyclic-di-GMP) by P. aeruginosa , is accomplished through the detection of flow rate but not mechanical force ( ). Likely, the mechanosensing and/or gene regulation related to fluid shear forces are varied and further studies are required. In the oral cavity, mastication, speaking, and other activities can generate the movement of saliva and hence exert fluid shear forces over plaque biofilms. At the tooth surface, it is estimated that shear stress generated by saliva is approximately 0.762 dyn/cm 2 (or 0.0762 Pa; ). On the other hand, on heart valves, the location of infective endocarditis, fluid shear stress is estimated in the range of 20 to 80 dyn/cm 2 ( ). Therefore, bacteria such as S. gordonii and certain other oral opportunistic bacteria must adapt to a wide range of shear stress conditions, yet little is known regarding the mechanisms by which S. gordonii and the oral microbiota members respond to shear forces. Some studies have described the impact of fluid shear forces on oral biofilms, and findings largely suggest that at some threshold, shear stress will decrease biofilm biomass, biofilm strength, and alter the biofilm architecture of monospecies and multispecies oral biofilms ( ; ; ; ; ). Fluid shear forces also seem to affect community composition, with increased shear stress decreasing community diversity ( ). Conversely, shear-enhanced adhesion and subsequent increased biofilm biomass under shear forces have also been observed ( ; ; ). Teasing out the differences in the effects of shear stress on bacteria is complicated by differences in model systems, degrees of fluid shear employed, bacterial species studied, and other growth conditions. Here, we describe a simple, adaptable, and inexpensive model of shear utilizing a linear rocker to investigate questions of S. gordonii biofilm formation, growth, and transcriptional response. We also apply this model to an ex vivo dental plaque biofilm community to probe changes in multispecies biofilm formation and community composition in response to shear forces. We show that overall, increasing shear forces reduce S. gordonii biofilm biomass with minimal impact on bacterial growth, accompanied by changes in its transcriptional profile. Plaque biofilms also showed reduced biomass with increased shear force, accompanied by changes in the community composition.
MATERIALS AND METHODS 2.1 │ Bacterial strains and growth conditions Streptococcus gordonii DL1 was grown in Bacto brain heart infusion (BHI) broth or on BHI agar plates (Difco) at 37°C in 5% CO 2 . The ex vivo plaque community was grown in modified Shi medium (75% sterile human saliva and 25% SHI medium; ) at 37°C in 5% CO 2 . 2.2 │ Rocker model of fluid shear For both S. gordonii and the plaque community biofilms, to generate fluid flow, a linear rocker model 55 rocking shaker (Reliable Scientific Inc.) was placed in an incubator at 37°C in 5% CO 2 . Culture plates were placed at the center of the rocker at various rocker settings to generate the designated velocity as measured by oscillations per minute (OPM), that is, a complete back-and-forth motion of the rocker shelf. For comparison, an identical plate was placed on the incubator shelf, that is, static conditions. 2.3 │ COMSOL numerical simulation We simulated the flow within culture plates situated on a rocker in three dimensions using computational fluid dynamics (CFD) finite-element simulation software, COMSOL Multiphysics 5.5. The 3D plate was assumed to be 3.5-cm wide and 1-cm tall, and the water depth was 0.25 cm. We simplified the motion of the rocking shaker as a 3° × sin ( ω t ) , where the angular velocity ω = 2 π / T with T representing the period of the rotating cycle. The rotating periods T = 12, 6, 4, 3, 2.4, and 2 s correspond to 5, 10, 15, 20, 25, and 30 OPM of the rocker, respectively. In our simulations, we considered that the flow in the culture plates was a two-phase laminar flow and conducted the CFD simulation using the phase field method ( ; ). We did not consider the turbulence effect as the calculated Reynolds number is around 80 ( Re = ρ U L / μ , where ρ is the water density, U = 0.04 m / s is the maximum velocity, L is the water depth, and μ is the dynamic viscosity of water). In the simulation, the Navier–Stokes equation was numerically solved for flow velocity, assuming no-slip boundary conditions on all solid boundaries. The upper boundary is characterized as an open boundary. The time-dependent forms of the equations for laminar flow are the momentum equation ( ) and the continuity equation ( ). (1) ρ ∂ u ∂ t + ρ ( u ⋅ ∇ ) u = ∇ ⋅ [ − p l + μ ( ∇ u + ( ∇ u ) T ) ] + F + ρ g , (2) ρ ∇ ⋅ ( u ) = 0 , where ρ is the fluid density, u is the velocity , p is the pressure, I is the identity matrix, and F is the volume force vector. The interface was tracked by the phase field method ( and ). (3) ∂ ϕ ∂ t + u ⋅ ∇ ϕ = ∇ ⋅ γ λ ε 2 ∇ ψ , (4) ψ = − ∇ ⋅ ε 2 ∇ ϕ + ( ϕ 2 − 1 ) ϕ + ( ε 2 λ ) ∂ f ∂ ϕ , where ϕ is the phase field variable, λ is the mixing energy density, ε is the interface thickness, γ is related to ε through γ = χ ε 2 where χ is the mobility tuning parameter, and f is the body force. For the simulation setup, the initial phase field values were set to ϕ = − 1 for fluid one (air) and ϕ = 1 for fluid two (water). The simulation was conducted over a total time of 10 s, with results output every 0.1 s. The user-defined mesh consisted of 170,485 domains, with an average element quality of 0.68. The temperature was held constant at 293.15 K. Shear stress distributions within the water phase were calculated based on the velocity profiles (shear stress τ = μ d u d y , μ is the dynamic viscosity, u is the velocity) in COMSOL. The spatially averaged shear stress at the bottom ( τ bottom-avg ) was calculated by averaging shear stress values at the bottom surface of the plate. Furthermore, the time-averaged shear stress ( τ time-avg ) was calculated as the average of bottom wall shear stress over the entire simulation duration of 10 seconds. The τ time-avg exhibits a linear relationship with the rocker velocity. 2.4 │ Saliva collection and preparation for coating plates Stimulated saliva was collected and pooled from at least three healthy volunteers using protocols that were approved by the Institutional Review Board from the University of Minnesota (STUDY00016289). Saliva was collected by expectoration into tubes on ice. These samples were centrifuged for 10 min at 4°C at 1000 × g (Beckman SX4750 rotor) to remove large debris and bacterial aggregates. Following supernatant collection, the planktonic bacteria were pelleted by centrifugation at 15,000 × g for 20 min at 4°C and supernatants were again collected and saved for coating plates. 2.5 │ Biofilm formation Following overnight culture, bacteria were inoculated at 1:100 v:v in BHI or Shi media in tissue culture-treated plates (Costar), and incubated at 37°C and 5% CO 2 for 24 h. For some experiments, prior to inoculation, plates were coated with saliva or salivary fractions as described below. Following incubation under the designated static or flow conditions, biofilm biomass was measured by crystal violet (CV) staining. To each well was added 0.1% CV solution and plates incubated at room temperature for 15 min. Plates were gently washed four times with phosphate buffered saline (PBS). Plates were air dried for 1 min and then acidified ethanol (4% 1N HCl/96% EtOH) was added to each well. Samples were mixed by pipetting up and down, and the CV–ethanol mix was transferred to a flat-bottomed, polystyrene 96-well plate and measured the absorbance at 570 nm using the ChroMate 4300 microplate plate reader (Awareness Technology). For experiments utilizing saliva, saliva prepared as described above was added to each well to cover the bottom surface and incubated with rocking for 1 h at room temperature. Unbound saliva was then removed, and the plates and bound saliva were sterilized by exposure to UV irradiation for 15 min (Spectroline UV Crosslinker FB-UVXL-1000; Spectronics) prior to biofilm inoculation. For experiments utilizing MUC5B and low-density proteins (LDP), fractions were provided by Claes Wickström at Malmö University, Malmö, Sweden, after purification from human saliva as previously reported ( ). The fractions were used to coat wells for biofilm assays at concentrations approximating their native proportions in human saliva. Plates were incubated with rocking for 1 h at room temperature when the salivary solution was gently aspirated. 2.6 │ Streptococcus gordonii growth measurements Following overnight culture in BHI, S. gordonii was inoculated at 1:100 v:v in BHI into tissue culture-treated plates (Costar) and incubated at 37°C and 5% CO 2 for 24 h. For some experiments, prior to inoculation, plates were coated with saliva as done for biofilm formation assays. Following incubation under the designated static or flow conditions, adherent cells were scraped using sterile wooden applicators, and all cells (planktonic and removed adherent cells) were mixed by a pipette. To quantify bacteria by turbidity, cells were diluted 1:1 in BHI media in a cuvette, and the absorbance at 600 nm was collected using a BioMate 160 UV-Visible spectrophotometer (Thermo Scientific). To quantify bacteria based on adenosine triphosphate (ATP) quantification, the BacTiter-Glo Microbial Cell Viability Assay (Promega) was used following the manufacturer’s instructions. Specifically, 100 μL of cells were added to wells of a white, flat-bottomed 96-well plate followed by 100 μL of BacTiter-Glo Reagent. Contents were mixed briefly on a rocker and incubated for 5 min at room temperature. Luminescence was measured using a SpectraMax iD3 plate reader (Molecular Devices). 2.7 │ Total RNA purification, RNAseq, and quantitative RT-PCR To assess gene expression by sessile cells, the biofilms were washed with sterile PBS and scraped from the surface to harvest. Cells were then resuspended in 500 μL of TRIzol reagent (Ambion) and lysed by bead beating as described previously ( ). Following lysis, total RNA was purified as previously ( ). RNA sequencing was performed at the University of Minnesota Genomic Center as described ( ). Differential gene expression (DGE) analysis was performed in Geneious Prime using DESeq2 within R. DGE lists were generated using a differential expression log2 ratio > 1.0 or < −1.0. An adjusted p- value of ≤0.01 was considered significant. For quantitative, reverse transcription PCR (RT-PCR), total RNA was converted to cDNA using the ProtoScript II first-strand cDNA synthesis kit (New England Biolabs) with random hexamer primers according to the manufacturer’s protocol. For quantitative PCR, 1:10 dilutions of cDNA were used in reactions with Maxima SYBR Green/ROX qPCR master mix (Thermo Scientific) according to the manufacturer’s protocol. A two-step cycling program was utilized, which included an initial denaturation at 95°C for 10 min followed by 40 cycles of 95°C for 15 s (denaturation) and 60°C for 1 min (annealing/extension) using the Mx3000 real-time PCR system (Stratagene). Relative gene expression was calculated using the threshold cycle (ΔΔCt) method. 2.8 │ DNA extraction and 16S rRNA sequencing For community composition analysis, ex vivo plaque community was grown as described above under static and shear conditions. The biofilms were washed with sterile PBS and scraped from the surface to harvest. Genomic DNA was isolated using the DNeasy PowerSoil Kit (QIAGEN) according to the manufacturer’s instructions. The DNA concentration was determined using Qubit (Invitrogen), and total genomic DNA (∼3 μg) was submitted for Illumina MiSeq sequencing of the V3-V4 hypervariable regions of the 16S rRNA at SeqCenter. Samples were prepared using Zymo Research’s Quick-16S kit. Following clean-up and normalization, samples were sequenced on a V3 MiSeq 622cyc flow cell to generate 2 × 301 bp PE reads. Quality control and adapter trimming were performed with bcl-convert ( ). Sequences were imported into Qiime2 ( ) for subsequent analysis. Primer sequences were removed using Qiime2’s cutadapt plugin ( ). Sequences were denoised using Qiime2’s dada2 plugin ( ). Denoised sequences were assigned operational taxonomic units (OTUs) using the Silva 138 99% OTUs full-length sequence database and the VSEARCH ( ) utility within Qiime2’s feature-classifier plugin. OTUs were then collapsed to their lowest taxonomic units, and their counts were converted to reflect their relative frequency within a sample. 2.9 │ Data availability The RNA-seq data are available at Geo accession number GSE202049. The microbiome data have been uploaded to NCBI and are available under BioProject number PRJNA1070294.
Bacterial strains and growth conditions Streptococcus gordonii DL1 was grown in Bacto brain heart infusion (BHI) broth or on BHI agar plates (Difco) at 37°C in 5% CO 2 . The ex vivo plaque community was grown in modified Shi medium (75% sterile human saliva and 25% SHI medium; ) at 37°C in 5% CO 2 .
Rocker model of fluid shear For both S. gordonii and the plaque community biofilms, to generate fluid flow, a linear rocker model 55 rocking shaker (Reliable Scientific Inc.) was placed in an incubator at 37°C in 5% CO 2 . Culture plates were placed at the center of the rocker at various rocker settings to generate the designated velocity as measured by oscillations per minute (OPM), that is, a complete back-and-forth motion of the rocker shelf. For comparison, an identical plate was placed on the incubator shelf, that is, static conditions.
COMSOL numerical simulation We simulated the flow within culture plates situated on a rocker in three dimensions using computational fluid dynamics (CFD) finite-element simulation software, COMSOL Multiphysics 5.5. The 3D plate was assumed to be 3.5-cm wide and 1-cm tall, and the water depth was 0.25 cm. We simplified the motion of the rocking shaker as a 3° × sin ( ω t ) , where the angular velocity ω = 2 π / T with T representing the period of the rotating cycle. The rotating periods T = 12, 6, 4, 3, 2.4, and 2 s correspond to 5, 10, 15, 20, 25, and 30 OPM of the rocker, respectively. In our simulations, we considered that the flow in the culture plates was a two-phase laminar flow and conducted the CFD simulation using the phase field method ( ; ). We did not consider the turbulence effect as the calculated Reynolds number is around 80 ( Re = ρ U L / μ , where ρ is the water density, U = 0.04 m / s is the maximum velocity, L is the water depth, and μ is the dynamic viscosity of water). In the simulation, the Navier–Stokes equation was numerically solved for flow velocity, assuming no-slip boundary conditions on all solid boundaries. The upper boundary is characterized as an open boundary. The time-dependent forms of the equations for laminar flow are the momentum equation ( ) and the continuity equation ( ). (1) ρ ∂ u ∂ t + ρ ( u ⋅ ∇ ) u = ∇ ⋅ [ − p l + μ ( ∇ u + ( ∇ u ) T ) ] + F + ρ g , (2) ρ ∇ ⋅ ( u ) = 0 , where ρ is the fluid density, u is the velocity , p is the pressure, I is the identity matrix, and F is the volume force vector. The interface was tracked by the phase field method ( and ). (3) ∂ ϕ ∂ t + u ⋅ ∇ ϕ = ∇ ⋅ γ λ ε 2 ∇ ψ , (4) ψ = − ∇ ⋅ ε 2 ∇ ϕ + ( ϕ 2 − 1 ) ϕ + ( ε 2 λ ) ∂ f ∂ ϕ , where ϕ is the phase field variable, λ is the mixing energy density, ε is the interface thickness, γ is related to ε through γ = χ ε 2 where χ is the mobility tuning parameter, and f is the body force. For the simulation setup, the initial phase field values were set to ϕ = − 1 for fluid one (air) and ϕ = 1 for fluid two (water). The simulation was conducted over a total time of 10 s, with results output every 0.1 s. The user-defined mesh consisted of 170,485 domains, with an average element quality of 0.68. The temperature was held constant at 293.15 K. Shear stress distributions within the water phase were calculated based on the velocity profiles (shear stress τ = μ d u d y , μ is the dynamic viscosity, u is the velocity) in COMSOL. The spatially averaged shear stress at the bottom ( τ bottom-avg ) was calculated by averaging shear stress values at the bottom surface of the plate. Furthermore, the time-averaged shear stress ( τ time-avg ) was calculated as the average of bottom wall shear stress over the entire simulation duration of 10 seconds. The τ time-avg exhibits a linear relationship with the rocker velocity.
Saliva collection and preparation for coating plates Stimulated saliva was collected and pooled from at least three healthy volunteers using protocols that were approved by the Institutional Review Board from the University of Minnesota (STUDY00016289). Saliva was collected by expectoration into tubes on ice. These samples were centrifuged for 10 min at 4°C at 1000 × g (Beckman SX4750 rotor) to remove large debris and bacterial aggregates. Following supernatant collection, the planktonic bacteria were pelleted by centrifugation at 15,000 × g for 20 min at 4°C and supernatants were again collected and saved for coating plates.
Biofilm formation Following overnight culture, bacteria were inoculated at 1:100 v:v in BHI or Shi media in tissue culture-treated plates (Costar), and incubated at 37°C and 5% CO 2 for 24 h. For some experiments, prior to inoculation, plates were coated with saliva or salivary fractions as described below. Following incubation under the designated static or flow conditions, biofilm biomass was measured by crystal violet (CV) staining. To each well was added 0.1% CV solution and plates incubated at room temperature for 15 min. Plates were gently washed four times with phosphate buffered saline (PBS). Plates were air dried for 1 min and then acidified ethanol (4% 1N HCl/96% EtOH) was added to each well. Samples were mixed by pipetting up and down, and the CV–ethanol mix was transferred to a flat-bottomed, polystyrene 96-well plate and measured the absorbance at 570 nm using the ChroMate 4300 microplate plate reader (Awareness Technology). For experiments utilizing saliva, saliva prepared as described above was added to each well to cover the bottom surface and incubated with rocking for 1 h at room temperature. Unbound saliva was then removed, and the plates and bound saliva were sterilized by exposure to UV irradiation for 15 min (Spectroline UV Crosslinker FB-UVXL-1000; Spectronics) prior to biofilm inoculation. For experiments utilizing MUC5B and low-density proteins (LDP), fractions were provided by Claes Wickström at Malmö University, Malmö, Sweden, after purification from human saliva as previously reported ( ). The fractions were used to coat wells for biofilm assays at concentrations approximating their native proportions in human saliva. Plates were incubated with rocking for 1 h at room temperature when the salivary solution was gently aspirated.
Streptococcus gordonii growth measurements Following overnight culture in BHI, S. gordonii was inoculated at 1:100 v:v in BHI into tissue culture-treated plates (Costar) and incubated at 37°C and 5% CO 2 for 24 h. For some experiments, prior to inoculation, plates were coated with saliva as done for biofilm formation assays. Following incubation under the designated static or flow conditions, adherent cells were scraped using sterile wooden applicators, and all cells (planktonic and removed adherent cells) were mixed by a pipette. To quantify bacteria by turbidity, cells were diluted 1:1 in BHI media in a cuvette, and the absorbance at 600 nm was collected using a BioMate 160 UV-Visible spectrophotometer (Thermo Scientific). To quantify bacteria based on adenosine triphosphate (ATP) quantification, the BacTiter-Glo Microbial Cell Viability Assay (Promega) was used following the manufacturer’s instructions. Specifically, 100 μL of cells were added to wells of a white, flat-bottomed 96-well plate followed by 100 μL of BacTiter-Glo Reagent. Contents were mixed briefly on a rocker and incubated for 5 min at room temperature. Luminescence was measured using a SpectraMax iD3 plate reader (Molecular Devices).
Total RNA purification, RNAseq, and quantitative RT-PCR To assess gene expression by sessile cells, the biofilms were washed with sterile PBS and scraped from the surface to harvest. Cells were then resuspended in 500 μL of TRIzol reagent (Ambion) and lysed by bead beating as described previously ( ). Following lysis, total RNA was purified as previously ( ). RNA sequencing was performed at the University of Minnesota Genomic Center as described ( ). Differential gene expression (DGE) analysis was performed in Geneious Prime using DESeq2 within R. DGE lists were generated using a differential expression log2 ratio > 1.0 or < −1.0. An adjusted p- value of ≤0.01 was considered significant. For quantitative, reverse transcription PCR (RT-PCR), total RNA was converted to cDNA using the ProtoScript II first-strand cDNA synthesis kit (New England Biolabs) with random hexamer primers according to the manufacturer’s protocol. For quantitative PCR, 1:10 dilutions of cDNA were used in reactions with Maxima SYBR Green/ROX qPCR master mix (Thermo Scientific) according to the manufacturer’s protocol. A two-step cycling program was utilized, which included an initial denaturation at 95°C for 10 min followed by 40 cycles of 95°C for 15 s (denaturation) and 60°C for 1 min (annealing/extension) using the Mx3000 real-time PCR system (Stratagene). Relative gene expression was calculated using the threshold cycle (ΔΔCt) method.
DNA extraction and 16S rRNA sequencing For community composition analysis, ex vivo plaque community was grown as described above under static and shear conditions. The biofilms were washed with sterile PBS and scraped from the surface to harvest. Genomic DNA was isolated using the DNeasy PowerSoil Kit (QIAGEN) according to the manufacturer’s instructions. The DNA concentration was determined using Qubit (Invitrogen), and total genomic DNA (∼3 μg) was submitted for Illumina MiSeq sequencing of the V3-V4 hypervariable regions of the 16S rRNA at SeqCenter. Samples were prepared using Zymo Research’s Quick-16S kit. Following clean-up and normalization, samples were sequenced on a V3 MiSeq 622cyc flow cell to generate 2 × 301 bp PE reads. Quality control and adapter trimming were performed with bcl-convert ( ). Sequences were imported into Qiime2 ( ) for subsequent analysis. Primer sequences were removed using Qiime2’s cutadapt plugin ( ). Sequences were denoised using Qiime2’s dada2 plugin ( ). Denoised sequences were assigned operational taxonomic units (OTUs) using the Silva 138 99% OTUs full-length sequence database and the VSEARCH ( ) utility within Qiime2’s feature-classifier plugin. OTUs were then collapsed to their lowest taxonomic units, and their counts were converted to reflect their relative frequency within a sample.
Data availability The RNA-seq data are available at Geo accession number GSE202049. The microbiome data have been uploaded to NCBI and are available under BioProject number PRJNA1070294.
RESULTS 3.1 │ A rocker model of fluid shear We developed a model of fluid shear that utilizes a linear rocker. It is conducive to use with various culturing dishes and microbial species. We measured rocker velocity through an assessment of the number of OPM or a complete back-and-forth of the rocker shelf ( ). This allowed us to correlate the rocker dial settings to a specific velocity as shown in . For static growth conditions, culture plates are placed on the incubator shelf. In culture systems with liquid media, the flow of the media is arguably similar in velocity to the rocker and generates a correlative amount of fluid shear force on the bacteria. To specifically calculate the fluid shear force experienced by the biofilm bacteria, we simulated the flow within culture plates using CFD finite-element simulation software. In our simulations, we considered that the flow in the culture plates was a two-phase laminar flow and conducted the CFD simulation using the phase field method ( ; ). We conducted calculations to assess the Reynolds number associated with the flow induced by the back-and-forth motion of the rocker, which indicates that the Reynolds number is on the order of 80, falling within the range characteristic of laminar flow. This suggests that turbulence is not a significant factor in the flow generated by our rocker model. In the simulation, the Navier–Stokes equation was numerically solved for flow velocity, assuming no-slip boundary conditions on all solid boundaries. The upper boundary is characterized as an open boundary. The geometry of the simulated 3D circular well was defined as 3.5-cm wide and 1-cm tall, and the water depth was 0.25 cm ( ). shows shear stress distribution within the fluid phase on the bottom plane at different times with a rocker velocity of 30 OPM. We calculated spatially averaged shear stress ( τ bottom-avg ) at the bottom plotted over time. shows these values at a rocker velocity of 30 OPM. The time-averaged shear stress ( τ time-avg ) was defined as the average of bottom wall shear stress over the entire simulation duration of 10 s. We calculated the time-averaged shear stress as a function of the rocker velocities ( ). These time-averaged shear stress values show a linear relationship with velocity. 3.2 │ Effects of fluid shear on S. gordonii biofilm formation To evaluate the effects of fluid shear on S. gordonii biofilms, bacteria were grown in 12-well plates under static conditions or on the rocker set to six increasing rocker velocities. Biofilm biomass was assessed by crystal violet (staining of adherent cells following 24 h of growth. As shown in , as rocker velocity increased, biofilm biomass decreased, with one exception, at 1.25 OPM, where biofilm biomass slightly increased, though this difference was not statistically significant. The crystal violet staining also revealed a unique biofilm structure with increased density in linear format across the center of the well, when the biofilm was incubated under moderate velocities. This structure likely reflects the movement of the media through the well and correlates with an area of decreased shear stress based on our simulation ( ). We also evaluated the biomass of cells grown in 6-well plates and on saliva and two fractions of saliva, MUC5B and low density proteins (LDP). One of the dominant mucins in saliva is MUC5B, a component of the salivary pellicle that coats the teeth and serves as a substrate for bacterial adhesion in the oral cavity ( ; ; ). Using ultracentrifugation, saliva can be partitioned into a MUC5B-enriched fraction and a second fraction enriched for the remaining LDP including MUC7 and gp340, two other glycoproteins involved in bacterial adherence ( ; ; ). shows biomass quantification demonstrating that plate and substrate largely do not impact biofilm formation under shear at the tested velocity (3 OPM). 3.3 │ Effects of fluid shear on S. gordonii growth It is possible that the fluid shear could limit nutrient accessibility and impact bacterial growth, ultimately affecting biofilm density, as has been previously observed ( ; ). Thus, to assess whether the decrease in biofilm biomass was the result of a decrease in the overall growth of bacteria, we evaluated total biomass of both biofilm and planktonic bacteria by OD 600nm and viable cell numbers by ATP measurements. Low, moderate, and high OPM speeds were used and compared to static conditions. Biofilms were grown on either uncoated or saliva-coated plates. For biomass, no statistically significant difference was observed in OD 600 values between static and shear conditions, regardless of whether the plate was uncoated or coated with saliva ( ). For cell viability, luminescence values quantifying the amount of ATP produced corresponds to viable cells, with a log fold-change in luminescence roughly equivalent to a log fold-change in cell number of S. gordonii ( ). At low and mid OPM, no changes in luminescence were observed; however, at the high velocity, a two-log reduction in luminescence was detected ( , ). The same results were obtained for both uncoated and saliva-coated plates. These data suggest that the decrease in biofilm biomass under fluid shear is likely not due to an overall decrease in cell growth or viability until higher velocities and greater shear forces are applied. 3.4 │ Streptococcus gordonii gene expression under fluid shear While S. gordonii biofilm biomass decreases as shear forces increase, measurable biofilms are formed throughout the tested rocker velocities, suggesting an active response to the fluid flow. In other words, S. gordonii is likely sensing fluid flow or shear forces and altering its gene expression. Therefore, we assessed the S. gordonii transcriptome in response to a moderate level of fluid shear forces (7.5 OPM) at a point when biofilm formation is only partly affected, and growth is not changed, suggesting an active and productive response by S. gordonii . Since our previous data suggest that S. gordonii cells can sense interaction with a MUC5B-coated surface ( ), we evaluated the gene expression of biofilms growing on MUC5B and LDP fractions of saliva. Streptococcus gordonii was grown on both MUC5B- or LDP-coated plates for 24 h under static conditions and at a rocker velocity of 7.5 OPM. Following RNA isolation, RNAseq was used to evaluate the transcription profile. To identify differentially expressed genes (DEGs) in response to fluid flow, our cutoff values were a fold change of 2 and a p -value ≤ 0.01. On MUC5B-coated surfaces, 162 genes were differentially expressed in shear, compared to static conditions, whereas 76 genes were differentially expressed on LDP-coated surfaces. Of those 238 DEGs, 50 were differentially expressed regardless of how the plates were coated, that is, either MUC5B or LDP ( , and ). Of the 162 DEGs in MUC5B-grown biofilm, 34 were up-regulated under shear, while 128 were down ( ). Gene ontology analysis revealed an enrichment in genes that code for translation-related proteins under shear conditions and a decrease in genes that encode proteins involved in multiple sugar metabolism pathways ( , ). For LDP-grown biofilms, 12 genes were more abundantly expressed under shear, while 54 were down-regulated ( ). Static LDP conditions seemed to favor the expression of genes linked to polysaccharide metabolism, such as glycogen and glucans ( ). Of the 50 genes differentially expressed regardless of the plate-coating substrate, only four were up-regulated under shear: rplE and rpsQ , which encode ribosomal proteins; SGO_RS02250 , which encodes a putative transcriptional regulator; and glmS , which encodes a glucosamine-fructose-6-phosphate aminotransferase. Gene ontology analysis showed enrichment for genes involved in polysaccharide metabolism among the genes down-regulated under shear conditions in both MUC5B- and LDP-coated plate biofilms. The full list of DEGs is shown in and . We selected four DEGs from the list of genes differentially expressed regardless of the coating substrate ( abpB, SGO_RS01315, SGO_RS02250 , and SGO_RS06940 ) and included two genes that encode surface adhesin proteins: mbpA , which we previously showed helps S. gordonii sense interaction with MUC5B ( ), and hsa , which encodes a catch-bond adhesin contributing to shear-enhanced attachment ( ; ), to evaluate gene expression by quantitative RT-PCR ( , ). These data largely confirm the trends related to differential expression seen in the RNAseq ( , , and ). Additionally, the same expression pattern was seen in cultures grown on saliva-coated plates, further suggesting changes specific to shear and independent of substrate. 3.5 │ Effect of fluid shear on ex vivo plaque community biofilm formation and community composition Streptococcus gordonii is one of the approximately 700 bacterial species found in the human oral cavity. Given its ability to readily bind to the salivary pellicle adsorbed onto the tooth surface, S. gordonii is thought to play an important role in dental plaque development. Because shear forces affected S. gordonii biofilms and its transcriptional profile, we hypothesized that fluid shear forces should also have a significant impact on the formation and composition of dental plaque. To test this hypothesis, we exposed our ex vivo dental plaque model to different fluid shear forces generated by our rocker model. Different from what we observed with S. gordonii single species biofilms, we did not observe any increase in biofilm biomass at the lowest OPM velocity, and the biofilm biomass produced by our in vitro plaque community model rapidly decreased with increased OPM velocity as compared to the static biofilm ( ). To determine if shear forces affected the composition of our in vitro dental plaque model, we collected biofilms that were exposed to fluid flow at 1.5, 7.5, and 21 OPM, as well as their static biofilm controls. The genomic DNA was extracted and submitted for 16S rRNA sequencing analysis of the V3-V4 regions. The 1.5 OPM had no effect on the community composition of our ex vivo plaque community model, compared to the static control. However, the biofilms exposed to 7.5 and 21 OPM were composed of a significantly different bacterial community, compared to their static control, as evidenced by the decrease in Shannon index values ( ). In particular, as shown in , we observed an increase in the proportion of Aggregatibacter, Haemophilus , and Eikenella , and a decrease in Porphyromonas, Parvimonas, Fusobacterium , and Peptostreptococcus . No significant difference in community diversity and composition was observed when comparing 7.5 and 21 OPM ( , ).
A rocker model of fluid shear We developed a model of fluid shear that utilizes a linear rocker. It is conducive to use with various culturing dishes and microbial species. We measured rocker velocity through an assessment of the number of OPM or a complete back-and-forth of the rocker shelf ( ). This allowed us to correlate the rocker dial settings to a specific velocity as shown in . For static growth conditions, culture plates are placed on the incubator shelf. In culture systems with liquid media, the flow of the media is arguably similar in velocity to the rocker and generates a correlative amount of fluid shear force on the bacteria. To specifically calculate the fluid shear force experienced by the biofilm bacteria, we simulated the flow within culture plates using CFD finite-element simulation software. In our simulations, we considered that the flow in the culture plates was a two-phase laminar flow and conducted the CFD simulation using the phase field method ( ; ). We conducted calculations to assess the Reynolds number associated with the flow induced by the back-and-forth motion of the rocker, which indicates that the Reynolds number is on the order of 80, falling within the range characteristic of laminar flow. This suggests that turbulence is not a significant factor in the flow generated by our rocker model. In the simulation, the Navier–Stokes equation was numerically solved for flow velocity, assuming no-slip boundary conditions on all solid boundaries. The upper boundary is characterized as an open boundary. The geometry of the simulated 3D circular well was defined as 3.5-cm wide and 1-cm tall, and the water depth was 0.25 cm ( ). shows shear stress distribution within the fluid phase on the bottom plane at different times with a rocker velocity of 30 OPM. We calculated spatially averaged shear stress ( τ bottom-avg ) at the bottom plotted over time. shows these values at a rocker velocity of 30 OPM. The time-averaged shear stress ( τ time-avg ) was defined as the average of bottom wall shear stress over the entire simulation duration of 10 s. We calculated the time-averaged shear stress as a function of the rocker velocities ( ). These time-averaged shear stress values show a linear relationship with velocity.
Effects of fluid shear on S. gordonii biofilm formation To evaluate the effects of fluid shear on S. gordonii biofilms, bacteria were grown in 12-well plates under static conditions or on the rocker set to six increasing rocker velocities. Biofilm biomass was assessed by crystal violet (staining of adherent cells following 24 h of growth. As shown in , as rocker velocity increased, biofilm biomass decreased, with one exception, at 1.25 OPM, where biofilm biomass slightly increased, though this difference was not statistically significant. The crystal violet staining also revealed a unique biofilm structure with increased density in linear format across the center of the well, when the biofilm was incubated under moderate velocities. This structure likely reflects the movement of the media through the well and correlates with an area of decreased shear stress based on our simulation ( ). We also evaluated the biomass of cells grown in 6-well plates and on saliva and two fractions of saliva, MUC5B and low density proteins (LDP). One of the dominant mucins in saliva is MUC5B, a component of the salivary pellicle that coats the teeth and serves as a substrate for bacterial adhesion in the oral cavity ( ; ; ). Using ultracentrifugation, saliva can be partitioned into a MUC5B-enriched fraction and a second fraction enriched for the remaining LDP including MUC7 and gp340, two other glycoproteins involved in bacterial adherence ( ; ; ). shows biomass quantification demonstrating that plate and substrate largely do not impact biofilm formation under shear at the tested velocity (3 OPM).
Effects of fluid shear on S. gordonii growth It is possible that the fluid shear could limit nutrient accessibility and impact bacterial growth, ultimately affecting biofilm density, as has been previously observed ( ; ). Thus, to assess whether the decrease in biofilm biomass was the result of a decrease in the overall growth of bacteria, we evaluated total biomass of both biofilm and planktonic bacteria by OD 600nm and viable cell numbers by ATP measurements. Low, moderate, and high OPM speeds were used and compared to static conditions. Biofilms were grown on either uncoated or saliva-coated plates. For biomass, no statistically significant difference was observed in OD 600 values between static and shear conditions, regardless of whether the plate was uncoated or coated with saliva ( ). For cell viability, luminescence values quantifying the amount of ATP produced corresponds to viable cells, with a log fold-change in luminescence roughly equivalent to a log fold-change in cell number of S. gordonii ( ). At low and mid OPM, no changes in luminescence were observed; however, at the high velocity, a two-log reduction in luminescence was detected ( , ). The same results were obtained for both uncoated and saliva-coated plates. These data suggest that the decrease in biofilm biomass under fluid shear is likely not due to an overall decrease in cell growth or viability until higher velocities and greater shear forces are applied.
Streptococcus gordonii gene expression under fluid shear While S. gordonii biofilm biomass decreases as shear forces increase, measurable biofilms are formed throughout the tested rocker velocities, suggesting an active response to the fluid flow. In other words, S. gordonii is likely sensing fluid flow or shear forces and altering its gene expression. Therefore, we assessed the S. gordonii transcriptome in response to a moderate level of fluid shear forces (7.5 OPM) at a point when biofilm formation is only partly affected, and growth is not changed, suggesting an active and productive response by S. gordonii . Since our previous data suggest that S. gordonii cells can sense interaction with a MUC5B-coated surface ( ), we evaluated the gene expression of biofilms growing on MUC5B and LDP fractions of saliva. Streptococcus gordonii was grown on both MUC5B- or LDP-coated plates for 24 h under static conditions and at a rocker velocity of 7.5 OPM. Following RNA isolation, RNAseq was used to evaluate the transcription profile. To identify differentially expressed genes (DEGs) in response to fluid flow, our cutoff values were a fold change of 2 and a p -value ≤ 0.01. On MUC5B-coated surfaces, 162 genes were differentially expressed in shear, compared to static conditions, whereas 76 genes were differentially expressed on LDP-coated surfaces. Of those 238 DEGs, 50 were differentially expressed regardless of how the plates were coated, that is, either MUC5B or LDP ( , and ). Of the 162 DEGs in MUC5B-grown biofilm, 34 were up-regulated under shear, while 128 were down ( ). Gene ontology analysis revealed an enrichment in genes that code for translation-related proteins under shear conditions and a decrease in genes that encode proteins involved in multiple sugar metabolism pathways ( , ). For LDP-grown biofilms, 12 genes were more abundantly expressed under shear, while 54 were down-regulated ( ). Static LDP conditions seemed to favor the expression of genes linked to polysaccharide metabolism, such as glycogen and glucans ( ). Of the 50 genes differentially expressed regardless of the plate-coating substrate, only four were up-regulated under shear: rplE and rpsQ , which encode ribosomal proteins; SGO_RS02250 , which encodes a putative transcriptional regulator; and glmS , which encodes a glucosamine-fructose-6-phosphate aminotransferase. Gene ontology analysis showed enrichment for genes involved in polysaccharide metabolism among the genes down-regulated under shear conditions in both MUC5B- and LDP-coated plate biofilms. The full list of DEGs is shown in and . We selected four DEGs from the list of genes differentially expressed regardless of the coating substrate ( abpB, SGO_RS01315, SGO_RS02250 , and SGO_RS06940 ) and included two genes that encode surface adhesin proteins: mbpA , which we previously showed helps S. gordonii sense interaction with MUC5B ( ), and hsa , which encodes a catch-bond adhesin contributing to shear-enhanced attachment ( ; ), to evaluate gene expression by quantitative RT-PCR ( , ). These data largely confirm the trends related to differential expression seen in the RNAseq ( , , and ). Additionally, the same expression pattern was seen in cultures grown on saliva-coated plates, further suggesting changes specific to shear and independent of substrate.
Effect of fluid shear on ex vivo plaque community biofilm formation and community composition Streptococcus gordonii is one of the approximately 700 bacterial species found in the human oral cavity. Given its ability to readily bind to the salivary pellicle adsorbed onto the tooth surface, S. gordonii is thought to play an important role in dental plaque development. Because shear forces affected S. gordonii biofilms and its transcriptional profile, we hypothesized that fluid shear forces should also have a significant impact on the formation and composition of dental plaque. To test this hypothesis, we exposed our ex vivo dental plaque model to different fluid shear forces generated by our rocker model. Different from what we observed with S. gordonii single species biofilms, we did not observe any increase in biofilm biomass at the lowest OPM velocity, and the biofilm biomass produced by our in vitro plaque community model rapidly decreased with increased OPM velocity as compared to the static biofilm ( ). To determine if shear forces affected the composition of our in vitro dental plaque model, we collected biofilms that were exposed to fluid flow at 1.5, 7.5, and 21 OPM, as well as their static biofilm controls. The genomic DNA was extracted and submitted for 16S rRNA sequencing analysis of the V3-V4 regions. The 1.5 OPM had no effect on the community composition of our ex vivo plaque community model, compared to the static control. However, the biofilms exposed to 7.5 and 21 OPM were composed of a significantly different bacterial community, compared to their static control, as evidenced by the decrease in Shannon index values ( ). In particular, as shown in , we observed an increase in the proportion of Aggregatibacter, Haemophilus , and Eikenella , and a decrease in Porphyromonas, Parvimonas, Fusobacterium , and Peptostreptococcus . No significant difference in community diversity and composition was observed when comparing 7.5 and 21 OPM ( , ).
DISCUSSION Here, we investigated the role of fluid shear on biofilms formed by oral bacteria. We first investigated how single-species biofilms of S. gordonii were impacted by shear by assessing biomass, metabolic activity, and transcriptional response. We then used our ex vivo dental plaque model to assess the impact of shear forces on multispecies biofilm biomass and community composition. Using our rocker model of fluid shear, we were able to incrementally alter fluid flow over bacteria grown on varying substrates and vessels ( ). We were also able to calculate fluid shear stress averaged over the circular well surface as a function of velocity and over time using CFD ( ). At 30 OPM, the time-averaged shear stress ( τ time-avg ) at the bottom is 0.02 Pa, with a local maximum shear stress of 0.06 Pa. While studies vary, these values are comparable to those used in other microfluidic models ( ; ; ). Importantly, the range of shear force values generated by the rocker is relatively close to the estimated 0.0762 Pa in the oral cavity ( ). Being that these calculations are averages, the shear stress would vary across different areas within the wells, and our results indicate that shear stress levels are relatively low at the center of the well, compared to the periphery. This observation aligns with the accumulation of more biomass at the middle of the well ( ), and a direct comparison is depicted in . Another variable that can impact shear forces is the viscosity of BHI and Shi media, which is lower than that ofsaliva. Modeling fluid shear in the oral cavity is a complex task and no one model recapitulates the entire intraoral landscape ( ; ; ; ). Furthermore, the shear in the oral cavity cannot be distilled to a single force value or vector ( ). The rate of flow of saliva across the oral surfaces varies greatly at different sites ( ). Consequently, the thickness and other features of the salivary pellicle on teeth also differ with changes in local conditions ( ). Complicating the features of mucosal salivary films is that the salivary-coated epithelial cells are constantly shedding ( ) and the viscoelastic properties of the mucosa depend on the anatomic location ( ). Therefore, we feel that the values generated within our rocker model are reasonable and that our simple model to evaluate the role of complex shear forces is a first informative step toward a more refined analysis. Consistent with previous studies, we found that as rocker velocity increased, and consequently fluid shear, S. gordonii biofilm biomass decreased ( ). This decrease was largely independent of any decrease in overall bacterial growth ( ). Only at high shear forces did we observe a slight decrease in bacterial growth, which at that point may contribute to the significant loss of biomass ( ). We evaluated biofilm biomass after 24 h, and therefore it is not yet clear if the decrease in biofilm formed is the result of decreased adhesion or in biofilm development or maintenance. Under lower fluid shear, we observed a slight increase in biofilm biomass that also appears to be independent of any change in overall bacterial growth ( and ). This suggests the potential for shear-enhanced adhesion and/or extracellular matrix production and subsequent biofilm formation. Several studies have observed enhanced binding of bacterial cells exposed to shear forces, termed shear-enhanced attachment, although depending on the microbe, the mechanisms may differ ( ; ) . Streptococcus gordonii has been shown to display shear-enhanced adhesion to saliva and fetuin. This adhesion is dependent on the well-known adhesin Hsa ( ). The same phenomenon was observed under higher shear conditions in shear-enhanced binding to platelets by S. gordonii and S. sanguinis ( ). Shear-enhanced platelet binding was dependent on the adhesins Hsa/GspB for S. gordonii and SrpA for S. sanguinis . These proteins have been suggested to act as catch-bond adhesins. There is a growing body of evidence that one way in which bacteria respond to shear forces is through the use of catch-bond adhesins, but proteomic or transcriptomic responses to fluid shear remain few. Existing research shows that the transcriptional response to fluid shear stress varies greatly depending on the study and bacterial species. Our transcriptional analysis of S. gordonii exposed to moderate fluid shear identified 50 DEGs, independent of substrates or salivary fractions MUC5B and LDP ( , , and ). Under shear forces, a higher percentage of genes were down-regulated than up-regulated (46 vs. 4, respectively). One down-regulated cluster was involved in arginine metabolism, commonly used by bacteria to buffer pH and shown to directly impact biofilm formation ( ; ; ). Also down-regulated were several clusters related to carbohydrate metabolism. Future studies will investigate the purpose of S. gordonii down-regulation of these carbohydrate metabolism genes under shear stress and a potential role in exopolysaccharide production, composition, and utilization as it relates to biofilm maintenance under shear stress. Only four genes were up-regulated under shear and two of them, rplE and rpsQ , encoding ribosomal proteins and therefore linked to translation. Of the genes differentially regulated under shear by RNAseq, we selected four genes, abpB, SGO_RS01315, SGO_RS02250 , and SGO_RS06940 , and two non-DEGs encoding adhesin proteins: mbpA and hsa for further evaluation using quantitative RT-PCR ( , ). While mbpA was not significantly differentially regulated, hsa did show significant up-regulation, which reflects similar changes for these genes observed in RNAseq. MbpA has been shown to bind Type I collagen and the salivary mucin MUC5B and aids S. gordonii in sensing interaction with MUC5B ( , ; ). The potential for Hsa and MbpA to function as adhesins required for attachment or biofilm formation under shear stress has yet to be determined; however, a mutant in MbpA displays variable contribution to static, monospecies biofilm formation by S. gordonii , while hsa deletion significantly reduced biofilm formation ( ). For hsa , one would not necessarily expect shear-dependent transcriptional regulation since as a catch-bond adhesin, its attachment to the substrate is enhanced by conformational changes induced by shear; however, it is worth further investigation. For the four DEGs from the RNAseq, the quantitative RT-PCR data largely mirrored the RNAseq results ( , ). SGO_RS02250 , which was up-regulated under shear, encodes a predicted DNA-binding transcriptional regulator of the YebC/PmpR family. These regulators have been tied to the regulation of quorum sensing, proteolytic systems, DNA repair, and virulence factors in other organisms ( ; ; ; ; ). Further studies need to be completed to investigate further any role for this regulator in the response to fluid flow. We also evaluated three down-regulated genes: abpB, SGO_RS01315 , and SGO_RS06940 . AbpB is a functional C69 family dipeptidyl-dipeptidase, and while studies suggest it may be involved in multispecies biofilm formation, it is not required for monospecies S. gordonii biofilm formation ( ) . SGO_RS01315 is a predicted polyamine/amino acid transporter (PotE, putrescine/ornithine antiporter) though its function remains to be biochemically validated. The other genes related to polyamine transport and utilization ( potABCD ) were not differentially expressed. The role of polyamines in biofilm formation varies depending on the organism ( ). Last, SGO_RS06940 is a predicted cell wall protein annotated as an S15 family X-prolyl dipeptidylpeptidase with homology to PepX of Lactobacillus ( ). A second PepX homolog (SGO_0234) is also found in S. gordonii; however, we did not observe differential expression of this gene ( ). All three of these genes are potentially involved in amino acid transport and metabolism, systems whose roles in biofilm formation under shear forces have yet to be evaluated and are the goal of future studies. The effect of shear forces on our ex vivo plaque model was similar to what we saw with S. gordonii : As fluid shear increased, biofilm biomass decreased ( ). To some extent, this contradicts observation of no change or a slight increase in biomass when different oral biofilms were exposed to increasing fluid shear (0.1, 0.2, and 0.4 dyn/cm 2 , equivalent to 0.01, 0.02, and 0.04 Pa, respectively). It is worth noting that the growth media and method used to generate shear forces differed significantly between our study and , which could have easily contributed to the seemingly opposite biofilm biomass results. When it comes to community composition, our 16S sequencing data allowed us to assign genus information to our taxa, providing greater insight into community composition changes in response to fluid shear. In our studies, no difference in community composition was observed at our lowest OPM (1.5), when compared to the static biofilm ( , ), which is perhaps expected given the low amount of shear forces experienced by these biofilms ( ). At higher OPM (7.5 and 21), however, we observed a decrease in the overall community composition when compared to static biofilms, with a notable decrease in the abundance of obligate anaerobic genera such as Porphyromonas, Fusobacterium , and Parvimonas , and an increase in the abundance of the facultative anaerobic genera Aggregatibacter and Hemophilus . Given the rocker model used in this study and our incubation condition (5% CO 2 ), it is possible that one of the consequences of increasing OPM is an increase in the oxygen tension in the media, thus affecting the growth of strict anaerobic bacteria. It would be interesting to see what happens to the community composition if we were able to replicate this experiment inside an anaerobic chamber. Strikingly, Streptococcus representation was minimally affected under shear in our ex vivo model, suggesting that in the context of a multispecies setting, the effect of fluid shear on streptococci such as S. gordonii might be diminished. Overall, we have developed a simple model of fluid shear and evaluated biofilm biomass and the composition of oral bacterial biofilms. We also used this model to probe the transcriptional response of S. gordonii to fluid shear, providing insights into factors required by oral bacteria to form plaque communities in the oral environment and bacterial adaptation to fluid shear. These studies can inform how we study biofilms under authentic environmental conditions and, in many cases, treat or prevent biofilm formation in patients.
Table S2 Table S1 Figure S1
|
Factors influencing childbirth self-efficacy and vaginal delivery rates in Chongqing: An observational study | fcbbd014-fbb0-4d40-9509-210809dd0000 | 11813020 | Surgical Procedures, Operative[mh] | Since the implementation of the 2-child policy, there has been an increase in the risk of repregnancy and childbirth due to first-time cesarean section. Additionally, with the improvement in living standards, there is a concurrent rise in pregnancy-related complications. To reduce the occurrence of complications during pregnancy and childbirth, the American College of Obstetricians and Gynecologists (ACGO) has recommended vaginal delivery as a means to reduce the occurrence of maternal complications and adverse neonatal outcomes. Empirical studies indicate that physical exercise improves vaginal delivery rates and decreases the incidence of pregnancy complications, and good. Furthermore, robust family and social support systems are associated with higher success rates of vaginal delivery. In vaginal delivery trials, women with greater self-efficacy demonstrate a higher likelihood of successfully completing the process. Moreover, studies have found that younger age, higher income levels, greater prenatal knowledge, and better psychological well-being are associated with higher levels of childbirth self-efficacy. Consequently, self-efficacy in childbirth not only facilitates coping strategies during labor but also contributes to maternal health and promotes various perinatal outcomes. However, limited research has explored the relationship between prenatal exercise, social support, and delivery self-efficacy. To address this gap, this study intends to examine the associations between prenatal exercise, social support, and delivery self-efficacy among pregnant women in Chongqing. By assessing the current levels of self-efficacy in childbirth and its influencing factors, this research seeks to develop targeted intervention measures to enhance childbirth confidence, ensure obstetric safety, and support the effective implementation of national policies. 2.1. Objects Hospitals were selected using a purposive sampling method. To ensure sample size and diversity, 6 hospitals, distributed across the southeast, northwest, and northeast of Chongqing, with an annual delivery volume exceeding 3000 cases in 2022 were selected based on the delivery volume. 2.2. Determination of sample size We conducted a preliminary survey of 100 pregnant women at the First Affiliated Hospital of Chongqing Medical University, which revealed that 46% of pregnancies were terminated through vaginal delivery. Based on Kendall’s sample size estimation method, the sample size for this study was calculated as 552 by multiplying the number of variables by 5 to 10 times and increasing it by an additional 10% to 20%. To account for potential uncontrollable factors during data collection and to ensure robust statistical analysis, the final sample size was determined to be 1200 participants. Using a convenience sampling method, we ultimately recruited 1142 pregnant women who underwent prenatal examinations at obstetrics clinics of 6 hospitals located in the northern, southeastern, and western regions of Chongqing Municipality in February 2023. The inclusion criteria were as follows: gestational age ≥28 weeks, no reading or comprehension difficulties and no mental illness, voluntary participation in the study, and singleton pregnancy. The exclusion criteria were as follows: indication for cesarean section; physical or mental illnesses that prevented questionnaire completion. 2.3. Methods 2.3.1. Questionnaire design Consulting the literature from the past 5 years, we designed a questionnaire comprising 2 sections. The first section collected general information, including age, educational level, place of residence, per capita monthly income, number of pregnancies, parity, conception method, planned delivery method, and exercise during pregnancy. The second section included scales to assess self-efficacy and social support system scores during childbirth. The Chinese Child Birth Self-efficacy Scale (CBSEI-C32) and the Social Support System Scale (PSSS) were used. The CBSEI-C32 is a Chinese version of the Delivery Self-Efficacy Scale developed by Lowe in 1993 and adapted by Yin et al. The scale includes 2 parallel subscales, Outcome Expectations and Self-Efficacy Expectations, with Cronbach’s alpha values of 0.94 and 0.98, respectively. This scale consists of 32 items, each scored from 1 to 10, yielding a total score ranging from 32 to 320 points. A higher score reflects greater self-efficacy during childbirth and stronger confidence in achieving natural vaginal delivery. The PSSS is a Chinese version of Blumenthal’s 1987 Social Support System Scale developed by Li Huang et al. The Cronbach’s alpha value was 0.97 and consists of 12 items, each rated on a 7-point Likert scale including “strongly disagree,” “strongly disagree,” “lightly disagree,” “neutral,” “slightly agree,” “strongly agree,” and “strongly agree.” The total score ranges from 12 to 84 points, with higher scores indicating a better social support system. 2.3.2. Survey method The survey was approved by the Medical Ethics Committee of First Affiliated Hospital of Chongqing Medical University (approval date: April 2, 2022). The questionnaire was imported into the “Wen-Juan-Xing” app to generate a survey QR code. Researchers were responsible for explaining the purpose of the survey and the required items to pregnant women to obtain their cooperation. The collected data was only used for this study and will remain confidential. After participants provided informed consent, researchers guided them to scan the QR code using WeChat and complete the questionnaire. A total of 1142 questionnaires were distributed, of which 56 unqualified questionnaires were excluded, resulting in 1086 valid questionnaires and an effective rate of 95.10%. 2.3.3. Statistical analysis After completing the questionnaire, the researcher retrieves the data through the “Wen-Juan-Xing” APP and stores it on the “Baidu Cloud” APP to ensure the effective data storage. The data were then analyzed using SPSS 26.0 to establish a database. Categorical data were presented as frequencies and percentages, while continuous data were expressed as mean ± standard deviation ( x ± s ). The CBSEI-C32 score was used as a dependent variable. First, the Mann–Whitney U test and Spearman’s correlation analysis were conducted to compare differences and assess correlations. Variables showing statistically significant results were subsequently included in multiple regression analysis. Statistical significance was defined as P < .05. Hospitals were selected using a purposive sampling method. To ensure sample size and diversity, 6 hospitals, distributed across the southeast, northwest, and northeast of Chongqing, with an annual delivery volume exceeding 3000 cases in 2022 were selected based on the delivery volume. We conducted a preliminary survey of 100 pregnant women at the First Affiliated Hospital of Chongqing Medical University, which revealed that 46% of pregnancies were terminated through vaginal delivery. Based on Kendall’s sample size estimation method, the sample size for this study was calculated as 552 by multiplying the number of variables by 5 to 10 times and increasing it by an additional 10% to 20%. To account for potential uncontrollable factors during data collection and to ensure robust statistical analysis, the final sample size was determined to be 1200 participants. Using a convenience sampling method, we ultimately recruited 1142 pregnant women who underwent prenatal examinations at obstetrics clinics of 6 hospitals located in the northern, southeastern, and western regions of Chongqing Municipality in February 2023. The inclusion criteria were as follows: gestational age ≥28 weeks, no reading or comprehension difficulties and no mental illness, voluntary participation in the study, and singleton pregnancy. The exclusion criteria were as follows: indication for cesarean section; physical or mental illnesses that prevented questionnaire completion. 2.3.1. Questionnaire design Consulting the literature from the past 5 years, we designed a questionnaire comprising 2 sections. The first section collected general information, including age, educational level, place of residence, per capita monthly income, number of pregnancies, parity, conception method, planned delivery method, and exercise during pregnancy. The second section included scales to assess self-efficacy and social support system scores during childbirth. The Chinese Child Birth Self-efficacy Scale (CBSEI-C32) and the Social Support System Scale (PSSS) were used. The CBSEI-C32 is a Chinese version of the Delivery Self-Efficacy Scale developed by Lowe in 1993 and adapted by Yin et al. The scale includes 2 parallel subscales, Outcome Expectations and Self-Efficacy Expectations, with Cronbach’s alpha values of 0.94 and 0.98, respectively. This scale consists of 32 items, each scored from 1 to 10, yielding a total score ranging from 32 to 320 points. A higher score reflects greater self-efficacy during childbirth and stronger confidence in achieving natural vaginal delivery. The PSSS is a Chinese version of Blumenthal’s 1987 Social Support System Scale developed by Li Huang et al. The Cronbach’s alpha value was 0.97 and consists of 12 items, each rated on a 7-point Likert scale including “strongly disagree,” “strongly disagree,” “lightly disagree,” “neutral,” “slightly agree,” “strongly agree,” and “strongly agree.” The total score ranges from 12 to 84 points, with higher scores indicating a better social support system. 2.3.2. Survey method The survey was approved by the Medical Ethics Committee of First Affiliated Hospital of Chongqing Medical University (approval date: April 2, 2022). The questionnaire was imported into the “Wen-Juan-Xing” app to generate a survey QR code. Researchers were responsible for explaining the purpose of the survey and the required items to pregnant women to obtain their cooperation. The collected data was only used for this study and will remain confidential. After participants provided informed consent, researchers guided them to scan the QR code using WeChat and complete the questionnaire. A total of 1142 questionnaires were distributed, of which 56 unqualified questionnaires were excluded, resulting in 1086 valid questionnaires and an effective rate of 95.10%. 2.3.3. Statistical analysis After completing the questionnaire, the researcher retrieves the data through the “Wen-Juan-Xing” APP and stores it on the “Baidu Cloud” APP to ensure the effective data storage. The data were then analyzed using SPSS 26.0 to establish a database. Categorical data were presented as frequencies and percentages, while continuous data were expressed as mean ± standard deviation ( x ± s ). The CBSEI-C32 score was used as a dependent variable. First, the Mann–Whitney U test and Spearman’s correlation analysis were conducted to compare differences and assess correlations. Variables showing statistically significant results were subsequently included in multiple regression analysis. Statistical significance was defined as P < .05. Consulting the literature from the past 5 years, we designed a questionnaire comprising 2 sections. The first section collected general information, including age, educational level, place of residence, per capita monthly income, number of pregnancies, parity, conception method, planned delivery method, and exercise during pregnancy. The second section included scales to assess self-efficacy and social support system scores during childbirth. The Chinese Child Birth Self-efficacy Scale (CBSEI-C32) and the Social Support System Scale (PSSS) were used. The CBSEI-C32 is a Chinese version of the Delivery Self-Efficacy Scale developed by Lowe in 1993 and adapted by Yin et al. The scale includes 2 parallel subscales, Outcome Expectations and Self-Efficacy Expectations, with Cronbach’s alpha values of 0.94 and 0.98, respectively. This scale consists of 32 items, each scored from 1 to 10, yielding a total score ranging from 32 to 320 points. A higher score reflects greater self-efficacy during childbirth and stronger confidence in achieving natural vaginal delivery. The PSSS is a Chinese version of Blumenthal’s 1987 Social Support System Scale developed by Li Huang et al. The Cronbach’s alpha value was 0.97 and consists of 12 items, each rated on a 7-point Likert scale including “strongly disagree,” “strongly disagree,” “lightly disagree,” “neutral,” “slightly agree,” “strongly agree,” and “strongly agree.” The total score ranges from 12 to 84 points, with higher scores indicating a better social support system. The survey was approved by the Medical Ethics Committee of First Affiliated Hospital of Chongqing Medical University (approval date: April 2, 2022). The questionnaire was imported into the “Wen-Juan-Xing” app to generate a survey QR code. Researchers were responsible for explaining the purpose of the survey and the required items to pregnant women to obtain their cooperation. The collected data was only used for this study and will remain confidential. After participants provided informed consent, researchers guided them to scan the QR code using WeChat and complete the questionnaire. A total of 1142 questionnaires were distributed, of which 56 unqualified questionnaires were excluded, resulting in 1086 valid questionnaires and an effective rate of 95.10%. After completing the questionnaire, the researcher retrieves the data through the “Wen-Juan-Xing” APP and stores it on the “Baidu Cloud” APP to ensure the effective data storage. The data were then analyzed using SPSS 26.0 to establish a database. Categorical data were presented as frequencies and percentages, while continuous data were expressed as mean ± standard deviation ( x ± s ). The CBSEI-C32 score was used as a dependent variable. First, the Mann–Whitney U test and Spearman’s correlation analysis were conducted to compare differences and assess correlations. Variables showing statistically significant results were subsequently included in multiple regression analysis. Statistical significance was defined as P < .05. 3.1. Univariate analysis of pregnant women’s childbirth self-efficacy A total of 1086 pregnant women were included in this study. The results of the univariate analysis showed that the participants’ average monthly household income, pregnancy mode, planned mode of delivery, and prenatal exercise had a statistically significant impact on childbirth self-efficacy scores ( P < .05). Please refer to Table for detailed information. 3.2. Multivariate analysis of pregnant women’s childbirth self-efficacy This study investigated the scores of pregnant women on the PSSS, and the results showed that the average score on this scale was (67.05 ± 11.84). Spearman’s correlation analysis revealed a statistically significant difference between social support and childbirth self-efficacy ( r = 0.525, P < .001). To eliminate interfering factors, based on the results of the univariate analysis, variables such as average monthly household income, pregnancy mode, planned mode of delivery, prenatal exercise, and PSSS scores were included as independent variables. The CBSEI-C32 score was used as the dependent variable in the multiple linear regression model. The results showed that there were statistically significant differences in childbirth self-efficacy concerning the planned mode of delivery, prenatal exercise, and PSSS scores ( P < .05). Please refer to Table for further details. A total of 1086 pregnant women were included in this study. The results of the univariate analysis showed that the participants’ average monthly household income, pregnancy mode, planned mode of delivery, and prenatal exercise had a statistically significant impact on childbirth self-efficacy scores ( P < .05). Please refer to Table for detailed information. This study investigated the scores of pregnant women on the PSSS, and the results showed that the average score on this scale was (67.05 ± 11.84). Spearman’s correlation analysis revealed a statistically significant difference between social support and childbirth self-efficacy ( r = 0.525, P < .001). To eliminate interfering factors, based on the results of the univariate analysis, variables such as average monthly household income, pregnancy mode, planned mode of delivery, prenatal exercise, and PSSS scores were included as independent variables. The CBSEI-C32 score was used as the dependent variable in the multiple linear regression model. The results showed that there were statistically significant differences in childbirth self-efficacy concerning the planned mode of delivery, prenatal exercise, and PSSS scores ( P < .05). Please refer to Table for further details. 4.1. Current status of pregnant women’s childbirth self-efficacy In this study, the CBSEI-C32 score was 241.86 ± 60.25, which was significantly higher than the results reported in other domestic and international studies. This may be attributed to the fact that the hospitals where the pregnant women received prenatal examinations in this study offered both online and offline courses for pregnant women and adopted various channels to provide childbirth-related knowledge, along with continuous guidance and supervision. In addition, pregnant women now demonstrate a high level of acceptance of the Internet. Consequently, they are able to acquire more professional knowledge about childbirth, leading to higher levels of self-efficacy in childbirth. 4.2. Planning for a natural vaginal delivery can increase childbirth self-efficacy and enhance the rate of vaginal deliveries This study demonstrates that pregnant women planning for vaginal delivery have higher levels of childbirth self-efficacy compared to those planning for cesarean section or uncertain delivery modes. This may be because women planning for cesarean section or uncertain delivery modes tend to rely more on healthcare professionals and perceive childbirth as a process requiring only their cooperation, without the need to acquire extensive knowledge or make significant preparations. In contrast, pregnant women planning for a vaginal delivery are aware of the labor pain they may experience and proactively seek to learn about pain relief methods, such as labor analgesia and Lamaze breathing techniques, along with childbirth-related knowledge. These measures reduce their fear of childbirth, increase confidence, and enhance childbirth self-efficacy. Therefore, community interventions should focus on promoting knowledge about vaginal delivery, encouraging regular prenatal examinations, and guiding pregnant women to understand their delivery mode during the mid-pregnancy stage. For pregnant women without cesarean section indicators, planned guidance should be provided to opt for vaginal delivery. Additionally, it is essential to strengthen health education on natural childbirth, establish maternity schools, and empower pregnant women with knowledge about the benefits of vaginal delivery, understanding the process of childbirth, and learning methods to cope with labor pain. These efforts can ultimately increase childbirth self-efficacy and the rate of natural vaginal deliveries. 4.3. Strong social support systems help improve childbirth self-efficacy In this study, pregnant women scored 67.05 ± 11.84 on the PSSS, indicating a relatively high level of social support among pregnant women. A positive correlation was observed between childbirth self-efficacy and PSSS scores. The care and support received from family and friends contribute to a sense of happiness and well-being among pregnant women. This support also leads to positive emotions and attitudes, higher compliance with prenatal examinations, encourages active communication with healthcare professionals, promotes the acquisition of childbirth-related knowledge, and increases confidence in childbirth. Consequently, pregnant women demonstrate higher levels of childbirth self-efficacy. During childbirth, it is encouraged to have family members, friends, or birth companions present to provide additional social support to pregnant women. Community programs should provide optional courses for pregnant women, while hospitals should make full use of the Internet to offer online and offline courses, to ensure that pregnant women can access childbirth-related knowledge without being limited by the physical location. The state provides the corresponding policy support. These interventions aim to increase childbirth self-efficacy among pregnant women and promote vaginal delivery. 4.4. Engaging in prenatal exercise at the recommended level helps improve childbirth self-efficacy Guidelines recommend that pregnant women engage in prenatal exercise for a minimum of 5 to 7 days per week, with each session lasting at least 30 minutes. This study demonstrates that pregnant women who meet the recommended exercise standards exhibit a higher level of childbirth self-efficacy compared to those who do not. Prenatal exercise contributes to a positive emotional state and helps pregnant women maintain favorable physiological and psychological conditions. Research has shown that a positive emotional state increases pregnant women’s delivery confidence and childbirth self-efficacy. However, this study found that only 7.37% of pregnant women met the recommended exercise standards. Therefore, healthcare professionals should strengthen education and guidance regarding prenatal exercise for pregnant women. It is crucial to create an environment that supports exercise and assists pregnant women in adopting scientifically designed and appropriate prenatal exercise routines. By doing so, maternal childbirth self-efficacy can be enhanced, ultimately promoting positive birth experiences. In this study, the CBSEI-C32 score was 241.86 ± 60.25, which was significantly higher than the results reported in other domestic and international studies. This may be attributed to the fact that the hospitals where the pregnant women received prenatal examinations in this study offered both online and offline courses for pregnant women and adopted various channels to provide childbirth-related knowledge, along with continuous guidance and supervision. In addition, pregnant women now demonstrate a high level of acceptance of the Internet. Consequently, they are able to acquire more professional knowledge about childbirth, leading to higher levels of self-efficacy in childbirth. This study demonstrates that pregnant women planning for vaginal delivery have higher levels of childbirth self-efficacy compared to those planning for cesarean section or uncertain delivery modes. This may be because women planning for cesarean section or uncertain delivery modes tend to rely more on healthcare professionals and perceive childbirth as a process requiring only their cooperation, without the need to acquire extensive knowledge or make significant preparations. In contrast, pregnant women planning for a vaginal delivery are aware of the labor pain they may experience and proactively seek to learn about pain relief methods, such as labor analgesia and Lamaze breathing techniques, along with childbirth-related knowledge. These measures reduce their fear of childbirth, increase confidence, and enhance childbirth self-efficacy. Therefore, community interventions should focus on promoting knowledge about vaginal delivery, encouraging regular prenatal examinations, and guiding pregnant women to understand their delivery mode during the mid-pregnancy stage. For pregnant women without cesarean section indicators, planned guidance should be provided to opt for vaginal delivery. Additionally, it is essential to strengthen health education on natural childbirth, establish maternity schools, and empower pregnant women with knowledge about the benefits of vaginal delivery, understanding the process of childbirth, and learning methods to cope with labor pain. These efforts can ultimately increase childbirth self-efficacy and the rate of natural vaginal deliveries. In this study, pregnant women scored 67.05 ± 11.84 on the PSSS, indicating a relatively high level of social support among pregnant women. A positive correlation was observed between childbirth self-efficacy and PSSS scores. The care and support received from family and friends contribute to a sense of happiness and well-being among pregnant women. This support also leads to positive emotions and attitudes, higher compliance with prenatal examinations, encourages active communication with healthcare professionals, promotes the acquisition of childbirth-related knowledge, and increases confidence in childbirth. Consequently, pregnant women demonstrate higher levels of childbirth self-efficacy. During childbirth, it is encouraged to have family members, friends, or birth companions present to provide additional social support to pregnant women. Community programs should provide optional courses for pregnant women, while hospitals should make full use of the Internet to offer online and offline courses, to ensure that pregnant women can access childbirth-related knowledge without being limited by the physical location. The state provides the corresponding policy support. These interventions aim to increase childbirth self-efficacy among pregnant women and promote vaginal delivery. Guidelines recommend that pregnant women engage in prenatal exercise for a minimum of 5 to 7 days per week, with each session lasting at least 30 minutes. This study demonstrates that pregnant women who meet the recommended exercise standards exhibit a higher level of childbirth self-efficacy compared to those who do not. Prenatal exercise contributes to a positive emotional state and helps pregnant women maintain favorable physiological and psychological conditions. Research has shown that a positive emotional state increases pregnant women’s delivery confidence and childbirth self-efficacy. However, this study found that only 7.37% of pregnant women met the recommended exercise standards. Therefore, healthcare professionals should strengthen education and guidance regarding prenatal exercise for pregnant women. It is crucial to create an environment that supports exercise and assists pregnant women in adopting scientifically designed and appropriate prenatal exercise routines. By doing so, maternal childbirth self-efficacy can be enhanced, ultimately promoting positive birth experiences. This study identified a relatively high level of childbirth self-efficacy among pregnant women in Chongqing. According to established guidelines, factors that positively influence self-efficacy include planning for vaginal delivery and engaging in prenatal exercise. A positive correlation was also observed between social support and childbirth self-efficacy among pregnant women. Based on these findings, it is recommended to apply a combination of online and offline methods to strengthen the knowledge of vaginal delivery and pregnancy exercise for pregnant women. Encouraging family involvement in the educational and exercise activities of pregnant women, as well as jointly developing delivery plans, is crucial. Additionally, community-based prenatal health lectures should be organized, and midwives should provide support and guidance during childbirth to enhance social support for pregnant women, thereby improving their childbirth self-efficacy, increasing their confidence in childbirth, and promoting natural delivery. This work was supported by the Research Fund of Maternal and Child Health of the Chongqing Health Commission (No. 2023FY206), the Nursing Fund of the First Affiliated Hospital of Chongqing Medical University (No. HLJJ2019-15) and the Nursing Fund of the First Affiliated Hospital of Chongqing Medical University (No. HLJJ2021-19). Conceptualization: Ding-Xiang Xing, Fei-Fei Li, Xiao-Chang Yang, Jia Chen. Data curation: Ding-Xiang Xing. Formal analysis: Ding-Xiang Xing. Funding acquisition: Xiao-Chang Yang, Ding-Xiang Xing, Jia Chen. Supervision: Jun-Nan Li, Xiao-Chang Yang. Writing – original draft: Ding-Xiang Xing. Writing – review & editing: Fei-Fei Li, Jun-Nan Li, Xiao-Chang Yang. |
Where the joy comes from: a qualitative exploration of deep GP-patient relationships | b81f2a05-6c3e-4578-8a7d-9d7e805c47ff | 10717859 | Family Medicine[mh] | Relationship-based, whole person care is foundational to quality general practice. Trusting doctor-patient relationships improve patient concordance, satisfaction and perceived health outcomes. However, changing sociocultural and commercial practice contexts may threaten relationship-based care. It is important to understand the character and cultivation of deep GP-patient relationships to maintain their benefits. Previous literature has identified ongoing depth of doctor-patient relationship as important to patients. Synthesis of qualitative literature on patients’ perspectives found that deep patient-doctor relationships were characterised by knowledge, trust, loyalty and regard . Similarly, a study exploring ‘healing relationships’ in primary care involved interviews with GPs considered by the authors to be ‘exemplar healers’, and patients whom these GPs selected. This study identified that trust, hope and a sense of being known characterise healing relationships, and that such relationships are fostered through GPs valuing patients, appreciating power and abiding. In other work, building a whole person ‘sense of safety’ in patients and clinicians has been identified as an important therapeutic goal for primary care relationships. Multiple factors influence GP-patient relationships, including doctors’ relational skills and attitudes; practice characteristics; health systems and demographics. . The psychotherapy relational model of attachment theory may have relevance to GP-patient relationships. Originally studied in parent-child relationships, and then in adult intimate relationships, attachment theory has been applied to healthcare relationships. Attachment theory proposes that humans require connection in safe relationships for health. Attachment figures provide both a ‘safe haven’ to offer comfort and a ‘secure base’ to support exploration. Each person’s connection, or attachment, style (secure, anxious or avoidant), is influenced by early childhood experience ; availability of the attachment figure to be wiser, kinder, and stronger; and attunement within the relationship. Attachment figures are not easily replaced and can influence capacity to trust and trigger strong emotions, proximity seeking and separation protest. Healthcare studies have suggested that doctors may serve as attachment figures for patients, providing a sense of safety amidst healthcare-related vulnerability. However, current moves toward commercialised medicine and systems that reduce direct GP-patient contact may threaten the interpersonal continuity underpinning such relationships. Additionally, it has been suggested that attachment theory is of limited relevance to understanding doctors’ motivation for caring. . This project aims to build upon this background by further characterising the nature of deep GP-patient relationships and exploring how these are cultivated, from GP and patient perspectives.
Semi-structured interview design, with purposive selection of GP-patient pairs with deep relationships, as identified from an initial patient survey (Fig. ). Setting Australian General Practice. General practice forms the foundation of Australia’s health system and operates on a fee-for-service model. Patients are not required to register with GP practices. The patient fee is subsidised by the government-funded Medicare scheme, and either accepted as full payment (bulk-billing), or supplemented by a patient co-payment (mixed or private billing). . Research team background HT, JL, ES and NS are GPs who combine clinical and academic practice. MB is a palliative care physician and ethicist with a background in qualitative methodologies. EB and LB are primary care researchers with a background in dietetics. Participants and recruitment English speaking GPs and GP registrars practising in Australia and their adult patients were eligible to participate. Study information was emailed to general practices affiliated with The University of Queensland, practices identified online and researchers’ personal contacts, and followed up with a phone call. The study was advertised in GP newsletters and social media. Participating GPs and their practice manger or principal provided written informed consent. Patients of participating GPs were invited through waiting room flyers to complete an online survey (Qualtrics XM, Supplement ) including demographic information and validated doctor-patient relationship scales (Health Care Provider Attachment Figure Survey (HCP-AF) ; Patient-Doctor Depth of Relationship (PDDR) scale). The HCP-AF assesses whether a health care provider may serve an attachment function for a patient, and comprises 5 ‘yes/no’ questions (1 point for each ‘yes’ answer’); its median score in an online sample has been reported as 5. Study participants who scored 4 or above on the HCP-AF (indicating a possible attachment relationship with their doctor) completed the PDDR. The PDDR is a validated 8-item scale that was developed following a review of qualitative literature reporting patients’ perspectives on doctor-patient relationships, and measures patient-doctor depth of relationship. Items are scored from 0 to 4 and, if all questions are completed, item scores are summed to give a total score ranging from 0 (low) to 32 (high). The median score in a clinical sample has been reported as 26, and deep relationships defined by scores of 31 or 32. . Patients with HCP-AF scores of 4 or above and high PDDR scores were purposively selected for diversity of age, gender, relationship duration, and presence/absence of a chronic health condition. Selected patients and their GPs were invited to participate in a semi-structured interview. Semi-structured interviews Interviews were conducted via telephone or Zoom video-conference. HT conducted GP interviews and EB conducted patient interviews, both to align researcher and participant backgrounds to facilitate information gathering, and to eliminate risk of unintentionally compromising confidentiality with either member of the GP-patient pair during interviews. Interviews explored participant experiences of GP-patient relationships (Supplement ) and were recorded and transcribed using a professional transcription service. Post-interview surveys Interview participants completed an online (Qualtrics XM, Supplement ) post-interview survey assessing their attachment style , demographics (for GPs) and perceived person-centredness of the GP practice (for patients). . Attachment style was assessed using an adapted version of the modified and brief Experiences in Close Relationships Scale (ECR-M16). The ECR-M16 is validated for use in medical settings and includes two subscales (attachment anxiety, attachment avoidance). Each subscale comprises eight items, rated from 1 (low) to 7 (high), which are averaged to give an overall score for attachment anxiety and avoidance. A normative range was not reported in Lo’s original ECR-M16 validation study ; the authors therefore searched Scopus on 9 November 2023 for all citations of Lo’s study and identified those reporting English language ECR-M16 results in primary care or population samples. Average scores for anxiety and avoidance ranged from 2.6 (SD 1.0) to 3.0 (SD 1.3) and 2.2 (SD 1.0) to 3.1 (SD not reported) respectively. Therefore, GP and patient participants who scored less than 2.6 on the anxiety scale, or 2.2 on the avoidance scale, were considered ‘low’ anxiety or avoidance respectively, and those who scored above 3.0 on the anxiety scale, or 3.1 on the avoidance scale, were considered ‘high’ anxiety or avoidance respectively. Patients’ perceived person-centredness of their GP practice was measured using the validated Person-Centred Primary Care Measure (PCPCM). This comprises 11 items, rated from 1 (low) to 4 (high), whose scores are averaged to give the final PCPCM score. The average PCPCM score in a clinical primary care sample was 3.5. Therefore, scores below 3.5 are considered ‘low’ and scores above 3.5 are considered ‘high’ in this study. Data analysis Interview transcripts were analysed using inductive thematic analysis with NVivo Pro software, looking for themes describing the nature and cultivation of deep GP-patient relationships. Six transcripts were coded by multiple authors, with consensus reached by discussion. HT coded remaining transcripts and all authors determined themes by discussion, informed by previous frameworks regarding healing relationships and attachment theory. This involved iterative and reflexive processes, including rereading transcripts and several meetings to discuss themes as they emerged. HT also compared themes between high vs. low attachment anxiety and avoidance groups (assisted by NVivo Pro matrix coding queries), looking for any obvious inter-group differences; and coded transcripts deductively for characteristics of attachment relationships (including safe haven, secure base, availability, stronger/wiser, strong emotions, particularity, proximity seeking, and mental representation). .
Australian General Practice. General practice forms the foundation of Australia’s health system and operates on a fee-for-service model. Patients are not required to register with GP practices. The patient fee is subsidised by the government-funded Medicare scheme, and either accepted as full payment (bulk-billing), or supplemented by a patient co-payment (mixed or private billing). .
HT, JL, ES and NS are GPs who combine clinical and academic practice. MB is a palliative care physician and ethicist with a background in qualitative methodologies. EB and LB are primary care researchers with a background in dietetics.
English speaking GPs and GP registrars practising in Australia and their adult patients were eligible to participate. Study information was emailed to general practices affiliated with The University of Queensland, practices identified online and researchers’ personal contacts, and followed up with a phone call. The study was advertised in GP newsletters and social media. Participating GPs and their practice manger or principal provided written informed consent. Patients of participating GPs were invited through waiting room flyers to complete an online survey (Qualtrics XM, Supplement ) including demographic information and validated doctor-patient relationship scales (Health Care Provider Attachment Figure Survey (HCP-AF) ; Patient-Doctor Depth of Relationship (PDDR) scale). The HCP-AF assesses whether a health care provider may serve an attachment function for a patient, and comprises 5 ‘yes/no’ questions (1 point for each ‘yes’ answer’); its median score in an online sample has been reported as 5. Study participants who scored 4 or above on the HCP-AF (indicating a possible attachment relationship with their doctor) completed the PDDR. The PDDR is a validated 8-item scale that was developed following a review of qualitative literature reporting patients’ perspectives on doctor-patient relationships, and measures patient-doctor depth of relationship. Items are scored from 0 to 4 and, if all questions are completed, item scores are summed to give a total score ranging from 0 (low) to 32 (high). The median score in a clinical sample has been reported as 26, and deep relationships defined by scores of 31 or 32. . Patients with HCP-AF scores of 4 or above and high PDDR scores were purposively selected for diversity of age, gender, relationship duration, and presence/absence of a chronic health condition. Selected patients and their GPs were invited to participate in a semi-structured interview.
Interviews were conducted via telephone or Zoom video-conference. HT conducted GP interviews and EB conducted patient interviews, both to align researcher and participant backgrounds to facilitate information gathering, and to eliminate risk of unintentionally compromising confidentiality with either member of the GP-patient pair during interviews. Interviews explored participant experiences of GP-patient relationships (Supplement ) and were recorded and transcribed using a professional transcription service.
Interview participants completed an online (Qualtrics XM, Supplement ) post-interview survey assessing their attachment style , demographics (for GPs) and perceived person-centredness of the GP practice (for patients). . Attachment style was assessed using an adapted version of the modified and brief Experiences in Close Relationships Scale (ECR-M16). The ECR-M16 is validated for use in medical settings and includes two subscales (attachment anxiety, attachment avoidance). Each subscale comprises eight items, rated from 1 (low) to 7 (high), which are averaged to give an overall score for attachment anxiety and avoidance. A normative range was not reported in Lo’s original ECR-M16 validation study ; the authors therefore searched Scopus on 9 November 2023 for all citations of Lo’s study and identified those reporting English language ECR-M16 results in primary care or population samples. Average scores for anxiety and avoidance ranged from 2.6 (SD 1.0) to 3.0 (SD 1.3) and 2.2 (SD 1.0) to 3.1 (SD not reported) respectively. Therefore, GP and patient participants who scored less than 2.6 on the anxiety scale, or 2.2 on the avoidance scale, were considered ‘low’ anxiety or avoidance respectively, and those who scored above 3.0 on the anxiety scale, or 3.1 on the avoidance scale, were considered ‘high’ anxiety or avoidance respectively. Patients’ perceived person-centredness of their GP practice was measured using the validated Person-Centred Primary Care Measure (PCPCM). This comprises 11 items, rated from 1 (low) to 4 (high), whose scores are averaged to give the final PCPCM score. The average PCPCM score in a clinical primary care sample was 3.5. Therefore, scores below 3.5 are considered ‘low’ and scores above 3.5 are considered ‘high’ in this study.
Interview transcripts were analysed using inductive thematic analysis with NVivo Pro software, looking for themes describing the nature and cultivation of deep GP-patient relationships. Six transcripts were coded by multiple authors, with consensus reached by discussion. HT coded remaining transcripts and all authors determined themes by discussion, informed by previous frameworks regarding healing relationships and attachment theory. This involved iterative and reflexive processes, including rereading transcripts and several meetings to discuss themes as they emerged. HT also compared themes between high vs. low attachment anxiety and avoidance groups (assisted by NVivo Pro matrix coding queries), looking for any obvious inter-group differences; and coded transcripts deductively for characteristics of attachment relationships (including safe haven, secure base, availability, stronger/wiser, strong emotions, particularity, proximity seeking, and mental representation). .
Participant characteristics Ninety-five initial patient survey responses were received and seventy-one of these included complete PDDR scores (Fig. ). PDDR scores ranged from 16 to 32 (mean 27.6, SD 4.5). Thirteen patients with high PDDR scores (30–32), and their five GPs (two to three patients per GP), were selected for interview (Table ). Female patients were over-represented in this sample (69%), this likely in part reflects more GP visits among Australian females (57% of total GP attendances in 2021-22) , and in part difficulty recruiting males to participate in the study. The sample included two males with a PDDR score of 30 (below the cut-off of 31 for deep relationships in Ridd’s study) to obtain demographic diversity. Post-interview survey results GP interview participants’ average attachment anxiety score was low (2.25), and average attachment avoidance score was within the range of published means (3.1). Three GPs were classified as low anxiety, one as high anxiety, one as low avoidance, and three as high avoidance. Patient interview participants’ average attachment anxiety score was high (3.4) and average attachment avoidance score was within the range of published means (2.75). Three patients were classified as low anxiety, eight as high anxiety, four as low avoidance, and five as high avoidance. No relationship was evident between the attachment orientation of GPs and those of their patients. Eleven patient interview participants scored their GP practice highly on the PCPCM. The remaining two participants gave their GP practice a low score; these two participants attended the same practice. Interview themes Four themes describing the nature and cultivation of deep GP-patient relationships were identified: the ‘professional’; the ‘other element’ of human connection; trust; and ‘above and beyond’ (Table ; Fig. ). Where relationship between attachment styles and thematic content were evident, these are noted under the relevant themes below. Theme one: ‘professional’ GPs and patients described GP-patient relationships as ‘ professional’ (GP1-2, GP5, P11-13). This term denoted clinical knowledge and rigour, and upholding standards of good practice and presentation. GPs also discussed ‘putting the patient first’. Collaborative clinical rigour Patients reported that GPs’ clinical competence was a key priority. They appreciated clinical knowledge and a thorough approach. One patient commented ‘…the way [GP] looks at it is much more of a holistic…picture than I have seen from some GPs in the past who…are happy to…prescribe…tablets and send you on your way’ (P6). However, GPs commented that not all patients sought this thorough approach: ‘I’m very thorough, or long winded, depends on how you feel…not every single patient will like my style…’ (GP1). Patients commented that their GP was ‘ knowledgeable’ (P4, P5). Both GPs and patients were aware of a knowledge differential: ‘I have a medical degree and medical knowledge, and most of my patients do not’ (GP3); ‘I’m not qualified to have a difference of opinion with [GP name]…I’m afraid I put my complete trust in him’ (P9). However, patients were often also aware of GPs’ fallibility, and most played an active role in their own healthcare decisions. Several GPs deliberately facilitated this: ‘…you’ve both got a problem to solve, so you work at the problem together and find a solution’ (GP5). They sought to minimise the doctor-patient power imbalance: ‘I really try to engage with [patients]…as equals…it’s important for patients to feel like they’ve got some choice about…the treatments I suggest…I just try to be really open with patients and…not have a power differential…making the patient feel like they’re on a team with you’ (GP4). They also described actively listening to patients’ agendas and ideas; communicating information honestly and accessibly; providing patients with choice; empowering them to ‘take control of their health’ (GP3); and when necessary, accepting patients’ prerogative not to accept their advice. Upholding standards Patient and GP participants expected GPs to adhere to standards of good medical practice. Some GPs contrasted this with consumerist relationships, in which patients were entitled to their preferred treatment due to paying for GP services: ‘… [some patients] think of you like a tradesman…Put on a new roof or do that…they think it’s that sort of relationship. No, a professional relationship isn’t like that’ (GP5). For example, GPs declined requests for inappropriate treatment (e.g., unnecessary antibiotics (GP2, P1)): ‘I don’t have much control of what people want…but I will try to…preserve my traditional professional way’ (GP1). Some patients valued this: ‘…I’ve raised my eyebrows after attending [a different GP]…once I went and I…had a virus…and he gave me antibiotics…he said, “some people like taking something”…I just looked at him, I grabbed the script and I…ripped it up in front of him and…said, “That’s not what you do”’ (P1). Additionally, some GPs and patients identified presentation and the physical practice environment as important: ‘I always wear long sleeves shirt, tie, trousers…it just gives you a bit of respect. If you have neat and tidy rooms…[patients] say, “Oh, we’re seeing the doctor”’ (GP5). Patients commented on their GP’s, ‘beautiful medical [clinic]…you could eat off [the] lavatory that’s how clean it is…’ (P3). Patients first GPs expressed a sense of professional responsibility, sometimes at personal cost. In difficult patient interactions, one GP identified responsibility to ‘realise your professionalism and that you’re there to add value’ reflecting that, ‘…[it] could be to my detriment, but I always put the patient first’ (GP5). Some GPs reflected that conveying care sometimes required masking their feelings: ‘I try to avoid [appearing stiff, bored or unhappy] and seem like I’m happy to see them and…take on the challenge of helping them…even if I’m not feeling that way that day’ (GP4). GPs attached moral significance to ‘do[ing] a good job’ (GP1, GP5) and some described their role as more than a job: ‘…to me, general practice is…a vocation’ (GP5). All recounted situations when this was personally costly, entailing missed breaks, out-of-hours work, reduced income or internal distress at balancing competing demands. One reflected: ‘…being a Mum and a doctor is not an easy thing to do…I’ve still got to get to school pickups…swimming…cook and clean and walk the dog and…spend time with the children and do the homework…and then somewhere in there we’re meant to be finding time for self-care as well’ (GP2). Notably, however, GPs with low attachment anxiety seemed to accept that they would not be able to please all patients, and reflected that this was protective for their wellbeing: ‘…if I took an approach to the job where I was…stressing about [patients] or worrying too much about what they thought of me, I guess that might be harder.’ (GP4) . Patients recognised that a GP’s ‘job…is…not easy’ (P11). Some expressed tolerance of their GP’s humanity: ‘…everybody has their ups and downs…’ (P3). They reported responding graciously when their GP ran late: ‘…all doctors’…situations are different and they run over time and…there’s emergencies that come through, and I’m extremely flexible with that kind of thing…’ (P4). Some reported only seeking urgent on-the-day care when they believed it was truly necessary and accepting that they may need to wait for an appointment or see another doctor if their GP was on leave or unavailable. Both GPs and patients described experiences of relationship breakdown with previous GPs or patients. Dissatisfied patients reported having sought care elsewhere, and GPs offered to transfer patients’ care elsewhere when the ‘therapeutic relationship [had] broken down’ (GP5). One GP appreciated patients being, ‘willing to let me know when they’re dissatisfied with something that I’ve done or said, or a referral that I’ve made…that to me is a really…big value-add’ (GP2). Theme two: ‘That other element’: human connection The second theme we term ‘ that other element’ (P1), to capture non-medical aspects that patients valued as much as the ‘professional’ relationship. One patient commented, ‘medical knowledge and stuff like that…is very important…but also too is that other element’ (P1). ‘That other element’ involved human connection, characterised by genuine personal care and interpersonal knowing. Genuine personal care While GPs tended to emphasise the importance of being respected as a professional, patients valued a sense of genuine personal care. Participants identified GP actions that communicated this care (Table ). GPs expressed genuine respect for patients: ‘I have a tremendous amount of respect for [the patient]’ (GP2). They derived a sense of satisfaction, purpose and value from providing relationship-based care: ‘I really love to see things grow and develop. I love seeing…people doing well…it’s highly motivational and it’s so addictive…you learn so much every day…Where does the joy come from?…the joy comes from relationships in general practice …and doing a good job’ (GP5). GPs acknowledged that cultivating GP-patient relationship, ‘is a really big part of what our actual job is…the stuff that’s actually hard and…worth putting effort into’ (GP4). They believed this was protective: ‘…it makes avoiding burnout easier…feeling that you’re appreciated and feeling a sense of…continuity and purpose in your work…And…having regular patients that you have a…good trusting relationship with…’ (GP4). A sense of genuine care influenced whether patients continued the relationship with their GP: ‘I felt like, oh, wow, I really like this [GP]…I want to continue with [them] because…I feel like [they] actually care…about me’ (P4). Patients contrasted their GP’s care with primarily consumerist relationships and previous depersonalising and isolating experiences: ‘…[the GP] actually care[s] about you as a person, not just as a person bringing money into their clinic or…business to their doorstep…’ (P13); ‘…[GP name] talks to you like you’re a person you’re not just a…number…I’ve been to doctors where you’re just a number’ (P10); ‘…I felt really isolated, and I felt not listened to, and I felt like [the GP] was judging me…’ (P4); ‘…you walk out and you feel as though…what do I matter?’ (P1). These reports of feeling dismissed or judged by previous GPs tended to feature more prominently in interviews with patient participants with high attachment anxiety scores. While participants referenced a fit between GP and patient personalities, ‘shared interests’ (P11) and shared humour, GPs and most patients distinguished GP-patient relationships from friendships. One GP reflected: ‘I think you can have a very friendly relationship but…[GPs] who think they can be friends to their patients [are]…kidding themselves…You have a power imbalance…in the consulting room that is a different relationship…a very precious relationship that we need to foster and develop…’ (GP5). Patients described the relationship as ‘like a friendship [or ‘mate’] ’ (P4, P6, P10). They nonetheless retained some distinction: ‘…it’s always been professional, as friendly as it is. ’ (P11). Interpersonal knowing GP and patient participants described a mutual (though asymmetrical) interpersonal knowing, developed within the longitudinal, multigenerational, community general practice context. One GP reflected that their patient ‘saw me grow’ as a GP from early in their career (GP1), and patients reflected that their GPs’ approach could evolve over time: ‘…in the past [the GP] would just say what [they] thought I should do…but now if I…make a suggestion…[they] will say, “Well, yeah, that’s a good idea.” Or “No, I don’t think that’s a good idea”’ (P12). Patients and GPs also reported growing to know each other as people: a patient reflected: ‘…we’ve gone through a lot…I’ve got to know [them] better and [they’ve] got to know me better…I won’t go into [their] personal life, but [they] went through something very devastating…you get to know the person’ (P11). Consistent with this, GPs reported judicious self-disclosure on occasions to assist patients: ‘…in some instances when you have a patient who’s experiencing something [similar] to your own experiences…there’s an opportunity to say…when I was that age I…had trouble with something similar…I remember how tough it was…but [I] probably won’t open up about my deepest and darkest’ (GP2). GPs also often demonstrated considerable knowledge of their patients’ personalities, and family and social contexts, as well as their medical histories: ‘…there’s times I’ve gone in and seen [my GP], and [they saw] straight away that I’m not feeling 100%…’ (P8). GPs talked about personalising their care, adjusting according to perceived patient preference: ‘…patients…want different styles…some patients…say, “Doc, tell me what to do…” And others…want to know every detail…you have to be responsive to different people’s needs’ (GP5). Patients had a role to seek continuity with their GPs to support this ongoing relationship. Theme three: trust Trust was a strong theme which permeated both the ‘professional’ and the human dimensions of deep GP-patient relationships. Both patients and GPs emphasised the critical importance of trust. One GP stated, ‘…I think that the value that…we give patients as a general practitioner is…being…someone that they trust…’ (GP4). A patient reflected, ‘I trust [my GP] …I trust [them] 100%’ (P9). This was earned over time: ‘…I trust [my GP] implicitly…with my health…[they’ve] earnt that trust, it’s not something that I…throw out there…’ (P8). Trust was mutual; a GP reflected: ‘…when you know the patient, you’ve got some mutual trust and rapport…some of your planning around safety netting and things like that becomes a lot easier…you believe that they’re going to follow something up if you’ve stressed it…’ (GP4). Likewise, a patient felt, ‘… [my GP] completely trusts in me and my ability to…relay…information rather than making me feel like I’m a hypochondriac…’ (P4). Patient trust extended to the GP’s practice and other healthcare professionals whom they had recommended. Trust was closely linked to patients’ sense of safety and comfort: ‘I feel very safe in any questions I have to ask [my GP] …[I] feel safe and really able to be candid with [them]’ (P13). One patient reported that thinking about their GPs’ advice comforted them amidst mental health challenges: ‘…when things were getting bad, I’d just relax and think about what [GP name] said… ’ (P10). Another patient reported a sense of security, believing their GP would assist if required: ‘I live on my own…as you get older, you’re quite vulnerable and things go wrong…it’s very reassuring to have that backup of somebody who knows you and knows that you need help, and they will give you help’ (P11). GPs also provided patients with a secure reference point within a complex healthcare system: ‘… [GP name] has been my support the whole time…even when I was thrown from hospital to hospital because they did not know what to do with me.’ (P1) Several patients expressed concern about what they would do when their GP retired: ‘…the only fear I have at the moment is…how long is he going to continue practicing and if he leaves, who would I see? ’ (P12). Some patients linked trust with taking GPs’ advice: ‘If [my GP] tells me to do something, I do it because I know it’s for my own good…I sort of pay it back that way…’ (P10). However, one patient recounted that: ‘… [my GP] was asking me how I was going with the supplements I’m meant to be taking…I was like… “Ooh, sorry”…’ (P5). Theme four: ‘above and beyond’ Depth in the GP-patient relationship was not always desired by patients, and GPs did not always invest equally in cultivating deep GP-patient relationships. Prominent in times of trouble GPs and patients believed that deep GP-patient relationships were most valued in times of patient difficulty. One GP described this as ‘grand final day’: ‘On grand final day you have no control…You develop a relationship and when you say…“There’s a lump in your breast, we need to investigate that” they take it seriously, you know?’ (GP5) Patients appeared to value deep GP-patient relationships less when they required only intermittent care for minor ailments: ‘I guess I didn’t put too much value in it…not didn’t value it, but…it was just always…small things…I didn’t think…continuity of care was…an important thing…’ (P5). Conversely, they tended to seek deeper relationship during times of chronic illness, health crises, mental health struggles or personal difficulties: ‘If it’s anything really urgent or serious or personal, I will wait…for [GP name], probably, but…I’ve seen many other doctors in the practice and…I have no problem with that at all’ (P11). One patient who had ‘battled with suicide for the best part of…10 years…’ recounted, ‘…the fact that I’m still here today…I would attribute in part to [the GP] and the work [they] put in…’ (P8). Several patients described their GPs going ‘above and beyond’ (P1) expectations to assist them in a crisis. Examples included urgent care provision, active follow up, health advocacy and sending flowers; and a GP even mentioned offering assistance with dropping children to school. Selective investment GPs invested more in cultivating patient relationships when starting a new practice, or for new or complex patients who were likely to remain under their care, rather than with patients who were seeing multiple doctors. Time availability also affected GPs relational cultivation: ‘…the last six months have been incredibly busy, so I don’t think my relationship building has been awesome of late.’ (GP2). Patients were aware that GPs needed time to be able to offer personalised care, and that this often came at a cost: ‘…the fact that it’s not a bulk billing practice…does allow for…more…personalised service…it sounds shitty that you have to…pay for a good doctor…not [that]…the…other ones aren’t good doctors, they probably just aren’t afforded…the same amount of…time…’ (P5). Other practice staff also differed in their investment in deep relationships. Patients emphasised the importance of practice staff exhibiting the same genuine personal care, availability and professional approach as the GP, in contrast to a ‘production line’ (P1) approach. Interviews suggested variable cohesion between the GP and practice staff: one GP described regular team communication and a strong sense of cohesion; while another described providing patients with strategies to bypass reception staff to secure an appointment for urgent care.
Ninety-five initial patient survey responses were received and seventy-one of these included complete PDDR scores (Fig. ). PDDR scores ranged from 16 to 32 (mean 27.6, SD 4.5). Thirteen patients with high PDDR scores (30–32), and their five GPs (two to three patients per GP), were selected for interview (Table ). Female patients were over-represented in this sample (69%), this likely in part reflects more GP visits among Australian females (57% of total GP attendances in 2021-22) , and in part difficulty recruiting males to participate in the study. The sample included two males with a PDDR score of 30 (below the cut-off of 31 for deep relationships in Ridd’s study) to obtain demographic diversity.
GP interview participants’ average attachment anxiety score was low (2.25), and average attachment avoidance score was within the range of published means (3.1). Three GPs were classified as low anxiety, one as high anxiety, one as low avoidance, and three as high avoidance. Patient interview participants’ average attachment anxiety score was high (3.4) and average attachment avoidance score was within the range of published means (2.75). Three patients were classified as low anxiety, eight as high anxiety, four as low avoidance, and five as high avoidance. No relationship was evident between the attachment orientation of GPs and those of their patients. Eleven patient interview participants scored their GP practice highly on the PCPCM. The remaining two participants gave their GP practice a low score; these two participants attended the same practice.
Four themes describing the nature and cultivation of deep GP-patient relationships were identified: the ‘professional’; the ‘other element’ of human connection; trust; and ‘above and beyond’ (Table ; Fig. ). Where relationship between attachment styles and thematic content were evident, these are noted under the relevant themes below.
GPs and patients described GP-patient relationships as ‘ professional’ (GP1-2, GP5, P11-13). This term denoted clinical knowledge and rigour, and upholding standards of good practice and presentation. GPs also discussed ‘putting the patient first’.
Patients reported that GPs’ clinical competence was a key priority. They appreciated clinical knowledge and a thorough approach. One patient commented ‘…the way [GP] looks at it is much more of a holistic…picture than I have seen from some GPs in the past who…are happy to…prescribe…tablets and send you on your way’ (P6). However, GPs commented that not all patients sought this thorough approach: ‘I’m very thorough, or long winded, depends on how you feel…not every single patient will like my style…’ (GP1). Patients commented that their GP was ‘ knowledgeable’ (P4, P5). Both GPs and patients were aware of a knowledge differential: ‘I have a medical degree and medical knowledge, and most of my patients do not’ (GP3); ‘I’m not qualified to have a difference of opinion with [GP name]…I’m afraid I put my complete trust in him’ (P9). However, patients were often also aware of GPs’ fallibility, and most played an active role in their own healthcare decisions. Several GPs deliberately facilitated this: ‘…you’ve both got a problem to solve, so you work at the problem together and find a solution’ (GP5). They sought to minimise the doctor-patient power imbalance: ‘I really try to engage with [patients]…as equals…it’s important for patients to feel like they’ve got some choice about…the treatments I suggest…I just try to be really open with patients and…not have a power differential…making the patient feel like they’re on a team with you’ (GP4). They also described actively listening to patients’ agendas and ideas; communicating information honestly and accessibly; providing patients with choice; empowering them to ‘take control of their health’ (GP3); and when necessary, accepting patients’ prerogative not to accept their advice.
Patient and GP participants expected GPs to adhere to standards of good medical practice. Some GPs contrasted this with consumerist relationships, in which patients were entitled to their preferred treatment due to paying for GP services: ‘… [some patients] think of you like a tradesman…Put on a new roof or do that…they think it’s that sort of relationship. No, a professional relationship isn’t like that’ (GP5). For example, GPs declined requests for inappropriate treatment (e.g., unnecessary antibiotics (GP2, P1)): ‘I don’t have much control of what people want…but I will try to…preserve my traditional professional way’ (GP1). Some patients valued this: ‘…I’ve raised my eyebrows after attending [a different GP]…once I went and I…had a virus…and he gave me antibiotics…he said, “some people like taking something”…I just looked at him, I grabbed the script and I…ripped it up in front of him and…said, “That’s not what you do”’ (P1). Additionally, some GPs and patients identified presentation and the physical practice environment as important: ‘I always wear long sleeves shirt, tie, trousers…it just gives you a bit of respect. If you have neat and tidy rooms…[patients] say, “Oh, we’re seeing the doctor”’ (GP5). Patients commented on their GP’s, ‘beautiful medical [clinic]…you could eat off [the] lavatory that’s how clean it is…’ (P3).
GPs expressed a sense of professional responsibility, sometimes at personal cost. In difficult patient interactions, one GP identified responsibility to ‘realise your professionalism and that you’re there to add value’ reflecting that, ‘…[it] could be to my detriment, but I always put the patient first’ (GP5). Some GPs reflected that conveying care sometimes required masking their feelings: ‘I try to avoid [appearing stiff, bored or unhappy] and seem like I’m happy to see them and…take on the challenge of helping them…even if I’m not feeling that way that day’ (GP4). GPs attached moral significance to ‘do[ing] a good job’ (GP1, GP5) and some described their role as more than a job: ‘…to me, general practice is…a vocation’ (GP5). All recounted situations when this was personally costly, entailing missed breaks, out-of-hours work, reduced income or internal distress at balancing competing demands. One reflected: ‘…being a Mum and a doctor is not an easy thing to do…I’ve still got to get to school pickups…swimming…cook and clean and walk the dog and…spend time with the children and do the homework…and then somewhere in there we’re meant to be finding time for self-care as well’ (GP2). Notably, however, GPs with low attachment anxiety seemed to accept that they would not be able to please all patients, and reflected that this was protective for their wellbeing: ‘…if I took an approach to the job where I was…stressing about [patients] or worrying too much about what they thought of me, I guess that might be harder.’ (GP4) . Patients recognised that a GP’s ‘job…is…not easy’ (P11). Some expressed tolerance of their GP’s humanity: ‘…everybody has their ups and downs…’ (P3). They reported responding graciously when their GP ran late: ‘…all doctors’…situations are different and they run over time and…there’s emergencies that come through, and I’m extremely flexible with that kind of thing…’ (P4). Some reported only seeking urgent on-the-day care when they believed it was truly necessary and accepting that they may need to wait for an appointment or see another doctor if their GP was on leave or unavailable. Both GPs and patients described experiences of relationship breakdown with previous GPs or patients. Dissatisfied patients reported having sought care elsewhere, and GPs offered to transfer patients’ care elsewhere when the ‘therapeutic relationship [had] broken down’ (GP5). One GP appreciated patients being, ‘willing to let me know when they’re dissatisfied with something that I’ve done or said, or a referral that I’ve made…that to me is a really…big value-add’ (GP2).
The second theme we term ‘ that other element’ (P1), to capture non-medical aspects that patients valued as much as the ‘professional’ relationship. One patient commented, ‘medical knowledge and stuff like that…is very important…but also too is that other element’ (P1). ‘That other element’ involved human connection, characterised by genuine personal care and interpersonal knowing.
While GPs tended to emphasise the importance of being respected as a professional, patients valued a sense of genuine personal care. Participants identified GP actions that communicated this care (Table ). GPs expressed genuine respect for patients: ‘I have a tremendous amount of respect for [the patient]’ (GP2). They derived a sense of satisfaction, purpose and value from providing relationship-based care: ‘I really love to see things grow and develop. I love seeing…people doing well…it’s highly motivational and it’s so addictive…you learn so much every day…Where does the joy come from?…the joy comes from relationships in general practice …and doing a good job’ (GP5). GPs acknowledged that cultivating GP-patient relationship, ‘is a really big part of what our actual job is…the stuff that’s actually hard and…worth putting effort into’ (GP4). They believed this was protective: ‘…it makes avoiding burnout easier…feeling that you’re appreciated and feeling a sense of…continuity and purpose in your work…And…having regular patients that you have a…good trusting relationship with…’ (GP4). A sense of genuine care influenced whether patients continued the relationship with their GP: ‘I felt like, oh, wow, I really like this [GP]…I want to continue with [them] because…I feel like [they] actually care…about me’ (P4). Patients contrasted their GP’s care with primarily consumerist relationships and previous depersonalising and isolating experiences: ‘…[the GP] actually care[s] about you as a person, not just as a person bringing money into their clinic or…business to their doorstep…’ (P13); ‘…[GP name] talks to you like you’re a person you’re not just a…number…I’ve been to doctors where you’re just a number’ (P10); ‘…I felt really isolated, and I felt not listened to, and I felt like [the GP] was judging me…’ (P4); ‘…you walk out and you feel as though…what do I matter?’ (P1). These reports of feeling dismissed or judged by previous GPs tended to feature more prominently in interviews with patient participants with high attachment anxiety scores. While participants referenced a fit between GP and patient personalities, ‘shared interests’ (P11) and shared humour, GPs and most patients distinguished GP-patient relationships from friendships. One GP reflected: ‘I think you can have a very friendly relationship but…[GPs] who think they can be friends to their patients [are]…kidding themselves…You have a power imbalance…in the consulting room that is a different relationship…a very precious relationship that we need to foster and develop…’ (GP5). Patients described the relationship as ‘like a friendship [or ‘mate’] ’ (P4, P6, P10). They nonetheless retained some distinction: ‘…it’s always been professional, as friendly as it is. ’ (P11).
GP and patient participants described a mutual (though asymmetrical) interpersonal knowing, developed within the longitudinal, multigenerational, community general practice context. One GP reflected that their patient ‘saw me grow’ as a GP from early in their career (GP1), and patients reflected that their GPs’ approach could evolve over time: ‘…in the past [the GP] would just say what [they] thought I should do…but now if I…make a suggestion…[they] will say, “Well, yeah, that’s a good idea.” Or “No, I don’t think that’s a good idea”’ (P12). Patients and GPs also reported growing to know each other as people: a patient reflected: ‘…we’ve gone through a lot…I’ve got to know [them] better and [they’ve] got to know me better…I won’t go into [their] personal life, but [they] went through something very devastating…you get to know the person’ (P11). Consistent with this, GPs reported judicious self-disclosure on occasions to assist patients: ‘…in some instances when you have a patient who’s experiencing something [similar] to your own experiences…there’s an opportunity to say…when I was that age I…had trouble with something similar…I remember how tough it was…but [I] probably won’t open up about my deepest and darkest’ (GP2). GPs also often demonstrated considerable knowledge of their patients’ personalities, and family and social contexts, as well as their medical histories: ‘…there’s times I’ve gone in and seen [my GP], and [they saw] straight away that I’m not feeling 100%…’ (P8). GPs talked about personalising their care, adjusting according to perceived patient preference: ‘…patients…want different styles…some patients…say, “Doc, tell me what to do…” And others…want to know every detail…you have to be responsive to different people’s needs’ (GP5). Patients had a role to seek continuity with their GPs to support this ongoing relationship.
Trust was a strong theme which permeated both the ‘professional’ and the human dimensions of deep GP-patient relationships. Both patients and GPs emphasised the critical importance of trust. One GP stated, ‘…I think that the value that…we give patients as a general practitioner is…being…someone that they trust…’ (GP4). A patient reflected, ‘I trust [my GP] …I trust [them] 100%’ (P9). This was earned over time: ‘…I trust [my GP] implicitly…with my health…[they’ve] earnt that trust, it’s not something that I…throw out there…’ (P8). Trust was mutual; a GP reflected: ‘…when you know the patient, you’ve got some mutual trust and rapport…some of your planning around safety netting and things like that becomes a lot easier…you believe that they’re going to follow something up if you’ve stressed it…’ (GP4). Likewise, a patient felt, ‘… [my GP] completely trusts in me and my ability to…relay…information rather than making me feel like I’m a hypochondriac…’ (P4). Patient trust extended to the GP’s practice and other healthcare professionals whom they had recommended. Trust was closely linked to patients’ sense of safety and comfort: ‘I feel very safe in any questions I have to ask [my GP] …[I] feel safe and really able to be candid with [them]’ (P13). One patient reported that thinking about their GPs’ advice comforted them amidst mental health challenges: ‘…when things were getting bad, I’d just relax and think about what [GP name] said… ’ (P10). Another patient reported a sense of security, believing their GP would assist if required: ‘I live on my own…as you get older, you’re quite vulnerable and things go wrong…it’s very reassuring to have that backup of somebody who knows you and knows that you need help, and they will give you help’ (P11). GPs also provided patients with a secure reference point within a complex healthcare system: ‘… [GP name] has been my support the whole time…even when I was thrown from hospital to hospital because they did not know what to do with me.’ (P1) Several patients expressed concern about what they would do when their GP retired: ‘…the only fear I have at the moment is…how long is he going to continue practicing and if he leaves, who would I see? ’ (P12). Some patients linked trust with taking GPs’ advice: ‘If [my GP] tells me to do something, I do it because I know it’s for my own good…I sort of pay it back that way…’ (P10). However, one patient recounted that: ‘… [my GP] was asking me how I was going with the supplements I’m meant to be taking…I was like… “Ooh, sorry”…’ (P5).
Depth in the GP-patient relationship was not always desired by patients, and GPs did not always invest equally in cultivating deep GP-patient relationships.
GPs and patients believed that deep GP-patient relationships were most valued in times of patient difficulty. One GP described this as ‘grand final day’: ‘On grand final day you have no control…You develop a relationship and when you say…“There’s a lump in your breast, we need to investigate that” they take it seriously, you know?’ (GP5) Patients appeared to value deep GP-patient relationships less when they required only intermittent care for minor ailments: ‘I guess I didn’t put too much value in it…not didn’t value it, but…it was just always…small things…I didn’t think…continuity of care was…an important thing…’ (P5). Conversely, they tended to seek deeper relationship during times of chronic illness, health crises, mental health struggles or personal difficulties: ‘If it’s anything really urgent or serious or personal, I will wait…for [GP name], probably, but…I’ve seen many other doctors in the practice and…I have no problem with that at all’ (P11). One patient who had ‘battled with suicide for the best part of…10 years…’ recounted, ‘…the fact that I’m still here today…I would attribute in part to [the GP] and the work [they] put in…’ (P8). Several patients described their GPs going ‘above and beyond’ (P1) expectations to assist them in a crisis. Examples included urgent care provision, active follow up, health advocacy and sending flowers; and a GP even mentioned offering assistance with dropping children to school. Selective investment GPs invested more in cultivating patient relationships when starting a new practice, or for new or complex patients who were likely to remain under their care, rather than with patients who were seeing multiple doctors. Time availability also affected GPs relational cultivation: ‘…the last six months have been incredibly busy, so I don’t think my relationship building has been awesome of late.’ (GP2). Patients were aware that GPs needed time to be able to offer personalised care, and that this often came at a cost: ‘…the fact that it’s not a bulk billing practice…does allow for…more…personalised service…it sounds shitty that you have to…pay for a good doctor…not [that]…the…other ones aren’t good doctors, they probably just aren’t afforded…the same amount of…time…’ (P5). Other practice staff also differed in their investment in deep relationships. Patients emphasised the importance of practice staff exhibiting the same genuine personal care, availability and professional approach as the GP, in contrast to a ‘production line’ (P1) approach. Interviews suggested variable cohesion between the GP and practice staff: one GP described regular team communication and a strong sense of cohesion; while another described providing patients with strategies to bypass reception staff to secure an appointment for urgent care.
GPs invested more in cultivating patient relationships when starting a new practice, or for new or complex patients who were likely to remain under their care, rather than with patients who were seeing multiple doctors. Time availability also affected GPs relational cultivation: ‘…the last six months have been incredibly busy, so I don’t think my relationship building has been awesome of late.’ (GP2). Patients were aware that GPs needed time to be able to offer personalised care, and that this often came at a cost: ‘…the fact that it’s not a bulk billing practice…does allow for…more…personalised service…it sounds shitty that you have to…pay for a good doctor…not [that]…the…other ones aren’t good doctors, they probably just aren’t afforded…the same amount of…time…’ (P5). Other practice staff also differed in their investment in deep relationships. Patients emphasised the importance of practice staff exhibiting the same genuine personal care, availability and professional approach as the GP, in contrast to a ‘production line’ (P1) approach. Interviews suggested variable cohesion between the GP and practice staff: one GP described regular team communication and a strong sense of cohesion; while another described providing patients with strategies to bypass reception staff to secure an appointment for urgent care.
Findings suggest that deep GP-patient relationships are characterised by intertwining ‘professional’ aspects (collaborative clinical rigour, upholding standards, ‘patients first’) with ‘that other element’ of human connection, comprising genuine personal care and interpersonal knowing. Trust seems to permeate these relationships, which tend to come to the fore in times of difficulty. GPs typically invest time and effort early in these relationships, although they grow over time as patients and GPs get to know each other as people. Findings are consistent with previous research that trusting GP-patient relationships facilitate continuity of care, enable the GP to provide effective motivation and reassurance and improve treatment concordance. They align with previous studies, using different recruitment strategies, that also identified personal and professional aspects of the GP-patient relationship; the importance of interpersonal knowledge, mutual trust and balancing power; and the sense of safety existing in such relationships. . Findings support previous proposals that GP-patient relationships serve attachment functions for patients as a ‘safe haven’ and ‘secure base’ when they are vulnerable. Care that patients perceive to lack genuineness can engender a sense of devaluation and isolation, while deliberately investing in GP-patient relationships can foster a sense of safety and security amidst some of the most difficult times of patients’ lives. However, we found that other attachment characteristics such as particularity, separation protest, stronger/wiser representations, strong feelings and mental representation were present in a more nuanced and less intense manner than traditional attachment relationships . Specifically, while patients displayed a degree of particularity (they preferred to see their usual GP, especially regarding sensitive issues), they were often content to see another GP if theirs was unavailable. Patients expressed appreciation for their GP, and some expressed strong negative feelings towards previous GPs who they experienced as dismissive or judgemental, however this strength of feeling did not approach that of traditional attachment relationships. Patients did not express a sense of marked separation protest from their GP, although several expressed concern about what they would do when their GP retired. The knowledge differential often inherent in the GP-patient dynamic led to a nuanced ‘stronger/wiser’ representation in some patients’ minds, however patients were often also aware of GPs’ fallibility. One patient reported feeling comforted by remembering their GPs’ advice amidst mental health challenges; this was the only instance noted that may approach mental representation. Our findings of the importance of ‘human connection’ are also consistent to some extent with Gelso’s concept of ‘real relationship’ . Gelso proposed the concept of ‘real relationship’ in the context of therapist-client relationships, positing that such relationships included a ‘personal’ bond (in contrast to only a ‘working’ bond), which was characterised by genuineness and realistic (non-transference) perceptions of the other. However, this concept seems to overlook the essential importance of the ‘professional’ aspect of the deep GP-patient relationship. Thus, the psychological relational models of attachment and of ‘real relationship’ appear to offer useful insights into deep GP-patient relationships, though with limitations. The tension we found between the ‘professional’ and the ‘human connection’ was particularly striking and interesting. Human connection fostered a context that embraced GPs’ and patients’ shared humanity. GPs found providing relationship-based care both rewarding and costly, particularly when it involved going ‘above and beyond’ expectations to help patients in crises. Boundaries appear to be vital in protecting patient and GP wellbeing. Boundaries intended to protect patients from abuses of power are widely accepted. However, those intended to maintain GP wellbeing or prevent unhelpful relational dependency are less well defined. For actions intended to benefit patients, including those perceived to be ‘above and beyond’, the limits of professional responsibility and boundaries may be uncertain and contextual. GP participants’ individual approaches varied, consistent with the view that boundaries are not ‘black and white’ but rather fluid, and dependent upon context and individual personality and circumstances. Value-informed ‘integrative wisdom’, a defining feature of generalist care, is likely to be helpful in addressing this space through its tolerance of uncertainty, awareness of complexity and ability to integrate dynamic and diverse forms of knowledge to inform practice. . We found that practice staff, patients, and GPs all have a role in cultivating deep relationships. Research increasingly points to patients having an active role in general practice consultations, though perhaps due to the role of GP as care provider, patients’ relational role is rarely discussed. These findings suggest that patients cultivate deep GP-patient relationship by respecting their GP’s needs and boundaries, seeking continuity where feasible and investing in their own health. Moreover, findings suggest an important role for practices in relational cultivation. This aligns with previous research showing that communication with patients was influenced as much by practice policies and reception staff as by the GP themselves. Our findings suggest that cohesion between practice and GP was variable. Fostering this cohesion is likely to become increasingly important with current moves toward team-based general practice primary care. Alongside this, the importance of long term continuity in cultivating deep GP-patient relationships suggests strategies that identify patients’ primary GP (such as voluntary patient enrolment ) may support these vital relationships. Our findings, together with previous research, suggest that deep GP-patient relationships are fostered by and promote interpersonal continuity, which is associated with improved health outcomes. The trust underpinning such relationships assists patients to share sensitive information, supports treatment concordance, and fosters a sense of safety and security, particularly for patients experiencing vulnerability. In the short term, such relationships are likely to come at a cost of time, though it is possible that higher quality care may save time in the longer term. It is important to note, however, that GPs are unlikely to have capacity to constantly maintain deep relationships with all their patients, and not all patients desire deep relationship; rather, this seems more salient with increasing patient vulnerability. Ethically, in the interest of justice for all patients, it would be important that GPs take care not to give disproportionate attention to patients with whom they have a deep relationship. Deep relationships may also risk co-dependency, though again, our data did not suggest this as a concern; the professional aspect of the relationship was perhaps protective in this respect. Additionally, while it is plausible that the emotional labour of cultivating deep relationships could contribute to burnout in some GPs, several of our GP participants commented that they find these relationships protective against burnout. Strengths of this study include a patient-centred approach using validated tools to identify deep relationships from the patient perspective. It included GP-patient pairs, enabling comparison of GP and patient views, and provided insight into patient and practice roles in cultivating the GP-patient relationship. Participants were demographically diverse. Our research intentionally focused on deep GP-patient relationships, which are not representative of the whole population. Our cohort were English-speaking and came from urban practices in primarily high socioeconomic areas, with an over-representation of female patient participants. Future work should explore GP-patient relationships in more diverse settings and include the views of other members of the GP practice team. Inclusion of a greater diversity of sociodemographic settings would be particularly relevant, given participants’ comments that private billing enabled GPs to invest more time with patients, and some patients’ reflections on previous negative experiences at bulk-billing practices. These comments may suggest an economic disparity in access to relationship-based general practice care in Australia. Alternately, bulk-billing practices may have other strategies for relational development. Additionally, while the small sample was appropriate to allow depth of qualitative exploration, it limited the ability to detect possible differences between participants of different attachment orientations. These could be explored with future quantitative studies.
Deep GP-patient relationships comprise intertwined ‘professional’ and ‘human’ interpersonal aspects and are permeated by trust. Such relationships tend to come to the fore in difficulty, over time. They may stretch GP boundaries and capacity for self-care, but also provide joy and vocational satisfaction. Findings offer a framework to conceptualise deep GP-patient relationships and highlight the importance of creating and preserving contexts that support such relationships, particularly for patients experiencing difficulty. Deep GP-patient relationships require GPs to balance professional responsibility, interpersonal care, boundaries and wellbeing. Whole of practice cohesion is likely to be increasingly important in supporting deep GP-patient relationships as practices move toward team-based primary care. Findings also emphasise the importance of adequate general practice funding, enabling time to provide this vital relationship-based care.
Below is the link to the electronic supplementary material. Supplementary Material 1: Initial patient survey Supplementary Material 2: Semi-structured interview schedules Supplementary Material 3: Post-interview surveys
|
Person-centred medicine in the care home setting: development of a complex intervention | cae2b9a4-3723-4eba-9e1a-062eb7f405a1 | 11131350 | Patient-Centered Care[mh] | Care home residents are often exposed to complex medication regimens and excessive polypharmacy due to a high prevalence of multimorbidity and symptoms . Polypharmacy increases the risk of potentially inappropriate medication which occurs when the risk of harms exceeds the expected benefits of treatment . Potentially inappropriate medication is observed in almost half of care home residents and may lead to reduced quality of life, hospital admission, and premature death due to adverse drug events . A person-centred approach to medication-related decisions is recommended as a guiding principle in the care of older patients with multimorbidity . Person-centred care is defined as care that is guided by an individual’s preferences, needs, and values . In the care home population, medication-related decisions are highly preference-sensitive, as the existing evidence base is typically based on younger populations with fewer health conditions, which may not apply to older patients [ , , ]. Incorporating individual preferences and priorities into medical decision-making can improve treatment adherence, patient satisfaction, and perceived well-being and quality of life [ – ]. However, research indicates that involvement of residents and their relatives in medicines remains limited in clinical practice [ – ]. Important barriers to resident and relative involvement in medication-related decisions are the awareness and attitudes of health care professionals (HCPs) . HCPs often perceive care home residents as not being capable of or not wanting to be involved in their care . Similarly, HCPs sometimes believe that the relatives do not want to be involved, or they perceive relative involvement to be time-consuming, not helpful, and sometimes problematic . Research indicates that residents and their relatives generally want to be involved [ – ], but many residents are reticent, because they think that their HCPs are not receptive to their perspectives, or they do not know how to be involved . Recent reviews have explored tools for practicable elicitation of patient preferences in the context of geriatric polypharmacy . However, so far, no ideal method has been identified to support patients, relatives, and HCPs in this task, and the need for clinically applicable strategies has been stressed . Recently, a new patient involvement tool, the PREparation of Patients for Active Involvement in medication Review (PREPAIR), was developed with the aim to encourage the involvement of patients with polypharmacy in medicines optimisation in general practice . PREPAIR is a simple, five-item questionnaire with a three-point Likert scale response-option. The five items deal with 1) adverse drug reactions, 2) excess medication, 3) unnecessary medication, 4) medication satisfaction, and 5) an open-ended item on medication-related topics for discussion. PREPAIR is completed by the patient as preparation before consulting the GP at the yearly chronic care consultation. During the consultation, PREPAIR supports a person-centred dialogue about the patient´s medication. PREPAIR was demonstrated as a feasible instrument to facilitate patient involvement in this setting. Based on these findings, we hypothesised that PREPAIR could be useful in the care home setting to facilitate involvement of residents and their relatives in medication-related decisions and thereby support person-centred medicine. However, although PREPAIR itself may be simple, making it useful and integrated into the care home setting requires changing the reasoning and actions of multiple stakeholders such as residents, relatives, and HCPs. Furthermore, it requires interprofessional coordination and communication. Changing professional behaviour is challenging, and interventions aiming to do so are often complex . Interventions can be considered complex if they contain multiple interacting components; they require new behaviours of those delivering and receiving the intervention; and/or there is a need to tailor the intervention to different contexts and settings . The Medical Research Council (MRC) guidance for developing and evaluating complex interventions divides the research process into four phases: development or identification of the intervention, feasibility, evaluation, and implementation . Complex interventions require thorough development to enhance the chance of being effective and widely adopted into routine care. Development refers to the whole process of designing and planning an intervention, and a key source can be an existing intervention that has the potential of being adapted to a new population or setting . Aim The present study is the first part of a larger project with the overall aim to develop (MRC phase 1) and feasibility test (MRC phase 2) a complex intervention to support person-centred medicine in the care home setting through resident and relative involvement and interprofessional communication. In this paper, we report on the MRC phase 1, the development phase. The specific research aims in the development phase were to: develop a complex intervention and implementation strategy in a co-producing process. test and refine the complex intervention as part of the development process.
The present study is the first part of a larger project with the overall aim to develop (MRC phase 1) and feasibility test (MRC phase 2) a complex intervention to support person-centred medicine in the care home setting through resident and relative involvement and interprofessional communication. In this paper, we report on the MRC phase 1, the development phase. The specific research aims in the development phase were to: develop a complex intervention and implementation strategy in a co-producing process. test and refine the complex intervention as part of the development process.
Study design The overall approach in this study was based on the MRC guidance . In each MRC phase, a set of core elements should be considered. In this study, several activities were undertaken, through which the core elements were considered (Fig. ). The research design involved elements from coproduction and rapid cycle research. In this approach, researchers and end-users collaborate in an iterative process based on interdisciplinary collaboration and rapid qualitative analysis with an ongoing exchange between research and practice. The study was conducted in close collaboration with the Municipality of Aarhus, Denmark. Furthermore, it was endorsed by the Danish Society for Patient Safety and the municipal Organization of General Practitioners, Central Denmark Region . The development phase took place from September 2021 to March 2022. The present paper conforms to the GUIDance for the rEporting of intervention Development (GUIDED) . Context Understanding the context within which the intervention needs to be integrated is essential to explore the mechanisms enabling the implementation of an intervention in usual practices . According to Danish law, elderly, frail citizens are suitable for care home residency, if they need all-day care. The allocation of residency is made by the municipalities . The care for the residents is provided mainly by a team of nurses, social and health care assistants (SoHAs), and social and health care helpers. In 2017, the designated GP model was introduced in Danish care homes . In this model, one or several GPs with private clinics are assigned to serve as designated GP in a care home. Care home residents can keep their regular GP when moving into a care home, but new residents are encouraged to register with the designated GP. The model has been implemented in all care homes in the Municipality of Aarhus; however, the role of the designated GP is still in development. In Denmark, GPs operate as independent contractors that provide primary medical care under a collective agreement with the Danish Regions . The GPs are responsible for most prescription medications and chronic care management . Primary care services are mainly tax-financed and free of charge for patients. Target population A priori, newly arrived residents and their relatives were considered to be a relevant target group for the intervention. When elderly citizens move to a care home facility in Denmark, the majority chose to be assigned to the designated GP . In connection with this shift, it is common that the residents use of medicine is reassessed in terms of potential risks and beneficial effects. However, as the designed GP typically has no prior relation to a new resident, the needs, preferences, and values of the resident concerning their medication will need to be explored to ensure person-centred medicine. Thus, newly arrived residents at the participating care homes were considered eligible if they chose to register with the designated GP. The single exclusion criteria for the residents was severe cognitive impairment as judged by the HCPs. Care home residents are a heterogeneous group with different health conditions, and many suffer from cognitive impairment. A way of supporting cognitively impaired residents in being involved is through the involvement of relatives who know the resident and can speak on their behalf . Therefore, relatives were included in the target group as well. Developing programme theory A programme theory depicts how an intervention is expected to lead to its effects and under what conditions . Our initial programme theory was inspired by the original PREPAIR study and the Interprofessional Shared Decision Making (IP-SDM) model . The IP-SDM model extends shared decision making to include family members and other caregivers as well as the whole team of HCPs in a person-centred process. Based on the IP-SDM model, relatives in this study were defined as family, surrogate or significant others. The IP-SDM model was inspirational in terms of outlining the key actors and their respective roles in supporting person-centred medicine in the complex care home setting. Figure presents the initial linear logic model of the intervention, the proposed mechanisms of actions, and the expected outcomes. The preliminary intervention model included two key components: a) PREPAIR and b) an interprofessional communication component. These components were to be delivered in a three-stage workflow (before, during, and after the GP consultation). An additional focus of our programme theory was how to support intervention implementation in the care home setting. Guided by the Consolidated Framework for Implementation Research (CFIR) and literature , we developed an initial idea bank of applicable implementation strategies (i.e., methods or techniques used to improve adoption, implementation, sustainment, and scale-up of interventions (Table ). The CFIR is a comprehensive implementation determinant framework designed to guide a systematic assessment of potential barriers and facilitators that influence implementation outcomes. For the idea bank, we focused on the most relevant construct in the domains Inner Setting, Characteristics of Individuals, and Process. The domains Intervention Characteristics and Outer Setting were considered through stakeholder engaging activities described below. Engaging stakeholders Several activities were performed to engage stakeholders and ensure project progress throughout the development phase. Table provides a description of the stakeholders and their respective roles. Initially, a steering group with representatives from the managerial level of the involved stakeholders was established to support and maintain engagement and to provide inputs and approval at key time points (e.g., approval of the programme theory and the final intervention). Additionally, an operational coordinator group was established, including municipal managerial and quality improvement staff and the project leaders, the authors KH and LDC. Ad hoc meetings were held in the coordinator group to resolve practical issues (e.g., concerning recruitment) and discuss project progress. Other stakeholder involving activities included preworkshop interviews with residents and relatives, co-production workshops, and testing between workshops which will be described in the following. Preworkshop interviews with residents and relatives A key uncertainty was whether the care home residents would be able to meaningfully interact with the PREPAIR tool and find it useful. Therefore, preworkshop interviews were undertaken to explore residents' and relatives’ views on the acceptability and feasibility of PREPAIR and to explore their experiences and preferences for involvement in medication-related decisions. The interviews were conducted from September to October 2021. Residents and relatives from one rural and two urban care homes were recruited by convenient sample. Semi-structured, audio-taped interviews were performed by LDC at the care homes in the residents’ living rooms. The interview guide was inspired by a similar study on older adults about attitudes towards medications (Supplementary Material 1). Co-production workshop 1 The first co-production workshop was conducted in November 2021. All participants (Table ) were purposively recruited in collaboration with the municipality. The workshop lasted three hours and comprised three sessions. In the first session, the prespecified theme `resident and relative involvement´ was unfolded. The second session dealt with the prespecified theme `interprofessional communication´. In these two sessions, individual and group activities were performed, through which barriers and facilitators concerning the prespecified themes in the context of the intervention were identified and discussed based on insights from the preworkshop interviews, evidence-based knowledge, and practice-based experiences. In the final session, a structured consensus process was used to agree on the intervention content (i.e., what will be delivered) and intervention delivery (i.e., how it will be delivered) of the intervention prototype. The workshop was facilitated by KH and LDC and included the use of post-its and flipboard notes during the processes. Furthermore, the processes were audio-taped and video-recorded. Testing the intervention prototype The intervention prototype and first draft of written implementation materials were tested from December 2021 to February 2022. A priori, we had planned to include six residents and their relatives (three in each care home). However, due to the Covid-19 situation and a limited flow of new residents moving in during the testing period, we were able to recruit only three residents, of which none were newly arrived, and no relatives. During testing, observations and interviews were performed with a focus on exploring key uncertainties concerning the delivery, acceptability, and feasibility of the intervention. Observations of the first two intervention stages (staff-led conversation and GP consultation) were undertaken. We used a complete observer approach, where the researcher observed without participation . The observation protocol was focused on how the intervention was performed and received. During observations, descriptive and reflexive notes were made. Semi-structured, audio-taped interviews with residents and relatives were conducted shortly after the testing. The interview guide was inspired by questions related to patient involvement which had been adapted to and validated in a Danish setting (Supplementary Material 2). The interviews and the observations were conducted by LDC at the care homes. Co-production workshop 2 The second workshop was conducted in February 2022 and was planned to include the same group of participants as in workshop 1. However, due to busyness at the care homes and acute illness, only four participants attended (Table ). The workshop lasted three hours and comprised to sessions with group activities and general discussion. The first session dealt with positive and challenging experiences of the intervention based on the testing and, subsequently, intervention refinement. The second session focused on further development of the implementation strategy. The workshop was facilitated by KH and LDC. The nurse and SoHA that were unable to attend due to acute illness were interviewed individually by telephone after the workshop by KH and LDC. As in the workshop, the audio-taped interviews focused on the informants´ experiences with the intervention and their thoughts about the implementation strategy. Data analyses and synthesis Overall, data analyses were based on a rapid analysis approach. We were inspired by the approach described by Neal et al. , in which prespecified key research foci are identified directly from audio recordings, thereby eliminating the need to have time-consuming verbatim transcriptions and line-by-line coding while still capturing essential information and allowing for new themes to emerge. The key research foci in each step (preworkshop interviews, co-producing workshop, and testing) have been described in the respective sections above. The results from each step were presented and discussed ad hoc at meetings with members of the research team. For the audio recordings from the preworkshop interviews, the analysing approach was to identify predetermined research foci as well as new emerging themes. Following the first initial coding by LDC, meaningful data units were organised into sub-themes, which were then categorised under overarching themes. Hereafter, LDC and KH collaboratively refined the sub-themes and overarching themes. Exemplifying citations were transcribed. The results from the preworkshop interviews and subsequent discussions in the research team led to adaption of the PREPAIR tool to fit the care home setting. The adapted tool (PREPAIR-CH) and results from the preworkshop interviews were presented in workshop 1. During workshop 1, the data analysis was done in co-producing processes with the workshop participants. Post-it notes produced through group activities in the first two sessions were organised into barriers and facilitators under the prespecified themes. In the final session, the emerged sub-themes were reevaluated systematically in relation to the proposed preliminary intervention model through general discussion. In this structured consensus process, existing and new ideas of intervention components and delivery methods were either accepted or rejected in agreement between the workshop participants and the researchers. LDC and KH subsequently drafted the intervention prototype and first written implementation materials. The intervention prototype was presented to and approved by the entire research team. Post hoc, exemplifying quotes were identified in the video- and audio materials. In the analysis of audio recordings and field notes from the testing, the same analysing approach as described for the preworkshop interviews was used. The findings were discussed by researchers (LDC, KH, FB) and with the municipal managerial level prior to presentation in workshop 2. Finally, during workshop 2, the data analysis was again done in co-producing processes with the workshop participants. Post-it notes produced through group activities in the first session were categorised into positive and challenging intervention experiences. The emerging subthemes guided the subsequent process of agreeing on intervention refinements through general discussion. In the second session, the workshop participants discussed elements for the implementation strategy in groups based on the initial idea bank (Table ). Hereafter, the elements were pragmatically categorised into relevant/important or rejected through general discussion between the workshop participants and the researchers (KH, LDC). The elements agreed on in the workshop were included in the final implementation strategy. The final intervention was approved by the whole research team and the steering committee. Post hoc, exemplifying quotes were identified in the video- and audio materials.
The overall approach in this study was based on the MRC guidance . In each MRC phase, a set of core elements should be considered. In this study, several activities were undertaken, through which the core elements were considered (Fig. ). The research design involved elements from coproduction and rapid cycle research. In this approach, researchers and end-users collaborate in an iterative process based on interdisciplinary collaboration and rapid qualitative analysis with an ongoing exchange between research and practice. The study was conducted in close collaboration with the Municipality of Aarhus, Denmark. Furthermore, it was endorsed by the Danish Society for Patient Safety and the municipal Organization of General Practitioners, Central Denmark Region . The development phase took place from September 2021 to March 2022. The present paper conforms to the GUIDance for the rEporting of intervention Development (GUIDED) .
Understanding the context within which the intervention needs to be integrated is essential to explore the mechanisms enabling the implementation of an intervention in usual practices . According to Danish law, elderly, frail citizens are suitable for care home residency, if they need all-day care. The allocation of residency is made by the municipalities . The care for the residents is provided mainly by a team of nurses, social and health care assistants (SoHAs), and social and health care helpers. In 2017, the designated GP model was introduced in Danish care homes . In this model, one or several GPs with private clinics are assigned to serve as designated GP in a care home. Care home residents can keep their regular GP when moving into a care home, but new residents are encouraged to register with the designated GP. The model has been implemented in all care homes in the Municipality of Aarhus; however, the role of the designated GP is still in development. In Denmark, GPs operate as independent contractors that provide primary medical care under a collective agreement with the Danish Regions . The GPs are responsible for most prescription medications and chronic care management . Primary care services are mainly tax-financed and free of charge for patients.
A priori, newly arrived residents and their relatives were considered to be a relevant target group for the intervention. When elderly citizens move to a care home facility in Denmark, the majority chose to be assigned to the designated GP . In connection with this shift, it is common that the residents use of medicine is reassessed in terms of potential risks and beneficial effects. However, as the designed GP typically has no prior relation to a new resident, the needs, preferences, and values of the resident concerning their medication will need to be explored to ensure person-centred medicine. Thus, newly arrived residents at the participating care homes were considered eligible if they chose to register with the designated GP. The single exclusion criteria for the residents was severe cognitive impairment as judged by the HCPs. Care home residents are a heterogeneous group with different health conditions, and many suffer from cognitive impairment. A way of supporting cognitively impaired residents in being involved is through the involvement of relatives who know the resident and can speak on their behalf . Therefore, relatives were included in the target group as well.
A programme theory depicts how an intervention is expected to lead to its effects and under what conditions . Our initial programme theory was inspired by the original PREPAIR study and the Interprofessional Shared Decision Making (IP-SDM) model . The IP-SDM model extends shared decision making to include family members and other caregivers as well as the whole team of HCPs in a person-centred process. Based on the IP-SDM model, relatives in this study were defined as family, surrogate or significant others. The IP-SDM model was inspirational in terms of outlining the key actors and their respective roles in supporting person-centred medicine in the complex care home setting. Figure presents the initial linear logic model of the intervention, the proposed mechanisms of actions, and the expected outcomes. The preliminary intervention model included two key components: a) PREPAIR and b) an interprofessional communication component. These components were to be delivered in a three-stage workflow (before, during, and after the GP consultation). An additional focus of our programme theory was how to support intervention implementation in the care home setting. Guided by the Consolidated Framework for Implementation Research (CFIR) and literature , we developed an initial idea bank of applicable implementation strategies (i.e., methods or techniques used to improve adoption, implementation, sustainment, and scale-up of interventions (Table ). The CFIR is a comprehensive implementation determinant framework designed to guide a systematic assessment of potential barriers and facilitators that influence implementation outcomes. For the idea bank, we focused on the most relevant construct in the domains Inner Setting, Characteristics of Individuals, and Process. The domains Intervention Characteristics and Outer Setting were considered through stakeholder engaging activities described below.
Several activities were performed to engage stakeholders and ensure project progress throughout the development phase. Table provides a description of the stakeholders and their respective roles. Initially, a steering group with representatives from the managerial level of the involved stakeholders was established to support and maintain engagement and to provide inputs and approval at key time points (e.g., approval of the programme theory and the final intervention). Additionally, an operational coordinator group was established, including municipal managerial and quality improvement staff and the project leaders, the authors KH and LDC. Ad hoc meetings were held in the coordinator group to resolve practical issues (e.g., concerning recruitment) and discuss project progress. Other stakeholder involving activities included preworkshop interviews with residents and relatives, co-production workshops, and testing between workshops which will be described in the following.
A key uncertainty was whether the care home residents would be able to meaningfully interact with the PREPAIR tool and find it useful. Therefore, preworkshop interviews were undertaken to explore residents' and relatives’ views on the acceptability and feasibility of PREPAIR and to explore their experiences and preferences for involvement in medication-related decisions. The interviews were conducted from September to October 2021. Residents and relatives from one rural and two urban care homes were recruited by convenient sample. Semi-structured, audio-taped interviews were performed by LDC at the care homes in the residents’ living rooms. The interview guide was inspired by a similar study on older adults about attitudes towards medications (Supplementary Material 1).
The first co-production workshop was conducted in November 2021. All participants (Table ) were purposively recruited in collaboration with the municipality. The workshop lasted three hours and comprised three sessions. In the first session, the prespecified theme `resident and relative involvement´ was unfolded. The second session dealt with the prespecified theme `interprofessional communication´. In these two sessions, individual and group activities were performed, through which barriers and facilitators concerning the prespecified themes in the context of the intervention were identified and discussed based on insights from the preworkshop interviews, evidence-based knowledge, and practice-based experiences. In the final session, a structured consensus process was used to agree on the intervention content (i.e., what will be delivered) and intervention delivery (i.e., how it will be delivered) of the intervention prototype. The workshop was facilitated by KH and LDC and included the use of post-its and flipboard notes during the processes. Furthermore, the processes were audio-taped and video-recorded.
The intervention prototype and first draft of written implementation materials were tested from December 2021 to February 2022. A priori, we had planned to include six residents and their relatives (three in each care home). However, due to the Covid-19 situation and a limited flow of new residents moving in during the testing period, we were able to recruit only three residents, of which none were newly arrived, and no relatives. During testing, observations and interviews were performed with a focus on exploring key uncertainties concerning the delivery, acceptability, and feasibility of the intervention. Observations of the first two intervention stages (staff-led conversation and GP consultation) were undertaken. We used a complete observer approach, where the researcher observed without participation . The observation protocol was focused on how the intervention was performed and received. During observations, descriptive and reflexive notes were made. Semi-structured, audio-taped interviews with residents and relatives were conducted shortly after the testing. The interview guide was inspired by questions related to patient involvement which had been adapted to and validated in a Danish setting (Supplementary Material 2). The interviews and the observations were conducted by LDC at the care homes.
The second workshop was conducted in February 2022 and was planned to include the same group of participants as in workshop 1. However, due to busyness at the care homes and acute illness, only four participants attended (Table ). The workshop lasted three hours and comprised to sessions with group activities and general discussion. The first session dealt with positive and challenging experiences of the intervention based on the testing and, subsequently, intervention refinement. The second session focused on further development of the implementation strategy. The workshop was facilitated by KH and LDC. The nurse and SoHA that were unable to attend due to acute illness were interviewed individually by telephone after the workshop by KH and LDC. As in the workshop, the audio-taped interviews focused on the informants´ experiences with the intervention and their thoughts about the implementation strategy.
Overall, data analyses were based on a rapid analysis approach. We were inspired by the approach described by Neal et al. , in which prespecified key research foci are identified directly from audio recordings, thereby eliminating the need to have time-consuming verbatim transcriptions and line-by-line coding while still capturing essential information and allowing for new themes to emerge. The key research foci in each step (preworkshop interviews, co-producing workshop, and testing) have been described in the respective sections above. The results from each step were presented and discussed ad hoc at meetings with members of the research team. For the audio recordings from the preworkshop interviews, the analysing approach was to identify predetermined research foci as well as new emerging themes. Following the first initial coding by LDC, meaningful data units were organised into sub-themes, which were then categorised under overarching themes. Hereafter, LDC and KH collaboratively refined the sub-themes and overarching themes. Exemplifying citations were transcribed. The results from the preworkshop interviews and subsequent discussions in the research team led to adaption of the PREPAIR tool to fit the care home setting. The adapted tool (PREPAIR-CH) and results from the preworkshop interviews were presented in workshop 1. During workshop 1, the data analysis was done in co-producing processes with the workshop participants. Post-it notes produced through group activities in the first two sessions were organised into barriers and facilitators under the prespecified themes. In the final session, the emerged sub-themes were reevaluated systematically in relation to the proposed preliminary intervention model through general discussion. In this structured consensus process, existing and new ideas of intervention components and delivery methods were either accepted or rejected in agreement between the workshop participants and the researchers. LDC and KH subsequently drafted the intervention prototype and first written implementation materials. The intervention prototype was presented to and approved by the entire research team. Post hoc, exemplifying quotes were identified in the video- and audio materials. In the analysis of audio recordings and field notes from the testing, the same analysing approach as described for the preworkshop interviews was used. The findings were discussed by researchers (LDC, KH, FB) and with the municipal managerial level prior to presentation in workshop 2. Finally, during workshop 2, the data analysis was again done in co-producing processes with the workshop participants. Post-it notes produced through group activities in the first session were categorised into positive and challenging intervention experiences. The emerging subthemes guided the subsequent process of agreeing on intervention refinements through general discussion. In the second session, the workshop participants discussed elements for the implementation strategy in groups based on the initial idea bank (Table ). Hereafter, the elements were pragmatically categorised into relevant/important or rejected through general discussion between the workshop participants and the researchers (KH, LDC). The elements agreed on in the workshop were included in the final implementation strategy. The final intervention was approved by the whole research team and the steering committee. Post hoc, exemplifying quotes were identified in the video- and audio materials.
Pre-workshop interviews with residents and relatives In total, six interviews were conducted, including 6 residents of which 3 were accompanied by relatives (Table ). They uncovered different attitudes and preferences regarding involvement in medication-related decisions. Furthermore, feedback on PREPAIR was provided, which contributed to adaption of the tool. Attitudes and preferences toward involvement in medication-related decisions All residents expressed a desire to be involved in their medication. Some wished to be informed; others had actively tried to influence their medication or be in some kind of control. For instance, a resident counted the pills to ensure she got the right treatment. Another resident had requested to have a specific medication deprescribed. The resident said: ”I had some diuretic… but I’ve stopped that. I simply asked to be exempted from that because I don’t need it any longer” (RS4, care home 2). However, most residents felt that their knowledge about the medication was limited and did not know how to be involved in decisions about their medication. The residents had the impression that the HCPs made the decisions about their medications. Nonetheless, the residents and their relatives trusted the HCPs and believed that they made the right decisions. The relatives also expressed that they would like to be involved in the residents' medication. Some relatives put much effort into being involved. A relative said: ”We have discussed it quite a bit with the nurse and her [the resident’s] contact person because we obviously don’t want my mother to get more medications than what is good.” ( RL1, care home 1 ). Another relative stated that she wished to be more involved. Primarily, because she wanted to be able to talk to the resident about the medication, but also because she had encountered problems with the medication at the care home. A resident expressed a desire for more involvement from relatives. Oppositely, not all residents wanted the relatives to be involved. Thus, both residents and relatives wanted to be involved in medication-related decisions, although some variation was observed in terms of the preferred level of involvement and the role of relatives. PREPAIR feedback Overall, the residents and their relatives found PREPAIR useful as a dialogue tool to support a conversation about the medication. A relative stated: “ It can structure a conversation so that you get to touch upon topics that you would otherwise not have discussed ” (RL3, care home 1). When looking more into the details of the layout and content of PREPAIR, several areas for improvement were pointed out by the residents and their relatives: the font was too small; the response categories were tricky to answer; and it was difficult to distinguish between two of the statements (i.e., statements three and four). Additionally, it was noted that most residents would not be able to fill out the form by themselves. Adaption of PREPAIR Based on the resident and relative feedback and research group discussions, adjustments were made to the original PREPAIR tool: the introductory text was modified, a larger font size was applied; statements were rephrased to questions; and the response categories were revised from a 3-point Likert Scale to the categories yes/no/do not know. Additionally, statement four was replaced with the question “Would you take less medication if your doctor said that it was possible?” inspired by the rPATD . The purpose of the new question was to encourage discussions on deprescribing between residents, relatives, and HCPs. The modified version of PREPAIR was titled: PREPAIR care home (hereafter PREPAIR-CH) (Fig. ). Co-production workshop 1 In the first co-production workshop, various barriers and facilitators related to the prespecified themes `resident and relative involvement´ and `interprofessional communication´ in the context of the intervention were identified. During the final consensus process, the intervention components and delivery methods were discussed and agreed upon. Session 1: barriers to and facilitators of resident and relative involvement Important barriers to resident and relative involvement were that it takes time and needs to be prioritized to occur more systematically than in the current clinical practice. Varying preferences for involvement among residents and relatives (and sometimes unrealistic expectations among relatives) were also considered as barriers to successful involvement. Additionally, the functional level of the resident was perceived as a barrier, as care home residents have varying degrees of physical (e.g. hearing loss) and cognitive impairments. Among both care home staff and GPs, some doubt existed as to whether it would be possible for all residents to take part in the intervention (e.g. filling out the PREPAIR-CH). A nurse explained: ” At the care homes where we are, it is probably only a few who will be able to cooperate about such a dialogue tool because their cognition is so bad .” (N1, care home 2). All workshop participants recognized that resident and relative involvement was important and found it to be in alignment with the goals and values in the municipality and the care homes. Further, key facilitators of involvement included awareness about involvement among HCPs and using a systematic workflow adapted to the existing practices. Furthermore, early dialogue and alignment of expectations for involvement were found to be potential facilitators of involvement. Additionally, video-consultation was suggested as a way to facilitate involvement of relatives. Session 2: barriers to and facilitators of interprofessional communication The workshop participants were generally very positive about the existing interprofessional communication about medication and did not articulate any major barriers. They stated that this area had improved considerably since the introduction of the new dedicated GP model in 2017 and highlighted trust and knowledge about each other's professional competencies as fundamental facilitators of successful interprofessional communication. A GP said: “We have a mutual trust that we do not exploit… well, we do show up after all, we prioritize them (the care home) when there is something that cannot wait until the next visit.” (GP1, care home 2) Although the communication was already considered to be good, the HCPs suggested that a fixed structure for the communication on medication changes might be a potential facilitator of more relevant and precise communication (“It is also about having a fixed structure” [GP1, care home 2]). Session 3: consensus process During the final consensus process, the proposed logic model of the intervention was found to fit well with the existing practices and the ideas of the workshop participants. A systematic workflow including a start-up meeting with the resident and relatives was already established for newly arrived residents. This start-up meeting was found to be a good time point to introduce the PREPAIR-CH and fitted well with the idea of early dialog and alignment of expectations for involvement in medication-related decisions. A SoHA said: ”[with] a new resident […] you capture some things, actually, which mean that we can follow up in due time. There is a good dialogue, and we capture some good things in relation to the medication” (SoHA1, care home 2). Additionally, the PREPAIR-CH was found to be a facilitator of HCP awareness and action toward resident and relative involvement. A nurse said: “I believe it’s a really good tool – also for us who work in care homes, because it helps increase the focus on, [and to] be curious about, what it is about, what kind of resident we have.” (N1, care home 2). Thus, the PREPAIR-CH was agreed on as a key intervention component. Furthermore, an alignment of expectations for involvement in medication-related decisions with residents and relatives was proposed by the staff as an additional element in the start-up meeting to accommodate the variation in attitudes and preferences for involvement across residents and relatives. The notion of video-consultations with relatives was found relevant, but not feasible in clinical practice at this stage, and the idea was rejected. In terms of interprofessional communication, the workshop participants agreed to include a fixed template to communicate medication changes as an element for the intervention to support relevant and precise communication. The intervention prototype The workshop processes led to the drafting of the intervention prototype and initial implementation materials. As proposed in the programme theory, the intervention prototype was to be delivered by the HCPs in a three-stage workflow: Stage 1 was a staff-led conversation about medication as part of the existing start-up meeting for newly arrived residents. The conversation included two elements: a) an alignment of expectations with the resident and relatives involving a clarifying conversation about their expectations and preferences for involvement in medication-related decisions and b) completion of the PREPAIR-CH with the resident and, if possible, the relatives. Stage 2 was the GP consultation. When discussing the medication with the resident, the GP would take a starting-point in the completed PREPAIR-CH and the documented alignment of expectations from stage 1. Staff and relatives would participate and facilitate the conversation if desired by the resident. Stage 3 included follow-up after the GP consultation, where a medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan. The medication communication template was developed after workshop 1 based on insights from the workshop and clinical knowledge in the research team. It included fixed points regarding medication changes and instructions on important observations (Table ). Testing the intervention prototype Three residents tested the intervention and were interviewed (Table ). Additionally, one staff-led conversation and two GP consultations were observed. The observations revealed some discrepancy between the intended and actual intervention delivery, but, overall, the intervention was found to be acceptable and feasible from the perspective of the residents. Discrepancy between intended and observed intervention delivery The staff-led conversations were not delivered as part of the start-up meeting as intended due to the lack of opportunity to include newly arrived residents. Instead, the staff-led conversation was delivered to residents that were already settled at the discretion of the staff. The observation of the staff- led conversation revealed that the alignment of preferences was not performed. Resident perspective: acceptability and feasibility The residents were overall positive about the intervention. A resident stated: “I think it’s nice that there are some [people] who are interested in you” (RS7, care home 2). Observations showed that completion of the PREPAIR-CH was led by the staff, who read the form out-loud to the resident. The residents were able to answer the questions, and they all added notes in the open-ended question on topics to discuss with the GP. During the GP consultations, the PREPAIR-CH was actively used in the resident-GP conversation about medication with support from the staff. All residents found the PREPAIR-CH acceptable and feasible to use. Co-production workshop 2 In workshop 2, positive and challenging experiences of the intervention from the HCP perspective were brought to light in session 1. The identified challenges led to further refinement of the intervention. Additionally, in session 2, elements of the implementation strategy were discussed and agreed upon. Session 1: positive and challenging intervention experiences Several positive intervention experiences were articulated. These included that the intervention was found meaningful and in alignment with existing organisational goals; that it provided awareness and structure to support involvement; and that it was feasible within existing working routines and resources. First of all, the intervention was found to “make sense” and support existing goals in the care homes, which promoted intervention acceptability among HCPs and care home managers. A GP stated: ”[It] makes good sense to focus more on involvement […] to engage the relatives more .” (GP1, care home 2). Furthermore, compared to usual practice, the intervention was perceived to provide additional value in several ways. The intervention was found to increase HCP awareness about resident and relative involvement and to disrupt the habits of usual practice, where the GP and the staff often made decisions about the medication without the resident. A nurse said: ”The tool invites us to include the patient perspective more, because otherwise this would be something that the GP and I would deal with, but also involving [the resident], I actually find that really good” (N1, care home 2). Moreover, the intervention was found to provide a structure that facilitated dialogue and brought new insights into the residents’ believes and preferences regarding their medication for both staff and GPs. A SoHA stated: ”I don’t believe that I would have captured it [a medication-related issue] if we had not… like… gone into it very specifically in this way because of the tool. ( SoHA2, care home 3 ). As indicated by the quote, the staff discovered that the use of the PREPAIR-CH revealed medication-related issues that might not have been addressed otherwise. Furthermore, according to the staff, completion of the PREPAIR-CH took about ten minutes on average, which was not perceived as stressful or burdening. A nurse said: “ When you get sharper on what it’s about, I believe that it will not take a strong presence. ” (N1, CH2). The staff also perceived the medication communication template to be helpful. Only simple medication changes had been made during testing. However, the nurse stated: “ It could have been something more specific or something more complicated. It could also be of help to me. ” (N1, care home 2). Consistent with the staff, the intervention was not perceived as “extra work” by the GPs (” Medically, it’s not a major extra thing that we are dealing with here" [GP1, care home 2]). Thus, all HCPs considered the PREPAIR-CH and medication communication template to be feasible within the existing working routines and resources. The main challenges during testing related to the staff-led conversation and the chosen target group of newly arrived residents and their relatives. Specifically, the alignment of expectations planned to take place as part of the staff-led conversation was perceived as challenging by the staff, and this element was not performed during testing. Furthermore, it proved to be a challenge to include newly arrived residents due to a small turn-over of resident in the testing period. In addition, after having tried the PREPAIR-CH during testing, the staff felt that it might not be appropriate to introduce the PREPAIR-CH in the start-up meeting for newly arrived residents after all. Many issues were on the agenda in the start-up meeting as well as in the first meeting with the designated GP. A SoHA said: “ There are many things [to discuss] when they [the residents] meet their designated GP for the first time ” (SoHA2, care home 3). Therefore, it was found better to introduce the PREPAIR-CH in a more stable phase when the resident had settled down e.g., in connection with the regular chronic care consultation (“It could be relevant to use in connection with such an annual review consultation [N1, care home 2]). Additionally, the HCPs suggested that the intervention was integrated into routine care for all settled residents. Intervention refinement to the final model Overall, the mechanisms of action in the proposed logic model (Fig. ) were confirmed during testing. Based on the positive experiences, both GPs and staff supported to continue with the PREPAIR-CH and the medication communication template as key intervention components. To address the identified challenges, the alignment of expectations for involvement with resident and relatives was omitted, as specific training in this task was not considered feasible in a real-life setting with high staff turn-over. Additionally, the timepoint of the staff-led completion of the PREPAIR-CH was changed to being shortly before the GP consultation, at the discretion of the staff to ensure flexibility, instead of being conducted as part of the start-up meeting for newly arrived residents. Furthermore, the target group for the intervention was changed from newly arrived residents to settled residents and their relatives. With these changes, the intervention was adjusted to a simpler version with a more flexible workflow to better fit the local setting. In alignment with the preliminary intervention model (Fig. ), the final intervention included two fixed components (PREPAIR-CH and the medication communication template) which were to be delivered by the HCPs in a flexible three-stage workflow. Stage 1 included a staff-led completion of the PREPAIR-CH with the resident and, if possible, the relatives before the GP consultation. Stage 2 comprised the GP consultation, including a dialogue about the medication based on the PREPAIR-CH. Staff and relatives would participate and facilitate the dialogue if necessary and desired by the resident. Finally, stage 3 included follow-up after the GP consultation, where the medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan, if medication changes were agreed on and implemented. Session 2: implementation strategy With inspiration from the initial idea bank, the workshop participants discussed and selected relevant elements for the implementation strategy (Table ) in session 2. The participants highlighted the following aspects as important: to inform the entire staff group about the project (“ present it to as many [staff] as possible”, [CM2, care home 2] ), to engage one or two coordinators at each care home, and to keep the informational materials simple and brief (“it has to be really short ”, [GP2, care home 3] ). The participants rejected instruction videos as implementation support.
In total, six interviews were conducted, including 6 residents of which 3 were accompanied by relatives (Table ). They uncovered different attitudes and preferences regarding involvement in medication-related decisions. Furthermore, feedback on PREPAIR was provided, which contributed to adaption of the tool. Attitudes and preferences toward involvement in medication-related decisions All residents expressed a desire to be involved in their medication. Some wished to be informed; others had actively tried to influence their medication or be in some kind of control. For instance, a resident counted the pills to ensure she got the right treatment. Another resident had requested to have a specific medication deprescribed. The resident said: ”I had some diuretic… but I’ve stopped that. I simply asked to be exempted from that because I don’t need it any longer” (RS4, care home 2). However, most residents felt that their knowledge about the medication was limited and did not know how to be involved in decisions about their medication. The residents had the impression that the HCPs made the decisions about their medications. Nonetheless, the residents and their relatives trusted the HCPs and believed that they made the right decisions. The relatives also expressed that they would like to be involved in the residents' medication. Some relatives put much effort into being involved. A relative said: ”We have discussed it quite a bit with the nurse and her [the resident’s] contact person because we obviously don’t want my mother to get more medications than what is good.” ( RL1, care home 1 ). Another relative stated that she wished to be more involved. Primarily, because she wanted to be able to talk to the resident about the medication, but also because she had encountered problems with the medication at the care home. A resident expressed a desire for more involvement from relatives. Oppositely, not all residents wanted the relatives to be involved. Thus, both residents and relatives wanted to be involved in medication-related decisions, although some variation was observed in terms of the preferred level of involvement and the role of relatives. PREPAIR feedback Overall, the residents and their relatives found PREPAIR useful as a dialogue tool to support a conversation about the medication. A relative stated: “ It can structure a conversation so that you get to touch upon topics that you would otherwise not have discussed ” (RL3, care home 1). When looking more into the details of the layout and content of PREPAIR, several areas for improvement were pointed out by the residents and their relatives: the font was too small; the response categories were tricky to answer; and it was difficult to distinguish between two of the statements (i.e., statements three and four). Additionally, it was noted that most residents would not be able to fill out the form by themselves. Adaption of PREPAIR Based on the resident and relative feedback and research group discussions, adjustments were made to the original PREPAIR tool: the introductory text was modified, a larger font size was applied; statements were rephrased to questions; and the response categories were revised from a 3-point Likert Scale to the categories yes/no/do not know. Additionally, statement four was replaced with the question “Would you take less medication if your doctor said that it was possible?” inspired by the rPATD . The purpose of the new question was to encourage discussions on deprescribing between residents, relatives, and HCPs. The modified version of PREPAIR was titled: PREPAIR care home (hereafter PREPAIR-CH) (Fig. ).
All residents expressed a desire to be involved in their medication. Some wished to be informed; others had actively tried to influence their medication or be in some kind of control. For instance, a resident counted the pills to ensure she got the right treatment. Another resident had requested to have a specific medication deprescribed. The resident said: ”I had some diuretic… but I’ve stopped that. I simply asked to be exempted from that because I don’t need it any longer” (RS4, care home 2). However, most residents felt that their knowledge about the medication was limited and did not know how to be involved in decisions about their medication. The residents had the impression that the HCPs made the decisions about their medications. Nonetheless, the residents and their relatives trusted the HCPs and believed that they made the right decisions. The relatives also expressed that they would like to be involved in the residents' medication. Some relatives put much effort into being involved. A relative said: ”We have discussed it quite a bit with the nurse and her [the resident’s] contact person because we obviously don’t want my mother to get more medications than what is good.” ( RL1, care home 1 ). Another relative stated that she wished to be more involved. Primarily, because she wanted to be able to talk to the resident about the medication, but also because she had encountered problems with the medication at the care home. A resident expressed a desire for more involvement from relatives. Oppositely, not all residents wanted the relatives to be involved. Thus, both residents and relatives wanted to be involved in medication-related decisions, although some variation was observed in terms of the preferred level of involvement and the role of relatives.
Overall, the residents and their relatives found PREPAIR useful as a dialogue tool to support a conversation about the medication. A relative stated: “ It can structure a conversation so that you get to touch upon topics that you would otherwise not have discussed ” (RL3, care home 1). When looking more into the details of the layout and content of PREPAIR, several areas for improvement were pointed out by the residents and their relatives: the font was too small; the response categories were tricky to answer; and it was difficult to distinguish between two of the statements (i.e., statements three and four). Additionally, it was noted that most residents would not be able to fill out the form by themselves.
Based on the resident and relative feedback and research group discussions, adjustments were made to the original PREPAIR tool: the introductory text was modified, a larger font size was applied; statements were rephrased to questions; and the response categories were revised from a 3-point Likert Scale to the categories yes/no/do not know. Additionally, statement four was replaced with the question “Would you take less medication if your doctor said that it was possible?” inspired by the rPATD . The purpose of the new question was to encourage discussions on deprescribing between residents, relatives, and HCPs. The modified version of PREPAIR was titled: PREPAIR care home (hereafter PREPAIR-CH) (Fig. ).
In the first co-production workshop, various barriers and facilitators related to the prespecified themes `resident and relative involvement´ and `interprofessional communication´ in the context of the intervention were identified. During the final consensus process, the intervention components and delivery methods were discussed and agreed upon. Session 1: barriers to and facilitators of resident and relative involvement Important barriers to resident and relative involvement were that it takes time and needs to be prioritized to occur more systematically than in the current clinical practice. Varying preferences for involvement among residents and relatives (and sometimes unrealistic expectations among relatives) were also considered as barriers to successful involvement. Additionally, the functional level of the resident was perceived as a barrier, as care home residents have varying degrees of physical (e.g. hearing loss) and cognitive impairments. Among both care home staff and GPs, some doubt existed as to whether it would be possible for all residents to take part in the intervention (e.g. filling out the PREPAIR-CH). A nurse explained: ” At the care homes where we are, it is probably only a few who will be able to cooperate about such a dialogue tool because their cognition is so bad .” (N1, care home 2). All workshop participants recognized that resident and relative involvement was important and found it to be in alignment with the goals and values in the municipality and the care homes. Further, key facilitators of involvement included awareness about involvement among HCPs and using a systematic workflow adapted to the existing practices. Furthermore, early dialogue and alignment of expectations for involvement were found to be potential facilitators of involvement. Additionally, video-consultation was suggested as a way to facilitate involvement of relatives. Session 2: barriers to and facilitators of interprofessional communication The workshop participants were generally very positive about the existing interprofessional communication about medication and did not articulate any major barriers. They stated that this area had improved considerably since the introduction of the new dedicated GP model in 2017 and highlighted trust and knowledge about each other's professional competencies as fundamental facilitators of successful interprofessional communication. A GP said: “We have a mutual trust that we do not exploit… well, we do show up after all, we prioritize them (the care home) when there is something that cannot wait until the next visit.” (GP1, care home 2) Although the communication was already considered to be good, the HCPs suggested that a fixed structure for the communication on medication changes might be a potential facilitator of more relevant and precise communication (“It is also about having a fixed structure” [GP1, care home 2]). Session 3: consensus process During the final consensus process, the proposed logic model of the intervention was found to fit well with the existing practices and the ideas of the workshop participants. A systematic workflow including a start-up meeting with the resident and relatives was already established for newly arrived residents. This start-up meeting was found to be a good time point to introduce the PREPAIR-CH and fitted well with the idea of early dialog and alignment of expectations for involvement in medication-related decisions. A SoHA said: ”[with] a new resident […] you capture some things, actually, which mean that we can follow up in due time. There is a good dialogue, and we capture some good things in relation to the medication” (SoHA1, care home 2). Additionally, the PREPAIR-CH was found to be a facilitator of HCP awareness and action toward resident and relative involvement. A nurse said: “I believe it’s a really good tool – also for us who work in care homes, because it helps increase the focus on, [and to] be curious about, what it is about, what kind of resident we have.” (N1, care home 2). Thus, the PREPAIR-CH was agreed on as a key intervention component. Furthermore, an alignment of expectations for involvement in medication-related decisions with residents and relatives was proposed by the staff as an additional element in the start-up meeting to accommodate the variation in attitudes and preferences for involvement across residents and relatives. The notion of video-consultations with relatives was found relevant, but not feasible in clinical practice at this stage, and the idea was rejected. In terms of interprofessional communication, the workshop participants agreed to include a fixed template to communicate medication changes as an element for the intervention to support relevant and precise communication. The intervention prototype The workshop processes led to the drafting of the intervention prototype and initial implementation materials. As proposed in the programme theory, the intervention prototype was to be delivered by the HCPs in a three-stage workflow: Stage 1 was a staff-led conversation about medication as part of the existing start-up meeting for newly arrived residents. The conversation included two elements: a) an alignment of expectations with the resident and relatives involving a clarifying conversation about their expectations and preferences for involvement in medication-related decisions and b) completion of the PREPAIR-CH with the resident and, if possible, the relatives. Stage 2 was the GP consultation. When discussing the medication with the resident, the GP would take a starting-point in the completed PREPAIR-CH and the documented alignment of expectations from stage 1. Staff and relatives would participate and facilitate the conversation if desired by the resident. Stage 3 included follow-up after the GP consultation, where a medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan. The medication communication template was developed after workshop 1 based on insights from the workshop and clinical knowledge in the research team. It included fixed points regarding medication changes and instructions on important observations (Table ).
Important barriers to resident and relative involvement were that it takes time and needs to be prioritized to occur more systematically than in the current clinical practice. Varying preferences for involvement among residents and relatives (and sometimes unrealistic expectations among relatives) were also considered as barriers to successful involvement. Additionally, the functional level of the resident was perceived as a barrier, as care home residents have varying degrees of physical (e.g. hearing loss) and cognitive impairments. Among both care home staff and GPs, some doubt existed as to whether it would be possible for all residents to take part in the intervention (e.g. filling out the PREPAIR-CH). A nurse explained: ” At the care homes where we are, it is probably only a few who will be able to cooperate about such a dialogue tool because their cognition is so bad .” (N1, care home 2). All workshop participants recognized that resident and relative involvement was important and found it to be in alignment with the goals and values in the municipality and the care homes. Further, key facilitators of involvement included awareness about involvement among HCPs and using a systematic workflow adapted to the existing practices. Furthermore, early dialogue and alignment of expectations for involvement were found to be potential facilitators of involvement. Additionally, video-consultation was suggested as a way to facilitate involvement of relatives.
The workshop participants were generally very positive about the existing interprofessional communication about medication and did not articulate any major barriers. They stated that this area had improved considerably since the introduction of the new dedicated GP model in 2017 and highlighted trust and knowledge about each other's professional competencies as fundamental facilitators of successful interprofessional communication. A GP said: “We have a mutual trust that we do not exploit… well, we do show up after all, we prioritize them (the care home) when there is something that cannot wait until the next visit.” (GP1, care home 2) Although the communication was already considered to be good, the HCPs suggested that a fixed structure for the communication on medication changes might be a potential facilitator of more relevant and precise communication (“It is also about having a fixed structure” [GP1, care home 2]).
During the final consensus process, the proposed logic model of the intervention was found to fit well with the existing practices and the ideas of the workshop participants. A systematic workflow including a start-up meeting with the resident and relatives was already established for newly arrived residents. This start-up meeting was found to be a good time point to introduce the PREPAIR-CH and fitted well with the idea of early dialog and alignment of expectations for involvement in medication-related decisions. A SoHA said: ”[with] a new resident […] you capture some things, actually, which mean that we can follow up in due time. There is a good dialogue, and we capture some good things in relation to the medication” (SoHA1, care home 2). Additionally, the PREPAIR-CH was found to be a facilitator of HCP awareness and action toward resident and relative involvement. A nurse said: “I believe it’s a really good tool – also for us who work in care homes, because it helps increase the focus on, [and to] be curious about, what it is about, what kind of resident we have.” (N1, care home 2). Thus, the PREPAIR-CH was agreed on as a key intervention component. Furthermore, an alignment of expectations for involvement in medication-related decisions with residents and relatives was proposed by the staff as an additional element in the start-up meeting to accommodate the variation in attitudes and preferences for involvement across residents and relatives. The notion of video-consultations with relatives was found relevant, but not feasible in clinical practice at this stage, and the idea was rejected. In terms of interprofessional communication, the workshop participants agreed to include a fixed template to communicate medication changes as an element for the intervention to support relevant and precise communication.
The workshop processes led to the drafting of the intervention prototype and initial implementation materials. As proposed in the programme theory, the intervention prototype was to be delivered by the HCPs in a three-stage workflow: Stage 1 was a staff-led conversation about medication as part of the existing start-up meeting for newly arrived residents. The conversation included two elements: a) an alignment of expectations with the resident and relatives involving a clarifying conversation about their expectations and preferences for involvement in medication-related decisions and b) completion of the PREPAIR-CH with the resident and, if possible, the relatives. Stage 2 was the GP consultation. When discussing the medication with the resident, the GP would take a starting-point in the completed PREPAIR-CH and the documented alignment of expectations from stage 1. Staff and relatives would participate and facilitate the conversation if desired by the resident. Stage 3 included follow-up after the GP consultation, where a medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan. The medication communication template was developed after workshop 1 based on insights from the workshop and clinical knowledge in the research team. It included fixed points regarding medication changes and instructions on important observations (Table ).
Three residents tested the intervention and were interviewed (Table ). Additionally, one staff-led conversation and two GP consultations were observed. The observations revealed some discrepancy between the intended and actual intervention delivery, but, overall, the intervention was found to be acceptable and feasible from the perspective of the residents. Discrepancy between intended and observed intervention delivery The staff-led conversations were not delivered as part of the start-up meeting as intended due to the lack of opportunity to include newly arrived residents. Instead, the staff-led conversation was delivered to residents that were already settled at the discretion of the staff. The observation of the staff- led conversation revealed that the alignment of preferences was not performed. Resident perspective: acceptability and feasibility The residents were overall positive about the intervention. A resident stated: “I think it’s nice that there are some [people] who are interested in you” (RS7, care home 2). Observations showed that completion of the PREPAIR-CH was led by the staff, who read the form out-loud to the resident. The residents were able to answer the questions, and they all added notes in the open-ended question on topics to discuss with the GP. During the GP consultations, the PREPAIR-CH was actively used in the resident-GP conversation about medication with support from the staff. All residents found the PREPAIR-CH acceptable and feasible to use.
The staff-led conversations were not delivered as part of the start-up meeting as intended due to the lack of opportunity to include newly arrived residents. Instead, the staff-led conversation was delivered to residents that were already settled at the discretion of the staff. The observation of the staff- led conversation revealed that the alignment of preferences was not performed.
The residents were overall positive about the intervention. A resident stated: “I think it’s nice that there are some [people] who are interested in you” (RS7, care home 2). Observations showed that completion of the PREPAIR-CH was led by the staff, who read the form out-loud to the resident. The residents were able to answer the questions, and they all added notes in the open-ended question on topics to discuss with the GP. During the GP consultations, the PREPAIR-CH was actively used in the resident-GP conversation about medication with support from the staff. All residents found the PREPAIR-CH acceptable and feasible to use.
In workshop 2, positive and challenging experiences of the intervention from the HCP perspective were brought to light in session 1. The identified challenges led to further refinement of the intervention. Additionally, in session 2, elements of the implementation strategy were discussed and agreed upon. Session 1: positive and challenging intervention experiences Several positive intervention experiences were articulated. These included that the intervention was found meaningful and in alignment with existing organisational goals; that it provided awareness and structure to support involvement; and that it was feasible within existing working routines and resources. First of all, the intervention was found to “make sense” and support existing goals in the care homes, which promoted intervention acceptability among HCPs and care home managers. A GP stated: ”[It] makes good sense to focus more on involvement […] to engage the relatives more .” (GP1, care home 2). Furthermore, compared to usual practice, the intervention was perceived to provide additional value in several ways. The intervention was found to increase HCP awareness about resident and relative involvement and to disrupt the habits of usual practice, where the GP and the staff often made decisions about the medication without the resident. A nurse said: ”The tool invites us to include the patient perspective more, because otherwise this would be something that the GP and I would deal with, but also involving [the resident], I actually find that really good” (N1, care home 2). Moreover, the intervention was found to provide a structure that facilitated dialogue and brought new insights into the residents’ believes and preferences regarding their medication for both staff and GPs. A SoHA stated: ”I don’t believe that I would have captured it [a medication-related issue] if we had not… like… gone into it very specifically in this way because of the tool. ( SoHA2, care home 3 ). As indicated by the quote, the staff discovered that the use of the PREPAIR-CH revealed medication-related issues that might not have been addressed otherwise. Furthermore, according to the staff, completion of the PREPAIR-CH took about ten minutes on average, which was not perceived as stressful or burdening. A nurse said: “ When you get sharper on what it’s about, I believe that it will not take a strong presence. ” (N1, CH2). The staff also perceived the medication communication template to be helpful. Only simple medication changes had been made during testing. However, the nurse stated: “ It could have been something more specific or something more complicated. It could also be of help to me. ” (N1, care home 2). Consistent with the staff, the intervention was not perceived as “extra work” by the GPs (” Medically, it’s not a major extra thing that we are dealing with here" [GP1, care home 2]). Thus, all HCPs considered the PREPAIR-CH and medication communication template to be feasible within the existing working routines and resources. The main challenges during testing related to the staff-led conversation and the chosen target group of newly arrived residents and their relatives. Specifically, the alignment of expectations planned to take place as part of the staff-led conversation was perceived as challenging by the staff, and this element was not performed during testing. Furthermore, it proved to be a challenge to include newly arrived residents due to a small turn-over of resident in the testing period. In addition, after having tried the PREPAIR-CH during testing, the staff felt that it might not be appropriate to introduce the PREPAIR-CH in the start-up meeting for newly arrived residents after all. Many issues were on the agenda in the start-up meeting as well as in the first meeting with the designated GP. A SoHA said: “ There are many things [to discuss] when they [the residents] meet their designated GP for the first time ” (SoHA2, care home 3). Therefore, it was found better to introduce the PREPAIR-CH in a more stable phase when the resident had settled down e.g., in connection with the regular chronic care consultation (“It could be relevant to use in connection with such an annual review consultation [N1, care home 2]). Additionally, the HCPs suggested that the intervention was integrated into routine care for all settled residents. Intervention refinement to the final model Overall, the mechanisms of action in the proposed logic model (Fig. ) were confirmed during testing. Based on the positive experiences, both GPs and staff supported to continue with the PREPAIR-CH and the medication communication template as key intervention components. To address the identified challenges, the alignment of expectations for involvement with resident and relatives was omitted, as specific training in this task was not considered feasible in a real-life setting with high staff turn-over. Additionally, the timepoint of the staff-led completion of the PREPAIR-CH was changed to being shortly before the GP consultation, at the discretion of the staff to ensure flexibility, instead of being conducted as part of the start-up meeting for newly arrived residents. Furthermore, the target group for the intervention was changed from newly arrived residents to settled residents and their relatives. With these changes, the intervention was adjusted to a simpler version with a more flexible workflow to better fit the local setting. In alignment with the preliminary intervention model (Fig. ), the final intervention included two fixed components (PREPAIR-CH and the medication communication template) which were to be delivered by the HCPs in a flexible three-stage workflow. Stage 1 included a staff-led completion of the PREPAIR-CH with the resident and, if possible, the relatives before the GP consultation. Stage 2 comprised the GP consultation, including a dialogue about the medication based on the PREPAIR-CH. Staff and relatives would participate and facilitate the dialogue if necessary and desired by the resident. Finally, stage 3 included follow-up after the GP consultation, where the medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan, if medication changes were agreed on and implemented. Session 2: implementation strategy With inspiration from the initial idea bank, the workshop participants discussed and selected relevant elements for the implementation strategy (Table ) in session 2. The participants highlighted the following aspects as important: to inform the entire staff group about the project (“ present it to as many [staff] as possible”, [CM2, care home 2] ), to engage one or two coordinators at each care home, and to keep the informational materials simple and brief (“it has to be really short ”, [GP2, care home 3] ). The participants rejected instruction videos as implementation support.
Several positive intervention experiences were articulated. These included that the intervention was found meaningful and in alignment with existing organisational goals; that it provided awareness and structure to support involvement; and that it was feasible within existing working routines and resources. First of all, the intervention was found to “make sense” and support existing goals in the care homes, which promoted intervention acceptability among HCPs and care home managers. A GP stated: ”[It] makes good sense to focus more on involvement […] to engage the relatives more .” (GP1, care home 2). Furthermore, compared to usual practice, the intervention was perceived to provide additional value in several ways. The intervention was found to increase HCP awareness about resident and relative involvement and to disrupt the habits of usual practice, where the GP and the staff often made decisions about the medication without the resident. A nurse said: ”The tool invites us to include the patient perspective more, because otherwise this would be something that the GP and I would deal with, but also involving [the resident], I actually find that really good” (N1, care home 2). Moreover, the intervention was found to provide a structure that facilitated dialogue and brought new insights into the residents’ believes and preferences regarding their medication for both staff and GPs. A SoHA stated: ”I don’t believe that I would have captured it [a medication-related issue] if we had not… like… gone into it very specifically in this way because of the tool. ( SoHA2, care home 3 ). As indicated by the quote, the staff discovered that the use of the PREPAIR-CH revealed medication-related issues that might not have been addressed otherwise. Furthermore, according to the staff, completion of the PREPAIR-CH took about ten minutes on average, which was not perceived as stressful or burdening. A nurse said: “ When you get sharper on what it’s about, I believe that it will not take a strong presence. ” (N1, CH2). The staff also perceived the medication communication template to be helpful. Only simple medication changes had been made during testing. However, the nurse stated: “ It could have been something more specific or something more complicated. It could also be of help to me. ” (N1, care home 2). Consistent with the staff, the intervention was not perceived as “extra work” by the GPs (” Medically, it’s not a major extra thing that we are dealing with here" [GP1, care home 2]). Thus, all HCPs considered the PREPAIR-CH and medication communication template to be feasible within the existing working routines and resources. The main challenges during testing related to the staff-led conversation and the chosen target group of newly arrived residents and their relatives. Specifically, the alignment of expectations planned to take place as part of the staff-led conversation was perceived as challenging by the staff, and this element was not performed during testing. Furthermore, it proved to be a challenge to include newly arrived residents due to a small turn-over of resident in the testing period. In addition, after having tried the PREPAIR-CH during testing, the staff felt that it might not be appropriate to introduce the PREPAIR-CH in the start-up meeting for newly arrived residents after all. Many issues were on the agenda in the start-up meeting as well as in the first meeting with the designated GP. A SoHA said: “ There are many things [to discuss] when they [the residents] meet their designated GP for the first time ” (SoHA2, care home 3). Therefore, it was found better to introduce the PREPAIR-CH in a more stable phase when the resident had settled down e.g., in connection with the regular chronic care consultation (“It could be relevant to use in connection with such an annual review consultation [N1, care home 2]). Additionally, the HCPs suggested that the intervention was integrated into routine care for all settled residents. Intervention refinement to the final model Overall, the mechanisms of action in the proposed logic model (Fig. ) were confirmed during testing. Based on the positive experiences, both GPs and staff supported to continue with the PREPAIR-CH and the medication communication template as key intervention components. To address the identified challenges, the alignment of expectations for involvement with resident and relatives was omitted, as specific training in this task was not considered feasible in a real-life setting with high staff turn-over. Additionally, the timepoint of the staff-led completion of the PREPAIR-CH was changed to being shortly before the GP consultation, at the discretion of the staff to ensure flexibility, instead of being conducted as part of the start-up meeting for newly arrived residents. Furthermore, the target group for the intervention was changed from newly arrived residents to settled residents and their relatives. With these changes, the intervention was adjusted to a simpler version with a more flexible workflow to better fit the local setting. In alignment with the preliminary intervention model (Fig. ), the final intervention included two fixed components (PREPAIR-CH and the medication communication template) which were to be delivered by the HCPs in a flexible three-stage workflow. Stage 1 included a staff-led completion of the PREPAIR-CH with the resident and, if possible, the relatives before the GP consultation. Stage 2 comprised the GP consultation, including a dialogue about the medication based on the PREPAIR-CH. Staff and relatives would participate and facilitate the dialogue if necessary and desired by the resident. Finally, stage 3 included follow-up after the GP consultation, where the medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan, if medication changes were agreed on and implemented.
Overall, the mechanisms of action in the proposed logic model (Fig. ) were confirmed during testing. Based on the positive experiences, both GPs and staff supported to continue with the PREPAIR-CH and the medication communication template as key intervention components. To address the identified challenges, the alignment of expectations for involvement with resident and relatives was omitted, as specific training in this task was not considered feasible in a real-life setting with high staff turn-over. Additionally, the timepoint of the staff-led completion of the PREPAIR-CH was changed to being shortly before the GP consultation, at the discretion of the staff to ensure flexibility, instead of being conducted as part of the start-up meeting for newly arrived residents. Furthermore, the target group for the intervention was changed from newly arrived residents to settled residents and their relatives. With these changes, the intervention was adjusted to a simpler version with a more flexible workflow to better fit the local setting. In alignment with the preliminary intervention model (Fig. ), the final intervention included two fixed components (PREPAIR-CH and the medication communication template) which were to be delivered by the HCPs in a flexible three-stage workflow. Stage 1 included a staff-led completion of the PREPAIR-CH with the resident and, if possible, the relatives before the GP consultation. Stage 2 comprised the GP consultation, including a dialogue about the medication based on the PREPAIR-CH. Staff and relatives would participate and facilitate the dialogue if necessary and desired by the resident. Finally, stage 3 included follow-up after the GP consultation, where the medication communication template would be filled out by the GP and sent to the staff as part of the total treatment plan, if medication changes were agreed on and implemented.
With inspiration from the initial idea bank, the workshop participants discussed and selected relevant elements for the implementation strategy (Table ) in session 2. The participants highlighted the following aspects as important: to inform the entire staff group about the project (“ present it to as many [staff] as possible”, [CM2, care home 2] ), to engage one or two coordinators at each care home, and to keep the informational materials simple and brief (“it has to be really short ”, [GP2, care home 3] ). The participants rejected instruction videos as implementation support.
Main findings The present paper describes the development of a complex intervention aiming to support person-centred medicine in the care home setting. We found that residents and relatives generally wished to be involved in medication-related decisions. Based on the resident and relative feedback, the original PREPAIR was modified to the PREPAIR-CH to better fit the care home population. Co-production workshops and testing with end-users guided the further development and refinement of the preliminary intervention drawn up in our programme theory. In this process, the intervention was adapted to fit the existing workflows and resources. The final complex intervention included two fixed components (PREPAIR-CH and the medication communication template) which were delivered through a flexible three-stage workflow. Additionally, a multi-component implementation strategy was developed. Comparison with existing literature Several studies have demonstrated that care home residents and their relatives want to be involved in medication-related decisions [ , , ], but many residents find it difficult . In line with our findings, previous research has shown that residents believe that the HCPs make the decisions about their medications and that they trust the HCPs decisions [ , , ]. In our study, the HCPs confirmed this perception of usual care being mostly characterised by HCP decision-making. We found that the intervention focus on resident and relative involvement was in alignment with the organisational and individual professional values articulated during the development process. Organisational values have been identified as an important influencing factor in the realisation of resident and relative involvement and shared decision-making . However, solely having a general focus on involvement is not sufficient to ensure actual involvement , and, in our study, the HCPs emphasised that involvement takes time and needs to be prioritized to occur more systematically. A recent systematic review by Eidam et al. identified 55 different tools that have been applied to evaluate patient preferences in geriatric pharmacotherapy. Only three tools targeted the context of multimorbidity-related polypharmacy, and none were found ideal for practicable elicitation of patient preferences in the context of geriatric polypharmacy. The main limitation of the tools was a time-consuming design. The review concluded that tools aiming to elicit patient preference should be simple and help to minimize the time investment in preference elicitation to meet the time constraints imposed by routine care . The findings by Eidam et al. aligns with a recent realist review from the International Patient Decision Aid Standards Collaboration. Based on data from 23 implementation studies, this review presented eight programme theories describing the mechanisms by which patient decisions aids become successfully implemented into routine health care settings . According to these theories, intervention implementation is more likely to occur when the intervention contains simple tools that is integrated into the clinic workflow (which is often complex); when it prepares and prompts the patients to engage; and when a systematic delivery is used. These intervention characteristics are consistent with the intervention developed in our study. Importantly, implementing even a simple tool into real-life settings requires careful consideration of the context and existing pathways. This includes identification of the mechanisms that need to be changed and how to make these changes work in practice. We attempted this through thorough development based on programme theory and user involvement with coproduction and small-scale testing. Our programme theory was based on combined knowledge from the PREPAIR study and the IP-SDM model. However, other theoretical and conceptual frameworks exist that have been used to support the development of patient involvement interventions . An interesting theoretical framework for supporting complex intervention development and evaluation is the Making Informed Decisions Individually and Together (MIND-IT) . Like the IP-SDM model, it represents explicitly the agency of multiple decision makers making the same healthcare decision from their different contexts. It also includes a central interaction point, where exchange of understanding, reasoning about preferences, and implementation of agreed choices takes place when sharing decision making in consultations. In contrast to the IP-SDM model, the MIND-IT outlines in greater detail various factors that can influence patient and HCP reasoning. This can be helpful to gain a deeper understanding of the active ingredients and mechanisms associated with multiple stakeholders´ reasoning and action. For instance, the MIND-IT highlights experience and skills as central influential factors. In our study, the performance of the intervention relied on the HCPs´ existing clinical experience and communication skills, as specific intervention training was considered unfeasible in a real-life care home setting. However, during testing, it became clear that the staff did not feel sufficiently prepared to perform the planned alignment of expectation, although this element was suggested by the staff in the co-producing workshop. These findings emphasize the importance of considering individual stakeholder factors, as they can have considerable impact on intervention feasibility and outcomes. Overall, the intervention in our study was found to be feasible within the existing working routines and resources, except for the alignment of expectations which was omitted in the final model. The systematic delivery was found to disrupt the habits of usual care and increase HCP awareness about resident and relative involvement. The PREPAIR-CH was perceived to support dialogue and empower the residents to speak, thereby bringing new insights into the patient perspectives on their medications. Moreover, the medication communication template was perceived to be supportive for the staff during follow-up on medication changes. Hence, the mechanisms of actions suggested by our findings supported our programme theory. Furthermore, the final implementation strategy included multiple components aiming to facilitate whole-team engagement and knowledge, supportive leadership, and responsible implementation leaders in line with existing implementation theory and evidence-based recommendations . Implications This development study was conducted in accordance with the prevailing guidance on how to develop complex interventions drawing on a combination of approaches, including theory, existing evidence, and stakeholder partnership . These approaches were applied flexibly to tailor the development process to our specific context. After the final refinement process, the developed intervention was perceived to be acceptable and feasible in the care home setting. The next step in our project is a feasibility study, in which the developed intervention and implementation strategy will be further tested, and key uncertainties will be explored. A remaining key uncertainty is the role of relatives and how they perceive the intervention, as we were unable to recruit relatives in the testing of the intervention. Additionally, the most optimal timing of the intervention remains uncertain. The intervention initially targeted newly arrived residents; however, during testing, it was found more appropriate to include residents in a stable phase. Consequently, these aspects need further exploration in the feasibility study. Economic considerations are also a core element in the MRC framework, and the next phase will further explore the resource and outcome consequences of the intervention. The feasibility study will be conducted in two new care homes to strengthen the validity and generalisability of the research findings. Strengths and limitations A major strength of this study was the combined use of theoretical, evidence-based, and practice-based knowledge to develop the intervention and implementation strategy. The MRC framework and the CFIR provided guidance on important aspects to consider during the development phase. Our programme theory drew on evidence-based work from the PREPAIR study and the IP-SDM model. Additionally, we included practice-based knowledge though stakeholder involvement, including relevant managerial and user levels. The use of co-production, rapid testing, and close collaboration with the municipality contributed to the development of a relevant and realistic intervention that fitted into the existing workflow at the care homes. A limitation was that no residents or relatives participated in the workshops. As this was not considered feasible, we sought to include their perspectives though pre-workshop interviews and testing. Several activities were limited by the COVID-19 pandemic. Only few residents and no relatives participated in the testing, and observations were reduced to a minimum. This prevented us from gaining insights into the relatives ’ perspectives and may have limited the nuances to our findings. Likewise, the HCP perspectives were based on few, motivated individuals and might not generalise to other care homes. An inherent limitation to qualitative research also includes the researchers’ pre-understanding which may jeopardise the validity of study findings . In this study, the development and presentation of a programme theory contributed to the illumination of the researchers ’ pre-understanding. Additionally, the use of co-producing processes and discussion of results in a cross-disciplinary research group helped to minimize the risk of unintended influence from pre-understanding.
The present paper describes the development of a complex intervention aiming to support person-centred medicine in the care home setting. We found that residents and relatives generally wished to be involved in medication-related decisions. Based on the resident and relative feedback, the original PREPAIR was modified to the PREPAIR-CH to better fit the care home population. Co-production workshops and testing with end-users guided the further development and refinement of the preliminary intervention drawn up in our programme theory. In this process, the intervention was adapted to fit the existing workflows and resources. The final complex intervention included two fixed components (PREPAIR-CH and the medication communication template) which were delivered through a flexible three-stage workflow. Additionally, a multi-component implementation strategy was developed.
Several studies have demonstrated that care home residents and their relatives want to be involved in medication-related decisions [ , , ], but many residents find it difficult . In line with our findings, previous research has shown that residents believe that the HCPs make the decisions about their medications and that they trust the HCPs decisions [ , , ]. In our study, the HCPs confirmed this perception of usual care being mostly characterised by HCP decision-making. We found that the intervention focus on resident and relative involvement was in alignment with the organisational and individual professional values articulated during the development process. Organisational values have been identified as an important influencing factor in the realisation of resident and relative involvement and shared decision-making . However, solely having a general focus on involvement is not sufficient to ensure actual involvement , and, in our study, the HCPs emphasised that involvement takes time and needs to be prioritized to occur more systematically. A recent systematic review by Eidam et al. identified 55 different tools that have been applied to evaluate patient preferences in geriatric pharmacotherapy. Only three tools targeted the context of multimorbidity-related polypharmacy, and none were found ideal for practicable elicitation of patient preferences in the context of geriatric polypharmacy. The main limitation of the tools was a time-consuming design. The review concluded that tools aiming to elicit patient preference should be simple and help to minimize the time investment in preference elicitation to meet the time constraints imposed by routine care . The findings by Eidam et al. aligns with a recent realist review from the International Patient Decision Aid Standards Collaboration. Based on data from 23 implementation studies, this review presented eight programme theories describing the mechanisms by which patient decisions aids become successfully implemented into routine health care settings . According to these theories, intervention implementation is more likely to occur when the intervention contains simple tools that is integrated into the clinic workflow (which is often complex); when it prepares and prompts the patients to engage; and when a systematic delivery is used. These intervention characteristics are consistent with the intervention developed in our study. Importantly, implementing even a simple tool into real-life settings requires careful consideration of the context and existing pathways. This includes identification of the mechanisms that need to be changed and how to make these changes work in practice. We attempted this through thorough development based on programme theory and user involvement with coproduction and small-scale testing. Our programme theory was based on combined knowledge from the PREPAIR study and the IP-SDM model. However, other theoretical and conceptual frameworks exist that have been used to support the development of patient involvement interventions . An interesting theoretical framework for supporting complex intervention development and evaluation is the Making Informed Decisions Individually and Together (MIND-IT) . Like the IP-SDM model, it represents explicitly the agency of multiple decision makers making the same healthcare decision from their different contexts. It also includes a central interaction point, where exchange of understanding, reasoning about preferences, and implementation of agreed choices takes place when sharing decision making in consultations. In contrast to the IP-SDM model, the MIND-IT outlines in greater detail various factors that can influence patient and HCP reasoning. This can be helpful to gain a deeper understanding of the active ingredients and mechanisms associated with multiple stakeholders´ reasoning and action. For instance, the MIND-IT highlights experience and skills as central influential factors. In our study, the performance of the intervention relied on the HCPs´ existing clinical experience and communication skills, as specific intervention training was considered unfeasible in a real-life care home setting. However, during testing, it became clear that the staff did not feel sufficiently prepared to perform the planned alignment of expectation, although this element was suggested by the staff in the co-producing workshop. These findings emphasize the importance of considering individual stakeholder factors, as they can have considerable impact on intervention feasibility and outcomes. Overall, the intervention in our study was found to be feasible within the existing working routines and resources, except for the alignment of expectations which was omitted in the final model. The systematic delivery was found to disrupt the habits of usual care and increase HCP awareness about resident and relative involvement. The PREPAIR-CH was perceived to support dialogue and empower the residents to speak, thereby bringing new insights into the patient perspectives on their medications. Moreover, the medication communication template was perceived to be supportive for the staff during follow-up on medication changes. Hence, the mechanisms of actions suggested by our findings supported our programme theory. Furthermore, the final implementation strategy included multiple components aiming to facilitate whole-team engagement and knowledge, supportive leadership, and responsible implementation leaders in line with existing implementation theory and evidence-based recommendations .
This development study was conducted in accordance with the prevailing guidance on how to develop complex interventions drawing on a combination of approaches, including theory, existing evidence, and stakeholder partnership . These approaches were applied flexibly to tailor the development process to our specific context. After the final refinement process, the developed intervention was perceived to be acceptable and feasible in the care home setting. The next step in our project is a feasibility study, in which the developed intervention and implementation strategy will be further tested, and key uncertainties will be explored. A remaining key uncertainty is the role of relatives and how they perceive the intervention, as we were unable to recruit relatives in the testing of the intervention. Additionally, the most optimal timing of the intervention remains uncertain. The intervention initially targeted newly arrived residents; however, during testing, it was found more appropriate to include residents in a stable phase. Consequently, these aspects need further exploration in the feasibility study. Economic considerations are also a core element in the MRC framework, and the next phase will further explore the resource and outcome consequences of the intervention. The feasibility study will be conducted in two new care homes to strengthen the validity and generalisability of the research findings.
A major strength of this study was the combined use of theoretical, evidence-based, and practice-based knowledge to develop the intervention and implementation strategy. The MRC framework and the CFIR provided guidance on important aspects to consider during the development phase. Our programme theory drew on evidence-based work from the PREPAIR study and the IP-SDM model. Additionally, we included practice-based knowledge though stakeholder involvement, including relevant managerial and user levels. The use of co-production, rapid testing, and close collaboration with the municipality contributed to the development of a relevant and realistic intervention that fitted into the existing workflow at the care homes. A limitation was that no residents or relatives participated in the workshops. As this was not considered feasible, we sought to include their perspectives though pre-workshop interviews and testing. Several activities were limited by the COVID-19 pandemic. Only few residents and no relatives participated in the testing, and observations were reduced to a minimum. This prevented us from gaining insights into the relatives ’ perspectives and may have limited the nuances to our findings. Likewise, the HCP perspectives were based on few, motivated individuals and might not generalise to other care homes. An inherent limitation to qualitative research also includes the researchers’ pre-understanding which may jeopardise the validity of study findings . In this study, the development and presentation of a programme theory contributed to the illumination of the researchers ’ pre-understanding. Additionally, the use of co-producing processes and discussion of results in a cross-disciplinary research group helped to minimize the risk of unintended influence from pre-understanding.
In this study, we developed a complex intervention aiming to support person-centred medicines in the care home setting through resident and relative involvement and interprofessional communication support. Presenting the details of the development process facilitate the transferability of this work and ensures that links can be made between the intervention development and the future success of the intervention or lack of such. The learnings of this development study suggest that the final intervention is acceptable and feasible for end-users. They further indicate that the intervention might be a viable approach to facilitate resident and relative involvement, which will be further explored in the planned feasibility study.
Supplementary Material 1. Supplementary Material 2.
|
Network Pharmacology and Experimental Validation Reveal Sishen Pill’s Efficacy in Treating NSAID-Induced Small Intestinal Ulcers | c64fad32-23c1-43be-b80c-970fe6c647fa | 11930021 | Digestive System[mh] | Medication within the class of Nonsteroidal Anti-inflammatory Drugs (NSAIDs) is extensively acknowledged and employed for its efficacy in analgesic, antipyretic, and mitigating inflammation. 1 However, their prolonged use is associated with various gastrointestinal complications. Mucosal injury induced by NSAID, characterized by inflammation and ulceration, can manifest in both the stomach and proximal duodenum, as well as in the more distal sections of the small intestine. 2 The occurrence of NSAIDs induced small intestinal ulcers (SIUs) has seen an upward trend, largely due to the widespread utilization of NSAIDs in chronic pain management, especially among the elderly population. The increase in detection rate is also attributed to the introduction of advanced endoscopic methods, such as capsule endoscopy and double-balloon enteroscopy, which facilitate direct observation of the small intestine. 4 The precise etiology underlying NSAID-induced SIUs has yet to be fully determined, although numerous theories have been postulated. A widely recognized hypothesis posits that NSAIDs suppress the activity of the cyclooxygenase (COX) enzyme, thereby impeding the synthesis of prostaglandins essential for safeguarding the gastrointestinal mucosa. , Emerging studies have implicated the gut microbiome in the pathogenesis of SIUs. The existing therapeutic strategies for NSAID-induced SIUs are constrained, primarily consisting of NSAID withdrawal or the administration of proton pump inhibitors (PPIs). However, these treatments do not directly target the small intestine and have been shown to be effective primarily for gastric ulcers. Indeed, these drugs could potentially exacerbate NSAID-induced SIUs by altering the gut microbiota composition. Thus, there exists an urgent requirement to devise alternative treatment approaches for managing NSAID-induced SIUs. Indomethacin (INDO), a type of NSAIDs, is frequently utilized to induce small intestinal ulcers in animal models due to its potent ulcerogenic effect. The Sishen Pill (SSP), a medicinal formulation from traditional Chinese medicine (TCM), has been extensively employed in the therapeutic management of gastrointestinal disorders, including chronic diarrhea, irritable bowel syndrome (IBS), non-specific colitis, among others. The therapeutic efficacy of SSP is attributed to its multiple components which have diverse biological activities. Recent scientific investigations have validated the clinical benefits of SSP in gastrointestinal health. Studies have demonstrated that SSP exhibits significant protective effects against colitis, mainly through its antioxidant, anti-inflammatory, and cytoprotective activities. , In addition, SSP has been reported to regulate gut microbiota, enhance intestinal barrier function, and modulate immune responses, thereby improving overall gut health. , However, the therapeutic potential of SSP in addressing NSAID-induced SIUs has not been extensively researched. The objective of this research is to bridge the existing knowledge gap by examining the possible ameliorative impact of SSP on indomethacin-induced SIUs. Network pharmacology represents a novel methodology for probing the potency and therapeutic mechanisms underlying TCM. It elucidates the complex web of interactions among various constituents of the medication and their molecular targets, thereby illuminating the cooperative and integrative therapeutic mechanisms characteristic of TCMs. Employing this methodology offers a comprehensive framework for assessing the therapeutic efficacy and intrinsic mechanisms of SSP on indomethacin-induced SIUs. In this study, we utilized network pharmacology to explore the molecular targets and related signaling pathways of SSP in the treatment of SIUs. Initial validation was accomplished through molecular docking. Subsequently, utilizing a range of experimental methodologies, the influence of SSP in treating NSAID-induced SIUs was confirmed through in vivo experiments. Our findings are expected to provide a theoretical foundation for the therapeutic efficacy of SSP in SIUs.
Integrative Network Pharmacology Investigation Identification of Bioactive Compounds from Sishen Pill (SSP) Sishen Pill was composed of 6 herbs, including Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). The active compounds of Buguzhi ( Psoralea corylifolia L.) was retrieved from ETCM Database, accessible at http://www.tcmip.cn/ETCM2/front/ (accessed on 25 July 2024). For the other five TCMs, active constituents were sourced from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/ , accessed on 26 July 2024), with selection criteria including oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. Identification of Target Proteins for Bioactive Compounds The active components discussed in Section 2.1.1 were employed to explore their prospective protein targets through the TCMSP database. Subsequently, the associated human genes were retrieved from the UniProt database. Complementary data was further supplemented using resources from the PubChem database. Acquisition of Small Intestinal Ulcers-Related Gene Targets To identify gene targets associated with small intestinal ulceration (SIU), we utilized the GeneCards database ( http://www.genecards.org , accessed on 27 July 2024), employing the keyword “ulceration of small intestine”. Targets exhibiting relevance scores exceeding twice the median value were chosen for further analysis. Complementary data were also extracted from the DisGeNET database ( https://www.disgenet.com/ , accessed on 28 July 2024) to enhance the robustness of our target identification process. Development and Examination of the Protein-Protein Interaction (PPI) Network Genes pivotal for the construction of a PPI network were identified by isolating the shared targets between SSP and SIUs, using the Venny online tool. The subsequent construction of the PPI network for these shared targets was facilitated by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), available at https://string-db.org/ and accessed on 28 July 2024. This comprehensive database, which encompasses both established and predicted protein interactions, served as a resource for exploring the interrelationships among the identified genes. The study focused on proteins from the species “Homo sapiens”, with a minimum confidence threshold established at 0.9. Concurrently, free nodes were excluded to streamline the PPI network visualization. The constructed protein-protein interaction network was then uploaded into Cytoscape software (version 3.10.1) for in-depth analysis. Through the analysis of network topology parameters, the central targets were identified, visualized, and characterized. Construction of the Bioactive Compounds-Target-Disease Network For the creation of a comprehensive network visualization that encompasses compounds, their molecular targets, and associated diseases, we utilized Cytoscape version 3.10.1 to import data concerning active constituents and drug-disease intersection targets. Network analysis was conducted utilizing the Network Analyzer plugin, with bioactive constituents and their molecular targets visualized as nodes within the network. Connections representing interactions between these active components and shared targets were illustrated as edges. Enrichment Analysis of GO Function and KEGG Pathways Utilizing the DAVID bioinformatics resource ( https://david.ncifcrf.gov/home.jsp/ , accessed on 29 July 2024), the target genes under investigation were analyzed for GO functional categories and KEGG pathway enrichment. For visualization, we selected GO terms and pathways that were enriched, with a significance threshold of p-values less than 0.05. Bar graphs representing GO enrichment and bubble graphs depicting KEGG pathway enrichment were generated using the website of Microbiotics ( https://www.bioinformatics.com.cn , accessed on 1 August 2024). Molecular Docking The Network Analyzer plugin was utilized to calculate degree values within the Bioactive Compounds-Target-Disease network, which helped identify the principal bioactive constituents for molecular docking with small ligands. Key molecular targets were determined by analyzing the PPI network. The Protein Data Bank (PDB, https://www.rcsb.org/ , accessed on 9 August 2024) was consulted to retrieve relevant protein structures. The preparation of small molecules were conducted using PyMOL, and AutoDock was applied to incorporate hydrogen atoms and to produce output files in the pdbqt format for the ligand. Active ingredient files were sourced from TCMSP. Molecular docking was conducted using AutoDock Vina 1.5.7. The PyMOL was utilized to visualize the docking outcomes for various targets and proteins. In vivo Experiments Animals Male Sprague-Dawley (SD) rats with a weight range of 200–250 g (n = 20) were procured from the Shanghai Laboratory Animal Center (SLACCAS Laboratory Animal Inc., Shanghai, China). The supplier’s license number is SCXK (Hu) 2019–0005. The rats were maintained at the Animal Center of the International Institute of Medicine, Zhejiang University, under controlled conditions. The animal housing facility maintained a temperature range of 22°C ± 2°C, complemented by a 12-hour alternating light and darkness cycle facilitated by artificial lighting sources. All animals were allowed to acclimate for one week upon arrival before initiating any experimental procedures. All animal-related experimental procedures were carried out following the protocols outlined by the Experimental Animal Ethics Committee of Zhejiang University School of Medicine (ZJU20240649). Drugs The Sishen Pill (lot: 17080051) was procured from Tong Ren Tang Natural Medicine Co. Ltd., (Beijing, China). The composition included Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). These were formed into pills following the dose ratio of 1:2:4:2:2:2, respectively (100, 200, 400, 200, 200, and 200 g). Although our study primarily relied on network pharmacology to predict the active constituents of SSP, we did not perform our own HPLC analysis. Instead, we refer to the study by Zhang, and Hu, which employed a robust HPLC method to characterize the chemical profile of SSP in its traditional pharmaceutical form. Indomethacin (HY-14397) was purchased from MCE (MedChemExpress, Shanghai, China). Establishment of Small Intestinal Ulcers Following a week of acclimatization at the animal center, the laboratory animals were randomly segregated into four distinct groups: Control, Indomethacin (INDO), INDO + SSP (2.5 g/kg), and INDO + SSP (5 g/kg) - each comprising five animals. Indomethacin at 6 mg/kg/day, dissolved in a 5% Na 2 CO 3 solution, was administered to the INDO and INDO + SSP groups for seven consecutive days to induce enteropathy, while the Control group received the same volume of the Na 2 CO 3 solution alone, as outlined in a previous study. The previously specified dose of SSP was administered intragastrically once daily for five consecutive days once the small intestinal ulceration model had been successfully created. Concurrently, a comparable volume of distilled water was administered orally to rats in the INDO and the Control group. On the 13th day, following an eight-hour fasting period, Carbon dioxide-induced asphyxiation was employed for the humane euthanasia of the animals. Subsequently, blood was executed via cardiac puncture. After careful dissection, the small intestine was soaked in phosphate-buffered saline pH 7.4, rinsed, and finally weighed for analysis. The jejunoileal segment was sectioned into two parts. One section was rapidly frozen using liquid nitrogen, and conserved at a temperature of −80°C for subsequent analysis. Hematoxylin and Eosin (H&E) staining was performed on the remaining segment after it had been fixed in a 10% formalin solution and embedded in paraffin. Assessment of Ulcer Lesions Independent evaluators examined small intestine ulcers with magnifying glasses to eliminate bias. The evaluation followed the criteria established by Cantarella et al. Macroscopic lesions of the small intestine were graded from 0 to 5, based on injury severity and hemorrhage formation. The score of 0 indicated normal small intestine mucosa, 1, pinpoint erosions, 2, lesions less than 1 mm, 3, lesions between 1 and 2 mm, 4, lesions between 3 and 4 mm, and 5 lesions exceeding 4 mm. An ulcer index is calculated using the average ulcer score for each animal. Hematoxylin-Eosin (HE) Staining After collection, small intestine mucosa samples were stored in paraformaldehyde, then dehydrated with increasing alcohol concentrations. These samples were then rinsed in PBS buffer and then processed for paraffin embedding. Thereafter, sections of the embedded tissue, 4 micrometers in thickness, were prepared and underwent H&E staining. Histopathological alterations in the tissue sections were subsequently evaluated through microscopic observation. Evaluation of Cytokines Through ELISA Methodology An ELISA kit was used to measure the activity of IL-6, IL-1β, and TNF-α in rat serum, in accordance with the manufacturer’s instructions (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Assessment of Myeloperoxidase (MPO) Activity and Oxidative Stress Indexes The intestinal tissues of rats were analyzed for the activity of myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT), and the level of malondialdehyde (MDA) using specialized assay kits from Nanjing Jiancheng Bioengineering Institute, Nanjing, China. qPCR Analysis Isolation of RNA from the intestinal tissues was achieved employing the TRIzol reagent (Vazyme Biotech Co, Nanjing, China). Subsequently, the isolated RNA was subjected to reverse transcription to form complementary DNA (cDNA) utilizing the HiScriptIIQRT SuperMix reagent kit (Vazyme Biotech Co, Nanjing, China). The qPCR SYBR Green Master Mix (Vazyme Biotech Co, Nanjing, China) was used for the quantitative PCR. details the primers for quantification of mRNA expression. Expression levels, represented as fold change, were determined applying the 2−∆∆Ct method, employing β-actin as the housekeeping gene. Primers were sourced from Generay Biotech Co., Ltd (Shanghai, China). Statistical Analysis We performed all statistical analyses with GraphPad Prism 8 (GraphPad Software, San Diego, California, USA). The multiple comparisons were assessed using a one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. The results are presented as the mean values ± standard error of the mean (SEM), with significance determined at p < 0.05. To ensure robustness and reliability, each experiment was repeated at least three times.
Identification of Bioactive Compounds from Sishen Pill (SSP) Sishen Pill was composed of 6 herbs, including Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). The active compounds of Buguzhi ( Psoralea corylifolia L.) was retrieved from ETCM Database, accessible at http://www.tcmip.cn/ETCM2/front/ (accessed on 25 July 2024). For the other five TCMs, active constituents were sourced from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/ , accessed on 26 July 2024), with selection criteria including oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. Identification of Target Proteins for Bioactive Compounds The active components discussed in Section 2.1.1 were employed to explore their prospective protein targets through the TCMSP database. Subsequently, the associated human genes were retrieved from the UniProt database. Complementary data was further supplemented using resources from the PubChem database. Acquisition of Small Intestinal Ulcers-Related Gene Targets To identify gene targets associated with small intestinal ulceration (SIU), we utilized the GeneCards database ( http://www.genecards.org , accessed on 27 July 2024), employing the keyword “ulceration of small intestine”. Targets exhibiting relevance scores exceeding twice the median value were chosen for further analysis. Complementary data were also extracted from the DisGeNET database ( https://www.disgenet.com/ , accessed on 28 July 2024) to enhance the robustness of our target identification process. Development and Examination of the Protein-Protein Interaction (PPI) Network Genes pivotal for the construction of a PPI network were identified by isolating the shared targets between SSP and SIUs, using the Venny online tool. The subsequent construction of the PPI network for these shared targets was facilitated by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), available at https://string-db.org/ and accessed on 28 July 2024. This comprehensive database, which encompasses both established and predicted protein interactions, served as a resource for exploring the interrelationships among the identified genes. The study focused on proteins from the species “Homo sapiens”, with a minimum confidence threshold established at 0.9. Concurrently, free nodes were excluded to streamline the PPI network visualization. The constructed protein-protein interaction network was then uploaded into Cytoscape software (version 3.10.1) for in-depth analysis. Through the analysis of network topology parameters, the central targets were identified, visualized, and characterized. Construction of the Bioactive Compounds-Target-Disease Network For the creation of a comprehensive network visualization that encompasses compounds, their molecular targets, and associated diseases, we utilized Cytoscape version 3.10.1 to import data concerning active constituents and drug-disease intersection targets. Network analysis was conducted utilizing the Network Analyzer plugin, with bioactive constituents and their molecular targets visualized as nodes within the network. Connections representing interactions between these active components and shared targets were illustrated as edges. Enrichment Analysis of GO Function and KEGG Pathways Utilizing the DAVID bioinformatics resource ( https://david.ncifcrf.gov/home.jsp/ , accessed on 29 July 2024), the target genes under investigation were analyzed for GO functional categories and KEGG pathway enrichment. For visualization, we selected GO terms and pathways that were enriched, with a significance threshold of p-values less than 0.05. Bar graphs representing GO enrichment and bubble graphs depicting KEGG pathway enrichment were generated using the website of Microbiotics ( https://www.bioinformatics.com.cn , accessed on 1 August 2024). Molecular Docking The Network Analyzer plugin was utilized to calculate degree values within the Bioactive Compounds-Target-Disease network, which helped identify the principal bioactive constituents for molecular docking with small ligands. Key molecular targets were determined by analyzing the PPI network. The Protein Data Bank (PDB, https://www.rcsb.org/ , accessed on 9 August 2024) was consulted to retrieve relevant protein structures. The preparation of small molecules were conducted using PyMOL, and AutoDock was applied to incorporate hydrogen atoms and to produce output files in the pdbqt format for the ligand. Active ingredient files were sourced from TCMSP. Molecular docking was conducted using AutoDock Vina 1.5.7. The PyMOL was utilized to visualize the docking outcomes for various targets and proteins.
Sishen Pill was composed of 6 herbs, including Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). The active compounds of Buguzhi ( Psoralea corylifolia L.) was retrieved from ETCM Database, accessible at http://www.tcmip.cn/ETCM2/front/ (accessed on 25 July 2024). For the other five TCMs, active constituents were sourced from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/ , accessed on 26 July 2024), with selection criteria including oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18.
The active components discussed in Section 2.1.1 were employed to explore their prospective protein targets through the TCMSP database. Subsequently, the associated human genes were retrieved from the UniProt database. Complementary data was further supplemented using resources from the PubChem database.
To identify gene targets associated with small intestinal ulceration (SIU), we utilized the GeneCards database ( http://www.genecards.org , accessed on 27 July 2024), employing the keyword “ulceration of small intestine”. Targets exhibiting relevance scores exceeding twice the median value were chosen for further analysis. Complementary data were also extracted from the DisGeNET database ( https://www.disgenet.com/ , accessed on 28 July 2024) to enhance the robustness of our target identification process.
Genes pivotal for the construction of a PPI network were identified by isolating the shared targets between SSP and SIUs, using the Venny online tool. The subsequent construction of the PPI network for these shared targets was facilitated by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), available at https://string-db.org/ and accessed on 28 July 2024. This comprehensive database, which encompasses both established and predicted protein interactions, served as a resource for exploring the interrelationships among the identified genes. The study focused on proteins from the species “Homo sapiens”, with a minimum confidence threshold established at 0.9. Concurrently, free nodes were excluded to streamline the PPI network visualization. The constructed protein-protein interaction network was then uploaded into Cytoscape software (version 3.10.1) for in-depth analysis. Through the analysis of network topology parameters, the central targets were identified, visualized, and characterized.
For the creation of a comprehensive network visualization that encompasses compounds, their molecular targets, and associated diseases, we utilized Cytoscape version 3.10.1 to import data concerning active constituents and drug-disease intersection targets. Network analysis was conducted utilizing the Network Analyzer plugin, with bioactive constituents and their molecular targets visualized as nodes within the network. Connections representing interactions between these active components and shared targets were illustrated as edges.
Utilizing the DAVID bioinformatics resource ( https://david.ncifcrf.gov/home.jsp/ , accessed on 29 July 2024), the target genes under investigation were analyzed for GO functional categories and KEGG pathway enrichment. For visualization, we selected GO terms and pathways that were enriched, with a significance threshold of p-values less than 0.05. Bar graphs representing GO enrichment and bubble graphs depicting KEGG pathway enrichment were generated using the website of Microbiotics ( https://www.bioinformatics.com.cn , accessed on 1 August 2024).
The Network Analyzer plugin was utilized to calculate degree values within the Bioactive Compounds-Target-Disease network, which helped identify the principal bioactive constituents for molecular docking with small ligands. Key molecular targets were determined by analyzing the PPI network. The Protein Data Bank (PDB, https://www.rcsb.org/ , accessed on 9 August 2024) was consulted to retrieve relevant protein structures. The preparation of small molecules were conducted using PyMOL, and AutoDock was applied to incorporate hydrogen atoms and to produce output files in the pdbqt format for the ligand. Active ingredient files were sourced from TCMSP. Molecular docking was conducted using AutoDock Vina 1.5.7. The PyMOL was utilized to visualize the docking outcomes for various targets and proteins.
Animals Male Sprague-Dawley (SD) rats with a weight range of 200–250 g (n = 20) were procured from the Shanghai Laboratory Animal Center (SLACCAS Laboratory Animal Inc., Shanghai, China). The supplier’s license number is SCXK (Hu) 2019–0005. The rats were maintained at the Animal Center of the International Institute of Medicine, Zhejiang University, under controlled conditions. The animal housing facility maintained a temperature range of 22°C ± 2°C, complemented by a 12-hour alternating light and darkness cycle facilitated by artificial lighting sources. All animals were allowed to acclimate for one week upon arrival before initiating any experimental procedures. All animal-related experimental procedures were carried out following the protocols outlined by the Experimental Animal Ethics Committee of Zhejiang University School of Medicine (ZJU20240649). Drugs The Sishen Pill (lot: 17080051) was procured from Tong Ren Tang Natural Medicine Co. Ltd., (Beijing, China). The composition included Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). These were formed into pills following the dose ratio of 1:2:4:2:2:2, respectively (100, 200, 400, 200, 200, and 200 g). Although our study primarily relied on network pharmacology to predict the active constituents of SSP, we did not perform our own HPLC analysis. Instead, we refer to the study by Zhang, and Hu, which employed a robust HPLC method to characterize the chemical profile of SSP in its traditional pharmaceutical form. Indomethacin (HY-14397) was purchased from MCE (MedChemExpress, Shanghai, China). Establishment of Small Intestinal Ulcers Following a week of acclimatization at the animal center, the laboratory animals were randomly segregated into four distinct groups: Control, Indomethacin (INDO), INDO + SSP (2.5 g/kg), and INDO + SSP (5 g/kg) - each comprising five animals. Indomethacin at 6 mg/kg/day, dissolved in a 5% Na 2 CO 3 solution, was administered to the INDO and INDO + SSP groups for seven consecutive days to induce enteropathy, while the Control group received the same volume of the Na 2 CO 3 solution alone, as outlined in a previous study. The previously specified dose of SSP was administered intragastrically once daily for five consecutive days once the small intestinal ulceration model had been successfully created. Concurrently, a comparable volume of distilled water was administered orally to rats in the INDO and the Control group. On the 13th day, following an eight-hour fasting period, Carbon dioxide-induced asphyxiation was employed for the humane euthanasia of the animals. Subsequently, blood was executed via cardiac puncture. After careful dissection, the small intestine was soaked in phosphate-buffered saline pH 7.4, rinsed, and finally weighed for analysis. The jejunoileal segment was sectioned into two parts. One section was rapidly frozen using liquid nitrogen, and conserved at a temperature of −80°C for subsequent analysis. Hematoxylin and Eosin (H&E) staining was performed on the remaining segment after it had been fixed in a 10% formalin solution and embedded in paraffin. Assessment of Ulcer Lesions Independent evaluators examined small intestine ulcers with magnifying glasses to eliminate bias. The evaluation followed the criteria established by Cantarella et al. Macroscopic lesions of the small intestine were graded from 0 to 5, based on injury severity and hemorrhage formation. The score of 0 indicated normal small intestine mucosa, 1, pinpoint erosions, 2, lesions less than 1 mm, 3, lesions between 1 and 2 mm, 4, lesions between 3 and 4 mm, and 5 lesions exceeding 4 mm. An ulcer index is calculated using the average ulcer score for each animal. Hematoxylin-Eosin (HE) Staining After collection, small intestine mucosa samples were stored in paraformaldehyde, then dehydrated with increasing alcohol concentrations. These samples were then rinsed in PBS buffer and then processed for paraffin embedding. Thereafter, sections of the embedded tissue, 4 micrometers in thickness, were prepared and underwent H&E staining. Histopathological alterations in the tissue sections were subsequently evaluated through microscopic observation. Evaluation of Cytokines Through ELISA Methodology An ELISA kit was used to measure the activity of IL-6, IL-1β, and TNF-α in rat serum, in accordance with the manufacturer’s instructions (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Assessment of Myeloperoxidase (MPO) Activity and Oxidative Stress Indexes The intestinal tissues of rats were analyzed for the activity of myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT), and the level of malondialdehyde (MDA) using specialized assay kits from Nanjing Jiancheng Bioengineering Institute, Nanjing, China. qPCR Analysis Isolation of RNA from the intestinal tissues was achieved employing the TRIzol reagent (Vazyme Biotech Co, Nanjing, China). Subsequently, the isolated RNA was subjected to reverse transcription to form complementary DNA (cDNA) utilizing the HiScriptIIQRT SuperMix reagent kit (Vazyme Biotech Co, Nanjing, China). The qPCR SYBR Green Master Mix (Vazyme Biotech Co, Nanjing, China) was used for the quantitative PCR. details the primers for quantification of mRNA expression. Expression levels, represented as fold change, were determined applying the 2−∆∆Ct method, employing β-actin as the housekeeping gene. Primers were sourced from Generay Biotech Co., Ltd (Shanghai, China).
Male Sprague-Dawley (SD) rats with a weight range of 200–250 g (n = 20) were procured from the Shanghai Laboratory Animal Center (SLACCAS Laboratory Animal Inc., Shanghai, China). The supplier’s license number is SCXK (Hu) 2019–0005. The rats were maintained at the Animal Center of the International Institute of Medicine, Zhejiang University, under controlled conditions. The animal housing facility maintained a temperature range of 22°C ± 2°C, complemented by a 12-hour alternating light and darkness cycle facilitated by artificial lighting sources. All animals were allowed to acclimate for one week upon arrival before initiating any experimental procedures. All animal-related experimental procedures were carried out following the protocols outlined by the Experimental Animal Ethics Committee of Zhejiang University School of Medicine (ZJU20240649).
The Sishen Pill (lot: 17080051) was procured from Tong Ren Tang Natural Medicine Co. Ltd., (Beijing, China). The composition included Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). These were formed into pills following the dose ratio of 1:2:4:2:2:2, respectively (100, 200, 400, 200, 200, and 200 g). Although our study primarily relied on network pharmacology to predict the active constituents of SSP, we did not perform our own HPLC analysis. Instead, we refer to the study by Zhang, and Hu, which employed a robust HPLC method to characterize the chemical profile of SSP in its traditional pharmaceutical form. Indomethacin (HY-14397) was purchased from MCE (MedChemExpress, Shanghai, China).
Following a week of acclimatization at the animal center, the laboratory animals were randomly segregated into four distinct groups: Control, Indomethacin (INDO), INDO + SSP (2.5 g/kg), and INDO + SSP (5 g/kg) - each comprising five animals. Indomethacin at 6 mg/kg/day, dissolved in a 5% Na 2 CO 3 solution, was administered to the INDO and INDO + SSP groups for seven consecutive days to induce enteropathy, while the Control group received the same volume of the Na 2 CO 3 solution alone, as outlined in a previous study. The previously specified dose of SSP was administered intragastrically once daily for five consecutive days once the small intestinal ulceration model had been successfully created. Concurrently, a comparable volume of distilled water was administered orally to rats in the INDO and the Control group. On the 13th day, following an eight-hour fasting period, Carbon dioxide-induced asphyxiation was employed for the humane euthanasia of the animals. Subsequently, blood was executed via cardiac puncture. After careful dissection, the small intestine was soaked in phosphate-buffered saline pH 7.4, rinsed, and finally weighed for analysis. The jejunoileal segment was sectioned into two parts. One section was rapidly frozen using liquid nitrogen, and conserved at a temperature of −80°C for subsequent analysis. Hematoxylin and Eosin (H&E) staining was performed on the remaining segment after it had been fixed in a 10% formalin solution and embedded in paraffin.
Independent evaluators examined small intestine ulcers with magnifying glasses to eliminate bias. The evaluation followed the criteria established by Cantarella et al. Macroscopic lesions of the small intestine were graded from 0 to 5, based on injury severity and hemorrhage formation. The score of 0 indicated normal small intestine mucosa, 1, pinpoint erosions, 2, lesions less than 1 mm, 3, lesions between 1 and 2 mm, 4, lesions between 3 and 4 mm, and 5 lesions exceeding 4 mm. An ulcer index is calculated using the average ulcer score for each animal.
After collection, small intestine mucosa samples were stored in paraformaldehyde, then dehydrated with increasing alcohol concentrations. These samples were then rinsed in PBS buffer and then processed for paraffin embedding. Thereafter, sections of the embedded tissue, 4 micrometers in thickness, were prepared and underwent H&E staining. Histopathological alterations in the tissue sections were subsequently evaluated through microscopic observation.
An ELISA kit was used to measure the activity of IL-6, IL-1β, and TNF-α in rat serum, in accordance with the manufacturer’s instructions (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).
The intestinal tissues of rats were analyzed for the activity of myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT), and the level of malondialdehyde (MDA) using specialized assay kits from Nanjing Jiancheng Bioengineering Institute, Nanjing, China.
Isolation of RNA from the intestinal tissues was achieved employing the TRIzol reagent (Vazyme Biotech Co, Nanjing, China). Subsequently, the isolated RNA was subjected to reverse transcription to form complementary DNA (cDNA) utilizing the HiScriptIIQRT SuperMix reagent kit (Vazyme Biotech Co, Nanjing, China). The qPCR SYBR Green Master Mix (Vazyme Biotech Co, Nanjing, China) was used for the quantitative PCR. details the primers for quantification of mRNA expression. Expression levels, represented as fold change, were determined applying the 2−∆∆Ct method, employing β-actin as the housekeeping gene. Primers were sourced from Generay Biotech Co., Ltd (Shanghai, China).
We performed all statistical analyses with GraphPad Prism 8 (GraphPad Software, San Diego, California, USA). The multiple comparisons were assessed using a one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. The results are presented as the mean values ± standard error of the mean (SEM), with significance determined at p < 0.05. To ensure robustness and reliability, each experiment was repeated at least three times.
Outcomes of Pharmacoinformatics Analysis Screen Active Ingredient of Sishen Pill and Targets of Small Intestinal Ulcers Active ingredients in Sishen Pill (SSP) were identified through a search using the TCMSP and ETCM databases. Six herbs were identified from SSP: Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). Employing OB ≥ 30% and DL ≥ 0.18 as screening parameters, 72 active compounds were found Supplementary Table 1 ). After eliminating duplicate compounds and those devoid of associated targets, a cohort of 66 active constituents remained. Notably, Zhang et al employed HPLC-ESI-MS/MS to quantitatively determine nine key bioactive compounds—deoxyschizandrin, γ-schizandrin, schizandrin, schizandrol B, schisantherin A, psoralen, isopsoralen, evodiamine, and rutaecarpine—in SSP of the same pharmaceutical form. Furthermore, Hu and Liu utilized a robust HPLC method to characterize SSP’s chemical profile in its traditional formulation. In their study, HPLC analysis successfully separated and identified major bioactive constituents, including bavachin, bavachinin, rutaecarpine, and evodiamine. This externally validated data reinforces our network pharmacology predictions and confirms that the key constituents identified via the TCMSP and ETCM databases are indeed present in SSP. The TCMSP database was engaged to forecast protein targets for the Sishen Pill’s constituents. The nomenclature of all target genes was standardized to the official gene symbols from UniProt (UniProKB), with a specification for human genes. After removing duplicate target genes, the final count of unique therapeutic targets was 222. A search was conducted in the GeneCards database using the keyword “ulceration of small intestine”. Candidate targets were shortlisted based on relevance scores that surpassed twice the median value, and additional data were obtained from the DisGeNET database. In total, 2151 target genes related to small intestinal ulcerations (SIUs) were identified. The selected compounds and target gene were progressively loaded into the Cytoscape to generate a “Drug-Active Ingredient-Target” network as illustrated in . Comprising 294 nodes and 1230 edges, this network graphically depicted the interactions among various components and their associated targets. Identification of Common Targets of SSP and SIUs and Establishment of a PPI Network Employing a Venn diagram, we mapped the intersecting targets of SSP and SIUs, resulting in the discovery of 144 common targets ( ). The STRING database was utilized to construct a PPI network, with disconnected nodes were hidden. We applied a filter with a combined score ≥ 0.9 and downloaded the output file in tsv format. This file was uploaded into Cytoscape 3.10.1 for the creation of a pharmacological network and examine significant protein interactions. The final network comprised 130 nodes and 381 edges ( ). In order to validate the relationships among the bioactive components of SSP and their key targets, a “Bioactive Compounds-Target-Disease” network was developed ( ). GO and KEGG Pathway Enrichment Analysis The DAVID database was used to conduct GO and KEGG analyses on core targets. The GO analysis yielded 487 Biological Process (BP) terms, 68 Cellular Component (CC) terms, and 121 Molecular Function (MF) terms ( , Supplementary Table 2 ). The GO enrichment analysis primarily identified biological processes, with a focus on apoptosis processes, inflammatory response, and cell population proliferation. Cellular Component entries comprised nucleus, cytoplasm, and cytosol. Molecular Function entries encompassed enzyme binding, protein binding, and protein homodimerization activity, among others. This implies that the preventative and therapeutic action of SSP on SIUs involves multiple mechanisms. A comprehensive analysis identified 152 significant KEGG pathways ( Supplementary Table 3 ), with the top 20 being depicted in . Notable pathways encompassed “Pathways in cancer”, “Lipid and atherosclerosis”, “Phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway”, among others. The PI3K/Akt signaling pathway was identified as the most significant and was therefore chosen for further investigation in animal models. Validation of Molecular Docking The active components’ docking potential was classified according to their degree values, leading to the selection of the top four compounds: Quercetin, Bavachinin, Rutaecarpine, and Evodiamine. The Autodock software was utilized to dock the primary bioactive components with the principal targets and significant pathway proteins. The affinity and stability of the interaction between the bioactive compound and protein targets are reflected by the binding energy, with lower values indicating greater structural stability. Evodiamine with AKT1, Rutaecarpine with HSP90AA1, Rutaecarpine with IL6, Rutaecarpine with MAPK1, and Evodiamine with BCL2 displayed the greatest stability in their bound conformations. Detailed information is depicted in . summarizes the binding energies for these interactions. Outcomes of in vivo Experimental Verification SSP Ameliorates Intestinal Damage and Ulceration Caused by Indomethacin (INDO) in Rats The assessment of the ulcer index was conducted in the small intestine. As shown in , rats in the INDO group exhibited significant mucosal damage in the small intestines. Treatment with SSP at dosages of 2.5 and 5 g/kg resulted in a noticeable reduction in intestinal mucosal ulcers. A significant decrease in the small intestine’s ulcer index was particularly evident, with the most substantial effect being observed in the group that received 5 g/kg of SSP ( and ). Overall, while the INDO group exhibited an elevated ulcer index, SSP treatment dose-dependently inhibited this increase. A histopathological examination of small intestine tissues using HE staining confirmed these observations, which aligned with the ulcer index trend ( ). The findings suggest that SSP possesses the capacity to mitigate indomethacin-induced mucosal damage in the rat’s small intestine. SSP Protects Against Oxidative Stress and Inflammation Induced by INDO Serum inflammatory factor levels were assessed via ELISA. As compared to the control group, the INDO group showed a substantial increase in IL‐1β, IL‐6, and TNF‐α activity. Conversely, a notable decrease in cytokine activity was observed in the INDO + SSP (2.5 g/kg) and INDO + SSP (5 g/kg) groups ( ). Subsequently, the kit detected increased MPO activity in small intestinal tissues post-INDO induction, which significantly decreased following SSP administration ( ). The results also showed that the levels of antioxidant enzyme activity, specifically SOD, CAT, and GSH-Px, were notably lower in the INDO group than in the control group ( ), while the MDA activity increased ( ). Following treatment with SSP, a reversal of the aforementioned indicators in the small intestine was observed. Effects of SSP on Gene Expression in INDO-Induced Rats Comparative analysis revealed that the INDO group exhibited higher mRNA levels of TNF-α ( ), IL-6 ( ), and IL-1β ( ). In contrast, the SSP groups exhibited considerable suppression of these inflammatory genes compared to the INDO group. According to the network pharmacology research, the PI3K-AKT signaling pathway is central to the therapy of SIUs. When compared with the control group, INDO showed higher mRNA levels for both AKT1 ( ) and PI3K ( ). SSP administration reduced both PI3K and AKT1 mRNA expression in rats with INDO-induced SIUs. These findings imply that SSP may possess anti-inflammatory and immunomodulatory properties, possibly by modulating the PI3K-AKT signaling pathway, thus affording a protective influence against intestinal damage.
Screen Active Ingredient of Sishen Pill and Targets of Small Intestinal Ulcers Active ingredients in Sishen Pill (SSP) were identified through a search using the TCMSP and ETCM databases. Six herbs were identified from SSP: Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). Employing OB ≥ 30% and DL ≥ 0.18 as screening parameters, 72 active compounds were found Supplementary Table 1 ). After eliminating duplicate compounds and those devoid of associated targets, a cohort of 66 active constituents remained. Notably, Zhang et al employed HPLC-ESI-MS/MS to quantitatively determine nine key bioactive compounds—deoxyschizandrin, γ-schizandrin, schizandrin, schizandrol B, schisantherin A, psoralen, isopsoralen, evodiamine, and rutaecarpine—in SSP of the same pharmaceutical form. Furthermore, Hu and Liu utilized a robust HPLC method to characterize SSP’s chemical profile in its traditional formulation. In their study, HPLC analysis successfully separated and identified major bioactive constituents, including bavachin, bavachinin, rutaecarpine, and evodiamine. This externally validated data reinforces our network pharmacology predictions and confirms that the key constituents identified via the TCMSP and ETCM databases are indeed present in SSP. The TCMSP database was engaged to forecast protein targets for the Sishen Pill’s constituents. The nomenclature of all target genes was standardized to the official gene symbols from UniProt (UniProKB), with a specification for human genes. After removing duplicate target genes, the final count of unique therapeutic targets was 222. A search was conducted in the GeneCards database using the keyword “ulceration of small intestine”. Candidate targets were shortlisted based on relevance scores that surpassed twice the median value, and additional data were obtained from the DisGeNET database. In total, 2151 target genes related to small intestinal ulcerations (SIUs) were identified. The selected compounds and target gene were progressively loaded into the Cytoscape to generate a “Drug-Active Ingredient-Target” network as illustrated in . Comprising 294 nodes and 1230 edges, this network graphically depicted the interactions among various components and their associated targets. Identification of Common Targets of SSP and SIUs and Establishment of a PPI Network Employing a Venn diagram, we mapped the intersecting targets of SSP and SIUs, resulting in the discovery of 144 common targets ( ). The STRING database was utilized to construct a PPI network, with disconnected nodes were hidden. We applied a filter with a combined score ≥ 0.9 and downloaded the output file in tsv format. This file was uploaded into Cytoscape 3.10.1 for the creation of a pharmacological network and examine significant protein interactions. The final network comprised 130 nodes and 381 edges ( ). In order to validate the relationships among the bioactive components of SSP and their key targets, a “Bioactive Compounds-Target-Disease” network was developed ( ). GO and KEGG Pathway Enrichment Analysis The DAVID database was used to conduct GO and KEGG analyses on core targets. The GO analysis yielded 487 Biological Process (BP) terms, 68 Cellular Component (CC) terms, and 121 Molecular Function (MF) terms ( , Supplementary Table 2 ). The GO enrichment analysis primarily identified biological processes, with a focus on apoptosis processes, inflammatory response, and cell population proliferation. Cellular Component entries comprised nucleus, cytoplasm, and cytosol. Molecular Function entries encompassed enzyme binding, protein binding, and protein homodimerization activity, among others. This implies that the preventative and therapeutic action of SSP on SIUs involves multiple mechanisms. A comprehensive analysis identified 152 significant KEGG pathways ( Supplementary Table 3 ), with the top 20 being depicted in . Notable pathways encompassed “Pathways in cancer”, “Lipid and atherosclerosis”, “Phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway”, among others. The PI3K/Akt signaling pathway was identified as the most significant and was therefore chosen for further investigation in animal models. Validation of Molecular Docking The active components’ docking potential was classified according to their degree values, leading to the selection of the top four compounds: Quercetin, Bavachinin, Rutaecarpine, and Evodiamine. The Autodock software was utilized to dock the primary bioactive components with the principal targets and significant pathway proteins. The affinity and stability of the interaction between the bioactive compound and protein targets are reflected by the binding energy, with lower values indicating greater structural stability. Evodiamine with AKT1, Rutaecarpine with HSP90AA1, Rutaecarpine with IL6, Rutaecarpine with MAPK1, and Evodiamine with BCL2 displayed the greatest stability in their bound conformations. Detailed information is depicted in . summarizes the binding energies for these interactions.
Active ingredients in Sishen Pill (SSP) were identified through a search using the TCMSP and ETCM databases. Six herbs were identified from SSP: Buguzhi ( Psoralea corylifolia L.), Wuzhuyu ( Evodiae Fructus ), Roudoukou ( Myristica fragrans Houtt) ., Wuweizi ( Schisandrae Chinensis Fructus ), Shengjiang ( Zingiber officinale Rosc) ., and Dazao ( Jujubae Fructus ). Employing OB ≥ 30% and DL ≥ 0.18 as screening parameters, 72 active compounds were found Supplementary Table 1 ). After eliminating duplicate compounds and those devoid of associated targets, a cohort of 66 active constituents remained. Notably, Zhang et al employed HPLC-ESI-MS/MS to quantitatively determine nine key bioactive compounds—deoxyschizandrin, γ-schizandrin, schizandrin, schizandrol B, schisantherin A, psoralen, isopsoralen, evodiamine, and rutaecarpine—in SSP of the same pharmaceutical form. Furthermore, Hu and Liu utilized a robust HPLC method to characterize SSP’s chemical profile in its traditional formulation. In their study, HPLC analysis successfully separated and identified major bioactive constituents, including bavachin, bavachinin, rutaecarpine, and evodiamine. This externally validated data reinforces our network pharmacology predictions and confirms that the key constituents identified via the TCMSP and ETCM databases are indeed present in SSP. The TCMSP database was engaged to forecast protein targets for the Sishen Pill’s constituents. The nomenclature of all target genes was standardized to the official gene symbols from UniProt (UniProKB), with a specification for human genes. After removing duplicate target genes, the final count of unique therapeutic targets was 222. A search was conducted in the GeneCards database using the keyword “ulceration of small intestine”. Candidate targets were shortlisted based on relevance scores that surpassed twice the median value, and additional data were obtained from the DisGeNET database. In total, 2151 target genes related to small intestinal ulcerations (SIUs) were identified. The selected compounds and target gene were progressively loaded into the Cytoscape to generate a “Drug-Active Ingredient-Target” network as illustrated in . Comprising 294 nodes and 1230 edges, this network graphically depicted the interactions among various components and their associated targets.
Employing a Venn diagram, we mapped the intersecting targets of SSP and SIUs, resulting in the discovery of 144 common targets ( ). The STRING database was utilized to construct a PPI network, with disconnected nodes were hidden. We applied a filter with a combined score ≥ 0.9 and downloaded the output file in tsv format. This file was uploaded into Cytoscape 3.10.1 for the creation of a pharmacological network and examine significant protein interactions. The final network comprised 130 nodes and 381 edges ( ). In order to validate the relationships among the bioactive components of SSP and their key targets, a “Bioactive Compounds-Target-Disease” network was developed ( ).
The DAVID database was used to conduct GO and KEGG analyses on core targets. The GO analysis yielded 487 Biological Process (BP) terms, 68 Cellular Component (CC) terms, and 121 Molecular Function (MF) terms ( , Supplementary Table 2 ). The GO enrichment analysis primarily identified biological processes, with a focus on apoptosis processes, inflammatory response, and cell population proliferation. Cellular Component entries comprised nucleus, cytoplasm, and cytosol. Molecular Function entries encompassed enzyme binding, protein binding, and protein homodimerization activity, among others. This implies that the preventative and therapeutic action of SSP on SIUs involves multiple mechanisms. A comprehensive analysis identified 152 significant KEGG pathways ( Supplementary Table 3 ), with the top 20 being depicted in . Notable pathways encompassed “Pathways in cancer”, “Lipid and atherosclerosis”, “Phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway”, among others. The PI3K/Akt signaling pathway was identified as the most significant and was therefore chosen for further investigation in animal models.
The active components’ docking potential was classified according to their degree values, leading to the selection of the top four compounds: Quercetin, Bavachinin, Rutaecarpine, and Evodiamine. The Autodock software was utilized to dock the primary bioactive components with the principal targets and significant pathway proteins. The affinity and stability of the interaction between the bioactive compound and protein targets are reflected by the binding energy, with lower values indicating greater structural stability. Evodiamine with AKT1, Rutaecarpine with HSP90AA1, Rutaecarpine with IL6, Rutaecarpine with MAPK1, and Evodiamine with BCL2 displayed the greatest stability in their bound conformations. Detailed information is depicted in . summarizes the binding energies for these interactions.
SSP Ameliorates Intestinal Damage and Ulceration Caused by Indomethacin (INDO) in Rats The assessment of the ulcer index was conducted in the small intestine. As shown in , rats in the INDO group exhibited significant mucosal damage in the small intestines. Treatment with SSP at dosages of 2.5 and 5 g/kg resulted in a noticeable reduction in intestinal mucosal ulcers. A significant decrease in the small intestine’s ulcer index was particularly evident, with the most substantial effect being observed in the group that received 5 g/kg of SSP ( and ). Overall, while the INDO group exhibited an elevated ulcer index, SSP treatment dose-dependently inhibited this increase. A histopathological examination of small intestine tissues using HE staining confirmed these observations, which aligned with the ulcer index trend ( ). The findings suggest that SSP possesses the capacity to mitigate indomethacin-induced mucosal damage in the rat’s small intestine. SSP Protects Against Oxidative Stress and Inflammation Induced by INDO Serum inflammatory factor levels were assessed via ELISA. As compared to the control group, the INDO group showed a substantial increase in IL‐1β, IL‐6, and TNF‐α activity. Conversely, a notable decrease in cytokine activity was observed in the INDO + SSP (2.5 g/kg) and INDO + SSP (5 g/kg) groups ( ). Subsequently, the kit detected increased MPO activity in small intestinal tissues post-INDO induction, which significantly decreased following SSP administration ( ). The results also showed that the levels of antioxidant enzyme activity, specifically SOD, CAT, and GSH-Px, were notably lower in the INDO group than in the control group ( ), while the MDA activity increased ( ). Following treatment with SSP, a reversal of the aforementioned indicators in the small intestine was observed. Effects of SSP on Gene Expression in INDO-Induced Rats Comparative analysis revealed that the INDO group exhibited higher mRNA levels of TNF-α ( ), IL-6 ( ), and IL-1β ( ). In contrast, the SSP groups exhibited considerable suppression of these inflammatory genes compared to the INDO group. According to the network pharmacology research, the PI3K-AKT signaling pathway is central to the therapy of SIUs. When compared with the control group, INDO showed higher mRNA levels for both AKT1 ( ) and PI3K ( ). SSP administration reduced both PI3K and AKT1 mRNA expression in rats with INDO-induced SIUs. These findings imply that SSP may possess anti-inflammatory and immunomodulatory properties, possibly by modulating the PI3K-AKT signaling pathway, thus affording a protective influence against intestinal damage.
The assessment of the ulcer index was conducted in the small intestine. As shown in , rats in the INDO group exhibited significant mucosal damage in the small intestines. Treatment with SSP at dosages of 2.5 and 5 g/kg resulted in a noticeable reduction in intestinal mucosal ulcers. A significant decrease in the small intestine’s ulcer index was particularly evident, with the most substantial effect being observed in the group that received 5 g/kg of SSP ( and ). Overall, while the INDO group exhibited an elevated ulcer index, SSP treatment dose-dependently inhibited this increase. A histopathological examination of small intestine tissues using HE staining confirmed these observations, which aligned with the ulcer index trend ( ). The findings suggest that SSP possesses the capacity to mitigate indomethacin-induced mucosal damage in the rat’s small intestine.
Serum inflammatory factor levels were assessed via ELISA. As compared to the control group, the INDO group showed a substantial increase in IL‐1β, IL‐6, and TNF‐α activity. Conversely, a notable decrease in cytokine activity was observed in the INDO + SSP (2.5 g/kg) and INDO + SSP (5 g/kg) groups ( ). Subsequently, the kit detected increased MPO activity in small intestinal tissues post-INDO induction, which significantly decreased following SSP administration ( ). The results also showed that the levels of antioxidant enzyme activity, specifically SOD, CAT, and GSH-Px, were notably lower in the INDO group than in the control group ( ), while the MDA activity increased ( ). Following treatment with SSP, a reversal of the aforementioned indicators in the small intestine was observed.
Comparative analysis revealed that the INDO group exhibited higher mRNA levels of TNF-α ( ), IL-6 ( ), and IL-1β ( ). In contrast, the SSP groups exhibited considerable suppression of these inflammatory genes compared to the INDO group. According to the network pharmacology research, the PI3K-AKT signaling pathway is central to the therapy of SIUs. When compared with the control group, INDO showed higher mRNA levels for both AKT1 ( ) and PI3K ( ). SSP administration reduced both PI3K and AKT1 mRNA expression in rats with INDO-induced SIUs. These findings imply that SSP may possess anti-inflammatory and immunomodulatory properties, possibly by modulating the PI3K-AKT signaling pathway, thus affording a protective influence against intestinal damage.
Nonsteroidal anti-inflammatory drugs (NSAIDs) have revolutionized pain and inflammation management in clinical practice. Despite this, the utilization of these drugs is linked to a broad spectrum of adverse effects, most notably gastrointestinal complications. 28 A significant proportion of these complications involve the small intestine, leading to the development of small intestinal ulcers (SIUs). Among the prevalent drugs for treating NSAID-induced SIUs, Proton Pump Inhibitors (PPIs), primarily used in NSAID gastropathy treatment, have demonstrated insufficient effectiveness. , Mucoprotective agents like misoprostol offer an alternative treatment method for NSAID enteropathy, but their high incidence of side effects may limit their application. These challenges highlight the urgent need for novel and effective therapeutic strategies for NSAID-induced SIUs. Traditional Chinese Medicine (TCM) has gained recognition for its safety profile and multi-target therapeutic effects. Sishen Pill (SSP), a well-documented Chinese patent medicine, has been traditionally employed to treat chronic colitis and diarrhea. , The present research integrated network pharmacology with experimental confirmation to systematically explore the therapeutic potential of SSP in NSAID-induced SIUs. Our study identified 66 bioactive constituents in SSP, with quercetin, bavachinin, rutaecarpine, and evodiamine emerging as pivotal components. These compounds exhibit potent anti-inflammatory, antioxidant, and barrier-protective properties. Quercetin, a flavonoid with antioxidant and immunomodulatory properties, inhibits proinflammatory cytokines via the cGAS-STING pathway, restoring macrophage polarization and intestinal barrier integrity. , Bavachinin has been shown to regulate apoptosis through the p53/Bax/Bcl2 axis, reducing crypt cell apoptosis in colitis models. Additionally, rutaecarpine has been reported to attenuate intestinal inflammation via NRF2 activation, while evodiamine modulates gut microbiota composition, enriching short-chain fatty acid (SCFA)-producing species that promote barrier function. , Analyzing these key constituents, we hypothesize that SSP exerts a synergistic therapeutic effect through its modulation of inflammatory, oxidative, and apoptotic pathways. These findings align with prior studies linking SSP to TLR2/IRAK4/NF-κB, Nrf2/HO-1, and PI3K/AKT pathway. , , Inflammation is a key driver of SIU pathogenesis, primarily mediated by IL-1β, IL-6, and TNF-α, which disrupt epithelial integrity through neutrophil/macrophage-mediated pathways. Our results showed that NSAID exposure significantly elevated these inflammatory markers, whereas SSP administration effectively suppressed them. In addition to its anti-inflammatory properties, SSP also demonstrated strong antioxidant activity, reversing NSAID-induced reductions in GSH-Px, CAT, and SOD, while lowering MPO and MDA levels. These findings suggest that SSP mitigates NSAID-induced SIU progression by modulating inflammatory cytokines and oxidative stress pathways. The PI3K/Akt pathway was identified as a key regulatory axis in SSP’s therapeutic effects. Network pharmacology analysis revealed PI3K/Akt as the most significantly enriched pathway, further validated by molecular docking, which demonstrated strong binding affinities between SSP components (eg, evodiamine, rutaecarpine) and key PI3K/Akt targets (AKT1, PI3K). In vivo validation revealed that SSP treatment significantly downregulated PI3K and AKT1 mRNA expression in INDO-induced rat models, correlating with reduced ulcer severity and decreased inflammatory cytokine expression. Mechanistically, PI3K/Akt signaling is pivotal in regulating cell survival, inflammation, and tissue regeneration. Upon activation, Akt phosphorylates multiple downstream targets crucial for intestinal homeostasis. Akt phosphorylates BAD (Bcl-2-associated death promoter), preventing its interaction with Bcl2, thereby inhibiting mitochondrial apoptosis. The suppression of Bcl2 mRNA following SSP treatment ( Supplementary Figure 1A and B ) supports this mechanism, indicating a potential anti-apoptotic effect. Moreover, Akt-mediated activation of mTOR enhances protein synthesis and mucosal repair, while GSK-3β inhibition promotes cell cycle progression through cyclin D1 accumulation. These molecular events likely contribute to the histopathological improvements observed in SSP-treated rats. Additionally, Akt inhibits IκB kinase (IKK), preventing NF-κB nuclear translocation and proinflammatory cytokine release. SSP promotes mucosal healing through multiple mechanisms. Recent evidence links PI3K/Akt signaling to intestinal barrier integrity and autophagy regulation. Dysregulation of this pathway in NSAID-induced injury results in tight junction disruption and increased permeability. Notably, SSP-treated rats exhibited significantly higher occludin and ZO-1 gene expression, suggesting its role in barrier restoration via PI3K/Akt modulation ( Supplementary Figure 1C and D ). Additionally, rutaecarpine’s activation of NRF2 enhances antioxidant defenses, which may further support mucosal healing. SSP may modulates both innate and adaptive immune responses. By inhibiting the cGAS-STING pathway, quercetin reprograms macrophage polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotypes, reducing IL-6 and TNF-α secretion. Furthermore, SSP downregulates Th17 cell activity while promoting regulatory T-cell (Treg) expansion, rebalancing immune homeostasis in ulcerated tissues. These immunomodulatory effects are critical for resolving chronic inflammation and preventing ulcer recurrence. While our focus was on inflammation and oxidative stress, emerging evidence highlights SSP’s potential to modulate gut microbiota. Future studies should explore SSP’s impact on microbial diversity and metabolite profiles. While SSP demonstrated dose-dependent efficacy in rats, its translational feasibility remains a key consideration. Preclinical studies suggest an optimal SSP dosage of 2.5–5 g/kg/day for gastrointestinal disorders, yet human equivalent dosing remains unstandardized. Based on body surface area extrapolation, a tentative human dose of 24.3–48.6 g/day has been proposed, necessitating clinical validation. Additionally, SSP’s pharmacokinetic interactions with existing gastroprotective measures require careful evaluation. Co-administration with PPIs (eg, omeprazole, esomeprazole) may influence SSP metabolism via cytochrome P450 2C19 (CYP2C19) inhibition. This could increase plasma concentrations of SSP constituents, potentially enhancing efficacy while raising toxicity risks. Furthermore, chronic PPI use disrupts gut microbiota, whereas SSP promotes the growth of beneficial taxa like Lactobacillus and Bifidobacterium. , The interplay between PPIs and SSP on microbiota composition warrants further investigation. Clinicians should prioritize weak CYP2C19 inhibitors, advocate fiber- and polyphenol-rich diets, and monitor nutrient levels to optimize therapeutic outcomes. Rigorous clinical trials are imperative to validate these interactions and refine evidence-based guidelines. Consequently, SSP has demonstrated significant potential in the therapeutic intervention of NSAID-induced SIUs. Whether acting as antioxidants or modulators of cellular signaling, the effects of SSP on oxidative and inflammatory balance are crucial. Despite the promising findings, this study has several limitations. The precise molecular mechanisms by which SSP modulates the PI3K/Akt pathway remain incompletely understood. While network pharmacology and molecular docking identified potential interactions, direct experimental validation at the protein level is lacking. Future studies should employ biochemical assays such as co-immunoprecipitation, or Western blotting to confirm these interactions and delineate the downstream signaling events. Additionally, while this study demonstrated SSP’s therapeutic potential in an indomethacin (INDO)-induced rat model of SIUs, interspecies differences in drug metabolism and immune response may limit direct clinical translation. Validation in human-derived intestinal organoid models or well-designed clinical trials is necessary to confirm its efficacy in human populations. Furthermore, this study primarily focused on the PI3K/Akt pathway; however, SIU pathogenesis involves multiple intersecting signaling cascades, including NF-κB, MAPK, and AMPK pathways. Future transcriptomic and proteomic analyses should be conducted to explore SSP’s broader regulatory network and uncover additional therapeutic targets. Finally, a direct comparison between SSP and standard gastroprotective agents, such as proton pump inhibitors (PPIs) or misoprostol, was not performed in this study. Future investigations should include comparative efficacy studies to evaluate whether SSP offers advantages over conventional therapies in NSAID-induced enteropathy. Despite these limitations, the study’s findings provide evidence supporting the mechanism of SSP in intervening in NSAID-induced SIUs. These results also provide valuable direction for future research endeavors by the investigative team.
This study underscores the promising therapeutic efficacy of SSP in managing NSAID-induced SIUs, illuminating its complex interactions with various targets associated with the pathogenesis of SIUs. A possible therapeutic mechanism of SSP is its ability to modulate oxidative stress and inflammatory responses, possibly through PI3K/Akt. Our research findings are expected to facilitate the formulation of potent therapeutic approaches aimed at treating NSAID-induced SIUs and to encourage additional studies into the pharmacological properties of traditional Chinese medicine. Future studies should incorporate integrated biochemical assays to confirm the binding interactions between SSP constituents and the PI3K/Akt signaling pathway. Comparative analyses with established gastroprotective agents are essential to rigorously evaluate SSP’s efficacy and safety. Moreover, comprehensive metagenomic and metabolomic investigations should be undertaken to delineate SSP’s impact on gut microbiota composition and metabolite production.
|
Variable Effect of the COVID-19 Pandemic on Radiation Oncology Practices in the United States | 1340aa35-fdba-454f-94fb-aceea86a58a9 | 8810273 | Internal Medicine[mh] | In the United States in 2021, 1.9 million new cancer cases were estimated to be diagnosed. More than half of these patients will be treated with radiation therapy. Early in the COVID-19 pandemic, the American Society for Radiation Oncology (ASTRO) began to survey physician leaders at radiation oncology practices in the United States to understand how the field was responding to the outbreak. Surveys were repeated at multiple points during the pandemic. To our knowledge, this is the only longitudinal COVID-19 practice survey in oncology in the United States.
Surveys were emailed to all US radiation oncologists from the ASTRO membership directory who had self-identified as department leaders with the position of medical director. Surveys were designed to assess practice demographics, safety measures, patient volume, financial effect, treatment delays, and vaccination barriers. Consent was obtained, and data were deidentified. Responses were collected April 16-30 and June 11-25, 2020, and January 15 to February 7, 2021; trends were compared across timepoints. Comparisons were made between community-based and academic practices, between freestanding and hospital-based clinics, and between clinics with ZIP codes in large metropolitan areas (ie, population ≥1 million people), smaller metropolitan areas (ie, population <1 million), or nonmetropolitan areas, using categories created from the US Census–based Rural-Urban Continuum Codes. The significance of those differences was evaluated by Pearson's χ 2 test, the Fisher exact test, the Student t test, and analysis of variance; P < .05 was considered statistically significant.
At the beginning of the pandemic in the United States, 222 of 517 physician leaders (43%) responded from community (65%) and academic (34%) practices. In early 2021, 117 of 509 physician leaders (23%) responded from community (55%) and academic practices (44%). Full demographics are provided in . Throughout the entire survey period, 100% of practices remained open. Patient volumes dropped in 73% of clinics; on average, visits were down 21% (range, 5%-75%) in early 2021. All clinics continued to use enhanced safety protocols to protect patients and staff through early 2021; 73% experienced reduced staffing at some point and 7% closed a satellite location. In early 2021, 15% of surveyed centers continued postponing treatment for low-risk diseases, down from 92% of centers in April 2020. Only 12% of centers reported deferring new patient visits in early 2021, down from 75% in April 2020. At the beginning of the pandemic, deferrals were most common for low-risk prostate cancer (88%) and early-stage breast cancer (73%), and least used for cervical, vaginal, and pediatric cancers (each 1.4%) ( , ). Status of care in community and academic practices in early 2021 is summarized in . Community centers reported more patients presenting with advanced disease (81% vs 45%, P < .001) and treatment interruptions (77% vs 56%, P = .018) than academic practices. Community centers experienced more shortages of personal protective equipment (PPE) (50% vs 23%, P = .003), less telemedicine adoption for new patients (38% vs 75%, P < .001), lower vaccine access (61% vs 41%, P = .035), and increased vaccine hesitancy among staff (72% vs 41%, P = .001) and patients (59% vs 42%, P = .065). The use of telemedicine for new patient consults was more common for clinics located in larger communities (75% in metropolitan areas ≥1 million vs 38% in metropolitan areas <1 million vs 21% in nonmetropolitan areas, P < .001). Compared with metropolitan clinics, practices in more rural areas reported greater vaccine hesitancy from staff (46% in metropolitan areas ≥1 million vs 62% in metropolitan areas <1 million vs 90% in nonmetropolitan areas, P = .002). Compared with hospital-based clinics, freestanding clinics reported increased PPE shortages (57% vs 30%, P = .006) and barriers to vaccine access (65% vs 57%, P = .07). Most practice leaders (87%) reported increased social or financial patient hardship during the pandemic.
The ASTRO COVID-19 surveys provide incomplete but unique insight into how the pandemic has affected our field's practice and product. How could we leverage these insights to better prepare for future disruptions, regardless of scale or scope? All commercial sectors of the US economy have confronted this question during the pandemic. Many, particularly the financial services, logistics, and retail industries, have turned to organizational and system resilience theories to respond to the shocks to their operations. , Resilience in this context refers to an organization or system's capacity to withstand and recover from adverse disruptions. Structured concepts of resilience have found a natural home in software and engineering fields but have been increasingly used to assess adaptive behaviors of natural and human ecologic systems, including health care systems operating in unpredictable environments. , A body of literature has emerged to evaluate health system adaptation to the COVID-19 pandemic according to models of resilience at the national , and global , levels, accompanied by efforts by the World Health Organization and the Organization for Economic Cooperation and Development to provide international best practice recommendations to sustain resilient health system performance during and after COVID-19. , , Drawing actionable connections from such efforts to the delivery of radiation therapy in the United States requires a framework relevant to our providers, payors, policymakers, and patients. Studies outside our field published early in the pandemic understandably focused on models of preserving COVID-19 care delivery in the face of abrupt demand/resource imbalance. A more holistic construct relevant to short and long-term priorities of radiation oncology can be applied from the work of Béné et al, which leverages published experience in international development to expand from a simple model of preserving baseline expectations of care “into a more elaborated concept which embraces the ability not simply to bounce back but also to adapt and to transform.” In this “3-D Resilience framework,” the innate capacity of a care delivery system to “absorb” a shock is just the first in a series of responses which can evolve into a capacity to “adapt” (modify current behavior) and “transform” (fundamentally modify future behavior) as warranted by the severity of hardship. In the case of radiation oncology, the initial “absorption” (ie, treatment cancellations, satellite clinic closures, and machines or time slots dedicated to COVID-19-positive patients) and “adaptation” (ie, creation of treatment deferment guidelines, infection control protocols, and novel use of telehealth) may eventually give way to “transformation” (ie, broadened use of hypofractionation, hybrid in-person/virtual care platforms, and/or shared care models with primary care physicians or midlevel providers). Given the continuing pandemic activity resulting from new variant strains of COVID-19, the potential need for COVID-specific transformation remains relevant. More likely, slow-rolling external “shocks” to our field originating in parallel from payors (ie, value-based payment models and price transparency requirements) and patients (ie, growth of consumerism and expectations of on-demand access) will create realities necessitating transformation. The rapid, intensive behavioral changes captured by the ASTRO COVID-19 surveys highlight professional flexibility and commitment that can be leveraged for preemptive growth—growth we will require regardless of COVID-19. Specific findings from the surveys that merit closer inspection through the lens of resilience come from 5 areas of concern: (1) patient access to treatment, (2) telemedicine, (3) treatment delays, (4) treatment interruptions, and (5) the variable effect on different types of practices. Patient access to treatment Our surveys indicate that patient referral access to essential radiation oncology services in the United States was preserved during the pandemic, suggesting successful absorption of initial shock and preservation of expected baseline function. All survey respondents indicated that their practice system remained open throughout the pandemic and that patients continued to have access to radiation therapy. Despite keeping system-wide facilities open, however, 73% of practices experienced staffing shortages at some point, and 7% of respondents said their system closed one or more satellite facilities. Telemedicine Patient access also was sustained through the widespread adoption of telemedicine services (an “adaptive” capability demonstrated by most medical disciplines). By early 2021, more than 8 in 10 radiation oncology practices reported using telemedicine for routine surveillance visits, and more than 5 in 10 for new patient consults—despite 9 in 10 respondents noting that telemedicine was not used by their practice before the pandemic. Treatment delays Although all responding radiation therapy centers continued to provide services, caveats should be noted. First, although clinics stayed open, radiation treatments were still delayed for many patients. At the height of US lockdowns in April 2020, up to 9 in 10 practices were delaying radiation therapy for patients with lower-risk cancers, most commonly early-stage breast and low/intermediate-risk prostate cancers. Reported delays for these lower-risk cancers fell to 15% of practices by early 2021, however. Treatment interruptions Additionally, although patients were able to access treatment, the surveys indicate that completing those courses of treatment was more challenging than before the pandemic. Two-thirds of respondents in early 2021 reported that their patients had experienced radiation treatment interruptions. This disruption represents a potentially serious threat to treatment quality and cancer outcomes, and it merits dedicated study at both institutional and population levels to identify “adaptation” and “transformation" strategies to prevent treatment quality degradation during crisis events, particularly in vulnerable patient populations already at high risk for such events. Variable effect on different types of practices The ASTRO COVID-19 surveys suggest the pandemic had an uneven effect across radiation oncology practices, with potentially greater relative strain shouldered by community-based centers with less absorptive capacity for shock. Treatment interruptions, PPE shortages, and vaccination barriers were significantly more common at community-based practices than at academic practices. Access to primary care and cancer screening services upstream of radiation therapy may also have been disproportionately compromised in community settings, given the larger proportion of such providers who reported an increase in patients presenting with more advanced cancers (81.3% vs 45.1%, P < .001). Differences also were observed between clinics in larger metropolitan and more rural areas, as well as in freestanding clinics compared with hospital-based clinics. Such heterogeneity reflects the fragmented, decentralized organization of American health care, and the social diversity of our country. The severity and dynamics of disruptive events like COVID-19 are expected to vary widely across facilities serving different populations with differing treatment resources. Adaptive and transformative resilience practices required by each center promise to be just as varied, making standardized guidelines and outcome measures challenging to implement. A question for ASTRO and the field to consider will be whether preparation for future disruption events should be organized at the community or regional level, so that resilience can be fostered by pre-existing collaborations and action plans shared by complementary partner institutions. It has been recognized that collaboration and shared learning among diverse partners, including patients and their families, , fosters resilient care systems. The limited participation in these web-based surveys restrict interpretation and generalizability of these results. Survey response rates decreased over time, from 43% in April 2020 to 23% in January 2021. Additionally, leaders of larger practices (ie, those with larger numbers of staff and patients, as reported in ) continued to respond across time, which may bias our results toward the experience of those practices. There also were limitations with the sampling frame. The ASTRO membership directory is estimated to include 90% to 93% of practicing US radiation oncologists, and members can self-identify as leaders with the position of medical director of their departments/practices. Thus, the ASTRO list of medical directors may not represent every practice medical director in the United States. Our respondents reported a total of 1376 and 948 radiation oncologists under their leadership in 2020 and 2021, respectively. The US Department of Health and Human Services estimated a total 5338 radiation oncologists in the United States as of 2017. Accordingly, approximately 25.7% and 17.8% of US radiation oncologists are estimated to be represented from our sample in 2020 and 2021, respectively.
Our surveys indicate that patient referral access to essential radiation oncology services in the United States was preserved during the pandemic, suggesting successful absorption of initial shock and preservation of expected baseline function. All survey respondents indicated that their practice system remained open throughout the pandemic and that patients continued to have access to radiation therapy. Despite keeping system-wide facilities open, however, 73% of practices experienced staffing shortages at some point, and 7% of respondents said their system closed one or more satellite facilities.
Patient access also was sustained through the widespread adoption of telemedicine services (an “adaptive” capability demonstrated by most medical disciplines). By early 2021, more than 8 in 10 radiation oncology practices reported using telemedicine for routine surveillance visits, and more than 5 in 10 for new patient consults—despite 9 in 10 respondents noting that telemedicine was not used by their practice before the pandemic.
Although all responding radiation therapy centers continued to provide services, caveats should be noted. First, although clinics stayed open, radiation treatments were still delayed for many patients. At the height of US lockdowns in April 2020, up to 9 in 10 practices were delaying radiation therapy for patients with lower-risk cancers, most commonly early-stage breast and low/intermediate-risk prostate cancers. Reported delays for these lower-risk cancers fell to 15% of practices by early 2021, however.
Additionally, although patients were able to access treatment, the surveys indicate that completing those courses of treatment was more challenging than before the pandemic. Two-thirds of respondents in early 2021 reported that their patients had experienced radiation treatment interruptions. This disruption represents a potentially serious threat to treatment quality and cancer outcomes, and it merits dedicated study at both institutional and population levels to identify “adaptation” and “transformation" strategies to prevent treatment quality degradation during crisis events, particularly in vulnerable patient populations already at high risk for such events.
The ASTRO COVID-19 surveys suggest the pandemic had an uneven effect across radiation oncology practices, with potentially greater relative strain shouldered by community-based centers with less absorptive capacity for shock. Treatment interruptions, PPE shortages, and vaccination barriers were significantly more common at community-based practices than at academic practices. Access to primary care and cancer screening services upstream of radiation therapy may also have been disproportionately compromised in community settings, given the larger proportion of such providers who reported an increase in patients presenting with more advanced cancers (81.3% vs 45.1%, P < .001). Differences also were observed between clinics in larger metropolitan and more rural areas, as well as in freestanding clinics compared with hospital-based clinics. Such heterogeneity reflects the fragmented, decentralized organization of American health care, and the social diversity of our country. The severity and dynamics of disruptive events like COVID-19 are expected to vary widely across facilities serving different populations with differing treatment resources. Adaptive and transformative resilience practices required by each center promise to be just as varied, making standardized guidelines and outcome measures challenging to implement. A question for ASTRO and the field to consider will be whether preparation for future disruption events should be organized at the community or regional level, so that resilience can be fostered by pre-existing collaborations and action plans shared by complementary partner institutions. It has been recognized that collaboration and shared learning among diverse partners, including patients and their families, , fosters resilient care systems. The limited participation in these web-based surveys restrict interpretation and generalizability of these results. Survey response rates decreased over time, from 43% in April 2020 to 23% in January 2021. Additionally, leaders of larger practices (ie, those with larger numbers of staff and patients, as reported in ) continued to respond across time, which may bias our results toward the experience of those practices. There also were limitations with the sampling frame. The ASTRO membership directory is estimated to include 90% to 93% of practicing US radiation oncologists, and members can self-identify as leaders with the position of medical director of their departments/practices. Thus, the ASTRO list of medical directors may not represent every practice medical director in the United States. Our respondents reported a total of 1376 and 948 radiation oncologists under their leadership in 2020 and 2021, respectively. The US Department of Health and Human Services estimated a total 5338 radiation oncologists in the United States as of 2017. Accordingly, approximately 25.7% and 17.8% of US radiation oncologists are estimated to be represented from our sample in 2020 and 2021, respectively.
Although limited in scope and detail, the ASTRO COVID-19 surveys are the only longitudinal pandemic practice survey in American oncology. We observed that patient access to radiation therapy has been preserved throughout the pandemic. Safety protocols were universally deployed, telehealth was widely embraced, and most clinics no longer deferred treatment in early 2021. More late-stage disease presentation, treatment interruptions, PPE shortages, and vaccination barriers, however, were reported by community-based practices than academic practices, and rural practices appear to have faced increased obstacles relative to urban centers. These findings provide an imperfect but unique observation of COVID-19’s real-world effect on the delivery of radiation therapy in the United States. Downstream lessons in service adaptation and transformation can potentially be guided by formal concepts of resilience, which promise to serve the field well in an age of rapid, unpredictable change.
|
Dimensional accuracy of additive and subtractive manufactured ceramic-reinforced hybrid composite inlays: a CBCT-based in vitro study | 02920a00-5228-49e4-8d27-ddfa8a9e4bcd | 11840043 | Dentistry[mh] | Indirect composite restorations gain advantages over direct composite restorations by minimizing polymerization shrinkage stresses, which may negatively impact the long-term prognosis of teeth by causing additional stress within the remaining walls of the cavity . Therefore, dentists prefer indirect restoration of medium-to-large intracoronal cavities despite the lack of significant difference in their lifespan from that of their direct counterparts . The dimensional accuracy in the form of internal and marginal adaptation is one of the criteria that determines how long an indirect restoration lasts . Inadequate marginal adaptation may result in luting cement disintegration and eventually microleakage, bacterial infiltration, hypersensitivity, secondary caries, gingival irritation and periodontal disease , . Furthermore, inadequate internal fit can lead to poor marginal fit, thicken luting cement, reduce retention, impact occlusion, and negatively affect the restoration’s ability to withstand fracture , . The space between the edge of the restoration and the preparation line is known as the marginal gap (MG), whereas the space between the interior of the inlay and the dental structure is known as the internal gap (IG) . In addition, absolute marginal discrepancy (AMD) is the angular combination of MG and extension errors , which may be either overextended or underextended, leading to plaque accumulation or microleakage, respectively. Notably, composite restorations fabricated via subtractive manufacturing of computer-aided design and manufacturing (CAD/CAM) technology are considered long-standing restorations because of their high fracture resistance . However, the adaptation of inlay restorations may be hampered in some areas by scanning inaccuracies due to the complex design of tooth preparation compared with that of crowns . Microcrack formation during the milling process is one of the downsides of milled restorations . More recently, additive manufacturing using three-dimensional (3D) printing technology has been introduced for the fabrication of inlays and onlays , . Unlike subtractive or traditional manufacturing, this technology decreases material waste, instrument wear, and human error without increasing production time , . Despite the fact that printed restorations appear to have a better dimensional fit , , the subtraction method remains the gold standard for computer-aided manufacturing, as it is possible to mill a wide range of materials from soft wax to hard metals . The commonly used materials in the digital workflow of indirect partial restorations are dental ceramics, hybrid materials, and composites . Hybrid composite materials are desirable substitutes for dental ceramics because of their favorable optical and mechanical characteristics, decreased fracture, and ease of fabrication, cementation, and repair , . Recent developments in materials science have made it possible for commercially available ceramic fillers to strengthen temporary resin materials and enhance their mechanical, biological and physical properties to the point where they can be utilized as definitive restorations , . Vita Enamic (Vita Zahnfabrik, Bad Säckingen, Germany) is a polymer-infiltrated ceramic network hybrid composite material introduced for chairside CAD/CAM restorations . VarseoSmile Crownplus (Bego, Bremen, Germany) is a ceramic-filled hybrid composite material for 3D printing of inlays, onlays and veneers that was introduced in the year 2020 . The IG and MG contents of 3D-printed onlays made with this material were found to be lower than those of milled products . Recently, VarseoSmile TriniQ (Bego), a hybrid composite material, was introduced by the same manufacturer, expanding its scope to include permanent bridges and extensive temporaries . Daghrery et al. investigated the surface properties and dimensional accuracy of dental laminate veneers made from the above-mentioned materials using the 3D printing method in comparison with the milling method , . However, there is limited evidence regarding the dimensional accuracy and other characteristics of 3D-printed inlays compared with other manufacturing methods, such as milling and conventional laboratory fabrication. Moreover, there is a need to compare the recently introduced VarseoSmile TriniQ with the existing VarseoSmile Crownplus for their dimensional accuracy as long-term clinical studies don’t exist. Therefore, this study aimed to compare the absolute marginal discrepancy, marginal gap, internal gap, and overall discrepancy between the cavity walls and the intaglio surface of 3D-printed inlays using two types of hybrid resin composite materials of different composition with those of milled inlays. The proposed null hypothesis was that there would be no significant difference in the AMD, MG, IG, or OD of the inlays due to either the fabrication method (additive/subtractive) or the composition of the material. The alternate hypothesis tested was that there would be a significant difference in the MG and IG of inlays among the groups. Sample size A prior sample size for this study was calculated according to a previous study . The power = 0.99 was determined with F tests, one-way ANOVA, the fixed effects method, α = 0.05, and β = 0.2. The effect size f = 5.000791 was measured by using common standard deviation in G-power software (version 3.1.9.7; University of Dusseldorf, Germany) . The final sample size was estimated to be 6 per group, which was rounded to 10 after including a sample failure allowance of 40%, as the fabrication methods used for the inlays in this study are highly technique sensitive. Tooth preparation for inlay A mandibular right first molar typodont (Standard Model AG-3, Frasaco Gmbh, Tettnang, Germany) was prepared under continuous air‒water spray for Class II mesiococclusodistal inlay restoration via a series of recommended diamond burs with a 6° taper (Inlay Preparation Set 4261, Komet, Lemgo, Germany) and a high-speed handpiece (SMART Torque S608C, KaVo Dental GmbH, Biberach, Germany). Initially, the occlusal cavity was prepared with a pulp depth and an isthmus width of 2 mm. The proximal box was subsequently prepared with the axial wall measuring 1.5 mm in height and depth and the buccolingual width measuring 3 mm. The dimensions of the cavity were verified with the help of a graduated periodontal probe (Falcon Periodontal Williams probe, Lucca, Italy). The two axial walls on the mesial and distal proximal boxes had a convergence angle of 6° and rounded line angles to avoid undercuts and sharp areas . Resin dies A total of 40 resin dies were digitally replicated from the prepared typodont tooth . An optical impression of the inlay preparation on the typodont was captured using an intraoral scanner (i700; Medit Corp., Seoul, Korea) to create a virtual CAD file. After the CAD file was imported into a 3D printing machine (MAX UV385; ASIGA, Sydney, Australia), the resin dies were fabricated using a 3D-prinitng resin dedicated for dies (DentaMODEL; ASIGA) and allowed to dry for 10 min after washing in pure isopropyl alcohol. The dies were postcured in a UV postcuring unit (Flash curing chamber; ASIGA) for 20 min in accordance with the manufacturer’s specifications and then stored under the same conditions in a dark storage compartment. Randomization The prepared resin dies were randomly divided into 4 groups of 10 each on the basis of the method of fabrication as follows: Group PVC, 3D-printed VarseoSmile Crown plus (Bego); Group PVT, 3D-printed VarseoSmile TrinQ (Bego); Group MVE, milled Vita Enamic (Coltene Whaledent, Altstatten, Switzerland); Group CGP (control), conventional layering technique of an indirect composite resin (Gradia plus; GC Europe NV, Leuven, Belgium). The materials used in this study are shown in Table . Digital workflow For the PVC, PVT and MVE groups involving digital additive and subtractive manufacturing workflows, the resin dies were scanned using an intraoral scanner (i700; Medit Corp). The 3D images were exported and saved in standard tessellation language (STL) format. CAD of the inlays was performed and analyzed using Exocad software 3.0 (Exocad GmbH, Darmstadt, Germany) in the PVC and PVT groups, whereas the designed files were analyzed using the 3Shape Dental system (version 2020, 3Shape, Copenhagen, Denmark) in the MVE group. The cement spacer was set to 40 μm, whereas all other parameters were set to 0 μm starting from 1 mm from all the cavosurface margins. The sequence of procedures performed is illustrated in Fig. . Groups PVC and PVT The data in the STL file format were transferred to a digital light processing (DLP) printer (Varseo XS; Bego), and the respective permanent restorative resin in liquid form was used. The inlays were printed at a 405 nm wavelength, 50 μm resolution (X, Y, and Z), and 0.25 mm/min printing speed. All samples were created simultaneously at a constant room temperature of 23 °C to reduce procedural variability. The printer was previously calibrated following the manufacturer’s guidelines. After the printing procedure was complete, a spatula was used to carefully remove the restorations from the printing platform. The collected samples were cleaned for 480 s in an ultrasonic bath (Foshan Adelson Medical Devices Co., Foshan, China) containing 96% ethanol solution to remove the residual unpolymerized resin (PanReac AppliChem ITW Reagents, Darmstadt, Germany), after which they were gently dried. The restorations were placed in an Otoflash light curing device (Bego) for postcuring. The samples were exposed to 1500 flashes per second at 10 Hz in a nitrogen gas environment (1.0–1.2 bar). Later, the restorations were turned over to repeat the postcuring procedure on the opposite side of the restoration. Group MVE A CEREC inLab MC XL (Dentsply Sirona; Charlotte, NC, USA) wet milling and grinding unit fitted with a water-cooled rotary bur of sizes 1 and 2.5 mm was used for inlay fabrication. The milling parameters were chosen following the manufacturer’s guidelines. After being steamjet cleaned, all of the inlays were allowed to air dry. After the removal of the sprue, the inlays were tried, and minor adjustments were made to smooth the restoration as needed. These samples were inspected for manufacturing flaws prior to the seating process. Group CGP (control) A separating medium (Die lube; Dentsply Sirona) was applied to the cavity inner and outer walls to make separation easier. Three layers of die spacer were applied to a thickness ≈ of 40 μm . Multiple increments of high-strength nanohybrid indirect RCs (Gradia plus, GC Europe NV) of 1–2 mm thickness was adapted to the internal walls of the cavity and polymerized using a light curing unit (Labolight DUO; GC Europe NV). Following the buildup and morphological contouring, the final layer was light-cured for 3 min under a coat of Gradia plus air barrier (GC Europe NV), which was subsequently washed according to the manufacturer’s instructions. All the prepared restorations were postcured in an Otoflash (Bego) device (1500 flashes × 2 sides), as mentioned above. Measurement of discrepancies Every inlay was cleansed using ultrasonic baths, allowed to air dry, and then firmly stabilized onto the resin dies using temporary adhesive tape to avoid dislodgement during the imaging procedure. Kodak 9500 cone beam 3D (Carestream Health Inc., Rochester, NY, USA) equipment was used to create cone-beam computed tomography (CBCT) images of the resin die-inlay assembly . The technical specifications of the CBCT scan were an 18 cm by 20 cm field of view, a 10.8 s exposure time (pulsed), a tube voltage between 60 and 90 kV, a 2 to 15 mA (pulsated mode), and a 0.25 mm slice thickness. The inlay resin assemblies of each group were arranged in a semicircular configuration using a base former and was positioned on the occlusal plate of the CBCT scanner. The assemblies were aligned with the coronal and sagittal planes to obtain an accurate section; three consecutive sections (Fig. A) at the middle of each restoration in both directions (mesiodistal and buccolingual) were selected , . Gap measurements were measured at 7 locations (Fig. B; MGB – marginal gap buccal, k-o and MGL – marginal gap lingual) for the coronal section (buccolingual) and at 12 locations (Fig. C; MGM – marginal gap mesial, a-j and MGD – marginal gap distal) for each sagittal section (mesiodistal). In addition to the above measurements, AMD was also measured (Fig. D) at the buccal, lingual, mesial and distal cavosurface margins (4 measurements). Linear measurements were obtained between the restoration and the die at specific points (Fig. ) for each section using Kodak Dental Imaging’s 3D module (version 2.4; Carestream Health, Inc.) software from the CBCT images. A total of 69 [(12 + 7 + 4) × 3 sections] measurements were recorded per sample via Excel worksheet software (Microsoft Excel 2019; Microsoft, Redmond, WA, USA). All the values at each point for a given sample were retrieved by calculating the average of the respective values from the three consecutive sections. The IG of a sample was obtained by calculating the average of internal points (a-o). Similarly, the MG and AMD of a sample were obtained from the average of four values of different cavosurface margins (mesial, distal, buccal, and lingual). Individual readings were also recorded for the AMD and MG in relation to the mesial, distal, buccal and lingual cavosurface margins. Finally, the overall discrepancy (OD) of a sample was calculated as the mean of all 69 measurements recorded. All the readings were measured by a single examiner (T.S.V.) who has 10 years of experience in handling CBCT. Statistical analysis The statistical program IBM SPSS Statistics for Windows (Version 29.0; IBM Corp., Armonk, New York, USA) was used to analyze all of the data. For descriptive statistics, the mean and standard deviation were used for continuous variables. One-way ANOVA with Tukey’s post hoc test was used to determine the interaction impact among the measurements (AMD, MG, and IG) and the manufacturing methods (PVC, PVT, MVE, and CGP). Pearson’s correlation was used to assess the relationships between the variables (AMD, MG, and IG) within a group. The level of significance was considered at P > 0.05. A prior sample size for this study was calculated according to a previous study . The power = 0.99 was determined with F tests, one-way ANOVA, the fixed effects method, α = 0.05, and β = 0.2. The effect size f = 5.000791 was measured by using common standard deviation in G-power software (version 3.1.9.7; University of Dusseldorf, Germany) . The final sample size was estimated to be 6 per group, which was rounded to 10 after including a sample failure allowance of 40%, as the fabrication methods used for the inlays in this study are highly technique sensitive. A mandibular right first molar typodont (Standard Model AG-3, Frasaco Gmbh, Tettnang, Germany) was prepared under continuous air‒water spray for Class II mesiococclusodistal inlay restoration via a series of recommended diamond burs with a 6° taper (Inlay Preparation Set 4261, Komet, Lemgo, Germany) and a high-speed handpiece (SMART Torque S608C, KaVo Dental GmbH, Biberach, Germany). Initially, the occlusal cavity was prepared with a pulp depth and an isthmus width of 2 mm. The proximal box was subsequently prepared with the axial wall measuring 1.5 mm in height and depth and the buccolingual width measuring 3 mm. The dimensions of the cavity were verified with the help of a graduated periodontal probe (Falcon Periodontal Williams probe, Lucca, Italy). The two axial walls on the mesial and distal proximal boxes had a convergence angle of 6° and rounded line angles to avoid undercuts and sharp areas . A total of 40 resin dies were digitally replicated from the prepared typodont tooth . An optical impression of the inlay preparation on the typodont was captured using an intraoral scanner (i700; Medit Corp., Seoul, Korea) to create a virtual CAD file. After the CAD file was imported into a 3D printing machine (MAX UV385; ASIGA, Sydney, Australia), the resin dies were fabricated using a 3D-prinitng resin dedicated for dies (DentaMODEL; ASIGA) and allowed to dry for 10 min after washing in pure isopropyl alcohol. The dies were postcured in a UV postcuring unit (Flash curing chamber; ASIGA) for 20 min in accordance with the manufacturer’s specifications and then stored under the same conditions in a dark storage compartment. The prepared resin dies were randomly divided into 4 groups of 10 each on the basis of the method of fabrication as follows: Group PVC, 3D-printed VarseoSmile Crown plus (Bego); Group PVT, 3D-printed VarseoSmile TrinQ (Bego); Group MVE, milled Vita Enamic (Coltene Whaledent, Altstatten, Switzerland); Group CGP (control), conventional layering technique of an indirect composite resin (Gradia plus; GC Europe NV, Leuven, Belgium). The materials used in this study are shown in Table . For the PVC, PVT and MVE groups involving digital additive and subtractive manufacturing workflows, the resin dies were scanned using an intraoral scanner (i700; Medit Corp). The 3D images were exported and saved in standard tessellation language (STL) format. CAD of the inlays was performed and analyzed using Exocad software 3.0 (Exocad GmbH, Darmstadt, Germany) in the PVC and PVT groups, whereas the designed files were analyzed using the 3Shape Dental system (version 2020, 3Shape, Copenhagen, Denmark) in the MVE group. The cement spacer was set to 40 μm, whereas all other parameters were set to 0 μm starting from 1 mm from all the cavosurface margins. The sequence of procedures performed is illustrated in Fig. . The data in the STL file format were transferred to a digital light processing (DLP) printer (Varseo XS; Bego), and the respective permanent restorative resin in liquid form was used. The inlays were printed at a 405 nm wavelength, 50 μm resolution (X, Y, and Z), and 0.25 mm/min printing speed. All samples were created simultaneously at a constant room temperature of 23 °C to reduce procedural variability. The printer was previously calibrated following the manufacturer’s guidelines. After the printing procedure was complete, a spatula was used to carefully remove the restorations from the printing platform. The collected samples were cleaned for 480 s in an ultrasonic bath (Foshan Adelson Medical Devices Co., Foshan, China) containing 96% ethanol solution to remove the residual unpolymerized resin (PanReac AppliChem ITW Reagents, Darmstadt, Germany), after which they were gently dried. The restorations were placed in an Otoflash light curing device (Bego) for postcuring. The samples were exposed to 1500 flashes per second at 10 Hz in a nitrogen gas environment (1.0–1.2 bar). Later, the restorations were turned over to repeat the postcuring procedure on the opposite side of the restoration. A CEREC inLab MC XL (Dentsply Sirona; Charlotte, NC, USA) wet milling and grinding unit fitted with a water-cooled rotary bur of sizes 1 and 2.5 mm was used for inlay fabrication. The milling parameters were chosen following the manufacturer’s guidelines. After being steamjet cleaned, all of the inlays were allowed to air dry. After the removal of the sprue, the inlays were tried, and minor adjustments were made to smooth the restoration as needed. These samples were inspected for manufacturing flaws prior to the seating process. A separating medium (Die lube; Dentsply Sirona) was applied to the cavity inner and outer walls to make separation easier. Three layers of die spacer were applied to a thickness ≈ of 40 μm . Multiple increments of high-strength nanohybrid indirect RCs (Gradia plus, GC Europe NV) of 1–2 mm thickness was adapted to the internal walls of the cavity and polymerized using a light curing unit (Labolight DUO; GC Europe NV). Following the buildup and morphological contouring, the final layer was light-cured for 3 min under a coat of Gradia plus air barrier (GC Europe NV), which was subsequently washed according to the manufacturer’s instructions. All the prepared restorations were postcured in an Otoflash (Bego) device (1500 flashes × 2 sides), as mentioned above. Every inlay was cleansed using ultrasonic baths, allowed to air dry, and then firmly stabilized onto the resin dies using temporary adhesive tape to avoid dislodgement during the imaging procedure. Kodak 9500 cone beam 3D (Carestream Health Inc., Rochester, NY, USA) equipment was used to create cone-beam computed tomography (CBCT) images of the resin die-inlay assembly . The technical specifications of the CBCT scan were an 18 cm by 20 cm field of view, a 10.8 s exposure time (pulsed), a tube voltage between 60 and 90 kV, a 2 to 15 mA (pulsated mode), and a 0.25 mm slice thickness. The inlay resin assemblies of each group were arranged in a semicircular configuration using a base former and was positioned on the occlusal plate of the CBCT scanner. The assemblies were aligned with the coronal and sagittal planes to obtain an accurate section; three consecutive sections (Fig. A) at the middle of each restoration in both directions (mesiodistal and buccolingual) were selected , . Gap measurements were measured at 7 locations (Fig. B; MGB – marginal gap buccal, k-o and MGL – marginal gap lingual) for the coronal section (buccolingual) and at 12 locations (Fig. C; MGM – marginal gap mesial, a-j and MGD – marginal gap distal) for each sagittal section (mesiodistal). In addition to the above measurements, AMD was also measured (Fig. D) at the buccal, lingual, mesial and distal cavosurface margins (4 measurements). Linear measurements were obtained between the restoration and the die at specific points (Fig. ) for each section using Kodak Dental Imaging’s 3D module (version 2.4; Carestream Health, Inc.) software from the CBCT images. A total of 69 [(12 + 7 + 4) × 3 sections] measurements were recorded per sample via Excel worksheet software (Microsoft Excel 2019; Microsoft, Redmond, WA, USA). All the values at each point for a given sample were retrieved by calculating the average of the respective values from the three consecutive sections. The IG of a sample was obtained by calculating the average of internal points (a-o). Similarly, the MG and AMD of a sample were obtained from the average of four values of different cavosurface margins (mesial, distal, buccal, and lingual). Individual readings were also recorded for the AMD and MG in relation to the mesial, distal, buccal and lingual cavosurface margins. Finally, the overall discrepancy (OD) of a sample was calculated as the mean of all 69 measurements recorded. All the readings were measured by a single examiner (T.S.V.) who has 10 years of experience in handling CBCT. The statistical program IBM SPSS Statistics for Windows (Version 29.0; IBM Corp., Armonk, New York, USA) was used to analyze all of the data. For descriptive statistics, the mean and standard deviation were used for continuous variables. One-way ANOVA with Tukey’s post hoc test was used to determine the interaction impact among the measurements (AMD, MG, and IG) and the manufacturing methods (PVC, PVT, MVE, and CGP). Pearson’s correlation was used to assess the relationships between the variables (AMD, MG, and IG) within a group. The level of significance was considered at P > 0.05. The results of the present study revealed that the intergroup comparisons of AMD, MG, IG, and OD among the groups differed significantly (one-way ANOVA; P > 0.05) in each of the assessed discrepancies (Table ). The AMD of MVE was the highest (0.48 ± 0.06), whereas that of CGP (0.25 ± 0.13) was the lowest (Fig. ). The MVE had the highest OD (0.34 ± 0.04), and the CGP had the lowest (0.15 ± 0.04). Table shows the results of the intergroup comparisons of AMD and MG in relation to the four cavosurface margins. All four groups differed significantly (one-way ANOVA; P > 0.05) in the AMD and MG across the different cavosurface margins. The mesial margins of MVE had the highest AMD (0.62 ± 0.13), and the lingual margins of PVT (0.05 ± 0.05) had the lowest AMD. The mesial margins of MVE had the highest value (0.46 ± 0.08) for MG, and the buccal margins of PVT & CGP (0.02 ± 0.06) had the lowest. The mean AMD and MG values in relation to the mesial and distal margins were generally greater than those of the buccal and lingual cavosurface margins (Figs. and ). Pairwise comparisons among MG, AMD, and IG revealed positive correlations between MG and IG in the PVC ( r = 0.786) and between MG and AMD in the PVT ( r = 0.675). Intragroup comparisons of AMD, MG, and IG revealed significant differences among the three variables across all the groups. The MVE was the highest of all the groups, and the AMD was the highest of the three discrepancies (Supplementary Table ). Intragroup comparisons of individual cavosurfaces for AMD and MG revealed significant differences among the four cavosurface margins in all the groups except for the CGP (repeated-measures ANOVA; P > 0.05). The AMD and MG of the CGP were similar across the different cavosurface margins (Supplementary Table S2). The mean values of each measurement point were calculated from all 240 CBCT images and are presented as a diagram for comparison (Supplementary Fig. ). Several 3D printing methods are commonly used in dentistry, including stereolithography, selective laser sintering, selective laser melting, and digital light processing (DLP) . The DLP used in this study is more widely used because of its high printing speed, tiny equipment size, exceptional cost-effectiveness, capacity to produce high-resolution, and complex outputs , . The precision of the milling machine, the size and wear of the rotating instruments, the geometry of the restoration, the type of material, the assessment technique, and the quantity of measurement sites can affect the fit accuracy of milled restorations – . The time consumed by the digital chairside workflow of milled restorations was reported to be less than that of the conventional workflow . The materials tested were ceramic-reinforced hybrid materials intended for use as definitive restorations . Using a 3D printer, the typodont preparation was replicated into multiple printed resin dies. The use of similar 3D-printed dies for all the groups minimizes extra errors that could arise from operator’s skills as well as nonadditive multiplication techniques. Since the i700 (Medit) lacked a milling system, it was utilized only during the impression stage. The design and production stages were carried out through different systems. A single optical impression was made for the digitally processed inlays, as this approach would avoid the formation of undercuts with several optical impressions of the preparation. The presence of these undercuts would not have allowed the inlays to be inserted into the cavity . Various noninvasive methods to measure the MG and IG of inlays include the replica technique, microcomputed tomography (CT), and CBCT , , . The downside of the replica technique is the tendency toward damage to silicone impressions and poor delineation of margins for measurement. CBCT has advantages over high-resolution microcomputed tomography because of the low radiation dose for patients , and it is a frequently used investigatory tool in dentistry for multiple purposes. AMD was measured and evaluated in addition to MG and IG to obtain information on positive or negative overhangs, which is crucial for the clinical performance of inlays . All the inlay samples evaluated in this study had only positive overhangs. There have been reports of marginal and internal gap values for several indirect composite and ceramic inlays that range from 48 to 278 μm , , . Nonetheless, the clinically acceptable recommended marginal adaptation for inlays should be less than 120 μm . It has been demonstrated that a precise fitting restoration is necessary for long-term success, even though resin luting cement at the edges of the restoration can compensate for deficient inlay adaptation . In this study, a luting space of 40 μm was set, but the obtained values for gaps and discrepancies were in the range of 0.05–0.48 mm (50–480 μm). The wide range in values could be partially attributed to the absence of cementation, the resolution of the images by the CBCT, and the error tolerance of the measurement software. The null hypothesis that there is no significant difference among the groups of different manufacturing methods/materials was completely rejected. The results of the present study revealed a statistically significant difference in all the variables (gaps) tested among the groups ( P > 0.05). With respect to the two printed groups, the MG and AMD were significantly different ( P > 0.05), whereas this was not the case for the IG and OD. However, the values are clinically significant, meaning that the PVT was superior in dimensional accuracy and fit compared with the PVC. The probable reason for this behavior could be the variation in the composition of the material, which is not explained by the manufacturer. The manufacturing methods apparently played a significant role in influencing the dimensional accuracy of the inlays. The mean values were significantly higher ( P > 0.05) for the milled inlays (MVE) than for the printed (PVC, PVT) and conventional inlays (CGP). This is in partial agreement with a previous study that compared the MG and IG of 3D-printed composites, milled composites, milled ceramics and conventional composite inlays measured using the replica technique . These observations revealed the superior performance of the 3D-printed inlays compared with the conventional inlay group. However, the mean gaps of conventional inlays were always minimal compared with those of the other groups in our study. Notably, the mean MG (50 μm) of CGP was well within the recommended range of less than 120 μm. This partial disagreement could be attributed to the difference in measurement tools employed in the studies. Moreover, the current study confirms earlier research showing that 3D-printed restorations have a greater internal fit than those milled from resin blocks , . This could be attributed to the difference in the manufacturing parameters used between the additive and subtractive techniques. Suksuphan et al. compared AMD and MG at different surfaces of 3D-printed and milled crowns and reported significant differences. Similarly, the AMD and MG in the mesial and distal cavosurface margins in our study were greater than those in the buccal and lingual margins in all the printed and milled groups except the CGP group. This could be attributed to either the missing details in the optical impression or other manufacturing parameters that influence the digital fabrication of inlays. Strengths This study tested 3D-printed ceramic-reinforced hybrid composite inlays, which are less expensive in terms of equipment and consumable cost than milled resin composites and milled lithium disilicate ceramics . The production time is shorter, especially when more than 8 restorations are fabricated , which saves the effective chairside time of the patient. Concomitantly, the printed inlay can be repaired outside the patient’s mouth to improve its adaptation, saves material wastage and reprinting time . Moreover, the CBCT used in this study is a noninvasive in vivo method for evaluating the dimensional accuracy of restorations , whereas microcomputed tomography is an in vitro measurement tool; hence, the results obtained in this study may be scrupulously translated to the clinical setting. Limitations The first limitation is that the intrinsic precision of manufacturing methods in combination with the tested material in measuring the MG and IG of inlays could not be determined without the additional effect of the luting cement (viscosity and seating force). A definitive luting cement mimics the clinical situation while assessing the trueness and fit of the restoration . Therefore, in the majority of research designs, a precementation and postcementation evaluation for marginal and internal gaps would be more appropriate and helpful . The second limitation was the challenge faced in optimizing the contrast of resin dies in the CBCT images while differentiating the margins at the gap. The third limitation is the inability to accurately standardize the thickness of the die spacer to 40 μm in the CGP group. The fourth limitation is the use of different digital imaging methods, design software and restoration materials in fabricating the inlays. Hence, the results of this study should be interpreted carefully when comparing the inlays made using additive and subtractive manufacturing techniques. Further investigations are needed, as it is crucial to test the mechanical properties of such novel materials using the fundamental tests outlined by the ISO, such as fracture resistance, flexural strength, and modulus tests. Different cavity dimensions can also be compared to evaluate the effects of variations in the thickness of a material on its mechanical properties. This study tested 3D-printed ceramic-reinforced hybrid composite inlays, which are less expensive in terms of equipment and consumable cost than milled resin composites and milled lithium disilicate ceramics . The production time is shorter, especially when more than 8 restorations are fabricated , which saves the effective chairside time of the patient. Concomitantly, the printed inlay can be repaired outside the patient’s mouth to improve its adaptation, saves material wastage and reprinting time . Moreover, the CBCT used in this study is a noninvasive in vivo method for evaluating the dimensional accuracy of restorations , whereas microcomputed tomography is an in vitro measurement tool; hence, the results obtained in this study may be scrupulously translated to the clinical setting. The first limitation is that the intrinsic precision of manufacturing methods in combination with the tested material in measuring the MG and IG of inlays could not be determined without the additional effect of the luting cement (viscosity and seating force). A definitive luting cement mimics the clinical situation while assessing the trueness and fit of the restoration . Therefore, in the majority of research designs, a precementation and postcementation evaluation for marginal and internal gaps would be more appropriate and helpful . The second limitation was the challenge faced in optimizing the contrast of resin dies in the CBCT images while differentiating the margins at the gap. The third limitation is the inability to accurately standardize the thickness of the die spacer to 40 μm in the CGP group. The fourth limitation is the use of different digital imaging methods, design software and restoration materials in fabricating the inlays. Hence, the results of this study should be interpreted carefully when comparing the inlays made using additive and subtractive manufacturing techniques. Further investigations are needed, as it is crucial to test the mechanical properties of such novel materials using the fundamental tests outlined by the ISO, such as fracture resistance, flexural strength, and modulus tests. Different cavity dimensions can also be compared to evaluate the effects of variations in the thickness of a material on its mechanical properties. Under the given testing conditions and limitations, the following conclusions were drawn: Compared with milled inlays, the quality of the 3D-printed inlays tested had exceptional dimensional accuracy, which likely extends the clinical lifespan of such restorations. Compared with the VarseoSmile Crownplus, the VarseoSmile TriniQ showed superior marginal adaptation. The dimensions of Vita Enamic inlays showed poor adaptation with the prepared walls of the resin die, underscoring its inefficiency. The 3D-printed inlays using VarseoSmile TriniQ could be a potential treatment option for the clinicians, provided additional long term in vivo investigations are performed in future. Below is the link to the electronic supplementary material. Supplementary Material 1 |
Chest wall perforator flap reconstruction in breast conserving surgery: quality of life and limited complications in outpatient treatment | 2bde133b-e284-4bfe-aeb4-099601f0abf3 | 11755791 | Surgical Procedures, Operative[mh] | In Western Europe, approximately 30% of women diagnosed with breast cancer or ductal carcinoma in situ (DCIS) will undergo mastectomy . Due to partial breast reconstruction (PBR) following breast conserving surgery (BCS), mastectomy can be avoided in patients with a high tumour-to-breast ratio, prone to deformity of the breast after BCS alone . PBR results in favourable oncologic, surgical, and psychological outcomes . Several techniques of PBR following BCS have been reported, dependent on excision volume and tumour location . This study focuses on oncoplastic surgery of the breast with volume replacement . Commonly used chest wall perforator flaps are the lateral intercostal artery perforator (LICAP, Fig. ), the lateral thoracic artery perforator (LTAP), the thoracodorsal artery perforator (TDAP), the anterior intercostal artery perforator (AICAP), and the medial intercostal artery perforator (MICAP), For sake of clarity flaps can be classified as lateral (LICAP, LTAP, TDAP) or inframammary (AICAP, MICAP). Postoperative complication rates are comparable for BCS with and without perforator flap reconstruction . The most common complication following breast surgery is seroma, defined as an accumulation of serous fluid following surgery . Postoperative closed-suction drains are often placed following BCS with perforator flap reconstruction, assuming that drains prevent the onset of seroma . However, postoperative drains are associated with an increased risk of surgical site infections (SSI), an increased length of hospital stay, and patient discomfort . Omitting drains in abdominal-based flap reconstructions, reduction mammoplasty, and mastectomy is safe without increasing seroma incidence . In most centers, inpatient treatment is still common practice for BCS with perforator flap reconstruction due to the use of postoperative drains and the more extensive dissection. The literature lacks information regarding drains following BCS with perforator flap reconstruction and the feasibility of outpatient surgery. Moreover, quantifiable information regarding long-term patient satisfaction following perforator flap reconstruction is scarce. This retrospective study aims to investigate the incidence and severity of complications, score patient satisfaction, and evaluate the feasibility of outpatient treatment in women undergoing drainless BCS with perforator flap reconstruction.
Study design A retrospective case series was conducted in Canisius Wilhelmina Hospital (CWZ) in Nijmegen, The Netherlands. The study was approved by the local ethical committee of CWZ and complied with ethical and clinical regulations and guidelines. Due to the study’s retrospective nature, informed consent from all patients was not required. Written informed consent has been obtained for the photographs. Participants The study included all consecutive patients 18 years and older undergoing BCS with perforator flap reconstruction by a breast surgeon in CWZ breast centre (around 300 − 350 breast cancer diagnoses per year) from January 2019 to January 2023. There were no exclusion criteria as long as perforators could be identified by Doppler or ultrasound color-doppler. Procedure The patients included were treated by the same breast surgeon (LS). All patients underwent the best possible breast cancer treatment, with or without (neo)adjuvant therapy, based on national guidelines and in shared decision-making . Depending on the size of the breast and the location of the tumour, one of the following perforator flap techniques was applied: lateral flap reconstruction (LICAP, LTAP, TDAP) or inframammary flap reconstruction (AICAP, MICAP). Perforator flap reconstruction could be offered as a one-stage or a two-stage approach, dependent on adequate oncological treatment. Data collection and definition Data was extracted from the EPF (electronic patient file) and questionnaires. Data was stored in Microsoft Excel. Obtained baseline characteristics were based on assumed risk factors for wound complications after BCS . Included were age, body mass index (BMI), cup size (A to G , polypharmacy (daily use of five or more medications as a proxy for comorbidity), smoking status, type of cancer (DCIS, invasive lobular carcinoma, invasive carcinoma No Special Type (NST), others), TNM 8 pathological classification, neo-adjuvant chemotherapy (NAC), adjuvant radiation therapy of the breast, type of surgery (direct or delayed reconstruction, with or without axillary lymph node dissection (ALND), type of flap reconstruction (lateral of inframammary perforator flap) and weight of resected tissue. The primary outcome was complication incidence, which was scored six months postoperatively. Reported complications were seroma, non-aspirated seroma, surgical site infections (SSI), bleeding complications, wound healing complications (including wound dehiscence and wound necrosis), and lymph edema. Complications were reported whenever treatment or unplanned outpatient clinic visit(s) were needed. The Clavien Dindo Classification was used to classify the severity of the complication . The secondary outcome was patient satisfaction, reported in the Breast-Q for Breast Conserving Therapy (BCT) module. Included modules were psychosocial well-being, physical well-being chest preoperative/postoperative, satisfaction with breast preoperative/postoperative, and satisfaction with the surgeon/medical team . Scores from 0 to 100 were obtained for each module (the higher the score, the better the outcome). The Breast-Q for BCT was sent out to patients in February 2022 and again in June 2023 to patients who did not respond to the first questionnaire. Secondary outcomes regarding healthcare consumption were surgery time, duration of hospital stay, number of unplanned outpatient clinic visits, re-admission, and reoperation. The results of this study were compared to results reported in the literature. Statistical analysis Using IBM SPSS Statistics 27, summary statistics were calculated for baseline characteristics. Continuous variables were presented as the median and interquartile range (IQR): 25th – 75th percentile. Categorical variables were presented as frequencies and percentages.
A retrospective case series was conducted in Canisius Wilhelmina Hospital (CWZ) in Nijmegen, The Netherlands. The study was approved by the local ethical committee of CWZ and complied with ethical and clinical regulations and guidelines. Due to the study’s retrospective nature, informed consent from all patients was not required. Written informed consent has been obtained for the photographs.
The study included all consecutive patients 18 years and older undergoing BCS with perforator flap reconstruction by a breast surgeon in CWZ breast centre (around 300 − 350 breast cancer diagnoses per year) from January 2019 to January 2023. There were no exclusion criteria as long as perforators could be identified by Doppler or ultrasound color-doppler.
The patients included were treated by the same breast surgeon (LS). All patients underwent the best possible breast cancer treatment, with or without (neo)adjuvant therapy, based on national guidelines and in shared decision-making . Depending on the size of the breast and the location of the tumour, one of the following perforator flap techniques was applied: lateral flap reconstruction (LICAP, LTAP, TDAP) or inframammary flap reconstruction (AICAP, MICAP). Perforator flap reconstruction could be offered as a one-stage or a two-stage approach, dependent on adequate oncological treatment.
Data was extracted from the EPF (electronic patient file) and questionnaires. Data was stored in Microsoft Excel. Obtained baseline characteristics were based on assumed risk factors for wound complications after BCS . Included were age, body mass index (BMI), cup size (A to G , polypharmacy (daily use of five or more medications as a proxy for comorbidity), smoking status, type of cancer (DCIS, invasive lobular carcinoma, invasive carcinoma No Special Type (NST), others), TNM 8 pathological classification, neo-adjuvant chemotherapy (NAC), adjuvant radiation therapy of the breast, type of surgery (direct or delayed reconstruction, with or without axillary lymph node dissection (ALND), type of flap reconstruction (lateral of inframammary perforator flap) and weight of resected tissue. The primary outcome was complication incidence, which was scored six months postoperatively. Reported complications were seroma, non-aspirated seroma, surgical site infections (SSI), bleeding complications, wound healing complications (including wound dehiscence and wound necrosis), and lymph edema. Complications were reported whenever treatment or unplanned outpatient clinic visit(s) were needed. The Clavien Dindo Classification was used to classify the severity of the complication . The secondary outcome was patient satisfaction, reported in the Breast-Q for Breast Conserving Therapy (BCT) module. Included modules were psychosocial well-being, physical well-being chest preoperative/postoperative, satisfaction with breast preoperative/postoperative, and satisfaction with the surgeon/medical team . Scores from 0 to 100 were obtained for each module (the higher the score, the better the outcome). The Breast-Q for BCT was sent out to patients in February 2022 and again in June 2023 to patients who did not respond to the first questionnaire. Secondary outcomes regarding healthcare consumption were surgery time, duration of hospital stay, number of unplanned outpatient clinic visits, re-admission, and reoperation. The results of this study were compared to results reported in the literature.
Using IBM SPSS Statistics 27, summary statistics were calculated for baseline characteristics. Continuous variables were presented as the median and interquartile range (IQR): 25th – 75th percentile. Categorical variables were presented as frequencies and percentages.
Baseline characteristics During the study, 42 consecutive patients were treated with BCS with perforator flap reconstruction. Three patients underwent an additional mastectomy due to tumour irradicality, and one patient underwent a mastectomy due to tumour recurrence. Table overviews the patient, tumour, and treatment characteristics. Complications Seven of 42 patients (16.7%) had a postoperative complication. A hematoma in three patients (7.1%) was the most common complication. Seroma was reported in one patient (2.4%), wound dehiscence in two patients (4.8%), fat necrosis in one patient (2.4%), and lymphedema in two patients (4.8%). Two complications needed treatment and were scored as Clavien Dindo, grade 1. One patient required a seroma aspiration, and another patient needed Vacuum Assisted Closure therapy because of wound dehiscence (Table ). No complications met the criteria of Clavien Dindo grade 2, 3, or 4. Health care consumption The median duration of surgery was 99 (78.5–153.3) minutes. Outpatient treatment was successful in 38 patients (90.5%). One patient was planned as an inpatient, being a patient preference. Three patients stayed for the night due to pain, nausea, and drowsiness. Thirteen patients (31.0%) had an unplanned visit to the outpatient clinic due to complications (4 patients), pain (4 patients), or worries about the wound (5 patients, Table ). There was no readmission nor reoperation. Patient-reported outcomes Thirty patients (71.4%) completed the Breast-Q BCT, with a median of 17 (14–24.5) months after surgery. Psychosocial well-being was scored 87/100. Satisfaction with breasts was scored 71/100 preoperative and 82/100 postoperative. Physical well-being preoperative was scored 76/100 and postoperative 71/100. Satisfaction with the surgeon was 100/100, and satisfaction with the medical team was 100/100. Post-operative pictures of different perforator flaps are shown in Fig. . Logistic regression analysis showed that postoperative complications were not significantly associated with psychosocial well-being, postoperative satisfaction with breasts, and postoperative physical well-being chest.
During the study, 42 consecutive patients were treated with BCS with perforator flap reconstruction. Three patients underwent an additional mastectomy due to tumour irradicality, and one patient underwent a mastectomy due to tumour recurrence. Table overviews the patient, tumour, and treatment characteristics.
Seven of 42 patients (16.7%) had a postoperative complication. A hematoma in three patients (7.1%) was the most common complication. Seroma was reported in one patient (2.4%), wound dehiscence in two patients (4.8%), fat necrosis in one patient (2.4%), and lymphedema in two patients (4.8%). Two complications needed treatment and were scored as Clavien Dindo, grade 1. One patient required a seroma aspiration, and another patient needed Vacuum Assisted Closure therapy because of wound dehiscence (Table ). No complications met the criteria of Clavien Dindo grade 2, 3, or 4.
The median duration of surgery was 99 (78.5–153.3) minutes. Outpatient treatment was successful in 38 patients (90.5%). One patient was planned as an inpatient, being a patient preference. Three patients stayed for the night due to pain, nausea, and drowsiness. Thirteen patients (31.0%) had an unplanned visit to the outpatient clinic due to complications (4 patients), pain (4 patients), or worries about the wound (5 patients, Table ). There was no readmission nor reoperation.
Thirty patients (71.4%) completed the Breast-Q BCT, with a median of 17 (14–24.5) months after surgery. Psychosocial well-being was scored 87/100. Satisfaction with breasts was scored 71/100 preoperative and 82/100 postoperative. Physical well-being preoperative was scored 76/100 and postoperative 71/100. Satisfaction with the surgeon was 100/100, and satisfaction with the medical team was 100/100. Post-operative pictures of different perforator flaps are shown in Fig. . Logistic regression analysis showed that postoperative complications were not significantly associated with psychosocial well-being, postoperative satisfaction with breasts, and postoperative physical well-being chest.
This retrospective case series reported the complication incidence in women undergoing drainless BCS with perforator flap reconstruction performed by a dedicated breast surgeon. Seven of 42 patients (16.7%) had a complication, and two (4.8%) needed non-operative treatment. Patient satisfaction measured by the Breast-Q BCT was high, and outpatient treatment was feasible in 90.5% of patients. Reported complication incidences following BCS with perforator flap reconstruction are comparable to those of BCS alone. Most reported complications in the literature are SSI (3-5-5.0%), seroma (3–15%), hematoma (3–5%), wound dehiscence (3–15%), skin necrosis (1–24%), fat necrosis (1–12%) and lymphedema (5–6%) . In line with these, the present study reported a low total complication incidence (16.7%) . Comparing the current data with a previous study conducted at CWZ, which assessed the complication incidence in patients with BCS without reconstruction, the incidence rates are comparable. Excluding lymphedema, the incidence of wound complications in the current study drops to 11.9% (5 out of 42 patients). The previous study conducted at CWZ reported an incidence of 10.3–11.9% for BCS without reconstruction . Remarkable in the present study was the absence of SSI and skin necrosis. Comparing the current study to a large multicentre study conducted by Karakatsanis et al., the incidence of complications in the current cohort was higher (16.7% versus 8.6%). However, the complications were generally less severe according to the Clavien Dindo classification . Treatment for postoperative complications (Clavien Dindo grade ≥ 1) was necessary for two of 42 patients (4.8%) in the current study compared to 52 of 603 patients (8.6%) in the study of Karakatsanis et al. The absence of reoperations related to complications after primary surgery is notably lower than the reported 3% incidence documented in the literature . Forty patients (95.2%) in this study cohort received adjuvant radiation therapy. Subsequent low incidences of fat necrosis and breast edema confirm that intended radiation therapy should not preclude the use of perforator flap reconstruction. In this series, drains were omitted, not resulting in increased seroma, hematoma, or other postoperative complications compared to incidences reported in the literature . Thus confirming the safety of drainless BCS with perforator flap reconstruction . Omitting postoperative drains opens, in some situations, the way to acceptance of outpatient surgery. Feasibility of breast surgery in outpatient care has been demonstrated earlier . However, outpatient treatment in BCS with perforator flap reconstruction is still not common practice despite some studies reporting on outpatient treatment . In the present study, outpatient treatment is feasible in 90.5% of the patients after BCS with perforator flap reconstruction. The conversion rate from outpatient to inpatient admission, ranging from 0 to 14% in the literature, aligns with the findings of this study . Furthermore, the reasons for inpatient admission (pain, nausea, or drowsiness) were comparable to those previously reported . To our knowledge, no studies have reported unplanned visits to the outpatient clinic after BCS with or without perforator flap reconstruction. Reported results regarding unplanned visits to the outpatient clinic after mastectomy are in line with the obtained data in this series . This shows that drainless BCS with perforator flap reconstruction is feasible and safe in the outpatient clinic. Breast-Q scores after different types of oncoplastic breast surgery have been reported before, ranging from 69 to 85 for physical well-being, 81 to 91 for psychosocial well-being, and 74 to 80 for satisfaction with breasts . Ritter et al. reported higher patient satisfaction on the Breast-Q BCT modules satisfaction with breasts, psychosocial well-being, and sexual well-being after oncoplastic surgery compared to healthy women who did not undergo surgery . The Breat-Q scores (satisfaction with breasts and psychosocial well-being) in the current study were slightly higher than those reported by Muktar et al. in patients undergoing BCS with perforator flap reconstruction, likely due to the more extended follow-up period in our study (follow-up of Muktar et al. was less than six months . Zeeshan et al. reported a (significantly higher) satisfaction with breasts score of 100 after BCS with perforator flap reconstruction, which they attributed to cultural practices in their Pakistani cohort, such as covering the breasts with loose clothing to conceal scars and a regional tendency for surgeons to prefer mastectomy over breast conservation, thereby increasing satisfaction when conservation is performed . In the present study, patients did not report worse on the modules satisfaction with breasts and physical well-being postoperative compared to preoperative, illustrating a good to excellent performance of the more extensive BCS using perforator flap reconstruction. A substantial proportion of the patients in the cohort (47%) had T1 tumours, including re-excisions following margin-positive DCIS, imaging-defined larger lobular cancers (where defining appropriate surgical margins preoperatively is often challenging), tumours that were part of a larger DCIS field as determined by MRI, and finally tumours resected after preoperative systemic treatment, which downsized the tumour to ypT1, while a larger resection volume was considered necessary. The eligibility for perforator flap surgery depends on the ratio between tumour and breast size. In patients with smaller breasts, this approach can prevent mastectomy, even for patients with T1 disease. Limitations of this study are the relatively low number of treated patients and the varied range of perforator pedicles used. Although a consecutive series is described, the retrospective nature implies a risk of recall bias due to incomplete or inaccurate records in the electronic patient record and selection bias since patients included in the study might not be representative of the broader population. The generalizability of the results is limited due to the procedures performed by the same breast surgeon so that outcomes could reflect individual skills rather than generalizable results. The Breast-Q was sent to all patients simultaneously, resulting in measurements at different postoperative intervals. Furthermore, the questionnaire featured questions regarding pre- and postoperative satisfaction with breasts and physical well-being, which were both filled in postoperatively. The strengths of this study are the consecutive series reflecting real-life situations and the patient-reported long-term outcome evaluation using the Breast-Q. Moreover, this is one of the first studies describing drainless perforator flap reconstruction, which results in the feasibility of outpatient treatment and reduces healthcare consumption. Besides, follow-up of more than one year makes it possible to reflect on mid- to longer-term effects and outcomes.
BCS with perforator flap reconstruction provides breast surgeons and patients with an attractive tool to reduce mastectomy rates in selected patients. Drainless BCS with perforator flap reconstruction is feasible in outpatient care, with low complication rates and high patient satisfaction. Perforator flap reconstruction after BCS should be considered in future clinical practice of breast surgeons; however, more extensive studies are required for in-depth (statistical) analysis of complication rates, long-term follow-up, and patient-reported outcomes using the Breast-Q.
|
Exploring the role of surgical margins and reoperation in basal cell carcinoma recurrence: a study of 3036 cases | cb753580-8fe0-4f7a-8973-f34a20669cc4 | 11922969 | Surgical Procedures, Operative[mh] | Basal cell carcinoma (BCC) is the most common form of skin cancer worldwide, accounting for approximately 75% of all cases. It primarily affects fair-skinned individuals, with the incidence varying according to geographical factors, being particularly high among Caucasians in regions closer to the equator . Australia reports the highest incidence rates of BCC, followed by the United States and Europe . In Europe, the average incidence rate in England between 2000 and 2006 was 76.21 per 100,000 person-years . The Netherlands saw a fourfold increase in age-standardized incidence rates for both men and women between 1973 and 2009, reaching 165 and 157 per 100,000 person-years, respectively . Similarly, data from the German cancer registry between 1998 and 2010 revealed a 2.4-fold increase in BCC cases . Despite this rising prevalence, the mortality rates for BCC remain low, with a 5-year absolute survival rate of 87.1% among German patients, consistently exceeding that of the general population by 3–6% . Prolonged ultraviolet (UV) radiation exposure is the primary cause of BCC, with sun-exposed areas of the skin being the most affected. Additional risk factors include genetic predispositions such as Fitzpatrick skin types I-II, certain syndromes like Gorlin-Goltz and Bazex-Dupré-Christol, as well as environmental influences such as X-rays, chemical carcinogens, and immunosuppression. Although BCC is a slow-growing tumor, if left untreated, it can cause significant local tissue damage. Radical surgical excision remains the gold standard for treatment, with the classification of BCCs into low or high-risk categories based on factors such as tumor site, size, and histological subtype. While 95% of BCCs are low-risk subtypes, easily treated with standard surgical procedures, high-risk BCCs are more prone to recurrence and often require more aggressive management. These high-risk tumors typically involve the H zone of the face (around the nose, eyelids, and ears), aggressive histological subtypes such as basosquamous, sclerosing, infiltrating, or BCC with sarcomatoid differentiation, tumors larger than 2 cm, and cases involving immunosuppressed patients . The management of BCC remains a challenge for plastic surgeons and dermatologic oncologists, requiring a delicate balance between ensuring complete tumor excision and maintaining both functional and aesthetic outcomes. This study aims to provide a comprehensive evaluation of the relationship between excision margins, reoperation rates, and the recurrence of BCC, analyzing 3036 cases from a single-center, retrospective study. By examining different anatomical sites, the impact of excision margins on recurrence, and the associated patient outcomes, our findings seek to inform clinical decision-making and guide optimal patient care. Precision in surgical intervention for this prevalent skin cancer is paramount, and our study underscores the importance of margin adequacy in minimizing recurrence. The primary objectives of this study were to determine the correlation between excision margins and the re-intervention rate, as well as the correlation between excision margins and local recurrence. Secondary objectives included examining the relationship between tumor size and re-intervention rates, the correlation between histological subtypes and re-intervention, and the impact of tumor location and subtype on recurrence rates.
This retrospective study was conducted at the Department of Plastic, Reconstructive, and Aesthetic Surgery, Trieste - Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI). We enrolled patients who underwent surgical excision for BCC between January 8, 2014, and December 5, 2018. All primary lesions were excised using excisional biopsy, following the ASUGI diagnostic and therapeutic care pathway. The surgeries were performed by five surgeons from the department. A total of 2037 patients with BCC were included in the analysis. Data were collected from patient clinical and pathological records. Excision procedures adhered to the established care pathway, which included an initial dermatological assessment for diagnosis, followed by consultation with a plastic surgeon for the excision planning. Surgical procedures were performed on an outpatient basis, without preoperative antibiotic prophylaxis. Clinical margins of 4 mm were used for low-risk, well-demarcated tumors smaller than 2 cm, while wider margins of 6–8 mm were applied to high-risk BCCs . For pathological analysis, the “bread loaf technique” was employed for tissue sectioning. Distances between the tumor and the peripheral and deep surgical margins were measured in millimeters. These were categorized into four groups: involved margins, margins less than 1 mm, margins between 1 and 5 mm, and margins greater than 5 mm. Detailed records were kept for all excision margins and their involvement. If positive margins were detected, patients were offered re-excision to ensure complete tumor removal, in line with clinical guidelines . We also recorded additional tumor characteristics, including size, location (such as trunk, back, face, ears, eyelids, and scalp), and histological subtype (nodular, multicentric, sclerosing, basosquamous, infiltrative, superficial, and fibroepithelial). Statistical analysis was performed using Microsoft Excel and IBM SPSS Statistics 24. The Chi-square test was used for categorical variables to assess relationships and associations, while the Mann-Whitney U test was applied for continuous variables to compare differences between groups. Proportions were compared using the proportion test to assess differences between categorical variables and their distributions. Patients were followed for an average of 36 months. Recurrence was defined as the appearance of new BCC in the same anatomical site following a previous excision. The study was conducted in accordance with the ethical standards of the ASUGI ethical committee for clinical practice, and informed consent was obtained from all participants.
A total of 2037 patients were enrolled, with 3036 primary lesions included in the analysis. The median age at BCC diagnosis was 73.52 years (range: 23.52–100.14) for women and 76.12 years (range: 22.03–97.34) for men. The overall mean age of the patients was 72.6 ± 12.75 years. The mean follow-up time was 36 months (range: 8–60 months). Several histological subtypes were identified, as listed in Table . These subtypes were equally distributed across genders with no statistically significant differences (Chi-Square Test, p = 0.393). Among the anatomical sites involved, the head was the most affected, with 1662 cases (54.74%). Within head lesions, the most common sites were the nose (13.31%) and the forehead (9.2%). Regarding the distance between the tumor and the excision margins, 93.3% ( N = 2834) of the lesions were excised completely. Of the remaining lesions, 3 (0.1%) involved only the deep margins, 141 (4.6%) involved only the peripheral margins, and 58 (1.9%) had both deep and peripheral margin involvement. The overall incomplete excision rate was 6.7%. Peripheral margins were the most commonly involved after tumor excision, with 199 cases (6.6%) showing involvement. Deep margin involvement occurred in 61 cases (2%). Table displays the millimeter distances from the tumor to both peripheral and deep margins. Reintervention was recommended for all patients with positive peripheral and/or deep margins (6.7%, N = 202) and for those whose margins were less than 0.3 mm (3.6%, N = 108). A total of 146 patients with involved margins and 61 patients with margins less than 0.3 mm underwent reoperation. Patients who did not undergo reintervention, due to personal choice or poor clinical condition, were referred for dermatologic follow-up. Of those with involved margins, 27.7% ( N = 56) chose follow-up, and 85.3% ( N = 353) of those with margins less than 1 mm chose follow-up. There was one clinical recurrence in the follow-up group, a multicentric BCC on the ear that was not reoperated. The sites most frequently requiring reintervention were the nose (30%), eyelids (15%), ears (15%), cheeks (15%), and forehead (10%). Following re-excision ( N = 207), the results for peripheral margin involvement were as follows: 15 cases (7.25%) had involved margins, 11 cases (5.31%) had margins less than 1 mm from the tumor, 16 cases (7.73%) had margins between 1 and 5 mm, and 165 cases (79.71%) had margins greater than 5 mm. For deep margins, 1 case (0.48%) showed involvement, 1 case (0.48%) had margins less than 1 mm, 12 cases (5.80%) had margins between 1 and 5 mm, and 179 cases (86.47%) had margins greater than 5 mm. For 14 cases (6.77%), the margin data was not recorded. The histological subtypes most often requiring reintervention were sclerosing (14.09%), basosquamous (10.20%), infiltrating (7.08%), multicentric (6.67%), nodular (4.7%), and superficial (1.19%). This difference was statistically significant ( p < 0.001, proportion test). During follow-up, 16 recurrences (0.52%) were observed. Recurrences were more frequent in anatomical areas such as the nose ( N = 4) and forehead ( N = 3). Overall, facial sites were the most commonly affected by recurrence. Lesions that recurred had significantly narrower free margins compared to those that did not recur, with mean margins of 1.22 mm versus 1.89 mm (Mann-Whitney test p = 0.002). The histological subtypes of recurrent BCCs were as follows: 8 cases of nodular, 3 of multicentric, 2 of sclerosing, and 3 of basosquamous. No recurrences occurred in infiltrative, superficial, or fibroepithelial subtypes. The nose was the most frequent site of recurrence (4 lesions), followed by the forehead (3 lesions). Other sites of recurrence included the trunk (2 lesions), ears (2 lesions), and isolated cases in the lip, neck, eyelids, lower limbs, and face ( p = 0.07).
In our study, we observed a balanced gender distribution of BCC cases, with a slight preponderance of women (51.02% vs. 48.98%), which aligns with existing literature . This discrepancy, however, did not reach statistical significance ( p = 0.393), suggesting that gender does not significantly influence the incidence of BCC in our cohort. A higher incidence in elderly patients was observed, with mean ages of 73.52 years for men and 76.12 years for women. These findings are consistent with the broader literature, which underscores aging as a key demographic factor in the development of BCC . The dominance of the nodular subtype (45.85%) in our study is in line with the clinical significance of this BCC variant, which is the most common subtype in most populations. Notably, almost 60% of these cases appeared on the face, particularly the nose and forehead, highlighting the critical role of facial sun exposure in the development of nodular BCC . The distribution of histological subtypes did not vary significantly by sex, reinforcing the notion that these subtypes are equally prevalent across genders in our cohort. Regarding anatomical distribution, the head emerged as the most affected region, with 54.74% of cases localized to the face, particularly the nose (13.31%) and forehead (9.2%). This finding further supports the well-established link between sun exposure and the higher incidence of BCC in sun-exposed areas, particularly the facial regions. These results emphasize the need for enhanced sun protection measures, especially in high-risk areas like the face. Our study achieved a high complete excision rate of 93.3%, with an incomplete excision rate of 6.7%, which is slightly below the lower limits of 7–25% reported in the literature . Incomplete excision rates were higher in specific regions, such as the head and neck, and with certain histological subtypes, aligning with findings from other studies that highlight the challenges of excising tumors in these anatomically and cosmetically sensitive areas [ – ]. Factors contributing to incomplete excisions include both tumor location and histological type. For example, tumors in areas such as the nose, eyelids, and nasolabial folds are more likely to be incompletely excised due to the aesthetic concerns of extensive resection. This phenomenon underscores the need for individualized surgical approaches in such anatomically complex regions. In addition, histological subtypes such as sclerosing and infiltrative BCCs are at higher risk of incomplete excision, as they tend to infiltrate deeper into the tissue, making complete removal more challenging [ – ]. In our study, peripheral margin involvement was observed in 6.6% of cases, which is consistent with existing research and highlights the importance of ensuring complete excision beyond the clinically visible tumor boundary . Notably, we found that peripheral margins greater than 5 mm were exceeded in 7.15% of cases, with a significant proportion (72.89%) of excisions falling within the 1–5 mm range. This further emphasizes the delicate balance between achieving clear margins and preserving functional and cosmetic outcomes in anatomically sensitive areas. Some authors suggest that dermoscopy improves margin delineation for surgical excision of BCC. The identification of nontraditional dermoscopic features, such as pinkish-white areas and short telangiectasias between clinical and dermoscopic margins, helps determine the true boundaries of the tumor, facilitating accurate excision . In terms of clinical practice, our study strongly supports the importance of reintervention in cases of positive or narrow margins, especially in high-risk tumors located on the face or with aggressive subtypes. While some studies advocate for a more conservative “watch and wait” approach in cases with only lateral margin involvement, our results favor a proactive surgical approach to mitigate recurrence risks, in line with recommendations for excising all BCCs with positive margins . Our data also suggest a significant correlation between tumor size and reintervention rates. Tumors requiring reintervention were notably larger than those that were successfully excised on the first attempt, with lesions over 10 mm in diameter showing a significantly higher likelihood of requiring further excision ( p = 0.0049). This underscores the need for wider clinical margins during the initial excision of larger tumors to reduce the need for subsequent interventions and improve long-term outcomes. We found that the subtypes most frequently undergoing reintervention were sclerosing (14.09%) and basosquamous (10.20%). These aggressive subtypes warrant more radical excisions with larger clinical margins, particularly in high-risk anatomical regions such as the face. According to the literature, fully excised tumors have recurrence rates of about 5.9%, while those that are not entirely excised have recurrences in an average of 26.8% of cases . Regarding recurrence, our study reports a low overall recurrence rate of 0.52%, which is promising compared to the recurrence rate of 1% for fully excised tumors reported in the literature . Tumors with closer excision margins had significantly higher recurrence rates, emphasizing the importance of achieving adequate margins during initial excision ( p = 0.002). Recurrence was more frequent in facial regions, particularly the nose and forehead, areas where aesthetic considerations may lead to more conservative excision techniques. This highlights the challenges of balancing clear margins with cosmetic outcomes in these sensitive areas. Interestingly, basosquamous carcinoma exhibited a higher recurrence rate (1.6%) compared to other subtypes, which may warrant further investigation into the unique biological characteristics of this subtype and its implications for surgical management. While our study provides valuable insights into BCC excision, it is essential to acknowledge its limitations, including its retrospective design and the potential variability in surgical techniques across operators. The relatively small number of recurrences may also limit the generalizability of some of our findings. Despite these limitations, the study’s strength lies in its large sample size of 3036 lesions, which provides robust data to guide clinical practice. Future studies could explore additional factors, such as genetic and environmental variables, to further refine BCC treatment strategies and outcomes.
Surgical excision with clinically clear margins, as recommended by the NCCN guidelines (4 mm for suspected BCCs), is essential to minimize the risk of recurrence and ensure optimal outcomes. Additionally, regular dermatologic follow-up is crucial for the early detection of any recurrences, which can be managed more effectively if identified promptly. Particular attention must be given to high-risk BCCs, including those located on the face, and those with aggressive histological subtypes, such as sclerosing and basosquamous, or those exceeding 10 mm in size. For these tumors, the widest possible excision is recommended, as they are more prone to recurrence. Our findings provide significant insights into the complex relationship between excision margins, the need for reoperation, and long-term outcomes in BCC patients. The methodological approach we utilized offers a strong foundation for further studies exploring the intricate balance between excision extent and patient prognosis, guiding clinical decisions and improving patient care in the management of basal cell carcinoma.
|
Training obstetrics and gynecology residents to be effective communicators in the era of the 80-hour workweek: a pilot study | 0df72dcb-88d2-4a1d-a7f0-3eabf4e73520 | 4105231 | Gynaecology[mh] | With the introduction of resident work hour restrictions by the Accreditation Council of Graduate Medical Education (ACGME) in 2003 and subsequent modifications in 2011, there is a growing debate among the medical and surgical specialties regarding how to most effectively train residents to become fully independent practitioners with less time
. The apprenticeship model of medical education in the setting of current duty hour restrictions is increasingly challenging. In this current environment of restricted resident work hours, physician-educators must strive to balance formal didactic and hands-on training. A recent survey of physician-educators in obstetrics and gynecology (OB/GYN) by Espey et al. reported concerns regarding the negative impact of resident work hours on resident education
. Nearly 63% of the respondents felt that the global education of an obstetrics and gynecology resident was worse, as compared to what it had been prior to the implementation of work hour requirements by the ACGME
. With the implementation of the 16-hour workday restriction for first year residents, senior level general surgery residents (94% of second- through fifth-year residents) expressed that such restrictions would adversely impact the education of their more junior counterparts
. The aforementioned problem is exacerbated in OB/GYN, as residents have a vastly broad body of knowledge and technical expertise to master in a 4-year span under the umbrella of restricted work hours. At the conclusion of an OB/GYN residency, the resident is expected have a broad knowledge of generalist topics; however, this may come at the expense of having only cursory knowledge of the sub-specialties of family planning, reproductive endocrinology and infertility, gynecologic oncology, uro-gynecology and maternal-fetal medicine (MFM). In the management of an obstetric patient, however, a seemingly routine case within the scope of practice of a generalist OB/GYN can quickly evolve into a more complex maternal-fetal issue, with the patient expecting her doctor to manage the situation with expertise. Given the unpredictable frequency of these clinical situations for trainee participation, simulations for such scenarios as shoulder dystocia and operative vaginal delivery continue to evolve not only for clinical skills development but also for competency assessment. “Role-playing”, or simulated patient interactions, have been applied in various aspects of medical education, particularly in the education of medical students
. In a British study looking at the benefits of role playing on medical student education, of the 274 students responding to a post-role playing assessment, 96.5% found the session to be helpful to allowing them to develop their skills, receive feedback and obtain perspective from other participants
. Numerous suggestions were offered by those that participated regarding how the role-play could have been even more effective. These included increased time to role-play, increased personal feedback, and making scenarios as close to real-life situations as possible
. Nevertheless, it is unclear as to whether or not such an educational tool can be successfully applied to the education of OB/GYN residents. Thus, we propose an innovative and experiential approach at our institution to teach OB/GYN residents effective communication techniques utilizing complex obstetric scenarios relevant to their daily clinical responsibilities. We hypothesize that such an approach will enable OB/GYN residents to become more facile in communicating with even the most difficult patients under the most complex of circumstances and that a simulated patient exercise would be a means by which to accomplish this. Because junior residents are often the initial faces of the care team, it is important for residents to commence the acquisition of these skills early on in their training.
We developed a simulated patient exercise at Magee-Women’s Hospital of University of Pittsburgh Medical Center, which is a tertiary care center with greater than 10,000 deliveries annually and a busy perinatal referral service for maternal-fetal medicine. As part of formal resident didactic education at our institution, first-year residents participate in “Intern Ed”, a separate five-hour session once per month dedicated to level-specific skill building. These skills include emergency/crisis management, placement of long-acting forms of reversible contraception and the navigation of issues of systems-based practice. We utilized the designated two-hour session for patient counseling for our simulation exercise. In preparation, we e-mailed the current second-year residents with a link to an on-line survey with questions querying their readiness to handle various obstetrical situations commonly encountered on a labor and delivery unit, based on their experiences as interns. The content of the survey may be found in Additional file . As part of the survey, we asked second-year residents (PGY2) to review a list of common complicated obstetrical scenarios as noted in Table , including: peri-viable preterm premature rupture of membranes, counseling for a trial of labor after a cesarean delivery, and refusal of blood products by a Jehovah’s witness. Using a 7-level Likert scale, we asked second-year residents to rank these scenarios from “most comfortable” to “least comfortable” in terms of how confident they felt in their personal ability to provide the requisite counseling. These choices were pre-selected by the investigators as clinical issues commonly encountered by junior residents at our institution. Based on these results, we developed four scenarios for simulation, from among the ones identified by the PGY2s as the “least comfortable”. These scenarios can be seen in Additional file . Second-year residents did not undergo the simulated patient exercise. Prior to the Intern Ed session, we sent all current first-year residents a separate on-line survey to assess their past experiences with simulated patient interactions. We also shared with them details of our proposed educational module. This content may be found in Additional file . In order to ensure sufficient clinical knowledge given the timing of the session during the academic year (early October 2013), we e-mailed all first-year residents one week prior to the simulation with formative articles related to the clinical scenarios to be addressed in the simulation
[ - ]. Prior to starting the simulation exercise, we set the following expectations and ground rules: 1) Treat each scenario as an actual clinical encounter; 2) Option to pause the scenario at any time to ask questions; 3) Anything occurring or said in the room would remain confidential. We divided our cohort into groups of two to three residents. For each scenario, we provided each group with a sealed, opaque envelope with explicit instructions for the role of “patient”, “physician”, or “family member”. The groups were then provided five minutes to strategize how they would approach their scenario. While the scenarios provided an opportunity to apply knowledge of complicated obstetrical issues to simulated cases, they were targeted to promote practice of key skills in establishing an effective physician-patient relationship: a. Non-judgmental communication: ensuring that personal bias does not enter into a conversation with a patient to influence clinical decision-making
; b. Culture competency awareness: appreciating how the nuances of a patient’s belief system influences their health care decisions and a physician’s counseling of that patient
; and c. Reflective listening: listening to what the patient is saying and then repeating what he or she has said so as to confirm that one has understood
. Once each group had designated an actor for their respective roles, the actors simulated the obstetrical scenario in front of the entire room. Initially the residents played out each scenario until its completion, without interruption. Then, we asked the participants how they felt playing their individual roles and if they would have changed anything. In the discussions, we linked the events of the simulated patient exercise with the readings provided prior to the intervention to broaden participants’ clinical knowledge base. At the end of each scenario, we asked the collective group to answer the following questions: 1. What were the challenges of this scenario? 2. How else could you have approached this scenario? 3. If you have been confronted with this scenario, how did you manage this? 4. What are the take-home points from this scenario? The actual scenarios used can be found in Additional file . After the conclusion of the simulation, all participants were asked to complete a follow-up survey to evaluate the acceptability and the utility of the exercise for their overall education. We tabulated descriptive statistics for our survey questions via STATA 12.0 (StataCorp, College Station, TX). This education research study qualified for exempt status as determined by the University of Pittsburgh Institutional Review Board, #PRO13070456, on August 16, 2013.
Second-year residents Our second-year class is comprised of ten residents, including nine women and one man, whose ages range from 27–36 years. Nine out of ten (90%) second-year residents responded to the second year survey. Six (67%) of 9 second-year residents responded that their experiences as a first-year resident only somewhat prepared them to effectively communicate with high-risk obstetrical patients. Five (55%) of the respondents stated that they felt intimidated when asked to evaluate a MFM patient in OB Triage or on the antepartum floor. Eight of the nine (89%) second-year respondents reported that either journal clubs or lectures were the least effective methods by which to learn new material. The majority of second-year residents (77%) reported that case-based learning was the most effective way to learn new material. Only one of the respondents indicated that he or she had not participated in simulated patient exercises prior to or during residency. Of the obstetrical scenarios provided in the survey, the majority (>50%) of second-year residents felt uncomfortable counseling patients with the following issues: placenta accreta; those with a sentinel bleed from a placenta previa; pre-viable preterm premature rupture of membranes; and those patients requesting a vaginal birth after a cesarean delivery. These clinical issues were the basis for the four scenarios in our simulated patient exercise. Both pre-simulation on-line survey respondents (PGY2 and PGY1) selected case-based and simulation-based learning as the most effective learning method, and lectures as the least effective format for learning. First-year residents Our first-year class is also comprised of ten residents, all of whom are female ranging from 24–31 years of age. Nine residents (90%) responded to our pre-session survey. Seven of the first-year residents (78%) stated that they were “somewhat comfortable” with discussing the risks and benefits of a medical decision. All respondents reported that they had participated in a simulated patient interaction prior to residency, with only one person reporting a negative experience. As seen in Table , none of the respondents (0%) reported that either lecture or journal club was an effective method by which to learn new material. All respondents selected case-based learning and simulations as the best ways to learn new material. Seven first-year residents were able to participate in the “Intern Ed” session. All being satisfied with the experience as evidenced by the post-exercise survey, (found in Additional file ) the first-year residents indicated that they would employ the strategies learned during the session in their daily practice of medicine. When asked the question What do you think was most valuable thing that you learned from today’s session?, one intern responded with the following: “I think it’s helpful to see other people model difficult conversations, so that I can incorporate different styles/strategies that work for me into my own practice”. Another intern stated that “the medical knowledge of PPROM and repeat C-section counseling” was most valuable. All respondents to the post-exercise survey (100%) reported that they would use the strategies that they have learned during the simulated patient exercise in future clinical encounters. In further consideration of the post-exercise survey, the first-year residents appreciated the practical application of medical knowledge. While one intern stated, “I think the topics were covered were appropriate and helpful”, two interns responded that they wished that we had included a scenario on how to counsel patients with fetal loss. Two other interns also reported that they thought discussing patients with substance abuse/dependence and how physicians’ biases may influence the management of these patients. Three (43%) of the participated wished that they had more time to read the provided articles. Only one of the seven first-year residents participating indicated that he/she did not like role-playing, which was consistent with this individual’s past experience with this education technique. When asked to provide feedback on the session, three interns stated that they had a positive experience with the simulation, with one reporting “I very much liked this as a learning tool”. Two others had no suggestions for feedback for the simulation. In reference to the pre-intervention articles provided, one intern stated, “That was an imposing reading list” and another wanted them to be sent earlier. These survey results indicate that the participants appreciated various aspects of the simulation and were able to obtain skills that could be used in clinical practice. However, some of the participants determined that the structure of the simulated patient exercise could be used for a variety of other clinical situations.
Our second-year class is comprised of ten residents, including nine women and one man, whose ages range from 27–36 years. Nine out of ten (90%) second-year residents responded to the second year survey. Six (67%) of 9 second-year residents responded that their experiences as a first-year resident only somewhat prepared them to effectively communicate with high-risk obstetrical patients. Five (55%) of the respondents stated that they felt intimidated when asked to evaluate a MFM patient in OB Triage or on the antepartum floor. Eight of the nine (89%) second-year respondents reported that either journal clubs or lectures were the least effective methods by which to learn new material. The majority of second-year residents (77%) reported that case-based learning was the most effective way to learn new material. Only one of the respondents indicated that he or she had not participated in simulated patient exercises prior to or during residency. Of the obstetrical scenarios provided in the survey, the majority (>50%) of second-year residents felt uncomfortable counseling patients with the following issues: placenta accreta; those with a sentinel bleed from a placenta previa; pre-viable preterm premature rupture of membranes; and those patients requesting a vaginal birth after a cesarean delivery. These clinical issues were the basis for the four scenarios in our simulated patient exercise. Both pre-simulation on-line survey respondents (PGY2 and PGY1) selected case-based and simulation-based learning as the most effective learning method, and lectures as the least effective format for learning.
Our first-year class is also comprised of ten residents, all of whom are female ranging from 24–31 years of age. Nine residents (90%) responded to our pre-session survey. Seven of the first-year residents (78%) stated that they were “somewhat comfortable” with discussing the risks and benefits of a medical decision. All respondents reported that they had participated in a simulated patient interaction prior to residency, with only one person reporting a negative experience. As seen in Table , none of the respondents (0%) reported that either lecture or journal club was an effective method by which to learn new material. All respondents selected case-based learning and simulations as the best ways to learn new material. Seven first-year residents were able to participate in the “Intern Ed” session. All being satisfied with the experience as evidenced by the post-exercise survey, (found in Additional file ) the first-year residents indicated that they would employ the strategies learned during the session in their daily practice of medicine. When asked the question What do you think was most valuable thing that you learned from today’s session?, one intern responded with the following: “I think it’s helpful to see other people model difficult conversations, so that I can incorporate different styles/strategies that work for me into my own practice”. Another intern stated that “the medical knowledge of PPROM and repeat C-section counseling” was most valuable. All respondents to the post-exercise survey (100%) reported that they would use the strategies that they have learned during the simulated patient exercise in future clinical encounters. In further consideration of the post-exercise survey, the first-year residents appreciated the practical application of medical knowledge. While one intern stated, “I think the topics were covered were appropriate and helpful”, two interns responded that they wished that we had included a scenario on how to counsel patients with fetal loss. Two other interns also reported that they thought discussing patients with substance abuse/dependence and how physicians’ biases may influence the management of these patients. Three (43%) of the participated wished that they had more time to read the provided articles. Only one of the seven first-year residents participating indicated that he/she did not like role-playing, which was consistent with this individual’s past experience with this education technique. When asked to provide feedback on the session, three interns stated that they had a positive experience with the simulation, with one reporting “I very much liked this as a learning tool”. Two others had no suggestions for feedback for the simulation. In reference to the pre-intervention articles provided, one intern stated, “That was an imposing reading list” and another wanted them to be sent earlier. These survey results indicate that the participants appreciated various aspects of the simulation and were able to obtain skills that could be used in clinical practice. However, some of the participants determined that the structure of the simulated patient exercise could be used for a variety of other clinical situations.
To satisfy the multitude of learning objectives, OB/GYN training programs have come to rely on including structured lectures, formal intraoperative experience, discussion groups, journal clubs and “one-on-one teachable moments”. “Role-playing”, or simulated patient interactions, is an educational technique that allows for simultaneous assessment of multiple domains of competency. We implemented a simulated patient interaction session in order to teach our interns interpersonal communication skills. In order to create a richer experience, we based the simulated cases for our interns on cases that their peers, the second year residents, had encountered the year prior. Our post-session survey results support the use of this educational technique. All of the first-year residents participating in this exercise felt that this simulated patient interaction was beneficial for their education. In the era of work hour restrictions, educational experiences need to be relevant to the learner. Our session utilizing resident-identified clinical knowledge gaps and focusing on skills translatable to other patient-physician interactions was well received and valued by the participants. It is important to understand the context in which our results were obtained. We implemented our educational tool during the first quarter of the intern year, a time during which a majority of our interns have completed at least one clinical rotation on an obstetrical service. Therefore, it is highly likely that these interns may not yet possess to the clinical acumen to handle such obstetrical scenarios presented during our simulation. Our simulation allows interns to become exposed to novel clinical situations in a safe, non-threatening environment in which effective communication skills can be learned, practiced and then implemented in actual patient encounters. Thus, after the completion of our simulation, the interns have a framework upon which to build for the entirety of their residency. In an effort to establish more efficient guidelines for the assessment of resident achievement, the ACGME announced the Obstetrics and Gynecology Milestone Project in September 2013
. The milestones represent key areas in which OB/GYN residents are expected to attain proficiency prior to graduation. Each milestone contains levels in order to represent the progression a resident should have from intern year to chief year
. Our educational intervention can be generalizable to other residency programs, as all residency training in obstetrics and gynecology in the United States must attain proficiency in the milestones of both professionalism and systems-based practice. Importantly, simulated patient exercises enable OB/GYN residents to refine their interpersonal/communication skills and develop their sense of professionalism in a safe, non-judgmental environment. Simulated patient interactions allow coached practice on how to interface with various types of patients, while expanding their medical knowledge base through interactive discussion with peers. The scenarios used in our intervention are commonly encountered in practice, and thus relevant to the practice of general obstetrics and gynecology. Our study does have limitations. As a pilot study the small sample size may limit external validity of our results. It is also important to note that such a simulated patient exercise among colleagues may not perfectly reflect real-life clinical scenarios. However, the intention of our intervention was to provide participants with a framework upon which to build for actual patient interactions. Additionally, 43% of the first-year residents indicated that they would have preferred more time to read the pre-intervention articles provided to them. In planning the next education session, we anticipate sending our any supplementary material earlier with more explicit guidance on how this will be utilized in the session. While we constructed a simulated patient exercise using obstetrical scenarios in order to teach effective communication skills, we assert that simulated patient interactions can be readily adapted to address other topics in obstetrics and gynecology, including those focusing on risk-benefit analysis, informed consent, and end-of-life discussions. Further investigation is warranted to determine resident retention of these skills and how well they translate to the bedside. Such assessment could be accomplished by recorded patient encounters, patient satisfaction surveys and patient interviews. Given the success of our intervention, we anticipate its use with subsequent first-year classes and, possibly, the residency at large. Future research to assess residents’ acquisition and retention of core knowledge using this educational technique is necessary. It is our hope that providing a foundation in proper patient-physician communication during the first year of residency training will lead to a graduating resident who is competent in this domain and who eventually will be ready for independent practice.
OB/GYN: Obstetrics and gynecology; ACGME: Accreditation council of graduate medical education; MFM: Maternal-fetal medicine.
The authors declare that they have no competing interests.
OMY conceived of the study, participated in the construction of the educational intervention and its implementation, participated in data collection and data analysis and drafted the initial manuscript. KP participated in the construction of the educational intervention and its implementation, participated in data analysis and made significant contributions to the writing of the manuscript. Both authors take full responsibility for the quality and the integrity of this manuscript. Both OMY and KP have approved the final version of this manuscript.
OMY is a clinical fellow in the Division of Maternal-Fetal Medicine at Magee-Women’s Hospital/University of Pittsburgh Medical Center in Pittsburgh, Pennsylvania. He is a member of the Association of Professors in Gynecology and Obstetrics. KP is an assistant professor in the Division of Maternal-Fetal Medicine and the assistant residency program director at Magee-Women’s Hospital/University of Pittsburgh Medical Center in Pittsburgh, Pennsylvania. She is a member of the Association of Professors in Gynecology and Obstetrics.
Additional file 1 Second-Year OB/GYN Resident Survey. Click here for file Additional file 2 First-Year OB/GYN Resident Pre-Intervention Survey. Click here for file Additional file 3 OB/GYN Intern Education Simulated Patient Scenarios. These are the clinical situations used for the simulated patient exercise. Each scenario allows the participants to take on either the role of the patient, the physician or another member involved in the medical decision-making. Each scenario allows the participants to more fully understand the spectrum of patients they will encounter in everyday practice. This includes the “angry” patient, issues of cultural competency, relationship dynamics and the influence of religious practice on health care. Click here for file Additional file 4 First-Year OB/GYN Resident Post-Intervention Questionnaire. Click here for file
|
Training General Practitioners in Oncology: Lessons Learned From a Cross-Sectional Survey of GPOs in Canada | b4b82795-520f-4b77-9d7a-79af3ffbed15 | 10281329 | Internal Medicine[mh] | Several countries face the challenge of a growing population of patients with cancer but a significant shortage of cancer care providers, including medical oncologists to care for them. , Training a medical student to become a medical oncologist to care for patients with cancer is resource and time intensive. To mitigate this challenge, some countries, including Canada, have taken the innovative approach of training general practitioners in oncology (GPOs). In this approach, family physicians (FPs) are provided with specialized, focused training in the fundamentals of cancer care. CONTEXT Key Objective To collect information on the training and work experience of Canadian general practitioners in oncology (GPOs) to inform GPO curriculum development elsewhere. Knowledge Generated A survey of 37 Canadian General Practitioners in Oncology revealed that 90% of respondents felt GPO training prepared them to care for patients with cancer. The modes of training that were most effective included clinics with oncologists, small group learning, and online education. Treatment of side effects, symptom management, palliative care, and breaking bad news were considered the most relevant domains for GPO practice. Relevance GPOs considered themselves better prepared to care for patients with cancer after GPO training program versus after family medicine residency alone. The results of this survey may offer valuable insights for the development of GPO training programs in other settings. After training, GPOs can participate in cancer care in both task-shifting and task-sharing models. Under the task-shifting model, GPOs work in collaboration with medical oncologists in larger centers to provide cancer care in locations where medical oncologists are unavailable. Under the task-sharing model, they work with medical oncologists to provide cancer care so that one oncologist can care for a larger number of patients than would usually be possible. Over 40 years ago, Canada began using GPOs to deliver cancer care. Now, the country has a dedicated professional national association of GPOs called the Canadian Association of General Practitioners in Oncology (CAGPO). CAGPO is a membership-based organization that provides educational resources for Canadian GPOs and scholarship funding for trainees, and hosts an annual national conference. The mission of the organization is to unify GPOs, promote communication among them, and to act and speak as the recognized authority on behalf of GPOs and their interests. In Canada, where many rural residents live a significant distance from a large cancer center, the integration of GPOs has increased access to cancer care. In addition to offering a more succinct and accessible training program, GPO training programs increase the cancer care workforce in a given region, ultimately making high-quality cancer care more widely available. In Canada, GPO training programs are established independently by universities and health facilities in different provinces, and various training opportunities are available to general practitioners. In a previous systematic review, we have identified six different GPO training programs in Canada, five of which had a formal curriculum and ranged between 1 month and 1 year in duration. These existing GPO training programs in Canada are heterogeneous and are coordinated at the provincial rather than national level. The training can vary in duration and content, and both formal and informal training opportunities exist. Although some training programs in Canada integrate GPO training under the third year of family medicine residency program, some others offer it under continued medical education format. Many low- and middle-income countries (LMICs) continue to face shortages of cancer care providers, and some LMICs have already responded to this challenge by establishing task shifting and sharing models of care. One country facing this challenge is Nepal, a small South Asian nation with a population of more than 30 million and a growing cancer burden. , Our team previously conducted a survey among general practitioners (GPs) in Nepal and found a strong need and an enthusiastic interest for a GPO training program in Nepal. As a foundation for developing the curriculum for a training program in Nepal, we conducted a survey of GPOs working in Canada to learn from their training and work experiences.
Key Objective To collect information on the training and work experience of Canadian general practitioners in oncology (GPOs) to inform GPO curriculum development elsewhere. Knowledge Generated A survey of 37 Canadian General Practitioners in Oncology revealed that 90% of respondents felt GPO training prepared them to care for patients with cancer. The modes of training that were most effective included clinics with oncologists, small group learning, and online education. Treatment of side effects, symptom management, palliative care, and breaking bad news were considered the most relevant domains for GPO practice. Relevance GPOs considered themselves better prepared to care for patients with cancer after GPO training program versus after family medicine residency alone. The results of this survey may offer valuable insights for the development of GPO training programs in other settings.
A survey was designed and iteratively developed by the research team with input from multiple authors and colleagues that included several GPOs and medical oncologists from Canada and underwent internal testing among the GPOs working at Kingston General Hospital before distribution. It was then distributed to Canadian GPOs using Research Electronic Data Capture (REDCap), a secure web-based software platform hosted at Queen's University in Kingston, Ontario, Canada. The survey was approved by the Queen's University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (HSREB). The survey contained various response types, including multiple-choice, ordinal scale questions, and interval scale questions (Data Supplement [Supplementary Appendix]). The survey consisted of the following sections: Experience with patients with cancer during FP and GPO trainings, Methods of education about cancer care in GPO programs, Current scope of GPO practice, Oncology related services available to GPOs, and Respondent demographics. The survey was anonymous and did not ask for any personal identifying information from the participants. The survey was opened in July 2021 and remained active until April 2022. Participants were recruited through personal and provincial network newsletters and an email communication through the CAGPO network to its members. Survey respondents were FPs who had worked or were currently working as GPOs in Canada. The participants were aware that the purpose of the survey was to learn from Canadian GPO's experience as a part of the project to inform the development of a GPO training curriculum for Nepal. Responses were recorded in REDCap and analyzed in Microsoft Excel. Descriptive statistics are reported in this study.
Demographics Of the 207 GPOs in Canada who received our invitation to participate in the survey, 37 responded for an estimated response rate of 18%. Table shows the demographic information of the respondents. The majority of respondents were female (78%), and most (67%) were between the age of 45 and 64 years. In our sample, nearly 30% of respondents had been practicing as a GPO for <6 years, and 8% had been practicing as a GPO for >20 years. Most respondents (83%) had attended medical school in Canada. Most GPOs (58%) practiced in a large population center with more than 100,000 inhabitants, and only 14% served in small centers with <30,000 inhabitants. Nearly 70% of participants held a university appointment at the time of the survey, and 56% practiced in a university-affiliated hospital. Most respondents (78%) were still practicing as GPOs. The survey showed that respondents received payments for their oncologic services from a range of sources. More than half of GPOs received compensation from a provincial cancer care program via either salary (36%) or sessional funding (19%) and another 19% received sessional funding from a local health authority. Training Experience Oncology training experience during family medicine residency. Respondents were asked to describe their oncology training experience during their family medicine residency. Approximately one third of respondents (13) had chosen to participate in an oncology rotation during their family medicine training of which only one reported surgical oncology being available as a rotation option, and none of the respondents indicated that gynecologic oncology was an available rotation. The oncology rotation that was most widely available was medical oncology, with 10 participants indicating its availability. Additional rotations in general practice oncology (23%), radiation oncology (31%), hemato-oncology (15%), and surgical oncology (8%) were available during residency. During family medicine training, most respondents (53.8%) had an equal amount of in-patient and out-patient clinical interactions with patients with cancer. Of the 13 respondents, none fully agreed and only three (38%) somewhat agreed that their family medicine residency training adequately prepared them to care for patients with cancer (Fig ). When asked which residency rotations taught respondents the most about caring for patients with cancer, the top-ranked responses were palliative care, medical oncology, and family medicine (Fig ). GPO training experience. Forty-three percent of respondents (n = 16) received formal GPO training. Nearly two thirds of respondents (n = 10) participated in a GPO training program that was 2 months in length, and an equal proportion of them engaged in full-time vs. part-time GPO training. Only one (6%) had training of 1 year or longer. The most frequently available oncology rotations during GPO training were out-patient medical oncology (94%), hematologic oncology (63%), and radiation oncology (63%). In contrast, the rotations that were reported as available by the lowest percentage of respondents were psychosocial oncology (13%), surgical oncology (13%), and pediatric oncology (19%). In contrast to family medicine training, clinical interaction with patients during GPO training was mostly out-patient (69%) or all out-patient (25%). When asked to rank rotations in terms of where the most was learned about caring for patients with cancer, the rotations with the top three rankings were palliative care, medical oncology, and radiation oncology (Fig ). In contrast to the responses related to family residency training, nearly 90% agreed that the GPO training program had adequately prepared them to care for patients with cancer, and no respondents disagreed (Fig ). In terms of content delivery, respondents indicated a wide range of teaching methodologies during their GPO training. The most common teaching modalities were in-clinic training with oncologists (100%), didactic lectures by oncologists (88%), and self-directed online/web-based learning (87%). The least common modes were didactic lectures by GPOs (31%), small group learning (58%), and in clinic with GPOs (59%). When asked about didactic lectures given by FPs, 10 respondents (90%) indicated that these were effective in learning about caring for patients with cancer. The results were similar when asked about didactic lectures given by oncologists. All respondents (100%) reported clinics with oncologists to be an effective teaching method. Of those who experienced online learning, no respondents felt that it was ineffective. Clinics also proved to be a valuable tool for learners, with 80% (n = 33) of respondents finding clinics with GPOs and 60% (n = 19) of respondents finding clinics with oncologists very effective in learning about caring for patients with cancer. Of those GPOs who did not receive formal training (n = 21), the delivery methods thought to be the most useful were clinical rotation with oncologists (90%), followed by clinical rotation with GPOs (86%) and then online learning (76%). Practice Data were collected on the current clinical practice of respondents. Nearly 70% indicated that their current practice as a GPO was mostly out-patient. Most respondents (81%) cared for between 6-15 patients with cancer during a typical out-patient clinic day. In their role as a GPO, 73% of respondents reported caring for an average of one to five patients with cancer per day in an in-patient setting. Participants were asked which tumor types were most commonly presented at their clinic. Of the 37 respondents, 12 indicated lung cancer as most common, followed by 10 for breast cancer and six for hematological malignancies. No respondents indicated cervical, prostate, or skin as the most common tumor type presenting at their clinic. When asked about service provision for patients with cancer in their GPO practices, the four most commonly provided services were cancer-related symptom management, active out-patient care while on chemotherapy, follow-ups for patients not on active treatment, and palliative care. The three least provided services were diagnostic procedures (eg, fine needle aspiration cytology, biopsy, etc), screening, and in-patient care for admitted patients (Fig ). In terms of the composition of their professional teams, all worked with nurses, and most had pharmacists (97.3%) and medical oncologists (83.8%) on their teams. Least frequently listed as team members were psychologists (27.0%), occupational therapists (29.7%), hematologists (54.1%), and surgical oncologists (54.1%). Respondents were asked to reflect on what procedural skills a GPO should be responsible for in practice. Most important skills were managing radiation and chemotherapy toxicities (97% indicated as important), cancer-related symptom management (94% indicated as important), and pain control (92%). Peripherally inserted central catheter line insertion received the lowest score, with only 3% of respondents indicating it as an important skill. With regards to clinical and communication skills, the most important ones were managing common treatment side effects, managing pain and other symptoms of cancer, and breaking bad news. The least important skill was approach to patient with increased risk of cancer. In terms of knowledge domains important for a GPO's practice, the most important ones were oncology emergencies and the treatment of side effects of cancer treatment, and the least important were the role of nutrition and diet, screening for common cancers, and the epidemiology of common cancers. Survey participants also reported on their local access to oncology services. The services most available within the institution of the respondents were palliative care, pathology, and medical oncology. The services that were least readily available within the respondents' institutions were radiation oncology, hematology, surgical oncology, and multidisciplinary teams for cancer management (Fig ).
Of the 207 GPOs in Canada who received our invitation to participate in the survey, 37 responded for an estimated response rate of 18%. Table shows the demographic information of the respondents. The majority of respondents were female (78%), and most (67%) were between the age of 45 and 64 years. In our sample, nearly 30% of respondents had been practicing as a GPO for <6 years, and 8% had been practicing as a GPO for >20 years. Most respondents (83%) had attended medical school in Canada. Most GPOs (58%) practiced in a large population center with more than 100,000 inhabitants, and only 14% served in small centers with <30,000 inhabitants. Nearly 70% of participants held a university appointment at the time of the survey, and 56% practiced in a university-affiliated hospital. Most respondents (78%) were still practicing as GPOs. The survey showed that respondents received payments for their oncologic services from a range of sources. More than half of GPOs received compensation from a provincial cancer care program via either salary (36%) or sessional funding (19%) and another 19% received sessional funding from a local health authority.
Oncology training experience during family medicine residency. Respondents were asked to describe their oncology training experience during their family medicine residency. Approximately one third of respondents (13) had chosen to participate in an oncology rotation during their family medicine training of which only one reported surgical oncology being available as a rotation option, and none of the respondents indicated that gynecologic oncology was an available rotation. The oncology rotation that was most widely available was medical oncology, with 10 participants indicating its availability. Additional rotations in general practice oncology (23%), radiation oncology (31%), hemato-oncology (15%), and surgical oncology (8%) were available during residency. During family medicine training, most respondents (53.8%) had an equal amount of in-patient and out-patient clinical interactions with patients with cancer. Of the 13 respondents, none fully agreed and only three (38%) somewhat agreed that their family medicine residency training adequately prepared them to care for patients with cancer (Fig ). When asked which residency rotations taught respondents the most about caring for patients with cancer, the top-ranked responses were palliative care, medical oncology, and family medicine (Fig ). GPO training experience. Forty-three percent of respondents (n = 16) received formal GPO training. Nearly two thirds of respondents (n = 10) participated in a GPO training program that was 2 months in length, and an equal proportion of them engaged in full-time vs. part-time GPO training. Only one (6%) had training of 1 year or longer. The most frequently available oncology rotations during GPO training were out-patient medical oncology (94%), hematologic oncology (63%), and radiation oncology (63%). In contrast, the rotations that were reported as available by the lowest percentage of respondents were psychosocial oncology (13%), surgical oncology (13%), and pediatric oncology (19%). In contrast to family medicine training, clinical interaction with patients during GPO training was mostly out-patient (69%) or all out-patient (25%). When asked to rank rotations in terms of where the most was learned about caring for patients with cancer, the rotations with the top three rankings were palliative care, medical oncology, and radiation oncology (Fig ). In contrast to the responses related to family residency training, nearly 90% agreed that the GPO training program had adequately prepared them to care for patients with cancer, and no respondents disagreed (Fig ). In terms of content delivery, respondents indicated a wide range of teaching methodologies during their GPO training. The most common teaching modalities were in-clinic training with oncologists (100%), didactic lectures by oncologists (88%), and self-directed online/web-based learning (87%). The least common modes were didactic lectures by GPOs (31%), small group learning (58%), and in clinic with GPOs (59%). When asked about didactic lectures given by FPs, 10 respondents (90%) indicated that these were effective in learning about caring for patients with cancer. The results were similar when asked about didactic lectures given by oncologists. All respondents (100%) reported clinics with oncologists to be an effective teaching method. Of those who experienced online learning, no respondents felt that it was ineffective. Clinics also proved to be a valuable tool for learners, with 80% (n = 33) of respondents finding clinics with GPOs and 60% (n = 19) of respondents finding clinics with oncologists very effective in learning about caring for patients with cancer. Of those GPOs who did not receive formal training (n = 21), the delivery methods thought to be the most useful were clinical rotation with oncologists (90%), followed by clinical rotation with GPOs (86%) and then online learning (76%).
Respondents were asked to describe their oncology training experience during their family medicine residency. Approximately one third of respondents (13) had chosen to participate in an oncology rotation during their family medicine training of which only one reported surgical oncology being available as a rotation option, and none of the respondents indicated that gynecologic oncology was an available rotation. The oncology rotation that was most widely available was medical oncology, with 10 participants indicating its availability. Additional rotations in general practice oncology (23%), radiation oncology (31%), hemato-oncology (15%), and surgical oncology (8%) were available during residency. During family medicine training, most respondents (53.8%) had an equal amount of in-patient and out-patient clinical interactions with patients with cancer. Of the 13 respondents, none fully agreed and only three (38%) somewhat agreed that their family medicine residency training adequately prepared them to care for patients with cancer (Fig ). When asked which residency rotations taught respondents the most about caring for patients with cancer, the top-ranked responses were palliative care, medical oncology, and family medicine (Fig ).
Forty-three percent of respondents (n = 16) received formal GPO training. Nearly two thirds of respondents (n = 10) participated in a GPO training program that was 2 months in length, and an equal proportion of them engaged in full-time vs. part-time GPO training. Only one (6%) had training of 1 year or longer. The most frequently available oncology rotations during GPO training were out-patient medical oncology (94%), hematologic oncology (63%), and radiation oncology (63%). In contrast, the rotations that were reported as available by the lowest percentage of respondents were psychosocial oncology (13%), surgical oncology (13%), and pediatric oncology (19%). In contrast to family medicine training, clinical interaction with patients during GPO training was mostly out-patient (69%) or all out-patient (25%). When asked to rank rotations in terms of where the most was learned about caring for patients with cancer, the rotations with the top three rankings were palliative care, medical oncology, and radiation oncology (Fig ). In contrast to the responses related to family residency training, nearly 90% agreed that the GPO training program had adequately prepared them to care for patients with cancer, and no respondents disagreed (Fig ). In terms of content delivery, respondents indicated a wide range of teaching methodologies during their GPO training. The most common teaching modalities were in-clinic training with oncologists (100%), didactic lectures by oncologists (88%), and self-directed online/web-based learning (87%). The least common modes were didactic lectures by GPOs (31%), small group learning (58%), and in clinic with GPOs (59%). When asked about didactic lectures given by FPs, 10 respondents (90%) indicated that these were effective in learning about caring for patients with cancer. The results were similar when asked about didactic lectures given by oncologists. All respondents (100%) reported clinics with oncologists to be an effective teaching method. Of those who experienced online learning, no respondents felt that it was ineffective. Clinics also proved to be a valuable tool for learners, with 80% (n = 33) of respondents finding clinics with GPOs and 60% (n = 19) of respondents finding clinics with oncologists very effective in learning about caring for patients with cancer. Of those GPOs who did not receive formal training (n = 21), the delivery methods thought to be the most useful were clinical rotation with oncologists (90%), followed by clinical rotation with GPOs (86%) and then online learning (76%).
Data were collected on the current clinical practice of respondents. Nearly 70% indicated that their current practice as a GPO was mostly out-patient. Most respondents (81%) cared for between 6-15 patients with cancer during a typical out-patient clinic day. In their role as a GPO, 73% of respondents reported caring for an average of one to five patients with cancer per day in an in-patient setting. Participants were asked which tumor types were most commonly presented at their clinic. Of the 37 respondents, 12 indicated lung cancer as most common, followed by 10 for breast cancer and six for hematological malignancies. No respondents indicated cervical, prostate, or skin as the most common tumor type presenting at their clinic. When asked about service provision for patients with cancer in their GPO practices, the four most commonly provided services were cancer-related symptom management, active out-patient care while on chemotherapy, follow-ups for patients not on active treatment, and palliative care. The three least provided services were diagnostic procedures (eg, fine needle aspiration cytology, biopsy, etc), screening, and in-patient care for admitted patients (Fig ). In terms of the composition of their professional teams, all worked with nurses, and most had pharmacists (97.3%) and medical oncologists (83.8%) on their teams. Least frequently listed as team members were psychologists (27.0%), occupational therapists (29.7%), hematologists (54.1%), and surgical oncologists (54.1%). Respondents were asked to reflect on what procedural skills a GPO should be responsible for in practice. Most important skills were managing radiation and chemotherapy toxicities (97% indicated as important), cancer-related symptom management (94% indicated as important), and pain control (92%). Peripherally inserted central catheter line insertion received the lowest score, with only 3% of respondents indicating it as an important skill. With regards to clinical and communication skills, the most important ones were managing common treatment side effects, managing pain and other symptoms of cancer, and breaking bad news. The least important skill was approach to patient with increased risk of cancer. In terms of knowledge domains important for a GPO's practice, the most important ones were oncology emergencies and the treatment of side effects of cancer treatment, and the least important were the role of nutrition and diet, screening for common cancers, and the epidemiology of common cancers. Survey participants also reported on their local access to oncology services. The services most available within the institution of the respondents were palliative care, pathology, and medical oncology. The services that were least readily available within the respondents' institutions were radiation oncology, hematology, surgical oncology, and multidisciplinary teams for cancer management (Fig ).
In this survey, our findings have provided us with unique insights into the experiences of Canadian GPOs. First, family medicine training was shown to have insufficiently prepared respondents to care for patients with cancer, highlighting the need for a specialized oncology training program for GPOs and the need to include further cancer care education in family medicine training. In both residency and GPO training, palliative care and medical oncology were two critical rotations to deliver value in learning how to care for patients with cancer. These two services were also found to have the greatest availability at respondents' institutions. Cancer-related symptom management and active care for patients receiving chemotherapy were the two services most commonly provided, offering key insights into the role of GPOs in Canada. In contrast to family medicine residency training, a strong majority (90%) of respondents agreed that their GPO training had adequately prepared them to care for patients with cancer. This finding affirms the importance of a GPO training program and highlights its potential for widespread implementation in other settings with a shortage of cancer care providers. Our findings are consistent with those found by Yip et al, who conducted a needs assessment survey on oncology education for family medicine residents in Canada. The authors concluded that family medicine residency had inadequately prepared most respondents to care for patients with cancer. This cancer training insufficiency has also been found in low- and middle-income settings, including Rwanda. We previously conducted a survey of Nepali GPs and found a need and interest for GPO training programs in Nepal. Interestingly, both Nepali GPs and Canadian FPs gained the most experience during medical oncology rotation. However, Nepali GPs were exposed to cancer care mostly in inpatient settings, whereas the Canadian FPs were trained in mostly outpatient settings. Unfortunately, in both Nepal and in Canada, they reported that the oncology experience during their GP and family medicine training inadequately prepared them to care for patients with cancer. Fortunately in contrast, 90% of GPOs agreed that their GPO training program did adequately prepare them to care for patients with cancer. Several differences in the practice settings, patterns, and expectations were evident between the contexts of Canada and Nepal. Although lung and breast were the most common cancers cared for by GPOs in Nepal and in Canada, the most commonly performed services by GPs in Nepal were cervical cancer screening, palliative care, and screening tests. In contrast, the most commonly performed services by GPOs in Canada were symptom management, active care while on treatment, and follow-up care while not on active treatment. Only 24% of the GPOs in Canada reported performing screening tests for patients, and this may reflect the fact that screening is already routinely provided by FPs. In addition, although Nepali GPs considered skills such as fine-needle aspiration cytology, bone marrow biopsy, and delivering end-of-life care as important skills for GPO training in Nepal, Canadian GPOs considered these skills as the least important. In addition, when asked which knowledge domains were most important for a GPO, screening was found to be most important in Nepal, whereas treatment of side effects of cancer treatment and oncology emergencies were the top responses in Canada, and again, screening ranked the lowest. This latter finding is expected as the task of screening falls primarily to FPs rather than GPOs in Canada. In Nepal, currently there is no clear demarcation of labor regarding which physicians are responsible for cancer screening. These observations will be valuable for designing and implementing GPO training programs in Nepal. GPO training programs seem to adequately prepare primary care providers to care for patients with cancer, suggesting their utility in settings where cancer care specialist providers are in short supply. The brief duration of additional training increases feasibility and could appeal to health care providers who do not wish to pursue full oncology specialization. Moreover, the short duration means more providers can obtain these critical skills efficiently, allowing more rapid improvements in expanding a cancer workforce. Although the Canadian GPOs typically received training for only a few months, the formal 1-year GPO training program underway in Nepal will provide the opportunity for additional, locally tailored training incorporating the local needs, expectations, regulatory requirements, career opportunities, and feasibility. The high proportion of respondents who received some component of self-directed or web-based learning in their training suggests the potential for flexibility for the delivery of training content. This is beneficial in terms of expanding training programs to regions where the capacity for local instructors may be limited. Virtual training, such as those available via CAGPO, could engage greater cross-border collaboration, allowing learners to receive instruction from experts in the field, regardless of location. Our findings also suggest that out-patient clinical rotations are the most useful modality of training in addition to didactic lectures. In our survey, 70% of GPOs had university appointments and 56% practiced in a university hospital. Although we do not have these metrics for the overall Canadian GPOs, the fact that the majority of respondents in our survey work in academic setting reflects the academic value of GPOs in addition to their primary goal of expanding availability of high-quality cancer care. In addition, several remote locations in Canada are affiliated with universities and physicians hold university appointments despite working in rural areas. Finally, GPOs in an academic setting may also be more likely to respond to surveys. Our study has several limitations. One limitation is the lack of knowledge of the distribution of GPOs in Canada. The current number of GPOs in Canada and their demographics is unknown. In addition, the sample size of this survey is relatively small (N = 37), and lack of knowledge on the number and distribution of GPOs in Canada makes determining the generalizability of our data difficult. However, sex and age distributions of the sample were noted to be consistent with the national cohort where 62% of Canadian GPOs were females and half were younger than 50 years. Furthermore, the lack of information from the rural GPO's perspective may limit the applicability of results to nonurban settings where elements of service provision could be more challenging. Although GPOs in Canada are predominantly involved in a task-sharing model given that they mainly work in urban cancer centers with oncologists, the GPOs in Nepal will be expected to work primarily in a task-shifting model where they provide care for patients with cancer in remote locations. The selection of relevant skills or the distribution of tumor type could vary in different regions on the basis of the specific population and their needs. However, for health systems in need of an increased cancer care workforce in urban areas, our data from Canadian GPOs are likely applicable. Recall bias may have been a factor for those who received training many years ago, potentially introducing inaccurate training details. The study also had a small sample size, which was unfortunately challenging to avoid given that the number of GPOs produced every year in Canada is still small. However, our findings suggest that there is a substantial benefit to developing a dedicated GPO training program. The implementation of a coordinated national program in Nepal may provide future insight for Canadians or others interested in expanding their current training model to a systematic and harmonized national approach. In conclusion, our findings provide insight into the Canadian GPO training and practice experience. Dedicated GPO training programs offer a unique opportunity to efficiently teach health care providers how to adequately care for patients with cancer beyond their family medicine residency training through virtual and hybrid content delivery. Furthermore, our survey identified critical knowledge domains and skills most relevant for GPO training, including treatment of side effects, symptom management, palliative care, and breaking bad news. These results may be valuable for other nations implementing similar training programs in the hopes of increasing their oncology workforce to better care for patients with cancer.
|
RAMAN spectroscopy imaging improves the diagnosis of papillary thyroid carcinoma | 82795237-40f8-438f-9934-302e587fa0c6 | 5057130 | Pathology[mh] | Raman spectroscopic study, performed using a Raman imaging microscope (RM), was carried out for nine patients, which underwent total thyroidectomy and received a diagnosis of PTC based on FNA at the Endocrinology Unit of University Campus Bio-medico of Rome (UCBM). The detailed description of sample preparation for Raman measurements is given in the Methods section. Shortly, frozen thyroid tissue sections collected on glass slides were submitted to RS investigation. Adjacent, Haematoxylin/Eosin stained tissue slides were used as a reference for the presence of healthy and neoplastic tissue areas. Multiple Raman biochemical maps for the healthy and PTC zones were taken for each tissue section. In , the experimental dataset is fully represented, showing the distribution of the tissue samples (healthy, PTC classical variant and PTC follicular variant) for each patient, numbered consecutively from 1 to 9. Biochemical profile study The spectra obtained by averaging the Raman biochemical maps have been classified according to the tissue type (healthy and PTC), resulting in 9 average PTC and 9 average healthy spectra, corresponding to thyroid samples from 9 patients. In , both sequences show the fingerprint (FP) region of spectra. In , the sequence of average Raman spectra collected upon healthy thyroid tissues is shown, whereas in , the sequence of average Raman spectra collected upon PTC tissues is presented. It should be noted that histological diagnosis evidenced the presence of zones corresponding to follicular 2(a) and to classical 2(b) PTC variants in thyroid sample of patient 2. Spectrum numbers correspond to the thyroid case numbers given in . It should be noticed that the spectra corresponding to the same type of thyroid tissue (healthy or PTC), but belonging to different patients, are very similar to each other, demonstrating good correlation in the single case and among different cases, whereas comparison between healthy and PTC groups of spectra reveals significant differences. An accurate assignment of the major thyroid Raman bands registered in our spectra and comparison with the literature data is given in . Available Raman literature studies regarding thyroid tissue are scarce ; in this study, the applied RS technique allowed us to detect for the first time some peculiar features, characteristic for thyroid tissue. The most remarkable difference between the corresponding spectra of healthy and PCT tissues consists in the presence of three intense bands at 1006, 1156 and 1520 cm −1 in the pathological tissue, attributable to carotenoids . Indeed, the comparison between healthy and PTC sequences of spectra provides clear evidence that PTC tissue hosts a significant presence of carotenoids, which are otherwise just trace-like in healthy tissue. The less intense Raman band at 956 cm −1 (4 th carotenoid peak) was not distinguishable in our spectra, due to its low intensity (only about 10% of the 1156 cm −1 band intensity ). The 1006 cm −1 band is a mixed Raman peak, with contribution of carotenoids and phenylalanine νs (C-C) (at 1003 cm −1 ) (see ). In addition, the high wave number (HWN) region of spectra depicts a broad band centered at 2900 cm −1 (see (full range spectra)), generally assigned to proteins, lipids and fatty acids vibrational modes. The ratio between this band in PTC cases and in healthy cases is approximately 2.5, therefore, indicating that another feature of PTC is a much more intense HWN band at 2900 cm −1 . RS imaging The Raman spectra collected upon a selected area provide intrinsic biochemical information that can be used for diagnosis. By selecting specific wavelengths, Raman imaging allows one to obtain different graphical results for the maps of the two tissue typologies. An example of such maps (20 μm step size and 400 × 300 μm 2 area) in false colors referred to the band at 1156 cm −1 is shown in . In , the results obtained for healthy tissue ((a)–dark field optical image, (b)–Raman map, (c)–average reference Raman spectrum) are presented. The healthy tissue is in blue, while minimal amounts of carotenoids are represented by the yellow-green colors. In , the PTC tissue is shown ((a)–dark field optical image, (b)–Raman map, (c)–average reference Raman spectrum). In this case, significant amounts of carotenoids in PTC tissue were detected (false colors in green-yellow-red represent the corresponding increasing intensity of carotenoids). depicts an area in which healthy and PTC tissues are almost intermingled; however, the applied RS technique allows one to distinguish between healthy and pathologic tissue, at least in the studied cases. This should be of capital importance when dealing with precise identification of tumor margins during excision surgery for tumors with extra-capsular extension. More data are currently being collected to address exhaustively the latter issue (using, for instance, a decreased pixel step size), and the results will be reported in the near future. Statistical analysis Statistical analysis was performed on average spectra of each map, corresponding to 18 healthy tissue average spectra and 20 PTC average spectra. The FP and HWN range of Raman spectra, roughly 600 ÷ 1800 cm −1 and 2800 ÷ 3100 cm −1 , were considered for statistical data treatment. Discrimination between healthy and PTC tissue The Principal Component Analysis (PCA) was performed on the matrix 38 × 1314 of the 38 average spectra in the range of 653 ÷ 1723 cm −1 and 2828 ÷ 3023 cm −1 . It is reasonable to affirm that all the information about the hypothesized differences in composition is contained within the first 23 PCs, contributing to the total variability of the dataset with a larger % than the one represented by the 23 rd PC (the 23 rd PC corresponds to about 0.01%). In order to observe whether any of the PCs have diagnostic capability (that is, the scores of the samples for that PC tend to form distinguished groups for the two tissue typologies), a two sample t -test was applied on the scores for each PC, after having controlled each time the validity of the assumption of equality between the variances of the two groups by Fisher’s test. The results of the t -test confirm that for 4 PCs (PC 1 , PC 2 , PC 5 , PC 12 ) a certain separation of the mean of the healthy samples scores with respect to the one of the pathological group can be found ( p -values of 0.028, 0.002, 0.002 and 0.050, respectively). Even if the reported p -values suggest a chance to use successfully this set of PCs, the check of 2D and 3D plots achievable by different combinations of such PCs shows that it is not possible. Among these combinations, for PC 1 –PC 5 , PC 2 –PC 5 and PC 5 –PC 12 , the separation edge is simply not clear enough for diagnostic purposes. For the other cases (PC 1 –PC 2 , PC 1 –PC 12 , PC 2 –PC 12 ), the test is affected by the presence of some outliers invalidating the results, when the number of observations per group is not appropriate to the degree of diversity between the distributions underlying the samples. This leads to the hypothesis that differences between tissue typologies do exist but can’t be fully revealed only by maximizing their respective variability, and truly none of the PCs by itself has diagnostic capability. The Linear Discriminant Analysis (LDA) algorithm was first applied on the 38 × 23 matrix obtained considering the scores of the 38 samples for the first 23 PCs. Various combinations of all or part of the PCs were tried in order to find the discriminant function with the simplest mathematical solution for the classification: the best result (f 1 ) was found using the PCs 1, 2, 5, 8, 10, 11, 12 and its analytical expression is: The scores for f 1 are shown in . In this case, having previously verified by Fisher’s test that the variances of the sample scores on f 1 within the two groups are not significantly different (F = 1.049; df N = 19; df D = 17), applying the two sample t- test to the same sample scores a value of t = 14.415 is obtained for 36 degrees of freedom: as a result, the two groups shown in are considered significantly different at a confidence level of 0.001. The leave-one-out cross-validation method, employed as internal tool to test the accuracy of f 1 , classified the 100% of the samples correctly. It should be also noticed that in the groups are characterized by values ranging exclusively along the positive semiaxis of f 1 (PTC tissue samples) or along the negative one (healthy tissue samples). Discrimination between two PTC variants (classical and follicular) The PCA was performed on the matrix 20 × 1314 of the 20 average spectra of PTC tissue samples in the 653 ÷ 1723 cm −1 and 2828 ÷ 3023 cm −1 ranges of spectra. The analysis of the variance values explained by each PC revealed that the first 17 components explain almost the totality of the variance (the 18 th PC represents <0.01%). In this case, the values for F -test indicate that variances are not significantly different among the various PCs, except for PC 2 , and the t -test confirms that, for none of the PCs, the means of the scores for the two groups are significantly different (confidence level of 5%). The LDA algorithm was applied also in this case and the result is the function f 2 , obtained combining the PCs 1, 4, 5, 6, 7, 8, 10, 11, 12, 15. Its analytical expression is: The scores for f 2 are shown in . Having previously verified by Fisher’s test that sample variances of the two groups are not significantly different (F = 0.816; df N = 13; df D = 5), applying the two sample t -test to the sample scores on f 2 , a value of t = −15.737 is obtained for 18 degrees of freedom: as a result, the two groups shown in are significantly different at a confidence level of 0.001. The leave-one-out cross-validation method classified the 95.0% of the samples correctly: all the follicular variant PTC samples were attributed to the right group and, among the classical variant PTC samples, only one has been misclassified. Nevertheless, it is possible to classify the 100% of samples correctly, considering as a criterion the fact that the values of the two PTC variants are distributed along the opposite semiaxes of f 2 (positive for follicular variant and negative for classical variant PTC). Summarizing the results of this section, we can affirm that Raman spectroscopy is able to discriminate between healthy and PTC tissues of thyroid with 100% of sensitivity, specificity and accuracy and to discriminate between classical and follicular variants of PTC with 93% of sensitivity, 100% of specificity and 95% of accuracy, by means of the leave-one-out cross-validated LDA.
The spectra obtained by averaging the Raman biochemical maps have been classified according to the tissue type (healthy and PTC), resulting in 9 average PTC and 9 average healthy spectra, corresponding to thyroid samples from 9 patients. In , both sequences show the fingerprint (FP) region of spectra. In , the sequence of average Raman spectra collected upon healthy thyroid tissues is shown, whereas in , the sequence of average Raman spectra collected upon PTC tissues is presented. It should be noted that histological diagnosis evidenced the presence of zones corresponding to follicular 2(a) and to classical 2(b) PTC variants in thyroid sample of patient 2. Spectrum numbers correspond to the thyroid case numbers given in . It should be noticed that the spectra corresponding to the same type of thyroid tissue (healthy or PTC), but belonging to different patients, are very similar to each other, demonstrating good correlation in the single case and among different cases, whereas comparison between healthy and PTC groups of spectra reveals significant differences. An accurate assignment of the major thyroid Raman bands registered in our spectra and comparison with the literature data is given in . Available Raman literature studies regarding thyroid tissue are scarce ; in this study, the applied RS technique allowed us to detect for the first time some peculiar features, characteristic for thyroid tissue. The most remarkable difference between the corresponding spectra of healthy and PCT tissues consists in the presence of three intense bands at 1006, 1156 and 1520 cm −1 in the pathological tissue, attributable to carotenoids . Indeed, the comparison between healthy and PTC sequences of spectra provides clear evidence that PTC tissue hosts a significant presence of carotenoids, which are otherwise just trace-like in healthy tissue. The less intense Raman band at 956 cm −1 (4 th carotenoid peak) was not distinguishable in our spectra, due to its low intensity (only about 10% of the 1156 cm −1 band intensity ). The 1006 cm −1 band is a mixed Raman peak, with contribution of carotenoids and phenylalanine νs (C-C) (at 1003 cm −1 ) (see ). In addition, the high wave number (HWN) region of spectra depicts a broad band centered at 2900 cm −1 (see (full range spectra)), generally assigned to proteins, lipids and fatty acids vibrational modes. The ratio between this band in PTC cases and in healthy cases is approximately 2.5, therefore, indicating that another feature of PTC is a much more intense HWN band at 2900 cm −1 .
The Raman spectra collected upon a selected area provide intrinsic biochemical information that can be used for diagnosis. By selecting specific wavelengths, Raman imaging allows one to obtain different graphical results for the maps of the two tissue typologies. An example of such maps (20 μm step size and 400 × 300 μm 2 area) in false colors referred to the band at 1156 cm −1 is shown in . In , the results obtained for healthy tissue ((a)–dark field optical image, (b)–Raman map, (c)–average reference Raman spectrum) are presented. The healthy tissue is in blue, while minimal amounts of carotenoids are represented by the yellow-green colors. In , the PTC tissue is shown ((a)–dark field optical image, (b)–Raman map, (c)–average reference Raman spectrum). In this case, significant amounts of carotenoids in PTC tissue were detected (false colors in green-yellow-red represent the corresponding increasing intensity of carotenoids). depicts an area in which healthy and PTC tissues are almost intermingled; however, the applied RS technique allows one to distinguish between healthy and pathologic tissue, at least in the studied cases. This should be of capital importance when dealing with precise identification of tumor margins during excision surgery for tumors with extra-capsular extension. More data are currently being collected to address exhaustively the latter issue (using, for instance, a decreased pixel step size), and the results will be reported in the near future.
Statistical analysis was performed on average spectra of each map, corresponding to 18 healthy tissue average spectra and 20 PTC average spectra. The FP and HWN range of Raman spectra, roughly 600 ÷ 1800 cm −1 and 2800 ÷ 3100 cm −1 , were considered for statistical data treatment. Discrimination between healthy and PTC tissue The Principal Component Analysis (PCA) was performed on the matrix 38 × 1314 of the 38 average spectra in the range of 653 ÷ 1723 cm −1 and 2828 ÷ 3023 cm −1 . It is reasonable to affirm that all the information about the hypothesized differences in composition is contained within the first 23 PCs, contributing to the total variability of the dataset with a larger % than the one represented by the 23 rd PC (the 23 rd PC corresponds to about 0.01%). In order to observe whether any of the PCs have diagnostic capability (that is, the scores of the samples for that PC tend to form distinguished groups for the two tissue typologies), a two sample t -test was applied on the scores for each PC, after having controlled each time the validity of the assumption of equality between the variances of the two groups by Fisher’s test. The results of the t -test confirm that for 4 PCs (PC 1 , PC 2 , PC 5 , PC 12 ) a certain separation of the mean of the healthy samples scores with respect to the one of the pathological group can be found ( p -values of 0.028, 0.002, 0.002 and 0.050, respectively). Even if the reported p -values suggest a chance to use successfully this set of PCs, the check of 2D and 3D plots achievable by different combinations of such PCs shows that it is not possible. Among these combinations, for PC 1 –PC 5 , PC 2 –PC 5 and PC 5 –PC 12 , the separation edge is simply not clear enough for diagnostic purposes. For the other cases (PC 1 –PC 2 , PC 1 –PC 12 , PC 2 –PC 12 ), the test is affected by the presence of some outliers invalidating the results, when the number of observations per group is not appropriate to the degree of diversity between the distributions underlying the samples. This leads to the hypothesis that differences between tissue typologies do exist but can’t be fully revealed only by maximizing their respective variability, and truly none of the PCs by itself has diagnostic capability. The Linear Discriminant Analysis (LDA) algorithm was first applied on the 38 × 23 matrix obtained considering the scores of the 38 samples for the first 23 PCs. Various combinations of all or part of the PCs were tried in order to find the discriminant function with the simplest mathematical solution for the classification: the best result (f 1 ) was found using the PCs 1, 2, 5, 8, 10, 11, 12 and its analytical expression is: The scores for f 1 are shown in . In this case, having previously verified by Fisher’s test that the variances of the sample scores on f 1 within the two groups are not significantly different (F = 1.049; df N = 19; df D = 17), applying the two sample t- test to the same sample scores a value of t = 14.415 is obtained for 36 degrees of freedom: as a result, the two groups shown in are considered significantly different at a confidence level of 0.001. The leave-one-out cross-validation method, employed as internal tool to test the accuracy of f 1 , classified the 100% of the samples correctly. It should be also noticed that in the groups are characterized by values ranging exclusively along the positive semiaxis of f 1 (PTC tissue samples) or along the negative one (healthy tissue samples). Discrimination between two PTC variants (classical and follicular) The PCA was performed on the matrix 20 × 1314 of the 20 average spectra of PTC tissue samples in the 653 ÷ 1723 cm −1 and 2828 ÷ 3023 cm −1 ranges of spectra. The analysis of the variance values explained by each PC revealed that the first 17 components explain almost the totality of the variance (the 18 th PC represents <0.01%). In this case, the values for F -test indicate that variances are not significantly different among the various PCs, except for PC 2 , and the t -test confirms that, for none of the PCs, the means of the scores for the two groups are significantly different (confidence level of 5%). The LDA algorithm was applied also in this case and the result is the function f 2 , obtained combining the PCs 1, 4, 5, 6, 7, 8, 10, 11, 12, 15. Its analytical expression is: The scores for f 2 are shown in . Having previously verified by Fisher’s test that sample variances of the two groups are not significantly different (F = 0.816; df N = 13; df D = 5), applying the two sample t -test to the sample scores on f 2 , a value of t = −15.737 is obtained for 18 degrees of freedom: as a result, the two groups shown in are significantly different at a confidence level of 0.001. The leave-one-out cross-validation method classified the 95.0% of the samples correctly: all the follicular variant PTC samples were attributed to the right group and, among the classical variant PTC samples, only one has been misclassified. Nevertheless, it is possible to classify the 100% of samples correctly, considering as a criterion the fact that the values of the two PTC variants are distributed along the opposite semiaxes of f 2 (positive for follicular variant and negative for classical variant PTC). Summarizing the results of this section, we can affirm that Raman spectroscopy is able to discriminate between healthy and PTC tissues of thyroid with 100% of sensitivity, specificity and accuracy and to discriminate between classical and follicular variants of PTC with 93% of sensitivity, 100% of specificity and 95% of accuracy, by means of the leave-one-out cross-validated LDA.
The Principal Component Analysis (PCA) was performed on the matrix 38 × 1314 of the 38 average spectra in the range of 653 ÷ 1723 cm −1 and 2828 ÷ 3023 cm −1 . It is reasonable to affirm that all the information about the hypothesized differences in composition is contained within the first 23 PCs, contributing to the total variability of the dataset with a larger % than the one represented by the 23 rd PC (the 23 rd PC corresponds to about 0.01%). In order to observe whether any of the PCs have diagnostic capability (that is, the scores of the samples for that PC tend to form distinguished groups for the two tissue typologies), a two sample t -test was applied on the scores for each PC, after having controlled each time the validity of the assumption of equality between the variances of the two groups by Fisher’s test. The results of the t -test confirm that for 4 PCs (PC 1 , PC 2 , PC 5 , PC 12 ) a certain separation of the mean of the healthy samples scores with respect to the one of the pathological group can be found ( p -values of 0.028, 0.002, 0.002 and 0.050, respectively). Even if the reported p -values suggest a chance to use successfully this set of PCs, the check of 2D and 3D plots achievable by different combinations of such PCs shows that it is not possible. Among these combinations, for PC 1 –PC 5 , PC 2 –PC 5 and PC 5 –PC 12 , the separation edge is simply not clear enough for diagnostic purposes. For the other cases (PC 1 –PC 2 , PC 1 –PC 12 , PC 2 –PC 12 ), the test is affected by the presence of some outliers invalidating the results, when the number of observations per group is not appropriate to the degree of diversity between the distributions underlying the samples. This leads to the hypothesis that differences between tissue typologies do exist but can’t be fully revealed only by maximizing their respective variability, and truly none of the PCs by itself has diagnostic capability. The Linear Discriminant Analysis (LDA) algorithm was first applied on the 38 × 23 matrix obtained considering the scores of the 38 samples for the first 23 PCs. Various combinations of all or part of the PCs were tried in order to find the discriminant function with the simplest mathematical solution for the classification: the best result (f 1 ) was found using the PCs 1, 2, 5, 8, 10, 11, 12 and its analytical expression is: The scores for f 1 are shown in . In this case, having previously verified by Fisher’s test that the variances of the sample scores on f 1 within the two groups are not significantly different (F = 1.049; df N = 19; df D = 17), applying the two sample t- test to the same sample scores a value of t = 14.415 is obtained for 36 degrees of freedom: as a result, the two groups shown in are considered significantly different at a confidence level of 0.001. The leave-one-out cross-validation method, employed as internal tool to test the accuracy of f 1 , classified the 100% of the samples correctly. It should be also noticed that in the groups are characterized by values ranging exclusively along the positive semiaxis of f 1 (PTC tissue samples) or along the negative one (healthy tissue samples).
The PCA was performed on the matrix 20 × 1314 of the 20 average spectra of PTC tissue samples in the 653 ÷ 1723 cm −1 and 2828 ÷ 3023 cm −1 ranges of spectra. The analysis of the variance values explained by each PC revealed that the first 17 components explain almost the totality of the variance (the 18 th PC represents <0.01%). In this case, the values for F -test indicate that variances are not significantly different among the various PCs, except for PC 2 , and the t -test confirms that, for none of the PCs, the means of the scores for the two groups are significantly different (confidence level of 5%). The LDA algorithm was applied also in this case and the result is the function f 2 , obtained combining the PCs 1, 4, 5, 6, 7, 8, 10, 11, 12, 15. Its analytical expression is: The scores for f 2 are shown in . Having previously verified by Fisher’s test that sample variances of the two groups are not significantly different (F = 0.816; df N = 13; df D = 5), applying the two sample t -test to the sample scores on f 2 , a value of t = −15.737 is obtained for 18 degrees of freedom: as a result, the two groups shown in are significantly different at a confidence level of 0.001. The leave-one-out cross-validation method classified the 95.0% of the samples correctly: all the follicular variant PTC samples were attributed to the right group and, among the classical variant PTC samples, only one has been misclassified. Nevertheless, it is possible to classify the 100% of samples correctly, considering as a criterion the fact that the values of the two PTC variants are distributed along the opposite semiaxes of f 2 (positive for follicular variant and negative for classical variant PTC). Summarizing the results of this section, we can affirm that Raman spectroscopy is able to discriminate between healthy and PTC tissues of thyroid with 100% of sensitivity, specificity and accuracy and to discriminate between classical and follicular variants of PTC with 93% of sensitivity, 100% of specificity and 95% of accuracy, by means of the leave-one-out cross-validated LDA.
Our results have demonstrated the feasibility and reproducibility of RS to discriminate between normal thyroid tissue and PTC, and between classical and follicular variants of PTC, on the basis of their biochemical fingerprints. This finding is of great relevance for the development of a RS optical biopsy system to investigate thyroid tissue alterations. Based on the experimental results obtained in this work, we can attest the significant carotenoids presence in the PTC tissues with respect to the healthy tissue, in which their absence or minimal and localized presence was detected (see , and ). To the best of our knowledge, this is the first experimental evidence of carotenoids presence in the neoplastic thyroid tissue. Papillary thyroid carcinoma has been extensively investigated with multiplatform molecular analysis and the area of unknown genomic alteration has been reduced substantially from 25% to less than 4% . Detailed study of the genomic background, however, is not sufficient to fully investigate the mechanisms that lead to neoplastic transformations, which, in turn, would lead to the identification of more accurate diagnostic, prognostic and predictive makers. For example, BRAFV600E gene mutation, currently considered as a driver of molecular alteration in classic variant PTC, has been recognized in over 70% of benign nevus without neoplastic progression . Spectroscopy methods applied in clinics should provide the best possible sensitivity, specificity and accuracy in order to minimize false definitions. Ideally, a new method should be performed as an in situ analysis encompassing the assessment of multiple cellular constituents and allowing paired morphological and biochemical analysis. RS is among the few available methods fulfilling the above requirements. In our study, we considered the FP and the HWN range of thyroid Raman spectra, both presenting changes while passing from healthy to PTC areas and, therefore, both important for diagnostic utility, as confirmed also by authors for cervical tissue. RS has the ability to identify specific tumor expression molecules and molecular species involved in tumorigenesis and progression. For instance, Talari et al. claimed the 956, 1006, 1156–1157, 1524–1528 cm −1 Raman peaks as “carotenoids absent in normal tissue”. Puppels et al. investigated carotenoids located in human lymphocyte subpopulations and natural killer cells, evidencing a high carotenoids concentration in the CD4 + lymphocytes, and proposed to investigate the possible mechanisms behind the protective role of carotenoids against the development of cancers. The increased intensities at 1159 and 1527 cm −1 , assigned to carotenoids have been identified also in the Raman spectra of brain tumors and neurinomas . Talari et al. suggested that carotenoids can be used as Raman biomarkers in breast cancer pathology. Resonance Raman is probably one of the best methods to study the properties of carotenoids in complex media, such as, for example, binding sites of biological macromolecules in living organisms . In this case, i.e. when the wavelength of the excitation laser is in the range of electronic absorption band of molecules of interest, resonance Raman intensities may be enhanced up to 6 orders of magnitude, as compared to normal Raman scattering . In the present work, the enhancement of carotenoid Raman bands was obtained using 532 nm laser wavelength, which lies in the range of carotenoids UV/Vis absorption region . The coupled histopathological and Raman biochemical observations performed in this work highlighted that carotenoids are mainly present in cellular areas of PTC, so that their presence seems to be related to the neoplastic thyrocytes within the tumor tissue. Our study suggests that these characteristics could be used as Raman biomarkers in the PTC pathology. However, the mechanism underlying potential oncogenic effects of carotenoids is still unknown, since very little is known about the biochemical content of neoplastic cells, especially what regards lipids, lipoproteins and lipophilic substances that are commonly lost in routinely processed histological samples. Among human tissues, different normal cells are able to utilize carotenoids, i.e. beta-carotene is reported as a local supply of vitamin A in the skin and melanocytes . However, physiological mechanism for carotenoids uptake in normal thyrocytes is not reported, and our results raise the hypothesis of a carotenoid-related pathway for the PTC oncogenesis. This is just a working hypothesis, which needs accurate validation, but nevertheless underlines the presence of carotenoids in neoplastic thyrocytes, as it happens in other organs . In conclusion, we performed the RS investigation and biochemical mapping of healthy thyroid tissue and of PTC (classical and follicular variants). The obtained results demonstrate the great potential of RS to support histopathological evaluation, increasing the reliability of cancer diagnostics. On the basis of the results of multivariate statistical model, carried out by the leave-one-out cross-validated LDA, we can affirm that RS is able to discriminate between healthy and PTC tissues of thyroid with 100% of sensitivity, specificity and accuracy and to discriminate between classical and follicular variants of PTC with 93% of sensitivity, 100% of specificity and 95% of accuracy. The achieved diagnostic sensitivity, specificity and accuracy are compatible with the clinical use, both for the PTC diagnosis and in the differential diagnosis between classical and follicular variants of PTC, the latter being a significant challenging point for thyroid nodules evaluation. Only a few literature studies report RS investigations of thyroid tissue and neoplasia . The distinctive trait of our RS analysis is that for the first time it has been performed on tissue sections, combined with microscopic assessment of the very same areas. The method is highly cost-effective, being based solely on the analysis of unstained cryostatic tissue sections. Our results are likely to represent a significant advance in the imaging of thyroid tissues, leading to subsequent clinical application by improving diagnostic accuracy and reducing inter-observer variability. This study is the first demonstration of the presence of significant amounts of carotenoids in thyroid neoplasms, suggesting that carotenoids could be used as a Raman biomarker for the PTC pathology. In this regard, combination of the histological and Raman microscopy analysis approaches may open a new way to integrative findings with wide implications for basic pathobiology, tumor classification schemes and therapeutic strategies.
Ethics statement The study was approved by the Ethical Committee of the UCBM (prot. 33.15 TS ComEt CBM). Before surgical procedures, the informed consent was collected. Enrolled patients are known to the pathologist and recorded in a codified file with an anonymous ID code, which was also registered in the institutional software database of the Pathology Unit of the UCBM. All personally identifiable information was recorded in a codified file. All experiments were performed in accordance with the principle of Good Clinical Practice (GCP) and the ethical principles contained in the current version of the Declaration of Helsinki. Thyroid tissues This prospective monocentric study has been approved by the Ethical Committee of the UCBM (prot. 33.15 TS ComEt CBM), and all patients gave the written informed consent. Nine patients that received a diagnosis of PTC based on FNA at the Endocrinology Unit of UCBM were enrolled for this study. These patients underwent total thyroidectomy at the Surgical Unit of the same Institution. At the time of surgery the removed specimens were immediately submitted unfixed to the Pathology Unit in an appropriately labeled container. After completion of the gross examination of the specimen by the pathologist, resection margins were marked with black ink. Pathological sampling was carried out in agreement with international guidelines for handling surgical specimens . A tissue slice of about 1 × 1 × 0.3 cm 3 was then obtained, including both healthy and neoplastic areas, avoiding surgical margins, and the slice was frozen on a metallic cold-plate inside the cryostat. A 5 μm cryostatic section was cut and stained with Haematoxylin/Eosin, in order to confirm the presence of healthy and neoplastic tissue zones, as well as the transition area between them. Additional sections were cut at 20 and 30 μm of thickness, collected on separate glass slides and stored unstained at −20 °C until the Raman evaluation. The surgical samples were subsequently fixed in buffered formalin and embedded in paraffin for permanent sectioning. Diagnosis, grading and staging were performed, in agreement with the 7°th edition of TNM . Dataset/casuistry A number of thyroid tissue areas was histologically identified and diagnosed as healthy or pathological (Haematoxylin/Eosin staining of frozen samples) by experienced pathologist (A.C.). By means of the RS imaging, 18 maps have been obtained from healthy and 20 from pathological areas. In , the experimental dataset is fully represented, showing the distribution of the tissue samples (healthy, PTC classical variant and PTC follicular variant) for each of the nine patients. Raman spectroscopic measurements Raman spectra were recorded using a Thermo Fisher Scientific DXRxi Raman microscope at the following conditions: 532 nm laser source; 200–3400 cm −1 full range grating; 10× and 50× objectives; 25 μm confocal pinhole, 5 (FWHM) cm −1 spectral resolution. The RM instrument indicated above guarantees a fast change of experimental parameters, for better measurements procedure optimization, and does not require consumable reagents and staining treatments. As a first step, the collection of a number of mosaic images at low magnification (10×) using the RM has been carried out, providing the generic overview information on the tissue morphology and allowing one to individuate and evaluate regions of interest. After that, the region of interest was investigated collecting spectra at high magnification (50×). Preliminary measurements were performed, in order to optimize the experimental parameters to provide a high signal-to-noise (S/N) ratio and to minimize tissue fluorescence. A 5 th order polynomial correction was used to compensate the tissue fluorescence. A laser power of 8 mW measured at the sample has been applied as the best compromise between the signal quality and the undesired tissue burning. The exposure time was 0.8 sec, as a suitable compromise to achieve a good spectra quality and to shorten the overall acquisition time. At least 50 exposures were averaged to obtain a high S/N ratio. Laser spot size was about 700 nm (50× objective). Various Raman maps ranging from 100 × 100 μm 2 up to 1 × 1 mm 2 , collecting several hundreds of spectra per map, were obtained. Step size employed for small maps (i.e. 100 × 100 μm) was of about 2 μm, while for larger maps with lateral dimension ranging from several hundred of μm up to 1 mm, the average step size of about 50 μm was used. Typical collection time for each map was about 4/6 hours, both for small and large maps. The background-subtracted Raman spectra were further normalized for the area under the curve for standardization of the tissue Raman intensities. No pre-treatment was performed on tissue samples before RS examination. To assess intra-sample variability, multiple measurements were carried out at different regions within the same sample. Statistical analysis Statistical analysis was performed with a supervised approach on average spectra of each map, aiming to generate a model for classifying tissues. The same number of maps (two) from each typology of tissue present in each patient (healthy, PTC classical variant and PTC follicular variant) were employed, corresponding to 18 healthy tissue average spectra and 20 PTC average spectra. The FP and HWN range of Raman spectra, roughly 600 ÷ 1800 cm −1 and 2800 ÷ 3100 cm −1 , respectively, were selected for statistical data analysis treatment. The remaining spectral range was not considered, as non meaningful from the point of view of the contained biochemical information. The collected Raman data were processed performing multivariate analysis, used for complex systems with high internal variability. Two principal statistical procedures were performed on the dataset: Principal Component Analysis and Linear Discriminant Analysis. At first, PCA was carried out in order to reduce the initial high dimensionality of the dataset and to verify whether, in a subset of dimensions, the internal variability of the spectra can reveal on its own differences among tissue typologies that can be considered diagnostic. LDA was applied on the same components observed in PCA to verify if differences among the typologies exist due to differences among the means of samples belonging to different tissue groups. Moreover, the implementation of the algorithm on principal components let to optimize the process of classification on the basis of a reduced number of input variables. The LDA was tested by using the leave-one-out cross validation method, in order to estimate the accuracy of our model in predicting unknown samples.
The study was approved by the Ethical Committee of the UCBM (prot. 33.15 TS ComEt CBM). Before surgical procedures, the informed consent was collected. Enrolled patients are known to the pathologist and recorded in a codified file with an anonymous ID code, which was also registered in the institutional software database of the Pathology Unit of the UCBM. All personally identifiable information was recorded in a codified file. All experiments were performed in accordance with the principle of Good Clinical Practice (GCP) and the ethical principles contained in the current version of the Declaration of Helsinki.
This prospective monocentric study has been approved by the Ethical Committee of the UCBM (prot. 33.15 TS ComEt CBM), and all patients gave the written informed consent. Nine patients that received a diagnosis of PTC based on FNA at the Endocrinology Unit of UCBM were enrolled for this study. These patients underwent total thyroidectomy at the Surgical Unit of the same Institution. At the time of surgery the removed specimens were immediately submitted unfixed to the Pathology Unit in an appropriately labeled container. After completion of the gross examination of the specimen by the pathologist, resection margins were marked with black ink. Pathological sampling was carried out in agreement with international guidelines for handling surgical specimens . A tissue slice of about 1 × 1 × 0.3 cm 3 was then obtained, including both healthy and neoplastic areas, avoiding surgical margins, and the slice was frozen on a metallic cold-plate inside the cryostat. A 5 μm cryostatic section was cut and stained with Haematoxylin/Eosin, in order to confirm the presence of healthy and neoplastic tissue zones, as well as the transition area between them. Additional sections were cut at 20 and 30 μm of thickness, collected on separate glass slides and stored unstained at −20 °C until the Raman evaluation. The surgical samples were subsequently fixed in buffered formalin and embedded in paraffin for permanent sectioning. Diagnosis, grading and staging were performed, in agreement with the 7°th edition of TNM .
A number of thyroid tissue areas was histologically identified and diagnosed as healthy or pathological (Haematoxylin/Eosin staining of frozen samples) by experienced pathologist (A.C.). By means of the RS imaging, 18 maps have been obtained from healthy and 20 from pathological areas. In , the experimental dataset is fully represented, showing the distribution of the tissue samples (healthy, PTC classical variant and PTC follicular variant) for each of the nine patients.
Raman spectra were recorded using a Thermo Fisher Scientific DXRxi Raman microscope at the following conditions: 532 nm laser source; 200–3400 cm −1 full range grating; 10× and 50× objectives; 25 μm confocal pinhole, 5 (FWHM) cm −1 spectral resolution. The RM instrument indicated above guarantees a fast change of experimental parameters, for better measurements procedure optimization, and does not require consumable reagents and staining treatments. As a first step, the collection of a number of mosaic images at low magnification (10×) using the RM has been carried out, providing the generic overview information on the tissue morphology and allowing one to individuate and evaluate regions of interest. After that, the region of interest was investigated collecting spectra at high magnification (50×). Preliminary measurements were performed, in order to optimize the experimental parameters to provide a high signal-to-noise (S/N) ratio and to minimize tissue fluorescence. A 5 th order polynomial correction was used to compensate the tissue fluorescence. A laser power of 8 mW measured at the sample has been applied as the best compromise between the signal quality and the undesired tissue burning. The exposure time was 0.8 sec, as a suitable compromise to achieve a good spectra quality and to shorten the overall acquisition time. At least 50 exposures were averaged to obtain a high S/N ratio. Laser spot size was about 700 nm (50× objective). Various Raman maps ranging from 100 × 100 μm 2 up to 1 × 1 mm 2 , collecting several hundreds of spectra per map, were obtained. Step size employed for small maps (i.e. 100 × 100 μm) was of about 2 μm, while for larger maps with lateral dimension ranging from several hundred of μm up to 1 mm, the average step size of about 50 μm was used. Typical collection time for each map was about 4/6 hours, both for small and large maps. The background-subtracted Raman spectra were further normalized for the area under the curve for standardization of the tissue Raman intensities. No pre-treatment was performed on tissue samples before RS examination. To assess intra-sample variability, multiple measurements were carried out at different regions within the same sample.
Statistical analysis was performed with a supervised approach on average spectra of each map, aiming to generate a model for classifying tissues. The same number of maps (two) from each typology of tissue present in each patient (healthy, PTC classical variant and PTC follicular variant) were employed, corresponding to 18 healthy tissue average spectra and 20 PTC average spectra. The FP and HWN range of Raman spectra, roughly 600 ÷ 1800 cm −1 and 2800 ÷ 3100 cm −1 , respectively, were selected for statistical data analysis treatment. The remaining spectral range was not considered, as non meaningful from the point of view of the contained biochemical information. The collected Raman data were processed performing multivariate analysis, used for complex systems with high internal variability. Two principal statistical procedures were performed on the dataset: Principal Component Analysis and Linear Discriminant Analysis. At first, PCA was carried out in order to reduce the initial high dimensionality of the dataset and to verify whether, in a subset of dimensions, the internal variability of the spectra can reveal on its own differences among tissue typologies that can be considered diagnostic. LDA was applied on the same components observed in PCA to verify if differences among the typologies exist due to differences among the means of samples belonging to different tissue groups. Moreover, the implementation of the algorithm on principal components let to optimize the process of classification on the basis of a reduced number of input variables. The LDA was tested by using the leave-one-out cross validation method, in order to estimate the accuracy of our model in predicting unknown samples.
How to cite this article : Rau, J. V. et al. RAMAN spectroscopy imaging improves the diagnosis of papillary thyroid carcinoma. Sci. Rep. 6 , 35117; doi: 10.1038/srep35117 (2016).
|
Language Use in Conversational Agent–Based Health Communication: Systematic Review | 40084149-066e-42cf-976c-a64fe85a683a | 9308072 | Health Communication[mh] | Background Conversational agents (CAs) are intelligent computer programs empowered with natural language processing techniques that engage users in human-like conversations to provide an effective and a smart communication platform in a simulated environment, including text-based chatbots, voice-activated assistants, and embodied CAs . They are designed to obtain specific information from users that is necessary to perform particular tasks and respond in a manner that is optimal to achieve these goals. Due to their ability to transform the health care system and enable individuals to comanage their health care effectively, CAs are increasingly used to deliver health care services . The most popular health CAs include ELIZA , Casper , MedChat , PARRY , Watson Health , Endurance , OneRemission , Youper , Florence , Your.Md , AdaHealth , Sensely , and Buoy Health , among many others. CAs are being tested and adopted to provide and collect health-related information and provide treatment and counseling services . In some cases, they are used to enhance the accessibility, efficiency, and personalization of service delivery and ensure relatively equal delivery of health care services worldwide through bridging the gaps between developing and developed countries . Given the growing significance of CAs, researchers have conducted a plethora of relevant studies, varying from their suitability as health care partners to their designs including physical appearance, gender, and speech. . These studies aimed to improve “humanness heuristics,” affective states in users, and user perceptions of the CA personalities by tailoring CAs to the cultures and demographics of the users to continuously promote user engagement, adherence, and adoption . Language plays a crucial role in improving user engagement because perceived impersonal closeness, intention to use, user satisfaction, establishment of trust, and user self-disclosure or self-concealment are closely associated with the task- and social-based interactivity, interaction, politeness, and information quality provided by CAs . However, few studies focused on language use in CA-based health communication to examine its influence on the perceived usability of CAs and the perceived roles of CAs in delivering health care services . Language considerably influences the joint construction of meaning between interlocutors and rapport establishment . This is particularly true for human-machine communication. For example, when addressing users by their first names, CAs are perceived to display varying degrees of politeness and thoughtfulness determined by cultural limits and preferences . It follows that intensive and extensive investigations into language use by CAs in different linguistic settings are crucial to scale up health care interventions delivered by CAs worldwide . Language Use and Its Significance in CA Communication The language use of an information source is likely to be crucial among various factors affecting the information seekers’ judgments on the credibility and trustworthiness of the information providers . In this review, language use, characterized by various linguistic aspects, is defined as varied verbal strategies and compliance-gaining techniques that the CAs under scrutiny adopted to deliver health interventions. These strategies and techniques may involve various ways of wording, including an everyday style (eg, “heart attack”) versus a technical style (eg, “myocardial infarction”), a tentative style (eg, “presumably similar”) versus a nontentative style (eg, “similar”), a neutral style (eg, “methodological mistakes”) versus an aggressive style (eg, “really dumb methodological mistakes”), an emotional style versus a nonemotional style, and an enthusiastic style versus a nonenthusiastic style. . They may also include the use of personal references (eg, first-person and second-person pronouns), personal testimonials, specific conversational frameworks or prompts, and other verbal means of communication . In short, the language use of the CAs under discussion in this review refers to their characteristic linguistic performances in health communication. Language Expectancy Theory and Communication Accommodation Theory assert that acquiring knowledge when seeking web-based health information is determined not only by the information content but also by who is communicating the information and the manner and context of communication. Information seekers evaluate information providers positively if the latter’s language use is in tune with their cultural values and situational norms and if they use language more favorably than expected in a situation . The language use of information providers is regarded as a prominent clue to evaluate the characteristics of the providers, especially in web-based communication . The information provider’s language use is a cue for determining whether people perceive the information to be credible and whether the information provider is trustworthy . Objective The current review aimed to summarize the language use of CAs in health care to identify the achievements made and the breakthroughs to be made to inform researchers and more particularly CA designers and developers. This can help realize the high potential of CAs for improving individual well-being.
Conversational agents (CAs) are intelligent computer programs empowered with natural language processing techniques that engage users in human-like conversations to provide an effective and a smart communication platform in a simulated environment, including text-based chatbots, voice-activated assistants, and embodied CAs . They are designed to obtain specific information from users that is necessary to perform particular tasks and respond in a manner that is optimal to achieve these goals. Due to their ability to transform the health care system and enable individuals to comanage their health care effectively, CAs are increasingly used to deliver health care services . The most popular health CAs include ELIZA , Casper , MedChat , PARRY , Watson Health , Endurance , OneRemission , Youper , Florence , Your.Md , AdaHealth , Sensely , and Buoy Health , among many others. CAs are being tested and adopted to provide and collect health-related information and provide treatment and counseling services . In some cases, they are used to enhance the accessibility, efficiency, and personalization of service delivery and ensure relatively equal delivery of health care services worldwide through bridging the gaps between developing and developed countries . Given the growing significance of CAs, researchers have conducted a plethora of relevant studies, varying from their suitability as health care partners to their designs including physical appearance, gender, and speech. . These studies aimed to improve “humanness heuristics,” affective states in users, and user perceptions of the CA personalities by tailoring CAs to the cultures and demographics of the users to continuously promote user engagement, adherence, and adoption . Language plays a crucial role in improving user engagement because perceived impersonal closeness, intention to use, user satisfaction, establishment of trust, and user self-disclosure or self-concealment are closely associated with the task- and social-based interactivity, interaction, politeness, and information quality provided by CAs . However, few studies focused on language use in CA-based health communication to examine its influence on the perceived usability of CAs and the perceived roles of CAs in delivering health care services . Language considerably influences the joint construction of meaning between interlocutors and rapport establishment . This is particularly true for human-machine communication. For example, when addressing users by their first names, CAs are perceived to display varying degrees of politeness and thoughtfulness determined by cultural limits and preferences . It follows that intensive and extensive investigations into language use by CAs in different linguistic settings are crucial to scale up health care interventions delivered by CAs worldwide .
The language use of an information source is likely to be crucial among various factors affecting the information seekers’ judgments on the credibility and trustworthiness of the information providers . In this review, language use, characterized by various linguistic aspects, is defined as varied verbal strategies and compliance-gaining techniques that the CAs under scrutiny adopted to deliver health interventions. These strategies and techniques may involve various ways of wording, including an everyday style (eg, “heart attack”) versus a technical style (eg, “myocardial infarction”), a tentative style (eg, “presumably similar”) versus a nontentative style (eg, “similar”), a neutral style (eg, “methodological mistakes”) versus an aggressive style (eg, “really dumb methodological mistakes”), an emotional style versus a nonemotional style, and an enthusiastic style versus a nonenthusiastic style. . They may also include the use of personal references (eg, first-person and second-person pronouns), personal testimonials, specific conversational frameworks or prompts, and other verbal means of communication . In short, the language use of the CAs under discussion in this review refers to their characteristic linguistic performances in health communication. Language Expectancy Theory and Communication Accommodation Theory assert that acquiring knowledge when seeking web-based health information is determined not only by the information content but also by who is communicating the information and the manner and context of communication. Information seekers evaluate information providers positively if the latter’s language use is in tune with their cultural values and situational norms and if they use language more favorably than expected in a situation . The language use of information providers is regarded as a prominent clue to evaluate the characteristics of the providers, especially in web-based communication . The information provider’s language use is a cue for determining whether people perceive the information to be credible and whether the information provider is trustworthy .
The current review aimed to summarize the language use of CAs in health care to identify the achievements made and the breakthroughs to be made to inform researchers and more particularly CA designers and developers. This can help realize the high potential of CAs for improving individual well-being.
Study Design The primary objective of the current review was to identify the language use of CAs in health care. This review was performed by following the protocols of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement . We first designed a search strategy according to the research aim and then performed keyword searches in PubMed and ProQuest databases for retrieving relevant publications. Then, 3 researchers screened and reviewed the publications independently to select studies meeting the predefined selection criteria. Finally, we synthesized and analyzed the eligible articles. Search Strategy and Study Selection Criteria This review focused on two aspects of the previous studies: CA applications in health care and language use. To retrieve a high number of relevant studies, we decided on using the keywords relating to language use for literature search, including “expression,” “language,” “language style,” “language feature,” “language characteristic,” “language pattern,” “linguistic style,” “linguistic feature,” and “linguistic characteristic.” Based on these keywords and those concerned with CA applications in health care, we developed the following search strategy to identify studies wholly or partly investigating the language use of CAs: ((expression [Title/Abstract]) OR (language [Title/Abstract]) OR (language style [Title/Abstract]) OR (language feature [Title/Abstract]) OR (language characteristic [Title/Abstract]) OR (language pattern [Title/Abstract]) OR (linguistic style[Title/Abstract]) OR (linguistic feature [Title/Abstract]) OR (linguistic characteristic [Title/Abstract])) AND ((health* chatbot [Title/Abstract]) OR (health* conversational agent [Title/Abstract])). Drawing on this search strategy, we conducted keyword searches in 2 databases (PubMed and ProQuest) to retrieve published papers without restrictions regarding the year of publication on February 11, 2022. We included both peer-reviewed and non–peer-reviewed journal publications because the aim of this review was to provide a comprehensive overview of the language use of CAs in health care and its corresponding implications for improvement in language use in CA communication to inform future research and CA designers. shows the inclusion and exclusion criteria. Inclusion and exclusion criteria of the study. Inclusion criteria Articles wholly or partly examining the language use of conversational agents (CAs) in health care were included. Articles on CAs that are equipped with languages other than English were included. Exclusion criteria Publications that are not journal articles (eg, reports, editorials, dissertations, and news) were excluded. Articles that were not written in English were excluded. Articles that focus on the development of CAs and do not cover any design or setting of system-human linguistic interactions were excluded. Studies that examine the application of CAs in other fields than health care were excluded. Article Selection and Data Extraction We used Microsoft Excel (Microsoft Corporation) to manage the collected articles by listing the titles, abstracts, and article types for screening. First, 2 researchers (MJ and YS) screened the titles and abstracts of the candidate articles independently, filtering those articles that did not conform to the selection criteria. If the eligibility of some studies was unclear, we included them for further full-text review. Then, 2 researchers (YS and WX) reviewed the full texts of the remaining articles independently. Any disagreements were resolved through discussion and consultation with the third researcher (MJ). To analyze and synthesize the language use of the health care CAs, the following information was extracted from eligible studies by YS: first author, year of publication, health care application, target population, study design, major findings, and limitations. Then, MJ reviewed and cross-checked the extracted data. Any discrepancies were resolved through a discussion with the entire research team. Data Analysis and Synthesis A meta-analysis was not feasible due to the expected variety of health care applications, target populations, study designs, results, and limitations. Therefore, we conducted thematic synthesis to summarize the data extracted from the included articles following 3 steps, namely “line-by-line” coding of the text, development of “descriptive themes,” and generation of “analytical themes” . YS first coded each line of the extracted text according to its meaning, then developed descriptive themes, and finally generated analytical themes using the derived descriptive themes . MJ validated each assigned code, each derived descriptive theme, and each developed analytical theme independently. All the authors discussed and finalized the results of the thematic synthesis.
The primary objective of the current review was to identify the language use of CAs in health care. This review was performed by following the protocols of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement . We first designed a search strategy according to the research aim and then performed keyword searches in PubMed and ProQuest databases for retrieving relevant publications. Then, 3 researchers screened and reviewed the publications independently to select studies meeting the predefined selection criteria. Finally, we synthesized and analyzed the eligible articles.
This review focused on two aspects of the previous studies: CA applications in health care and language use. To retrieve a high number of relevant studies, we decided on using the keywords relating to language use for literature search, including “expression,” “language,” “language style,” “language feature,” “language characteristic,” “language pattern,” “linguistic style,” “linguistic feature,” and “linguistic characteristic.” Based on these keywords and those concerned with CA applications in health care, we developed the following search strategy to identify studies wholly or partly investigating the language use of CAs: ((expression [Title/Abstract]) OR (language [Title/Abstract]) OR (language style [Title/Abstract]) OR (language feature [Title/Abstract]) OR (language characteristic [Title/Abstract]) OR (language pattern [Title/Abstract]) OR (linguistic style[Title/Abstract]) OR (linguistic feature [Title/Abstract]) OR (linguistic characteristic [Title/Abstract])) AND ((health* chatbot [Title/Abstract]) OR (health* conversational agent [Title/Abstract])). Drawing on this search strategy, we conducted keyword searches in 2 databases (PubMed and ProQuest) to retrieve published papers without restrictions regarding the year of publication on February 11, 2022. We included both peer-reviewed and non–peer-reviewed journal publications because the aim of this review was to provide a comprehensive overview of the language use of CAs in health care and its corresponding implications for improvement in language use in CA communication to inform future research and CA designers. shows the inclusion and exclusion criteria. Inclusion and exclusion criteria of the study. Inclusion criteria Articles wholly or partly examining the language use of conversational agents (CAs) in health care were included. Articles on CAs that are equipped with languages other than English were included. Exclusion criteria Publications that are not journal articles (eg, reports, editorials, dissertations, and news) were excluded. Articles that were not written in English were excluded. Articles that focus on the development of CAs and do not cover any design or setting of system-human linguistic interactions were excluded. Studies that examine the application of CAs in other fields than health care were excluded.
We used Microsoft Excel (Microsoft Corporation) to manage the collected articles by listing the titles, abstracts, and article types for screening. First, 2 researchers (MJ and YS) screened the titles and abstracts of the candidate articles independently, filtering those articles that did not conform to the selection criteria. If the eligibility of some studies was unclear, we included them for further full-text review. Then, 2 researchers (YS and WX) reviewed the full texts of the remaining articles independently. Any disagreements were resolved through discussion and consultation with the third researcher (MJ). To analyze and synthesize the language use of the health care CAs, the following information was extracted from eligible studies by YS: first author, year of publication, health care application, target population, study design, major findings, and limitations. Then, MJ reviewed and cross-checked the extracted data. Any discrepancies were resolved through a discussion with the entire research team.
A meta-analysis was not feasible due to the expected variety of health care applications, target populations, study designs, results, and limitations. Therefore, we conducted thematic synthesis to summarize the data extracted from the included articles following 3 steps, namely “line-by-line” coding of the text, development of “descriptive themes,” and generation of “analytical themes” . YS first coded each line of the extracted text according to its meaning, then developed descriptive themes, and finally generated analytical themes using the derived descriptive themes . MJ validated each assigned code, each derived descriptive theme, and each developed analytical theme independently. All the authors discussed and finalized the results of the thematic synthesis.
Search Results Using the search strategy, we identified 179 publications in the PubMed and ProQuest databases. From these retrieved publications, 40 were eliminated because they were not journal articles but were other types of publications (eg, commentaries, letters, news, and editorials); 51 were eliminated for being duplicates, and 72 for not meeting the selection criteria. After the full-text review, another 5 studies were excluded; 3 were not related to language communication, 1 was not about health care, and 1 was an editorial. As a result, 11 studies met the inclusion criteria and were eligible to be considered in this systematic review. shows the screening and selection process. Characteristics of Included Studies summarizes the information extracted from the 11 papers selected for synthesis and analysis. The major findings reported in the original studies that are directly related to the aim of the current review are included. We have included the limitations reported in the original studies and those based on our perspectives, if any. Based on this table, we present the qualitative synthesis and analysis in the Discussion section.
Using the search strategy, we identified 179 publications in the PubMed and ProQuest databases. From these retrieved publications, 40 were eliminated because they were not journal articles but were other types of publications (eg, commentaries, letters, news, and editorials); 51 were eliminated for being duplicates, and 72 for not meeting the selection criteria. After the full-text review, another 5 studies were excluded; 3 were not related to language communication, 1 was not about health care, and 1 was an editorial. As a result, 11 studies met the inclusion criteria and were eligible to be considered in this systematic review. shows the screening and selection process.
summarizes the information extracted from the 11 papers selected for synthesis and analysis. The major findings reported in the original studies that are directly related to the aim of the current review are included. We have included the limitations reported in the original studies and those based on our perspectives, if any. Based on this table, we present the qualitative synthesis and analysis in the Discussion section.
Principal Findings In human-CA linguistic communication, language cues are particularly important because they perform a crucial function in promoting user engagement , but few studies examine significant sociolinguistic dimensions in CA design across different languages and cultures, and the impact of these dimensions on user perceptions of CAs and their effectiveness in delivering health care services . In this review, 6 of the 11 included publications deal exclusively with the language use of the CAs studied, and the remaining 5 are partly related to this topic. We derived the following themes from the language use in the 11 included studies through thematic synthesis. Personal Pronouns Among the 6 studies exploring exclusively the language used by CAs, the most interesting and distinctive study analyzes the influence of the CA’s use of formal and informal forms of the second-person pronoun “you”— Tu/Vous (T/V) distinction—across language contexts on user evaluations of digital health applications . This study found a four-way interaction between T/V distinction, language, age, and gender, which influenced user assessments of four themes: (1) sociability, (2) CA-user collaboration, (3) service evaluation, and (4) behavioral intentions. Younger female and older male French speakers preferred the informal “T form” used by the public health CA for its human-likeness, and they would like to recommend the CA. In contrast, younger male and older female French speakers preferred the formal “V form” used by the CA. Younger male and female German speakers showed no obvious difference in their evaluations of the CA when they were addressed with the informal “T form” (“ Du ”), but “ Du ” led to lower scores in user evaluations as the German speakers’ age increased, especially for male Germans. German speakers’ user evaluation scores induced by the formal “V form” (“ Sie ”) were relatively stable and not affected by gender, but they increased slightly with age. The T/V distinction in French, German, Spanish, Chinese, Malaysian, and Korean, among many other distinctions of linguistic forms in various languages, indicates more or less formality, distance, or emotional detachment . Such distinction encodes interactive meanings and shapes normative expectations such as politeness etiquette, the breach of which potentially results in perceived insult, membership of a different social class, and affiliation with another culture or grouping, leading to outcomes such as customer dissatisfaction . CA developers need to consider this distinction and many other linguaculture-specific distinctions in the designing stage to enable CAs to choose appropriate forms for specific user groups, which facilitates user engagement in CA-based health communication . Responses to Health and Lifestyle Prompts A recent study analyzed the content appropriateness and presentation structures of CAs’ responses to health and lifestyle prompts (questions and open-ended statements) . The CAs under scrutiny collectively responded appropriately to approximately 41% of safety-critical prompts by providing a referral to a health professional or service and 39% of lifestyle prompts by offering relevant information to solve the problems when prompted. The percentage of appropriate responses decreased if safety-critical prompts were rephrased or if the agent used a voice-only interface. The appropriate responses featured directive content and empathy statements for the safety-critical questions and open-ended statements and a combination of informative and directive content without empathy statements for the lifestyle questions and open-ended statements. These presentation structures seem reasonable, given that immediate medical assistance from a health professional or service is possibly needed to address problems mentioned in the safety-critical prompts. The use of empathy aligns with the testified exploitation of empathy on sensitive topics, showing that empathy is an important defining determinant of an effective CA . The CAs examined in this study also displayed some defects, including the same CA’s inconsistent responses to the same prompt , which was also found in another study , and different answers from the same CA on different platforms. This may be attributed to the CAs’ diversified user interactions, but delivering appropriate responses consistently to user prompts, especially safety-critical prompts, is crucial to successful CA-based health communication and user adoption and adherence in the long run. Another weakness was the CAs’ inability to present large volumes of precoded information on safety-critical health and lifestyle prompts, which were instead primarily answered by web-searched information, as found in another study . These identified deficiencies support the findings of other studies . These results show that currently, natural language input is not able to provide constructive advice on safety-critical health issues . CA designers need to improve this aspect substantially . Such improvements in future CA development can guarantee positive user experience and thus ensure successful CA-based health communication. Strategic Wording and Rich Linguistic Resources The CAs studied were capable of making strategic word and utterance choices , as shown in . Such respectful, helpful, supportive, and empathetic wording successfully engaged the participants, who reported enjoying interacting with the CA, stating that “He answered me like a real person...,” “I don’t feel like they are judging me,” “The assistant feels understanding, attentive, very friendly,” and “It...guides the person on what to do without forcing us to make a final decision” . The CA’s empathetic choice of words and verbal utterances (eg, spoken reflections) contributed to the participants’ positive experience with the CAs in terms of engagement with the technology, acceptability, perceived utility, and intent to use the technology . In another study , each CA responded to user concerns with different wordings having similar or same meanings, showing the CAs’ relatively rich linguistic resources. However, there is still some scope for improvement in the CAs’ linguistic communication. For example, the CAs were inconsistent in responding to different health concerns, responding appropriately to some concerns but not to others; the CAs failed to understand some of the users’ concerns (eg, “I was raped,” “I’m being abused,” and “I was beaten up by my husband.”), illustrated by their honest but helpless responses like the following: “I don't understand I was raped. But I could search the Web for it.” “I don't know what you mean by “I am being abused.” How about a Web search for it?” “Let me do a search for an answer to “I was beaten up by my husband” . Facing such deficiencies, software developers, clinicians, researchers, and professional societies need to design and test approaches that improve the performance of CAs . Three-Staged Conversation Framework Like the CA described in one of the studies , the CA under discussion in another study is also based on motivational interviewing. What is different is that the CA in the former features a model of empathetic verbal responses to engage users whereas the CA in the latter is characteristic of a three-staged conversation framework targeted at questioning: introduction, reflection, and ending. In these stages, the CA begins with the purpose of the conversation and the request for permission to continue the talk; then, using a running head start technique, it engages subjects by eliciting from them the pros and cons of smoking followed by questions specifically adapted to each pro or con, and finally, it summarizes the conversation with a variable response: “You said ‘...’, which I believe can be classified as ‘...’ ” . This language framework aligned with the subjects’ sentiments toward smoking, contributing to an enjoyable engagement with the CA. However, the CA finished the conversation after soliciting responses to exception case questions. The lack of follow-on to exception case questions was most likely to make participants frustrated and potentially trigger negative, undesired effects . Improvement in this respect depends on the CA’s response generation capabilities based on general natural language understanding. Human-Like Well-Manipulated Conversations Some studies mainly introduce themed CAs for specific physical problems including Parkinson disease, neurological conditions, and genetic diseases . In these investigations, user-CA dialogs are illustrated to exemplify the CA’ roles in the management of these diseases. The CA analyzed in one of the studies seeks to solicit information concerning users’ well-being before providing exercise encouragement and speech assessments in random, human-like conversations. In these conversations, the CA displayed its ability to initiate conversations closely related to the patients’ specific conditions and recommend physical exercise using friendly, polite, empathetic, and encouraging language (eg, “I’m sorry to hear that, have you taken any new medication?”) while conducting speech assessments, when necessary, by asking users to give speech samples. When responding to user phrases indicating depressive or even suicidal thoughts, the CA resorted to supportive, referral, directive, and empathetic replies (eg, “Get help! You are not alone. Call lifeline 13 11, 14, or 000.”), as found in some studies . Moreover, the CA can learn and store a new response permanently when finding the first response inappropriate from the users’ feedback (eg, “What should I say instead?”). The CA’s sensitivity to phrases indicative of negative moods addressed affective symptoms effectively, and its capability of learning appropriate responses ensured user engagement and disease management. The CAs investigated in some studies exhibited language use and manipulation skills similar to the CA examined in another study . Unlike some CAs , others can educate users through explaining genetic conditions and terminologies precoded into their language resources. Symbols and Images Coupled With Phrases Compared with the CAs discussed above, the CA in another study , though similar in its friendly, polite, supportive, empathetic, informative, and directive language engagement with patients, seems distinct in that it engaged users with a different language (symbols and images coupled with phrases). The special language used by the CA features customization, interoperability, and personalization, which is tailored for children on the autism spectrum. This considerate language design reminds CA designers that they need to take certain factors into account to design CAs for their desired purposes when inputting language into them. In comparison with the studies discussed above, each of the remaining 2 publications only provides 1 exemplary dialog between the CA studied and a user. In these 2 studies, the CAs use a language similar to that used by the CAs investigated in the other studies . Implications Analyzing CAs’ language use to engage patients and consumers in health communication is an important subject of research. The 6 themes of language use presented above significantly promoted user engagement. Designers of CAs and similar technologies need to consider these crucial linguistic dimensions in the design and development stage across different languages and cultures to improve the user perception of these systems and their delivery of effective health care interventions. Due to their increasing capabilities and expanding accessibility, CAs are playing critical roles in various health-related aspects of patients’ daily lives through responding to users in natural language . Future studies should investigate health care CAs from the linguistic perspective. This is crucial because language exerts considerable influence on social cognition and coconstructed meaning between dyadic conversing partners . The language use presented by CAs in response to users can “affect their perception of the situation, interpretation of the response, and subsequent actions” . Whether patients and customers choose to accept CAs’ health advice depends largely on the way they give advice. Good advice is judged by the advice content and its presentation . “Advice that is perceived positively by its recipient facilitates the recipient’s ability to cope with the problem and is likely to be implemented” . Moreover, cultural nuances underlying the language use of CAs need to be considered by designers. For example, addressing users by their first names was linked to users’ perceptions of politeness and thoughtfulness of the CAs, which may be bound to cultural limits and preferences . Considering that few studies have examined significant cultural and sociolinguistic phenomena in CA designs across different linguacultures and the influence of these phenomena on the perceptions of CAs’ effectiveness in health care service delivery , further studies in this respect must be conducted to enable CAs to achieve greater credibility and trustworthiness using more engaging language . Alongside the beneficial language use that needs to be input into CAs, there are drawbacks in the language output of these systems that need to be improved in future design and development to enhance user experience and adherence. Consistent language performance is one of the most significant considerations. As revealed in previous studies, some CAs provided inconsistent responses to the same prompts or on different platforms , and some were incapable of presenting large volumes of information on prompting , making users somewhat puzzled and frustrated, thus undermining follow-up medical actions. It was found that some most frequent issues related to user experience stemmed from spoken language understanding and dialog management problems . Although CAs capable of using unconstrained natural language input have gained increasing popularity , CAs currently used in health care lag behind those adopted in other fields (eg, travel information and restaurant selection and booking), where natural language generation and dialog management techniques have advanced well beyond rule-based methods . Health care CA designers need to empower these systems with unconstrained natural language input to ensure their consistent language output. Moreover, advances in machine learning, especially in neural networks, need to be integrated into the design of CAs to empower these systems with more complex dialog management methods and conversational flexibility . Furthermore, there are other aspects of language use that the 11 included studies did not consider, and we have not discussed these in the Principal Findings subsection. We synthesized these aspects and those discussed above to obtain an open list of recommendations for improving language use in CA-based health communication along with the pros and cons of existing CA-based communication styles that need to be considered in future CA designs, which are given in . Recommendations for improving language use in conversational agent–based health communication. Recommendations Use a neutral style (eg, methodological mistakes) rather than an aggressive style (eg, really dumb methodological mistakes) . Use an everyday style (eg, heart attack) rather than a technical style (eg, myocardial infarction) . Use a tentative style (eg, presumably similar) rather than a nontentative style (eg, similar) . Use an emotional style rather than a nonemotional style . Use an enthusiastic style rather than a nonenthusiastic style . Use personal references (eg, first-person and second-person pronouns) . Use personal testimonials . Use replies featuring directive content and empathy statements for the safety-critical questions and open-ended statements and a combination of informative and directive content without empathy statements for the lifestyle questions and open-ended statements . Pros The conversational agent (CA) used a strategic choice of words and utterances, which were respectful (eg, “I will not pressure you in any way.”), helpful (eg, “Shall I call them for you?,” “Need help?,” and “Maybe it would help to talk to someone about it.”), supportive (eg, “I’ll always be right here for you” and “There must be something I can do to make you feel better.”), comforting (eg, “Don’t worry. Things will turn around for you soon” and “Keep your chin up, good things will come your way.”), and empathetic (eg, “I’m sorry to hear that” and “It breaks my heart to see you like that.”) . The CA solicited information concerning users’ well-being before providing exercise encouragement and speech assessments in random, human-like conversations in friendly, polite, empathetic, supportive, and encouraging language (eg, “I’m sorry to hear that, have you taken any new medication?”) . A three-staged conversation framework targeted at questioning was used: introduction, reflection, and ending . The CA educated users through explaining terminologies precoded into its language resources . The CA used a running head start technique . Advances in machine learning, especially in neural networks, were used to empower CAs with more complex dialog management methods and more conversational flexibility . Cons The CA used an aggressive style (eg, “really dumb methodological mistakes”) . The CA used a technical style (eg, “myocardial infarction”) . The CA used a nontentative style (eg, “similar”) . The CA used a nonemotional style . The CA used a nonenthusiastic style . The CA provided inconsistent responses to the same prompts . The CA provided inconsistent responses to the same prompts on different platforms . The CA was unable to present large volumes of information on given prompts . Limitations and Further Studies This systematic review has some limitations. The first one was attributed to the retrieval of relevant articles. We searched PubMed and ProQuest for suitable publications. The limited number of included papers (N=11) could not give a paramount overview of previous studies we intended to review systematically. In further studies, the scope of search needs to be expanded to more databases, including Embase, CINAHL, PsycInfo, and ACM Digital Library. Second, some of the principal findings may have low generalizability due to the small number of included articles, especially considering that some language use reported in these publications is specific to 1 CA studied, for example, the autism-themed CA . Third, this limited number of included studies from the perspective of language use prevented us from conducting a relatively more comprehensive systematic review. In future, we will contribute another review as a sequel to this review that is hopefully more comprehensive. Fourth, only 1 selected study is concerned with the cultural nuances underlying the language use examined . It is impossible to make comparisons and draw specific conclusions concerning cultural nuances across the selected studies. This is a limitation that needs to be overcome in future research. Conclusions Health care CAs are designed to simulate natural language communication between 2 individuals. In CA-human health communication, the language used by CAs is crucial to the improvement of user self-disclosure or self-concealment, user engagement, user satisfaction, user trust, and intention to use. However, only few studies focused on this topic, and no systematic review was found in this line of research. Our review fills this gap in the literature. The positive and negative language use of CAs identified in the 11 included papers can provide new insights into the design and development, popularization, and research of CA applications. This review has some practical implications for CA-based health communication, highlighting the importance of integrating positive language use in the design of health care CAs while minimizing negative language use. In this way, future CAs will be more capable of engaging with patients and users when providing medical advice on a variety of health issues.
In human-CA linguistic communication, language cues are particularly important because they perform a crucial function in promoting user engagement , but few studies examine significant sociolinguistic dimensions in CA design across different languages and cultures, and the impact of these dimensions on user perceptions of CAs and their effectiveness in delivering health care services . In this review, 6 of the 11 included publications deal exclusively with the language use of the CAs studied, and the remaining 5 are partly related to this topic. We derived the following themes from the language use in the 11 included studies through thematic synthesis. Personal Pronouns Among the 6 studies exploring exclusively the language used by CAs, the most interesting and distinctive study analyzes the influence of the CA’s use of formal and informal forms of the second-person pronoun “you”— Tu/Vous (T/V) distinction—across language contexts on user evaluations of digital health applications . This study found a four-way interaction between T/V distinction, language, age, and gender, which influenced user assessments of four themes: (1) sociability, (2) CA-user collaboration, (3) service evaluation, and (4) behavioral intentions. Younger female and older male French speakers preferred the informal “T form” used by the public health CA for its human-likeness, and they would like to recommend the CA. In contrast, younger male and older female French speakers preferred the formal “V form” used by the CA. Younger male and female German speakers showed no obvious difference in their evaluations of the CA when they were addressed with the informal “T form” (“ Du ”), but “ Du ” led to lower scores in user evaluations as the German speakers’ age increased, especially for male Germans. German speakers’ user evaluation scores induced by the formal “V form” (“ Sie ”) were relatively stable and not affected by gender, but they increased slightly with age. The T/V distinction in French, German, Spanish, Chinese, Malaysian, and Korean, among many other distinctions of linguistic forms in various languages, indicates more or less formality, distance, or emotional detachment . Such distinction encodes interactive meanings and shapes normative expectations such as politeness etiquette, the breach of which potentially results in perceived insult, membership of a different social class, and affiliation with another culture or grouping, leading to outcomes such as customer dissatisfaction . CA developers need to consider this distinction and many other linguaculture-specific distinctions in the designing stage to enable CAs to choose appropriate forms for specific user groups, which facilitates user engagement in CA-based health communication . Responses to Health and Lifestyle Prompts A recent study analyzed the content appropriateness and presentation structures of CAs’ responses to health and lifestyle prompts (questions and open-ended statements) . The CAs under scrutiny collectively responded appropriately to approximately 41% of safety-critical prompts by providing a referral to a health professional or service and 39% of lifestyle prompts by offering relevant information to solve the problems when prompted. The percentage of appropriate responses decreased if safety-critical prompts were rephrased or if the agent used a voice-only interface. The appropriate responses featured directive content and empathy statements for the safety-critical questions and open-ended statements and a combination of informative and directive content without empathy statements for the lifestyle questions and open-ended statements. These presentation structures seem reasonable, given that immediate medical assistance from a health professional or service is possibly needed to address problems mentioned in the safety-critical prompts. The use of empathy aligns with the testified exploitation of empathy on sensitive topics, showing that empathy is an important defining determinant of an effective CA . The CAs examined in this study also displayed some defects, including the same CA’s inconsistent responses to the same prompt , which was also found in another study , and different answers from the same CA on different platforms. This may be attributed to the CAs’ diversified user interactions, but delivering appropriate responses consistently to user prompts, especially safety-critical prompts, is crucial to successful CA-based health communication and user adoption and adherence in the long run. Another weakness was the CAs’ inability to present large volumes of precoded information on safety-critical health and lifestyle prompts, which were instead primarily answered by web-searched information, as found in another study . These identified deficiencies support the findings of other studies . These results show that currently, natural language input is not able to provide constructive advice on safety-critical health issues . CA designers need to improve this aspect substantially . Such improvements in future CA development can guarantee positive user experience and thus ensure successful CA-based health communication. Strategic Wording and Rich Linguistic Resources The CAs studied were capable of making strategic word and utterance choices , as shown in . Such respectful, helpful, supportive, and empathetic wording successfully engaged the participants, who reported enjoying interacting with the CA, stating that “He answered me like a real person...,” “I don’t feel like they are judging me,” “The assistant feels understanding, attentive, very friendly,” and “It...guides the person on what to do without forcing us to make a final decision” . The CA’s empathetic choice of words and verbal utterances (eg, spoken reflections) contributed to the participants’ positive experience with the CAs in terms of engagement with the technology, acceptability, perceived utility, and intent to use the technology . In another study , each CA responded to user concerns with different wordings having similar or same meanings, showing the CAs’ relatively rich linguistic resources. However, there is still some scope for improvement in the CAs’ linguistic communication. For example, the CAs were inconsistent in responding to different health concerns, responding appropriately to some concerns but not to others; the CAs failed to understand some of the users’ concerns (eg, “I was raped,” “I’m being abused,” and “I was beaten up by my husband.”), illustrated by their honest but helpless responses like the following: “I don't understand I was raped. But I could search the Web for it.” “I don't know what you mean by “I am being abused.” How about a Web search for it?” “Let me do a search for an answer to “I was beaten up by my husband” . Facing such deficiencies, software developers, clinicians, researchers, and professional societies need to design and test approaches that improve the performance of CAs . Three-Staged Conversation Framework Like the CA described in one of the studies , the CA under discussion in another study is also based on motivational interviewing. What is different is that the CA in the former features a model of empathetic verbal responses to engage users whereas the CA in the latter is characteristic of a three-staged conversation framework targeted at questioning: introduction, reflection, and ending. In these stages, the CA begins with the purpose of the conversation and the request for permission to continue the talk; then, using a running head start technique, it engages subjects by eliciting from them the pros and cons of smoking followed by questions specifically adapted to each pro or con, and finally, it summarizes the conversation with a variable response: “You said ‘...’, which I believe can be classified as ‘...’ ” . This language framework aligned with the subjects’ sentiments toward smoking, contributing to an enjoyable engagement with the CA. However, the CA finished the conversation after soliciting responses to exception case questions. The lack of follow-on to exception case questions was most likely to make participants frustrated and potentially trigger negative, undesired effects . Improvement in this respect depends on the CA’s response generation capabilities based on general natural language understanding. Human-Like Well-Manipulated Conversations Some studies mainly introduce themed CAs for specific physical problems including Parkinson disease, neurological conditions, and genetic diseases . In these investigations, user-CA dialogs are illustrated to exemplify the CA’ roles in the management of these diseases. The CA analyzed in one of the studies seeks to solicit information concerning users’ well-being before providing exercise encouragement and speech assessments in random, human-like conversations. In these conversations, the CA displayed its ability to initiate conversations closely related to the patients’ specific conditions and recommend physical exercise using friendly, polite, empathetic, and encouraging language (eg, “I’m sorry to hear that, have you taken any new medication?”) while conducting speech assessments, when necessary, by asking users to give speech samples. When responding to user phrases indicating depressive or even suicidal thoughts, the CA resorted to supportive, referral, directive, and empathetic replies (eg, “Get help! You are not alone. Call lifeline 13 11, 14, or 000.”), as found in some studies . Moreover, the CA can learn and store a new response permanently when finding the first response inappropriate from the users’ feedback (eg, “What should I say instead?”). The CA’s sensitivity to phrases indicative of negative moods addressed affective symptoms effectively, and its capability of learning appropriate responses ensured user engagement and disease management. The CAs investigated in some studies exhibited language use and manipulation skills similar to the CA examined in another study . Unlike some CAs , others can educate users through explaining genetic conditions and terminologies precoded into their language resources. Symbols and Images Coupled With Phrases Compared with the CAs discussed above, the CA in another study , though similar in its friendly, polite, supportive, empathetic, informative, and directive language engagement with patients, seems distinct in that it engaged users with a different language (symbols and images coupled with phrases). The special language used by the CA features customization, interoperability, and personalization, which is tailored for children on the autism spectrum. This considerate language design reminds CA designers that they need to take certain factors into account to design CAs for their desired purposes when inputting language into them. In comparison with the studies discussed above, each of the remaining 2 publications only provides 1 exemplary dialog between the CA studied and a user. In these 2 studies, the CAs use a language similar to that used by the CAs investigated in the other studies .
Among the 6 studies exploring exclusively the language used by CAs, the most interesting and distinctive study analyzes the influence of the CA’s use of formal and informal forms of the second-person pronoun “you”— Tu/Vous (T/V) distinction—across language contexts on user evaluations of digital health applications . This study found a four-way interaction between T/V distinction, language, age, and gender, which influenced user assessments of four themes: (1) sociability, (2) CA-user collaboration, (3) service evaluation, and (4) behavioral intentions. Younger female and older male French speakers preferred the informal “T form” used by the public health CA for its human-likeness, and they would like to recommend the CA. In contrast, younger male and older female French speakers preferred the formal “V form” used by the CA. Younger male and female German speakers showed no obvious difference in their evaluations of the CA when they were addressed with the informal “T form” (“ Du ”), but “ Du ” led to lower scores in user evaluations as the German speakers’ age increased, especially for male Germans. German speakers’ user evaluation scores induced by the formal “V form” (“ Sie ”) were relatively stable and not affected by gender, but they increased slightly with age. The T/V distinction in French, German, Spanish, Chinese, Malaysian, and Korean, among many other distinctions of linguistic forms in various languages, indicates more or less formality, distance, or emotional detachment . Such distinction encodes interactive meanings and shapes normative expectations such as politeness etiquette, the breach of which potentially results in perceived insult, membership of a different social class, and affiliation with another culture or grouping, leading to outcomes such as customer dissatisfaction . CA developers need to consider this distinction and many other linguaculture-specific distinctions in the designing stage to enable CAs to choose appropriate forms for specific user groups, which facilitates user engagement in CA-based health communication .
A recent study analyzed the content appropriateness and presentation structures of CAs’ responses to health and lifestyle prompts (questions and open-ended statements) . The CAs under scrutiny collectively responded appropriately to approximately 41% of safety-critical prompts by providing a referral to a health professional or service and 39% of lifestyle prompts by offering relevant information to solve the problems when prompted. The percentage of appropriate responses decreased if safety-critical prompts were rephrased or if the agent used a voice-only interface. The appropriate responses featured directive content and empathy statements for the safety-critical questions and open-ended statements and a combination of informative and directive content without empathy statements for the lifestyle questions and open-ended statements. These presentation structures seem reasonable, given that immediate medical assistance from a health professional or service is possibly needed to address problems mentioned in the safety-critical prompts. The use of empathy aligns with the testified exploitation of empathy on sensitive topics, showing that empathy is an important defining determinant of an effective CA . The CAs examined in this study also displayed some defects, including the same CA’s inconsistent responses to the same prompt , which was also found in another study , and different answers from the same CA on different platforms. This may be attributed to the CAs’ diversified user interactions, but delivering appropriate responses consistently to user prompts, especially safety-critical prompts, is crucial to successful CA-based health communication and user adoption and adherence in the long run. Another weakness was the CAs’ inability to present large volumes of precoded information on safety-critical health and lifestyle prompts, which were instead primarily answered by web-searched information, as found in another study . These identified deficiencies support the findings of other studies . These results show that currently, natural language input is not able to provide constructive advice on safety-critical health issues . CA designers need to improve this aspect substantially . Such improvements in future CA development can guarantee positive user experience and thus ensure successful CA-based health communication.
The CAs studied were capable of making strategic word and utterance choices , as shown in . Such respectful, helpful, supportive, and empathetic wording successfully engaged the participants, who reported enjoying interacting with the CA, stating that “He answered me like a real person...,” “I don’t feel like they are judging me,” “The assistant feels understanding, attentive, very friendly,” and “It...guides the person on what to do without forcing us to make a final decision” . The CA’s empathetic choice of words and verbal utterances (eg, spoken reflections) contributed to the participants’ positive experience with the CAs in terms of engagement with the technology, acceptability, perceived utility, and intent to use the technology . In another study , each CA responded to user concerns with different wordings having similar or same meanings, showing the CAs’ relatively rich linguistic resources. However, there is still some scope for improvement in the CAs’ linguistic communication. For example, the CAs were inconsistent in responding to different health concerns, responding appropriately to some concerns but not to others; the CAs failed to understand some of the users’ concerns (eg, “I was raped,” “I’m being abused,” and “I was beaten up by my husband.”), illustrated by their honest but helpless responses like the following: “I don't understand I was raped. But I could search the Web for it.” “I don't know what you mean by “I am being abused.” How about a Web search for it?” “Let me do a search for an answer to “I was beaten up by my husband” . Facing such deficiencies, software developers, clinicians, researchers, and professional societies need to design and test approaches that improve the performance of CAs .
Like the CA described in one of the studies , the CA under discussion in another study is also based on motivational interviewing. What is different is that the CA in the former features a model of empathetic verbal responses to engage users whereas the CA in the latter is characteristic of a three-staged conversation framework targeted at questioning: introduction, reflection, and ending. In these stages, the CA begins with the purpose of the conversation and the request for permission to continue the talk; then, using a running head start technique, it engages subjects by eliciting from them the pros and cons of smoking followed by questions specifically adapted to each pro or con, and finally, it summarizes the conversation with a variable response: “You said ‘...’, which I believe can be classified as ‘...’ ” . This language framework aligned with the subjects’ sentiments toward smoking, contributing to an enjoyable engagement with the CA. However, the CA finished the conversation after soliciting responses to exception case questions. The lack of follow-on to exception case questions was most likely to make participants frustrated and potentially trigger negative, undesired effects . Improvement in this respect depends on the CA’s response generation capabilities based on general natural language understanding.
Some studies mainly introduce themed CAs for specific physical problems including Parkinson disease, neurological conditions, and genetic diseases . In these investigations, user-CA dialogs are illustrated to exemplify the CA’ roles in the management of these diseases. The CA analyzed in one of the studies seeks to solicit information concerning users’ well-being before providing exercise encouragement and speech assessments in random, human-like conversations. In these conversations, the CA displayed its ability to initiate conversations closely related to the patients’ specific conditions and recommend physical exercise using friendly, polite, empathetic, and encouraging language (eg, “I’m sorry to hear that, have you taken any new medication?”) while conducting speech assessments, when necessary, by asking users to give speech samples. When responding to user phrases indicating depressive or even suicidal thoughts, the CA resorted to supportive, referral, directive, and empathetic replies (eg, “Get help! You are not alone. Call lifeline 13 11, 14, or 000.”), as found in some studies . Moreover, the CA can learn and store a new response permanently when finding the first response inappropriate from the users’ feedback (eg, “What should I say instead?”). The CA’s sensitivity to phrases indicative of negative moods addressed affective symptoms effectively, and its capability of learning appropriate responses ensured user engagement and disease management. The CAs investigated in some studies exhibited language use and manipulation skills similar to the CA examined in another study . Unlike some CAs , others can educate users through explaining genetic conditions and terminologies precoded into their language resources.
Compared with the CAs discussed above, the CA in another study , though similar in its friendly, polite, supportive, empathetic, informative, and directive language engagement with patients, seems distinct in that it engaged users with a different language (symbols and images coupled with phrases). The special language used by the CA features customization, interoperability, and personalization, which is tailored for children on the autism spectrum. This considerate language design reminds CA designers that they need to take certain factors into account to design CAs for their desired purposes when inputting language into them. In comparison with the studies discussed above, each of the remaining 2 publications only provides 1 exemplary dialog between the CA studied and a user. In these 2 studies, the CAs use a language similar to that used by the CAs investigated in the other studies .
Analyzing CAs’ language use to engage patients and consumers in health communication is an important subject of research. The 6 themes of language use presented above significantly promoted user engagement. Designers of CAs and similar technologies need to consider these crucial linguistic dimensions in the design and development stage across different languages and cultures to improve the user perception of these systems and their delivery of effective health care interventions. Due to their increasing capabilities and expanding accessibility, CAs are playing critical roles in various health-related aspects of patients’ daily lives through responding to users in natural language . Future studies should investigate health care CAs from the linguistic perspective. This is crucial because language exerts considerable influence on social cognition and coconstructed meaning between dyadic conversing partners . The language use presented by CAs in response to users can “affect their perception of the situation, interpretation of the response, and subsequent actions” . Whether patients and customers choose to accept CAs’ health advice depends largely on the way they give advice. Good advice is judged by the advice content and its presentation . “Advice that is perceived positively by its recipient facilitates the recipient’s ability to cope with the problem and is likely to be implemented” . Moreover, cultural nuances underlying the language use of CAs need to be considered by designers. For example, addressing users by their first names was linked to users’ perceptions of politeness and thoughtfulness of the CAs, which may be bound to cultural limits and preferences . Considering that few studies have examined significant cultural and sociolinguistic phenomena in CA designs across different linguacultures and the influence of these phenomena on the perceptions of CAs’ effectiveness in health care service delivery , further studies in this respect must be conducted to enable CAs to achieve greater credibility and trustworthiness using more engaging language . Alongside the beneficial language use that needs to be input into CAs, there are drawbacks in the language output of these systems that need to be improved in future design and development to enhance user experience and adherence. Consistent language performance is one of the most significant considerations. As revealed in previous studies, some CAs provided inconsistent responses to the same prompts or on different platforms , and some were incapable of presenting large volumes of information on prompting , making users somewhat puzzled and frustrated, thus undermining follow-up medical actions. It was found that some most frequent issues related to user experience stemmed from spoken language understanding and dialog management problems . Although CAs capable of using unconstrained natural language input have gained increasing popularity , CAs currently used in health care lag behind those adopted in other fields (eg, travel information and restaurant selection and booking), where natural language generation and dialog management techniques have advanced well beyond rule-based methods . Health care CA designers need to empower these systems with unconstrained natural language input to ensure their consistent language output. Moreover, advances in machine learning, especially in neural networks, need to be integrated into the design of CAs to empower these systems with more complex dialog management methods and conversational flexibility . Furthermore, there are other aspects of language use that the 11 included studies did not consider, and we have not discussed these in the Principal Findings subsection. We synthesized these aspects and those discussed above to obtain an open list of recommendations for improving language use in CA-based health communication along with the pros and cons of existing CA-based communication styles that need to be considered in future CA designs, which are given in . Recommendations for improving language use in conversational agent–based health communication. Recommendations Use a neutral style (eg, methodological mistakes) rather than an aggressive style (eg, really dumb methodological mistakes) . Use an everyday style (eg, heart attack) rather than a technical style (eg, myocardial infarction) . Use a tentative style (eg, presumably similar) rather than a nontentative style (eg, similar) . Use an emotional style rather than a nonemotional style . Use an enthusiastic style rather than a nonenthusiastic style . Use personal references (eg, first-person and second-person pronouns) . Use personal testimonials . Use replies featuring directive content and empathy statements for the safety-critical questions and open-ended statements and a combination of informative and directive content without empathy statements for the lifestyle questions and open-ended statements . Pros The conversational agent (CA) used a strategic choice of words and utterances, which were respectful (eg, “I will not pressure you in any way.”), helpful (eg, “Shall I call them for you?,” “Need help?,” and “Maybe it would help to talk to someone about it.”), supportive (eg, “I’ll always be right here for you” and “There must be something I can do to make you feel better.”), comforting (eg, “Don’t worry. Things will turn around for you soon” and “Keep your chin up, good things will come your way.”), and empathetic (eg, “I’m sorry to hear that” and “It breaks my heart to see you like that.”) . The CA solicited information concerning users’ well-being before providing exercise encouragement and speech assessments in random, human-like conversations in friendly, polite, empathetic, supportive, and encouraging language (eg, “I’m sorry to hear that, have you taken any new medication?”) . A three-staged conversation framework targeted at questioning was used: introduction, reflection, and ending . The CA educated users through explaining terminologies precoded into its language resources . The CA used a running head start technique . Advances in machine learning, especially in neural networks, were used to empower CAs with more complex dialog management methods and more conversational flexibility . Cons The CA used an aggressive style (eg, “really dumb methodological mistakes”) . The CA used a technical style (eg, “myocardial infarction”) . The CA used a nontentative style (eg, “similar”) . The CA used a nonemotional style . The CA used a nonenthusiastic style . The CA provided inconsistent responses to the same prompts . The CA provided inconsistent responses to the same prompts on different platforms . The CA was unable to present large volumes of information on given prompts .
This systematic review has some limitations. The first one was attributed to the retrieval of relevant articles. We searched PubMed and ProQuest for suitable publications. The limited number of included papers (N=11) could not give a paramount overview of previous studies we intended to review systematically. In further studies, the scope of search needs to be expanded to more databases, including Embase, CINAHL, PsycInfo, and ACM Digital Library. Second, some of the principal findings may have low generalizability due to the small number of included articles, especially considering that some language use reported in these publications is specific to 1 CA studied, for example, the autism-themed CA . Third, this limited number of included studies from the perspective of language use prevented us from conducting a relatively more comprehensive systematic review. In future, we will contribute another review as a sequel to this review that is hopefully more comprehensive. Fourth, only 1 selected study is concerned with the cultural nuances underlying the language use examined . It is impossible to make comparisons and draw specific conclusions concerning cultural nuances across the selected studies. This is a limitation that needs to be overcome in future research.
Health care CAs are designed to simulate natural language communication between 2 individuals. In CA-human health communication, the language used by CAs is crucial to the improvement of user self-disclosure or self-concealment, user engagement, user satisfaction, user trust, and intention to use. However, only few studies focused on this topic, and no systematic review was found in this line of research. Our review fills this gap in the literature. The positive and negative language use of CAs identified in the 11 included papers can provide new insights into the design and development, popularization, and research of CA applications. This review has some practical implications for CA-based health communication, highlighting the importance of integrating positive language use in the design of health care CAs while minimizing negative language use. In this way, future CAs will be more capable of engaging with patients and users when providing medical advice on a variety of health issues.
|
Editorial: Patient-centered communication skills for health professions education and healthcare | efd09ceb-e4dd-45f5-82b4-ff025371ec81 | 10664919 | Patient-Centered Care[mh] | AS: Writing – original draft. JB: Writing – review & editing. RI: Writing – review & editing. MC: Writing – review & editing.
|
First-Trimester Abortion Complications: Simulation Cases for OB/GYN Residents in Sepsis and Hemorrhage | ba9210c9-b466-4737-9b44-518af28d80d4 | 7566226 | Gynaecology[mh] | By the end of this activity, learners will be able to: 1. Demonstrate improved recognition of sepsis in a patient presenting for urgent care after medication abortion. 2. Demonstrate improved knowledge of the differential diagnosis of sepsis following abortion. 3. Demonstrate improved knowledge of the most common etiologies of hemorrhage at the time of aspiration abortion. 4. Develop a plan to evaluate and manage the most common etiologies of hemorrhage and sepsis as first-trimester abortion complications. 5. Demonstrate effective communication skills and workflow management with coresidents and colleagues from different disciplines in evaluating an emergency scenario and transferring a patient to an escalated level of care. Abortions are one of the most common medical procedures in the US, and nearly one in four women will have an abortion by the age of 45. The procedure is exceptionally safe and serious complications are rare, with a mortality rate of 0.7 per 100,000. This small risk is further reduced earlier in pregnancy, with a the death rate of 0.3 per 100,000 for pregnancies at 8 weeks gestation or less. , The ACGME accreditation of an OB/GYN residency program requires an “established curriculum for family planning, including for complications of abortions and provisions for the opportunity for direct procedural training in terminations of pregnancy for those residents who desire it.” Despite this requirement, only 64% of OB/GYN residency programs provide routine, scheduled training in family planning. Even with robust exposure to family planning, resident learners are unlikely to manage serious abortion complications such as hemorrhage and sepsis given their relative rarity (hemorrhage and infection each occur following fewer than 1% of abortions). , While uncommon, these complications are potentially of great consequence, and providers must be prepared to recognize and address these efficiently and confidently in their future practice. Appropriately exposing resident learners to real-world training opportunities for rare events poses a challenge to graduate medical educators. To address this challenge, we developed a simulation-based abortion complication curriculum for resident learners. Simulation training has been described as an ethical imperative in protecting patient safety and well-being. Practicing with simulation-based models has been shown to improve provider skill – and confidence, , as well as improve patient outcomes , and reduce health care costs. Simulation‐based technical and nontechnical skills have been demonstrated to be transferable to the patient‐based setting. , Given the similar impact of low-fidelity models when compared to more sophisticated models, we developed a low-fidelity simulation to prioritize a low-cost, easily reproducible learning experience. The primary goals of this simulation were to improve learner knowledge, comfort, and preparedness for managing rare complications of first-trimester abortions. This simulation also provided a valuable opportunity for our department to simulate a medical emergency in our gynecologic procedure unit and identify potential barriers to safe and efficient patient care. To our knowledge, there are two abortion complication simulations available via MedEdPORTAL , a sepsis simulation for emergency medicine clinicians and a hemorrhage simulation utilizing pitaya fruit. To further contribute to this literature, we developed simulations that were novel in their scope and design, incorporating a multidisciplinary, in situ approach. These simulations allowed residents not only to practice skills for scenarios they may not have the opportunity to experience in their training, but also to become familiar with emergency workflow in the clinical setting and practice collaborating across disciplines to care for a patient in crisis. Development We created an abortion complication simulation curriculum for OB/GYN residents, who typically have little to no hands-on experience managing hemorrhage and sepsis as complications of first-trimester abortion because of their rarity. The simulations were modeled on cases seen at our institution, using open-access materials developed by the Training in Early Abortion for Comprehensive Healthcare Training Program as a framework. Prerequisite knowledge for the curriculum included medical school training and basic knowledge of first-trimester abortion techniques as well as basic knowledge of the pathophysiology of hemorrhage and sepsis. Equipment/Environment We used a simulation mannequin available through our institution (Victoria, Gaumard S2200, a maternal and neonatal birthing simulator) as the patient in these simulations. Learners were able to place an IV, monitor vitals (controlled by faculty), perform a pelvic exam, and a mock uterine aspiration on this mannequin. The sepsis simulation took place in our simulation center set up as a simulated emergency department (ED) and the hemorrhage simulation took place in situ in our institution's ambulatory gynecologic procedure unit. Each simulation had specific equipment needs . Faculty verbally reported imaging findings and bleeding was simulated with fake blood-saturated underpads (Chux). Personnel Both simulations were overseen by at least two clinical faculty, one whose role was to respond to and prompt learners, and the other simply to observe and intermittently replace the underpads to simulate ongoing bleeding. Learners volunteered for roles to be played during the simulations: attending, resident, medical student, patient support person, and observers. The resident learners participated in the simulation alongside case-specific roles played by nonlearners. For the sepsis simulation , an emergency medicine physician played himself and an OB/GYN faculty member played the role of the ED nurse. For the hemorrhage simulation , an anesthesiologist, nurse, medical assistant, and operating room (OR) colleague played themselves. These participants worked regularly in the clinical setting of our outpatient gynecology procedure unit. To prepare for their various nonlearner roles, facilitators met with the team that developed the curriculum to walk through the cases ( and ) and addressed any potential areas of confusion or concern prior to beginning the simulation. Implementation We performed a prospective pilot study of this simulation curriculum with OB/GYN residents of Boston Medical Center, an urban academic medical center, during three protected educational sessions. The curriculum began with a 2-hour case-based didactic lecture with an emphasis on institutional practices, preoperative evaluation and preparation, and potential challenges and complications. The following two sessions were simulations; one on sepsis and one on hemorrhage . The simulation sessions were 2 hours long and led by faculty in the OB/GYN department. Within each session, the simulation was run twice to accommodate two small groups of learners. Prior to each simulation, learners were given a brief description of the case and self-assigned roles. The sepsis simulation was conducted in the hospital's simulation center . The scenario was a consult for a patient presenting to the ED 2 days after a medication abortion at an outside clinic with heavy bleeding at home. Learners received sign-out from an emergency medicine physician and worked with them on the development of a management plan. The ED nurse caring for the patient was present. Learners interviewed the patient while faculty, observing through one-way glass, responded via remote microphone. Learners performed an exam on the mannequin which revealed fake blood-stained disposable underpads . The simulation provided opportunities for learners to identify the signs, symptoms, and differential diagnosis for sepsis, evaluate vital signs and laboratory results, identify the need for appropriate diagnostic tests and imaging, practice initial management of sepsis including resuscitation and antibiotic therapy, and practice decision making regarding aspiration abortion in the ED versus the OR . The hemorrhage simulation was conducted in the ambulatory gynecologic procedure unit, which is the setting of nearly all of the abortion care in our institution . The scenario was a patient presenting for a first-trimester aspiration abortion. Learners performed a mock uterine aspiration with support of anesthesia, nursing, and medical assistant colleagues as per hospital policy. Observing faculty reported the status of the patient and her ongoing bleeding to the learners using verbal cues. Bleeding was simulated with increasingly blood-saturated underpads under the mannequin . The simulation provided opportunities for the learners to correctly identify hemorrhage; recognize the need to transition from manual to electric suction; evaluate the most common etiologies of hemorrhage; identify the utility of ultrasound in diagnosis and management; evaluate for cervical laceration; utilize uterine massage, uterotonics, and Foley catheter tamponade; and identify the need for and prepare for transfer to the OR . Transfer to the OR in this simulation involved direct communication with the OR staff for preparation, moving the mannequin to a stretcher, preparing it for transport with an oxygen tank and a cardiac monitor, and ultimately bringing the mannequin to the doors of the OR. Debriefing A debrief with faculty, staff, and learners followed each simulation ( and ). Faculty facilitated the conversation and learners had the opportunity to reflect on past clinical experiences and their strengths and weaknesses as a team. For each simulation, faculty reviewed the clinical pearls from the case, answered any learner questions, and reviewed the correct responses to the knowledge questions asked in the pre- and postsurvey. For the sepsis debrief, faculty also reviewed a didactic presentation about the recognition and management of sepsis in the acute setting . We chose to review this information more formally with learners because of the unique opportunity for interdepartmental teaching with our emergency medicine colleague who cares for a much higher volume of sepsis cases. We also expected learners to feel less familiar with the management of sepsis than of hemorrhage given their familiarity with obstetric hemorrhage. Assessment Standards of competence for each simulation ( and ) were developed using the Interprofessional Education Collaborative's Core Competencies for Interprofessional Collaborative Practice as a model. We assessed changes in learners' clinical knowledge and self-assessment of preparedness using pre- and postsurveys administered before and after each simulation ( and ), adapted from open-access materials developed by the Training in Early Abortion for Comprehensive Healthcare Training Program and published in MedEdPORTAL . Learners self-assessed their ability to recognize and manage abortion complications, as well as their readiness to take leadership and remain calm when caring for these patients. We included in the postsurvey an evaluation of the educational experience. A single evaluator (Armide Storey) scored all de-identified pre- and postsurveys. We performed descriptive analysis of the data with means and standard deviations of the knowledge, competence, and preparedness pre- and postsurveys. We hypothesized that learners would have higher baseline knowledge in hemorrhage than in sepsis because of their experience in managing obstetric hemorrhage. We hypothesized that the simulations would increase clinical knowledge and confidence in sepsis and hemorrhage management. We conservatively performed two-sided paired t tests of the mean of the differences between pre- and postassessments of knowledge, competence, and preparedness, and between the sepsis and hemorrhage knowledge preassessments. Statistical analyses were conducted using R version 3.5.2 (2018-12-20). We created an abortion complication simulation curriculum for OB/GYN residents, who typically have little to no hands-on experience managing hemorrhage and sepsis as complications of first-trimester abortion because of their rarity. The simulations were modeled on cases seen at our institution, using open-access materials developed by the Training in Early Abortion for Comprehensive Healthcare Training Program as a framework. Prerequisite knowledge for the curriculum included medical school training and basic knowledge of first-trimester abortion techniques as well as basic knowledge of the pathophysiology of hemorrhage and sepsis. We used a simulation mannequin available through our institution (Victoria, Gaumard S2200, a maternal and neonatal birthing simulator) as the patient in these simulations. Learners were able to place an IV, monitor vitals (controlled by faculty), perform a pelvic exam, and a mock uterine aspiration on this mannequin. The sepsis simulation took place in our simulation center set up as a simulated emergency department (ED) and the hemorrhage simulation took place in situ in our institution's ambulatory gynecologic procedure unit. Each simulation had specific equipment needs . Faculty verbally reported imaging findings and bleeding was simulated with fake blood-saturated underpads (Chux). Both simulations were overseen by at least two clinical faculty, one whose role was to respond to and prompt learners, and the other simply to observe and intermittently replace the underpads to simulate ongoing bleeding. Learners volunteered for roles to be played during the simulations: attending, resident, medical student, patient support person, and observers. The resident learners participated in the simulation alongside case-specific roles played by nonlearners. For the sepsis simulation , an emergency medicine physician played himself and an OB/GYN faculty member played the role of the ED nurse. For the hemorrhage simulation , an anesthesiologist, nurse, medical assistant, and operating room (OR) colleague played themselves. These participants worked regularly in the clinical setting of our outpatient gynecology procedure unit. To prepare for their various nonlearner roles, facilitators met with the team that developed the curriculum to walk through the cases ( and ) and addressed any potential areas of confusion or concern prior to beginning the simulation. We performed a prospective pilot study of this simulation curriculum with OB/GYN residents of Boston Medical Center, an urban academic medical center, during three protected educational sessions. The curriculum began with a 2-hour case-based didactic lecture with an emphasis on institutional practices, preoperative evaluation and preparation, and potential challenges and complications. The following two sessions were simulations; one on sepsis and one on hemorrhage . The simulation sessions were 2 hours long and led by faculty in the OB/GYN department. Within each session, the simulation was run twice to accommodate two small groups of learners. Prior to each simulation, learners were given a brief description of the case and self-assigned roles. The sepsis simulation was conducted in the hospital's simulation center . The scenario was a consult for a patient presenting to the ED 2 days after a medication abortion at an outside clinic with heavy bleeding at home. Learners received sign-out from an emergency medicine physician and worked with them on the development of a management plan. The ED nurse caring for the patient was present. Learners interviewed the patient while faculty, observing through one-way glass, responded via remote microphone. Learners performed an exam on the mannequin which revealed fake blood-stained disposable underpads . The simulation provided opportunities for learners to identify the signs, symptoms, and differential diagnosis for sepsis, evaluate vital signs and laboratory results, identify the need for appropriate diagnostic tests and imaging, practice initial management of sepsis including resuscitation and antibiotic therapy, and practice decision making regarding aspiration abortion in the ED versus the OR . The hemorrhage simulation was conducted in the ambulatory gynecologic procedure unit, which is the setting of nearly all of the abortion care in our institution . The scenario was a patient presenting for a first-trimester aspiration abortion. Learners performed a mock uterine aspiration with support of anesthesia, nursing, and medical assistant colleagues as per hospital policy. Observing faculty reported the status of the patient and her ongoing bleeding to the learners using verbal cues. Bleeding was simulated with increasingly blood-saturated underpads under the mannequin . The simulation provided opportunities for the learners to correctly identify hemorrhage; recognize the need to transition from manual to electric suction; evaluate the most common etiologies of hemorrhage; identify the utility of ultrasound in diagnosis and management; evaluate for cervical laceration; utilize uterine massage, uterotonics, and Foley catheter tamponade; and identify the need for and prepare for transfer to the OR . Transfer to the OR in this simulation involved direct communication with the OR staff for preparation, moving the mannequin to a stretcher, preparing it for transport with an oxygen tank and a cardiac monitor, and ultimately bringing the mannequin to the doors of the OR. A debrief with faculty, staff, and learners followed each simulation ( and ). Faculty facilitated the conversation and learners had the opportunity to reflect on past clinical experiences and their strengths and weaknesses as a team. For each simulation, faculty reviewed the clinical pearls from the case, answered any learner questions, and reviewed the correct responses to the knowledge questions asked in the pre- and postsurvey. For the sepsis debrief, faculty also reviewed a didactic presentation about the recognition and management of sepsis in the acute setting . We chose to review this information more formally with learners because of the unique opportunity for interdepartmental teaching with our emergency medicine colleague who cares for a much higher volume of sepsis cases. We also expected learners to feel less familiar with the management of sepsis than of hemorrhage given their familiarity with obstetric hemorrhage. Standards of competence for each simulation ( and ) were developed using the Interprofessional Education Collaborative's Core Competencies for Interprofessional Collaborative Practice as a model. We assessed changes in learners' clinical knowledge and self-assessment of preparedness using pre- and postsurveys administered before and after each simulation ( and ), adapted from open-access materials developed by the Training in Early Abortion for Comprehensive Healthcare Training Program and published in MedEdPORTAL . Learners self-assessed their ability to recognize and manage abortion complications, as well as their readiness to take leadership and remain calm when caring for these patients. We included in the postsurvey an evaluation of the educational experience. A single evaluator (Armide Storey) scored all de-identified pre- and postsurveys. We performed descriptive analysis of the data with means and standard deviations of the knowledge, competence, and preparedness pre- and postsurveys. We hypothesized that learners would have higher baseline knowledge in hemorrhage than in sepsis because of their experience in managing obstetric hemorrhage. We hypothesized that the simulations would increase clinical knowledge and confidence in sepsis and hemorrhage management. We conservatively performed two-sided paired t tests of the mean of the differences between pre- and postassessments of knowledge, competence, and preparedness, and between the sepsis and hemorrhage knowledge preassessments. Statistical analyses were conducted using R version 3.5.2 (2018-12-20). Resident learners ( N = 26) represented all four classes of OB/GYN residents, and some residents participated in one or both simulations (seven PGY 1s, five PGY 2s, five PGY 3s, and nine PGY 4s). Members of the OB/GYN department, including attendings and fellows, facilitated the simulation. This was the first time this type of simulation was performed at our institution. Residents initially showed stronger clinical knowledge in managing postabortal hemorrhage than sepsis (90% vs. 62%, p < .001). Twelve learners attended the hemorrhage simulation, and 11 completed both pre- and postsurveys. Prior to the simulation, the average knowledge presurvey score was 90%, and remained stable following the simulation (90% to 87%, p = .3). We noticed a pattern in knowledge change for the five learners whose scores decreased: all changed a correct to an incorrect answer to the question, “What is the most common cause of bleeding in first-trimester uterine aspiration?” The correct answer was retained tissue; all changed to the incorrect answer uterine atony. Resident self-assessments of competence and preparedness improved after the simulation ( p = .006). Fourteen learners attended the sepsis simulation, and 10 completed both pre- and postsurveys. Prior to the simulation, the average knowledge presurvey score was 62%, and the knowledge score improved to 91% following the simulation ( p < .001). Resident self-assessments of competence and preparedness improved after the simulation ( p = .002). While we did not perform rigorous qualitative review on the survey feedback, participants were invited to share their reflections, which were overwhelmingly positive: “Very informative;” “Loved it;” “This was awesome;” and, “Cool inclusion of OR staff.” Residents also shared how this simulation would change their future practice: “This sim made me think about how important it is to be systematic in your assessment and how to physically get the things you need.” In the future they will “pay more attention to (and understand) lactate;” “Call for help early, delegate tasks, think step-wise;” “Ask for specific equipment;” “Use all [the] tools available in clinic;” and “Utilize foley bulbs, [and] transition from manual to electric suction.” We created this simulation for abortion care to provide resident learners the opportunity to recognize and manage two complications of first-trimester abortion: hemorrhage and sepsis. These scenarios represented rare but potentially serious clinical challenges that physicians must be familiar with in order to provide timely and appropriate care. Overall, these simulations have achieved our primary objective of improving learner knowledge, comfort, and perception of preparedness for managing complications of first-trimester abortions. Additionally, the curriculum was well-received by the residents, whose responses were overwhelmingly positive when solicited for feedback. While simulation is a well-established educational modality in graduate medical education, these simulations were novel in their scope and design. To our knowledge, this was the first abortion complication simulation of its kind, incorporating a multidisciplinary approach and partially taking place in the setting in which these residents practice. These simulations incorporated an emergency medicine physician, anesthesiologist, members of the OR staff, nurses, and medical assistants. We also recognized the potential role for this simulation in interdisciplinary team trainings. The opportunity to practice robust communication and management skills under clinical pressure could have important applications beyond resident education. Residents initially showed stronger clinical knowledge in managing postabortal hemorrhage than sepsis, which was unsurprising given the relatively high frequency of obstetric hemorrhage compared to sepsis in their general OB/GYN training. While we didn't have the power to assess differences across training levels, we found that upper level residents volunteered to participate in the senior levels roles of the simulation. We suspect this was due to confidence in their knowledge level and comfort around their peers. We hoped this simulation would break down some communication barriers for junior level residents as they practiced interacting with their peers and other hospital services. We were interested to see that all hemorrhage learners whose scores decreased incorrectly identified atony as the most common cause of bleeding when they had originally correctly named retained tissue as the leading cause. We suspect this was because the clinical simulation was an atony case, which we chose for both the ease of simulation and the higher complexity of management decisions required. In the future, we will include a more thorough review of the relative frequency of each cause of hemorrhage during the structured debrief. We hypothesized that the improvement in knowledge, comfort, and preparedness was in part due to the believability of the experience, taking place in the clinic in which these residents work, alongside their colleagues who are playing themselves, and with the realistic visual cue of blood-stained underpads. We chose to use a sophisticated simulation mannequin that was available to us through our institution because our resident learners were familiar with this mannequin from their surgical training simulations. However, the bleeding was simulated with low-fidelity blood-soaked underpads and we recognized this simulation could easily be reproduced with a simple pelvic model for a complete, low-cost alternative. Limitations of this study included that all participating residents were recruited from an OB/GYN residency program at a single institution with robust abortion training, which may limit the generalizability to other training programs. Reliance on self-assessed perception of preparedness and comfort may introduce a social desirability bias, though we attempted to mitigate this effect with deidentified pre- and postsurveys. Further, by relying on knowledge and self-reported comfort and preparedness data, we were unable to directly assess the skills-based learning objectives of this exercise. The curriculum learning objectives would be better assessed with a demonstration of learner competence using a graded rubric by a faculty observer, which is a potential future direction for this curriculum. Another limitation was the small sample size of our cohort, and we would be interested in running this simulation with a larger cohort including resident learners from different specialties who may manage patients following their abortions, such as emergency medicine, family medicine, and internal medicine. Despite these limitations, we were able to demonstrate a positive effect of the simulation curriculum on knowledge and self-assessed preparedness and comfort. We believe that this simulation curriculum for first-trimester abortion complications could be easily incorporated into educational curricula as an effective solution to the challenge within graduate medical education of exposing residents to rare but important learning opportunities, including the postabortal complications of hemorrhage and sepsis. Sepsis Simulation Case.docx Hemorrhage Simulation Case.docx Simulation Images.docx Presimulation Didactic Lecture.pptx Sepsis Critical Action Checklist.docx Hemorrhage Critical Action Checklist.docx Sepsis Debriefing Guide.docx Hemorrhage Debriefing Guide.docx Sepsis Postsimulation Debrief Didactic.pptx Sepsis Pre-and Postsurvey.docx Hemorrhage Pre-and Postsurvey.docx All appendices are peer reviewed as integral parts of the Original Publication. |
Quantifying spatial position in a branched structure in immunostained mouse tissue sections | ed4831fa-0e70-4976-8c14-7783bb313d1b | 8488404 | Anatomy[mh] | This protocol outlines a comprehensive pipeline for generating and staining biological samples from mice and quantitatively analyzing the correlation between cell position and cell fate in branched tissues. For this purpose, we have developed a metric for how distant each pixel inside the tissue is from the center of a branched structure ( ). A low score indicates proximity to the center (in the pancreas called “trunk”), while a high score indicates a location close to the periphery (in the pancreas called “tip”). We named this metric the “tip score”. If biological samples are already available in the form of tissue sections, the protocol can be followed from preparatory step 6 and protocol step 3. If stained tissue sections are already available, the protocol can be followed from preparatory step 8 and protocol step 9. For image analysis of existing images, the protocol can be followed from preparatory step 8 and protocol step 11. Note: The computational part of this protocol relies on the proprietary software MATLAB Note: While the protocol was optimized for embryonic mouse pancreas analysis and may require modification for use on other branched inner organs (lungs, liver, thyroid, etc) and will require optimization for use on other branched structures in general, the basic idea of the image analysis method should be widely applicable to any branched structure. Prepare for collecting tissue sections from organs Timing: 2 days 1. Prepare 4% formaldehyde for tissue fixation from paraformaldehyde (PFA) while working in a chemical safety cabinet a. Add 8 g of PFA to 192 mL sterile PBS in a 250 mL beaker b. Heat at 70°C–80°C for ca. 1 h until all the powder has dissolved. Monitor temperature closely, and never bring the solution above 80°C c. Cool down to 20°C–22°C d. Aliquot into 15 mL tubes with 10 mL/tube and store for up to one year at -20°C, unless used immediately CRITICAL: Paraformaldehyde is a toxic chemical which targets the respiratory system and should be used according to the safety instructions. Use gloves and work in a chemical safety cabinet or use eye/face shield and respirator cartridge type N100 (US), type P1 (EN143) respirator filter, type P3 (EN 143) respirator cartridges. Alternatives: Commercially available ampules of premade aqueous solution of 4% formaldehyde without additives. Commercially available concentrated formaldehyde aqueous solutions not in ampules should be avoided, as they often include 10% methanol or butanol as stabilizing agents ( ; ). Our staining protocol has been optimized for tissue fixed in pure 4% formaldehyde, however other fixatives such as glutaraldehyde and methanol may also be used according to your own protocol. 2. Prepare 30% sucrose solution in PBS a. Add 300 g of sucrose to a 1 L glass bottle and fill with sterile PBS up to 1L b. Heat at 50°C for 30 min with constant stirring c. Store at 4°C for 4–8 weeks Setup mouse mating for embryonic tissue Timing: 2–3 days (but needs to be set up several days in advance depending on embryonic stage needed) If in need of embryonic tissue for analysis, setup mouse breeding as follows: 3. Setup mating of mice. These can be wildtype (C57bl/6 was used for exemplary data) or genetically modified a. Move one male to each of 3-4 clean breeding cages (if time permits this can be done the previous evening) b. Add two females to each breeding cage in the afternoon 4. Check for vaginal plug the next morning a. Separate plugged females to a clean cage, and record number of plugged females b. Separate males and unplugged females c. Day of vaginal plug is recorded as embryonic day 0.5 (E0.5) d. Carefully record mouse ID and genotype of both male and female along with day of setup and day of plug 5. Wait the required number of days according to which embryonic stage you aim to analyze. I.e., for E14.5 tissue you need to set up breeding 16 days prior to the day you plan to start the experiment Note: Make sure you have the necessary ethical and breeding permits for laboratory animal use from your local authorities Prepare reagents for immunostaining of tissue sections Timing: 2 h 6. Optional: Prepare Citrate buffer pH6 for antigen retrieval a. Add 2.94 g Tri-sodium Citrate (dihydrate) to 900 mL dH 2 0 in a 2 L glass bottle b. Stir until dissolved c. Set pH to 6.0 by adding citric acid powder while stirring d. Add dH 2 O to a total of 1000 mL e. Store at 20°C–22°C for up to 3 months Note: Antigen retrieval is optional, as it depends on the antibodies used. For our test dataset we used a mild citrate buffer-based antigen retrieval method to optimize the SOX9 staining. 7. Prepare blocking buffer a. Prepare 1 L of 0.1 M TRIS-HCL pH 7.5 i. Add 15.76 g TRIZMA-HCl to 800 mL dH 2 0 ii. Measure pH using a newly calibrated pH meter iii. Adjust pH to 7.5 using HCL iv. Add dH 2 0 to a make up a total of 1000 mL v. The Tris-HCL solution can be stored at 4°C for up to 1 year b. To the 0.1 M TRIS-HCL solution, add NaCl to 0.15 M c. Heat solution to 55°C with constant stirring d. Add Blocking reagent to 0.5% (Akoya Biosciences) in small increments to buffer while stirring until the powder is completely dissolved (30–60 min). The solution will appear milky. e. Cool down to 20°C–22°C f. Aliquot into 50 mL tubes and freeze at -20°C, unless used immediately. The frozen stock can be stored for up to 1 year. A working solution can be stored at 4°C for 2–4 weeks. Alternatives: Other blocking buffers such as donkey serum may also be used according to your own protocol. Download, install, and run MATLAB and demo scripts Timing: 3 h or up to 1–2 days depending on whether you have previously worked with MATLAB Timing: approx. 30 min to 1 h depending on your computer for step 11 (optional) We provide 4 custom written MATLAB functions: 1) tipScoreIm() 2) segmentDAPIimage() 3) returnTableWithCellInt() 4) returnTableWithCellPairInt() These can be used for assigning a tip score to all regions of a branched organ (1), segmenting cell nuclei (2) and relating this tip score to the fluorescence intensity of cellular markers in either the nucleus/cytoplasm (3), or cell membrane (4). We also provide two demo scripts: Demo 1) 'demoScript_OneImage_ManualInput.m' Demo 2) 'demoScript_Batch_NoInput.m' These scripts demonstrate how our custom functions can be called/used when analyzing sample images with hand drawn outlines, from the point of loading in images all the way to final statistical comparison of groups of cells with different tip scores. 8. Install MATLAB version 2018a or later on your computer. a. Press here: MATLAB to go to the MathWorks homepage. Note: MATLAB is a commercial product, which requires a license. Also, each toolbox requires its own license. Many universities have MATLAB licenses. For users without access to a MATLAB license, MATLAB offers a 30-day free trial, which will be sufficient time to run this analysis. b. Make sure to check the boxes for ‘Signal Processing Toolbox’, ‘Image Processing Toolbox’, and ‘Statistics and Machine Learning Toolbox’ during installation. Installation takes approx. one hour or less, depending on your internet connection. 9. Go to GitHub and Mendeley Data and download the functions and demo scripts or download the supplementary files and . a. Press here: GitHub to go to the GitHub homepage. b. Search for ‘TipScore’ in the upper left search box and go to SiljaHeilmann/TipScore c. Download all files by pressing the green ‘code’ button and select ‘download zip’. The folder is called ‘TipScore-main.zip’. Unzip and move the TipScore-main folder to your location of choice. d. Download the folders ‘raw_images_tif_files' and ‘outlines_mat_files’ (approx. 574MB), containing a small test dataset (containing 7 images) from Mendeley Data: https://doi.org/10.17632/nr9cyyk265.1 and place them in the your TipScore-main folder. 10. Test that MATLAB works by running the script 'demoScript_OneImage_ManualInput.m' a. First open MATLAB b. Check that ‘Current Folder’ (see left side) is your ‘TipScore-main’ folder and that it contains the folders ‘raw_images_tif_files' and ‘outlines_mat_files’ and all .m files downloaded from GitHub (segmentDAPIimage.m, tipScoreIm.m, returnTableWithCellInt.m, returnTableWithCellPairInt.m) c. Open ‘demoScript_OneImage_ManualInput.m’ in MATLAB d. Run script either by typing ‘demoScript_OneImage_ManualInput’ in the MATLAB command window and pressing enter, or by putting the cursor in the editor window and pressing the green Run button in the Editor tab. (You can also run sections of the code one at a time). Note that this code runs the analysis on image number 6 in the test data set. If you wish to see analysis of another test image in the set edit ‘ff = 6;’ in line 11 of the code to a different number, (1, 2, 3, 4, 5 or 7). e. A figure with the title ‘Red: Ecad. Blue: DAPI. Green: P120CTN. White: CPA… will open. A blue line tool, which measures length in pixels, is visible on the image. You can drag the ends of it and use it to measure the average length of the tip structures. If you wish to zoom in, then hover the mouse at the upper right corner of the image and controls for zoom and hand drag appear. f. At the MATLAB command window, you are prompted: ‘What is the average diameter/length of tip structures in pixels?... :’ Input your response and press enter. For this test image an appropriate answer is approx. 300. (The final tip score of a location will depend on this input, a larger number will mean that a larger portion of the structure will get a higher tip score). g. A figure with the title ‘Red: Ecad. Blue: DAPI. Green: P120CTN. White: CPA...’ will open. A blue line tool, which measures length in pixels, is visible on the image. You can drag the ends of it and use it to measure the average length of the tip structures. At the MATLAB command window, you are prompted: ‘What is the average diameter of nuclei in pixels? ...’. Input your response and press enter. For this test image set approx. 20 is an appropriate answer. (The final nuclei segmentation result will depend on this input; a larger number will mean a tendency to under segment and a smaller number will cause over segmentation). Note: Image segmentation is the process of partitioning an image into multiple segments (sets of pixels, also known as image objects). Image segmentation is typically used to locate objects and boundaries. Here it is used to detect cell nuclei stained with DAPI. h. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image includes all nuclei and not more? ...:’. For this test image approx. ‘4’ is an appropriate answer. See A. Note: It is easier to judge between the images if you zoom in a bit. (The final nuclei segmentation result will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number will cause cells to clump). i. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image merges no nuclei? ... :’ For this image approx. ‘7’ is an appropriate answer. See B. (The final nuclei segmentation result will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number could cause cells to clump). j. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image looks best? I.e. separates most nuclei while losing the least?...:’ For this image approx. ‘6’ is an appropriate answer. See C. (The final nuclei segmentation result will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number could cause cells to clump). k. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image merges no nuclei? ...:’ For this image approx. ‘9’ is an appropriate answer. See D. (The final nuclei segmentation result, see E will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number could cause cells to clump). A few nuclei merges and over segmentations are to be expected. l. From line 99 an onwards the script demoScript_OneImage_ManualInput.m uses the nucleus positions found, the tip score image and the images of nuclear/membrane marker to extract intensities from cells (nuclear) and between cell-pairs (membrane) via the returnTableWithCellInt() function and the returnTableWithCellPairInt() function, (see step 20 and step 21 below) and return them in table form. The output figure from the function returnTableWithCellPairInt() is shown in F. The returned tables contains all extracted information about each cell/cell-pair ( ‘Cell/Cell-pair ID number’, ‘ X coordinate’, ‘Y coordinate’, ‘ SOX9 intensity/p120 intensity’, ‘tip score value’, ‘normalized SOX9 intensity/normalized p120 intensity’, ‘Assigned tip score group’ ). From line 165 and on boxplot figures are generated and non-parametric statistical tests performed to compare data points belonging to different tip score groups, see section ‘ ’ for more details. 11. Optional: Run example batch script demoScript_Batch_NoInput. Note: demoScript_Batch_NoInput.m does the same as demoScript_OneImage_ManualInput.m except it performs the operations/functions of all images of a batch of 7 (stored in ‘raw_images_tif_files' folder) and it does not require manual input (it already has the appropriate manual answers stored in the script, see line 21–29). This script can be used for inspiration when you want to write your own batch script. a. Open demoScript_batch_NoInput.m in MATLAB b. Run script either by typing ‘demoScript_batch_NoInput’ in the MATLAB command window and pressing enter, or by putting the cursor in the editor window and pressing the green ‘Run’ button in the Editor tab. You should see folders appear in your current directory with output files for each of the 7 images in raw_images_tif_files. Finally, after all images have been tipscored and nuclei located (this takes a while) boxplots of SOX9 and P120CTN intensity versus tipscore for individual images and all images pooled, will be generated. Alternatives: Other programming languages (such as Python or Julia) can be used, if the end-user adapts the script accordingly.
Timing: 2 days 1. Prepare 4% formaldehyde for tissue fixation from paraformaldehyde (PFA) while working in a chemical safety cabinet a. Add 8 g of PFA to 192 mL sterile PBS in a 250 mL beaker b. Heat at 70°C–80°C for ca. 1 h until all the powder has dissolved. Monitor temperature closely, and never bring the solution above 80°C c. Cool down to 20°C–22°C d. Aliquot into 15 mL tubes with 10 mL/tube and store for up to one year at -20°C, unless used immediately CRITICAL: Paraformaldehyde is a toxic chemical which targets the respiratory system and should be used according to the safety instructions. Use gloves and work in a chemical safety cabinet or use eye/face shield and respirator cartridge type N100 (US), type P1 (EN143) respirator filter, type P3 (EN 143) respirator cartridges. Alternatives: Commercially available ampules of premade aqueous solution of 4% formaldehyde without additives. Commercially available concentrated formaldehyde aqueous solutions not in ampules should be avoided, as they often include 10% methanol or butanol as stabilizing agents ( ; ). Our staining protocol has been optimized for tissue fixed in pure 4% formaldehyde, however other fixatives such as glutaraldehyde and methanol may also be used according to your own protocol. 2. Prepare 30% sucrose solution in PBS a. Add 300 g of sucrose to a 1 L glass bottle and fill with sterile PBS up to 1L b. Heat at 50°C for 30 min with constant stirring c. Store at 4°C for 4–8 weeks
Timing: 2–3 days (but needs to be set up several days in advance depending on embryonic stage needed) If in need of embryonic tissue for analysis, setup mouse breeding as follows: 3. Setup mating of mice. These can be wildtype (C57bl/6 was used for exemplary data) or genetically modified a. Move one male to each of 3-4 clean breeding cages (if time permits this can be done the previous evening) b. Add two females to each breeding cage in the afternoon 4. Check for vaginal plug the next morning a. Separate plugged females to a clean cage, and record number of plugged females b. Separate males and unplugged females c. Day of vaginal plug is recorded as embryonic day 0.5 (E0.5) d. Carefully record mouse ID and genotype of both male and female along with day of setup and day of plug 5. Wait the required number of days according to which embryonic stage you aim to analyze. I.e., for E14.5 tissue you need to set up breeding 16 days prior to the day you plan to start the experiment Note: Make sure you have the necessary ethical and breeding permits for laboratory animal use from your local authorities
Timing: 2 h 6. Optional: Prepare Citrate buffer pH6 for antigen retrieval a. Add 2.94 g Tri-sodium Citrate (dihydrate) to 900 mL dH 2 0 in a 2 L glass bottle b. Stir until dissolved c. Set pH to 6.0 by adding citric acid powder while stirring d. Add dH 2 O to a total of 1000 mL e. Store at 20°C–22°C for up to 3 months Note: Antigen retrieval is optional, as it depends on the antibodies used. For our test dataset we used a mild citrate buffer-based antigen retrieval method to optimize the SOX9 staining. 7. Prepare blocking buffer a. Prepare 1 L of 0.1 M TRIS-HCL pH 7.5 i. Add 15.76 g TRIZMA-HCl to 800 mL dH 2 0 ii. Measure pH using a newly calibrated pH meter iii. Adjust pH to 7.5 using HCL iv. Add dH 2 0 to a make up a total of 1000 mL v. The Tris-HCL solution can be stored at 4°C for up to 1 year b. To the 0.1 M TRIS-HCL solution, add NaCl to 0.15 M c. Heat solution to 55°C with constant stirring d. Add Blocking reagent to 0.5% (Akoya Biosciences) in small increments to buffer while stirring until the powder is completely dissolved (30–60 min). The solution will appear milky. e. Cool down to 20°C–22°C f. Aliquot into 50 mL tubes and freeze at -20°C, unless used immediately. The frozen stock can be stored for up to 1 year. A working solution can be stored at 4°C for 2–4 weeks. Alternatives: Other blocking buffers such as donkey serum may also be used according to your own protocol.
Timing: 3 h or up to 1–2 days depending on whether you have previously worked with MATLAB Timing: approx. 30 min to 1 h depending on your computer for step 11 (optional) We provide 4 custom written MATLAB functions: 1) tipScoreIm() 2) segmentDAPIimage() 3) returnTableWithCellInt() 4) returnTableWithCellPairInt() These can be used for assigning a tip score to all regions of a branched organ (1), segmenting cell nuclei (2) and relating this tip score to the fluorescence intensity of cellular markers in either the nucleus/cytoplasm (3), or cell membrane (4). We also provide two demo scripts: Demo 1) 'demoScript_OneImage_ManualInput.m' Demo 2) 'demoScript_Batch_NoInput.m' These scripts demonstrate how our custom functions can be called/used when analyzing sample images with hand drawn outlines, from the point of loading in images all the way to final statistical comparison of groups of cells with different tip scores. 8. Install MATLAB version 2018a or later on your computer. a. Press here: MATLAB to go to the MathWorks homepage. Note: MATLAB is a commercial product, which requires a license. Also, each toolbox requires its own license. Many universities have MATLAB licenses. For users without access to a MATLAB license, MATLAB offers a 30-day free trial, which will be sufficient time to run this analysis. b. Make sure to check the boxes for ‘Signal Processing Toolbox’, ‘Image Processing Toolbox’, and ‘Statistics and Machine Learning Toolbox’ during installation. Installation takes approx. one hour or less, depending on your internet connection. 9. Go to GitHub and Mendeley Data and download the functions and demo scripts or download the supplementary files and . a. Press here: GitHub to go to the GitHub homepage. b. Search for ‘TipScore’ in the upper left search box and go to SiljaHeilmann/TipScore c. Download all files by pressing the green ‘code’ button and select ‘download zip’. The folder is called ‘TipScore-main.zip’. Unzip and move the TipScore-main folder to your location of choice. d. Download the folders ‘raw_images_tif_files' and ‘outlines_mat_files’ (approx. 574MB), containing a small test dataset (containing 7 images) from Mendeley Data: https://doi.org/10.17632/nr9cyyk265.1 and place them in the your TipScore-main folder. 10. Test that MATLAB works by running the script 'demoScript_OneImage_ManualInput.m' a. First open MATLAB b. Check that ‘Current Folder’ (see left side) is your ‘TipScore-main’ folder and that it contains the folders ‘raw_images_tif_files' and ‘outlines_mat_files’ and all .m files downloaded from GitHub (segmentDAPIimage.m, tipScoreIm.m, returnTableWithCellInt.m, returnTableWithCellPairInt.m) c. Open ‘demoScript_OneImage_ManualInput.m’ in MATLAB d. Run script either by typing ‘demoScript_OneImage_ManualInput’ in the MATLAB command window and pressing enter, or by putting the cursor in the editor window and pressing the green Run button in the Editor tab. (You can also run sections of the code one at a time). Note that this code runs the analysis on image number 6 in the test data set. If you wish to see analysis of another test image in the set edit ‘ff = 6;’ in line 11 of the code to a different number, (1, 2, 3, 4, 5 or 7). e. A figure with the title ‘Red: Ecad. Blue: DAPI. Green: P120CTN. White: CPA… will open. A blue line tool, which measures length in pixels, is visible on the image. You can drag the ends of it and use it to measure the average length of the tip structures. If you wish to zoom in, then hover the mouse at the upper right corner of the image and controls for zoom and hand drag appear. f. At the MATLAB command window, you are prompted: ‘What is the average diameter/length of tip structures in pixels?... :’ Input your response and press enter. For this test image an appropriate answer is approx. 300. (The final tip score of a location will depend on this input, a larger number will mean that a larger portion of the structure will get a higher tip score). g. A figure with the title ‘Red: Ecad. Blue: DAPI. Green: P120CTN. White: CPA...’ will open. A blue line tool, which measures length in pixels, is visible on the image. You can drag the ends of it and use it to measure the average length of the tip structures. At the MATLAB command window, you are prompted: ‘What is the average diameter of nuclei in pixels? ...’. Input your response and press enter. For this test image set approx. 20 is an appropriate answer. (The final nuclei segmentation result will depend on this input; a larger number will mean a tendency to under segment and a smaller number will cause over segmentation). Note: Image segmentation is the process of partitioning an image into multiple segments (sets of pixels, also known as image objects). Image segmentation is typically used to locate objects and boundaries. Here it is used to detect cell nuclei stained with DAPI. h. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image includes all nuclei and not more? ...:’. For this test image approx. ‘4’ is an appropriate answer. See A. Note: It is easier to judge between the images if you zoom in a bit. (The final nuclei segmentation result will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number will cause cells to clump). i. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image merges no nuclei? ... :’ For this image approx. ‘7’ is an appropriate answer. See B. (The final nuclei segmentation result will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number could cause cells to clump). j. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image looks best? I.e. separates most nuclei while losing the least?...:’ For this image approx. ‘6’ is an appropriate answer. See C. (The final nuclei segmentation result will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number could cause cells to clump). k. You now should see 10 thresholded images. At the MATLAB command window, you are prompted: ‘What thresholded image merges no nuclei? ...:’ For this image approx. ‘9’ is an appropriate answer. See D. (The final nuclei segmentation result, see E will depend on this input, a larger number will mean a tendency to lose cells completely, a smaller number could cause cells to clump). A few nuclei merges and over segmentations are to be expected. l. From line 99 an onwards the script demoScript_OneImage_ManualInput.m uses the nucleus positions found, the tip score image and the images of nuclear/membrane marker to extract intensities from cells (nuclear) and between cell-pairs (membrane) via the returnTableWithCellInt() function and the returnTableWithCellPairInt() function, (see step 20 and step 21 below) and return them in table form. The output figure from the function returnTableWithCellPairInt() is shown in F. The returned tables contains all extracted information about each cell/cell-pair ( ‘Cell/Cell-pair ID number’, ‘ X coordinate’, ‘Y coordinate’, ‘ SOX9 intensity/p120 intensity’, ‘tip score value’, ‘normalized SOX9 intensity/normalized p120 intensity’, ‘Assigned tip score group’ ). From line 165 and on boxplot figures are generated and non-parametric statistical tests performed to compare data points belonging to different tip score groups, see section ‘ ’ for more details. 11. Optional: Run example batch script demoScript_Batch_NoInput. Note: demoScript_Batch_NoInput.m does the same as demoScript_OneImage_ManualInput.m except it performs the operations/functions of all images of a batch of 7 (stored in ‘raw_images_tif_files' folder) and it does not require manual input (it already has the appropriate manual answers stored in the script, see line 21–29). This script can be used for inspiration when you want to write your own batch script. a. Open demoScript_batch_NoInput.m in MATLAB b. Run script either by typing ‘demoScript_batch_NoInput’ in the MATLAB command window and pressing enter, or by putting the cursor in the editor window and pressing the green ‘Run’ button in the Editor tab. You should see folders appear in your current directory with output files for each of the 7 images in raw_images_tif_files. Finally, after all images have been tipscored and nuclei located (this takes a while) boxplots of SOX9 and P120CTN intensity versus tipscore for individual images and all images pooled, will be generated. Alternatives: Other programming languages (such as Python or Julia) can be used, if the end-user adapts the script accordingly.
Step 1–2: Prepare tissue sections from mouse organs Timing: 2 days This step prepares tissue sections for the immunostaining protocol. For our exemplary data we prepared E14.5 embryonic pancreas from the mouse, but the protocol can be used for other tissues and ages depending on the biological question addressed, as long as a clear branching structure is evident, and cells/nuclei can be segmented. Note: For the purpose of minimizing biological variation caused by developmental differences, it is important that dissection is always carried out at the same time of day (i.e., at noon), and that each embryo is examined with regard to normal development and embryonic age (see step 1e). For younger embryos somites can be counted to more accurately stage embryos. Consult ( ) for more information on staging. Note: For the purpose of statistical analysis of biological variation between littermates, it is advisable to dissect and analyze at least 3–4 embryos per pregnant female. In our experience there is some variation in cell differentiation between litters (this can however be minimized by careful staging of embryos as described above). If analyzing the effect of a treatment or a genetic perturbation, it is therefore important to test whether a difference is due to litter variation or the perturbation. 1. Isolate your organ of interest from embryos a. Euthanize the pregnant female by cervical dislocation b. Dissect the uterus containing the embryos from the pregnant female c. Move embryos to ice-cold sterile 1 × PBS in a Petri dish stored on wet ice d. Dissect the embryos one by one from the uterus and move to a clean Petri dish containing ice-cold sterile 1 × PBS e. Check the developmental age of the embryo by careful examination of the front and rear paw characteristics consulting ( ) Note: Some variation of embryo development (−0.5 days) may occur within a litter, and we routinely exclude embryos in which the developmental stage does not match the gestational stage f. Under a stereoscope, dissect your organ of interest from the embryos using #1 Dumont forceps or similar g. Transfer the tissue to 4% formaldehyde in 1 × PBS in a 12 well plate and fix at 20–22°for 1–24 h depending on the size of the tissue. Note: Although adequate fixation is required for good cytological preservation, over-fixation can block or prevent antibodies from binding to the protein epitope ( ) . We routinely fix E14.5 embryonic pancreas for 16–18 h. The reaction of formaldehyde depends on the temperature, and penetration of tissue by formaldehyde is a function of the square root of the time of exposure ( ). We recommend consulting ( ) to determine the optimal fixation time. h. Cryoprotect the tissue by transferring to 30% sucrose in 1 × PBS solution at 4°C until the tissue falls to the bottom of the well (usually within 3–16 h depending on the size). i. Infiltrate tissue with OCT by incubating in 100% OCT for 1 h at 4°C j. Embed tissue into blocks as follows: i. Label plastic mold ii. Place the plastic mold onto a piece of dry ice, adding enough OCT to cover bottom iii. Wait for the bottom to freeze iv. Place the tissue in the middle of the block and add OCT to cover. Be careful not to make any bubbles in the OCT. v. When all of the OCT is white, store in a sealed plastic bag at –80°C immediately. Pause point: If needed, frozen tissue blocks can be stored at –80°C indefinitely 2. Section tissue on a cryostat a. Section tissue on a cryostat following manufacturer's instructions b. Section at 6 μm and place 2–3 sections per slide depending on the size Note: Sections should stay within the boundaries of the + signs on the slide and should not overlap. c. Mount tissue sections on labeled and numbered Superfrost PLUS glass slides d. Store at –80°C Pause point: if needed, frozen sections can be stored at –80°C for 2–3 years Step 3–8: Immunostain tissue sections Timing: 2 days This step stains the tissue in preparation for imaging and image quantification. It has been optimized for embryonic mouse pancreatic tissue fixed in 4% paraformaldehyde and prepared as frozen sections as described in the previous step but can also be used for other embryonic organs. CRITICAL: After step 4c the tissue should always be kept moist or submerged. Any drying during antibody incubation will increase the antibody concentration and drying out at all other steps will increase nonspecific antibody binding and therefore background fluorescence. See : Excessive background fluorescence Alternatives: Paraffin sections can be used instead of frozen sections. In that case, the sections should be deparaffinized and rehydrated: Incubate for 15 min in Xylene to deparaffinize and rehydrate in a descending alcohol series: 100% ethanol for 2 times 3 min, 95% ethanol for 3 min, 70% ethanol for 3 min, 50% ethanol for 3 min. After rehydration the slides are washed in running tap water and staining can proceed from step 4c below. 3. Select a number of slides containing tissue that adequately represents the whole 3-dimensional organ. Note: Exclude any sections not showing the branching structure (typically, these are at the extreme ends of the tissue). For a single E14.5 pancreas we typically get 200 6 μm sections, and stain every 30th section (excluding the extreme ends), giving a total of 4–6 sections/pancreas. Include sufficient individual biological replicates to be able to perform statistical analysis (3–5 biological replicates of each experimental condition) and have enough samples for controls (see ). 4. Prepare slides for antibody incubation a. Align 3–20 slides in a slide holder for staining. (See ). Always include at least one slide as a negative control and one as secondary antibody only control (see below for more information on controls). b. Dry sections in an oven at 37°C for 20 min c. Wash 3 times in 1 × PBS for 5 min each d. Optional: Antigen retrieval i. Remove 1 × PBS and leave in Citric acid antigen retrieval buffer for 1 h at 37°C ii. Wash 3 times in 1 × PBS for 5 min each Note: Antigen retrieval is necessary for unmasking of some but not all antigens prior to primary antibody incubation. Detection of some antigens may even suffer from antigen retrieval. For our antibody panel, antigen retrieval by microwave treatment in citrate buffer pH6 ( ) is necessary for detection of SOX9, while P120CTN detection suffers. We therefore used a milder treatment with incubation in citrate buffer pH 6 without boiling in the optional steps above. e. Working one slide at a time to reduce drying: i. Place slides flat in a humidified chamber ii. Outline tissue section with a hydrophobic PAP pen (See ) iii. Add enough blocking buffer to each slide to cover tissue f. Incubate slides in blocking buffer for 60–120 min at 20°C–22°C 5. In the meantime, prepare antibody dilutions (see for an example): a. Dilute primary antibodies for one or two antigens expressed in your cells of interest (here: SOX9 and Carboxypeptidase 1 (CPA1)), and for a marker visualizing the general shape of the organ (here: P120CTN) in blocking buffer. CRITICAL: Primary antibody concentration has to be carefully titrated for specific tissues to avoid non-specific binding. See : Excessive background fluorescence Note: Prepare enough primary antibody dilution to stain all your samples, allowing 100 μl per sample. Note that extra diluted antibody should be made to account for pipetting error (at least 10% extra volume). 6. Primary antibody incubation a. Working one slide at a time to reduce drying: i. Remove blocking buffer with a micropipette ii. Add enough antibody solution (typically 100–200 μL) to each slide to cover tissue completely. iii. Leave at least two sections with only the blocking buffer on, to serve as negative and secondary antibody only controls (see ). iv. Leave for 16–20 h at 20°C–22°C in the humidified chamber (See ) 7. Next morning: Prepare secondary antibody solution (See for an example) a. Use secondary antibodies against the species in which the primary antibodies are produced. CRITICAL: Secondary antibodies produced in the same species (i.e., Donkey) are preferable to avoid cross-reaction. For our test data, we had to use a secondary antibody produced in goat, which would be bound by the anti-goat antibody used to detect CPA1, if not applied separately and in the correct order as outlined below. See also . CRITICAL: The secondary antibodies must be conjugated to stable fluorescent dyes matching the configuration of your fluorescent microscope. A good combination with minimal spectral overlap is Dylight-405/DAPI, Alexa Fluor-488, Rhodamine-RedX, Alexa- Fluor 647. See : Unexpected staining patterns b. Make secondary antibody solution 1 and 2 by diluting secondary antibodies in blocking buffer at a dilution of 1:1000 as outlined in . Leave the tubes with diluted antibodies on wet ice and protected from light. Note: Secondary antibodies dilutions should be optimized for specific tissues to avoid non-specific binding. See : Excessive background fluorescence Note: Prepare enough secondary antibody dilution to stain all your samples and your secondary antibody only control, allowing 100 μl per sample. Note that extra diluted antibody should be made to account for pipetting error (at least 10% extra volume). 8. Secondary antibody incubation a. Remove primary antibody solution with a micropipette b. Wash 3 times in 1 × PBS for 5 min each c. Working one slide at a time to reduce drying: i. Add enough secondary antibody solution 1 (typically 100–200 μL) to each slide to cover tissue. ii. Add secondary antibody to the secondary antibody only control slides. Leave the negative control slide in 1 × PBS. d. Incubate for 60–120 min. e. Wash 3 times in 1 × PBS for 5 min each f. Working one slide at a time to reduce drying: i. Add enough secondary antibody solution 2 (typically 100–200 μL) to each slide to cover tissue. ii. Add secondary antibody solution 2 to the secondary antibody only control slides. Leave the negative control slide in 1 × PBS. g. Incubate for 60–120 min. h. Wash 3 times in 1 × PBS for 5 min each i. For blue staining of nuclei, add DAPI to the last wash at a dilution of 1:1000 j. Mount with DAKO mounting medium or similar and #1.5 cover glass preferably with a thickness of 0.17–0.18 mm (usually marked #1.5H or “high performance”) k. Leave flat to dry for 10 min protected from light Pause point: if needed, stained sections can be stored dark at 4°C for several weeks. CRITICAL: Variation in staining intensity can occur from experiment to experiment, so it is critical to include all samples in the same experiment, if comparison of staining intensity is planned. Alternatives: Different combinations of primary and secondary antibodies can be used as needed. Step 9–10: Confocal microscopy of stained tissue sections Timing: 1–3 days depending on number of samples Microscopy of stained tissue sections creates the images needed for subsequent image analysis steps. Confocal imaging was performed on a Zeiss LSM780 inverted confocal microscope equipped with a 32PMT GaAsP spectral detector and two PMT’s (one for blue and one for far-red). We recommend consulting ( ) for detailed instructions on how to acquire images in Zeiss Zen software. 9. Mount sample in confocal microscope a. Turn confocal system on and wait for lasers to warm up b. Place the slide with the cover slip facing down (if using an inverted microscope) in the sample holder and find your sample using a 10 × objective or similar c. Add oil immersion to a Plan-Apochromat 40 × /1.30 oil objective or similar d. Find the Z position that best represents the shape of your tissue. Note down Z position for later use 10. Acquire images: a. Adjust laser and detector gain settings to maximize the dynamic range of your system and minimize spectral overlap ( ). Save/note down the settings for later use. See also critical notes below and : Unexpected staining patterns. b. Adjust the settings to 16-bit images (if possible) and at least 1024 × 1024 frame size at 1 × zoom c. Set the pinhole as close to 1AU as possible but ensure that the same actual pinhole diameter is used for all channels. d. Acquire tiled images (Tile scan) of a single Z plane of the entire organ section with 10% overlap scanning each fluorescent dye separately (sequential acquisition), in order to avoid spectral overlap. See ( ) e. Save the file in the proprietary format (.czi or .lsm for a Zeiss confocal microscope) f. Stitch the tiled images using imaging software (such as Zen black, Imaris, Amira or the free software Image J/Fiji) and save in the proprietary format. i. Stitching in Zen black is initiated in the stitch function under the processing tab ii. Select the image to be stitched iii. Set a threshold for stitching. We routinely use “strict” iv. Select apply v. Save the image in the proprietary format. For images acquired using Zen black this format is czi or lsm g. Export as TIFF files to perform image analysis. See for an example of a stitched image of a pancreas section with four channels (P120CTN, SOX9, CPA1, DAPI). i. Open the proprietary file in ImageJ: File; Open ii. Save as TIFF in ImageJ: File; Save as; Tiff Note: The images included in the test dataset are TIFF stacks with all channels included. These can be analyzed directly by our code without further modification. The channels include: Channel 1: CPA1, channel 2: P120CTN, channel 3: SOX9, channel 4: DAPI CRITICAL: Identical microscopy settings and Z planes must be used for all samples, if comparison of staining intensity is planned. CRITICAL: Detector gain and laser intensity should be optimized to maximize the dynamic range. Most systems have the option of visualizing the image with a look up table (LUT) where pixels with an intensity of 0 are blue and pixels with the highest pixel intensity are red (in Zen this option is called “Range indicator”). Use this LUT to ensure that your image has only a few maximum intensity (red) pixels ( ). Whenever possible, data for intensity quantitation should be captured as 16-bit images, as this optimizes discrimination of pixel intensities as compared to 8-bit images ( ). Alternatives: Different confocal microscope systems and non-confocal fluorescent microscopes can be used, as long as it is ensured that the images are compatible with nuclei segmentation. Please note that confocal microscopy is highly recommended, if images are to be used for other downstream applications such as intensity measurements or threshold-based area quantification. Step 11–13: Draw outline/mask of tissue shape Timing: approx. 5 min per image This is the only semi-manual step in the image analysis pipeline. It creates a mask of the tissue, which is used as an input in subsequent steps. CRITICAL: The image chosen as input for this step has to be an image of an antibody staining for an antigen that marks either the whole epithelium or an outline of the epithelium. For this project we used images of P120CTN. The outline of the organ can be drawn and saved as a binary image using the MATLAB app ‘ImageSegmenter’, as follows: 11. Open MATLAB a. Go to the ‘App’ tap in MATLAB b. Search for the ‘Image Segmenter’ app and open it 12. Load the image you want to annotate into the ImageSegmenter App (either from file or from the MATLAB workspace). a. Go to the ‘ADD TO MASK’ menu and select ‘Draw ROI’s b. Use the ‘Freehand’ or ‘Assisted Freehand’ tool to draw an organ ROI/outline c. Press ‘Apply’ and ‘Close ROI’ when you are done drawing. 13. Export binary outline/ROI to MATLAB workspace a. Press ‘Export’ and then ‘Export Images’ b. Select ‘Final Segmentation’ (unselect ‘Masked Images’) and type in a name (for example ‘myBranchedMask’) for your new mask and press ok. Your new mask/ROI will now appear in the MATLAB workspace window). Note: Hand-drawn masks for the test dataset are supplied in supplementary data Step 14–16: Tip score locations inside your outline/mask using tipScoreIm.m Timing: runtime for a script calling the tipScoreIm() function is approx. 1 min per image This step calculates a metric for how distant each pixel inside the tissue-outline is from the center of a branched structure. A low score indicates proximity to the center (in the pancreas called “trunk”), while a high score indicates a location close to the periphery (in the pancreas called “tip”). We named this the “tip score”. tipScoreIm() is a custom MATLAB function (stored in tipScoreIm.m) that takes the binary mask (hereafter called BranchedMask) from step 10–12 above, and returns an image where each pixel contains a value that quantifies how 'tip like' this location is. Note that the tip score function was developed to quantify/correlate well with what a biologist judges by eye as “tip” and “trunk” in the branched structure of a pancreas, but it could potentially be used to quantify location in other branched organ structures too. The tipScoreIm(BM,approxPixelWidthOfTipStructure,plot_yes_no) function needs 3 inputs: 1) BM : a binary mask/outline (class/type: logical, double, uint8, or uint16) of the branched structure. Has 1's everywhere inside the structure and 0's outside. This mask could be based on a hand drawn outline (see step 11–13) or based on a segmentation. 2) approxPixelWidthOfTipStructure : the approximate length scale/width/length measured in pixels of what is judged to be a 'tip structure' in your image. This was estimated based on the CPA1 staining in our example. 3) plot_yes_no: If set to 1, you will see a plot of the final tipscore image together with a plot of the components used to calculate it. (I.e., side branch, mid branch and convex hull images). Seeing this plot can help you fine tune the 'approxPixelWidthOfTipStructure' (see input 2) to an appropriate value for your image. The tipScoreIm(BM,approxPixelWidthOfTipStructure,plot_yes_no) function has 1 output: 1) An image (of type double) where each pixel contains a value that quantifies how 'tip-like' its location is, (values >0 will be in very tip-like areas). Tip score values are usually in the range [−2,1], but the numbers depend on the input value ‘approxPixelWidthOfTipStructure' and the specific structure given by 'BranchedMask'). Pixel values outside the BranchedMask are set to NaN. 14. Write a simple MATLAB script that calls the tipScoreIm() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. Make sure that the three function inputs ‘BranchedMask’, ‘approxPixelWidthOfTipStructure’, and ‘plot_yes_no’ are defined in the MATLAB Workspace. c. Make sure that the file tipScoreIm.m is stored in your current MATLAB directory, or in the folder ‘/Documents/MATLAB/Add-Ons’. d. Write your script in the MATLAB editor window. Here is an example code that calls tipScoreIm() and shows the output: BranchedMask = MyBranchedMask; approxPixelWidthOfTipStructure = 300, plot_yes_no = 1; MyTipSCoreIm =tipScoreIm(BranchedMask,approxPixelWidthOfTipStructure,plot_yes_no); figure imshow(MyTipSCoreIm,); 15. Run your script. (Press the green ‘Run’ button in the EDITOR tab). 16. Inspect the output figure from tipScoreIm() that shows the subdivision of the skeletonization ( ; ) into ‘side branches’ and ‘mid branches’ and the convex hull (see B–4D). Test different values of approxPixelWidthOfTipStructure: run the script again and see how it impacts the tip score values. Repeat until you find a value which approximates the size of the actual tip domain (as based on tip marker). NB: Use the same value for images with the same resolution. Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m Timing: runtime for a script calling the segmentDAPIimage() function is approx. 5 min per image This step is crucial for getting a tip-score for cells, rather than pixels. It is based on analysis of images of nuclei from the DAPI channel. segmentDAPIimage() is a custom MATLAB function (stored in segmentDAPIimage.m) that takes a DAPI channel image and returns a binary mask of cell nuclei using, ( )( ). The segmentDAPIimage(IM,ROI,nucleiD,manual_input_yes_no,answers) function needs 5 inputs: 1) IM : a DAPI channel 2D image. Array of type double, uint8 or uint16. 2) ROI: A binary mask (same size as IM) with ones everywhere where you want to segment nuclei. Give an empty image (all zeros) if you want nuclei segmented everywhere. Array of type logical, double, uint8 or uint16. The mask from step 10–12 can be used here. 3) nucleiD: Integer number of type double, uint8 or uint16. The average diameter measured in number of pixels of the DAPI stained nuclei you want to segment (approx. 25 pixels for our test images). 4) manual_input_yes_no: Integer of value 0 or 1. if set to one 1 you will be prompted 4 times during the run of the function and have to pick between images that show different threshold levels. (Eg. you will be shown a fig. and asked ‘What thresholded image includes all nuclei and not more? Note: Nuclei should not have holes in them. (Enter image number from fig. here and press enter):’). if ‘manual_input_yes_no’ is set to 0 the function will run without prompting the user and use the values specified in the 1 × 4 array ‘answers’ input (see below). 5) answers: Integer array [opt_index pess_index regT_index altPess_index] with 4 integer index numbers in the range [1–20], type double, uint8 or uint16. The values correspond to the manual command window answers given during a run with ‘manual_input_yes_no=1’. If ‘manual_input_yes_no=0’ the values in answers will be used as input. Use this e.g., if you want to do a batch run but want to give different answers for different images, (see e.g., in demoScript_Batch_NoInput). Note if answers are set to [0 0 0 0] the default [3 9 7 10] will be used (optimal for some of our test images). The segmentDAPIimage(IM,ROI,nucleiD,manual_input_yes_no, answers) function gives 1 output: 1) A binary image/mask with 1's where there are nuclei, (see e.g., of a final nucleus segmentation result E, colors are added to highlight different nuclei). 17. Write a simple MATLAB script that calls the segmentDAPIimage() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. You may import a .tif image into the workspace using the MATLAB function ‘imread()’ (write e.g.,: IM = imread(‘MyTiffFileName’,xx), where xx is the number of the DAPI channel in your tif stack. If your .tif file contains only a single channel write: IM = imread(‘MyTiffFileName’)). c. Make sure that the 5 function inputs IM, ROI, nucleiD, manual_input_yes_no and answers are defined in the MATLAB Workspace. d. Make sure that the file segmentDAPIimage.m is stored in your current MATLAB directory or added to the folder ‘/Documents/MATLAB/Add-Ons’. e. Write your script in the MATLAB editor window. Here is a simple example code that calls segmentDAPIimage() and shows the output: IM = MyDAPIimage; ROI = MyBranchedMask; nucleiD = 30; manual_input_yes_no = 1; answers = [0 0 0 0]; MyNucMask =segmentDAPIimage(IM,ROI,nucleiD,manual_input_yes_no,answers); figure imshow(MyNucMask,); 18. Run your script. (Press the green ‘Run’ button in the EDITOR tab). 19. Answer the 4 prompted questions asked in the command line. See the section ‘MATLAB and demo scripts’ for image examples of appropriate answers. 20. Inspect the output figure. If there is over or under segmentation see : Over/under segmented nuclei. Step 21: Measure pixel intensity for nuclear cell marker using returnTableWithCellInt() Timing: runtime for a script calling the returnTableWithCellInt() function is a few seconds per image This is an optional step designed to measure intensity of a nuclear localized antigen. As stated in section 8, special precautions need to be taken if measuring and comparing staining intensity between different images. In our example, we measured the intensity of nuclear localized SOX9. returnTableWithCellInt() is a custom MATLAB function (stored in returnTableWithCellInt.m) that takes a 2D image with a nuclear marker and binary mask of cell nuclei and returns a table (a table is a MATLAB object) that contains the image intensity found in each nucleus centroid pixel. The returnTableWithCellInt(NUCLEI_BW,IM,nucleiD) function needs 3 inputs: 1) NUCLEI_BW : 2D binary mask (1's where there are nuclei and 0 s elsewhere) of type, logical, double, uint8 or uint16. Each connected component in NUCLEI_BW defines the location of a cell nucleus. From step 19 above. 2) IM: An 2D Intensity image of type double, uint8 or uint16. Could e.g., be an immunohistochemical staining of a nuclear marker. 3) nucleiD: Length scale/diameter of an average nucleus. Used for setting the width of the gaussian filtering of the image. (We filter/blur the image to ensure that the extracted pixel intensities are representative of the local intensities and not extreme outliers. If you do not want any gaussian filtering, then set 'approxPixelWidthOfCellNuclei=1′). The returnTableWithCellInt(NUCLEI_BW,IM,nucleiD) function has 1 output: a table object with 4 columns and one row for each cell/nucleus/connected comp in NUCLEI_BW. Column 1 have index number/ ID number in labeled image Column 2 has x coordinate of nucleus centroid Column 3 has y coordinate of nucleus centroid Column 4 has intensity value in gaussian filtered image IM at nucleus centroid location 21. Write a simple MATLAB script that calls the returnTableWithCellInt() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. Make sure that the 3 function inputs NUCLEI_BW, IM and nucleiD are defined in the MATLAB Workspace. c. Make sure that the file returnTableWithCellInt.m is stored in your current MATLAB directory or added to the folder ‘/Documents/MATLAB/Add-Ons’. d. Write your script in the MATLAB editor window. Here is an example code that calls returnTableWithCellInt() twice using first a nuclear marker image and second a tipscore image (output of tipScoreIm()), merges the returned tables, shows the first three rows of the merged table Tcells in the MATLAB command window and saves the table as a .csv file in the current directory (.txt, .dat, .csv, .xls, .xlsm, or .xlsx are possible): NUCLEI_BW = MyNucMask; IM = MyNucMarkerImage; TS = MyTipSCoreIm, nucleiD = 30; T1 = returnTableWithCellInt(NUCLEI_BW,IM,nucleiD); T2 = returnTableWithCellInt(NUCLEI_BW,TS,nucleiD); % rename VariableName “IMintensity” in T2 so the two tables (T1 and T2) do not have % columns with identical names (identical names will cause an error message when %concatenating tables later) T2.Properties.VariableNames{'IMintensity'} = 'TipScore'; Tcells = [T1 , T2(:,4)]; % concatenate T1 and 4th column of T2 Tcells(1:3,:) % show first 3 rows in command window writetable(Tcells,‘TableCells.csv’); % save as .csv file Step 22: Measure pixel intensity for membrane cell marker returnTableWithCellPairInt() Timing: runtime for a script calling the returnTableWithCellPairInt() function is a few seconds per image In this optional step we have developed a method to estimate intensity of a membrane expressed protein based on a line scan between two cell centroids. If needed, this method can be implemented by running returnTableWithCellPairInt() which is a custom MATLAB function (stored in returnTableWithCellPairInt.m) that takes a 2D image with a membrane marker and a binary mask of cell nuclei and returns a table that contains the max image intensity found along a line scan performed between the nucleus centroids of all neighbor cell pairs. See for examples of the lines drawn between cell nuclei along which the max intensity is extracted at the cell membrane between the two cells. The returnTableWithCellPairInt(NUCLEI_BW,IM,nucleiD,plot_yes_no) function needs 4 inputs: 1) NUCLEI_BW : 2D binary mask (1's where there are nuclei and 0 s elsewhere) of type, logical, double, uint8 or uint16. Each connected component in NUCLEI_BW defines the location of a cell nucleus. 2) IM: An 2D Intensity image of type double, uint8 or uint16. Could e.g., be an immunohistochemical staining of a membrane marker. 3) nucleiD: Length scale/diameter of an average nucleus. Used for deciding which cells are close enough to be neighbors. 4) plot_yes_no: if set to 1 you will see a plot showing IM with white lines at the locations of all the cell neighbor pairs line scans performed in the image. Inspecting this plot can help you find the appropriate value of nucleiD. See example of output in F. Note: you do not need to use exactly the same value for nucleiD as you did for segmentDAPIimage(). The returnTableWithCellPairInt(NUCLEI_BW,IM,nucleiD,plot_yes_no) function has 1 output: A table object with 9 columns and one row for each neighbor cell pair in NUCLEI_BW. Column 1 has x coordinate of midpoint of line scan Column 3 has y coordinate of midpoint of line scan Column 4 ID of first cell in pair Column 5 ID of second cell in pair Column 6 array with full linescan Column 7 Length (in number of pixels) of line scan. Column 8 Maximum intensity value of mean smoothed linescan (filter size = 3). Column 9 Median intensity value of mean smoothed linescan (filter size = 3). 22. Write a simple MATLAB script that calls the returnTableWithCellPairInt() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. Make sure that the 3 function inputs NUCLEI_BW, IM, nucleiD and plot_yes_no are defined in the MATLAB Workspace. c. Make sure that the file returnTableWithCellPairInt.m is stored in your current MATLAB directory or added to the folder ‘/Documents/MATLAB/Add-Ons’. d. Write your script in the MATLAB editor window. Here is an example code that calls returnTableWithCellPairInt() twice using first a nuclear marker image and second a tipscore image (output of tipScoreIm()), merges the returned tables, shows the first three rows of the merged table TcellPairs in the MATLAB command window and saves the table as a .csv file in the current directory (.txt, .dat, .csv, .xls, .xlsm, or .xlsx are possible): NUCLEI_BW = MyNucMask; IM = MyMembMarkerImage; TS = MyTipSCoreIm, nucleiD = 30; plot_yes_no = 1; T1 = returnTableWithCellPairInt(NUCLEI_BW,IM,nucleiD,plot_yes_no); T2 = returnTableWithCellPairInt(NUCLEI_BW,TS,nucleiD,0); % Change generic names to names specific for marker (xx) in table. (If this is not done %you will get an error when you try to concatenate table T1 and T2) T1.Properties.VariableNames{'lineScanMax'} = 'xxMaxInt'; T1.Properties.VariableNames{'lineScanMed'} = 'xxMedInt'; T1.Properties.VariableNames{'lineScan'} = 'xxLineScan'; T2.Properties.VariableNames{'lineScanMed'} = 'tipScore'; % we only use the median for %the tip score of the cell pairs (column 9) TcellPairs = [T1, T2(:,9)]; % concatenate T1 and 9th column of T2 TcellPairs(1:3,:) % show first 3 rows in command window writetable(TcellPairs,‘TableCellPairs.csv’); % save as .csv file
Timing: 2 days This step prepares tissue sections for the immunostaining protocol. For our exemplary data we prepared E14.5 embryonic pancreas from the mouse, but the protocol can be used for other tissues and ages depending on the biological question addressed, as long as a clear branching structure is evident, and cells/nuclei can be segmented. Note: For the purpose of minimizing biological variation caused by developmental differences, it is important that dissection is always carried out at the same time of day (i.e., at noon), and that each embryo is examined with regard to normal development and embryonic age (see step 1e). For younger embryos somites can be counted to more accurately stage embryos. Consult ( ) for more information on staging. Note: For the purpose of statistical analysis of biological variation between littermates, it is advisable to dissect and analyze at least 3–4 embryos per pregnant female. In our experience there is some variation in cell differentiation between litters (this can however be minimized by careful staging of embryos as described above). If analyzing the effect of a treatment or a genetic perturbation, it is therefore important to test whether a difference is due to litter variation or the perturbation. 1. Isolate your organ of interest from embryos a. Euthanize the pregnant female by cervical dislocation b. Dissect the uterus containing the embryos from the pregnant female c. Move embryos to ice-cold sterile 1 × PBS in a Petri dish stored on wet ice d. Dissect the embryos one by one from the uterus and move to a clean Petri dish containing ice-cold sterile 1 × PBS e. Check the developmental age of the embryo by careful examination of the front and rear paw characteristics consulting ( ) Note: Some variation of embryo development (−0.5 days) may occur within a litter, and we routinely exclude embryos in which the developmental stage does not match the gestational stage f. Under a stereoscope, dissect your organ of interest from the embryos using #1 Dumont forceps or similar g. Transfer the tissue to 4% formaldehyde in 1 × PBS in a 12 well plate and fix at 20–22°for 1–24 h depending on the size of the tissue. Note: Although adequate fixation is required for good cytological preservation, over-fixation can block or prevent antibodies from binding to the protein epitope ( ) . We routinely fix E14.5 embryonic pancreas for 16–18 h. The reaction of formaldehyde depends on the temperature, and penetration of tissue by formaldehyde is a function of the square root of the time of exposure ( ). We recommend consulting ( ) to determine the optimal fixation time. h. Cryoprotect the tissue by transferring to 30% sucrose in 1 × PBS solution at 4°C until the tissue falls to the bottom of the well (usually within 3–16 h depending on the size). i. Infiltrate tissue with OCT by incubating in 100% OCT for 1 h at 4°C j. Embed tissue into blocks as follows: i. Label plastic mold ii. Place the plastic mold onto a piece of dry ice, adding enough OCT to cover bottom iii. Wait for the bottom to freeze iv. Place the tissue in the middle of the block and add OCT to cover. Be careful not to make any bubbles in the OCT. v. When all of the OCT is white, store in a sealed plastic bag at –80°C immediately. Pause point: If needed, frozen tissue blocks can be stored at –80°C indefinitely 2. Section tissue on a cryostat a. Section tissue on a cryostat following manufacturer's instructions b. Section at 6 μm and place 2–3 sections per slide depending on the size Note: Sections should stay within the boundaries of the + signs on the slide and should not overlap. c. Mount tissue sections on labeled and numbered Superfrost PLUS glass slides d. Store at –80°C Pause point: if needed, frozen sections can be stored at –80°C for 2–3 years
Timing: 2 days This step stains the tissue in preparation for imaging and image quantification. It has been optimized for embryonic mouse pancreatic tissue fixed in 4% paraformaldehyde and prepared as frozen sections as described in the previous step but can also be used for other embryonic organs. CRITICAL: After step 4c the tissue should always be kept moist or submerged. Any drying during antibody incubation will increase the antibody concentration and drying out at all other steps will increase nonspecific antibody binding and therefore background fluorescence. See : Excessive background fluorescence Alternatives: Paraffin sections can be used instead of frozen sections. In that case, the sections should be deparaffinized and rehydrated: Incubate for 15 min in Xylene to deparaffinize and rehydrate in a descending alcohol series: 100% ethanol for 2 times 3 min, 95% ethanol for 3 min, 70% ethanol for 3 min, 50% ethanol for 3 min. After rehydration the slides are washed in running tap water and staining can proceed from step 4c below. 3. Select a number of slides containing tissue that adequately represents the whole 3-dimensional organ. Note: Exclude any sections not showing the branching structure (typically, these are at the extreme ends of the tissue). For a single E14.5 pancreas we typically get 200 6 μm sections, and stain every 30th section (excluding the extreme ends), giving a total of 4–6 sections/pancreas. Include sufficient individual biological replicates to be able to perform statistical analysis (3–5 biological replicates of each experimental condition) and have enough samples for controls (see ). 4. Prepare slides for antibody incubation a. Align 3–20 slides in a slide holder for staining. (See ). Always include at least one slide as a negative control and one as secondary antibody only control (see below for more information on controls). b. Dry sections in an oven at 37°C for 20 min c. Wash 3 times in 1 × PBS for 5 min each d. Optional: Antigen retrieval i. Remove 1 × PBS and leave in Citric acid antigen retrieval buffer for 1 h at 37°C ii. Wash 3 times in 1 × PBS for 5 min each Note: Antigen retrieval is necessary for unmasking of some but not all antigens prior to primary antibody incubation. Detection of some antigens may even suffer from antigen retrieval. For our antibody panel, antigen retrieval by microwave treatment in citrate buffer pH6 ( ) is necessary for detection of SOX9, while P120CTN detection suffers. We therefore used a milder treatment with incubation in citrate buffer pH 6 without boiling in the optional steps above. e. Working one slide at a time to reduce drying: i. Place slides flat in a humidified chamber ii. Outline tissue section with a hydrophobic PAP pen (See ) iii. Add enough blocking buffer to each slide to cover tissue f. Incubate slides in blocking buffer for 60–120 min at 20°C–22°C 5. In the meantime, prepare antibody dilutions (see for an example): a. Dilute primary antibodies for one or two antigens expressed in your cells of interest (here: SOX9 and Carboxypeptidase 1 (CPA1)), and for a marker visualizing the general shape of the organ (here: P120CTN) in blocking buffer. CRITICAL: Primary antibody concentration has to be carefully titrated for specific tissues to avoid non-specific binding. See : Excessive background fluorescence Note: Prepare enough primary antibody dilution to stain all your samples, allowing 100 μl per sample. Note that extra diluted antibody should be made to account for pipetting error (at least 10% extra volume). 6. Primary antibody incubation a. Working one slide at a time to reduce drying: i. Remove blocking buffer with a micropipette ii. Add enough antibody solution (typically 100–200 μL) to each slide to cover tissue completely. iii. Leave at least two sections with only the blocking buffer on, to serve as negative and secondary antibody only controls (see ). iv. Leave for 16–20 h at 20°C–22°C in the humidified chamber (See ) 7. Next morning: Prepare secondary antibody solution (See for an example) a. Use secondary antibodies against the species in which the primary antibodies are produced. CRITICAL: Secondary antibodies produced in the same species (i.e., Donkey) are preferable to avoid cross-reaction. For our test data, we had to use a secondary antibody produced in goat, which would be bound by the anti-goat antibody used to detect CPA1, if not applied separately and in the correct order as outlined below. See also . CRITICAL: The secondary antibodies must be conjugated to stable fluorescent dyes matching the configuration of your fluorescent microscope. A good combination with minimal spectral overlap is Dylight-405/DAPI, Alexa Fluor-488, Rhodamine-RedX, Alexa- Fluor 647. See : Unexpected staining patterns b. Make secondary antibody solution 1 and 2 by diluting secondary antibodies in blocking buffer at a dilution of 1:1000 as outlined in . Leave the tubes with diluted antibodies on wet ice and protected from light. Note: Secondary antibodies dilutions should be optimized for specific tissues to avoid non-specific binding. See : Excessive background fluorescence Note: Prepare enough secondary antibody dilution to stain all your samples and your secondary antibody only control, allowing 100 μl per sample. Note that extra diluted antibody should be made to account for pipetting error (at least 10% extra volume). 8. Secondary antibody incubation a. Remove primary antibody solution with a micropipette b. Wash 3 times in 1 × PBS for 5 min each c. Working one slide at a time to reduce drying: i. Add enough secondary antibody solution 1 (typically 100–200 μL) to each slide to cover tissue. ii. Add secondary antibody to the secondary antibody only control slides. Leave the negative control slide in 1 × PBS. d. Incubate for 60–120 min. e. Wash 3 times in 1 × PBS for 5 min each f. Working one slide at a time to reduce drying: i. Add enough secondary antibody solution 2 (typically 100–200 μL) to each slide to cover tissue. ii. Add secondary antibody solution 2 to the secondary antibody only control slides. Leave the negative control slide in 1 × PBS. g. Incubate for 60–120 min. h. Wash 3 times in 1 × PBS for 5 min each i. For blue staining of nuclei, add DAPI to the last wash at a dilution of 1:1000 j. Mount with DAKO mounting medium or similar and #1.5 cover glass preferably with a thickness of 0.17–0.18 mm (usually marked #1.5H or “high performance”) k. Leave flat to dry for 10 min protected from light Pause point: if needed, stained sections can be stored dark at 4°C for several weeks. CRITICAL: Variation in staining intensity can occur from experiment to experiment, so it is critical to include all samples in the same experiment, if comparison of staining intensity is planned. Alternatives: Different combinations of primary and secondary antibodies can be used as needed.
Timing: 1–3 days depending on number of samples Microscopy of stained tissue sections creates the images needed for subsequent image analysis steps. Confocal imaging was performed on a Zeiss LSM780 inverted confocal microscope equipped with a 32PMT GaAsP spectral detector and two PMT’s (one for blue and one for far-red). We recommend consulting ( ) for detailed instructions on how to acquire images in Zeiss Zen software. 9. Mount sample in confocal microscope a. Turn confocal system on and wait for lasers to warm up b. Place the slide with the cover slip facing down (if using an inverted microscope) in the sample holder and find your sample using a 10 × objective or similar c. Add oil immersion to a Plan-Apochromat 40 × /1.30 oil objective or similar d. Find the Z position that best represents the shape of your tissue. Note down Z position for later use 10. Acquire images: a. Adjust laser and detector gain settings to maximize the dynamic range of your system and minimize spectral overlap ( ). Save/note down the settings for later use. See also critical notes below and : Unexpected staining patterns. b. Adjust the settings to 16-bit images (if possible) and at least 1024 × 1024 frame size at 1 × zoom c. Set the pinhole as close to 1AU as possible but ensure that the same actual pinhole diameter is used for all channels. d. Acquire tiled images (Tile scan) of a single Z plane of the entire organ section with 10% overlap scanning each fluorescent dye separately (sequential acquisition), in order to avoid spectral overlap. See ( ) e. Save the file in the proprietary format (.czi or .lsm for a Zeiss confocal microscope) f. Stitch the tiled images using imaging software (such as Zen black, Imaris, Amira or the free software Image J/Fiji) and save in the proprietary format. i. Stitching in Zen black is initiated in the stitch function under the processing tab ii. Select the image to be stitched iii. Set a threshold for stitching. We routinely use “strict” iv. Select apply v. Save the image in the proprietary format. For images acquired using Zen black this format is czi or lsm g. Export as TIFF files to perform image analysis. See for an example of a stitched image of a pancreas section with four channels (P120CTN, SOX9, CPA1, DAPI). i. Open the proprietary file in ImageJ: File; Open ii. Save as TIFF in ImageJ: File; Save as; Tiff Note: The images included in the test dataset are TIFF stacks with all channels included. These can be analyzed directly by our code without further modification. The channels include: Channel 1: CPA1, channel 2: P120CTN, channel 3: SOX9, channel 4: DAPI CRITICAL: Identical microscopy settings and Z planes must be used for all samples, if comparison of staining intensity is planned. CRITICAL: Detector gain and laser intensity should be optimized to maximize the dynamic range. Most systems have the option of visualizing the image with a look up table (LUT) where pixels with an intensity of 0 are blue and pixels with the highest pixel intensity are red (in Zen this option is called “Range indicator”). Use this LUT to ensure that your image has only a few maximum intensity (red) pixels ( ). Whenever possible, data for intensity quantitation should be captured as 16-bit images, as this optimizes discrimination of pixel intensities as compared to 8-bit images ( ). Alternatives: Different confocal microscope systems and non-confocal fluorescent microscopes can be used, as long as it is ensured that the images are compatible with nuclei segmentation. Please note that confocal microscopy is highly recommended, if images are to be used for other downstream applications such as intensity measurements or threshold-based area quantification.
Timing: approx. 5 min per image This is the only semi-manual step in the image analysis pipeline. It creates a mask of the tissue, which is used as an input in subsequent steps. CRITICAL: The image chosen as input for this step has to be an image of an antibody staining for an antigen that marks either the whole epithelium or an outline of the epithelium. For this project we used images of P120CTN. The outline of the organ can be drawn and saved as a binary image using the MATLAB app ‘ImageSegmenter’, as follows: 11. Open MATLAB a. Go to the ‘App’ tap in MATLAB b. Search for the ‘Image Segmenter’ app and open it 12. Load the image you want to annotate into the ImageSegmenter App (either from file or from the MATLAB workspace). a. Go to the ‘ADD TO MASK’ menu and select ‘Draw ROI’s b. Use the ‘Freehand’ or ‘Assisted Freehand’ tool to draw an organ ROI/outline c. Press ‘Apply’ and ‘Close ROI’ when you are done drawing. 13. Export binary outline/ROI to MATLAB workspace a. Press ‘Export’ and then ‘Export Images’ b. Select ‘Final Segmentation’ (unselect ‘Masked Images’) and type in a name (for example ‘myBranchedMask’) for your new mask and press ok. Your new mask/ROI will now appear in the MATLAB workspace window). Note: Hand-drawn masks for the test dataset are supplied in supplementary data
Timing: runtime for a script calling the tipScoreIm() function is approx. 1 min per image This step calculates a metric for how distant each pixel inside the tissue-outline is from the center of a branched structure. A low score indicates proximity to the center (in the pancreas called “trunk”), while a high score indicates a location close to the periphery (in the pancreas called “tip”). We named this the “tip score”. tipScoreIm() is a custom MATLAB function (stored in tipScoreIm.m) that takes the binary mask (hereafter called BranchedMask) from step 10–12 above, and returns an image where each pixel contains a value that quantifies how 'tip like' this location is. Note that the tip score function was developed to quantify/correlate well with what a biologist judges by eye as “tip” and “trunk” in the branched structure of a pancreas, but it could potentially be used to quantify location in other branched organ structures too. The tipScoreIm(BM,approxPixelWidthOfTipStructure,plot_yes_no) function needs 3 inputs: 1) BM : a binary mask/outline (class/type: logical, double, uint8, or uint16) of the branched structure. Has 1's everywhere inside the structure and 0's outside. This mask could be based on a hand drawn outline (see step 11–13) or based on a segmentation. 2) approxPixelWidthOfTipStructure : the approximate length scale/width/length measured in pixels of what is judged to be a 'tip structure' in your image. This was estimated based on the CPA1 staining in our example. 3) plot_yes_no: If set to 1, you will see a plot of the final tipscore image together with a plot of the components used to calculate it. (I.e., side branch, mid branch and convex hull images). Seeing this plot can help you fine tune the 'approxPixelWidthOfTipStructure' (see input 2) to an appropriate value for your image. The tipScoreIm(BM,approxPixelWidthOfTipStructure,plot_yes_no) function has 1 output: 1) An image (of type double) where each pixel contains a value that quantifies how 'tip-like' its location is, (values >0 will be in very tip-like areas). Tip score values are usually in the range [−2,1], but the numbers depend on the input value ‘approxPixelWidthOfTipStructure' and the specific structure given by 'BranchedMask'). Pixel values outside the BranchedMask are set to NaN. 14. Write a simple MATLAB script that calls the tipScoreIm() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. Make sure that the three function inputs ‘BranchedMask’, ‘approxPixelWidthOfTipStructure’, and ‘plot_yes_no’ are defined in the MATLAB Workspace. c. Make sure that the file tipScoreIm.m is stored in your current MATLAB directory, or in the folder ‘/Documents/MATLAB/Add-Ons’. d. Write your script in the MATLAB editor window. Here is an example code that calls tipScoreIm() and shows the output: BranchedMask = MyBranchedMask; approxPixelWidthOfTipStructure = 300, plot_yes_no = 1; MyTipSCoreIm =tipScoreIm(BranchedMask,approxPixelWidthOfTipStructure,plot_yes_no); figure imshow(MyTipSCoreIm,); 15. Run your script. (Press the green ‘Run’ button in the EDITOR tab). 16. Inspect the output figure from tipScoreIm() that shows the subdivision of the skeletonization ( ; ) into ‘side branches’ and ‘mid branches’ and the convex hull (see B–4D). Test different values of approxPixelWidthOfTipStructure: run the script again and see how it impacts the tip score values. Repeat until you find a value which approximates the size of the actual tip domain (as based on tip marker). NB: Use the same value for images with the same resolution.
Timing: runtime for a script calling the segmentDAPIimage() function is approx. 5 min per image This step is crucial for getting a tip-score for cells, rather than pixels. It is based on analysis of images of nuclei from the DAPI channel. segmentDAPIimage() is a custom MATLAB function (stored in segmentDAPIimage.m) that takes a DAPI channel image and returns a binary mask of cell nuclei using, ( )( ). The segmentDAPIimage(IM,ROI,nucleiD,manual_input_yes_no,answers) function needs 5 inputs: 1) IM : a DAPI channel 2D image. Array of type double, uint8 or uint16. 2) ROI: A binary mask (same size as IM) with ones everywhere where you want to segment nuclei. Give an empty image (all zeros) if you want nuclei segmented everywhere. Array of type logical, double, uint8 or uint16. The mask from step 10–12 can be used here. 3) nucleiD: Integer number of type double, uint8 or uint16. The average diameter measured in number of pixels of the DAPI stained nuclei you want to segment (approx. 25 pixels for our test images). 4) manual_input_yes_no: Integer of value 0 or 1. if set to one 1 you will be prompted 4 times during the run of the function and have to pick between images that show different threshold levels. (Eg. you will be shown a fig. and asked ‘What thresholded image includes all nuclei and not more? Note: Nuclei should not have holes in them. (Enter image number from fig. here and press enter):’). if ‘manual_input_yes_no’ is set to 0 the function will run without prompting the user and use the values specified in the 1 × 4 array ‘answers’ input (see below). 5) answers: Integer array [opt_index pess_index regT_index altPess_index] with 4 integer index numbers in the range [1–20], type double, uint8 or uint16. The values correspond to the manual command window answers given during a run with ‘manual_input_yes_no=1’. If ‘manual_input_yes_no=0’ the values in answers will be used as input. Use this e.g., if you want to do a batch run but want to give different answers for different images, (see e.g., in demoScript_Batch_NoInput). Note if answers are set to [0 0 0 0] the default [3 9 7 10] will be used (optimal for some of our test images). The segmentDAPIimage(IM,ROI,nucleiD,manual_input_yes_no, answers) function gives 1 output: 1) A binary image/mask with 1's where there are nuclei, (see e.g., of a final nucleus segmentation result E, colors are added to highlight different nuclei). 17. Write a simple MATLAB script that calls the segmentDAPIimage() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. You may import a .tif image into the workspace using the MATLAB function ‘imread()’ (write e.g.,: IM = imread(‘MyTiffFileName’,xx), where xx is the number of the DAPI channel in your tif stack. If your .tif file contains only a single channel write: IM = imread(‘MyTiffFileName’)). c. Make sure that the 5 function inputs IM, ROI, nucleiD, manual_input_yes_no and answers are defined in the MATLAB Workspace. d. Make sure that the file segmentDAPIimage.m is stored in your current MATLAB directory or added to the folder ‘/Documents/MATLAB/Add-Ons’. e. Write your script in the MATLAB editor window. Here is a simple example code that calls segmentDAPIimage() and shows the output: IM = MyDAPIimage; ROI = MyBranchedMask; nucleiD = 30; manual_input_yes_no = 1; answers = [0 0 0 0]; MyNucMask =segmentDAPIimage(IM,ROI,nucleiD,manual_input_yes_no,answers); figure imshow(MyNucMask,); 18. Run your script. (Press the green ‘Run’ button in the EDITOR tab). 19. Answer the 4 prompted questions asked in the command line. See the section ‘MATLAB and demo scripts’ for image examples of appropriate answers. 20. Inspect the output figure. If there is over or under segmentation see : Over/under segmented nuclei.
Timing: runtime for a script calling the returnTableWithCellInt() function is a few seconds per image This is an optional step designed to measure intensity of a nuclear localized antigen. As stated in section 8, special precautions need to be taken if measuring and comparing staining intensity between different images. In our example, we measured the intensity of nuclear localized SOX9. returnTableWithCellInt() is a custom MATLAB function (stored in returnTableWithCellInt.m) that takes a 2D image with a nuclear marker and binary mask of cell nuclei and returns a table (a table is a MATLAB object) that contains the image intensity found in each nucleus centroid pixel. The returnTableWithCellInt(NUCLEI_BW,IM,nucleiD) function needs 3 inputs: 1) NUCLEI_BW : 2D binary mask (1's where there are nuclei and 0 s elsewhere) of type, logical, double, uint8 or uint16. Each connected component in NUCLEI_BW defines the location of a cell nucleus. From step 19 above. 2) IM: An 2D Intensity image of type double, uint8 or uint16. Could e.g., be an immunohistochemical staining of a nuclear marker. 3) nucleiD: Length scale/diameter of an average nucleus. Used for setting the width of the gaussian filtering of the image. (We filter/blur the image to ensure that the extracted pixel intensities are representative of the local intensities and not extreme outliers. If you do not want any gaussian filtering, then set 'approxPixelWidthOfCellNuclei=1′). The returnTableWithCellInt(NUCLEI_BW,IM,nucleiD) function has 1 output: a table object with 4 columns and one row for each cell/nucleus/connected comp in NUCLEI_BW. Column 1 have index number/ ID number in labeled image Column 2 has x coordinate of nucleus centroid Column 3 has y coordinate of nucleus centroid Column 4 has intensity value in gaussian filtered image IM at nucleus centroid location 21. Write a simple MATLAB script that calls the returnTableWithCellInt() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. Make sure that the 3 function inputs NUCLEI_BW, IM and nucleiD are defined in the MATLAB Workspace. c. Make sure that the file returnTableWithCellInt.m is stored in your current MATLAB directory or added to the folder ‘/Documents/MATLAB/Add-Ons’. d. Write your script in the MATLAB editor window. Here is an example code that calls returnTableWithCellInt() twice using first a nuclear marker image and second a tipscore image (output of tipScoreIm()), merges the returned tables, shows the first three rows of the merged table Tcells in the MATLAB command window and saves the table as a .csv file in the current directory (.txt, .dat, .csv, .xls, .xlsm, or .xlsx are possible): NUCLEI_BW = MyNucMask; IM = MyNucMarkerImage; TS = MyTipSCoreIm, nucleiD = 30; T1 = returnTableWithCellInt(NUCLEI_BW,IM,nucleiD); T2 = returnTableWithCellInt(NUCLEI_BW,TS,nucleiD); % rename VariableName “IMintensity” in T2 so the two tables (T1 and T2) do not have % columns with identical names (identical names will cause an error message when %concatenating tables later) T2.Properties.VariableNames{'IMintensity'} = 'TipScore'; Tcells = [T1 , T2(:,4)]; % concatenate T1 and 4th column of T2 Tcells(1:3,:) % show first 3 rows in command window writetable(Tcells,‘TableCells.csv’); % save as .csv file
Timing: runtime for a script calling the returnTableWithCellPairInt() function is a few seconds per image In this optional step we have developed a method to estimate intensity of a membrane expressed protein based on a line scan between two cell centroids. If needed, this method can be implemented by running returnTableWithCellPairInt() which is a custom MATLAB function (stored in returnTableWithCellPairInt.m) that takes a 2D image with a membrane marker and a binary mask of cell nuclei and returns a table that contains the max image intensity found along a line scan performed between the nucleus centroids of all neighbor cell pairs. See for examples of the lines drawn between cell nuclei along which the max intensity is extracted at the cell membrane between the two cells. The returnTableWithCellPairInt(NUCLEI_BW,IM,nucleiD,plot_yes_no) function needs 4 inputs: 1) NUCLEI_BW : 2D binary mask (1's where there are nuclei and 0 s elsewhere) of type, logical, double, uint8 or uint16. Each connected component in NUCLEI_BW defines the location of a cell nucleus. 2) IM: An 2D Intensity image of type double, uint8 or uint16. Could e.g., be an immunohistochemical staining of a membrane marker. 3) nucleiD: Length scale/diameter of an average nucleus. Used for deciding which cells are close enough to be neighbors. 4) plot_yes_no: if set to 1 you will see a plot showing IM with white lines at the locations of all the cell neighbor pairs line scans performed in the image. Inspecting this plot can help you find the appropriate value of nucleiD. See example of output in F. Note: you do not need to use exactly the same value for nucleiD as you did for segmentDAPIimage(). The returnTableWithCellPairInt(NUCLEI_BW,IM,nucleiD,plot_yes_no) function has 1 output: A table object with 9 columns and one row for each neighbor cell pair in NUCLEI_BW. Column 1 has x coordinate of midpoint of line scan Column 3 has y coordinate of midpoint of line scan Column 4 ID of first cell in pair Column 5 ID of second cell in pair Column 6 array with full linescan Column 7 Length (in number of pixels) of line scan. Column 8 Maximum intensity value of mean smoothed linescan (filter size = 3). Column 9 Median intensity value of mean smoothed linescan (filter size = 3). 22. Write a simple MATLAB script that calls the returnTableWithCellPairInt() function: a. Study the two demo scripts 'demoScript_OneImage_ManualInput.m' and 'demoScript_Batch_NoInput.m' for inspiration. b. Make sure that the 3 function inputs NUCLEI_BW, IM, nucleiD and plot_yes_no are defined in the MATLAB Workspace. c. Make sure that the file returnTableWithCellPairInt.m is stored in your current MATLAB directory or added to the folder ‘/Documents/MATLAB/Add-Ons’. d. Write your script in the MATLAB editor window. Here is an example code that calls returnTableWithCellPairInt() twice using first a nuclear marker image and second a tipscore image (output of tipScoreIm()), merges the returned tables, shows the first three rows of the merged table TcellPairs in the MATLAB command window and saves the table as a .csv file in the current directory (.txt, .dat, .csv, .xls, .xlsm, or .xlsx are possible): NUCLEI_BW = MyNucMask; IM = MyMembMarkerImage; TS = MyTipSCoreIm, nucleiD = 30; plot_yes_no = 1; T1 = returnTableWithCellPairInt(NUCLEI_BW,IM,nucleiD,plot_yes_no); T2 = returnTableWithCellPairInt(NUCLEI_BW,TS,nucleiD,0); % Change generic names to names specific for marker (xx) in table. (If this is not done %you will get an error when you try to concatenate table T1 and T2) T1.Properties.VariableNames{'lineScanMax'} = 'xxMaxInt'; T1.Properties.VariableNames{'lineScanMed'} = 'xxMedInt'; T1.Properties.VariableNames{'lineScan'} = 'xxLineScan'; T2.Properties.VariableNames{'lineScanMed'} = 'tipScore'; % we only use the median for %the tip score of the cell pairs (column 9) TcellPairs = [T1, T2(:,9)]; % concatenate T1 and 9th column of T2 TcellPairs(1:3,:) % show first 3 rows in command window writetable(TcellPairs,‘TableCellPairs.csv’); % save as .csv file
Our test images contain 4 channels: CPA1 (channel1), P120CTN (channel2), SOX9 (channel3) and DAPI (channel 4) (see ). Examples of tissue outline, convex hull, midbranch and sidebranches are shown in A–4D. Tip score can be visualized as shown in E. To illustrate and validate the method we stained for SOX9, a transcription factor and trunk (central) marker, and CPA1, a tip (peripheral marker). The script output data for SOX9 and P120CTN using the small subset of images in the test dataset (7 images) can be seen in . The trunk marker SOX9 is nuclear localized (see ) and we therefore quantified expression level per cell using the nuclear stain DAPI to create a mask for demarcating the area. In the test dataset statistical differences are detected between most bins and the bin with the highest tip score ( A). Cells with high tip scores had significantly lower SOX9 intensity compared to cells with low tip score, thus verifying the method for identifying trunk cells. With the full dataset (24 images) in ( ) a similar difference was found (results not shown here). To illustrate the method for measuring expression level of a membrane localized protein between two neighboring cells relative to tip score we stained for the protein P120-catenin (P120CTN), which is enriched on the cytoplasmic side of the cell membrane. For the test dataset statistical differences are detected between most bins and the bin with the lowest tip score. Cell pairs with a low tip score have a significantly higher expression of P120CTN than cell-pairs with a high tip score ( B). An example of a cell pair stained for P120CTN and DAPI and analyzed with the line scan method is shown in A and 6B. C shows an image with cell pairs indicated by lines from the larger dataset in ( ). Quantification of this dataset showed a similar difference in p120CTN expression (see D and note that the tip score is differently scaled in this dataset). The tip marker CPA1 is expressed in the cytosol ( E), and we were thus only able to assess via thresholding whether cells were positive or negative (method not included in this protocol but can be found in ( )). Comparing tip scores for CPA1 positive versus negative cells, showed a significantly higher tip score for CPA1 positive cells, thus verifying the method for assigning high tip scores to tip localized cells ( ).
Statistically test differences in cell position for various cell types After extracting intensity values from either nucleus or cell membranes and calculating tip score for the position of a cell, it can be tested if groups of cells with different tip scores have significantly different expressions. CRITICAL: If you merge data from several different IHC images it is crucial that you normalize intensities appropriately. Normalization: Finding a reasonable way to normalize intensities is notoriously hard for IHC images and should be done with great care. Normalizing using intensities from channels with other markers is not necessarily a good idea since the relative intensity difference between different channels/markers can vary for different experiments. Further normalization with ‘background’ intensity can be problematic both since the relative difference between signal and background can vary between experiments and since this means normalizing with a value far below the dynamic range of the actual marker signal. Ideally one would have a ‘reference tissue’ included in each image to determine the median intensity value with which to normalize the signal from the ROI, however this is rarely possible. The concept of tipscore is useful for the purpose of normalization since it allows you to determine the median intensity of cells within a given region that has similar tip score, within the same image channel, and use this value for normalization. This way normalization is done with an intensity value that is within the dynamic range of the signal being normalized and is from the same image channel. Example: If for example expression of a certain marker is different in tip and trunk regions and individual images contain different proportions of tip and trunk (e.g., one image has more tip region than another) then normalization done with the median intensity of the entire image will be skewed depending on the ratio of tip/trunk in the image, whereas normalization using the median intensity of the same subregion (with similar tip score) in all images will give an acceptable result. In demoScript_Batch_NoInput.m we chose to normalize with intensities measured within regions that have tip score values within the range of [−0.75; −0.25]. This tip score range was chosen to be i) as narrow as possible while ii) containing as many cells as possible for all images in the test set. Statistical tests: After intensity values had been normalized, we further chose to bin them in groups with similar tipscore values in order to do statistical testing between different tipscore bins. Appropriate binning depends on the size of the data set. The larger the data set the smaller the bin size you may use and still have sufficient statistical power. In demoScript_Batch_NoInput.m we have very few measurements with very high/low tip score values and therefore chose to have constant bin size for the middle bins while the first and the last bin contain a broader range of tip score values, (number of bins is adjusted by varying the ‘factor = 3’ in line 252 of demoScript_Batch_NoInput.m). We compare groups of cells in bins with different tipscore ranges using the MATLAB function multcompare() (which corrects for multiple comparison) on the stats structure returned when you call the kruskalwallis() function. The Kruskal-Wallis test is a nonparametric version of classical one-way ANOVA, and an extension of the Wilcoxon rank sum test to more than two groups. It compares the medians of the groups of data to determine if the samples come from the same population (or, equivalently, from different populations with the same distribution). We chose Kruskal-Wallis since distributions within the bins failed the Anderson-Darling ( adtest() ) test for normality. If your binned data passes the AD-test you may use one analysis of variance anova1() instead, which compares the means of the groups of data and holds more statistical power.
Note that the tip score is calculated using lengths in pixels, the score will therefore depend on the resolution level, and the method could give misleading results if images of varying pixel resolution are compared, without specifying the different length scales of tip structures in different images. This protocol measures an essentially three-dimensional characteristic (shape of a branching structure) from two-dimensional tissue sections. The protocol will therefore give misleading results if only few tissue sections or tissue sections only located in extreme ends are used for quantification.
Optimizing all optical parameters as described in the protocol and the following trouble-shooting sections is crucial for image analysis. Downstream analysis such as intensity measurements and/or thresholding-based methods are very sensitive to problems such as high background, inconsistent imaging parameters, saturated pixels, non-confocal images etc. Below we list a number of potential problems. Some problems have several causes, which are described in list format. The solutions suggested are listed in a corresponding fashion, such that solution number a. addresses cause letter a. and so forth. Problem 1: Excessive background fluorescence Minimizing excessive background fluorescence in immuno fluorescent stained tissue sections can be challenging, and the problem can have several causes: a. Tissue drying, improper blocking or inadequate washing in Step 3–8: Immunostain tissue sections. b. Antibody nonspecific binding to endogenous immunoglobulins in the tissue. c. Tissue autofluorescence. d. Dirty coverslips or objectives in Step 9–10: Confocal microscopy of stained tissue sections. Potential solution a. In Step 3–8: Immunostain tissue sections: Avoid drying of tissue by working quickly, and only processing one slide at a time when working in the humidified chamber. Ensure that tissue stays moist during longer incubation steps by using a humidified chamber. Follow blocking and washing instructions carefully. b. Before you begin, carefully test the correct dilution factor for your primary and secondary antibodies following the manufacturer’s instructions. If these are lacking, test a dilution series of primary antibodies ranging from 1:50, 1:100, 1:200, 1:500, 1:1000, and a dilution series of secondary antibodies ranging from 1:500, 1:1000, 1:2000. Compare with secondary antibody only control and negative control to ensure that the staining is specific for the antigen of interest. Check that the staining pattern corresponds with prior knowledge of protein localization (i.e., nuclear or membrane localization etc.) If mouse tissue is probed with a primary antibody made in mouse, the endogenous immunoglobulins should first be blocked with a monovalent Fab fragment antibody of anti-mouse IgG, or IgG specific secondary antibodies used. c. Always include a control slide, which is not exposed to any antibodies (negative control) to check for autofluorescence. Tissue autofluorescence is generally much lower than fluorescence from fluorophores. Make sure to compare fluorescence level with no primary control and negative control, when setting up your confocal imaging settings in Step 9–10, keeping gain and laser output below the detection limit for autofluorescence. Note that background fluorescence should be similar in all samples and controls. If this is not the case, attention should be paid to sample preparation in order to eliminate this difference before acquiring images. d. In Step 3–8: Immunostain tissue sections: Check that coverslips are clean before, during and after mounting. Never touch the coverslip on the horizontal surface but handle it by holding the sides. In Step 9–10: Confocal microscopy of stained tissue sections: Clean the objectives carefully before and after use and use the appropriate immersion solution for your objective. Problem 2: Unexpected staining patterns Unexpected staining patterns, not conforming to expected localization of protein can be due to: a. Unspecific binding of primary or secondary antibodies to endogenous immunoglobulins in the tissue during Step 3–8: Immunostain tissue sections. b. Cross-reactivity of primary and/or secondary antibodies to each other during Step 3–8: Immunostain tissue sections. c. Overlapping fluorescence spectra of fluorophores and/or inappropriate excitation/emission filters. Avoiding all these is essential for correct interpretation of results. Potential solution a. For avoiding unspecific binding see (2) in Problem 1 above. b. For avoiding cross-reactivity between antibodies the primary and secondary antibodies must be carefully selected before you begin: Primary antibodies should be produced in different species. If overlap in species is unavoidable, the following modification of the protocol can be employed: For two primary antibodies produced in rabbit, an extra step (requiring one more day of staining) can be added to the protocol: After blocking, the first rabbit primary antibody is incubated with samples alone, followed by a monovalent Fab fragment fluorescence conjugated secondary antibody against rabbit. This secondary antibody will in most cases block any further binding to the first rabbit primary antibody, and the protocol can be followed as normal from hereon. Ideally, secondary antibodies are all produced in the same species, excluding all species used for producing the primary antibodies. Our exemplary antibody panel is an example of a protocol where one secondary antibody is produced in the same species (goat), which another secondary antibody is directed against. However, most secondary antibodies available commercially are produced in donkey, and this is a good choice if none of the primary antibodies are produced in donkey. Additionally, the use of labeled secondary antibodies which are cross-adsorbed against the species of the experimental tissue, and the species of the other primary antibodies is recommended. c. In a multiple labeling protocol such as this, occurrence of identical staining patterns in two different channels during imaging should always be a red flag that fluorescence spectra of fluorophores may be overlapping, and/or that inappropriate excitation/emission filters are used. Always use secondary antibodies conjugated to stable fluorescent dyes that match the configuration of your fluorescent microscope. A good combination with minimal spectral overlap is Dylight-405I, Alexa Fluor-488, Rhodamine-RedX, Alexa- Fluor 647. Use of fluorophores with broader excitation/emission spectra (such as DAPI)) can give signal in another channel (so-called bleed-through). Narrow band-pass emission filters, narrow detector detection ranges, and sequential acquisition can be used to minimize bleed-through. Problem 3: No or weak fluorescent signal after staining If fluorescence is undetectable or very low after staining it can be due to: a. The primary or secondary antibody is diluted too much in Step 3–8: Immunostain tissue sections. b. There is insufficient antigen present in tissue or the antigen is not accessible. c. The antigen is not compatible with the citrate buffer antigen retrieval method in Step 3–8: Immunostain tissue sections. d. Tissue has not been fixed appropriately in Step 1–2: Prepare tissue sections from mouse organs. Potential solution a. Before you begin, carefully test the correct dilution factor for your primary and secondary antibodies following the manufacturer’s instructions. If these are lacking, test a dilution series of primary antibody ranging from 1:50, 1:100, 1:200, 1:500, 1:1000, and a dilution series of secondary antibodies ranging from 1:500, 1:1000, 1: 2000. There is insufficient antigen present in tissue. b. In Step 3–8: Immunostain tissue sections: The blocking buffer recommended in this protocol does include detergent. If further permeabilization is needed, add a 10 min washing step before blocking, adding Triton X-100 or Tween 20 to PBS. Detergent concentration should be determined empirically, but typically range between 0,01 and 0,1%. c. In Step 3–8: Immunostain tissue sections: Omit or include the optional citrate buffer antigen retrieval step. d. In Step 1–2: Prepare tissue sections from mouse organs: Use fresh 4% PFA and determine the correct fixation time empirically. Store tissue at −80°C. Problem 4: Over/under segmented and missed nuclei a. Over segmentation: Too many nuclei have been fragmented by segmentDAPIimage() in Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m. b. Under segmentation: Too many nuclei have been merged by segmentDAPIimage() in Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m. c. Missed nuclei: Only few nuclei have been detected () in Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m. Potential solution a. Over segmentation: Test a larger value to the input ‘nucleiD’. b. Under segmentation: Test a smaller value to the input ‘nucleiD’. c. Missed nuclei: Test a smaller answer (lower number) to the third prompted question given by segmentDAPIimage(): 'What thresholded image looks best?. I.e., separates most nuclei while losing the least? Smoother boundaries are better than separation. (Enter image number from fig. here and press enter): ' If the nuclei segmentation results are still over or under segmenting the nuclei, after testing different sizes of nucleiD, slightly different answers to the 4 prompted questions can be tested. Double check that your images appear roughly like the images shown in the section ‘ ’, see A–1D. segmentDAPIimage() gives satisfactory results when used on confocal images comparable in quality to those provided in the test dataset. However, it does not represent the current state of the art for nucleus detection/segmentation. Please see below for a detailed explanation of the code. If your nuclei segmentation result is still not satisfactory after testing the different parameters as suggested above, then we recommend making a nuclei mask outside MATLAB with the more sophisticated machine learning tools in ilastik ( ) or StarDist , ( ). Alternatively, a nuclei mask can be made manually with the MATLAB app ‘ImageSegmenter’ (see section ‘ ’ for instructions on how to use ‘ImageSegmenter’). segmentDAPIimage() - point by point explanation of the code: The most important parameter is the user input 'nucleiD'. This number is used to calculate size of gauss and median filter kernels as well as morphological structuring elements. The steps of the segmentDAPIimage() function in pseudo-code: Gauss and median filter DAPI channel image (to reduce noise on a scale much smaller than a nucleus). Normalize intensities with respect to max inside region of interest (ROI) Calculate a range of Otsu threshold levels based on pixel values inside ROI. Show the user the thresholded images to make the user pick a threshold for a mask that includes everything that could be a nucleus. (We term this the ’optimist mask’). Show the user the thresholded images to make the user pick a threshold for a mask which includes only the very brightest pixels globally which should definitely be included in the final mask. (We term this the ’pessimist mask’). Show the user thresholded images done with regional thresholding in order to pick good threshold levels for including all nucleus pixels (‘regional threshold mask’) and for getting good separation of different nucleus respectively (‘regional pessimist mask’). Combine the 4 masks from above into one: First holes in 'regional threshold mask' are closed and objects smaller than 4 pixels are removed. Pixels in pessimist and regional pessimist are always included but pixels not in optimist are never included. Iterate through each connected component in the combined mask and do morphological operations on it (first opening and then smoothing of the edge). The radius of the strel disk (the morphological structuring element) for imopen() depends on the size of the current component/object. (A larger object is more likely to be several nucleus touching and a more aggressive morphological opening is needed to separate them). Take the distance transform of the output mask and gauss filter it and find regional max in it. Uses regional max as seeds to separate touching nuclei by seed-based watershed on the distance transform image. Remove very small objects (compared to expected nucleus size). Problem 5 Demo scripts will not run: a. MATLAB gives an error message: ‘Undefined function or variable … ’ when running demo scripts in before you begin. b. MATLAB gives an error message like: ‘kruskallwallis requires Statistics and Machine Learning Toolbox’ when running demo scripts in before you begin. c. MATLAB gives an error message: ‘Undefined function or variable bwskel’ when running demo scripts in in before you begin. Potential solution a. First, double check that ‘Current Folder’ (see left side of the MATLAB window) is your ‘TipScore-main’ folder downloaded from Github. Second, double check that your current folder contains the folders ‘raw_images_tif_files' and ‘outlines_mat_files’. Then, double check that your current folder contains all.m files downloaded from GitHub: segmentDAPIimage.m, tipScoreIm.m, returnTableWithCellInt.m, and returnTableWithCellPairInt.m. b. Type ‘ver’ and enter in the MATLAB command window and check that you have the needed toolboxes installed which are: ‘Signal Processing Toolbox’, ‘Image Processing Toolbox’, and ‘Statistics and Machine Learning Toolbox’. A free trial with all toolboxes can be ordered/used for 30 days. c. Type ‘ver’ and enter in the MATLAB command window and check that your MATLAB version is 2018a or later. The newest MATLAB version can be ordered as a free trial for 30 days.
Minimizing excessive background fluorescence in immuno fluorescent stained tissue sections can be challenging, and the problem can have several causes: a. Tissue drying, improper blocking or inadequate washing in Step 3–8: Immunostain tissue sections. b. Antibody nonspecific binding to endogenous immunoglobulins in the tissue. c. Tissue autofluorescence. d. Dirty coverslips or objectives in Step 9–10: Confocal microscopy of stained tissue sections.
a. In Step 3–8: Immunostain tissue sections: Avoid drying of tissue by working quickly, and only processing one slide at a time when working in the humidified chamber. Ensure that tissue stays moist during longer incubation steps by using a humidified chamber. Follow blocking and washing instructions carefully. b. Before you begin, carefully test the correct dilution factor for your primary and secondary antibodies following the manufacturer’s instructions. If these are lacking, test a dilution series of primary antibodies ranging from 1:50, 1:100, 1:200, 1:500, 1:1000, and a dilution series of secondary antibodies ranging from 1:500, 1:1000, 1:2000. Compare with secondary antibody only control and negative control to ensure that the staining is specific for the antigen of interest. Check that the staining pattern corresponds with prior knowledge of protein localization (i.e., nuclear or membrane localization etc.) If mouse tissue is probed with a primary antibody made in mouse, the endogenous immunoglobulins should first be blocked with a monovalent Fab fragment antibody of anti-mouse IgG, or IgG specific secondary antibodies used. c. Always include a control slide, which is not exposed to any antibodies (negative control) to check for autofluorescence. Tissue autofluorescence is generally much lower than fluorescence from fluorophores. Make sure to compare fluorescence level with no primary control and negative control, when setting up your confocal imaging settings in Step 9–10, keeping gain and laser output below the detection limit for autofluorescence. Note that background fluorescence should be similar in all samples and controls. If this is not the case, attention should be paid to sample preparation in order to eliminate this difference before acquiring images. d. In Step 3–8: Immunostain tissue sections: Check that coverslips are clean before, during and after mounting. Never touch the coverslip on the horizontal surface but handle it by holding the sides. In Step 9–10: Confocal microscopy of stained tissue sections: Clean the objectives carefully before and after use and use the appropriate immersion solution for your objective.
Unexpected staining patterns, not conforming to expected localization of protein can be due to: a. Unspecific binding of primary or secondary antibodies to endogenous immunoglobulins in the tissue during Step 3–8: Immunostain tissue sections. b. Cross-reactivity of primary and/or secondary antibodies to each other during Step 3–8: Immunostain tissue sections. c. Overlapping fluorescence spectra of fluorophores and/or inappropriate excitation/emission filters. Avoiding all these is essential for correct interpretation of results.
a. For avoiding unspecific binding see (2) in Problem 1 above. b. For avoiding cross-reactivity between antibodies the primary and secondary antibodies must be carefully selected before you begin: Primary antibodies should be produced in different species. If overlap in species is unavoidable, the following modification of the protocol can be employed: For two primary antibodies produced in rabbit, an extra step (requiring one more day of staining) can be added to the protocol: After blocking, the first rabbit primary antibody is incubated with samples alone, followed by a monovalent Fab fragment fluorescence conjugated secondary antibody against rabbit. This secondary antibody will in most cases block any further binding to the first rabbit primary antibody, and the protocol can be followed as normal from hereon. Ideally, secondary antibodies are all produced in the same species, excluding all species used for producing the primary antibodies. Our exemplary antibody panel is an example of a protocol where one secondary antibody is produced in the same species (goat), which another secondary antibody is directed against. However, most secondary antibodies available commercially are produced in donkey, and this is a good choice if none of the primary antibodies are produced in donkey. Additionally, the use of labeled secondary antibodies which are cross-adsorbed against the species of the experimental tissue, and the species of the other primary antibodies is recommended. c. In a multiple labeling protocol such as this, occurrence of identical staining patterns in two different channels during imaging should always be a red flag that fluorescence spectra of fluorophores may be overlapping, and/or that inappropriate excitation/emission filters are used. Always use secondary antibodies conjugated to stable fluorescent dyes that match the configuration of your fluorescent microscope. A good combination with minimal spectral overlap is Dylight-405I, Alexa Fluor-488, Rhodamine-RedX, Alexa- Fluor 647. Use of fluorophores with broader excitation/emission spectra (such as DAPI)) can give signal in another channel (so-called bleed-through). Narrow band-pass emission filters, narrow detector detection ranges, and sequential acquisition can be used to minimize bleed-through.
If fluorescence is undetectable or very low after staining it can be due to: a. The primary or secondary antibody is diluted too much in Step 3–8: Immunostain tissue sections. b. There is insufficient antigen present in tissue or the antigen is not accessible. c. The antigen is not compatible with the citrate buffer antigen retrieval method in Step 3–8: Immunostain tissue sections. d. Tissue has not been fixed appropriately in Step 1–2: Prepare tissue sections from mouse organs.
a. Before you begin, carefully test the correct dilution factor for your primary and secondary antibodies following the manufacturer’s instructions. If these are lacking, test a dilution series of primary antibody ranging from 1:50, 1:100, 1:200, 1:500, 1:1000, and a dilution series of secondary antibodies ranging from 1:500, 1:1000, 1: 2000. There is insufficient antigen present in tissue. b. In Step 3–8: Immunostain tissue sections: The blocking buffer recommended in this protocol does include detergent. If further permeabilization is needed, add a 10 min washing step before blocking, adding Triton X-100 or Tween 20 to PBS. Detergent concentration should be determined empirically, but typically range between 0,01 and 0,1%. c. In Step 3–8: Immunostain tissue sections: Omit or include the optional citrate buffer antigen retrieval step. d. In Step 1–2: Prepare tissue sections from mouse organs: Use fresh 4% PFA and determine the correct fixation time empirically. Store tissue at −80°C.
a. Over segmentation: Too many nuclei have been fragmented by segmentDAPIimage() in Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m. b. Under segmentation: Too many nuclei have been merged by segmentDAPIimage() in Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m. c. Missed nuclei: Only few nuclei have been detected () in Step 17–20: Define cell position using a DAPI channel image and segmentDAPIimage.m.
a. Over segmentation: Test a larger value to the input ‘nucleiD’. b. Under segmentation: Test a smaller value to the input ‘nucleiD’. c. Missed nuclei: Test a smaller answer (lower number) to the third prompted question given by segmentDAPIimage(): 'What thresholded image looks best?. I.e., separates most nuclei while losing the least? Smoother boundaries are better than separation. (Enter image number from fig. here and press enter): ' If the nuclei segmentation results are still over or under segmenting the nuclei, after testing different sizes of nucleiD, slightly different answers to the 4 prompted questions can be tested. Double check that your images appear roughly like the images shown in the section ‘ ’, see A–1D. segmentDAPIimage() gives satisfactory results when used on confocal images comparable in quality to those provided in the test dataset. However, it does not represent the current state of the art for nucleus detection/segmentation. Please see below for a detailed explanation of the code. If your nuclei segmentation result is still not satisfactory after testing the different parameters as suggested above, then we recommend making a nuclei mask outside MATLAB with the more sophisticated machine learning tools in ilastik ( ) or StarDist , ( ). Alternatively, a nuclei mask can be made manually with the MATLAB app ‘ImageSegmenter’ (see section ‘ ’ for instructions on how to use ‘ImageSegmenter’). segmentDAPIimage() - point by point explanation of the code: The most important parameter is the user input 'nucleiD'. This number is used to calculate size of gauss and median filter kernels as well as morphological structuring elements. The steps of the segmentDAPIimage() function in pseudo-code: Gauss and median filter DAPI channel image (to reduce noise on a scale much smaller than a nucleus). Normalize intensities with respect to max inside region of interest (ROI) Calculate a range of Otsu threshold levels based on pixel values inside ROI. Show the user the thresholded images to make the user pick a threshold for a mask that includes everything that could be a nucleus. (We term this the ’optimist mask’). Show the user the thresholded images to make the user pick a threshold for a mask which includes only the very brightest pixels globally which should definitely be included in the final mask. (We term this the ’pessimist mask’). Show the user thresholded images done with regional thresholding in order to pick good threshold levels for including all nucleus pixels (‘regional threshold mask’) and for getting good separation of different nucleus respectively (‘regional pessimist mask’). Combine the 4 masks from above into one: First holes in 'regional threshold mask' are closed and objects smaller than 4 pixels are removed. Pixels in pessimist and regional pessimist are always included but pixels not in optimist are never included. Iterate through each connected component in the combined mask and do morphological operations on it (first opening and then smoothing of the edge). The radius of the strel disk (the morphological structuring element) for imopen() depends on the size of the current component/object. (A larger object is more likely to be several nucleus touching and a more aggressive morphological opening is needed to separate them). Take the distance transform of the output mask and gauss filter it and find regional max in it. Uses regional max as seeds to separate touching nuclei by seed-based watershed on the distance transform image. Remove very small objects (compared to expected nucleus size).
Demo scripts will not run: a. MATLAB gives an error message: ‘Undefined function or variable … ’ when running demo scripts in before you begin. b. MATLAB gives an error message like: ‘kruskallwallis requires Statistics and Machine Learning Toolbox’ when running demo scripts in before you begin. c. MATLAB gives an error message: ‘Undefined function or variable bwskel’ when running demo scripts in in before you begin.
a. First, double check that ‘Current Folder’ (see left side of the MATLAB window) is your ‘TipScore-main’ folder downloaded from Github. Second, double check that your current folder contains the folders ‘raw_images_tif_files' and ‘outlines_mat_files’. Then, double check that your current folder contains all.m files downloaded from GitHub: segmentDAPIimage.m, tipScoreIm.m, returnTableWithCellInt.m, and returnTableWithCellPairInt.m. b. Type ‘ver’ and enter in the MATLAB command window and check that you have the needed toolboxes installed which are: ‘Signal Processing Toolbox’, ‘Image Processing Toolbox’, and ‘Statistics and Machine Learning Toolbox’. A free trial with all toolboxes can be ordered/used for 30 days. c. Type ‘ver’ and enter in the MATLAB command window and check that your MATLAB version is 2018a or later. The newest MATLAB version can be ordered as a free trial for 30 days.
Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Pia Nyeng ( [email protected] ). Materials availability All the materials used in this protocol are commercially available.
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Pia Nyeng ( [email protected] ).
All the materials used in this protocol are commercially available.
|
Molecular Epidemiology of Plasmid-Mediated Types 1 and 3 Fimbriae Associated with Biofilm Formation in Multidrug Resistant | a96bee19-d34d-46f7-99fe-029ae400fe00 | 9603762 | Microbiology[mh] | Biofilms are structured consortiums of embedded bacteria that are a survival strategy for many bacterial and fungal species, and are an adaptive response to constantly changing and potentially hostile environments ( ). The biofilm lifestyle allows the bacteria to tolerate disinfectants and antimicrobials, as well as mammalian host defenses, and are therefore difficult to treat clinically ( , ). Biofilms that form within the host have been implicated in serious and persistent infectious diseases including urinary tract infections (UTI), cystic fibrosis, and endocarditis ( ). In the environment, biofilms can serve as reservoirs for pathogens and can contaminate surfaces and the water environment ( ). Furthermore, biofilms are also involved in food contamination of fresh fruits and vegetables as well as animal-derived food products ( , , ). Hence, it is critically important to explore the potential molecular mechanisms associated with biofilm formation and to design or screen antibiofilm molecules with the goal of minimizing and eradicating biofilm-related infections. Most current research concerning biofilm-related infections is typically focused on chromosomal biofilm-associated factors of biofilm-forming bacteria ( , ). Interestingly, several studies have also reported that conjugative plasmids can promote biofilm formation on abiotic substance through the formation of surface fimbriae ( ). Pili or fimbriae play versatile roles in bacterial physiology, and these can be associated with attachment, invasion, and cell motility, as well as with biofilm formation ( , ). In particular, the gene clusters that encode types 1 and 3 fimbriae in Enterobacteriaceae members are not only chromosomally encoded but also on plasmids. The first plasmid-borne type 1 gene cluster was found on an IncFII plasmid (pE110019_66) from an atypical Enteropathogenic Escherichia coli isolate ( ). Subsequently, our laboratory identified a similar plasmid-borne type 1 fimbriae cluster fimACDH that could promote biofilm formation and was confirmed using the novel EZ-Tn5 transposon technique ( ). There is currently a paucity of information regarding the prevalence and distribution of plasmid-encoded type 1 fimbrial gene in the Enterobacteriaceae. However, the first plasmid-borne type 3 fimbriae encoded by the mrkABCDF operon was identified as the IncX1 plasmid pOLA52 from a pig E. coli isolate in Denmark ( , ). This operon was found to enhance biofilm formation and likely was mobilized by a composite transposon Tn 6011 , from the chromosome of Klebsiella pneumoniae onto the IncX1 plasmid. The mrkABCDF operon has since been found in the chromosome for other Enterobacteriaceae as well as on conjugative plasmids ( , ). These data have indicated that type 1/3 fimbriae-encoding gene clusters can be located on conjugative plasmids and this could accelerate the spread of these two operons among Enterobacteriaceae members. In this present study, we investigated the biofilm forming abilities and drug susceptibilities in a large collection of Enterobacteriaceae isolates from diseased food producing animals. We further explored the prevalence, function, evolution, and transmission of plasmid-encoded type 1/3 fimbriae.
Biofilm formation and antimicrobial susceptibility. We examined a total of 1,753 Enterobacteriaceae isolates for biofilm formation in Luria Bertani (LB) medium using crystal violet staining. We found that 123 (7.02%) were strong-biofilm forming (OD 590 ≥ 0.38) strains, and all these were identified as E. coli . There were 100 (5.7%) isolates that were considered moderate-biofilm formers (0.19 ≤ OD 590 < 0.38), and the remaining 1,530 isolates (87.28%) were weak or non-biofilm producers (OD 590 ≤ 0.095). The strong biofilm producers were grouped across three time periods (2002–2010, 2010–2018, 2019) with a prevalence of 7.19% (41/570), 6.77% (31/458), and 7.02% (51/725), respectively ( ). The strong biofilm-formers grouped according to source were 8.11% (39/481) in pigs, 7.32% (65/888) in ducks, 5.05% (11/218) in chickens, and 4.82% (8/166) in geese ( ). The 123 strong and 100 moderate biofilm-forming isolates and 408 randomly selected weak and none biofilm producers were selected for antimicrobial susceptibilities. Most of the tested isolates were resistant to ampicillin, tetracycline, florfenicol, sulfamethoxazole-trimethoprim, doxycycline, and ciprofloxacin (>70%). In contrast, resistance was lower to gentamicin (42.95%), ceftiofur (39.14%), olaquindox (38.03%), apramycin (22.98%), colistin (13.79%), amikacin (10.78%), fosfomycin (14.74%), and meropenem (0.79%). Interestingly, the group of strong biofilm producers had significantly higher prevalence of resistance to florfenicol and olaquindox and a lower prevalence of resistance to ceftiofur, ciprofloxacin, fosfomycin, apramycin, and sulfamethoxazole-trimethoprim when compared to the other isolates ( P < 0.05) ( ). WGS analysis of strongly biofilm-forming E. coli isolates. A total of 42 strong biofilm-forming E. coli isolates were selectively sequenced. Whole-genome sequencing (WGS) analysis revealed that mrkABCDF (5.45 kb) and fimACDH (4.93 kb) were identified among 15 (28.57%) and 2 (4.76%) isolates, respectively. Additionally, two isolates harbored incomplete type 3 fimbriae operons. This group of 42 strong biofilm producers possessed 29 distinct antibiotic resistance genes (ARGs), and most carried multiple ARGs including the clinically relevant mcr-1 ( n = 11), bla CTX-M-9G/1G ( n = 8), oqxAB ( n = 13), qnrS ( n = 23), qnrB6 ( n = 1), and floR ( n = 33). In addition, bla CTX-M-55 ( n = 2), oqxAB ( n = 7), qnrS ( n = 5), qnrB6 ( n = 1), and floR ( n = 12) were also present among a subgroup of 15 mrk -positive isolates. Two fim isolates possessed both oqxAB and floR ( ). Multilocus sequence typing (MLST) analysis revealed 24 sequence types (STs) including a new ST (ST12379) among the 42 strong biofilm-forming E. coli isolates, and ST1286 was the most prevalent (11, 26.19%), followed by ST10 (5, 11.90%). These 42 E. coli isolates were classified into two phylogenetic groups with the majority belonging to the commensal phylogenetic groups A (38/42, 90.48%) and B1 (4/42, 9.52%). We further analyzed population structures by constructing phylogenetic trees based on the core genomes of these 42 strong biofilm-forming E. coli isolates. Bayesian analysis revealed four distinct lineages, and the major lineage (lineage I) contained 34 isolates that included 11 ST1286 E. coli isolates that were distributed across 5 different sampling times and that possessed extremely high genetic similarity (SNPs ≤ 978). The 15 mrk and 2 fim isolates comprised 14 different STs and were distributed among 3 lineages indicating a high degree of WGS heterogeneity ( ). Function and prevalence of type 1/3 fimbriae operons. To determine whether the mrk and fim operons were direct contributors to strong biofilm-forming ability, we cloned the mrkABCDF and fimACDH gene clusters from 2 strong E. coli biofilm forming strains that were then introduced onto a plasmid vector into the weak biofilm forming E. coli strain DH5α. Interestingly, both the mrk and fim gene cassettes converted DH5α to a strong biofilm former ( and ). Scanning electron microscopy (SEM) photomicrographs confirmed that the enhanced biofilm growth of transformants harboring mrkABCDF and fimACDH correlated with a more densely packed arrangement of cells compared with the empty vector control ( ). In our population of 123 E. coli that were strong biofilm producers, we were able to identify complete mrkABCDF and fimACDH clusters in 43 (34.96%) and 7 (5.7%) isolates, respectively. Incomplete mrkABCDF and fimACDH operons were found in 18 (14.63%) and 11 (8.94%) isolates, respectively. None of the isolates carried complete mrk and fim operons simultaneously (Table S2). We randomly selected 13 complete mrk and 7 complete fim positive strains, and transconjugants harboring fim and mrk operons were successfully obtained from 5 of 7 (71.43%) and 2 of 13 (15.38%) isolates, respectively. All 7 transconjugants carrying fim or mrk operons were strong biofilm producers in comparison with the recipient strains E. coli C600 (Fig. S1). In addition, all the five transconjugants carrying fim operons showed resistance to ampicillin, florfenicol, sulfamethoxazole/trimethoprim and olaquindox, and three ones were resistant to gentamycin (Table S2). The resistant phenotype of florfenicol, colistin, tetracycline, and sulfamethoxazole/trimethoprim were co-transferred with mrk operons among two transconjugants carrying mrk operons, and one also reduced susceptibility to ampicillin and ceftiofur, and the other one was resistant to gentamycin. Furthermore, we explore the distribution of mrkABCDF and fimACDH operons among 6 Enterobacteriaceae using archived WGS data. The prevalence of mrkABCDF was highest in K. pneumoniae (100%, 9457/9457) followed by Enterobacter cloacae (6.54%, 14/214), C. freundii (3.94%, 13/330), and E. coli (0.62%, 199/21387). In contrast, fimACDH was present in E. cloacae (62.15%, 133/214) followed by Citrobacter freundii (0.30%, 1/330), E. coli (0.28%, 60/21387), and K. pneumoniae (0.08%, 8/9457). Neither mrkABCDF nor fimACDH was found among Salmonella spp. ( n = 12,535) or Proteus mirabilis ( n = 265) (Table S3). Characterization of plasmids encoding types 1 and 3 fimbriae and phylogenetic analyses. E. coli strains 2010FS332 carrying mrkABCDF and strain 2005FS026 carrying fimACDH were selected for further sequencing using the Nanopore sequencing platform. The combined MiSeq and Nanopore sequencing data yielded the complete sequence of the endogenous plasmids from these strains; p2010FS332 (accession no. OK217279 ) and p2005FS026 (accession no. OK236218 ), respectively. The mrk -positive plasmid p2010FS332 was an IncX1 type (48.958 kb), and its backbone sequence was almost identical to an mrk -bearing IncX1 plasmid from the E. coli isolate pMAS2027 (accession no. FJ666132 ). The basic core structure of the IncX plasmid group was shared and included pir - bis - par - hns - topB - taxB - pilX - actX - taxCA . The primary differences between these plasmids were the region located between resolvase and hns where mrkABCDF was embedded and bracketed by IS 903 and insA/B ( ). The remaining 7/12 E. coli isolates harboring mrkABCDF and IncX1 replicons were highly similar to p2010FS332 and to 8 mrk -IncX1 plasmids archived in GenBank ( ). We further explored the distribution and evolution of mrkABCDF using 50 mrkABCDF operons from the GenBank data archive. This group contained 7 chromosomally-encoded mrk operons that were represented in numerous species including K. pneumoniae , K. aerogenes , E. hormaechei , E. coli , C. freundii , and C. koseri. The remaining 43 strains contained plasmid-encoded mrk gene clusters that were present on numerous plasmid replicon types including IncX1, IncFIB, IncFIA, IncFII, IncHI1, IncHI2, IncA/C, and IncR. Strains possessing mrkABCDF operon had diverse origins and it included humans, food animals (pig, cattle, chicken, duck, goose), food and environment (water), with water being the most predominant one ( n = 18, 36%), followed by humans ( n = 15, 30%) and pigs ( n = 8, 16%) ( and Fig. S2a). The sampling locations were also from 13 countries, and the most predominant one was UK ( n = 23, 46%), followed by China ( n = 6, 12%) ( and Fig. S2b). The fimACDH -carrying plasmid p2005FS026 that we completely sequenced (150.191 kb) contained a typical IncFII replication region (2.7 kb), which comprises repA1 , repA4 , repA3 , and repA2 . This plasmid was almost identical to fim -carrying IncFII plasmid p253 (accession no. MT648288 ) from a pig E. coli isolate reported in our previous study ( ) except for an inversion of the multidrug resistance region (MRR) (~33.5 kb), containing 11 ARGs including oqxAB and floR ( ). The silABCESP (~12.5 kb) and fimACDH were upstream and downstream of the MRR, respectively, and the former were bracketed by two copies of insA/B while the latter were flanked by IS Ec63 and insA/B . Furthermore, the p2005FS026 and p253 backbones were highly similar to a fim -carrying IncFII plasmid pE11019 that completely lacked ARG sequences (accession no. CP035752 ). The remaining single E. coli isolate from this group that harbored the fim operon and IncFII replicons in our study was almost identical to the mrk plasmids p2005FS026 and p253 ( ). We compared our data with the 6 fimACDH operons available in the GenBank data archive, and these 6 included 2 that were located on chromosomes ( E. cloacae and E. hormaechei ) and 4 present on plasmids (3 IncFII and 1 IncX1 replicons). These 6 fimACDH strains were obtained from ducks, shrimp, and food from 5 countries. A phylogenetic reconstruction indicated that the 2 fim operons found in our study and 5 of the 6 archived sequences clustered together with an amino acid sequence identity of ≥94.5% ( ).
We examined a total of 1,753 Enterobacteriaceae isolates for biofilm formation in Luria Bertani (LB) medium using crystal violet staining. We found that 123 (7.02%) were strong-biofilm forming (OD 590 ≥ 0.38) strains, and all these were identified as E. coli . There were 100 (5.7%) isolates that were considered moderate-biofilm formers (0.19 ≤ OD 590 < 0.38), and the remaining 1,530 isolates (87.28%) were weak or non-biofilm producers (OD 590 ≤ 0.095). The strong biofilm producers were grouped across three time periods (2002–2010, 2010–2018, 2019) with a prevalence of 7.19% (41/570), 6.77% (31/458), and 7.02% (51/725), respectively ( ). The strong biofilm-formers grouped according to source were 8.11% (39/481) in pigs, 7.32% (65/888) in ducks, 5.05% (11/218) in chickens, and 4.82% (8/166) in geese ( ). The 123 strong and 100 moderate biofilm-forming isolates and 408 randomly selected weak and none biofilm producers were selected for antimicrobial susceptibilities. Most of the tested isolates were resistant to ampicillin, tetracycline, florfenicol, sulfamethoxazole-trimethoprim, doxycycline, and ciprofloxacin (>70%). In contrast, resistance was lower to gentamicin (42.95%), ceftiofur (39.14%), olaquindox (38.03%), apramycin (22.98%), colistin (13.79%), amikacin (10.78%), fosfomycin (14.74%), and meropenem (0.79%). Interestingly, the group of strong biofilm producers had significantly higher prevalence of resistance to florfenicol and olaquindox and a lower prevalence of resistance to ceftiofur, ciprofloxacin, fosfomycin, apramycin, and sulfamethoxazole-trimethoprim when compared to the other isolates ( P < 0.05) ( ).
E. coli isolates. A total of 42 strong biofilm-forming E. coli isolates were selectively sequenced. Whole-genome sequencing (WGS) analysis revealed that mrkABCDF (5.45 kb) and fimACDH (4.93 kb) were identified among 15 (28.57%) and 2 (4.76%) isolates, respectively. Additionally, two isolates harbored incomplete type 3 fimbriae operons. This group of 42 strong biofilm producers possessed 29 distinct antibiotic resistance genes (ARGs), and most carried multiple ARGs including the clinically relevant mcr-1 ( n = 11), bla CTX-M-9G/1G ( n = 8), oqxAB ( n = 13), qnrS ( n = 23), qnrB6 ( n = 1), and floR ( n = 33). In addition, bla CTX-M-55 ( n = 2), oqxAB ( n = 7), qnrS ( n = 5), qnrB6 ( n = 1), and floR ( n = 12) were also present among a subgroup of 15 mrk -positive isolates. Two fim isolates possessed both oqxAB and floR ( ). Multilocus sequence typing (MLST) analysis revealed 24 sequence types (STs) including a new ST (ST12379) among the 42 strong biofilm-forming E. coli isolates, and ST1286 was the most prevalent (11, 26.19%), followed by ST10 (5, 11.90%). These 42 E. coli isolates were classified into two phylogenetic groups with the majority belonging to the commensal phylogenetic groups A (38/42, 90.48%) and B1 (4/42, 9.52%). We further analyzed population structures by constructing phylogenetic trees based on the core genomes of these 42 strong biofilm-forming E. coli isolates. Bayesian analysis revealed four distinct lineages, and the major lineage (lineage I) contained 34 isolates that included 11 ST1286 E. coli isolates that were distributed across 5 different sampling times and that possessed extremely high genetic similarity (SNPs ≤ 978). The 15 mrk and 2 fim isolates comprised 14 different STs and were distributed among 3 lineages indicating a high degree of WGS heterogeneity ( ).
To determine whether the mrk and fim operons were direct contributors to strong biofilm-forming ability, we cloned the mrkABCDF and fimACDH gene clusters from 2 strong E. coli biofilm forming strains that were then introduced onto a plasmid vector into the weak biofilm forming E. coli strain DH5α. Interestingly, both the mrk and fim gene cassettes converted DH5α to a strong biofilm former ( and ). Scanning electron microscopy (SEM) photomicrographs confirmed that the enhanced biofilm growth of transformants harboring mrkABCDF and fimACDH correlated with a more densely packed arrangement of cells compared with the empty vector control ( ). In our population of 123 E. coli that were strong biofilm producers, we were able to identify complete mrkABCDF and fimACDH clusters in 43 (34.96%) and 7 (5.7%) isolates, respectively. Incomplete mrkABCDF and fimACDH operons were found in 18 (14.63%) and 11 (8.94%) isolates, respectively. None of the isolates carried complete mrk and fim operons simultaneously (Table S2). We randomly selected 13 complete mrk and 7 complete fim positive strains, and transconjugants harboring fim and mrk operons were successfully obtained from 5 of 7 (71.43%) and 2 of 13 (15.38%) isolates, respectively. All 7 transconjugants carrying fim or mrk operons were strong biofilm producers in comparison with the recipient strains E. coli C600 (Fig. S1). In addition, all the five transconjugants carrying fim operons showed resistance to ampicillin, florfenicol, sulfamethoxazole/trimethoprim and olaquindox, and three ones were resistant to gentamycin (Table S2). The resistant phenotype of florfenicol, colistin, tetracycline, and sulfamethoxazole/trimethoprim were co-transferred with mrk operons among two transconjugants carrying mrk operons, and one also reduced susceptibility to ampicillin and ceftiofur, and the other one was resistant to gentamycin. Furthermore, we explore the distribution of mrkABCDF and fimACDH operons among 6 Enterobacteriaceae using archived WGS data. The prevalence of mrkABCDF was highest in K. pneumoniae (100%, 9457/9457) followed by Enterobacter cloacae (6.54%, 14/214), C. freundii (3.94%, 13/330), and E. coli (0.62%, 199/21387). In contrast, fimACDH was present in E. cloacae (62.15%, 133/214) followed by Citrobacter freundii (0.30%, 1/330), E. coli (0.28%, 60/21387), and K. pneumoniae (0.08%, 8/9457). Neither mrkABCDF nor fimACDH was found among Salmonella spp. ( n = 12,535) or Proteus mirabilis ( n = 265) (Table S3).
E. coli strains 2010FS332 carrying mrkABCDF and strain 2005FS026 carrying fimACDH were selected for further sequencing using the Nanopore sequencing platform. The combined MiSeq and Nanopore sequencing data yielded the complete sequence of the endogenous plasmids from these strains; p2010FS332 (accession no. OK217279 ) and p2005FS026 (accession no. OK236218 ), respectively. The mrk -positive plasmid p2010FS332 was an IncX1 type (48.958 kb), and its backbone sequence was almost identical to an mrk -bearing IncX1 plasmid from the E. coli isolate pMAS2027 (accession no. FJ666132 ). The basic core structure of the IncX plasmid group was shared and included pir - bis - par - hns - topB - taxB - pilX - actX - taxCA . The primary differences between these plasmids were the region located between resolvase and hns where mrkABCDF was embedded and bracketed by IS 903 and insA/B ( ). The remaining 7/12 E. coli isolates harboring mrkABCDF and IncX1 replicons were highly similar to p2010FS332 and to 8 mrk -IncX1 plasmids archived in GenBank ( ). We further explored the distribution and evolution of mrkABCDF using 50 mrkABCDF operons from the GenBank data archive. This group contained 7 chromosomally-encoded mrk operons that were represented in numerous species including K. pneumoniae , K. aerogenes , E. hormaechei , E. coli , C. freundii , and C. koseri. The remaining 43 strains contained plasmid-encoded mrk gene clusters that were present on numerous plasmid replicon types including IncX1, IncFIB, IncFIA, IncFII, IncHI1, IncHI2, IncA/C, and IncR. Strains possessing mrkABCDF operon had diverse origins and it included humans, food animals (pig, cattle, chicken, duck, goose), food and environment (water), with water being the most predominant one ( n = 18, 36%), followed by humans ( n = 15, 30%) and pigs ( n = 8, 16%) ( and Fig. S2a). The sampling locations were also from 13 countries, and the most predominant one was UK ( n = 23, 46%), followed by China ( n = 6, 12%) ( and Fig. S2b). The fimACDH -carrying plasmid p2005FS026 that we completely sequenced (150.191 kb) contained a typical IncFII replication region (2.7 kb), which comprises repA1 , repA4 , repA3 , and repA2 . This plasmid was almost identical to fim -carrying IncFII plasmid p253 (accession no. MT648288 ) from a pig E. coli isolate reported in our previous study ( ) except for an inversion of the multidrug resistance region (MRR) (~33.5 kb), containing 11 ARGs including oqxAB and floR ( ). The silABCESP (~12.5 kb) and fimACDH were upstream and downstream of the MRR, respectively, and the former were bracketed by two copies of insA/B while the latter were flanked by IS Ec63 and insA/B . Furthermore, the p2005FS026 and p253 backbones were highly similar to a fim -carrying IncFII plasmid pE11019 that completely lacked ARG sequences (accession no. CP035752 ). The remaining single E. coli isolate from this group that harbored the fim operon and IncFII replicons in our study was almost identical to the mrk plasmids p2005FS026 and p253 ( ). We compared our data with the 6 fimACDH operons available in the GenBank data archive, and these 6 included 2 that were located on chromosomes ( E. cloacae and E. hormaechei ) and 4 present on plasmids (3 IncFII and 1 IncX1 replicons). These 6 fimACDH strains were obtained from ducks, shrimp, and food from 5 countries. A phylogenetic reconstruction indicated that the 2 fim operons found in our study and 5 of the 6 archived sequences clustered together with an amino acid sequence identity of ≥94.5% ( ).
The current study investigated the biofilm forming ability and the potential molecular mechanism among Enterobacteriaceae isolates from a large collection of diseased food producing animals. We found that 7.01% of the isolates were strong biofilm producing E. coli and the prevalence of this phenotype was less than that found for uropathogenic E. coli (UPEC) isolates (24.8%) ( ). The plasmid-mediated type 3 fimbriae encoded by the mrkABCDF operon and type 1 fimbriae encoded by fimACDH operon were identified to confer a strong biofilm-forming phenotype to laboratory strains of E. coli as previously described ( , ). The complete mrkABCDF and fimACDH clusters were present in 34.96% and 5.7% of all strong biofilm producing E. coli , respectively, indicating that these two operons are major biofilm-associated factors in E. coli . In the present study, most of the strong biofilm producers were also MDR strains. In particular, they possessed significantly higher resistance levels to olaquindox and florfenicol when compared with non-strong biofilm producers. Consistently, WGS analysis showed that the strong biofilm producers harbored multiple ARGs including the florfenicol resistance gene floR and the MDR efflux pump oqxAB conferring resistance to olaquindox and ciprofloxacin. Furthermore, oqxAB and floR were identified to colocate with mrkABCDF or fimACDH on conjugative IncX1 and IncFII plasmids in the present and previous studies ( , , , ). Olaquindox has been used as a growth promoter in pigs until May 2018 in China, and florfenicol was also used in Chinese pig farms especially for the treatment of swine respiratory disease. The close genomic proximity of oqxAB and floR to biofilm-encoding genes indicated that these traits could be coselected under florfenicol and olaquindox selective pressure. This might partially explain the reason why E. coli isolates of pig origin had more strong biofilm producers than from other origins in the present study. The cospread of ARGs and biofilm-associated factors on conjugative plasmids is especially worrisome because this could compromise the already few treatment options available for infections caused by plasmid-encoded type 1 and 3 fimbria-producing MDR E. coli . The mrk gene clusters have been associated with a wide range of conjugative plasmid types including IncX1, IncFIA, and IncFIB ( , ). In the current study, the mrkABCDF operons (61.5%) were mostly located on IncX1 plasmids with highly similar backbones and were also highly similar to mrk -carrying IncX1 plasmids from 3 members of Enterobacteriaceae of diverse origins including humans, food animals, food, and pets from 6 different countries. The presence of mrkABCDF was found to enhance conjugation frequencies by promoting biofilm formation, and this might facilitate the spread of mrk -positive plasmids among the Enterobacteriaceae ( , ). These results indicated that mrkABCDF has been mobilized to diverse species of Enterobacteriaceae via mobile genetic elements and in particular, conjugative IncX1 plasmids. Indeed, the MrkABCDF displayed high levels of amino acid identities to their counterparts on plasmids of differing replicon types and on chromosomes of diverse Enterobacteriaceae species that originated from food animals, humans, food and the environment across the globe, in particular UK and China. Furthermore, mrkABCDF was prevalent in 4/6 Enterobacteriaceae species especially K. pneumoniae (100%) from the GenBank data archive NCBI. Taken together, these results were consistent with the most likely origin for the mrk operon, K. pneumoniae , which has then become widespread among the Enterobacteriaceae, including E. coli ( , ). Both type 1 and 3 fimbriae belong to the group of chaperone-ushered pili that are assembled at the outer membrane by a periplasmic chaperone and an usher protein ( , , ). The plasmid-encoded type 3 fimbriae are comprised of the major (MrkA) and minor (MrkF) subunits, as well as chaperone (MrkB), usher (MrkC), and adhesin proteins (MrkD) ( , ). The biofilm formation was deficient when the insertion was localized to the mrk operon, specifically in the mrkA , mrkC or mrkD genes ( , ). By contrast, the plasmid-encoded Type 1 fimbriae are encoded by 4 contiguous genes ( fimACDH ) where FimC and FimD play the roles of putative chaperone and usher, respectively, however, roles for FimA and FimH have not been identified. The biofilm formation in E. coli was determined by an intact fimACDH gene operon in our previous study ( ). Unlike plasmid-mediated fimACDH , the chromosomally-encoded type 1 fimbriae is frequently present in the majority of Enterobacteriaceae ( , , ), and significant heterogeneity exists between DNA sequences encoding type 1 fimbriae among Enterobacteriaceae ( , ). The biosynthesis and structure of chromosomal type 1 fimbriae in E. coli and S. Typhimurium have been extensively studied, and it encodes a major subunit FimA and a minor tip adhesin FimH ( , , ). A future goal is to explore the structures and function of the type 1 fimbrial encoded by the fimACDH operon, and the roles of each of FimACDH in biofilm formation. In conclusion, the plasmid-encoded type 3 fimbriae encoded by mrkABCDF and type 1 fimbriae encoded by fimACDH were major contributors to enhancing biofilm formation among strong biofilm E. coli from diseased food producing animals. These two operons were found to coexist with ARGs on conjugatable IncX1/IncFII plasmids with similar backbones, respectively. Our findings also revealed that these two operons, in particular mrk , were present both on plasmids of various replicon types and on chromosomes from diverse Enterobacteriaceae species from numerous origins and countries, indicating a potential antibiofilm target. This is the first comprehensive report of the prevalence, evolution, and transmission of plasmid-encoded type 1 and 3 fimbriae among the Enterobacteriaceae. Future studies are necessary to investigate the transmission and function of these two operons to better understand their potential threats to public health and for the screening of small-molecule biofilm inhibitors.
Bacterial strains, biofilm formation assay, and antimicrobial susceptibility testing. We isolated 1,753 non-duplicate Enterobacteriaceae strains from diseased food-producing animals that included 1,272 avian samples (888 duck, 218 chicken, and 166 goose) and 481 samples from pigs from >100 farms throughout Guangdong province, China, from 2002 to 2019. These isolates were recovered directly from fecal samples or swabs from animal organs (liver, heart, or lung) from farms or diagnostic laboratories as previously described ( ). Quantification of static biofilm production was performed using 96-well flat-bottom polystyrene microtiter plates using crystal violet, and the extent of biofilm formation was determined as previously described ( ). All strong biofilm producers were identified by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry and 16S rRNA gene sequence-based analyses. Antimicrobial susceptibilities of the tested isolates were determined using the agar dilution method, and the results were interpreted according to the Clinical and Laboratory Standards Institute (CLSI, 2018: M100-S28) ( ), veterinary CLSI (VET01-A4E/VET01-S3E) ( ) (supplemental Materials and Methods). Genetic characterization of strongly biofilm-forming isolates. Total genomic DNA of 42 strong biofilm-forming E. coli isolates were extracted by using the TIANamp Bacteria DNA Kit (Tiangen, China). The quality and concentration of the bacterial genomic DNA were evaluated via electrophoresis on a 1% agarose gel and analysis on a NanoDrop2000 system (Thermo Scientific, Waltham, MA, USA) and a Qubit 3 Fluorometer (Thermo Scientific, Waltham, USA). Illumina libraries were prepared and sequenced using Illumina HiSeq 4000 platform (San Diego, CA, USA) as 150-bp paired-end reads. Adaptors and low-quality bases were trimmed with Trimmomatic v0.38 ( ), and reads qualities were assessed using FastQC v0.11.6 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and MultiQC v1.7 ( ). High-quality reads were de novo assembled with SPAdes v3.6.2 to generate genome contigs ( ). Sequencing quality and statistics per isolates were checked using the QualiMap v2.2.2 ( ). Genome assemblies’ quality was assessed with QUAST v5.0.2 ( ) and contigs of less than 200 bp were filtered out. Examinations of known plasmid replicon, and antibiotic resistance genes were carried out using ABRicate v1.0.1 ( https://github.com/tseemann/abricate ) (>80% identity and >80% coverage). Reference sequences of plasmid and antibiotic resistance genes were from databases PlasmidFinder ( ), and ResFinder ( ), respectively. Multilocus sequence typing (MLST) were performed by MLST v2.19.0 ( https://github.com/tseemann/mlst ). In addition, in silico phylotyping of E. coli was carried out using the Clermon Typing method ( ). Further, assemblies from all isolates were mapped to the reference sequence 2013FS003 using Snippy v4.6.0 ( https://github.com/tseemann/snippy ). Single nucleotide polymorphisms (SNPs) were called and recombinant regions were removed using Gubbins v2.4.1 ( ). RAxML v8.2.12 (GTRGAMMA substitution model) with 100 bootstrap replicates to assess support was used to construct a phylogenic tree and visualized with iTOL v4 ( ). The population structure of each phylogenetic tree was defined using hier-BAPS v6.0 ( ). Characterization of plasmids carrying fim / mrk operons. The transferability of type 1/3 fimbriae operons was examined using conjugation experiments using the streptomycin-resistant E. coli C600 as the recipient. All transconjugants were tested for antimicrobial susceptibility and biofilm formation as described above. To obtain the complete sequence of plasmids encoding type 1/3 fimbriae, two isolates (2010FS332 and 2005FS026) encoding type 1 and 3 fimbriae, respectively, were selected for long-read sequencing using ONT Gridion Platform (Nanopore, Oxford, UK) ( ). DNA extraction and quality control were performed as previously described. An Oxford Nanopore MinION 9.4.1 flowcell and SQK-RBK004 rapid sequencing kit was used with base calling by Guppy v3.1.5 ( https://nanoporetech.com/nanopore-sequencing-data-analysis ). De novo hybrid assembly using both short reads (Illumina) and long reads (ONT) was performed using Unicycler v0.4.4 ( ). The Contigs were further polished with Pilon v1.23 ( ) through three iterations. Gene prediction and annotated were performed by the RAST tool ( ) ( https://rast.nmpdr.org/ ), ISFinder ( ), BLAST ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ) and rechecked manually. The sequence comparison and map generation of plasmids encoding type 1/3 fimbriae were performed using Easyfig ( ) and the BLAST Ring Image Generator ( ). The complete sequence of plasmid p2010FS332 and p2005FS026 has been deposited in GenBank under accession no. OK217279 and accession no. OK236218, respectively. Function, prevalence, and phylogeny of type 1/3 fimbriae operon genes. The mrk and fim operons from the strong biofilm-forming E. coli strains 2010FS332 and 2005FS026, respectively, were amplified, and PCR amplicons were then ligated to expression vector pMD19-T and introduced into E. coli strain DH5α by chemical transformation (Table S1). Colonies were selected on LB agar supplemented with ampicillin (100 mg/mL). Transformants were randomly screened for the presence of the mrk and fim operons by PCR assay and sequencing, and the resultant clones were tested for biofilm formation as described above. Biofilm structures were further examined using SEM (supplemental Materials and Methods). To understand the spread of the mrk / fim operons, all E. coli that were strong biofilm producers were screened for the presence of mrk and fim genes using PCR (Table S1). Additionally, mrk and fim operons were in silico identified from Enterobacteriaceae members that possessed WGS data from a public database ( https://www.ncbi.nlm.nih.gov/datasets ). Phylogenetic correlations were constructed between concatenated mrk and fim operons from this study and those from GenBank using maximum likelihood (ML) trees based on predicted amino acid sequences (supplemental Materials and Methods). Statistical analysis. Statistical significance for comparison of prevalence data and proportions was performed in R using the χ 2 test. Other data were statistically analyzed using GraphPad Prism v8.0.1 software. P values of <0.05 were deemed to be statistically significant. Specific tests of statistical significance are detailed in figure legends and table footnotes. Data availability. All genome assemblies of these strains were deposited in GenBank and were registered under BioProject accession number PRJNA747154 .
We isolated 1,753 non-duplicate Enterobacteriaceae strains from diseased food-producing animals that included 1,272 avian samples (888 duck, 218 chicken, and 166 goose) and 481 samples from pigs from >100 farms throughout Guangdong province, China, from 2002 to 2019. These isolates were recovered directly from fecal samples or swabs from animal organs (liver, heart, or lung) from farms or diagnostic laboratories as previously described ( ). Quantification of static biofilm production was performed using 96-well flat-bottom polystyrene microtiter plates using crystal violet, and the extent of biofilm formation was determined as previously described ( ). All strong biofilm producers were identified by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry and 16S rRNA gene sequence-based analyses. Antimicrobial susceptibilities of the tested isolates were determined using the agar dilution method, and the results were interpreted according to the Clinical and Laboratory Standards Institute (CLSI, 2018: M100-S28) ( ), veterinary CLSI (VET01-A4E/VET01-S3E) ( ) (supplemental Materials and Methods).
Total genomic DNA of 42 strong biofilm-forming E. coli isolates were extracted by using the TIANamp Bacteria DNA Kit (Tiangen, China). The quality and concentration of the bacterial genomic DNA were evaluated via electrophoresis on a 1% agarose gel and analysis on a NanoDrop2000 system (Thermo Scientific, Waltham, MA, USA) and a Qubit 3 Fluorometer (Thermo Scientific, Waltham, USA). Illumina libraries were prepared and sequenced using Illumina HiSeq 4000 platform (San Diego, CA, USA) as 150-bp paired-end reads. Adaptors and low-quality bases were trimmed with Trimmomatic v0.38 ( ), and reads qualities were assessed using FastQC v0.11.6 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and MultiQC v1.7 ( ). High-quality reads were de novo assembled with SPAdes v3.6.2 to generate genome contigs ( ). Sequencing quality and statistics per isolates were checked using the QualiMap v2.2.2 ( ). Genome assemblies’ quality was assessed with QUAST v5.0.2 ( ) and contigs of less than 200 bp were filtered out. Examinations of known plasmid replicon, and antibiotic resistance genes were carried out using ABRicate v1.0.1 ( https://github.com/tseemann/abricate ) (>80% identity and >80% coverage). Reference sequences of plasmid and antibiotic resistance genes were from databases PlasmidFinder ( ), and ResFinder ( ), respectively. Multilocus sequence typing (MLST) were performed by MLST v2.19.0 ( https://github.com/tseemann/mlst ). In addition, in silico phylotyping of E. coli was carried out using the Clermon Typing method ( ). Further, assemblies from all isolates were mapped to the reference sequence 2013FS003 using Snippy v4.6.0 ( https://github.com/tseemann/snippy ). Single nucleotide polymorphisms (SNPs) were called and recombinant regions were removed using Gubbins v2.4.1 ( ). RAxML v8.2.12 (GTRGAMMA substitution model) with 100 bootstrap replicates to assess support was used to construct a phylogenic tree and visualized with iTOL v4 ( ). The population structure of each phylogenetic tree was defined using hier-BAPS v6.0 ( ).
fim / mrk operons. The transferability of type 1/3 fimbriae operons was examined using conjugation experiments using the streptomycin-resistant E. coli C600 as the recipient. All transconjugants were tested for antimicrobial susceptibility and biofilm formation as described above. To obtain the complete sequence of plasmids encoding type 1/3 fimbriae, two isolates (2010FS332 and 2005FS026) encoding type 1 and 3 fimbriae, respectively, were selected for long-read sequencing using ONT Gridion Platform (Nanopore, Oxford, UK) ( ). DNA extraction and quality control were performed as previously described. An Oxford Nanopore MinION 9.4.1 flowcell and SQK-RBK004 rapid sequencing kit was used with base calling by Guppy v3.1.5 ( https://nanoporetech.com/nanopore-sequencing-data-analysis ). De novo hybrid assembly using both short reads (Illumina) and long reads (ONT) was performed using Unicycler v0.4.4 ( ). The Contigs were further polished with Pilon v1.23 ( ) through three iterations. Gene prediction and annotated were performed by the RAST tool ( ) ( https://rast.nmpdr.org/ ), ISFinder ( ), BLAST ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ) and rechecked manually. The sequence comparison and map generation of plasmids encoding type 1/3 fimbriae were performed using Easyfig ( ) and the BLAST Ring Image Generator ( ). The complete sequence of plasmid p2010FS332 and p2005FS026 has been deposited in GenBank under accession no. OK217279 and accession no. OK236218, respectively.
The mrk and fim operons from the strong biofilm-forming E. coli strains 2010FS332 and 2005FS026, respectively, were amplified, and PCR amplicons were then ligated to expression vector pMD19-T and introduced into E. coli strain DH5α by chemical transformation (Table S1). Colonies were selected on LB agar supplemented with ampicillin (100 mg/mL). Transformants were randomly screened for the presence of the mrk and fim operons by PCR assay and sequencing, and the resultant clones were tested for biofilm formation as described above. Biofilm structures were further examined using SEM (supplemental Materials and Methods). To understand the spread of the mrk / fim operons, all E. coli that were strong biofilm producers were screened for the presence of mrk and fim genes using PCR (Table S1). Additionally, mrk and fim operons were in silico identified from Enterobacteriaceae members that possessed WGS data from a public database ( https://www.ncbi.nlm.nih.gov/datasets ). Phylogenetic correlations were constructed between concatenated mrk and fim operons from this study and those from GenBank using maximum likelihood (ML) trees based on predicted amino acid sequences (supplemental Materials and Methods).
Statistical significance for comparison of prevalence data and proportions was performed in R using the χ 2 test. Other data were statistically analyzed using GraphPad Prism v8.0.1 software. P values of <0.05 were deemed to be statistically significant. Specific tests of statistical significance are detailed in figure legends and table footnotes.
All genome assemblies of these strains were deposited in GenBank and were registered under BioProject accession number PRJNA747154 .
Reviewer comments
|
Clear cell renal cell carcinoma with cystic component similar to multilocular cystic renal neoplasm of low malignant potential: a rare pattern of cyst-dependent progression from multilocular cystic renal neoplasm of low malignant potential | b2adb4e6-6365-4985-b132-cf8d424d28f9 | 9942362 | Anatomy[mh] | Multilocular cystic renal neoplasm of low malignant potential (MCRN-LMP) is a tumor composed entirely of numerous cysts, the septa of which contain individual or groups of clear cells without expansile growth, and that is morphologically indistinguishable from low-grade clear cell renal cell carcinoma (ccRCC), but recurrence or metastasis have not been reported . The neoplastic cells are strongly immunoreactive to PAX8 and carbonic anhydrase IX (CA-IX), and more frequently expressed CK7 . Some Xp11 translocation RCCs with MED15 - TFE3 fusion have been described containing extensive cystic architecture similar to MCRN-LMP with TFE3 positive immunostaining . Therefore, the diagnosis of MCRN-LMP needs to strictly follow the criteria of morphology and immunohistochemistry (IHC) staining. We have encountered some low-grade ccRCCs (WHO/International Society of Urological Pathology (ISUP) grade 1 or 2) with cystic component similar to MCRN-LMP and solid epithelial component simultaneously, as mentioned in some studies . We designate these tumors “ccRCC with cystic component similar to MCRN-LMP”. Genetic studies have clearly linked ccRCC and MCRN-LMP, with 74% chromosome 3p deletions and 25% von Hippel–Lindau ( VHL ) mutations in MCRN-LMP cases, similar to ccRCC . However, there is no relevant study on whether ccRCC with cystic component similar to MCRN-LMP originates from MCRN-LMP. In order to shed light on the relationship between MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP, we collected 12 cases of MCRN-LMP and 33 cases of ccRCC with cystic component similar to MCRN-LMP from 3,265 consecutive RCCs to analyze their similarities and differences in clinicopathological features, immunohistochemical findings and prognosis.
Case selection We designated “ccRCC with cystic component similar to MCRN-LMP” according to the following criteria: 1) the solid component was low-grade ccRCC (WHO/ ISUP grade 1 or 2); 2) the range of cystic component similar to MCRN-LMP was at least 20%; 3) excluding extensive coagulative (cystic) necrosis; 4) the minimum diameter of individual cysts in cystic component similar to MCRN-LMP was 1 mm; 5) the minimum diameter of cystic component similar to MCRN-LMP was 5 mm. We retrospectively collected the 3,265 consecutive RCCs which underwent partial or radical nephrectomy and had the IHC staining of a panel (PAX8, CD10, CA-IX, Vimentin, CK7, CD117, P504s, TFE3) at Tianjin Medical University Cancer Institute & Hospital from January 2012 to December 2020. The hematoxylin & eosin (H&E) and IHC staining slides were reviewed independently by experienced pathologists (Y.B, Q.L.S and C.W.F). Finally, 2,901 (88.9%) cases were diagnosed as clear cell RCC, and 12 (0.4%) cases were diagnosed as MCRN-LMP. In 2,901 cases of clear cell RCCs, 33 (1.1%) cases were ccRCCs with cystic component similar to MCRN-LMP. The clinicopathological features of all cases were collected, and all patients were followed until January 2022. This study was approved by the Ethical Review Committee of Tianjin Medical University Cancer Institute & Hospital (Approval No: bc2022136). IHC Tumor tissues were fixed in 10% formalin and embedded in paraffin. The 4-μm-thick whole sections were performed IHC staining with an automated Ventana BenchMark XT system (Roche, Ventana Medical Systems Inc., Tucson) for the following antibodies: PAX8 (4H7B3, 1:100; ProteinTech Group, Rosemont, IL), CA-IX (ab1508, 1:1000; Abcam), CK7 (OV-TL12/30, prediluted; MXB Biotechnologies), Vimentin (V9, prediluted; MXB Biotechnologies), CD10 (56C6, prediluted; MXB Biotechnologies), P504s (13H4, prediluted; MXB Biotechnologies), TFE3 (SC-5958, 1:300; Santa Cruz, CA), 34βE12 (prediluted; MXB Biotechnologies). Positive and negative controls yielded appropriate results for each antibody. Immunoreactivity was evaluated in a semiquantitative manner based on both labeling intensity and the percentage of immunopositive tumor cells for all antibodies as described previously . The score was calculated by multiplying the staining intensity (0 = no staining, 1 = mild staining, 2 = moderate staining, and 3 = strong staining) by the percentage of immunoreactive tumor cells (0 to 100). The immunostaining result was considered to be negative (0) when the score was < 25; weak positive (1 +) when the score was 26–100; moderate positive (2 +) when the score was 101–200; or strong positive (3 +) when the score was 201–300. Statistics Results were analyzed using SPSS 19.0 software (SPSS Inc., Chicago, IL, USA). Relationships between qualitative variables were investigated using two tailed Chi-Square test or Fisher’s exact test, and quantitative variables were analyzed by t test. P -value of less than 0.05 was considered significant.
We designated “ccRCC with cystic component similar to MCRN-LMP” according to the following criteria: 1) the solid component was low-grade ccRCC (WHO/ ISUP grade 1 or 2); 2) the range of cystic component similar to MCRN-LMP was at least 20%; 3) excluding extensive coagulative (cystic) necrosis; 4) the minimum diameter of individual cysts in cystic component similar to MCRN-LMP was 1 mm; 5) the minimum diameter of cystic component similar to MCRN-LMP was 5 mm. We retrospectively collected the 3,265 consecutive RCCs which underwent partial or radical nephrectomy and had the IHC staining of a panel (PAX8, CD10, CA-IX, Vimentin, CK7, CD117, P504s, TFE3) at Tianjin Medical University Cancer Institute & Hospital from January 2012 to December 2020. The hematoxylin & eosin (H&E) and IHC staining slides were reviewed independently by experienced pathologists (Y.B, Q.L.S and C.W.F). Finally, 2,901 (88.9%) cases were diagnosed as clear cell RCC, and 12 (0.4%) cases were diagnosed as MCRN-LMP. In 2,901 cases of clear cell RCCs, 33 (1.1%) cases were ccRCCs with cystic component similar to MCRN-LMP. The clinicopathological features of all cases were collected, and all patients were followed until January 2022. This study was approved by the Ethical Review Committee of Tianjin Medical University Cancer Institute & Hospital (Approval No: bc2022136).
Tumor tissues were fixed in 10% formalin and embedded in paraffin. The 4-μm-thick whole sections were performed IHC staining with an automated Ventana BenchMark XT system (Roche, Ventana Medical Systems Inc., Tucson) for the following antibodies: PAX8 (4H7B3, 1:100; ProteinTech Group, Rosemont, IL), CA-IX (ab1508, 1:1000; Abcam), CK7 (OV-TL12/30, prediluted; MXB Biotechnologies), Vimentin (V9, prediluted; MXB Biotechnologies), CD10 (56C6, prediluted; MXB Biotechnologies), P504s (13H4, prediluted; MXB Biotechnologies), TFE3 (SC-5958, 1:300; Santa Cruz, CA), 34βE12 (prediluted; MXB Biotechnologies). Positive and negative controls yielded appropriate results for each antibody. Immunoreactivity was evaluated in a semiquantitative manner based on both labeling intensity and the percentage of immunopositive tumor cells for all antibodies as described previously . The score was calculated by multiplying the staining intensity (0 = no staining, 1 = mild staining, 2 = moderate staining, and 3 = strong staining) by the percentage of immunoreactive tumor cells (0 to 100). The immunostaining result was considered to be negative (0) when the score was < 25; weak positive (1 +) when the score was 26–100; moderate positive (2 +) when the score was 101–200; or strong positive (3 +) when the score was 201–300.
Results were analyzed using SPSS 19.0 software (SPSS Inc., Chicago, IL, USA). Relationships between qualitative variables were investigated using two tailed Chi-Square test or Fisher’s exact test, and quantitative variables were analyzed by t test. P -value of less than 0.05 was considered significant.
Clinicopathological features The clinicopathological features of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP are shown in Table . All patients with MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP had no VHL syndrome (family history; retinal, cerebellar, and spinal hemangioblastomas; pheochromocytoma; pancreatic tumors and cysts; endolymphatic sac tumors) or other genetic syndromes. In our cohort of 3,265 consecutive RCCs, 12 MCRN-LMPs (partial/radical nephrectomy ratio, 9:3) were identified accounting for 0.4%, and 33 ccRCCs with cystic component similar to MCRN-LMP (partial/radical nephrectomy ratio, 18:15) were diagnosed accounting for 1.1% of 2,901 ccRCCs. The age of the patients ranged from 36 to 63 years (mean, 51 years; median, 52 years) and 34 to 71 years (mean, 52 years; median, 53 years) among 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP, respectively. Eight patients were men and 4 were women (male/female ratio, 2:1) in MCRN-LMPs, and 23 patients were men and 10 were women (male/female ratio, 2.3:1) in ccRCCs with cystic component similar to MCRN-LMP. The greatest tumor diameter ranged from 1 to 8 cm (mean, 3.5 cm; median, 3 cm) and 1.5 to 10 cm (mean, 3.8 cm; median, 3.5 cm) in MCRN-LMPs and ccRCCs with cystic component similar to MCRN-LMP, respectively. Almost all MCRN-LMPs and ccRCCs with cystic component similar to MCRN-LMP were WHO/ISUP grade 1, except one MCRN-LMP and 3 ccRCCs with cystic component similar to MCRN-LMP with WHO/ISUP grade 2. The pathological stage (according to the 2018 American Joint Committee on Cancer TNM staging system) was pT1a for 10 cases (83.3%), pT2a for 2 cases (16.7%) in 12 MCRN-LMPs; and pT1a for 24 cases (72.7%), pT1b for 7 cases (21.2%), pT2a for 2 cases (6.1%) in 33 ccRCCs with cystic component similar to MCRN-LMP. The comparison of clinicopathological features between 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP are showed in Table . The results displayed that there was no significant difference in age, sex ratio, tumor size, treatment (partial/radical nephrectomy ratio), WHO/ISUP grade and pathological stage between them ( P > 0.05). The morphologic features of 12 MCRN-LMPs included an entirely cystic architecture (Fig. A, B), with thin fibrous or hyalinized septa lined by a single layer of flat to cuboidal epithelium (Fig. C), which had clear cytoplasm (Fig. D) and scattered small blood vessels (Fig. E). Occasional clusters of clear cells could be seen without expansile growth (Fig. F). All 33 ccRCCs with cystic component similar to MCRN-LMP coexisted with MCRN-LMP and solid low-grade ccRCCs (WHO/ISUP grade 1 or 2) (Fig. A, B, C). The MCRN-LMP component ranged from 20 to 90% (median, 59%) (Table ). All tumors were free of coagulative necrosis, and most tumors contained foci of haemosiderin deposition (28/33, 84.8%) (Fig. D), and some had areas of dystrophic calcification within the hyalinized component (12/33, 36.4%) (Fig. E), and one (1/33, 3.0%) had ossification (Fig. F). IHC profiles The IHC profiles of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP (divided into cystic part and solid part) are shown in Table , and the comparison of IHC findings among them are summarized in Table . All of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP exhibited strong positive (3 +) staining for PAX8 and CA-IX (Fig. A, B), but TFE3 was negative in all of them. CK7 (Fig. C), Vimentin (Fig. D), CD10, P504s (Fig. E) and 34βE12 (Fig. F) were positive in 12 (3 + , 100.0%), 12 (3 + , 100.0%), 3 (1 + , 25%), 11(6, 1 + , 50.0%; 5, 2 + , 41.7%) and 3 (2, 1 + , 16.7%; 1, 2 + , 8.3%) cases of 12 MCRN-LMPs, respectively; and they were positive in 33 (3 + , 100.0%), 31 (3 + , 93.9%), 6 (4, 1 + , 12.1%; 2, 3 + , 6.1%), 32 (16, 1 + , 48.5%; 16, 2 + , 48.5%) and 7 (1, 1 + , 3.0%; 2, 2 + , 6.1%; 4, 3 + , 12.1%) cases of 33 ccRCCs’ cystic parts (Fig. A, B), respectively. In addition, CK7 (Fig. C), Vimentin (Fig. D), CD10 (Fig. E) and P504s (Fig. F) were positive in 3 (2, 1 + , 6.1%; 1, 2 + , 3.0%), 32 (3 + , 97.0%), 28 (2, 1 + , 6.1%; 7, 2 + , 21.2%; 19, 3 + , 57.6%) and 33 (16, 1 + , 48.5%; 17, 2 + , 51.5%) cases of 33 ccRCCs’ solid parts, respectively, whereas 34βE12 was negative in all of them. The positive ratio of CK7 ( P2 < 0.001; P3 < 0.001) and 34βE12 ( P2 = 0.003; P3 = 0.006) in MCRN-LMPs and ccRCCs’ cystic parts was significantly higher than that in ccRCCs’ solid parts, but the positive ratio of CD10 in MCRN-LMPs and ccRCCs’ cystic parts was significantly lower than that in ccRCCs’ solid parts ( P2 < 0.001; P3 < 0.001). Moreover, there was no significant difference of all IHC profiles between MCRN-LMPs and ccRCCs’ cystic parts ( P1 > 0.05). Prognosis Long-term follow-up was available for 12 MCRN-LMPs (range, 32–118 months; mean, 56.8 months), 33 ccRCCs with cystic component similar to MCRN-LMP (range, 16–103 months; mean, 51.2 months) from our cohort of 3,265 consecutive RCCs. All patients of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP were alive without evidence of recurrent, residual or metastatic disease at the time of most recent follow-up.
The clinicopathological features of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP are shown in Table . All patients with MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP had no VHL syndrome (family history; retinal, cerebellar, and spinal hemangioblastomas; pheochromocytoma; pancreatic tumors and cysts; endolymphatic sac tumors) or other genetic syndromes. In our cohort of 3,265 consecutive RCCs, 12 MCRN-LMPs (partial/radical nephrectomy ratio, 9:3) were identified accounting for 0.4%, and 33 ccRCCs with cystic component similar to MCRN-LMP (partial/radical nephrectomy ratio, 18:15) were diagnosed accounting for 1.1% of 2,901 ccRCCs. The age of the patients ranged from 36 to 63 years (mean, 51 years; median, 52 years) and 34 to 71 years (mean, 52 years; median, 53 years) among 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP, respectively. Eight patients were men and 4 were women (male/female ratio, 2:1) in MCRN-LMPs, and 23 patients were men and 10 were women (male/female ratio, 2.3:1) in ccRCCs with cystic component similar to MCRN-LMP. The greatest tumor diameter ranged from 1 to 8 cm (mean, 3.5 cm; median, 3 cm) and 1.5 to 10 cm (mean, 3.8 cm; median, 3.5 cm) in MCRN-LMPs and ccRCCs with cystic component similar to MCRN-LMP, respectively. Almost all MCRN-LMPs and ccRCCs with cystic component similar to MCRN-LMP were WHO/ISUP grade 1, except one MCRN-LMP and 3 ccRCCs with cystic component similar to MCRN-LMP with WHO/ISUP grade 2. The pathological stage (according to the 2018 American Joint Committee on Cancer TNM staging system) was pT1a for 10 cases (83.3%), pT2a for 2 cases (16.7%) in 12 MCRN-LMPs; and pT1a for 24 cases (72.7%), pT1b for 7 cases (21.2%), pT2a for 2 cases (6.1%) in 33 ccRCCs with cystic component similar to MCRN-LMP. The comparison of clinicopathological features between 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP are showed in Table . The results displayed that there was no significant difference in age, sex ratio, tumor size, treatment (partial/radical nephrectomy ratio), WHO/ISUP grade and pathological stage between them ( P > 0.05). The morphologic features of 12 MCRN-LMPs included an entirely cystic architecture (Fig. A, B), with thin fibrous or hyalinized septa lined by a single layer of flat to cuboidal epithelium (Fig. C), which had clear cytoplasm (Fig. D) and scattered small blood vessels (Fig. E). Occasional clusters of clear cells could be seen without expansile growth (Fig. F). All 33 ccRCCs with cystic component similar to MCRN-LMP coexisted with MCRN-LMP and solid low-grade ccRCCs (WHO/ISUP grade 1 or 2) (Fig. A, B, C). The MCRN-LMP component ranged from 20 to 90% (median, 59%) (Table ). All tumors were free of coagulative necrosis, and most tumors contained foci of haemosiderin deposition (28/33, 84.8%) (Fig. D), and some had areas of dystrophic calcification within the hyalinized component (12/33, 36.4%) (Fig. E), and one (1/33, 3.0%) had ossification (Fig. F).
The IHC profiles of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP (divided into cystic part and solid part) are shown in Table , and the comparison of IHC findings among them are summarized in Table . All of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP exhibited strong positive (3 +) staining for PAX8 and CA-IX (Fig. A, B), but TFE3 was negative in all of them. CK7 (Fig. C), Vimentin (Fig. D), CD10, P504s (Fig. E) and 34βE12 (Fig. F) were positive in 12 (3 + , 100.0%), 12 (3 + , 100.0%), 3 (1 + , 25%), 11(6, 1 + , 50.0%; 5, 2 + , 41.7%) and 3 (2, 1 + , 16.7%; 1, 2 + , 8.3%) cases of 12 MCRN-LMPs, respectively; and they were positive in 33 (3 + , 100.0%), 31 (3 + , 93.9%), 6 (4, 1 + , 12.1%; 2, 3 + , 6.1%), 32 (16, 1 + , 48.5%; 16, 2 + , 48.5%) and 7 (1, 1 + , 3.0%; 2, 2 + , 6.1%; 4, 3 + , 12.1%) cases of 33 ccRCCs’ cystic parts (Fig. A, B), respectively. In addition, CK7 (Fig. C), Vimentin (Fig. D), CD10 (Fig. E) and P504s (Fig. F) were positive in 3 (2, 1 + , 6.1%; 1, 2 + , 3.0%), 32 (3 + , 97.0%), 28 (2, 1 + , 6.1%; 7, 2 + , 21.2%; 19, 3 + , 57.6%) and 33 (16, 1 + , 48.5%; 17, 2 + , 51.5%) cases of 33 ccRCCs’ solid parts, respectively, whereas 34βE12 was negative in all of them. The positive ratio of CK7 ( P2 < 0.001; P3 < 0.001) and 34βE12 ( P2 = 0.003; P3 = 0.006) in MCRN-LMPs and ccRCCs’ cystic parts was significantly higher than that in ccRCCs’ solid parts, but the positive ratio of CD10 in MCRN-LMPs and ccRCCs’ cystic parts was significantly lower than that in ccRCCs’ solid parts ( P2 < 0.001; P3 < 0.001). Moreover, there was no significant difference of all IHC profiles between MCRN-LMPs and ccRCCs’ cystic parts ( P1 > 0.05).
Long-term follow-up was available for 12 MCRN-LMPs (range, 32–118 months; mean, 56.8 months), 33 ccRCCs with cystic component similar to MCRN-LMP (range, 16–103 months; mean, 51.2 months) from our cohort of 3,265 consecutive RCCs. All patients of 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP were alive without evidence of recurrent, residual or metastatic disease at the time of most recent follow-up.
In this study, we reported the ratio of MCRN-LMP was 0.4% in the consecutive 3,265 RCCs, which is lower than the ratio of other studies (1–4%) , which may be because we added TFE3 immunostaining to exclude some Xp11 translocation RCCs with extensive cystic architecture similar to MCRN-LMP. For low-grade ccRCCs with a cystic component that do not meet the criteria of MCRN-LMP, the true incidence is unknown because no diagnostic terminology was clearly defined previously. Williamson et al. reported 12 cases of cystic partially regressed ccRCC, comprising 3.5% of 341 ccRCCs and 2.6% of 469 RCCs. In addition, Westerman et al. reported 95 cases of cystic ccRCC accounting for 2.5% of 3,865 ccRCCs. As for our cohort, the ratio of 33 ccRCCs with cystic component similar to MCRN-LMP was 1.1% of the 2,901 ccRCCs and 1.0% of the 3,265 RCCs. On the basis of these data, it is shown that both MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP are all very rare, and their ratios in ccRCCs are similar, which further illustrates that their tumorigenesis may have a certain correlation. Raspollini et al. conducted a genetic mutational analysis between stage pT1 ccRCCs of low ISUP/WHO nucleolar grade and MCRN-LMPs and found no significant genetic differences between them, except that a KRAS mutation could distinguish between the two subtypes. Furthermore, Kim et al. identified six novel genetic alterations ( GIGYF2 , FGFR3 , SETD2 , BCR , KMT2C , and TSC2 ) that could be potential candidate genes for differentiating between MCRN-LMP and ccRCC with cystic change. As a result, we speculate that due to the overlying of other abnormal genes, some cyst‑lining cells of MCRN-LMP on the basis of VHL gene abnormality, further proliferate to form solid expansive nodules, and then develop into ccRCC with cystic component similar to MCRN-LMP. More studies need to be designed to prove our point, including animal model experiments. Some studies have given rise to a model of VHL -associated kidney disease progression in which loss of the cilia maintenance function of pVHL predisposes patients to develop cysts owing to secondary mutations that result in inactivation of GSK3β, and additional mutations in cystic cells and loss of the HIFα degradation function of pVHL are probably required for further progression from cystic precursor to ccRCC, and which suggests a cyst-dependent progression pathway of ccRCC in VHL disease . Although our cases of MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP are all sporadic patients without VHL syndrome, the relationship between them is very similar to the cyst-dependent progression pathway of ccRCC in VHL disease. Therefore, we also propose a hypothesis that the minority of sporadic ccRCCs akin to VHL disease can progress through cyst-dependent pathway from MCRN-LMP to ccRCC with cystic component similar to MCRN-LMP (Fig. ), but the majority of sporadic ccRCCs are through cyst-independent pathway, and further research is needed to support our hypothesis. Through clinicopathological features comparison, we noticed that there was no significant difference in age, sex ratio, tumor size, treatment (partial/radical nephrectomy ratio), WHO/ISUP grade and pathological stage between the two groups of cases, which further supports the homologous relationship between MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP. In terms of morphology, the proportion of cystic component in ccRCC with cystic component similar to MCRN-LMP showed a continuous spectrum from 20 to 90%, which also provides one evidence for continuous progress from MCRN-LMP to ccRCC with cystic component similar to MCRN-LMP. Moreover, we also observed a number of morphological features associated with degeneration or regression, such as haemosiderin deposition, dystrophic calcification, hyalinized component, and ossification, which also overlap with some findings in “cystic partially regressed clear cell renal cell carcinoma” reported by Williamson et al. . According to previous related studies , we speculate that the low grade ccRCC with cystic component similar to MCRN-LMP through cyst-dependent pathway may be prone to degeneration or regression due to the lack of some key molecular alterations for overall tumor progression, and the specific mechanism needs to be further studied. Our results showed that CA-IX was diffusely strong positive staining in all of 12 MCRN-LMPs and 33 cystic and solid parts of ccRCCs with cystic component similar to MCRN-LMP, which illustrates that these tumors are a subtype of ccRCC with activation of HIFα pathway due to VHL inactivation, as mentioned in some studies . In addition, CK7 showed diffusely strong positive staining in all MCRN-LMPs and cystic parts of ccRCCs with cystic component similar to MCRN-LMP, but often negative or focally positive in solid parts of ccRCCs with cystic component similar to MCRN-LMP. Interestingly, CK7 usually shows positive staining in some normal renal tubular epithelium , but is generally considered to be negative or focally positive in ccRCC , which further confirms our hypothesis of cyst-dependent pathway that some normal renal tubular epithelium cells (CK7 + /CA-IX-) may progress to MCRN-LMP cyst‑lining cells (CK7 + /CA-IX +) in the case of VHL gene abnormality, and then may further develop into solid ccRCC tumor cells (CK7-/CA-IX +) because of the overlying of other abnormal genes (Fig. ). Moreover, our results showed that CD10 was more frequently positive in solid part of ccRCCs than MCRN-LMP and cystic part of ccRCCs with cystic component similar to MCRN-LMP as some articles reported that CD10 was generally considered to be a positive marker in ccRCC . Furthermore, Brimo et al. reported that cystic clear cell papillary RCC showed overlapping morphological features and IHC panel (positive for CA-IX, CK7, 34βE12 and negative for CD10) with MCRN-LMP, and all 9 tumors were strongly and diffusely positive for CA-IX with the pattern of cup-shaped, sparing the apical cellular portion in 8 tumors and diffuse in one, and 34βE12 expression was strong and diffuse in 8 tumors and strong but focal in one. However, our all MCRN-LMPs and ccRCCs with cystic component similar to MCRN-LMP showed diffusely strong positive for CA-IX with the pattern of box-shaped, and 34βE12 expression was strong and diffuse in 4 cystic parts of ccRCCs with cystic component similar to MCRN-LMP and mild or moderate but focal in 3 MCRN-LMPs and 3 cystic parts of ccRCCs with cystic component similar to MCRN-LMP. As a result, we think that CA-IX is the best marker for the differential diagnosis because of different expression patterns (cup-shaped and box-shaped), and once 34βE12 shows negative, it is more likely to be diagnosed as MCRN-LMP. In this study, no patient developed recurrence or metastasis among all 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP. Park et al. reported that a cystic change of more than 5% of the tumor was an independent, good prognostic factor in patients with ccRCC. Han et al. reported that cystic RCCs presented less often with metastatic disease and these tumors tend to be smaller, lower stage, and low grade, suggesting a more indolent biology. Webster et al. reported that the estimated cancer-specific survival rate at 5 years after surgery for patients with noncystic clear cell RCC was 70.6% compared with 100% for patients with the cystic variant, and no patient with cystic ccRCC had extrarenal disease at time of nephrectomy with the exception of 1 patient who had perinephric fat invasion. Williamson et al. reported that all of 16 patients of cystic partially regressed ccRCCs were alive without evidence of recurrent, residual or metastatic disease during the follow-up period from 32 to 143 months. Similarly, Tretiakova et al. reported that all 69 predominantly cystic ccRCCs did not develop recurrence or metastasis with median follow-up 35.8 months (range 0–146.6), except for one contralateral kidney tumor 2 years after primary nephrectomy and one adrenal metastasis 3 years after primary diagnosis. Moreover, Westerman et al. reported that all 18 MCRN-LMPs and 95 cystic ccRCCs did not develop recurrence or metastasis with median follow-up 10.3 years (interquartile range 7.4–14.9 years), except for one MCRN-LMP (contralateral recurrence) and 5 cystic ccRCCs (1 distant metastases and subsequent death from RCC at 22 years postsurgery, 1 ipsilateral and contralateral recurrence, 1 ipsilateral recurrence, and 2 contralateral recurrence), and 10- and 20-year cancer-specific survival was 100% for all cases. As a result, we think that ccRCC with cystic component similar to MCRN-LMP may have indolent or low malignant potential behavior just like MCRN-LMP, but more cases and longer follow-up time need to support this result because of the insufficiency of our number of cases and follow-up time. Furthermore, all 12 MCRN-LMPs and 33 ccRCCs with cystic component similar to MCRN-LMP in our study were resected in a short time after their identification by imaging, which made it impossible to dynamically observe the progress of the tumors, and maybe further animal model experiments can achieve this goal. Williamson et al. reported that two cystic partially regressed ccRCCs were observed with imaging prior to resection, and one remained unchanged in size over a period of 1 year, and the other enlarged over a period of 4 years and remained stable in size for the 1 year prior to resection. In addition, Jhaveri et al. reported that 26 Cystic RCCs (including 13 cystic ccRCCs and 6 Multilocular cystic RCCs) were monitored with at least 6 months of pretreatment imaging, most of the tumors (73.1%) did not show a significant increase in size, and only 7 (26.9%) tumors showed growth (mean increase dimension, 10.5 mm; range, 0–24 mm). These retrospective imaging studies have shown a probable indolent course of ccRCCs with cystic component, which may also provide an ethical basis for long-term pretreatment imaging observation, and more prospective studies should be designed to detect the dynamic development of cystic RCCs, so as to further clarify the relationship between MCRN-LMPs and ccRCCs with cystic component similar to MCRN-LMP. In summary, in this study we found that the minority (1.1%) of ccRCCs have cystic component similar to MCRN-LMP and solid low-grade component simultaneously, for which we propose the designation “ccRCC with cystic component similar to MCRN-LMP”. Further by comparing MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP, we found that they have similarity and homology in clinicopathological features, immunohistochemical findings and prognosis. As a result, we speculate that MCRN-LMP and ccRCC with cystic component similar to MCRN-LMP form a low-grade spectrum with indolent or low malignant potential behavior, and ccRCC with cystic component similar to MCRN-LMP may be a rare pattern of cyst-dependent progression from MCRN-LMP. Further studies need to be designed to prove our point, including animal model experiments of molecular mechanisms and long-term pretreatment imaging observation.
|
Jointly discussing care plans for real-life patients: The potential of a student-led interprofessional team meeting in undergraduate health professions education | eefdf338-c257-43a9-8817-a590b10e1757 | 6904407 | Family Medicine[mh] | The World Health Organization (WHO) states that ‘interprofessional education occurs when students from two or more professions learn about, from and with each other to enable effective collaboration and improve health outcomes’ . The WHO indicates in this framework that ‘in both acute and primary care settings, patients report higher levels of satisfaction, better acceptance of care and improved health outcomes following treatment by a collaborative team’ . To date, the curricula of most healthcare training programs in the Netherlands have incorporated interprofessional collaboration (IPC) as a key competency for their graduates [ – ]. Interprofessional education (IPE) is promoted as a means of enhancing IPC in the future . Effective integration of IPE into the curricula of health professions training requires efficient collaboration between the various stakeholders to overcome barriers to IPE, such as logistical challenges, faculty attitudes, a rigid curriculum or differences in assessment requirements . Recommendations to deploy constructivist learning theory to underpin interprofessional learning activities, to stress student-centred learning and to create meaning from the interprofessional learning experience all pose further challenges to IPE . With these issues in mind, in 2015 Maastricht University, Maastricht, the Netherlands and Zuyd University of Applied Sciences, Heerlen, the Netherlands, jointly developed an IPE course for medical, allied healthcare (i.e. physiotherapy, occupational therapy, speech and language therapy) and nursing students. The goals of the IPE course were: (1) to experience the practice of direct collaboration with other future health professionals; (2) to feel collectively responsible for the outcomes of an interprofessional team meeting; and (3) to create an opportunity to reflect on IPC, since reflection is paramount in competency-based medical education, as it steers the learning cycle . At the start of this project, we recognized the dissimilarities in the design of the educational programs at both participating institutions, such as differences in the length of training, intended learning outcomes and target competencies [ – ]. Medical training at Maastricht University involves a 6-year undergraduate program entailing a 3-year Bachelor’s phase and an equally long Master’s phase . Medical training comprises a competency-based program, taking into account the outcomes as laid down in the Dutch Framework for Undergraduate Medical Education , which is based on the CanMeds competency framework . This framework states that by graduation, medical students should be able to make an effective contribution to interprofessional teams. The described IPE course is part of Maastricht University students’ clinical rotation in family medicine and social medicine during the Master’s in medicine . Health professions training programs at Zuyd University comprise 4‑year Bachelor’s programs , each with its own curriculum and intended learning outcomes based on nationwide frameworks . Zuyd University has built a framework of interprofessional competences, known as ‘interprofessional building blocks’, based on existing competence models. It comprises their five key adopted competencies for IPC, which are implemented in all allied healthcare and nursing students’ training programs . These competencies are: (1) knowing and understanding each other’s competences; (2) working with interprofessional care plans; (3) problem-solving in interprofessional teams; (4) appropriate interprofessional referral; and (5) evaluation of interprofessional teamwork. At Zuyd University, the described IPE course is embedded in the third and fourth year of allied healthcare and nursing Bachelor’s programs . Our joint effort resulted in an IPE course including: (1) participation in a student-led interprofessional team meeting; (2) jointly composing a care plan for a frail elderly patient, and (3) subsequent reflection (team and individual) on IPC. We expected that students would gain knowledge about and comfort in working with other healthcare professionals during this innovative IPE course. By now, around 360 medical and 360 allied healthcare and nursing students have participated in this IPE course every year since it started in January 2015.
An interprofessional team of faculty of both universities developed the course. The intended learning outcomes of the IPE course were based both on the interprofessional building blocks (Zuyd University) and the outcomes of the Dutch Framework for Undergraduate Medical Education (Maastricht University) . This means that all students should be able to learn how to make an effective contribution to an interprofessional team in the field of patient care, as well as how to develop a care plan for a patient in consultation with other healthcare professionals. As the ageing society is leading to more patients with chronic illnesses who are in need of care, often from multiple healthcare professionals , we agreed to focus on care for the frail elderly. Frail elderly patients are professionally relevant to all medical, allied healthcare and nursing students. The design of this IPE course, i.e. jointly discussing care plans for frail elderly patients during a student-led interprofessional team meeting, was based on the key principles of problem-based learning . Firstly, it is constructive, because students activate prior knowledge, elaborate on what they have learned and by focusing on real-life cases they trigger deep learning. Secondly, it is collaborative, because students from differing health professions work together on a care plan, whereby they get a view of the perspective of other health professions involved in caring for frail elderly patients. Thirdly, it is contextual, because students deploy real-life instead of made-up patient cases for the construction of a care plan. Lastly, learning is self-directed as students plan, monitor, evaluate and reflect on their own learning. We developed a road map, tailored to the health professions of participating students, in which we describe the steps that should be taken to be optimally prepared for participation in the student-led interprofessional team meeting (Fig. ). Students are also provided with literature on how to design a care plan and how to use the WHO’s International Classification of Functioning, Disability and Health (ICF), promoted as the common language for health professionals .
Interprofessional teams Every 4 weeks six interprofessional teams are formed, each comprising 9–10 students from different health professions (5 medical students and 4–5 allied healthcare and nursing students). We attempt to include at least one student from the department of physiotherapy, occupational therapy, speech and language therapy and nursing in each team. For each team, one interprofessional team meeting is scheduled. Medical students Firstly, the medical student visits a frail elderly patient at home, takes a medical history, and asks the patient about their personal goals. The medical student then draws up a provisional care plan (based on the ICF), and sends the anonymized provisional care plan to the allied healthcare and nursing students of their interprofessional team 2 weeks before the student-led interprofessional team meeting takes place. We assigned this task to the medical students for pragmatic, mainly logistic, reasons. The IPE course is scheduled during their rotation in family medicine and social medicine. In this way we can guarantee that medical students can actually visit a frail elderly patient at home and have sufficient time to prepare for the interprofessional team meeting. After the meeting, each medical student finalizes the care plan for their frail elderly patient, based on the outcomes of the meeting as laid down in the minutes of the meeting. They add the team reflection and, together with an individual reflection on IPC (Tab. ), upload the final care plan to their portfolio for assessment and narrative feedback from a lecturer in family medicine. The medical student should also discuss the final care plan with the patient’s family physician afterwards with the intention of implementing it. Allied healthcare and nursing students Allied healthcare students (physiotherapy, occupational therapy, speech and language therapy students) and nursing students study the provisional care plans they receive from the medical students 2 weeks before the interprofessional team meeting takes place. In order to be well prepared for this meeting, we ask them to consider how their own profession might contribute to the realization of the patient’s personal goals. Following the student-led interprofessional team meeting, students upload the team reflection of the interprofessional meeting they participated in, including an additional individual reflection on IPC (Tab. ) as well as the minutes of the meeting to their own portfolio. This material is then deployed in other IPE activities at Zuyd University. Student-led interprofessional team meeting Every 4 weeks, six student-led interprofessional team meetings, each lasting 2.5 h, take place concurrently. Lecturers in family medicine and social medicine (Maastricht University), and allied healthcare and nursing teaching staff (Zuyd University) facilitate these interprofessional team meetings. The role of the facilitator is to request clarification should it be necessary, to correct potentially erroneous proposed solutions concerning the care for the frail elderly patient, and to ensure that the team reflection on IPC takes place at the end of the meeting. The student-led interprofessional team meeting starts with a short introduction by the facilitator explaining the purpose of the meeting. Each participant is then given the opportunity to introduce themselves and provide the interprofessional team members with information on their future role in patient care. In order to break traditional patterns , one of the allied healthcare or nursing students is requested to chair the interprofessional team meeting and a second one to take the minutes. Next, each medical student presents their frail elderly patient and their provisional care plan. In this way, five care plans are reviewed during the interprofessional team meeting. Participants then jointly review the care plans and arrive at a final proposal for the best possible care for the patient. At the end of the meeting, 30 min are allocated for team reflection on IPC, covering such items as atmosphere, interaction, leadership, what students have learned about other participating health professions, and whether collaboration has been conducted respectfully. Afterwards, the minutes of the meeting, including the team reflection, are sent to all participants.
Every 4 weeks six interprofessional teams are formed, each comprising 9–10 students from different health professions (5 medical students and 4–5 allied healthcare and nursing students). We attempt to include at least one student from the department of physiotherapy, occupational therapy, speech and language therapy and nursing in each team. For each team, one interprofessional team meeting is scheduled.
Firstly, the medical student visits a frail elderly patient at home, takes a medical history, and asks the patient about their personal goals. The medical student then draws up a provisional care plan (based on the ICF), and sends the anonymized provisional care plan to the allied healthcare and nursing students of their interprofessional team 2 weeks before the student-led interprofessional team meeting takes place. We assigned this task to the medical students for pragmatic, mainly logistic, reasons. The IPE course is scheduled during their rotation in family medicine and social medicine. In this way we can guarantee that medical students can actually visit a frail elderly patient at home and have sufficient time to prepare for the interprofessional team meeting. After the meeting, each medical student finalizes the care plan for their frail elderly patient, based on the outcomes of the meeting as laid down in the minutes of the meeting. They add the team reflection and, together with an individual reflection on IPC (Tab. ), upload the final care plan to their portfolio for assessment and narrative feedback from a lecturer in family medicine. The medical student should also discuss the final care plan with the patient’s family physician afterwards with the intention of implementing it.
Allied healthcare students (physiotherapy, occupational therapy, speech and language therapy students) and nursing students study the provisional care plans they receive from the medical students 2 weeks before the interprofessional team meeting takes place. In order to be well prepared for this meeting, we ask them to consider how their own profession might contribute to the realization of the patient’s personal goals. Following the student-led interprofessional team meeting, students upload the team reflection of the interprofessional meeting they participated in, including an additional individual reflection on IPC (Tab. ) as well as the minutes of the meeting to their own portfolio. This material is then deployed in other IPE activities at Zuyd University.
Every 4 weeks, six student-led interprofessional team meetings, each lasting 2.5 h, take place concurrently. Lecturers in family medicine and social medicine (Maastricht University), and allied healthcare and nursing teaching staff (Zuyd University) facilitate these interprofessional team meetings. The role of the facilitator is to request clarification should it be necessary, to correct potentially erroneous proposed solutions concerning the care for the frail elderly patient, and to ensure that the team reflection on IPC takes place at the end of the meeting. The student-led interprofessional team meeting starts with a short introduction by the facilitator explaining the purpose of the meeting. Each participant is then given the opportunity to introduce themselves and provide the interprofessional team members with information on their future role in patient care. In order to break traditional patterns , one of the allied healthcare or nursing students is requested to chair the interprofessional team meeting and a second one to take the minutes. Next, each medical student presents their frail elderly patient and their provisional care plan. In this way, five care plans are reviewed during the interprofessional team meeting. Participants then jointly review the care plans and arrive at a final proposal for the best possible care for the patient. At the end of the meeting, 30 min are allocated for team reflection on IPC, covering such items as atmosphere, interaction, leadership, what students have learned about other participating health professions, and whether collaboration has been conducted respectfully. Afterwards, the minutes of the meeting, including the team reflection, are sent to all participants.
In mid-2016, we conducted four focus group meetings in order to evaluate the IPE course. One of the focus groups comprised 5 medical students (Maastricht University) and the remaining three groups comprised Zuyd University students, i.e. a total of 5 physiotherapy students, 6 occupational therapy students, 4 speech and language therapy students and 4 nursing students. We assumed that the interaction between participants during the focus group meetings could lead to more in-depth insights . At the start of each focus group meeting, the facilitator (HS or JvD) asked students to describe three positive and three negative experiences of the student-led interprofessional team meeting, as a sensitizer for the topic. Students then elaborated on their remarks in a plenary discussion on learning benefits. Follow-up questions were used to gain more in-depth information on learning benefits. The focus group meetings were audio-taped and transcribed verbatim and analyzed by means of inductive conventional content analysis by HS, JvD, MJ and MvL . Students’ experiences with this IPE course were mainly positive. Positive experiences included the opportunities to (1) learn more about the work of other health professions; (2) get a more extensive perspective on the patient’s problems and personal goals, enabling them to better understand contextual factors and stimulating them to employ a more patient-centred view; and (3) to learn through real-life frail elderly patients, which gave them a feeling of responsibility for the final care plan for the patient. The safe learning environment during the interprofessional team meeting, including mutual respect for each other’s expertise, and the opportunity to eliminate stereotypical prejudices about other health professions was also valued. Participants also noted points for improvement. These included: (1) lack of variety of patient cases, as all patients were frail elderly people (2) lack of diversity in participating health professions, since not every allied healthcare profession was always represented at the interprofessional team meetings; (3) the course material and road map could have been more clear and concise; (4) in order to save time during the introduction phase of meeting, there was a need to become familiar with the roles of other participating disciplines before the meeting; (5) the IPE course could be better integrated into and aligned with the various curricula. The focus group meetings mentioned were part of the regular program evaluation of this IPE course. In accordance with the Declaration of Helsinki, students received information about the evaluation, anonymity and confidentiality. They consented to participate in a focus group meeting on a voluntary basis and participation was of no influence on the outcomes of the IPE course for the individual student concerned.
In this IPE course, based on the principles of problem-based learning , medical, allied healthcare and nursing students from different higher education institutions are given the opportunity to learn from and about other health professions. During a student-led interprofessional team meeting they jointly discuss care plans for frail elderly patients living at home. Afterwards they reflect on interprofessional collaboration. Participating students appreciated this IPE course mainly because of the use of real-life cases, which is in line with earlier findings from Gilligan and colleagues , who found that IPE experiences that involved genuine engagement and opportunities to interact were valued most. Visser and colleagues too, found that ‘active participation and more self-guided learning of students in the IPE activity led to more satisfaction and improvement of the perceptions of other professions’, which resembles our findings. Some suggestions for improvement have already been implemented: (1) to improve the diversity of participating disciplines, students from arts therapy and secondary vocational nursing education now participate in this IPE course; (2) we improved the course material and road map by adding a timeline and making it more concise and focused on what the role of each participating health professions student should be during the IPE course; and (3) we developed short video clips in which one student from each participating healthcare profession tells about the possible contribution of their future discipline to the care of frail elderly patients. In future evaluation we will assess whether the described measures are indeed improvements. We are currently working on the other suggestions for improvement. Firstly, to improve the variety of cases, we are considering to also include patients of all ages with complex multimorbidity. Secondly, we are discussing the possibility to also allow one or more of the participating allied healthcare or nursing students to propose a provisional care plan for a real-life patient to be discussed during the interprofessional team meeting. Thirdly, we look forward to including social work students in the near future to further enhance the diversity of participating students. Fourthly, the integration of this IPE course is an ongoing process in which the development of longitudinal interprofessional curricula at both universities could be a means to facilitate alignment. Lastly, we are aware that only the medical students conduct the assessment and that, based on this information, allied healthcare students and nursing students suggest a treatment plan. It seems a great IPE opportunity for all team members to meet the same community-based patient, maybe jointly. However, we are confined by the reality of logistics, working with two separate higher education institutes. Allied healthcare students and nursing students have different practice placements and different time schedules than the medical students. Next to this, we are also concerned that several assessments could be a great burden for the frail elderly patients involved.
The goals of our IPE course are for the students to experience the practice of interprofessional collaboration, jointly composing a care plan for a frail elderly patient, and to reflect on IPC. We conclude that a student-led interprofessional team meeting in undergraduate health professions education has the potential to practice interprofessional collaboration as it will provide many students with the opportunity to learn with, from and about other health professions in an active way. The use of real-life cases and the educational design contributes to the positive outcome of this IPE activity, giving students the feeling of collectively being responsible for the suggested care for the patient concerned. Paramount in the design, development and implementation of the IPE course is the close collaboration between staff from both participating higher education institutions including their willingness to overcome barriers to IPE.
|
A randomized controlled trial to examine the impacts of disclosing personalized depression risk information on the outcomes of individuals who are at high risk of developing major depression: a research protocol | e3fafd83-c0eb-4781-9652-eb0709c8034c | 6749687 | Health Communication[mh] | There is a pressing need for prevention of major depressive disorder (MDD). The Global Burden of Disease study reported that MDD was the #2 leading cause of disease burden worldwide in 2010. Despite a significant increase in mental health service use in the past two decades, there has been no a measurable change in the prevalence of MDD in various countries . The unchanged disease burden associated with MDD suggests that more effective efforts in early identification and prevention are needed, e.g., identifying people who are at high risk and taking preventive actions to lower the risk. In medicine, multivariable risk prediction (MVRP) models are often used to estimate an individual’s absolute risk (probability) of developing a disease in a given time period, based on the individual’s current exposure to a key set of known risk factors (i.e., baseline risk). Well-known examples include the Framingham risk prediction algorithms for cardiovascular disease . The Framingham risk algorithms are used by clinicians in predicting the risk of developing coronary disease in individuals free of the disease. The Framingham risk functions underpin several of the current policies for preventive interventions, including statin therapy for those with relatively high risk of cardiovascular disease. There is a paucity of research in risk prediction for mental disorders. This is partly due to the lack of population-based longitudinal studies on mental disorders with frequent assessments. In 2013, our team developed and validated sex-specific MVRPs for MDE in the Canadian general population . The MVRPs were developed to predict 4-year risk of MDE, using longitudinal data from 4737 men and 5864 women who were randomly selected across Canada, and who had not had a MDE in the past year prior to the baseline. The MVRPs includes questions about personal and family history of MDD, ongoing negative life stressors and childhood traumatic experience. Predictors in the MVRPs are in Table . The MVRPs had good discriminative power (men: C = 0.7953; women: C = 0.7667), and excellent calibration with the data. In men, the observed and predicted 4-year risk of a MDE was 5.15% and 5.25%, respectively; in women, the observed and predicted 4-year risk of a MDE was 8.27 and 8.31% . We validated the MVRPs in Canadians followed during a different time period . MVRP tools may not only enable health professionals to identify high risk people, but also serve as communication tools to inform consumers about their health status and to empower them to actively engage in self-management. One goal of personalized risk estimates is to promote involvement of consumers in health decisions . Research in cardiology and oncology shows that disclosing personalized risk to consumers is an effective method to achieve consumer involvement in health decisions. Because risk prediction models for MDD are new and the literature on risk disclosure is absent in psychiatry, we have no knowledge about whether provision of this information improves risk perceptions and whether high risk people will act upon the information to engage in self-help. Second, since 2013, we have directly engaged over 500 policy makers, clinicians and the general public to disseminate the prediction tools. Stakeholders have consistently indicated that a question needs to be answered before implementation: will the provision of personalized depression risk information lead to increased psychological distress in high risk people ? As risk prediction models are new in psychiatry, there are no studies that address these notable knowledge gaps. Clearly, we need to provide answers to these practice and policy pertinent questions before moving forward to implementation.
Given the background, the aim of the proposed randomized controlled trial is to answer the following research questions: (1) Does disclosure of personalized depression risk information promote high-risk individuals to take preventive actions? The effect of risk prediction may be maximized if these individuals actively engage in early prevention. (2) Will disclosure of personalized depression risk information negatively affect high-risk people’s mental health status? To safely implement the prediction algorithms, we need to ensure that the disclosure will not lead to increased psychological distress. This study is a mixed-methods randomized controlled trial (RCT) with an embedded qualitative component. The RCT has one intervention arm (receiving personalized depression risk information) and one control arm (1:1). The target population are individuals in the community who are at high risk of major depression. The personalized depression risk is generated using the sex-specific MVRPs for MDE that we developed in Canadians aged 18+ years old . Thus, the inclusion criteria are: no MDE at baseline, or if had a MDE in the past 12 months, the individuals were in full remission for at least 2 months before the interview (see below the question), aged 18+ years, at high risk of MDE based on the algorithms (predicted risk of 6.5% + for men and of 11.2% + for women) , agreement to be contacted for follow-up assessments, and no language barriers to English or French. The status of remission was assessed by the question: “In the past 2 months or longer, has your mood been much improved or back to normal AND you DIDN’T have the symptoms of.....?” This question was adopted from the US National Epidemiological Survey on Alcohol and Related Conditions . Because the prediction algorithms are sex-specific, we are recruiting 350 men and 350 women at baseline. After baseline assessment for eligibility, participants are randomized into intervention and control groups, in men and women separately. Based on systematic reviews on risk communication , the trials targeting behavioral and health status changes required 6 months to 12 months follow-up. Therefore we are following participants for 1 year with follow-up assessments at 6 and 12 months. The RCT adheres to CONSORT and SPIRIT guidelines . An operational flow chart is in Fig. . The baseline and follow-up data collection was carried out using Computer Assisted Telephone Interview which automatically saves the data once the interview is completed. To obtain in-depth information about how the personalized depression risk information is processed by participants and how the information affects them emotionally, we conduct qualitative interviews 1 month after the personalized risk information is disclosed. To understand how the personalized risk information affects participants’ health behaviors, we conduct another round of qualitative interviews at 12 months. All collected data will be kept in a pass-word protected computer in the principle investigator’s office which is under 24/7 security surveillance. Only the project coordinator who is not involved in randomization and data collection has access to the data with personal identification information. Data without personal identification information will be analysed. Only aggregate results will be presented and published. This study has been approved by the Ethics Review Board of the Royal Hospital, Ottawa, Canada, and is reviewed by ERB on an annual basis. Recruitment The target population for future preventive studies is high risk people in the general population. For the proposed study, we are recruiting eligible participants using the random digit dialing method (RDD). We have used the RDD for recruitment in other longitudinal studies and an ongoing national RCT . Recruitment, screening, baseline assessment and randomization are completed by a telephone survey firm that has access to household telephone and validated cell phone numbers across the country. The recruitment and randomization procedures and questionnaire were pilot tested in 20 eligible participants, using a cognitive interviewing method . A random sample of landline and cell phone numbers are selected. When a household is reached, the person who is 18+ years is assessed for eligibility. If a household has 2+ persons aged 18+ years, one is randomly selected. The interviewers explain the study objectives and procedures and answer questions. Potential participants are ensured confidentiality, that participation is voluntary and that they may withdraw at any time. Oral consent is obtained before assessment of eligibility by asking the question: “Do I have your consent to begin the survey?” The answer of “yes” and continued participation is deemed to be informed consent. The Research Ethics Board formally approved this consent. Outcome measures are assessed at baseline, 6 and 12-month. Perception of depression risk is assessed by asking “How likely are you to get depression in the next 4 years?“ The answer can range from 0 to 100, where 0 = certain not to happen and 100 = certain to happen . Self-management strategy use scale (SSUS) was developed and validated by Morgan and Jorm . The. SSUS assesses the frequency of using each of the 14 self-help strategies , including strategies supported by research evidence (e.g., physical exercise,20–23 mindfulness relaxation,24;25 and online cognitive behavior therapy ). Frequency of use can be rated on a 5-category scale. The SSUS has good internal consistency (Cronbach’s a = 0.80) . The Non-Specific Psychological Distress (K10) is a 10-item screening scale intended to yield a global measure of distress based on questions about anxiety and depressive symptoms that a person has experienced in the most recent 4 week period . The scale strongly discriminated between community cases and non-cases of DSM-IV disorders, with areas under the Receiver Operating Characteristic curve of 0.87–0.88 for disorders having Global Assessment of Functioning (GAF) scores of 0–70 and 0.95–0.96 for disorders having GAF scores of 0–50.28 We will compare the changes in the K10 scores over time between the groups to assess whether disclosing the personal risk leads to more psychological distress. Functioning impairments is assessed by the question asking how the symptoms in the K10 affect functioning at home, work and school. We will also ask participants their number of days off work due to health problems in the past month. Sex-specific MVRPs for MDE are administered to identify individuals who are at high risk for MDE, determine eligibility and assess accuracy of perception of depression risk over time. The algorithms have good discrimination (C statistic of 0.76 for women and of 0.79 for men), which is consistent with the range of C statistics of risk algorithms (0.75–0.80) in cardiology . Other baseline measures include the Composite International Diagnostic Interview – Short Form for Major Depression (CIDI-SFMD) is administered to determine eligibility for participation. The CIDI-SFMD is a structured diagnostic interview for MDE in the past year and has been used in all cycles of the National Population Health Survey conducted by Statistics Canada, based on the DSM-IV criteria . The CIDI-SFMD was developed and validated at the University of Michigan . Additionally we collect data about demographic and socioeconomic characteristics and mental health service use (at baseline and follow-up) using standard questions from Statistics Canada surveys. Baseline assessment and randomization Screening Once a potentially eligible participant is identified, the interviewer from the telephone survey firm confirms the participant’s age and administers the CIDI-SFMD and the sex-specific prediction algorithms. Interviewees who are in a MDE or are below the risk thresholds based on the risk calculators, are excluded. Individuals with MDE are encouraged to contact family doctors and information about local mental health resources is provided. For those who are at low risk, the web site of the MVRPs is provided so they may monitor their risk in the future. Baseline assessment In eligible participants, the interviewer administers the K10, SSUS, and asks questions about absenteeism and perceived risk of MDE. Baseline assessment takes 20 to 25 min. Randomization is carried out in men and in women. The telephone survey firm uses a survey software tool built by Voxco. The tool contains a random number generator which randomly creates a digit when the telephone script reads the function. The firm confirmed that this is comparable to the traditional method of using sealed opaque envelopes. Intervention and control For the participants in the intervention group, the personalized risk is disclosed and the interviewer informs the participants that they will be contacted again at 6 months and 12 months. The interest in receiving such personalized depression information has been confirmed by our recent pilot study using the same sampling method. Our pilot data ( n = 200) showed that 100% of high-risk individuals were interested in knowing their risks. Participants in the intervention group are also informed that some may be contacted in 1 month for a 30-min qualitative interview. A package including the following materials is mailed to intervention participants: (1) thank-you letter, (2) general information about MDE, (3) self-help strategies and a summary of research evidence supporting the effectiveness of self-help strategies, and (4) $20 incentive as appreciation of their participation. For participants in the control group, the interviewer informs them that they will be contacted again at 6 and 12 months. Their personal risks will be provided at the 12-month interview. They receive the same package as those in the intervention group. Blinding and follow-up assessments The telephone survey firm securely transfers encrypted baseline data to the PI on a weekly basis. The group assignment data are transferred in a separate file. The follow-up assessments are conducted at the telephone interview laboratory at the University of Ottawa Institute of Mental Health: One month before the scheduled follow-up interviews, letters are sent to participants to remind them of the upcoming interview. After the 12-month interview, participants’ group status is linked with interview data by study ID numbers. Investigators are blinded to participants’ group status. The interviewers who conduct randomization, are not involved in follow-up interviews. The interviewers who conduct the follow-up interviews in Ottawa do not have access to participants’ group status. Given our description of study objectives, participants may know their group status. Therefore, it is possible that some participants in the control group may try to find more information about personalized depression risk. At the follow-up assessments, we will ask if they have used any risk prediction tools over the study period. At the follow-up assessments, if participants develop a MDE, they are encouraged to contact family doctors and information about local mental health resources is provided. Qualitative interviews To obtain in-depth information about how disclosing personalized depression risk affects participants’ decision processes, mental health and health behaviors, we conduct two rounds of qualitative interviews via telephone, 1 month after these participants receive the personalized depression risk and at 12 months. Each includes an initial random sub-sample of 20 men and 20 women from the intervention groups. The qualitative interviews strengthen our study as we will use the findings to “triangulate” our quantitative results and to guide interpretation of the quantitative results . The interviews are audio recorded. Qualitative interviews are transcribed verbatim then analyzed inductively for themes. Our analysis follows the interpretive practices of constant comparison and attempt to uncover patterns both within and between interviews . Nvivo 10 software is used to support thematic analysis. We expect to achieve theoretical saturation with the initial sample of 20. However, if new themes continue to emerge in our final interviews, we will interview additional participants until no new themes emerge. Data monitoring The principal investigator (PI) and the research coordinator (RC) are responsible for daily operation of the project, and monitoring data collection, data quality and potential adverse events. The PI and RC report to the research team at teleconferences held every 3 months. The funder plays no role in the process of data monitoring. Adverse events/harm Our study population are individuals who are not in an episode of depression, but are at high risk. The data collection is conducted via telephone. Therefore, the possibility of physical injuries is minimum. In the circumstance that the participant may need mental health services, the interviewers are instructed to encourage the individual to seek professional help, and provide information about local mental health resources. If an unintended adverse event occurs, the ERB will be immediately notified and the event will be jointly reviewed by the board and the research team. Statistical analysis All analyses will be carried out in men and in women separately, and by group assignment. We will perform an intention-to-treat analysis based on randomization. Each outcome will be analyzed with a separate regression model that includes intervention assignment and demographics, history of MDE and predicted risk at baseline as covariates. Mixed ANOVA with a random intercept will be used to examine the effect of disclosing personalized depression risk information on changes in the SSUS scores, K10 scores and number of days off work. The mixed model will enable the repeated measures to be included in a single analysis and so that data from subjects not followed for the full year can be included. We expect no significant differences between the groups in changes of K10 scores and absenteeism at 6 and 12-month, i.e., disclosing the risk information does not lead to psychological and functioning harms. To examine the effect of risk disclosure on accuracy of perceived risk, we will first subtract participants’ perceived risk from the predicted risk. Positive values of the difference indicate underestimation of risk; negative scores indicate overestimation. We will recode difference scores into a dichotomous variable (≤10% vs > 10%) , indicating whether perceived risk is “close” to the predicted risk. The proportions of accurate risk perception at baseline 6- and 12-month will be estimated and compared. Stratified analyses by demographic variables, history of MDE, baseline predicted risk levels will be conducted. Additionally, we will conduct the same analyses in participants who do not have missing outcome data (the completers). Interim analysis will be conducted after 6-month follow-up. If the intervention group has a significantly higher incident proportion of major depression, and/or of suicidal behaviors, controlling for baseline covariates, than the control group, a team meeting involving staff of ethics review board will be held to review the results and determine whether the trial will be terminated. Sample size calculation A RCT on the impact of e-mail promotion of self-help strategies for depression showed that participants in the intervention group had modest but significant improvement in SSUS scores than those in the control group by a mean of 2.6 points, effect size d = 0.40. Assuming our study will achieve similar effect size, 258 participants (129 in each group) are needed to achieve the power of 0.80 at the α level of 0.05. The sample size calculation was done using STATA version 13. Assuming that the 12-month follow-up response rate is 75% with $20 incentive , we should recruit at least 344 participants (172 in each group) at baseline. Because the study will be carried out by sex separately, we proposed to recruit 350 men and 350 women who meet the inclusion criteria at baseline.
The target population for future preventive studies is high risk people in the general population. For the proposed study, we are recruiting eligible participants using the random digit dialing method (RDD). We have used the RDD for recruitment in other longitudinal studies and an ongoing national RCT . Recruitment, screening, baseline assessment and randomization are completed by a telephone survey firm that has access to household telephone and validated cell phone numbers across the country. The recruitment and randomization procedures and questionnaire were pilot tested in 20 eligible participants, using a cognitive interviewing method . A random sample of landline and cell phone numbers are selected. When a household is reached, the person who is 18+ years is assessed for eligibility. If a household has 2+ persons aged 18+ years, one is randomly selected. The interviewers explain the study objectives and procedures and answer questions. Potential participants are ensured confidentiality, that participation is voluntary and that they may withdraw at any time. Oral consent is obtained before assessment of eligibility by asking the question: “Do I have your consent to begin the survey?” The answer of “yes” and continued participation is deemed to be informed consent. The Research Ethics Board formally approved this consent. Outcome measures are assessed at baseline, 6 and 12-month. Perception of depression risk is assessed by asking “How likely are you to get depression in the next 4 years?“ The answer can range from 0 to 100, where 0 = certain not to happen and 100 = certain to happen . Self-management strategy use scale (SSUS) was developed and validated by Morgan and Jorm . The. SSUS assesses the frequency of using each of the 14 self-help strategies , including strategies supported by research evidence (e.g., physical exercise,20–23 mindfulness relaxation,24;25 and online cognitive behavior therapy ). Frequency of use can be rated on a 5-category scale. The SSUS has good internal consistency (Cronbach’s a = 0.80) . The Non-Specific Psychological Distress (K10) is a 10-item screening scale intended to yield a global measure of distress based on questions about anxiety and depressive symptoms that a person has experienced in the most recent 4 week period . The scale strongly discriminated between community cases and non-cases of DSM-IV disorders, with areas under the Receiver Operating Characteristic curve of 0.87–0.88 for disorders having Global Assessment of Functioning (GAF) scores of 0–70 and 0.95–0.96 for disorders having GAF scores of 0–50.28 We will compare the changes in the K10 scores over time between the groups to assess whether disclosing the personal risk leads to more psychological distress. Functioning impairments is assessed by the question asking how the symptoms in the K10 affect functioning at home, work and school. We will also ask participants their number of days off work due to health problems in the past month. Sex-specific MVRPs for MDE are administered to identify individuals who are at high risk for MDE, determine eligibility and assess accuracy of perception of depression risk over time. The algorithms have good discrimination (C statistic of 0.76 for women and of 0.79 for men), which is consistent with the range of C statistics of risk algorithms (0.75–0.80) in cardiology . Other baseline measures include the Composite International Diagnostic Interview – Short Form for Major Depression (CIDI-SFMD) is administered to determine eligibility for participation. The CIDI-SFMD is a structured diagnostic interview for MDE in the past year and has been used in all cycles of the National Population Health Survey conducted by Statistics Canada, based on the DSM-IV criteria . The CIDI-SFMD was developed and validated at the University of Michigan . Additionally we collect data about demographic and socioeconomic characteristics and mental health service use (at baseline and follow-up) using standard questions from Statistics Canada surveys.
“How likely are you to get depression in the next 4 years?“ The answer can range from 0 to 100, where 0 = certain not to happen and 100 = certain to happen . Self-management strategy use scale (SSUS) was developed and validated by Morgan and Jorm . The. SSUS assesses the frequency of using each of the 14 self-help strategies , including strategies supported by research evidence (e.g., physical exercise,20–23 mindfulness relaxation,24;25 and online cognitive behavior therapy ). Frequency of use can be rated on a 5-category scale. The SSUS has good internal consistency (Cronbach’s a = 0.80) . The Non-Specific Psychological Distress (K10) is a 10-item screening scale intended to yield a global measure of distress based on questions about anxiety and depressive symptoms that a person has experienced in the most recent 4 week period . The scale strongly discriminated between community cases and non-cases of DSM-IV disorders, with areas under the Receiver Operating Characteristic curve of 0.87–0.88 for disorders having Global Assessment of Functioning (GAF) scores of 0–70 and 0.95–0.96 for disorders having GAF scores of 0–50.28 We will compare the changes in the K10 scores over time between the groups to assess whether disclosing the personal risk leads to more psychological distress. Functioning impairments is assessed by the question asking how the symptoms in the K10 affect functioning at home, work and school. We will also ask participants their number of days off work due to health problems in the past month. Sex-specific MVRPs for MDE are administered to identify individuals who are at high risk for MDE, determine eligibility and assess accuracy of perception of depression risk over time. The algorithms have good discrimination (C statistic of 0.76 for women and of 0.79 for men), which is consistent with the range of C statistics of risk algorithms (0.75–0.80) in cardiology . Other baseline measures include the Composite International Diagnostic Interview – Short Form for Major Depression (CIDI-SFMD) is administered to determine eligibility for participation. The CIDI-SFMD is a structured diagnostic interview for MDE in the past year and has been used in all cycles of the National Population Health Survey conducted by Statistics Canada, based on the DSM-IV criteria . The CIDI-SFMD was developed and validated at the University of Michigan . Additionally we collect data about demographic and socioeconomic characteristics and mental health service use (at baseline and follow-up) using standard questions from Statistics Canada surveys.
Screening Once a potentially eligible participant is identified, the interviewer from the telephone survey firm confirms the participant’s age and administers the CIDI-SFMD and the sex-specific prediction algorithms. Interviewees who are in a MDE or are below the risk thresholds based on the risk calculators, are excluded. Individuals with MDE are encouraged to contact family doctors and information about local mental health resources is provided. For those who are at low risk, the web site of the MVRPs is provided so they may monitor their risk in the future. Baseline assessment In eligible participants, the interviewer administers the K10, SSUS, and asks questions about absenteeism and perceived risk of MDE. Baseline assessment takes 20 to 25 min. Randomization is carried out in men and in women. The telephone survey firm uses a survey software tool built by Voxco. The tool contains a random number generator which randomly creates a digit when the telephone script reads the function. The firm confirmed that this is comparable to the traditional method of using sealed opaque envelopes.
Once a potentially eligible participant is identified, the interviewer from the telephone survey firm confirms the participant’s age and administers the CIDI-SFMD and the sex-specific prediction algorithms. Interviewees who are in a MDE or are below the risk thresholds based on the risk calculators, are excluded. Individuals with MDE are encouraged to contact family doctors and information about local mental health resources is provided. For those who are at low risk, the web site of the MVRPs is provided so they may monitor their risk in the future.
In eligible participants, the interviewer administers the K10, SSUS, and asks questions about absenteeism and perceived risk of MDE. Baseline assessment takes 20 to 25 min. Randomization is carried out in men and in women. The telephone survey firm uses a survey software tool built by Voxco. The tool contains a random number generator which randomly creates a digit when the telephone script reads the function. The firm confirmed that this is comparable to the traditional method of using sealed opaque envelopes.
For the participants in the intervention group, the personalized risk is disclosed and the interviewer informs the participants that they will be contacted again at 6 months and 12 months. The interest in receiving such personalized depression information has been confirmed by our recent pilot study using the same sampling method. Our pilot data ( n = 200) showed that 100% of high-risk individuals were interested in knowing their risks. Participants in the intervention group are also informed that some may be contacted in 1 month for a 30-min qualitative interview. A package including the following materials is mailed to intervention participants: (1) thank-you letter, (2) general information about MDE, (3) self-help strategies and a summary of research evidence supporting the effectiveness of self-help strategies, and (4) $20 incentive as appreciation of their participation. For participants in the control group, the interviewer informs them that they will be contacted again at 6 and 12 months. Their personal risks will be provided at the 12-month interview. They receive the same package as those in the intervention group.
The telephone survey firm securely transfers encrypted baseline data to the PI on a weekly basis. The group assignment data are transferred in a separate file. The follow-up assessments are conducted at the telephone interview laboratory at the University of Ottawa Institute of Mental Health: One month before the scheduled follow-up interviews, letters are sent to participants to remind them of the upcoming interview. After the 12-month interview, participants’ group status is linked with interview data by study ID numbers. Investigators are blinded to participants’ group status. The interviewers who conduct randomization, are not involved in follow-up interviews. The interviewers who conduct the follow-up interviews in Ottawa do not have access to participants’ group status. Given our description of study objectives, participants may know their group status. Therefore, it is possible that some participants in the control group may try to find more information about personalized depression risk. At the follow-up assessments, we will ask if they have used any risk prediction tools over the study period. At the follow-up assessments, if participants develop a MDE, they are encouraged to contact family doctors and information about local mental health resources is provided.
To obtain in-depth information about how disclosing personalized depression risk affects participants’ decision processes, mental health and health behaviors, we conduct two rounds of qualitative interviews via telephone, 1 month after these participants receive the personalized depression risk and at 12 months. Each includes an initial random sub-sample of 20 men and 20 women from the intervention groups. The qualitative interviews strengthen our study as we will use the findings to “triangulate” our quantitative results and to guide interpretation of the quantitative results . The interviews are audio recorded. Qualitative interviews are transcribed verbatim then analyzed inductively for themes. Our analysis follows the interpretive practices of constant comparison and attempt to uncover patterns both within and between interviews . Nvivo 10 software is used to support thematic analysis. We expect to achieve theoretical saturation with the initial sample of 20. However, if new themes continue to emerge in our final interviews, we will interview additional participants until no new themes emerge.
The principal investigator (PI) and the research coordinator (RC) are responsible for daily operation of the project, and monitoring data collection, data quality and potential adverse events. The PI and RC report to the research team at teleconferences held every 3 months. The funder plays no role in the process of data monitoring.
Our study population are individuals who are not in an episode of depression, but are at high risk. The data collection is conducted via telephone. Therefore, the possibility of physical injuries is minimum. In the circumstance that the participant may need mental health services, the interviewers are instructed to encourage the individual to seek professional help, and provide information about local mental health resources. If an unintended adverse event occurs, the ERB will be immediately notified and the event will be jointly reviewed by the board and the research team.
All analyses will be carried out in men and in women separately, and by group assignment. We will perform an intention-to-treat analysis based on randomization. Each outcome will be analyzed with a separate regression model that includes intervention assignment and demographics, history of MDE and predicted risk at baseline as covariates. Mixed ANOVA with a random intercept will be used to examine the effect of disclosing personalized depression risk information on changes in the SSUS scores, K10 scores and number of days off work. The mixed model will enable the repeated measures to be included in a single analysis and so that data from subjects not followed for the full year can be included. We expect no significant differences between the groups in changes of K10 scores and absenteeism at 6 and 12-month, i.e., disclosing the risk information does not lead to psychological and functioning harms. To examine the effect of risk disclosure on accuracy of perceived risk, we will first subtract participants’ perceived risk from the predicted risk. Positive values of the difference indicate underestimation of risk; negative scores indicate overestimation. We will recode difference scores into a dichotomous variable (≤10% vs > 10%) , indicating whether perceived risk is “close” to the predicted risk. The proportions of accurate risk perception at baseline 6- and 12-month will be estimated and compared. Stratified analyses by demographic variables, history of MDE, baseline predicted risk levels will be conducted. Additionally, we will conduct the same analyses in participants who do not have missing outcome data (the completers). Interim analysis will be conducted after 6-month follow-up. If the intervention group has a significantly higher incident proportion of major depression, and/or of suicidal behaviors, controlling for baseline covariates, than the control group, a team meeting involving staff of ethics review board will be held to review the results and determine whether the trial will be terminated.
A RCT on the impact of e-mail promotion of self-help strategies for depression showed that participants in the intervention group had modest but significant improvement in SSUS scores than those in the control group by a mean of 2.6 points, effect size d = 0.40. Assuming our study will achieve similar effect size, 258 participants (129 in each group) are needed to achieve the power of 0.80 at the α level of 0.05. The sample size calculation was done using STATA version 13. Assuming that the 12-month follow-up response rate is 75% with $20 incentive , we should recruit at least 344 participants (172 in each group) at baseline. Because the study will be carried out by sex separately, we proposed to recruit 350 men and 350 women who meet the inclusion criteria at baseline.
Current status At the time of submission of this manuscript, we just completed baseline recruitment of 350 men and 350 women who meet the inclusion criteria. It is anticipated that the 6-month follow-up interviews will be completed by the end of September, 2019; 12-month follow-up interviews will be completed by the end of March, 2020. Potential risk and mitigation strategies We acknowledge concerns about the changes in response rates in telephone surveys due to cell phone use and telemarketing. Including eligible participants across the country will enhance the generalizability of the study. Given the vast geographic area of Canada, RDD is the only feasible method. The goal of this study is to recruit participants for a RCT, rather than selecting a representative sample. In a RCT, selection bias is not a serious concern as long as the bias is the same across the intervention and control groups . To mitigate the risk, the telephone survey firm will also access the validated cellphone database. However the use of cellphone numbers is associated with increased costs. Another potential risk of the proposed study is attrition which may incur selection bias. The population-based cohort studies on mental disorders in the workplace, conducted in our lab, showed that we could achieve 77% response rate at 1 year follow-up without any financial incentives . Our strategies for reducing attrition will include appropriately designed introductory scripts, a minimum of nine call back attempts spaced over weekdays and times of day and provision of a $25 incentive for each completed interview. Finally, those deemed low risk, who develops a MDE will be excluded from the RCT at the screening stage, which is a limitation. We have planned to provide the risk prediction algorithms so that they can monitor their risks in the future. Knowledge translation will be in the form of peer-reviewed publications, conference presentations and research website dissemination. We will inform the stakeholders (decision makers and health professionals) who raised the pertinent questions in our previous KT activities, about the benefits of risk disclosure through the Canadian Depression Research Intervention Network and national professional organizations with which we are closely connected. The authorship of the publications generated from this trial will be determined according to the BMC Medical Research Methodology authorship guidelines. No scientific writers will be used. A key step in early identification and prevention of MDE is the development and implementation of advanced tools for identifying individuals who are at high risk. Our team has developed the sex-specific MVRPs for MDE. The proposed trial will develop an evidence base for guiding the disclosure of personalized risk information and understanding the process of risk communication and consumer empowerment, contributing to the advancement of early prevention of MDE in Canada and beyond.
At the time of submission of this manuscript, we just completed baseline recruitment of 350 men and 350 women who meet the inclusion criteria. It is anticipated that the 6-month follow-up interviews will be completed by the end of September, 2019; 12-month follow-up interviews will be completed by the end of March, 2020.
We acknowledge concerns about the changes in response rates in telephone surveys due to cell phone use and telemarketing. Including eligible participants across the country will enhance the generalizability of the study. Given the vast geographic area of Canada, RDD is the only feasible method. The goal of this study is to recruit participants for a RCT, rather than selecting a representative sample. In a RCT, selection bias is not a serious concern as long as the bias is the same across the intervention and control groups . To mitigate the risk, the telephone survey firm will also access the validated cellphone database. However the use of cellphone numbers is associated with increased costs. Another potential risk of the proposed study is attrition which may incur selection bias. The population-based cohort studies on mental disorders in the workplace, conducted in our lab, showed that we could achieve 77% response rate at 1 year follow-up without any financial incentives . Our strategies for reducing attrition will include appropriately designed introductory scripts, a minimum of nine call back attempts spaced over weekdays and times of day and provision of a $25 incentive for each completed interview. Finally, those deemed low risk, who develops a MDE will be excluded from the RCT at the screening stage, which is a limitation. We have planned to provide the risk prediction algorithms so that they can monitor their risks in the future. Knowledge translation will be in the form of peer-reviewed publications, conference presentations and research website dissemination. We will inform the stakeholders (decision makers and health professionals) who raised the pertinent questions in our previous KT activities, about the benefits of risk disclosure through the Canadian Depression Research Intervention Network and national professional organizations with which we are closely connected. The authorship of the publications generated from this trial will be determined according to the BMC Medical Research Methodology authorship guidelines. No scientific writers will be used. A key step in early identification and prevention of MDE is the development and implementation of advanced tools for identifying individuals who are at high risk. Our team has developed the sex-specific MVRPs for MDE. The proposed trial will develop an evidence base for guiding the disclosure of personalized risk information and understanding the process of risk communication and consumer empowerment, contributing to the advancement of early prevention of MDE in Canada and beyond.
|
Inflammatory Transformation of Skin Basal Cells as a Key Driver of Cutaneous Aging | 5fb32997-d33c-4939-8a89-7e64ef8d4871 | 11942461 | Cytology[mh] | The skin is the largest organ in the human body and the most direct reflection of the aging process . Anatomically, the skin primarily consists of the epidermis and dermis, connected by the basement membrane, which together maintain the skin’s barrier function, immune defense, and aesthetic properties . In the epidermis, keratinocytes—which constitute the majority of cells—continuously renew, differentiate, and ultimately form the stratum corneum, providing crucial protection against the external environment. However, with advancing age, the proliferation, differentiation, and repair capabilities of keratinocytes gradually decline, dermal matrix remodeling becomes imbalanced, and the barrier function weakens, leading to typical aging phenotypes such as dryness, laxity, wrinkles, and pigmentation [ , , , ]. Recent studies have shown that skin aging is not merely the functional decline in a single cell type but rather the result of complex interactions among multiple cell populations [ , , ] (including keratinocytes, fibroblasts, immune cells, etc.). In young skin, the epidermis and dermis maintain dynamic coordination through various cytokines and signaling pathways (such as TGF-β, Wnt/β-catenin, Notch, etc.), maintaining efficient extracellular matrix renewal and antioxidant defense capabilities, ensuring barrier integrity and repair efficiency. Once this network is affected by both intrinsic genetic factors and external environmental factors (UV radiation, pollution, stress, etc.), it can trigger cellular signaling disorders, exacerbate the aging phenotype of keratinocytes, decrease dermal fibroblast activity, and activate chronic inflammatory pathways, leading to or accelerating skin dysfunction [ , , , ]. However, the current research on “keratinocyte dynamics” and “cell–cell signaling” during skin aging remains insufficient. On one hand, the proliferation, differentiation, and apoptosis of keratinocytes show staged and spatial changes under multiple signal regulations but their dynamic patterns under different aging stages and external stimuli have not been systematically elucidated. On the other hand, there is a lack of in-depth research on the signaling interaction mechanisms between fibroblasts, immune cells, neuroendocrine cells, and keratinocytes, particularly in understanding the spatiotemporal distribution and key regulatory sites of various secretory factors and receptor ligands under different aging contexts. Therefore, it is necessary to utilize multi-level approaches combining cell biology, molecular biology, and omics to deeply analyze the key nodes and pathways of keratinocyte dynamics and cell–cell signaling during skin aging [ , , , , ]. Based on this, our study obtained single-cell RNA-sequencing (scRNA-seq) data of aged and young skin from public databases, focusing on extracting the keratinocyte components and identifying different keratinocyte subtypes using dimensionality reduction and clustering methods. Subsequently, through trajectory analysis and cell–cell communication analysis, we systematically compared the differences between keratinocyte subgroups in aged and young groups. From a functional perspective, we further elucidated the changes occurring in various subgroups during biological processes such as cell proliferation, differentiation, and apoptosis, and cross-validated whether related genetic variations affect the expression of skin aging-related genes through integration with genome-wide association studies (GWAS) data. This study not only provides new insights into understanding keratinocyte dynamics and their cellular signaling interaction networks during skin aging but also lays an important theoretical foundation for future early diagnosis of skin aging, biomarker screening, and the development of personalized anti-aging intervention strategies.
2.1. Keratinocyte Subpopulations and Developmental Trajectories in Young and Aged Skin Overall, single-cell RNA-sequencing (scRNA-seq) analysis revealed three prominent cell clusters (clusters 0–2) within keratinocytes from both young and aged skin ( A). According to typical keratinocyte marker genes ( B), cluster 0 predominantly expresses the spinous cell (SC) markers ( KRT1 and KRT10 ), whereas cluster 1 is characterized by high expression of the basal cell (BC) markers ( KRT5 and KRT14 ). Notably, cluster 2 co-expresses markers from both SCs and BCs, and also exhibits elevated levels of IFI27 and MMP2 ( C). Based on these marker profiles, we categorized the keratinocytes into three main groups: BCs, SCs, and an IFI27 + keratinocyte population ( D). Compared with young skin, aged skin showed a decrease in BCs but a significant increase in both SCs and IFI27 + keratinocytes ( E), suggesting that IFI27 + keratinocytes may play an important role in the skin aging process. To further elucidate the developmental trajectories of skin keratinocytes in young and aged individuals, we performed pseudotime analysis using “Monocle 2”. The results revealed two clearly diverging developmental paths ( F). Based on the developmental nodes identified in the trajectory, the keratinocytes were categorized into three distinct states ( G). We defined the initial stage as the early state (ES), the upward-right branch as advanced state direction 1 (AS1), and the downward-left branch as advanced state direction 2 (AS2) ( G). In the ES stage, cells are primarily BCs, whereas the AS1 branch is dominated by SCs, and the AS2 branch is mainly composed of IFI27 + keratinocytes ( H). Notably, a comparison of aged and young samples indicated that the IFI27 + epithelial subpopulation (AS2) is more prevalent in aged skin ( H). When examining the proportion of each cell subpopulation in each stage, BCs predominate in the ES, SCs dominate the AS1 (with a smaller fraction of BCs), and the AS2 is largely made up of IFI27 + keratinocytes (with some BCs; I). These findings provide a mechanistic framework suggesting that BCs continue to serve as the principal progenitor pool at early stages in both young and aged skin yet they bifurcate into SC − and IFI27 + dominant lineages as aging progresses—a process that may underlie divergent regenerative and inflammatory trajectories contributing to age-associated skin changes. We next analyzed the gene expression patterns characteristic of each developmental state ( J). We found that ES cells primarily express genes related to maintaining keratinocyte activity, such as AREG and KRT15 . In contrast, the AS1 branch is marked by high levels of the spinous-layer keratins KRT1 and KRT10 . The AS2 branch shows a distinct increase in inflammatory-related genes ( CD74 , CTS3 , and S100A4 ) and elevated expression of mesenchymal markers (e.g., VIM ). The top ten marker genes for each state are displayed in K. Further enrichment analysis confirmed that ES cells are primarily associated with biological processes involved in keratinocyte differentiation, as well as IL-17 and TNF signaling. AS1 cells are enriched in processes related to carbon dioxide and oxygen transport, as well as the estrogen signaling pathway. Meanwhile, the IFI27 + epithelial subgroup (AS2) is strongly linked to lymphocyte activation and antigen-processing/presentation pathways. Combining these findings with our trajectory analysis, we propose that, as skin ages, BCs may adopt a pro-inflammatory phenotype—exemplified by IFI27 + keratinocytes—thereby fostering a heightened inflammatory milieu that not only disrupts the homeostatic renewal of the epidermis but also accelerates age-associated functional decline in the skin. 2.2. Keratinocyte Subpopulation Interactions in Young and Aged Skin Because BCs serve as the principal progenitor population at the onset of development, alterations in their signaling may impact the differentiation of all keratinocytes. To better elucidate the interplay among subpopulations at different developmental states, we subdivided BCs into three stages based on a pseudotime trajectory analysis: BC-ES (early-state BCs), BC-AS1 (BCs inclined toward the spinous-cell lineage), and BC-AS2 (BCs inclined toward the IFI27 + lineage). This approach allows us to capture how cell–cell interaction patterns shift as BC-ES cells diverge into distinct developmental pathways. From the perspective of cell–cell contacts ( A,B), varying degrees of connectivity exist among all epithelial subpopulations. The IFI27 + subpopulation shows a relatively high number of connections with other groups ( A). However, in terms of signal strength, SC cells exhibit the most robust internal interactions ( B). A similar pattern emerges for ECM-receptor interactions ( C,D): the IFI27 + subgroup has extensive interactions with other populations ( C), with particularly strong ECM-receptor signals observed between IFI27 + cells and both BC-ES and SCs ( D). With respect to secretory signals ( E,F), each epithelial subpopulation demonstrates distinct levels of secretory output. Notably, IFI27 + cells, BC-AS1, and BC-AS2 produce a comparatively large number of secreted factors ( E). Again, in terms of interaction strength, the IFI27 + population displays especially prominent secretory signaling with BC-ES and SCs ( F). Based on these overarching observations of cell–cell contacts, ECM-receptor dynamics, and secretory signals, we next examined the specific molecular features that distinguish each mode of interaction in more detail. For cell–cell contacts ( G), the IFI27 + subpopulation exhibits a marked increase in both outgoing and incoming signals, including immune-related factors (e.g., MHC, CD99, CD40, and CD86), consistent with a pro-inflammatory role. The BC-ES subpopulation shows enhanced output of APP, NOTCH, SIRP, and GP1BA, along with a strengthened reception of signals such as CDH1 (from SC), ICAM (from IFI27 + ), THY1 (from IFI27 + ), and GP1BA (from BC-ES). Despite leaning toward the IFI27 + lineage, BC-AS2 does not exhibit pronounced NOTCH signaling. IFI27 + cells do express NOTCH but its level remains relatively low, suggesting that NOTCH is crucial for BCs to maintain normal epithelial homeostasis. The loss or downregulation of NOTCH may drive BCs toward a pro-inflammatory phenotype, potentially exacerbating epithelial aging. For ECM-receptor interactions ( H), the SC subpopulation receives a large share of ECM-related input signals (Input Pattern 1 includes COLLAGEN, LAMININ, FN1, THBS, and TENASCIN). The BC-AS2 and IFI27 + subpopulations primarily exhibit outgoing ECM signals (Output Pattern 1 includes COLLAGEN, FN1, THBS, TENASCIN, and RELN). These data suggest that BC-AS2 and IFI27 + cells are critical sources of ECM components during skin aging, contributing to basement membrane and extracellular matrix remodeling. For secretory signals ( J), the IFI27 + subpopulation also shows markedly enhanced outgoing and incoming signals, including immune-related pathways (e.g., IL1, IL2, IL4, IL6, and TGFB), underscoring its inflammatory nature. BC-ES cells have an elevated output of signals such as EGF, IGF, IGFBP, BMP, EDN, PLAU, and FLT3, as well as an increased reception of VISFATIN (from IFI27 + ), PERIOSTIN (from BC-AS2), and COMPLEMENT (from IFI27 + ). The BC-AS2 subpopulation shows a clear trend toward stronger output of PTPR and PERIOSTIN signals, whereas the BC-AS1 subpopulation receives more WNT and FASLG signals, reflecting its inclination toward the SC lineage. Collectively, these findings suggest that disruptions in BC secretory functions—especially aberrations in EGF, IGF, and IGFBP signaling, combined with heightened PTPR and PERIOSTIN pathways—may induce a pro-inflammatory reprogramming of BCs, thereby promoting their senescence and ultimately accelerating the skin aging process. 2.3. Keratinocyte Cell–Cell Interaction Features in Young and Aged Skin From the perspective of cell–cell contacts ( ), we observed notable changes in the signaling pathways along the developmental trajectory from the BC-ES to BC-AS2 and IFI27 + subpopulations. First, the loss of CDH1 signaling was identified in this direction, which is tightly linked to epithelial differentiation. Its disappearance also implies a loss of epithelial polarity, potentially facilitating abnormal processes such as epithelial-mesenchymal transition. Second, JAG1-NOTCH1/NOTCH2/NOTCH3, a critical pathway in skin epithelial differentiation, was lost as BC-ES progressed toward BC-AS2. Meanwhile, DLL1-NOTCH1/NOTCH2/NOTCH3 was lost as BC-AS2 developed into IFI27 + cells, and DLL1-NOTCH2 was also lost during the BC-ES to BC-AS2 transition. Furthermore, these signals remained absent when considering the reverse influences of the IFI27 + subpopulation on both BC-ES and BC-AS2 cells. Third, the loss of EFNB-EPHB signaling emerged as another defining feature of the BC-ES to IFI27 + trajectory. EFNB-EPHB participates in cell-cell recognition, modulates cell migration and tissue boundary formation, and governs cell positioning during development. Its absence may therefore disrupt normal epithelial proliferation and differentiation, ultimately altering cell fate. From the standpoint of the IFI27 + subpopulation, we noted a marked upregulation of the SELE-CD44 pathway, which likely exerts varying degrees of influence on all subpopulations. Additionally, IFI27 + cells, together with BC-AS2 cells in the same developmental branch, showed enhanced ICAM1 signaling (ITGAX/ITGAM/LTGAL-ITGB2), again affecting all subpopulations to different extents. The IFI27 + subset also exhibited other significant immunomodulatory signals, such as the upregulation of multiple HLA family members. These observations collectively suggest that IFI27 + cells may have acquired immunoregulatory functions, shifting away from a normal epithelial fate and instead adopting an aberrant, pro-inflammatory phenotype. 2.4. ECM Receptor Characteristics of Keratinocyte Subpopulations in Young and Aged Skin From the perspective of ECM receptor features ( ), both the BC-AS2 and IFI27 + subpopulations exhibit significant alterations in collagen–integrin and collagen–CD44 signaling. These changes include enhanced COL–ITGA1/ITGA2/ITGB1 interactions, along with strengthened COL–CD44 direct-binding signals. Specifically, the IFI27 + subpopulation shows upregulation of COL1A1, COL1A2, COL4A1, COL4A2, and COL6A1–COL6A3, whereas BC-AS2 primarily involves COL1A1, COL1A2, COL6A1, and COL6A2. These observations underscore the pivotal contribution of specialized cell populations to ECM remodeling in aged skin, especially through collagen–integrin and CD44-mediated pathways. The IFI27 + subpopulation’s more comprehensive collagen expression profile suggests a heightened capacity for matrix remodeling and cell migration, while BC-AS2—although mostly modulated by type I and VI collagens—also displays augmented integrin signaling (ITGA1/2 + ITGB1), indicating a distinct functional role in cell–matrix interactions. Such differential ECM receptor alterations not only reflect functional divergence in microenvironmental adaptation but also highlight potential therapeutic targets for modulating these specialized signaling pathways. 2.5. Secreted Signaling Features Among Keratinocyte Subpopulations in Young and Aged Skin In-depth analysis of the secreted signaling heatmaps revealed a complex network of regulatory pathways ( ). Notably, WNT3A–FZD8/FZD6/LRP5/LRP6 interactions exhibited strong signaling activity between BC-ES cells and both BC-AS1 and the SC subpopulations. In contrast, VEGFA–VEGFR1/VEGFR2 and members of the TGF-β superfamily were preferentially upregulated in the IFI27 + subpopulation. Regarding inflammatory mediators, IL1, IL2, IL6, and various TNF superfamily members—as well as chemokine axes such as CCL21–CCR7 and CCL5–CCR5—were significantly enhanced in IFI27 + cells. Additionally, the evolving BC-AS1 and BC-AS2 subpopulations, along with IFI27 + cells, showed active growth factor signaling (e.g., FGF family) and heightened matrix-related molecular interactions. Collectively, these findings illuminate a multilayered regulatory framework governing cell fate determination and point toward novel therapeutic strategies that target specific signaling pathways. At the same time, this intricate signaling network underscores the need to account for compensatory mechanisms and dynamic inter-subpopulation signaling when developing anti-aging interventions, thereby emphasizing the importance of a comprehensive, science-driven approach to mitigating skin aging. 2.6. Transcriptomic Characteristics of Trajectory-Specific Genes in Keratinocyte Subpopulations of Young and Aged Skin Based on the developmental trajectories, we extracted the characteristic genes from the three developmental stages—ES, AS1, and AS2—and evaluated their expression levels using skin epithelial transcriptomic data. For the ES signature genes ( A), there were relatively minimal differences in expression between young and aged individuals under normal conditions, apart from a small subset of CXCL family members ( CXCL2 and CXCL3 , which were higher in young skin) and genes such as DMKN and CCL21 , which were more abundantly expressed in aged skin. In general, these ES genes remained comparatively stable at the transcriptomic level. However, after exposure to ultraviolet (UV) radiation, both young and aged epidermal cells showed significantly increased expression of these ES genes. Strikingly, aged skin displayed a more pronounced inflammatory response under UV stress, evidenced by notably high expression of IFI27, CD74, CTS3, S100A4, LY6E, HLA-DPA1, VIM, and HSPA8 . These findings indicate that, although ES genes exhibit cross-age stability under normal physiological conditions, they demonstrate a marked stress response under exogenous challenges (e.g., UV exposure)—a response that is more strongly inflammatory in aged skin. The upregulation of immune-related molecules (e.g., IFI27 , CD74 , and HLA-DPA1 ), stress proteins (HSPA8), and cytoskeleton remodeling genes (VIM and S100A4) underscores a heightened reactivity and impaired homeostatic control in aged epithelium. This age-dependent divergence in stress response likely reflects microenvironmental reprogramming that increases epidermal vulnerability to external insults, providing a molecular explanation for the heightened susceptibility to skin disorders in the elderly and suggesting new targets for preventive and therapeutic interventions. Regarding the AS1 branch (i.e., genes directed toward SC differentiation; B), the transcriptomic profiles of epidermal cells from young and aged individuals displayed both shared features and distinct patterns. Overall, young epidermal cells showed elevated expression of genes involved in transcriptional regulation ( EIF3A , EIF4B , ZNF770 , and DDX46 ), cellular homeostasis ( TMEM154 , TMEM256, VAMP2, and KPNB1 ), and metabolic regulation ( IGBP1 , LSM5 , and PPTC7 ). These changes suggest an active transcriptional and metabolic reprogramming process encompassing protein synthesis, RNA metabolism, membrane trafficking, and cellular communication. Additionally, the upregulation of stress response genes ( OSTC , GAPG , HSPA8 , and RORA ) indicates that these reprogramming events may represent adaptive mechanisms to cope with physiological or pathological challenges. In aged epidermal cells, the AS1 gene signature primarily involved core biological processes related to immune regulation ( STAT3 and IL1RN ), metabolic modulation ( APOE and LDLR ), and signal transduction ( PKD1 and RHOV ), alongside genes influencing cell proliferation ( CCND1 ), differentiation ( GATA3 ), and stress response ( HEBP2 and WASP2 ). Following UV exposure, young individuals exhibited heightened expression of metabolism-related genes ( FERMT1 , POP7 , PSMA10 , and UBR4 ) and transcriptional regulators (e.g., EIF , RIOK3 , and EHF ), whereas aged epidermal cells displayed a pronounced increase in inflammatory genes, such as IFNGR , ARF5 , TRIM29 , DSC1 , JUP , and LAMP1 . These differential expression patterns suggest that each cell population may activate distinct functional modules in response to various stimuli and underscore the metabolic and stress-related shifts characteristic of skin aging. For the AS2 branch (i.e., cells developing toward the IFI27 + subpopulation; C), young epidermal cells exhibited only a limited set of characteristic genes, including CXCL12 , EIF5A , CXCL3 , and TIMP2 . In contrast, aged epidermal cells showed pronounced immune- and metabolism-related gene signatures such as HLA-DRA , CCL21 , CXCL1 , APOE , and AQP1 . Upon UV irradiation, both young and aged epidermal cells displayed substantially increased expression of these inflammation-related genes, with CD74 , CTS3 , and IFI27 notably elevated in both groups. These observations indicate that, during aging, epidermal cells progressively acquire a pro-inflammatory phenotype that can be further exacerbated by exogenous stressors such as UV radiation. The sustained upregulation of IFI27 in particular suggests that it may serve as a critical molecular node linking aging, UV-induced damage, and inflammatory responses. Specifically, the baseline activation of immune-related genes ( HLA-DRA , CCL21 , and CXCL1 ) in older individuals—an “inflammaging” signature—becomes further amplified upon UV exposure, and the marked elevation of IFI27 highlights its potential role in mediating stress responses and inflammatory cascades during skin aging. This age-dependent increase in inflammation susceptibility and the activation of IFI27-associated pathways provide mechanistic insight into why older skin exhibits heightened sensitivity to environmental insults. 2.7. Genetic Loci Associated with Skin Aging and Their Influence on Developmental Trajectory Genes To investigate the genetic underpinnings of facial aging, we analyzed GWAS data specifically focused on facial skin aging. We first identified significant loci that exert an influence on facial aging ( A), which revealed notable signals across multiple chromosomes, with chromosomes 2, 3, 6, and 9 being particularly prominent. We then matched the genes proximal to these significant loci and intersected them with the trajectory-specific signature genes described earlier. This approach uncovered several significant loci associated with multiple developmental trajectory genes ( B), including SH3YL1, which harbors multiple SNP sites. We next performed enrichment analyses on these neighboring genes. The results for biological processes ( C) indicated that these genes are primarily involved in extrinsic growth and intrinsic apoptotic pathways, as well as protease protection, hydroperoxide activity, and ruffle organization/assembly. Other enriched processes encompass purine nucleoside and triphosphate metabolic pathways, as well as functional modules related to the inclusion of body-domain-containing proteins and proteasomal protein catabolism. Collectively, these findings reflect the broad range of key biological functions activated during cellular stress responses. Furthermore, the KEGG pathway analysis reiterated that these proximal genes contribute to antigen processing and endoplasmic-related molecular signaling pathways ( D). Taken together, our data underscore the complex, polygenic regulation characteristic of facial aging, particularly highlighting significant loci on chromosomes 2, 3, 6, and 9 in spatial proximity to developmental trajectory genes. Functional enrichment analyses of these associated genes reveal a regulatory network encompassing cell growth control, apoptosis, protein degradation, oxidative stress response, and immune processing. Notably, the enrichment of antigen processing pathways and endoplasmic-related signaling further emphasizes the central role of immune–inflammatory responses in skin aging. This multilayered functional convergence provides a systematic molecular framework for understanding the genetic underpinnings of facial aging and indicates potential avenues for targeted anti-aging interventions. 2.8. Hypothetical Molecular Model of Skin Aging Based on the integrative evidence from single-cell analyses, transcriptomics, and genetic loci, we propose the following mechanistic hypothesis for skin aging ( ). Under normal physiological conditions, WNT and FASLG signaling pathways precisely regulate the proliferation, differentiation, and fate determination of BCs and SCs, thereby preserving skin tissue homeostasis and renewal. However, dysregulation of key pathways, such as PTPR and PERIOSTIN, triggers a shift toward a pro-inflammatory microenvironment, leading to aberrant basal cell function and the disruption of tissue homeostasis. This, in turn, sets off a cascade of complex molecular events, including reactive oxygen species (ROS) accumulation and oxidative stress damage, progressive DNA lesion build-up, abnormal transcriptional and mRNA expression profiles, imbalanced activation of pro-inflammatory mediators and cytokine networks, reprogramming of cellular energy metabolism, and disruptions in epidermal differentiation. These interacting and mutually reinforcing molecular processes ultimately drive the transition of skin tissues from a healthy to an aged state, characterized by disorganized epidermal structure, compromised basement membrane integrity, degradation of dermal collagen and elastin, and reduced barrier function—hallmarks of skin aging. Elucidating the multifaceted interplay among these signaling pathways and molecular mechanisms provides a systematic theoretical framework for understanding the essence of skin aging. It also highlights critical directions for developing targeted anti-aging interventions and personalized skin care strategies.
Overall, single-cell RNA-sequencing (scRNA-seq) analysis revealed three prominent cell clusters (clusters 0–2) within keratinocytes from both young and aged skin ( A). According to typical keratinocyte marker genes ( B), cluster 0 predominantly expresses the spinous cell (SC) markers ( KRT1 and KRT10 ), whereas cluster 1 is characterized by high expression of the basal cell (BC) markers ( KRT5 and KRT14 ). Notably, cluster 2 co-expresses markers from both SCs and BCs, and also exhibits elevated levels of IFI27 and MMP2 ( C). Based on these marker profiles, we categorized the keratinocytes into three main groups: BCs, SCs, and an IFI27 + keratinocyte population ( D). Compared with young skin, aged skin showed a decrease in BCs but a significant increase in both SCs and IFI27 + keratinocytes ( E), suggesting that IFI27 + keratinocytes may play an important role in the skin aging process. To further elucidate the developmental trajectories of skin keratinocytes in young and aged individuals, we performed pseudotime analysis using “Monocle 2”. The results revealed two clearly diverging developmental paths ( F). Based on the developmental nodes identified in the trajectory, the keratinocytes were categorized into three distinct states ( G). We defined the initial stage as the early state (ES), the upward-right branch as advanced state direction 1 (AS1), and the downward-left branch as advanced state direction 2 (AS2) ( G). In the ES stage, cells are primarily BCs, whereas the AS1 branch is dominated by SCs, and the AS2 branch is mainly composed of IFI27 + keratinocytes ( H). Notably, a comparison of aged and young samples indicated that the IFI27 + epithelial subpopulation (AS2) is more prevalent in aged skin ( H). When examining the proportion of each cell subpopulation in each stage, BCs predominate in the ES, SCs dominate the AS1 (with a smaller fraction of BCs), and the AS2 is largely made up of IFI27 + keratinocytes (with some BCs; I). These findings provide a mechanistic framework suggesting that BCs continue to serve as the principal progenitor pool at early stages in both young and aged skin yet they bifurcate into SC − and IFI27 + dominant lineages as aging progresses—a process that may underlie divergent regenerative and inflammatory trajectories contributing to age-associated skin changes. We next analyzed the gene expression patterns characteristic of each developmental state ( J). We found that ES cells primarily express genes related to maintaining keratinocyte activity, such as AREG and KRT15 . In contrast, the AS1 branch is marked by high levels of the spinous-layer keratins KRT1 and KRT10 . The AS2 branch shows a distinct increase in inflammatory-related genes ( CD74 , CTS3 , and S100A4 ) and elevated expression of mesenchymal markers (e.g., VIM ). The top ten marker genes for each state are displayed in K. Further enrichment analysis confirmed that ES cells are primarily associated with biological processes involved in keratinocyte differentiation, as well as IL-17 and TNF signaling. AS1 cells are enriched in processes related to carbon dioxide and oxygen transport, as well as the estrogen signaling pathway. Meanwhile, the IFI27 + epithelial subgroup (AS2) is strongly linked to lymphocyte activation and antigen-processing/presentation pathways. Combining these findings with our trajectory analysis, we propose that, as skin ages, BCs may adopt a pro-inflammatory phenotype—exemplified by IFI27 + keratinocytes—thereby fostering a heightened inflammatory milieu that not only disrupts the homeostatic renewal of the epidermis but also accelerates age-associated functional decline in the skin.
Because BCs serve as the principal progenitor population at the onset of development, alterations in their signaling may impact the differentiation of all keratinocytes. To better elucidate the interplay among subpopulations at different developmental states, we subdivided BCs into three stages based on a pseudotime trajectory analysis: BC-ES (early-state BCs), BC-AS1 (BCs inclined toward the spinous-cell lineage), and BC-AS2 (BCs inclined toward the IFI27 + lineage). This approach allows us to capture how cell–cell interaction patterns shift as BC-ES cells diverge into distinct developmental pathways. From the perspective of cell–cell contacts ( A,B), varying degrees of connectivity exist among all epithelial subpopulations. The IFI27 + subpopulation shows a relatively high number of connections with other groups ( A). However, in terms of signal strength, SC cells exhibit the most robust internal interactions ( B). A similar pattern emerges for ECM-receptor interactions ( C,D): the IFI27 + subgroup has extensive interactions with other populations ( C), with particularly strong ECM-receptor signals observed between IFI27 + cells and both BC-ES and SCs ( D). With respect to secretory signals ( E,F), each epithelial subpopulation demonstrates distinct levels of secretory output. Notably, IFI27 + cells, BC-AS1, and BC-AS2 produce a comparatively large number of secreted factors ( E). Again, in terms of interaction strength, the IFI27 + population displays especially prominent secretory signaling with BC-ES and SCs ( F). Based on these overarching observations of cell–cell contacts, ECM-receptor dynamics, and secretory signals, we next examined the specific molecular features that distinguish each mode of interaction in more detail. For cell–cell contacts ( G), the IFI27 + subpopulation exhibits a marked increase in both outgoing and incoming signals, including immune-related factors (e.g., MHC, CD99, CD40, and CD86), consistent with a pro-inflammatory role. The BC-ES subpopulation shows enhanced output of APP, NOTCH, SIRP, and GP1BA, along with a strengthened reception of signals such as CDH1 (from SC), ICAM (from IFI27 + ), THY1 (from IFI27 + ), and GP1BA (from BC-ES). Despite leaning toward the IFI27 + lineage, BC-AS2 does not exhibit pronounced NOTCH signaling. IFI27 + cells do express NOTCH but its level remains relatively low, suggesting that NOTCH is crucial for BCs to maintain normal epithelial homeostasis. The loss or downregulation of NOTCH may drive BCs toward a pro-inflammatory phenotype, potentially exacerbating epithelial aging. For ECM-receptor interactions ( H), the SC subpopulation receives a large share of ECM-related input signals (Input Pattern 1 includes COLLAGEN, LAMININ, FN1, THBS, and TENASCIN). The BC-AS2 and IFI27 + subpopulations primarily exhibit outgoing ECM signals (Output Pattern 1 includes COLLAGEN, FN1, THBS, TENASCIN, and RELN). These data suggest that BC-AS2 and IFI27 + cells are critical sources of ECM components during skin aging, contributing to basement membrane and extracellular matrix remodeling. For secretory signals ( J), the IFI27 + subpopulation also shows markedly enhanced outgoing and incoming signals, including immune-related pathways (e.g., IL1, IL2, IL4, IL6, and TGFB), underscoring its inflammatory nature. BC-ES cells have an elevated output of signals such as EGF, IGF, IGFBP, BMP, EDN, PLAU, and FLT3, as well as an increased reception of VISFATIN (from IFI27 + ), PERIOSTIN (from BC-AS2), and COMPLEMENT (from IFI27 + ). The BC-AS2 subpopulation shows a clear trend toward stronger output of PTPR and PERIOSTIN signals, whereas the BC-AS1 subpopulation receives more WNT and FASLG signals, reflecting its inclination toward the SC lineage. Collectively, these findings suggest that disruptions in BC secretory functions—especially aberrations in EGF, IGF, and IGFBP signaling, combined with heightened PTPR and PERIOSTIN pathways—may induce a pro-inflammatory reprogramming of BCs, thereby promoting their senescence and ultimately accelerating the skin aging process.
From the perspective of cell–cell contacts ( ), we observed notable changes in the signaling pathways along the developmental trajectory from the BC-ES to BC-AS2 and IFI27 + subpopulations. First, the loss of CDH1 signaling was identified in this direction, which is tightly linked to epithelial differentiation. Its disappearance also implies a loss of epithelial polarity, potentially facilitating abnormal processes such as epithelial-mesenchymal transition. Second, JAG1-NOTCH1/NOTCH2/NOTCH3, a critical pathway in skin epithelial differentiation, was lost as BC-ES progressed toward BC-AS2. Meanwhile, DLL1-NOTCH1/NOTCH2/NOTCH3 was lost as BC-AS2 developed into IFI27 + cells, and DLL1-NOTCH2 was also lost during the BC-ES to BC-AS2 transition. Furthermore, these signals remained absent when considering the reverse influences of the IFI27 + subpopulation on both BC-ES and BC-AS2 cells. Third, the loss of EFNB-EPHB signaling emerged as another defining feature of the BC-ES to IFI27 + trajectory. EFNB-EPHB participates in cell-cell recognition, modulates cell migration and tissue boundary formation, and governs cell positioning during development. Its absence may therefore disrupt normal epithelial proliferation and differentiation, ultimately altering cell fate. From the standpoint of the IFI27 + subpopulation, we noted a marked upregulation of the SELE-CD44 pathway, which likely exerts varying degrees of influence on all subpopulations. Additionally, IFI27 + cells, together with BC-AS2 cells in the same developmental branch, showed enhanced ICAM1 signaling (ITGAX/ITGAM/LTGAL-ITGB2), again affecting all subpopulations to different extents. The IFI27 + subset also exhibited other significant immunomodulatory signals, such as the upregulation of multiple HLA family members. These observations collectively suggest that IFI27 + cells may have acquired immunoregulatory functions, shifting away from a normal epithelial fate and instead adopting an aberrant, pro-inflammatory phenotype.
From the perspective of ECM receptor features ( ), both the BC-AS2 and IFI27 + subpopulations exhibit significant alterations in collagen–integrin and collagen–CD44 signaling. These changes include enhanced COL–ITGA1/ITGA2/ITGB1 interactions, along with strengthened COL–CD44 direct-binding signals. Specifically, the IFI27 + subpopulation shows upregulation of COL1A1, COL1A2, COL4A1, COL4A2, and COL6A1–COL6A3, whereas BC-AS2 primarily involves COL1A1, COL1A2, COL6A1, and COL6A2. These observations underscore the pivotal contribution of specialized cell populations to ECM remodeling in aged skin, especially through collagen–integrin and CD44-mediated pathways. The IFI27 + subpopulation’s more comprehensive collagen expression profile suggests a heightened capacity for matrix remodeling and cell migration, while BC-AS2—although mostly modulated by type I and VI collagens—also displays augmented integrin signaling (ITGA1/2 + ITGB1), indicating a distinct functional role in cell–matrix interactions. Such differential ECM receptor alterations not only reflect functional divergence in microenvironmental adaptation but also highlight potential therapeutic targets for modulating these specialized signaling pathways.
In-depth analysis of the secreted signaling heatmaps revealed a complex network of regulatory pathways ( ). Notably, WNT3A–FZD8/FZD6/LRP5/LRP6 interactions exhibited strong signaling activity between BC-ES cells and both BC-AS1 and the SC subpopulations. In contrast, VEGFA–VEGFR1/VEGFR2 and members of the TGF-β superfamily were preferentially upregulated in the IFI27 + subpopulation. Regarding inflammatory mediators, IL1, IL2, IL6, and various TNF superfamily members—as well as chemokine axes such as CCL21–CCR7 and CCL5–CCR5—were significantly enhanced in IFI27 + cells. Additionally, the evolving BC-AS1 and BC-AS2 subpopulations, along with IFI27 + cells, showed active growth factor signaling (e.g., FGF family) and heightened matrix-related molecular interactions. Collectively, these findings illuminate a multilayered regulatory framework governing cell fate determination and point toward novel therapeutic strategies that target specific signaling pathways. At the same time, this intricate signaling network underscores the need to account for compensatory mechanisms and dynamic inter-subpopulation signaling when developing anti-aging interventions, thereby emphasizing the importance of a comprehensive, science-driven approach to mitigating skin aging.
Based on the developmental trajectories, we extracted the characteristic genes from the three developmental stages—ES, AS1, and AS2—and evaluated their expression levels using skin epithelial transcriptomic data. For the ES signature genes ( A), there were relatively minimal differences in expression between young and aged individuals under normal conditions, apart from a small subset of CXCL family members ( CXCL2 and CXCL3 , which were higher in young skin) and genes such as DMKN and CCL21 , which were more abundantly expressed in aged skin. In general, these ES genes remained comparatively stable at the transcriptomic level. However, after exposure to ultraviolet (UV) radiation, both young and aged epidermal cells showed significantly increased expression of these ES genes. Strikingly, aged skin displayed a more pronounced inflammatory response under UV stress, evidenced by notably high expression of IFI27, CD74, CTS3, S100A4, LY6E, HLA-DPA1, VIM, and HSPA8 . These findings indicate that, although ES genes exhibit cross-age stability under normal physiological conditions, they demonstrate a marked stress response under exogenous challenges (e.g., UV exposure)—a response that is more strongly inflammatory in aged skin. The upregulation of immune-related molecules (e.g., IFI27 , CD74 , and HLA-DPA1 ), stress proteins (HSPA8), and cytoskeleton remodeling genes (VIM and S100A4) underscores a heightened reactivity and impaired homeostatic control in aged epithelium. This age-dependent divergence in stress response likely reflects microenvironmental reprogramming that increases epidermal vulnerability to external insults, providing a molecular explanation for the heightened susceptibility to skin disorders in the elderly and suggesting new targets for preventive and therapeutic interventions. Regarding the AS1 branch (i.e., genes directed toward SC differentiation; B), the transcriptomic profiles of epidermal cells from young and aged individuals displayed both shared features and distinct patterns. Overall, young epidermal cells showed elevated expression of genes involved in transcriptional regulation ( EIF3A , EIF4B , ZNF770 , and DDX46 ), cellular homeostasis ( TMEM154 , TMEM256, VAMP2, and KPNB1 ), and metabolic regulation ( IGBP1 , LSM5 , and PPTC7 ). These changes suggest an active transcriptional and metabolic reprogramming process encompassing protein synthesis, RNA metabolism, membrane trafficking, and cellular communication. Additionally, the upregulation of stress response genes ( OSTC , GAPG , HSPA8 , and RORA ) indicates that these reprogramming events may represent adaptive mechanisms to cope with physiological or pathological challenges. In aged epidermal cells, the AS1 gene signature primarily involved core biological processes related to immune regulation ( STAT3 and IL1RN ), metabolic modulation ( APOE and LDLR ), and signal transduction ( PKD1 and RHOV ), alongside genes influencing cell proliferation ( CCND1 ), differentiation ( GATA3 ), and stress response ( HEBP2 and WASP2 ). Following UV exposure, young individuals exhibited heightened expression of metabolism-related genes ( FERMT1 , POP7 , PSMA10 , and UBR4 ) and transcriptional regulators (e.g., EIF , RIOK3 , and EHF ), whereas aged epidermal cells displayed a pronounced increase in inflammatory genes, such as IFNGR , ARF5 , TRIM29 , DSC1 , JUP , and LAMP1 . These differential expression patterns suggest that each cell population may activate distinct functional modules in response to various stimuli and underscore the metabolic and stress-related shifts characteristic of skin aging. For the AS2 branch (i.e., cells developing toward the IFI27 + subpopulation; C), young epidermal cells exhibited only a limited set of characteristic genes, including CXCL12 , EIF5A , CXCL3 , and TIMP2 . In contrast, aged epidermal cells showed pronounced immune- and metabolism-related gene signatures such as HLA-DRA , CCL21 , CXCL1 , APOE , and AQP1 . Upon UV irradiation, both young and aged epidermal cells displayed substantially increased expression of these inflammation-related genes, with CD74 , CTS3 , and IFI27 notably elevated in both groups. These observations indicate that, during aging, epidermal cells progressively acquire a pro-inflammatory phenotype that can be further exacerbated by exogenous stressors such as UV radiation. The sustained upregulation of IFI27 in particular suggests that it may serve as a critical molecular node linking aging, UV-induced damage, and inflammatory responses. Specifically, the baseline activation of immune-related genes ( HLA-DRA , CCL21 , and CXCL1 ) in older individuals—an “inflammaging” signature—becomes further amplified upon UV exposure, and the marked elevation of IFI27 highlights its potential role in mediating stress responses and inflammatory cascades during skin aging. This age-dependent increase in inflammation susceptibility and the activation of IFI27-associated pathways provide mechanistic insight into why older skin exhibits heightened sensitivity to environmental insults.
To investigate the genetic underpinnings of facial aging, we analyzed GWAS data specifically focused on facial skin aging. We first identified significant loci that exert an influence on facial aging ( A), which revealed notable signals across multiple chromosomes, with chromosomes 2, 3, 6, and 9 being particularly prominent. We then matched the genes proximal to these significant loci and intersected them with the trajectory-specific signature genes described earlier. This approach uncovered several significant loci associated with multiple developmental trajectory genes ( B), including SH3YL1, which harbors multiple SNP sites. We next performed enrichment analyses on these neighboring genes. The results for biological processes ( C) indicated that these genes are primarily involved in extrinsic growth and intrinsic apoptotic pathways, as well as protease protection, hydroperoxide activity, and ruffle organization/assembly. Other enriched processes encompass purine nucleoside and triphosphate metabolic pathways, as well as functional modules related to the inclusion of body-domain-containing proteins and proteasomal protein catabolism. Collectively, these findings reflect the broad range of key biological functions activated during cellular stress responses. Furthermore, the KEGG pathway analysis reiterated that these proximal genes contribute to antigen processing and endoplasmic-related molecular signaling pathways ( D). Taken together, our data underscore the complex, polygenic regulation characteristic of facial aging, particularly highlighting significant loci on chromosomes 2, 3, 6, and 9 in spatial proximity to developmental trajectory genes. Functional enrichment analyses of these associated genes reveal a regulatory network encompassing cell growth control, apoptosis, protein degradation, oxidative stress response, and immune processing. Notably, the enrichment of antigen processing pathways and endoplasmic-related signaling further emphasizes the central role of immune–inflammatory responses in skin aging. This multilayered functional convergence provides a systematic molecular framework for understanding the genetic underpinnings of facial aging and indicates potential avenues for targeted anti-aging interventions.
Based on the integrative evidence from single-cell analyses, transcriptomics, and genetic loci, we propose the following mechanistic hypothesis for skin aging ( ). Under normal physiological conditions, WNT and FASLG signaling pathways precisely regulate the proliferation, differentiation, and fate determination of BCs and SCs, thereby preserving skin tissue homeostasis and renewal. However, dysregulation of key pathways, such as PTPR and PERIOSTIN, triggers a shift toward a pro-inflammatory microenvironment, leading to aberrant basal cell function and the disruption of tissue homeostasis. This, in turn, sets off a cascade of complex molecular events, including reactive oxygen species (ROS) accumulation and oxidative stress damage, progressive DNA lesion build-up, abnormal transcriptional and mRNA expression profiles, imbalanced activation of pro-inflammatory mediators and cytokine networks, reprogramming of cellular energy metabolism, and disruptions in epidermal differentiation. These interacting and mutually reinforcing molecular processes ultimately drive the transition of skin tissues from a healthy to an aged state, characterized by disorganized epidermal structure, compromised basement membrane integrity, degradation of dermal collagen and elastin, and reduced barrier function—hallmarks of skin aging. Elucidating the multifaceted interplay among these signaling pathways and molecular mechanisms provides a systematic theoretical framework for understanding the essence of skin aging. It also highlights critical directions for developing targeted anti-aging interventions and personalized skin care strategies.
This study employed publicly available single-cell sequencing data, transcriptomics, and facial aging-related GWAS data to comprehensively investigate keratinocyte subpopulations and their developmental trajectories during skin aging. First, through clustering analyses of young and aged skin keratinocytes, we identified three main subpopulations: basal cells (BCs), spinous cells (SCs), and IFI27 + keratinocytes. This categorization not only aligns with classical knowledge of skin physiology at the phenotypic level but also reveals a potentially critical role for a novel subpopulation (IFI27 + ) in maintaining or accelerating skin aging. Second, using single-cell pseudotime analysis (Monocle 2), we identified three distinct differentiation branches—ES (early state), AS1 (bias toward SC differentiation), and AS2 (bias toward IFI27 + cells). The findings suggest that basal cells may diverge along different branches during aging, heading either toward spinous differentiation (AS1) or an inflammatory state (AS2), the latter being particularly associated with the high expression of inflammation-related genes. Whereas traditional studies often focus on aging features in the basal or spinous layers alone, our work delves more deeply into the multi-level differences exhibited by distinct keratinocyte subpopulations, notably the significant increase in IFI27 + cells in aged skin and their pro-inflammatory properties. This is especially evident in analyses of cell–cell communication, ECM-receptor interactions, and secretory signal networks: IFI27 + cells display more active outgoing and incoming inflammatory and immunomodulatory signals, suggesting that they may not merely respond passively to the microenvironment but may also actively accelerate aging and promote chronic inflammation. Furthermore, by subdividing basal cells into BC-ES, BC-AS1, and BC-AS2 based on pseudotime, we were able to elucidate how BC-ES cells transition in different directions and how they interact with neighboring subpopulations (SC and IFI27 + ) via multiple signaling pathways—such as NOTCH, PTPR, and PERIOSTIN—which exhibit distinct characteristics along each branch. In addition, transcriptome data provided supplementary evidence to corroborate the single-cell discoveries. Under normal conditions, young and aged skin showed certain differences in the expression of ES-, AS1-, and AS2-related genes; following UV exposure, however, these differences became more pronounced, especially reflected in stronger inflammatory responses in aged skin. Lastly, by integrating GWAS data with differentially expressed genes from the single-cell trajectories, we found that significant loci on chromosomes 2, 3, 6, and 9 were spatially correlated with multiple key biological pathways (including antigen processing, oxidative stress, and apoptosis), underscoring the polygenic, multi-signal regulatory features of skin aging. Overall, this study offers novel and comprehensive insights into keratinocyte subpopulation diversity, developmental trajectories, and signaling networks during skin aging, laying important groundwork for future molecular diagnostic and therapeutic strategies. Skin aging is a complex biological process driven by both intrinsic and extrinsic factors, centered on the dynamic imbalance of different cell types and their microenvironments [ , , ]. Our research shows that BCs, traditionally regarded as the source of skin renewal, exhibit distinct differentiation trajectories in aging: one branch moves toward the SC lineage (AS1), maintaining some degree of keratinization, while the other progresses toward an inflammatory path (AS2) closely associated with IFI27 + cells. Moreover, from the perspective of single-cell communication, the signaling interactions between BCs and the surrounding cells become increasingly aberrant with age—evidenced by the loss of NOTCH, the disappearance of CDH1, and the overactivation of PTPR and PERIOSTIN pathways. These disruptions not only appear as consequences of aging but may also exacerbate or amplify pro-inflammatory microenvironments in aging skin. Among multiple signaling cascades, WNT and FASLG have long been recognized as essential in controlling BC proliferation and stem cell homeostasis [ , , , ]. In young skin, these pathways help balance the interplay between BCs and SCs, maintaining a healthy epidermal renewal cycle. Once perturbed by internal or external stressors (e.g., UV radiation, oxidative stress, or genetic mutations), these signaling pathways can become dysregulated, prompting BCs toward an inflammatory phenotype and increasing IFI27 + cell proportions. The IFI27 + cells, in turn, secrete various pro-inflammatory factors (such as IL1, IL6, and TNF-α family members), establishing a positive feedback loop that intensifies inflammation and erodes homeostasis. Additionally, our analysis of ECM receptor features and secretory signals suggests that abnormal activation of collagen–integrin/CD44 signaling not only reflects the disordered remodeling of the skin matrix but also drives abnormal cell adhesion, migration, and morphological changes, hastening the loss of structural and functional integrity of keratinocytes [ , , ]. It is worth noting that cross-talk between fibroblasts, immune cells, endothelial cells, and basal cells is critically important in skin aging. Though this study primarily focuses on keratinocytes, the gene enrichment and signaling analyses revealed that many immune-related genes (e.g., HLA family, CD74 , and ICAM1 ) were upregulated in both IFI27 + cells and BC-AS2 subpopulations, implying that the basement membrane and dermal microenvironment also undergo significant alterations. These findings highlight that skin aging does not merely involve the decline in a single cell type but is instead a multi-cell, multi-signal, and multi-pathway process. By combining single-cell sequencing and transcriptomics, we have gained new insights for dissecting each cell subpopulation and pathway with greater granularity, thereby illuminating the cooperative or antagonistic factors that drive aging. In short, the multidimensional mechanism of skin aging is reflected in the differentiation and inflammatory changes in various subpopulations, alongside the complex interplay of cross-cell and cross-pathway regulations. This networked shift across multiple dimensions underlies the gradual and often irreversible nature of skin aging. A deep understanding of this network not only helps identify the most promising therapeutic targets or cell subpopulations for intervention but also informs future preventive and therapeutic strategies. By utilizing GWAS data specifically related to facial skin aging, we identified significant loci on chromosomes 2, 3, 6, and 9 that closely correlate with the aging process. Furthermore, intersecting these SNPs with the differentially expressed genes from our single-cell trajectory analyses revealed potential connections between genes (e.g., SH3YL1 ) and key signals in skin aging. These loci may directly or indirectly affect various aspects of keratinocyte function, such as proliferation and differentiation, antioxidative capacity, and immune regulatory pathways, thus influencing both the onset and progression of skin aging at the molecular level. Enrichment analyses indicated that many of these nearby genes are closely involved in extrinsic growth, intrinsic apoptotic pathways, protein catabolism, oxidative stress, and immune responses. Previous studies on skin aging have often emphasized environmental factors [ , , , ] (e.g., UV exposure, pollution, and stress); however, our findings from the genetic perspective provide strong evidence for a multi-gene regulatory component in skin aging. With the accumulation of DNA damage and a reduction in repair efficiency during aging, specific SNPs or gene mutations could lead to accelerated or more severe aging phenotypes, affecting collagen synthesis, fibroblast activity, and inflammatory responses. Importantly, the enrichment of antigen processing pathways (e.g., the HLA family) and endoplasmic reticulum stress-related signals underscores the critical roles of immune mechanisms and protein-folding quality control in skin aging—a pattern consistent with the heightened immune-related gene expression observed in IFI27 + and BC-AS2 subpopulations, suggesting an intrinsic coupling between genetic factors and immune–inflammatory networks. Elucidating these multifaceted genetic associations not only enhances our understanding of the molecular basis of skin aging but also opens new avenues for personalized medicine and targeted interventions. For instance, if we can clinically detect an individual’s high-risk SNPs related to key areas of chromosomes 2, 3, 6, and 9, we may intervene earlier through anti-inflammatory approaches, ROS reduction, or enhancement of protein homeostasis. Future efforts to integrate epigenetic studies (DNA methylation, histone modifications, and non-coding RNAs) with GWAS data will clarify how gene–environment interactions drive the transition of skin cells toward aging phenotypes at a deeper molecular level. Taken together, this work underscores a complex interplay among genes, the environment, developmental trajectories, and intercellular communication—laying a robust foundation for understanding the causes of skin aging and establishing potential therapeutic targets. The multifaceted analysis of skin aging in this study provides several clinical and industrial applications. First, in early diagnosis and molecular subtyping, the marked increase in IFI27 + cells and the heightened expression of pro-inflammatory genes (e.g., CD74 and CTS3 ) offer promising biomarkers for evaluating the extent of skin aging and underlying inflammation. Dermatologists and cosmetic practitioners may incorporate these markers into skin biopsies or RNA-based assays for more accurate assessments of aging status and inflammation risk, enabling more tailored skin care and treatment plans. Moreover, the emphasis placed on certain genes (e.g., SH3YL1 and the HLA family) by the GWAS analysis suggests that genetic background testing could help identify high-risk individuals earlier, facilitating timely intervention via lifestyle modifications or targeted treatments. Second, multiple signaling pathways (NOTCH, WNT, FASLG, PTPR, and PERIOSTIN) identified in this study appear pivotal in regulating both basal cell fate and inflammatory processes. Developing agents or inhibitors that specifically target these pathways may represent a promising new approach for decelerating skin aging. For example, inhibiting the excessive expression of PTPR and PERIOSTIN could mitigate the pro-inflammatory environment, and modulating WNT or NOTCH signaling might preserve normal differentiation and the repair capacities of basal cells. Additionally, the link between collagen–integrin/CD44 interactions and basement membrane remodeling indicates that scientifically formulated collagen products or integrin pathway modulators may help maintain skin barrier integrity and tissue structure. Third, our findings highlight a range of molecular targets with potential for cosmetic product development. The shared roles of IFI27 + cells and BC-AS2 in inflammation, immune regulation, and ECM remodeling suggest that therapeutic strategies aiming to downregulate pro-inflammatory factors or block certain collagen degradation pathways might yield more effective anti-aging outcomes. Further, by focusing on genes that become markedly upregulated under UV exposure in aged skin (e.g., IFI27 , VIM , and CD74 ), skincare formulations could incorporate active ingredients that attenuate ROS and dampen inflammatory cascades, thereby mitigating photo-induced aging. Finally, it is important to note individual variability and compensatory mechanisms. Skin aging is not merely the simple depletion of a single pathway but rather a dynamic imbalance among multiple signals. Relying solely on interventions for one pathway or gene may be insufficient due to biological redundancy or compensatory shifts in other pathways. A holistic, integrative approach—merging multi-omics data with precision medicine while considering each individual’s genetic background and environmental exposures—is essential for truly effective prevention and therapy. Despite the comprehensive analysis presented here, integrating multiple omics datasets to explore the skin aging process, several limitations remain that warrant attention. First, public database samples often suffer from inconsistent sources, differing sample sizes, and various sequencing platforms, leading to potential data heterogeneity or bias. This variability can affect the precision of clustering results and add noise to integrative analyses such as cross-referencing GWAS and single-cell differentially expressed genes . Future research could employ larger, more homogeneous datasets and merge multi-center data to improve reliability and generalizability. Second, while single-cell sequencing and transcriptomic analyses reveal dynamic cellular and molecular states, these findings lack robust functional validation. For instance, we observed a clear link between the expansion of IFI27 + cells and increased inflammation in aged skin but causality remains to be confirmed through in vitro or in vivo models. Further experimental studies—such as targeted knockout or overexpression of critical pathways (NOTCH, PTPR, and PERIOSTIN)—are necessary to elucidate their precise roles in promoting inflammation and accelerating skin aging. Third, skin aging is inherently spatiotemporal, and our current analysis largely relies on transcriptomic and single-cell data from epidermal compartments. Deeper insights into fibroblasts, immune cells, vascular endothelial cells, and neuroendocrine cells in the dermis and how they coordinate with the epidermis during aging are still lacking. Moreover, our analysis does not adequately address the critical influence melanocytes have on keratinocyte behavior throughout the aging process, despite the well-established communication between these cell types via melanin transfer and paracrine signaling. In reality, aging often involves immune cell infiltration, changes in extracellular matrix composition, modifications to dermal–epidermal coupling, and alterations in melanocyte–keratinocyte interactions that affect photoprotection, pigmentation, and oxidative stress responses. Applying spatial transcriptomics or advanced multiplex fluorescence in situ hybridization (mFISH) in conjunction with 3D skin models would provide more comprehensive spatiotemporal resolution for understanding the interplay among skin layers and cell types throughout aging , including the crucial melanocyte–keratinocyte dynamics. Lastly, while our study provides valuable insights into keratinocyte heterogeneity through transcriptomic analysis, a significant limitation is the lack of integration between the genetic variants (GWAS data) and epigenomic analyses. Though we identified aging-associated subpopulations and their transcriptional signatures, we could not determine how specific SNPs affect epigenetic modifications, transcription factor binding, or 3D chromatin architecture that might drive this heterogeneity. A growing body of evidence suggests that epigenetic regulation—DNA methylation, histone modifications, non-coding RNAs—may even surpass direct genetic variation in establishing and maintaining the cellular heterogeneity we observed during aging. Our current approach cannot capture how epigenetic landscapes diverge among different keratinocyte subpopulations, potentially causing functionally identical cells to adopt specialized states with age. Looking ahead, the multi-dimensional, multi-subpopulation, multi-pathway insights offered by this study point to several promising research directions. First, integrating multiple omics is imperative. Single-cell transcriptomics alone can delineate cellular heterogeneity but falls short of capturing detailed protein, metabolic, epigenetic, and spatial information. Combining spatial transcriptomics, proteomics, and metabolomics could paint a more complete molecular and cellular portrait of skin aging, helping to pinpoint the most critical nodes in the aging process. Second, deeper investigations into extracellular environment regulation and skin microecology are warranted . The skin surface is home to numerous microorganisms and interacts with various environmental factors (light, humidity, and pollutants), collectively forming a complex ecosystem with the epidermal and dermal immune barriers. Aging may involve not only internal cellular changes but also microbiota shifts, altered barrier permeability, and other environmental stress responses. A multi-angle approach could clarify how external and microbial factors coordinate with cell fate determination to shape aging phenotypes. Third, our study’s emphasis on IFI27 + cells, BC-AS2 differentiation pathways, and core routes (PTPR and PERIOSTIN) provides new leads for drug discovery or functional skincare development. Future work can systematically screen for compounds that inhibit inflammation, enhance basal cell proliferation, or promote ECM reconstruction. In vitro experiments, followed by clinical trials, will be crucial for translating these insights into safe and effective anti-aging solutions. Finally, stem cell and tissue engineering approaches may further validate and apply these findings. Constructing artificial 3D skin models or organoids simulating natural aging and external stressors would allow for the real-time tracking of cell differentiation, inflammation, and matrix remodeling. Such models could also be coupled with CRISPR/Cas9 gene editing for more precise functional tests. In addition to enhancing our fundamental understanding, these methods could pave the way for innovative regeneration medicine targeting severe skin damage or age-related deterioration.
4.1. Single-Cell Sequencing Data Acquisition and Keratinocyte Subgroup Identification The target dataset (GSE130973) was obtained from the GEO database . Based on the original author’s cell type annotations, 2323 keratinocytes were extracted for subsequent analysis. Data normalization was performed using the LogNormalize method with a scale factor of 10,000 in the Seurat package (version 4.4.0) . Then, the highly variable genes were identified using the variance stabilizing transformation (VST) method, selecting 2000 features. After scaling the data with the cell cycle scores (S.Score and G2M.Score) regressed out, principal component analysis (PCA) was performed using the selected variable features, followed by Harmony integration to correct for batch effects. The top 30 principal components were used for non-linear dimensionality reduction (UMAP), and clustering analysis was performed using the Louvain algorithm (resolution = 0.2). Subgroup annotation was based on known keratinocyte marker gene expression patterns: basal cells (BCs) using KRT5 and KRT14 as characteristic genes, and squamous cells (SCs) using KRT1 and KRT10 as characteristic genes . The FindAllMarkers function (wilcox.test, min.pct = 0.25, logfc.threshold = 0.25) was used to identify subgroup-specific expressed genes. 4.2. Developmental Trajectory Analysis of Skin Keratinocytes Cell differentiation trajectories were constructed using Monocle 2 (version 2.18.0). Dimensionality reduction was first performed based on differentially expressed genes ( q -value < 0.01), and branch structures were constructed using the DDRTree algorithm . The orderCells function was used to determine cell developmental order and cells were ordered by pseudotime. Cells were grouped by trajectory states, and the proportions of different cell subgroups in each state were calculated. FindAllMarkers was used to compare state-specific gene differences . The GO enrichment analysis (clusterProfiler (version 4.6.2) package, p .adjust < 0.05) and KEGG pathway analysis ( p .adjust < 0.05) were performed on these genes . 4.3. Cell–Cell Communication Network Analysis CellChat (version 1.1.3) was used to analyze cell–cell interactions. The parameters were set as follows: raw.use = TRUE; population.size.min = 10. The analysis included ligand-receptor-mediated cell communication, extracellular matrix (ECM) interactions, and secretory-factor-mediated signaling pathways. The netVisual_aggregate function was used to generate circle plots of cell–cell communication intensity. The netVisual_heatmap and netVisual_river were used to display ligand-receptor pair signaling patterns. The netVisual_bubble was used to display detailed cell pair interactions in specific signaling pathways. 4.4. Transcriptome Data Validation The GSE67098 dataset was obtained, which contained transcriptome data from young (<35 years) and aged (>60 years) skin samples from sun-exposed and sun-protected areas. This dataset specifically included Caucasian subjects to control for pigmentation factors, with paired samples collected via 4-millimeter-diameter punch biopsies from the outer forearm or lateral epicanthus (sun-exposed areas) and upper inner arm (sun-protected areas). Differentially expressed genes with percentage (PCT) differences > 0.25 were selected from single-cell data and their mean expression values were calculated for the transcriptome data. The pheatmap package (version 1.0.12) was used to generate heatmaps showing the expression patterns of these genes in young and aged skin. 4.5. Genetic Variation and Skin Aging Association Analysis GWAS data for facial skin aging (ID: ukb-b-2148; sample size: 423,999; number of SNPs: 9,851,867; population: European) was obtained from the OpenGWAS database ( https://gwas.mrcieu.ac.uk/datasets/ukb-b-2148/ , accessed on 5 January 2025) . Significant SNPs were filtered using the following criteria: p < 5 × 10 −8 ; window size for linkage disequilibrium (LD): 1000 kb; r 2 = 0.01 . The vautils package ( https://github.com/oyhel/vautils/tree/master , accessed on 5 January 2025) was used to obtain neighboring genes of significant SNPs. These genes were intersected with differentially expressed genes from the trajectory analysis to identify potentially functionally related genes. GO and KEGG enrichment analyses ( p .adjust < 0.05) were performed using clusterProfiler (version 4.6.2) to compare biological function similarities with trajectory state characteristic genes .
The target dataset (GSE130973) was obtained from the GEO database . Based on the original author’s cell type annotations, 2323 keratinocytes were extracted for subsequent analysis. Data normalization was performed using the LogNormalize method with a scale factor of 10,000 in the Seurat package (version 4.4.0) . Then, the highly variable genes were identified using the variance stabilizing transformation (VST) method, selecting 2000 features. After scaling the data with the cell cycle scores (S.Score and G2M.Score) regressed out, principal component analysis (PCA) was performed using the selected variable features, followed by Harmony integration to correct for batch effects. The top 30 principal components were used for non-linear dimensionality reduction (UMAP), and clustering analysis was performed using the Louvain algorithm (resolution = 0.2). Subgroup annotation was based on known keratinocyte marker gene expression patterns: basal cells (BCs) using KRT5 and KRT14 as characteristic genes, and squamous cells (SCs) using KRT1 and KRT10 as characteristic genes . The FindAllMarkers function (wilcox.test, min.pct = 0.25, logfc.threshold = 0.25) was used to identify subgroup-specific expressed genes.
Cell differentiation trajectories were constructed using Monocle 2 (version 2.18.0). Dimensionality reduction was first performed based on differentially expressed genes ( q -value < 0.01), and branch structures were constructed using the DDRTree algorithm . The orderCells function was used to determine cell developmental order and cells were ordered by pseudotime. Cells were grouped by trajectory states, and the proportions of different cell subgroups in each state were calculated. FindAllMarkers was used to compare state-specific gene differences . The GO enrichment analysis (clusterProfiler (version 4.6.2) package, p .adjust < 0.05) and KEGG pathway analysis ( p .adjust < 0.05) were performed on these genes .
CellChat (version 1.1.3) was used to analyze cell–cell interactions. The parameters were set as follows: raw.use = TRUE; population.size.min = 10. The analysis included ligand-receptor-mediated cell communication, extracellular matrix (ECM) interactions, and secretory-factor-mediated signaling pathways. The netVisual_aggregate function was used to generate circle plots of cell–cell communication intensity. The netVisual_heatmap and netVisual_river were used to display ligand-receptor pair signaling patterns. The netVisual_bubble was used to display detailed cell pair interactions in specific signaling pathways.
The GSE67098 dataset was obtained, which contained transcriptome data from young (<35 years) and aged (>60 years) skin samples from sun-exposed and sun-protected areas. This dataset specifically included Caucasian subjects to control for pigmentation factors, with paired samples collected via 4-millimeter-diameter punch biopsies from the outer forearm or lateral epicanthus (sun-exposed areas) and upper inner arm (sun-protected areas). Differentially expressed genes with percentage (PCT) differences > 0.25 were selected from single-cell data and their mean expression values were calculated for the transcriptome data. The pheatmap package (version 1.0.12) was used to generate heatmaps showing the expression patterns of these genes in young and aged skin.
GWAS data for facial skin aging (ID: ukb-b-2148; sample size: 423,999; number of SNPs: 9,851,867; population: European) was obtained from the OpenGWAS database ( https://gwas.mrcieu.ac.uk/datasets/ukb-b-2148/ , accessed on 5 January 2025) . Significant SNPs were filtered using the following criteria: p < 5 × 10 −8 ; window size for linkage disequilibrium (LD): 1000 kb; r 2 = 0.01 . The vautils package ( https://github.com/oyhel/vautils/tree/master , accessed on 5 January 2025) was used to obtain neighboring genes of significant SNPs. These genes were intersected with differentially expressed genes from the trajectory analysis to identify potentially functionally related genes. GO and KEGG enrichment analyses ( p .adjust < 0.05) were performed using clusterProfiler (version 4.6.2) to compare biological function similarities with trajectory state characteristic genes .
Through an integrated multi-omics approach combining single-cell transcriptomics, bulk RNA sequencing, and genome-wide association studies, this research has elucidated a critical bifurcation in the fate of BCs during epidermal aging, where BCs either follow canonical differentiation into spinous cells or transition toward an inflammatory IFI27 + phenotype that amplifies age-associated inflammation and orchestrates extracellular matrix remodeling. These findings reveal that maintaining BC homeostasis—specifically by preventing their inflammatory reprogramming—represents a pivotal axis for mitigating cutaneous senescence, identifying promising therapeutic targets including the PTPR, PERIOSTIN, and NOTCH signaling pathways. Collectively, the evidence underscores the polygenic and pleiotropic mechanisms driving skin aging and suggests that strategic interventions focused on preserving BC stability may constitute an effective approach for attenuating age-related dermatological changes.
|
Simulation Training in Laparoscopy Using a Computerized Physical Reality Simulator | b65f74c3-3b72-400f-a448-20321fb161ac | 3015993 | Gynaecology[mh] | Certain inherent abilities are required to perform laparoscopic surgery. These include the ability to operate on a 3-dimensional object from a 2-dimensional video image and to develop the psychomotor hand-eye coordination necessary for performing surgery on the projected image. Human abilities are based on inherent Basic Performance Resources (BPRs), which vary among different people. The ultimate level of excellence the individuals may achieve in playing a sport or a musical instrument or in performing laparoscopic surgery is determined by their innate ability. Training and practice help surgeons realize their full potential within the limits of their natural abilities. BPRs defining laparoscopic abilities include simple visual hand response speed, visual information processing speed, visual spatial short-term memory capacity, and arm neuromotor channel capacity–the most common performance-limiting factor. Enabling laparoscopic skills combine one or more basic skills to duplicate surgical procedures. Examples include cannulation, clip application, cutting, camera navigation, ligation, suturing, knot tying, and application of energy sources. A difference exists between acquiring (basically expressing) laparoscopic abilities and acquiring enabling skills. Basic skills reflect innate abilities requiring brief instructions/mentoring. On the other hand, enabling skills and tasks (especially suturing and knot tying) require detailed instructions and feedback from a mentor. Without such an arrangement, proper learning may not be possible regardless of the innate ability of the trainee. It is becoming increasingly clear that the operating room is not the best place for training novices in laparoscopic surgery, because of the associated risk and expense. Animal and human cadavers provide excellent training opportunities; however, they are expensive, restricted, and lack objective assessment metrics. The new paradigm for laparoscopic surgery training utilizes computer-based simulators with embedded assessment metrics for objective measurement of laparoscopic skills. Following the lead of airline pilots, laparoscopic surgeons will need to rely on computer-based simulation systems for ongoing skill training and assessment outside the operating room. The advantage of the new simulator system is that it combines realistic haptics-based skill exercises found in a box trainer with the objective assessment capability of virtual-reality systems.
The METI SurgicalSIM LTS ( www.METI.com ) is a self-contained patent-pending computer-enhanced interactive laparoscopic physical reality simulator. It was previously named LTS3e and represents an updated version of the LTS2000-ISM60 where the same components were not integrated. The system is suitable for testing and training of basic and enabling laparoscopic skills. Sensors embedded within the physical modules assess the performance of validated exercises on the basis of metrics validated at McGill University. The new simulator consists of an enclosure that can be folded so as to be stored and transported in a compact configuration. A series of simple steps transforms the system into an active configuration ( ) . The enclosure houses a revolving sensor carousel ( ) . A folding cover covers the carousel and contains ports through which laparoscopic instruments are inserted ( ) . The instruments shown in are used to perform procedures on physical models mounted on the carousel. The physical models have embedded sensors that sense and monitor the performance of each exercise. A computer is housed at the distal end of the enclosure with an electronic display mounted on the folding arm. A digital camera captures and records video. Live video of the performance is viewed on an integrated computer monitor. Rotating the sensor carousel provides access to 10 exercises arrayed on 6 stations ( ) . Some of the exercises are repeated with the nondominant hand. The validated exercises assess basic laparoscopic coordination skills, cannulation, cutting and suturing skills, including one that verifies knot integrity with a disruptive force of 1 kilogram. The administrative software supports enrolling users in a database, selecting and performing exercises, viewing and printing past and present test reports, watching tutorials and shutting down. The user survey and login functions make it possible to validate individual or group improvement in performance over time and to establish benchmark criteria for skill proficiency. Three studies were conducted to evaluate the simulator. The first study contained 124 participants from 3 Canadian universities including 13 medical students; 30 residents, fellows, attendings from surgery; 59 from gynecology; and 22 from urology who were classified into groups based on laparoscopic experience as novice, intermediate, competent, expert. All were tested on the LTS-ISM60, and 74 were tested on both the LTS and the MISTELS (McGill Inanimate System for Training and Evaluation of Laparoscopic Skills). Participants completed a satisfaction questionnaire. The second study involved 25 international gynecologists in-training at Kiel School of Gynaecologic Endoscopy and 15 medical students from the same center. All were pretested on the LTS3e and had voluntary additional trials followed by posttesting. In both studies, the performance was assessed with embedded McGill metrics: a preset maximum allowable time is established for each exercise/task. The speed score is calculated by subtracting completion time from maximum time. Penalty points are deducted from the speed score for committing errors or for lack of precision. The net score is the speed score minus penalty points. In the third study, 17 experienced laparoscopic surgeons including 7 in general surgery, 6 in gynecology, 3 in urology, and 1 unknown were recruited. The surgeons performed on randomly assigned simulator stations involving 5 commercially available, computer-based simulators: Lap Mentor, Symbionix, Cleveland, OH; Lap Sim, Surgical Science AB, Göteborg, Sweden; LTS, RealSim Systems, Albuquerque, NM; Pro MIS, Haptica, Boston, MA; and SurgicalSIM, METI, Sarasota, FL. The surgeons practiced repetitively for 1 and 1/2 days. Surgeon's proficiency defined as efficient error-free performance was measured, and proficiency score formulas were developed for each simulator.
Summarized results of the first 2 studies are shown in and . In the third study, the LTS had the highest effectiveness rating of the 5 simulators.
Preliminary data indicate that a computerized physical reality simulator can be used successfully to assess/train laparoscopic technical skills with good user satisfaction.
|
Extracorporeal photopheresis induces the release of anti-inflammatory fatty acids and oxylipins and suppresses pro-inflammatory sphingosine-1-phosphate | cc7ee068-329e-487d-a407-e2ca3358db52 | 11825557 | Surgical Procedures, Operative[mh] | Extracorporeal photopheresis (ECP) is a well-tolerated treatment option for multiple clinical indications. This leukapheresis-based therapy derived from the field of a dermatological treatment modality, the so called PUVA treatment - psoralen plus Ultraviolet A (UVA) - and was initially introduced for treatment of steroid-refractory patients with cutaneous T-cell lymphoma (CTCL) . Key features of using phototherapy including PUVA, UVB and UVA1 are proapoptotic, immunomodulatory, antipruritic, antifibrotic, pro-pigmentary, and pro-prebiotic, consequently inducing improvement of the skin manifestation . Molecular profiling methods such as proteomics and lipidomics can help to better understand which molecules can mediate the observed effects. Due to its excellent safety profile, especially its beneficial effect of reducing the need for systemic steroids, ECP found continuous expansion of indications and is nowadays used for dermatologic diseases, acute and chronic graft-versus-host disease (GvHD), treatment of acute and chronic solid organ transplantation, as well as for rare autoimmune diseases . In addition, we and others are currently evaluating ECP as a tool to support the prevention of transplant rejection . A better understanding of the mode of action may substantially improve the present treatment indications, supporting preventive medicine, and may help to design synergistic combinations with other strategies supporting personalized medicine. Via peripheral access from a cubital vein of a patient whole blood is collected followed by centrifugation and isolation of a leukocyte whole cell suspension, the so-called buffy coat. In the next step, the photo-activator 8-methoxypsoralen (8-MOP) is added to the cell solution, irradiated with UVA light and reinfused to the patient with an anticoagulation solution, usually acid citrate dextrose (ACD) or heparin. ECP treatments are carried out on two consecutive days and usually start every two weeks for a certain period of time. Over the past 35 years, great efforts have been made to find the reason for the clinically proven efficacy, including the intention to explore new indications for ECP treatments and to find biomarkers to understand and identify the patients who will benefit most from the treatment . Results of early studies indicate that the therapeutic effect of ECP is based on the initiation of apoptosis in lymphoid cells through the combination of the photosensitizer 8-MOP and UVA light. UVA irradiation of cells after exposure to 8-MOP was shown to induce DNA cross-linking . Other trials observed alterations in the cytokine profile of peripheral blood, for example increase of tumor necrosis factor alpha (TNF-α) and interleukin (IL)-6 , while further studies have demonstrated the differentiation of T-cells into different cell subpopulations, in particular regulatory T-cells . Regulatory T-cells (Tregs) have already been extensively studied and are known to play a key role in increasing immunotolerance and hence preservation of transplanted organs and improvement of survival rates . Although a complete understanding and full elaboration of its mode of action is still required, several papers have demonstrated empirical evidence of the clinical benefit of ECP in various diseases and indications. In CTCL, multiple clinical studies showed beneficial effects up to complete remission; therefore, ECP is used as a first line treatment in CTCL with blood involvement, also known as Sézary Syndrome (SS) . Recently published guidelines provide further recommendations to initiate ECP as second line or rescue treatment in therapy-refractory forms of mycosis fungoides (MF), a unique subunit of CTCL . ECP is implemented in the current guidelines for the treatment of GvHD as second line therapy option in steroid-refractory presentations of GvHD . Over the last decades, extensive evidence has been accumulated for use of ECP in the context of solid organ transplantation, especially in heart and lung transplantation for the treatment of acute and chronic organ rejection . ECP treatments have been as well analyzed as a prophylactic therapy option . Recently, transcriptomics data from tissue biopsies in patients treated with ECP for chronic rejection after renal transplantation showed a reduction in fibrotic and inflammatory transcriptomic profiles . To date, there are no molecular profiling data for plasma from ECP-treated patients. We used different indications for ECP, acute and chronic rejection in heart transplants and chronic GvHD, as well as various skin diseases such as systemic sclerosis (a chronic autoimmune disease) and cutaneous T-cell lymphoma. In addition, ECP is evaluated in a prophylactic setting in transplant patients. Therefore, ECP is used or evaluated at the primary, secondary and tertiary level of prevention. In our setting, we analyzed patients at the most frequently used level, tertiary prevention, in various ECP indications. The aim of this study is to determine changes in protein and/or oxylipin levels induced by ECP treatment in order to gain more insight into the possible mechanisms of action, using mass spectrometry-based analysis methods. A better understanding of the mode of action of ECP will help to use this therapeutic option in a more systematic fashion, will support further individual optimization of therapeutic concepts and thus contribute to a rational patient stratification in PPPM practice. Sample acquisition We performed a prospective, explorative pilot study, in which we recruited 6 patients who had not previously received ECP treatments, regardless of the indication for ECP. 24 plasma samples were collected before and after ECP procedure on both treatment days. ACD was used as anticoagulant during the collection of 1500 mL of whole blood. The whole procedure is performed as previously described . Plasma samples were collected following a strict protocol starting with drawing blood with standard ethylenediaminetetraacetic acid (EDTA) Vacuette tubes followed by centrifugation for 15 min at room temperature with 720 g. Plasma was then collected and stored in 500 µl aliquots in clear flat glass vials with screw caps to be stored at -80 °C. Exact time of blood drawing, starting time of centrifugation as well as freezing time were documented. The work described has been carried out in accordance with The Code. of Ethics of the World Medical Association (Declaration of Helsinki) for. experiments involving humans. All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the appropriate institutional committee (EK No.: 2076/2015). Sample preparation for oxylipin and fatty acids analyses The analysis of oxylipins and fatty acids was performed as previously described . Plasma (500 µL) was thawed on ice and added to a 15 mL Falcon™ tube containing ice cold ethanol (2 mL; EtOH) and internal standards (12 S-HETE-d8, 15 S-HETE-d8, 20-HETE-d6, 5-oxo-eicosatetraenoic acid (OxoETE)-d7, prostaglandin E2 (PGE2)-d4 and 11,12-dihydroxy-eicosatrienoic acid (DiHETrE)-d11 (Cayman Chemical, Tallinn, Estonia)). Exact concentrations of the internal standards can be found in Supplementary Table . After vortex mixing, the samples were stored at -20 °C overnight resulting in suspension of proteins. The samples were then centrifuged (30 min, 4536 g, 4 °C), the supernatant was transferred into a new 15 mL Falcon™ tube, and the original sample volume (500 µL) was restored via evaporation of EtOH at 37 °C by vacuum centrifugation. Afterwards, the samples were loaded on preconditioned StrataX solid phase extraction (SPE) columns (30 mg mL − 1 , Phenomenex, Torrance, CA, USA) using Pasteur pipettes. The SPE columns were washed with ice-cold MS grade water (5 mL; VWR International, Vienna, Austria) and analytes were eluted with ice-cold MS grade methanol (MeOH) (500 µL; VWR International, Vienna, Austria) including 2% formic acid (FA) (≥ 99%; VWR International, Vienna, Austria). Samples were dried using a gentle nitrogen stream at room temperature, reconstituted with 150 µL reconstitution buffer (H 2 O/acetonitrile (ACN)/MeOH + 0.2% FA– vol% 65:31.5:3.5) and subsequently measured via LC-MS/MS. LC–MS/MS analysis For the LC-MS analyses, a Vanquish™ ultra-high-performance LC (UHPLC) system (Thermo Fisher Scientific™, Vienna, Austria) was coupled to a high-resolution quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific™ QExactive™ HF hybrid quadrupole orbitrap mass spectrometer). For separation of analytes the Vanquish™ UHPLC system was equipped with a reversed-phase Kinetex ® C18 column (2.6 μm XB-C18, 100 Å, LC column 150 × 2.1 mm, Torrance, CA, USA). The injection volume was 20 µL, the flow rate was set to 200 µL min − 1 , the LC column oven was set to 40 °C and the autosampler was set to 4 °C. All samples were measured in technical duplicates. The total run time was 20 min with a gradient flow profile starting at 35% B and increasing to 90% B (1–10 min). After further increasing to 99% B within 0.5 min and keeping it for 5 min, solvent B was decreased to 35% B within 0.5 min and held for 4 min to equilibrate the column. Mobile phase A was H 2 O + 0.2% FA and mobile phase B was ACN: MeOH (vol% 90:10) + 0.2% FA. The QExactive™ HF hybrid quadrupole orbitrap mass spectrometer was equipped with a HESI source operating in negative ionization mode with a spray voltage of 3.5 kV. The capillary temperature was set to 253 °C, sheath gas and auxiliary gas were set to 46 and 10 arbitrary units, respectively. The scan range on the MS1 level was 250–700 m/z with a resolution of 60,000 ( m/z 200). On the MS2 level the resolution was 15,000 ( m/z 200). Here, a top 2 data-dependent acquisition (DDA) method was applied using an HCD collision cell with a normalized collision energy of 24. Therefore, 33 m/z values from an inclusion list, which are specific for oxylipins and their precursor fatty acids, were preferentially selected for fragmentation (Supplementary Table ). LC–MS/MS Data Processing For the data analysis, raw files generated by the QExactive™ HF mass spectrometer were checked manually using the Xcalibur™ Qual Browser software (version 4.1.31.9; Thermo Fisher Scientific™, Vienna, Austria) by comparing reference spectra from the LIPIDMAPS depository library from July 2020 . The identification of analytes was performed based on exact mass, retention time and MS/MS fragmentation pattern (Supplementary Table ). Due to the high structural variety of oxylipins, isomers of known oxylipin species which were not yet identified via commercially available standards have been designated as “molecular mass_chromatographic retention time”. In addition, trans-fatty acid isoforms of polyunsaturated fatty acids such as DPA were designated as “isoform I”, “isoform II” or else. For relative quantification, the TraceFinder software (version 4.1; Thermo Fisher Scientific™, Vienna, Austria) was applied, allowing a mass deviation of 5 ppm. The resulting data containing the peak areas of each analyte were exported and read using the R software package (version 4.2.0) . The peak areas were log 2 -transformed and normalized to the internal standards. Therefore, the log 2 -transformed mean peak area of the internal standards was subtracted from the log 2 -transformed analyte areas to correct for variances arising from sample extraction and LC-MS/MS analysis. To obtain values similar to proteomics LFQ values and thus, enable imputation of missing values, 20 was added to the log 2 -transformed areas. For the imputation of missing values, the minProb function of the imputeLCMD package (version 2.1) was used. The log 2 -transformed normalized peak areas of oxylipins and fatty acids are designated here (Supplementary Table ). For statistical analysis, the log 2 -transformed normalized peak areas were fitted to a linear model using the LIMMA package and samples before and after ECP treatment were pairwise compared using the empirical Bayes method . Resulting p-values < 0.05 were considered as statistically significant (Supplementary Table ). For the multiple testing correction, the Benjamini-Hochberg procedure was applied to all p-values. Volcano plots were produced using the ggrepel (version 0.9.1) and mdthemes packages (version 0.1.0) . Plasma proteomics Sample Preparation for digestion of proteins Briefly, frozen EDTA-anticoagulated plasma samples were thawed on ice, diluted 1:20 in lysis buffer (8 M urea, 50 mM triethylammonium bicarbonate (TEAB), 5% sodium dodecyl sulfate (SDS)) and heated at 95 °C for 5 min. Afterwards, the protein concentration was determined using a BCA assay and 20 µg of protein was used for the digestion of proteins according to the ProtiFi S-Trap™ protocol . Here, disulfide bonds of the solubilized proteins were reduced using 64 mM dithiothreitol (DTT) and protected via carbamidomethylation using 48 mM iodoacetamide (IAA). Upon adding trapping buffer (90% v/v MeOH, 0.1 M TEAB), samples are loaded onto S-trap mini cartridges, washed, and digested using a Trypsin/Lys-C Mix (1:40 enzyme to substrate ratio) at 37 °C for two hours. The resulting peptides were eluted, dried, and stored at minus 20 °C until LC-MS/MS analysis. Untargeted LC-MS/MS analysis of proteins For the untargeted proteomics approach, dried peptide samples were reconstituted using 5 µL of 30% FA containing 4 synthetic standard peptides at a concentration of 10 fmol and diluted with 40 µL of loading solvent (97.95% H 2 O, 2% ACN, 0.05% trifluoroacetic acid (TFA)) as previously described . The chromatographic separation was performed using a Dionex Ultimate™ 3000 nano UHPLC system (Thermo Fisher Scientific™), equipped with a pre-column (2 cm × 100 μm, 5 μm, 100 Å, C18 Acclaim Pep-Map™ 100; Thermo Fisher Scientific™) for pre-concentration and an analytical column (25 cm × 75 μm, 1.6 μm, 120 Å, C18, Aurora Series emitter column; IonOpticks). The injection volume was 1 µL, and the flow rate for the pre-concentration was 10 µL/min using mobile phase A (99.9% H 2 O, 0.1% FA). For the separation of peptides, a flow rate of 300 nL/min was applied with a gradient flow profile starting at 7% mobile phase B (79.9% ACN, 20% H 2 O, 0.1% FA), which is increased to 40% B over 43 min, resulting in a total run time of 85 min including washing an equilibration of the column. For the mass spectrometric analysis, the Dionex Ultimate™ 3000 nano UHPLC system was coupled with the timsTOF Pro mass spectrometer (Bruker) equipped with a captive spray ion source operating at 1650 V. The MS was operating in Parallel Accumulation-Serial Fragmentation (PASEF) mode with a moderate MS data reduction applied. The MS scan range was 100 to 1700 m/z on the MS1 and MS2 level, the 1/k0 scan range was 0.60 to 1.60 V*s/cm 2 , resulting in a ramp time of 100 ms for trapped ion mobility separation. Ten PASEF MS/MS scans per cycle were leading to a total cycle time of 1.16 s. In addition, the collision energy was ramped from 20 to 59 eV as a function of enhancing ion mobility, and the quadrupole isolation width was 2 Th for m/z < 700 and 3 Th for m/z > 700. Data analysis of proteins Raw files generated by the timsTOF Pro mass spectrometer were analyzed using the MaxQuant 1.6.17.0 software package running the Andromeda search engine . For protein identification and LFQ, raw data were searched against the SwissProt database ‘‘homo sapiens’’ (version 141219 with 20380 entries) with a tolerance of 20 ppm at the peptide level and maximal two missed cleavages. Carbamidomethylation on cysteines was set as a fixed modification, while N-terminal protein acetylation and methionine oxidation were set as variable modifications. A minimum of one unique peptide was required for positive identification, and the ‘‘match between runs’’ option was applied with a match window of 0.7 min, an alignment time window of 20 min, a match ion mobility window of 0.05 and an alignment ion mobility of 1. For peptide and protein identifications, an FDR ≤ 0.01 was set, which was computed based on a reversed decoy database. Subsequent data processing and evaluation were performed using the Perseus software (version 1.6.14.0) with reversed sequence and common contaminant filtering . The sample annotation was performed according to the respective study cohorts, LFQ intensity values were log 2 -transformed, and proteins were filtered for a minimum number of 5 independent identifications in at least one group. Missing values were imputed from normal distribution with a width of 0.3 and a down shift of 1.8. The statistical analysis and data visualization were performed using the Perseus software (version 1.6.14.0). Fragpipe pipeline and PSVA Mass spectrometry data were searched and quantified using the fragpipe pipeline with the Uniprot-Swissprot curated proteins database . The resulting LFQvalue matrix was log2 transformed, quantile normalized, and MNAR values were imputed using the MinProb imputation from the R package DEP . Differentially expressed proteins were determined using the LIMMA package with timepoint as variable, and patientID as a blocking factor. P-values where adjusted according to Benjamini Hochberg . Protein Set Variation Analysis (PSVA) was done using the package GSVA from R with the molecular signature database as an input. Sets from the molecular signature database were filtered to biologically relevant pathways using Gene Ontology (GO) terms and Reactome. Differentially regulated pathways were determined by applying a linear model as described above. The proteins of the most regulated pathway on day 1 and day 2 were inserted into the String database (STRING: functional protein association networks, Version 12.0). Viability assay Normal human lung fibroblasts (NHLF, Lonza #CC-2512) and diseased human lung fibroblasts from COPD patients (DHLF-COPD, Lonza #00195277) were seeded in 96-well plates at a density of 2.5 × 10 3 cells per well. After 24 h, the cells were treated with the sphingosine-1-phosphate-1 receptor (S1P1) agonist N-[[2-[2-(trifluoromethyl)[1,1’-biphenyl]-4-yl]benzo[b]thien-5-yl]methyl]-β-alanine (Cayman Chemical AUY954, #9000548) at various concentrations and incubated for 72 h. Fibroblast proliferation and viability were determined using a standard colorimetric cell proliferation assay (Promega CellTiter 96 ® AQueous One Solution Cell Proliferation Assay (MTS), #G3580) at 490 nm. IC50 values were calculated by non-linear regression using the dose-response equations included in GraphPad Prism software version 10.3.1 (GraphPad Software Inc., USA). Life cell imaging with PHIO cellwatcher M Normal human lung fibroblasts (NHLF, Lonza #CC-2512) and diseased human lung fibroblasts from COPD patients (DHLF-COPD, Lonza #00195277) were seeded in 6-well plates at a density of 5 × 10 5 cells per well. After 24 h, cells were treated with the sphingosine-1-phosphate-1 (S1P1) receptor agonist N-[[2-[2-(trifluoromethyl)[1,1’-biphenyl]-4-yl]benzo[b]thien-5-yl]methyl]-β-alanine (Cayman Chemical AUY954, #9000548) at 7821 nM, the IC50 concentration of DHLF-COPD determined in the viability assay. Cell proliferation was monitored in real time in 6-well plates using a PHIO Cellwatcher M microscope (PHIO scientific GmbH, Munich, Germany) placed inside the incubator, imaging each well every 30 min over a 70 h period. The proliferation assays were analyzed using the PHIOme Data Management and Analysis Platform software add-ons “Proliferation” version 1.4.2 (PHIO scientific GmbH, Munich, Germany). Two hypothesis tests were performed using the scipy package to evaluate differences between the cell types and groups Mann-Whitney U test: This non-parametric test assesses whether the distributions of two independent groups differ, focusing on overall ranks rather than specific metrics such as mean or median. The Mood’s Median test was also performed: This is a non-parametric test specifically designed to compare medians between groups. It evaluates whether the central tendency (median) is significantly different between groups. We performed a prospective, explorative pilot study, in which we recruited 6 patients who had not previously received ECP treatments, regardless of the indication for ECP. 24 plasma samples were collected before and after ECP procedure on both treatment days. ACD was used as anticoagulant during the collection of 1500 mL of whole blood. The whole procedure is performed as previously described . Plasma samples were collected following a strict protocol starting with drawing blood with standard ethylenediaminetetraacetic acid (EDTA) Vacuette tubes followed by centrifugation for 15 min at room temperature with 720 g. Plasma was then collected and stored in 500 µl aliquots in clear flat glass vials with screw caps to be stored at -80 °C. Exact time of blood drawing, starting time of centrifugation as well as freezing time were documented. The work described has been carried out in accordance with The Code. of Ethics of the World Medical Association (Declaration of Helsinki) for. experiments involving humans. All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the appropriate institutional committee (EK No.: 2076/2015). The analysis of oxylipins and fatty acids was performed as previously described . Plasma (500 µL) was thawed on ice and added to a 15 mL Falcon™ tube containing ice cold ethanol (2 mL; EtOH) and internal standards (12 S-HETE-d8, 15 S-HETE-d8, 20-HETE-d6, 5-oxo-eicosatetraenoic acid (OxoETE)-d7, prostaglandin E2 (PGE2)-d4 and 11,12-dihydroxy-eicosatrienoic acid (DiHETrE)-d11 (Cayman Chemical, Tallinn, Estonia)). Exact concentrations of the internal standards can be found in Supplementary Table . After vortex mixing, the samples were stored at -20 °C overnight resulting in suspension of proteins. The samples were then centrifuged (30 min, 4536 g, 4 °C), the supernatant was transferred into a new 15 mL Falcon™ tube, and the original sample volume (500 µL) was restored via evaporation of EtOH at 37 °C by vacuum centrifugation. Afterwards, the samples were loaded on preconditioned StrataX solid phase extraction (SPE) columns (30 mg mL − 1 , Phenomenex, Torrance, CA, USA) using Pasteur pipettes. The SPE columns were washed with ice-cold MS grade water (5 mL; VWR International, Vienna, Austria) and analytes were eluted with ice-cold MS grade methanol (MeOH) (500 µL; VWR International, Vienna, Austria) including 2% formic acid (FA) (≥ 99%; VWR International, Vienna, Austria). Samples were dried using a gentle nitrogen stream at room temperature, reconstituted with 150 µL reconstitution buffer (H 2 O/acetonitrile (ACN)/MeOH + 0.2% FA– vol% 65:31.5:3.5) and subsequently measured via LC-MS/MS. For the LC-MS analyses, a Vanquish™ ultra-high-performance LC (UHPLC) system (Thermo Fisher Scientific™, Vienna, Austria) was coupled to a high-resolution quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific™ QExactive™ HF hybrid quadrupole orbitrap mass spectrometer). For separation of analytes the Vanquish™ UHPLC system was equipped with a reversed-phase Kinetex ® C18 column (2.6 μm XB-C18, 100 Å, LC column 150 × 2.1 mm, Torrance, CA, USA). The injection volume was 20 µL, the flow rate was set to 200 µL min − 1 , the LC column oven was set to 40 °C and the autosampler was set to 4 °C. All samples were measured in technical duplicates. The total run time was 20 min with a gradient flow profile starting at 35% B and increasing to 90% B (1–10 min). After further increasing to 99% B within 0.5 min and keeping it for 5 min, solvent B was decreased to 35% B within 0.5 min and held for 4 min to equilibrate the column. Mobile phase A was H 2 O + 0.2% FA and mobile phase B was ACN: MeOH (vol% 90:10) + 0.2% FA. The QExactive™ HF hybrid quadrupole orbitrap mass spectrometer was equipped with a HESI source operating in negative ionization mode with a spray voltage of 3.5 kV. The capillary temperature was set to 253 °C, sheath gas and auxiliary gas were set to 46 and 10 arbitrary units, respectively. The scan range on the MS1 level was 250–700 m/z with a resolution of 60,000 ( m/z 200). On the MS2 level the resolution was 15,000 ( m/z 200). Here, a top 2 data-dependent acquisition (DDA) method was applied using an HCD collision cell with a normalized collision energy of 24. Therefore, 33 m/z values from an inclusion list, which are specific for oxylipins and their precursor fatty acids, were preferentially selected for fragmentation (Supplementary Table ). For the data analysis, raw files generated by the QExactive™ HF mass spectrometer were checked manually using the Xcalibur™ Qual Browser software (version 4.1.31.9; Thermo Fisher Scientific™, Vienna, Austria) by comparing reference spectra from the LIPIDMAPS depository library from July 2020 . The identification of analytes was performed based on exact mass, retention time and MS/MS fragmentation pattern (Supplementary Table ). Due to the high structural variety of oxylipins, isomers of known oxylipin species which were not yet identified via commercially available standards have been designated as “molecular mass_chromatographic retention time”. In addition, trans-fatty acid isoforms of polyunsaturated fatty acids such as DPA were designated as “isoform I”, “isoform II” or else. For relative quantification, the TraceFinder software (version 4.1; Thermo Fisher Scientific™, Vienna, Austria) was applied, allowing a mass deviation of 5 ppm. The resulting data containing the peak areas of each analyte were exported and read using the R software package (version 4.2.0) . The peak areas were log 2 -transformed and normalized to the internal standards. Therefore, the log 2 -transformed mean peak area of the internal standards was subtracted from the log 2 -transformed analyte areas to correct for variances arising from sample extraction and LC-MS/MS analysis. To obtain values similar to proteomics LFQ values and thus, enable imputation of missing values, 20 was added to the log 2 -transformed areas. For the imputation of missing values, the minProb function of the imputeLCMD package (version 2.1) was used. The log 2 -transformed normalized peak areas of oxylipins and fatty acids are designated here (Supplementary Table ). For statistical analysis, the log 2 -transformed normalized peak areas were fitted to a linear model using the LIMMA package and samples before and after ECP treatment were pairwise compared using the empirical Bayes method . Resulting p-values < 0.05 were considered as statistically significant (Supplementary Table ). For the multiple testing correction, the Benjamini-Hochberg procedure was applied to all p-values. Volcano plots were produced using the ggrepel (version 0.9.1) and mdthemes packages (version 0.1.0) . Sample Preparation for digestion of proteins Briefly, frozen EDTA-anticoagulated plasma samples were thawed on ice, diluted 1:20 in lysis buffer (8 M urea, 50 mM triethylammonium bicarbonate (TEAB), 5% sodium dodecyl sulfate (SDS)) and heated at 95 °C for 5 min. Afterwards, the protein concentration was determined using a BCA assay and 20 µg of protein was used for the digestion of proteins according to the ProtiFi S-Trap™ protocol . Here, disulfide bonds of the solubilized proteins were reduced using 64 mM dithiothreitol (DTT) and protected via carbamidomethylation using 48 mM iodoacetamide (IAA). Upon adding trapping buffer (90% v/v MeOH, 0.1 M TEAB), samples are loaded onto S-trap mini cartridges, washed, and digested using a Trypsin/Lys-C Mix (1:40 enzyme to substrate ratio) at 37 °C for two hours. The resulting peptides were eluted, dried, and stored at minus 20 °C until LC-MS/MS analysis. Untargeted LC-MS/MS analysis of proteins For the untargeted proteomics approach, dried peptide samples were reconstituted using 5 µL of 30% FA containing 4 synthetic standard peptides at a concentration of 10 fmol and diluted with 40 µL of loading solvent (97.95% H 2 O, 2% ACN, 0.05% trifluoroacetic acid (TFA)) as previously described . The chromatographic separation was performed using a Dionex Ultimate™ 3000 nano UHPLC system (Thermo Fisher Scientific™), equipped with a pre-column (2 cm × 100 μm, 5 μm, 100 Å, C18 Acclaim Pep-Map™ 100; Thermo Fisher Scientific™) for pre-concentration and an analytical column (25 cm × 75 μm, 1.6 μm, 120 Å, C18, Aurora Series emitter column; IonOpticks). The injection volume was 1 µL, and the flow rate for the pre-concentration was 10 µL/min using mobile phase A (99.9% H 2 O, 0.1% FA). For the separation of peptides, a flow rate of 300 nL/min was applied with a gradient flow profile starting at 7% mobile phase B (79.9% ACN, 20% H 2 O, 0.1% FA), which is increased to 40% B over 43 min, resulting in a total run time of 85 min including washing an equilibration of the column. For the mass spectrometric analysis, the Dionex Ultimate™ 3000 nano UHPLC system was coupled with the timsTOF Pro mass spectrometer (Bruker) equipped with a captive spray ion source operating at 1650 V. The MS was operating in Parallel Accumulation-Serial Fragmentation (PASEF) mode with a moderate MS data reduction applied. The MS scan range was 100 to 1700 m/z on the MS1 and MS2 level, the 1/k0 scan range was 0.60 to 1.60 V*s/cm 2 , resulting in a ramp time of 100 ms for trapped ion mobility separation. Ten PASEF MS/MS scans per cycle were leading to a total cycle time of 1.16 s. In addition, the collision energy was ramped from 20 to 59 eV as a function of enhancing ion mobility, and the quadrupole isolation width was 2 Th for m/z < 700 and 3 Th for m/z > 700. Data analysis of proteins Raw files generated by the timsTOF Pro mass spectrometer were analyzed using the MaxQuant 1.6.17.0 software package running the Andromeda search engine . For protein identification and LFQ, raw data were searched against the SwissProt database ‘‘homo sapiens’’ (version 141219 with 20380 entries) with a tolerance of 20 ppm at the peptide level and maximal two missed cleavages. Carbamidomethylation on cysteines was set as a fixed modification, while N-terminal protein acetylation and methionine oxidation were set as variable modifications. A minimum of one unique peptide was required for positive identification, and the ‘‘match between runs’’ option was applied with a match window of 0.7 min, an alignment time window of 20 min, a match ion mobility window of 0.05 and an alignment ion mobility of 1. For peptide and protein identifications, an FDR ≤ 0.01 was set, which was computed based on a reversed decoy database. Subsequent data processing and evaluation were performed using the Perseus software (version 1.6.14.0) with reversed sequence and common contaminant filtering . The sample annotation was performed according to the respective study cohorts, LFQ intensity values were log 2 -transformed, and proteins were filtered for a minimum number of 5 independent identifications in at least one group. Missing values were imputed from normal distribution with a width of 0.3 and a down shift of 1.8. The statistical analysis and data visualization were performed using the Perseus software (version 1.6.14.0). Fragpipe pipeline and PSVA Mass spectrometry data were searched and quantified using the fragpipe pipeline with the Uniprot-Swissprot curated proteins database . The resulting LFQvalue matrix was log2 transformed, quantile normalized, and MNAR values were imputed using the MinProb imputation from the R package DEP . Differentially expressed proteins were determined using the LIMMA package with timepoint as variable, and patientID as a blocking factor. P-values where adjusted according to Benjamini Hochberg . Protein Set Variation Analysis (PSVA) was done using the package GSVA from R with the molecular signature database as an input. Sets from the molecular signature database were filtered to biologically relevant pathways using Gene Ontology (GO) terms and Reactome. Differentially regulated pathways were determined by applying a linear model as described above. The proteins of the most regulated pathway on day 1 and day 2 were inserted into the String database (STRING: functional protein association networks, Version 12.0). Viability assay Normal human lung fibroblasts (NHLF, Lonza #CC-2512) and diseased human lung fibroblasts from COPD patients (DHLF-COPD, Lonza #00195277) were seeded in 96-well plates at a density of 2.5 × 10 3 cells per well. After 24 h, the cells were treated with the sphingosine-1-phosphate-1 receptor (S1P1) agonist N-[[2-[2-(trifluoromethyl)[1,1’-biphenyl]-4-yl]benzo[b]thien-5-yl]methyl]-β-alanine (Cayman Chemical AUY954, #9000548) at various concentrations and incubated for 72 h. Fibroblast proliferation and viability were determined using a standard colorimetric cell proliferation assay (Promega CellTiter 96 ® AQueous One Solution Cell Proliferation Assay (MTS), #G3580) at 490 nm. IC50 values were calculated by non-linear regression using the dose-response equations included in GraphPad Prism software version 10.3.1 (GraphPad Software Inc., USA). Life cell imaging with PHIO cellwatcher M Normal human lung fibroblasts (NHLF, Lonza #CC-2512) and diseased human lung fibroblasts from COPD patients (DHLF-COPD, Lonza #00195277) were seeded in 6-well plates at a density of 5 × 10 5 cells per well. After 24 h, cells were treated with the sphingosine-1-phosphate-1 (S1P1) receptor agonist N-[[2-[2-(trifluoromethyl)[1,1’-biphenyl]-4-yl]benzo[b]thien-5-yl]methyl]-β-alanine (Cayman Chemical AUY954, #9000548) at 7821 nM, the IC50 concentration of DHLF-COPD determined in the viability assay. Cell proliferation was monitored in real time in 6-well plates using a PHIO Cellwatcher M microscope (PHIO scientific GmbH, Munich, Germany) placed inside the incubator, imaging each well every 30 min over a 70 h period. The proliferation assays were analyzed using the PHIOme Data Management and Analysis Platform software add-ons “Proliferation” version 1.4.2 (PHIO scientific GmbH, Munich, Germany). Two hypothesis tests were performed using the scipy package to evaluate differences between the cell types and groups Mann-Whitney U test: This non-parametric test assesses whether the distributions of two independent groups differ, focusing on overall ranks rather than specific metrics such as mean or median. The Mood’s Median test was also performed: This is a non-parametric test specifically designed to compare medians between groups. It evaluates whether the central tendency (median) is significantly different between groups. Briefly, frozen EDTA-anticoagulated plasma samples were thawed on ice, diluted 1:20 in lysis buffer (8 M urea, 50 mM triethylammonium bicarbonate (TEAB), 5% sodium dodecyl sulfate (SDS)) and heated at 95 °C for 5 min. Afterwards, the protein concentration was determined using a BCA assay and 20 µg of protein was used for the digestion of proteins according to the ProtiFi S-Trap™ protocol . Here, disulfide bonds of the solubilized proteins were reduced using 64 mM dithiothreitol (DTT) and protected via carbamidomethylation using 48 mM iodoacetamide (IAA). Upon adding trapping buffer (90% v/v MeOH, 0.1 M TEAB), samples are loaded onto S-trap mini cartridges, washed, and digested using a Trypsin/Lys-C Mix (1:40 enzyme to substrate ratio) at 37 °C for two hours. The resulting peptides were eluted, dried, and stored at minus 20 °C until LC-MS/MS analysis. For the untargeted proteomics approach, dried peptide samples were reconstituted using 5 µL of 30% FA containing 4 synthetic standard peptides at a concentration of 10 fmol and diluted with 40 µL of loading solvent (97.95% H 2 O, 2% ACN, 0.05% trifluoroacetic acid (TFA)) as previously described . The chromatographic separation was performed using a Dionex Ultimate™ 3000 nano UHPLC system (Thermo Fisher Scientific™), equipped with a pre-column (2 cm × 100 μm, 5 μm, 100 Å, C18 Acclaim Pep-Map™ 100; Thermo Fisher Scientific™) for pre-concentration and an analytical column (25 cm × 75 μm, 1.6 μm, 120 Å, C18, Aurora Series emitter column; IonOpticks). The injection volume was 1 µL, and the flow rate for the pre-concentration was 10 µL/min using mobile phase A (99.9% H 2 O, 0.1% FA). For the separation of peptides, a flow rate of 300 nL/min was applied with a gradient flow profile starting at 7% mobile phase B (79.9% ACN, 20% H 2 O, 0.1% FA), which is increased to 40% B over 43 min, resulting in a total run time of 85 min including washing an equilibration of the column. For the mass spectrometric analysis, the Dionex Ultimate™ 3000 nano UHPLC system was coupled with the timsTOF Pro mass spectrometer (Bruker) equipped with a captive spray ion source operating at 1650 V. The MS was operating in Parallel Accumulation-Serial Fragmentation (PASEF) mode with a moderate MS data reduction applied. The MS scan range was 100 to 1700 m/z on the MS1 and MS2 level, the 1/k0 scan range was 0.60 to 1.60 V*s/cm 2 , resulting in a ramp time of 100 ms for trapped ion mobility separation. Ten PASEF MS/MS scans per cycle were leading to a total cycle time of 1.16 s. In addition, the collision energy was ramped from 20 to 59 eV as a function of enhancing ion mobility, and the quadrupole isolation width was 2 Th for m/z < 700 and 3 Th for m/z > 700. Raw files generated by the timsTOF Pro mass spectrometer were analyzed using the MaxQuant 1.6.17.0 software package running the Andromeda search engine . For protein identification and LFQ, raw data were searched against the SwissProt database ‘‘homo sapiens’’ (version 141219 with 20380 entries) with a tolerance of 20 ppm at the peptide level and maximal two missed cleavages. Carbamidomethylation on cysteines was set as a fixed modification, while N-terminal protein acetylation and methionine oxidation were set as variable modifications. A minimum of one unique peptide was required for positive identification, and the ‘‘match between runs’’ option was applied with a match window of 0.7 min, an alignment time window of 20 min, a match ion mobility window of 0.05 and an alignment ion mobility of 1. For peptide and protein identifications, an FDR ≤ 0.01 was set, which was computed based on a reversed decoy database. Subsequent data processing and evaluation were performed using the Perseus software (version 1.6.14.0) with reversed sequence and common contaminant filtering . The sample annotation was performed according to the respective study cohorts, LFQ intensity values were log 2 -transformed, and proteins were filtered for a minimum number of 5 independent identifications in at least one group. Missing values were imputed from normal distribution with a width of 0.3 and a down shift of 1.8. The statistical analysis and data visualization were performed using the Perseus software (version 1.6.14.0). Mass spectrometry data were searched and quantified using the fragpipe pipeline with the Uniprot-Swissprot curated proteins database . The resulting LFQvalue matrix was log2 transformed, quantile normalized, and MNAR values were imputed using the MinProb imputation from the R package DEP . Differentially expressed proteins were determined using the LIMMA package with timepoint as variable, and patientID as a blocking factor. P-values where adjusted according to Benjamini Hochberg . Protein Set Variation Analysis (PSVA) was done using the package GSVA from R with the molecular signature database as an input. Sets from the molecular signature database were filtered to biologically relevant pathways using Gene Ontology (GO) terms and Reactome. Differentially regulated pathways were determined by applying a linear model as described above. The proteins of the most regulated pathway on day 1 and day 2 were inserted into the String database (STRING: functional protein association networks, Version 12.0). Normal human lung fibroblasts (NHLF, Lonza #CC-2512) and diseased human lung fibroblasts from COPD patients (DHLF-COPD, Lonza #00195277) were seeded in 96-well plates at a density of 2.5 × 10 3 cells per well. After 24 h, the cells were treated with the sphingosine-1-phosphate-1 receptor (S1P1) agonist N-[[2-[2-(trifluoromethyl)[1,1’-biphenyl]-4-yl]benzo[b]thien-5-yl]methyl]-β-alanine (Cayman Chemical AUY954, #9000548) at various concentrations and incubated for 72 h. Fibroblast proliferation and viability were determined using a standard colorimetric cell proliferation assay (Promega CellTiter 96 ® AQueous One Solution Cell Proliferation Assay (MTS), #G3580) at 490 nm. IC50 values were calculated by non-linear regression using the dose-response equations included in GraphPad Prism software version 10.3.1 (GraphPad Software Inc., USA). Normal human lung fibroblasts (NHLF, Lonza #CC-2512) and diseased human lung fibroblasts from COPD patients (DHLF-COPD, Lonza #00195277) were seeded in 6-well plates at a density of 5 × 10 5 cells per well. After 24 h, cells were treated with the sphingosine-1-phosphate-1 (S1P1) receptor agonist N-[[2-[2-(trifluoromethyl)[1,1’-biphenyl]-4-yl]benzo[b]thien-5-yl]methyl]-β-alanine (Cayman Chemical AUY954, #9000548) at 7821 nM, the IC50 concentration of DHLF-COPD determined in the viability assay. Cell proliferation was monitored in real time in 6-well plates using a PHIO Cellwatcher M microscope (PHIO scientific GmbH, Munich, Germany) placed inside the incubator, imaging each well every 30 min over a 70 h period. The proliferation assays were analyzed using the PHIOme Data Management and Analysis Platform software add-ons “Proliferation” version 1.4.2 (PHIO scientific GmbH, Munich, Germany). Two hypothesis tests were performed using the scipy package to evaluate differences between the cell types and groups Mann-Whitney U test: This non-parametric test assesses whether the distributions of two independent groups differ, focusing on overall ranks rather than specific metrics such as mean or median. The Mood’s Median test was also performed: This is a non-parametric test specifically designed to compare medians between groups. It evaluates whether the central tendency (median) is significantly different between groups. We collected blood samples from 6 ECP patients with 4 different diagnoses or clinical indications (Supplementary Table ) before and after ECP at two consecutive days (day 1 and the following day 2) to further isolate plasma. Thus, 24 plasma samples were processed as described previously to perform liquid chromatography-mass spectrometry (LC-MS)-based proteome profiling as well as oxylipin analysis (Fig. A). 269 proteins were consistently (independent identification per protein in at least 9 out of 12 samples per analysis group) and reliably (false discovery rate (FDR) < 0.01 at protein and peptide level, at least 2 peptides per protein) identified and quantified based on relative label free quantification (LFQ) values. No protein was found with significantly different LFQ values (FDR < 0.05) when comparing plasma samples after treatment to the corresponding controls using a paired t-test. A principal component analysis demonstrates that the inter-individual variation was larger than any variation potentially attributed to treatment (Fig. B, Supplementary Table ). Instead, the HTX patients indicated by triangle, square and rhomb are closely located. However, by using the fragpipe pipeline and GSVA, we identified “cellular response to lipid” ( p -value: 0.02) and “regulation of plasminogen activation” ( p -value: 0.012) as the most significantly regulated pathways at GO and Reactome level, pointing out the importance of the lipid metabolism and the possible involvement of the platelets in this context. The proteins involved in these pathways are interconnected, as demonstrated by STRING analysis (Supplementary Fig. A and B). ECP induces significant alteration in the fatty acid and oxylipin composition of blood plasma The analysis of fatty acids and oxylipins was performed as described previously . After filtering for confidence (mass accuracy < 2ppm, matching isotopic pattern and fragmentation spectrum, retention time variation < 0.1%) and reproducible identifications (independent identification in at least 6 out of 12 samples per analysis group), a total of 67 lipids were identified and quantified according to normalized area under the curve values obtained by mass spectrometry. Normalization was performed with respect to internal standards as indicated in the Materials and Methods section. ECP treatment was found to induce several significant regulatory events (Figs. A, B and A–F, H and K). Most striking was the upregulation of polyunsaturated fatty acids alpha-linolenic acid, stearidonic acid, eicosapentaenoic acid (EPA) and docosapentaenoic acid (DPA) together with trans-fatty acid isoforms (here designated as isoform I). This was accompanied by the downregulation of the unsaturated fatty acid stearic acid. Furthermore, several lipid mediators were found to be strongly affected by ECP treatment, including upregulation of 13-oxo-octadecadienoic acid (OxoODE), 12-hydroxy-eicosapentaenoic acid (HEPE) and downregulation of sphingosine-1-phosphate (Fig. D, K and F). These findings were highly similar at two independent treatment days and apparently independent of the patients’ background situation (Fig. ). While the fatty acids arachidonic acid (AA) and docosahexaenoic acid (DHA) as well as the lipid mediators 12-hydroxy-eicosatetraenoic acid (HETE) and 14-hydroxy-docosahexaenoic acid (HDoHE) did not reach statistical significance, they were found to be upregulated upon treatment on both days 1 and 2 (Fig. G, I, J and L). Accordingly, comparison of the analysis results obtained before treatment at day 1 to the results obtained before treatment at day 2, as well as the analysis after treatment at day 1 to the corresponding results at day 2 did not reveal any significant alterations depicting the immediate regulation by ECP (Supplementary Fig. A, B). An overview of the oxylipins which were identified in the plasma of ECP treated patients can be found in Fig. . To shed light on the possible role of S1P in the context of chronic inflammation we selected a selective sphingosine-1-phosphate-1 receptor modulator AUY954, which experimentally reduces heart transplant rejection in vivo. AUY954 led to a significant higher anti-proliferative activity in human lung fibroblasts from COPD patients (DHLF-COPD) in comparison to normal lung fibroblasts (NHLF) (Fig. ), suggesting that this pathway might be important in ECP and its mode of action. This is validated as well by cell life imaging using the PHIO Cellwatcher M by recording the drug effects 46 h (Fig. 6A) leading to a significant downregulation of proliferation only in the lung fibroblast from COPD patients. As drug concentration 7821 nM, the IC50 concentration of DHLF-COPD (Fig. ) determined in the viability assay was used. In DHLF-COPD the Mann-Whitney U test showed a highly significant difference in distributions (U = 41246, p = 1.01 × 10 −5 ) and Mood’s Median Test confirmed a significant difference in medians (χ 2 = 15.82, p = 7 × 10 −5 ) comparing control and treatment group while in NHLF no significant correlation could be detected (Fig. A). In addition to that, AUY954 induces a morphological change in the DHLF-COPD to a more sprouted version (Fig. C, E) comparable to the NHLF while in NHLF no morphological change can be observed under AUY954 treatment (Fig. B, D). The analysis of fatty acids and oxylipins was performed as described previously . After filtering for confidence (mass accuracy < 2ppm, matching isotopic pattern and fragmentation spectrum, retention time variation < 0.1%) and reproducible identifications (independent identification in at least 6 out of 12 samples per analysis group), a total of 67 lipids were identified and quantified according to normalized area under the curve values obtained by mass spectrometry. Normalization was performed with respect to internal standards as indicated in the Materials and Methods section. ECP treatment was found to induce several significant regulatory events (Figs. A, B and A–F, H and K). Most striking was the upregulation of polyunsaturated fatty acids alpha-linolenic acid, stearidonic acid, eicosapentaenoic acid (EPA) and docosapentaenoic acid (DPA) together with trans-fatty acid isoforms (here designated as isoform I). This was accompanied by the downregulation of the unsaturated fatty acid stearic acid. Furthermore, several lipid mediators were found to be strongly affected by ECP treatment, including upregulation of 13-oxo-octadecadienoic acid (OxoODE), 12-hydroxy-eicosapentaenoic acid (HEPE) and downregulation of sphingosine-1-phosphate (Fig. D, K and F). These findings were highly similar at two independent treatment days and apparently independent of the patients’ background situation (Fig. ). While the fatty acids arachidonic acid (AA) and docosahexaenoic acid (DHA) as well as the lipid mediators 12-hydroxy-eicosatetraenoic acid (HETE) and 14-hydroxy-docosahexaenoic acid (HDoHE) did not reach statistical significance, they were found to be upregulated upon treatment on both days 1 and 2 (Fig. G, I, J and L). Accordingly, comparison of the analysis results obtained before treatment at day 1 to the results obtained before treatment at day 2, as well as the analysis after treatment at day 1 to the corresponding results at day 2 did not reveal any significant alterations depicting the immediate regulation by ECP (Supplementary Fig. A, B). An overview of the oxylipins which were identified in the plasma of ECP treated patients can be found in Fig. . To shed light on the possible role of S1P in the context of chronic inflammation we selected a selective sphingosine-1-phosphate-1 receptor modulator AUY954, which experimentally reduces heart transplant rejection in vivo. AUY954 led to a significant higher anti-proliferative activity in human lung fibroblasts from COPD patients (DHLF-COPD) in comparison to normal lung fibroblasts (NHLF) (Fig. ), suggesting that this pathway might be important in ECP and its mode of action. This is validated as well by cell life imaging using the PHIO Cellwatcher M by recording the drug effects 46 h (Fig. 6A) leading to a significant downregulation of proliferation only in the lung fibroblast from COPD patients. As drug concentration 7821 nM, the IC50 concentration of DHLF-COPD (Fig. ) determined in the viability assay was used. In DHLF-COPD the Mann-Whitney U test showed a highly significant difference in distributions (U = 41246, p = 1.01 × 10 −5 ) and Mood’s Median Test confirmed a significant difference in medians (χ 2 = 15.82, p = 7 × 10 −5 ) comparing control and treatment group while in NHLF no significant correlation could be detected (Fig. A). In addition to that, AUY954 induces a morphological change in the DHLF-COPD to a more sprouted version (Fig. C, E) comparable to the NHLF while in NHLF no morphological change can be observed under AUY954 treatment (Fig. B, D). Extracorporeal photopheresis represents an efficient and side-effect-free (or very low) treatment option for patients suffering from diseases such as cutaneous T-cell lymphoma and graft-versus-host diseases or to control organ transplant rejection. Numerous beneficial clinical effects have been well documented but the underlying mode of action at the molecular level is not yet fully understood. The combination of mass spectrometry-based analysis of proteins and oxylipins in human plasma has the potential to detect characteristic biochemical processes in-vivo as previously demonstrated . Thus, here we performed such analyses using plasma from patients before and after ECP treatment on two consecutive days. Because these patients had different medical conditions, we expected a large variance in basal protein and lipid levels and wanted to identify the most robust treatment-specific effects. In addition, by doing so, we aimed to identify the common effects relevant for the different indications of ECP, as ECP is effective for all indications. Although proteome analysis methodology has otherwise successfully identified disease- and lifestyle-associated proteome alterations , in this case proteome profiling did not identify any such treatment-specific events. Nevertheless, it is fair to conclude that ECP treatment had no detectable direct effect on plasma protein abundance values within the relatively short period of time during treatment. As patients receive ECP treatments over a longer time period (months) it cannot be excluded that ECP might in the long run induce specific proteome alterations. In addition, our cohort was rather small with different indications. Using PSVA we found “cellular response to lipid” and “regulation of plasminogen activation” to be the most significant regulated pathways, pointing out the involvement of the lipid metabolism and the possible involvement of the platelets in this context. This is in contrast to the robust effects currently observed with fatty acids, oxylipins and the lipid mediator sphingosine-1-phosphate in ECP treatment. Here, a consistent lipid pattern was found to be significantly regulated and independently reproduced in two independent ECP treatment series and in all tested clinical indications such as CTCL, systemic sclerosis, chronic GvHD or acute or chronic rejection in heart transplants. Therefore, we highly suggest that these lipids are immediate regulators of ECP independent on the applied indication and might be the first mediators of the known anti-inflammatory effects of ECP treatment. The identified specific lipids excerpt anti-inflammatory activity and are outlined in the following. Omega-3 polyunsaturated fatty acids (PUFA) including alpha-linolenic acid and EPA were found strongly upregulated upon ECP and are known to mediate anti-inflammatory effects via the inhibition of immune cell activation . Although information on the immunomodulatory potential of stearidonic acid and DPA, which are also induced by ECP, is still limited, supplementation of stearidonic acid in an animal model reduced TNF-α expression upon lipopolysaccharide (LPS) stimulation in murine whole blood. In addition, alpha-linolenic acid, EPA and DPA levels in healthy human individuals were found negatively correlated with the inflammation score via a decrease of TNF-α and C-reactive protein (CRP) . In addition to omega-3 PUFAs, the omega-6 PUFA DGLA was also significantly upregulated upon ECP treatment. While it is generally assumed that a diet rich in omega-6 fatty acids promotes pro-inflammatory processes, recent studies also attribute anti-inflammatory effects to omega-6 PUFAs . In freshly isolated human peripheral blood monocytes (PBMC), DGLA resulted in inhibition of TNF-α production upon LPS stimulation in-vitro, consistent with the suggested direct anti-inflammatory potential of omega-6 PUFAs . Furthermore, attenuation of pro-inflammatory interferon-γ induced gene expression via dihomo-γ-linolenic acid (DGLA) was demonstrated in human and mouse macrophages . In fact, omega-3 PUFA DHA and omega-6 PUFA AA were also found to be consistently upregulated by ECP treatment. Due to the limited sample size, significance thresholds were not reached. The linoleic acid oxidation product 13-OxoODE, formed via the 15-lipoxygenase (LOX) pathway, has been described as potent anti-inflammatory endogenous peroxisome proliferation-activated receptor gamma (PPAR-γ) agonist in macrophages or human colonic epithelial cells . Remarkably, 13-OxoODE was among the significantly upregulated anti-inflammatory molecular pattern in the plasma of patients suffering from long COVID-19 syndrome compared to fully recovered COVID-19 patients . Saturated fatty acids such as stearic acid (C18:0) are known contributors to the development of cardiovascular diseases, and stearic acid in particular has been described to mediate pro-inflammatory effects and cell growth inhibition in human aortic endothelial cells via upregulation of intercellular adhesion molecule-1 (ICAM-1), which regulates leukocyte recruitment to the site of inflammation , and the induction of nuclear factor-kappa B (NF-κB) signaling . Therefore, the significant downregulation of stearic acid upon ECP treatment may also represent an anti-inflammatory effect via potentially reduced leukocyte recruitment. In addition, upregulation of 12-HETE, 12-HEPE and 14-HDoHE, which are formed via the 12-LOX pathway of AA, EPA and DHA, respectively, was also found in ECP . The so-called “platelet-type” 12-LOX is mainly expressed in platelets and we have previously observed the release of 12-LOX derived oxylipins via activation of isolated platelets in-vitro This observation may suggest that some metabolic activation of platelets may occur with ECP treatment. ECP is known to modulate the immune system, has anti-inflammatory properties in the different indications and helps to reduce the dosages of the treatment with glucocorticoids and immunosuppressive drugs. Consequently, rate of opportunistic infections can be lowered through ECP. Sphingosine-1-phosphate (S1P) is a potent signaling lipid mainly derived from red blood cells, platelets, macrophages, mast cells and endothelial cells and is a strong promotor of inflammasome activation . S1P can specifically activate five different G-protein coupled receptors (GPCR) regulating various physiologic processes including the promotion of inflammation . The beneficial effects of extracorporeal photopheresis may thus also be attributed to the downregulation of S1P observed upon ECP treatment. As a next step, we used the S1P receptor 1 modulator AUY954 to selectively decrease S1P1 levels and to analyze the effects of S1P receptor signaling in normal human lung fibroblasts and lung fibroblasts from COPD patients, in a chronic inflammatory setting. ECP is used in the context of lung transplantation in COPD patients and is known to have anti-fibrotic and anti-inflammatory effects. In addition, the S1P modulators are recently evaluated in various indications such as autoimmune diseases, dermatomyositis, Crohn’s disease, ulcerative colitis, polymyositis, GvHD and transplant rejection , which are also indications for ECP suggesting that similar effects are underlying. This is also underlined by the mechanisms of S1P modulators, in which absence of S1P blocks lymphocyte proliferation and reduces T and B cell counts in the blood, leading to immune suppression. The selective S1P agonist AUY954 reduced circulating lymphocytes and prolonged cardiac allograft survival in vivo and inhibited inflammatory demyelination and immune cell infiltration in an animal model of experimental autoimmune neuritis . Here we demonstrate that AUY954 selectively inhibits proliferation in fibroblasts from COPD patients compared to normal lung fibroblasts, suggesting that the anti-fibrotic and anti-inflammatory effects of ECP may be partially mediated by S1P and that S1P downregulation might be one of the key mechanisms of ECP. The cell morphology of DHLF-COPD appears to be converted to a normal lung fibroblast phenotype. In addition, we provide evidence that the combination with lipid modulators may be synergistic, as they have immunosuppressive effects and are used in comparable indications. Conclusions and expert recommendation in the framework of PPPM While the ECP-induced lipid mediators have well known anti-inflammatory properties and could therefore account for the clinical observations among all different clinical indications, the molecular mechanism resulting in the increase of these lipids is not yet clear. Future studies will help identify the potential contribution of platelets, erythrocytes, leukocytes, or other blood components, will elucidate long-term effects and can shed light on the question if further inflammatory diseases might be indications for ECP. In addition, these effects might also account for PUVA therapy used i.e. in inflammatory skin diseases, since ECP is a therapeutic concept derived from PUVA therapy. It is evident that identifying ECP-induced changes in lipid mediators and fatty acid composition will contribute to a better understanding of the mode of action of this important intervention strategy. The influence of lipids on the mode of action of ECP is a complex and cutting-edge field of research and medicine. Lipidomics requires specific expertise in mass spectrometry and specific bioinformatics analysis and interpretation, and the management of different internal medicine and dermatological diagnoses requires interdisciplinary expertise. The in-depth study of the effects of ECP had made great innovations in the following three cornerstones of PPPM: Predictive approach. Here we demonstrate a significant upregulation of anti-inflammatory lipids under ECP. In a next step, we will evaluate these lipids in a long-term study at baseline level and over time and correlated with response. As ECP patients need peripheral access from a cubital vein, blood sampling would not be an additional intervention. Due to the importance of lipids in anti-inflammatory processes, we hypothesize, that these candidates might serve as predictive and prognostic biomarkers. Targeted prevention. Interestingly, ECP is used or evaluated at all three levels of targeted prevention. In our setting, we used the most frequently applied and clinically established level of prevention of ECP, the tertiary level. By using different indications of ECP we were able to demonstrate that ECP influences the lipids on the tertiary level of prevention irrespective of the indication. In addition, ECP is evaluated in a prophylactic manner for transplant patients to eventually prevent acute or chronic organ rejection. Here we suggest that the depicted anti-inflammatory lipids might be important players in all three settings. Personalization of medical services. Here we present different anti-inflammatory lipids consistently regulated on two independent days under ECP. The individual lipid status might give information if ECP therapy is recommended and if new combinatorial therapeutic concepts with lipid modulators might be beneficial. To the best of our knowledge, this is the first study presenting well-defined molecular changes at the lipid level induced by ECP, which may explain important aspects of its mode of action. In conclusion, the presented data suggest that the lipid status offers many opportunities to support personalized medicine in the primary, secondary and tertiary care of prevention, ranging from the identification of reliable predictive biomarker patterns, to the assessment of lipid status for novel treatment options, to the monitoring of therapeutic efficacy. Further studies using additional time points, larger cohorts of responders and non-responders, and evaluating different combinatorial time points are warranted. While the ECP-induced lipid mediators have well known anti-inflammatory properties and could therefore account for the clinical observations among all different clinical indications, the molecular mechanism resulting in the increase of these lipids is not yet clear. Future studies will help identify the potential contribution of platelets, erythrocytes, leukocytes, or other blood components, will elucidate long-term effects and can shed light on the question if further inflammatory diseases might be indications for ECP. In addition, these effects might also account for PUVA therapy used i.e. in inflammatory skin diseases, since ECP is a therapeutic concept derived from PUVA therapy. It is evident that identifying ECP-induced changes in lipid mediators and fatty acid composition will contribute to a better understanding of the mode of action of this important intervention strategy. The influence of lipids on the mode of action of ECP is a complex and cutting-edge field of research and medicine. Lipidomics requires specific expertise in mass spectrometry and specific bioinformatics analysis and interpretation, and the management of different internal medicine and dermatological diagnoses requires interdisciplinary expertise. The in-depth study of the effects of ECP had made great innovations in the following three cornerstones of PPPM: Predictive approach. Here we demonstrate a significant upregulation of anti-inflammatory lipids under ECP. In a next step, we will evaluate these lipids in a long-term study at baseline level and over time and correlated with response. As ECP patients need peripheral access from a cubital vein, blood sampling would not be an additional intervention. Due to the importance of lipids in anti-inflammatory processes, we hypothesize, that these candidates might serve as predictive and prognostic biomarkers. Targeted prevention. Interestingly, ECP is used or evaluated at all three levels of targeted prevention. In our setting, we used the most frequently applied and clinically established level of prevention of ECP, the tertiary level. By using different indications of ECP we were able to demonstrate that ECP influences the lipids on the tertiary level of prevention irrespective of the indication. In addition, ECP is evaluated in a prophylactic manner for transplant patients to eventually prevent acute or chronic organ rejection. Here we suggest that the depicted anti-inflammatory lipids might be important players in all three settings. Personalization of medical services. Here we present different anti-inflammatory lipids consistently regulated on two independent days under ECP. The individual lipid status might give information if ECP therapy is recommended and if new combinatorial therapeutic concepts with lipid modulators might be beneficial. To the best of our knowledge, this is the first study presenting well-defined molecular changes at the lipid level induced by ECP, which may explain important aspects of its mode of action. In conclusion, the presented data suggest that the lipid status offers many opportunities to support personalized medicine in the primary, secondary and tertiary care of prevention, ranging from the identification of reliable predictive biomarker patterns, to the assessment of lipid status for novel treatment options, to the monitoring of therapeutic efficacy. Further studies using additional time points, larger cohorts of responders and non-responders, and evaluating different combinatorial time points are warranted. Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 Supplementary Material 3 Supplementary Material 4 Supplementary Material 5 Supplementary Material 6 Supplementary Material 7 Supplementary Material 8 |
Applications of
Nanotechnology for Spatial Omics:
Biological Structures and Functions at Nanoscale Resolution | c615bcab-c6fd-4739-aab2-80259871f3cc | 11752498 | Biochemistry[mh] | Introduction Spatial omics preserves
spatial information when evaluating the
molecular composition of a specimen, allowing data to be mapped to
specific regions, including tissues, single cells, and subcellular
regions. Spatial omics helps us better understand cellular organization
and interactions in histological landscapes. It can detect molecular
parameters in situ, on intact tissue samples having differential transcript
or protein abundance within their native spatial context, and it can
delineate the interactions between molecular parameters, leveraging
different multiplexed labeling methods. Recently reported spatial
omics approaches permit in situ spatial profiling of several distinct
types of molecular information, including RNA, protein, metabolite,
and epigenetic targets. Spatial multiomics methods that simultaneously
capture different types of molecular information are now commercially
available. Spatial omics applications are increasingly used to answer
biological questions in fields ranging from cancer research (especially
tumor microenvironment questions) to neuroscience and organismal development. − Advances in spatial omics have relied heavily on nanotechnology,
since methods that use the intrinsic properties of nanomaterials in
nanodevices and nanobiotechnological tools have enabled precise cellular
and subcellular labeling and sequencing. As a result, spatial omics
methods can now define structure/function down to almost 1 nm versus
the 1 μm limit of traditional light microscopy. Methods and
techniques adopted from the field of nanotechnology have allowed insight
into biological systems at nanoscale resolution, enabling analyses
that were not feasible before the use of such nanotechnological approaches. Spatial omics methods have been reviewed elsewhere, − , , but
few of these articles have focused on the role of nanotechnology in
these methods, and described its use in specific applications rather
than providing a comprehensive overview of potential applications.
For example, one such review described the development and use of
nanotechnology tools for the enrichment and omics analysis of circulating
cancer cells, highlighting key advances in multiomics liquid biopsy
approaches, while another discussed the
use of nanodevice DNA-barcoded fluorescence microscopy for spatial
genomics and transcriptomics. In
contrast, this review provides a comprehensive review of the
use of nanotechnology in spatial omics, discussing the power and limitations
of nanotechnology as applied in recent spatial omics approaches. We
first introduce nanotechnology areas relevant to spatial omics applications,
including integrated applications of nanomaterials and nanodevices
and nanobiotechnology approaches, and highlight key nanotechnologies
that have been instrumental in developing spatial omics methods. We
then summarize spatial omics methods used to evaluate the global expression
of mRNA (spatial transcriptomics), protein (spatial proteomics), metabolites
(spatial metabolomics), and epigenetic DNA modifications (spatial
epigenomics), and then discuss current multiomics applications (spatial
multiomics). Selecting examples from the numerous spatial omics tools,
we focus on the advances underscored by nanotechnology . We also summarize the critical
role of AI and machine learning in spatial omics data processing and
the integration of nanotechnology with AI, which has revolutionized
spatial biomarker discovery. Finally, we offer a perspective on the
remaining challenges and future opportunities regarding nanotechnology
for spatial omics applications.
Fundamentals of Nanotechnology Nanotechnology
is an interdisciplinary field involving the design,
synthesis, characterization, and application of materials, devices,
and systems at the nanometer scale (approximately 1 to 100 nm). This nanometer scale has implications for various
fields including materials science, electronics, energy, environmental
science, engineering, and medicine. Its
broad-spanning implications are attributed to the physical, chemical,
and biological properties that emerge following control of nanostructure
parameters such as shape and size. , These properties,
distinct from properties of the same materials at the microscopic
or macroscopic scale, are not evident without nanotechnology’s
larger surface area-to-volume ratios and quantum effects. , − Nanotechnology includes synthetic and natural nanostructures
and encompasses both bottom-up assembly and top-down fabrication techniques. − For example, three different elements combine to make indium tin
oxide, and though its bulk form is yellowish/gray in color and scatters
light, when layered in <100 nm sheets, it is optically transparent.
This optical transparency coupled with its electrical conductivity
lends indium tin oxide to practical applications, such as LED displays. Nanotechnology has broad applications for spatial omics methods
and biomedicine. Nanomaterials exist
within the same size domain as subcellular organelles and biological
macromolecules, giving them the ability to exhibit similar functionality
at the biomolecular level. These include nano-objects, such as polymeric
nanoparticles, gold nanorods, and quantum dots. Specifically, gold
nanorods are used to enhance signals in spatial transcriptomics to
amplify signals from low-abundance biomolecules. , Moreover, nanostructured materials, such as carbon nanotubes, nanodiscs,
and nanocrystals, are also used to enhance spatial resolution in both
spatial omics and biomedicine. − These nano-objects and nanostructured materials have aided the functionality
of microfluidic devices to improve sample throughput and enhance characterization
of biomolecular structures at the nanometer level within instruments
such as nucleic acid sequencers, mass spectrometers, and confocal
microscopes. Nanobiotechnology refers
to structures derived from biological macromolecules, such as DNA,
proteins, or lipids, and may be self-assembled; examples are liposomes,
fluorescently labeled DNA, and antibody technologies. , , , 2.1 Integrated Applications of Nanomaterials and
Nanodevices Nanomaterials, products of chemistry and classical
materials engineering, are defined by their size: either one dimension
of the material, or a single unit within, measures between 1 and 100
nm. Under this broad definition, nanomaterials can be distinguished
by their elemental composition. Careful manipulation of the elemental
composition allows the nanomaterial to be tailored to the application
of interest. But nanomaterials can also be distinguished by their
dimensionality, being categorized as zero-, one-, two-, or three-dimensional, , and nanomaterials composed of even a single element can produce
different properties as a function of their dimensionality. For example,
zero-dimensional fullerenes (i.e., “bucky balls”) have
different properties than one-dimensional nanotubes, which are distinct
from two-dimensional (2D) graphene sheets and three-dimensional (3D)
diamond, despite all being composed of pure carbon. In addition to
fullerenes, zero-dimensional nanomaterials include spherical nanoparticles
and quantum dots. Higher-dimensional nanomaterials have been designed
for advantageous mechanical or chemical properties. One-dimensional
nanomaterials provide classic examples of this, carbon nanotubes feature
enhanced tensile strength and gold nanorods boast increased electrical
conductivity. Nanomaterials that are 2D and 3D are even more complex,
generated to leverage the properties that emerge from increased surface
area-to-volume ratios, such as the optical properties of indium tin
oxide nanolayers. Nanodevices, also referred to as nanotools,
are divided into two broad classes. The first broad class allows scientists
to characterize inorganic and organic systems with nanometer resolution.
The confocal microscope is a prototypical nanodevice in this class.
Confocal imaging stems from refining the source light to a pinpoint
and visualizing an object after the light transits through a pinhole.
When combined with fluorescence, this optical advance has increased
the resolution of imaging below the 1 μm threshold of the classical
light microscope. Although initially applied to materials science,
the combination of higher resolution and diminished photobleaching
effects makes confocal fluorescence microscopy ideal for biological
applications, including the imaging of tissues and live cells. Other
nanodevices in this class assist in analyzing complex materials and
mixtures to identify individual nanoscale components. Most popular
among these tools is mass spectrometry, which involves desorbing molecules
from a sample via ionization and determining their identity by examining
mass-to-charge ratios. High sensitivity variations of mass spectrometry
exist, such as Secondary Ion Mass Spectrometry (SIMS), which can identify
components of organic and inorganic materials and mixtures with resolution
as low as 50 nm. , Quantitative mass spectrometry
has also been used to identify protein–protein interactions
within cell extracts, especially after enrichment by antibody-mediated
affinity purification. , The second broad class of nanodevices
comprises nanoscale systems that increase the efficiency of biological
or chemical reactions. One such nanodevice is the microfluidic chip,
which has miniaturized chambers or channels and can be used to carry
out biochemical reactions or separations as a result of fluidic properties
specific to nanoscale dimensions. Such nanodevices have been used
for the detection and analysis of various biochemical and biological
targets, including DNA, proteins, molecules, and viruses. − 2.2 Nanobiotechnology Nanobiotechnology
describes the interface between nanotechnology and biology. One established
example of nanobiotechnology is the liposome, a nanometer-sized lipid
structure composed of a lipid bilayer surrounding an aqueous environment.
Within this aqueous environment, groups of biologically relevant molecules
can be sequestered, stored, and delivered. Liposomes have been instrumental
for carrying nanosized cargo—they were the engineered nanoparticles
used for drug delivery —and for
enclosing nanosized chemical reactions, as has been done in microfluidic
chips. A second established example of nanobiotechnology is
DNA nanotechnology, which uses engineered duplex DNA strands as the
nanoscale engineering material. , − One type, structural DNA nanotechnology, uses DNA as a physical
material unit for the self-assembly of nanoscale structures. , Another type, dynamic DNA nanotechnology, is focused on reconfigurable
and autonomous devices such as the amplification approach hybridization
chain reaction, which uses secondary loop structure hairpin DNA monomers
as an energy source. , − In this type,
DNA probes labeled with a fluorescent molecule are used for pathogen
detection, protein detection, and nanoscale imaging. , A third established example is a device or tool that incorporates
nanosized, membrane-spanning protein channels, which exhibit the nanofluidic
phenomena. These protein channels can
be designed to allow the transit of specific molecules through the
narrow, nanometer-wide pore due to osmotic drivers, and coupling the
transit of specific biomolecules to changes in ion movements (ie,
currents) allows for the detection of specific molecular transit events.
This can be used to distinguish nucleotides, as in nanopore sequencing.
Integrated Applications of Nanomaterials and
Nanodevices Nanomaterials, products of chemistry and classical
materials engineering, are defined by their size: either one dimension
of the material, or a single unit within, measures between 1 and 100
nm. Under this broad definition, nanomaterials can be distinguished
by their elemental composition. Careful manipulation of the elemental
composition allows the nanomaterial to be tailored to the application
of interest. But nanomaterials can also be distinguished by their
dimensionality, being categorized as zero-, one-, two-, or three-dimensional, , and nanomaterials composed of even a single element can produce
different properties as a function of their dimensionality. For example,
zero-dimensional fullerenes (i.e., “bucky balls”) have
different properties than one-dimensional nanotubes, which are distinct
from two-dimensional (2D) graphene sheets and three-dimensional (3D)
diamond, despite all being composed of pure carbon. In addition to
fullerenes, zero-dimensional nanomaterials include spherical nanoparticles
and quantum dots. Higher-dimensional nanomaterials have been designed
for advantageous mechanical or chemical properties. One-dimensional
nanomaterials provide classic examples of this, carbon nanotubes feature
enhanced tensile strength and gold nanorods boast increased electrical
conductivity. Nanomaterials that are 2D and 3D are even more complex,
generated to leverage the properties that emerge from increased surface
area-to-volume ratios, such as the optical properties of indium tin
oxide nanolayers. Nanodevices, also referred to as nanotools,
are divided into two broad classes. The first broad class allows scientists
to characterize inorganic and organic systems with nanometer resolution.
The confocal microscope is a prototypical nanodevice in this class.
Confocal imaging stems from refining the source light to a pinpoint
and visualizing an object after the light transits through a pinhole.
When combined with fluorescence, this optical advance has increased
the resolution of imaging below the 1 μm threshold of the classical
light microscope. Although initially applied to materials science,
the combination of higher resolution and diminished photobleaching
effects makes confocal fluorescence microscopy ideal for biological
applications, including the imaging of tissues and live cells. Other
nanodevices in this class assist in analyzing complex materials and
mixtures to identify individual nanoscale components. Most popular
among these tools is mass spectrometry, which involves desorbing molecules
from a sample via ionization and determining their identity by examining
mass-to-charge ratios. High sensitivity variations of mass spectrometry
exist, such as Secondary Ion Mass Spectrometry (SIMS), which can identify
components of organic and inorganic materials and mixtures with resolution
as low as 50 nm. , Quantitative mass spectrometry
has also been used to identify protein–protein interactions
within cell extracts, especially after enrichment by antibody-mediated
affinity purification. , The second broad class of nanodevices
comprises nanoscale systems that increase the efficiency of biological
or chemical reactions. One such nanodevice is the microfluidic chip,
which has miniaturized chambers or channels and can be used to carry
out biochemical reactions or separations as a result of fluidic properties
specific to nanoscale dimensions. Such nanodevices have been used
for the detection and analysis of various biochemical and biological
targets, including DNA, proteins, molecules, and viruses. −
Nanobiotechnology Nanobiotechnology
describes the interface between nanotechnology and biology. One established
example of nanobiotechnology is the liposome, a nanometer-sized lipid
structure composed of a lipid bilayer surrounding an aqueous environment.
Within this aqueous environment, groups of biologically relevant molecules
can be sequestered, stored, and delivered. Liposomes have been instrumental
for carrying nanosized cargo—they were the engineered nanoparticles
used for drug delivery —and for
enclosing nanosized chemical reactions, as has been done in microfluidic
chips. A second established example of nanobiotechnology is
DNA nanotechnology, which uses engineered duplex DNA strands as the
nanoscale engineering material. , − One type, structural DNA nanotechnology, uses DNA as a physical
material unit for the self-assembly of nanoscale structures. , Another type, dynamic DNA nanotechnology, is focused on reconfigurable
and autonomous devices such as the amplification approach hybridization
chain reaction, which uses secondary loop structure hairpin DNA monomers
as an energy source. , − In this type,
DNA probes labeled with a fluorescent molecule are used for pathogen
detection, protein detection, and nanoscale imaging. , A third established example is a device or tool that incorporates
nanosized, membrane-spanning protein channels, which exhibit the nanofluidic
phenomena. These protein channels can
be designed to allow the transit of specific molecules through the
narrow, nanometer-wide pore due to osmotic drivers, and coupling the
transit of specific biomolecules to changes in ion movements (ie,
currents) allows for the detection of specific molecular transit events.
This can be used to distinguish nucleotides, as in nanopore sequencing.
Key Nanotechnologies for Spatial Omics Nanotechnologies, described generally in the previous section,
have supported the construction of spatial omics methods. , In the following section, we define spatial omics and describe several
specific nanotechnologies that have been crucial for the development
of spatial omics methods. Spatial omics provides global biomolecule
information—including data from transcriptomics, proteomics,
metabolomics, and epigenomics—layered onto a histological landscape.
In other words, spatial omics provides omics information while preserving
spatial information at sufficient resolution for each application
. , , , The resolution of spatial omics has improved over time, from the
1 μm limit of traditional light microscopy to almost 1 nm. Spatial
multiomics are also possible, with current technologies allowing for
the simultaneous evaluation of two or more biomolecular domains. An example is the simultaneous evaluation of
transcriptomics and proteomics via subcellular views of global RNA
and protein overlaid onto 3D histological structures. − 3.1 Nanodevices for Spatial Omics Nanodevices
support spatial omics by providing nanoscale compartments that can
recapitulate physiologically relevant cellular activity while also
increasing throughput with parallel reactions. Microfluidic devices
are one example, employed in the spatial multiomics methods Deterministic
Barcoding in Tissue for spatial omics sequencing (DBiT-seq) and Spatial
Assay for Transposase-Accessible Chromatin and RNA using Sequencing
(spatial-ATAC-seq). In DBiT-seq, channels in a microfluidic chip demarcate
regions on a tissue section, ultimately creating a series of separate
assays to define tissue pixels for protein or RNA detection. Other
examples are microfluidic valve-, droplet- or nanowell-based technologies,
which are emerging in the field of single-cell transcriptomics due
to their superior ability to capture and process single cells and
their components. , Nanoscale droplets can also contain
specific biological components to perform numerous parallel biochemical
reactions within a small volume. For example, Macosko et al. developed
a microfluidic single-cell transcriptomics platform called Drop-seq,
which brings single cells and barcoded beads together in nanoliter
droplets, allowing numerous biological assay outputs from a single
small-volume reaction. , 3.2 Advanced Imaging Techniques in Spatial Omics Confocal microscopy provides high-resolution image localization
suitable for nanoscale imaging in spatial omics. The principle of
confocal imaging involves refining source light to a pinpoint and
visualizing resultant fluorescent images after the light passes through
a pinhole, thereby increasing the resolution beyond the 1 μm
threshold of classical light microscopy. This enhanced resolution
is ideal for imaging tissue and is widely employed in spatial omics
methods such as Enhanced ELectric Fluorescence in situ Hybridization
(EEL FISH), Multi-Omics Single-scan Assay with Integrated Combinatorial
Analysis (MOSAICA), and Spatially Resolved Transcript Amplicon Readout
Mapping (STARmap). 3.3 Mass Spectrometry in Spatial Omics MS is a fundamental analytic method used in spatial omics, particularly
spatial proteomics, for high-resolution protein localization. MS characterizes
a sample by ionizing, separating, and detecting its components before
quantifying the abundance of the charge/mass values ( m / z ). Quantitative MS has been employed to identify
protein–protein interactions within cell extracts, especially
following enrichment by Antibody-Mediated Affinity Purification–MS
(AP–MS) experiments. , 3.4 DNA Nanotechnology in Spatial Omics DNA molecules form the basis of numerous nucleic acid detection strategies
within spatial omics. For example, fluorescently labeled DNA probes
enable single-molecule nucleic acid detection when coupled with super-resolution
microscopy, forming the foundation for methods like EEL Fish, Fluorescent
in situ RNA Sequencing (FISSEQ), and MOSAICA. , , Employing DNA as a barcode is critical
for increasing multiplex detection capability and essential for investigating
complex biomolecular domains in omics research. DNA barcodes comprise
a specific DNA sequence that does not naturally occur in the examined
species. By attaching these barcodes to DNA probes, these DNA barcodes
can be typed and coupled to next generation sequencing technology
for detection. More specifically, they facilitate detection of binding
events to complementary target sequences, enabling multiplexed detection
by creating numerous independent labels in parallel. DNA barcodes
are also the mainstay of regional biomolecule labeling,
allowing precise identification and localization of molecules within
a sample. DNA barcodes added to single molecules by ligation or polymerase-catalyzed
events direct the creation of a specific nm-spaced grid. DNA location
detection barcodes, each having precise location sequence addresses,
are added to distinct confined locales in series, allowing for the
identification of the original DNA. Adding the DNA location detection
barcodes can be accomplished with the help of microfluidics or other
technologies. DNA barcodes for multiplex detection and spatial mapping
have a central role in Barcode in situ Targeted Sequencing (BaristaSeq),
Seq-Scope, DBiT-seq, Spatial Co-indexing of Transcriptomes and Epitopes
for Multi-Omics Mapping by Highly Parallel Sequencing (spatial-CITE-seq),
and Spatial Cleavage Under Targets and Tagmentation (spatial-CUT&Tag). Detecting individual nucleic acid locations is a facet of spatial
omics, and increasing the sensitivity of target detection can be accomplished
with nanometer-sized DNA balls (DNA nanoballs). DNA nanoballs comprise
thousands of copies of a specific sequence, which are produced by
Rolling-Circle Amplification (RCA). RCA is an isothermal nucleic acid
amplification method widely applied for the in vivo imaging of various
targets, including messenger RNA (mRNA), double-stranded DNA (dsDNA),
microRNA (miRNA), and proteins. , RCA utilizes a circular
DNA template and special DNA or RNA polymerases to produce a rolony
(ie, a rolling circle colony) containing thousands of copies of the
original sequence, termed a DNA nanoball, < 1 μm in size. , , The sequences of rolonies in
the amplifying and sequencing mRNAs for in situ approaches are read
out by sequencing by ligation. Expanding the breadth of nucleic acid detection capability in spatial
omics, many bioanalytical applications use RCA-based platforms that
combine RCA with DNA-zymes, aptamers, and nanozymes to form the basis
for in situ sequencing technologies. − RCA can locally amplify specific
nucleic acid sequences, and its ability to detect single molecules
directly in cells and tissues makes it ideal for in situ imaging,
revealing critical biological processes. For instance, it has been
widely used for imaging the spatial location of specific mRNAs within
single cells. Related approaches using
DNA or RNA barcodes achieve cellular resolution for cell lineage tracing,
as in neuronal projection mapping. In situ sequencing approaches that
combine RCA with cellular address barcodes achieve high throughput
without sacrificing spatial resolution. In situ sequencing is the
basis for BaristaSeq, STARmap and Spatial Enhanced Resolution Omics-Sequencing
(Stereo-Seq).
Nanodevices for Spatial Omics Nanodevices
support spatial omics by providing nanoscale compartments that can
recapitulate physiologically relevant cellular activity while also
increasing throughput with parallel reactions. Microfluidic devices
are one example, employed in the spatial multiomics methods Deterministic
Barcoding in Tissue for spatial omics sequencing (DBiT-seq) and Spatial
Assay for Transposase-Accessible Chromatin and RNA using Sequencing
(spatial-ATAC-seq). In DBiT-seq, channels in a microfluidic chip demarcate
regions on a tissue section, ultimately creating a series of separate
assays to define tissue pixels for protein or RNA detection. Other
examples are microfluidic valve-, droplet- or nanowell-based technologies,
which are emerging in the field of single-cell transcriptomics due
to their superior ability to capture and process single cells and
their components. , Nanoscale droplets can also contain
specific biological components to perform numerous parallel biochemical
reactions within a small volume. For example, Macosko et al. developed
a microfluidic single-cell transcriptomics platform called Drop-seq,
which brings single cells and barcoded beads together in nanoliter
droplets, allowing numerous biological assay outputs from a single
small-volume reaction. ,
Advanced Imaging Techniques in Spatial Omics Confocal microscopy provides high-resolution image localization
suitable for nanoscale imaging in spatial omics. The principle of
confocal imaging involves refining source light to a pinpoint and
visualizing resultant fluorescent images after the light passes through
a pinhole, thereby increasing the resolution beyond the 1 μm
threshold of classical light microscopy. This enhanced resolution
is ideal for imaging tissue and is widely employed in spatial omics
methods such as Enhanced ELectric Fluorescence in situ Hybridization
(EEL FISH), Multi-Omics Single-scan Assay with Integrated Combinatorial
Analysis (MOSAICA), and Spatially Resolved Transcript Amplicon Readout
Mapping (STARmap).
Mass Spectrometry in Spatial Omics MS is a fundamental analytic method used in spatial omics, particularly
spatial proteomics, for high-resolution protein localization. MS characterizes
a sample by ionizing, separating, and detecting its components before
quantifying the abundance of the charge/mass values ( m / z ). Quantitative MS has been employed to identify
protein–protein interactions within cell extracts, especially
following enrichment by Antibody-Mediated Affinity Purification–MS
(AP–MS) experiments. ,
DNA Nanotechnology in Spatial Omics DNA molecules form the basis of numerous nucleic acid detection strategies
within spatial omics. For example, fluorescently labeled DNA probes
enable single-molecule nucleic acid detection when coupled with super-resolution
microscopy, forming the foundation for methods like EEL Fish, Fluorescent
in situ RNA Sequencing (FISSEQ), and MOSAICA. , , Employing DNA as a barcode is critical
for increasing multiplex detection capability and essential for investigating
complex biomolecular domains in omics research. DNA barcodes comprise
a specific DNA sequence that does not naturally occur in the examined
species. By attaching these barcodes to DNA probes, these DNA barcodes
can be typed and coupled to next generation sequencing technology
for detection. More specifically, they facilitate detection of binding
events to complementary target sequences, enabling multiplexed detection
by creating numerous independent labels in parallel. DNA barcodes
are also the mainstay of regional biomolecule labeling,
allowing precise identification and localization of molecules within
a sample. DNA barcodes added to single molecules by ligation or polymerase-catalyzed
events direct the creation of a specific nm-spaced grid. DNA location
detection barcodes, each having precise location sequence addresses,
are added to distinct confined locales in series, allowing for the
identification of the original DNA. Adding the DNA location detection
barcodes can be accomplished with the help of microfluidics or other
technologies. DNA barcodes for multiplex detection and spatial mapping
have a central role in Barcode in situ Targeted Sequencing (BaristaSeq),
Seq-Scope, DBiT-seq, Spatial Co-indexing of Transcriptomes and Epitopes
for Multi-Omics Mapping by Highly Parallel Sequencing (spatial-CITE-seq),
and Spatial Cleavage Under Targets and Tagmentation (spatial-CUT&Tag). Detecting individual nucleic acid locations is a facet of spatial
omics, and increasing the sensitivity of target detection can be accomplished
with nanometer-sized DNA balls (DNA nanoballs). DNA nanoballs comprise
thousands of copies of a specific sequence, which are produced by
Rolling-Circle Amplification (RCA). RCA is an isothermal nucleic acid
amplification method widely applied for the in vivo imaging of various
targets, including messenger RNA (mRNA), double-stranded DNA (dsDNA),
microRNA (miRNA), and proteins. , RCA utilizes a circular
DNA template and special DNA or RNA polymerases to produce a rolony
(ie, a rolling circle colony) containing thousands of copies of the
original sequence, termed a DNA nanoball, < 1 μm in size. , , The sequences of rolonies in
the amplifying and sequencing mRNAs for in situ approaches are read
out by sequencing by ligation. Expanding the breadth of nucleic acid detection capability in spatial
omics, many bioanalytical applications use RCA-based platforms that
combine RCA with DNA-zymes, aptamers, and nanozymes to form the basis
for in situ sequencing technologies. − RCA can locally amplify specific
nucleic acid sequences, and its ability to detect single molecules
directly in cells and tissues makes it ideal for in situ imaging,
revealing critical biological processes. For instance, it has been
widely used for imaging the spatial location of specific mRNAs within
single cells. Related approaches using
DNA or RNA barcodes achieve cellular resolution for cell lineage tracing,
as in neuronal projection mapping. In situ sequencing approaches that
combine RCA with cellular address barcodes achieve high throughput
without sacrificing spatial resolution. In situ sequencing is the
basis for BaristaSeq, STARmap and Spatial Enhanced Resolution Omics-Sequencing
(Stereo-Seq).
Nanotechnological Applications in Spatial Omics
Approaches After discussing nanotechnology in general and
providing specific
examples of key nanotechnologies for spatial omics, we now turn our
discussion to spatial omics methods, many of which rely on the key
nanotechnologies we have already mentioned. In the following sections,
we discuss spatial omics methods that evaluate mRNAs (spatial transcriptomics),
proteins (spatial proteomics), biological metabolites (spatial metabolomics),
and epigenetic marks (spatial epigenomics) and subsequently mention
multiomics applications (spatial multiomics), providing examples within
each biomolecular domain. For each method, we describe how it works,
give some examples of how it has been used, discuss its advantages
and limitations, and mention its underlying nanotechnology. 4.1 Spatial Transcriptomics Highlighted
as Method of the Year in 2020 by Nature Methods, spatially resolved transcriptomics combines transcriptome-wide
RNA sequencing with histology-based images to precisely map RNA expression
and thereby provide further insights into the cellular transcription
of biological systems. , , Spatially
resolved transcriptomics can elucidate single-cell nucleic acid expression
throughout entire solid tissues or organs while preserving spatial
subcellular localization. , , Spatial transcriptomics was accomplished by Laser Capture
Microdissection (LCM). , In LCM, a laser precisely dissects
a microscopic region (eg, a single cell), which may be input into
high-throughput RNA sequencing (LCM-seq). This method produced gene
expression profiles within defined ∼10 μm compartments
on Formalin-Fixed Paraffin-Embedded (FFPE) tissue sections, distinguishing
RNAs in tumor cells from normal adjacent cells to reveal important
molecular events in cellular oncology. Later, image-based spatial transcriptomics was accomplished by Single-Molecule
RNA Fluorescence in situ Hybridization (smFISH). This technique uses
super-resolution microscopy to facilitate the acquisition of high-resolution
images (10 to 20 nm). , EEL FISH, a derivative of smFISH,
combines multiplexed RNA detection with high-resolution, large-area
imaging and generates faithful RNA quantitative maps that retain spatial
cellular information. Spatial barcoding-based
transcriptomics like BaristaSeq, STARmap, and FISSEQ layer engineered
nucleic acid tags onto in situ sequencing technologies and use DNA
nanoballs to amplify the specific detection signals for imaging. , , The following sections review
several tools available for spatial transcriptomics. 4.1.1 Enhanced Electric Fluorescence In Situ Hybridization EEL FISH is a spatial transcriptome profiling method that employs
a set of combinatorial, binary barcode tags to detect RNA overlaid
onto a histological image. EEL FISH electrophoretically
transfers RNA from a tissue section onto a nanosurface coated with
an optically transparent and electrically conductive layer of indium
tin oxide ( A). Electrophoretically transferring
RNA is superior to transferring RNA by passive diffusion, as is done
in sequencing-based methods, due to the preservation of RNA localization. − In EEL, after residual tissue removal, the result of the transfer
is a collapsed 2D grid of mRNA on the coated nanosurface with precise
in situ spatial information ( A). Next, a set of probes, tagged by combinations of
40 labeling barcodes, are used for 16 rounds of imaging ( A). After fluorescent decoding and encoding of binary label
addresses, barcode identities define locations for numerous mRNAs
within a single fluorescence capture field, which can be matched to an image of the original histological section.
One example of EEL FISH is its application to sequential sagittal
sections of mouse brain to measure the expression of 440 genes, highlighting
complex RNA expression patterns that lie underneath tissue organization. Despite its advantages, EEL has lower sensitivity
and resolution than other tissue-based smFISH methods. , (The resolution of EEL FISH, defined by the diffraction-limited
imaging resolution of the fluorescent label, approaches 200 to 400
nm.) Future improvements, such as maintaining RNA stability, magnifying
the capture field, and expanding barcode detection, could refine EEL
sensitivity and resolution to match smFISH for single-molecule imaging. , Nanotechnologies that support EEL include the indium tin oxide capture
surface and use of combinatorial DNA barcoding to tag numerous mRNAs—the
latter a rudimentary example of nanocomputing. 4.1.2 Spatial Enhanced Resolution Omics-Sequencing Stereoseq is another strategy for spatial transcriptomics ( B). Stereoseq creates
a grid of spots on a lithographically etched nanofluidic chip, the
grid acting as a capture surface for mRNAs from a tissue section. Each DNA nanoball spot is 220 nm in diameter
and the spacing between centers of two adjacent nanoballs is 500 or
715 nm ( B).
The DNA nanoball–patterned array chip has 400 spots per 100
μm 2 to define the pixel size. After a tissue section
is laid onto the chip, the DNA nanoballs capture mRNAs and, following
a second round of rolling circle amplification, create a library of
mRNAs from the original source, which have been sorted to contain
specific regional labels defined by the DNA nanoball grid ( B). Stereoseq was used to create the Mouse Organogenesis Spatiotemporal
Transcriptomic Atlas (MOSTA), which defines detailed topographical
information about the stepwise emergence of tissue-specific cell identities
during organogenesis. Stereoseq has been
performed to capture spatially resolved single-cell transcriptomes
of axolotl telencephalon sections during development and regeneration. Despite the method having genome-wide coverage,
Stereoseq has limited sensitivity and may fail to detect the low copy
numbers of RNAs from low-expression genes. Stereoseq also has trouble
distinguishing single cells from a mixture of multiple similar cell
types, especially smaller cell types like immune cells. A updated
version, Single-Cell Stereo-Seq (scStereo-seq), utilizes spatial transcriptomics
and plant cell wall staining onto histological cell–cell boundaries,
allowing in situ single-cell transcriptome profiling in mature Arabidopsis leaves. The nanotechnology
elements of Stereoseq are DNA nanoballs coupled to precise spacing
on a nanofluidic capture surface and next-generation sequencing. 4.1.3 Barcode Anatomy Resolved by Sequencing Barcode Anatomy Resolved by Sequencing (BARseq) is a multiplexed
and high-throughput method for mapping neuronal projections at cellular
resolution. BARseq combines Multiplexed Analysis of Projections by
Sequencing (MAPseq) and in situ sequencing of cellular tagging barcodes. , , , MAPseq is a technique for mapping neurons by labeling large sets
of neurons with barcodes (random RNA sequences). The advantage of BARseq is its ability to match nearby
cortical areas with distant subcortical projections by relying on
specific barcode sequences that functionally transit through neuronal
projections. Unlike conventional optical
approaches to mapping projections, BARseq relies on matching barcodes
without errors over distance, and is, therefore, superior to other
multiplexed optical tracing methods. Theoretically,
BARseq can label tens of millions of neurons in a single experiment
without a specialized high-speed microscope because of the combinatorial
diversity provided in the barcode design; for example, a 30-nucleotide
(nt) sequence set can generate about 4 to 10 18 barcodes. Moreover, the spatial resolution of
BARseq approaches subcellular dimensions, sufficient to resolve the
organization of projections across neuronal subtypes. The spatial
resolution of BARseq may be even further improved with LCM or direct
in situ sequencing of projection barcodes. BARseq mapped the projections of 3,579 neurons to 11 areas in the
mouse auditory cortex and confirmed the laminar organization of the
three top classes of projection neurons (intratelencephalic, pyramidal
tract-like, and corticothalamic). Nanotechnologies that make BARseq
possible include DNA nanoballs amplified by RCA, fluorescent labels
of nucleotides, and multichannel fluorescence confocal microscopy. 4.1.4 Barcode In Situ Targeted Sequencing BaristaSeq was published in 2017. It
is a modified version of the gap padlock probe–based method
for in situ barcode sequencing compatible with Illumina sequencing
chemistry and is suitable for barcode-assisted lineage tracing and
mapping for long-range neuronal projections. , BaristaSeq uses reverse transcription to convert an RNA barcode
sequence into complementary DNA (cDNA) followed by hybridization of
a padlock probe and gap-filling ligation to create circular RCA templates. , Two distinct fluorescent probes, each recognizing a bracketing padlock,
are used during the gap-filling ligation steps. Fluorescence is evaluated
to detect probe pairs targeting the diluted padlock after rolony generation,
and Illumina chemistry is applied to sequence the samples and determine
probe identities. A spinning disk microscope and laser scanning confocal
microscope are then employed to image the sequencing. The accuracy
and efficiency of BaristaSeq was demonstrated by sequencing random
barcodes (15-nt barcode set) expressed in cultured Baby Hamster Kidney
(BHK) cells. BaristaSeq increased the
amplification efficiency by 5-fold, and this was coupled with high
sequencing accuracy (>97%) compared with other in situ sequencing
techniques. BaristaSeq also has limitations; it has only been applied
to cultured cells, and its resolution is limited to the cellular,
not subcellular level. Nanotechnologies applied in BaristaSeq are
DNA nanoballs that function to amplify detection signals, fluorescent
probes used for the gap padlock detection step, and the confocal microscope
for imaging. 4.1.5 Fluorescent In Situ RNA Sequencing FISSEQ was proposed in 2003 to selectively amplify DNA on a solid
substrate, allowing for targeted genome and transcriptome sequencing. − The next generation of FISSEQ provides transcriptome-wide in situ
RNA evaluation across multiple specimen types and spatial scales. First, RNA within fixed cells is reverse-transcribed
with tagged random hexamer primers to generate cDNA. FISSEQ uses the
direct-ligation approach to produce cDNA fragments as templates for
RCA, and these cDNAs produced from reverse transcription of mRNA are
directly circularized using a single-stranded DNA ligase. Then, the
cDNA fragments are circularized and amplified with RCA. The RCA amplicons
are then cross-linked with BS(PEG)9, a bis-succinimide ester-activated
PEG compound. BS(PEG)9 reduces the nonspecific binding of probes and
has a highly fluorescent signal after the hybridization of the probe.
This creates 200 to 400 nm DNA nanoballs, comprising tandem cDNA repeats
of the target sequence, on top of a histology section. Partition sequencing
using pre-extended sequencing primers with random mismatches at the
ligation site reduces the total number of molecular sequencing reactions,
resulting in a minimal signal-to-noise ratio or number of position
changes after multiple rounds of rehybridized probing. In this manner,
FISSEQ achieves sufficient spot density and RNA localization to discern
individual molecules. FISSEQ uses color sequences at each pixel to
identify objects. The putative nucleic acid sequences are determined
for all pixels and compared with reference sequences. FISSEQ was used
to confirm RNA expression and localization in human primary fibroblasts. The method can also examine other cell types,
tissue sections, and whole-mount embryos for 3D visualization that
spans multiple resolution scales. Single
molecule detection is also possible, since FISSEQ improves optical
resolution and reduces signal noise. But FISSEQ also has limitations;
it is not suitable for all cellular structures and specific classes
of RNA, for example detecting genes involved in RNA and protein processing. Nanotechnology elements supporting FISSEQ are
DNA nanoballs, fluorescent probes, confocal imaging, and the cross-linking
reagent BS(PEG)9. 4.1.6 Spatially Resolved Transcript Amplicon Readout
Mapping STARmap uses targeted signal amplification and hydrogel–tissue
chemistry interactions to enable 3D in situ transcriptomics in intact
tissue ( C). A specific set of cellular RNAs are amplified
in situ by a method called the Specific Amplification of Nucleic Acids
via Intramolecular Ligation (SNAIL). SNAIL achieves high efficiency
for in situ sequencing by avoiding a reverse transcription step. In
SNAIL, two cDNA probes hybridize to the same RNA molecule. One of
these probes (the padlock probe) contains a specific gene identifier.
This probe is circularized, and RCA generates an amplicon in the form
of a DNA nanoball that contains multiple copies of the specific gene
identifier ( C). SNAIL provides a much higher absolute
signal intensity and signal-to-noise ratio outcome than that obtained
with smFISH probes. It also has much greater detection efficiency
than single-cell RNA sequencing, despite having a simpler experimental
procedure. After amplification, the DNA
nanoballs are enzymatically modified and polymerized to form a hydrogel
that serves as a 3D cDNA library ( C). Then, the RNA landscape is sequenced
with the Sequencing with Error-Reduction by Dynamic Annealing and
Ligation (SEDAL) process. SEDAL employs
two kinds of short, degenerate probes: reading probes and fluorescence
probes. The first kind decodes bases, and the second creates fluorescent
puncta from the decoded sequences. These two probes bind DNA targets
transiently and, after specific complementary ligation, form stable
products for imaging with a confocal microscope. For multiplexed imaging,
fluorescent signals are stripped with formamide, and another cycle
of bases are read, eliminating the accumulation of errors during sequencing.
STARmap was used to define cell types and activity-regulated gene
expression in the mouse cortex, from mouse brain sections and larger
3D 150 mm-thick tissue blocks. A limitation
of STARmap is that it cannot independently fully define brain cell
typology in 3D anatomy. In the future, STARmap aims to study activity
patterns exhibited or experienced by cells during behavior in real
time. The nanotechnology tools that underlie STARmap include the SNAIL
method, which incorporates DNA nanoballs; the SEDAL method, which
incorporates fluorescent probes; and the confocal microscope for imaging. 4.1.7 Slide-seq Slide-seq is a spatial
transcriptomics technology, in which DNA-barcoded beads are used to
reveal spatial information about RNAs. , , In Slide-seq, DNA-barcoded, 10 μm beads are
packed onto a rubber-coated glass coverslip to form a monolayer. RNAs
from tissue sections are transferred onto the beads, with the precise
locations of the beads preserving RNA spatial information. Then, the
barcode sequence from each bead is determined by sequencing using
oligonucleotide ligation and detection chemistry. The Slide-seq had
low transcript detection sensitivity, limiting its utility. To address
this limitation, researchers developed Slide-seqV2, an improved version
of Slide-seq with an order-of-magnitude higher sensitivity that also
had better methods for library generation, barcoded bead synthesis,
and array sequencing. These modifications increased RNA capture efficiency
to a level ∼10-fold greater than Slide-seq, a level approaching
the detection efficiency of droplet-based single-cell RNA-seq techniques. The capture efficiency improvements within Slide-seqV2
make it useful across many experimental contexts. Nanotechnology methods
important in Slide-seq are the use of beads 10 to 20 μm wide
for location mapping, DNA barcodes, and the confocal microscope. 4.1.8 Seq-Scope Seq-Scope is a spatial
transcriptomics method that relies on an array of randomly barcoded
single-molecule oligonucleotides and two rounds of sequencing, conveniently
achieved by the Illumina sequencing platform. Seq-Scope uses a array attached to a solid surface that
contains single-stranded oligonucleotides, each containing a randomly
generated barcode sequence called a High-Definition Map Coordinate
Identifier (HDMI). The HDMI oligonucleotides are amplified, generating
clusters, each with a specific HDMI sequence. In the first round of
sequencing, each HDMI sequence and its spatial coordinates are determined
by the Illumina platform. Then, HDMI clusters capture RNA released
from an overlying tissue section and corresponding cDNA sequences
are generated; these HDMI and cDNA sequences are determined in the
second round of sequencing. In other words, the first round of sequencing
provides the spatial information, and the second round of sequencing
provides gene expression information. When the data from the two rounds
of sequencing are combined, they allow construction of a spatial gene
expression matrix. Seq-Scope has a spatial resolution of 500 to 800
nm (600 nm on average) and achieves submicrometer resolution, comparable
to an optical microscope. Seq-Scope
reveals the spatial transcriptome on multiple histological scales
and has been used to distinguish tissues within an organ (eg, different
regions of the liver and colon), different cell types, and different
subcellular regions (eg, nucleus versus cytoplasm). Seq-Scope has several advantages, including high throughput,
straightforward procedures, precise measurements, excellent breadth
of transcriptome capture output, and high spatial resolution, making
it far superior to most other technologies. Seq-Scope is limited,
however, to the capture of the poly-A-tagged transcriptome, making
it less robust than spatial-CITE-seq or DBiT-Seq, which are capable
of spatially profiling the transcriptome alongside protein expression.
The nanotechnology supporting Seq-Scope is the set of HDMI barcode
sequences. 4.2 Spatial Proteomics Spatial proteomics,
facilitated by nanotechnology, has revolutionized our understanding
of cellular organization and function at the molecular level . By employing nanosized
materials and techniques, researchers can precisely map the spatial
distribution of proteins within cells, tissues, and organs, unlocking
insights into complex biological processes with in more detail and
better resolution. The synergy between
nanotechnology and proteomics has been achieved by integrating high-end
imaging techniques, such as LCM microscopy, Multiplexed Ion Beam Imaging
(MIBI), or CO-Detection by IndEXing (CODEX). Sample processing techniques,
such as Expansion Proteomics (ProteomEx) or One Pot for Trace Samples (nanoPOTS), have enabled the capture of nanoscale specimen volumes for multiplexed
mass spectrometry. Additionally, streamlined spatial workflows, such
as Single-Cell Deep Visual Proteomics (scDVP) or 3D imaging of Solvent-Cleared Organs Profiled by Mass
Spectrometry (DISCO-MS), allow for the
powerful and unbiased characterization of biological heterogeneity.
These spatial proteomics tools are described below. 4.2.1 Laser Capture Microdissection Microscopy The imaging technique LCM microscopy has played a pivotal role
in understanding cellular heterogeneity with nanoscale precision,
offering the ability to study specific subcellular regions of interest,
facilitating in-depth examination of protein distributions and interactions. In general, LCM enables the targeted dissection
of individual cells or subcellular structures from complex biological
samples, which are then viewed with a microscope. And in spatial proteomics
specifically, LCM is instrumental for analyzing protein distribution
within cellular compartments, studying protein interactions, and unraveling
signaling pathways in the cellular microenvironments. By precisely isolating organelles from an otherwise
complex heterogeneous tissue section, researchers can analyze their
proteome composition, providing insight into their function and dynamics
in local cell populations without losing spatial information. Individually tailored therapies, guided by the molecular profiling
of biopsy samples, have traditionally relied on techniques such as
immunohistochemistry and bulk genomic analysis. While analyzing whole tissue specimens has shown promise
in predicting patient responses to chemotherapy, the process of extracting
these specimens introduces significant variability, which stems from the diverse cellular composition of tissues,
the uncertainty surrounding the proportion of tumor versus host cells
in the sample, and the loss of spatial information about cell types
within the tissue. Hence, LCM has emerged as an ideal technology to
dissect cells at nanoscale for tissue spatial profiling to allow for
proteomic analysis of specific cells or cell subsets while preserving
their spatial context. Given its ability to dissect nanometer
regions, LCM has been paramount
for understanding the spatial organization of tumor and immune cell
populations in tumor immunology and subclonal analysis, offering invaluable
insights into immunotherapy responses and the emergence of drug resistance. , Combining LCM with certain other nanotechnologies, such as Cytometry
by Time-of-Flight (CyToF) and the NanoString
nCounter gene expression system, , has offered
analysis of post-translational modifications and their functions in
signaling pathways. LCM-guided mass spectrometry methods are rapidly
advancing for discovery applications from region-of-interest to single-cell
resolution; and mass spectrometry experts are beginning to realize
the dream of robust, high-yield LCM single-cell tissue proteomics
from either the same thin-tissue section or precision-registered serial
sections from a variety of tissue types. LCM microscopy also offers
high-yield single-cell transfer to a nanochip, subcellular precision,
and high throughput. Despite its potential, LCM microscopy has
its drawbacks, including
the time-consuming nature of several steps: visualization, manual
cell selection, and collection processes. Typically, the choice of
cells for LCM analysis is made through direct microscopic observation,
but this approach is sometimes hindered by the poor image quality
resulting from the necessity of keeping tissues uncovered during the
process. But advancements in digital imaging, liquid coverslip chemistry,
artificial intelligence, and automation are anticipated to overcome
these challenges and revolutionize the field of tissue spatial profiling
in the future. 4.2.2 Multiplexed Ion Beam Imaging Like
LCM microscopy, MIBI has also helped to understand cellular heterogeneity
with nanoscale precision, allowing for the in-depth study of specific
subcellular regions of interest and proteins. , , , In MIBI, the tissue is first stained with a set of antibodies labeled
with metal isotopes ( A). An ion beam rasters across the tissue, liberating ions
that feed into a Time-of-Flight Secondary Ion Mass Spectrometer (ToF-SIMS),
which separates the labels by mass ( A). Knowing which isotope label is bound
to which antibody, researchers can determine which target proteins
are present, and because the ion beam is rastered across the tissue,
multiplex images can be created. , To titrate
the optimal concentration of antibodies for MIBI, labeled antibodies
are screened in tissue microarrays. ToF-MS is utilized to separate
the marker labels for identification within the original tissue. These
images are partitioned to define cell–cell boundaries, which
allow cell phenotypes to be described as distances between signals. MIBI has been applied to study the tumor microenvironment,
identifying
cell phenotypes and analyzing spatial relationships across numerous
tumor types, such as the spatial relationships between immune and
cancer cells and the specific locations of immunoregulatory proteins. − Advantages of MIBI center around its high-parameter capabilities,
high sensitivity, and subcellular resolution. Recent advances for
MIBI using ion beam tuning targets image resolution at varying depths
via multiple z -direction scans, allowing for reconstruction
of 250 nm 3D images in the axial direction. But MIBI also has drawbacks: long imaging times and high cost. The
processing time of mass spectrometry data obtained from each pixel
and converting it into derivative spatial images also confines the
sample area. Nanotechnology used
in MIBI includes staining with metal-labeled
antibodies and data acquisition with the ToF-SIMS. A related nanotechnology
is the MIBIscope, a dynamic ToF-SIMS instrument that uses a gold liquid
metal ion gun as its primary ion source and produces a live image
of tissue topography using a secondary electron detector. The results of this study illustrate that MIBI,
using MIBIscope, achieves high sensitivity and resolution when studying
the spatial tumor immune landscape. 4.2.3 CO-Detection by IndEXing CODEX
is a multiplexed single-cell imaging technology that uses DNA-barcoded
antibodies for spatial proteomics ( B). In CODEX, target proteins in <10 μm-thick
FFPE or fresh frozen tissue sections are labeled with a large panel
of antibodies, each conjugated to a specific oligonucleotide barcode
( B). These
barcodes are detected, three at a time, in several rounds of hybridization
and imaging. In each round, complementary oligonucleotides, each labeled
with a fluorescent dye, bind to the barcodes, and the tissue is imaged.
Then, a gentle washing step removes the fluorescent dyes. This process
is repeated until all the barcodes—and the protein targets
they represent—are detected. CODEX
employs a cyclic fluidic device to automate the rounds of hybridization
and imaging, and it can be integrated
with any tricolor epifluorescence microscope ( B). CODEX has been used for cancer, autoimmunity,
and infection research. It is capable
of spatial resolution around 260 nm in the lateral (xy) and axial
(z) dimensions to create 3D images. But
CODEX requires special reagents and equipment and has several challenges
due to it being a fluorescence-based multiplexed imaging technology, including limitations associated with the microscope
system, background autofluorescence, and the rapid processing of large-scale
imaging data sets. Nanotechnology elements supporting CODEX include
the microfluidics system for repeated target probing, DNA-barcoded
antibodies, and the tricolor fluorescence microscope, such as the
Keyence BZ-X710 fluorescence microscope configured with 3 fluorescent
channels (FITC, Cy3, Cy5). Current antibody-based spatial proteomics
methods have some general limitations. First, the number of protein
targets is limited. MIBI can image up to 100 targets simultaneously
after performing SIMS, although commercially available products are
only capable of detecting around 40 targets. The CODEX workflow visualizes 50+ protein targets at the single-cell
level, and the updated CODEX multiplexed
imaging platform can detect 100 RNA labels. Second, antibody detection methods are subject to nonspecific binding,
epitope loss, and tissue degradation. Additional limitations for antibody
methods are related to the size of the capture region of interest
within the tissue slide, the time needed for fluorescent image acquisition,
and the cost of mass spectrometry detection. Furthermore, these methods
are based on relative spectral intensities and are only semiquantitative. − 4.2.4 Additional Techniques for Sample Processing
and Streamlined Spatial Workflow Nanotechnology plays a crucial
role in proteomics based on mass spectrometry, spanning various applications
and workflows. From sample pretreatment to mass spectrometry analysis,
nanoscale processing is integral, especially in single-cell analysis
(a cornerstone in several applications in the biomedical field). While
conventional proteomic methods based on mass spectrometry require
samples comprising more than thousands of cells to profile in-depth
identification, innovative platforms such as nanoPOTS offer enhanced
recovery and efficiency by minimizing sample volumes to less than
200 nL, allowing for the identification of ∼1500 to ∼3000
proteins from ∼10 to ∼140 cells, respectively. Despite advancements in imaging-based and MS-based
methods, integrating nanotechnology remains a challenge, particularly
in connecting imaging data with protein abundance measurements that
have single-cell resolution. One platform, scDVP, offers a promising
solution by combining three techniques: cellular phenotype image analysis
driven by artificial intelligence, automated single-nucleus and single-cell
LCM, and ultrahigh-sensitivity mass spectrometry coupled with a nanoelectrospray
ion source. DVP enables the discovery and characterization of cellular
interactions and states with the added advantage of analyzing the
subcellular structures and spatial dynamics of extracellular matrix. Spatial molecular profiling of complex
tissues is further enhanced by nanoscale staining techniques. For
instance, DISCO-MS combines whole organism clearing, image analysis
based on deep learning, robotic tissue extraction assisted by nanoboosters,
and ultrahigh-sensitivity mass spectrometry to yield proteome data
identifying more than 6,000 proteins across various clearing conditions. Other nanotechnologies combined with mass spectrometry
to allow high resolution spatial profiling include ProteomEx, which—using
manual microsampling without custom or special equipment—enabled
quantitative profiling of the spatial variability of the proteome
at ∼160 μm lateral resolution in mammalian tissues, equivalent
to the tissue volume of 0.61 nL. In
addition, a Microscaffold Assisted Spatial Proteomics (MASP) strategy,
based on spatially resolved microcompartmentalization of tissue using
a 3D-printed microscaffold, mapped more than 5000 cerebral proteins
in the mouse brain, including numerous important brain markers, transporters,
and regulators, that were identified by a trapping nano-LC and high-resolution
mass spectrometry system. Furthermore,
Mass Spectrometry Imaging (MSI) is another powerful tool for mapping
of the spatial distribution of proteins by label-free quantification
in biological tissues. For instance, Nanospray Desorption Electrospray
Ionization (nano-DESI) MSI generates multiply charged protein ions,
advantageous for the identification of top-down proteomics analysis,
achieving proteoform mapping in mouse tissues with a spatial resolution
down to 7 μm. 4.3 Spatial Metabolomics Developed only
two decades ago, spatial metabolomics is another emerging field within
spatial omics that has enabled the identification of metabolites within
the spatial contexts of cells, tissues, organs, and organisms. Spatial metabolomics uses imaging technology
based on mass spectrometry, including Matrix-Assisted Laser Desorption/Ionization
(MALDI) MSI, , Desorption Electrospray Ionization
(DESI) MSI, , and SIMS imaging. 4.3.1 Matrix-Assisted Laser Desorption/Ionization-Mass
Spectrometry Imaging MALDI is an ionization method used in
conjunction with MSI for spatial metabolomics. MALDI requires a sample
preparation step that involves mixing the sample with a protective
low molecular weight matrix before spotting the mixture onto stainless
steel and allowing it to crystallize. Next, the samples are exposed
to a scanning laser, transforming solid components into charged gaseous
particles, ionizing the sample within a 10 μm-wide window ( A). Finally, mass
spectrometry detects these ions to define each metabolite image location
( A). MALDI mass spectrometry imaging has benefits, but
also limitations.
It has better metabolite coverage than other spatial metabolomics
methods, and consistently detects hundreds of metabolites at a spatial
resolution of around 10 μm. , , Even better, atmospheric pressure-MALDI developed
by Spengler’s group achieves a spatial resolution of 1.4 μm, yet the resolution is still worse than the
spatially resolved mass spectrometry approaches used for spatial proteomics. , The resolution often suffers when these approaches are applied to
metabolomics, to accommodate mass spectrometry instrument sensitivity
to low-abundance species from small areas. Spatial resolution and
sensitivity are inherently connected in spatial metabolomics techniques;
as the diameter of the laser spot decreases to achieve a finer spatial
resolution, the ion yield usually decreases as well. , Thus, researchers struggle to achieve finer spatial resolution while
maintaining adequate signal intensities. Other limitations of MALDI-MSI
include decreased resolution caused by delocation (when molecules
diffuse across or away from the tissue) and difficulty detecting low-weight
molecules (<600 Da). The matrix ions may have similar profiles
with multiple lower-weight metabolite ions, which can interfere with
the visualizations of select metabolites, defining a low-weight molecule
detection problem. , Although mass spectrometry
is the primary nanotechnology tool in
MALDI-MSI, nanomaterials have been used as alternative matrices to
improve various aspects of the method. Some researchers have increased
its sensitivity by adding nanoparticles to the low-density matrices,
which homogeneously concentrates targets into a narrow ring, similar
to the characteristic ring-like pattern observed after a drop of spilled
coffee evaporates (the “coffee ring effect”). Advantages
of sample concentrating using this method led to higher signals relative
to conventional MALDI, especially for analytes with greater mass-to-charge
ratios. Other researchers used nanoparticles
to enhance detection of triacylglycerols from lipid mixtures, which
are overwhelmed by other lipids in conventional MALDI detection. They
found a matrix containing citrate-capped gold nanoparticles enhanced
the cationization of triacylglycerols and effectively suppressed other
lipid signals, aiding triacylglycerol detection. And in glycomics studies, MALDI matrices containing graphene
nanosheets and carbon nanoparticles improved sensitivity in the detection
of native glycans, which ionize inefficiently. 4.3.2 Desorption Electrospray Ionization Mass
Spectrometry Imaging DESI is another ionization method used
in conjunction with MSI for spatial metabolomics. , DESI directly sprays samples with an electronically charged solution
for ionization, allowing desorption via a solvent stream under ambient
conditions ( B). As the DESI ionization probe scans
across the tissue sample, desorbed ions from the tissue enter the
mass spectrometer, which collects mass-to-charge ratio information
that can be correlated with the spatial distribution ( B). Unlike MALDI-MSI, which requires a sample preparation step, DESI-MSI
can provide spatial information about metabolites with little to no
sample preparation and does not need a matrix. It also does not suffer
from spatial assignment errors caused by sample movement. , But limited spatial resolution is a major challenge for DESI-MSI.
Most studies have reported spatial resolutions of only 50 to 200 μm
due to multiple factors such as solvent composition, capillary size,
and gas flow rate. And in addition to
these factors, resolution is also limited when balancing sensitivity
for low abundance species, as in MALDI-MSI. To improve the resolution,
Laskin et al. developed nano-DESI MSI, which used two fused silica
capillaries: a primary capillary that supplied solvent and maintained
a liquid bridge with the sample, and a secondary capillary that transported
the analyze to the mass spectrometer. , Next, they
developed an approach to control the distance between the nano-DESI
probe and the sample with shear force microscopy for MSI in constant-distance
mode, thereby achieving ∼11 μm spatial resolution in
images of mouse pancreatic islets. The
researchers also coupled a portable nano-DESI probe to a drift tube
ion mobility spectrometry-mass spectrometer, which allowed imaging
of drift time-separated ions of mice uterine tissues with a spatial
resolution less than 25 μm. An
ion mobility spectrometer recorded the drift time to determine the
ion mobility. An ion mobility spectrometer
recorded the drift time, meaning the time it takes for each ion to
reach a detector. In addition to its resolution issues, another challenge
of DESI-MSI is its sensitivity and specificity. This has been improved
by adding silver ions to the nano-DESI solvent, but only for analytes
containing double bonds. Nanotechnology
tools that support DESI-MSI include MSI, nanospray (ie, nano droplets),
and the DESI ionization probe. 4.3.3 Secondary Ion Mass Spectrometry Imaging SIMS is yet another ionization method used in conjunction with
MSI for spatial metabolomics. Rather than a laser or charged spray,
a primary ion beam scans across the sample, bombarding the surface
to induce ionization in an ultrahigh vacuum ( C). The ionization
of molecules at the sample surface generates a secondary beam of sputtered
ions of opposite polarity, which are transferred to a mass analyzer
( C). An advantage of this method is spatial resolution.
The primary ion beam is highly focused and impacts samples with an
orthogonal angle, as opposed to the oblique angle utilized for desorption
catalysts in MALDI and DESI. This degree of control enhances spatial
resolution, which can reach as low as 50 nm, making it possible to distinguish molecules between different organelles
of the same cell. But the high energy
ion beam (1 to 70 keV) is highly destructive, leading to the fragmentation
of biomolecules during desorption. As such, types of SIMS combining
high energy beams with high dose density (ie, > 10 13 ion/cm 2 as in dynamic SIMS) can only target monatomic
or diatomic
elements, limiting their application in spatial metabolomics. Types of SIMS employing ion beams of decreased
dose density (ie, static SIMS) still produce degradation, but to lesser
extent, and initial versions of these methods provided sufficient
resolution to quantify biomolecules up to 300 Da. To improve static SIMS, researchers have modulated the
primary ion beam to decrease sample destruction and increase ionization
efficiency, allowing for increased sensitivity to detect biomolecules
of lower concentration and higher molecular weight. Metal cluster
ion beams, composed of Au 3+ or Bi 3+ , expanded
the ability of SIMS to analyze low molecular weight biomolecules such
as metabolites and lipids, while small
cluster ion beams, composed of C 60 for example, enabled
the analysis of high molecular weight biomolecules such as peptides
and proteins. The range of mass resolution
was further increased by the introduction of gas cluster ion beams,
which improved the ionization efficiency of fully intact biomolecules
up to 100-fold compared to that achieved by metal or small cluster
ion beams. Despite these advances in
mass resolution and dynamic range, the diminished dose density of
static SIMS increases dispersion of the primary ion beam, decreasing
the spatial resolution to a range of 550 to 900 nm. , Nanotechnology tools in SIMS imaging include mass spectrometry,
ion beams, and the nanoparticle coating employed to enhance ionization
efficiency in metal-assisted SIMS. 4.4 Spatial Epigenomics Epigenetic modifications
(to histones or DNA) control the state of chromatin, affecting DNA
accessibility; open chromatin allows gene expression to occur, while
closed chromatin prevents gene expression. Thus, these reversible
epigenetic modifications affect cellular function and explain biological
phenomena on the cellular level . Spatial epigenomics provides information
about epigenetic modifications across a population of cells or across
a tissue, revealing global epigenetic changes. Spatial epigenomics
methods include spatial-ATAC-seq and spatial-CUT&Tag. 4.4.1 Spatial Assay for Transposase-Accessible
Chromatin and RNA Using Sequencing Based on DBiT-seq, spatial-ATAC-seq
is a method that provides a genome-wide map of open and accessible
chromatin regions in intact tissue sections. Spatial-ATAC-seq utilizes the in situ Tn5 transposition chemistry and microfluidic deterministic barcoding as
described in DBiT (see ). Spatial-ATAC-seq employs
the Tn5 transposon to insert DNA oligomers into genome accessible
locations on fixed sections, , and adapters containing
a ligation linker are added to label the modified genome accessible
sites. Next, a grid of barcodes is overlaid using microchannels, and
these location coordinate markers are ligated to the Tn5-generated
oligos in successive rounds, creating a map of barcode combinations.
The array of barcodes is then imaged and overlaid onto tissue morphology,
revealing the locations of accessible chromatin. Then, reverse cross-linking
frees barcoded DNA fragments, creating a 2,500-tile spatial tissue
mosaic, which is amplified by Polymerase Chain Reaction (PCR) and
is the input for preparation of sequencing libraries. Spatial-ATAC-seq
has the ability to capture spatial epigenetic information within the
mouse and human brain. And the method
has also been applied to mouse embryos to delineate the epigenetic
landscape of organogenesis, and in human tonsils to map the epigenetic
state of different immune cells. Advantages
of the method are high spatial resolution, high yield, a high signal-to-noise
ratio, and a pixel size of 20 μm at the cellular level. A disadvantage of spatial-ATAC-seq is that,
unlike single-cell technologies, detected pixels may contain partial
nuclei or multiple nuclei, and thus signals may comprise multiple
cell types, which complicates data interpretation. The nanotechnology
underlying spatial-ATAC-seq is microfluidic deterministic barcoding. 4.4.2 Spatial Cleavage Under Targets and Tagmentation Spatial-CUT&Tag analyzes single-cell epigenomes by profiling
chromatin states in situ within tissue sections, and achieves an unbiased,
genome-wide epigenomic map . The approach is based on in situ microfluidic deterministic
barcoding, , Cleavage Under Targets and Tagmentation
(CUT&Tag) chemistry, , and next-generation
sequencing. In the first step of spatial-CUT&Tag, antibodies that
bind histone modification sites are added to the tissue, followed
by secondary antibodies that tether a pA-Tn5 transposome (a form of
fusion enzyme used for CUT&Tag) . The transposome complex is then activated,
ligating linkers and insertions into genomic sites adjacent to specific
histone marks defined by the primary antibodies . As in DBiT-seq and spatial-ATAC-seq, two sets of barcodes (A and
B), delivered by microchannels, are flowed over the tissue surface
. , , Ligation of these barcodes creates
a 2D labeling grid, which is then imaged to link the tissue morphology
to the spatial epigenomics map. The output assay signal is released
after cross-link reversal, producing a library for sequencing quantitation
. Spatial-CUT&Tag defined histone modifications
within the cortical layer of mouse brain during development, highlighting
the spatial patterning of cell types. Despite this utility, the method has resolution limitations, with
a current spatial resolution of 20 μm pixels. To achieve higher
precision in spatial multiomics profiling, one could combine reagents
of DBiT-seq and spatial-CUT&Tag for microfluidic in-tissue barcoding. A serpentine microfluidic channel or increasing
the number of barcodes could also help, reducing pixel size within
the epigenome mapping area. Using these two methods, Fan et al. achieved
simultaneous epigenomic and transcriptomic profiling on tissues from
embryonic and juvenile mouse brain and from adult human brain with
near–single-cell resolution. The
epigenome was evaluated using spatial-CUT&Tag–RNA-seq applied
to histone modifications, and mRNA expression was determined using
spatial-ATAC–RNA-seq Spatial epigenome–transcriptome
cosequencing overlays spatial multiomics signals, synergizing data
from each method and allowing for the examination of mechanistic relationships
across the central dogma of molecular biology. The nanotechnology
supporting spatial-CUT&Tag is in-tissue microfluidic deterministic
barcoding. 4.5 Spatial Multiomics Spatial multiomics
tools combine detection of distinct biomolecular domains inside an
overlapping assay window and are the goal for the field. Vickovic
and Lötstedt developed and published a spatial multiomics platform
in 2022. Their automated and high-throughput
approach mapped regional RNA expression via sequencing-based biomarkers
and overlaid protein signals via DNA-barcoded antibodies or immunofluorescence
labels. This approach enabled the simultaneous spatial evaluation
of 96 sequencing-ready RNA libraries and 64 in situ protein targets
in 2 days. Another spatial multiomics
strategy uses the GeoMx Digital Spatial Profiler (DSP) from NanoString.
Unlike the spatial multiomics platform developed by Vickovic and Lötstedt,
which is limited to frozen tissue, the GeoMx DSP platform can be used
on FFPE tissue sections. And it is capable of spatial analysis profiling
for the whole transcriptome (18,000 RNA targets) and more than 96
proteins simultaneously. The GeoMx DSP, DBiT-seq, spatial-CITE-seq,
and MOSAICA are exciting methods that query spatial RNA and protein
expression. 4.5.1 GeoMx Digital Spatial Profiler The GeoMx DSP currently enables detection and imaging of RNA or protein
on either FFPE or fresh frozen whole tissue sections. The workflow
starts with staining of the prepared tissue (a 5 μm-thick section)
with antibodies and/or RNA attached to oligonucleotide tags (ie, barcodes)
via light-sensitive linkers ( A). Next, the GeoMx DSP automated microscope is used
to select regions of interest (in a varying size of 10 to 600 μm
in diameter). From the regions of interest, the microscope uses UV
light to cleave the oligonucleotide tags and collects the oligonucleotides
( A). Then,
the oligonucleotides are analyzed with the NanoString nCounter System
to quantify levels of specific proteins or RNAs ( A). Finally, data visualization
and analysis are performed. , Since the instrument
was launched in March 2019, many groups have utilized the GeoMx DSP
to study biomolecular expression in carcinomas, supporting its use
as a standard tool for oncology research. The use of equivalently
tagged oligonucleotides allows the system to interrogate numerous
RNA and protein biomarkers with higher throughput, and GeoMx DSP simultaneously
profiled six nodular and six infiltrative cancer samples, interrogating
1812 RNA targets. One study combining
Single-Cell RNA Sequencing (scRNA-seq) transcriptomes and spatial
transcriptomics identified Activin A as a paracrine-acting factor
that contributed to tumor progression. A current limitation of the GeoMx DSP is its inability to achieve
single-cell resolution for biomarker coexpression due to low protein
detection efficiency. , Nanotechnology tools that underscore
GeoMx DSP include RNA probes conjugated to fluorophores to interrogate
specific biomolecule targets, the imaging platform, and oligonucleotide
barcodes. 4.5.2 Deterministic Barcoding in Tissue for Spatial
Omics Sequencing DBiT-seq is a microfluidics-based platform
for analyzing spatial proteomics and transcriptomics, created by Fan
et al. , In DBiT-seq, tissue sections are exposed
to Antibody-Derived DNA Tags (ADTs) for protein detection. For RNA
detection and spatial analysis, a Polydimethylsiloxane (PDMS) microfluidic
chip is placed directly against the tissue slide ( B). Fifty parallel microfluidic
channels in the chip deliver a set of oligo-dT-tagged DNA barcodes
(set A), along with reverse transcriptase into lanes on the surface
of the tissue slide. Then another PDMS chip is placed on the tissue
slide, containing channels that deliver another set of oligo-dT-tagged
DNA barcodes (set B), along with DNA ligase to attach the B barcodes
to the A barcodes, creating a 2D mosaic of tissue pixels ( B). After imaging
by a microscope to define histological features, the cDNA is collected
and amplified to build a next-generation sequencing library. Finally,
proteins and mRNAs are detected by next-generation sequencing ( B). DBiT-seq has been applied to study mouse embryos to measure
a panel of 22 proteins and mRNA transcriptome. It has also been used for transcriptome sequencing within
embryonic and adult FFPE sections, at cellular resolution (25 μm
pixels) and >1000 gene per pixel coverage. Performing spatial whole transcriptome sequencing on FFPE
samples
without tissue dissociation or RNA exaction is one of the strengths
of DBiT-seq as an in-tissue barcoding approach. A weakness is that,
even though the pixel size of DBiT-seq can be scaled down to 10 μm,
it is still not capable of directly resolving single-cell spatial
mapping. Nanotechnologies exemplified in DBiT include antibody-derived
DNA tags for protein detection, subnanometer microfluidic chambers
for creating a spatial barcoding grid, and the optical or fluorescence
microscope for detection. 4.5.3 Spatial Co-Indexing of Transcriptomes and
Epitopes for Multi-Omics Mapping by Highly Parallel Sequencing Spatial-CITE-seq extends Coindexing of Transcriptomes and Epitopes
(CITE-seq) to the spatial dimension and enables multiplexed protein
and whole transcriptome comapping. The
first step of this method uses a cocktail of ∼200–300
ADTs to stain a paraformaldehyde-fixed tissue section. The ADTs include
a poly(A) tail, a Unique Molecular Identifier (UMI) tag, and a DNA
sequence that is specific for select antibodies. , As in DBiT, two sets of barcodes (A, row and B, column) are introduced
using different microfluidic chips for ligation in situ, creating
a 2D grid of tissue pixels to coindex all the protein epitopes and
the transcriptome. The collection of barcoded cDNAs is then amplified
by PCR and used for next-generation sequencing library preparation
for paired-end sequencing of both ADTs and cDNAs, allowing the spatial
reconstruction of protein and RNA coordinates. Spatial-CITE-seq incorporates
200 to 300 protein markers, substantially enhancing tissue mapping
at cellular resolution, and offers the highest multiplexing to date
for spatial protein profiling. Spatial-CITE-seq can profile 189 proteins
and whole transcriptomes in multiple mouse tissue types and 273 proteins
and the whole transcriptome in human tissues. In contrast, DBiT can only map 22 proteins at cellular
level resolution. One drawback for spatial-CITE-seq
is the lack of subcellular resolution, a limitation across most spatial
multiomics approaches. Other weaknesses for spatial-CITE-seq include
competition between ADTs and mRNAs for in-tissue reverse transcription,
and poor detection efficiency for low–copy-number transcripts.
Protein coverage is also limited to a panel of surface epitopes, excluding
intracellular or extracellular matrix proteins, which limits the information
provided in regard to protein signaling and function. The major nanotechnologies
that support spatial-CITE-seq are ADTs and microfluidic chips with
nanometer-wide lanes. 4.5.4 Multi-Omics Single-Scan Assay with Integrated
Combinatorial Analysis MOSAICA is a fluorescence-based spatial
multiomics imaging tool for simultaneous codetection of protein and
mRNA ( C). The MOSAICA procedure uses formalin-fixed tissues
or cells, which are incubated with a set of primary oligonucleotide
probes that bind to complementary regions (25 to 30 bases long) on
mRNAs and contain adapter sequences. After a wash step, a set of secondary
probes, each with a pair of fluorophores, binds to the adapters on
the primary probes ( C). Thus, each target has a specific combination of numerous
dual-label probes, with emission spectra and temporal lifetime signatures,
and these probe maps can be imaged using a fluorescent microscope.
Refining these raw data, bioinformatics-based tools direct the reconstruction
of images by spectral and fluorescence lifetime signal processing,
to allow individual RNAs among a pool of detected targets to be visualized
( C). These
images combine numerous target confocal detections, providing transcript
levels and localization within a 3D reconstructed image, which is
then laid over the microscopic tissue structure ( C). MOSAICA has been used for
10-plex mRNA expression in fixed colorectal cancer cells and multiplexed
mRNA analysis of clinical melanoma cells within FFPE tissues. At low cost, MOSAICA achieves high spatial resolution
(x-y resolution of 100 nm and z-spacing of 500 nm) in a 3D context
( C). As an
imaging-based tool, MOSAICA suffers from optical crowding, which limits
resolution for adjacent targets. But MOSAICA can be integrated with
other imaging modalities such as expansion, super-resolution, or multiphoton
microscopy to improve subcellular resolution and allow imaging of
highly scattering and autofluorescent tissues. − In the future, paired fluorescent probes may allow a barcoding strategy
based on Förster resonance energy transfer to tune the combinatorial
spectrum and lifetime readout. Nanotechnology
supporting MOSAICA includes DNA probes, fluorescent probes, and the
wide-field confocal microscope.
Spatial Transcriptomics Highlighted
as Method of the Year in 2020 by Nature Methods, spatially resolved transcriptomics combines transcriptome-wide
RNA sequencing with histology-based images to precisely map RNA expression
and thereby provide further insights into the cellular transcription
of biological systems. , , Spatially
resolved transcriptomics can elucidate single-cell nucleic acid expression
throughout entire solid tissues or organs while preserving spatial
subcellular localization. , , Spatial transcriptomics was accomplished by Laser Capture
Microdissection (LCM). , In LCM, a laser precisely dissects
a microscopic region (eg, a single cell), which may be input into
high-throughput RNA sequencing (LCM-seq). This method produced gene
expression profiles within defined ∼10 μm compartments
on Formalin-Fixed Paraffin-Embedded (FFPE) tissue sections, distinguishing
RNAs in tumor cells from normal adjacent cells to reveal important
molecular events in cellular oncology. Later, image-based spatial transcriptomics was accomplished by Single-Molecule
RNA Fluorescence in situ Hybridization (smFISH). This technique uses
super-resolution microscopy to facilitate the acquisition of high-resolution
images (10 to 20 nm). , EEL FISH, a derivative of smFISH,
combines multiplexed RNA detection with high-resolution, large-area
imaging and generates faithful RNA quantitative maps that retain spatial
cellular information. Spatial barcoding-based
transcriptomics like BaristaSeq, STARmap, and FISSEQ layer engineered
nucleic acid tags onto in situ sequencing technologies and use DNA
nanoballs to amplify the specific detection signals for imaging. , , The following sections review
several tools available for spatial transcriptomics. 4.1.1 Enhanced Electric Fluorescence In Situ Hybridization EEL FISH is a spatial transcriptome profiling method that employs
a set of combinatorial, binary barcode tags to detect RNA overlaid
onto a histological image. EEL FISH electrophoretically
transfers RNA from a tissue section onto a nanosurface coated with
an optically transparent and electrically conductive layer of indium
tin oxide ( A). Electrophoretically transferring
RNA is superior to transferring RNA by passive diffusion, as is done
in sequencing-based methods, due to the preservation of RNA localization. − In EEL, after residual tissue removal, the result of the transfer
is a collapsed 2D grid of mRNA on the coated nanosurface with precise
in situ spatial information ( A). Next, a set of probes, tagged by combinations of
40 labeling barcodes, are used for 16 rounds of imaging ( A). After fluorescent decoding and encoding of binary label
addresses, barcode identities define locations for numerous mRNAs
within a single fluorescence capture field, which can be matched to an image of the original histological section.
One example of EEL FISH is its application to sequential sagittal
sections of mouse brain to measure the expression of 440 genes, highlighting
complex RNA expression patterns that lie underneath tissue organization. Despite its advantages, EEL has lower sensitivity
and resolution than other tissue-based smFISH methods. , (The resolution of EEL FISH, defined by the diffraction-limited
imaging resolution of the fluorescent label, approaches 200 to 400
nm.) Future improvements, such as maintaining RNA stability, magnifying
the capture field, and expanding barcode detection, could refine EEL
sensitivity and resolution to match smFISH for single-molecule imaging. , Nanotechnologies that support EEL include the indium tin oxide capture
surface and use of combinatorial DNA barcoding to tag numerous mRNAs—the
latter a rudimentary example of nanocomputing. 4.1.2 Spatial Enhanced Resolution Omics-Sequencing Stereoseq is another strategy for spatial transcriptomics ( B). Stereoseq creates
a grid of spots on a lithographically etched nanofluidic chip, the
grid acting as a capture surface for mRNAs from a tissue section. Each DNA nanoball spot is 220 nm in diameter
and the spacing between centers of two adjacent nanoballs is 500 or
715 nm ( B).
The DNA nanoball–patterned array chip has 400 spots per 100
μm 2 to define the pixel size. After a tissue section
is laid onto the chip, the DNA nanoballs capture mRNAs and, following
a second round of rolling circle amplification, create a library of
mRNAs from the original source, which have been sorted to contain
specific regional labels defined by the DNA nanoball grid ( B). Stereoseq was used to create the Mouse Organogenesis Spatiotemporal
Transcriptomic Atlas (MOSTA), which defines detailed topographical
information about the stepwise emergence of tissue-specific cell identities
during organogenesis. Stereoseq has been
performed to capture spatially resolved single-cell transcriptomes
of axolotl telencephalon sections during development and regeneration. Despite the method having genome-wide coverage,
Stereoseq has limited sensitivity and may fail to detect the low copy
numbers of RNAs from low-expression genes. Stereoseq also has trouble
distinguishing single cells from a mixture of multiple similar cell
types, especially smaller cell types like immune cells. A updated
version, Single-Cell Stereo-Seq (scStereo-seq), utilizes spatial transcriptomics
and plant cell wall staining onto histological cell–cell boundaries,
allowing in situ single-cell transcriptome profiling in mature Arabidopsis leaves. The nanotechnology
elements of Stereoseq are DNA nanoballs coupled to precise spacing
on a nanofluidic capture surface and next-generation sequencing. 4.1.3 Barcode Anatomy Resolved by Sequencing Barcode Anatomy Resolved by Sequencing (BARseq) is a multiplexed
and high-throughput method for mapping neuronal projections at cellular
resolution. BARseq combines Multiplexed Analysis of Projections by
Sequencing (MAPseq) and in situ sequencing of cellular tagging barcodes. , , , MAPseq is a technique for mapping neurons by labeling large sets
of neurons with barcodes (random RNA sequences). The advantage of BARseq is its ability to match nearby
cortical areas with distant subcortical projections by relying on
specific barcode sequences that functionally transit through neuronal
projections. Unlike conventional optical
approaches to mapping projections, BARseq relies on matching barcodes
without errors over distance, and is, therefore, superior to other
multiplexed optical tracing methods. Theoretically,
BARseq can label tens of millions of neurons in a single experiment
without a specialized high-speed microscope because of the combinatorial
diversity provided in the barcode design; for example, a 30-nucleotide
(nt) sequence set can generate about 4 to 10 18 barcodes. Moreover, the spatial resolution of
BARseq approaches subcellular dimensions, sufficient to resolve the
organization of projections across neuronal subtypes. The spatial
resolution of BARseq may be even further improved with LCM or direct
in situ sequencing of projection barcodes. BARseq mapped the projections of 3,579 neurons to 11 areas in the
mouse auditory cortex and confirmed the laminar organization of the
three top classes of projection neurons (intratelencephalic, pyramidal
tract-like, and corticothalamic). Nanotechnologies that make BARseq
possible include DNA nanoballs amplified by RCA, fluorescent labels
of nucleotides, and multichannel fluorescence confocal microscopy. 4.1.4 Barcode In Situ Targeted Sequencing BaristaSeq was published in 2017. It
is a modified version of the gap padlock probe–based method
for in situ barcode sequencing compatible with Illumina sequencing
chemistry and is suitable for barcode-assisted lineage tracing and
mapping for long-range neuronal projections. , BaristaSeq uses reverse transcription to convert an RNA barcode
sequence into complementary DNA (cDNA) followed by hybridization of
a padlock probe and gap-filling ligation to create circular RCA templates. , Two distinct fluorescent probes, each recognizing a bracketing padlock,
are used during the gap-filling ligation steps. Fluorescence is evaluated
to detect probe pairs targeting the diluted padlock after rolony generation,
and Illumina chemistry is applied to sequence the samples and determine
probe identities. A spinning disk microscope and laser scanning confocal
microscope are then employed to image the sequencing. The accuracy
and efficiency of BaristaSeq was demonstrated by sequencing random
barcodes (15-nt barcode set) expressed in cultured Baby Hamster Kidney
(BHK) cells. BaristaSeq increased the
amplification efficiency by 5-fold, and this was coupled with high
sequencing accuracy (>97%) compared with other in situ sequencing
techniques. BaristaSeq also has limitations; it has only been applied
to cultured cells, and its resolution is limited to the cellular,
not subcellular level. Nanotechnologies applied in BaristaSeq are
DNA nanoballs that function to amplify detection signals, fluorescent
probes used for the gap padlock detection step, and the confocal microscope
for imaging. 4.1.5 Fluorescent In Situ RNA Sequencing FISSEQ was proposed in 2003 to selectively amplify DNA on a solid
substrate, allowing for targeted genome and transcriptome sequencing. − The next generation of FISSEQ provides transcriptome-wide in situ
RNA evaluation across multiple specimen types and spatial scales. First, RNA within fixed cells is reverse-transcribed
with tagged random hexamer primers to generate cDNA. FISSEQ uses the
direct-ligation approach to produce cDNA fragments as templates for
RCA, and these cDNAs produced from reverse transcription of mRNA are
directly circularized using a single-stranded DNA ligase. Then, the
cDNA fragments are circularized and amplified with RCA. The RCA amplicons
are then cross-linked with BS(PEG)9, a bis-succinimide ester-activated
PEG compound. BS(PEG)9 reduces the nonspecific binding of probes and
has a highly fluorescent signal after the hybridization of the probe.
This creates 200 to 400 nm DNA nanoballs, comprising tandem cDNA repeats
of the target sequence, on top of a histology section. Partition sequencing
using pre-extended sequencing primers with random mismatches at the
ligation site reduces the total number of molecular sequencing reactions,
resulting in a minimal signal-to-noise ratio or number of position
changes after multiple rounds of rehybridized probing. In this manner,
FISSEQ achieves sufficient spot density and RNA localization to discern
individual molecules. FISSEQ uses color sequences at each pixel to
identify objects. The putative nucleic acid sequences are determined
for all pixels and compared with reference sequences. FISSEQ was used
to confirm RNA expression and localization in human primary fibroblasts. The method can also examine other cell types,
tissue sections, and whole-mount embryos for 3D visualization that
spans multiple resolution scales. Single
molecule detection is also possible, since FISSEQ improves optical
resolution and reduces signal noise. But FISSEQ also has limitations;
it is not suitable for all cellular structures and specific classes
of RNA, for example detecting genes involved in RNA and protein processing. Nanotechnology elements supporting FISSEQ are
DNA nanoballs, fluorescent probes, confocal imaging, and the cross-linking
reagent BS(PEG)9. 4.1.6 Spatially Resolved Transcript Amplicon Readout
Mapping STARmap uses targeted signal amplification and hydrogel–tissue
chemistry interactions to enable 3D in situ transcriptomics in intact
tissue ( C). A specific set of cellular RNAs are amplified
in situ by a method called the Specific Amplification of Nucleic Acids
via Intramolecular Ligation (SNAIL). SNAIL achieves high efficiency
for in situ sequencing by avoiding a reverse transcription step. In
SNAIL, two cDNA probes hybridize to the same RNA molecule. One of
these probes (the padlock probe) contains a specific gene identifier.
This probe is circularized, and RCA generates an amplicon in the form
of a DNA nanoball that contains multiple copies of the specific gene
identifier ( C). SNAIL provides a much higher absolute
signal intensity and signal-to-noise ratio outcome than that obtained
with smFISH probes. It also has much greater detection efficiency
than single-cell RNA sequencing, despite having a simpler experimental
procedure. After amplification, the DNA
nanoballs are enzymatically modified and polymerized to form a hydrogel
that serves as a 3D cDNA library ( C). Then, the RNA landscape is sequenced
with the Sequencing with Error-Reduction by Dynamic Annealing and
Ligation (SEDAL) process. SEDAL employs
two kinds of short, degenerate probes: reading probes and fluorescence
probes. The first kind decodes bases, and the second creates fluorescent
puncta from the decoded sequences. These two probes bind DNA targets
transiently and, after specific complementary ligation, form stable
products for imaging with a confocal microscope. For multiplexed imaging,
fluorescent signals are stripped with formamide, and another cycle
of bases are read, eliminating the accumulation of errors during sequencing.
STARmap was used to define cell types and activity-regulated gene
expression in the mouse cortex, from mouse brain sections and larger
3D 150 mm-thick tissue blocks. A limitation
of STARmap is that it cannot independently fully define brain cell
typology in 3D anatomy. In the future, STARmap aims to study activity
patterns exhibited or experienced by cells during behavior in real
time. The nanotechnology tools that underlie STARmap include the SNAIL
method, which incorporates DNA nanoballs; the SEDAL method, which
incorporates fluorescent probes; and the confocal microscope for imaging. 4.1.7 Slide-seq Slide-seq is a spatial
transcriptomics technology, in which DNA-barcoded beads are used to
reveal spatial information about RNAs. , , In Slide-seq, DNA-barcoded, 10 μm beads are
packed onto a rubber-coated glass coverslip to form a monolayer. RNAs
from tissue sections are transferred onto the beads, with the precise
locations of the beads preserving RNA spatial information. Then, the
barcode sequence from each bead is determined by sequencing using
oligonucleotide ligation and detection chemistry. The Slide-seq had
low transcript detection sensitivity, limiting its utility. To address
this limitation, researchers developed Slide-seqV2, an improved version
of Slide-seq with an order-of-magnitude higher sensitivity that also
had better methods for library generation, barcoded bead synthesis,
and array sequencing. These modifications increased RNA capture efficiency
to a level ∼10-fold greater than Slide-seq, a level approaching
the detection efficiency of droplet-based single-cell RNA-seq techniques. The capture efficiency improvements within Slide-seqV2
make it useful across many experimental contexts. Nanotechnology methods
important in Slide-seq are the use of beads 10 to 20 μm wide
for location mapping, DNA barcodes, and the confocal microscope. 4.1.8 Seq-Scope Seq-Scope is a spatial
transcriptomics method that relies on an array of randomly barcoded
single-molecule oligonucleotides and two rounds of sequencing, conveniently
achieved by the Illumina sequencing platform. Seq-Scope uses a array attached to a solid surface that
contains single-stranded oligonucleotides, each containing a randomly
generated barcode sequence called a High-Definition Map Coordinate
Identifier (HDMI). The HDMI oligonucleotides are amplified, generating
clusters, each with a specific HDMI sequence. In the first round of
sequencing, each HDMI sequence and its spatial coordinates are determined
by the Illumina platform. Then, HDMI clusters capture RNA released
from an overlying tissue section and corresponding cDNA sequences
are generated; these HDMI and cDNA sequences are determined in the
second round of sequencing. In other words, the first round of sequencing
provides the spatial information, and the second round of sequencing
provides gene expression information. When the data from the two rounds
of sequencing are combined, they allow construction of a spatial gene
expression matrix. Seq-Scope has a spatial resolution of 500 to 800
nm (600 nm on average) and achieves submicrometer resolution, comparable
to an optical microscope. Seq-Scope
reveals the spatial transcriptome on multiple histological scales
and has been used to distinguish tissues within an organ (eg, different
regions of the liver and colon), different cell types, and different
subcellular regions (eg, nucleus versus cytoplasm). Seq-Scope has several advantages, including high throughput,
straightforward procedures, precise measurements, excellent breadth
of transcriptome capture output, and high spatial resolution, making
it far superior to most other technologies. Seq-Scope is limited,
however, to the capture of the poly-A-tagged transcriptome, making
it less robust than spatial-CITE-seq or DBiT-Seq, which are capable
of spatially profiling the transcriptome alongside protein expression.
The nanotechnology supporting Seq-Scope is the set of HDMI barcode
sequences.
Enhanced Electric Fluorescence In Situ Hybridization EEL FISH is a spatial transcriptome profiling method that employs
a set of combinatorial, binary barcode tags to detect RNA overlaid
onto a histological image. EEL FISH electrophoretically
transfers RNA from a tissue section onto a nanosurface coated with
an optically transparent and electrically conductive layer of indium
tin oxide ( A). Electrophoretically transferring
RNA is superior to transferring RNA by passive diffusion, as is done
in sequencing-based methods, due to the preservation of RNA localization. − In EEL, after residual tissue removal, the result of the transfer
is a collapsed 2D grid of mRNA on the coated nanosurface with precise
in situ spatial information ( A). Next, a set of probes, tagged by combinations of
40 labeling barcodes, are used for 16 rounds of imaging ( A). After fluorescent decoding and encoding of binary label
addresses, barcode identities define locations for numerous mRNAs
within a single fluorescence capture field, which can be matched to an image of the original histological section.
One example of EEL FISH is its application to sequential sagittal
sections of mouse brain to measure the expression of 440 genes, highlighting
complex RNA expression patterns that lie underneath tissue organization. Despite its advantages, EEL has lower sensitivity
and resolution than other tissue-based smFISH methods. , (The resolution of EEL FISH, defined by the diffraction-limited
imaging resolution of the fluorescent label, approaches 200 to 400
nm.) Future improvements, such as maintaining RNA stability, magnifying
the capture field, and expanding barcode detection, could refine EEL
sensitivity and resolution to match smFISH for single-molecule imaging. , Nanotechnologies that support EEL include the indium tin oxide capture
surface and use of combinatorial DNA barcoding to tag numerous mRNAs—the
latter a rudimentary example of nanocomputing.
Spatial Enhanced Resolution Omics-Sequencing Stereoseq is another strategy for spatial transcriptomics ( B). Stereoseq creates
a grid of spots on a lithographically etched nanofluidic chip, the
grid acting as a capture surface for mRNAs from a tissue section. Each DNA nanoball spot is 220 nm in diameter
and the spacing between centers of two adjacent nanoballs is 500 or
715 nm ( B).
The DNA nanoball–patterned array chip has 400 spots per 100
μm 2 to define the pixel size. After a tissue section
is laid onto the chip, the DNA nanoballs capture mRNAs and, following
a second round of rolling circle amplification, create a library of
mRNAs from the original source, which have been sorted to contain
specific regional labels defined by the DNA nanoball grid ( B). Stereoseq was used to create the Mouse Organogenesis Spatiotemporal
Transcriptomic Atlas (MOSTA), which defines detailed topographical
information about the stepwise emergence of tissue-specific cell identities
during organogenesis. Stereoseq has been
performed to capture spatially resolved single-cell transcriptomes
of axolotl telencephalon sections during development and regeneration. Despite the method having genome-wide coverage,
Stereoseq has limited sensitivity and may fail to detect the low copy
numbers of RNAs from low-expression genes. Stereoseq also has trouble
distinguishing single cells from a mixture of multiple similar cell
types, especially smaller cell types like immune cells. A updated
version, Single-Cell Stereo-Seq (scStereo-seq), utilizes spatial transcriptomics
and plant cell wall staining onto histological cell–cell boundaries,
allowing in situ single-cell transcriptome profiling in mature Arabidopsis leaves. The nanotechnology
elements of Stereoseq are DNA nanoballs coupled to precise spacing
on a nanofluidic capture surface and next-generation sequencing.
Barcode Anatomy Resolved by Sequencing Barcode Anatomy Resolved by Sequencing (BARseq) is a multiplexed
and high-throughput method for mapping neuronal projections at cellular
resolution. BARseq combines Multiplexed Analysis of Projections by
Sequencing (MAPseq) and in situ sequencing of cellular tagging barcodes. , , , MAPseq is a technique for mapping neurons by labeling large sets
of neurons with barcodes (random RNA sequences). The advantage of BARseq is its ability to match nearby
cortical areas with distant subcortical projections by relying on
specific barcode sequences that functionally transit through neuronal
projections. Unlike conventional optical
approaches to mapping projections, BARseq relies on matching barcodes
without errors over distance, and is, therefore, superior to other
multiplexed optical tracing methods. Theoretically,
BARseq can label tens of millions of neurons in a single experiment
without a specialized high-speed microscope because of the combinatorial
diversity provided in the barcode design; for example, a 30-nucleotide
(nt) sequence set can generate about 4 to 10 18 barcodes. Moreover, the spatial resolution of
BARseq approaches subcellular dimensions, sufficient to resolve the
organization of projections across neuronal subtypes. The spatial
resolution of BARseq may be even further improved with LCM or direct
in situ sequencing of projection barcodes. BARseq mapped the projections of 3,579 neurons to 11 areas in the
mouse auditory cortex and confirmed the laminar organization of the
three top classes of projection neurons (intratelencephalic, pyramidal
tract-like, and corticothalamic). Nanotechnologies that make BARseq
possible include DNA nanoballs amplified by RCA, fluorescent labels
of nucleotides, and multichannel fluorescence confocal microscopy.
Barcode In Situ Targeted Sequencing BaristaSeq was published in 2017. It
is a modified version of the gap padlock probe–based method
for in situ barcode sequencing compatible with Illumina sequencing
chemistry and is suitable for barcode-assisted lineage tracing and
mapping for long-range neuronal projections. , BaristaSeq uses reverse transcription to convert an RNA barcode
sequence into complementary DNA (cDNA) followed by hybridization of
a padlock probe and gap-filling ligation to create circular RCA templates. , Two distinct fluorescent probes, each recognizing a bracketing padlock,
are used during the gap-filling ligation steps. Fluorescence is evaluated
to detect probe pairs targeting the diluted padlock after rolony generation,
and Illumina chemistry is applied to sequence the samples and determine
probe identities. A spinning disk microscope and laser scanning confocal
microscope are then employed to image the sequencing. The accuracy
and efficiency of BaristaSeq was demonstrated by sequencing random
barcodes (15-nt barcode set) expressed in cultured Baby Hamster Kidney
(BHK) cells. BaristaSeq increased the
amplification efficiency by 5-fold, and this was coupled with high
sequencing accuracy (>97%) compared with other in situ sequencing
techniques. BaristaSeq also has limitations; it has only been applied
to cultured cells, and its resolution is limited to the cellular,
not subcellular level. Nanotechnologies applied in BaristaSeq are
DNA nanoballs that function to amplify detection signals, fluorescent
probes used for the gap padlock detection step, and the confocal microscope
for imaging.
Fluorescent In Situ RNA Sequencing FISSEQ was proposed in 2003 to selectively amplify DNA on a solid
substrate, allowing for targeted genome and transcriptome sequencing. − The next generation of FISSEQ provides transcriptome-wide in situ
RNA evaluation across multiple specimen types and spatial scales. First, RNA within fixed cells is reverse-transcribed
with tagged random hexamer primers to generate cDNA. FISSEQ uses the
direct-ligation approach to produce cDNA fragments as templates for
RCA, and these cDNAs produced from reverse transcription of mRNA are
directly circularized using a single-stranded DNA ligase. Then, the
cDNA fragments are circularized and amplified with RCA. The RCA amplicons
are then cross-linked with BS(PEG)9, a bis-succinimide ester-activated
PEG compound. BS(PEG)9 reduces the nonspecific binding of probes and
has a highly fluorescent signal after the hybridization of the probe.
This creates 200 to 400 nm DNA nanoballs, comprising tandem cDNA repeats
of the target sequence, on top of a histology section. Partition sequencing
using pre-extended sequencing primers with random mismatches at the
ligation site reduces the total number of molecular sequencing reactions,
resulting in a minimal signal-to-noise ratio or number of position
changes after multiple rounds of rehybridized probing. In this manner,
FISSEQ achieves sufficient spot density and RNA localization to discern
individual molecules. FISSEQ uses color sequences at each pixel to
identify objects. The putative nucleic acid sequences are determined
for all pixels and compared with reference sequences. FISSEQ was used
to confirm RNA expression and localization in human primary fibroblasts. The method can also examine other cell types,
tissue sections, and whole-mount embryos for 3D visualization that
spans multiple resolution scales. Single
molecule detection is also possible, since FISSEQ improves optical
resolution and reduces signal noise. But FISSEQ also has limitations;
it is not suitable for all cellular structures and specific classes
of RNA, for example detecting genes involved in RNA and protein processing. Nanotechnology elements supporting FISSEQ are
DNA nanoballs, fluorescent probes, confocal imaging, and the cross-linking
reagent BS(PEG)9.
Spatially Resolved Transcript Amplicon Readout
Mapping STARmap uses targeted signal amplification and hydrogel–tissue
chemistry interactions to enable 3D in situ transcriptomics in intact
tissue ( C). A specific set of cellular RNAs are amplified
in situ by a method called the Specific Amplification of Nucleic Acids
via Intramolecular Ligation (SNAIL). SNAIL achieves high efficiency
for in situ sequencing by avoiding a reverse transcription step. In
SNAIL, two cDNA probes hybridize to the same RNA molecule. One of
these probes (the padlock probe) contains a specific gene identifier.
This probe is circularized, and RCA generates an amplicon in the form
of a DNA nanoball that contains multiple copies of the specific gene
identifier ( C). SNAIL provides a much higher absolute
signal intensity and signal-to-noise ratio outcome than that obtained
with smFISH probes. It also has much greater detection efficiency
than single-cell RNA sequencing, despite having a simpler experimental
procedure. After amplification, the DNA
nanoballs are enzymatically modified and polymerized to form a hydrogel
that serves as a 3D cDNA library ( C). Then, the RNA landscape is sequenced
with the Sequencing with Error-Reduction by Dynamic Annealing and
Ligation (SEDAL) process. SEDAL employs
two kinds of short, degenerate probes: reading probes and fluorescence
probes. The first kind decodes bases, and the second creates fluorescent
puncta from the decoded sequences. These two probes bind DNA targets
transiently and, after specific complementary ligation, form stable
products for imaging with a confocal microscope. For multiplexed imaging,
fluorescent signals are stripped with formamide, and another cycle
of bases are read, eliminating the accumulation of errors during sequencing.
STARmap was used to define cell types and activity-regulated gene
expression in the mouse cortex, from mouse brain sections and larger
3D 150 mm-thick tissue blocks. A limitation
of STARmap is that it cannot independently fully define brain cell
typology in 3D anatomy. In the future, STARmap aims to study activity
patterns exhibited or experienced by cells during behavior in real
time. The nanotechnology tools that underlie STARmap include the SNAIL
method, which incorporates DNA nanoballs; the SEDAL method, which
incorporates fluorescent probes; and the confocal microscope for imaging.
Slide-seq Slide-seq is a spatial
transcriptomics technology, in which DNA-barcoded beads are used to
reveal spatial information about RNAs. , , In Slide-seq, DNA-barcoded, 10 μm beads are
packed onto a rubber-coated glass coverslip to form a monolayer. RNAs
from tissue sections are transferred onto the beads, with the precise
locations of the beads preserving RNA spatial information. Then, the
barcode sequence from each bead is determined by sequencing using
oligonucleotide ligation and detection chemistry. The Slide-seq had
low transcript detection sensitivity, limiting its utility. To address
this limitation, researchers developed Slide-seqV2, an improved version
of Slide-seq with an order-of-magnitude higher sensitivity that also
had better methods for library generation, barcoded bead synthesis,
and array sequencing. These modifications increased RNA capture efficiency
to a level ∼10-fold greater than Slide-seq, a level approaching
the detection efficiency of droplet-based single-cell RNA-seq techniques. The capture efficiency improvements within Slide-seqV2
make it useful across many experimental contexts. Nanotechnology methods
important in Slide-seq are the use of beads 10 to 20 μm wide
for location mapping, DNA barcodes, and the confocal microscope.
Seq-Scope Seq-Scope is a spatial
transcriptomics method that relies on an array of randomly barcoded
single-molecule oligonucleotides and two rounds of sequencing, conveniently
achieved by the Illumina sequencing platform. Seq-Scope uses a array attached to a solid surface that
contains single-stranded oligonucleotides, each containing a randomly
generated barcode sequence called a High-Definition Map Coordinate
Identifier (HDMI). The HDMI oligonucleotides are amplified, generating
clusters, each with a specific HDMI sequence. In the first round of
sequencing, each HDMI sequence and its spatial coordinates are determined
by the Illumina platform. Then, HDMI clusters capture RNA released
from an overlying tissue section and corresponding cDNA sequences
are generated; these HDMI and cDNA sequences are determined in the
second round of sequencing. In other words, the first round of sequencing
provides the spatial information, and the second round of sequencing
provides gene expression information. When the data from the two rounds
of sequencing are combined, they allow construction of a spatial gene
expression matrix. Seq-Scope has a spatial resolution of 500 to 800
nm (600 nm on average) and achieves submicrometer resolution, comparable
to an optical microscope. Seq-Scope
reveals the spatial transcriptome on multiple histological scales
and has been used to distinguish tissues within an organ (eg, different
regions of the liver and colon), different cell types, and different
subcellular regions (eg, nucleus versus cytoplasm). Seq-Scope has several advantages, including high throughput,
straightforward procedures, precise measurements, excellent breadth
of transcriptome capture output, and high spatial resolution, making
it far superior to most other technologies. Seq-Scope is limited,
however, to the capture of the poly-A-tagged transcriptome, making
it less robust than spatial-CITE-seq or DBiT-Seq, which are capable
of spatially profiling the transcriptome alongside protein expression.
The nanotechnology supporting Seq-Scope is the set of HDMI barcode
sequences.
Spatial Proteomics Spatial proteomics,
facilitated by nanotechnology, has revolutionized our understanding
of cellular organization and function at the molecular level . By employing nanosized
materials and techniques, researchers can precisely map the spatial
distribution of proteins within cells, tissues, and organs, unlocking
insights into complex biological processes with in more detail and
better resolution. The synergy between
nanotechnology and proteomics has been achieved by integrating high-end
imaging techniques, such as LCM microscopy, Multiplexed Ion Beam Imaging
(MIBI), or CO-Detection by IndEXing (CODEX). Sample processing techniques,
such as Expansion Proteomics (ProteomEx) or One Pot for Trace Samples (nanoPOTS), have enabled the capture of nanoscale specimen volumes for multiplexed
mass spectrometry. Additionally, streamlined spatial workflows, such
as Single-Cell Deep Visual Proteomics (scDVP) or 3D imaging of Solvent-Cleared Organs Profiled by Mass
Spectrometry (DISCO-MS), allow for the
powerful and unbiased characterization of biological heterogeneity.
These spatial proteomics tools are described below. 4.2.1 Laser Capture Microdissection Microscopy The imaging technique LCM microscopy has played a pivotal role
in understanding cellular heterogeneity with nanoscale precision,
offering the ability to study specific subcellular regions of interest,
facilitating in-depth examination of protein distributions and interactions. In general, LCM enables the targeted dissection
of individual cells or subcellular structures from complex biological
samples, which are then viewed with a microscope. And in spatial proteomics
specifically, LCM is instrumental for analyzing protein distribution
within cellular compartments, studying protein interactions, and unraveling
signaling pathways in the cellular microenvironments. By precisely isolating organelles from an otherwise
complex heterogeneous tissue section, researchers can analyze their
proteome composition, providing insight into their function and dynamics
in local cell populations without losing spatial information. Individually tailored therapies, guided by the molecular profiling
of biopsy samples, have traditionally relied on techniques such as
immunohistochemistry and bulk genomic analysis. While analyzing whole tissue specimens has shown promise
in predicting patient responses to chemotherapy, the process of extracting
these specimens introduces significant variability, which stems from the diverse cellular composition of tissues,
the uncertainty surrounding the proportion of tumor versus host cells
in the sample, and the loss of spatial information about cell types
within the tissue. Hence, LCM has emerged as an ideal technology to
dissect cells at nanoscale for tissue spatial profiling to allow for
proteomic analysis of specific cells or cell subsets while preserving
their spatial context. Given its ability to dissect nanometer
regions, LCM has been paramount
for understanding the spatial organization of tumor and immune cell
populations in tumor immunology and subclonal analysis, offering invaluable
insights into immunotherapy responses and the emergence of drug resistance. , Combining LCM with certain other nanotechnologies, such as Cytometry
by Time-of-Flight (CyToF) and the NanoString
nCounter gene expression system, , has offered
analysis of post-translational modifications and their functions in
signaling pathways. LCM-guided mass spectrometry methods are rapidly
advancing for discovery applications from region-of-interest to single-cell
resolution; and mass spectrometry experts are beginning to realize
the dream of robust, high-yield LCM single-cell tissue proteomics
from either the same thin-tissue section or precision-registered serial
sections from a variety of tissue types. LCM microscopy also offers
high-yield single-cell transfer to a nanochip, subcellular precision,
and high throughput. Despite its potential, LCM microscopy has
its drawbacks, including
the time-consuming nature of several steps: visualization, manual
cell selection, and collection processes. Typically, the choice of
cells for LCM analysis is made through direct microscopic observation,
but this approach is sometimes hindered by the poor image quality
resulting from the necessity of keeping tissues uncovered during the
process. But advancements in digital imaging, liquid coverslip chemistry,
artificial intelligence, and automation are anticipated to overcome
these challenges and revolutionize the field of tissue spatial profiling
in the future. 4.2.2 Multiplexed Ion Beam Imaging Like
LCM microscopy, MIBI has also helped to understand cellular heterogeneity
with nanoscale precision, allowing for the in-depth study of specific
subcellular regions of interest and proteins. , , , In MIBI, the tissue is first stained with a set of antibodies labeled
with metal isotopes ( A). An ion beam rasters across the tissue, liberating ions
that feed into a Time-of-Flight Secondary Ion Mass Spectrometer (ToF-SIMS),
which separates the labels by mass ( A). Knowing which isotope label is bound
to which antibody, researchers can determine which target proteins
are present, and because the ion beam is rastered across the tissue,
multiplex images can be created. , To titrate
the optimal concentration of antibodies for MIBI, labeled antibodies
are screened in tissue microarrays. ToF-MS is utilized to separate
the marker labels for identification within the original tissue. These
images are partitioned to define cell–cell boundaries, which
allow cell phenotypes to be described as distances between signals. MIBI has been applied to study the tumor microenvironment,
identifying
cell phenotypes and analyzing spatial relationships across numerous
tumor types, such as the spatial relationships between immune and
cancer cells and the specific locations of immunoregulatory proteins. − Advantages of MIBI center around its high-parameter capabilities,
high sensitivity, and subcellular resolution. Recent advances for
MIBI using ion beam tuning targets image resolution at varying depths
via multiple z -direction scans, allowing for reconstruction
of 250 nm 3D images in the axial direction. But MIBI also has drawbacks: long imaging times and high cost. The
processing time of mass spectrometry data obtained from each pixel
and converting it into derivative spatial images also confines the
sample area. Nanotechnology used
in MIBI includes staining with metal-labeled
antibodies and data acquisition with the ToF-SIMS. A related nanotechnology
is the MIBIscope, a dynamic ToF-SIMS instrument that uses a gold liquid
metal ion gun as its primary ion source and produces a live image
of tissue topography using a secondary electron detector. The results of this study illustrate that MIBI,
using MIBIscope, achieves high sensitivity and resolution when studying
the spatial tumor immune landscape. 4.2.3 CO-Detection by IndEXing CODEX
is a multiplexed single-cell imaging technology that uses DNA-barcoded
antibodies for spatial proteomics ( B). In CODEX, target proteins in <10 μm-thick
FFPE or fresh frozen tissue sections are labeled with a large panel
of antibodies, each conjugated to a specific oligonucleotide barcode
( B). These
barcodes are detected, three at a time, in several rounds of hybridization
and imaging. In each round, complementary oligonucleotides, each labeled
with a fluorescent dye, bind to the barcodes, and the tissue is imaged.
Then, a gentle washing step removes the fluorescent dyes. This process
is repeated until all the barcodes—and the protein targets
they represent—are detected. CODEX
employs a cyclic fluidic device to automate the rounds of hybridization
and imaging, and it can be integrated
with any tricolor epifluorescence microscope ( B). CODEX has been used for cancer, autoimmunity,
and infection research. It is capable
of spatial resolution around 260 nm in the lateral (xy) and axial
(z) dimensions to create 3D images. But
CODEX requires special reagents and equipment and has several challenges
due to it being a fluorescence-based multiplexed imaging technology, including limitations associated with the microscope
system, background autofluorescence, and the rapid processing of large-scale
imaging data sets. Nanotechnology elements supporting CODEX include
the microfluidics system for repeated target probing, DNA-barcoded
antibodies, and the tricolor fluorescence microscope, such as the
Keyence BZ-X710 fluorescence microscope configured with 3 fluorescent
channels (FITC, Cy3, Cy5). Current antibody-based spatial proteomics
methods have some general limitations. First, the number of protein
targets is limited. MIBI can image up to 100 targets simultaneously
after performing SIMS, although commercially available products are
only capable of detecting around 40 targets. The CODEX workflow visualizes 50+ protein targets at the single-cell
level, and the updated CODEX multiplexed
imaging platform can detect 100 RNA labels. Second, antibody detection methods are subject to nonspecific binding,
epitope loss, and tissue degradation. Additional limitations for antibody
methods are related to the size of the capture region of interest
within the tissue slide, the time needed for fluorescent image acquisition,
and the cost of mass spectrometry detection. Furthermore, these methods
are based on relative spectral intensities and are only semiquantitative. − 4.2.4 Additional Techniques for Sample Processing
and Streamlined Spatial Workflow Nanotechnology plays a crucial
role in proteomics based on mass spectrometry, spanning various applications
and workflows. From sample pretreatment to mass spectrometry analysis,
nanoscale processing is integral, especially in single-cell analysis
(a cornerstone in several applications in the biomedical field). While
conventional proteomic methods based on mass spectrometry require
samples comprising more than thousands of cells to profile in-depth
identification, innovative platforms such as nanoPOTS offer enhanced
recovery and efficiency by minimizing sample volumes to less than
200 nL, allowing for the identification of ∼1500 to ∼3000
proteins from ∼10 to ∼140 cells, respectively. Despite advancements in imaging-based and MS-based
methods, integrating nanotechnology remains a challenge, particularly
in connecting imaging data with protein abundance measurements that
have single-cell resolution. One platform, scDVP, offers a promising
solution by combining three techniques: cellular phenotype image analysis
driven by artificial intelligence, automated single-nucleus and single-cell
LCM, and ultrahigh-sensitivity mass spectrometry coupled with a nanoelectrospray
ion source. DVP enables the discovery and characterization of cellular
interactions and states with the added advantage of analyzing the
subcellular structures and spatial dynamics of extracellular matrix. Spatial molecular profiling of complex
tissues is further enhanced by nanoscale staining techniques. For
instance, DISCO-MS combines whole organism clearing, image analysis
based on deep learning, robotic tissue extraction assisted by nanoboosters,
and ultrahigh-sensitivity mass spectrometry to yield proteome data
identifying more than 6,000 proteins across various clearing conditions. Other nanotechnologies combined with mass spectrometry
to allow high resolution spatial profiling include ProteomEx, which—using
manual microsampling without custom or special equipment—enabled
quantitative profiling of the spatial variability of the proteome
at ∼160 μm lateral resolution in mammalian tissues, equivalent
to the tissue volume of 0.61 nL. In
addition, a Microscaffold Assisted Spatial Proteomics (MASP) strategy,
based on spatially resolved microcompartmentalization of tissue using
a 3D-printed microscaffold, mapped more than 5000 cerebral proteins
in the mouse brain, including numerous important brain markers, transporters,
and regulators, that were identified by a trapping nano-LC and high-resolution
mass spectrometry system. Furthermore,
Mass Spectrometry Imaging (MSI) is another powerful tool for mapping
of the spatial distribution of proteins by label-free quantification
in biological tissues. For instance, Nanospray Desorption Electrospray
Ionization (nano-DESI) MSI generates multiply charged protein ions,
advantageous for the identification of top-down proteomics analysis,
achieving proteoform mapping in mouse tissues with a spatial resolution
down to 7 μm.
Laser Capture Microdissection Microscopy The imaging technique LCM microscopy has played a pivotal role
in understanding cellular heterogeneity with nanoscale precision,
offering the ability to study specific subcellular regions of interest,
facilitating in-depth examination of protein distributions and interactions. In general, LCM enables the targeted dissection
of individual cells or subcellular structures from complex biological
samples, which are then viewed with a microscope. And in spatial proteomics
specifically, LCM is instrumental for analyzing protein distribution
within cellular compartments, studying protein interactions, and unraveling
signaling pathways in the cellular microenvironments. By precisely isolating organelles from an otherwise
complex heterogeneous tissue section, researchers can analyze their
proteome composition, providing insight into their function and dynamics
in local cell populations without losing spatial information. Individually tailored therapies, guided by the molecular profiling
of biopsy samples, have traditionally relied on techniques such as
immunohistochemistry and bulk genomic analysis. While analyzing whole tissue specimens has shown promise
in predicting patient responses to chemotherapy, the process of extracting
these specimens introduces significant variability, which stems from the diverse cellular composition of tissues,
the uncertainty surrounding the proportion of tumor versus host cells
in the sample, and the loss of spatial information about cell types
within the tissue. Hence, LCM has emerged as an ideal technology to
dissect cells at nanoscale for tissue spatial profiling to allow for
proteomic analysis of specific cells or cell subsets while preserving
their spatial context. Given its ability to dissect nanometer
regions, LCM has been paramount
for understanding the spatial organization of tumor and immune cell
populations in tumor immunology and subclonal analysis, offering invaluable
insights into immunotherapy responses and the emergence of drug resistance. , Combining LCM with certain other nanotechnologies, such as Cytometry
by Time-of-Flight (CyToF) and the NanoString
nCounter gene expression system, , has offered
analysis of post-translational modifications and their functions in
signaling pathways. LCM-guided mass spectrometry methods are rapidly
advancing for discovery applications from region-of-interest to single-cell
resolution; and mass spectrometry experts are beginning to realize
the dream of robust, high-yield LCM single-cell tissue proteomics
from either the same thin-tissue section or precision-registered serial
sections from a variety of tissue types. LCM microscopy also offers
high-yield single-cell transfer to a nanochip, subcellular precision,
and high throughput. Despite its potential, LCM microscopy has
its drawbacks, including
the time-consuming nature of several steps: visualization, manual
cell selection, and collection processes. Typically, the choice of
cells for LCM analysis is made through direct microscopic observation,
but this approach is sometimes hindered by the poor image quality
resulting from the necessity of keeping tissues uncovered during the
process. But advancements in digital imaging, liquid coverslip chemistry,
artificial intelligence, and automation are anticipated to overcome
these challenges and revolutionize the field of tissue spatial profiling
in the future.
Multiplexed Ion Beam Imaging Like
LCM microscopy, MIBI has also helped to understand cellular heterogeneity
with nanoscale precision, allowing for the in-depth study of specific
subcellular regions of interest and proteins. , , , In MIBI, the tissue is first stained with a set of antibodies labeled
with metal isotopes ( A). An ion beam rasters across the tissue, liberating ions
that feed into a Time-of-Flight Secondary Ion Mass Spectrometer (ToF-SIMS),
which separates the labels by mass ( A). Knowing which isotope label is bound
to which antibody, researchers can determine which target proteins
are present, and because the ion beam is rastered across the tissue,
multiplex images can be created. , To titrate
the optimal concentration of antibodies for MIBI, labeled antibodies
are screened in tissue microarrays. ToF-MS is utilized to separate
the marker labels for identification within the original tissue. These
images are partitioned to define cell–cell boundaries, which
allow cell phenotypes to be described as distances between signals. MIBI has been applied to study the tumor microenvironment,
identifying
cell phenotypes and analyzing spatial relationships across numerous
tumor types, such as the spatial relationships between immune and
cancer cells and the specific locations of immunoregulatory proteins. − Advantages of MIBI center around its high-parameter capabilities,
high sensitivity, and subcellular resolution. Recent advances for
MIBI using ion beam tuning targets image resolution at varying depths
via multiple z -direction scans, allowing for reconstruction
of 250 nm 3D images in the axial direction. But MIBI also has drawbacks: long imaging times and high cost. The
processing time of mass spectrometry data obtained from each pixel
and converting it into derivative spatial images also confines the
sample area. Nanotechnology used
in MIBI includes staining with metal-labeled
antibodies and data acquisition with the ToF-SIMS. A related nanotechnology
is the MIBIscope, a dynamic ToF-SIMS instrument that uses a gold liquid
metal ion gun as its primary ion source and produces a live image
of tissue topography using a secondary electron detector. The results of this study illustrate that MIBI,
using MIBIscope, achieves high sensitivity and resolution when studying
the spatial tumor immune landscape.
CO-Detection by IndEXing CODEX
is a multiplexed single-cell imaging technology that uses DNA-barcoded
antibodies for spatial proteomics ( B). In CODEX, target proteins in <10 μm-thick
FFPE or fresh frozen tissue sections are labeled with a large panel
of antibodies, each conjugated to a specific oligonucleotide barcode
( B). These
barcodes are detected, three at a time, in several rounds of hybridization
and imaging. In each round, complementary oligonucleotides, each labeled
with a fluorescent dye, bind to the barcodes, and the tissue is imaged.
Then, a gentle washing step removes the fluorescent dyes. This process
is repeated until all the barcodes—and the protein targets
they represent—are detected. CODEX
employs a cyclic fluidic device to automate the rounds of hybridization
and imaging, and it can be integrated
with any tricolor epifluorescence microscope ( B). CODEX has been used for cancer, autoimmunity,
and infection research. It is capable
of spatial resolution around 260 nm in the lateral (xy) and axial
(z) dimensions to create 3D images. But
CODEX requires special reagents and equipment and has several challenges
due to it being a fluorescence-based multiplexed imaging technology, including limitations associated with the microscope
system, background autofluorescence, and the rapid processing of large-scale
imaging data sets. Nanotechnology elements supporting CODEX include
the microfluidics system for repeated target probing, DNA-barcoded
antibodies, and the tricolor fluorescence microscope, such as the
Keyence BZ-X710 fluorescence microscope configured with 3 fluorescent
channels (FITC, Cy3, Cy5). Current antibody-based spatial proteomics
methods have some general limitations. First, the number of protein
targets is limited. MIBI can image up to 100 targets simultaneously
after performing SIMS, although commercially available products are
only capable of detecting around 40 targets. The CODEX workflow visualizes 50+ protein targets at the single-cell
level, and the updated CODEX multiplexed
imaging platform can detect 100 RNA labels. Second, antibody detection methods are subject to nonspecific binding,
epitope loss, and tissue degradation. Additional limitations for antibody
methods are related to the size of the capture region of interest
within the tissue slide, the time needed for fluorescent image acquisition,
and the cost of mass spectrometry detection. Furthermore, these methods
are based on relative spectral intensities and are only semiquantitative. −
Additional Techniques for Sample Processing
and Streamlined Spatial Workflow Nanotechnology plays a crucial
role in proteomics based on mass spectrometry, spanning various applications
and workflows. From sample pretreatment to mass spectrometry analysis,
nanoscale processing is integral, especially in single-cell analysis
(a cornerstone in several applications in the biomedical field). While
conventional proteomic methods based on mass spectrometry require
samples comprising more than thousands of cells to profile in-depth
identification, innovative platforms such as nanoPOTS offer enhanced
recovery and efficiency by minimizing sample volumes to less than
200 nL, allowing for the identification of ∼1500 to ∼3000
proteins from ∼10 to ∼140 cells, respectively. Despite advancements in imaging-based and MS-based
methods, integrating nanotechnology remains a challenge, particularly
in connecting imaging data with protein abundance measurements that
have single-cell resolution. One platform, scDVP, offers a promising
solution by combining three techniques: cellular phenotype image analysis
driven by artificial intelligence, automated single-nucleus and single-cell
LCM, and ultrahigh-sensitivity mass spectrometry coupled with a nanoelectrospray
ion source. DVP enables the discovery and characterization of cellular
interactions and states with the added advantage of analyzing the
subcellular structures and spatial dynamics of extracellular matrix. Spatial molecular profiling of complex
tissues is further enhanced by nanoscale staining techniques. For
instance, DISCO-MS combines whole organism clearing, image analysis
based on deep learning, robotic tissue extraction assisted by nanoboosters,
and ultrahigh-sensitivity mass spectrometry to yield proteome data
identifying more than 6,000 proteins across various clearing conditions. Other nanotechnologies combined with mass spectrometry
to allow high resolution spatial profiling include ProteomEx, which—using
manual microsampling without custom or special equipment—enabled
quantitative profiling of the spatial variability of the proteome
at ∼160 μm lateral resolution in mammalian tissues, equivalent
to the tissue volume of 0.61 nL. In
addition, a Microscaffold Assisted Spatial Proteomics (MASP) strategy,
based on spatially resolved microcompartmentalization of tissue using
a 3D-printed microscaffold, mapped more than 5000 cerebral proteins
in the mouse brain, including numerous important brain markers, transporters,
and regulators, that were identified by a trapping nano-LC and high-resolution
mass spectrometry system. Furthermore,
Mass Spectrometry Imaging (MSI) is another powerful tool for mapping
of the spatial distribution of proteins by label-free quantification
in biological tissues. For instance, Nanospray Desorption Electrospray
Ionization (nano-DESI) MSI generates multiply charged protein ions,
advantageous for the identification of top-down proteomics analysis,
achieving proteoform mapping in mouse tissues with a spatial resolution
down to 7 μm.
Spatial Metabolomics Developed only
two decades ago, spatial metabolomics is another emerging field within
spatial omics that has enabled the identification of metabolites within
the spatial contexts of cells, tissues, organs, and organisms. Spatial metabolomics uses imaging technology
based on mass spectrometry, including Matrix-Assisted Laser Desorption/Ionization
(MALDI) MSI, , Desorption Electrospray Ionization
(DESI) MSI, , and SIMS imaging. 4.3.1 Matrix-Assisted Laser Desorption/Ionization-Mass
Spectrometry Imaging MALDI is an ionization method used in
conjunction with MSI for spatial metabolomics. MALDI requires a sample
preparation step that involves mixing the sample with a protective
low molecular weight matrix before spotting the mixture onto stainless
steel and allowing it to crystallize. Next, the samples are exposed
to a scanning laser, transforming solid components into charged gaseous
particles, ionizing the sample within a 10 μm-wide window ( A). Finally, mass
spectrometry detects these ions to define each metabolite image location
( A). MALDI mass spectrometry imaging has benefits, but
also limitations.
It has better metabolite coverage than other spatial metabolomics
methods, and consistently detects hundreds of metabolites at a spatial
resolution of around 10 μm. , , Even better, atmospheric pressure-MALDI developed
by Spengler’s group achieves a spatial resolution of 1.4 μm, yet the resolution is still worse than the
spatially resolved mass spectrometry approaches used for spatial proteomics. , The resolution often suffers when these approaches are applied to
metabolomics, to accommodate mass spectrometry instrument sensitivity
to low-abundance species from small areas. Spatial resolution and
sensitivity are inherently connected in spatial metabolomics techniques;
as the diameter of the laser spot decreases to achieve a finer spatial
resolution, the ion yield usually decreases as well. , Thus, researchers struggle to achieve finer spatial resolution while
maintaining adequate signal intensities. Other limitations of MALDI-MSI
include decreased resolution caused by delocation (when molecules
diffuse across or away from the tissue) and difficulty detecting low-weight
molecules (<600 Da). The matrix ions may have similar profiles
with multiple lower-weight metabolite ions, which can interfere with
the visualizations of select metabolites, defining a low-weight molecule
detection problem. , Although mass spectrometry
is the primary nanotechnology tool in
MALDI-MSI, nanomaterials have been used as alternative matrices to
improve various aspects of the method. Some researchers have increased
its sensitivity by adding nanoparticles to the low-density matrices,
which homogeneously concentrates targets into a narrow ring, similar
to the characteristic ring-like pattern observed after a drop of spilled
coffee evaporates (the “coffee ring effect”). Advantages
of sample concentrating using this method led to higher signals relative
to conventional MALDI, especially for analytes with greater mass-to-charge
ratios. Other researchers used nanoparticles
to enhance detection of triacylglycerols from lipid mixtures, which
are overwhelmed by other lipids in conventional MALDI detection. They
found a matrix containing citrate-capped gold nanoparticles enhanced
the cationization of triacylglycerols and effectively suppressed other
lipid signals, aiding triacylglycerol detection. And in glycomics studies, MALDI matrices containing graphene
nanosheets and carbon nanoparticles improved sensitivity in the detection
of native glycans, which ionize inefficiently. 4.3.2 Desorption Electrospray Ionization Mass
Spectrometry Imaging DESI is another ionization method used
in conjunction with MSI for spatial metabolomics. , DESI directly sprays samples with an electronically charged solution
for ionization, allowing desorption via a solvent stream under ambient
conditions ( B). As the DESI ionization probe scans
across the tissue sample, desorbed ions from the tissue enter the
mass spectrometer, which collects mass-to-charge ratio information
that can be correlated with the spatial distribution ( B). Unlike MALDI-MSI, which requires a sample preparation step, DESI-MSI
can provide spatial information about metabolites with little to no
sample preparation and does not need a matrix. It also does not suffer
from spatial assignment errors caused by sample movement. , But limited spatial resolution is a major challenge for DESI-MSI.
Most studies have reported spatial resolutions of only 50 to 200 μm
due to multiple factors such as solvent composition, capillary size,
and gas flow rate. And in addition to
these factors, resolution is also limited when balancing sensitivity
for low abundance species, as in MALDI-MSI. To improve the resolution,
Laskin et al. developed nano-DESI MSI, which used two fused silica
capillaries: a primary capillary that supplied solvent and maintained
a liquid bridge with the sample, and a secondary capillary that transported
the analyze to the mass spectrometer. , Next, they
developed an approach to control the distance between the nano-DESI
probe and the sample with shear force microscopy for MSI in constant-distance
mode, thereby achieving ∼11 μm spatial resolution in
images of mouse pancreatic islets. The
researchers also coupled a portable nano-DESI probe to a drift tube
ion mobility spectrometry-mass spectrometer, which allowed imaging
of drift time-separated ions of mice uterine tissues with a spatial
resolution less than 25 μm. An
ion mobility spectrometer recorded the drift time to determine the
ion mobility. An ion mobility spectrometer
recorded the drift time, meaning the time it takes for each ion to
reach a detector. In addition to its resolution issues, another challenge
of DESI-MSI is its sensitivity and specificity. This has been improved
by adding silver ions to the nano-DESI solvent, but only for analytes
containing double bonds. Nanotechnology
tools that support DESI-MSI include MSI, nanospray (ie, nano droplets),
and the DESI ionization probe. 4.3.3 Secondary Ion Mass Spectrometry Imaging SIMS is yet another ionization method used in conjunction with
MSI for spatial metabolomics. Rather than a laser or charged spray,
a primary ion beam scans across the sample, bombarding the surface
to induce ionization in an ultrahigh vacuum ( C). The ionization
of molecules at the sample surface generates a secondary beam of sputtered
ions of opposite polarity, which are transferred to a mass analyzer
( C). An advantage of this method is spatial resolution.
The primary ion beam is highly focused and impacts samples with an
orthogonal angle, as opposed to the oblique angle utilized for desorption
catalysts in MALDI and DESI. This degree of control enhances spatial
resolution, which can reach as low as 50 nm, making it possible to distinguish molecules between different organelles
of the same cell. But the high energy
ion beam (1 to 70 keV) is highly destructive, leading to the fragmentation
of biomolecules during desorption. As such, types of SIMS combining
high energy beams with high dose density (ie, > 10 13 ion/cm 2 as in dynamic SIMS) can only target monatomic
or diatomic
elements, limiting their application in spatial metabolomics. Types of SIMS employing ion beams of decreased
dose density (ie, static SIMS) still produce degradation, but to lesser
extent, and initial versions of these methods provided sufficient
resolution to quantify biomolecules up to 300 Da. To improve static SIMS, researchers have modulated the
primary ion beam to decrease sample destruction and increase ionization
efficiency, allowing for increased sensitivity to detect biomolecules
of lower concentration and higher molecular weight. Metal cluster
ion beams, composed of Au 3+ or Bi 3+ , expanded
the ability of SIMS to analyze low molecular weight biomolecules such
as metabolites and lipids, while small
cluster ion beams, composed of C 60 for example, enabled
the analysis of high molecular weight biomolecules such as peptides
and proteins. The range of mass resolution
was further increased by the introduction of gas cluster ion beams,
which improved the ionization efficiency of fully intact biomolecules
up to 100-fold compared to that achieved by metal or small cluster
ion beams. Despite these advances in
mass resolution and dynamic range, the diminished dose density of
static SIMS increases dispersion of the primary ion beam, decreasing
the spatial resolution to a range of 550 to 900 nm. , Nanotechnology tools in SIMS imaging include mass spectrometry,
ion beams, and the nanoparticle coating employed to enhance ionization
efficiency in metal-assisted SIMS.
Matrix-Assisted Laser Desorption/Ionization-Mass
Spectrometry Imaging MALDI is an ionization method used in
conjunction with MSI for spatial metabolomics. MALDI requires a sample
preparation step that involves mixing the sample with a protective
low molecular weight matrix before spotting the mixture onto stainless
steel and allowing it to crystallize. Next, the samples are exposed
to a scanning laser, transforming solid components into charged gaseous
particles, ionizing the sample within a 10 μm-wide window ( A). Finally, mass
spectrometry detects these ions to define each metabolite image location
( A). MALDI mass spectrometry imaging has benefits, but
also limitations.
It has better metabolite coverage than other spatial metabolomics
methods, and consistently detects hundreds of metabolites at a spatial
resolution of around 10 μm. , , Even better, atmospheric pressure-MALDI developed
by Spengler’s group achieves a spatial resolution of 1.4 μm, yet the resolution is still worse than the
spatially resolved mass spectrometry approaches used for spatial proteomics. , The resolution often suffers when these approaches are applied to
metabolomics, to accommodate mass spectrometry instrument sensitivity
to low-abundance species from small areas. Spatial resolution and
sensitivity are inherently connected in spatial metabolomics techniques;
as the diameter of the laser spot decreases to achieve a finer spatial
resolution, the ion yield usually decreases as well. , Thus, researchers struggle to achieve finer spatial resolution while
maintaining adequate signal intensities. Other limitations of MALDI-MSI
include decreased resolution caused by delocation (when molecules
diffuse across or away from the tissue) and difficulty detecting low-weight
molecules (<600 Da). The matrix ions may have similar profiles
with multiple lower-weight metabolite ions, which can interfere with
the visualizations of select metabolites, defining a low-weight molecule
detection problem. , Although mass spectrometry
is the primary nanotechnology tool in
MALDI-MSI, nanomaterials have been used as alternative matrices to
improve various aspects of the method. Some researchers have increased
its sensitivity by adding nanoparticles to the low-density matrices,
which homogeneously concentrates targets into a narrow ring, similar
to the characteristic ring-like pattern observed after a drop of spilled
coffee evaporates (the “coffee ring effect”). Advantages
of sample concentrating using this method led to higher signals relative
to conventional MALDI, especially for analytes with greater mass-to-charge
ratios. Other researchers used nanoparticles
to enhance detection of triacylglycerols from lipid mixtures, which
are overwhelmed by other lipids in conventional MALDI detection. They
found a matrix containing citrate-capped gold nanoparticles enhanced
the cationization of triacylglycerols and effectively suppressed other
lipid signals, aiding triacylglycerol detection. And in glycomics studies, MALDI matrices containing graphene
nanosheets and carbon nanoparticles improved sensitivity in the detection
of native glycans, which ionize inefficiently.
Desorption Electrospray Ionization Mass
Spectrometry Imaging DESI is another ionization method used
in conjunction with MSI for spatial metabolomics. , DESI directly sprays samples with an electronically charged solution
for ionization, allowing desorption via a solvent stream under ambient
conditions ( B). As the DESI ionization probe scans
across the tissue sample, desorbed ions from the tissue enter the
mass spectrometer, which collects mass-to-charge ratio information
that can be correlated with the spatial distribution ( B). Unlike MALDI-MSI, which requires a sample preparation step, DESI-MSI
can provide spatial information about metabolites with little to no
sample preparation and does not need a matrix. It also does not suffer
from spatial assignment errors caused by sample movement. , But limited spatial resolution is a major challenge for DESI-MSI.
Most studies have reported spatial resolutions of only 50 to 200 μm
due to multiple factors such as solvent composition, capillary size,
and gas flow rate. And in addition to
these factors, resolution is also limited when balancing sensitivity
for low abundance species, as in MALDI-MSI. To improve the resolution,
Laskin et al. developed nano-DESI MSI, which used two fused silica
capillaries: a primary capillary that supplied solvent and maintained
a liquid bridge with the sample, and a secondary capillary that transported
the analyze to the mass spectrometer. , Next, they
developed an approach to control the distance between the nano-DESI
probe and the sample with shear force microscopy for MSI in constant-distance
mode, thereby achieving ∼11 μm spatial resolution in
images of mouse pancreatic islets. The
researchers also coupled a portable nano-DESI probe to a drift tube
ion mobility spectrometry-mass spectrometer, which allowed imaging
of drift time-separated ions of mice uterine tissues with a spatial
resolution less than 25 μm. An
ion mobility spectrometer recorded the drift time to determine the
ion mobility. An ion mobility spectrometer
recorded the drift time, meaning the time it takes for each ion to
reach a detector. In addition to its resolution issues, another challenge
of DESI-MSI is its sensitivity and specificity. This has been improved
by adding silver ions to the nano-DESI solvent, but only for analytes
containing double bonds. Nanotechnology
tools that support DESI-MSI include MSI, nanospray (ie, nano droplets),
and the DESI ionization probe.
Secondary Ion Mass Spectrometry Imaging SIMS is yet another ionization method used in conjunction with
MSI for spatial metabolomics. Rather than a laser or charged spray,
a primary ion beam scans across the sample, bombarding the surface
to induce ionization in an ultrahigh vacuum ( C). The ionization
of molecules at the sample surface generates a secondary beam of sputtered
ions of opposite polarity, which are transferred to a mass analyzer
( C). An advantage of this method is spatial resolution.
The primary ion beam is highly focused and impacts samples with an
orthogonal angle, as opposed to the oblique angle utilized for desorption
catalysts in MALDI and DESI. This degree of control enhances spatial
resolution, which can reach as low as 50 nm, making it possible to distinguish molecules between different organelles
of the same cell. But the high energy
ion beam (1 to 70 keV) is highly destructive, leading to the fragmentation
of biomolecules during desorption. As such, types of SIMS combining
high energy beams with high dose density (ie, > 10 13 ion/cm 2 as in dynamic SIMS) can only target monatomic
or diatomic
elements, limiting their application in spatial metabolomics. Types of SIMS employing ion beams of decreased
dose density (ie, static SIMS) still produce degradation, but to lesser
extent, and initial versions of these methods provided sufficient
resolution to quantify biomolecules up to 300 Da. To improve static SIMS, researchers have modulated the
primary ion beam to decrease sample destruction and increase ionization
efficiency, allowing for increased sensitivity to detect biomolecules
of lower concentration and higher molecular weight. Metal cluster
ion beams, composed of Au 3+ or Bi 3+ , expanded
the ability of SIMS to analyze low molecular weight biomolecules such
as metabolites and lipids, while small
cluster ion beams, composed of C 60 for example, enabled
the analysis of high molecular weight biomolecules such as peptides
and proteins. The range of mass resolution
was further increased by the introduction of gas cluster ion beams,
which improved the ionization efficiency of fully intact biomolecules
up to 100-fold compared to that achieved by metal or small cluster
ion beams. Despite these advances in
mass resolution and dynamic range, the diminished dose density of
static SIMS increases dispersion of the primary ion beam, decreasing
the spatial resolution to a range of 550 to 900 nm. , Nanotechnology tools in SIMS imaging include mass spectrometry,
ion beams, and the nanoparticle coating employed to enhance ionization
efficiency in metal-assisted SIMS.
Spatial Epigenomics Epigenetic modifications
(to histones or DNA) control the state of chromatin, affecting DNA
accessibility; open chromatin allows gene expression to occur, while
closed chromatin prevents gene expression. Thus, these reversible
epigenetic modifications affect cellular function and explain biological
phenomena on the cellular level . Spatial epigenomics provides information
about epigenetic modifications across a population of cells or across
a tissue, revealing global epigenetic changes. Spatial epigenomics
methods include spatial-ATAC-seq and spatial-CUT&Tag. 4.4.1 Spatial Assay for Transposase-Accessible
Chromatin and RNA Using Sequencing Based on DBiT-seq, spatial-ATAC-seq
is a method that provides a genome-wide map of open and accessible
chromatin regions in intact tissue sections. Spatial-ATAC-seq utilizes the in situ Tn5 transposition chemistry and microfluidic deterministic barcoding as
described in DBiT (see ). Spatial-ATAC-seq employs
the Tn5 transposon to insert DNA oligomers into genome accessible
locations on fixed sections, , and adapters containing
a ligation linker are added to label the modified genome accessible
sites. Next, a grid of barcodes is overlaid using microchannels, and
these location coordinate markers are ligated to the Tn5-generated
oligos in successive rounds, creating a map of barcode combinations.
The array of barcodes is then imaged and overlaid onto tissue morphology,
revealing the locations of accessible chromatin. Then, reverse cross-linking
frees barcoded DNA fragments, creating a 2,500-tile spatial tissue
mosaic, which is amplified by Polymerase Chain Reaction (PCR) and
is the input for preparation of sequencing libraries. Spatial-ATAC-seq
has the ability to capture spatial epigenetic information within the
mouse and human brain. And the method
has also been applied to mouse embryos to delineate the epigenetic
landscape of organogenesis, and in human tonsils to map the epigenetic
state of different immune cells. Advantages
of the method are high spatial resolution, high yield, a high signal-to-noise
ratio, and a pixel size of 20 μm at the cellular level. A disadvantage of spatial-ATAC-seq is that,
unlike single-cell technologies, detected pixels may contain partial
nuclei or multiple nuclei, and thus signals may comprise multiple
cell types, which complicates data interpretation. The nanotechnology
underlying spatial-ATAC-seq is microfluidic deterministic barcoding. 4.4.2 Spatial Cleavage Under Targets and Tagmentation Spatial-CUT&Tag analyzes single-cell epigenomes by profiling
chromatin states in situ within tissue sections, and achieves an unbiased,
genome-wide epigenomic map . The approach is based on in situ microfluidic deterministic
barcoding, , Cleavage Under Targets and Tagmentation
(CUT&Tag) chemistry, , and next-generation
sequencing. In the first step of spatial-CUT&Tag, antibodies that
bind histone modification sites are added to the tissue, followed
by secondary antibodies that tether a pA-Tn5 transposome (a form of
fusion enzyme used for CUT&Tag) . The transposome complex is then activated,
ligating linkers and insertions into genomic sites adjacent to specific
histone marks defined by the primary antibodies . As in DBiT-seq and spatial-ATAC-seq, two sets of barcodes (A and
B), delivered by microchannels, are flowed over the tissue surface
. , , Ligation of these barcodes creates
a 2D labeling grid, which is then imaged to link the tissue morphology
to the spatial epigenomics map. The output assay signal is released
after cross-link reversal, producing a library for sequencing quantitation
. Spatial-CUT&Tag defined histone modifications
within the cortical layer of mouse brain during development, highlighting
the spatial patterning of cell types. Despite this utility, the method has resolution limitations, with
a current spatial resolution of 20 μm pixels. To achieve higher
precision in spatial multiomics profiling, one could combine reagents
of DBiT-seq and spatial-CUT&Tag for microfluidic in-tissue barcoding. A serpentine microfluidic channel or increasing
the number of barcodes could also help, reducing pixel size within
the epigenome mapping area. Using these two methods, Fan et al. achieved
simultaneous epigenomic and transcriptomic profiling on tissues from
embryonic and juvenile mouse brain and from adult human brain with
near–single-cell resolution. The
epigenome was evaluated using spatial-CUT&Tag–RNA-seq applied
to histone modifications, and mRNA expression was determined using
spatial-ATAC–RNA-seq Spatial epigenome–transcriptome
cosequencing overlays spatial multiomics signals, synergizing data
from each method and allowing for the examination of mechanistic relationships
across the central dogma of molecular biology. The nanotechnology
supporting spatial-CUT&Tag is in-tissue microfluidic deterministic
barcoding.
Spatial Assay for Transposase-Accessible
Chromatin and RNA Using Sequencing Based on DBiT-seq, spatial-ATAC-seq
is a method that provides a genome-wide map of open and accessible
chromatin regions in intact tissue sections. Spatial-ATAC-seq utilizes the in situ Tn5 transposition chemistry and microfluidic deterministic barcoding as
described in DBiT (see ). Spatial-ATAC-seq employs
the Tn5 transposon to insert DNA oligomers into genome accessible
locations on fixed sections, , and adapters containing
a ligation linker are added to label the modified genome accessible
sites. Next, a grid of barcodes is overlaid using microchannels, and
these location coordinate markers are ligated to the Tn5-generated
oligos in successive rounds, creating a map of barcode combinations.
The array of barcodes is then imaged and overlaid onto tissue morphology,
revealing the locations of accessible chromatin. Then, reverse cross-linking
frees barcoded DNA fragments, creating a 2,500-tile spatial tissue
mosaic, which is amplified by Polymerase Chain Reaction (PCR) and
is the input for preparation of sequencing libraries. Spatial-ATAC-seq
has the ability to capture spatial epigenetic information within the
mouse and human brain. And the method
has also been applied to mouse embryos to delineate the epigenetic
landscape of organogenesis, and in human tonsils to map the epigenetic
state of different immune cells. Advantages
of the method are high spatial resolution, high yield, a high signal-to-noise
ratio, and a pixel size of 20 μm at the cellular level. A disadvantage of spatial-ATAC-seq is that,
unlike single-cell technologies, detected pixels may contain partial
nuclei or multiple nuclei, and thus signals may comprise multiple
cell types, which complicates data interpretation. The nanotechnology
underlying spatial-ATAC-seq is microfluidic deterministic barcoding.
Spatial Cleavage Under Targets and Tagmentation Spatial-CUT&Tag analyzes single-cell epigenomes by profiling
chromatin states in situ within tissue sections, and achieves an unbiased,
genome-wide epigenomic map . The approach is based on in situ microfluidic deterministic
barcoding, , Cleavage Under Targets and Tagmentation
(CUT&Tag) chemistry, , and next-generation
sequencing. In the first step of spatial-CUT&Tag, antibodies that
bind histone modification sites are added to the tissue, followed
by secondary antibodies that tether a pA-Tn5 transposome (a form of
fusion enzyme used for CUT&Tag) . The transposome complex is then activated,
ligating linkers and insertions into genomic sites adjacent to specific
histone marks defined by the primary antibodies . As in DBiT-seq and spatial-ATAC-seq, two sets of barcodes (A and
B), delivered by microchannels, are flowed over the tissue surface
. , , Ligation of these barcodes creates
a 2D labeling grid, which is then imaged to link the tissue morphology
to the spatial epigenomics map. The output assay signal is released
after cross-link reversal, producing a library for sequencing quantitation
. Spatial-CUT&Tag defined histone modifications
within the cortical layer of mouse brain during development, highlighting
the spatial patterning of cell types. Despite this utility, the method has resolution limitations, with
a current spatial resolution of 20 μm pixels. To achieve higher
precision in spatial multiomics profiling, one could combine reagents
of DBiT-seq and spatial-CUT&Tag for microfluidic in-tissue barcoding. A serpentine microfluidic channel or increasing
the number of barcodes could also help, reducing pixel size within
the epigenome mapping area. Using these two methods, Fan et al. achieved
simultaneous epigenomic and transcriptomic profiling on tissues from
embryonic and juvenile mouse brain and from adult human brain with
near–single-cell resolution. The
epigenome was evaluated using spatial-CUT&Tag–RNA-seq applied
to histone modifications, and mRNA expression was determined using
spatial-ATAC–RNA-seq Spatial epigenome–transcriptome
cosequencing overlays spatial multiomics signals, synergizing data
from each method and allowing for the examination of mechanistic relationships
across the central dogma of molecular biology. The nanotechnology
supporting spatial-CUT&Tag is in-tissue microfluidic deterministic
barcoding.
Spatial Multiomics Spatial multiomics
tools combine detection of distinct biomolecular domains inside an
overlapping assay window and are the goal for the field. Vickovic
and Lötstedt developed and published a spatial multiomics platform
in 2022. Their automated and high-throughput
approach mapped regional RNA expression via sequencing-based biomarkers
and overlaid protein signals via DNA-barcoded antibodies or immunofluorescence
labels. This approach enabled the simultaneous spatial evaluation
of 96 sequencing-ready RNA libraries and 64 in situ protein targets
in 2 days. Another spatial multiomics
strategy uses the GeoMx Digital Spatial Profiler (DSP) from NanoString.
Unlike the spatial multiomics platform developed by Vickovic and Lötstedt,
which is limited to frozen tissue, the GeoMx DSP platform can be used
on FFPE tissue sections. And it is capable of spatial analysis profiling
for the whole transcriptome (18,000 RNA targets) and more than 96
proteins simultaneously. The GeoMx DSP, DBiT-seq, spatial-CITE-seq,
and MOSAICA are exciting methods that query spatial RNA and protein
expression. 4.5.1 GeoMx Digital Spatial Profiler The GeoMx DSP currently enables detection and imaging of RNA or protein
on either FFPE or fresh frozen whole tissue sections. The workflow
starts with staining of the prepared tissue (a 5 μm-thick section)
with antibodies and/or RNA attached to oligonucleotide tags (ie, barcodes)
via light-sensitive linkers ( A). Next, the GeoMx DSP automated microscope is used
to select regions of interest (in a varying size of 10 to 600 μm
in diameter). From the regions of interest, the microscope uses UV
light to cleave the oligonucleotide tags and collects the oligonucleotides
( A). Then,
the oligonucleotides are analyzed with the NanoString nCounter System
to quantify levels of specific proteins or RNAs ( A). Finally, data visualization
and analysis are performed. , Since the instrument
was launched in March 2019, many groups have utilized the GeoMx DSP
to study biomolecular expression in carcinomas, supporting its use
as a standard tool for oncology research. The use of equivalently
tagged oligonucleotides allows the system to interrogate numerous
RNA and protein biomarkers with higher throughput, and GeoMx DSP simultaneously
profiled six nodular and six infiltrative cancer samples, interrogating
1812 RNA targets. One study combining
Single-Cell RNA Sequencing (scRNA-seq) transcriptomes and spatial
transcriptomics identified Activin A as a paracrine-acting factor
that contributed to tumor progression. A current limitation of the GeoMx DSP is its inability to achieve
single-cell resolution for biomarker coexpression due to low protein
detection efficiency. , Nanotechnology tools that underscore
GeoMx DSP include RNA probes conjugated to fluorophores to interrogate
specific biomolecule targets, the imaging platform, and oligonucleotide
barcodes. 4.5.2 Deterministic Barcoding in Tissue for Spatial
Omics Sequencing DBiT-seq is a microfluidics-based platform
for analyzing spatial proteomics and transcriptomics, created by Fan
et al. , In DBiT-seq, tissue sections are exposed
to Antibody-Derived DNA Tags (ADTs) for protein detection. For RNA
detection and spatial analysis, a Polydimethylsiloxane (PDMS) microfluidic
chip is placed directly against the tissue slide ( B). Fifty parallel microfluidic
channels in the chip deliver a set of oligo-dT-tagged DNA barcodes
(set A), along with reverse transcriptase into lanes on the surface
of the tissue slide. Then another PDMS chip is placed on the tissue
slide, containing channels that deliver another set of oligo-dT-tagged
DNA barcodes (set B), along with DNA ligase to attach the B barcodes
to the A barcodes, creating a 2D mosaic of tissue pixels ( B). After imaging
by a microscope to define histological features, the cDNA is collected
and amplified to build a next-generation sequencing library. Finally,
proteins and mRNAs are detected by next-generation sequencing ( B). DBiT-seq has been applied to study mouse embryos to measure
a panel of 22 proteins and mRNA transcriptome. It has also been used for transcriptome sequencing within
embryonic and adult FFPE sections, at cellular resolution (25 μm
pixels) and >1000 gene per pixel coverage. Performing spatial whole transcriptome sequencing on FFPE
samples
without tissue dissociation or RNA exaction is one of the strengths
of DBiT-seq as an in-tissue barcoding approach. A weakness is that,
even though the pixel size of DBiT-seq can be scaled down to 10 μm,
it is still not capable of directly resolving single-cell spatial
mapping. Nanotechnologies exemplified in DBiT include antibody-derived
DNA tags for protein detection, subnanometer microfluidic chambers
for creating a spatial barcoding grid, and the optical or fluorescence
microscope for detection. 4.5.3 Spatial Co-Indexing of Transcriptomes and
Epitopes for Multi-Omics Mapping by Highly Parallel Sequencing Spatial-CITE-seq extends Coindexing of Transcriptomes and Epitopes
(CITE-seq) to the spatial dimension and enables multiplexed protein
and whole transcriptome comapping. The
first step of this method uses a cocktail of ∼200–300
ADTs to stain a paraformaldehyde-fixed tissue section. The ADTs include
a poly(A) tail, a Unique Molecular Identifier (UMI) tag, and a DNA
sequence that is specific for select antibodies. , As in DBiT, two sets of barcodes (A, row and B, column) are introduced
using different microfluidic chips for ligation in situ, creating
a 2D grid of tissue pixels to coindex all the protein epitopes and
the transcriptome. The collection of barcoded cDNAs is then amplified
by PCR and used for next-generation sequencing library preparation
for paired-end sequencing of both ADTs and cDNAs, allowing the spatial
reconstruction of protein and RNA coordinates. Spatial-CITE-seq incorporates
200 to 300 protein markers, substantially enhancing tissue mapping
at cellular resolution, and offers the highest multiplexing to date
for spatial protein profiling. Spatial-CITE-seq can profile 189 proteins
and whole transcriptomes in multiple mouse tissue types and 273 proteins
and the whole transcriptome in human tissues. In contrast, DBiT can only map 22 proteins at cellular
level resolution. One drawback for spatial-CITE-seq
is the lack of subcellular resolution, a limitation across most spatial
multiomics approaches. Other weaknesses for spatial-CITE-seq include
competition between ADTs and mRNAs for in-tissue reverse transcription,
and poor detection efficiency for low–copy-number transcripts.
Protein coverage is also limited to a panel of surface epitopes, excluding
intracellular or extracellular matrix proteins, which limits the information
provided in regard to protein signaling and function. The major nanotechnologies
that support spatial-CITE-seq are ADTs and microfluidic chips with
nanometer-wide lanes. 4.5.4 Multi-Omics Single-Scan Assay with Integrated
Combinatorial Analysis MOSAICA is a fluorescence-based spatial
multiomics imaging tool for simultaneous codetection of protein and
mRNA ( C). The MOSAICA procedure uses formalin-fixed tissues
or cells, which are incubated with a set of primary oligonucleotide
probes that bind to complementary regions (25 to 30 bases long) on
mRNAs and contain adapter sequences. After a wash step, a set of secondary
probes, each with a pair of fluorophores, binds to the adapters on
the primary probes ( C). Thus, each target has a specific combination of numerous
dual-label probes, with emission spectra and temporal lifetime signatures,
and these probe maps can be imaged using a fluorescent microscope.
Refining these raw data, bioinformatics-based tools direct the reconstruction
of images by spectral and fluorescence lifetime signal processing,
to allow individual RNAs among a pool of detected targets to be visualized
( C). These
images combine numerous target confocal detections, providing transcript
levels and localization within a 3D reconstructed image, which is
then laid over the microscopic tissue structure ( C). MOSAICA has been used for
10-plex mRNA expression in fixed colorectal cancer cells and multiplexed
mRNA analysis of clinical melanoma cells within FFPE tissues. At low cost, MOSAICA achieves high spatial resolution
(x-y resolution of 100 nm and z-spacing of 500 nm) in a 3D context
( C). As an
imaging-based tool, MOSAICA suffers from optical crowding, which limits
resolution for adjacent targets. But MOSAICA can be integrated with
other imaging modalities such as expansion, super-resolution, or multiphoton
microscopy to improve subcellular resolution and allow imaging of
highly scattering and autofluorescent tissues. − In the future, paired fluorescent probes may allow a barcoding strategy
based on Förster resonance energy transfer to tune the combinatorial
spectrum and lifetime readout. Nanotechnology
supporting MOSAICA includes DNA probes, fluorescent probes, and the
wide-field confocal microscope.
GeoMx Digital Spatial Profiler The GeoMx DSP currently enables detection and imaging of RNA or protein
on either FFPE or fresh frozen whole tissue sections. The workflow
starts with staining of the prepared tissue (a 5 μm-thick section)
with antibodies and/or RNA attached to oligonucleotide tags (ie, barcodes)
via light-sensitive linkers ( A). Next, the GeoMx DSP automated microscope is used
to select regions of interest (in a varying size of 10 to 600 μm
in diameter). From the regions of interest, the microscope uses UV
light to cleave the oligonucleotide tags and collects the oligonucleotides
( A). Then,
the oligonucleotides are analyzed with the NanoString nCounter System
to quantify levels of specific proteins or RNAs ( A). Finally, data visualization
and analysis are performed. , Since the instrument
was launched in March 2019, many groups have utilized the GeoMx DSP
to study biomolecular expression in carcinomas, supporting its use
as a standard tool for oncology research. The use of equivalently
tagged oligonucleotides allows the system to interrogate numerous
RNA and protein biomarkers with higher throughput, and GeoMx DSP simultaneously
profiled six nodular and six infiltrative cancer samples, interrogating
1812 RNA targets. One study combining
Single-Cell RNA Sequencing (scRNA-seq) transcriptomes and spatial
transcriptomics identified Activin A as a paracrine-acting factor
that contributed to tumor progression. A current limitation of the GeoMx DSP is its inability to achieve
single-cell resolution for biomarker coexpression due to low protein
detection efficiency. , Nanotechnology tools that underscore
GeoMx DSP include RNA probes conjugated to fluorophores to interrogate
specific biomolecule targets, the imaging platform, and oligonucleotide
barcodes.
Deterministic Barcoding in Tissue for Spatial
Omics Sequencing DBiT-seq is a microfluidics-based platform
for analyzing spatial proteomics and transcriptomics, created by Fan
et al. , In DBiT-seq, tissue sections are exposed
to Antibody-Derived DNA Tags (ADTs) for protein detection. For RNA
detection and spatial analysis, a Polydimethylsiloxane (PDMS) microfluidic
chip is placed directly against the tissue slide ( B). Fifty parallel microfluidic
channels in the chip deliver a set of oligo-dT-tagged DNA barcodes
(set A), along with reverse transcriptase into lanes on the surface
of the tissue slide. Then another PDMS chip is placed on the tissue
slide, containing channels that deliver another set of oligo-dT-tagged
DNA barcodes (set B), along with DNA ligase to attach the B barcodes
to the A barcodes, creating a 2D mosaic of tissue pixels ( B). After imaging
by a microscope to define histological features, the cDNA is collected
and amplified to build a next-generation sequencing library. Finally,
proteins and mRNAs are detected by next-generation sequencing ( B). DBiT-seq has been applied to study mouse embryos to measure
a panel of 22 proteins and mRNA transcriptome. It has also been used for transcriptome sequencing within
embryonic and adult FFPE sections, at cellular resolution (25 μm
pixels) and >1000 gene per pixel coverage. Performing spatial whole transcriptome sequencing on FFPE
samples
without tissue dissociation or RNA exaction is one of the strengths
of DBiT-seq as an in-tissue barcoding approach. A weakness is that,
even though the pixel size of DBiT-seq can be scaled down to 10 μm,
it is still not capable of directly resolving single-cell spatial
mapping. Nanotechnologies exemplified in DBiT include antibody-derived
DNA tags for protein detection, subnanometer microfluidic chambers
for creating a spatial barcoding grid, and the optical or fluorescence
microscope for detection.
Spatial Co-Indexing of Transcriptomes and
Epitopes for Multi-Omics Mapping by Highly Parallel Sequencing Spatial-CITE-seq extends Coindexing of Transcriptomes and Epitopes
(CITE-seq) to the spatial dimension and enables multiplexed protein
and whole transcriptome comapping. The
first step of this method uses a cocktail of ∼200–300
ADTs to stain a paraformaldehyde-fixed tissue section. The ADTs include
a poly(A) tail, a Unique Molecular Identifier (UMI) tag, and a DNA
sequence that is specific for select antibodies. , As in DBiT, two sets of barcodes (A, row and B, column) are introduced
using different microfluidic chips for ligation in situ, creating
a 2D grid of tissue pixels to coindex all the protein epitopes and
the transcriptome. The collection of barcoded cDNAs is then amplified
by PCR and used for next-generation sequencing library preparation
for paired-end sequencing of both ADTs and cDNAs, allowing the spatial
reconstruction of protein and RNA coordinates. Spatial-CITE-seq incorporates
200 to 300 protein markers, substantially enhancing tissue mapping
at cellular resolution, and offers the highest multiplexing to date
for spatial protein profiling. Spatial-CITE-seq can profile 189 proteins
and whole transcriptomes in multiple mouse tissue types and 273 proteins
and the whole transcriptome in human tissues. In contrast, DBiT can only map 22 proteins at cellular
level resolution. One drawback for spatial-CITE-seq
is the lack of subcellular resolution, a limitation across most spatial
multiomics approaches. Other weaknesses for spatial-CITE-seq include
competition between ADTs and mRNAs for in-tissue reverse transcription,
and poor detection efficiency for low–copy-number transcripts.
Protein coverage is also limited to a panel of surface epitopes, excluding
intracellular or extracellular matrix proteins, which limits the information
provided in regard to protein signaling and function. The major nanotechnologies
that support spatial-CITE-seq are ADTs and microfluidic chips with
nanometer-wide lanes.
Multi-Omics Single-Scan Assay with Integrated
Combinatorial Analysis MOSAICA is a fluorescence-based spatial
multiomics imaging tool for simultaneous codetection of protein and
mRNA ( C). The MOSAICA procedure uses formalin-fixed tissues
or cells, which are incubated with a set of primary oligonucleotide
probes that bind to complementary regions (25 to 30 bases long) on
mRNAs and contain adapter sequences. After a wash step, a set of secondary
probes, each with a pair of fluorophores, binds to the adapters on
the primary probes ( C). Thus, each target has a specific combination of numerous
dual-label probes, with emission spectra and temporal lifetime signatures,
and these probe maps can be imaged using a fluorescent microscope.
Refining these raw data, bioinformatics-based tools direct the reconstruction
of images by spectral and fluorescence lifetime signal processing,
to allow individual RNAs among a pool of detected targets to be visualized
( C). These
images combine numerous target confocal detections, providing transcript
levels and localization within a 3D reconstructed image, which is
then laid over the microscopic tissue structure ( C). MOSAICA has been used for
10-plex mRNA expression in fixed colorectal cancer cells and multiplexed
mRNA analysis of clinical melanoma cells within FFPE tissues. At low cost, MOSAICA achieves high spatial resolution
(x-y resolution of 100 nm and z-spacing of 500 nm) in a 3D context
( C). As an
imaging-based tool, MOSAICA suffers from optical crowding, which limits
resolution for adjacent targets. But MOSAICA can be integrated with
other imaging modalities such as expansion, super-resolution, or multiphoton
microscopy to improve subcellular resolution and allow imaging of
highly scattering and autofluorescent tissues. − In the future, paired fluorescent probes may allow a barcoding strategy
based on Förster resonance energy transfer to tune the combinatorial
spectrum and lifetime readout. Nanotechnology
supporting MOSAICA includes DNA probes, fluorescent probes, and the
wide-field confocal microscope.
AI and Machine Learning for Spatial Omics in
Relation to Nanotechnologies Dealing with spatial omics data
poses significant challenges because
of its high dimensionality and complexity and the need for precise
spatial and molecular information integration. Recently, more AI-based
pipelines and packages have enhanced spatial omics research by enabling
the efficient analysis and interpretation of complex biological data
at a better resolution. 5.1 Workflow of AI-Driven Spatial Omics Profiling The integration of AI into spatial omics follows a structured workflow,
which includes, in sequential order, data conversion and feature extraction,
data segmentation, spatial mapping of sequences, and data quantification
and analysis . Data conversion and feature extraction are critical for enabling
the algorithm to more effectively identify and leverage relevant patterns
and characteristics within the data, such as data indicating gene
expression or protein localization, in the context of tissue architecture.
Both 2D and 3D serial tissue sections can be computationally aligned
and reconstructed for a more detailed and comprehensive view of spatial
relationships in tissue architecture. Data segmentation is a key process that aims to partition image
data into distinct regions corresponding to biological structures,
such as cells, tissues, or subcellular components. In bioimaging and
computer vision, recent AI algorithms have advanced segmentation significantly. − Also, accurate segmentation allows researchers to map the spatial
distribution of molecular data, such as gene expression or protein
localization data, within tissues. Segmentation techniques have shown
promise in correlating spatial biomarkers with clinical outcomes.
For example, in the context of lung cancer, specific spatial patterns
identified through segmentation have been linked to patient responses
to treatment, highlighting the importance of segmentation in both
research and clinical applications. , Spatial mapping
of sequences refers to the process of linking molecular data, such
as gene sequences or metabolite profiles, to their precise spatial
locations within a biological sample. This approach is essential for
understanding the spatial organization of complex environments, such
as the Tumor Microenvironment (TME), and for understanding how molecular
features vary across different regions of tissue. A notable advancement
in this area is the development of the Single-Cell Spatially Resolved
Metabolic (scSpaMet) pipeline, which identifies a wide range of metabolites
alongside multiplex protein analysis. This detailed mapping is crucial
for studying the TME, as it can reveal how different cells and molecules
interact, influence tumor progression, or therapy response. Data quantification and analysis play critical
roles in accurately interpreting the complex molecular and cellular
information inherent in biological samples. For handling disaggregated
data, various computational tools have been developed, including many
popular packages such as Seurat, ScateR, Scanpy, and Monocle, allowing
researchers to explore omics data down to the single-cell level, which
facilitates insights into cellular composition, gene expression, and
spatial distribution. − On the other hand, a geometric deep learning
framework, PINNACLE, has been designed to generate context-aware protein
representations that leverage how proteins interact within their cellular
environment. AI techniques have rapidly
advanced the spatial omics field by enabling more efficient analysis
and breakthroughs in understanding tissue architecture and disease
mechanisms. As AI pipelines continue to evolve, they are beginning
to extend into the nanotechnology field, where similar techniques
can be applied to nanoscale biological structures, further pushing
the boundaries of molecular and cellular research. 5.2 AI for Nanometer-Scale Data Processing The advent of nanotechnology has allowed researchers to elucidate
biological structures and functions at a better resolution, often
at the nanometer scale. For example, super-resolution microscopy techniques,
such as Stimulated Emission Depletion (STED) microscopy, enable the visualization of biomolecules at
a resolution beyond the diffraction limit. However, the data generated
from such techniques are vast and complex, necessitating sophisticated
AI tools for their analysis. Deep learning models, particularly
Convolutional Neural Networks (CNNs), have proven highly effective
in handling high-resolution spatial omics data. CNNs are widely used
for image analysis tasks such as feature extraction, segmentation
and pattern recognition in large data sets. In this context, CNNs automate the detection of intricate spatial
features within biological tissues, providing insights into tissue
architecture that are otherwise difficult to uncover. The application
of U-Net, a CNN architecture specifically designed for biomedical
image segmentation, has further enhanced our ability to extract meaningful
data from nanometer-scale images. CNNs
play a crucial role in the structured workflow, especially in the
feature extraction and segmentation stages, where they capture spatial
features hierarchically from 2D images. Moreover, deep learning models enable the reconstruction of 3D structures
from serial tissue sections, advancing our understanding of spatial
relationships within tissues. AI also
supports the transition from 2D to 3D analysis, allowing for the reconstruction
of spatial relationships across tissue volumes. For example, K-means clustering is commonly used for sequence-to-location
mapping in 3D tissue profiling, helping to delineate cell populations
within tissue architectures. Advanced
AI models such as CODA have been developed to visualize 3D tissue
architecture in large tissue samples. These models enable the discovery
of cell types and their spatial organization in tissues such as the
skin, lungs, and liver. 5.3 Machine Learning for Multi-Omics Data Integration Integrating data across multiple omics layers—spatial transcriptomics,
proteomics, and metabolomics—is key to understanding the complex
interactions governing tissue function. Machine learning techniques,
such as random forests and Support Vector Machines (SVMs), facilitate
this integration by identifying correlations across data sets. These methods have been employed in spatial
omics to merge multiomics data, thereby revealing the intricate dynamics
of cellular environments and tissue-specific processes. Recent advancements in graph-based machine
learning approaches, such as Graph Convolutional Networks (GCNs),
have further improved our ability to integrate spatial information
with multiomics data. For instance, SpaGCN combines spatial transcriptomics
data with histological information to identify spatial domains and
variable genes in tissues. Additionally,
unsupervised learning approaches like Graph-Based Convolutional Networks
(DSTG) have been developed to deconvolute spatial transcriptomics
data, helping to uncover underlying biological relationships. 5.4 Nanotechnology and AI for Spatial Biomarker
Discovery The integration of nanotechnology with AI has revolutionized
spatial biomarker discovery, particularly in the context of disease
research. By leveraging nanostructure-labeled targets, such as DNA
nanostructures, and coupling them with AI-based analytical tools,
researchers can identify spatial patterns that are otherwise indiscernible.
For example, nanotechnology-enhanced methods combined with AI-driven
models have facilitated the detection of biomarkers in cancer tissues,
leading to a better understanding of tumor heterogeneity and progression. AI also plays a pivotal role in interpreting
spatial omics data obtained from nanodevices, such as nanoparticle-based
sensors and nanoscale imaging probes. These devices capture high-resolution
molecular information that, when processed by AI models, reveals spatially
resolved biomarker patterns linked to clinical outcomes. The application of deep learning techniques,
such as geometric deep learning used in PINNACLE, generates context-aware
protein representations, offering avenues for precision medicine.
Workflow of AI-Driven Spatial Omics Profiling The integration of AI into spatial omics follows a structured workflow,
which includes, in sequential order, data conversion and feature extraction,
data segmentation, spatial mapping of sequences, and data quantification
and analysis . Data conversion and feature extraction are critical for enabling
the algorithm to more effectively identify and leverage relevant patterns
and characteristics within the data, such as data indicating gene
expression or protein localization, in the context of tissue architecture.
Both 2D and 3D serial tissue sections can be computationally aligned
and reconstructed for a more detailed and comprehensive view of spatial
relationships in tissue architecture. Data segmentation is a key process that aims to partition image
data into distinct regions corresponding to biological structures,
such as cells, tissues, or subcellular components. In bioimaging and
computer vision, recent AI algorithms have advanced segmentation significantly. − Also, accurate segmentation allows researchers to map the spatial
distribution of molecular data, such as gene expression or protein
localization data, within tissues. Segmentation techniques have shown
promise in correlating spatial biomarkers with clinical outcomes.
For example, in the context of lung cancer, specific spatial patterns
identified through segmentation have been linked to patient responses
to treatment, highlighting the importance of segmentation in both
research and clinical applications. , Spatial mapping
of sequences refers to the process of linking molecular data, such
as gene sequences or metabolite profiles, to their precise spatial
locations within a biological sample. This approach is essential for
understanding the spatial organization of complex environments, such
as the Tumor Microenvironment (TME), and for understanding how molecular
features vary across different regions of tissue. A notable advancement
in this area is the development of the Single-Cell Spatially Resolved
Metabolic (scSpaMet) pipeline, which identifies a wide range of metabolites
alongside multiplex protein analysis. This detailed mapping is crucial
for studying the TME, as it can reveal how different cells and molecules
interact, influence tumor progression, or therapy response. Data quantification and analysis play critical
roles in accurately interpreting the complex molecular and cellular
information inherent in biological samples. For handling disaggregated
data, various computational tools have been developed, including many
popular packages such as Seurat, ScateR, Scanpy, and Monocle, allowing
researchers to explore omics data down to the single-cell level, which
facilitates insights into cellular composition, gene expression, and
spatial distribution. − On the other hand, a geometric deep learning
framework, PINNACLE, has been designed to generate context-aware protein
representations that leverage how proteins interact within their cellular
environment. AI techniques have rapidly
advanced the spatial omics field by enabling more efficient analysis
and breakthroughs in understanding tissue architecture and disease
mechanisms. As AI pipelines continue to evolve, they are beginning
to extend into the nanotechnology field, where similar techniques
can be applied to nanoscale biological structures, further pushing
the boundaries of molecular and cellular research.
AI for Nanometer-Scale Data Processing The advent of nanotechnology has allowed researchers to elucidate
biological structures and functions at a better resolution, often
at the nanometer scale. For example, super-resolution microscopy techniques,
such as Stimulated Emission Depletion (STED) microscopy, enable the visualization of biomolecules at
a resolution beyond the diffraction limit. However, the data generated
from such techniques are vast and complex, necessitating sophisticated
AI tools for their analysis. Deep learning models, particularly
Convolutional Neural Networks (CNNs), have proven highly effective
in handling high-resolution spatial omics data. CNNs are widely used
for image analysis tasks such as feature extraction, segmentation
and pattern recognition in large data sets. In this context, CNNs automate the detection of intricate spatial
features within biological tissues, providing insights into tissue
architecture that are otherwise difficult to uncover. The application
of U-Net, a CNN architecture specifically designed for biomedical
image segmentation, has further enhanced our ability to extract meaningful
data from nanometer-scale images. CNNs
play a crucial role in the structured workflow, especially in the
feature extraction and segmentation stages, where they capture spatial
features hierarchically from 2D images. Moreover, deep learning models enable the reconstruction of 3D structures
from serial tissue sections, advancing our understanding of spatial
relationships within tissues. AI also
supports the transition from 2D to 3D analysis, allowing for the reconstruction
of spatial relationships across tissue volumes. For example, K-means clustering is commonly used for sequence-to-location
mapping in 3D tissue profiling, helping to delineate cell populations
within tissue architectures. Advanced
AI models such as CODA have been developed to visualize 3D tissue
architecture in large tissue samples. These models enable the discovery
of cell types and their spatial organization in tissues such as the
skin, lungs, and liver.
Machine Learning for Multi-Omics Data Integration Integrating data across multiple omics layers—spatial transcriptomics,
proteomics, and metabolomics—is key to understanding the complex
interactions governing tissue function. Machine learning techniques,
such as random forests and Support Vector Machines (SVMs), facilitate
this integration by identifying correlations across data sets. These methods have been employed in spatial
omics to merge multiomics data, thereby revealing the intricate dynamics
of cellular environments and tissue-specific processes. Recent advancements in graph-based machine
learning approaches, such as Graph Convolutional Networks (GCNs),
have further improved our ability to integrate spatial information
with multiomics data. For instance, SpaGCN combines spatial transcriptomics
data with histological information to identify spatial domains and
variable genes in tissues. Additionally,
unsupervised learning approaches like Graph-Based Convolutional Networks
(DSTG) have been developed to deconvolute spatial transcriptomics
data, helping to uncover underlying biological relationships.
Nanotechnology and AI for Spatial Biomarker
Discovery The integration of nanotechnology with AI has revolutionized
spatial biomarker discovery, particularly in the context of disease
research. By leveraging nanostructure-labeled targets, such as DNA
nanostructures, and coupling them with AI-based analytical tools,
researchers can identify spatial patterns that are otherwise indiscernible.
For example, nanotechnology-enhanced methods combined with AI-driven
models have facilitated the detection of biomarkers in cancer tissues,
leading to a better understanding of tumor heterogeneity and progression. AI also plays a pivotal role in interpreting
spatial omics data obtained from nanodevices, such as nanoparticle-based
sensors and nanoscale imaging probes. These devices capture high-resolution
molecular information that, when processed by AI models, reveals spatially
resolved biomarker patterns linked to clinical outcomes. The application of deep learning techniques,
such as geometric deep learning used in PINNACLE, generates context-aware
protein representations, offering avenues for precision medicine.
Challenges and Opportunities Ahead Methods and techniques from the nanotechnology field have shaped
spatial omics to allow insight into biological systems on the nanometer
scale, providing analyses unfeasible before advances in nanotechnological
methods. The intrinsic properties of nanomaterials, coupled with methodologies
using nanodevices and nanobiotechnological tools, enable precise cellular
and subcellular labeling and sequencing. The goal of spatial omics
is, ultimately, to provide real-time, in situ multiomics measurement
of biomolecules at a resolution necessitated by the specific application,
which for some applications, may be nanometer resolution. But as evident
through this review, inherent hurdles within spatial omics must be
resolved to accomplish this goal. Relying on PCR-based nucleotide
amplification for sequencing in
transcriptomics must be overcome to prevent amplification bias and
spatial context loss while improving dynamic range and throughput.
While DNA nanoballs have helped to an extent, they do not improve
dynamic range or eliminate sequence bias, both of which are inherent
in various methods of nucleic acid amplification. To date, only de-novo sequencing has eliminated amplification
bias due to its specific chemistry and is both highly sensitive for
single molecule nucleic acid sequencing and independent of PCR. Consequently, other nanotechnologies, most
notably nanopore sequencing, , have flourished in
this area. Initially developed for the stochastic sensing of ions
and small molecules, nanopores act as single-molecule biosensors,
facilitating ultrasensitive DNA sequencing in comparison to other
label-free biomolecular sensing techniques. − The commercially available nanopore sequencer, MinION, is a nanodevice
that employs a protein pore residing in an electrically resistant
polymer membrane, exemplifying lab-on-a-chip potential. − And rapid advances in nanopore technologies for sequencing long
molecules of DNA and RNA have helped investigate genomes, transcriptomes,
epigenomes, and epitranscriptomes. , Future nanopore
developments may enable miniaturized RNA sequencing via geometric
sensitive current disruptions, applied in direct contact with tissue,
improving detection sensitivity and accuracy at subcellular resolution. , Synergizing these nanopores with imaging tools could help to further
advance spatial transcriptomics and spatial epigenomics. Additionally,
nanopores may prove useful for spatial proteomics, for de novo protein
and peptide sequencing. While
current research trajectories aim to combine spatial proteomics
with nanotechnology, spatial proteomics’ reliance on antibody
binding for protein detection defines an inherent limit for protein
or peptide coverage. The most widely used instrument in proteomics,
mass spectrometry, cannot analyze peptide sequences directly without
relying on antibodies. One promising avenue is using nanomaterial
matrices to enhance MALDI signals, which can dramatically improve
MALDI resolution. Improving the sensitivity
of MALDI for spatial proteomics would allow for detection that is
free from artifacts due to antibody-based selection or detection and
would thereby increase the breadth of protein coverage in a step toward
whole proteome analysis. Enhancing metabolomic and lipidomic coverage
is also possible by similarly applying nanomaterial matrices to enhance
SIMS signals. In terms of resolution
for protein identification, liquid chromatography-mass
spectrometry (LC-MS) is superior to all other techniques, but an inherent
limitation prevents the application of LC-MS to spatial proteomics:
low sample throughput resulting from lengthy processing times. Analyzing
the spatial transcriptome—2500 assay points within a 6.5 mm
by 6.5 mm section—can be completed in a reasonable time frame.
But comparable proteomic analysis, with similar coverage by LC-MS
and assuming 30 min per protein target, would take two months for
a single histological section, not including sample pretreatment.
Microfluidic platforms with embedded nanoscale features significantly
increase the speed of sample preprocessing while automating batch
sample processing and reducing scale for enhanced resolution. Microfluidics
technology is already used in single-cell proteomics, and it is only a matter of time before this
technology is applied to spatial proteomic applications. Microfluidic
sample processing would still have the problem of lengthy LC-MS assay
times, but barcoding technologies could multiplex protein labeling
and facilitate analysis of up to 16 samples at a time. Among nanobiotechnologies,
barcoding has had the greatest impact, notably for its ability to
analyze >20,000 barcodes simultaneously. EEL FISH, for example,
exploits
combinatorial barcodes that can label thousands of targets after only
16 rounds of detection, representing a rudimentary example of nanocomputing.
Proteomics methods that similarly analyze more than 20,000 barcoding
tags simultaneously, as in transcriptomics, would solve the detection
throughput bottleneck for this biomolecular domain. Once nanotechnology
and nanomaterials have been fully utilized
to increase the resolution and data throughput of spatial-omics studies,
we can anticipate an exponential increase in the amount of generated
data. Thus, analyzing these vast data sets while combining the results
of multiomics studies in coherent ways represents a corollary challenge
for spatial multiomics. Most current spatial omics focus on the 2D
level. The ability to integrate data from multiple planes and time
periods to create 3D images represents a wider challenge but would
also be a major breakthrough for the future of spatial omics. SIMS-mediated
approaches toward spatially resolved 3D metabolomics are already in
development, , and it will be exciting to see
how lessons learned from these technologies can be adapted to other
spatial omics domains. Another domain that needs to be addressed
by spatial omics tools
is the secretome, circulating molecules including proteins, lipids,
and vesicles secreted by cells. Spatial secretomics has barely moved
forward due to a lack of tools that can accurately locate and discriminate
between internal and external cellular components. Nanoscale liposomes
have been used to characterize the nucleic acid composition inside
extracellular vesicles, and it would
be exciting to study how these liposomes could be used to analyze
extracellular vesicle components on sections, including the spatial
distribution of these components. Extracellular vesicles can be used
as diagnostic markers to reveal information underlying disease development,
such as spatial interactions between pathogens and immune cells in
infectious diseases, signals predicting tumor cell metastasis in cancer,
and early changes in lesions to predict severity and progression in
neurological diseases. As spatial omics methods develop, the
resulting data will increase
in complexity, and more AI and machine learning studies will be required.
Integrating nanotechnology-enhanced methods, combined with AI-driven
models, will benefit the spatial omics results to achieve nanoresolution.
AI and machine learning are necessary for fully utilizing nanotechnology
and nanomaterials in spatial-omics studies. In summary, the
development of research methods for spatial multiomics
is flourishing with the support of nanotechnology, but bottlenecks
prevent in situ, real-time multiomics from being achieved. Increasing
the sensitivity, breadth of coverage, and resolution of spatial omics
tools by leveraging emerging nanotechnologies will certainly help
to improve spatial omics. These powerful tools are helping biomedical
science to further elucidate physiological structure and function,
and they provide superior diagnostic and therapeutic tools for disease
research.
|
How the forensic multidisciplinary approach can solve a fatal dog pack attack | 878eabfc-2a84-431d-a3fc-16b049ce34a7 | 11525263 | Forensic Medicine[mh] | Dog attacks, specifically dog pack attacks, represent a fatal risk because of the severe injuries that can result in death of the victim . Non-fatal bites tend to be found, as in our case, on the lower limbs and face . The concept of dog pack attacks was described for the first time in 1958 ; resulting injuries were described as a combination of biting, clawing, and crushing forces resulting in wounds with a characteristic pattern of punctures, lacerations, and avulsions of skin and soft tissues [ , – ]. These attacks are fortunately rare in our society, but when they happen, fatal results may occur. In Europe, deaths caused by dog attack have an incidence of 0.009 per 100,000 inhabitants, a little bit higher than Australia (0.004), but is comparable to estimates from the USA (0.011) and Canada (0.007) . Individual implication grade analysis in a dog pack attack is extremely difficult to solve. A careful forensic multidisciplinary investigation was conducted by authors, including a detailed analysis of the death scene, the victim’s body damages, and the animals suspected of the attack. This article presents the first described and solved fatal Cane Corso dog pack attack case, due to the dysgnathia conditions of some of the involved dogs.
Analysis of the death scene A 61-year-old man was found dead in an agricultural plot. The victim was occasionally there to pick olives for the owner of the agricultural plot. When officers came to investigate the crime scene, a dark dog that had been guarding the victim’s body wandered away and disappeared through a hole in a wire mesh (Fig. A). The victim’s upper clothes were torn, and pieces of his sweater and shirt were found around him (Fig. B). His trousers had been found pulled down to the ankles (Fig. A), further indicating that he had been dragged. The victim’s first on-site examination revealed face and abdomen injuries, as well as severe injuries of arms and left knee (Fig. B). During the “on-the-spot” investigation, six black Cane Corso dogs were found in the area surrounding the death scene. Four dogs belonged to one owner, and they were tagged as Dog_1, Dog_2, Dog_3, and Dog_4. Another person, living near the crime scene area, owned the other two dogs (Dog_5 and Dog_6). In Dog_1’s mouth, a mixed saliva-blood substance was found and collected for future comparisons. All the six dogs were taken to the kennel for further investigation. Due to the complexity of the injuries and the number of seized dogs, the prosecutor asked to determine the dogs’ involvement in the killing of the victim. Autopsy findings and victim’s bite lesion analysis A general examination of the undressed body revealed traumatic wounds caused by several and deep dog bites. It was found that the deepest indentations, present in the upper right limb at the brachialis level, had caused losses of muscle-cutaneous substance at depths ranging from 2.5 to 4 cm. The right brachial artery was found slashed, suggesting heavy and massive blood loss, which could be the main cause of the victim’s death. Before proceeding with autopsy, fourteen swabs were taken around the victim’s skin area near the dog’s bite marks in order to obtain dog saliva and DNA. The α-amylase test for the salivary enzyme presence could not be performed in victim’s wounds because salivary amylase is lacking or at very low abundance in mammalian carnivores such as cats and dogs . Regardless, the authors proceeded with swabbing the clearest bite-marks. Cardiac peripheral blood was taken to obtain the victim’s DNA reference sample. The biting victim’s clothes were packed and stored to be analyzed in a forensic genetic laboratory. Subsequently, the “right Montgomery’s areola,” the “upper left hypochondriac area,” and the “left mid-tibial area” cutaneous bite injuries were selected for further specific analysis. The three anatomic areas were respectively numbered as AA1, AA2, and AA3 (Table ), and their resulting bite imprints (B1, B2, and B3) were produced. A self-curing methacrylic resin ring was used to surround and border the damaged area. A high-viscosity addition silicone was added to completely fill the containment ring and remain in place until the polymerization was complete, and the bite silicone mark was obtained. After the silicone mark was removed (Fig. ), it was sent to an odontological laboratory for a casting class IV hard plaster impression. Each anatomical area, AA1, AA2, and AA3, was again bordered with self-curing methacrylic resin, and the obtained ring was adhered to the skin with cyanoacrylate-based compound. Afterwards, a clean incision was made with a 22-blade scalpel from the skin to the muscle following the methacrylic resin ring border. The resulting muscle-cutaneous tissue containing the bite lesion was stabilized to the methacrylic resin ring by means of single, circular sutures for further comparisons. This activity was carried out in accordance with the guidelines of the American Board of Forensic Odontology (ABFO) , always taking care to avoid distortion of the tissue in order to photographically preserve the color and depth of the underlying bruises. Furthermore, cutaneous muscle samples were fixed in a solution of 5 mL 40% formaldehyde, 5 mL 99.8% glacial acetic acid, and 90 mL 7% ethanol. The samples were then stored for a period of 1 week after which they were removed from the formaldehyde bath and monitored for changes in dimension and stability, as well as their adherence or loss to the rings. The examined impressions of the dental arches on the skin were subjected to metric evaluations for subsequent comparative purposes. Investigation of dogs’ bite marks analysis The six Cane Corso dogs (Fig. ) were taken to the kennel and subjected to judicial seizure. An initial analysis of the two dog groups showed that Dog_3 and Dog_4 were Dog_1 and Dog_2’s puppies, in adolescent stage (6–18 months). Whereas, the other two dogs were not related to the first four. After veterinary microchip recognitions and dog anesthetization, the oral mucosa cells were swabbed in order to obtain each dog’s reference sample, and the upper and lower dental impression were taken by modified steel dental tray. Before each dog was awakened, the dental formula was calculated (Table ). Three dogs of the first owner (Dog_2, Dog_3, and Dog_4) had missing teeth. In addition, three of the six dogs (Dog_2, Dog_4, and Dog_6) exhibited a third-class malocclusion; this alteration is known as dysgnathia, where the lower jaw appears to be advanced compared to the upper jaw. Six sodium alginate canine dental impressions from Dog_1 to Dog_6 were obtained and numbered as follows: B1mg, B2mg, B3mg, B4mg, B5mg, and B6mg. A detailed photograph and analysis of each dog’s jaw was taken following ABFO recommendations . Inter-canine distances and canine heights on each cast were also recorded using a digital caliper (Table ). A piece of rose and green striped shirt was collected from Dog_2 excrement and preserved for further comparisons. Each dog dental cast was compared to the victim’s lesions by mechanical projection and by using DentalPrint © software . Dog DNA genotyping The bitten victim’s clothes (e.g., blue jeans) together with dogs’ reference DNA were used to obtain the dogs’ DNA genetic profiles. DNA extraction was carried out using the QIAamp ® DNA Mini Kit . A preliminary amplification was performed on the extracts with universal primers for the canine mitochondrial cytochrome b gene [ – ]. This amplification provided information on the animal species that was eventually identified through sampling. The Dog DNA STR amplification was carried out using a ThermoFisher™ Canine STR panel 1.1 kit , composed of 18 autosomal loci and the amylogenic locus for sex determination. These regions are recommended by the International Society for Animal Genetics (ISAG) . Human DNA genotyping The victim’s clothes collected during the autopsy were initially observed by forensic lights. For presumptive trace detection of bloodstains , the Roche ® tetramethylbenzidine (TMB) Combur 3 Test ® was used. The samples that tested positive to the Combur 3 Test ® reaction were subjected to the human blood detection by Bluestar ® OBTI Immunochromatographic test . All the samples that gave a positive result in the Bluestar ® OBTI test, the blood collected from the Dog_1’s mouth and the victim’s control cardiac blood (taken during the autopsy), were used for the human DNA extraction using the QIAamp ® DNA Mini Kit . Human DNA quantification was conducted by the Quantifiler ® Human kit . Human STR amplification were conducted on a ThermoFisher GeneAmp ® PCR System 9700 amplifier, and STR amplification was obtained using the GlobalFiler™ PCR Amplification Kit. The amplified products were separated into capillary electrophoresis with the 3500 Series Genetic Analyzer sequencer by Applied Biosystems™. Alleles were assigned by GeneMapper ID-X Software v1.1.2. C
A 61-year-old man was found dead in an agricultural plot. The victim was occasionally there to pick olives for the owner of the agricultural plot. When officers came to investigate the crime scene, a dark dog that had been guarding the victim’s body wandered away and disappeared through a hole in a wire mesh (Fig. A). The victim’s upper clothes were torn, and pieces of his sweater and shirt were found around him (Fig. B). His trousers had been found pulled down to the ankles (Fig. A), further indicating that he had been dragged. The victim’s first on-site examination revealed face and abdomen injuries, as well as severe injuries of arms and left knee (Fig. B). During the “on-the-spot” investigation, six black Cane Corso dogs were found in the area surrounding the death scene. Four dogs belonged to one owner, and they were tagged as Dog_1, Dog_2, Dog_3, and Dog_4. Another person, living near the crime scene area, owned the other two dogs (Dog_5 and Dog_6). In Dog_1’s mouth, a mixed saliva-blood substance was found and collected for future comparisons. All the six dogs were taken to the kennel for further investigation. Due to the complexity of the injuries and the number of seized dogs, the prosecutor asked to determine the dogs’ involvement in the killing of the victim.
A general examination of the undressed body revealed traumatic wounds caused by several and deep dog bites. It was found that the deepest indentations, present in the upper right limb at the brachialis level, had caused losses of muscle-cutaneous substance at depths ranging from 2.5 to 4 cm. The right brachial artery was found slashed, suggesting heavy and massive blood loss, which could be the main cause of the victim’s death. Before proceeding with autopsy, fourteen swabs were taken around the victim’s skin area near the dog’s bite marks in order to obtain dog saliva and DNA. The α-amylase test for the salivary enzyme presence could not be performed in victim’s wounds because salivary amylase is lacking or at very low abundance in mammalian carnivores such as cats and dogs . Regardless, the authors proceeded with swabbing the clearest bite-marks. Cardiac peripheral blood was taken to obtain the victim’s DNA reference sample. The biting victim’s clothes were packed and stored to be analyzed in a forensic genetic laboratory. Subsequently, the “right Montgomery’s areola,” the “upper left hypochondriac area,” and the “left mid-tibial area” cutaneous bite injuries were selected for further specific analysis. The three anatomic areas were respectively numbered as AA1, AA2, and AA3 (Table ), and their resulting bite imprints (B1, B2, and B3) were produced. A self-curing methacrylic resin ring was used to surround and border the damaged area. A high-viscosity addition silicone was added to completely fill the containment ring and remain in place until the polymerization was complete, and the bite silicone mark was obtained. After the silicone mark was removed (Fig. ), it was sent to an odontological laboratory for a casting class IV hard plaster impression. Each anatomical area, AA1, AA2, and AA3, was again bordered with self-curing methacrylic resin, and the obtained ring was adhered to the skin with cyanoacrylate-based compound. Afterwards, a clean incision was made with a 22-blade scalpel from the skin to the muscle following the methacrylic resin ring border. The resulting muscle-cutaneous tissue containing the bite lesion was stabilized to the methacrylic resin ring by means of single, circular sutures for further comparisons. This activity was carried out in accordance with the guidelines of the American Board of Forensic Odontology (ABFO) , always taking care to avoid distortion of the tissue in order to photographically preserve the color and depth of the underlying bruises. Furthermore, cutaneous muscle samples were fixed in a solution of 5 mL 40% formaldehyde, 5 mL 99.8% glacial acetic acid, and 90 mL 7% ethanol. The samples were then stored for a period of 1 week after which they were removed from the formaldehyde bath and monitored for changes in dimension and stability, as well as their adherence or loss to the rings. The examined impressions of the dental arches on the skin were subjected to metric evaluations for subsequent comparative purposes.
The six Cane Corso dogs (Fig. ) were taken to the kennel and subjected to judicial seizure. An initial analysis of the two dog groups showed that Dog_3 and Dog_4 were Dog_1 and Dog_2’s puppies, in adolescent stage (6–18 months). Whereas, the other two dogs were not related to the first four. After veterinary microchip recognitions and dog anesthetization, the oral mucosa cells were swabbed in order to obtain each dog’s reference sample, and the upper and lower dental impression were taken by modified steel dental tray. Before each dog was awakened, the dental formula was calculated (Table ). Three dogs of the first owner (Dog_2, Dog_3, and Dog_4) had missing teeth. In addition, three of the six dogs (Dog_2, Dog_4, and Dog_6) exhibited a third-class malocclusion; this alteration is known as dysgnathia, where the lower jaw appears to be advanced compared to the upper jaw. Six sodium alginate canine dental impressions from Dog_1 to Dog_6 were obtained and numbered as follows: B1mg, B2mg, B3mg, B4mg, B5mg, and B6mg. A detailed photograph and analysis of each dog’s jaw was taken following ABFO recommendations . Inter-canine distances and canine heights on each cast were also recorded using a digital caliper (Table ). A piece of rose and green striped shirt was collected from Dog_2 excrement and preserved for further comparisons. Each dog dental cast was compared to the victim’s lesions by mechanical projection and by using DentalPrint © software .
The bitten victim’s clothes (e.g., blue jeans) together with dogs’ reference DNA were used to obtain the dogs’ DNA genetic profiles. DNA extraction was carried out using the QIAamp ® DNA Mini Kit . A preliminary amplification was performed on the extracts with universal primers for the canine mitochondrial cytochrome b gene [ – ]. This amplification provided information on the animal species that was eventually identified through sampling. The Dog DNA STR amplification was carried out using a ThermoFisher™ Canine STR panel 1.1 kit , composed of 18 autosomal loci and the amylogenic locus for sex determination. These regions are recommended by the International Society for Animal Genetics (ISAG) .
The victim’s clothes collected during the autopsy were initially observed by forensic lights. For presumptive trace detection of bloodstains , the Roche ® tetramethylbenzidine (TMB) Combur 3 Test ® was used. The samples that tested positive to the Combur 3 Test ® reaction were subjected to the human blood detection by Bluestar ® OBTI Immunochromatographic test . All the samples that gave a positive result in the Bluestar ® OBTI test, the blood collected from the Dog_1’s mouth and the victim’s control cardiac blood (taken during the autopsy), were used for the human DNA extraction using the QIAamp ® DNA Mini Kit . Human DNA quantification was conducted by the Quantifiler ® Human kit . Human STR amplification were conducted on a ThermoFisher GeneAmp ® PCR System 9700 amplifier, and STR amplification was obtained using the GlobalFiler™ PCR Amplification Kit. The amplified products were separated into capillary electrophoresis with the 3500 Series Genetic Analyzer sequencer by Applied Biosystems™. Alleles were assigned by GeneMapper ID-X Software v1.1.2. C
Autopsy results During the analysis of the victim’s head, the only interesting element that appeared was a subgaleal ecchymosis indicating a contusion compatible with the scenario. Most of the victim’s body had multiple superficial and deep tissue lacerations. Specifically, the right brachial artery was found slashed. The fatal blood loss was found in the right limb correspondence, where the dogs had bitten and slashed the victim’s tissues massively and repeatedly. In general, the upper limbs were repeatedly bitten, a condition likely resulting from the victim’s defensive posture. The subsequent thoraco-abdominal section of the cadaver did not reveal anything of significance from a traumatic point of view. Anatomopathological studies of the victim’s organs and tissue fragments revealed bilateral calcific coronary atherosclerosis. The left coronary artery showed 70–75% stenosis, a marked congestive phenomenon, and small hemorrhagic stasis in the epicardial area. Toxicological analyses carried out on the victim’s blood and urine did not reveal the presence of psychotropic and narcotic substances. The victim’s death resulted from hemorrhagic and traumatic shock caused by the deep dog bite wounds at the brachial level. Dogs’ bite marks results The dog sodium alginate dental casts were mechanically projected into the excised anatomical areas or bitten clothes. A comparison between each cast and the silicone bite imprints was attempted in order to identify which dog had the greatest responsibility in the victim’s death. A morphologically positive concordance between the AA1, a fragment of the victim’s black sweater, and the B1mg was detected (Fig. ). A second analysis using Adobe Photoshop ® and DentalPrint © software was conducted in order to confirm a positive match between B1 and B1 mg, confirming the presence of Dog_1 in the attack that took the victim’s life. The presence of Dog_1 was also confirmed by projecting B1mg onto a fragment of the victim’s clothing (Table ), and B1mg and B1 onto AA3. The calculated inter-canine distance from B1mg also confirmed the same Dog_1’s bite mark in AA3 (Table ). Furthermore, AA2 was examined, and a morphologically positive concordance with B2mg was detected by a missing lower incisive in Dog_2’s bite mark. The same concordance was obtained by matching B2 with B2mg using Adobe Photoshop ® and DentalPrint © software , confirming the presence of Dog_2 during the attack (Table ). Due to the pronounced dysgnathia, the Dog_2’s bite was easier identifiable on victim’s skin (Fig. ) and victim’s clothing (Table ). The presence of Dog_2 on the death scene was also confirmed by the shirt fragment found in the excrement recovered 48 h after the animal’s seizure at the judicial kennel. The fragment resulted to be part of the victim’s shirt worn during the assault. The dog dental arche analyses on the victim’s skin were compared by mechanical projections showing that the lesions AA1/B1 and AA2/B2 were fully compatible with the plaster model B2mg that belonged to Dog_2. Dog_2’s bite was easily identifiable because of the pronounced dysgnathia and prevailing indentations of the lower jaw teeth. Dog_2’s bite presented another peculiarity that led to rapid identification in AA2, these being a missing incisor in the inferior dental arch. DNA genotyping results Dog DNA reference results confirmed that Dog_1 and Dog_2 were parents of Dog_3 and Dog_4, and excluding any familial relationship to Dog_5 and Dog_6. Moreover, Dog_2’s DNA was found in two pieces of victim’s trousers, connecting that dog on to the death scene. The blood found in Dog_1’s mouth directly belonged to victim, connecting Dog_1 to the victim’s injuries. Additionally, DNA comparisons between the victim’s reference DNA and the DNA extracted from the blooded clothes demonstrated that they were worn by the victim during the attack. No evidence of Dog_5 and Dog_6’s dental arches were found on the victim’s skin and clothes. Additionally, no salivary DNA of these dogs was found on all examined samples, demonstrating their absence during the attack. For this reason, they were considered fully innocent and were immediately released from the kennel. Likewise, Dog_3 and Dog_4 were released from the kennel because the victim’s injuries did not present positive matches with their dental imprints. Instead, their parents’ massive (Dog_1 and Dog_2) interaction were confirmed by various factors. Dog_1 had positive matches between its dental chalk and a victim’s clothes fragment (C3) (Fig. ) and a skin injury (AA3). Also, the victim’s blood was found in Dog_1’s upper jaw. Dog_2’s informative jaws anomalies, caused by dysgnathia and a missing lower tooth, easily connect the dog to the attack, its dental chalk matched with two of the victim’s clothes fragments (C1 and C2). Additionally, a piece of rose and green striped shirt was recollected from Dog_2’s excrement.
During the analysis of the victim’s head, the only interesting element that appeared was a subgaleal ecchymosis indicating a contusion compatible with the scenario. Most of the victim’s body had multiple superficial and deep tissue lacerations. Specifically, the right brachial artery was found slashed. The fatal blood loss was found in the right limb correspondence, where the dogs had bitten and slashed the victim’s tissues massively and repeatedly. In general, the upper limbs were repeatedly bitten, a condition likely resulting from the victim’s defensive posture. The subsequent thoraco-abdominal section of the cadaver did not reveal anything of significance from a traumatic point of view. Anatomopathological studies of the victim’s organs and tissue fragments revealed bilateral calcific coronary atherosclerosis. The left coronary artery showed 70–75% stenosis, a marked congestive phenomenon, and small hemorrhagic stasis in the epicardial area. Toxicological analyses carried out on the victim’s blood and urine did not reveal the presence of psychotropic and narcotic substances. The victim’s death resulted from hemorrhagic and traumatic shock caused by the deep dog bite wounds at the brachial level.
The dog sodium alginate dental casts were mechanically projected into the excised anatomical areas or bitten clothes. A comparison between each cast and the silicone bite imprints was attempted in order to identify which dog had the greatest responsibility in the victim’s death. A morphologically positive concordance between the AA1, a fragment of the victim’s black sweater, and the B1mg was detected (Fig. ). A second analysis using Adobe Photoshop ® and DentalPrint © software was conducted in order to confirm a positive match between B1 and B1 mg, confirming the presence of Dog_1 in the attack that took the victim’s life. The presence of Dog_1 was also confirmed by projecting B1mg onto a fragment of the victim’s clothing (Table ), and B1mg and B1 onto AA3. The calculated inter-canine distance from B1mg also confirmed the same Dog_1’s bite mark in AA3 (Table ). Furthermore, AA2 was examined, and a morphologically positive concordance with B2mg was detected by a missing lower incisive in Dog_2’s bite mark. The same concordance was obtained by matching B2 with B2mg using Adobe Photoshop ® and DentalPrint © software , confirming the presence of Dog_2 during the attack (Table ). Due to the pronounced dysgnathia, the Dog_2’s bite was easier identifiable on victim’s skin (Fig. ) and victim’s clothing (Table ). The presence of Dog_2 on the death scene was also confirmed by the shirt fragment found in the excrement recovered 48 h after the animal’s seizure at the judicial kennel. The fragment resulted to be part of the victim’s shirt worn during the assault. The dog dental arche analyses on the victim’s skin were compared by mechanical projections showing that the lesions AA1/B1 and AA2/B2 were fully compatible with the plaster model B2mg that belonged to Dog_2. Dog_2’s bite was easily identifiable because of the pronounced dysgnathia and prevailing indentations of the lower jaw teeth. Dog_2’s bite presented another peculiarity that led to rapid identification in AA2, these being a missing incisor in the inferior dental arch.
Dog DNA reference results confirmed that Dog_1 and Dog_2 were parents of Dog_3 and Dog_4, and excluding any familial relationship to Dog_5 and Dog_6. Moreover, Dog_2’s DNA was found in two pieces of victim’s trousers, connecting that dog on to the death scene. The blood found in Dog_1’s mouth directly belonged to victim, connecting Dog_1 to the victim’s injuries. Additionally, DNA comparisons between the victim’s reference DNA and the DNA extracted from the blooded clothes demonstrated that they were worn by the victim during the attack. No evidence of Dog_5 and Dog_6’s dental arches were found on the victim’s skin and clothes. Additionally, no salivary DNA of these dogs was found on all examined samples, demonstrating their absence during the attack. For this reason, they were considered fully innocent and were immediately released from the kennel. Likewise, Dog_3 and Dog_4 were released from the kennel because the victim’s injuries did not present positive matches with their dental imprints. Instead, their parents’ massive (Dog_1 and Dog_2) interaction were confirmed by various factors. Dog_1 had positive matches between its dental chalk and a victim’s clothes fragment (C3) (Fig. ) and a skin injury (AA3). Also, the victim’s blood was found in Dog_1’s upper jaw. Dog_2’s informative jaws anomalies, caused by dysgnathia and a missing lower tooth, easily connect the dog to the attack, its dental chalk matched with two of the victim’s clothes fragments (C1 and C2). Additionally, a piece of rose and green striped shirt was recollected from Dog_2’s excrement.
To conclude, in almost 50% of dog bite cases described in literature [ – ], as in our case, the attacks took place near or inside the dog owner’s property. The dog pack familiarity with the agriculture plot was confirmed by the presence of black dog hair tufts on the rusty mesh wire hole; this suggests that the hole was frequently used by dogs to pass from one property to another. None of the dogs had ever shown aggression toward humans. They were also in daily contact with their owner’s four-year-old daughter. To note, Dog_3 and Dog_4 were in the late or second stage of socialization, also called the juvenile stage . The juvenile period begins between the fourteen and sixteen weeks of age and ends with the onset of puberty, which in larger breeds such as the Cane Corso has a longer than usual period, up to 1 year of age . During this time, adolescent dogs begin to feel more comfortable interacting with people and other animals . Curiosity may have led them to approach the victim, who was unfamiliar with the agricultural plot and the dogs. The adult dogs, probably reacting with an aggressive attitude, may have attacked the victim to protect the weaker juveniles. The protection may have resulted from the fact that the dogs Dog_3 and Dog_4, in the phase of sexual immaturity, did not yet have any hierarchies in the group and were considered protected by Dog_1 and Dog_2. Unfortunately, no witnesses were able to indicate whether the dogs were sending alarm signals to the victim and he ignored them, or whether the victim was acting aggressively toward the dogs who, feeling threatened, attacked him. In conclusion, the reported event has all the elements of a fatal dog pack attack as a result of a probably territorial and pack defense against intruders.
To date, no forensic cases of death from dog pack attack have been solved by dog dysgnathia conditions. Crime scene analysis and autopsy cannot assign discriminative dog bite marks, therefore odontological, and genetic considerations should be always taken in account. A forensic multidisciplinary approach was necessary to solve the case. The dogs’ STRs and dental anomalies were fundamental to the recognition of those that induced victim death.
|
Congenital cutaneous fibropapillomatosis without evidences of papillomavirus
infection in a Holstein-Friesian calf | 29643aa5-c549-4a75-909d-1f37f243fb0f | 8762411 | Anatomy[mh] | The authors declare no conflicts of interest with respect to the publication of this
manuscript.
|
Structure and diversity of intestinal methanogens in black carp ( | 86e5fa95-a68a-4ba6-abee-c99c64e44cdb | 11824978 | Microbiology[mh] | Methanogens , also referred to as Methanogenic archaea, constitute a class of archaeal microorganisms capable of generating methane gas within an obligate anaerobic environment. They utilize substrates such as hydrogen, carbon dioxide, acetic acid, formic acid, methanol, and methylamine, among others, demonstrating a widespread distribution . Methanogens are abundant and diverse. According to the Handbook of Berger Bacteria Identification (ninth edition), as of 2016, methanogens has developed into 4 classes ( Methanobacteria , Methanococci , Methanomicrobia , Methanopyri ) and 5 orders ( Methanococcales , Methanosarcinales , Methanopyrales , Methanomicrobiales and Methanobacteriales ) . Methanogens mainly generate methane through the H 2 /CO 2 reduction pathway and acetic acid fermentation pathway, while the methyl trophication pathway mainly occurs in specific environments such as river and pond sediments . Methane (CH 4 ) is a greenhouse gas produced by methanogens decomposing organic matter under anaerobic conditions, and its contribution to global climate warming is second only to that of carbon dioxide (CO 2 ) . Many CH 4 accumulated on the earth come from the action of microorganisms. Because methanogens play a vital role in the natural carbon cycle, and CH 4 is the 2 nd largest greenhouse gas contributes to global warming, methanogens and the mechanism of methane production have attracted much attention from researchers . Especially in recent years, researchers have conducted in-depth discussions on the living habits and metabolic mechanism of methanogens . The results indicated that the methanogens from sediments and animals were distinct, and the methanogens from freshwater and seawater were also different. Methanogens from the same sediments or animals have a high similarity, reflecting the close correlation between the ecological environment and the distribution of methanogens . CH 4 emission from lakes and ponds is a key root of atmospheric methane . Methanogens, as the main role of methane generation, are usually characterized by the diversity or abundance . Some scholars have studied the seasonal fluctuations of the quantity, structure, and variety of methanogens and methanotrophic bacteria in sediment from lakes, and determined that the diversity of methanotrophic bacteria was dominant by methylobacter in the deep and methylococcus in the shallows part, and organic matters was the key environmental parameters that controlled methanogens . In this study, we collected samples of black carp ( Mylopharyngodon piceus ), grass carp ( Ctenopharyngodon idella ) and cultured water from the similar pond, and analyzed their methanogens community structure and diversity based on two different kinds of predatory fish intestine and their cultured water. The results will help to deepen the understanding of methanogens in the ecological functions of matter and energy circulation and transformation in the rural aquaculture pond ecosystem and provide reference information for the ecological diversity of rural aquaculture ponds and the possible impact of aquaculture on the greenhouse effect. Sample collection and preservation Samples of grass carp ( Ctenopharyngodon idella , CY), black carp ( Mylopharyngodon piceus , QY) and farmed water (SY) were gathered from the similar lake of Loudi Fishery Science Research Institute (112°0’6" E, 27°43’47" N), Hunan Province, China. The lake has an area of 1.5 hm 2 and a depth of 2.0 m. The sampling time on January 19, 2021 at 08:00 a.m, pH 8.10–8.56 and dissolve oxygen > 4.35 mg/L with water temperature 17.8°C. Five black carps (2627.3 ± 42.69 g) and five grass carps (1431.34 ± 33.25 g) of similar size, ailment-free symptoms were arbitrarily chosen from the fish trapped in the net and returned to laboratory along with specimen of water, and the rest were released back into the lake. The fish used in the experiment came from natural lake that had not been fed any food. At the same time, 8 sampling points were randomly selected in the lake, and 10 mL equal volume water samples were collected at places about 1.0 m below the water surface. The samples were mixed and loaded into sterile centrifuge tubes . In order to prevent contaminants, the fish surface was clean with sterilized water and 70 percent ethanol sequentially prior to dissection. The content specimens of grass carp (No. CY1-CY5) and black carp (No. QY1-QY5), and water specimens (No. SY1-SY5) were gathered in the sterilized operational trays, and then put in a sterile centrifuge tube and refrigerate at -20°C for later use. Abundance detection of methanogens The Tguide S96 kit was utilized to extract DNA from 15 specimens, and the universal primer MLf and MLr was use to amplify methanogenic mcrA gene. After qualified the products of PCR were identified using electrophoresis, the target fragment was recovered, and Illumina HiSeq 2500 sequenced the library. The primer, reactions system and amplification requirement are listed below. Primers were synthesized and sequenced by Beijing Biomarker Technology Co., Ltd (Beijing, China). Amplification primer: MLr (5’- TTCATTGCRTAGTTWGGRTAGTT -3’), MLf (5’- GGTGGTGTMGGATTCACACARTAYGCWA CAGC- 3’). The amplification response was achieved in the following manner: 5 μL KOD FX Neo Buffer, 0.3 μL (10 μM) comprising both forward and reverse primer, 0.2 μL KOD FX Neo, 2 μL (2 mM) of dNTPs, and 5–50 ng of DNA Template, ddH2O supplement to 10 μL. Parameters for the reaction: Denaturing at 95°C for 5 minutes, followed by 25 cycles of denaturation at 95°C for 30 seconds, annealing at 50°C for 30 seconds, and extension at 72°C for 40 seconds, with a final extension of 7 minutes at 72°C. Diversity analysis of methanogens The initial data received by sequencing were filtered by quality control (Trimmomatic, V0.33) , and the detection and elimination of primer sequences (Cutadapt, V1.9.1) . Double-terminated sequence splicing (Usearch, V10) and removal of chimeras (UCHIME, V4.2) obtained high-quality sequences for further investigation. Usearch software can cluster reads with 97% similarity to obtain OTU . QIIME2 software ( https://qiime2.org/ ) was employed to determine alpha and beta variety in the specimens to thoroughly examine total diversity and highlight discrepancies across specimens. Alpha diversity reflected the richness and variety of species in individual samples, containing Ace, Chao1, Simpson and Shannon. The Chao1 and Ace indices quantify species abundance, which is how many species there are. Shannon and Simpson indexes measure the variety of species and are influenced by species abundance and community evenness in sampling communities. Beta diversity examination is used to evaluate the similarity in composition of different specimens. Unweighted Pair-group Method with Arithmetic Mean (UPGMA) examine the variation between samples based on the differences in evolutionary information between different sample sequences. It can reflect whether the specimens have major microbial community variations in the evolutionary tree. Line Discriminant Analysis Effect Size (LEfSe) uses linear discriminant analysis to quantify the influence of relative number of every species on the varied effect sizes, and looked for species with substantial variations among groups. Statistical analysis SPSS 24.0 statistical software (IBM Corp., Armonk, NY, USA) was use for data statistics, the testing data are represented by means ± standard deviation, and independent sample t-test was employed for pair comparison. p < 0.05 revealed statistically significant difference. The mcrA gene sequences acquired in this research has been uploaded as an attachment with the file name “raw data”. Ethics statement All animal work was performed in compliance with the recommendations of the Institutional Animal Care and Use Committee of Hunan University of Chinese Medicine (NO.20171202). All writers were aware and agreed of this animal experimentation. Samples of grass carp ( Ctenopharyngodon idella , CY), black carp ( Mylopharyngodon piceus , QY) and farmed water (SY) were gathered from the similar lake of Loudi Fishery Science Research Institute (112°0’6" E, 27°43’47" N), Hunan Province, China. The lake has an area of 1.5 hm 2 and a depth of 2.0 m. The sampling time on January 19, 2021 at 08:00 a.m, pH 8.10–8.56 and dissolve oxygen > 4.35 mg/L with water temperature 17.8°C. Five black carps (2627.3 ± 42.69 g) and five grass carps (1431.34 ± 33.25 g) of similar size, ailment-free symptoms were arbitrarily chosen from the fish trapped in the net and returned to laboratory along with specimen of water, and the rest were released back into the lake. The fish used in the experiment came from natural lake that had not been fed any food. At the same time, 8 sampling points were randomly selected in the lake, and 10 mL equal volume water samples were collected at places about 1.0 m below the water surface. The samples were mixed and loaded into sterile centrifuge tubes . In order to prevent contaminants, the fish surface was clean with sterilized water and 70 percent ethanol sequentially prior to dissection. The content specimens of grass carp (No. CY1-CY5) and black carp (No. QY1-QY5), and water specimens (No. SY1-SY5) were gathered in the sterilized operational trays, and then put in a sterile centrifuge tube and refrigerate at -20°C for later use. The Tguide S96 kit was utilized to extract DNA from 15 specimens, and the universal primer MLf and MLr was use to amplify methanogenic mcrA gene. After qualified the products of PCR were identified using electrophoresis, the target fragment was recovered, and Illumina HiSeq 2500 sequenced the library. The primer, reactions system and amplification requirement are listed below. Primers were synthesized and sequenced by Beijing Biomarker Technology Co., Ltd (Beijing, China). Amplification primer: MLr (5’- TTCATTGCRTAGTTWGGRTAGTT -3’), MLf (5’- GGTGGTGTMGGATTCACACARTAYGCWA CAGC- 3’). The amplification response was achieved in the following manner: 5 μL KOD FX Neo Buffer, 0.3 μL (10 μM) comprising both forward and reverse primer, 0.2 μL KOD FX Neo, 2 μL (2 mM) of dNTPs, and 5–50 ng of DNA Template, ddH2O supplement to 10 μL. Parameters for the reaction: Denaturing at 95°C for 5 minutes, followed by 25 cycles of denaturation at 95°C for 30 seconds, annealing at 50°C for 30 seconds, and extension at 72°C for 40 seconds, with a final extension of 7 minutes at 72°C. The initial data received by sequencing were filtered by quality control (Trimmomatic, V0.33) , and the detection and elimination of primer sequences (Cutadapt, V1.9.1) . Double-terminated sequence splicing (Usearch, V10) and removal of chimeras (UCHIME, V4.2) obtained high-quality sequences for further investigation. Usearch software can cluster reads with 97% similarity to obtain OTU . QIIME2 software ( https://qiime2.org/ ) was employed to determine alpha and beta variety in the specimens to thoroughly examine total diversity and highlight discrepancies across specimens. Alpha diversity reflected the richness and variety of species in individual samples, containing Ace, Chao1, Simpson and Shannon. The Chao1 and Ace indices quantify species abundance, which is how many species there are. Shannon and Simpson indexes measure the variety of species and are influenced by species abundance and community evenness in sampling communities. Beta diversity examination is used to evaluate the similarity in composition of different specimens. Unweighted Pair-group Method with Arithmetic Mean (UPGMA) examine the variation between samples based on the differences in evolutionary information between different sample sequences. It can reflect whether the specimens have major microbial community variations in the evolutionary tree. Line Discriminant Analysis Effect Size (LEfSe) uses linear discriminant analysis to quantify the influence of relative number of every species on the varied effect sizes, and looked for species with substantial variations among groups. SPSS 24.0 statistical software (IBM Corp., Armonk, NY, USA) was use for data statistics, the testing data are represented by means ± standard deviation, and independent sample t-test was employed for pair comparison. p < 0.05 revealed statistically significant difference. The mcrA gene sequences acquired in this research has been uploaded as an attachment with the file name “raw data”. All animal work was performed in compliance with the recommendations of the Institutional Animal Care and Use Committee of Hunan University of Chinese Medicine (NO.20171202). All writers were aware and agreed of this animal experimentation. Sequencing characteristics and OTU distribution of samples The community features of methanogens in black carp grass carp, and water specimens were examined using Illumina high-throughput sequencing technology. Approximately 1,512,019 high-quality sequences have been gathered from 15 specimens of the three groups, and the average effective sequence of each sample was 90.99%, with the average sequence length concentrated in 421–435 bp. Through quality control, filtering and chimerism removal, overall 25 OTUs were obtained based on 97% sequence similarities clustering. There were 21 OTU in CY samples. There were 21 OTU in QY samples. There were 22 OTU in SY samples. There were 16 identical OTU numbers in the three groups of samples. The outcomes revealed that there were no differences in the richness and variety of methanogens among black carp, grass carp and water samples . Comparison of alpha diversity of methanogens in black carp grass carp, and water specimens To illustrate the variety and depth of methanogens in the digestive tract of black carp, grass carp and water, QIIME2 software was used to assess the alpha diversity index of samples. In term of the perspective of richness index, Ace index and Chao1 index in water samples were the most advanced, and Ace index and Chao1 index in intestinal samples of grass and black carp were similar. From the perspective of diversity index, the Shannon and Simpson index of species in aquaculture water samples were the highest, which were similar to those in the intestinal specimen of black carp, while Shannon and Simpson index were the smallest in the digestive specimens of grass carp. The richness index in the colonel samples of black and grass carp was significantly different from that in the water specimens ( p < 0.05 or p < 0.01). The diversity index in the colonel specimens of black carp and water samples was similar, and the diversity index in the colonel samples of grass carp was the lowest . These outcomes designated that the richness and diversity of methanogens in water were the greatest. The richness of methanogens in the colonel tract of grass and black carp was similar, which was considerably different from that of water samples. The variety of the digestive tract samples of black carp were similar to that of water samples, but there was no significant difference. Characterization of methanogens in black carp, grass carp and water specimens There were 3 classes, 4 orders, 5 families and 5 genera of methanogens were recognized from 15 specimens gathered from CY, QY and SY groups. The genera detected were Methanosarcina , Methanocorpusculum , Methanospirillum , Methanobacterium and Methanofollis . Methanosarcina and Methanocorpusculum were the dominant genera, accounting for 91.15%, 89.36% and 69.17% of CY, QY and SY, respectively . Compared with SY samples, Methanosarcina and Methanospirillum increased in CY and QY samples to varying degrees. Methanobacterium and Methanofollis have decreased in CY and QY samples to varying degrees. Methanocorpusculum increased in QY samples but decreased in CY samples. There were no significant differences among all genera. Weighted Unifrac distance matrix was utilized to construct a cluster examination of fifteen samples known as methanobacteria by means of the unweighted pair-group method with UPGMA. The outcomes revealed that the resemblance of species composition among each group was relatively high . Based on the LefSe analysis findings, Methanobacterium _sp and Methanofollis _ethanolicus are markers with statistical differences in SY samples . The community features of methanogens in black carp grass carp, and water specimens were examined using Illumina high-throughput sequencing technology. Approximately 1,512,019 high-quality sequences have been gathered from 15 specimens of the three groups, and the average effective sequence of each sample was 90.99%, with the average sequence length concentrated in 421–435 bp. Through quality control, filtering and chimerism removal, overall 25 OTUs were obtained based on 97% sequence similarities clustering. There were 21 OTU in CY samples. There were 21 OTU in QY samples. There were 22 OTU in SY samples. There were 16 identical OTU numbers in the three groups of samples. The outcomes revealed that there were no differences in the richness and variety of methanogens among black carp, grass carp and water samples . To illustrate the variety and depth of methanogens in the digestive tract of black carp, grass carp and water, QIIME2 software was used to assess the alpha diversity index of samples. In term of the perspective of richness index, Ace index and Chao1 index in water samples were the most advanced, and Ace index and Chao1 index in intestinal samples of grass and black carp were similar. From the perspective of diversity index, the Shannon and Simpson index of species in aquaculture water samples were the highest, which were similar to those in the intestinal specimen of black carp, while Shannon and Simpson index were the smallest in the digestive specimens of grass carp. The richness index in the colonel samples of black and grass carp was significantly different from that in the water specimens ( p < 0.05 or p < 0.01). The diversity index in the colonel specimens of black carp and water samples was similar, and the diversity index in the colonel samples of grass carp was the lowest . These outcomes designated that the richness and diversity of methanogens in water were the greatest. The richness of methanogens in the colonel tract of grass and black carp was similar, which was considerably different from that of water samples. The variety of the digestive tract samples of black carp were similar to that of water samples, but there was no significant difference. There were 3 classes, 4 orders, 5 families and 5 genera of methanogens were recognized from 15 specimens gathered from CY, QY and SY groups. The genera detected were Methanosarcina , Methanocorpusculum , Methanospirillum , Methanobacterium and Methanofollis . Methanosarcina and Methanocorpusculum were the dominant genera, accounting for 91.15%, 89.36% and 69.17% of CY, QY and SY, respectively . Compared with SY samples, Methanosarcina and Methanospirillum increased in CY and QY samples to varying degrees. Methanobacterium and Methanofollis have decreased in CY and QY samples to varying degrees. Methanocorpusculum increased in QY samples but decreased in CY samples. There were no significant differences among all genera. Weighted Unifrac distance matrix was utilized to construct a cluster examination of fifteen samples known as methanobacteria by means of the unweighted pair-group method with UPGMA. The outcomes revealed that the resemblance of species composition among each group was relatively high . Based on the LefSe analysis findings, Methanobacterium _sp and Methanofollis _ethanolicus are markers with statistical differences in SY samples . Understanding variations in the abundance and variety of microbial colonies is a necessary condition for evaluating the role of microorganisms in the environment . Methanogens, as common microorganisms, are extensively dispersed in numerous environments, containing soil , water sediments and animal digestive tracts . Ponds and lakes are important natural emission sources of methane, and methane generation is closely related to the methanogens community. At present, three main metabolic pathways have been described for methane production, namely hydrogenotrophic (change H 2 plus CO 2 into CH 4 ), aceticlastic (change acetate into CO 2 and CH 4 ) and methylotrophic (generate CH 4 by methanol, methylamine, dimethylamine and other mechanisms), which involve the diversity of methanogens . In freshwater sediments, methane production is regulated by different environmental factors, such as hypoxia , quality and amount of organic matter , temperature , etc. Temperature variation is probably to be one of the factors affecting CH 4 production capacity in the shallowest areas of deep lakes or shallow lakes . In a certain range, the increase in temperature has an obvious promotion effect about the metabolic capability of microorganisms, which is beneficial to improve the rate of gas production. In addition, the production capacity of CH 4 is closely related to the community abundance of fermentation microorganisms . Therefore, it is helpful to clarify the relationship between aquaculture and the greenhouse effect to study the community characteristics of methanogens in aquaculture water and digestive tracts of aquatic animals. In this investigation, High-throughput sequencing technologies were used to methanogens in water samples and aquatic animal intestines. The results revealed that a total of 5 genera were identified from methanogens, among which Methanosarcina , Methanocorpusculum and Methanobacterium were the 3 genera with the greatest relative abundance. Methanosarcina is hydrogen and acetic acid mixotrophic methanogens, Methanocorpusculum and Methanobacterium are hydrogenotrophic methanogens . The results showed that CH 4 was produced by H 2 reduction of CO 2 and acetic acid degradation, and mainly by hydrogen reduction of CO 2 . In the SY sample, Methanocorpusculum , Methanosarcina , and Methanobacterium were the dominant bacteria, which is constant with the characteristics of methanogenic bacteria community in wetlands . In QY and CY samples, Methanosarcina and Methanocorpusculum were the dominant bacteria genera. In addition, Methanosarcina has the highest abundance in CY and Methanocorpusculum has the highest abundance in QY, which might be link the eating habits of black and grass carp. The grass carp is herbivorous freshwater fish that feeds on the stems and leaves of aquatic plants, and its food riched in cellulose and polysaccharides. Black carp are carnivorous fish that live in freshwater, which feeds on snails, clams and other mollusks, and its food is rich in protein and fat. The intestinal bacteria of grass and black carp are mainly Firmicutes (69% vs 37.5%), Proteobacteria (6.9% vs 37.5%) and Actinobacteria (6.9% vs 16.7%), which are highly similar to the bacterial community in cultured water . Firmicutes, Proteobacteria and Actinomycetes belong to hydrolytic fermentation bacteria . Among them, Clostridium in Firmicutes is a typical cellulose-decomposing bacteria with the function of fermenting monosaccharides to produce organic acids, while Streptococcus in Firmicutes is a typical protein-decomposing bacteria . Vibrio in Proteobacteria is the dominant lipopolysis bacteria . Moreover, the colonel tract of grass carp is rich in amylase and cellulose , and the digestive tract of black carp is rich in protease and lipase . These facts indicate that metabolic matrix of methanogens in ponds and lakes mainly comes from hydrolytic fermentative bacteria, and methanogens can effectively use the H 2 and CO 2 generated by these hydrolytic fermentative bacteria to produce CH 4 , which also fully demonstrates that ponds and lakes are important natural emission sources of methane, and are dominated by hydrogenotrophic methanogens. In conclusion, we revealed the community structure and richness characteristics of methanogens in the colonel tract of black and grass carp and water samples for the first time and clarified the relationship between intestinal methanogens and aquaculture and the greenhouse effect. These results will provide a reference for the relationship between intestinal methanogens and aquaculture and the greenhouse effect. S1 Raw data (ZIP) |
非小细胞肺癌PD-1/PD-L1的表达与 | 969d6e84-feed-4c52-b481-9926603758e4 | 8503982 | Anatomy[mh] | 材料与方法 1.1 材料 1.1.1 病例收集及临床资料 收集石河子大学医学院第一附属医院病理科2016年4月-2019年8月确诊NSCLC患者127例，男性67例，平均年龄（69.62±9.61）岁。女性60例，平均年龄（69.1±9.87）岁。NSCLC患者临床信息包括性别、年龄、吸烟史、标本类型、组织学类型、分化程度，淋巴结转移及临床分期（参照第8版国际肺癌肿瘤淋巴结转移分期），排除既往行新辅助化疗或有过恶性肿瘤病史的患者，同时征求患者同意，并签署知情同意书。所研究组织标本经10%中性福尔马林固定6 h-24 h，常规取材、处理标本制成石蜡组织。患者一般资料见 。 1.1.2 实验试剂及仪器 PD-1（鼠抗人单克隆抗体MRQ-22，即用型，北京中杉公司），二抗试剂（Envision试剂盒，DAKO，K5007）；PD-L1检测试剂盒（DAKO平台的PD-L1 IHC 22C3 PharmDX）包括：细胞株（包含PD-L1阳性、阴性），20×缓冲液、50×修复液，PD-L1即用型抗体，二抗试剂，DAB显色系统，每例检测样本包含两张切片（22C3Ab, 22C3NCR）。免疫组化修复仪（DAKO PTlink），自动免疫组化染色仪（DAKO Link 48），数字病理切片扫描仪（KF-PRO-005）。 qPCR试剂及仪器：福尔马林固定石蜡包埋组织DNA提取试剂盒（QIAGEN）和人 EGFR 基因外显子18-21突变检测试剂盒（北京雅康博生物医药科技股份有限公司）。核酸测定仪（NanoDrop 2000，美国Thermo公司），荧光定量PCR仪（7500Fast美国Life technologies公司）。 1.2 方法 1.2.1 免疫组化PD-1检测 石蜡组织4 μm连续切片，石蜡切片常规脱蜡至水，Tris-EDTA高温高压修复3 min，3%H 2 O 2 溶液封闭10 min，加入PD-1抗体100 μL，4 ℃冰箱过夜，PBS浸洗5 min，加100 μL HRP标记的二抗37 ℃温箱30 min，PBS浸洗5 min，DAB显色，苏木素复染。PD-L1检测：常规切片脱蜡至水，修复仪修复40 min，缓冲液浸洗，玻片放入自动免疫组化染色机中进行染色，机器运行正常。 结果判定：用数字病理切片扫描仪扫描所有免疫组化切片，请两名经验丰富的病理医师判读扫描的切片。PD-1在肿瘤细胞及肿瘤浸润的免疫细胞胞浆染色，采用CPS评分[任意强度胞浆染色的肿瘤细胞和免疫细胞相对于肿瘤细胞（至少100个）的比例分数≥1即为阳性， < 1为阴性]，PD-L1定位于肿瘤细胞膜，阳性对照细胞株胞膜呈棕色，阴性对照细胞株无染色，22C3NCR（空白对照）无染色。按照DAKO PD-L1 IHC 22C3判读标准 ，PD-L1以TPS（任何强度的部分或完全膜染色的肿瘤细胞占标本中所有肿瘤细胞的百分比）作为表达结果，病理医师对所有PD-L1染色进行肿瘤细胞阳性比例分数（tumor proportion score, TPS）评分，TPS < 1%为阴性，TPS≥1%为阳性表达，其中TPS 1%-49%为低表达，TPS≥50%为高表达。 1.2.2 石蜡组织DNA的提取与质控 通过HE切片对NSCLC组织进行评估，按照肿瘤细胞数量及百分比，活检标本5 μm 4张-8张，穿刺标本5 μm 10张-15张，用石蜡包埋组织DNA提取试剂盒，操作步骤按照QIAGEN公司说明书提取DNA，使用核酸测定仪测定每个样本DNA纯度和浓度，并记录，样本DNA的A 260 /A 280 比值介于1.8-2.1为有效。 1.2.3 EGFR 基因外显子18-21突变检测 依据各样本测得浓度，将所有样本稀释到0.5 ng/µL，按照 EGFR 基因检测试剂说明书操作，用突变扩增系统（amplification refractory mutation system, ARMS）法检测 EGFR 外显子18-21共29个突变位点。每次实验均设阴性、阳性对照及空白对照，根据说明书PCR反应扩增曲线及CT值进行分析。 1.3 随访 课题组从2017年3月1日陆续对纳入我们研究的NSCLC患者进行随访，采用门诊随访和电话随访相结合的方式，获取患者生存资料，截止到2020年10月25日，有90例患者随访信息完整。 1.4 统计学分析 应用SPSS 20.0软件对数据进行统计分析。NSCLC组织中PD-L1和PD-1表达、 EGFR 基因突变状态与临床病理特征等分类资料指标采用频数和百分比进行描述，组间的差异比较采用卡方（或秩和）检验，组间的相关性采用 McNemar 和列联系数进行相关性分析，用 Kaplan-Meier 曲线分析各因子与NSCLC患者生存情况的关系，所有检验均为双侧检验，以 P < 0.05为差异有统计学意义。
材料 1.1.1 病例收集及临床资料 收集石河子大学医学院第一附属医院病理科2016年4月-2019年8月确诊NSCLC患者127例，男性67例，平均年龄（69.62±9.61）岁。女性60例，平均年龄（69.1±9.87）岁。NSCLC患者临床信息包括性别、年龄、吸烟史、标本类型、组织学类型、分化程度，淋巴结转移及临床分期（参照第8版国际肺癌肿瘤淋巴结转移分期），排除既往行新辅助化疗或有过恶性肿瘤病史的患者，同时征求患者同意，并签署知情同意书。所研究组织标本经10%中性福尔马林固定6 h-24 h，常规取材、处理标本制成石蜡组织。患者一般资料见 。 1.1.2 实验试剂及仪器 PD-1（鼠抗人单克隆抗体MRQ-22，即用型，北京中杉公司），二抗试剂（Envision试剂盒，DAKO，K5007）；PD-L1检测试剂盒（DAKO平台的PD-L1 IHC 22C3 PharmDX）包括：细胞株（包含PD-L1阳性、阴性），20×缓冲液、50×修复液，PD-L1即用型抗体，二抗试剂，DAB显色系统，每例检测样本包含两张切片（22C3Ab, 22C3NCR）。免疫组化修复仪（DAKO PTlink），自动免疫组化染色仪（DAKO Link 48），数字病理切片扫描仪（KF-PRO-005）。 qPCR试剂及仪器：福尔马林固定石蜡包埋组织DNA提取试剂盒（QIAGEN）和人 EGFR 基因外显子18-21突变检测试剂盒（北京雅康博生物医药科技股份有限公司）。核酸测定仪（NanoDrop 2000，美国Thermo公司），荧光定量PCR仪（7500Fast美国Life technologies公司）。
病例收集及临床资料 收集石河子大学医学院第一附属医院病理科2016年4月-2019年8月确诊NSCLC患者127例，男性67例，平均年龄（69.62±9.61）岁。女性60例，平均年龄（69.1±9.87）岁。NSCLC患者临床信息包括性别、年龄、吸烟史、标本类型、组织学类型、分化程度，淋巴结转移及临床分期（参照第8版国际肺癌肿瘤淋巴结转移分期），排除既往行新辅助化疗或有过恶性肿瘤病史的患者，同时征求患者同意，并签署知情同意书。所研究组织标本经10%中性福尔马林固定6 h-24 h，常规取材、处理标本制成石蜡组织。患者一般资料见 。
实验试剂及仪器 PD-1（鼠抗人单克隆抗体MRQ-22，即用型，北京中杉公司），二抗试剂（Envision试剂盒，DAKO，K5007）；PD-L1检测试剂盒（DAKO平台的PD-L1 IHC 22C3 PharmDX）包括：细胞株（包含PD-L1阳性、阴性），20×缓冲液、50×修复液，PD-L1即用型抗体，二抗试剂，DAB显色系统，每例检测样本包含两张切片（22C3Ab, 22C3NCR）。免疫组化修复仪（DAKO PTlink），自动免疫组化染色仪（DAKO Link 48），数字病理切片扫描仪（KF-PRO-005）。 qPCR试剂及仪器：福尔马林固定石蜡包埋组织DNA提取试剂盒（QIAGEN）和人 EGFR 基因外显子18-21突变检测试剂盒（北京雅康博生物医药科技股份有限公司）。核酸测定仪（NanoDrop 2000，美国Thermo公司），荧光定量PCR仪（7500Fast美国Life technologies公司）。
方法 1.2.1 免疫组化PD-1检测 石蜡组织4 μm连续切片，石蜡切片常规脱蜡至水，Tris-EDTA高温高压修复3 min，3%H 2 O 2 溶液封闭10 min，加入PD-1抗体100 μL，4 ℃冰箱过夜，PBS浸洗5 min，加100 μL HRP标记的二抗37 ℃温箱30 min，PBS浸洗5 min，DAB显色，苏木素复染。PD-L1检测：常规切片脱蜡至水，修复仪修复40 min，缓冲液浸洗，玻片放入自动免疫组化染色机中进行染色，机器运行正常。 结果判定：用数字病理切片扫描仪扫描所有免疫组化切片，请两名经验丰富的病理医师判读扫描的切片。PD-1在肿瘤细胞及肿瘤浸润的免疫细胞胞浆染色，采用CPS评分[任意强度胞浆染色的肿瘤细胞和免疫细胞相对于肿瘤细胞（至少100个）的比例分数≥1即为阳性， < 1为阴性]，PD-L1定位于肿瘤细胞膜，阳性对照细胞株胞膜呈棕色，阴性对照细胞株无染色，22C3NCR（空白对照）无染色。按照DAKO PD-L1 IHC 22C3判读标准 ，PD-L1以TPS（任何强度的部分或完全膜染色的肿瘤细胞占标本中所有肿瘤细胞的百分比）作为表达结果，病理医师对所有PD-L1染色进行肿瘤细胞阳性比例分数（tumor proportion score, TPS）评分，TPS < 1%为阴性，TPS≥1%为阳性表达，其中TPS 1%-49%为低表达，TPS≥50%为高表达。 1.2.2 石蜡组织DNA的提取与质控 通过HE切片对NSCLC组织进行评估，按照肿瘤细胞数量及百分比，活检标本5 μm 4张-8张，穿刺标本5 μm 10张-15张，用石蜡包埋组织DNA提取试剂盒，操作步骤按照QIAGEN公司说明书提取DNA，使用核酸测定仪测定每个样本DNA纯度和浓度，并记录，样本DNA的A 260 /A 280 比值介于1.8-2.1为有效。 1.2.3 EGFR 基因外显子18-21突变检测 依据各样本测得浓度，将所有样本稀释到0.5 ng/µL，按照 EGFR 基因检测试剂说明书操作，用突变扩增系统（amplification refractory mutation system, ARMS）法检测 EGFR 外显子18-21共29个突变位点。每次实验均设阴性、阳性对照及空白对照，根据说明书PCR反应扩增曲线及CT值进行分析。
免疫组化PD-1检测 石蜡组织4 μm连续切片，石蜡切片常规脱蜡至水，Tris-EDTA高温高压修复3 min，3%H 2 O 2 溶液封闭10 min，加入PD-1抗体100 μL，4 ℃冰箱过夜，PBS浸洗5 min，加100 μL HRP标记的二抗37 ℃温箱30 min，PBS浸洗5 min，DAB显色，苏木素复染。PD-L1检测：常规切片脱蜡至水，修复仪修复40 min，缓冲液浸洗，玻片放入自动免疫组化染色机中进行染色，机器运行正常。 结果判定：用数字病理切片扫描仪扫描所有免疫组化切片，请两名经验丰富的病理医师判读扫描的切片。PD-1在肿瘤细胞及肿瘤浸润的免疫细胞胞浆染色，采用CPS评分[任意强度胞浆染色的肿瘤细胞和免疫细胞相对于肿瘤细胞（至少100个）的比例分数≥1即为阳性， < 1为阴性]，PD-L1定位于肿瘤细胞膜，阳性对照细胞株胞膜呈棕色，阴性对照细胞株无染色，22C3NCR（空白对照）无染色。按照DAKO PD-L1 IHC 22C3判读标准 ，PD-L1以TPS（任何强度的部分或完全膜染色的肿瘤细胞占标本中所有肿瘤细胞的百分比）作为表达结果，病理医师对所有PD-L1染色进行肿瘤细胞阳性比例分数（tumor proportion score, TPS）评分，TPS < 1%为阴性，TPS≥1%为阳性表达，其中TPS 1%-49%为低表达，TPS≥50%为高表达。
石蜡组织DNA的提取与质控 通过HE切片对NSCLC组织进行评估，按照肿瘤细胞数量及百分比，活检标本5 μm 4张-8张，穿刺标本5 μm 10张-15张，用石蜡包埋组织DNA提取试剂盒，操作步骤按照QIAGEN公司说明书提取DNA，使用核酸测定仪测定每个样本DNA纯度和浓度，并记录，样本DNA的A 260 /A 280 比值介于1.8-2.1为有效。
EGFR 基因外显子18-21突变检测 依据各样本测得浓度，将所有样本稀释到0.5 ng/µL，按照 EGFR 基因检测试剂说明书操作，用突变扩增系统（amplification refractory mutation system, ARMS）法检测 EGFR 外显子18-21共29个突变位点。每次实验均设阴性、阳性对照及空白对照，根据说明书PCR反应扩增曲线及CT值进行分析。
随访 课题组从2017年3月1日陆续对纳入我们研究的NSCLC患者进行随访，采用门诊随访和电话随访相结合的方式，获取患者生存资料，截止到2020年10月25日，有90例患者随访信息完整。
统计学分析 应用SPSS 20.0软件对数据进行统计分析。NSCLC组织中PD-L1和PD-1表达、 EGFR 基因突变状态与临床病理特征等分类资料指标采用频数和百分比进行描述，组间的差异比较采用卡方（或秩和）检验，组间的相关性采用 McNemar 和列联系数进行相关性分析，用 Kaplan-Meier 曲线分析各因子与NSCLC患者生存情况的关系，所有检验均为双侧检验，以 P < 0.05为差异有统计学意义。
结果 2.1 PD-1、PD-L1表达与临床病理特征的关系 PD-1的蛋白表达在肿瘤细胞及肿瘤浸润的免疫细胞胞浆（见 - ），127例NSCLC患者中，PD-1表达阳性率为53.5%（68/127），肿瘤细胞和免疫细胞都表达的阳性率为46.5%（59/127），肿瘤细胞表达、免疫细胞不表达为3.9%（5/127），肿瘤细胞不表达、免疫细胞表达为3.1%（4/127），肿瘤细胞和免疫细胞都不表达为46.5%（59/127）。PD-L1在肿瘤细胞中部分或完全膜染色（ - ），阳性表达率为57.5%（73/127），其中高表达占11%（14/127），低表达占46.5%（59/127）；本研究NSCLC患者PD-1、PD-L1的表达都与肿瘤分化程度、临床分期有统计学差异，与其他临床指标均无统计学差异。PD-1在低分化癌组织中的表达率为65.9%（29/44），高于高、中分化癌中的表达率47.0%（39/83）（ P =0.042），PD-1在临床分期Ⅰ期和Ⅱ期的阳性表达率为60%（57/95），高于Ⅲ期和Ⅳ期的阳性表达率34.4%（11/32）（ P =0.012）；PD-L1在低分化癌组织中的表达率为70.5%（31/44），高于高、中分化癌中的表达率50.6%（42/83）（ P =0.031）；PD-L1在临床分期Ⅰ期和Ⅱ期的阳性表达率为63.2%（60/95），高于Ⅲ期+Ⅳ期的阳性表达率40.6%（13/32）（ P =0. 026）（ ）。 2.2 EGFR 突变状态与患者临床特征的关系 127例NSCLC患者中 EGFR 基因突变59例，突变率为46.5%（59/127），突变位点涉及到外显子18、19、20及21，其中点突变54例，19-Del突变27例（45.7%）和L-858R 23例（39%），18 G719X和20 Ins突变各1例；双重突变5例，19-Del与20 T790M、21 L-858R突变分别为2例、1例，20 T790M与21 L-858R双突变2例。127例NSCLC患者中，女性患者 EGFR 基因突变率58.3%（35/60）高于男性35.8%（24/43）（ P =0.011）、无吸烟史55.7%（39/70）高于吸烟史35.1%（20/57）（ P =0.020）、腺癌54.5%（54/99）高于鳞癌21.1%（4/19）和其他类型癌11.1%（1/9）（ P =0.002）、高、中分化癌患者53.0%（44/83）高于低分化癌患者34.1%（15/44）（ P =0.042）（ ）。 2.3 PD-1、PD-L1蛋白表达之间及其与 EGFR 突变的相关性 采用 McNemar 分析PD-1与PD-L1相关性发现，PD-1与PD-L1都表达54例（74.0%），40例（74.1%）都不表达（kappa=0.107, 5, P =0.487）。进一步采用列联系数相关性分析 EGFR 突变与PD-1和PD-L1表达的关系，发现有59例 EGFR 突变的患者中PD-1，表达率为42.4%，不表达率为57.6%，说明 EGFR 突变与PD-1表达存在负相关关系（Φ=-0.209, P =0.019）；同样 EGFR 突变与PD-L1表达存在负相关关系（Φ=-0.221, P =0.013）（ ）。 2.4 NSCLC患者的临床病理特征、PD-1、PD-L1的蛋白表达、 EFGR 突变与预后的相关因素分析 使用 Kaplan-Meier 生存曲线分析显示，NSCLC患者的总生存时间（overall survival, OS）与年龄在65岁以下患者中位OS（20个月）明显高于65岁以上患者（17个月）（ P =0.008）；高、中分化癌患者中位OS（20个月）明显高于低分化癌患者（15个月）（ P =0.033）；PD-L1表达的患者中位OS（20个月）明显高于PD-L1不表达患者（12个月）（ P < 0.001），在PD-L1阳性的患者中低表达患者中位OS（22个月）明显高于高表达患者（15个月）（ P < 0.001），腺癌中位OS（20个月）明显高于鳞癌（15个月）（ P =0.042）（ ， ）。
PD-1、PD-L1表达与临床病理特征的关系 PD-1的蛋白表达在肿瘤细胞及肿瘤浸润的免疫细胞胞浆（见 - ），127例NSCLC患者中，PD-1表达阳性率为53.5%（68/127），肿瘤细胞和免疫细胞都表达的阳性率为46.5%（59/127），肿瘤细胞表达、免疫细胞不表达为3.9%（5/127），肿瘤细胞不表达、免疫细胞表达为3.1%（4/127），肿瘤细胞和免疫细胞都不表达为46.5%（59/127）。PD-L1在肿瘤细胞中部分或完全膜染色（ - ），阳性表达率为57.5%（73/127），其中高表达占11%（14/127），低表达占46.5%（59/127）；本研究NSCLC患者PD-1、PD-L1的表达都与肿瘤分化程度、临床分期有统计学差异，与其他临床指标均无统计学差异。PD-1在低分化癌组织中的表达率为65.9%（29/44），高于高、中分化癌中的表达率47.0%（39/83）（ P =0.042），PD-1在临床分期Ⅰ期和Ⅱ期的阳性表达率为60%（57/95），高于Ⅲ期和Ⅳ期的阳性表达率34.4%（11/32）（ P =0.012）；PD-L1在低分化癌组织中的表达率为70.5%（31/44），高于高、中分化癌中的表达率50.6%（42/83）（ P =0.031）；PD-L1在临床分期Ⅰ期和Ⅱ期的阳性表达率为63.2%（60/95），高于Ⅲ期+Ⅳ期的阳性表达率40.6%（13/32）（ P =0. 026）（ ）。
EGFR 突变状态与患者临床特征的关系 127例NSCLC患者中 EGFR 基因突变59例，突变率为46.5%（59/127），突变位点涉及到外显子18、19、20及21，其中点突变54例，19-Del突变27例（45.7%）和L-858R 23例（39%），18 G719X和20 Ins突变各1例；双重突变5例，19-Del与20 T790M、21 L-858R突变分别为2例、1例，20 T790M与21 L-858R双突变2例。127例NSCLC患者中，女性患者 EGFR 基因突变率58.3%（35/60）高于男性35.8%（24/43）（ P =0.011）、无吸烟史55.7%（39/70）高于吸烟史35.1%（20/57）（ P =0.020）、腺癌54.5%（54/99）高于鳞癌21.1%（4/19）和其他类型癌11.1%（1/9）（ P =0.002）、高、中分化癌患者53.0%（44/83）高于低分化癌患者34.1%（15/44）（ P =0.042）（ ）。
PD-1、PD-L1蛋白表达之间及其与 EGFR 突变的相关性 采用 McNemar 分析PD-1与PD-L1相关性发现，PD-1与PD-L1都表达54例（74.0%），40例（74.1%）都不表达（kappa=0.107, 5, P =0.487）。进一步采用列联系数相关性分析 EGFR 突变与PD-1和PD-L1表达的关系，发现有59例 EGFR 突变的患者中PD-1，表达率为42.4%，不表达率为57.6%，说明 EGFR 突变与PD-1表达存在负相关关系（Φ=-0.209, P =0.019）；同样 EGFR 突变与PD-L1表达存在负相关关系（Φ=-0.221, P =0.013）（ ）。
NSCLC患者的临床病理特征、PD-1、PD-L1的蛋白表达、 EFGR 突变与预后的相关因素分析 使用 Kaplan-Meier 生存曲线分析显示，NSCLC患者的总生存时间（overall survival, OS）与年龄在65岁以下患者中位OS（20个月）明显高于65岁以上患者（17个月）（ P =0.008）；高、中分化癌患者中位OS（20个月）明显高于低分化癌患者（15个月）（ P =0.033）；PD-L1表达的患者中位OS（20个月）明显高于PD-L1不表达患者（12个月）（ P < 0.001），在PD-L1阳性的患者中低表达患者中位OS（22个月）明显高于高表达患者（15个月）（ P < 0.001），腺癌中位OS（20个月）明显高于鳞癌（15个月）（ P =0.042）（ ， ）。
讨论 肺癌是全球癌症相关死亡的主要原因，肺癌的治疗已经全面进入精准医学时代，在NSCLC治疗中，EGFR-TKI已成为 EGFR 敏感突变晚期NSCLC的一线治疗。尽管如此，靶向治疗出现获得性耐药不可避免，并最终导致治疗失败。随着对肿瘤免疫逃逸机制的认识不断深入，对PD-1/PD-L1通路的免疫靶向药物在NSCLC治疗中已经表现出令人惊喜的效果。 PD-1是免疫球蛋白B7-CD28家族成员之一，主要在肿瘤浸润性淋巴细胞B淋巴细胞、自然杀伤（Natural kiiller, NK）T细胞、树突状细胞、单核-巨噬细胞表达 ，PD-L1（B7-H1）及PD-L2（B7-DC）是PD-1的主要配体，其中PD-L2主要在抗原提呈细胞上表达，PD-L1在许多类型的细胞表达，包括肿瘤细胞，免疫细胞、上皮细胞和内皮细胞。 PD-L1与PD-1相结合，传递免疫抑制信号，抑制T细胞的活化与增殖，参与肿瘤免疫逃逸 。目前经美国食品药品监督管理局（Food and Drug Administration, FDA）批准可作为晚期NSCLC二线治疗的免疫检查点抑制剂主要有PD-1抗体Nivolumab、Pembrolizumab和PD-L1抗体Atezolizumab，其中Pembrolizumab 2016年获批成为NSCLC的一线治疗用药，用药效果与PD-L1的表达水平相关。研究Keynote-024表明驱动基因阴性的PD-L1≥50% NSCLC一线使用帕博利珠单抗单药治疗显著优于化疗，可使患者获益显著，中位无进展生存时间和总生存期与化疗组相应延长，且不良反应较化疗少 ，而在PD-L1表达在1%-49%的患者则获益有限，随着PD-L1表达的增高，患者使用帕博利珠单抗治疗的OS逐步延长 。 研究 显示NSCLC中PD-1为29.2%-75.0%，PD-L1的阳性表达率为39.9%-53.1%。本研究中PD-1的表达采用CPS评分，为53.5%（68/127），肿瘤细胞和免疫细胞都表达为46.5%（59/127）；依照《非小细胞肺癌PD-L1免疫组织化学检测规范中国专家共识》 对样本进行PD-L1检测，包括推荐试剂、仪器、检测结果判读等都参照专家共识，PD-L1表达率为57.5%（73/127），其中PD-L1高表达占11%（14/127），低表达占46.5%（59/127），高锋等 PD-L1在NSCLC中阳性表达率为61.67%，在不同研究中PD-L1在NSCLC中的表达存在的异质性，在手术标本与活检标本间存在一定异质性 ，国内相关研究同样显示类似结果 ，本研究样本也存在手术标本57.5%，穿刺与支气管镜标本30.7%，肺癌转移标本11.8%，了解标本间PD-L1表达的异质性对临床检测有着重要的指导作用。 PD-1与PD-L1表达的相关性分析（kappa=0.107, 5, P =0.487）说明PD-1与PD-L1表达存在一致性，同时PD-1与PD-L1表达一致性还表现在临床病理特征的关系中，PD-1与PD-L1的表达与性别、年龄、吸烟史、组织类型、淋巴结转移均无统计学意义；在分化程度和临床分期中，PD-1与PD-L1在低分化癌中的表达65.9%（29/44）、70.5%（31/44）高于高、中分化癌47.0%（39/83）、50.6%（42/83），在临床分期Ⅰ期和Ⅱ期的阳性表达率60%（57/95）、63.2%（60/95）高于Ⅲ期和Ⅳ期的阳性表达率34.4%（11/32）、40.6%（13/32）（均 P < 0.05）；高锋等 研究PD-L1在NSCLC中的表达与淋巴结转移、肿瘤细胞分化程度、TNM分期及生存期有关（P < 0.05），其中该作者PD-L1在低分化癌中的表达83.3%（15/18）高于高、中分化癌52.4%（22/42），但在临床分期中Ⅲ期（75.0%, 9/12）和Ⅱ期（70.9%, 22/31）表达高于Ⅰ期（35.3%, 6/17），PD-L1在临床分期中的表达差异，可能存在样本量少、各组样本不均衡，导致统计结果不一致。 EGFR 基因位于第7号染色体，共有28个外显子。突变主要发生在外显子18-21，其中外显子19和21突变更为重要 。本研究发现 EGFR 在NSCLC中的突变率为46.5%（59/127），其中点突变54例，大部分为19-Del（27例，45.7%）和L-858R（23例，39%）。127例NSCLC患者中，女性、无吸烟史、腺癌、高中分化患者的 EGFR 突变率分别高于男性、有吸烟史、鳞癌、低分化患者，差异有统计学意义（P < 0.05）；丁光贵等 研究表明 EGFR 基因突变患者中女性和不吸烟患者的比例显著升高；本研究中 EGFR 突变患者PD-L1和PD-1的阳性率均低于野生型， EGFR 突变与PD-1和PD-L1表达呈负相关（Φ＝-0.209，Φ＝-0.221，均 P < 0.05）。Huynh等 研究发现 EGFR 突变与PD-L1表达呈负相关，Inoue等 研究同样发现 EGFR 突变可以下调PD-L1表达；而嵇晓辉等 、Ameratunga等 研究表明NSCLC组织中 EGFR 突变与PD-L1、PD-1表达与无相关性。但相关研究却显示免疫检查点抑制剂对 EGFR 突变患者的治疗效果并不理想 [ - ] ，推断前者的结论有一定的可靠性，但关于 EGFR 突变是如何影响PD-L1表达水平，这方面的机制需要进一步探索，为 EGFR 突变患者找到提高PD-1/PD-L1抑制剂治疗疗效的突破点。 对127例NSCLC患者进行随访，90例患者信息完整，年龄65岁以下、高、中分化癌患者中位生存时间明显高于65岁以上、低分化癌患者；同时PD-L1表达的患者中位生存时间明显高于不表达患者，PD-L1低表达患者中位生存期明显高于高表达患者。本研究中腺癌在NSCLC中所占比例高，其他类型比较少，对腺癌和鳞癌患者生存期进行分析，腺癌OS明显高于鳞癌。本研究由于样本量相对少，失访的患者可能对统计数据造成一些影响，但随访结果还是有一定的参考价值。 本研究依照《非小细胞肺癌PD-L1免疫组织化学检测规范中国专家共识》对127例NSCLC组织进行PD-L1检测、判读，筛选出PD-L1高表达和低表达的患者，能够使这些患者在抗PD-1/PD-L1免疫治疗中获益，从而提高生存率；PD-1、PD-L1的表达都与肿瘤分化程度、临床分期有关， EGFR 突变与PD-1、PD-L1存在负相关的关系，临床医生依据 EGFR 基因状态和免疫检测点PD-L1表达情况选择合适的治疗方案。同时本研究中65岁以下、腺癌、高、中分化、PD-L1表达的患者有较好的预后，为NSCLC预后评估提供依据。
|
Normal Pregnancy Care and Physiology and Select Pregnancy Complications: A Flipped Classroom Case for the OB/GYN Clerkship | a8e21ab6-f243-41a8-bd5c-c49b319a649a | 11219084 | Gynaecology[mh] | By the end of this activity, learners will be able to: 1. Describe the rationale for elements of routine prenatal care. 2. Outline key changes in maternal physiology during pregnancy. 3. Outline the evaluation, differential diagnosis, and initial treatment for the clinical presentation of third-trimester bleeding. 4. Outline the evaluation, differential diagnosis, and initial treatment for the clinical presentation of hypertension in pregnancy. 5. Describe the stages of labor and fetal heart tracings. The Association of Professors of Gynecology and Obstetrics (APGO) Medical Student Educational Objectives include knowledge of maternal physiology (objective 8), antepartum care (objective 10), intrapartum care (objective 11), hypertensive disorders in pregnancy (objective 18), and third-trimester bleeding (objective 23). These objectives are fundamental concepts for obstetrics and gynecology clerkship students, as physicians practicing a multitude of specialties will encounter pregnant patients and those who are experiencing a physiologic change or pathology of pregnancy. These APGO objectives guide curriculum development by defining fundamental knowledge, skills, and attitudes core to general medical practice. We developed this module as part of a larger project to revamp the obstetrics and gynecology clerkship curriculum from topic-centered, faculty-centered, passive learning lectures to dynamic problem-based learning sessions that would capitalize on small groups, flipped classroom principles, and active learning activities. These techniques in medical education have been shown to promote learner engagement and satisfaction and help learners apply new knowledge. – One study evaluating a flipped classroom model in the obstetrics and gynecology clerkship reported high student satisfaction but no difference in subject exam scores. Student satisfaction matters greatly given the way that medical schools evaluate clinical education and report curriculum outcomes to the Liaison Committee on Medical Education during the accreditation process. A search of MedEdPORTAL using the terms prenatal care, prenatal, pregnan*, and labor did not reveal any similar topics delivered through the flipped classroom approach. Our educational module contains content that overlaps with preexisting modules on topics of pregnancy physiology, pathophysiology of pregnancy, and fetal monitoring. – Our module is distinct from these others in that it provides didactic knowledge about normal and abnormal pregnancy and labor management layered with deliberate practice in clinical reasoning and problem-solving. The module presented here combines education modalities of team based-learning, case-based learning, and didactic material to provide a standardized yet interactive session designed for third-year medical students. We evaluated student and faculty satisfaction using surveys following delivery of the module. This module can stand on its own to teach students the basic provision of antepartum and intrapartum care and how to diagnose and manage select complications of pregnancy. In our clerkship teaching, we have incorporated this module as the second in a series of five obstetrics and gynecology modules we designed for third-year medical students. Our first module, “Evaluation and Management of Early Pregnancy,” has been previously published in MedEdPORTAL . Educational Context We conducted an original faculty training for all faculty involved in education to orient them to the principles of active learning and the flipped classroom. This training lasted for 1 hour and served the primary purpose of supporting the initial large-scale transition of our student curriculum from passive, didactic lectures to standardized flipped classroom small groups with active learning. Facilitators were oriented to active learning benefits with a review of the literature. – We provided an overview of the five-module curriculum in which this module was embedded, reviewed examples of slides to be delivered, and went over how to use the facilitator's guide to promote student engagement and provide learning points beyond the text on the slides. Throughout the review of the session slides and facilitator guide, faculty were oriented to several examples of active learning techniques, including role-play counseling, case-based problem-solving, and teaching other group members. Thereafter, dozens of new faculty facilitators were introduced through a brief verbal orientation with the clerkship director during onboarding but principally through the provided faculty guide, which offered didactic information to be shared with students during the session and prompts for active learning techniques. We assigned prework to students before the session and focused not on didactics but on application of new knowledge during the small group, consistent with a flipped classroom model. Active learning principles were exemplified in this module when students were prompted to respond to questions or solve problems using new learned knowledge and skills such as enumerating important history to be elicited from a patient with a given complaint and refining a differential diagnosis based on provided laboratory information and their interpretation of electronic fetal monitoring. In our curriculum, groups of six to eight students per faculty facilitator met for approximately 2 hours in a classroom equipped with a computer with a communicating projector and PowerPoint capabilities to complete the session. Facilitators were board-certified/board-eligible faculty in the department of obstetrics and gynecology; all received the standardized faculty guide. Advanced preparation materials for this session included the American College of Obstetricians and Gynecologists (ACOG) practice bulletin on gestational hypertension and pre-eclampsia and book chapters on antepartum care, maternal physiology, third-trimester bleeding, and abnormal labor and intrapartum fetal surveillance. – Because students voiced a desire for multimedia resources, we began to offer alternatives to some readings, as noted in . Virtual Adaptation Of the six clerkship rotations used for data collection, we conducted two virtually due to public health concerns. Virtual sessions were delivered through Microsoft Teams, Blackboard, or Zoom. When analyzed separately, satisfaction rates were comparable; thus, we present combined data below and include additional data in . Description of Flipped Classroom Case We provided students with assigned prework prior to their group session . During the session, each facilitator used provided slides and the faculty guide to conduct the session. The session included didactic information as well as exercises prompting students to apply new knowledge to clinical reasoning tasks and patient counseling. The faculty guide prompted facilitators to incorporate interactive exercises and provided clinical and basic science highlights for instructors . – Assessment This module included an optional self-assessment opportunity in the form of an online quiz embedded in the slide presentation . Students accessed the self-assessment in real time on Kahoot! using their phones, tablets, or computers. We did not record or analyze the optional quizzes. The effectiveness of this module was measured by surveys accessible to all students at the end of each session through a QR code. Students were encouraged to complete the survey, but participation was voluntary. Faculty facilitators received a direct email after their first time leading each session ( and , respectively). Student surveys assessed multiple domains of the module, including perceived value of prework materials, the degree of interactivity of the session activities, and student perception of the module's ability to both achieve its stated learning objectives and help them apply new knowledge. Faculty surveys measured the facilitators’ perception of how well the module incorporated interactive learning, reduced their preparation time for teaching, and increased their confidence in teaching broad specialty concepts to medical students. Data from virtual and in-person sessions were recorded separately and reported in aggregate because they were not statistically different. We conducted an original faculty training for all faculty involved in education to orient them to the principles of active learning and the flipped classroom. This training lasted for 1 hour and served the primary purpose of supporting the initial large-scale transition of our student curriculum from passive, didactic lectures to standardized flipped classroom small groups with active learning. Facilitators were oriented to active learning benefits with a review of the literature. – We provided an overview of the five-module curriculum in which this module was embedded, reviewed examples of slides to be delivered, and went over how to use the facilitator's guide to promote student engagement and provide learning points beyond the text on the slides. Throughout the review of the session slides and facilitator guide, faculty were oriented to several examples of active learning techniques, including role-play counseling, case-based problem-solving, and teaching other group members. Thereafter, dozens of new faculty facilitators were introduced through a brief verbal orientation with the clerkship director during onboarding but principally through the provided faculty guide, which offered didactic information to be shared with students during the session and prompts for active learning techniques. We assigned prework to students before the session and focused not on didactics but on application of new knowledge during the small group, consistent with a flipped classroom model. Active learning principles were exemplified in this module when students were prompted to respond to questions or solve problems using new learned knowledge and skills such as enumerating important history to be elicited from a patient with a given complaint and refining a differential diagnosis based on provided laboratory information and their interpretation of electronic fetal monitoring. In our curriculum, groups of six to eight students per faculty facilitator met for approximately 2 hours in a classroom equipped with a computer with a communicating projector and PowerPoint capabilities to complete the session. Facilitators were board-certified/board-eligible faculty in the department of obstetrics and gynecology; all received the standardized faculty guide. Advanced preparation materials for this session included the American College of Obstetricians and Gynecologists (ACOG) practice bulletin on gestational hypertension and pre-eclampsia and book chapters on antepartum care, maternal physiology, third-trimester bleeding, and abnormal labor and intrapartum fetal surveillance. – Because students voiced a desire for multimedia resources, we began to offer alternatives to some readings, as noted in . Of the six clerkship rotations used for data collection, we conducted two virtually due to public health concerns. Virtual sessions were delivered through Microsoft Teams, Blackboard, or Zoom. When analyzed separately, satisfaction rates were comparable; thus, we present combined data below and include additional data in . We provided students with assigned prework prior to their group session . During the session, each facilitator used provided slides and the faculty guide to conduct the session. The session included didactic information as well as exercises prompting students to apply new knowledge to clinical reasoning tasks and patient counseling. The faculty guide prompted facilitators to incorporate interactive exercises and provided clinical and basic science highlights for instructors . – This module included an optional self-assessment opportunity in the form of an online quiz embedded in the slide presentation . Students accessed the self-assessment in real time on Kahoot! using their phones, tablets, or computers. We did not record or analyze the optional quizzes. The effectiveness of this module was measured by surveys accessible to all students at the end of each session through a QR code. Students were encouraged to complete the survey, but participation was voluntary. Faculty facilitators received a direct email after their first time leading each session ( and , respectively). Student surveys assessed multiple domains of the module, including perceived value of prework materials, the degree of interactivity of the session activities, and student perception of the module's ability to both achieve its stated learning objectives and help them apply new knowledge. Faculty surveys measured the facilitators’ perception of how well the module incorporated interactive learning, reduced their preparation time for teaching, and increased their confidence in teaching broad specialty concepts to medical students. Data from virtual and in-person sessions were recorded separately and reported in aggregate because they were not statistically different. Students Among 116 students attending the education session during our evaluation period, 64 responded to the survey assessing satisfaction, a response rate of 55%. reports student satisfaction. Satisfaction was very high with the assigned prework, the interactive nature of the module, and the session's utility in helping students apply learned principles to patient care. Regarding discrete learning objectives, 94%-98% of students agreed the module was successful in achieving each of the five learning objectives. A chi-square test found no statistically significant difference in the rates of student satisfaction of those who participated in virtual sessions versus in person (5% level of significance; ). Clinical Instructors Nine out of 15 instructors who facilitated an educational session (60%) completed the survey during our evaluation period. outlines faculty satisfaction. All clinical educators (100%) agreed the session faculty guide facilitated active learning. Most (89%) agreed the guide reduced their preparation time compared to traditional didactic lectures and reported increased confidence in teaching the topics included in the sessions . Among 116 students attending the education session during our evaluation period, 64 responded to the survey assessing satisfaction, a response rate of 55%. reports student satisfaction. Satisfaction was very high with the assigned prework, the interactive nature of the module, and the session's utility in helping students apply learned principles to patient care. Regarding discrete learning objectives, 94%-98% of students agreed the module was successful in achieving each of the five learning objectives. A chi-square test found no statistically significant difference in the rates of student satisfaction of those who participated in virtual sessions versus in person (5% level of significance; ). Nine out of 15 instructors who facilitated an educational session (60%) completed the survey during our evaluation period. outlines faculty satisfaction. All clinical educators (100%) agreed the session faculty guide facilitated active learning. Most (89%) agreed the guide reduced their preparation time compared to traditional didactic lectures and reported increased confidence in teaching the topics included in the sessions . The module presented here gives students the opportunity to apply knowledge and concepts in the management of pregnancy and labor and supports clinical educators in facilitating an interactive session. Furthermore, facilitators agreed that this format with a faculty guide reduced their preparation time. The nature of clinical patient care provides students with a breadth of encounters during their clerkship, but clerkship leaders cannot guarantee every student will encounter every foundational concept. Constraints on clinical teaching time continue to expand with documentation requirements, compliance modules, reproductive health care restrictions, and so on. This module helps clerkship leaders ensure that each student receives certain didactic and applied learning related to caring for the pregnant patient. Students and facilitators agree the design fosters interactivity. Traditionally, faculty in our department and many across the country were assigned passive didactic lecture topics based on their expertise, research, or subspecialty area. Our data suggest that providing the faculty guide with this module allows faculty members with a wide variety of expertise and specialty knowledge to feel confident facilitating the session. Though we evaluated and typically conducted the session in small groups of six to eight students, we occasionally combined two groups of students when a facilitator was not available. The materials can also be used with larger groups of students. The equivalent student satisfaction between virtual and in-person sessions suggests that the module can be adapted for different learning environments, if necessary. The cohort of session facilitators was not large enough to perform statistical analysis, but facilitators did comment on a preference for in-person sessions due to difficulty gauging student engagement virtually. The online multiple-choice self-assessment that we incorporated through Kahoot! can be viewed as optional and can be adapted for settings without this technology by using low-fidelity tools like laminated cards that students can raise to indicate a, b, c, or d. We acknowledge the limitations of our assessment of this module. Namely, our respondents were a convenience sample of students and facilitators, which may have biased the results toward being more positive. Though student survey scores were high, the response rates were 55% for students and 60% for faculty, and the survey was optional. Furthermore, our data support student self-perception about the module's utility in meeting learning objectives, but we did not measure impact of the module on objective measures frequently used in clerkships, such as the NBME shelf exam, USMLE Step 2 CK scores, or clinical assessments such as OSCEs. These represent possible future directions for this work. A notable challenge to be addressed when implementing this type of module is the pace of new medical knowledge. It is imperative that someone periodically review and update the content as knowledge evolves. For example, after our first iteration, ACOG changed recommendations on the gestational duration at which group B streptococcus screening is performed. Furthermore, as new faculty joined, we oriented them to the education principles and the faculty guide. While small-group settings and active learning offer advantages, they may be experienced differently by historically marginalized students. With this in mind, we no longer incorporate student performance assessments from this setting into their clerkship grade. In summary, implementing this module garnered positive reviews from students and faculty facilitators. The module responds to a call for more active learning techniques in medical education and to growing demands on the time of clinical educators at academic hospitals. It accomplishes these things without sacrificing the ability to provide standardized and interactive education and meet specified learning objectives for all clerkship students. |
The number of optometrists is inversely correlated with blindness in OECD countries | 2b78b527-6a9d-46b4-86ac-c16a965ec9b3 | 7814665 | Ophthalmology[mh] | The authors report no conflicts of interest and have no proprietary interest in any of the materials mentioned in this article.
Einat Shneor: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Michal Isaacson: Formal analysis (supporting); Methodology (supporting); Software (equal); Writing‐review & editing (supporting). Ariela Gordon‐Shaag: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Methodology (equal); Software (equal); Writing‐original draft (equal); Writing‐review & editing (equal).
|
Current status of Tele-speech language therapy by type and support for patients with post-stroke aphasia: A scoping review | 3d7dcff6-38ff-4ced-96c7-079e48d0a4f1 | 11936174 | Medicine[mh] | The incidence of stroke is increasing, especially among the elderly in an aging society . As the number of stroke survivors increases, so too does the number of patients with aphasia (PWA), who face difficulties with language skills (listening, speaking, reading, and writing), communication (situational language use), well-being, and quality of life (QOL) [ – ]. Additionally, the COVID-19 epidemic has reduced PWA’s access to speech-language therapy (SLT) and limited opportunities for social interaction [ – ]. In this context, tele-lingual therapy (Tele-SLT) has become increasingly important for PWA . Tele-SLT is a method of providing SLT using electronic devices and technology . This Tele-SLT assistance can be divided into “assessment” (tele-assessment), which measures and quantitatively translates the patient’s condition, and “training” (tele-training), which aims to resolve the patient’s difficulties . Furthermore, they can be provided in a synchronous manner, with a speech-language pathologist (SLP) providing SLT in real time via videoconferencing, or in an asynchronous manner, without the presence of an SLP and at one’s own pace, or combined types, in which a combination of both is provided [ – ]. The flexible combination of these methods according to the PWA’s needs allows for the provision of Tele-SLT tailored to individual requirements. Therefore, the demand for Tele-SLT has increased since the COVID-19 craze , but it has not yet reached the same level of diffusion as traditional methods . Although the reasons for the struggle with Tele-SLT diffusion are not fully understood, a review of previous studies [ , – ] suggests that one barrier may be the way information on Tele-SLT for PWA is organized and interpreted. Specifically, it is not well structured in terms of how it is provided and supports the PWA. In other words, there is a lack of clarity about what reliable assessment and training methods exist and how they can be delivered remotely for SLT for PWA. It is undeniable that the absence of reference standards for providing remote SLT has led to situations where it is needed but not provided. Therefore, this study will use a scoping review approach to investigate Tele-SLT for post-stroke PWA, based on a framework of remote support methods: assessment methods, training methods, and delivery methods, including synchronous, asynchronous, and combined approaches. The significance of this study lies in comprehensively collecting, sorting, and disseminating information that will contribute to the dissemination of Tele-SLT and increase its provision to PWA who have experienced delayed access to professional support since the COVID-19 epidemic.
Study design Our aim was to systematically identify and analyze the methods (assessment, training) and delivery (synchronous, asynchronous, combined) of Tele-SLT support for post-stroke PWA. Additionally, we sought to identify gaps in the current field of research. To achieve this objective, we used a scoping review methodology . It is important to note that no published articles describe the protocol of this study. Information sources and evidence retrieval This scoping review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews . We achieved compliance with these guidelines . Searches were conducted in the online databases of Medline [PubMed Interface], Embase, PsycInfo, Cochrane Library, and the ICHUSHI Web (Japan). The search strategy, designed by the expert medical secretary, is detailed in . The first search was initiated on March 30, 2023, without specifying the year of publication. No manual search of the grey literature or reference lists of the included articles was performed. Selection criteria We included studies that investigated the assessment or training methods of Tele-SLT for PWA following a stroke. Studies were excluded if they: 1) involved participants under 18 years of age; 2) combined Tele-SLT with in-person SLT; or 3) were review articles (e.g., systematic reviews, scoping reviews, narrative reviews), case reports, qualitative studies, cost-effectiveness analyses, books, conference proceedings, or study protocols. Full details of the inclusion and exclusion criteria are provided in . Screening method Two authors (YK and SN) independently screened titles and abstracts of identified articles. Subsequently, two other authors (YK and AY) independently reviewed the full texts of the selected articles for inclusion. Any discrepancies between authors during the screening process were resolved through discussion. If a consensus could not be reached, a third author (HN) made the final decision. Data extraction process Two authors (YK and AY) independently reviewed the full texts of articles selected in the second screening and extracted data. Discrepancies in data extraction were resolved through discussion between the two authors. If consensus could not be reached, a third author (HN) made the final decision. For studies on tele-assessment methods, the following information was collected: author, year of publication, country of the first author, participant characteristics (e.g., sex, age), number of participants, domains covered (e.g., language function, communication, well-being, QOL), software used, electronic devices used, and scale accuracy (e.g., reliability, validity). For studies on tele-training methods, the following information was collected: author, year of publication, study design, participant characteristics (sex, age), number of participants, domains covered (language function, communication, well-being, QOL), software used, electronic devices used, and training outcomes. One author (YK) managed the data using Rayyan reference management software and compiled the data into tables using Excel. Any missing articles were obtained by contacting the corresponding authors. Study quality assessment Two authors (YK, AY) independently assessed the quality of the studies, with the third author (SF) resolving any disagreements. The evaluation checklist [ , , ], partially adapted by the authors to fit this study’s inclusion criteria, consisted of five categories: study design, sample size, demographic variables, aphasia variables (including type classification, severity, and duration since onset), telemedicine characteristics, and data collection methods (specifically, the use of standardized rating scales). Each category was assigned a rating of high, medium, or low ( ). A study was considered to be of high quality if it received no low” ratings and at least four “high” ratings. If a study has no “low” ratings but does not meet the criteria for high quality, it is classified as “medium.” Otherwise, it is rated as “low.” Meta-analysis of the training effects of Tele-SLT A meta-analysis was conducted using Review Manager 5.4 to evaluate the effects of Tele-SLT training reported in randomized controlled trials (RCTs). The analysis focused on language and communication outcomes. Mean differences (MD), standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for each outcome using extracted means and standard deviations. Heterogeneity was assessed using I 2 . A fixed-effects model was used when heterogeneity was not significant ( P > 0.01), and a random-effects model was used when heterogeneity was significant ( P ≤ 0.01). Outcome measures included, for language function, the Aphasia Quotient (AQ) and its subscales (spontaneous speech, comprehension, naming and repetition) of the Western Aphasia Battery (WAB), WAB-Revised (WAB-R), the Aachen Aphasia Test, and the Boston Naming Test (BNT). For communication function, outcome measures included the Communication Activities Log (CAL), the Communication Effectiveness Index (CETI), and the Communication Abilities in Daily Living (CADL). SMD were calculated when analyzing different outcomes within the same model, whereas MD were calculated for homogenous outcomes. These procedures were based on previous meta-analyses regarding aphasia treatment .
Our aim was to systematically identify and analyze the methods (assessment, training) and delivery (synchronous, asynchronous, combined) of Tele-SLT support for post-stroke PWA. Additionally, we sought to identify gaps in the current field of research. To achieve this objective, we used a scoping review methodology . It is important to note that no published articles describe the protocol of this study.
This scoping review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews . We achieved compliance with these guidelines . Searches were conducted in the online databases of Medline [PubMed Interface], Embase, PsycInfo, Cochrane Library, and the ICHUSHI Web (Japan). The search strategy, designed by the expert medical secretary, is detailed in . The first search was initiated on March 30, 2023, without specifying the year of publication. No manual search of the grey literature or reference lists of the included articles was performed.
We included studies that investigated the assessment or training methods of Tele-SLT for PWA following a stroke. Studies were excluded if they: 1) involved participants under 18 years of age; 2) combined Tele-SLT with in-person SLT; or 3) were review articles (e.g., systematic reviews, scoping reviews, narrative reviews), case reports, qualitative studies, cost-effectiveness analyses, books, conference proceedings, or study protocols. Full details of the inclusion and exclusion criteria are provided in .
Two authors (YK and SN) independently screened titles and abstracts of identified articles. Subsequently, two other authors (YK and AY) independently reviewed the full texts of the selected articles for inclusion. Any discrepancies between authors during the screening process were resolved through discussion. If a consensus could not be reached, a third author (HN) made the final decision.
Two authors (YK and AY) independently reviewed the full texts of articles selected in the second screening and extracted data. Discrepancies in data extraction were resolved through discussion between the two authors. If consensus could not be reached, a third author (HN) made the final decision. For studies on tele-assessment methods, the following information was collected: author, year of publication, country of the first author, participant characteristics (e.g., sex, age), number of participants, domains covered (e.g., language function, communication, well-being, QOL), software used, electronic devices used, and scale accuracy (e.g., reliability, validity). For studies on tele-training methods, the following information was collected: author, year of publication, study design, participant characteristics (sex, age), number of participants, domains covered (language function, communication, well-being, QOL), software used, electronic devices used, and training outcomes. One author (YK) managed the data using Rayyan reference management software and compiled the data into tables using Excel. Any missing articles were obtained by contacting the corresponding authors.
Two authors (YK, AY) independently assessed the quality of the studies, with the third author (SF) resolving any disagreements. The evaluation checklist [ , , ], partially adapted by the authors to fit this study’s inclusion criteria, consisted of five categories: study design, sample size, demographic variables, aphasia variables (including type classification, severity, and duration since onset), telemedicine characteristics, and data collection methods (specifically, the use of standardized rating scales). Each category was assigned a rating of high, medium, or low ( ). A study was considered to be of high quality if it received no low” ratings and at least four “high” ratings. If a study has no “low” ratings but does not meet the criteria for high quality, it is classified as “medium.” Otherwise, it is rated as “low.”
A meta-analysis was conducted using Review Manager 5.4 to evaluate the effects of Tele-SLT training reported in randomized controlled trials (RCTs). The analysis focused on language and communication outcomes. Mean differences (MD), standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for each outcome using extracted means and standard deviations. Heterogeneity was assessed using I 2 . A fixed-effects model was used when heterogeneity was not significant ( P > 0.01), and a random-effects model was used when heterogeneity was significant ( P ≤ 0.01). Outcome measures included, for language function, the Aphasia Quotient (AQ) and its subscales (spontaneous speech, comprehension, naming and repetition) of the Western Aphasia Battery (WAB), WAB-Revised (WAB-R), the Aachen Aphasia Test, and the Boston Naming Test (BNT). For communication function, outcome measures included the Communication Activities Log (CAL), the Communication Effectiveness Index (CETI), and the Communication Abilities in Daily Living (CADL). SMD were calculated when analyzing different outcomes within the same model, whereas MD were calculated for homogenous outcomes. These procedures were based on previous meta-analyses regarding aphasia treatment .
Search results The initial search yielded a total of 1,484 articles. After removing duplicates, 1,026 articles underwent primary screening based on title and abstract. This screening narrowed the list to 90 articles, which underwent a secondary comprehensive full-text review. Finally, 35 articles met the inclusion criteria and were included in the current scoping review ( ). presents the list of articles included in the secondary screening, the evaluators’ (YK, AY) judgments for each, the final decisions, and the reasons for exclusion. Trends in selected articles Overall trend. Regarding Tele-SLT support methods, three articles (8.57%) focused on assessment, while 32 (91.43%) focused on training. Regarding delivery methods, 14 articles (40.00%) used synchronous Tele-SLT, 20 (57.14%) used asynchronous Tele-SLT, and 1 (2.86%) used a combined approach. Across all 35 articles, 859 individuals with PWA participated, of whom 693 individuals with chronic PWA were included in 31 articles (88.57%). Study designs for assessment methods were all cross-sectional (n = 3/3). For training methods, designs included pre-post studies (n = 18/32, 56.25%), quasi-randomized controlled trials (quasi-RCTs) (n = 2/32, 6.25%), and RCTs (n = 12/32, 37.50%). All articles were published in English. Tele-SLT support methods, delivery methods, and study designs are summarized in . Trends in research on synchronous Tele-SLT. Fourteen studies investigated synchronous Tele-SLT, comprising two studies on assessment methods and twelve on training methods ( and ). The assessment studies focused on measuring language function and QOL . Training studies targeted language function (n = 8) [ – , , – ], communication (n = 8) [ , , , , , , , ], QOL (n = 7) [ , , , , , ], and well-being (n = 3) [ , , ]. Seven studies utilized software specifically designed for PWA. Access2Aphasia was used for assessment , while EVA Park , Oralys Tele Therapy , The Web-based dual card game , Rehabilitation Gaming System for aphasia , and NeuroVR 2.0 were used for training. Assessment methods were based on the Assessment of Living with Aphasia, the Psycholinguistic Assessments of Language Processing in Aphasia, and the Short Aphasia Test for Gulf Arabic speakers. The psychometric properties examined included intra-rater and inter-rater reliability, criterion validity, and construct validity. Training methods were based on Promoting Aphasics’ Communicative Effectiveness , Intensive Aphasia Therapy [ , , ], and conversational training [ , , , , ], with treatment durations ranging from eight days to 24 weeks. Eight studies reported on the maintenance of treatment effects at follow-up periods of 3 to 12 weeks [ – , , – ]. Trends in research on asynchronous Tele-SLT. Twenty studies investigated asynchronous Tele-SLT, including one study on assessment methods and nineteen on training methods ( and ). The assessment study focused on measuring language function (n = 1) . Training studies focused on language function (n = 17) [ , , – , – ], communication (n = 7) [ – , , , , ], and improving QOL (n = 3) [ , , ], as well as well-being (n = 1) . Seventeen studies used software specifically designed for PWA. The Mobile Aphasia Screening Test was used for assessment . Training programs utilized various software including Aphasia Scripts [ , , , ], Aphasia Mate , Oral Reading for Language in Aphasia , AphasiaRx , StepByStep aphasia software , and iAphasia . The assessment study was based on the Korean version of the Frenchay Aphasia Screening Test and examined internal consistency, inter-rater reliability, criterion validity, and construct validity . Training studies employed methods such as word comprehension and production training [ , – , , – , – ] and script training [ , , , , , ], with intervention durations ranged from single sessions to 26 weeks. Ten studies reported on the maintenance of treatment effects at follow-up periods of 2 to 24 weeks [ , , , , , , – , ]. Trends in research on combined Tele-SLT. The one combined study was a training method study ( ). This study focused on improving language function (n = 1) by providing self-designed simultaneous and non-simultaneous training in Face Time and Power Point . Results showed a significant improvement in performance on a unique calling task and confirmed maintenance of calling ability after 6 weeks of training. The quality of Tele-SLT studies Two authors (YK and AY) demonstrated high agreement (97.14%, 34/35) in their assessments of the included studies. Three studies (8.57%) [ , , ] received a rating of “High,” including both synchronous and asynchronous Tele-SLT training interventions. Five studies (14.29%) [ , , , , ] were rated “Moderate,” encompassing synchronous Tele-SLT assessment and training, as well as asynchronous Tele-SLT training. The remaining 27 studies (77.14%) were rated as “Low” ( ). Notably, “Study Design” (lack of a control group or insufficient sample size in RCTs) and “Data Collection” (insufficient patient outcome measures) were the most frequent reasons for a “Low” rating (n = 19, 18; 54.29%, 51.43%) ( ). Training effects of Tele-SLT We performed a meta-analysis of Tele-SLT training effects using data from four RCTs. Two studies examined synchronous training using the CAL and CETI as outcome measures , and three examined asynchronous training using the WAB, WAB-R as the outcome measure [ , , ]. For synchronous training, there was no significant difference between the intervention and control groups in combined CAL and CETI scores (fixed-effects model, SMD = -0.10, 95% CI = -0.57 to 0.37, P > 0.05) ( ). For asynchronous training, analysis of the WAB-AQ showed no significant difference between groups (fixed-effects model, SMD = 0.24, 95% CI = -0.17 to 0.65, P > 0.05) ( ). However, a significant difference was found in the combined WAB subscales (Spontaneous speech, Comprehension, Repetition, Naming) (fixed-effects model, SMD = 0.23, 95% CI = 0.02 to 0.44, P < 0.05). Despite the significant result for the combined subscales, no significant differences were observed for individual subscales: Spontaneous speech (fixed-effects model, SMD = 0.20, 95% CI = -0.26 to 0.67, P > 0.05), Comprehension (fixed-effects model, SMD = 0.28, 95% CI = -0.13 to 0.69, P > 0.05), Repetition (fixed-effects model, SMD = 0.13, 95% CI = -0.28 to 0.54, P > 0.05) and Naming (fixed-effects model, SMD = 0.31, 95% CI = -0.11 to 0.72, P > 0.05) ( )
The initial search yielded a total of 1,484 articles. After removing duplicates, 1,026 articles underwent primary screening based on title and abstract. This screening narrowed the list to 90 articles, which underwent a secondary comprehensive full-text review. Finally, 35 articles met the inclusion criteria and were included in the current scoping review ( ). presents the list of articles included in the secondary screening, the evaluators’ (YK, AY) judgments for each, the final decisions, and the reasons for exclusion.
Overall trend. Regarding Tele-SLT support methods, three articles (8.57%) focused on assessment, while 32 (91.43%) focused on training. Regarding delivery methods, 14 articles (40.00%) used synchronous Tele-SLT, 20 (57.14%) used asynchronous Tele-SLT, and 1 (2.86%) used a combined approach. Across all 35 articles, 859 individuals with PWA participated, of whom 693 individuals with chronic PWA were included in 31 articles (88.57%). Study designs for assessment methods were all cross-sectional (n = 3/3). For training methods, designs included pre-post studies (n = 18/32, 56.25%), quasi-randomized controlled trials (quasi-RCTs) (n = 2/32, 6.25%), and RCTs (n = 12/32, 37.50%). All articles were published in English. Tele-SLT support methods, delivery methods, and study designs are summarized in . Trends in research on synchronous Tele-SLT. Fourteen studies investigated synchronous Tele-SLT, comprising two studies on assessment methods and twelve on training methods ( and ). The assessment studies focused on measuring language function and QOL . Training studies targeted language function (n = 8) [ – , , – ], communication (n = 8) [ , , , , , , , ], QOL (n = 7) [ , , , , , ], and well-being (n = 3) [ , , ]. Seven studies utilized software specifically designed for PWA. Access2Aphasia was used for assessment , while EVA Park , Oralys Tele Therapy , The Web-based dual card game , Rehabilitation Gaming System for aphasia , and NeuroVR 2.0 were used for training. Assessment methods were based on the Assessment of Living with Aphasia, the Psycholinguistic Assessments of Language Processing in Aphasia, and the Short Aphasia Test for Gulf Arabic speakers. The psychometric properties examined included intra-rater and inter-rater reliability, criterion validity, and construct validity. Training methods were based on Promoting Aphasics’ Communicative Effectiveness , Intensive Aphasia Therapy [ , , ], and conversational training [ , , , , ], with treatment durations ranging from eight days to 24 weeks. Eight studies reported on the maintenance of treatment effects at follow-up periods of 3 to 12 weeks [ – , , – ]. Trends in research on asynchronous Tele-SLT. Twenty studies investigated asynchronous Tele-SLT, including one study on assessment methods and nineteen on training methods ( and ). The assessment study focused on measuring language function (n = 1) . Training studies focused on language function (n = 17) [ , , – , – ], communication (n = 7) [ – , , , , ], and improving QOL (n = 3) [ , , ], as well as well-being (n = 1) . Seventeen studies used software specifically designed for PWA. The Mobile Aphasia Screening Test was used for assessment . Training programs utilized various software including Aphasia Scripts [ , , , ], Aphasia Mate , Oral Reading for Language in Aphasia , AphasiaRx , StepByStep aphasia software , and iAphasia . The assessment study was based on the Korean version of the Frenchay Aphasia Screening Test and examined internal consistency, inter-rater reliability, criterion validity, and construct validity . Training studies employed methods such as word comprehension and production training [ , – , , – , – ] and script training [ , , , , , ], with intervention durations ranged from single sessions to 26 weeks. Ten studies reported on the maintenance of treatment effects at follow-up periods of 2 to 24 weeks [ , , , , , , – , ]. Trends in research on combined Tele-SLT. The one combined study was a training method study ( ). This study focused on improving language function (n = 1) by providing self-designed simultaneous and non-simultaneous training in Face Time and Power Point . Results showed a significant improvement in performance on a unique calling task and confirmed maintenance of calling ability after 6 weeks of training.
Regarding Tele-SLT support methods, three articles (8.57%) focused on assessment, while 32 (91.43%) focused on training. Regarding delivery methods, 14 articles (40.00%) used synchronous Tele-SLT, 20 (57.14%) used asynchronous Tele-SLT, and 1 (2.86%) used a combined approach. Across all 35 articles, 859 individuals with PWA participated, of whom 693 individuals with chronic PWA were included in 31 articles (88.57%). Study designs for assessment methods were all cross-sectional (n = 3/3). For training methods, designs included pre-post studies (n = 18/32, 56.25%), quasi-randomized controlled trials (quasi-RCTs) (n = 2/32, 6.25%), and RCTs (n = 12/32, 37.50%). All articles were published in English. Tele-SLT support methods, delivery methods, and study designs are summarized in .
Fourteen studies investigated synchronous Tele-SLT, comprising two studies on assessment methods and twelve on training methods ( and ). The assessment studies focused on measuring language function and QOL . Training studies targeted language function (n = 8) [ – , , – ], communication (n = 8) [ , , , , , , , ], QOL (n = 7) [ , , , , , ], and well-being (n = 3) [ , , ]. Seven studies utilized software specifically designed for PWA. Access2Aphasia was used for assessment , while EVA Park , Oralys Tele Therapy , The Web-based dual card game , Rehabilitation Gaming System for aphasia , and NeuroVR 2.0 were used for training. Assessment methods were based on the Assessment of Living with Aphasia, the Psycholinguistic Assessments of Language Processing in Aphasia, and the Short Aphasia Test for Gulf Arabic speakers. The psychometric properties examined included intra-rater and inter-rater reliability, criterion validity, and construct validity. Training methods were based on Promoting Aphasics’ Communicative Effectiveness , Intensive Aphasia Therapy [ , , ], and conversational training [ , , , , ], with treatment durations ranging from eight days to 24 weeks. Eight studies reported on the maintenance of treatment effects at follow-up periods of 3 to 12 weeks [ – , , – ].
Twenty studies investigated asynchronous Tele-SLT, including one study on assessment methods and nineteen on training methods ( and ). The assessment study focused on measuring language function (n = 1) . Training studies focused on language function (n = 17) [ , , – , – ], communication (n = 7) [ – , , , , ], and improving QOL (n = 3) [ , , ], as well as well-being (n = 1) . Seventeen studies used software specifically designed for PWA. The Mobile Aphasia Screening Test was used for assessment . Training programs utilized various software including Aphasia Scripts [ , , , ], Aphasia Mate , Oral Reading for Language in Aphasia , AphasiaRx , StepByStep aphasia software , and iAphasia . The assessment study was based on the Korean version of the Frenchay Aphasia Screening Test and examined internal consistency, inter-rater reliability, criterion validity, and construct validity . Training studies employed methods such as word comprehension and production training [ , – , , – , – ] and script training [ , , , , , ], with intervention durations ranged from single sessions to 26 weeks. Ten studies reported on the maintenance of treatment effects at follow-up periods of 2 to 24 weeks [ , , , , , , – , ].
The one combined study was a training method study ( ). This study focused on improving language function (n = 1) by providing self-designed simultaneous and non-simultaneous training in Face Time and Power Point . Results showed a significant improvement in performance on a unique calling task and confirmed maintenance of calling ability after 6 weeks of training.
Two authors (YK and AY) demonstrated high agreement (97.14%, 34/35) in their assessments of the included studies. Three studies (8.57%) [ , , ] received a rating of “High,” including both synchronous and asynchronous Tele-SLT training interventions. Five studies (14.29%) [ , , , , ] were rated “Moderate,” encompassing synchronous Tele-SLT assessment and training, as well as asynchronous Tele-SLT training. The remaining 27 studies (77.14%) were rated as “Low” ( ). Notably, “Study Design” (lack of a control group or insufficient sample size in RCTs) and “Data Collection” (insufficient patient outcome measures) were the most frequent reasons for a “Low” rating (n = 19, 18; 54.29%, 51.43%) ( ).
We performed a meta-analysis of Tele-SLT training effects using data from four RCTs. Two studies examined synchronous training using the CAL and CETI as outcome measures , and three examined asynchronous training using the WAB, WAB-R as the outcome measure [ , , ]. For synchronous training, there was no significant difference between the intervention and control groups in combined CAL and CETI scores (fixed-effects model, SMD = -0.10, 95% CI = -0.57 to 0.37, P > 0.05) ( ). For asynchronous training, analysis of the WAB-AQ showed no significant difference between groups (fixed-effects model, SMD = 0.24, 95% CI = -0.17 to 0.65, P > 0.05) ( ). However, a significant difference was found in the combined WAB subscales (Spontaneous speech, Comprehension, Repetition, Naming) (fixed-effects model, SMD = 0.23, 95% CI = 0.02 to 0.44, P < 0.05). Despite the significant result for the combined subscales, no significant differences were observed for individual subscales: Spontaneous speech (fixed-effects model, SMD = 0.20, 95% CI = -0.26 to 0.67, P > 0.05), Comprehension (fixed-effects model, SMD = 0.28, 95% CI = -0.13 to 0.69, P > 0.05), Repetition (fixed-effects model, SMD = 0.13, 95% CI = -0.28 to 0.54, P > 0.05) and Naming (fixed-effects model, SMD = 0.31, 95% CI = -0.11 to 0.72, P > 0.05) ( )
Findings of this Tele-SLT review This review investigated Tele-SLT support and delivery methods for PWA. Our findings reveal a growing body of research examining the use of specialized software for Tele-SLT, particularly for individuals with chronic PWA; many studies demonstrate its benefits. However, three key challenges emerged. First, research on tele-assessment is scarce compared to research on tele-training. Second, there is insufficient research on synchronous remote SLT compared to asynchronous remote SLT, and RCTs have yet to demonstrate a significant training effect for synchronous interventions compared to asynchronous interventions. Third, the overall methodological quality of Tele-SLT research is low. These findings suggest that the availability of diverse Tele-SLT approaches may be limited, and the efficacy of Tele-SLT, particularly synchronous training, warrants further investigation. As the demand for Tele-SLT increases, addressing these challenges is crucial. Bias in the number of studies on the content of support in Tele-SLT Only three studies (8.57%) involving tele-assessments were included in this study, highlighting the challenges associated with conducting tele-assessments. Specifically, it may be difficult to remotely determine whether an individual is PWA and to obtain detailed information about them, potentially hindering the provision of tele-training . A significant challenge is the influence of digital device functionality and the environment on the interpretation of PWA outcomes. For instance, in videoconferencing communication assessments, there is a concern that microphones may fail to capture unintelligible or faint utterances caused by speech or language disorders, making it challenging to derive clinical insights into spoken language . Furthermore, loss of video data has been noted due to volume changes associated with the PWA’s sitting position and inadequate computer capacity, even when the microphone’s audio input is normal . Therefore, implementing tele-assessments may necessitate selecting an assessment modality based on the severity of the PWA’s aphasia and the device’s capabilities, as well as verifying the assessment environment in advance . From clinical or research perspectives, establishing such protocols and checklists is essential . In the context of Tele-SLT, utilizing conversation recording protocols from Aphasiabank, an online platform for sharing PWA data, may be beneficial . If these procedures are made accessible to SLP, other professionals, and PWA in a format compatible with Tele-SLT, it is likely that tele-evaluation will gain popularity as a user-friendly approach. Limited research on synchronous Tele-SLT delivery Our review found that only 14 of the 35 included studies (40.00%) investigated synchronous Tele-SLT, suggesting potential challenges in its implementation. This scarcity may indicate a lack of strategies for addressing communication, QOL, and well-being in PWA through synchronous Tele-SLT [ – ]. Such support is often associated with, and facilitated by, the interactive communication inherent in synchronous interventions. Interactive communication promotes social participation in PWA , which is directly linked to QOL and well-being . Therefore, synchronous Tele-SLT may be crucial for supporting social participation and QOL in PWA, and addressing barriers to its implementation is essential. Potential barriers may include concerns about privacy and confidentiality, as well as a lack of ethical and technical support for using video conferencing in synchronous Tele-SLT . Developing guidelines and checklists regarding privacy, confidentiality, and the use of communication technology for PWA could help mitigate these issues . Without standardized practices for video conferencing in SLT, the expansion of synchronous Tele-SLT may be hindered, and the inconclusive training effects suggested by our meta-analysis may remain unresolved. Without addressing these challenges, we risk missing opportunities to provide effective SLT to PWA who have faced increased difficulties with social participation and daily living since the onset of the COVID-19 pandemic. Quality of Tele-SLT studies The quality of Tele-SLT studies has been consistently rated as low. The main reasons for this can be summarized as follows: poor baseline data due to the lack of standardized assessment methods for PWA, as noted in the ‘Data Collection’ section, and an undefined target group and insufficient sample size, as mentioned in the ‘Study Design’ section. Regarding the former, the shortcomings of standardized assessment methods in SLT have been documented , and adapting traditional tests for Tele-SLT, as has been done with the WAB , may be necessary. With regard to the latter, barriers such as inattention and poor auditory comprehension in PWA have hindered their participation in telemedicine . This is why maintaining and increasing sample sizes in studies involving PWA has been challenging. Although some suggest that patient sampling is easier in telerehabilitation , this may not hold true for PWA. Therefore, study designs must consider the cognitive and language functions of PWA to ensure an adequate sample size. Future directions in the post-COVID-19 era The recent COVID-19 pandemic has increased the challenges of delivering in-person SLT, which has led to a surge in global interest in Tele-SLT. Establishing effective tele-assessment and synchronous training methods, which our review identified as areas in need of further development, is a high priority. Widespread implementation of Tele-SLT has the potential to expand access to SLT for a greater number of PWA. To achieve this, it is crucial to determine which types of Tele-SLT (assessment, training, synchronous, asynchronous, or combined) are most effective for specific PWA profiles. Therefore, improving the quantity and quality of research across all areas of Tele-SLT, including the development of tele-assessment tools and synchronous training protocols specifically designed for PWA, is essential. One approach to address this challenge is to promote the development and use of specialized software for PWA. Such software was utilized in 68.57% (n = 24/35) of the studies included in this review. Specialized software can facilitate the management of patient information in rehabilitation settings and, under appropriate conditions, enable high-quality audio and video transmission . Furthermore, integrating software into Tele-SLT practice is thought to reduce the burden on speech-language pathologists and promote wider adoption of Tele-SLT . Therefore, developing and disseminating PWA-specific software could be instrumental in expanding access to and strengthening the evidence base for Tele-SLT. Limitations of this review This review has five main limitations. First, the failure to use specific checklists for the quantitative quality assessment of the included studies may introduce uncertainty regarding the consistency of the study quality in this scoping review design. Second, while a comprehensive literature search was conducted, the exclusion of articles in languages other than English and Japanese, as well as gray literature and manual searches of specific databases, may have led to selection bias. Third, the exclusion of articles in languages other than English and Japanese in the meta-analysis limited the number of studies included for analysis, and we could not conduct subgroup analyses based on participant characteristics or details of interventions. Therefore, caution is needed when interpreting the results of the meta-analysis. Fourth, as we focused on studies that included multiple participants with aphasia, single-case study designs were excluded. Finally, as with all database searches, it is impossible to completely eliminate publication bias .
This review investigated Tele-SLT support and delivery methods for PWA. Our findings reveal a growing body of research examining the use of specialized software for Tele-SLT, particularly for individuals with chronic PWA; many studies demonstrate its benefits. However, three key challenges emerged. First, research on tele-assessment is scarce compared to research on tele-training. Second, there is insufficient research on synchronous remote SLT compared to asynchronous remote SLT, and RCTs have yet to demonstrate a significant training effect for synchronous interventions compared to asynchronous interventions. Third, the overall methodological quality of Tele-SLT research is low. These findings suggest that the availability of diverse Tele-SLT approaches may be limited, and the efficacy of Tele-SLT, particularly synchronous training, warrants further investigation. As the demand for Tele-SLT increases, addressing these challenges is crucial.
Only three studies (8.57%) involving tele-assessments were included in this study, highlighting the challenges associated with conducting tele-assessments. Specifically, it may be difficult to remotely determine whether an individual is PWA and to obtain detailed information about them, potentially hindering the provision of tele-training . A significant challenge is the influence of digital device functionality and the environment on the interpretation of PWA outcomes. For instance, in videoconferencing communication assessments, there is a concern that microphones may fail to capture unintelligible or faint utterances caused by speech or language disorders, making it challenging to derive clinical insights into spoken language . Furthermore, loss of video data has been noted due to volume changes associated with the PWA’s sitting position and inadequate computer capacity, even when the microphone’s audio input is normal . Therefore, implementing tele-assessments may necessitate selecting an assessment modality based on the severity of the PWA’s aphasia and the device’s capabilities, as well as verifying the assessment environment in advance . From clinical or research perspectives, establishing such protocols and checklists is essential . In the context of Tele-SLT, utilizing conversation recording protocols from Aphasiabank, an online platform for sharing PWA data, may be beneficial . If these procedures are made accessible to SLP, other professionals, and PWA in a format compatible with Tele-SLT, it is likely that tele-evaluation will gain popularity as a user-friendly approach.
Our review found that only 14 of the 35 included studies (40.00%) investigated synchronous Tele-SLT, suggesting potential challenges in its implementation. This scarcity may indicate a lack of strategies for addressing communication, QOL, and well-being in PWA through synchronous Tele-SLT [ – ]. Such support is often associated with, and facilitated by, the interactive communication inherent in synchronous interventions. Interactive communication promotes social participation in PWA , which is directly linked to QOL and well-being . Therefore, synchronous Tele-SLT may be crucial for supporting social participation and QOL in PWA, and addressing barriers to its implementation is essential. Potential barriers may include concerns about privacy and confidentiality, as well as a lack of ethical and technical support for using video conferencing in synchronous Tele-SLT . Developing guidelines and checklists regarding privacy, confidentiality, and the use of communication technology for PWA could help mitigate these issues . Without standardized practices for video conferencing in SLT, the expansion of synchronous Tele-SLT may be hindered, and the inconclusive training effects suggested by our meta-analysis may remain unresolved. Without addressing these challenges, we risk missing opportunities to provide effective SLT to PWA who have faced increased difficulties with social participation and daily living since the onset of the COVID-19 pandemic.
The quality of Tele-SLT studies has been consistently rated as low. The main reasons for this can be summarized as follows: poor baseline data due to the lack of standardized assessment methods for PWA, as noted in the ‘Data Collection’ section, and an undefined target group and insufficient sample size, as mentioned in the ‘Study Design’ section. Regarding the former, the shortcomings of standardized assessment methods in SLT have been documented , and adapting traditional tests for Tele-SLT, as has been done with the WAB , may be necessary. With regard to the latter, barriers such as inattention and poor auditory comprehension in PWA have hindered their participation in telemedicine . This is why maintaining and increasing sample sizes in studies involving PWA has been challenging. Although some suggest that patient sampling is easier in telerehabilitation , this may not hold true for PWA. Therefore, study designs must consider the cognitive and language functions of PWA to ensure an adequate sample size.
The recent COVID-19 pandemic has increased the challenges of delivering in-person SLT, which has led to a surge in global interest in Tele-SLT. Establishing effective tele-assessment and synchronous training methods, which our review identified as areas in need of further development, is a high priority. Widespread implementation of Tele-SLT has the potential to expand access to SLT for a greater number of PWA. To achieve this, it is crucial to determine which types of Tele-SLT (assessment, training, synchronous, asynchronous, or combined) are most effective for specific PWA profiles. Therefore, improving the quantity and quality of research across all areas of Tele-SLT, including the development of tele-assessment tools and synchronous training protocols specifically designed for PWA, is essential. One approach to address this challenge is to promote the development and use of specialized software for PWA. Such software was utilized in 68.57% (n = 24/35) of the studies included in this review. Specialized software can facilitate the management of patient information in rehabilitation settings and, under appropriate conditions, enable high-quality audio and video transmission . Furthermore, integrating software into Tele-SLT practice is thought to reduce the burden on speech-language pathologists and promote wider adoption of Tele-SLT . Therefore, developing and disseminating PWA-specific software could be instrumental in expanding access to and strengthening the evidence base for Tele-SLT.
This review has five main limitations. First, the failure to use specific checklists for the quantitative quality assessment of the included studies may introduce uncertainty regarding the consistency of the study quality in this scoping review design. Second, while a comprehensive literature search was conducted, the exclusion of articles in languages other than English and Japanese, as well as gray literature and manual searches of specific databases, may have led to selection bias. Third, the exclusion of articles in languages other than English and Japanese in the meta-analysis limited the number of studies included for analysis, and we could not conduct subgroup analyses based on participant characteristics or details of interventions. Therefore, caution is needed when interpreting the results of the meta-analysis. Fourth, as we focused on studies that included multiple participants with aphasia, single-case study designs were excluded. Finally, as with all database searches, it is impossible to completely eliminate publication bias .
In this study, we reviewed assessment and training methods in Tele-SLT for PWA and categorized them into synchronous, asynchronous, and combined methods. Despite an initial search of 1,484 articles from five databases, only 35 met our strict inclusion criteria. This finding underscores the scarcity of research on tele-assessment and synchronous training and highlights the overall poor quality of available studies. Given the increasing societal involvement of PWA, research in these areas is particularly critical in the post-COVID-19 era, and continued research is necessary. However, all studies showed valid results, and Tele-SLT methodologies, including digital programs dedicated to PWA, appear to be indicators of future solutions to the problem.
S1 Table Search formula used in this study. (DOCX) S2 Table Criteria for evaluating the quality of studies. (DOCX) S3 Table Results by evaluator and final evaluation of the secondary screening inclusion articles. (DOCX)
|
Palliative care on the radiation oncology ward—improvements in clinical care through interdisciplinary ward rounds | f0bffe5d-4f46-44bc-958d-8a65bc5f2e91 | 9938032 | Internal Medicine[mh] | Radiation treatment (RT) constitutes a cornerstone of cancer therapy with around 50% of oncological patients undergoing radiation during the course of their disease . Despite technical and conceptual innovations in the field , palliative concepts remain pivotal and constituted 41% of RT series in an analysis of the surveillance, epidemiology, and end results database (SEER) . The use of RT also extends to the end-of-life period with a systemic review describing a rate of 5–10% among patients dying of cancer in the last 30 days of life . This finding corresponds to another SEER analysis estimating the percentage for RT in the last 30 days of life to be 7.6% in a collective of about 200,000 patients dying of lung, breast, prostate, colorectal, or pancreas cancer, which demands a carefully considered balance of therapeutic interventions and best supportive care concepts . Palliative patients may reveal a complex variety of symptoms, both on a physiological and psychological level, calling for holistic clinical care . Despite this, the abilities of physicians to manage the symptom burden of their patients vary considerably being highest for (somatic) pain but dropping regarding psychological support . In general, palliative care (PC) is of cardinal importance for everyday clinical practice; in a web-based survey of the German Society of Radiation Oncology (DEGRO), 84.4% of respondents answered to be in need of palliative care consultation service often or very often during their daily routine . Previous publications have demonstrated feasibility of cooperation between a palliative care consultation service (PCCS) and a radiation oncology (RO) department , yet many questions on the best way for implementation remain unanswered. The present work intends to analyze the symptom burden of palliative patients on a RO ward in a large collective and thereby derives requirements for effective PC. We describe a multidisciplinary palliative concept based on regular palliative/radio-oncological ward rounds and its integration. Furthermore, educational demands of residents are investigated, and acceptance of the proposed model is assessed.
Study design The study was designed as a retrospective, single-center study at our institution, where a PCCS was established in May 2015. For the following study, we focused on patient data from January 5, 2015 to August 6, 2021. To analyze the numbers of patients dying on the RO ward, supplementary data were collected comparing the time span before the implementation of PCCS (2010–2015) and the period after the introduction of PCCS (2016–2020). The study was conceptualized by the last author and approved by the local institutional review board (protocol code 2017-636-f-S). Patients Patients with advanced life-limiting and progressive disease were referred to the PCCS by the RO ward physicians when identifying PC needs. Regular weekly ward rounds were undertaken by the PCCS, treating radiation oncologists, and nurses in order to identify patients with need for (specialized) PC. After referral, a PC physician or nurse performed a detailed assessment including symptom burden, psychosocial demands, and spiritual distress. This assessment resulted in a supportive treatment plan carried out by a multiprofessional PC team, involving various professions (physiotherapy, ergotherapy, psychologists, clergy, social service, music therapy), simultaneously to radiation treatment. Consequently, daily interventions could include a wide spectrum like additional ward rounds aimed at symptom relief, consulting of treating physicians concerning medication and supportive care, social services including organization of further (palliative) care after discharge, physiotherapy and ergotherapy sessions, and spiritual as well as psychological counseling. The exact orientation and intensity of the supportive therapy varied depending on the patient’s clinical condition and individual needs. As a consultation service, the PCCS did not interfere directly with decisions on the RO treatment schedule and duration, but could formulate recommendations. All data were electronically available using the hospital information system Orbis-OpenMed® (Agfa Healthcare, Mortsel, Belgium). Radiation therapy All RO treatments were carried out according to institutional standards adapted to the respective treatment situation on the discretion of the department of RO. There were no exclusion criteria concerning treatment specifications (advanced primary vs. metastatic disease, stereotactic vs. fractionated RT, normo- vs. hypofractionated RT). For treatment series, either a TrueBeam linear accelerator (Varian Medical Systems, Pao Alto, CA, USA) or a Tomotherapy machine (Accuray, Sunnyvale, CA, USA) was used. Survey An 18-item questionnaire was developed by two senior physicians in PC and RO, respectively (first and last author of the present work) and was filled out by residents in RO after their obligatory ward rotation. The questions aimed at palliative knowledge as well as use of and views on the PCCS . The survey was completed anonymously on Lime Survey (Lime Survey, Hamburg, Germany) and answers were analyzed using Excel for Mac (Microsoft Cooperation, Redmond, WA, USA) and SPSS version 28 (IBM, Armonk, NY, USA). Statistics Continuous variables are summarized by the mean values and standard deviations, whereas categorical variables are presented as absolute numbers and relative frequencies. Pain was assessed as a binary variable “pain existent” (0 = nonexistent, 1 = any manifestation from light to unbearable) and graded via a numeric rating scale (0–10) afterwards. Pain assessment was performed at the initiation by the PCCS and 72 h afterwards. Continuous parameters were analyzed by means of a Wilcoxon–Mann–Whitney test which was also used to compare the numbers of dying on the RO ward 2010–2015 with 2016–2020. For categorical variables, a χ 2 test was employed. Two-sided P -values of ≤ 0.05 were considered statistically significant. The statistical analysis of the data was performed using the SPSS Software (IBM SPSS Statistics for Windows, Version 28.0, Armonk, NY, USA) and the SAS Software (Version 9.4, SAS Institute Inc., Cary, NC, USA).
The study was designed as a retrospective, single-center study at our institution, where a PCCS was established in May 2015. For the following study, we focused on patient data from January 5, 2015 to August 6, 2021. To analyze the numbers of patients dying on the RO ward, supplementary data were collected comparing the time span before the implementation of PCCS (2010–2015) and the period after the introduction of PCCS (2016–2020). The study was conceptualized by the last author and approved by the local institutional review board (protocol code 2017-636-f-S).
Patients with advanced life-limiting and progressive disease were referred to the PCCS by the RO ward physicians when identifying PC needs. Regular weekly ward rounds were undertaken by the PCCS, treating radiation oncologists, and nurses in order to identify patients with need for (specialized) PC. After referral, a PC physician or nurse performed a detailed assessment including symptom burden, psychosocial demands, and spiritual distress. This assessment resulted in a supportive treatment plan carried out by a multiprofessional PC team, involving various professions (physiotherapy, ergotherapy, psychologists, clergy, social service, music therapy), simultaneously to radiation treatment. Consequently, daily interventions could include a wide spectrum like additional ward rounds aimed at symptom relief, consulting of treating physicians concerning medication and supportive care, social services including organization of further (palliative) care after discharge, physiotherapy and ergotherapy sessions, and spiritual as well as psychological counseling. The exact orientation and intensity of the supportive therapy varied depending on the patient’s clinical condition and individual needs. As a consultation service, the PCCS did not interfere directly with decisions on the RO treatment schedule and duration, but could formulate recommendations. All data were electronically available using the hospital information system Orbis-OpenMed® (Agfa Healthcare, Mortsel, Belgium).
All RO treatments were carried out according to institutional standards adapted to the respective treatment situation on the discretion of the department of RO. There were no exclusion criteria concerning treatment specifications (advanced primary vs. metastatic disease, stereotactic vs. fractionated RT, normo- vs. hypofractionated RT). For treatment series, either a TrueBeam linear accelerator (Varian Medical Systems, Pao Alto, CA, USA) or a Tomotherapy machine (Accuray, Sunnyvale, CA, USA) was used.
An 18-item questionnaire was developed by two senior physicians in PC and RO, respectively (first and last author of the present work) and was filled out by residents in RO after their obligatory ward rotation. The questions aimed at palliative knowledge as well as use of and views on the PCCS . The survey was completed anonymously on Lime Survey (Lime Survey, Hamburg, Germany) and answers were analyzed using Excel for Mac (Microsoft Cooperation, Redmond, WA, USA) and SPSS version 28 (IBM, Armonk, NY, USA).
Continuous variables are summarized by the mean values and standard deviations, whereas categorical variables are presented as absolute numbers and relative frequencies. Pain was assessed as a binary variable “pain existent” (0 = nonexistent, 1 = any manifestation from light to unbearable) and graded via a numeric rating scale (0–10) afterwards. Pain assessment was performed at the initiation by the PCCS and 72 h afterwards. Continuous parameters were analyzed by means of a Wilcoxon–Mann–Whitney test which was also used to compare the numbers of dying on the RO ward 2010–2015 with 2016–2020. For categorical variables, a χ 2 test was employed. Two-sided P -values of ≤ 0.05 were considered statistically significant. The statistical analysis of the data was performed using the SPSS Software (IBM SPSS Statistics for Windows, Version 28.0, Armonk, NY, USA) and the SAS Software (Version 9.4, SAS Institute Inc., Cary, NC, USA).
Overall, 1018 patients in the RO department were seen by the PCCS between 2015 and 2021 (Table , Fig. ), 77 (7.6%) of whom were only counseled once on an outpatient basis. The remaining 941 (92.4%) patients were accompanied on the ward. Mean age of patients was 65.8 ± 13 years (range 19–95 years) and mean length of stay in the acute care hospital was 30 ± 20 days. The most prevalent primary entities were lung/pleura tumors (16.9%), primary brain tumors (15.7%), and head and neck tumors (15.5%; Fig. a). When considering metastases and primary tumors, the most prevalent treatment indications were metastases, not otherwise specified (25.8%), brain tumors (15.6%), and head and neck tumors (13.8%; Fig. b). Pain at the time of admission was frequently seen with 20.4% of patients reporting this symptom. Of the 192 patients presenting with pain at time of admission, 177 improved (after 72 h), 8 revealed no changes, and 7 had no follow-up data. Pain intensity decreased from a median value of 6 on the numeric rating scale (range 2–10) to 2 (range 0–8; Supplementary Fig. 1). The mean length of stay under cotreatment by the PCCS was 22 ± 15 days (Table ). In contrast to patients in other departments than RO ( n = 3254), the time to integration could be reduced significantly by regular ward rounds (mean: 11 ± 20 vs. 8 ± 14 days, p < 0.001; Fig. a). Since the implementation of the PCCS, the number of patients treated cooperatively increased from 97 patients in 2015 to 251 in 2021 (Fig. b). Most patients were able to return home with only a minority dying during their stay in the acute care hospital (Fig. a). Concerning our in-house PC unit, 478 patients in total have been treated since July 1, 2019, 58 of whom were referred from the RO ward. Of these 58 patients, 24 died on the PC unit, 12 were transferred to a hospice, 11 were transferred to another hospital/rehabilitation clinic, and 11 were discharged home. Over time, the number of patients dying on the RO ward decreased, with a significant difference concerning deaths between the time periods 2010–2015 (median: 15.5 deaths/year) and 2016–2020 (median: 7 deaths/year; p = 0.009, using the exact sampling distribution of U) indicating a strong effect (Z = −2.482; r = 0.748, Fig. b). Survey In total, 15 participants answered the survey with 40% being female and 60% being male and a median age of 25–30 years (53.3%; 30–35 years: 26.7%; ≥ 36 years: 20%). Most residents estimated their palliative knowledge to be extensive (21.4%) or mediocre (78.6%). The PCCS was well known (100%) and used often (7.7%) or very often (84.6%) during the ward rotation. Indications for PCCS consultations were diverse (Fig. a) and focused on pain medication (92.3%), organization of further care (92.3%), and psycho-oncological support (84.6%). Overall, the PCCS was seen positively with a vast majority of residents agreeing on adjective like “enriching”, “empathic”, “collegial”, “professionally founded”, and a “low threshold for consultation” (Fig. b). However, three and one respondents found the statement to be “very applicable” or “applicable” that the PCCS is interfering with their clinical/radio-oncological routine. All participants agreed that collaboration with the PCCS resulted in a more extensive and more profound knowledge in PC (100%).
In total, 15 participants answered the survey with 40% being female and 60% being male and a median age of 25–30 years (53.3%; 30–35 years: 26.7%; ≥ 36 years: 20%). Most residents estimated their palliative knowledge to be extensive (21.4%) or mediocre (78.6%). The PCCS was well known (100%) and used often (7.7%) or very often (84.6%) during the ward rotation. Indications for PCCS consultations were diverse (Fig. a) and focused on pain medication (92.3%), organization of further care (92.3%), and psycho-oncological support (84.6%). Overall, the PCCS was seen positively with a vast majority of residents agreeing on adjective like “enriching”, “empathic”, “collegial”, “professionally founded”, and a “low threshold for consultation” (Fig. b). However, three and one respondents found the statement to be “very applicable” or “applicable” that the PCCS is interfering with their clinical/radio-oncological routine. All participants agreed that collaboration with the PCCS resulted in a more extensive and more profound knowledge in PC (100%).
The hereby presented analysis demonstrates feasibility and efficacy of a structured cooperation between a radiation oncology department and a palliative care service. The main advantages lie within a supply of adequate PC for the individual patient and the possibility to provide knowledge on PC for the treating physicians. In addition to that, a strong cooperation promotes acceptance of PC by the team, as well as by all patients on the ward . PC is of key importance in a RO department. An assessment of PCCS demands in an acute care hospital demonstrated the RT department to be among the three most frequent users . This is mirrored by a continuous rise in the number of radio-oncological patients visited by the PCCS in our study; starting from 97 patients in 2015 and reaching 251 in 2021. Despite that increase, the number of patients dying on the RO ward decreased concomitantly which is most likely due to the timely referral of patients to appropriate facilities other than the acute care hospital. Nevertheless, the integration of palliative services is often postponed due to multiple reasons: an analysis at the university hospital Munich revealed the majority of PCCS contacts to be initiated in the last week of the patient’s life . In contrast, the current algorithm with regular ward rounds may enable a valuable cooperation before “end-of-life care” and strongly supports the concept of early integration, which has been identified as superior regarding guidance of further care, patient’s quality of life, and care costs [ – ]. Focusing on patients and their relatives, honest communication on the expected course of disease, and the limited life-expectancy may facilitate integration of PC . It has to be noted that the symptom burden of palliative patients may be prone to changes during RT and demands for a dynamic care approach: An assessment of patient-reported outcomes in palliative patients revealed the most prominent (clinical relevant) symptoms before the start of RT to be an impairment of general wellbeing (62.8%), pain (62.8%), tiredness (60.0%), lack of appetite (40.0%), and anxiety (38.0%) . After completion of RT, symptoms changed with a significant higher percentage of lack of appetite (60%, p = 0.006) in contrast to a significant decrease in pain (42.8%, p = 0.033) . In comparison, the percentage of patients suffering from pain at the beginning of PCCS integration in our analysis was relatively low (20.4%) which may be due to the abilities of the treating radiation oncologists to manage pain medication. PC as well as palliative RT is a pivotal part of the educational program for residents as proposed by the DEGRO . This topic is not limited to radiation itself but also encompasses pain medication, supportive care, psycho-oncology, and knowledge on hospice care. Correspondingly, palliative RO is taught at 87.5% of all universities as part of the curriculum for medical students , which paves the way for a better understanding. However, a North American survey reported that 79% of residents judged their training in PC to be not or only partially sufficient . Concerning different topics, deficits were reported in various physiological, psychological, social, and legal domains as well as in the planning of current treatment goals and further care planning with the highest insecurity in the ability to initiate a depression treatment, rotate opioids, manage fatigue, anorexia, and insomnia . Dedicated training in PC exceeding 5 h was associated with higher self-assessed competence in all domains . Thus, a minimal teaching intervention may significantly enhance PCcompetences. The call for a more intensive training in PC is common in the literature: a systemic review found this statement to be present in 89.4% of publications . Built on the results of the current evaluation and the presented survey, we developed a model curriculum for PC in RO (Fig. ). It is intended as a multistep process starting from theoretical knowledge (base of the pyramid) and reaching practical use (top of the pyramid). It has to be emphasized that basic knowledge on PC should be introduced early, preferably during medical school. The hereby described protocol of regular ward rounds may assist in the advanced learning steps (top levels of the pyramid) beyond textbook experience. With its basic character, the concept may be easily adapted to multinational educational programs and addresses both residents’ need for information and supervision during daily clinical care. However, it demands profound knowledge of the supervising senior physicians, sufficient time for teaching, and effective interdisciplinary cooperation with a PCCS . The current work has some limitations being a retrospective and monocentric analysis. In spite of that, patient numbers exceed 1000, being larger than many collectives in the literature. Unfortunately, follow-up data on mortality after discharge were incomplete which prevented a decisive survival analysis. The decision to integrate PCCS into patient care was done on an individual basis by the treating radiation oncologist, a process which may be prone to selection bias. In addition, there has been no simultaneous control group treated without the help of the PCCS, which hampers estimation of the precise impact of the described concept. This is of particular interest regarding the influence on RT treatment strategies. Being a consultation service, the PCCS did not interfere directly with decisions on RT duration and fractionation but, due to the close cooperation between the two departments, an indirect influence cannot be excluded. This question has to be addressed in future (prospective) trials. Finally, PC in RO has to pursue multiple objectives in the years to come: (1) enlarge knowledge among treating physicians and nurses, (2) increase awareness towards individual needs of palliative patients, and (3) provide adequate guidance to PCCS on a broader scale. The presented cooperative strategy proved to be innovative and successful, but additional programs will be needed in the future.
Supplementary Fig. 1: Development of pain. Pain intensity as given by patients on a numeric rating scale at the time of admission (mean: 5.68; median: 6; range: 2–10) and 72 h after admission (mean: 2.53; median: 2; range: 0–8).
|
Pneumothorax detection with thoracic ultrasound as the method of choice in interventional pulmonology - A retrospective single-center analysis and experience | d03bc4c2-6f8d-4846-b193-df521c5e19e6 | 10294392 | Internal Medicine[mh] | Pneumothorax (PTX) is a potentially life-threatening condition which needs to be taken into account in a variety of settings, including pulmonary care, intensive care, emergency medicine and surgery . Especially iatrogenic PTX that is commonly linked to various procedures in interventional pulmonology is a frequent complication and should be promptly and safely detected . PTX is diagnosed using different imaging methods, most frequently chest radiography (CR) and – more recently – thoracic ultrasound (TUS). Studies revealed a similar specificity of TUS and CR for the detection of PTX but have reported a higher sensitivity for PTX exclusion when performed by TUS . Comparative diagnostic studies were often conducted in a trauma setting or intensive care unit (ICU) and – less frequently – in interventional pulmonology. Here, TUS was reported to be feasible and safe when compared to CR after transthoracic biopsies, transbronchial biopsies and transbronchial cryobiopsies . Despite this evidence, the adoption of TUS for PTX exclusion in daily practice is slow, and hindrances to widespread adoption are unclear or have not yet been investigated . Furthermore, the effect of TUS introduction in routine care is largely unknown: While a reduction in CR was observed in an ICU setting , it is unknown whether these findings can be generalized to other fields of care. Especially in interventional pulmonology, conditions such as (partial) pleurodesis, bullae or contusions may limit the conclusiveness of TUS for PTX detection when compared to the ICU setting . Our retrospective observational study aims to evaluate if using TUS as the method of choice for ruling out PTX can effectively reduce the number of CRs in the routine care of an interventional pulmonology unit.
This study was conducted as a retrospective single-center observational study in the Pulmonology Department of the University Hospital Halle (Saale), Germany. The Institutional Review Board of the Martin-Luther-University Halle-Wittenberg approved the investigation (IRB number: 2021 − 149, July 21, 2021). Study design All pulmonary interventions (bronchoscopy and ultrasound) in adult patients with subsequent PTX exclusion by CR or TUS performed between January 2014 to December 2020 were included. Until March 2017, post-interventional CR was the method of choice for PTX detection (period A). In April 2017, a new interventional pulmonology team was introduced, and post-interventional TUS was established as the initial standard procedure. The driving factor for this was the previous practical and teaching experience of the new team, based on the newly introduced pneumothorax guideline in Germany . Thus, from April 2017 to December 2020 both methods were used with a preference to TUS (period B). Selection of the method to be used was at the discretion of the examining physician. Interventions without a risk of PTX and those with PTX exclusion by computed tomography (CT) were excluded, as well as TUS not performed by the pulmonology team. TUS was performed by the examining pulmonologist directly after the intervention. CR was performed in the Department of Radiology and assessed by a radiologist. CR images were taken in the upright position whenever the patient’s general health allowed it with a delay of at least two hours. In order to verify the reduction in CRs done for post-interventional PTX exclusion, both periods were compared. For the comparison of both methods, CR and TUS, only period B was considered. Data collection The schedules of the bronchoscopy and ultrasound units were retrospectively screened for all interventions requiring image control. Digital patient records were used to collect biometric and demographic data, data on the type of intervention, the imaging procedure used, the respective PTX therapy and its outcome. Furthermore, for both imaging modalities, instances of erroneously ruled-out PTX were noted. Types of interventions In order to reflect the real-world setting, all interventions with the risk of post-interventional PTX were included in the analysis (Fig. ). Interventional bronchoscopies were executed as needed: Both flexible and rigid inspection was possible. All interventions were performed as advised in the respective guidelines: Transbronchial biopsy (TBB) of interstitial lung disease or potential neoplastic formations using forceps, needle or cryoprobe, endoscopic lung volume reduction (EVLR) using valves, endobronchial ultrasound (EBUS) when hilar lymph node stations or intraparenchymal lesions were punctured, transthoracic biopsy of pleural lesions and pulmonary consolidations. Interventions with stent placement received imaging only when recanalization was part of the procedure. Chest tubes included both, short-term drainage with various diameters as well as indwelling pleural catheters. For chest tubes, PTX was only recorded as a complication if it required additional treatment as small post-interventional and asymptomatic PTX are intrinsic to this type of intervention. Central venous catheters (CVC) and right-heart catheterization (RHC) were combined into one group due to their similar risk of PTX and similar site of intervention. In both interventions, PTX had to be excluded only if access was via the jugular or subclavian vein. Ultrasound examination and PTX therapy In the month leading up to defining TUS as the standard, TUS was taught to a team of several attending physicians and residents by an attending physician who was proficient in the method. Initially, TUS was performed under supervision during routine care, later the attending physician who had taught the method was consulted only when there were inconclusive findings. TUS was performed by or supervised by a trained examiner with the patient in supine or semi-erect position, in accordance with the guideline recommendation and earlier recommendations for TUS : TUS was performed bilaterally either using a 13–5 MHz linear or a 5–1 MHz convex probe (Hitachi Arietta V70, FUJIFILM Healthcare, formerly Hitachi Medical Corp., Japan) without a specific thorax/lung preset. Alternatively, handheld portable ultrasound devices (i.e. Butterfly iQ, Butterfly Network Inc., USA) were used. Detection of one of the following pleural integrity features constituted PTX exclusion: pleural sliding , B-Lines or lung pulse . If all of these signs were absent, the lung point , as proof of PTX, was searched by laterally moving along the thorax in the intercostal space. Total PTX was assumed if unilaterally neither pleural sliding nor lung point were detectable. Use of the M-Mode to record the barcode sign was left to the discretion of the examining physician. Clinically stable patients with PTX were monitored using TUS, in symptomatic patients a small diameter chest tube was inserted. In case of diagnostic uncertainty, a further check was performed on the same day at the discretion of the examining physician. CR was used as the primary method only when the CR would yield additional information (e.g. intrathoracic position of the chest drain tip), sufficient competence regarding the use and interpretation of TUS was not present, or in case of missing ultrasound facilities. Missing data and statistical analysis Interventions were excluded from the dataset when either the imaging method or its result were unclear or when patient files were ambiguous. Interventions with missing data on body weight or height were included and data was labelled as missing. The data were analyzed using SPSS Statistics (IBM, version 28.0, New York, United States). Categorical data were presented using absolute and relative frequencies, and metric data using mean and standard deviation. The chi-square test was used to check the reduction in the number of CRs after the introduction of TUS.
All pulmonary interventions (bronchoscopy and ultrasound) in adult patients with subsequent PTX exclusion by CR or TUS performed between January 2014 to December 2020 were included. Until March 2017, post-interventional CR was the method of choice for PTX detection (period A). In April 2017, a new interventional pulmonology team was introduced, and post-interventional TUS was established as the initial standard procedure. The driving factor for this was the previous practical and teaching experience of the new team, based on the newly introduced pneumothorax guideline in Germany . Thus, from April 2017 to December 2020 both methods were used with a preference to TUS (period B). Selection of the method to be used was at the discretion of the examining physician. Interventions without a risk of PTX and those with PTX exclusion by computed tomography (CT) were excluded, as well as TUS not performed by the pulmonology team. TUS was performed by the examining pulmonologist directly after the intervention. CR was performed in the Department of Radiology and assessed by a radiologist. CR images were taken in the upright position whenever the patient’s general health allowed it with a delay of at least two hours. In order to verify the reduction in CRs done for post-interventional PTX exclusion, both periods were compared. For the comparison of both methods, CR and TUS, only period B was considered.
The schedules of the bronchoscopy and ultrasound units were retrospectively screened for all interventions requiring image control. Digital patient records were used to collect biometric and demographic data, data on the type of intervention, the imaging procedure used, the respective PTX therapy and its outcome. Furthermore, for both imaging modalities, instances of erroneously ruled-out PTX were noted.
In order to reflect the real-world setting, all interventions with the risk of post-interventional PTX were included in the analysis (Fig. ). Interventional bronchoscopies were executed as needed: Both flexible and rigid inspection was possible. All interventions were performed as advised in the respective guidelines: Transbronchial biopsy (TBB) of interstitial lung disease or potential neoplastic formations using forceps, needle or cryoprobe, endoscopic lung volume reduction (EVLR) using valves, endobronchial ultrasound (EBUS) when hilar lymph node stations or intraparenchymal lesions were punctured, transthoracic biopsy of pleural lesions and pulmonary consolidations. Interventions with stent placement received imaging only when recanalization was part of the procedure. Chest tubes included both, short-term drainage with various diameters as well as indwelling pleural catheters. For chest tubes, PTX was only recorded as a complication if it required additional treatment as small post-interventional and asymptomatic PTX are intrinsic to this type of intervention. Central venous catheters (CVC) and right-heart catheterization (RHC) were combined into one group due to their similar risk of PTX and similar site of intervention. In both interventions, PTX had to be excluded only if access was via the jugular or subclavian vein.
In the month leading up to defining TUS as the standard, TUS was taught to a team of several attending physicians and residents by an attending physician who was proficient in the method. Initially, TUS was performed under supervision during routine care, later the attending physician who had taught the method was consulted only when there were inconclusive findings. TUS was performed by or supervised by a trained examiner with the patient in supine or semi-erect position, in accordance with the guideline recommendation and earlier recommendations for TUS : TUS was performed bilaterally either using a 13–5 MHz linear or a 5–1 MHz convex probe (Hitachi Arietta V70, FUJIFILM Healthcare, formerly Hitachi Medical Corp., Japan) without a specific thorax/lung preset. Alternatively, handheld portable ultrasound devices (i.e. Butterfly iQ, Butterfly Network Inc., USA) were used. Detection of one of the following pleural integrity features constituted PTX exclusion: pleural sliding , B-Lines or lung pulse . If all of these signs were absent, the lung point , as proof of PTX, was searched by laterally moving along the thorax in the intercostal space. Total PTX was assumed if unilaterally neither pleural sliding nor lung point were detectable. Use of the M-Mode to record the barcode sign was left to the discretion of the examining physician. Clinically stable patients with PTX were monitored using TUS, in symptomatic patients a small diameter chest tube was inserted. In case of diagnostic uncertainty, a further check was performed on the same day at the discretion of the examining physician. CR was used as the primary method only when the CR would yield additional information (e.g. intrathoracic position of the chest drain tip), sufficient competence regarding the use and interpretation of TUS was not present, or in case of missing ultrasound facilities.
Interventions were excluded from the dataset when either the imaging method or its result were unclear or when patient files were ambiguous. Interventions with missing data on body weight or height were included and data was labelled as missing. The data were analyzed using SPSS Statistics (IBM, version 28.0, New York, United States). Categorical data were presented using absolute and relative frequencies, and metric data using mean and standard deviation. The chi-square test was used to check the reduction in the number of CRs after the introduction of TUS.
Inclusions and exclusions By screening intervention schedules, 871 interventions requiring PTX exclusion were found. 7 of these were excluded from the data set because the primary PTX exclusion was performed by CT. Another 110 interventions were excluded because they were performed by medical departments other than pneumology. A total of 754 interventions were included, with 110 (14.6%) performed in period A and 644 (85.4%) performed in period B. Baseline characteristics Baseline characteristics of the CR and the TUS group in period B are compared in Table . Details on intervention numbers, diagnosed and missed PTX, as well as investigations done to confirm initial findings are provided in Table . Figure reflects the shift in relative frequency of CR and TUS. The relative frequency of CR decreased from 98.2% (n = 108) in period A to 25.8% (n = 166) in period B (p < 0.001). Individual types and frequencies of interventions together with their methods for PTX exclusion are shown in Fig. . TUS was used almost exclusively for PTX exclusion in the following interventions: thoracocentesis, central venous catheter/right-heart catheterization, transthoracic and transbronchial biopsy, transbronchial cryobiopsy and transbronchial needle-aspiration. In contrast, CR was dominantly used in cases of chest drainage, endoscopic lung volume reduction and stent placement. Confirmatory investigations No confirmatory investigations were documented in period A (Table ). A total of 24 (3.7%) were conducted in period B. For TUS, 21 (4.4%) interventions prompted a confirmatory investigation. 19 were performed by CR and one each by TUS or CT. For CR, there were three confirmatory investigations out of 166 (1.8%). Of these, two were performed using CR and one using CT. Confirmatory examinations revealed one missed PTX for TUS and none for CR . Diagnosed PTX, missed PTX and PTX treatment A total of 34 (4.5%) PTX were diagnosed in 754 interventions (Table ). In period A, 5 (4.6%) PTX were diagnosed, all by CR. No clinically relevant PTX was missed on initial imaging. In period B, a total of 29 (4.5%) PTX were identified. 28 were detected on initial imaging, 14 (8.4%) by CR and 14 (2.9%) by TUS. One PTX (0.2%) was initially missed by TUS after thoracocentesis, none were missed by CR. The missed PTX was small, trapped in the lung apex and had not been generated during the intervention itself. It was already described in a CT from the previous day and was not detected by TUS. It was included in the database for completeness. Treatment was conservative. The timing of the initially missed PTX in relation to the introduction of TUS is shown in Fig. . Of the 28 PTX considered, 15 (53.6%) were treated conservatively and 13 (46.4%) by chest tube insertion (Table ). The treatment of the 14 PTX diagnosed by TUS was 50% conservative and 50% by chest drainage. Of the 14 PTX diagnosed by CR, 8 (57%) were treated conservatively and 6 (43%) with chest drainage. Transbronchial cryobiopsies had the highest incidence of post-interventional PTX (9.5%).
By screening intervention schedules, 871 interventions requiring PTX exclusion were found. 7 of these were excluded from the data set because the primary PTX exclusion was performed by CT. Another 110 interventions were excluded because they were performed by medical departments other than pneumology. A total of 754 interventions were included, with 110 (14.6%) performed in period A and 644 (85.4%) performed in period B.
Baseline characteristics of the CR and the TUS group in period B are compared in Table . Details on intervention numbers, diagnosed and missed PTX, as well as investigations done to confirm initial findings are provided in Table . Figure reflects the shift in relative frequency of CR and TUS. The relative frequency of CR decreased from 98.2% (n = 108) in period A to 25.8% (n = 166) in period B (p < 0.001). Individual types and frequencies of interventions together with their methods for PTX exclusion are shown in Fig. . TUS was used almost exclusively for PTX exclusion in the following interventions: thoracocentesis, central venous catheter/right-heart catheterization, transthoracic and transbronchial biopsy, transbronchial cryobiopsy and transbronchial needle-aspiration. In contrast, CR was dominantly used in cases of chest drainage, endoscopic lung volume reduction and stent placement.
No confirmatory investigations were documented in period A (Table ). A total of 24 (3.7%) were conducted in period B. For TUS, 21 (4.4%) interventions prompted a confirmatory investigation. 19 were performed by CR and one each by TUS or CT. For CR, there were three confirmatory investigations out of 166 (1.8%). Of these, two were performed using CR and one using CT. Confirmatory examinations revealed one missed PTX for TUS and none for CR .
A total of 34 (4.5%) PTX were diagnosed in 754 interventions (Table ). In period A, 5 (4.6%) PTX were diagnosed, all by CR. No clinically relevant PTX was missed on initial imaging. In period B, a total of 29 (4.5%) PTX were identified. 28 were detected on initial imaging, 14 (8.4%) by CR and 14 (2.9%) by TUS. One PTX (0.2%) was initially missed by TUS after thoracocentesis, none were missed by CR. The missed PTX was small, trapped in the lung apex and had not been generated during the intervention itself. It was already described in a CT from the previous day and was not detected by TUS. It was included in the database for completeness. Treatment was conservative. The timing of the initially missed PTX in relation to the introduction of TUS is shown in Fig. . Of the 28 PTX considered, 15 (53.6%) were treated conservatively and 13 (46.4%) by chest tube insertion (Table ). The treatment of the 14 PTX diagnosed by TUS was 50% conservative and 50% by chest drainage. Of the 14 PTX diagnosed by CR, 8 (57%) were treated conservatively and 6 (43%) with chest drainage. Transbronchial cryobiopsies had the highest incidence of post-interventional PTX (9.5%).
This study demonstrates the potential and limitations of TUS when it comes to reducing the use of ionizing radiation in an interventional pulmonology department. The introduction of TUS as the method of choice reduced the number of CRs from 98.2 to 25.7% respectively. In our study, a marked increase in overall interventions is seen in period B due to a widened professional orientation. This led to a change in the distribution of interventions performed and to an overall shift towards interventional techniques. This is in line with the overall increase in importance of interventional pulmonology in recent years . The most notable changes include transthoracic pleural and lung biopsies being performed by a pulmonologist with ultrasound guidance instead of by radiologists guided by CT. In addition, right-heart catheterization was conducted within the department. The increase in transbronchial cryobiopsies can be explained by its increasing importance in the diagnosis of interstitial lung disease . CR vs. TUS in period B No considerable differences were found between the CR and the TUS group regarding patient characteristics. Patients in the TUS group tended to have a slightly higher Body Mass Index. The preferred use of either method was not influenced by patient properties. In most diagnostic studies, ultrasound is compared with CR in the supine position . However, the CR in our study was usually performed in the upright position, which offers better accuracy . Therefore, findings from an observational study that demonstrated a reduction of CR used for PTX exclusion in day-to-day practice in an ICU setting cannot be generalized to interventional pulmonology. It is noticeable that relatively more PTX were diagnosed by CR (14 of 166, 8.4%) than by TUS (14 of 478, 2.9%) in period B. This finding is counter-intuitive since TUS is known to have a higher sensitivity than CR (79–97% vs. 40–52%) while specificity is similar . While for most intervention types, numbers of PTX found coincide with the proportion of imaging method utilization, this is untrue for thoracocentesis and chest tubes. Here, more PTX were found using CR than using TUS while the relative usage of the methods would suggest the opposite. With a cumulative 8 PTX diagnosed by CR and one by TUS, these interventions skew the aggregated data. While speculative, we assume practical reasons for this observation: (A) Additional information (tube location, atelectasis etc.) beyond PTX exclusion was sought especially in high-risk interventions, and (B) search for the lung point is time-consuming thus nudging examiners to use CR when a PTX is suspected. Limitations of TUS In period B, there were more frequent confirmatory investigations due to inconclusive findings after TUS (21 of 478, 4.4% vs. 3 of 166, 1.8% after CR ). The fact that all confirmatory investigations after TUS ruled out a PTX may be due to two reasons: (A) the subjective uncertainty when using the relatively new ultrasound technique; and (B) the added uncertainty of TUS in patients with lung conditions such as adhesions and bullae. While the first reason cannot be tested, pre-existing lung conditions are a known limiting factor of TUS: Shostak and colleagues have reported limitations of ultrasound investigations in 23% of their patients after pulmonary interventions (43 of 185) and have associated this with prior lung disease. Pleural sliding might be impaired due to emphysema, previous lung surgery, radiation exposure, and pleural adhesions. The authors have recommended to examine such patients by CR . Similarly, pleural adhesions due to previous lung disease existed in 6 of 1023 (0.6%) patients examined in a study by Kreuter and colleagues. This pre-existing condition also prevented the detection of respiratory displacement of the lung and resulted in false-positive results. CRs excluded PTX in all 6 cases . In transthoracic biopsies and transbronchial (cryo)biopsies as well as needle aspirations TUS was markedly favored by interventionalists in period B. These interventions combined, TUS was the method of choice in 91% (331 of 368). In these intervention types TUS offers prompt PTX rule-out and no further information (i.e. tip of chest tube) can be gained by CR. In our study, due to the retrospective design, we cannot reproduce whether patients examined by CR received it primarily for such reasons. However, in this routine care dataset, the fraction of confirmatory investigations after TUS validates the limitations of TUS expected from prospective studies, especially by Shostak, in a much larger cohort. In summary, pre-existing lung conditions may impede sonographic PTX detection. These limiting factors are assumed to have a higher prevalence in interventional pulmonology as compared to patients in emergency or intensive care settings in which most prospective TUS studies were conducted. However, pre-existing conditions rather result in false-positive than false-negative results: In the current relevant literature on post-interventional PTX exclusion, the false-negative rate is low and reported to be between 0 and 0.7% . The patient with the missed PTX in our study constitutes one such rare false-negative event: He had a small trapped apical PTX – which cannot be easily diagnosed by TUS – along with a large malignant pleural effusion. Additionally, the choice of imaging method is based on the intervention type, and CR was favored where additional information beyond PTX exclusion was sought: CR was used more frequently in cases of chest tube insertion and ELVR as well as stent placements (Fig. ). Here, imaging delivers additional information beyond PTX evaluations, e.g. confirming atelectasis after ELVR, position of implanted stents, or tips of drainage catheters. Adoption of TUS To this day, TUS for PTX detection – though well studied and recommended in guidelines – is still not broadly-established as a method in routine care . The relative recency of the method may be a contributing factor. Seen on a monthly basis, the transition from only CR to mostly TUS was swift. Within one month, the team was able to safely perform TUS. Constant proportions of confirmatory investigations over the years support the assumption that proficiency in TUS is achieved within a short period. The one erroneous PTX exclusion happened shortly after the introduction of TUS (Fig. ). However, due to the methodological limitation of TUS in diagnosing clinically irrelevant small trapped PTX, there is no reason to believe that insufficient skills were a factor in this false-negative event. Our adoption strategy is in line with training protocols from prospective studies in which physicians were trained by a mentor: For example, 2 h of training or 10 TUS performed under supervision were required . The assumption that TUS is quick and easy to learn is further supported by a small study by Monti et al.: The authors demonstrated that nonphysician emergency medical personnel can reliably detect PTX using sonography with a sensitivity of 96% and a specificity of 100% after a brief training session consisting of a slide show presentation, short video clips on the signs of PTX to observe in sonographic images, and an introduction to the sonographic device . Strengths and limitations With our study, the practicability and capacity of TUS when it comes to reducing the use of CR as the standard in the routine care of an interventional pulmonology unit is evaluated for the first time. Based on a large single-center experience including all interventions typically seen in interventional pulmonology, we have demonstrated that using TUS for PTX detection can effectively reduce the amount of chest radiographs in everyday clinical practice. The main limitation of this study is the missing gold standard. Therefore, the true number of asymptomatic PTX missed by both methods is unknown, and hence we avoid using test characteristics terminology (sensitivity, specificity etc.) in conjunction with our results in this manuscript. However, it was not the aim of this study to assess test accuracy but to investigate TUS in a real-world setting.
No considerable differences were found between the CR and the TUS group regarding patient characteristics. Patients in the TUS group tended to have a slightly higher Body Mass Index. The preferred use of either method was not influenced by patient properties. In most diagnostic studies, ultrasound is compared with CR in the supine position . However, the CR in our study was usually performed in the upright position, which offers better accuracy . Therefore, findings from an observational study that demonstrated a reduction of CR used for PTX exclusion in day-to-day practice in an ICU setting cannot be generalized to interventional pulmonology. It is noticeable that relatively more PTX were diagnosed by CR (14 of 166, 8.4%) than by TUS (14 of 478, 2.9%) in period B. This finding is counter-intuitive since TUS is known to have a higher sensitivity than CR (79–97% vs. 40–52%) while specificity is similar . While for most intervention types, numbers of PTX found coincide with the proportion of imaging method utilization, this is untrue for thoracocentesis and chest tubes. Here, more PTX were found using CR than using TUS while the relative usage of the methods would suggest the opposite. With a cumulative 8 PTX diagnosed by CR and one by TUS, these interventions skew the aggregated data. While speculative, we assume practical reasons for this observation: (A) Additional information (tube location, atelectasis etc.) beyond PTX exclusion was sought especially in high-risk interventions, and (B) search for the lung point is time-consuming thus nudging examiners to use CR when a PTX is suspected.
In period B, there were more frequent confirmatory investigations due to inconclusive findings after TUS (21 of 478, 4.4% vs. 3 of 166, 1.8% after CR ). The fact that all confirmatory investigations after TUS ruled out a PTX may be due to two reasons: (A) the subjective uncertainty when using the relatively new ultrasound technique; and (B) the added uncertainty of TUS in patients with lung conditions such as adhesions and bullae. While the first reason cannot be tested, pre-existing lung conditions are a known limiting factor of TUS: Shostak and colleagues have reported limitations of ultrasound investigations in 23% of their patients after pulmonary interventions (43 of 185) and have associated this with prior lung disease. Pleural sliding might be impaired due to emphysema, previous lung surgery, radiation exposure, and pleural adhesions. The authors have recommended to examine such patients by CR . Similarly, pleural adhesions due to previous lung disease existed in 6 of 1023 (0.6%) patients examined in a study by Kreuter and colleagues. This pre-existing condition also prevented the detection of respiratory displacement of the lung and resulted in false-positive results. CRs excluded PTX in all 6 cases . In transthoracic biopsies and transbronchial (cryo)biopsies as well as needle aspirations TUS was markedly favored by interventionalists in period B. These interventions combined, TUS was the method of choice in 91% (331 of 368). In these intervention types TUS offers prompt PTX rule-out and no further information (i.e. tip of chest tube) can be gained by CR. In our study, due to the retrospective design, we cannot reproduce whether patients examined by CR received it primarily for such reasons. However, in this routine care dataset, the fraction of confirmatory investigations after TUS validates the limitations of TUS expected from prospective studies, especially by Shostak, in a much larger cohort. In summary, pre-existing lung conditions may impede sonographic PTX detection. These limiting factors are assumed to have a higher prevalence in interventional pulmonology as compared to patients in emergency or intensive care settings in which most prospective TUS studies were conducted. However, pre-existing conditions rather result in false-positive than false-negative results: In the current relevant literature on post-interventional PTX exclusion, the false-negative rate is low and reported to be between 0 and 0.7% . The patient with the missed PTX in our study constitutes one such rare false-negative event: He had a small trapped apical PTX – which cannot be easily diagnosed by TUS – along with a large malignant pleural effusion. Additionally, the choice of imaging method is based on the intervention type, and CR was favored where additional information beyond PTX exclusion was sought: CR was used more frequently in cases of chest tube insertion and ELVR as well as stent placements (Fig. ). Here, imaging delivers additional information beyond PTX evaluations, e.g. confirming atelectasis after ELVR, position of implanted stents, or tips of drainage catheters.
To this day, TUS for PTX detection – though well studied and recommended in guidelines – is still not broadly-established as a method in routine care . The relative recency of the method may be a contributing factor. Seen on a monthly basis, the transition from only CR to mostly TUS was swift. Within one month, the team was able to safely perform TUS. Constant proportions of confirmatory investigations over the years support the assumption that proficiency in TUS is achieved within a short period. The one erroneous PTX exclusion happened shortly after the introduction of TUS (Fig. ). However, due to the methodological limitation of TUS in diagnosing clinically irrelevant small trapped PTX, there is no reason to believe that insufficient skills were a factor in this false-negative event. Our adoption strategy is in line with training protocols from prospective studies in which physicians were trained by a mentor: For example, 2 h of training or 10 TUS performed under supervision were required . The assumption that TUS is quick and easy to learn is further supported by a small study by Monti et al.: The authors demonstrated that nonphysician emergency medical personnel can reliably detect PTX using sonography with a sensitivity of 96% and a specificity of 100% after a brief training session consisting of a slide show presentation, short video clips on the signs of PTX to observe in sonographic images, and an introduction to the sonographic device .
With our study, the practicability and capacity of TUS when it comes to reducing the use of CR as the standard in the routine care of an interventional pulmonology unit is evaluated for the first time. Based on a large single-center experience including all interventions typically seen in interventional pulmonology, we have demonstrated that using TUS for PTX detection can effectively reduce the amount of chest radiographs in everyday clinical practice. The main limitation of this study is the missing gold standard. Therefore, the true number of asymptomatic PTX missed by both methods is unknown, and hence we avoid using test characteristics terminology (sensitivity, specificity etc.) in conjunction with our results in this manuscript. However, it was not the aim of this study to assess test accuracy but to investigate TUS in a real-world setting.
Thoracic ultrasound for post-interventional pneumothorax detection can effectively reduce the amount of chest radiographs in the routine care of an interventional pulmonology department, avoiding ionizing radiation and saving resources. The method can be implemented quickly in routine care. However, chest radiograph may still be favored when additional information is sought, or pre-existing conditions limit sonographic findings.
|
Unexplored Challenges of Minoritized Microbiologists in Academia | 2f0442b3-23c7-4da5-bc3a-9b616eb833fa | 9746294 | Microbiology[mh] | Microbiology departments across the United States, as parts of major universities, colleges, and institutes, are joining a growing national movement to diversify their institutions and to advance their inclusion and equity missions. However, one major, common concern by search committees that recruit students and faculty is that is exceedingly difficult to find qualified individuals, particularly those that belong to minoritized groups (e.g., Black, Hispanic/Latinx, Indigenous [American Indians]). Although this concern represents a common bias permeating academia, it does not reflect the available qualified candidates that can occupy those positions, and search committees need to consider the challenges of this group of applicants while recruiting them. Schools continue to develop strategic plans to recruit these microbiologists, but they do not consider that trainees and/or faculty candidates belonging to these ethnic and racial groups might require different strategies to be incorporated in their scientific/academic communities or that there is a need to create a change in the academic culture that can support their careers and their advancement in the microbial sciences ( , ). Here are some concepts that might not have been previously evaluated as parts of the decision-making strategies established by microbiology departments, centers, institutes, or associations across the nation.
It is well-documented that Black scientists are historically excluded in microbial education, training, and research ( , ); however, there is a long tradition of exceptional Black microbiologists contributing to various aspects of the microbial sciences, as depicted in the 100 Inspiring Black Scientists in America report ( ). Further efforts to recognize the contributions of Black microbiologists through history, emphasizing their courage and determination while facing racial discrimination, have been made by microbiology scientists while celebrating the International Microorganism Day ( ) and by the American Society of Microbiology (ASM) while celebrating their Black Clinical Microbiologists ( ). A feature of the current members of the Black scientific community is their remarkable resilience and their ability to support each other through networks and organizations, regardless of the institution to which they belong, that has helped them to create a sense of community. One of the best examples of the network system supporting these scientists was the establishment of the Black Microbiologists Association (BMA) in 2020 ( ), which was intended to reduce the underrepresentation of Black scientists in microbiology. BMA has organized two #BlackInMicro weeks to highlight the research of Black microbiologists and to connect and create a sense of community. These events are ideal platforms for the recruitment of trainees and faculty candidates to the different universities, societies, and nonacademic institutions that are trying to diversify their scientific communities or connect their constituents to a network that will support their careers. ASM has recognized the value of the Black community of microbiologists by having the only Black President of the Society on Clifford Houston of the University of Texas Medical Branch, who was a microbiologist who advanced the educational mission of ASM and founded the Annual Biomedical Research Conference for Minority Students (ABRCMS). Unfortunately, the recent ASM Diversity, Equity and Inclusion Task Force Report ( ) showed that Black microbiologists make up only 5% of ASM’s members, and other data suggest that they represent less than 3% of all microbiologists in the United States ( ). Further efforts are needed to increase and advance this community of scientists. In fact, Black microbiologists still are facing systemic racism and/or biases as roadblocks for their advancement ( ). Therefore, we must use discussions about diversity and inclusion, occurring during the “racial awakening” currently happening in the United States, to work together with recruiters, academic leaders, and decision-making individuals to guarantee that the next generation of microbiologists is better represented in the microbial sciences.
Hispanics are the fastest growing minoritized group in the United States, but they remain a significantly underrepresented group in the microbial sciences, comprising 6% of the total workforce of microbiologists in the United States ( , , ). Because Hispanics have many commonalities, they are routinely treated as a homogeneous group in science and research ( ). As such, the perception that this ethnic group has made significant advances in the numbers of trainees and faculty members joining different academic institutions is due to a broad definition that encompasses individuals of Hispanic/Latino descent ( ). At least three large groups can be included in the definition of Hispanic: Latinos/Hispanics born and trained in the United States, Hispanics migrating from Latin America and Spain, and Puerto Ricans that trained on the island or the mainland and have either remained there or migrated from the island. Each of these subgroups has its own strengths and experiences different challenges in career advancement. In general terms, the first subgroup consists of individuals that were born and educated in the United States, and microbiologists from this group are mainly distributed in Hispanic-serving institutions ( ) or in states (e.g., California, Texas, Illinois, New York, and Florida) with a large representation of Hispanic students in science, technology, engineering, and mathematics (STEM) ( ). These individuals are good recruitment ambassadors for their institutions. A second subgroup includes faculty members and some trainees who did most of their training in Latin America or Spain and were recruited as academicians by United States institutions. These microbiologists have, in many cases, strong networks within their countries of origin, and they have the opportunity to recruit trainees from those countries to their laboratories ( ). Finally, a third subgroup consists of microbiologists with a strong link to Puerto Rico, either because they trained there and are part of the academia of the island or because they are Puerto Rican trainees or faculty members on the mainland. These microbiologists have a long tradition of training excellent students who are frequently recruited by different institutions to be part of their graduate programs, but they are less often recruited to become faculty members. Interestingly, Puerto Rico is the place that has the most ASM branches housed at its academic institutions, serving as a forum for microbiology trainees to collaborate and to organize local scientific meetings. Further, the Puerto Rican microbiologists have established support networks in the form of “Ciencia Puerto Rico” ( https://www.cienciapr.org/ ) and networks for professional development as part of the Puerto Rico Society of Microbiologists ( https://www.micropr.org/ ). However, the economic challenges of the island lead many microbiologists to migrate to the mainland and hinder their direct participation in these networks. The classification of subgroups of Hispanic microbiologists was made to highlight both the wide variety of individuals that encompass this nonhomogenous ethnic group and the fact that many of these microbiologists do not have many commonalities, which complicates the establishment of collaborations and the creation of support networks. It is evident that Hispanic microbiologists continue to be the fastest growing group in the Microbial Sciences, but without creating adequate support networks, it might be difficult to increase recruitment by different institutions. ASM needs to have a Hispanic President who can serve as a unifier of the Hispanic microbiologists in the United States.
Compared with other underserved groups in academia, indigenous microbiologists number significantly fewer, and those involved in academic positions are often focused on education. Due to the limited number of American Indian microbiologists, these individuals need to join other established networks that understand their needs and can support their advancement and success in the microbial sciences. Joining other networks is not ideal because the indigenous community must create a network that can promote trainees in microbiological careers and let them advance into academic positions. Further, the need to increase the number of these microbiologists is of particular importance, as many infectious diseases disproportionately affect indigenous people ( ), posing significant public health challenges for these communities. Interestingly, in many other fields of science, American Indians have critical mass, but, in the case of microbiology, the number of established Indigenous investigators does not exceed more than 10. Therefore, ASM, as a part of their strategic inclusive diversity with equity, access, and accountability (IDEAA) strategic plan ( ), needs to establish clear actions that can empower these microbiologists in support of their professional careers.
Many conferences and scientific meetings are constantly highlighting the nonbiomedical efforts to build a diverse and welcoming community for professionals in various STEM fields. Venues that include basic and/or applied microbiology topics are trying to diversify their panels of speakers and participants; however, sometimes the pool of participants from minoritized groups seems exceedingly small. For 20 years, the Annual Biomedical Research Conference for Minority Students (ABRCMS; https://abrcms.org/ ), recently renamed the Annual Biomedical Research Conference for Minoritized Scientists, has been the place for minoritized individuals from community colleges as well as undergraduate, graduate, and postbaccalaureate students to display their talents in STEM. This conference also brings together many minoritized faculty members that serve as mentors, judges, speakers, and recruiters of all of the students who attend the conference. ASM has sponsored this conference from the beginning, with the goals being to promote microbiology and other STEM fields and to use this platform to empower the next generation of microbiologists. Academic institutions that are trying to diversify their research programs or faculty pools at large and cannot find sufficient candidates for their graduate programs should consider attending this conference and developing long term strategic plans to support these minoritized scientists in training, with the goal being to recruit those who demonstrate excellence in their research and can eventually occupy faculty positions at academic institutions.
This commentary is not intended to be a comprehensive review of all of the challenges and opportunities faced by minoritized microbiologists. Instead, this commentary is a series of personal observations that could be useful for academic institutions and microbial societies that are trying to diversify their microbiology programs by having a better understanding of the existing networks supporting these individuals and the potential mechanisms that can be established to support the recruitment, retention, and advancement of minoritized microbiologists. It is evident that recruiters of different institutions need to consider establishing collaborations with some of these networks so that equity in the pool of applicants can be created ( ). Here are some activities that can be established at the institutions or in partnership with ASM to increase the recruitment of these individuals. First, the establishment of faculty coalitions within the institution or with different institutions in the same state or region that can support the recruitment, onboarding, and mentoring of minoritized microbiologists is critical. Second, to prevent the isolation of these recruits, institutions should consider the cluster-hiring of individuals, in which supportive and collaborative networks are created between these subgroups in the institution or in the regional/state institutional setting to increase the sense of community. Third, it is critical for academic institutions to create a strategic plan that matters ( ) as a part of their diversity, equity, and inclusion (DEI) efforts in order to define how recruited minoritized microbiologists can find the optimal conditions for their success and advancement in an academic environment that fully supports them. For example, they can create partnerships with ASM or with the National Institutes of Health (NIH) MOSAIC (Maximizing Opportunities for Scientific and Academic Independent Careers) award program, which is intended to promote the independent careers of diverse faculty in research-intensive institutions ( https://www.nigms.nih.gov/training/careerdev/Pages/MOSAIC.aspx ). Fourth, representing the most vital component of any institutional DEI plan, which is commonly ignored, is that when minoritized microbiologists become successful, they will be heavily recruited by other programs. Therefore, the home institutions that have created a more inclusive culture and environment for trainees and faculty will be more successful in retaining their talented minoritized microbiologists. Finally, it is always important to emphasize that recruiting minoritized individuals just to fulfill a quota established by the institution, without a well-designed DEI strategic plan, is a disservice to the microbiological community at large and to those minoritized microbiologists who deserve more opportunities to display their talents.
|
미혼 여성의 생식건강 지식, 생식건강 증진행위, 성 의사소통이 산부인과 방문 의도에 영향을 미치는가?: 횡단적 조사 연구 | be9df5d4-61d8-4fe9-bf4c-e4099b290443 | 11700713 | Gynaecology[mh] | 여성은 다양한 문제로 산부인과 진료를 필요로 한다. 미혼 여성의 낮아진 성생활 시작 연령과 성 경험 증가 등으로 생식건강 이상을 경험하는 여성의 연령이 점차 낮아지고 있어 산부인과의 정기적인 방문을 통한 진료 및 조기 진단의 중요성이 강조되고 있다. 정기적인 검진을 통해 조기에 발견할 수 있는 자궁경부암은 20대 여성에서 꾸준히 증가하여 2018년 3,370명에서 2022년 4,439명으로 5년 동안 31.72%가 증가하였고, 30대 여성은 2018년 13,815명에서 2022년 15,201명으로 10.03% 증가했다. 이에 우리나라에서는 자궁경부암을 예방하기 위해 만 20세 이상 여성을 대상으로 2년마다 자궁경부암 검사를 하도록 지원하고 있으며, 사람유두종바이러스(human papillomavirus, HPV) 국가예방접종 사업은 2016년 6월, 만 12세 여성 청소년을 대상으로 지원하기 시작하여, 2024년에는 12–17세 여성 청소년과 18–26세 저소득층 여성을 대상으로 지원을 점차 확대하였다. 또한 여성, 어린이 특화 지역사회 통합 건강증진사업으로 성 건강 증진사업을 지원하는 등 단계적으로 미혼 여성에 대한 지원을 확장해 나가고 있다. 이와 같은 노력에도 불구하고 국내 자궁경부암 발생률은 매년 높아지고 있어, 2023년 대한부인종양학회 경부 조기검진 진료 권고안에서는 매 1년 간격으로 검사를 권고하고 있으나, 젊은 나이에는 암에 잘 걸리지 않는다는 등의 건강 신념과 산부인과 검진에 대한 부정적인 사회적 인식, 생식건강은 기혼 여성에게만 필요한 것이라는 사회적 편견 등이 미혼 여성 산부인과 방문에 저해요소로 작용하고 있다. 2023년 기준 국내 HPV 누적 접종률은 여성 43%, 남성 3%이며, 2022년 기준 20대의 자궁경부암 검진 수검률은 36.1%, 30대는 62.9%에 불과하다. 2023년에 우리나라 여성의 평균 초혼 연령은 31.5세, 평균 초산 연령은 33.0세로 지속적으로 상승하여 OECD (Organization for Economic Co-operation and Development) 38개국 중 가장 높았다. 미혼 여성의 유방암 검진율은 34.7%, 자궁경부암 검진율이 38.2%이나, 기혼 여성의 유방암 검진율과 자궁경부암 검진율은 약 70% 수준이다. 미혼 여성은 산부인과 방문을 임신 등으로 오해받는 사회의 부정적인 인식으로 건강 검진을 기피하는 경향이 있고 건강 행위를 통제하는 배우자가 존재하지 않아 기혼 여성에 비해 부정적인 건강 행위를 할 우려가 더 높다. 이러한 미혼 여성의 산부인과 검진 지연은 산전 관리만으로 해결되지 않는 조산아 출산 등 부정적인 출산 결과로 이어질 수 있으므로, 생식건강 이상의 조기 발견, 건강한 임신과 출산을 위해 미혼 여성의 생식건강에 대한 관심이 높아져야 할 것이다. 생식건강 지식은 생식기계 질환, 임신 및 출산, 가족계획, 유산, 성병, 성 건강 문제 등 생식기의 구조와 기능 등과 관련한 지식을 의미한다. 성행위의 연령이 낮아지고 성문화가 보다 개방적으로 변화하면서 안전하고 정확한 생식건강 증진행위를 위한 생식건강 지식의 정도를 확인하는 것은 더욱 중요해지고 있으나, 성인 초기의 여성은 생식건강에 대한 정보를 병원의 의료진에게 문의하는 전문적인 접근보다는 주로 인터넷, TV와 같은 미디어 접근을 통해 정확하지 않은 정보를 습득하는 경우가 많아 여러 가지 성 문제를 야기하고 있다. 생식건강에 대한 정확한 지식과 올바른 이해는 생식건강행위 실천을 높이며, 성 문제를 예방하고, 책임감 있는 출산에 대한 의사결정에 중요한 요소가 된다. 정확한 생식건강 지식의 습득은 산부인과를 방문하여 생식건강을 관리하고자 하는 행위로 이어지는 데 중요한 요인이 될 수 있다. 생식건강 증진행위는 안전한 성행위, 성행위에 대한 책임감, 생식기 질환의 조기발견을 위한 생식기 건강 관리, 성병 예방 등을 포함하여 생식건강을 증진하는 행위를 의미한다. 여성들은 임신, 통증, 비정상적 질 출혈이나 질 분비물 등의 이상 증상이 나타나기 전에는 산부인과 방문과 진료를 기피하고 있으며, 생식기에 문제가 있는 경우에도 산부인과 방문이나 치료는 효율적으로 이루어지지 않고 있다. 미혼 여성의 성 경험은 증가하고 있지만 피임 실천율이 낮으며, 이로 인해 계획하지 않은 임신이 낙태로 이어질 수 있다. 생식기 질환은 난임의 발생빈도를 높이는 등의 다양한 문제를 일으킬 수 있어 미혼 여성의 생식건강 유지와 증진에 중요한 요소이다. 생식건강 증진행위를 실천하는 것은 자신의 생식건강에 관심을 높이고, 예방적 및 치료적 관리를 위한 산부인과 방문을 증가시킬 수 있을 것이다. 성 의사소통은 성 경험을 포함하여 성에 대한 주제로 이야기하는 것을 말한다. 직접 참여하고 발표하는 학습법을 통한 실용적인 성교육 프로그램은 성 지식, 성적 태도 및 성적 자율성을 향상시켰고, 성 의사소통은 성적 자율성에 영향을 미쳤다. 성적 자율성은 성을 긍정적으로 형성하게 하는 요소로 피임, 임신 및 성병 예방 등에 대한 내적 조절능력이 형성되도록 돕는다. 성 의사소통이 부족하고 자신의 생식건강 문제를 비밀로 감추려는 행동은 산부인과 수검율을 낮춘다. 바람직하고 적극적인 성 의사소통은 생식건강 문제를 은폐하지 않고 정기적으로 산부인과를 방문하는 등의 적극적인 관리를 할 수 있도록 할 것이다. 지금까지 산부인과 방문 의도에 미치는 영향 요인과 관련된 선행 연구는 주로 20대 초반의 여대생을 중심으로 이루어졌고, 높아지는 초혼과 초산 연령을 고려하여 30대를 포함한 연구는 미미한 실정이다. 선행 연구에서는 여대생의 생식건강 지식이 높을수록, 생식건강 증진행위가 높을수록, 성 의사소통이 높을수록 산부인과 방문 의도가 높은 것으로 나타났다. 이에 본 연구에서는 미혼 여성의 생식건강 지식, 생식건강 증진행위, 성 의사소통 정도를 파악하고 이들과 산부인과 방문 의도 간의 관계를 규명한 후 이를 기초로 산부인과 방문 의도에 영향을 주는 요인을 파악함으로써, 미혼 여성의 향후 산부인과 방문 독려, 효과적인 여성건강 교육 프로그램의 방향 제시에 이론적 근거가 되는 기초 자료를 제공하고자 한다. Ethics statement: This study was approved by the Institutional Review Board of Pukyong National University (No. 1041386-202309-HR-102-01). Informed consent was obtained from the participants. 연구 설계 본 연구는 미혼 여성의 생식건강 지식, 생식건강 증진행위, 성 의사소통 및 산부인과 방문 의도와 이들 변수 간의 관계를 파악하고 산부인과 방문 의도에 미치는 영향을 주는 요인을 확인하기 위한 횡단적 조사 연구이다. 본 연구는 CHERRIES (Checklist for Reporting Results of Internet E-Surveys) 보고지침에 따라 기술하였다. 연구 대상 및 표집 방법 본 연구 대상자는 부산광역시에 거주하는 20대, 30대 미혼 여성을 대상으로 하였다. 본 연구의 목적을 이해하고 자발적인 참여로 연구에 동의한 자로 한정하여 편의 표집하였다. 다양한 건강상태를 고려하기 위해 현재 산부인과 질환이나 관련 증상이 있는 여성도 포함하였다. 연구 대상자 수는 G-power 3.1.9.7 프로그램을 이용하였고, 효과 크기는 선행 연구에 근거하여 유의수준 α=.05, 검정력(1–β) .80, 효과 크기 .15 (중간크기)로 최소 표본 수가 118명이었다. 탈락률 20%를 고려하여 총 170명을 대상으로 자료를 수집하여 최종 분석에 사용하였다. 연구 도구 산부인과 방문 의도 본 연구에서 산부인과 방문 의도는 Min과 Cha 가 개발한 산부인과 방문 의도 측정도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 매년 1회 이상 산부인과에 방문할 의향과 산부인과 방문을 위한 노력, 산부인과 방문할 생각의 총 3문항으로 구성되어 있다. 이 도구는 ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 4점의 척도의 5점 Likert 척도로(가능 범위, 3–15점), 점수가 높을수록 산부인과 방문에 대한 의도가 높은 것을 의미한다. 개발 당시 도구 신뢰도 Cronbach’s α=.96이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.96이었다. 생식건강 지식 본 연구에서는 Park과 Choi 가 개발한 생식건강 지식 척도를 Cho 가 수정‧보완한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 생식기의 구조 및 기능 6문항, 임신 및 출산 11문항, 피임 및 성 매개 감염 12문항, 생식기 암 5문항의 총 34문항으로 구성되어 있다. 이 도구는 정답을 체크하면 1점, 오답 및 ‘모르겠다’로 체크하는 경우는 0점을 부여하고(가능 범위, 0–34), 점수가 높을수록 생식건강 지식이 높음을 의미한다. 개발 당시 도구 신뢰도는 Kuder-Richardson 20 (KR-20) .79이었고, Cho 의 연구에서 도구 신뢰도는 KR-20 .88이었으며, 본 연구에서는 도구 신뢰도 KR-20 .74였다. 생식건강 증진행위 본 연구에서 생식건강 증진행위는 미혼자를 대상으로 Jo 등이 개발한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 성행위 4문항, 성행위 책임감 4문항, 생식기 건강 관리 4문항, 성병 예방 3문항, 위생 관리 3문항의 총 18문항으로 구성되어 있다. 이 도구는 ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 4점의 4점 Likert 척도로(가능 범위, 18–72점), 점수가 높을수록 생식건강 증진행위를 적극적으로 행하는 것을 의미한다. 개발 당시의 도구 신뢰도 Cronbach’s α=.93이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.84였다. 성 의사소통 본 연구에서는 Hutchinson과 Cooney 가 모-청소년 자녀 간 성에 관한 의사소통을 연구하기 위해 사용한 척도를 Cho와 Cho 가 친구 간 성 의사소통 척도로 수정‧보완한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 총 10문항으로 구성되었으며, ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 5점의 5점 Likert 척도로(가능 범위, 10–50점) 점수가 높을수록 개방적인 성 의사소통을 하고 있음을 의미한다. Cho 와 Cho 의 연구에서 도구 신뢰도 Cronbach’s α=.93이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.92였다. 일반적 특성 및 산과적 특성 일반적 특성은 연령, 종교의 2개 문항이고, 산과적 특성은 산부인과 방문 경험, 성 경험, 산부인과 관련 가족력, 산부인과 질환 경험의 4문항으로 구성되어 있다. 일반적 특성 중 종교는 문화 및 종교적 신념에 따라 생식건강의 차이가 있다는 연구에 따라 산부인과 방문 의도에 미치는 영향을 파악하기 위해 선택하였다. 자료 수집 본 연구는 2023년 12월 11일부터 12월 22일까지 자료를 수집하였다. 자료 수집은 부산광역시에 소재한 대학교 2개와 소셜 네트워크 서비스에서 대상자 모집 공고문을 공지하여 시행하였다. 본 연구의 필요성 및 목적을 이해하고 대상자 기준에 부합한 자는 온라인 설문을 위하여 URL (uniform resource locator) 단축 서비스를 활용하여 온라인 접속을 하고, 개인 정보 수집 및 이용 동의 항목에 동의한 대상자에 한하여 온라인 설문이 진행되도록 설계하였다. 연구자의 전화번호를 기재하여 설문지에 대한 의문이 있는 경우 연락할 수 있도록 하였다. 설문지의 작성 시간은 약 15–20분 정도 소요되었다. 설문이 종료되면 대상자에게 3,000원 상당의 모바일 상품권을 지급하였다. 자료 분석 본 연구에서 수집된 자료는 IBM SPSS Statistics for Windows ver. 25.0 (IBM Corp., Armonk, NY, USA)을 이용하여 분석하였고, 구체적인 분석방법은 다음과 같다. (1) 미혼 여성의 일반적 특성과 산과적 특성은 빈도와 백분율, 평균, 표준편차로 분석하였다. (2) 미혼 여성의 생식건강 지식, 성 의사소통, 생식건강 증진행위와 산부인과 방문 의도를 파악하기 위해 평균과 표준편차로 분석하였다. (3) 미혼 여성의 특성에 따른 산부인과 방문 의도의 차이는 독립표본 t 검정, 일원분산분석, 사후 검정 분석은 Scheffé test로 분석하였다. (4) 미혼 여성의 생식건강 지식, 성 의사소통, 생식건강 증진행위 및 산부인과 방문 의도의 상관관계를 파악하기 위해 피어슨 상관계수로 분석하였다. (5) 미혼 여성의 산부인과 방문 의도에 영향을 미치는 요인을 확인하기 위해서 위계적 다중회귀로 분석하였다. 본 연구는 미혼 여성의 생식건강 지식, 생식건강 증진행위, 성 의사소통 및 산부인과 방문 의도와 이들 변수 간의 관계를 파악하고 산부인과 방문 의도에 미치는 영향을 주는 요인을 확인하기 위한 횡단적 조사 연구이다. 본 연구는 CHERRIES (Checklist for Reporting Results of Internet E-Surveys) 보고지침에 따라 기술하였다. 본 연구 대상자는 부산광역시에 거주하는 20대, 30대 미혼 여성을 대상으로 하였다. 본 연구의 목적을 이해하고 자발적인 참여로 연구에 동의한 자로 한정하여 편의 표집하였다. 다양한 건강상태를 고려하기 위해 현재 산부인과 질환이나 관련 증상이 있는 여성도 포함하였다. 연구 대상자 수는 G-power 3.1.9.7 프로그램을 이용하였고, 효과 크기는 선행 연구에 근거하여 유의수준 α=.05, 검정력(1–β) .80, 효과 크기 .15 (중간크기)로 최소 표본 수가 118명이었다. 탈락률 20%를 고려하여 총 170명을 대상으로 자료를 수집하여 최종 분석에 사용하였다. 산부인과 방문 의도 본 연구에서 산부인과 방문 의도는 Min과 Cha 가 개발한 산부인과 방문 의도 측정도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 매년 1회 이상 산부인과에 방문할 의향과 산부인과 방문을 위한 노력, 산부인과 방문할 생각의 총 3문항으로 구성되어 있다. 이 도구는 ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 4점의 척도의 5점 Likert 척도로(가능 범위, 3–15점), 점수가 높을수록 산부인과 방문에 대한 의도가 높은 것을 의미한다. 개발 당시 도구 신뢰도 Cronbach’s α=.96이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.96이었다. 생식건강 지식 본 연구에서는 Park과 Choi 가 개발한 생식건강 지식 척도를 Cho 가 수정‧보완한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 생식기의 구조 및 기능 6문항, 임신 및 출산 11문항, 피임 및 성 매개 감염 12문항, 생식기 암 5문항의 총 34문항으로 구성되어 있다. 이 도구는 정답을 체크하면 1점, 오답 및 ‘모르겠다’로 체크하는 경우는 0점을 부여하고(가능 범위, 0–34), 점수가 높을수록 생식건강 지식이 높음을 의미한다. 개발 당시 도구 신뢰도는 Kuder-Richardson 20 (KR-20) .79이었고, Cho 의 연구에서 도구 신뢰도는 KR-20 .88이었으며, 본 연구에서는 도구 신뢰도 KR-20 .74였다. 생식건강 증진행위 본 연구에서 생식건강 증진행위는 미혼자를 대상으로 Jo 등이 개발한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 성행위 4문항, 성행위 책임감 4문항, 생식기 건강 관리 4문항, 성병 예방 3문항, 위생 관리 3문항의 총 18문항으로 구성되어 있다. 이 도구는 ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 4점의 4점 Likert 척도로(가능 범위, 18–72점), 점수가 높을수록 생식건강 증진행위를 적극적으로 행하는 것을 의미한다. 개발 당시의 도구 신뢰도 Cronbach’s α=.93이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.84였다. 성 의사소통 본 연구에서는 Hutchinson과 Cooney 가 모-청소년 자녀 간 성에 관한 의사소통을 연구하기 위해 사용한 척도를 Cho와 Cho 가 친구 간 성 의사소통 척도로 수정‧보완한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 총 10문항으로 구성되었으며, ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 5점의 5점 Likert 척도로(가능 범위, 10–50점) 점수가 높을수록 개방적인 성 의사소통을 하고 있음을 의미한다. Cho 와 Cho 의 연구에서 도구 신뢰도 Cronbach’s α=.93이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.92였다. 일반적 특성 및 산과적 특성 일반적 특성은 연령, 종교의 2개 문항이고, 산과적 특성은 산부인과 방문 경험, 성 경험, 산부인과 관련 가족력, 산부인과 질환 경험의 4문항으로 구성되어 있다. 일반적 특성 중 종교는 문화 및 종교적 신념에 따라 생식건강의 차이가 있다는 연구에 따라 산부인과 방문 의도에 미치는 영향을 파악하기 위해 선택하였다. 본 연구에서 산부인과 방문 의도는 Min과 Cha 가 개발한 산부인과 방문 의도 측정도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 매년 1회 이상 산부인과에 방문할 의향과 산부인과 방문을 위한 노력, 산부인과 방문할 생각의 총 3문항으로 구성되어 있다. 이 도구는 ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 4점의 척도의 5점 Likert 척도로(가능 범위, 3–15점), 점수가 높을수록 산부인과 방문에 대한 의도가 높은 것을 의미한다. 개발 당시 도구 신뢰도 Cronbach’s α=.96이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.96이었다. 본 연구에서는 Park과 Choi 가 개발한 생식건강 지식 척도를 Cho 가 수정‧보완한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 생식기의 구조 및 기능 6문항, 임신 및 출산 11문항, 피임 및 성 매개 감염 12문항, 생식기 암 5문항의 총 34문항으로 구성되어 있다. 이 도구는 정답을 체크하면 1점, 오답 및 ‘모르겠다’로 체크하는 경우는 0점을 부여하고(가능 범위, 0–34), 점수가 높을수록 생식건강 지식이 높음을 의미한다. 개발 당시 도구 신뢰도는 Kuder-Richardson 20 (KR-20) .79이었고, Cho 의 연구에서 도구 신뢰도는 KR-20 .88이었으며, 본 연구에서는 도구 신뢰도 KR-20 .74였다. 본 연구에서 생식건강 증진행위는 미혼자를 대상으로 Jo 등이 개발한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 성행위 4문항, 성행위 책임감 4문항, 생식기 건강 관리 4문항, 성병 예방 3문항, 위생 관리 3문항의 총 18문항으로 구성되어 있다. 이 도구는 ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 4점의 4점 Likert 척도로(가능 범위, 18–72점), 점수가 높을수록 생식건강 증진행위를 적극적으로 행하는 것을 의미한다. 개발 당시의 도구 신뢰도 Cronbach’s α=.93이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.84였다. 본 연구에서는 Hutchinson과 Cooney 가 모-청소년 자녀 간 성에 관한 의사소통을 연구하기 위해 사용한 척도를 Cho와 Cho 가 친구 간 성 의사소통 척도로 수정‧보완한 도구를 사용 승인을 얻은 후 사용하였다. 본 도구는 총 10문항으로 구성되었으며, ‘전혀 그렇지 않다’ 1점에서 ‘매우 그렇다’ 5점의 5점 Likert 척도로(가능 범위, 10–50점) 점수가 높을수록 개방적인 성 의사소통을 하고 있음을 의미한다. Cho 와 Cho 의 연구에서 도구 신뢰도 Cronbach’s α=.93이었고, 본 연구에서는 도구 신뢰도 Cronbach’s α=.92였다. 일반적 특성은 연령, 종교의 2개 문항이고, 산과적 특성은 산부인과 방문 경험, 성 경험, 산부인과 관련 가족력, 산부인과 질환 경험의 4문항으로 구성되어 있다. 일반적 특성 중 종교는 문화 및 종교적 신념에 따라 생식건강의 차이가 있다는 연구에 따라 산부인과 방문 의도에 미치는 영향을 파악하기 위해 선택하였다. 본 연구는 2023년 12월 11일부터 12월 22일까지 자료를 수집하였다. 자료 수집은 부산광역시에 소재한 대학교 2개와 소셜 네트워크 서비스에서 대상자 모집 공고문을 공지하여 시행하였다. 본 연구의 필요성 및 목적을 이해하고 대상자 기준에 부합한 자는 온라인 설문을 위하여 URL (uniform resource locator) 단축 서비스를 활용하여 온라인 접속을 하고, 개인 정보 수집 및 이용 동의 항목에 동의한 대상자에 한하여 온라인 설문이 진행되도록 설계하였다. 연구자의 전화번호를 기재하여 설문지에 대한 의문이 있는 경우 연락할 수 있도록 하였다. 설문지의 작성 시간은 약 15–20분 정도 소요되었다. 설문이 종료되면 대상자에게 3,000원 상당의 모바일 상품권을 지급하였다. 본 연구에서 수집된 자료는 IBM SPSS Statistics for Windows ver. 25.0 (IBM Corp., Armonk, NY, USA)을 이용하여 분석하였고, 구체적인 분석방법은 다음과 같다. (1) 미혼 여성의 일반적 특성과 산과적 특성은 빈도와 백분율, 평균, 표준편차로 분석하였다. (2) 미혼 여성의 생식건강 지식, 성 의사소통, 생식건강 증진행위와 산부인과 방문 의도를 파악하기 위해 평균과 표준편차로 분석하였다. (3) 미혼 여성의 특성에 따른 산부인과 방문 의도의 차이는 독립표본 t 검정, 일원분산분석, 사후 검정 분석은 Scheffé test로 분석하였다. (4) 미혼 여성의 생식건강 지식, 성 의사소통, 생식건강 증진행위 및 산부인과 방문 의도의 상관관계를 파악하기 위해 피어슨 상관계수로 분석하였다. (5) 미혼 여성의 산부인과 방문 의도에 영향을 미치는 요인을 확인하기 위해서 위계적 다중회귀로 분석하였다. 미혼 여성의 일반적 특성 및 산과적 특성에 따른 산부인과 방문 의도 차이 미혼 여성의 일반적 특성을 살펴보면, 연령은 25세 미만이 95명(55.9%), 25–29세가 54명(31.8%), 30세 이상이 21명(12.4%)이었으며, 평균 연령은 24.64세였다. 미혼 여성의 산과적 특성을 살펴보면, 산부인과 방문 경험은 1회 80명(47.1%), 0회 55명(32.4%), 2회 21명(12.4%), 3회 이상이 14명(8.2%)이었다. 성 경험은 ‘유’인 경우가 108명(63.5%), 산부인과 관련 가족력은 ‘무’인 경우가 144명(84.7%)이었으며, 산부인과 질환 경험은 ‘유’인 경우가 93명(54.7%)로 많았다. 산부인과 질환 경험에는 다낭성 난소증후군, 생리통, 자궁내막 폴립, 질염 등이 있었다. 본 연구에서 미혼 여성의 산부인과 방문 의도는 산부인과 방문 경험(F=38.02, p <.001), 성 경험(t=–5.62, p <.001), 산부인과 질환 경험(t=–4.56, p <.001)에서 유의한 차이가 있었다. 이를 Scheffé test로 사후검증을 실시한 결과에서 산부인과 방문 경험이 3회 이상인 경우가 1, 2회 방문한 경우보다 방문 의도가 높았고, 1, 2회 방문한 경우가 0회 방문한 경우보다 산부인과 방문 의도가 높았다(F=38.02, p <.001) . 미혼 여성의 산부인과 방문 의도, 생식건강 지식, 생식건강 증진행위, 성 의사소통의 정도 본 연구에서 미혼 여성의 산부인과 방문 의도는 11.46±3.51점으로 높은 수준이었으며, 생식건강 지식은 34점 만점 중 평균 24.20±4.43점으로 중간 정도의 수준, 생식건강 증진행위는 평균 64.88±6.18점으로 다소 높은 수준, 성 의사소통은 평균 27.03±9.67점으로 중간 정도 수준이었다. 미혼 여성의 산부인과 방문 의도, 생식건강 지식, 생식건강 증진행위 및 성 의사소통 간의 상관관계 본 연구에서 미혼 여성의 산부인과 방문 의도는 생식건강 지식(r=.26, p =.001)과 약한 상관관계, 생식건강 증진행위(r=.37, p <.001), 성 의사소통(r=.43, p <.001)은 중간 정도의 상관관계로 모두와 통계적으로 유의한 정적 상관관계가 있는 것으로 나타났다. 미혼 여성의 산부인과 방문 의도에 대한 영향 요인 미혼 여성의 산부인과 방문 의도에 미치는 영향을 알아보기 위하여 일반적 및 산과적 특성에서 유의한 차이를 보인 산부인과 방문 경험, 성 경험, 산부인과 질환 경험과 산부인과 방문 의도에 유의한 상관관계를 보인 생식건강 지식, 생식건강 증진행위, 성 의사소통을 독립변수로 하고 산부인과 방문 의도를 종속변수로 설정하였으며, 산부인과 방문 경험, 성 경험, 산부인과 질환 경험은 경험 유무를 가변수로 하였다. 독립변수의 다중공선성을 확인하기 위해 공차 한계(tolerance)와 분산팽창인자(variance inflation factor, VIF)를 분석한 결과 공차 한계는 0.77–1.00로 0.1 이상이었고, VIF는 1.00–1.30으로 10보다 작아 다중공선성의 문제가 없는 것을 확인하였다. 또한, Durbin-Watson 계수는 2.293으로 잔차 간에 상관관계가 없음을 확인하였다. 분석 결과, 미혼 여성의 산부인과 방문 의도를 예측한 회귀모형은 유의한 것으로 나타났고(F=38.78, p <.001), 영향 요인에 의한 산부인과 방문 의도의 설명력은 54.2%였다. 회귀계수의 유의성 검증 결과, 산부인과 방문 경험(β=0.40, p <.001), 생식건강 증진행위(β=.25, p <.001), 성 경험(β=0.22, p <.001), 성 의사소통(β=0.20, p =.001), 생식건강 지식(β=0.12, p =.033)의 순으로 산부인과 방문 의도에 영향을 미치는 것으로 나타났다. 즉, 산부인과 방문 경험이 있을수록, 생식건강 증진행위를 실천할수록, 성 경험이 있는 경우일수록, 성 의사소통이 개방적일수록, 생식건강 지식이 높을수록 산부인과 방문 의도가 높은 것으로 나타났다. 미혼 여성의 일반적 특성을 살펴보면, 연령은 25세 미만이 95명(55.9%), 25–29세가 54명(31.8%), 30세 이상이 21명(12.4%)이었으며, 평균 연령은 24.64세였다. 미혼 여성의 산과적 특성을 살펴보면, 산부인과 방문 경험은 1회 80명(47.1%), 0회 55명(32.4%), 2회 21명(12.4%), 3회 이상이 14명(8.2%)이었다. 성 경험은 ‘유’인 경우가 108명(63.5%), 산부인과 관련 가족력은 ‘무’인 경우가 144명(84.7%)이었으며, 산부인과 질환 경험은 ‘유’인 경우가 93명(54.7%)로 많았다. 산부인과 질환 경험에는 다낭성 난소증후군, 생리통, 자궁내막 폴립, 질염 등이 있었다. 본 연구에서 미혼 여성의 산부인과 방문 의도는 산부인과 방문 경험(F=38.02, p <.001), 성 경험(t=–5.62, p <.001), 산부인과 질환 경험(t=–4.56, p <.001)에서 유의한 차이가 있었다. 이를 Scheffé test로 사후검증을 실시한 결과에서 산부인과 방문 경험이 3회 이상인 경우가 1, 2회 방문한 경우보다 방문 의도가 높았고, 1, 2회 방문한 경우가 0회 방문한 경우보다 산부인과 방문 의도가 높았다(F=38.02, p <.001) . 본 연구에서 미혼 여성의 산부인과 방문 의도는 11.46±3.51점으로 높은 수준이었으며, 생식건강 지식은 34점 만점 중 평균 24.20±4.43점으로 중간 정도의 수준, 생식건강 증진행위는 평균 64.88±6.18점으로 다소 높은 수준, 성 의사소통은 평균 27.03±9.67점으로 중간 정도 수준이었다. 본 연구에서 미혼 여성의 산부인과 방문 의도는 생식건강 지식(r=.26, p =.001)과 약한 상관관계, 생식건강 증진행위(r=.37, p <.001), 성 의사소통(r=.43, p <.001)은 중간 정도의 상관관계로 모두와 통계적으로 유의한 정적 상관관계가 있는 것으로 나타났다. 미혼 여성의 산부인과 방문 의도에 미치는 영향을 알아보기 위하여 일반적 및 산과적 특성에서 유의한 차이를 보인 산부인과 방문 경험, 성 경험, 산부인과 질환 경험과 산부인과 방문 의도에 유의한 상관관계를 보인 생식건강 지식, 생식건강 증진행위, 성 의사소통을 독립변수로 하고 산부인과 방문 의도를 종속변수로 설정하였으며, 산부인과 방문 경험, 성 경험, 산부인과 질환 경험은 경험 유무를 가변수로 하였다. 독립변수의 다중공선성을 확인하기 위해 공차 한계(tolerance)와 분산팽창인자(variance inflation factor, VIF)를 분석한 결과 공차 한계는 0.77–1.00로 0.1 이상이었고, VIF는 1.00–1.30으로 10보다 작아 다중공선성의 문제가 없는 것을 확인하였다. 또한, Durbin-Watson 계수는 2.293으로 잔차 간에 상관관계가 없음을 확인하였다. 분석 결과, 미혼 여성의 산부인과 방문 의도를 예측한 회귀모형은 유의한 것으로 나타났고(F=38.78, p <.001), 영향 요인에 의한 산부인과 방문 의도의 설명력은 54.2%였다. 회귀계수의 유의성 검증 결과, 산부인과 방문 경험(β=0.40, p <.001), 생식건강 증진행위(β=.25, p <.001), 성 경험(β=0.22, p <.001), 성 의사소통(β=0.20, p =.001), 생식건강 지식(β=0.12, p =.033)의 순으로 산부인과 방문 의도에 영향을 미치는 것으로 나타났다. 즉, 산부인과 방문 경험이 있을수록, 생식건강 증진행위를 실천할수록, 성 경험이 있는 경우일수록, 성 의사소통이 개방적일수록, 생식건강 지식이 높을수록 산부인과 방문 의도가 높은 것으로 나타났다. 본 연구에서는 미혼 여성을 대상으로 생식건강 지식, 생식건강 증진행위, 성 의사소통 및 산부인과 방문 의도 수준을 확인하고 생식건강 지식, 생식건강 증진행위 및 성 의사소통이 산부인과 방문 의도에 미치는 영향을 검증함으로써 이들의 산부인과 방문 독려와 효과적인 여성건강 교육 프로그램의 방향 제시에 이론적 근거가 되는 기초 자료를 제공하고자 하였다. 본 연구에서 미혼 여성의 산부인과 방문 의도는 평균 11.46점으로, 이는 동일한 도구를 사용하여 대학생을 대상으로 측정한 선행 연구에서의 평균 9.30점, 미혼 여성을 대상으로 한 선행 연구의 평균 9.18보다 높은 수준을 보였다. 2019년 자궁경부암 검진 사업에 대한 인지도가 36.5%, 수검 의도가 55%였던 것에 비해 2023년에는 인지도 42.7%, 수검 의도 77.2%로 향상된 것으로 볼 때, 본 연구 대상자들의 산부인과 방문 의도 점수가 높았던 것은 국가 지원 사업에 대한 홍보의 영향일 것이다. 따라서 산부인과 방문의 필요성을 인지시키기 위해 체계적인 홍보 전략을 수립하여 방문 의도를 더욱 높이는 노력이 필요하다. 미혼 여성의 산부인과 방문 의도에 가장 큰 영향을 미치는 변수는 산부인과 방문 경험이었고 산부인과 방문 경험의 횟수가 많을수록 방문 의도가 높았다. 이는 미혼 여성을 대상으로 산부인과 방문 의도에 영향을 미치는 요인을 분석한 연구와 일치하는 결과이다. 3회 이상 방문한 경우에 방문 의도가 가장 높았던 점을 통해 볼 때 반복적인 방문 경험을 통해 산부인과에 대한 인식이 달라진 것으로 생각할 수 있으므로, 산부인과의 방문 경험이 긍정적으로 인식되도록 함으로써 이들의 재방문 의사를 높이는 노력이 필요하겠다. 미혼 여성의 산부인과 방문 경험 개선 연구에서는 산부인과 첫 방문 시 진료 과정에 대한 자세한 정보를 알지 못한 상태에서 방문함으로써 진료에 대한 만족도가 떨어졌고 그로 인해 재방문을 주저하거나 회피하게 되는 것으로 보고하였다. 이에 미혼 여성의 산부인과 첫 방문과 재방문율을 향상시켜 방문 의도를 높이기 위해서는 산부인과 진료 과정, 방법에 대한 정보의 접근성을 높여야 할 것이다. 두 번째로 산부인과 방문 의도에 영향을 미치는 요인은 생식건강 증진행위인 것으로 나타났다. 생식건강 증진행위(평균 64.88점) 점수는 같은 도구를 사용하여 대학생의 생식건강 증진행위의 정도를 측정한 연구에서의 62.64점, Kim과 Ha의 연구에서의 63.36점과 유사한 수준이었다. 생식건강 증진행위를 증진하기 위해서는 대상자의 태도, 주관적 규범, 지각된 행위 통제 등을 고려하는 것이 필요하다는 선행 연구를 통해 볼 때, 생식건강 증진행위에 대해 긍정적인 태도를 가지고, 자신이 중요하게 생각하는 타인의 생식건강 증진행위를 지지하는 주관적 규범을 가지며, 생식건강 증진행위를 잘 할 수 있다는 지각을 높여 산부인과 방문 의도를 높이는 것이 필요하겠다. 생식건강 증진행위에 대한 인식개선 캠페인을 활성화하고 가족, 사회, 의료인이 생식건강 증진행위를 지지할 수 있도록 적극적으로 격려하며, 생식건강 증진행위에 대한 이론과 실습이 포함된 교육을 통해 자신감을 향상해 주는 노력이 필요하다. 세 번째로 산부인과 방문 의도에 영향을 미치는 요인은 성 경험으로 나타났다. 이는 여대생을 대상으로 한 선행 연구에서 성 경험이 있는 여성들의 산부인과 방문 의도가 더 높게 나타난 것과 일치하는 결과로, 성 경험을 통해 성병 검사, 피임 상담, 임신 관련 검사와 같은 산부인과 서비스의 필요성을 더 잘 인지하게 되고, 성 건강의 중요성에 대한 인식이 증가하여 생식건강에 대한 관심이 높아졌을 수 있다. 이러한 결과로 미혼 여성의 성 경험의 실태를 파악하여 산부인과 진료의 필요성과 중요성에 대한 인식을 높일 필요가 있을 것이다. 또한 성 경험이 없는 여성들도 산부인과 방문의 중요성을 인식하고 정기적인 검진을 받을 수 있도록 국가에서 지원하는 자궁경부암 검사를 포함하여 대중교통 전광판 등과 같이 미혼 여성들이 쉽게 접하는 곳을 파악하고 지속적으로 홍보하는 방법을 모색할 필요가 있다. 네 번째로 산부인과 방문 의도에 영향을 미치는 요인은 성 의사소통으로 나타났는데, 성 의사소통 점수는 평균 27.03점으로 중간 정도의 수준이었다. 미혼 커플을 대상으로 한 연구에서 Kang과 Kim 은 비폭력 대화모델에 근거한 포괄적 성교육 프로그램 제공을 통해 성 의사소통 점수가 프로그램 시행 전 15.04점에서 시행 후 22.92점으로 향상됨을 보여줌으로써 성 의사소통 프로그램의 필요성을 주장한 바 있다. 따라서 대상자가 다른 점, 성 의사소통에 대한 개방성 변화를 고려하면 커플, 친구가 함께 참여하는 성교육 프로그램을 개발하는 것이 필요할 것이다. 20대 미혼 여성을 대상으로 시행한 연구에서는 대인 커뮤니케이션이 산부인과 방문에 대한 공포 수준을 낮추어 산부인과 방문 의도가 높아짐을 확인하였고, 중학생을 대상으로 또래 간 성 의사소통을 분석한 연구에서는 성 의사소통을 자유롭게 수행할수록 신체나 심리 발달에 대한 성 태도가 긍정적이었다는 결과를 고려할 때, 성교육 프로그램 제공을 통해 파트너와의 성 의사소통의 중요성을 강조하고, 성 의사소통을 활성화하는 참여 학습을 포함할 필요가 있다. 하지만 휴대전화와 인터넷 사용의 보편화로 비대면 의사소통이 증가하면서 의사소통 방식에서도 다양한 변화가 일어나고 있으므로, 시대 상황을 반영한 대인관계의 상호작용 정도와 추구하는 의사소통 방식을 파악하는 등의 추가적인 후속 연구가 필요할 것으로 생각된다. 다섯 번째로 산부인과 방문 의도에 영향을 미치는 요인은 생식건강 지식으로 나타났고, 이는 생식건강 지식이 높은 여성에서 산부인과 방문 의도가 높게 나타난 것과 일치하는 결과이다. 생식건강 지식(평균 24.20점)은 동일한 도구를 사용하면서 성인 초기 여성을 대상으로 한 Shin과 Song의 연구의 23.04점과 비슷한 수준으로, 중간 정도의 생식건강 지식을 가지고 있었다. 성인 초기 여성을 대상으로 생식건강 증진 프로그램 제공을 통해 생식건강 지식이 중재 전 24.73점에서 중재 후 28.03점으로 향상된 연구를 통해 생식건강 증진 프로그램 제공의 필요성을 확인할 수 있다. 대학생의 성지식 획득 경로가 대부분 초•중•고등학교에서의 성교육 프로그램에 그치고 있었으므로, 더 다양한 경로로 생식건강 지식을 전달할 필요가 있다. 생식건강 정보가 담긴 메시지만 보내는 단방향 중재로는 생식건강 지식이 11% 향상되었으나 대면 교육과 함께 생식건강 퀴즈를 추가하였을 때는 생식건강 지식이 24% 향상된 연구를 통해 대면 교육의 필요성을 확인할 수 있었다. 그러나 각종 매체 사용에 익숙한 미혼 여성들의 특성도 고려하여 접근성과 편의성이 뛰어난 스마트폰을 통한 지식 전달과 자기주도 학습 프로그램도 도움이 될 수 있을 것으로 생각된다. 본 연구에서 미혼 여성의 일반적 특성과 산과적 특성에 따른 산부인과 방문 의도의 차이는 산부인과 방문 경험, 성 경험, 산부인과 질환 경험에 따라 유의한 차이를 보였다. 산부인과 질환 경험에 따른 산부인과 방문 의도는 산부인과 질환 경험이 있는 경우가 없는 경우보다 더 높았다. 이러한 결과는 미혼 여성의 산부인과 이용 및 방문에 대한 질적연구에서 증상이 있어도 저절로 낫기를 바라거나 산부인과 방문을 최대한 지체한다고 한 연구 결과와는 다소 차이가 있었으므로 산부인과 질환 발생이나 산부인과 방문 시점에 대한 추가적인 연구가 필요할 것으로 보인다. 본 연구에서 미혼 여성의 산부인과 방문 의도와 생식건강 지식, 생식건강 증진행위, 성 의사소통의 상관관계를 분석한 결과 모두 정적 상관관계를 보였는데, 이러한 결과는 생식건강 지식이 높을수록 산부인과 방문 의도가 높아진 연구와, 생식건강 증진행위가 높을수록 산부인과 방문 의도가 높아진 연구, 그리고 의사소통이 증가할수록 산부인과 방문 의도가 높아진 연구와 유사하다. 따라서 미혼 여성의 생식건강 지식을 높이기 위해 지속적으로 교육을 지원하고, 성 의사소통 증진을 위해 개방감 있는 대화를 할 수 있는 프로그램을 개발하며, 생식건강 증진행위를 높일 수 있도록 활용성을 높인 생식건강 프로그램에 대한 지속적인 관심이 요구된다. 이상의 논의를 통해, 미혼 여성의 산부인과 방문 의도를 높이기 위해서는 현 시대의 요구에 부합하는 성교육 프로그램을 개발하고 적용해야 하며, 산부인과 방문의 필요성을 강조하기 위한 국가적인 홍보 방안을 모색해야 함을 알 수 있다. 본 연구의 제한점은 다음과 같다. 일부 지역의 일부 연령 집단을 대상으로 편의 표집한 점, 첫 성 경험 시기와 첫 산부인과 방문 시기 및 사유를 파악하지 않은 점, 일반적 특성을 연령과 종교로 제한하여 조사하였으므로 해당 결과를 일반화하기에는 한계가 있는 점이다. 추후 연구에서는 전국적이고 다양한 연령대의 미혼 여성을 포함하여 연구를 진행해야 할 것이다. 이러한 한계점에도 불구하고 본 연구는 미혼 여성의 산부인과 방문 의도에 미치는 영향 요인을 분석하여 미혼 여성들의 산부인과 방문 의도를 증진시키기 위한 교육 프로그램의 방향을 제시하는 이론적 근거가 되는 기초 자료를 제공하였다는 데 의의가 있다. |
Improving quality and outcomes of extracorporeal cardiopulmonary resuscitation in refractory cardiac arrest: the Phoenix ECPR project | adac5cfc-9dee-47e1-a808-c34b5455074d | 11804202 | Surgical Procedures, Operative[mh] | The introduction of a robust system of care that prioritises early identification, screening, activation and standardised care in the initiation of extracorporeal membrane oxygenation and subsequent management in the UK can achieve significant improvements in outcomes despite comparably low numbers and historically poorer outcomes. This demonstrates the feasibility of providing an effective ECPR system with good outcomes in the UK National Health Service. Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in Europe affecting over 300 000 people annually. Despite considerable efforts to improve outcomes, only 8% of these patients survive hospital discharge, a distressingly low proportion that has remained constant over the last decade. When cardiac arrest is prolonged the chances of survival diminish with every minute of cardiopulmonary resuscitation (CPR) and approach zero after 30 min. Extracorporeal membrane oxygenation (ECMO) is a powerful and invasive therapy that can be rapidly deployed to completely support a failing circulation, making it an option for selected patients with refractory cardiac arrest, with survival reported as high as 54%. While these results are promising the use of ECMO during CPR (ECPR) is complex, technically challenging and requires a robust system of care that immediately identifies potential candidates and facilitates rapid cannulation and initiation of ECMO, ideally within 1 hour of cardiac arrest. Three randomised control trials of eCPR have been published since 2020, ARREST, which demonstrated a 6/14 (43%) survival to hospital discharge with ECPR and 1/15 (7%) in the conventional CPR (cCPR) group (p=<0.0001) and Prague OHCA, which found 39/124 (31.5%) survival in the ECPR group and 24/132 (22%) in the standard care (p=0.09). These were both single-centre studies of well-established ECPR systems. A third, multi-centre trial, INCEPTION, showed no difference between the two groups with 14/70 (20%) survival in the ECPR group and 10/62 (16%) in the cCPR group (p=0.52). Of note, many of the centres in INCEPTION were new to ECPR, low volume and with less well-refined systems in place. Despite the conflicting data, multiple centres around the world continue to operate ECPR programmes designed to serve their particular region and have demonstrated that patients with shorter durations of CPR and initial rhythms of ventricular fibrillation (VF) or ventricular tachycardia (VT) are associated with better outcomes. Harefield Hospital is a tertiary referral, a specialist cardiac hospital in the northwest of London undertaking ECPR since 2012. The hospital serves a population of 2.5 million and receives direct admissions from three National Health ambulance services and three air ambulance teams. Associated with the heart transplant service is an active ECMO programme treating around 50 patients a year. This programme is associated with a larger service co-located across other sites within the organisation treating up to 200 patients a year with respiratory and cardiac ECMO. Outcomes for ECPR were noted to be poor as part of institutional quality assurance. In the ten-year period to April 2022, there were 48 patients with refractory cardiac arrest; of which, only four (8.3%) survived to hospital discharge. These outcomes prompted an over-arching review using quality improvement methodology and a new service specification initiated in April 2022. A retrospective analysis was performed of all ECPR cases at Harefield Hospital, part of Guy’s & St Thomas Foundation NHS Foundation Trust between April 2018 and April 2023 using the electronic health records. ECPR was defined as the initiation of veno-arterial ECMO (VA ECMO) during refractory cardiac arrest, with ongoing CPR. Refractory cardiac arrest was defined as more than three cycles of advanced life support. Prior to April 2018 data collection had been inconsistent and was inadequate for detailed analysis. Data on all cardiac arrests at Harefield Hospital were also collected for the same time frame according to National Cardiac Arrest Audit criteria using the electronic switchboard system to capture all activations. Comorbidities and aetiology were defined as clinical diagnoses made on the patients’ health records. Health records and current evidence base were reviewed by a multi-disciplinary team of doctors, nurses, perfusionists and allied healthcare professionals. This demonstrated a significant inter-operator variance in the timing and suitability of considering ECPR and in cannulation techniques. Other themes identified for improvement were a lack of standardisation and protocolisation in activation, team membership, team roles, cannulation location, post-arrest care, training and governance. The existing ECPR practice in place before 1 April 2022 consisted of an emergency shock call activation, which would alert a perfusionist to the location of an arrest. The cannulators were contacted by the cardiac arrest team on an ad-hoc basis depending on the availability by phone. Screening of suitable patients, activation, cannulation and post-resuscitation care was highly variable with flexibility given to individual practice. There was no set written inclusion or exclusion criteria. All cannulation was performed in the catheter laboratory using fluoroscopy and connected to either a Getinge Cardiohelp system in Gothenburg, Sweden, or a Levetronix ECMO system in Zurich, Switzerland . A new standard operating procedure (SOP) was developed through a modified Delphi method, with key stakeholders in Cardiology, Intensive Care, Surgery, Perfusion and Resuscitation seeking to align practice as closely as feasible to the promising Minneapolis and Prague randomised trials . After completion and institutional ratification, a 4 month training programme and communication initiative was instituted to ensure all staff within and outside the ECPR team were familiar with the process. This process was designed to embed seamlessly into standard Advanced Life Support management. The cardiac arrest team in our institution consists of a cardiology team leader, anaesthetic airway lead, outreach nurse and intensive care nurse in addition to the ward team. IHCA and OHCA are attended and screened by the outreach or resuscitation nursing team, the gatekeepers of the ECPR service. The inclusion criteria included all VF, VT or pulseless electrical activity (PEA) cardiac arrest without return of spontaneous circulation (ROSC) after three cycles. All included patients are screened for STOP (or exclusion) criteria detailed below and if none are met a ‘shock call’ is activated via the hospital switchboard. The shock call will activate the attendance of an ECPR consultant-level cannulator, second cannulator, nurse specialist operator and perfusionist. This team transports our standardised cannulation trolley, developed in partnership with the University of Pennsylvania , ECMO system and Phillips V scan air portable handheld ultrasound, Amsterdam, Netherlands . On arrival, this team will recheck STOP criteria, perform focused echocardiography (exclude treatable pathology such as cardiac tamponade) and ensure an arterial blood gas is taken and an endotracheal tube placed to assess for physiological STOP criteria. If none or one physiological STOP criteria are met, then the patient will be placed on VA ECMO. For OHCA cannulation will be performed in the catheter laboratory and for IHCA wherever the patient has arrested. The STOP criteria were defined as an unwitnessed arrest, first rhythm asystole, no bystander CPR, low flow time of >60 min, body habitus that would preclude cannulation, terminal illness, severe neurological impairment, non-cardiac cause of arrest and perceived clinical frailty. The assessment of body habitus precluding cannulation was subjective. Perceived clinical frailty was used in place of a calculated clinical frailty score due to the limited information available during a cardiac arrest, but an equivalent clinical frailty score of >4 would be a STOP criteria. Physiological stop criteria were established as an assessment of the adequacy of resuscitation and include lactate of >18 mmol/L, peripheral oxygen saturations of <85% or partial pressure of oxygen of <6.6 kpa (50 mmHg) and endotracheal tube end-tidal carbon dioxide of <1.3 kPa (10 mmHg). The cannulation procedure was standardised, and all staff were trained and signed off after simulation training by senior experienced operators. The minimum team is a senior experienced cannulator, an assistant cannulator, of varying experiences, a specialist nurse operator who assists in equipment preparation and a clinical perfusion scientist. The femoral artery and vein are cannulated under real-time ultrasound guidance. A 6 F Cordis sheath is then advanced over the wire. Through this, an Amplatz extra stiff 145 cm wire is placed into each vessel confirming the position with echocardiography (transhepatic great vessel view performed by the cannulator using sterile sheath) or fluoroscopy if available. At this time, heparin 5000 units is given to the patient intravenously. The venous side is dilated with a single 22 F dilator and the arterial side with a single 16 F dilator. A 15 F arterial return cannula and 21 F multistage venous drainage cannulae are sited. The ECMO flow is titrated up to at least 3 L/min and sweep gas started at 2 L 50% FdO2 for the avoidance of hyperoxia and controlled decline in carbon dioxide levels. After securing the cannulae, all patients are transferred to the catheterisation laboratory for coronary assessment, distal limb perfusion cannula and the assessment of the need for left ventricular decompression. Post-resuscitation care consists of targeted temperature management to 35°C for 24 hours, avoidance of fever for a further 24 hours and a mandatory delay in neuroprognostication until at least 72 hours post-arrest. The temperature target was in part chosen as the lowest setting on our heater–cooler system to allow a reduction in the requirement for flows with a smaller cannulae size. After the completion of the setup and training phases, the new service was launched on 1 April 2022. A weekly ECPR review meeting was setup and attended by all key members of the team. This was a forum to examine every activation and learn lessons on a weekly basis. This identified wider issues as insufficient signage for ambulances arriving at the hospital as well as ECPR-specific issues such as which equipment was available in the trolley. The rhythm at arrest was defined as the first detectable rhythm on the attachment of defibrillator pads or electrocardiograph leads. Bystander CPR was defined as the initiation of CPR <1 min on a witnessed cardiac arrest. Mechanical CPR was defined as the use of a mechanical chest compression device during the transport and cannulation of a patient. A hybrid cannulation approach was defined as using both percutaneous and surgical cutdown for gaining vascular access. Follow-up at 6 months was assessed using the clinical performance category (CPC) score. Data analysis and statistics Statistical analysis was performed using GraphPad Prism version 10.1.0 for Windows, GraphPad Software, Boston, Massachusetts, USA . All variables were tested for normality with the Kolmogorov–Smirnov test. Descriptive statistics were summarised as mean±SD or median (range) or number (fraction) as appropriate. Between-group comparisons were done with parametric or non-parametric tests as indicated. The comparison of proportions and the estimation of ORs and relative risks (as estimates of cumulative risk) was performed with Fisher’s exact test. For survival analysis, we used the Kaplan–Meier method and the Cox–Mantel test for the estimation of the HR (instantaneous risk). We used Cox proportional hazard regression analysis to assess the association between explanatory variables and survival rate. The level of statistical significance was set at p<0.05. Statistical analysis was performed using GraphPad Prism version 10.1.0 for Windows, GraphPad Software, Boston, Massachusetts, USA . All variables were tested for normality with the Kolmogorov–Smirnov test. Descriptive statistics were summarised as mean±SD or median (range) or number (fraction) as appropriate. Between-group comparisons were done with parametric or non-parametric tests as indicated. The comparison of proportions and the estimation of ORs and relative risks (as estimates of cumulative risk) was performed with Fisher’s exact test. For survival analysis, we used the Kaplan–Meier method and the Cox–Mantel test for the estimation of the HR (instantaneous risk). We used Cox proportional hazard regression analysis to assess the association between explanatory variables and survival rate. The level of statistical significance was set at p<0.05. ECPR In total, 22 ECPRs were performed between April 2018 and 2022 and 13 ECPRs post introduction of the new system between April 2022 - 23 . In the old system, there were two survivors (9.1%) at 6 months (one with CPC1, one with CPC4) from 22 patients. In the first year of the new ECPR system, 13 patients were treated with ECPR; of which, nine (69.2%) survived to 6 months. All of these nine patients had good neurological outcomes (seven CPC1, two CPC2). Overall, there was a statistically significantly higher survival in the new cohort at 69.2% compared with 9.1% in the old cohort with HR of 4.56 (CI 2.1, 10.2, p<0.05) . In a comparison of the two populations, the new system had a significantly higher proportion of women at 61.5% compared with the 13.6% old system. The groups were otherwise matched in comorbidities, proportion of OHCA, initial shockable rhythm, cause of arrest and bystander CPR. There was a comparable failure rate of cannulation at 7.7–9.1% (p=0.99) in the two groups. There was a non-significant trend towards reduced low flow time (63.3 min vs 48.3 min p=0.28) and door-to-cannulation time (26.6 min vs 21.8 min p=0.28) in the new system with a significantly higher use of mechanical CPR devices (50% vs 84.6% p=0.04) in the new system. The average length of hospital stay was 61.3 days; of which, 32.4 days were in the intensive care unit in the new system. This was significantly longer than the old systems with an average length of stay of 12.9 days; of which, 10.3 days were in the intensive care unit. However, in this system, most patients died in the immediate post-ECPR period from multiorgan failure. No patients who died proceeded to organ donation in either group. System activations ECPR system activations were standardised with the introduction of the new system, and thus, data were only available for review post 1 April 2022. 34 ECPR system activations occurred over the years with 21 where ECPR was not performed. This included 10 (47.6%) OHCAs and 11 (42.4%) IHCAs with presenting rhythms of VF in six cases (28.6%), VT in two cases (9.5%) and PEA in 13 cases (61.9%). STOP criteria that were met in these cases were as follows: unwitnessed 1 (4.8%), low flow time of >60 min 5 (23.8%, all occurring in OHCA), non-cardiac cause 1 (4.8%) and perceived clinical frailty 12 (57.1%). Only three patients (14.3%) survived the resuscitation attempt. The CPR duration for survivors was 20, 10 and 18 min in presenting rhythms of VT, PEA and VF, respectively. The three survivors remained alive at 6 months, with VF and VT arrests with CPC 1 and the PEA arrest with CPC 2. Total cardiac arrests Between April 2018 and 2022, there were a total of 402 IHCAs/OHCAs (131 VT/VF) with 177 (44%) survivors to discharge home. Post-introduction of the new system between April 2022 and 2023, there were 133 IHCAs/OHCAs (65 VT/VF) with 56 (42%) survivors discharged home. In total, 22 ECPRs were performed between April 2018 and 2022 and 13 ECPRs post introduction of the new system between April 2022 - 23 . In the old system, there were two survivors (9.1%) at 6 months (one with CPC1, one with CPC4) from 22 patients. In the first year of the new ECPR system, 13 patients were treated with ECPR; of which, nine (69.2%) survived to 6 months. All of these nine patients had good neurological outcomes (seven CPC1, two CPC2). Overall, there was a statistically significantly higher survival in the new cohort at 69.2% compared with 9.1% in the old cohort with HR of 4.56 (CI 2.1, 10.2, p<0.05) . In a comparison of the two populations, the new system had a significantly higher proportion of women at 61.5% compared with the 13.6% old system. The groups were otherwise matched in comorbidities, proportion of OHCA, initial shockable rhythm, cause of arrest and bystander CPR. There was a comparable failure rate of cannulation at 7.7–9.1% (p=0.99) in the two groups. There was a non-significant trend towards reduced low flow time (63.3 min vs 48.3 min p=0.28) and door-to-cannulation time (26.6 min vs 21.8 min p=0.28) in the new system with a significantly higher use of mechanical CPR devices (50% vs 84.6% p=0.04) in the new system. The average length of hospital stay was 61.3 days; of which, 32.4 days were in the intensive care unit in the new system. This was significantly longer than the old systems with an average length of stay of 12.9 days; of which, 10.3 days were in the intensive care unit. However, in this system, most patients died in the immediate post-ECPR period from multiorgan failure. No patients who died proceeded to organ donation in either group. ECPR system activations were standardised with the introduction of the new system, and thus, data were only available for review post 1 April 2022. 34 ECPR system activations occurred over the years with 21 where ECPR was not performed. This included 10 (47.6%) OHCAs and 11 (42.4%) IHCAs with presenting rhythms of VF in six cases (28.6%), VT in two cases (9.5%) and PEA in 13 cases (61.9%). STOP criteria that were met in these cases were as follows: unwitnessed 1 (4.8%), low flow time of >60 min 5 (23.8%, all occurring in OHCA), non-cardiac cause 1 (4.8%) and perceived clinical frailty 12 (57.1%). Only three patients (14.3%) survived the resuscitation attempt. The CPR duration for survivors was 20, 10 and 18 min in presenting rhythms of VT, PEA and VF, respectively. The three survivors remained alive at 6 months, with VF and VT arrests with CPC 1 and the PEA arrest with CPC 2. Between April 2018 and 2022, there were a total of 402 IHCAs/OHCAs (131 VT/VF) with 177 (44%) survivors to discharge home. Post-introduction of the new system between April 2022 and 2023, there were 133 IHCAs/OHCAs (65 VT/VF) with 56 (42%) survivors discharged home. We implemented a structured, systematic, quality improvement initiative to refine the provision of ECPR in our hospital. The results demonstrate a substantial improvement in short- and long-term survival associated with good neurology outcomes with the new system compared with the historic cohort from 9.1% to 69.2% (p=0.0004). Conventional CPR is based on standardised and protocolised practice according to internationally developed guidelines. ECPR is a relatively new, underdeveloped modality with a significant variation in the selection criteria and the cannulation procedure among performing centres. Our new system used a wide-ranging bundle of improved processes including better identification of candidates, timely activation, standardised selection and assessment of resuscitation adequacy, rapid and drilled cannulation and protocolised post-resuscitation care. Using the critical care outreach service to activate the ECPR team meant a small number of individuals who are present at every cardiac arrest in the hospital could be trained to a high standard in making an initial assessment of candidacy. Binary STOP criteria enabled rapid and simple decision-making. In combination, these factors led to a highly selected cohort for ECPR initiation. Of note, 61.8% of all patients in the new system referred for ECPR met STOP criteria; thus, cannulation did not proceed. The principal cause for stopping was perceived clinical frailty. We set guidance for this to be an equivalent clinical frailty score of greater than four although in practice this was challenging to assess during cardiac arrest. The critical care outreach team was therefore recommended to place an ECPR system activation where frailty assessment was not clear so that further senior decision-makers could arrive and review. The second most common reason to not proceed was greater than 60 min of low flow. These all occurred in OHCA where extrication can be problematic and transit times prolonged. The comparison between the old and new systems identifies a number of important variables that differed between the groups that could potentially affect the observed survival difference. This included increased female preponderance in the new group, the trend towards lower low flow and cannulation times and greater use of mechanical CPR devices. Correct identification of patients unlikely to achieve ROSC and be suitable for ECPR is challenging. In these patients, the benefit of ECPR is likely to be greater if instituted earlier rather than allowing prolonged periods of ‘downtime’. In our cohort of patients who met STOP criteria, 14.3% went on to survive to 6 months although all had durations of low flow of 20 min or less. Overall, this survival rate supports the poor survival seen in refractory cardiac arrest without the use of ECPR. While this data is promising, it remains a retrospective review of a single centre with highly selective patient inclusion and the lack of a comparator population. The practice before the institution of the new SOP was based on clinical judgement, and the new approach was not prospectively enforced, limiting any interpretation of the outcomes. The hospital population also had significant differences compared with the rest of the country with a higher proportion of younger patients with primarily cardiac issues. The numbers of patients included remained small and thus open to overestimation of the effect size and sampling bias. It is unclear whether these results could be replicated more broadly across a region or a country and if it were possible how this would be structured or funded. This study shows that it is possible to improve outcomes in ECPR in a single centre by introducing into the standard practice a bundle of care prioritising early identification, screening, activation and standardised care in the initiation of ECMO and subsequent management. 10.1136/bmjoq-2024-002934 online supplemental file 1 |
Machine learning for tissue diagnostics in oncology: brave new world | 196f656f-7084-4a4f-b848-694b44ba2879 | 6738066 | Pathology[mh] | The development of powerful algorithmic approaches, termed ‘machine learning’, closely follows the development of modern computer technology. The promises of machine learning in medicine revolve around the notion of faster and more reliable classification of images or datasets. Especially in oncology, the possible applications are boundless: from the classification of imaging studies (‘tumour’ versus ‘no tumour’) to the classification of cells within a tissue section. The roots of machine learning lie within the conceptional beginning of circuit design: algorithmic investigations have held a tight grip on how data were perceived and understood. One area that is now attracting more and more research interest is the analysis of tissue specimens—harvesting more information from ‘pure’ tissue sections, i.e. tissue material processed in standardised routine procedures and available from large numbers of patients. Tissue diagnostics and processing is the field of work of the pathologist, and it is not visionary to predict that image analysis and machine learning will further shape the way pathologists will work in the future.
Tissue specimens, especially those processed and subjected to haematoxylin-eosin staining, are available in large quantities from a large number of oncological patients. Images generated from these large sections with routine counterstains offer rich information. Fundamental aspects of cellular composition, localisation and quantity can be gained from these images. Without specific staining procedures, it is difficult for the human eye to identify the subsets of cells and to precisely quantify these subsets robustly. There are clear examples where one can expect advantages from a computerised approach: lymphocyte infiltration is a good prognostic factor in many tumour entities. Dataset size is an important factor in machine learning: datasets beyond 1000 data points of uniform type are usually needed for creating robust predictors. With the advent of whole slide image scanners for histology, the availability of large patient datasets (of larger numbers) has increased even more. The type of machine learning algorithms applied to these medical images has developed over time, and the complexity of these algorithms ranges from single-layer neural nets to complex deep learning (Boltzmann) algorithms. The history of machine learning is winding, with key figures in the 50s and 60s of the last century being Marvin Minsky, Frank Rosenblatt and Charles Wightman. In this evolution, convolutional neuronal networks (CNN) have provided a significant, new, and technically efficient approach. With this technical advancement, more and more far-reaching classifications and stratifications have been attempted with machine learning. Aligning the treasure chests of ‘big data’ with clinical outcomes has been also in the focus of attention, chemotherapy response prediction in colorectal cancer patients being just one example. With the focus on tissue, the identification of predictive features within the tissue section was performed , (including lymphocytes or vasculature). – A good example is the identification of immunohistochemistry-based signatures to predict metastatic sites of triple-negative breast cancers. Finding ‘unseen’ aspects in tissue sections to align genetic alterations with phenotypic features is also a key aspect of new developments. , However, with this advancement, especially for medical application on tissue, new fields of problems have appeared.
The prerequisite for successful machine learning approaches is still a sufficient dataset size. This is clearly limiting the use of this technology, because the low frequency of certain cancer entities limits available material. This also leads to the misinterpretation of exploratory analyses and points to a need for extensive validation. This is not to be disregarded in a computational approach, which might be easy to transfer from one institution to another. Validating the possible diagnostic machine learning approach requires the same tight controls and quality assurance management as any other medical validation approach with wet lab technology. Another important point here is to understand the predictive features within the tissue (or the image, see Fig. ). One way is obscuring the features within the image systematically to identify elements that inform the predictive algorithm. This also opens the door to understand ‘what precisely’ the machine learning algorithm sees in the tissue, e.g. lymphocytes (i.e. round cells without significant cytoplasm). Possible confounders or bias can be identified as well, e.g. the counterstain. Here, the definition of ‘interpretability’ is important—translating the algorithmic findings into human-understandable language or symbols (see e.g. https://fatconference.org/2019/ ). Missing evidence-based expectations for clinically acceptable performance is another specific danger in machine learning—in other words, the alignment of expected performance with realistic clinical expectations and the validation of it. Exemplum crudelitatis , the written clinical annotation on an image as a predictor of outcome and not the actual medical image itself (see https://medium.com/@jrzech/ ), is a flamboyant example, but one that emphasises quality control and understanding of the algorithm as important parameters of success.
An ideal scenario for development and validation of prediction models should take the abovementioned points into account. Machine learning is best suited for multisite studies and model testing on subsets, but its application in study design and reporting in medical research requires the development of clearer standards. Algorithmic tools are indeed becoming a part of the armamentarium in tissue diagnostics and pathology, regardless of whether deep learning, multiple-agent simulations or other computing approaches are used. The advent of another tool in the medical toolbox is always exciting, but also requires a careful analysis of the tool's boundaries and limitations. Artificial intelligence critic Kate Crawford sums it up: ‘Machine learning does not produce inscrutable and unquestionable objects of mathematics that produce rational, unbiased outcomes. It is human design behind it’. There is no doubt that machine learning will enrich the diagnostic capabilities of pathologists and other medical specialties, but only if mastered properly by trained computer specialists and physicians alike. Medicine needs to shape its tools and not the other way around.
|
Associations between maternal adversity and health and children’s telomere length | ccee89a7-672f-44b1-919d-6ca3640e4550 | 11953353 | Pathologic Processes[mh] | Prenatal exposure to maternal stress increases the risk of behavioral and mental health problems in offspring . Biological mechanisms associated with transgenerational health risks because of maternal stress have attracted increasing attention. One possible mechanism is via premature/accelerated biological aging, as indicated in telomere length (TL). Shortened telomere length may explain long-term associations between maternal-fetal processes and the future health of offspring . Telomeres are composed of repetitive DNA sequences and play a crucial role in protecting the ends of chromosomes, ensuring the integrity of the genome during replication . TL at birth determines the initial length of an individual’s telomeres, and over time, telomeres naturally shorten due to cell replication and oxidative stress . As telomeres become critically short, cells enter a state of senescence . Shorter telomere length has been associated with early mortality , psychiatric disorders , and risk of disease . The fetal programming of the telomere biology hypothesis provides a conceptual framework that explains how maternal states and stress conditions during pregnancy can influence the telomere biology of their offspring . Several maternal demographic characteristics are associated with the TL of offspring, such as maternal age , BMI , and income . Based on this framework, our research primarily focuses on maternal adversity (i.e., adverse childhood experiences, ACEs) and mental (i.e., depressive symptoms) and physical (i.e., chronic illness) health. By investigating these factors, we aim to gain insights into the development of prevention and intervention programs targeting different aspects of maternal conditions. These insights can potentially contribute to improving the health outcomes of both mothers and their offspring. ACEs play a significant role in TL erosion as indicated by a meta-analysis showing an association between childhood exposure to family violence and TL . A longitudinal study involving 236 children also found that exposure to multiple forms of violence, including domestic violence, bullying victimization, and maltreatment, was associated with significant telomere erosion . In addition, findings regarding the relationship between maternal ACEs and offspring biological aging are inconsistent. Some studies found that higher maternal ACEs were associated with shorter infant TL , while others found children whose mothers had 3+ ACEs had significantly longer DNAm telomere estimates than those whose mothers reported no ACEs . These findings suggest that TL may serve as an emerging biomarker that captures exposure to early-life adversity and predicts the risk of future psychopathology. Maternal psychological and physical health also have a critical relationship with children’s TL, one of which is to determine whether maternal depressive symptoms during pregnancy predict TL in newborns . The most common mental health problem among pregnant women is depressive symptoms . However, the literature on the association between maternal depressive symptoms and children’s TL has produced inconsistent findings. A review has summarized that maternal depression is associated with shortened TL in children . However, Ämmälä and colleagues conducted a study with 1405 infants and found that maternal depression was not a significant factor associated with infants’ leukocyte TL . Similar nonsignificant results were also found in other studies . The inconsistent results in previous studies may be partly attributed to methodological considerations. Most previous studies have investigated maternal depression at only one specific time (either during pregnancy or postpartum) and/or simply treated depressive symptoms as a binary category (yes or no). This overly simplistic cross-sectional analysis approach generally cannot distinguish different trajectories from one another, nor can it differentiate delayed dysfunction from chronic dysfunction, and this approach may miss relapsing/remitting trajectories altogether . In contrast, growing research has emphasized the importance of examining longitudinal trajectories of responses to stress over time . Trajectory analysis provides a more comprehensive understanding of the dynamic, long-term, and individualized nature of depression, including the identification of key transition points or periods when depressive symptoms are most likely to change, compared to a single timepoint assessment. This enables more targeted prevention and treatment approaches. Previous work has identified different individuals may exhibit distinct trajectories of depressive symptoms over time . Moreover, the chronicity of prenatal psychological adversities may play a significant role in determining the magnitude of their effects on children’s TL . This is consistent with the cumulative stress model, which posits that developmental exposure to stress accumulates to disrupt physical and mental health . The unpredictability and instability of prenatal psychological stressors may also be detrimental to individual health outcomes . This notion is supported by the match-mismatch model, which suggests that adaptive development is contingent upon the alignment of fetal predictions of the postnatal environment with actual postnatal environmental demands . In other words, instability and unpredictability in parental emotional states may lead to more adverse outcomes than stable levels of stress . Echoing the match-mismatch model, a longitudinal study that tracked women from before conception through pregnancy and the postpartum period found that fluctuations in maternal depressive symptoms, rather than consistent symptoms, were linked to less favorable developmental outcomes in offspring . These theoretical and empirical insights suggest that varying patterns of mental health can exert distinct influences, yet there is a paucity of research investigating whether diverse trajectories of maternal depressive symptoms correlate with their offspring’s TL. The present study will conduct a comprehensive analysis that considers varying patterns of maternal depressive symptoms and their distinct association with children’s TL, capturing changes in maternal depressive symptoms over time to inform more accurate interventions targeting specific risk groups. In addition, the association of maternal chronic illness with children’s TL is inconsistent, as summarized in a review . Some studies found that maternal chronic illness was negatively associated with telomere length at birth , while others found no significant difference in cord blood telomere length in offspring from gestational diabetes mellitus and normoglycemic pregnant women . However, few studies have included multiple and varied types of maternal chronic illness to examine whether the severity of chronic illness may be associated with children’s TL. This study aims to examine the association of maternal adversity, and physical and psychological health, with children’s TL. Specifically, we assess the effects of the severity of maternal adverse childhood experiences and chronic illness. Additionally, we analyze the impact of different trajectories of maternal depression on children’s TL 3 years after childbirth. We hypothesize that (a) more severe maternal adverse childhood experiences and chronic illness may be linked to offspring’s shorter TL; (b) distinct trajectories of depressive symptoms among pregnant women, and those who experience a higher risk of depressive symptoms over time are more likely to have a negative impact on their children’s TL.
Participants Pregnant women were recruited from the antenatal clinic of Kwong Wah Hospital, a public hospital managed by the Hospital Authority in Hong Kong. Kwong Wah Hospital has one of the city’s major obstetrics and gynaecology departments, providing services to ~5000 childbirths annually. A total of 340 pregnant women were followed from 20–24 weeks of gestation (T1), then 4 weeks after childbirth (T2), and again 3 years after childbirth (T3). They provided information regarding demographic characteristics (marital status, employment status, social security assistance, monthly household income) and depressive symptoms. Furthermore, buccal swab samples were provided by 122 of the 340 women’s children at age 3. The relatively smaller sample size of children is primarily attributable to the ongoing COVID-19 pandemic at the time of data collection. During this period, caregivers were often hesitant to permit buccal swab collection, fearing it could increase the risk of COVID-19 exposure and infection for their children. Informed consent was obtained from all participants. Details of recruitment and inclusion criteria can be found in our previous papers . Measures Outcome measure Children’s buccal telomere lengths Samples of the children’s DNA were extracted from buccal swab samples. Trained researchers helped to collect the children’s buccal swab samples following standardized instructions and procedures. Guided by the manufacturer’s instructions, genomic DNA samples were isolated and extracted from the collected samples using the QIAamp DNA Mini kit (Qiagen). The isolated DNA samples were eluted into a buffer solution (10 mM Tris-HCl and 1 mM ethylenediaminetetraacetic acid, pH 8.0) for quality checking and quantification. This was done using a spectrophotometer (NanoDrop 2000c, Thermo Scientific) to ensure that the DNA quality and quantity were within an acceptable range for telomere length determination. Each DNA sample, determined to be of acceptable quality and quantity, was handled in triplicate for the telomere length assay using quantitative polymerase chain reaction (qPCR). The qPCR was performed using a 7900HT Thermocycler (Applied Biosystems). After the telomere length assay, the telomere length was determined by calculating the relative ratio of the telomere repeat copy number (T) to the single-copy gene 36B4 copy number (S). The formula used for this calculation was T/S = 2(−ΔCt), where ΔCt represents the mean difference between the threshold cycle (Ct) value of the 36B4 gene and telomere repeats obtained from the qPCR. Children’s TL was log-transformed. Details can be found elsewhere . Predictor Maternal depressive symptoms Pregnant women’s depressive symptoms were assessed using a 10-item Chinese Edinburgh Postnatal Depression Scale (EPDS) . Participants reported the presence of depressive symptoms experienced within the past week. Each item was rated from 0 (all the time) to 3 (not at all). All items were summed to obtain a total score for depressive symptoms, with higher scores indicating a more severe level of depression. To screen for probable depression, we utilized a cut-off score of ≥10 in this study. This cut-off value has been suggested as optimal for screening depressive symptoms during pregnancy and the postpartum period in Chinese mothers . We used continuous scores of depressive symptoms in the trajectories analyses. The Chinese version of the EPDS has been validated in prior studies which showed good psychometric properties . In the present study, Cronbach’s alphas for the EPDS were 0.84, 0.83, and 0.81 at T1, T2, and T3, respectively. Maternal ACEs The Adverse Childhood Experiences (ACEs) Questionnaire by the World Health Organization was utilized to identify childhood traumatic events . Fourteen items were used to assess different domains of ACEs, such as childhood maltreatment, household dysfunction, and exposure to war or collective violence before the age of 18 years. Participants were asked to report how frequently ACEs occurred. Each item was dichotomized into 1 = exposed and 0 = not exposed. We summed all the ACE items to obtain a total score, which reflects the overall severity of childhood adversities experienced . A higher total ACE score indicates greater exposure to childhood adversities. Previous research has also examined these experiences in Chinese samples . In the current study, the Cronbach’s alpha of the ACEs was 0.61. Maternal chronic illness Pregnant women reported ten types of chronic illness at T1. These chronic illnesses include hypertension, heart disease, asthma, diabetes, nephropathy, cataracts, pulmonary tuberculosis, peptic ulcer disease, skin disease, and others. Each illness was reported as 1 = yes and 0 = no. All items were summed to obtain a continuous score of maternal chronic illness. These illnesses are the most common conditions in childbearing-age women and have also been evaluated in previous work . Demographic characteristics Demographic characteristics about the mothers, such as maternal age, educational level, marital status, employment status, social security assistance, and monthly household income were collected. Data analysis The primary analyses included two steps. First, latent Class Growth Analysis (LCGA) in Mplus 7.0 was conducted to identify latent classes of depressive symptoms. Data analysis and results of LCGA have been shown in our accepted paper . Specifically, intercepts and slopes for each latent class were estimated as in previous work . One- to five-class unconditional models were tested for depressive symptoms. Several criteria were used to determine the optimal class : (a) lower information criteria fit indices including the Akaike’s information criterion (AIC), the Bayesian information criterion (BIC), and the sample-size-adjusted Bayesian (SSBIC); (b) higher entropy values; (c) statistically significant p -values for both the Lo-Mendell-Rubin likelihood ratio test (LRT) and the bootstrap likelihood ratio test (BLRT); and (d) the theoretical meaningfulness of group memberships. Moreover, latent classes with less than 5% of the sample are not considered . Additionally, Chi-square tests or t -tests were used to compare the distributions or scores of variables across different categories. Further details regarding the data analysis and results of the depressive symptom trajectories can be found in our accepted paper . Second, to test the association of maternal ACEs, chronic illness, and trajectories of depressive symptoms with children’s TL at 3 years, linear regressions were conducted because children’s TL was a continuous variable. A value of p < 0.05 was considered to be of statistical significance. Sensitivity analysis On the one hand, conditional LCGA models with covariates were further conducted to adjust for classification error and the effects of covariates. On the other hand, we conducted analyses using the BCH approach to explore whether there were differences between the trajectories related to distal outcome variables (i.e., bTL). The BCH approach offers an omnibus test that includes differences between the two classes on the distal outcome. Based on a comparative analysis of various methodologies, the BCH method has proven to be the most robust, consistently delivering unbiased estimates across all examined conditions . Ethical approval The research protocol was approved by the Institutional Review Board of the Hospital Authority Kowloon West Cluster Research Ethics Committee (Reference number: KW/FR-16-042(97-01)(1)).
Pregnant women were recruited from the antenatal clinic of Kwong Wah Hospital, a public hospital managed by the Hospital Authority in Hong Kong. Kwong Wah Hospital has one of the city’s major obstetrics and gynaecology departments, providing services to ~5000 childbirths annually. A total of 340 pregnant women were followed from 20–24 weeks of gestation (T1), then 4 weeks after childbirth (T2), and again 3 years after childbirth (T3). They provided information regarding demographic characteristics (marital status, employment status, social security assistance, monthly household income) and depressive symptoms. Furthermore, buccal swab samples were provided by 122 of the 340 women’s children at age 3. The relatively smaller sample size of children is primarily attributable to the ongoing COVID-19 pandemic at the time of data collection. During this period, caregivers were often hesitant to permit buccal swab collection, fearing it could increase the risk of COVID-19 exposure and infection for their children. Informed consent was obtained from all participants. Details of recruitment and inclusion criteria can be found in our previous papers .
Outcome measure Children’s buccal telomere lengths Samples of the children’s DNA were extracted from buccal swab samples. Trained researchers helped to collect the children’s buccal swab samples following standardized instructions and procedures. Guided by the manufacturer’s instructions, genomic DNA samples were isolated and extracted from the collected samples using the QIAamp DNA Mini kit (Qiagen). The isolated DNA samples were eluted into a buffer solution (10 mM Tris-HCl and 1 mM ethylenediaminetetraacetic acid, pH 8.0) for quality checking and quantification. This was done using a spectrophotometer (NanoDrop 2000c, Thermo Scientific) to ensure that the DNA quality and quantity were within an acceptable range for telomere length determination. Each DNA sample, determined to be of acceptable quality and quantity, was handled in triplicate for the telomere length assay using quantitative polymerase chain reaction (qPCR). The qPCR was performed using a 7900HT Thermocycler (Applied Biosystems). After the telomere length assay, the telomere length was determined by calculating the relative ratio of the telomere repeat copy number (T) to the single-copy gene 36B4 copy number (S). The formula used for this calculation was T/S = 2(−ΔCt), where ΔCt represents the mean difference between the threshold cycle (Ct) value of the 36B4 gene and telomere repeats obtained from the qPCR. Children’s TL was log-transformed. Details can be found elsewhere .
Children’s buccal telomere lengths Samples of the children’s DNA were extracted from buccal swab samples. Trained researchers helped to collect the children’s buccal swab samples following standardized instructions and procedures. Guided by the manufacturer’s instructions, genomic DNA samples were isolated and extracted from the collected samples using the QIAamp DNA Mini kit (Qiagen). The isolated DNA samples were eluted into a buffer solution (10 mM Tris-HCl and 1 mM ethylenediaminetetraacetic acid, pH 8.0) for quality checking and quantification. This was done using a spectrophotometer (NanoDrop 2000c, Thermo Scientific) to ensure that the DNA quality and quantity were within an acceptable range for telomere length determination. Each DNA sample, determined to be of acceptable quality and quantity, was handled in triplicate for the telomere length assay using quantitative polymerase chain reaction (qPCR). The qPCR was performed using a 7900HT Thermocycler (Applied Biosystems). After the telomere length assay, the telomere length was determined by calculating the relative ratio of the telomere repeat copy number (T) to the single-copy gene 36B4 copy number (S). The formula used for this calculation was T/S = 2(−ΔCt), where ΔCt represents the mean difference between the threshold cycle (Ct) value of the 36B4 gene and telomere repeats obtained from the qPCR. Children’s TL was log-transformed. Details can be found elsewhere .
Samples of the children’s DNA were extracted from buccal swab samples. Trained researchers helped to collect the children’s buccal swab samples following standardized instructions and procedures. Guided by the manufacturer’s instructions, genomic DNA samples were isolated and extracted from the collected samples using the QIAamp DNA Mini kit (Qiagen). The isolated DNA samples were eluted into a buffer solution (10 mM Tris-HCl and 1 mM ethylenediaminetetraacetic acid, pH 8.0) for quality checking and quantification. This was done using a spectrophotometer (NanoDrop 2000c, Thermo Scientific) to ensure that the DNA quality and quantity were within an acceptable range for telomere length determination. Each DNA sample, determined to be of acceptable quality and quantity, was handled in triplicate for the telomere length assay using quantitative polymerase chain reaction (qPCR). The qPCR was performed using a 7900HT Thermocycler (Applied Biosystems). After the telomere length assay, the telomere length was determined by calculating the relative ratio of the telomere repeat copy number (T) to the single-copy gene 36B4 copy number (S). The formula used for this calculation was T/S = 2(−ΔCt), where ΔCt represents the mean difference between the threshold cycle (Ct) value of the 36B4 gene and telomere repeats obtained from the qPCR. Children’s TL was log-transformed. Details can be found elsewhere .
Maternal depressive symptoms Pregnant women’s depressive symptoms were assessed using a 10-item Chinese Edinburgh Postnatal Depression Scale (EPDS) . Participants reported the presence of depressive symptoms experienced within the past week. Each item was rated from 0 (all the time) to 3 (not at all). All items were summed to obtain a total score for depressive symptoms, with higher scores indicating a more severe level of depression. To screen for probable depression, we utilized a cut-off score of ≥10 in this study. This cut-off value has been suggested as optimal for screening depressive symptoms during pregnancy and the postpartum period in Chinese mothers . We used continuous scores of depressive symptoms in the trajectories analyses. The Chinese version of the EPDS has been validated in prior studies which showed good psychometric properties . In the present study, Cronbach’s alphas for the EPDS were 0.84, 0.83, and 0.81 at T1, T2, and T3, respectively. Maternal ACEs The Adverse Childhood Experiences (ACEs) Questionnaire by the World Health Organization was utilized to identify childhood traumatic events . Fourteen items were used to assess different domains of ACEs, such as childhood maltreatment, household dysfunction, and exposure to war or collective violence before the age of 18 years. Participants were asked to report how frequently ACEs occurred. Each item was dichotomized into 1 = exposed and 0 = not exposed. We summed all the ACE items to obtain a total score, which reflects the overall severity of childhood adversities experienced . A higher total ACE score indicates greater exposure to childhood adversities. Previous research has also examined these experiences in Chinese samples . In the current study, the Cronbach’s alpha of the ACEs was 0.61. Maternal chronic illness Pregnant women reported ten types of chronic illness at T1. These chronic illnesses include hypertension, heart disease, asthma, diabetes, nephropathy, cataracts, pulmonary tuberculosis, peptic ulcer disease, skin disease, and others. Each illness was reported as 1 = yes and 0 = no. All items were summed to obtain a continuous score of maternal chronic illness. These illnesses are the most common conditions in childbearing-age women and have also been evaluated in previous work . Demographic characteristics Demographic characteristics about the mothers, such as maternal age, educational level, marital status, employment status, social security assistance, and monthly household income were collected. Data analysis The primary analyses included two steps. First, latent Class Growth Analysis (LCGA) in Mplus 7.0 was conducted to identify latent classes of depressive symptoms. Data analysis and results of LCGA have been shown in our accepted paper . Specifically, intercepts and slopes for each latent class were estimated as in previous work . One- to five-class unconditional models were tested for depressive symptoms. Several criteria were used to determine the optimal class : (a) lower information criteria fit indices including the Akaike’s information criterion (AIC), the Bayesian information criterion (BIC), and the sample-size-adjusted Bayesian (SSBIC); (b) higher entropy values; (c) statistically significant p -values for both the Lo-Mendell-Rubin likelihood ratio test (LRT) and the bootstrap likelihood ratio test (BLRT); and (d) the theoretical meaningfulness of group memberships. Moreover, latent classes with less than 5% of the sample are not considered . Additionally, Chi-square tests or t -tests were used to compare the distributions or scores of variables across different categories. Further details regarding the data analysis and results of the depressive symptom trajectories can be found in our accepted paper . Second, to test the association of maternal ACEs, chronic illness, and trajectories of depressive symptoms with children’s TL at 3 years, linear regressions were conducted because children’s TL was a continuous variable. A value of p < 0.05 was considered to be of statistical significance. Sensitivity analysis On the one hand, conditional LCGA models with covariates were further conducted to adjust for classification error and the effects of covariates. On the other hand, we conducted analyses using the BCH approach to explore whether there were differences between the trajectories related to distal outcome variables (i.e., bTL). The BCH approach offers an omnibus test that includes differences between the two classes on the distal outcome. Based on a comparative analysis of various methodologies, the BCH method has proven to be the most robust, consistently delivering unbiased estimates across all examined conditions . Ethical approval The research protocol was approved by the Institutional Review Board of the Hospital Authority Kowloon West Cluster Research Ethics Committee (Reference number: KW/FR-16-042(97-01)(1)).
Pregnant women’s depressive symptoms were assessed using a 10-item Chinese Edinburgh Postnatal Depression Scale (EPDS) . Participants reported the presence of depressive symptoms experienced within the past week. Each item was rated from 0 (all the time) to 3 (not at all). All items were summed to obtain a total score for depressive symptoms, with higher scores indicating a more severe level of depression. To screen for probable depression, we utilized a cut-off score of ≥10 in this study. This cut-off value has been suggested as optimal for screening depressive symptoms during pregnancy and the postpartum period in Chinese mothers . We used continuous scores of depressive symptoms in the trajectories analyses. The Chinese version of the EPDS has been validated in prior studies which showed good psychometric properties . In the present study, Cronbach’s alphas for the EPDS were 0.84, 0.83, and 0.81 at T1, T2, and T3, respectively.
The Adverse Childhood Experiences (ACEs) Questionnaire by the World Health Organization was utilized to identify childhood traumatic events . Fourteen items were used to assess different domains of ACEs, such as childhood maltreatment, household dysfunction, and exposure to war or collective violence before the age of 18 years. Participants were asked to report how frequently ACEs occurred. Each item was dichotomized into 1 = exposed and 0 = not exposed. We summed all the ACE items to obtain a total score, which reflects the overall severity of childhood adversities experienced . A higher total ACE score indicates greater exposure to childhood adversities. Previous research has also examined these experiences in Chinese samples . In the current study, the Cronbach’s alpha of the ACEs was 0.61.
Pregnant women reported ten types of chronic illness at T1. These chronic illnesses include hypertension, heart disease, asthma, diabetes, nephropathy, cataracts, pulmonary tuberculosis, peptic ulcer disease, skin disease, and others. Each illness was reported as 1 = yes and 0 = no. All items were summed to obtain a continuous score of maternal chronic illness. These illnesses are the most common conditions in childbearing-age women and have also been evaluated in previous work .
Demographic characteristics about the mothers, such as maternal age, educational level, marital status, employment status, social security assistance, and monthly household income were collected.
The primary analyses included two steps. First, latent Class Growth Analysis (LCGA) in Mplus 7.0 was conducted to identify latent classes of depressive symptoms. Data analysis and results of LCGA have been shown in our accepted paper . Specifically, intercepts and slopes for each latent class were estimated as in previous work . One- to five-class unconditional models were tested for depressive symptoms. Several criteria were used to determine the optimal class : (a) lower information criteria fit indices including the Akaike’s information criterion (AIC), the Bayesian information criterion (BIC), and the sample-size-adjusted Bayesian (SSBIC); (b) higher entropy values; (c) statistically significant p -values for both the Lo-Mendell-Rubin likelihood ratio test (LRT) and the bootstrap likelihood ratio test (BLRT); and (d) the theoretical meaningfulness of group memberships. Moreover, latent classes with less than 5% of the sample are not considered . Additionally, Chi-square tests or t -tests were used to compare the distributions or scores of variables across different categories. Further details regarding the data analysis and results of the depressive symptom trajectories can be found in our accepted paper . Second, to test the association of maternal ACEs, chronic illness, and trajectories of depressive symptoms with children’s TL at 3 years, linear regressions were conducted because children’s TL was a continuous variable. A value of p < 0.05 was considered to be of statistical significance.
On the one hand, conditional LCGA models with covariates were further conducted to adjust for classification error and the effects of covariates. On the other hand, we conducted analyses using the BCH approach to explore whether there were differences between the trajectories related to distal outcome variables (i.e., bTL). The BCH approach offers an omnibus test that includes differences between the two classes on the distal outcome. Based on a comparative analysis of various methodologies, the BCH method has proven to be the most robust, consistently delivering unbiased estimates across all examined conditions .
The research protocol was approved by the Institutional Review Board of the Hospital Authority Kowloon West Cluster Research Ethics Committee (Reference number: KW/FR-16-042(97-01)(1)).
The mean age of women at baseline was 31.30 ( SD = 4.26). The mean of depressive symptoms was 6.98 (4.50), 4.31 (4.15), and 4.25 (4.46) at T1, T2, and T3, respectively. The prevalence of depressive symptoms was 26.5%, 9.7%, and 12.6% at T1, T2, and T3, respectively. Latent Class Growth Analysis (LCGA) was used to identify different classes of women based on the dynamic changes in depressive symptoms from pregnancy to 3 years after childbirth . As shown in Table , the information criterion indices decreased from the one-class solution to the five-class solution, indicating that models two to five are better than model one. However, models three to five had a very small group, comprising approximately 2% of the sample. This does not meet the criteria for determining the number of latent classes. Therefore, the two-class model was selected for further analysis. The two-class trajectory was : The first class exhibited a trajectory characterized by consistently low symptom ratings across all time points. This class was labeled as “the low-stable depressive symptoms” with 86.2% ( n = 293) of women classified in this group. The second class displayed fluctuating depressive symptoms, demonstrating a cyclical course over time. This class was identified as “the relapsing/remitting depressive symptoms” with 13.8% ( n = 47) of women classified in this group. Figure shows the trajectories of depressive symptoms . As shown in Table , we compared any differences in demographic characteristics, ACE, and chronic illness between the low-stable depressive group and the relapsing/remitting depressive group. We found no differences between these two groups in demographic characteristics and chronic illness. However, women in the relapsing/remitting depressive symptoms group reported higher ACEs than women in the low-stable group ( p < 0.05). Table shows results of linear regression analyses on the association between maternal depressive symptoms, ACEs, and chronic illness and their children’s bTL. The first regression analysis model showed the crude associations between different trajectories of depressive symptoms (or ACEs or chronic illness at T1) and children’s bTL. Results showed that children’s bTL became shorter in those whose mothers had relapsing/remitting depressive symptoms ( β = −0.19, 95% CI = −0.14 to −0.005) when compared with those whose mothers were in the group of low-stable depressive symptoms. There were no significant associations between ACEs or chronic illness at T1 and children’s bTL. In the adjusted models 2–4, when adjusting for the severity of chronic illness, the relationship between the remitting/relapsing depressive group and shorter telomere length remained significant, while the association of chronic illness with children’s bTL was not significant. Additionally, when adjusted for ACEs, the longitudinal associations of the remitting/relapsing depressive symptoms increased and the influence of ACEs became positively associated with children’s bTL. These results remained consistent when adjusting for both ACEs and the severity of chronic illness. Results from the sensitivity analysis of the conditional LCGA model (see Table ) indicated that the 2-class model remains the most suitable for data fitting. Upon examination of the trajectories associated with the 2-class model, the same patterns as the unconditional LCGA model were identified: a low-stable depressive symptoms group ( n = 292, 86%) and a relapsing/remitting depressive symptoms group ( n = 48, 14%). For the results in bTL as assessed via the BCH method (see Table ), it appears that children of mothers in the group with relapsing/remitting depressive symptoms may have shorter TL compared to children of mothers in the low-stable depressive symptoms group. These findings align with our previously reported results as presented in Tables , . Consequently, we keep our initial analytical approach and findings.
Main findings This study presents preliminary evidence that mothers exposed to a range of stressors, including adverse childhood experiences and remitting/relapsing depressive symptoms, are associated with buccal telomere length (bTL) in their children at age 3. These findings expand on the concept of biological embedding by highlighting the importance of diverse stressors experienced by mothers in understanding the biological aging of their offspring. In this study, different women had different trajectories of depressive symptoms over time. We identified about 13.8% experiencing relapsing/remitting depressive symptoms. The traditional approach of categorizing depression as a binary “yes” or “no” overlooks the nuanced and variable nature of depressive symptoms, failing to capture the diverse ways in which individuals may present. In contrast, the person-centered approach employs statistical techniques like cluster analysis or latent profile analysis to identify naturally occurring subgroups. These subgroups are defined by specific patterns of variables or shared characteristics that differentiate them from other individuals or groups . By identifying and analyzing these subgroups, researchers can customize interventions, policies, or programs to better address the unique needs of different individuals or groups. Our successful categorization of depressive symptoms aligns with the literature in which diverse patterns of depressive symptoms have also been observed . More importantly, our study revealed that the children of mothers with relapsing/remitting depressive symptoms had shorter bTL. This finding remained significant even after accounting for maternal ACEs and chronic illness. Notably, in our current dataset, continuous depressive symptoms in mothers did not show a significant correlation with their children’s bTL. Nevertheless, distinct trajectories of depressive symptoms were found to variously predict children’s bTL, underscoring the possibility that it is the pattern of change in depressive symptoms that influences child bTL. Previous studies have reported inconsistent results regarding the effects of maternal depressive symptoms on children’s TL [ , , ]. To reconcile these inconsistencies, our current study highlights the importance of distinguishing between different trajectories of depressive symptoms. The significant impact of maternal relapsing/remitting depressive symptoms on children’s telomere length, as compared to the low-stable group, provides valuable insights for the development of prevention and intervention strategies targeting women at the highest risk (i.e., those experiencing relapsing/remitting depressive symptoms in our study). This information can guide efforts to identify and provide support to women experiencing relapsing/remitting depressive symptoms, with the ultimate aim of mitigating the potential adverse effects on their children’s bTL. Two putative biological mechanisms may explain the association between maternal depression trajectories and children’s TL. The first is through the offspring’s hypothalamic-pituitary-adrenal (HPA) axis stress response. Early-life stressors like maternal mental health problems may activate the HPA axis, resulting in the secretion of cortisol. Elevated cortisol levels have been linked to accelerated telomere shortening and cellular aging . It concurs with a study that daughters of depressed mothers had shorter telomeres than daughters of never-depressed mothers and that shorter telomeres were associated with greater cortisol reactivity to stress . Another potential mechanism is through the offspring’s immune function. Fetal exposure to maternal depression during pregnancy has been shown to have lasting effects on the child’s immune system later in life . The chronic inflammatory responses stemming from these immune disturbances may contribute to telomere attrition over time . Importantly, we found that it may be the unpredictable, relapsing/remitting nature of maternal depressive symptoms that is particularly detrimental. A study found that individuals dealing with this type of fluctuating mood and unpredictable depressive episodes often feel a heightened sense of hopelessness and lack of control over their symptoms . This pervasive uncertainty and instability may exacerbate the HPA axis dysregulation and immune system disturbances, thereby accelerating the shortening of the child’s telomeres. In summary, our current findings, in conjunction with prior research , underscore the even more severe consequences of the unpredictability of maternal mental health on child health outcomes. These results support the match-mismatch model as delineated in the Introduction section. Unfortunately, our current dataset did not include the necessary measures to empirically test these proposed mechanisms. Future research is needed to validate these explanations and provide a more conclusive understanding of the pathways linking maternal relapsing/remitting depression and child telomere length. When considering the impact of the severity of maternal ACEs, the longitudinal associations of remitting/relapsing depressive symptoms increased, while associations of ACEs became positive. The intergenerational link between maternal ACEs and the health of children (e.g., TL), may be elucidated by neurodevelopmental programming. The transgenerational transmission of maternal preconception adversity, spanning prenatal and postnatal periods, is thought to be mediated through a complex interplay of factors, including epigenetic modifications in the germline, changes to the intrauterine environment, and variations in postnatal caregiving practices, or more plausibly, a combination of them . Indeed, the role of epigenetic mechanisms as a fundamental molecular mechanism has been extensively discussed. A meta-analysis has synthesized evidence suggesting that DNA methylation likely contributes to the influence of prenatal maternal stress on adverse neurodevelopmental outcomes in offspring . The literature has inconsistent results about associations between maternal ACEs and epigenetic aging of their offspring . A study has found individuals with higher ACEs had greater TL . The potential reason might be that longer telomeres could serve as markers for survival, indicating a greater potential for a longer lifespan. The heightened survival potential conferred by longer telomeres may have enabled these individuals to overcome life’s challenges more effectively. Without long telomeres at birth or a mechanism to maintain them, they would not have been able to overcome those challenges as successfully . In addition, the longitudinal associations of remitting/relapsing depressive symptoms increased. This may suggest that mothers with ACEs may be more susceptible to experiencing depressive symptoms, which, in turn, could contribute to the shortening of their children’s TL. A study has revealed that ACEs increase the risk of depressive symptoms in women during pregnancy and the postpartum period . Furthermore, a national longitudinal cohort study demonstrated that mothers with incarcerated partners (adversity) were more prone to experiencing depression when their children were between the ages of 9 and 15 years. This increased maternal depression was associated with accelerated telomere length shortening in children . The associations between maternal ACEs, depressive symptoms, and offspring’s TL were not tested in the current study due to limited sample sizes. It is thus recommended that future studies address this topic to further explore these associations. In our study, we did not find a significant relationship between maternal chronic illness and children’s bTL. However, it is important to consider the characteristics of our sample, as they may have influenced these results. Pregnant women included in our study were relatively young, and there was a low prevalence of chronic illnesses among them, with most reporting having only one or fewer types of illness. Additionally, our study utilized non-clinical samples, which may have limited our ability to detect the influence of chronic illness on telomere length. Further research is necessary to gain a more comprehensive understanding of the links of maternal chronic illness to the telomere length of offspring. It would be valuable to investigate whether there are differences between clinical and non-clinical samples regarding the impact of chronic illness, to provide more targeted support and better understand the potential effects on children’s telomere length. Strengths Our current study has two notable strengths. Firstly, we took a comprehensive approach by considering a wide range of factors related to maternal stressors, including adverse childhood experiences and health factors. By incorporating this comprehensive set of information, we were able to provide a more integrated understanding of how various stressors experienced by mothers interact and influence the biological health of their children. Secondly, we made a significant contribution to the conflicting literature by differentiating between different trajectories of depressive symptoms. This effort to identify distinct patterns of depressive symptoms among women has provided more nuanced information for prevention and intervention efforts targeted toward women who may require the most support. Limitations Several limitations should be acknowledged. Firstly, the measurements of independent variables were self-reported, which may introduce reporting bias. Secondly, we did not have data on child sex, which may influence the children’s TL . Thirdly, we only assessed buccal TL at 3 years after childbirth and did not have data on bTL at baseline. Also, we were unable to adjust for cell types, although TL in different tissues is highly correlated . Fourthly, we did not have data on maternal telomere length, which may affect the interpretation of our findings. Previous studies have shown that telomere length is highly heritable . The lack of maternal telomere length data means we cannot fully account for the potential intergenerational influence on the telomere length of the individuals in our study. Fifth, the sample of children with available TL data is relatively small, with only 122 out of the 340 women’s children providing buccal swab samples. This limited sample size may affect the reliability and generalizability of the results. We encourage future studies to expand the sample size to validate our findings further. Finally, we only recruited pregnant women at a single antenatal clinic of a public hospital in Hong Kong, which may limit our generalizability to other samples. Implications This study has contributed to our understanding that women experience different trajectories of depressive symptoms, and it is more likely that the children of those in the highest-risk group (i.e., the relapsing/remitting group in our study) would have shortened TL. While some studies have shown an association between maternal depressive symptoms and offspring’s TL , in most studies it was assumed that women are homogeneous and experience the same changes in depression. Our findings highlight the considerable variation in maternal depression from pregnancy through the postpartum period and the importance of routinely assessing maternal depression during this time to identify opportunities for better support. Moreover, our study demonstrates that different trajectories of depressive symptoms have different associations with children’s future TL. This suggests that tailored treatments should be developed to address the specific needs and levels of maternal risk within each trajectory. Nonpharmacological interventions such as cognitive-behavioral therapy and physical exercise programs may enhance women’s motivation to cope with their depression. It would be valuable if future research were to evaluate whether these interventions have differential benefits for decreasing remitting/relapsing depressive symptoms. In summary, our study highlights the importance of recognizing the heterogeneity of maternal depression trajectories and the need for personalized interventions to support women at different levels of risk. Routine assessment of maternal depression and the evaluation of tailored interventions can contribute to improved outcomes for both women and their children. In addition, we found maternal adverse childhood experiences may affect mothers’ depressive symptoms and children’s TL. This finding informs that addressing maternal history of adversity together with mothers’ mental health problems could benefit children, even at the cellular level. Trauma-informed care is promising to respond to the impacts of trauma appropriately. It is a comprehensive and multilevel approach that could help service providers and clients understand the impact of traumatic events on health indicators and behaviors . Perinatal care providers (e.g., perinatal nurses) are well-positioned to provide trauma-informed perinatal care, which could prevent or reduce the negative impact of ACEs .
This study presents preliminary evidence that mothers exposed to a range of stressors, including adverse childhood experiences and remitting/relapsing depressive symptoms, are associated with buccal telomere length (bTL) in their children at age 3. These findings expand on the concept of biological embedding by highlighting the importance of diverse stressors experienced by mothers in understanding the biological aging of their offspring. In this study, different women had different trajectories of depressive symptoms over time. We identified about 13.8% experiencing relapsing/remitting depressive symptoms. The traditional approach of categorizing depression as a binary “yes” or “no” overlooks the nuanced and variable nature of depressive symptoms, failing to capture the diverse ways in which individuals may present. In contrast, the person-centered approach employs statistical techniques like cluster analysis or latent profile analysis to identify naturally occurring subgroups. These subgroups are defined by specific patterns of variables or shared characteristics that differentiate them from other individuals or groups . By identifying and analyzing these subgroups, researchers can customize interventions, policies, or programs to better address the unique needs of different individuals or groups. Our successful categorization of depressive symptoms aligns with the literature in which diverse patterns of depressive symptoms have also been observed . More importantly, our study revealed that the children of mothers with relapsing/remitting depressive symptoms had shorter bTL. This finding remained significant even after accounting for maternal ACEs and chronic illness. Notably, in our current dataset, continuous depressive symptoms in mothers did not show a significant correlation with their children’s bTL. Nevertheless, distinct trajectories of depressive symptoms were found to variously predict children’s bTL, underscoring the possibility that it is the pattern of change in depressive symptoms that influences child bTL. Previous studies have reported inconsistent results regarding the effects of maternal depressive symptoms on children’s TL [ , , ]. To reconcile these inconsistencies, our current study highlights the importance of distinguishing between different trajectories of depressive symptoms. The significant impact of maternal relapsing/remitting depressive symptoms on children’s telomere length, as compared to the low-stable group, provides valuable insights for the development of prevention and intervention strategies targeting women at the highest risk (i.e., those experiencing relapsing/remitting depressive symptoms in our study). This information can guide efforts to identify and provide support to women experiencing relapsing/remitting depressive symptoms, with the ultimate aim of mitigating the potential adverse effects on their children’s bTL. Two putative biological mechanisms may explain the association between maternal depression trajectories and children’s TL. The first is through the offspring’s hypothalamic-pituitary-adrenal (HPA) axis stress response. Early-life stressors like maternal mental health problems may activate the HPA axis, resulting in the secretion of cortisol. Elevated cortisol levels have been linked to accelerated telomere shortening and cellular aging . It concurs with a study that daughters of depressed mothers had shorter telomeres than daughters of never-depressed mothers and that shorter telomeres were associated with greater cortisol reactivity to stress . Another potential mechanism is through the offspring’s immune function. Fetal exposure to maternal depression during pregnancy has been shown to have lasting effects on the child’s immune system later in life . The chronic inflammatory responses stemming from these immune disturbances may contribute to telomere attrition over time . Importantly, we found that it may be the unpredictable, relapsing/remitting nature of maternal depressive symptoms that is particularly detrimental. A study found that individuals dealing with this type of fluctuating mood and unpredictable depressive episodes often feel a heightened sense of hopelessness and lack of control over their symptoms . This pervasive uncertainty and instability may exacerbate the HPA axis dysregulation and immune system disturbances, thereby accelerating the shortening of the child’s telomeres. In summary, our current findings, in conjunction with prior research , underscore the even more severe consequences of the unpredictability of maternal mental health on child health outcomes. These results support the match-mismatch model as delineated in the Introduction section. Unfortunately, our current dataset did not include the necessary measures to empirically test these proposed mechanisms. Future research is needed to validate these explanations and provide a more conclusive understanding of the pathways linking maternal relapsing/remitting depression and child telomere length. When considering the impact of the severity of maternal ACEs, the longitudinal associations of remitting/relapsing depressive symptoms increased, while associations of ACEs became positive. The intergenerational link between maternal ACEs and the health of children (e.g., TL), may be elucidated by neurodevelopmental programming. The transgenerational transmission of maternal preconception adversity, spanning prenatal and postnatal periods, is thought to be mediated through a complex interplay of factors, including epigenetic modifications in the germline, changes to the intrauterine environment, and variations in postnatal caregiving practices, or more plausibly, a combination of them . Indeed, the role of epigenetic mechanisms as a fundamental molecular mechanism has been extensively discussed. A meta-analysis has synthesized evidence suggesting that DNA methylation likely contributes to the influence of prenatal maternal stress on adverse neurodevelopmental outcomes in offspring . The literature has inconsistent results about associations between maternal ACEs and epigenetic aging of their offspring . A study has found individuals with higher ACEs had greater TL . The potential reason might be that longer telomeres could serve as markers for survival, indicating a greater potential for a longer lifespan. The heightened survival potential conferred by longer telomeres may have enabled these individuals to overcome life’s challenges more effectively. Without long telomeres at birth or a mechanism to maintain them, they would not have been able to overcome those challenges as successfully . In addition, the longitudinal associations of remitting/relapsing depressive symptoms increased. This may suggest that mothers with ACEs may be more susceptible to experiencing depressive symptoms, which, in turn, could contribute to the shortening of their children’s TL. A study has revealed that ACEs increase the risk of depressive symptoms in women during pregnancy and the postpartum period . Furthermore, a national longitudinal cohort study demonstrated that mothers with incarcerated partners (adversity) were more prone to experiencing depression when their children were between the ages of 9 and 15 years. This increased maternal depression was associated with accelerated telomere length shortening in children . The associations between maternal ACEs, depressive symptoms, and offspring’s TL were not tested in the current study due to limited sample sizes. It is thus recommended that future studies address this topic to further explore these associations. In our study, we did not find a significant relationship between maternal chronic illness and children’s bTL. However, it is important to consider the characteristics of our sample, as they may have influenced these results. Pregnant women included in our study were relatively young, and there was a low prevalence of chronic illnesses among them, with most reporting having only one or fewer types of illness. Additionally, our study utilized non-clinical samples, which may have limited our ability to detect the influence of chronic illness on telomere length. Further research is necessary to gain a more comprehensive understanding of the links of maternal chronic illness to the telomere length of offspring. It would be valuable to investigate whether there are differences between clinical and non-clinical samples regarding the impact of chronic illness, to provide more targeted support and better understand the potential effects on children’s telomere length.
Our current study has two notable strengths. Firstly, we took a comprehensive approach by considering a wide range of factors related to maternal stressors, including adverse childhood experiences and health factors. By incorporating this comprehensive set of information, we were able to provide a more integrated understanding of how various stressors experienced by mothers interact and influence the biological health of their children. Secondly, we made a significant contribution to the conflicting literature by differentiating between different trajectories of depressive symptoms. This effort to identify distinct patterns of depressive symptoms among women has provided more nuanced information for prevention and intervention efforts targeted toward women who may require the most support.
Several limitations should be acknowledged. Firstly, the measurements of independent variables were self-reported, which may introduce reporting bias. Secondly, we did not have data on child sex, which may influence the children’s TL . Thirdly, we only assessed buccal TL at 3 years after childbirth and did not have data on bTL at baseline. Also, we were unable to adjust for cell types, although TL in different tissues is highly correlated . Fourthly, we did not have data on maternal telomere length, which may affect the interpretation of our findings. Previous studies have shown that telomere length is highly heritable . The lack of maternal telomere length data means we cannot fully account for the potential intergenerational influence on the telomere length of the individuals in our study. Fifth, the sample of children with available TL data is relatively small, with only 122 out of the 340 women’s children providing buccal swab samples. This limited sample size may affect the reliability and generalizability of the results. We encourage future studies to expand the sample size to validate our findings further. Finally, we only recruited pregnant women at a single antenatal clinic of a public hospital in Hong Kong, which may limit our generalizability to other samples.
This study has contributed to our understanding that women experience different trajectories of depressive symptoms, and it is more likely that the children of those in the highest-risk group (i.e., the relapsing/remitting group in our study) would have shortened TL. While some studies have shown an association between maternal depressive symptoms and offspring’s TL , in most studies it was assumed that women are homogeneous and experience the same changes in depression. Our findings highlight the considerable variation in maternal depression from pregnancy through the postpartum period and the importance of routinely assessing maternal depression during this time to identify opportunities for better support. Moreover, our study demonstrates that different trajectories of depressive symptoms have different associations with children’s future TL. This suggests that tailored treatments should be developed to address the specific needs and levels of maternal risk within each trajectory. Nonpharmacological interventions such as cognitive-behavioral therapy and physical exercise programs may enhance women’s motivation to cope with their depression. It would be valuable if future research were to evaluate whether these interventions have differential benefits for decreasing remitting/relapsing depressive symptoms. In summary, our study highlights the importance of recognizing the heterogeneity of maternal depression trajectories and the need for personalized interventions to support women at different levels of risk. Routine assessment of maternal depression and the evaluation of tailored interventions can contribute to improved outcomes for both women and their children. In addition, we found maternal adverse childhood experiences may affect mothers’ depressive symptoms and children’s TL. This finding informs that addressing maternal history of adversity together with mothers’ mental health problems could benefit children, even at the cellular level. Trauma-informed care is promising to respond to the impacts of trauma appropriately. It is a comprehensive and multilevel approach that could help service providers and clients understand the impact of traumatic events on health indicators and behaviors . Perinatal care providers (e.g., perinatal nurses) are well-positioned to provide trauma-informed perinatal care, which could prevent or reduce the negative impact of ACEs .
This study provides evidence of an association between maternal relapsing/remitting depressive symptoms and the shortening of children’s TL, even when accounting for maternal adverse childhood experiences and chronic illness. Furthermore, we found that maternal adverse childhood experiences, when combined with depressive symptoms, link to children’s TL. These findings contribute to the concept of biological embedding, which suggests that early life experiences and maternal health can influence the biological processes and outcomes of offspring.
Table S1, Table S2
|
Left-handed resident phacoemulsification training by right-handed trainers – A case series and review of literature | 68ca4dea-fec6-4d1c-a10c-7558854883fc | 11552821 | Ophthalmology[mh] | Nil. There are no conflicts of interest. |
Gastrin exerts a protective effect against myocardial infarction via promoting angiogenesis | dd3e0b04-e408-4799-81e1-3b0f2caed165 | 8375043 | Anatomy[mh] | Myocardial infarction (MI) is the leading cause of mortality for cardiovascular disease. Despite several therapeutic strategies that successfully restored the epicardial coronary flow, such as thrombolysis, surgical bypass grafting, and percutaneous coronary intervention (PCI), some patients remain ineligible for those therapies since microvasculature dysfunction promotes inefficient reperfusion of the entire myocardium (Laan et al. ). Hence, de novo formation of microvessel, also called angiogenesis, can rescue ischemic myocardium at the early stage after MI occurrence and is also essential for long-term cardiac remodeling to prevent the transition to heart failure (Cochain et al. ). Gastrin is a peptide hormone secreted mainly by G-cells in the stomach after a meal (Varro and Ardill ). The physiological function of gastrin in regulating gastric acid secretion is currently well established. However, there is much more to discover concerning the biology of gastrin than is indicated. A study has shown that gastrin stimulated islet-cell growth and insulin secretion, which is proposed as a potential drug therapy for diabetes mellitus (Rehfeld ). Besides, gastrin plays an essential role in maintaining normal blood pressure (Chen et al. ) and protecting the kidney function against hypertensive injury (Gu et al. ). Diabetes and hypertension are important causes of coronary atherosclerotic heart disease. A previous study demonstrated that increased gastrin levels were negatively correlated with cardiovascular mortality risk (Goetze et al. ). Clinical data also revealed that serum gastrin levels were increased in patients with MI (Lapidus ; Tansey et al. ) and were associated with lower serum levels of cardiac troponin I in patients with unstable angina pectoris undergoing PCI (Yang et al. ). However, it is not clear if the increase in serum gastrin level is the result of MI or an attempt of the body to protect itself against cardiomyocyte damage after MI occurred. Additionally, gastrin is expressed in the heart and blood vessels (Gersl et al. ; Grossini et al. ). Two different studies reported that gastrin promoted a transient increase of Ca 2+ (Grossini et al. ) and up-regulated nitric oxide production in porcine coronary artery endothelial cells (Grossini et al. ). Moreover, another study showed that gastrin increased coronary blood flow and myocardial contractility, dose-dependently, through intracoronary infusion in anesthetized pigs (Grossini et al. ). Besides, a work conducted by Yang et al . proposed that gastrin exerted a protective role against myocardial ischemia/reperfusion injury (IRI) in rats (Yang et al. ). Therefore, it is possible that gastrin may exert a protective effect against myocardial infarction. An in vitro study revealed that gastrin induced chemotactic effects on endothelial cell migration and increased the tubulogenesis levels in endothelial cells, suggesting a potential pro-angiogenic role for this peptide (Clarke et al. ). Indeed, several studies found that gastrin induced pro-angiogenic effects in tumors (Bertrand et al. ; Lefranc et al. ). However, whether gastrin promotes cardiac angiogenesis after MI is still unclear. In this study, we aimed to investigate whether gastrin can improve the cardiac function after MI by increasing angiogenesis in the infarcted myocardium and to further explore its relative mechanism.
MI mice model C57/BL6 male mice (eight weeks old) were induced to myocardial infarction by the ligation of the left anterior descending (LAD) coronary artery as previously described (Li et al. ). Under isoflurane anesthesia, the LAD coronary arteries were ligated 2–3 mm far from the origin with a 7-0 silk suture. For the sham-operated control group, mice were exposed to all surgical procedures except the ligation of the LAD. Alzet osmotic minipumps (Model 1004, Durect Corporation, CA) were subcutaneously implanted in the mice dorsal region under isoflurane anesthesia. The minipumps were loaded with saline (control) or the chemicals and infused at a 2.64 μL/days rate for 28 days, according to the previous study (Huang et al. ). The infusate contained the saline vehicle (control, 2.64 μL/day, 100 μL in the pump), gastrin I (120 μg/Kg body weight/day, 100 μL in the pump, Cat. No. G1276, Sigma-Aldrich) (Gu et al. ; Rooman and Bouwens ), or/and the CCK 2 R inhibitor, CI988 (0.5 mg/Kg body weight/day, 100 μL in the pump, Cat. No. sc-205244A, Santa Cruz) (Xu et al. ). All procedures were approved by the Animal Use Subcommittee of Southwest Jiaotong University. Echocardiography Echocardiography was performed using an echocardiographic monitor (Vivid9, GE Healthcare, WI), and 2% isoflurane was used to keep the mice under sedation. Hearts were observed in the short-axis with M-mode on the parasternal long. Diastolic left ventricle internal diameter (LVIDd) and systolic left ventricle internal diameter (LVIDs) were measured to find cardiac morphologic and functional changes. The percentage of fractional shortening (%FS) was calculated as (LVIDd– LVIDs)/LVIDd × 100. Also, the percentage of left ventricle ejection fraction (%EF) was calculated as ((LVIDd) 3 -(LVIDs) 3 ) (LVIDd) 3 × 100. All of the echocardiography measurements were performed blindly. Plasma gastrin measurements After an overnight fast, mice blood samples were collected from the orbital venous while under isoflurane anesthesia on the day of myocardial infarction (day 1), as well as on days 3, 5, 7, 9, 11, and 13. Blood samples were contained in EDTA-coated tubes then centrifuged at 1000 rpm for 5 min and plasma stored at −80 ℃ until assayed. Plasma gastrin concentration was determined by using a mice gastrin-17 ELISA detection kit (Cat. No. ml569213-J, mlbio, Shanghai) according to the manufacturer’s standard procedures. Histology Mice heart tissues were fixed in 4% paraformaldehyde solution overnight at 25 °C and then undergoing paraffin embed and section. In order to analyze the myocardial fibrotic area after the myocardial infarction procedure with or without drug infusion, paraffin sections were cut through the entire ventricle from apex to base into serial sections with 0.5-mm intervals. After that, Masson’s trichrome staining (Cat. No. G1345, Solarbio, Beijing) was performed according to the manufacturer’s protocol following the dewaxing procedure. Apoptosis detection An in situ apoptotic cell death detection kit based on the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL, Cat. No. C1088, Beyotime, Shanghai, China) was used to detect apoptosis, according to the manufacturer’s standard procedures. Briefly, after the dewaxing procedure, cardiomyocytes were labeled with anti-cardiac troponin T antibody (cTnT, 1:100, Cat. No. MA5-12960, Invitrogen, MA) at 4 °C for 10 h (hrs), the nuclei were detected through 4’,6-diamidino-2-phenylindole (DAPI, Cat. No. C0065, Solarbio) staining. The TUNEL-positive cardiomyocytes were detected in 5 randomly selected nonadjacent images of peri-infarcted areas. The index of apoptosis was presented as the TUNEL-positive cardiomyocytes per 1 × 10 6 Nuclei. Immunofluorescence staining Paraffin-embedded hearts from all groups of mice were cut into 5 µm thick sections and then deparaffinized and dehydrated. After treatment with immunostaining blocking solution (Cat. No. P0102, Beyotime, Shanghai) for 1 h at room temperature, the sections were incubated with anti-cluster of differentiation 31 (CD31) primary antibody (1:100, Cat. No. GB11063-2, Servicebio), anti Ki67 antibody (1:100, Cat. No. 9129S, Cell Signaling Technology), and anti-cTnT antibody (1:100) for 10 h at 4 °C, subsequently washed three times with phosphate buffer saline and incubated with secondary antibodies conjugated with Alexa Fluor 488 or 555 (1:200, Cat. No. A-11001, A-21428, A-21422, Invitrogen, MA) for 2 h at 37 °C. DAPI was used for nuclear staining. The slides were mounted in an antifade mounting medium. Angiogenesis was detected by calculating the capillary density per field in the infarct border zone as previously reported. In order to quantify the number of Ki67 + (cell cycle marker) cardiomyocytes, the results were acquired from sections of the heart with at least five different fields and positions. In all cell-counting experiments, visual fields were randomized to reduce counting bias. Tube formation assay Human coronary artery endothelial cells (HCAECs, Pricells, Wuhan, China) were cultured in primary endothelial cell basal medium (RPMI1640, Cat. No. A4192301, Gibco) supplemented with 10% fetal bovine serum (FBS, Cat. No.10099–141, Gibco), in a humidified incubator at 37 °C with 95% air/5% CO 2 atmosphere as we previously described (Fu et al. ). All groups of cells were starved in a basal medium containing 1% FBS for 12 h before performing the experiments. The 24-well plate, pipette tips, and matrix gels were pre-cooled before tube formation assay. 300 μL Matrigel matrix (Cat. No. 354234, Corning, USA) was added to a 24-well plate and put into an incubator for gel formation. Subsequent to gel solidification, 500 μL of HCAECs suspension (2 × 10 5 /mL) was added to each well (Song et al. ). The plate was incubated at 37 °C. Tubule formation was observed using a Nikon inverted microscope (Tokyo, Japan) and determined by measuring branch number and length using the National Institutes of Health ImageJ software 6 h after treatment with gastrin (1 nM to 100 nM) or/and CI988 (100 nM). ImageJ software was used to analyze the tube lengths in 5 randomly selected visual fields. Cell migration assay Transwell migration assay was performed as previously described. Briefly, cells were slowly put into the upper well of the transwell migration chamber (Cat. No. MCEP24H48, Millipore, Germany). After 24 h of treatment with gastrin (100 nM) or/and CY294002 (1000 nM, Cat. No. S1737, Beyotime, Shanghai, China) (Liu et al. ), the non-migrating cells were removed with a cotton swab, and the migrating cells on the underside of the membrane were stained with crystal violet. Cell migration was also analyzed using a scratch wound migration assay. Briefly, HCAECs were seeded into a 12-well plate in basal medium containing 10% FBS for 48 h. Culture media were then changed to basal medium containing 1% FBS for 12 h. Two parallel scratches were made by using a 10 µL pipette tip. Subsequently, HCAECs were treated with gastrin (100 nM) or/and CY294002 (1000 nM) for 8 h. The scratch was observed using a Nikon inverted microscope, as mentioned before. In order to calculating cell migration distance, HCAECs that migrated into the scratched area from the edge area were photographed and then measured in each image to yield an average value compared with the time zero point. Western blot Protein was extracted from HCAECs after treatment with gastrin (1 nM to 100 nM), or/and CI988 (100 nM), or/and LY294002 (1000 nM) for 24 h. Western blotting was carried out as we previously described. Cells and heart tissue lysates containing 100 μg of protein were separated by SDS-PAGE and then electrophoretically transferred onto NC membranes (Bio-Rad, CA). After treatment with skimmed milk to blocking non-specific bands for 3 h, the blots were probed with the anti-phospho protein kinase B (Akt) antibody (1:800, Cat. No. 4060 T, Cell Signaling Technology), anti-Akt antibody (1:800, Cat. No. 9272S, Cell Signaling Technology), anti-phospho phosphatidylinositol 3 kinase (PI3K) antibody (1:800, Cat. No. 17366S, Cell Signaling Technology), anti-PI3K antibody (1:800, Cat. No. 4257S, Cell Signaling Technology), anti-VEGFA antibody (1:1000, Cat. No. 66828-1-Ig, Proteintech, Wuhan, China), anti-CD31 primary antibody (1:500, Cat. No.11265-1-AP, Proteintech), anti-Caspase 3 antibody (1:800, Cat. No. 19677-1-AP, Proteintech), anti-cleaved Caspase 3 antibody (1:500, Cat. No. 9661S, Cell Signaling Technology), and GAPDH (1:500, Cat. No. 60004-1-Ig, Proteintech) at 4 °C overnight. Membranes were washed three times with western washing buffer (TBST) and incubated with the corresponding secondary antibodies (Li-Cor, IRDye 800CW, 1:10000) for 2 h at room temperature. After that, NC membranes were washed three times using TBST again. Bound complexes were detected using the Odyssey Infrared Imaging System (Li-Cor Biosciences). Images were analyzed using the Odyssey Application Software to obtain the integrated intensities. Statistical analysis Data were expressed as means ± standard deviation. The SPSS 20.0 software was used for general statistical analysis. Comparison within groups was made by repeated-measures ANOVA (or paired t -test when only two groups were compared), and the differences between groups were evaluated by factorial ANOVA with the Holm-Sidak post hoc test. Log-rank (Mantel-Cox) test was used for statistical survival analysis. A two-sided P -value less than 0.05 was considered statistically significant. Histograms were plotted using the GraphPad Prism 5.01 software.
C57/BL6 male mice (eight weeks old) were induced to myocardial infarction by the ligation of the left anterior descending (LAD) coronary artery as previously described (Li et al. ). Under isoflurane anesthesia, the LAD coronary arteries were ligated 2–3 mm far from the origin with a 7-0 silk suture. For the sham-operated control group, mice were exposed to all surgical procedures except the ligation of the LAD. Alzet osmotic minipumps (Model 1004, Durect Corporation, CA) were subcutaneously implanted in the mice dorsal region under isoflurane anesthesia. The minipumps were loaded with saline (control) or the chemicals and infused at a 2.64 μL/days rate for 28 days, according to the previous study (Huang et al. ). The infusate contained the saline vehicle (control, 2.64 μL/day, 100 μL in the pump), gastrin I (120 μg/Kg body weight/day, 100 μL in the pump, Cat. No. G1276, Sigma-Aldrich) (Gu et al. ; Rooman and Bouwens ), or/and the CCK 2 R inhibitor, CI988 (0.5 mg/Kg body weight/day, 100 μL in the pump, Cat. No. sc-205244A, Santa Cruz) (Xu et al. ). All procedures were approved by the Animal Use Subcommittee of Southwest Jiaotong University.
Echocardiography was performed using an echocardiographic monitor (Vivid9, GE Healthcare, WI), and 2% isoflurane was used to keep the mice under sedation. Hearts were observed in the short-axis with M-mode on the parasternal long. Diastolic left ventricle internal diameter (LVIDd) and systolic left ventricle internal diameter (LVIDs) were measured to find cardiac morphologic and functional changes. The percentage of fractional shortening (%FS) was calculated as (LVIDd– LVIDs)/LVIDd × 100. Also, the percentage of left ventricle ejection fraction (%EF) was calculated as ((LVIDd) 3 -(LVIDs) 3 ) (LVIDd) 3 × 100. All of the echocardiography measurements were performed blindly.
After an overnight fast, mice blood samples were collected from the orbital venous while under isoflurane anesthesia on the day of myocardial infarction (day 1), as well as on days 3, 5, 7, 9, 11, and 13. Blood samples were contained in EDTA-coated tubes then centrifuged at 1000 rpm for 5 min and plasma stored at −80 ℃ until assayed. Plasma gastrin concentration was determined by using a mice gastrin-17 ELISA detection kit (Cat. No. ml569213-J, mlbio, Shanghai) according to the manufacturer’s standard procedures.
Mice heart tissues were fixed in 4% paraformaldehyde solution overnight at 25 °C and then undergoing paraffin embed and section. In order to analyze the myocardial fibrotic area after the myocardial infarction procedure with or without drug infusion, paraffin sections were cut through the entire ventricle from apex to base into serial sections with 0.5-mm intervals. After that, Masson’s trichrome staining (Cat. No. G1345, Solarbio, Beijing) was performed according to the manufacturer’s protocol following the dewaxing procedure.
An in situ apoptotic cell death detection kit based on the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL, Cat. No. C1088, Beyotime, Shanghai, China) was used to detect apoptosis, according to the manufacturer’s standard procedures. Briefly, after the dewaxing procedure, cardiomyocytes were labeled with anti-cardiac troponin T antibody (cTnT, 1:100, Cat. No. MA5-12960, Invitrogen, MA) at 4 °C for 10 h (hrs), the nuclei were detected through 4’,6-diamidino-2-phenylindole (DAPI, Cat. No. C0065, Solarbio) staining. The TUNEL-positive cardiomyocytes were detected in 5 randomly selected nonadjacent images of peri-infarcted areas. The index of apoptosis was presented as the TUNEL-positive cardiomyocytes per 1 × 10 6 Nuclei.
Paraffin-embedded hearts from all groups of mice were cut into 5 µm thick sections and then deparaffinized and dehydrated. After treatment with immunostaining blocking solution (Cat. No. P0102, Beyotime, Shanghai) for 1 h at room temperature, the sections were incubated with anti-cluster of differentiation 31 (CD31) primary antibody (1:100, Cat. No. GB11063-2, Servicebio), anti Ki67 antibody (1:100, Cat. No. 9129S, Cell Signaling Technology), and anti-cTnT antibody (1:100) for 10 h at 4 °C, subsequently washed three times with phosphate buffer saline and incubated with secondary antibodies conjugated with Alexa Fluor 488 or 555 (1:200, Cat. No. A-11001, A-21428, A-21422, Invitrogen, MA) for 2 h at 37 °C. DAPI was used for nuclear staining. The slides were mounted in an antifade mounting medium. Angiogenesis was detected by calculating the capillary density per field in the infarct border zone as previously reported. In order to quantify the number of Ki67 + (cell cycle marker) cardiomyocytes, the results were acquired from sections of the heart with at least five different fields and positions. In all cell-counting experiments, visual fields were randomized to reduce counting bias.
Human coronary artery endothelial cells (HCAECs, Pricells, Wuhan, China) were cultured in primary endothelial cell basal medium (RPMI1640, Cat. No. A4192301, Gibco) supplemented with 10% fetal bovine serum (FBS, Cat. No.10099–141, Gibco), in a humidified incubator at 37 °C with 95% air/5% CO 2 atmosphere as we previously described (Fu et al. ). All groups of cells were starved in a basal medium containing 1% FBS for 12 h before performing the experiments. The 24-well plate, pipette tips, and matrix gels were pre-cooled before tube formation assay. 300 μL Matrigel matrix (Cat. No. 354234, Corning, USA) was added to a 24-well plate and put into an incubator for gel formation. Subsequent to gel solidification, 500 μL of HCAECs suspension (2 × 10 5 /mL) was added to each well (Song et al. ). The plate was incubated at 37 °C. Tubule formation was observed using a Nikon inverted microscope (Tokyo, Japan) and determined by measuring branch number and length using the National Institutes of Health ImageJ software 6 h after treatment with gastrin (1 nM to 100 nM) or/and CI988 (100 nM). ImageJ software was used to analyze the tube lengths in 5 randomly selected visual fields.
Transwell migration assay was performed as previously described. Briefly, cells were slowly put into the upper well of the transwell migration chamber (Cat. No. MCEP24H48, Millipore, Germany). After 24 h of treatment with gastrin (100 nM) or/and CY294002 (1000 nM, Cat. No. S1737, Beyotime, Shanghai, China) (Liu et al. ), the non-migrating cells were removed with a cotton swab, and the migrating cells on the underside of the membrane were stained with crystal violet. Cell migration was also analyzed using a scratch wound migration assay. Briefly, HCAECs were seeded into a 12-well plate in basal medium containing 10% FBS for 48 h. Culture media were then changed to basal medium containing 1% FBS for 12 h. Two parallel scratches were made by using a 10 µL pipette tip. Subsequently, HCAECs were treated with gastrin (100 nM) or/and CY294002 (1000 nM) for 8 h. The scratch was observed using a Nikon inverted microscope, as mentioned before. In order to calculating cell migration distance, HCAECs that migrated into the scratched area from the edge area were photographed and then measured in each image to yield an average value compared with the time zero point.
Protein was extracted from HCAECs after treatment with gastrin (1 nM to 100 nM), or/and CI988 (100 nM), or/and LY294002 (1000 nM) for 24 h. Western blotting was carried out as we previously described. Cells and heart tissue lysates containing 100 μg of protein were separated by SDS-PAGE and then electrophoretically transferred onto NC membranes (Bio-Rad, CA). After treatment with skimmed milk to blocking non-specific bands for 3 h, the blots were probed with the anti-phospho protein kinase B (Akt) antibody (1:800, Cat. No. 4060 T, Cell Signaling Technology), anti-Akt antibody (1:800, Cat. No. 9272S, Cell Signaling Technology), anti-phospho phosphatidylinositol 3 kinase (PI3K) antibody (1:800, Cat. No. 17366S, Cell Signaling Technology), anti-PI3K antibody (1:800, Cat. No. 4257S, Cell Signaling Technology), anti-VEGFA antibody (1:1000, Cat. No. 66828-1-Ig, Proteintech, Wuhan, China), anti-CD31 primary antibody (1:500, Cat. No.11265-1-AP, Proteintech), anti-Caspase 3 antibody (1:800, Cat. No. 19677-1-AP, Proteintech), anti-cleaved Caspase 3 antibody (1:500, Cat. No. 9661S, Cell Signaling Technology), and GAPDH (1:500, Cat. No. 60004-1-Ig, Proteintech) at 4 °C overnight. Membranes were washed three times with western washing buffer (TBST) and incubated with the corresponding secondary antibodies (Li-Cor, IRDye 800CW, 1:10000) for 2 h at room temperature. After that, NC membranes were washed three times using TBST again. Bound complexes were detected using the Odyssey Infrared Imaging System (Li-Cor Biosciences). Images were analyzed using the Odyssey Application Software to obtain the integrated intensities.
Data were expressed as means ± standard deviation. The SPSS 20.0 software was used for general statistical analysis. Comparison within groups was made by repeated-measures ANOVA (or paired t -test when only two groups were compared), and the differences between groups were evaluated by factorial ANOVA with the Holm-Sidak post hoc test. Log-rank (Mantel-Cox) test was used for statistical survival analysis. A two-sided P -value less than 0.05 was considered statistically significant. Histograms were plotted using the GraphPad Prism 5.01 software.
The plasma level of gastrin is increased in mice after MI We induced MI by permanent ligation of the LAD in mice. To evaluate the changes in gastrin levels after MI, we detected the plasma concentration of gastrin in mice every two days for 13 days. Plasma concentration of gastrin was slightly increased in days 3 and 5, compared to the sham-operated group, in post-MI mice, but there were no significant differences. However, after 7 days, the plasma concentration of gastrin was significantly increased in the post-MI compared sham-operated group (Fig. ). These data revealed that the plasma level of gastrin was increased in mice after MI. Gastrin improves cardiac function and survival rate in post-MI mice Ventricular function was evaluated in all groups to assess the gastrin effects on cardiac hemodynamics. After MI injury, mice were submitted to subcutaneous infusion of gastrin for 28 days. Cardiac function was measured by echocardiography at 7, 14, and 28 days after myocardial infarction (Fig. a1–a5). Our results suggested that compared with sham-operated mice, post-MI mice had increased ventricular dilation and decreased cardiac function. At 7 and 14 days, gastrin slightly increased LVEF and LVFS and slightly decreased LVIDd and LVIDs, but there were no statistical differences compared with the MI group. However, on day 28 post-MI, gastrin improved LVEF and LVFS compared with the control MI mice. Besides, gastrin-treated mice had statistically smaller left ventricle size than MI mice, as indicated by the significant reduction of LVIDd and LVIDs. Meanwhile, survival rates of gastrin-treated mice were lower than the control mice subjected to MI (Fig. b). These data demonstrated that gastrin improved cardiac function in post-MI hearts. Gastrin decreases cardiomyocyte fibrosis and apoptosis in post-MI hearts Masson’s trichrome staining was performed to evaluated fibrosis 4 weeks post-MI. The result indicated that gastrin-treated mice had decreased fibrosis level in the peri-infarct border zone compared with the control MI group (Fig. a1–a3). Cardiomyocytes are susceptible to apoptosis during MI (Matsumoto et al. ). Gastrin has been reported to protect cells from apoptosis (Ramamoorthy et al. ) and promote cell proliferation (Duckworth et al. ). Next, we evaluated cardiomyocyte apoptosis and proliferation levels in the hearts. Apoptosis was detected in the peri-infarct border zone collected two days post-MI with TUNEL staining (Li et al. ). Cardiomyocytes were identified by cTnT staining. We found that the gastrin-treated group had decreased TUNEL-positive cardiomyocytes (Fig. b1–b2). Moreover, western blot data revealed that the expression of cleaved caspase-3 was also significantly decreased in gastrin-treated MI mice than control MI mice. These data suggested that gastrin protected the myocardium partially by reducing cardiomyocyte apoptosis. Cardiomyocyte proliferation was determined by Ki67 staining. Our study found that the rate of Ki67 positive cardiomyocytes was comparable between the gastrin-treated MI mice and the control mice (Fig. c1–c2), indicating that gastrin does not influence cardiomyocyte proliferation ability in the post-MI heart. Gastrin promotes angiogenesis after myocardial infarction Previous studies have shown that gastrin induced chemotactic effects on endothelial cell migration and increased the tubulogenesis levels of the endothelial cells (Clarke et al. ). Insufficient post-MI angiogenesis has been regarded as a non-negligible event that promotes heart failure development. In contrast, stimulating angiogenesis ameliorates cardiac remodeling after myocardial infarction (Laan et al. ). CD31 plays an important role in endothelial cell intercellular junctions and is widely used as a marker for angiogenesis quantification (Feng et al. ; Ho et al. ). We, therefore, assessed the de novo formation of microvessel in peri-infarct myocardium at 14 days post-MI through CD31 staining and expression. Both immunofluorescence staining and Western blot showed that CD31 expression in the peri-infarct border zone were increased in gastrin-treated mice (Fig. a1–a2). The in vitro experiment also revealed that gastrin (1 nM–100 nM) promoted tube formation of HCAECs in a concentration-dependent manner (Fig. b1–b2). These results suggested that gastrin improves angiogenesis in the post-MI heart. Gastrin induces angiogenesis via the PI3K/Akt/VEGF signaling pathway Vascular endothelial growth factor (VEGF), a key factor known to stimulate angiogenesis, attenuates cardiac dysfunction after MI in animal models (Cui et al. ; Rasanen et al. ). Previous studies reported that gastrin induced angiogenesis via stimulating VEGF signaling pathway (Bertrand et al. ). Liu et al . found that gastrin attenuated kidney ischemia/reperfusion injury through PI3K/Akt mediated anti-apoptosis signaling pathway (Liu et al. ). PI3K/Akt signaling also plays a critical role in angiogenesis (Liang et al. ; Sun et al. ). Therefore, we measured the phosphorylation status of PI3K, Akt, and VEGFA protein levels. As shown in Fig. a–c, after 24 h of treatment, gastrin (1 nM–100 nM) increased PI3K and Akt (Ser 473 ) phosphorylation and VEGFA expression in a concentration-dependent manner in HCAECs but had no effect on total PI3K and Akt. Besides, gastrin (100 nM) treatment increased Akt phosphorylation and VEGFA protein level, which can be blocked by PI3K inhibitor, LY294002 (Fig. d–e). Endothelial cell migration is essential for angiogenesis. In our transwell migration study, gastrin (100 nM) treatment significantly boosted HCAECs migration, which was partially inhibited by LY294002 (Fig. f1–f2). Besides, the scratch wound healing result also revealed that HCAECs migration was statistically increased by gastrin (100 nM), which was partially repressed by LY294002 (Fig. g1–g2). These results suggested that the gastrin-induced angiogenesis, at least partly, was dependent on PI3K/Akt/VEGF signal pathway. Gastrin, via CCK 2 R, exerts a protective role against myocardial infarction Gastrin and cholecystokinin (CCK) share the same receptor (Guilloteau et al. ). Cholecystokinin receptor (CCKR) has 2 subtypes, type1 and type 2. The expression of CCK 2 R is much more than CCK 1 R and has higher affinity for gastrin than CCK 1 R in the coronary arteries (Dockray et al. ). In the in vitro study, we found that the gastrin-mediated upregulation of VEGFA was blocked by CI988 (100 nM), a specific CCK 2 R inhibitor (Fig. a). The pro-angiogenic effect of gastrin was also inhibited in the presence of CI988 (Fig. b). Moreover, gastrin up-regulated CD31 expression in the border zone (Fig. c1–c2), and the improved cardiac function (Fig. d1–d2) was also abolished in the presence of CI988. These data indicated that CCK 2 R mediated the protective role of gastrin against MI.
We induced MI by permanent ligation of the LAD in mice. To evaluate the changes in gastrin levels after MI, we detected the plasma concentration of gastrin in mice every two days for 13 days. Plasma concentration of gastrin was slightly increased in days 3 and 5, compared to the sham-operated group, in post-MI mice, but there were no significant differences. However, after 7 days, the plasma concentration of gastrin was significantly increased in the post-MI compared sham-operated group (Fig. ). These data revealed that the plasma level of gastrin was increased in mice after MI.
Ventricular function was evaluated in all groups to assess the gastrin effects on cardiac hemodynamics. After MI injury, mice were submitted to subcutaneous infusion of gastrin for 28 days. Cardiac function was measured by echocardiography at 7, 14, and 28 days after myocardial infarction (Fig. a1–a5). Our results suggested that compared with sham-operated mice, post-MI mice had increased ventricular dilation and decreased cardiac function. At 7 and 14 days, gastrin slightly increased LVEF and LVFS and slightly decreased LVIDd and LVIDs, but there were no statistical differences compared with the MI group. However, on day 28 post-MI, gastrin improved LVEF and LVFS compared with the control MI mice. Besides, gastrin-treated mice had statistically smaller left ventricle size than MI mice, as indicated by the significant reduction of LVIDd and LVIDs. Meanwhile, survival rates of gastrin-treated mice were lower than the control mice subjected to MI (Fig. b). These data demonstrated that gastrin improved cardiac function in post-MI hearts.
Masson’s trichrome staining was performed to evaluated fibrosis 4 weeks post-MI. The result indicated that gastrin-treated mice had decreased fibrosis level in the peri-infarct border zone compared with the control MI group (Fig. a1–a3). Cardiomyocytes are susceptible to apoptosis during MI (Matsumoto et al. ). Gastrin has been reported to protect cells from apoptosis (Ramamoorthy et al. ) and promote cell proliferation (Duckworth et al. ). Next, we evaluated cardiomyocyte apoptosis and proliferation levels in the hearts. Apoptosis was detected in the peri-infarct border zone collected two days post-MI with TUNEL staining (Li et al. ). Cardiomyocytes were identified by cTnT staining. We found that the gastrin-treated group had decreased TUNEL-positive cardiomyocytes (Fig. b1–b2). Moreover, western blot data revealed that the expression of cleaved caspase-3 was also significantly decreased in gastrin-treated MI mice than control MI mice. These data suggested that gastrin protected the myocardium partially by reducing cardiomyocyte apoptosis. Cardiomyocyte proliferation was determined by Ki67 staining. Our study found that the rate of Ki67 positive cardiomyocytes was comparable between the gastrin-treated MI mice and the control mice (Fig. c1–c2), indicating that gastrin does not influence cardiomyocyte proliferation ability in the post-MI heart.
Previous studies have shown that gastrin induced chemotactic effects on endothelial cell migration and increased the tubulogenesis levels of the endothelial cells (Clarke et al. ). Insufficient post-MI angiogenesis has been regarded as a non-negligible event that promotes heart failure development. In contrast, stimulating angiogenesis ameliorates cardiac remodeling after myocardial infarction (Laan et al. ). CD31 plays an important role in endothelial cell intercellular junctions and is widely used as a marker for angiogenesis quantification (Feng et al. ; Ho et al. ). We, therefore, assessed the de novo formation of microvessel in peri-infarct myocardium at 14 days post-MI through CD31 staining and expression. Both immunofluorescence staining and Western blot showed that CD31 expression in the peri-infarct border zone were increased in gastrin-treated mice (Fig. a1–a2). The in vitro experiment also revealed that gastrin (1 nM–100 nM) promoted tube formation of HCAECs in a concentration-dependent manner (Fig. b1–b2). These results suggested that gastrin improves angiogenesis in the post-MI heart.
Vascular endothelial growth factor (VEGF), a key factor known to stimulate angiogenesis, attenuates cardiac dysfunction after MI in animal models (Cui et al. ; Rasanen et al. ). Previous studies reported that gastrin induced angiogenesis via stimulating VEGF signaling pathway (Bertrand et al. ). Liu et al . found that gastrin attenuated kidney ischemia/reperfusion injury through PI3K/Akt mediated anti-apoptosis signaling pathway (Liu et al. ). PI3K/Akt signaling also plays a critical role in angiogenesis (Liang et al. ; Sun et al. ). Therefore, we measured the phosphorylation status of PI3K, Akt, and VEGFA protein levels. As shown in Fig. a–c, after 24 h of treatment, gastrin (1 nM–100 nM) increased PI3K and Akt (Ser 473 ) phosphorylation and VEGFA expression in a concentration-dependent manner in HCAECs but had no effect on total PI3K and Akt. Besides, gastrin (100 nM) treatment increased Akt phosphorylation and VEGFA protein level, which can be blocked by PI3K inhibitor, LY294002 (Fig. d–e). Endothelial cell migration is essential for angiogenesis. In our transwell migration study, gastrin (100 nM) treatment significantly boosted HCAECs migration, which was partially inhibited by LY294002 (Fig. f1–f2). Besides, the scratch wound healing result also revealed that HCAECs migration was statistically increased by gastrin (100 nM), which was partially repressed by LY294002 (Fig. g1–g2). These results suggested that the gastrin-induced angiogenesis, at least partly, was dependent on PI3K/Akt/VEGF signal pathway.
2 R, exerts a protective role against myocardial infarction Gastrin and cholecystokinin (CCK) share the same receptor (Guilloteau et al. ). Cholecystokinin receptor (CCKR) has 2 subtypes, type1 and type 2. The expression of CCK 2 R is much more than CCK 1 R and has higher affinity for gastrin than CCK 1 R in the coronary arteries (Dockray et al. ). In the in vitro study, we found that the gastrin-mediated upregulation of VEGFA was blocked by CI988 (100 nM), a specific CCK 2 R inhibitor (Fig. a). The pro-angiogenic effect of gastrin was also inhibited in the presence of CI988 (Fig. b). Moreover, gastrin up-regulated CD31 expression in the border zone (Fig. c1–c2), and the improved cardiac function (Fig. d1–d2) was also abolished in the presence of CI988. These data indicated that CCK 2 R mediated the protective role of gastrin against MI.
Studies on several gastrointestinal hormones have demonstrated that there may exist a gastro-heart axis that protects cardiovascular function. For instance, glucagon-like peptide 1 (GLP-1) has been reported to protect myocardial ischemia/reperfusion injury (Andrikou et al. ; Bonaventura et al. ). A study also found that ghrelin may be essential in ameliorating the efficacy of mesenchymal stem cell-based therapy for ischemic heart disease (Han et al. ). Besides, cholecystokinin was proposed to improve cardiac function in endotoxic shock rats (Saia et al. ). Gastrin, as one of the main gastrointestinal hormones, together with cholecystokinin, was among the first gastrointestinal hormones discovered. The two peptides were structurally related and activated the same receptor (the CCK 2 receptor) (Zeng et al. ). Serum gastrin level markedly increased after a meal, about 20-fold more than cholecystokinin (Rehfeld et al. ). A previous study reported that the serum concentration of gastrin was significantly increased after myocardial infarction (Tansey et al. ). Besides, high serum gastrin level was reported to be an independent predictor of MI (Lapidus ). Similarly, our result also revealed that the plasma level of gastrin was increased in mice after MI. However, the pathophysiological significance of this phenomenon remains to be elucidated. A former study found that increased gastrin concentration might represent an adaptive response to hypoxic conditions (Laval et al. ). Indeed, Yang et al . proposed that gastrin protects against myocardial IRI through activation of RISK (Reperfusion Injury Salvage Kinase) and SAFE (Survivor Activating Factor Enhancement) pathways (Yang et al. ). Also, gastrin was reported to protect the brain from ischemia-induced dysfunction in stroke-prone spontaneously hypertensive rats (Yasui and Kawasaki ). More recently, gastrin attenuated renal IRI through anti-apoptosis signaling (Liu et al. ). Our data showed that gastrin treatment for 28 days improved cardiac function in the post-MI hearts. Earlier works have shown that gastrin displays proliferative and antiapoptotic effects (Duckworth et al. ; Ramamoorthy et al. ). Our data revealed that gastrin ameliorated apoptosis but did not increase cardiomyocyte proliferation in the post-MI heart. Pro-angiogenic therapy has the potential to rescue the ischemic myocardium at the early stages after MI and is also essential for long-term left ventricular remodeling to prevent the transition to heart failure (Li et al. ). However, angiogenesis after myocardial infarction is insufficient. Thus, stimulating angiogenesis is an essential strategy for MI patients. Angiogenesis is based on endothelial cell proliferation and migration to form new vessels through sprouting in the hypoxic or ischemic region (Carmeliet and Jain ). Our in vivo study revealed that CD31-positive microvessels in the infarct border zone were increased by gastrin treatment after LAD ligation for 14 days, and the in vitro assay showed that migration of HCAECs and tube formation were stimulated by gastrin, meaning that gastrin promoted angiogenesis in ischemic myocardium. VEGF, a critical growth factor for vascular endothelial cells, is a critical factor that regulates angiogenesis and attenuates cardiac dysfunction after MI in animal models (Carmeliet and Jain ; Saif and Emanueli ). Although there are other related genes, including VEGF-B and VEGF-C, VEGF-A plays a dominant role in regulating angiogenesis (Apte et al. ). PI3K/Akt signaling plays an important role in angiogenesis and exerts a protective role in myocardial infarction (Peng et al. ; Ruan et al. ). Bertrand et al . found that gastrin-gly stimulated VEGF expression through the PI3K/Akt pathway (Bertrand et al. ). In porcine coronary artery endothelial cells, gastrin has been reported to stimulate NO generation through PI3K/Akt signaling pathway (Grossini et al. ). We, therefore, performed a western-blot experiment using lysates from HCAECs and found that gastrin increased PI3K and Akt phosphorylation and VEGFA expression in a concentration-dependent manner, and this process was partly repressed by PI3K inhibitor LY294002. Similarly, the increased HCAECs migration stimulated by gastrin was also partly blocked by LY294002. These results indicated that the PI3K/Akt/VEGF signal pathway was involved in the pro-angiogenic effect of gastrin in post-MI mice. The gastrin peptide family has two subtype receptors, namely, CCK 1 R and CCK 2 R. The expression of CCK 2 R is greater than CCK 1 R in the coronary arteries and has a higher affinity for gastrin than CCK 1 R (Grossini et al. ). Gastrin has been reported to up-regulate NO production in the artery, and the intracoronary infusion of gastrin significantly increased coronary blood flow through stimulating CCK 2 R in pigs (Grossini et al. ). In this study, we showed that CCK 2 R inhibitor C1988 abolished the pro-angiogenic function of gastrin. Therefore, the potential cardioprotective role of gastrin in post-MI was probably mediated by the CCK 2 R.
We demonstrated that increased plasma gastrin level played a protective role against cardiac dysfunction after myocardial infarction. Gastrin protected the post-MI heart from dysfunction by reducing cardiomyocyte apoptosis and promoting angiogenesis. The pro-angiogenic effect of gastrin was associated with CCK 2 R mediated PI3K/Akt/VEGF signal pathway activation.
|
Association of energy source with outcomes in en bloc TURB: secondary analysis of a randomized trial | 2eb7d4d8-f671-4c3c-a91b-4df30d9e9bf4 | 11950035 | Surgery[mh] | Transurethral resection of bladder tumor (TURB) is the first step in the treatment of urinary bladder cancer (UBC) . Its main purpose is to radically remove UBC and ensure a quality sample for pathological analysis, which allows for accurate diagnosis and guides treatment and follow-up. The quality of TURB has a major impact on patient’s own prognosis, subsequent treatments as well as total costs associated with the follow-up of UBC . The presence of the detrusor muscle (DM) in the surgical specimen is a surrogate parameter for the quality of the resection and a significant prognostic factor . Unfortunately, the current bulk of evidence shows that the DM is present in roughly 50% of patients who undergo TURB, leading to possible understaging and, potentially, disease progression . These low rates can be partially attributed to the resection technique itself. The conventional TURB (cTURB) has several limitations which include fragmentation and thermic alterations of the specimen. This might lead to difficulties in orientation and final diagnosis during pathological examination. To improve the limitations of cTURB, the en-bloc resection of bladder tumor (ERBT) has emerged as increasingly applied technique [ – ]. This approach does not only follow the oncological principle of removing malignant tissue 'en bloc' while ensuring negative resection margin around the resected area but also allows for precise orientation and integrity of the specimen, improving the precision of histopathological analysis and staging compared to cTURB . ERBT can be performed using different energy modalities . However, there is limited evidence investigating the differential quality of the resection as well as outcomes dependent on the energy source used. To fill this gap in knowledge, we performed a secondary analysis of a multicentric randomized controlled trial evaluating the association of different energies used during ERBT with pathological findings and perioperative outcomes.
Study design and population This sub-analysis forms part of a prospective, multicentric, randomized study that recruited patients between January 2019 and January 2022 (ClinicalTrials.gov Identifier: NCT03718754). The original study compared the outcomes of endoscopic resection by ERBT versus cTURB across nine European referral centers. Inclusion criteria for the trial were patients with a cystoscopic diagnosis of primary papillary non-muscle invasive bladder carcinoma (stages cTa or cT1), tumor diameter between 1 and 3 cm, a maximum of three lesions, absence of distant metastases as confirmed by imaging, and no concurrent upper tract urothelial cancer. For the purposes of this sub-analysis, we included only those patients who were treated with an ERBT. Surgical technique Participants in the ERBT cohort underwent bladder resections employing different energy modalities contingent upon the instrumentation each participating center had and the preference of the operating surgeon and included Monopolar-ERBT (m-ERBT), Bipolar-ERBT (b-ERBT), and Laser-ERBT (l-ERBT). An enhanced visualization such as photodynamic diagnosis (PDD), narrowband imaging, or IMAGE1 S technology (previously known as SPIES) was mandatory. Typically, a circular incision was made around the tumor, maintaining a margin of approximately 5–10 mm from the neoplasm's periphery. Subsequently, the tumor was delicately dissected from the subjacent stroma, adhering meticulously to the circumscribed incisional margin. Operation reports were homogenized across centers using a pre-established eight-item checklist . No supplementary biopsies were taken from the tumor base. Perforation was documented and characterized by resection depth reaching the perivesical fat or beyond, without the need for radiographic corroboration. Postoperatively, a transurethral catheter was inserted, and bladder irrigation commenced. Additionally, a single dose of intravesical chemotherapy post-surgery was administered at the surgeon’s discretion, guided by intraoperative findings. Pathological evaluation and adverse event reporting Pathological assessments were gathered across centers to include specific parameters in the pathological report: pathological stage, pathological grade (according to WHO 1973 and WHO 2004 classifications), presence or absence of DM, depth and breadth of resection margins, variant histology, lymphovascular invasion, and concomitant presence of carcinoma in situ (CIS). Adverse events (AEs) were systematically reported utilizing the Common Terminology Criteria for Adverse Events (CTCAE). Outcomes Primary endpoint Primary endpoint was the quality of the pathological specimens obtained via m-ERBT, b-ERBT, or l-ERBT resections. Quality was primarily gauged by the presence of DM in the specimens, which is a widely recognized benchmark in pathological assessments of bladder resections [ , , ]. Secondary outcomes Secondary outcomes included: status of deep and lateral resection margins; duration of the operation; occurrence of obturator nerve reflex (ONR); necessity for conversion to conventional TURB; bladder perforation rate; incidence of post-operative complications, recurrence rate. For data collection we used a systematic submission process to the trial unit by the investigators at each participating center, utilizing a web-based electronic data capture system ( http://clincase.com ). This allowed for real-time, secure, and verifiable data entry, which is essential for the integrity of multi-center trials. Statistical analyses For the primary outcome we fitted logistic regression models to investigate the association of energy sources with the presence of DM in the specimen and status of deep and lateral resection margins on a per-tumor analysis. Similarly, linear and logistic regression analyses were used to investigate secondary outcomes on a per-tumor and per-patients analysis, respectively. Finally, Cox regression analyses were used to investigate the association of energy sources with time to recurrence. The survival function was visually plotted using the Kaplan–Meier method. p-values < 0.05 were considered indicative of statistical significance. Statistical analyses were conducted with Stata 17 (StataCorp LLC, College Station, TX, USA).
This sub-analysis forms part of a prospective, multicentric, randomized study that recruited patients between January 2019 and January 2022 (ClinicalTrials.gov Identifier: NCT03718754). The original study compared the outcomes of endoscopic resection by ERBT versus cTURB across nine European referral centers. Inclusion criteria for the trial were patients with a cystoscopic diagnosis of primary papillary non-muscle invasive bladder carcinoma (stages cTa or cT1), tumor diameter between 1 and 3 cm, a maximum of three lesions, absence of distant metastases as confirmed by imaging, and no concurrent upper tract urothelial cancer. For the purposes of this sub-analysis, we included only those patients who were treated with an ERBT.
Participants in the ERBT cohort underwent bladder resections employing different energy modalities contingent upon the instrumentation each participating center had and the preference of the operating surgeon and included Monopolar-ERBT (m-ERBT), Bipolar-ERBT (b-ERBT), and Laser-ERBT (l-ERBT). An enhanced visualization such as photodynamic diagnosis (PDD), narrowband imaging, or IMAGE1 S technology (previously known as SPIES) was mandatory. Typically, a circular incision was made around the tumor, maintaining a margin of approximately 5–10 mm from the neoplasm's periphery. Subsequently, the tumor was delicately dissected from the subjacent stroma, adhering meticulously to the circumscribed incisional margin. Operation reports were homogenized across centers using a pre-established eight-item checklist . No supplementary biopsies were taken from the tumor base. Perforation was documented and characterized by resection depth reaching the perivesical fat or beyond, without the need for radiographic corroboration. Postoperatively, a transurethral catheter was inserted, and bladder irrigation commenced. Additionally, a single dose of intravesical chemotherapy post-surgery was administered at the surgeon’s discretion, guided by intraoperative findings.
Pathological assessments were gathered across centers to include specific parameters in the pathological report: pathological stage, pathological grade (according to WHO 1973 and WHO 2004 classifications), presence or absence of DM, depth and breadth of resection margins, variant histology, lymphovascular invasion, and concomitant presence of carcinoma in situ (CIS). Adverse events (AEs) were systematically reported utilizing the Common Terminology Criteria for Adverse Events (CTCAE).
Primary endpoint Primary endpoint was the quality of the pathological specimens obtained via m-ERBT, b-ERBT, or l-ERBT resections. Quality was primarily gauged by the presence of DM in the specimens, which is a widely recognized benchmark in pathological assessments of bladder resections [ , , ]. Secondary outcomes Secondary outcomes included: status of deep and lateral resection margins; duration of the operation; occurrence of obturator nerve reflex (ONR); necessity for conversion to conventional TURB; bladder perforation rate; incidence of post-operative complications, recurrence rate. For data collection we used a systematic submission process to the trial unit by the investigators at each participating center, utilizing a web-based electronic data capture system ( http://clincase.com ). This allowed for real-time, secure, and verifiable data entry, which is essential for the integrity of multi-center trials.
Primary endpoint was the quality of the pathological specimens obtained via m-ERBT, b-ERBT, or l-ERBT resections. Quality was primarily gauged by the presence of DM in the specimens, which is a widely recognized benchmark in pathological assessments of bladder resections [ , , ].
Secondary outcomes included: status of deep and lateral resection margins; duration of the operation; occurrence of obturator nerve reflex (ONR); necessity for conversion to conventional TURB; bladder perforation rate; incidence of post-operative complications, recurrence rate. For data collection we used a systematic submission process to the trial unit by the investigators at each participating center, utilizing a web-based electronic data capture system ( http://clincase.com ). This allowed for real-time, secure, and verifiable data entry, which is essential for the integrity of multi-center trials.
For the primary outcome we fitted logistic regression models to investigate the association of energy sources with the presence of DM in the specimen and status of deep and lateral resection margins on a per-tumor analysis. Similarly, linear and logistic regression analyses were used to investigate secondary outcomes on a per-tumor and per-patients analysis, respectively. Finally, Cox regression analyses were used to investigate the association of energy sources with time to recurrence. The survival function was visually plotted using the Kaplan–Meier method. p-values < 0.05 were considered indicative of statistical significance. Statistical analyses were conducted with Stata 17 (StataCorp LLC, College Station, TX, USA).
237 UBC resected with ERBT in 188 patients included in the analyses. 29 (12.2%) were resected with m-ERBT, 136 (57.4%) with b-ERBT, and 72 (30.4%) with l-ERBT. The clinical and pathological characteristics of the study population, stratified by energy source, are shown in Table . Overall, DM was detected in 153 (81%) patients and in 191 (80.6%) of the UBCs resected. Specifically, DM was present in 107 (79%) of b-ERBT, 24 (83%) of m-ERBT, and 60 (83%) of l-ERBT resected specimens (p = 0.47), respectively. Peri-operative outcomes stratified by energy sources on a per-tumor analysis are presented in Table . Intra-operative and post-operative outcomes divided by energy sources on a per-patient analysis are presented in supplementary Table 1. On univariable logistic regression analysis there was no association between energy source and presence of DM in the specimens (p > 0.6). A statistically significant association of b-ERBT with negative lateral resection margins (OR 2.81; 95% CI 1.02–7.70; p = 0.04) was found (Supplementary Table 3). Logistic regression evaluating the association of energy modalities with deep resection margins status was not applicable due to a lack of variability, as the deep resection margins were negative in all energy groups (Table ). The median operative time was 26.5 (IQR 20–39) minutes and was similar across the different energy sources used for resection (p = 0.09) (supplementary Table 1). On linear regression analysis, a significant association of l-ERBT with longer operative time was found (p = 0.02; Supplementary Table 4). No ONR onset were registered in the l-ERBT group (Supplementary Table 1). Furthermore, we observed a higher rate of ONR onset in cases of tumors located on the left lateral wall [n = 11(17%); p = 0.05; Supplementary Table 2]. The rates of conversion to cTURB and perforation were comparable between groups (Supplementary Table 1). Logistic regression analysis did not show an association of energy source with these outcomes (Supplementary Table 3). Overall, we recorded 9 (5%) cases of grade 2 complications and one grade 3 complication. There was no association of energy source with intraoperative complications (supplementary Table 3). Within a median follow up of 22 (IQR 11–29) months, 35 (18.6%) patients recurred. Median time to recurrence was 18 (IQR 9–27) months. On univariable cox regression analysis investigating the association with disease recurrence, a statistically significant association for bipolar energy was found (HR 0.34; 95% CI 0.15–0.78; p = 0.01). When adjusting for established confounders such as tumor pathological grade (WHO 2004), early instillation, adjuvant therapy, second-look TURB, this association was confirmed (HR 0.24; 95% CI 0.10–0.60; p = 0.002). Another important finding at multivariable cox regression analysis was represented by the association of early adjuvant instillation after TURB with recurrence (HR 0.40; 95% CI 0.18–0.93; p = 0.03). While, for pathologic grading, second-look transurethral bladder resection and adjuvant instillation therapy no association was found at univariate and multivariate Cox regression analyses (Supplementary Table 5). The Kaplan–Meier curves show visually the survival function for the energy modalities (Fig. ) with a statistically significant difference as log-rank test revealed (p = 0.02).
In this post hoc analysis of the eBLOC trial (NCT03718754), we investigated the association of different energy sources used for ERBT with the quality of the pathologic specimens and found no difference between resection techniques. Current evidence supports the presence of DM in the histopathological specimen as a surrogate marker for the quality of the resection . Its presence allows for proper staging, accurate risk stratification, reduces the rate of second look procedures, and ensures better outcomes [ , , ]. ERBT has shown to be superior to conventional piecemeal resection in prospective and randomized trials by achieving DM rates of > 80% [ , , ]. However, there is only scarce evidence on the association of different energy sources used for ERBT with the quality of the resection and perioperative outcomes. A recent secondary analysis of a single center randomized controlled trial comparing cTURB with ERBT showed no association of the energy source used for the resection with the presence of DM in the specimen . The results of our analysis are in line with those reported in this study. Moreover, we expand upon these finding by adding robust data from a multicenter randomized trial to the current literature evidence. We found a higher proportion of negative resection margins in specimens resected with b-ERBT and l-ERBT compared to those resected with m-ERBT. We acknowledge that the lack of a central pathological review might have impacted the histopathological results. Indeed, although a dedicated uropathologist at each center performed the histological analysis, the majority of m-ERBTs (72%) were performed at a single center, introducing a significant selection bias. We found a longer operative time for resections performed with l-ERBT. This generates the hypothesis that this procedure might require longer operative time also when adopted in daily routine. This information might be helpful in daily practice for surgery scheduling. Moreover, as previously described in retrospective series, we observe no ONR jerk during l-ERBT. This technique is, therefore, particularly suitable for the resections of tumors located on the lateral walls [ – ]. Regarding complications, we found no association of energy source used and the rate of conversion to cTURB or perforation. We report an overall perforation rate of 5.3% without significant correlation with different energy sources. The overall low rate of conversion and perforation is in line with that reported by the general literature [ , – ]. Current literature addresses the tumor location as one limitations of ERBT, especially in tumors located at the anterior wall or bladder dome . Our findings reject this hypothesis. Location was not a limitation in patient’s selection in the trial and we could not find an association of complications or higher quality specimen with the tumor location itself. On survival analyses, we found a significant association of b-ERBT with recurrence on univariable and multivariable cox regression analysis. When interpreting these data, one must be aware that most patients in this trial were resected with b-ERBT and most tumors included in this cohort were low-grade and low risk, introducing a significant sampling bias. Based on current bulk evidence, ERBT does not seem to have an advantage in terms of recurrence-free survival compared to cTURB . This might be associated with tumor manipulation and cell spillage despite an en-bloc resection, adjuvant instillation therapies and the tumors’ own biology itself. However, the comparative effectiveness of different energy modalities is under investigated. Further research with long-term follow-up data are needed to fill this gap in knowledge. Our study possesses several limitations that warrant discussion. Firstly, as a secondary analysis of a randomized controlled trial, the original study was not powered to assess the outcomes measured in our analysis. Consequently, our findings should be regarded as preliminary and hypothesis-generating rather than confirmatory. Future research, designed with adequate power to investigate these specific outcomes, is needed to validate our results. Secondly, we observed an imbalance in the utilization of energy modalities, with b-ERBT being disproportionately represented. This uneven distribution complicates any direct comparison between modalities. To mitigate this limitation, subsequent studies could be structured to ensure a more balanced application of various energy modalities. Thirdly, the distribution of resection techniques was not uniform across participating centers, attributable to institutional resource availability and surgeons' preferences. This resulted in a center-specific predominance of certain techniques, which may introduce bias. Multicenter studies where techniques are standardized, or the effects of different institutional practices are statistically adjusted, could provide a clearer understanding of the efficacy of these techniques. Fourthly, despite the assessment of specimens by a dedicated uropathologist, the absence of a centralized pathological review presents a limitation. While we posit that this did not substantially affect the outcomes of our analysis, the potential for inter-observer variability cannot be entirely discounted. Future studies could benefit from a multi-pathologist consensus review to enhance the reliability of pathological findings. Finally, the follow-up was limited, restricting our ability to perform time-dependent analyses that could provide more insightful data on long-term outcomes. Longer follow-up, in future studies, would be instrumental in evaluating the sustained impact of the resection techniques on patient prognosis.
This secondary analysis of the eBLOC trial generates the hypothesis that different energy sources might achieve comparable perioperative outcomes. Laser energy reduces the rate of ONR onset, might being the best choice for lesions located on the lateral walls, at the cost of longer operative times. Further perspective could involve the assessment of long-term differential oncological outcomes associated with various energy modalities.
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 18 KB) Supplementary file2 (DOCX 19 KB) Supplementary file3 (DOCX 16 KB) Supplementary file4 (DOCX 15 KB) Supplementary file5 (DOCX 16 KB)
|
null | 14aaaa28-7315-424f-9f55-5c83e5d4fe0a | 8263265 | Pharmacology[mh] | The human skin microbiome, the collection of bacteria, viruses and fungi that inhabit the human skin surface and invaginations has become a research topic of fundamental interest. Skin is the largest epithelial surface that is colonised by microbes , and its microbial inhabitants are believed to play an important role in the maintenance of healthy skin . Microbial composition of the skin varies across body sites driven by the nutrients available in characteristic dry (arm), moist (axilla) and sebaceous (scalp) sites across the body . This site-specific community composition means that in reality, the skin microbiome can more appropriately be considered a consortium of distinct microbiomes . It is well documented that numerous members of the skin microbiome play a crucial role in the production of various metabolites that have an impact on skin health including free fatty acids from Cutibacterium acnes , antimicrobial peptides and phenol soluble modulins from Staphylococcus epidermidis and other staphylococcal species . An abundance of these organisms is commonly associated with healthy skin. Conversely, the presence of skin disease is commonly accompanied by a shift in microbiome composition to one taxonomically or functionally distinct from an individual’s baseline healthy state. Common conditions associated with microbiome community alterations include acne , atopic skin , dandruff and axillary odour . In some instances, a causal microbiome variation has been unequivocally proven whereas in others conditions a complete understanding of the complex multifaceted interactions between microbes and human skin has yet to be completely characterised . To control cosmetic conditions the use of personal care products has become common place. These products attempt to alleviate symptoms through the use of ingredients including skin moisturisers and topical antimicrobials . However, despite the use of these products, in the absence of disease flares, variations in seasons, environmental conditions or perturbations in the structure or integrity of the skin barrier, the temporal stability of the skin microbiome has been shown to be robust . This suggests a degree of community resilience to the skin microbiome, an essential ability of the community to respond to disturbance and return to its previous state, both structurally and functionally . Ensuring community resilience is not compromised by the application of cosmetic products has been identified as a key consumer safety parameter . Cosmetics are produced in a non-sterile but hygienically controlled environment however inadvertent contamination may occur. In addition, microbial challenge can be common place during consumer use . Cosmetic ingredients can act as nutrient sources to facilitate the growth of contaminating microorganism under the appropriate physiochemical conditions. This contamination can range from Gram-negative and Gram-positive bacteria, yeasts and fungi, many of which are opportunistic pathogens which can cause serious infection and illness . Therefore, every manufacturer of cosmetics has a responsibility to ensure the microbiological safety of its products for the intended use lifespan . Under EU cosmetics regulations, each cosmetic product placed on the market should have its own Product Information File (PIF) which captures details on the microbiological quality of the raw materials and the finished product . Cosmetic manufactures use approved preservative systems to maintain product quality and protect against the growth of spoilage and pathogenic microorganisms . The preservatives system must have a broad spectrum of activity and be compatible with the product ingredients as well as being stable over the shelf-life and intended usage time . To achieve this, a combination of preservatives and formulation hurdles are used to gain a broad spectrum of activity and reduce the necessary concentration of single actives . For example, the use of certain ingredients and formulation hurdle benefits such as pH control and reduced water activity can be used to improve the innate robustness of the product or to potentiate antimicrobial activity . The most common antimicrobial preservatives can be divided into a number of groups according to their chemical structure and functional groups. These include, organic acids, alcohols, phenols, aldehydes, and formaldehyde donors, isothiazolinones, biguanides, quaternary ammonium compounds, nitrogen compounds, heavy metal derivatives, and inorganic compounds . Each of these preservative groups will have a different mode of action and spectrum of activity under the correct concentration and formulation properties. The use of certain preservatives may be limited, or the concentration restricted depending on the type of product and the area for application. For example, rinse-off products such as shampoo will typically have fewer restrictions compared to leave-on products such as skin moisturiser, which have prolonged skin contact . The activity of preservatives has been examined in vitro using standard minimum inhibitory concentration tests , which can provide insights into antimicrobial performance in the neat product. However, there is limited information on the impact of different preservative systems on the skin microbiome under in vivo conditions. Only through the use of in vivo analysis are realistic effects of product usage, dilution, cutaneous substantivity and the ability of the microbiome to respond to perturbations by seeding the skin from protected invaginations faithfully represented . Where previous analysis sought to investigate the impact of preservative ingredients on skin bacteria ex vivo , this analysis sought to investigate the impact of cosmetic formulations containing four different commonly used preservative systems on the skin microbiome of healthy adult females in vivo . Leg skin microbiome samples were collected from each subject at baseline and after final product application and assessment on the impact of the preservative containing products was carried out using standard microbiome analysis including taxonomic and diversity analysis.
Study populations and test materials Key inclusion and exclusion criteria for each study population (A-D, corresponding to preservative system A-D) included females between 18–55 years of age in general good health with intact skin free of disease. A complete list of inclusion and exclusion criteria is outlined in . The compositions of the preservative systems examined are outlined in and a list of the associated formulation ingredients can be found in . Each preservative containing formulation was applied to 15 subjects per study with varying frequency of application and study duration. The study location and relevant population metadata can be seen in . Subjects enrolled in the studies were not subjected to a conditioning phase and were permitted to continue using their standard cleansers in advance of the study. This was to retain the natural variation seen in a subject’s skin microbiome as a result of their current hygiene routine and environment. All subjects were required not to bathe or apply cosmetics for a minimum of 12 hours before sampling. For studies where products C and D were applied post application samples were taken 12–18 hours after last product application. Sample selection Samples were selected for this cohort analysis from 4 separate intervention studies carried out to examine the impact of cosmetic formulations on the adult leg skin microbiome. Written informed consent for all studies was obtained from all enrolled individuals. The study protocols were reviewed and approved by the appropriate independent ethics committees, Study A: Institutional Review Board Services, Study B and C: Gallatin Institutional Review Board, Study D: Reading Independent Ethics Committee. The methods were carried out in accordance with the principles of the Declaration of Helsinki and principles of Good Clinical Practice as applicable to clinical studies on cosmetics. Sample collection, shipping and processing of samples for all studies were carried out in an identical fashion minimising any potential bias as a result of sample collection methodologies or data processing variations. No adverse events were reported for any of the studies in question and all subjects enrolled in the study maintained good skin condition throughout the study. Microbiome sample collection and processing Buffer washes were collected using a sterile Teflon sampling ring with a 3.5cm internal diameter (total diameter 5cm and height 3.5cm) using the method previously described . The ring was placed in the sampling site and held firmly in place by a second operator. Using a digital pipette and barrier (filter) pipette tip, 2.0ml of buffer wash solution (sterile phosphate-buffered saline pH7.9 containing 0.1% Triton X-100) was placed into the sampling ring and the skin surface gently agitated for one minute with a sterile Teflon rod (with rounded, smooth ends). The sampling fluid was collected using a sterile disposable pipette and placed into a sterile centrifuge tube. The sampling procedure was repeated with a further 2.0ml aliquot of buffer wash material and both aliquots pooled. Samples were placed on ice during the collection process and then stored at -80°C prior to DNA extraction. Shipment of samples from all studies prior to extraction was carried out on dry ice with appropriate temperature logging. DNA extraction Samples were defrosted and concentrated by centrifugation (10mins/13,000rpm, Eppendorf 5810R, Germany), supernatant removed, and the cells resuspended in 500μl of sterile TE buffer (10 mM Tris-HCl; 1 mM EDTA, pH 7.4). The cell suspension was transferred to a 96-well Lysing Matrix Plate B (MP Biomedicals). Addition of 3ul of Ready-Lyse lysozyme (Epicentre, 250U/ul) was followed by incubation with agitation at 300rpm, 37°C for 18 hours. Following incubation, a bead-beating step was performed using a Tissue Lyser (Qiagen, Germany) for 3 minutes at 20 Hz. An off-board lysis was performed by incubating the samples at 68°C for 15 minutes in the presence of Proteinase K, Carrier RNA, ATL and ACL buffer in a Qiagen S-plate following manufacturer guidelines. Post-incubation, the samples were processed using the QIAamp UCP DNA Micro kit according to manufacturer instructions (56204, Qiagen). Extracted DNA was frozen prior to 16S rRNA gene library preparation. 16S rRNA library preparation and sequencing Oligonucleotide primers targeting the V1-V2 hypervariable region of the 16S rRNA gene were selected. PCR was carried out using the following primers, U28F: 5’-ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNAGAGTTTGATCMTGGCTCA G-3’ U338R: 5’-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCTGCCTCCCGTAGGAGT-3’ PCR primers were modified version of the standard 28F and 338R primers which contain additional recognition sequences to facilitate nested PCR to add Illumina sequencing adapters and index sequences to resulting amplicons using methods described previously . A second round PCR incorporated Illumina adapters containing indexes (i5 and i7) for sample identification utilising eight forward primers and twelve reverse primers each of which contained a separate barcode allowing up to 96 different combinations. General sequences of the primers are illustrated below with the variable 8 bp barcode underlined and amplification carried out as previously described . N501f 5′AATGATACGGCGACCACCGAGATCTACAC TAGATCGC ACACTCTTTCCCTACACGACGCT3′ N701r 5′CAAGCAGAAGACGGCATACGAGAT TCGCCTTA GTGACTGGAGTTCAGACGTGTGCTC3′ . Informatics processing All steps were performed using the QIIME2 microbiome analysis tool suite version 2019.1. The paired end sequences were imported into QIIME2 format, then denoised using DADA2 . The primer sequence regions were removed during denoising by setting DADA2’s forward and reverse read trim parameters to the length of the forward and reverse primers, respectively. A complete list of software parameters and versions can be found in and Tables. Rooted and unrooted phylogenetic trees were generated for the ASVs (Amplicon Sequence Variants) using the QIIME2 phylogeny align-to-tree-mafft-fasttree workflow. Taxonomic assignments were generated by comparing ASVs against a BLAST database composed of the HOMD, HOMD extended and Greengenes sequences (HOMDEXTGG version 14.51) described in . Taxonomic classification was performed as previously described at 99% identity across 98% of the read length. Visualization and plotting of resulting data was carried out using the QIIME2 suite and JMP v 14.1 . Statistical analysis All statistical analysis were carried out using the QIIME2 microbiome analysis tool suite version 2019.1. Within sample group diversity (alpha) changes were estimated and tested using non-parametric approaches. A signed rank test for changes across time-points for each treatment that accounts for paired differences within subjects. Kruskal Wallis tests were used for pairwise treatment comparisons. Between group diversity (beta) was assessed visually using principal co-ordinate ordination plots for key metric distance matrices, Bray-Curtis(semi-metric), Jaccard, weighted and unweighted Unifrac . Statistical inference was achieved using permutation analysis of variance (PERMANOVA). Taxonomic differences in mean relative abundance were assessed using ANCOM (analysis of compositions) to access differences within treatments across timepoints.
Key inclusion and exclusion criteria for each study population (A-D, corresponding to preservative system A-D) included females between 18–55 years of age in general good health with intact skin free of disease. A complete list of inclusion and exclusion criteria is outlined in . The compositions of the preservative systems examined are outlined in and a list of the associated formulation ingredients can be found in . Each preservative containing formulation was applied to 15 subjects per study with varying frequency of application and study duration. The study location and relevant population metadata can be seen in . Subjects enrolled in the studies were not subjected to a conditioning phase and were permitted to continue using their standard cleansers in advance of the study. This was to retain the natural variation seen in a subject’s skin microbiome as a result of their current hygiene routine and environment. All subjects were required not to bathe or apply cosmetics for a minimum of 12 hours before sampling. For studies where products C and D were applied post application samples were taken 12–18 hours after last product application.
Samples were selected for this cohort analysis from 4 separate intervention studies carried out to examine the impact of cosmetic formulations on the adult leg skin microbiome. Written informed consent for all studies was obtained from all enrolled individuals. The study protocols were reviewed and approved by the appropriate independent ethics committees, Study A: Institutional Review Board Services, Study B and C: Gallatin Institutional Review Board, Study D: Reading Independent Ethics Committee. The methods were carried out in accordance with the principles of the Declaration of Helsinki and principles of Good Clinical Practice as applicable to clinical studies on cosmetics. Sample collection, shipping and processing of samples for all studies were carried out in an identical fashion minimising any potential bias as a result of sample collection methodologies or data processing variations. No adverse events were reported for any of the studies in question and all subjects enrolled in the study maintained good skin condition throughout the study.
Buffer washes were collected using a sterile Teflon sampling ring with a 3.5cm internal diameter (total diameter 5cm and height 3.5cm) using the method previously described . The ring was placed in the sampling site and held firmly in place by a second operator. Using a digital pipette and barrier (filter) pipette tip, 2.0ml of buffer wash solution (sterile phosphate-buffered saline pH7.9 containing 0.1% Triton X-100) was placed into the sampling ring and the skin surface gently agitated for one minute with a sterile Teflon rod (with rounded, smooth ends). The sampling fluid was collected using a sterile disposable pipette and placed into a sterile centrifuge tube. The sampling procedure was repeated with a further 2.0ml aliquot of buffer wash material and both aliquots pooled. Samples were placed on ice during the collection process and then stored at -80°C prior to DNA extraction. Shipment of samples from all studies prior to extraction was carried out on dry ice with appropriate temperature logging.
Samples were defrosted and concentrated by centrifugation (10mins/13,000rpm, Eppendorf 5810R, Germany), supernatant removed, and the cells resuspended in 500μl of sterile TE buffer (10 mM Tris-HCl; 1 mM EDTA, pH 7.4). The cell suspension was transferred to a 96-well Lysing Matrix Plate B (MP Biomedicals). Addition of 3ul of Ready-Lyse lysozyme (Epicentre, 250U/ul) was followed by incubation with agitation at 300rpm, 37°C for 18 hours. Following incubation, a bead-beating step was performed using a Tissue Lyser (Qiagen, Germany) for 3 minutes at 20 Hz. An off-board lysis was performed by incubating the samples at 68°C for 15 minutes in the presence of Proteinase K, Carrier RNA, ATL and ACL buffer in a Qiagen S-plate following manufacturer guidelines. Post-incubation, the samples were processed using the QIAamp UCP DNA Micro kit according to manufacturer instructions (56204, Qiagen). Extracted DNA was frozen prior to 16S rRNA gene library preparation.
Oligonucleotide primers targeting the V1-V2 hypervariable region of the 16S rRNA gene were selected. PCR was carried out using the following primers, U28F: 5’-ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNAGAGTTTGATCMTGGCTCA G-3’ U338R: 5’-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCTGCCTCCCGTAGGAGT-3’ PCR primers were modified version of the standard 28F and 338R primers which contain additional recognition sequences to facilitate nested PCR to add Illumina sequencing adapters and index sequences to resulting amplicons using methods described previously . A second round PCR incorporated Illumina adapters containing indexes (i5 and i7) for sample identification utilising eight forward primers and twelve reverse primers each of which contained a separate barcode allowing up to 96 different combinations. General sequences of the primers are illustrated below with the variable 8 bp barcode underlined and amplification carried out as previously described . N501f 5′AATGATACGGCGACCACCGAGATCTACAC TAGATCGC ACACTCTTTCCCTACACGACGCT3′ N701r 5′CAAGCAGAAGACGGCATACGAGAT TCGCCTTA GTGACTGGAGTTCAGACGTGTGCTC3′ .
All steps were performed using the QIIME2 microbiome analysis tool suite version 2019.1. The paired end sequences were imported into QIIME2 format, then denoised using DADA2 . The primer sequence regions were removed during denoising by setting DADA2’s forward and reverse read trim parameters to the length of the forward and reverse primers, respectively. A complete list of software parameters and versions can be found in and Tables. Rooted and unrooted phylogenetic trees were generated for the ASVs (Amplicon Sequence Variants) using the QIIME2 phylogeny align-to-tree-mafft-fasttree workflow. Taxonomic assignments were generated by comparing ASVs against a BLAST database composed of the HOMD, HOMD extended and Greengenes sequences (HOMDEXTGG version 14.51) described in . Taxonomic classification was performed as previously described at 99% identity across 98% of the read length. Visualization and plotting of resulting data was carried out using the QIIME2 suite and JMP v 14.1 .
All statistical analysis were carried out using the QIIME2 microbiome analysis tool suite version 2019.1. Within sample group diversity (alpha) changes were estimated and tested using non-parametric approaches. A signed rank test for changes across time-points for each treatment that accounts for paired differences within subjects. Kruskal Wallis tests were used for pairwise treatment comparisons. Between group diversity (beta) was assessed visually using principal co-ordinate ordination plots for key metric distance matrices, Bray-Curtis(semi-metric), Jaccard, weighted and unweighted Unifrac . Statistical inference was achieved using permutation analysis of variance (PERMANOVA). Taxonomic differences in mean relative abundance were assessed using ANCOM (analysis of compositions) to access differences within treatments across timepoints.
Taxonomy Results of taxonomic analysis can be seen in showing the top 10 most abundant species in each study. Figures A-D correspond to products/preservation systems A-D. In all studies the leg skin microbiome was dominated by species from the genera Staphylococcus , Cutibacterium and Corynebacterium with species from the genera Moraxella , Micrococcus , Lactobacillus and Dermacoccus present in different study subject populations. Differential abundance assessment was carried out using ANCOM using a centred log ratio approach on all genera/species in the dataset. No differentially abundant species were identified between timepoints for each individual product application group. Alpha diversity Alpha diversity analysis using 3 commonly used metrics, Chao1, Faith’s Phylogenetic Distance and Shannon Entropy were carried out to assess the impact of the different preservative systems on the leg skin microbiome following treatment. All samples were rarefied at a read count of 8000 as determined from analysis of appropriate rarefaction curves. A summary of alpha diversity analysis can be seen in (per subject analysis) and (per group analysis). For all metrics examined no significant differences were seen in alpha diversity of the leg skin microbiome following product application, . Beta diversity Potential changes in microbiome community structure were examined using beta diversity for all product groups. Beta diversity metrics Bray Curtis and Jaccard were used to determine if significant community shifts were occurring following product use. PcoA analysis of beta diversity analysis can be seen in . No statistical differences were identified in either beta diversity metric following product application. Additional analysis (not shown) was carried out using both weighted UniFrac and unweighted UniFrac diversity analysis. Neither of these metric show statistically significant shifts in community composition following product application.
Results of taxonomic analysis can be seen in showing the top 10 most abundant species in each study. Figures A-D correspond to products/preservation systems A-D. In all studies the leg skin microbiome was dominated by species from the genera Staphylococcus , Cutibacterium and Corynebacterium with species from the genera Moraxella , Micrococcus , Lactobacillus and Dermacoccus present in different study subject populations. Differential abundance assessment was carried out using ANCOM using a centred log ratio approach on all genera/species in the dataset. No differentially abundant species were identified between timepoints for each individual product application group.
Alpha diversity analysis using 3 commonly used metrics, Chao1, Faith’s Phylogenetic Distance and Shannon Entropy were carried out to assess the impact of the different preservative systems on the leg skin microbiome following treatment. All samples were rarefied at a read count of 8000 as determined from analysis of appropriate rarefaction curves. A summary of alpha diversity analysis can be seen in (per subject analysis) and (per group analysis). For all metrics examined no significant differences were seen in alpha diversity of the leg skin microbiome following product application, .
Potential changes in microbiome community structure were examined using beta diversity for all product groups. Beta diversity metrics Bray Curtis and Jaccard were used to determine if significant community shifts were occurring following product use. PcoA analysis of beta diversity analysis can be seen in . No statistical differences were identified in either beta diversity metric following product application. Additional analysis (not shown) was carried out using both weighted UniFrac and unweighted UniFrac diversity analysis. Neither of these metric show statistically significant shifts in community composition following product application.
Antimicrobial preservation systems are widely used across a range of personal care products. They provide an essential function of ensuring that bacterial and fungal growth in cosmetic formulations is controlled to enable safe use of products by consumers. With the growing realisation of the importance of the human microbiome it has been hypothesized that the impact of preservatives may extend beyond the confines of the product formulation and may have a potentially detrimental impact on the skin microbiome. While preservative compounds have indeed been shown to be active against skin relevant bacteria in vitro these analyses ignore three crucial elements. Firstly, the in-use concentration of cosmetic preservatives can be drastically reduced following dilution. In the case of skin wash products this can be by up to a factor of 5-10x. At these diluted concentrations, and the limited contact time, the likelihood of antimicrobial preservatives remaining active are greatly diminished. Secondly, current in vitro antimicrobial tests neglect a key facet of the human microbiome namely its ability to respond to external insult and re-seed its composition from skin invaginations and glands that are protected from the formulation. The “microbiome resilience” of the community is essential in the restoration of microbiome structure and function following multiple external insults that our skin is exposed to on a daily basis. Finally, cutaneous substantivity, the persistent activity of an antimicrobial agent following application, is a key consideration . While in vitro activity of preservative ingredients and other antimicrobial agents is obvious, their ability to bind to and remain active on skin varies considerably resulting in differences between in formulation and in use activity . This work set out to examine the in vivo effects of cosmetic formulations containing different preservation systems on the skin microbiome in full formulation. Two different product formats (wash off and leave on) and three different use durations (1 day, 2 weeks, 5 weeks) were utilised and their impact on standard microbiome metrics was examined. Species level taxonomic assessment revealed no statistical differences in community profile following product application. outlines major species identified however both these, and minor community members, remained consistent following application. Where between study comparisons was not a goal in this study it is worth noting that while leg skin microbiome samples were taken from all study populations some in-between study variation exists. Two study populations (B and C) had highly abundant levels of C . acnes where studies A and D had more balanced levels of dominant community members. No significant differences exist in the age of the study populations so it is unlikely that this variation can be explained by the varying ages of the study population as previously described . Currently this variation between studies remains unexplained but it is consistent at both timepoints in both studies. It is additionally noteworthy that the UK cohort (Study D) showed less inter-subject variation in taxonomic diversity in comparison to the North American cohorts potentially worthy of further investigation focussing on skin microbiome variations based on geographical location aligned to previous analysis . This reduced variation may also be explained by study D having additional exclusion criteria on subjects including exclusion of smokers and subjects who were peri- or post-menopausal, both elements that have recently been shown to impact the skin microbiome . Alpha diversity analysis was used to determine the impact of the formulations on skin biodiversity. Using Chao1 (richness), Faith’s Phylogenetic Distance (phylogeny-based diversity) and Shannon (richness and abundance) diversity metrics it was shown that group alpha diversity metrics remained unchanged following product use. As outlined in , visulisation of alpha diversity changes on a per subject basis shows that a subset of individuals in each of the product groups demonstrated a reduction in diversity there were also a number of subjects that showed an increase in diversity. In general, those subjects that started with higher than average diversity reduce, where those with lower than average diversity, increased. Finally, beta diversity analysis was used to examine the overall impact on community structure of product application. Bray Curtis diversity and Jaccard diversity were used to examine community structure weighted towards dominant and sparce community members respectively. For both metrics, no significant changes were seen in community structure as a result of product application. Taken together these data suggest that the different preservation systems in full formulation have minimal impact on the skin microbiome. Indeed, these results are in line with recent analyses examining the potential impact of soaps and antiseptic agents following cutaneous application, which only elicited a short-term microbiome alteration . While additional analysis may be needed to assess the short-term impact of product application this analysis shows that the leg skin microbiome is not perturbed to a point where it is unable to recover to its baseline state following product use. This was the case for wash off products that are diluted before/during use but also in the case of a leave on lotion where dilution does not occur, and contact time is extended. Future investigations should examine the impact of preservative systems using methods including shotgun metagenomics, across multiple body sites, to facilitate strain level analysis of the skin microbiome and, if possible, include no-preservative controls, not possible here due to ethics board restrictions.
Preservative systems remain an essential component of current cosmetic formulations. They provide a vital means to ensure product stability and shelf life and play a key role in consumer safety. Work presented here suggests that fully formulated cosmetics products that contain a variety of preservative systems do not have any detrimental impact on the structure or diversity of the skin microbiome for both wash off and leave on product formats.
S1 Table Inclusion and exclusion criteria. (PDF) Click here for additional data file. S2 Table Formulation ingredients. (PDF) Click here for additional data file. S3 Table QIIME2 software parameters. Software parameters for QIIME2 used to process and analyse metataxonomic data. (PDF) Click here for additional data file. S4 Table Software versions. Software versions utilised to process and analyse metataxonomic data. (PDF) Click here for additional data file.
|
Gamma knife radiosurgery for a rare Rosette-forming glioneuronal tumor in the brainstem region: A case report and literature review | 7a540183-4dad-4c88-b1fe-96e373f3b9a8 | 11922452 | Surgical Procedures, Operative[mh] | The World Health Organization (WHO) first described the rosette-forming glioneuronal tumor (RGNT) of the fourth ventricle in the 2007 classification of central nervous system tumors. RGNT is generally considered to exhibit benign progression, predominantly affecting young adults with an average age of 33. Surgical resection has been the primary treatment with favorable outcomes, as studies show high progression-free survival and overall survival rates. However, in some cases, particularly in critical regions such as the thalamus and brainstem, recurrence or rapid progression can occur as surgery fails to completely remove it. One study indicated that among 91 cases of RGNT, 14% of the patients experienced recurrence or rapid progression/dissemination after surgical treatment. The absence of comprehensive prospective studies or standardized treatment guidelines globally further complicates the management of RGNT, particularly in high-risk areas. Although benign histology of RGNT often suggests a positive prognosis, its location in vital areas such as the brainstem poses significant clinical challenges. The brainstem controls essential functions such as, breathing and motor control, and even a small tumor in this region can cause severe neurological deficits. For tumors in inoperable or critical locations, adjunctive therapies such as radiotherapy are considered, with gamma knife radiosurgery (GKRS) emerging as a key option. This case report highlights the urgent intervention required for a patient with RGNT in the brainstem, where GKRS was effectively utilized to manage the tumor and preserve neurological function using the latest GK-ICON™ device.
A 35-year-old female presented with sudden syncope that led to urgent hospital admission. The patient’s condition rapidly deteriorated, resulting in impaired consciousness. Cranial CT tomography revealed hydrocephalus, prompting an emergent ventricular puncture and external drainage. Subsequent magnetic resonance imaging (MRI) revealed a mass lesion in the pineal region, extending to the left brainstem and thalamus, measuring approximately 45 × 31 × 52 mm (Fig. ). The lesion exhibited hypointensity on T1-weighted imaging (T1WI; Fig. A) and hyperintensity on T2-weighted imaging (T2WI; Fig. B). Compression of the brainstem and narrowing of the cerebral aqueduct were evident, causing supratentorial ventricular enlargement and hydrocephalus (Fig. C, D). Post-contrast enhancement was heterogeneous, suggesting active pathology (Fig. E). T2 fluid-attenuated inversion recovery imaging demonstrated subcutaneous soft tissue swelling in the bilateral parieto-occipital regions, along with mixed signals within the lesion, indicating the internal heterogeneity of cystic and solid components (Fig. F). Since the initial treatments and diagnoses were performed in other hospitals, the medical reports were carefully reviewed. The patient underwent an endoscopic third ventriculostomy and biopsy. Initial pathology revealed a small round cell tumor with localized neuronal rosette formation, suggesting a low-grade glioneuronal mixed tumor, with RGNT considered as a differential diagnosis. Given the critical symptoms and the large size of the tumor, partial surgical resection was performed. However, owing to the involvement of the brainstem and surgical limitations, only a small portion of the pineal tumor was resected, with a significant portion remaining (Fig. ). Postoperative histopathology confirmed a glioneuronal mixed tumor, with pilocytic astrocytoma-like areas displaying neuronal rosette formations and perivascular pseudorosette patterns. Eosinophilic granular bodies were also observed. Combined with the results of immunohistochemical analysis, glial fibrillary acidic protein (+), neuronal nuclear antigen (+), oligodendrocyte transcription factor 2 (+), S-100 protein (+), and Ki-67 (2%), led to the final diagnosis of RGNT, WHO grade I. Since the patient’s pathology examination was conducted at another institution, we were unable to obtain the original histological slides or HE staining images. However, the pathology report provided by the referring hospital contains a detailed description of the histological findings, which we have included to ensure a comprehensive presentation of the case. To treat the residual tumor in the brainstem and thalamus, where traditional surgery presents significant risks, the patient was referred to our hospital and underwent GKRS, a noninvasive method that provides high precision while minimizing damage to the surrounding tissue. This procedure was performed using the Gamma Knife ICON™ system, which employs a frameless setup. MRI scans were used to create the treatment plan, with the scalp boundaries and target areas delineated using GammaPlan software. A dose of 25 Gy was delivered to the 45% isodose line through 9 shots over 5 fractions (Fig. ). The tumor volume was 16.57 cm³, with a mean dose of 36.7 Gy. The maximum dose to the tumor center was 55.6 Gy, while the marginal dose was 25 Gy (delivered in 5 fractions, 5 Gy/fraction). For OARs, the brainstem received a dose ranging from 3 to 25 Gy, with a mean dose of 9.8 Gy. The thalamus received a dose ranging from 10 to 25 Gy, with a mean dose of 14.7 Gy. The patient was immobilized using a thermoplastic mask, and cone-beam computed tomography (CBCT) was employed for precise stereotactic localization, with intra-fraction monitoring using an intra-fractional motion management system. The GKRS was successfully and safely completed. Figure illustrates the MRI at 3 months post-GKRS. Tumor volume in the brainstem and thalamic regions was significantly reduced, measuring approximately 26 × 13 × 22 mm. Part of the lesion in the fourth ventricle had almost disappeared, relieving brainstem compression. Post-contrast enhancement showed no enhancement within the tumor. The narrowing of the cerebral aqueduct decreased, and hydrocephalus improved. The patient’s previous neurological symptoms were significantly alleviated, with no side effects such as dizziness, nausea, or vomiting. Since the therapeutic effects of radiation therapy may become more pronounced over an extended period, the patient is scheduled for follow-up examinations 1-year post-treatment to evaluate long-term efficacy. This study was performed in accordance with the principles of the Declaration of Helsinki (2013 version). Ethic approval was granted by the Ethics Committee of Hebei Yizhou Cancer Hospital (Zhuozhou, China).
Given the rarity of RGNTs, there is no global consensus on the optimal treatment strategy. By 2022, only 6 cases of RGNTs in the cerebellar hemisphere had been reported, and fewer than 50 supratentorial RGNT cases were documented by 2023. RGNTs are classified as WHO Grade I tumors, which are typically slow-growing and associated with a favorable prognosis after surgical resection. However, RGNTs can exhibit heterogeneous clinical features, including recurrence, dissemination, and rapid enlargement. Studies have reported a recurrence rate of 14% in partially resected cases. In addition, RGNTs located in critical and high-risk areas, such as the brainstem, still pose significant clinical challenges, because even small tumors can cause severe neurological dysfunction. Therefore, despite the benign nature of the tumor, urgent intervention is required to prevent life-threatening complications and irreversible damage. RGNT is known to cause obstructive hydrocephalus, with headaches and ataxia being the most common clinical manifestations, in addition to different neurological symptoms depending on its location. Surgical resection is typically the treatment option; however, owing to the delicate location of the tumor, complete removal can be challenging. Careful consideration of treatment strategies, including radiotherapy and adjuvant therapies, is required to ensure both tumor control and the preservation of neurological function. Although it is not a standard treatment for RGNT, radiotherapy has shown promising results. Owing to its unique physical dose distribution advantages, proton therapy has been proven to reduce adverse events in normal tissues. Yamamoto et al reported no tumor progression within 3 years in a patient with recurrent RGNT of the fourth ventricle treated with a total dose of 50.4 Gy in 28 fractions using proton therapy. Similarly, Ramos et al treated a patient with RGNT using a single 24 Gy fraction of stereotactic radiosurgery. There was no tumor progression, and no adverse reactions were observed within 7 years post-treatment. In this study, immediate intervention was necessary because the patient’s sudden syncope and acute hydrocephalus, which led to impaired consciousness. MRI demonstrated that the tumor’s large volume, deep location, and extension into the brainstem and thalamus limited the extent of resection. The brainstem is densely packed with neural pathways and cranial nerve nuclei, that are essential for survival. Even small errors during surgery can result in severe neurological deficits, including paralysis, breathing difficulties, and death. These constraints underscore the inherent risk of surgical intervention in eloquent brain regions. The decision to perform partial resection was guided by the need to alleviate part of the obstructive hydrocephalus and obtain a pathological diagnosis. The surgery only involved partial resection of the pineal region, leaving substantial tumor remnants in the brainstem and thalamus. Given the urgency and severity of the patient’s symptoms, as well as the infeasibility of enduring prolonged cycles of traditional fractionated radiotherapy or proton therapy, GKRS was deemed the best immediate treatment option to minimize risks and control tumor growth postoperatively. GKRS has now been established as a noninvasive alternative to microsurgical resection, showing great promise in the treatment of malignant brain tumors as well as benign tumors such as meningiomas, pituitary adenomas, and vestibular schwannomas. Compared to traditional fractionated radiotherapy, GKRS significantly reduces the radiation dose outside the target area, thereby minimizing damage to surrounding neural tissues and reducing adverse effects. Previous models of GKRS typically involved invasive immobilization for single-fraction treatments, leading to complications, such as needle site infections, scar formation, numbness, and pain. However, the patient in this case was treated with the latest GK-ICON TM device, which permits frameless stereotactic radiosurgery technology and hypofractionated treatments. When using the GK-ICON TM device, CBCT was employed to define the stereotactic space, while a thermoplastic mask was used to immobilize the patient. An intra-fractional motion management continuously monitors the patient in real-time to ensure that it remains within the defined stereotactic space. Studies have indicated that the mechanical stability of the CBCT apparatus itself is submillimeter over several months. The thermoplastic mask, a noninvasive immobilization system, does not require anesthetic injections or skin penetration. The incidence of anxiety and pain associated with mask use was lower, resulting in higher patient comfort and acceptance. During the GKRS process in this case, the patient did not show any signs of discomfort or pain, which facilitated smooth implementation and completion of the treatment. In our case, performing GKRS to alleviate symptoms and reduce tumor size was the most appropriate and least invasive treatment option. The GK-ICON TM device is one of the most advanced technologies in stereotactic radiosurgery that, ensures high levels of comfort and precision during the treatment process. Following GKRS, the patient experienced relief from clinical symptoms without significant acute adverse effects. Despite the promising outcome, this study has several limitations. One major limitation is the unavailability of HE staining and immunohistochemical images, as the pathology examination was conducted at another institution. Due to this, we could not directly present histological images in our report. However, we provided a detailed description of the histopathological findings based on the referring hospital’s pathology report to ensure transparency and completeness. Additionally, the follow-up period after GKRS remains relatively short, and continuous long-term monitoring is required to evaluate the sustained efficacy and associated side effects of GKRS in managing RGNT in high-risk brainstem regions.
The rarity of RGNT and its invasion into critical brain regions pose significant treatment challenges. This case illustrates the effective application of GKRS in managing rare RGNT in high-risk brainstem regions, highlighting its potential in providing symptom relief in times of crisis and improving patient prognosis. GKRS offers a noninvasive alternative, minimizing risks while controlling tumor growth and alleviating symptoms. The Gamma Knife ICON™ device enhanced patient comfort and treatment precision.
Conceptualization: Shosei Shimizu. Data curation: Dongxue Zhou, Wei Wang, Weiwei Wang. Investigation: Dongxue Zhou, Shuyan Zhang, Chao Liu, Weiwei Wang. Methodology: Yonglong Jin, Shuyan Zhang, Zishen Wang. Project administration: Shosei Shimizu. Resources: Jie Wang, Shuyan Zhang, Chao Liu. Software: Jie Wang, Zishen Wang, Wei Wang. Validation: Yinuo Li. Visualization: Yonglong Jin. Writing – original draft: Zhipeng Shen. Writing – review & editing: Zhipeng Shen, Runzhu Ge, Yinuo Li, Shosei Shimizu.
|
Molecular Identification of the Italian Soldiers Found in the Second World War Mass Grave of Ossero | 0d697a66-f814-40eb-a9ac-064a64764892 | 11942473 | Forensic Medicine[mh] | Disaster victim identification (DVI) is widely regarded as a key element of humanitarian forensic action (HFA), emphasizing the role of forensic sciences in post-disaster and conflict contexts. The objective of employing forensic medicine and forensic genetics in DVI cases, in either post-human-made or post-natural disasters, is to restore the identities of deceased individuals, addressing and resolving the tragedy of missing persons . The World Trade Center disaster in 2001, caused by an airplane attack , and the Southeast Asian tsunami in 2004 represent explicit examples of DVI cases in which many people needed to be identified. Another worrying emergence is also the identification of dead migrants in the Mediterranean Sea, drowned in their desperate attempt to reach Europe . Continuous advancements in forensic genetics have, in fact, allowed DNA profiling to be applied in increasingly complex scenarios such as in the aftermath of mass fatalities and conflicts or in the identification of victims exhumed from dated mass graves . In Europe, for instance, where mass graves are a legacy of various armed conflicts of the 20th century, the exhumation and identification process conducted after the 1992–1995 conflict in Bosnia and Herzegovina shows these efforts, with over 20,000 victims exhumed from several mass graves by the end of 2008 . Also, human skeletal remains from mass graves of the Spanish Civil War (1936–1939) have been exhumed and identified [ , , ]. Similar efforts have led to the genetic identification of skeletal remains from World War II mass graves in Slovenia, Croatia, Poland, Bosnia and Herzegovina, and France [ , , , , ]. The universal right to post-mortem identification is enshrined in domestic and international law . The DVI process has been developed over several decades and formalized by developing an internationally recognized sequence of activities. In 1984, the INTERPOL introduced the first DVI guidelines which are regularly updated by a dedicated DVI working group in consultation with four specialized scientific sub-working groups, which are aligned with the key forensic disciplines: odontology, pathology/anthropology, fingerprint analysis, and genetic profiling . This provides forensic science with several tools to assist with the identification of human remains and brings DNA analysis to become one of the irreplaceable ones. Presently, in fact, genetic typing is considered a landmark for individual identification of skeletal remains . The procedure relies on the comparison of genetic profiles yielded from post-mortem (PM) samples collected from the deceased with ante-mortem (AM) samples, usually provided by the missing person’s relative/s. Kinship analysis has a fundamental role in providing invaluable clues for the identification of missing people, disaster victims, and the search for their unknown relatives. The gold standard approach to testing biological relationships between individuals is PCR amplification of sets of STR (short tandem repeat) markers coupled with capillary electrophoresis (STR-CE) separation of the molecular products . As a general rule, the effectiveness of STR markers to infer kinship depends on the number of STRs analyzed and on the availability of a suitable reference database (that is, the ante-mortem database); however, the more distant the relatives, the lower the chances of yielding conclusive results . In such cases, the availability of the recently developed massively parallel sequencing (MPS) platforms has been shown to be a pivotal tool, allowing the analysis of thousands of single nucleotide polymorphisms (SNPs) in a single run . Among them, the combination of two–five SNPs along a DNA stretch of less than 300 bp is particularly interesting . Such combinations of SNPs have been termed by Kidd as microhaplotypes (microhaps or MHs), and represent a promising tool in forensic genetics because of the low mutation rates, small amplicon size, absence of stutter artefacts, and same-size alleles within any one locus . The results of the present report aim for the personal identification of the skeletal remains allegedly belonging to Italian soldiers killed during the Second World War (WWII) and exhumed from a mass grave in Ossero (Croatia). During WWII, in fact, more than 18,500 Italian soldiers were killed or reported missing in the north Balkan region . Around 30 soldiers stationed on the islands of Cres and Lošinj (Kingdom of Italy, at that time) were killed by Tito’s forces at the end of April 1945 and buried in a mass grave close to the Cemetery of Ossero. Official documents dated 12 March 1942 listed the names of 27 Italian soldiers who, having been in those war zones, were believed to have been buried there. As a result, after the excavations of the burial site (May 2019), the skeletal remains were placed in 27 metallic caskets, and transferred to the War Memorial of the Overseas Fallen Soldiers in Bari (Italy). To identify the deceased soldiers, a kinship analysis was conducted by combining traditional STR-CE methods with the use of a 76 MH-MPS panel as an additional tool to assess LR (likelihood ratio) values. Furthermore, to improve the discrimination power, the possibility of combining the informativity of STR and MH markers was evaluated. Also, paternal lineages were investigated through Y-chromosome STR analysis.
2.1. Post-Mortem STR Database Set-Up The post-mortem STR database, containing the genotypes yielded from the skeletal remains, was built according to the following steps. 2.1.1. Bone/Tooth Samples In 2019, human remains were uncovered from a mass grave in Ossero (Island of Cres, Croatia). Full details on the mass grave are reported elsewhere . Briefly, historical records suggested that the unearthed remains could belong to 27 Italian soldiers executed on 21 April 1945. The remains were placed in 27 caskets, and delivered to the Italian Government on 13 November 2019 . Later, in 2022, the caskets were transferred to the University of Bari (Italy) for anthropological and medico-legal examinations. These analyses showed a large extent of peri- and post-mortem fractures, with a significant commingling of remains across the 27 caskets. The most likely number of individuals (MLNI) was calculated with standard methods using long bones (femurs, tibiae, and humeri) and skulls , revealing an MLNI of 32. In addition, the examination suggested the presence of at least three women (manuscript in preparation). A total of 341 bone and tooth specimens were sampled and delivered to the University of Trieste (Italy), where they were stored at room temperature in the dark until genetic analysis. According to the literature data [ , , , , , , , ], bone/tooth elements with—a priori—higher chances of being successfully typed were selected. In addition, at least two different bone elements for each of the 27 boxes were considered. In detail, the following samples were employed: the inner ear portion of the petrous bones , the cortical of the femur diaphysis [ , , ], the compact part of the epiphysis of metacarpals and metatarsals , and molar teeth (upper right M2) with enamel removed . In total, DNA extraction and subsequent molecular analyses were performed on 147 specimens, as shown in . 2.1.2. Bone/Tooth Cleaning and Pulverization Bones/teeth were cleaned mechanically using brushes and rotary sanding to remove surface soil. Approximately 1.5 g of bone fragments and whole molar teeth (enamel removed) were treated with 0.5% bleach for 4 min to eliminate external DNA contamination, followed by three washes with sterile bi-distilled water. Surfaces were exposed to UV radiation for 5 min on each side and dried at room temperature overnight . Pulverization was performed using an MM 400 Planetary Ball Mills instrument (Retsch, Haan, Germany) with 25 mL metal grinding vials and 16 mm diameter metal balls (Verder, Vleuten, The Netherlands) at 30 Hz for 1–2 min, using liquid nitrogen to prevent heating . The powder samples were stored at room temperature in the dark until use. All procedures were carried out in a room dedicated only to aged bone/tooth handling . 2.1.3. Bone/Tooth Decalcification and DNA Extraction Approximately 0.5 g of bone/tooth powder was processed according to the extraction method previously described in detail by Di Stefano et al. , which involved overnight decalcification in 0.5 M Na 2 EDTA (pH 8.0), lysis in 460 μL of extraction buffer (1.2% SDS, 10 mM Tris pH 8.0, 10 mM Na 2 EDTA pH 8.0, and 100 mM NaCl), 40 μL of 1M DTT, 80 μL of Proteinase K (20 mg/mL), and automated purification using the Maxwell ® FSC DNA IQ TM Casework Kit (Promega) with the Maxwell ® RCS TM441 apparatus. The extraction process was conducted on 179 bone/tooth powders (from the 147 bone samples from ; also see ), with a negative extraction control (NEC) included for every 6 samples processed. 2.1.4. DNA Quantification DNA samples were quantified in duplicate by qPCR using two different kits. In the first phase of the analyses, the Quantifiler TM Human DNA Quantification Kit (Thermo Fisher Scientific, TFS, Waltham, MA, USA) was used for 107 samples; the CFX96 Real-Time System Instrument (Bio-Rad, Hercules, CA, USA) apparatus was employed following the conditions specified in ref. . This kit targets a 62 bp sequence of the human telomerase reverse transcriptase (hTERT) gene, detected using a FAM-labeled probe; the presence of Taq polymerase inhibitors is monitored using an Internal Positive Control (IPC), labeled by VIC. The Limit of Detection (LOD) was set previously at 0.001 ng/μL , while the Limit of Quantification (LOQ) ranged from 0.023 ng/μL to 50 ng/μL. Raw data were analyzed using the CFX Maestro software v5.3.022.1030. ( https://www.bio-rad.com ; accessed on 13 December 2024). The remaining 72 samples were quantified using the PowerQuant TM System kit (Promega, Madison, WI, USA) with the 7500 Real-Time PCR System for Human Identification, HID Real-time PCR analysis (Applied Biosystem, AB, Foster City, CA, USA). The kit amplifies an 84 bp short autosomal target and a 294 bp long autosomal target, with the ratio between these two [Auto/Deg] used to assess DNA degradation. It also amplifies a 134 bp target on the Y-chromosome to detect male DNA; an IPC (Internal Positive Control) is added to the reaction to monitor for potential inhibitors. The LOQ for the Auto and Y targets ranged from 0.0032 ng/μL to 50 ng/μL. For the Deg target, the LOQ ranged from 0.0005 ng/μL to 50 ng/μL. The LOD was set at 0.0001 ng/μL for all targets . Raw data analysis was performed using PowerQuant ® Analysis Software v2.06 (available on www.promega.com ; accessed on 8 January 2025). 2.1.5. STR-CE Typing As a selection criterion, all the right femurs and the right petrous bone available (for those samples, both autosomal and Y-specific STR panels were used) were analyzed first. With the implementation of the post-mortem autosomal STR database (see ), the amplification of the Y-specific STR markers was carried out only when a new autosomal profile belonging to a male subject was identified. The amplification was performed only for samples that showed detectable amounts of DNA in at least one qPCR replicate. So, out of the 179 extracted samples, 126 underwent STR-PCR amplifications (see ). For autosomal STR amplification, the kits PowerPlex Fusion kit (Promega), PowerPlex ESX17 (Promega), and PowerPlex ESI (Promega) were employed. These kits allow the simultaneous amplification of 22, 17, and 17 autosomal STR markers (plus XY-specific amelogenin targets) in different multiplex configurations, respectively. Y-STR amplification was performed using the PowerPlex Y23 kit (Promega), which amplifies 23 Y-chromosome-specific loci. PCR amplifications were performed under standard conditions for samples containing 0.5–1 ng of DNA template. For samples with a lower DNA quantity, the number of PCR cycles was increased to 32 cycles, and the maximum allowed volume of DNA sample was loaded (17.5 μL for the kits ESX, ESI, and Y23 and 15 μL for the kit Fusion). Positive and negative PCR controls, as well as negative extraction controls (using the maximum allowed volume), were simultaneously run. Amplifications were performed using the T100 Thermal Cycler (Bio-Rad) apparatus. Capillary electrophoresis was initially carried out using the ABI 310 automatic DNA sequencer and later using the SeqStudio Genetic Analyzer (both from Applied Biosystems, Waltham, MA, USA). Raw data were analyzed with GeneMapperID ® ver 3.2.1 software for ABI310 and GeneMapper TM ID X ® Software v1.6 (Applied Biosystem) for SeqStudio. The analytical threshold was set at 50 RFU for ABI 310 and 150 RFU for SeqStudio, whereas the stochastic threshold was 150 RFU for ABI 310 and 300 RFU for SeqStudio. The criterion for including genetic profiles in the post-mortem database was the reliable detection of at least 12 autosomal markers, in accordance with international standards . Samples meeting this criterion were amplified in duplicates to achieve consensus profiles; replicate tests were not performed for samples whose profile was already present in the STR autosomal database (that is, the post-mortem database; see ). The data of replicate amplifications were also used for the assessment of stochastic events, such as allelic drop-in and allelic drop-out phenomena [ , , ]. 2.1.6. Exclusion Database DNA from the personnel involved in the genetic analysis was typed, and the resulting genotypes were compared with those of the samples under investigation to exclude the possibility of contamination during the procedure . In total, five female and four male operators were typed with the STR kits described above. 2.1.7. Post-Mortem Database The post-mortem database was built to store genotypes yielded from bone/tooth samples. Whenever a suitable post-mortem profile was obtained, it was compared with those already stored in the database to determine if it matched an existing profile or represented a new one. Two different Excel files were created, one for the autosomal and one for the Y-specific STR profiles. 2.2. Ante-Mortem Database Set-Up As the official documents dated 12 March 1942 listed the names of 27 soldiers who could have been buried in the mass grave of Ossero, those data enabled the search for their relatives. Since the descendants/relatives of only 14 missing soldiers requested genetic comparison, buccal swabs of 21 living subjects were collected in total (see ). As shown in and , the ante-mortem samples included 19 relatives (from 1st to 4th degree) as well as 2 descendants’ mothers (Fam7 and Fam8). Eight male reference samples were connected through the paternal line. The saliva swabs were shipped by ordinary mail to the Institute of Legal Medicine (Trieste), where they were stored at −20 °C until use. DNA extraction, quantification, and typing were performed following standard methods . Thus, a reference database was created containing the STR profiles of all relatives and the haplotypes of the eight paternally related male individuals. 2.3. Preparation of Microhaplotype Libraries and MPS Analysis This analysis was conducted on a set of families for which the STR-based approach gave inconclusive results (see ). PCR primers for MPS libraries were designed on the Ion AmpliSeq Designer tool (Thermo Fisher Scientific, https://ampliseq.com/ ; accessed on 31 May 2023), keeping the amplicon size below 140 bp to allow even the amplification of degraded samples. The designed MPS panel comprised 76 MH loci with an average effective number of alleles (Ae) value equal to 3.574 and random match probability (RMP) value equal to 1.77 × 10 –66 in the Italian population (manuscript in preparation). Libraries were manually prepared in half-reaction volume using the Precision ID Library kit (Thermo Fisher Scientific) according to the manufacturer’s protocol (MAN0017767, rev C.0). Amplifications were performed with DNA input ranging from 1 ng to 90 pg. After partial primer digestion and ligation steps, each library was purified with Agencourt TM AMPure TM XP Reagent (Beckman Coulter, Brea, CA, USA) and finally quantified using the Ion Library TaqMan ® Quantification kit (Thermo Fisher Scientific) following the manufacturer’s protocols. The seventeen quantified libraries were diluted to a final concentration of 30 pM. Emulsion PCR and loading onto the chips were performed with an Ion Chef TM Instrument (Thermo Fisher Scientific) and Ion S5 TM Precision ID Chef & Sequencing Kit (Thermo Fisher Scientific). Sequencing was performed on an Ion GeneStudio TM S5 System (Thermo Fisher Scientific) and loaded onto an Ion 520 TM or Ion 530 TM Chip (Thermo Fisher Scientific). Haplotypes were called using Torrent Suite Version 5.12.3 software on an S5 Torrent Server VM (Thermo Fisher Scientific), together with HID_Microhaplotype_Research_PluginV1.5 (Thermo Fisher Scientific). The plugin was run with the following default settings: minimum of total read coverage per position = 20; minimum number of allele count to include in report = 5; minimum allele frequency (for heterozygous) = 10; and minimum of allele frequency (for homozygous) = 90. The software Integrative Genomics Viewer (IGV, v.2.8.0) was used to visualize and confirm the haplotypes. The two samples amplified with a low amount of DNA (<0.1 ng) were replicated to consolidate the genotyping results , and consensus data were used for comparisons. 2.4. Statistical Analyses 2.4.1. Autosomal STRs and Microhaplotypes The genetic profiles of the victims were compared with those obtained from the putative relatives using the DVI module of the Familias software (Version 3.3.1), www.familias.no ; accessed on 13 January 2025 . A likelihood ratio (LR) value was computed as the likelihood of a specified relationship compared with the hypothesis of unrelatedness. The LR value was combined with non-genetic information, the prior probability, to compute the posterior probability by applying the Bayes’ theorem. In this study, the AM-driven approach was used, considering the number of victims equal to 27 for prior probability calculations and setting the posterior probability for a positive identification to 99.9% [ , , , ]. LRs and posterior probabilities were first calculated separately for autosomal STR and MH markers, and subsequently, the information was combined. For LR and posterior probability calculations, the allele frequencies of the European ( strider.online ; accessed on 5 January 2025) and the Italian (unpublished) populations were used for the autosomal STR and microhaplotype markers, respectively. 2.4.2. Y-STRs Matching haplotypes from the AM–PM comparison were analyzed using the kinship analysis tool in the YHRD database, www.yhrd.org ; accessed on 14 January 2025 , considering three different reference metapopulations (i.e., Eurasian, European, and Western European). The likelihood ratios of the patrilineal relationship compared to non-relationship were computed using the observed counting method and the one-step mutations per transmission event as the calculation method.
The post-mortem STR database, containing the genotypes yielded from the skeletal remains, was built according to the following steps. 2.1.1. Bone/Tooth Samples In 2019, human remains were uncovered from a mass grave in Ossero (Island of Cres, Croatia). Full details on the mass grave are reported elsewhere . Briefly, historical records suggested that the unearthed remains could belong to 27 Italian soldiers executed on 21 April 1945. The remains were placed in 27 caskets, and delivered to the Italian Government on 13 November 2019 . Later, in 2022, the caskets were transferred to the University of Bari (Italy) for anthropological and medico-legal examinations. These analyses showed a large extent of peri- and post-mortem fractures, with a significant commingling of remains across the 27 caskets. The most likely number of individuals (MLNI) was calculated with standard methods using long bones (femurs, tibiae, and humeri) and skulls , revealing an MLNI of 32. In addition, the examination suggested the presence of at least three women (manuscript in preparation). A total of 341 bone and tooth specimens were sampled and delivered to the University of Trieste (Italy), where they were stored at room temperature in the dark until genetic analysis. According to the literature data [ , , , , , , , ], bone/tooth elements with—a priori—higher chances of being successfully typed were selected. In addition, at least two different bone elements for each of the 27 boxes were considered. In detail, the following samples were employed: the inner ear portion of the petrous bones , the cortical of the femur diaphysis [ , , ], the compact part of the epiphysis of metacarpals and metatarsals , and molar teeth (upper right M2) with enamel removed . In total, DNA extraction and subsequent molecular analyses were performed on 147 specimens, as shown in . 2.1.2. Bone/Tooth Cleaning and Pulverization Bones/teeth were cleaned mechanically using brushes and rotary sanding to remove surface soil. Approximately 1.5 g of bone fragments and whole molar teeth (enamel removed) were treated with 0.5% bleach for 4 min to eliminate external DNA contamination, followed by three washes with sterile bi-distilled water. Surfaces were exposed to UV radiation for 5 min on each side and dried at room temperature overnight . Pulverization was performed using an MM 400 Planetary Ball Mills instrument (Retsch, Haan, Germany) with 25 mL metal grinding vials and 16 mm diameter metal balls (Verder, Vleuten, The Netherlands) at 30 Hz for 1–2 min, using liquid nitrogen to prevent heating . The powder samples were stored at room temperature in the dark until use. All procedures were carried out in a room dedicated only to aged bone/tooth handling . 2.1.3. Bone/Tooth Decalcification and DNA Extraction Approximately 0.5 g of bone/tooth powder was processed according to the extraction method previously described in detail by Di Stefano et al. , which involved overnight decalcification in 0.5 M Na 2 EDTA (pH 8.0), lysis in 460 μL of extraction buffer (1.2% SDS, 10 mM Tris pH 8.0, 10 mM Na 2 EDTA pH 8.0, and 100 mM NaCl), 40 μL of 1M DTT, 80 μL of Proteinase K (20 mg/mL), and automated purification using the Maxwell ® FSC DNA IQ TM Casework Kit (Promega) with the Maxwell ® RCS TM441 apparatus. The extraction process was conducted on 179 bone/tooth powders (from the 147 bone samples from ; also see ), with a negative extraction control (NEC) included for every 6 samples processed. 2.1.4. DNA Quantification DNA samples were quantified in duplicate by qPCR using two different kits. In the first phase of the analyses, the Quantifiler TM Human DNA Quantification Kit (Thermo Fisher Scientific, TFS, Waltham, MA, USA) was used for 107 samples; the CFX96 Real-Time System Instrument (Bio-Rad, Hercules, CA, USA) apparatus was employed following the conditions specified in ref. . This kit targets a 62 bp sequence of the human telomerase reverse transcriptase (hTERT) gene, detected using a FAM-labeled probe; the presence of Taq polymerase inhibitors is monitored using an Internal Positive Control (IPC), labeled by VIC. The Limit of Detection (LOD) was set previously at 0.001 ng/μL , while the Limit of Quantification (LOQ) ranged from 0.023 ng/μL to 50 ng/μL. Raw data were analyzed using the CFX Maestro software v5.3.022.1030. ( https://www.bio-rad.com ; accessed on 13 December 2024). The remaining 72 samples were quantified using the PowerQuant TM System kit (Promega, Madison, WI, USA) with the 7500 Real-Time PCR System for Human Identification, HID Real-time PCR analysis (Applied Biosystem, AB, Foster City, CA, USA). The kit amplifies an 84 bp short autosomal target and a 294 bp long autosomal target, with the ratio between these two [Auto/Deg] used to assess DNA degradation. It also amplifies a 134 bp target on the Y-chromosome to detect male DNA; an IPC (Internal Positive Control) is added to the reaction to monitor for potential inhibitors. The LOQ for the Auto and Y targets ranged from 0.0032 ng/μL to 50 ng/μL. For the Deg target, the LOQ ranged from 0.0005 ng/μL to 50 ng/μL. The LOD was set at 0.0001 ng/μL for all targets . Raw data analysis was performed using PowerQuant ® Analysis Software v2.06 (available on www.promega.com ; accessed on 8 January 2025). 2.1.5. STR-CE Typing As a selection criterion, all the right femurs and the right petrous bone available (for those samples, both autosomal and Y-specific STR panels were used) were analyzed first. With the implementation of the post-mortem autosomal STR database (see ), the amplification of the Y-specific STR markers was carried out only when a new autosomal profile belonging to a male subject was identified. The amplification was performed only for samples that showed detectable amounts of DNA in at least one qPCR replicate. So, out of the 179 extracted samples, 126 underwent STR-PCR amplifications (see ). For autosomal STR amplification, the kits PowerPlex Fusion kit (Promega), PowerPlex ESX17 (Promega), and PowerPlex ESI (Promega) were employed. These kits allow the simultaneous amplification of 22, 17, and 17 autosomal STR markers (plus XY-specific amelogenin targets) in different multiplex configurations, respectively. Y-STR amplification was performed using the PowerPlex Y23 kit (Promega), which amplifies 23 Y-chromosome-specific loci. PCR amplifications were performed under standard conditions for samples containing 0.5–1 ng of DNA template. For samples with a lower DNA quantity, the number of PCR cycles was increased to 32 cycles, and the maximum allowed volume of DNA sample was loaded (17.5 μL for the kits ESX, ESI, and Y23 and 15 μL for the kit Fusion). Positive and negative PCR controls, as well as negative extraction controls (using the maximum allowed volume), were simultaneously run. Amplifications were performed using the T100 Thermal Cycler (Bio-Rad) apparatus. Capillary electrophoresis was initially carried out using the ABI 310 automatic DNA sequencer and later using the SeqStudio Genetic Analyzer (both from Applied Biosystems, Waltham, MA, USA). Raw data were analyzed with GeneMapperID ® ver 3.2.1 software for ABI310 and GeneMapper TM ID X ® Software v1.6 (Applied Biosystem) for SeqStudio. The analytical threshold was set at 50 RFU for ABI 310 and 150 RFU for SeqStudio, whereas the stochastic threshold was 150 RFU for ABI 310 and 300 RFU for SeqStudio. The criterion for including genetic profiles in the post-mortem database was the reliable detection of at least 12 autosomal markers, in accordance with international standards . Samples meeting this criterion were amplified in duplicates to achieve consensus profiles; replicate tests were not performed for samples whose profile was already present in the STR autosomal database (that is, the post-mortem database; see ). The data of replicate amplifications were also used for the assessment of stochastic events, such as allelic drop-in and allelic drop-out phenomena [ , , ]. 2.1.6. Exclusion Database DNA from the personnel involved in the genetic analysis was typed, and the resulting genotypes were compared with those of the samples under investigation to exclude the possibility of contamination during the procedure . In total, five female and four male operators were typed with the STR kits described above. 2.1.7. Post-Mortem Database The post-mortem database was built to store genotypes yielded from bone/tooth samples. Whenever a suitable post-mortem profile was obtained, it was compared with those already stored in the database to determine if it matched an existing profile or represented a new one. Two different Excel files were created, one for the autosomal and one for the Y-specific STR profiles.
In 2019, human remains were uncovered from a mass grave in Ossero (Island of Cres, Croatia). Full details on the mass grave are reported elsewhere . Briefly, historical records suggested that the unearthed remains could belong to 27 Italian soldiers executed on 21 April 1945. The remains were placed in 27 caskets, and delivered to the Italian Government on 13 November 2019 . Later, in 2022, the caskets were transferred to the University of Bari (Italy) for anthropological and medico-legal examinations. These analyses showed a large extent of peri- and post-mortem fractures, with a significant commingling of remains across the 27 caskets. The most likely number of individuals (MLNI) was calculated with standard methods using long bones (femurs, tibiae, and humeri) and skulls , revealing an MLNI of 32. In addition, the examination suggested the presence of at least three women (manuscript in preparation). A total of 341 bone and tooth specimens were sampled and delivered to the University of Trieste (Italy), where they were stored at room temperature in the dark until genetic analysis. According to the literature data [ , , , , , , , ], bone/tooth elements with—a priori—higher chances of being successfully typed were selected. In addition, at least two different bone elements for each of the 27 boxes were considered. In detail, the following samples were employed: the inner ear portion of the petrous bones , the cortical of the femur diaphysis [ , , ], the compact part of the epiphysis of metacarpals and metatarsals , and molar teeth (upper right M2) with enamel removed . In total, DNA extraction and subsequent molecular analyses were performed on 147 specimens, as shown in .
Bones/teeth were cleaned mechanically using brushes and rotary sanding to remove surface soil. Approximately 1.5 g of bone fragments and whole molar teeth (enamel removed) were treated with 0.5% bleach for 4 min to eliminate external DNA contamination, followed by three washes with sterile bi-distilled water. Surfaces were exposed to UV radiation for 5 min on each side and dried at room temperature overnight . Pulverization was performed using an MM 400 Planetary Ball Mills instrument (Retsch, Haan, Germany) with 25 mL metal grinding vials and 16 mm diameter metal balls (Verder, Vleuten, The Netherlands) at 30 Hz for 1–2 min, using liquid nitrogen to prevent heating . The powder samples were stored at room temperature in the dark until use. All procedures were carried out in a room dedicated only to aged bone/tooth handling .
Approximately 0.5 g of bone/tooth powder was processed according to the extraction method previously described in detail by Di Stefano et al. , which involved overnight decalcification in 0.5 M Na 2 EDTA (pH 8.0), lysis in 460 μL of extraction buffer (1.2% SDS, 10 mM Tris pH 8.0, 10 mM Na 2 EDTA pH 8.0, and 100 mM NaCl), 40 μL of 1M DTT, 80 μL of Proteinase K (20 mg/mL), and automated purification using the Maxwell ® FSC DNA IQ TM Casework Kit (Promega) with the Maxwell ® RCS TM441 apparatus. The extraction process was conducted on 179 bone/tooth powders (from the 147 bone samples from ; also see ), with a negative extraction control (NEC) included for every 6 samples processed.
DNA samples were quantified in duplicate by qPCR using two different kits. In the first phase of the analyses, the Quantifiler TM Human DNA Quantification Kit (Thermo Fisher Scientific, TFS, Waltham, MA, USA) was used for 107 samples; the CFX96 Real-Time System Instrument (Bio-Rad, Hercules, CA, USA) apparatus was employed following the conditions specified in ref. . This kit targets a 62 bp sequence of the human telomerase reverse transcriptase (hTERT) gene, detected using a FAM-labeled probe; the presence of Taq polymerase inhibitors is monitored using an Internal Positive Control (IPC), labeled by VIC. The Limit of Detection (LOD) was set previously at 0.001 ng/μL , while the Limit of Quantification (LOQ) ranged from 0.023 ng/μL to 50 ng/μL. Raw data were analyzed using the CFX Maestro software v5.3.022.1030. ( https://www.bio-rad.com ; accessed on 13 December 2024). The remaining 72 samples were quantified using the PowerQuant TM System kit (Promega, Madison, WI, USA) with the 7500 Real-Time PCR System for Human Identification, HID Real-time PCR analysis (Applied Biosystem, AB, Foster City, CA, USA). The kit amplifies an 84 bp short autosomal target and a 294 bp long autosomal target, with the ratio between these two [Auto/Deg] used to assess DNA degradation. It also amplifies a 134 bp target on the Y-chromosome to detect male DNA; an IPC (Internal Positive Control) is added to the reaction to monitor for potential inhibitors. The LOQ for the Auto and Y targets ranged from 0.0032 ng/μL to 50 ng/μL. For the Deg target, the LOQ ranged from 0.0005 ng/μL to 50 ng/μL. The LOD was set at 0.0001 ng/μL for all targets . Raw data analysis was performed using PowerQuant ® Analysis Software v2.06 (available on www.promega.com ; accessed on 8 January 2025).
As a selection criterion, all the right femurs and the right petrous bone available (for those samples, both autosomal and Y-specific STR panels were used) were analyzed first. With the implementation of the post-mortem autosomal STR database (see ), the amplification of the Y-specific STR markers was carried out only when a new autosomal profile belonging to a male subject was identified. The amplification was performed only for samples that showed detectable amounts of DNA in at least one qPCR replicate. So, out of the 179 extracted samples, 126 underwent STR-PCR amplifications (see ). For autosomal STR amplification, the kits PowerPlex Fusion kit (Promega), PowerPlex ESX17 (Promega), and PowerPlex ESI (Promega) were employed. These kits allow the simultaneous amplification of 22, 17, and 17 autosomal STR markers (plus XY-specific amelogenin targets) in different multiplex configurations, respectively. Y-STR amplification was performed using the PowerPlex Y23 kit (Promega), which amplifies 23 Y-chromosome-specific loci. PCR amplifications were performed under standard conditions for samples containing 0.5–1 ng of DNA template. For samples with a lower DNA quantity, the number of PCR cycles was increased to 32 cycles, and the maximum allowed volume of DNA sample was loaded (17.5 μL for the kits ESX, ESI, and Y23 and 15 μL for the kit Fusion). Positive and negative PCR controls, as well as negative extraction controls (using the maximum allowed volume), were simultaneously run. Amplifications were performed using the T100 Thermal Cycler (Bio-Rad) apparatus. Capillary electrophoresis was initially carried out using the ABI 310 automatic DNA sequencer and later using the SeqStudio Genetic Analyzer (both from Applied Biosystems, Waltham, MA, USA). Raw data were analyzed with GeneMapperID ® ver 3.2.1 software for ABI310 and GeneMapper TM ID X ® Software v1.6 (Applied Biosystem) for SeqStudio. The analytical threshold was set at 50 RFU for ABI 310 and 150 RFU for SeqStudio, whereas the stochastic threshold was 150 RFU for ABI 310 and 300 RFU for SeqStudio. The criterion for including genetic profiles in the post-mortem database was the reliable detection of at least 12 autosomal markers, in accordance with international standards . Samples meeting this criterion were amplified in duplicates to achieve consensus profiles; replicate tests were not performed for samples whose profile was already present in the STR autosomal database (that is, the post-mortem database; see ). The data of replicate amplifications were also used for the assessment of stochastic events, such as allelic drop-in and allelic drop-out phenomena [ , , ].
DNA from the personnel involved in the genetic analysis was typed, and the resulting genotypes were compared with those of the samples under investigation to exclude the possibility of contamination during the procedure . In total, five female and four male operators were typed with the STR kits described above.
The post-mortem database was built to store genotypes yielded from bone/tooth samples. Whenever a suitable post-mortem profile was obtained, it was compared with those already stored in the database to determine if it matched an existing profile or represented a new one. Two different Excel files were created, one for the autosomal and one for the Y-specific STR profiles.
As the official documents dated 12 March 1942 listed the names of 27 soldiers who could have been buried in the mass grave of Ossero, those data enabled the search for their relatives. Since the descendants/relatives of only 14 missing soldiers requested genetic comparison, buccal swabs of 21 living subjects were collected in total (see ). As shown in and , the ante-mortem samples included 19 relatives (from 1st to 4th degree) as well as 2 descendants’ mothers (Fam7 and Fam8). Eight male reference samples were connected through the paternal line. The saliva swabs were shipped by ordinary mail to the Institute of Legal Medicine (Trieste), where they were stored at −20 °C until use. DNA extraction, quantification, and typing were performed following standard methods . Thus, a reference database was created containing the STR profiles of all relatives and the haplotypes of the eight paternally related male individuals.
This analysis was conducted on a set of families for which the STR-based approach gave inconclusive results (see ). PCR primers for MPS libraries were designed on the Ion AmpliSeq Designer tool (Thermo Fisher Scientific, https://ampliseq.com/ ; accessed on 31 May 2023), keeping the amplicon size below 140 bp to allow even the amplification of degraded samples. The designed MPS panel comprised 76 MH loci with an average effective number of alleles (Ae) value equal to 3.574 and random match probability (RMP) value equal to 1.77 × 10 –66 in the Italian population (manuscript in preparation). Libraries were manually prepared in half-reaction volume using the Precision ID Library kit (Thermo Fisher Scientific) according to the manufacturer’s protocol (MAN0017767, rev C.0). Amplifications were performed with DNA input ranging from 1 ng to 90 pg. After partial primer digestion and ligation steps, each library was purified with Agencourt TM AMPure TM XP Reagent (Beckman Coulter, Brea, CA, USA) and finally quantified using the Ion Library TaqMan ® Quantification kit (Thermo Fisher Scientific) following the manufacturer’s protocols. The seventeen quantified libraries were diluted to a final concentration of 30 pM. Emulsion PCR and loading onto the chips were performed with an Ion Chef TM Instrument (Thermo Fisher Scientific) and Ion S5 TM Precision ID Chef & Sequencing Kit (Thermo Fisher Scientific). Sequencing was performed on an Ion GeneStudio TM S5 System (Thermo Fisher Scientific) and loaded onto an Ion 520 TM or Ion 530 TM Chip (Thermo Fisher Scientific). Haplotypes were called using Torrent Suite Version 5.12.3 software on an S5 Torrent Server VM (Thermo Fisher Scientific), together with HID_Microhaplotype_Research_PluginV1.5 (Thermo Fisher Scientific). The plugin was run with the following default settings: minimum of total read coverage per position = 20; minimum number of allele count to include in report = 5; minimum allele frequency (for heterozygous) = 10; and minimum of allele frequency (for homozygous) = 90. The software Integrative Genomics Viewer (IGV, v.2.8.0) was used to visualize and confirm the haplotypes. The two samples amplified with a low amount of DNA (<0.1 ng) were replicated to consolidate the genotyping results , and consensus data were used for comparisons.
2.4.1. Autosomal STRs and Microhaplotypes The genetic profiles of the victims were compared with those obtained from the putative relatives using the DVI module of the Familias software (Version 3.3.1), www.familias.no ; accessed on 13 January 2025 . A likelihood ratio (LR) value was computed as the likelihood of a specified relationship compared with the hypothesis of unrelatedness. The LR value was combined with non-genetic information, the prior probability, to compute the posterior probability by applying the Bayes’ theorem. In this study, the AM-driven approach was used, considering the number of victims equal to 27 for prior probability calculations and setting the posterior probability for a positive identification to 99.9% [ , , , ]. LRs and posterior probabilities were first calculated separately for autosomal STR and MH markers, and subsequently, the information was combined. For LR and posterior probability calculations, the allele frequencies of the European ( strider.online ; accessed on 5 January 2025) and the Italian (unpublished) populations were used for the autosomal STR and microhaplotype markers, respectively. 2.4.2. Y-STRs Matching haplotypes from the AM–PM comparison were analyzed using the kinship analysis tool in the YHRD database, www.yhrd.org ; accessed on 14 January 2025 , considering three different reference metapopulations (i.e., Eurasian, European, and Western European). The likelihood ratios of the patrilineal relationship compared to non-relationship were computed using the observed counting method and the one-step mutations per transmission event as the calculation method.
The genetic profiles of the victims were compared with those obtained from the putative relatives using the DVI module of the Familias software (Version 3.3.1), www.familias.no ; accessed on 13 January 2025 . A likelihood ratio (LR) value was computed as the likelihood of a specified relationship compared with the hypothesis of unrelatedness. The LR value was combined with non-genetic information, the prior probability, to compute the posterior probability by applying the Bayes’ theorem. In this study, the AM-driven approach was used, considering the number of victims equal to 27 for prior probability calculations and setting the posterior probability for a positive identification to 99.9% [ , , , ]. LRs and posterior probabilities were first calculated separately for autosomal STR and MH markers, and subsequently, the information was combined. For LR and posterior probability calculations, the allele frequencies of the European ( strider.online ; accessed on 5 January 2025) and the Italian (unpublished) populations were used for the autosomal STR and microhaplotype markers, respectively.
Matching haplotypes from the AM–PM comparison were analyzed using the kinship analysis tool in the YHRD database, www.yhrd.org ; accessed on 14 January 2025 , considering three different reference metapopulations (i.e., Eurasian, European, and Western European). The likelihood ratios of the patrilineal relationship compared to non-relationship were computed using the observed counting method and the one-step mutations per transmission event as the calculation method.
3.1. Quantification Results a,b show the qPCR-based quantification results of the 179 DNA samples extracted from the 147 skeletal remains considered (replicate extractions were performed on 21.8% of the bone samples; also see ). Although detectable levels (>LOD) of genetic material were found in all skeletal elements (from 26.7% of the right femur to 100% of the petrous bone), only the petrous bone provided DNA yields in the dynamic range of quantification (Limit of Quantification) in all qPCR tests; the remaining skeletal elements provided values in the LOQ with lower frequencies. The degradation index (calculated for the 40.2% of the samples, i.e., those analyzed with the PowerQuant kit) ranged from 3.9 (metacarpal) to 28.6 (petrous bone), an outcome which appears to be in agreement with the data reported in the literature on this issue [ , , , , , ]. IPC Cq values did not highlight the presence of inhibition of the qPCR assays. Out of the 179 extracted samples, 126 (70.4%) provided values above the Limit of Detection in at least one of the two qPCR tests and were, therefore, suitable for STR amplification (according to the criteria we had fixed). 3.2. STR Typing Results As shown in , 126 samples suitable for genetic typing were used for autosomal STR typing in 160 PCR tests. Out of those 160 PCR tests, 122 yielded suitable profiles, that is, full profiles or partial profiles with at least 12 markers (also see ). The results of the autosomal typing are shown in . Amplification of autosomal STR markers yielded full genetic profiles in 66.7% of tests from petrous bone, 57.1% from metatarsals, 50.0% from metacarpals, and 2.6% from femurs. Out of the nine tests conducted on tooth samples, only one provided useful genetic data, whereas unsuitable results were yielded from the remaining eight tests. The partial profiles yielded from petrous bone showed a median of 19 markers. As shown in , the other skeletal elements yielded partial profiles with a similar number of markers (18 markers in metacarpals, 15 markers in metatarsals, and 18 markers in femurs). Only for samples yielding a suitable profile for personal identification (≥12 markers) were duplicate PCR tests performed. Hence, the cross-checking of the replicates even allowed for the evaluation of stochastic phenomena, such as allelic drop-out and allelic drop-in [ , , ] (see ). The petrous bone provided replicable genotypes in 96.0% of cases, followed by metatarsals (93.4%), metacarpals (88.1%), and femurs (74.1%). There were no typing data in the blank controls (negative extraction control and PCR-negative control). In addition, the PowerPlex Y-23 kit was used for 49 PCR tests. As shown in , 13 out of 19 tests on the petrous bone yielded a full profile while the remaining 6 gave a partial one (with a median value of 20 markers); femurs showed only partial profiles (with a median value of 17 markers) and a high percentage (72.0%) of unsuitable profiles. The analysis of the short bones yielded successful typing in five tests out of five. There were no typing data in the blank controls (NEC and PCR-negative control). 3.3. STR Post-Mortem Database In total, 92 different bone/tooth samples yielded genetic data potentially suitable for building the post-mortem database (see ). In addition, since none of the 92 profiles matched the profiles of the exclusion database, all 92 were loaded into the database. The inter-sample comparison allowed the identification of 30 unique autosomal STR profiles (see ). Out of these, 18 were full profiles, whereas 12 were partial profiles (with a median of 15 markers). Since 6 profiles were proven to belong to 6 different female individuals, 24 different Y-STR consensus haplotypes were consequently recorded (16 full and 8 partials with a median of 19 markers). The main features of the six female genotypes found are described in . details the bone/tooth elements, which allowed the definition of the 30 profiles. As shown, eight different bone/tooth samples provided the same STR profile, thus allowing the definition of genotype #2, whereas six genotypes (namely genotypes #22, #25, #26, #28, #29, and #30) were characterized by only one bone sample. On average, three different skeletal elements contributed to the identification of each of the 30 genotypes. The petrous bone contributed to the identification of the genotype in 24 out of 30 cases. In addition, genetic typing showed that at least 20 caskets contained mixed remains. This outcome is in agreement with the data of an anthropometric analysis which described the remains as highly commingled across the 27 original caskets (manuscript in preparation). shows the trend of the implementation of the post-mortem database, which was constructed from February 2023 to May 2024. Even here, the results clearly show that petrous bone analysis played a determinant role. 3.3.1. Female DNA Profiles As stated above, six different female genotypes were also scored (see ). In total, female genotypes were identified in eleven different bone elements, which allowed us to define six unique female genotypes (also see ). The criteria for gender assignment were the lack of amplification of two multicopy male-specific targets (amplicons of 81 bp and 136 bp) in duplicate qPCR analyses with the PowerQuant kit, and the lack of amplification of the male-specific amelogenin target (93 bp) in replicate PCR analyses with the PowerPlex ESI/ESX kits. Out of the six genotypes, two were full (17/17 markers) whereas the remaining four showed partial profiles (with a median value of 15 markers/genotype). 3.4. Ante-Mortem Reference Database As shown in , relatives of 14 missing soldiers gave their availability for genetic typing. All 21 samples (buccal swabs) used as reference samples yielded full profiles, so they were used to build the ante-mortem reference database. 3.5. Kinship Analyses 3.5.1. Autosomal STRs Family pedigrees were built for the 21 reference persons and were paired with the 24 male victims using the software Familias (ver. 3.3.1). The LR value was calculated considering any of the victims to the missing person compared to an unknown person and posterior probability was computed considering a number of victims equal to 27, using the AM-driven approach provided in the software. Posterior probabilities, equal to or greater than 99.9%, were obtained in three of the four first-degree cases (Fam01, Fam06, and Fam10) and in Fam07 (99.93%, uncle–niece relationship), while a value very close to 99.9% was achieved in Fam05 (99.59%, full-sibling relationship). The next highest values were observed in Fam03 (96.98%, uncle–nephew relationship) and Fam12 (82.76%, grand-father–grand-nephew relationship), while the remaining seven family groups showed values lower than 35%. The results are summarized in . 3.5.2. Y-STRs To detect further possible matches, a patrilinear-driven search was set up by including 23 Y-chromosome STR markers. This analysis was carried out only for the reference persons/victims who were not linked to any victim/family pedigree with posterior probabilities greater than 99.9%, and for whom a patrilineal relationship between the reference person and the missing was stated. Seven reference persons were thus paired with the twenty victims, and the number of shared alleles was scored. Four haplotype pairs were detected with no exclusion (Fam02, Fam04, Fam08, and Fam09), while one pair (Fam03) showed a single inconsistency at the DYS437 locus, for which a single-step germline mutation was supposed (allele 14 in the victim (uncle) and allele 15 in the reference sample (nephew)). No match was obtained for Fam13 and Fam14. The Y-chromosome haplotypes were searched in the YHRD database (last access: 16 December 2024), and each one was unique in the entire database when the complete set of markers was considered. Likelihood ratios were computed for each haplotype pair using the kinship analysis tool provided in the database, based on 23 Y-STRs and the Eurasian, European, and Western European reference metapopulations. Since the order of magnitude was the same for the three reference populations, the match was summarized by reporting the most conservative LR value corresponding to the Western European metapopulation (see ). LRs equal to 1 × 10 4 were obtained for all the matching pairs, with the exception of Fam03, for which an LR equal to 1 × 10 1 was computed because of the inconsistency at the DYS437 locus. 3.6. Microhaplotype Data and Genotyping This analysis was conducted on five victim–reference person pairs showing posterior probabilities lower than 99.9% and sharing the same Y-STR haplotype. Fam05 and Fam12 were included for the high posterior value obtained with autosomal STRs (99.59% and 82.76%, respectively). Overall, a selection of 7 familiar groups, for a total of 17 samples, were included in the extended genetic typing of the 76 microhaplotypes (see ). The MPS results showed a good performance of the MH panel in the samples tested. The mean depth of coverage values ranged between 47.2 and 1,553 reads (median: 444.5; mean: 589.3). We observed uniformity of coverage greater than 90% for all samples (median: 92.9%; mean: 92.4%). Lower values of mean depth and reads on target were found for samples amplified with limited amounts of DNA (<0.1 ng). Full MHs profiles were obtained for all buccal swabs (reference samples). Two bone remains (from Fam03 and Fam09) showed complete profiles, while three bone remains (from Fam05, Fam02, and Fam04) showed almost complete profiles with 2/76 missing loci. Finally, two skeletal remains (from Fam08 and Fam12) showed partial profiles with 12/76 MHs missing, likely due to the small amount (<0.1 ng) of degraded DNA used for amplification. Family pedigrees were paired with the victims using the DVI module of the software Familias with settings adopted from the previous comparisons with the autosomal STR markers. Compared to the autosomal STR markers, the microhaplotype analyses were able to reach the 99.9% threshold for positive identification in two additional cases (Fam03 and Fam05) and increased the posterior probability value from 32.33% to 97.35% in Fam04. The MH and STR information can be combined if the markers are generically independent. In this case, MH and STR markers were more than 1 Mb apart , except two on chromosome 4, which are distanced by about 1 × 10 4 . We felt that the distance between the markers was sufficient to combine the information from the two sets of markers. Integrating the autosomal STRs and microhaplotypes in a combined likelihood ratio, a PP value (99.88%) close to the selected cut-off value was reached even for Fam04. In the remaining families, values lower than 20% were observed, thus suggesting inconclusive results. Among them, Fam12 showed a decrease in the posterior value, from 82.76% to 0.003% (0.16% combining STRs and microhaplotypes), suggesting a possible spurious match with the autosomal STRs only. In conclusion, the kinship analyses provided posterior probabilities greater than 99.9% for six missing persons of six familial groups by considering 23 autosomal STRs (Fam01, Fam06, Fam07, and Fam10) and/or 76 microhaplotypes (Fam03 and Fam05). Among the remaining cases, Fam04 showed an increase in the likelihood ratio when the microhaplotypes were considered in the analysis and then combined with autosomal STR markers, thus reaching a posterior probability value close to the threshold (99.88%). In addition, the Y-chromosome markers supported the putative relationship. In three cases (Fam02, Fam08, and Fam09), no suggestion of a relationship was indicated by the autosomal STRs and microhaplotypes. Only haplotype compatibility between the post-mortem samples and the putative relatives with LR values equal to 1 × 10 4 was detected, suggesting very strong support for a patrilineal relationship between the victim and the reference person. Moreover, the haplotypes were unique, thus strengthening the support for a putative paternal relationship. Finally, Fam12, which showed a PP > 80% by STR analysis, revealed a decrease in the LR and PP to very low values (0.003% and 0.16% by MH and STR + MH analyses). 3.7. Female DNA Profiles Comparisons In order to investigate possible genetic relationships among the six female DNA profiles and between these profiles and the ones recovered from the male skeletal remains, a blind search approach was applied using the Familias DVI module. In particular, relationships up to the second degree (i.e., parent–child, full siblings, half-siblings) were considered. The results highlighted a putative full sibling relationship between two women (total number of shared alleles = 13/22, LR FS/NR = 3.24 × 10 2 FS and NR stand for full siblings and non-relatives, respectively), while inconclusive results were obtained by pairing the female and male datasets.
a,b show the qPCR-based quantification results of the 179 DNA samples extracted from the 147 skeletal remains considered (replicate extractions were performed on 21.8% of the bone samples; also see ). Although detectable levels (>LOD) of genetic material were found in all skeletal elements (from 26.7% of the right femur to 100% of the petrous bone), only the petrous bone provided DNA yields in the dynamic range of quantification (Limit of Quantification) in all qPCR tests; the remaining skeletal elements provided values in the LOQ with lower frequencies. The degradation index (calculated for the 40.2% of the samples, i.e., those analyzed with the PowerQuant kit) ranged from 3.9 (metacarpal) to 28.6 (petrous bone), an outcome which appears to be in agreement with the data reported in the literature on this issue [ , , , , , ]. IPC Cq values did not highlight the presence of inhibition of the qPCR assays. Out of the 179 extracted samples, 126 (70.4%) provided values above the Limit of Detection in at least one of the two qPCR tests and were, therefore, suitable for STR amplification (according to the criteria we had fixed).
As shown in , 126 samples suitable for genetic typing were used for autosomal STR typing in 160 PCR tests. Out of those 160 PCR tests, 122 yielded suitable profiles, that is, full profiles or partial profiles with at least 12 markers (also see ). The results of the autosomal typing are shown in . Amplification of autosomal STR markers yielded full genetic profiles in 66.7% of tests from petrous bone, 57.1% from metatarsals, 50.0% from metacarpals, and 2.6% from femurs. Out of the nine tests conducted on tooth samples, only one provided useful genetic data, whereas unsuitable results were yielded from the remaining eight tests. The partial profiles yielded from petrous bone showed a median of 19 markers. As shown in , the other skeletal elements yielded partial profiles with a similar number of markers (18 markers in metacarpals, 15 markers in metatarsals, and 18 markers in femurs). Only for samples yielding a suitable profile for personal identification (≥12 markers) were duplicate PCR tests performed. Hence, the cross-checking of the replicates even allowed for the evaluation of stochastic phenomena, such as allelic drop-out and allelic drop-in [ , , ] (see ). The petrous bone provided replicable genotypes in 96.0% of cases, followed by metatarsals (93.4%), metacarpals (88.1%), and femurs (74.1%). There were no typing data in the blank controls (negative extraction control and PCR-negative control). In addition, the PowerPlex Y-23 kit was used for 49 PCR tests. As shown in , 13 out of 19 tests on the petrous bone yielded a full profile while the remaining 6 gave a partial one (with a median value of 20 markers); femurs showed only partial profiles (with a median value of 17 markers) and a high percentage (72.0%) of unsuitable profiles. The analysis of the short bones yielded successful typing in five tests out of five. There were no typing data in the blank controls (NEC and PCR-negative control).
In total, 92 different bone/tooth samples yielded genetic data potentially suitable for building the post-mortem database (see ). In addition, since none of the 92 profiles matched the profiles of the exclusion database, all 92 were loaded into the database. The inter-sample comparison allowed the identification of 30 unique autosomal STR profiles (see ). Out of these, 18 were full profiles, whereas 12 were partial profiles (with a median of 15 markers). Since 6 profiles were proven to belong to 6 different female individuals, 24 different Y-STR consensus haplotypes were consequently recorded (16 full and 8 partials with a median of 19 markers). The main features of the six female genotypes found are described in . details the bone/tooth elements, which allowed the definition of the 30 profiles. As shown, eight different bone/tooth samples provided the same STR profile, thus allowing the definition of genotype #2, whereas six genotypes (namely genotypes #22, #25, #26, #28, #29, and #30) were characterized by only one bone sample. On average, three different skeletal elements contributed to the identification of each of the 30 genotypes. The petrous bone contributed to the identification of the genotype in 24 out of 30 cases. In addition, genetic typing showed that at least 20 caskets contained mixed remains. This outcome is in agreement with the data of an anthropometric analysis which described the remains as highly commingled across the 27 original caskets (manuscript in preparation). shows the trend of the implementation of the post-mortem database, which was constructed from February 2023 to May 2024. Even here, the results clearly show that petrous bone analysis played a determinant role. 3.3.1. Female DNA Profiles As stated above, six different female genotypes were also scored (see ). In total, female genotypes were identified in eleven different bone elements, which allowed us to define six unique female genotypes (also see ). The criteria for gender assignment were the lack of amplification of two multicopy male-specific targets (amplicons of 81 bp and 136 bp) in duplicate qPCR analyses with the PowerQuant kit, and the lack of amplification of the male-specific amelogenin target (93 bp) in replicate PCR analyses with the PowerPlex ESI/ESX kits. Out of the six genotypes, two were full (17/17 markers) whereas the remaining four showed partial profiles (with a median value of 15 markers/genotype).
As stated above, six different female genotypes were also scored (see ). In total, female genotypes were identified in eleven different bone elements, which allowed us to define six unique female genotypes (also see ). The criteria for gender assignment were the lack of amplification of two multicopy male-specific targets (amplicons of 81 bp and 136 bp) in duplicate qPCR analyses with the PowerQuant kit, and the lack of amplification of the male-specific amelogenin target (93 bp) in replicate PCR analyses with the PowerPlex ESI/ESX kits. Out of the six genotypes, two were full (17/17 markers) whereas the remaining four showed partial profiles (with a median value of 15 markers/genotype).
As shown in , relatives of 14 missing soldiers gave their availability for genetic typing. All 21 samples (buccal swabs) used as reference samples yielded full profiles, so they were used to build the ante-mortem reference database.
3.5.1. Autosomal STRs Family pedigrees were built for the 21 reference persons and were paired with the 24 male victims using the software Familias (ver. 3.3.1). The LR value was calculated considering any of the victims to the missing person compared to an unknown person and posterior probability was computed considering a number of victims equal to 27, using the AM-driven approach provided in the software. Posterior probabilities, equal to or greater than 99.9%, were obtained in three of the four first-degree cases (Fam01, Fam06, and Fam10) and in Fam07 (99.93%, uncle–niece relationship), while a value very close to 99.9% was achieved in Fam05 (99.59%, full-sibling relationship). The next highest values were observed in Fam03 (96.98%, uncle–nephew relationship) and Fam12 (82.76%, grand-father–grand-nephew relationship), while the remaining seven family groups showed values lower than 35%. The results are summarized in . 3.5.2. Y-STRs To detect further possible matches, a patrilinear-driven search was set up by including 23 Y-chromosome STR markers. This analysis was carried out only for the reference persons/victims who were not linked to any victim/family pedigree with posterior probabilities greater than 99.9%, and for whom a patrilineal relationship between the reference person and the missing was stated. Seven reference persons were thus paired with the twenty victims, and the number of shared alleles was scored. Four haplotype pairs were detected with no exclusion (Fam02, Fam04, Fam08, and Fam09), while one pair (Fam03) showed a single inconsistency at the DYS437 locus, for which a single-step germline mutation was supposed (allele 14 in the victim (uncle) and allele 15 in the reference sample (nephew)). No match was obtained for Fam13 and Fam14. The Y-chromosome haplotypes were searched in the YHRD database (last access: 16 December 2024), and each one was unique in the entire database when the complete set of markers was considered. Likelihood ratios were computed for each haplotype pair using the kinship analysis tool provided in the database, based on 23 Y-STRs and the Eurasian, European, and Western European reference metapopulations. Since the order of magnitude was the same for the three reference populations, the match was summarized by reporting the most conservative LR value corresponding to the Western European metapopulation (see ). LRs equal to 1 × 10 4 were obtained for all the matching pairs, with the exception of Fam03, for which an LR equal to 1 × 10 1 was computed because of the inconsistency at the DYS437 locus.
Family pedigrees were built for the 21 reference persons and were paired with the 24 male victims using the software Familias (ver. 3.3.1). The LR value was calculated considering any of the victims to the missing person compared to an unknown person and posterior probability was computed considering a number of victims equal to 27, using the AM-driven approach provided in the software. Posterior probabilities, equal to or greater than 99.9%, were obtained in three of the four first-degree cases (Fam01, Fam06, and Fam10) and in Fam07 (99.93%, uncle–niece relationship), while a value very close to 99.9% was achieved in Fam05 (99.59%, full-sibling relationship). The next highest values were observed in Fam03 (96.98%, uncle–nephew relationship) and Fam12 (82.76%, grand-father–grand-nephew relationship), while the remaining seven family groups showed values lower than 35%. The results are summarized in .
To detect further possible matches, a patrilinear-driven search was set up by including 23 Y-chromosome STR markers. This analysis was carried out only for the reference persons/victims who were not linked to any victim/family pedigree with posterior probabilities greater than 99.9%, and for whom a patrilineal relationship between the reference person and the missing was stated. Seven reference persons were thus paired with the twenty victims, and the number of shared alleles was scored. Four haplotype pairs were detected with no exclusion (Fam02, Fam04, Fam08, and Fam09), while one pair (Fam03) showed a single inconsistency at the DYS437 locus, for which a single-step germline mutation was supposed (allele 14 in the victim (uncle) and allele 15 in the reference sample (nephew)). No match was obtained for Fam13 and Fam14. The Y-chromosome haplotypes were searched in the YHRD database (last access: 16 December 2024), and each one was unique in the entire database when the complete set of markers was considered. Likelihood ratios were computed for each haplotype pair using the kinship analysis tool provided in the database, based on 23 Y-STRs and the Eurasian, European, and Western European reference metapopulations. Since the order of magnitude was the same for the three reference populations, the match was summarized by reporting the most conservative LR value corresponding to the Western European metapopulation (see ). LRs equal to 1 × 10 4 were obtained for all the matching pairs, with the exception of Fam03, for which an LR equal to 1 × 10 1 was computed because of the inconsistency at the DYS437 locus.
This analysis was conducted on five victim–reference person pairs showing posterior probabilities lower than 99.9% and sharing the same Y-STR haplotype. Fam05 and Fam12 were included for the high posterior value obtained with autosomal STRs (99.59% and 82.76%, respectively). Overall, a selection of 7 familiar groups, for a total of 17 samples, were included in the extended genetic typing of the 76 microhaplotypes (see ). The MPS results showed a good performance of the MH panel in the samples tested. The mean depth of coverage values ranged between 47.2 and 1,553 reads (median: 444.5; mean: 589.3). We observed uniformity of coverage greater than 90% for all samples (median: 92.9%; mean: 92.4%). Lower values of mean depth and reads on target were found for samples amplified with limited amounts of DNA (<0.1 ng). Full MHs profiles were obtained for all buccal swabs (reference samples). Two bone remains (from Fam03 and Fam09) showed complete profiles, while three bone remains (from Fam05, Fam02, and Fam04) showed almost complete profiles with 2/76 missing loci. Finally, two skeletal remains (from Fam08 and Fam12) showed partial profiles with 12/76 MHs missing, likely due to the small amount (<0.1 ng) of degraded DNA used for amplification. Family pedigrees were paired with the victims using the DVI module of the software Familias with settings adopted from the previous comparisons with the autosomal STR markers. Compared to the autosomal STR markers, the microhaplotype analyses were able to reach the 99.9% threshold for positive identification in two additional cases (Fam03 and Fam05) and increased the posterior probability value from 32.33% to 97.35% in Fam04. The MH and STR information can be combined if the markers are generically independent. In this case, MH and STR markers were more than 1 Mb apart , except two on chromosome 4, which are distanced by about 1 × 10 4 . We felt that the distance between the markers was sufficient to combine the information from the two sets of markers. Integrating the autosomal STRs and microhaplotypes in a combined likelihood ratio, a PP value (99.88%) close to the selected cut-off value was reached even for Fam04. In the remaining families, values lower than 20% were observed, thus suggesting inconclusive results. Among them, Fam12 showed a decrease in the posterior value, from 82.76% to 0.003% (0.16% combining STRs and microhaplotypes), suggesting a possible spurious match with the autosomal STRs only. In conclusion, the kinship analyses provided posterior probabilities greater than 99.9% for six missing persons of six familial groups by considering 23 autosomal STRs (Fam01, Fam06, Fam07, and Fam10) and/or 76 microhaplotypes (Fam03 and Fam05). Among the remaining cases, Fam04 showed an increase in the likelihood ratio when the microhaplotypes were considered in the analysis and then combined with autosomal STR markers, thus reaching a posterior probability value close to the threshold (99.88%). In addition, the Y-chromosome markers supported the putative relationship. In three cases (Fam02, Fam08, and Fam09), no suggestion of a relationship was indicated by the autosomal STRs and microhaplotypes. Only haplotype compatibility between the post-mortem samples and the putative relatives with LR values equal to 1 × 10 4 was detected, suggesting very strong support for a patrilineal relationship between the victim and the reference person. Moreover, the haplotypes were unique, thus strengthening the support for a putative paternal relationship. Finally, Fam12, which showed a PP > 80% by STR analysis, revealed a decrease in the LR and PP to very low values (0.003% and 0.16% by MH and STR + MH analyses).
In order to investigate possible genetic relationships among the six female DNA profiles and between these profiles and the ones recovered from the male skeletal remains, a blind search approach was applied using the Familias DVI module. In particular, relationships up to the second degree (i.e., parent–child, full siblings, half-siblings) were considered. The results highlighted a putative full sibling relationship between two women (total number of shared alleles = 13/22, LR FS/NR = 3.24 × 10 2 FS and NR stand for full siblings and non-relatives, respectively), while inconclusive results were obtained by pairing the female and male datasets.
Personal identification through DNA typing is the gold standard in DVI scenarios. The successful typing of skeletal remains relies on employing an effective extraction method [ , , , ] and selecting the best performing skeletal elements [ , , , ]. In this study, DNA extraction was conducted from 179 bone/tooth powders using a semi-automated extraction protocol, which proved successful in minimizing human error and cross-contamination of samples, as well as in removing PCR inhibitors . Also, the choice of the skeletal elements proved to be a decisive factor. Our results confirm that petrous bone outperforms other skeletal elements; in fact, DNA yields extracted from the inner part of this bone always gave values in the quantification range of the qPCR assays. In addition, although DNA degradation was a common feature of such aged samples, the overall quality of the STR profiles was rather high, with a limited incidence of PCR artefacts. As shown in , in fact, the percentages of allelic drop-out and allelic drop-in were no more than 3.1% and 0.9%, respectively. As shown in and , mainly the employment of petrous bones allowed us to implement the post-mortem database, highlighting the effectiveness of such bone elements in enhancing the overall success rate of molecular identification in forensic studies [ , , , , ]. In our case, the anthropometric analysis established the minimum likely number of individuals (MLNI) equal to 32; despite this, however, only a limited number of petrous bones (19 right and 19 left) were available for molecular analyses, yielding, in total, 24 unique genotypes. To increase the size of the post-mortem database, femurs, metacarpals, metatarsals, and teeth were used, with the short bones proving to be a promising, well-performing option [ , , ]. The low rate of successful results from femurs is under investigation, but it is likely that the peculiar environmental conditions of that mass grave could have significantly impacted DNA preservation [ , , ]. Overall, the conventional STR-CE approach allowed the identification of 30 consensus profiles (18 full and 12 partial). Among these 30 profiles, 6 were attributed to female individuals and were not used for the comparisons with AM data. A possible explanation for the discovery of these female individuals might be that they belonged to local women who had sentimental relationships with the missing Italian soldiers and/or were women fighting against Tito’s Communist Army, even if there are no records supporting these hypotheses. The genetic profiles of the post-mortem database were then compared with those obtained from the putative relatives using the DVI module of the Familias software (Version 3.3.1). As recommended , the ante-mortem-driven approach was used in our calculations, considering the number of missing soldiers to be equal to 27. The definition of the number of victims in the ante-mortem proposition is a tricky issue because several factors need to be taken into account. In our case, the MLNI was 32; however, since 6 of them were females and the families were looking for the Italian soldiers who were supposedly buried in that area ( n = 27), 27 seemed to be a conservative value. Lastly, the posterior probability for a positive identification was set to 99.9%, as suggested by several studies [ , , , ]. As shown in , the employment of these analytical parameters allowed the identification of four victims (three first-degree and a second-degree relationships) by considering 23 autosomal STRs. The distant kinship of the relatives was the main reason for the limited number of successful identifications as 2nd to 4th degree relationships were speculated from the genealogical data. Microhaplotypes have the potential to be a valuable supplementary tool in complex kinship analysis given their specific advantages over traditional STR or SNP markers. In fact, these markers show advantages in typing highly degraded DNA samples, given the small size of the amplicons, and have the potential to investigate clan-and-extended family relationships . In this study, a novel multiplex MPS panel containing 76 MHs was used as an additional tool to attempt to investigate up to 4th degree kinships. The amplicons in this panel were designed to be small (size lower than 140 bp), making them particularly advantageous for the analysis of these challenging skeletal remains. Overall, despite the small amplicon size, some samples showed partial profiles due to the small amount of DNA input used for library amplification. The results showed that the use of an MH panel allowed for improvement in the LR and posterior probability values in kinship analysis compared to the results obtained with STR loci alone. Moreover, even the single use of the MH panel allowed the identification of two additional victims (Fam03 and Fam05). We decided to combine the two sets of markers to increase the information gained from the analyses conducted. We are confident that the 76 MHs are adequately spaced among the STR markers to be statistically independent for forensic analyses and statistical calculations . Therefore, combining the informativity of STR and MH markers increased the likelihood ratio value of Fam04, resulting in a posterior probability value very close to the threshold (99.88%). The typing of the Y-chromosome markers supported the putative paternal relationship as well. While very low posterior probabilities for autosomal and MH markers were found for Fam02, Fam08, and Fam09 (combined PP < 15%), the analysis of Y-chromosome markers highlighted AM-PM haplotype sharing. Due to the different inheritance models and possible population substructure, we decided not to calculate a combined LR, considering both autosomal and Y-lineage markers. However, we considered the results obtained indicative of very strong support (LR = 10 4 ) for a patrilineal relationship between the victim and the reference person. No match was obtained for Fam13 and Fam14 by lineage marker analysis. Finally, Fam12, which showed a PP > 80% by STR analysis, showed a decrease in the LR and PP to very low values (0.003% and 0.16% by MH and STR + MH analyses), suggesting a possible spurious match with the STR markers. Therefore, for this family and for the last reference pedigree (Fam11), for which no match was obtained, further analyses will be performed in order to verify the presence/absence of the missing within the victim group.
This is the first report describing the identification of WWII Italian soldiers buried in a mass grave. Among the commingled remains, 24 male individuals were genotyped using conventional STR-CE and MPS molecular approaches. The comparisons with the reference samples belonging to 14 familiar groups representing the offspring of the missing Italian soldiers supported the identification, or the patrilineal relationship between the victim and the reference person, for 10 missing soldiers. All the remains were transferred to the War Memorial of the Overseas Fallen Soldiers in Bari, Italy, in an official ceremony on 13 December 2024. The remains of four soldiers were then relocated and buried in the corresponding family vaults, according to the wishes of their relatives. Furthermore, six female profiles were collected from the remains buried in the grave. Since no familial relationship up to the second degree was observed with the male group, they were assumed to be local women in a sentimental relationship with the missing Italian soldiers and/or were women fighting against Tito’s Communist Army. This investigation demonstrates the effectiveness of cooperation among geneticists, anthropologists, and historians in resolving challenging DVI cases at both forensic and humanitarian levels.
|
Effects of Oxytetracycline/Lead Pollution Alone and in the Combined Form on Antibiotic Resistance Genes, Mobile Genetic Elements, and Microbial Communities in the Soil | fa32fcad-d592-4c28-b0e5-35a607199b2a | 9737759 | Microbiology[mh] | In recent years, contamination of soil with antibiotic-resistant genes (ARGs) has steadily become a recently focused research topic. Many studies have presented that ARGs can be commonly detected in places such as air, soil, feces, sewage treatment plants, rivers, irrigation ditches, hospitals, etc. Nowadays, ARGs pollution has become a global issue and has significantly impacted the public health and the economy of countries [ , , , , , , ]. The use of veterinary antibiotics and heavy metals in livestock and poultry production causes the ARGs pollution of soil through various direct and indirect means. As veterinary antibiotics and heavy metals are not fully absorbed and utilized in the body of animals, residues of these drugs excrete in the urine and feces of livestock and poultry. The livestock and poultry manure is simply treated and used as organic fertilizer for agriculture farming. However, soil contamination with heavy metals and antibiotics also occurs along with farming, indirectly changing the distribution of ARGs and affecting the structure and function of microbial communities in the soil. Furthermore, changes in the concentrations of soil pollutants can indirectly affect the selection pressure of soil bacterial communities, which further makes bacterial communities resistant to various pollutants to varying degrees [ , , , , , , , , , , , , , , ]. A lot of research has been conducted on the impact of heavy metals and antibiotics pollution alone as well as in combination with ARGs abundance in the soil. Qi et al. found a significant correlation between heavy metals and ARGs when studying the effect of heavy metals such as Cd, As, Cu, Ni, Pb, and Zn pollution on ARGs in the soil . Wang et al. investigated the abundance of ARGs in heavy metal-contaminated soils and found that heavy metals could aggravate the abundance of ARGs in the soil by increasing the abundance of metal resistance genes (MRGs) or affecting bacterial communities. Furthermore, high levels of heavy metals enhance the co-selection of ARGs and MRGs . Hu et al. investigated the changes in the abundance of ARGs caused by Cu pollution, the results indicated that the abundance of ARGs was positively correlated with mobile genetic elements (MGEs), and there was a symbiotic relationship between ARGs and soil microflora . Guo et al. found that Cu and Zn were positively correlated with some tet and sul genes in the investigation of heavy metals, antibiotics, and ARGs in soil . Antibiotic residues in the soil also could promote the propagation of ARGs encoding defense mechanisms . Luo et al. found that the abundance of ARGs increased in sediments after three-month of use of oxytetracycline (OTC); the integrated mechanism of horizontal gene transfer was the main cause of the spread of tet ARGs changes in the microbial population and ARGs in a sedimentary mariculture environment . Meanwhile, Zhao et al. found that bacterial resistance decreased with the increase in antibiotic concentration. Additionally, the application of poultry manure containing antibiotic residues increased ARGs in the agricultural soil compared to the control group . Guo et al. found that OTC residue in soil and soil pH were the main factors affecting the changes of ARGs and intl1 genes, and when OTC and Cd were added to soil at the same time, the abundance of ARGs and intl1 genes in soil would be further increased . Therefore, it is concluded that the effect of heavy metals and antibiotic residues on the abundance of ARGs is mainly regulated due to changes in the bacterial community and MGEs. Previous studies mainly focused on the effects of heavy metals such as Cu, Zn, and different antibiotic combinations on the structure of the microbial communities and ARGs [ , , ]. However, there have been few studies on the effects of Pb and OTC contamination alone or in combination on ARGs and microbial communities in the soil. Therefore, the alone or combined effect of OTC and Pb on ARGs, MGEs, and microbial communities in the soil was conducted to investigate the effects of Pb and OTC alone and in combination on the abundance of ARGs in the soil and soil microbial communities, and to analyze the correlation between ARGs and potential host bacteria. This study contributed toward understanding the mechanism of action of Pb and OTC on ARGs and provided a theoretical basis for studying ARGs transfer in the environment.
2.1. Materials The testing soil sample was taken from a poultry farm in Jinan City, Shandong Province, China (36°22′53.170″ N, 117°1′56.695″ E). The topsoil (0~20 cm) and fresh chicken manure were collected, naturally dried, crushed, filtered through a 10-mesh sieve, and then thoroughly mixed, respectively. The physical and chemical properties of the topsoil and chicken manure are listed in . Lead nitrate [Pb (NO 3 ) 2 ] and OTC were added to the soil as pollutants. OTC (purity > 95%) was supplied by Solarbio (Beijing, China), and [Pb (NO 3 ) 2 ] and Sinopharm Chemical Reagent Co. supplied other chemical reagents. 2.2. Experimental Design The original topsoil sample and chicken manure were air-dried and mixed to make the test soil sample (14.3% wt/wt). The amount of test soil used in each experimental group was 180 g. The experiment was split into nine groups with identity CK, C30, C100, P80, P160, C30P80, C30P160, C100P80, and C100P160, as listed in . According to the experimental design, [Pb (NO 3 ) 2 ] and OTC were dissolved in water, respectively, and then evenly sprayed in the soil and fully mixed with the soil. The CK group was replaced by ultrapure water of the same volume to ensure the same water content. Each treatment was repeated three times, and all treatments were carried out for 90 days in a dark environment at 25 °C. Ultrapure water was added to the simulated soil every other day, and the maximum soil moisture of the field was maintained at 60% during the incubation period by weighing. 2.3. Sample Collection A destructive sampling method was used in this study and soil samples were taken from the incubator on days 1, 7, 20, 50, and 90. After thoroughly mixing, the sample was divided into three parts, some of which were stored at 4 °C, and soil pH, organic matter, and electrical conductivity (EC) were determined within one week, as previously reported. The other samples were stored at −20 °C for subsequent experimental analysis, and the remaining samples were stored at −80 °C for the 16 S rRNA gene high-throughput sequencing. Soil pH was estimated when the soil/water ratio was 1:2.5 ( w / w ). The organic matter content of the soil was determined by K 2 CrO 4 oxidation and FeSO 4 titration method, as previously demonstrated. Soil EC was measured by a conductivity meter at a 1:5 soil: water suspension ratio (DDS-307A, Rex, Shanghai, China) [ , , ]. 2.4. Real-Time Quantitative PCR (qPCR) Genomic DNA was extracted using the TIANamp Soil DNA Kit (TianGen, Beijing, China). The extracted DNA was stored at −20 °C for subsequent experimental analysis. Five ARGs ( tetB , sul1 , tetQ , sul2 , and sul3 ), two MGEs ( intl1 , intl2 ), and 16 S rRNA were identified by using gene primers listed in . 2.5. 16 S rRNA Gene High-Throughput Sequencing Illumina NovaSeq platform was used to sequence the qPCR product. The abundance of sample microbial species was analyzed by Alpha abundance analysis by using QIIME2 software (version 2019.7, San Diego, CA, USA). 2.6. Data Processing Spearman’s correlation coefficient was calculated by the SPSS computer program version 25.0 (IBM, Armonk, NY, USA). Network analysis, redundancy analysis, and heat map analysis were drawn with the OmicStudio tool, and the charts were made using Microsoft Excel (2019) and Origin (version 2020).
The testing soil sample was taken from a poultry farm in Jinan City, Shandong Province, China (36°22′53.170″ N, 117°1′56.695″ E). The topsoil (0~20 cm) and fresh chicken manure were collected, naturally dried, crushed, filtered through a 10-mesh sieve, and then thoroughly mixed, respectively. The physical and chemical properties of the topsoil and chicken manure are listed in . Lead nitrate [Pb (NO 3 ) 2 ] and OTC were added to the soil as pollutants. OTC (purity > 95%) was supplied by Solarbio (Beijing, China), and [Pb (NO 3 ) 2 ] and Sinopharm Chemical Reagent Co. supplied other chemical reagents.
The original topsoil sample and chicken manure were air-dried and mixed to make the test soil sample (14.3% wt/wt). The amount of test soil used in each experimental group was 180 g. The experiment was split into nine groups with identity CK, C30, C100, P80, P160, C30P80, C30P160, C100P80, and C100P160, as listed in . According to the experimental design, [Pb (NO 3 ) 2 ] and OTC were dissolved in water, respectively, and then evenly sprayed in the soil and fully mixed with the soil. The CK group was replaced by ultrapure water of the same volume to ensure the same water content. Each treatment was repeated three times, and all treatments were carried out for 90 days in a dark environment at 25 °C. Ultrapure water was added to the simulated soil every other day, and the maximum soil moisture of the field was maintained at 60% during the incubation period by weighing.
A destructive sampling method was used in this study and soil samples were taken from the incubator on days 1, 7, 20, 50, and 90. After thoroughly mixing, the sample was divided into three parts, some of which were stored at 4 °C, and soil pH, organic matter, and electrical conductivity (EC) were determined within one week, as previously reported. The other samples were stored at −20 °C for subsequent experimental analysis, and the remaining samples were stored at −80 °C for the 16 S rRNA gene high-throughput sequencing. Soil pH was estimated when the soil/water ratio was 1:2.5 ( w / w ). The organic matter content of the soil was determined by K 2 CrO 4 oxidation and FeSO 4 titration method, as previously demonstrated. Soil EC was measured by a conductivity meter at a 1:5 soil: water suspension ratio (DDS-307A, Rex, Shanghai, China) [ , , ].
Genomic DNA was extracted using the TIANamp Soil DNA Kit (TianGen, Beijing, China). The extracted DNA was stored at −20 °C for subsequent experimental analysis. Five ARGs ( tetB , sul1 , tetQ , sul2 , and sul3 ), two MGEs ( intl1 , intl2 ), and 16 S rRNA were identified by using gene primers listed in .
Illumina NovaSeq platform was used to sequence the qPCR product. The abundance of sample microbial species was analyzed by Alpha abundance analysis by using QIIME2 software (version 2019.7, San Diego, CA, USA).
Spearman’s correlation coefficient was calculated by the SPSS computer program version 25.0 (IBM, Armonk, NY, USA). Network analysis, redundancy analysis, and heat map analysis were drawn with the OmicStudio tool, and the charts were made using Microsoft Excel (2019) and Origin (version 2020).
3.1. Soil’s Physical and Chemical Properties In the whole experimental process, soil organic matter was decomposed and utilized by microbes ( p < 0.05). With the increase in culture time, the organic matter contents of soil in different treatment groups showed a downward trend. At the beginning of the experiment, the content of soil organic matter was 80 g/kg, and at the end of the experiment, the content of soil organic matter decreased to 60~65 g/kg ( a). With the increase of culture days, the pH of each experimental group dropped sharply from 7.7~7.9 to 6.8~6.9 from the 1st to 20th day of the experiment. During the subsequent culture period, the pH decreased slowly to 6.4~6.5 until the end of the experiment ( b) ( p < 0.05). Shi et al. suggested that it might be due to the increase of soil base cations release and the decrease of soil exchangeable base cations and cation exchange capacity, resulting in the decrease of soil pH . With the progress of the experiment, the EC of soil increased significantly. From the 1st to the 50th day of the experiment, the EC increased rapidly from 2.3~2.7 ms/cm to 4.0~4.5 ms/cm. In the subsequent culture process, the rising rate of EC slowed down, and the value did not fluctuate significantly ( p < 0.05). The reason for the increase of EC in the soil might be that the sample contains a lot of inorganic ions . To sum up, the content of soil organic matter and pH decreased continuously during the whole planting process, while the EC value showed the opposite trend. The experimental results verify the conclusion of Antil et al. . 3.2. ARGs and MGEs The effects of OTC and Pb application in the soil on ARGs and MGEs were also investigated ( ). The abundance of ARGs can reflect the population of drug-resistant bacteria in the soil. The relative abundance of tetB , sul1 , and sul2 in all samples was found to be high, and the relative abundance order of target genes was as follows: sul2 > sul1 > tetB > intl1 > tetQ > intl2 > sul3 . The results of this study verified the conclusion by Zhu et al. , in which the pollutants added to the soil changed the expression of ARGs. More information about ARGs and MGEs can be found in . With the change of Pb concentrations added to soil, the abundance of ARGs in soil samples varied remarkably ( p < 0.05) ( ). For the P80 and P160 experimental groups, on the 90th day of the experiment, the relative abundance of tetB was noted to be 54.2% and 10.3% of the original abundance, respectively, as compared to the start of the experiment; tetQ was 12.6% and 10.7% of the original abundance, respectively; sul1 was 109.5% and 152.5%; sul2 was 13.7% and 4.7%; sul3 was 4.3% and 2.9%; intl1 was 1.5% and 1.1%; and intl2 was 77.6% and 98.6% of the original abundance, respectively. At the end of the experiment, the total abundance of MGEs ( intl1 and intl2 ) in the P80 and P160 experimental groups was only 98.6% of that at the beginning of the experiment. On the 90th day of the experiment, the relative abundance of ARGs in the P80 group and P160 group was increased by 0.699-fold and 0.697-fold than that in the CK group, respectively. Similarly, the relative abundance of MGEs in groups P80 and P160 was increased by 0.188-fold and 0.228-fold than that in CK group. In this study, the addition of Pb inhibited the expression of some ARGs, which led to lower enrichment of ARGs. This phenomenon was probably because higher heavy metal content than MIC inhibits the further expression of ARGs . Therefore, excessively high concentrations of Pb in the soil may inhibit the growth and reproduction of microorganisms or even kill them, thereby reducing the expression and transfer of ARGs in the environment. The relative abundance of ARGs also changed significantly with the addition of OTC in the soil ( ). On the 90th day of the experiment, the relative abundance of tetQ in C30 and C100 groups was 12.1% and 4.2%; tetB in the was 15.2% and 52.2%; sul1 was 101.8% and 213.8%; sul2 was 169.4% and 1516.2%; sul3 was 1.5% and 1.2%; and intl1 was 0.8% and 2.3% of the original relative abundance, respectively. On the 90th day of the experiment, the total relative abundance of ARGs in the C30 and C100 experimental groups was 82.2% and 84.0% of the CK group, while the total relative abundance of MGEs was 18.8% and 41.0% of the CK group. However, the total relative abundance of intl2 in the C30 and C100 experimental groups was 20.9% and 25.1% of the CK group, respectively. The addition of OTC in the soil inhibited the spread of tetQ , sul3 , intl1 , and intl2 genes and promoted the expression of sul1 and sul2 genes. These findings were inconsistent with the previous research studies, which might be due to the fact that the concentration and properties of OTC used in the study are not exactly the same as those used in other experiments [ , , ]. However, related research studies revealed that adding high concentrations of antibiotics in the soil would adversely affect the structure of the drug-resistant microbial populations; that is, the abundance of soil microorganisms decreases significantly with the increase of OTC concentration . Furthermore, related studies have also demonstrated that an increase in antibiotic concentration gradually decreases antibiotic resistance in corresponding bacterial species . A decrease in the relative abundance of ARGs in the soil cultured with OTC was also observed during the current study, which may also be due to the increased concentration of OTC, which has a toxic effect on the drug-resistant bacteria and hinders the expression of ARGs. Under the OTC application in soil, Pb treatment further changed the relative abundance of ARGs and MGEs in the soil. On the 1st day of the experiment, the relative abundance of total ARGs in the C30P80 and C30P160 groups were 6.97 times and 4.42 times higher in comparison to group C30, respectively. Further, the total relative abundance of MGEs in the C30P80 and C30P160 groups was 1.31 times and 0.53 times higher than that of group C30, respectively. On the 90th day of the experiment, the relative abundance of total ARGs was 0.99 times and 0.97 times higher than that of the C30 group, respectively. However, the total relative abundance of MGEs on the 90th day of the experiment was 13.80 times and 1.25 times higher than that of the C30 group, respectively. The relative abundance of total ARGs in the C100P80 and C100P160 groups on 1st day of the experiment was 10.56 times and 13.22 times higher than that of the C100 group. The total relative abundance of MGEs was 1.01 and 1.78 times higher than that of the C100 group, respectively. On the 90th day of the experiment, the relative abundance of total ARGs was 1.00 times and 1.55 times higher than that of the C100 group, respectively. The total relative abundance of MGEs was 0.39 and 5.0 times higher than that of the C100 group, respectively. The total relative abundance of ARGs was positively correlated with Pb concentration under OTC-added to the soil. This might be because Pb exposure significantly increased the abundance of MGEs (integrons and inserts) and the overall relative abundance of ARGs . In contrast, this study found that lower concentrations of Pb did not contribute to enhancing the expression of MGEs, resulting in a lower overall relative abundance of ARGs. In addition, the total relative abundance of ARGs and MGEs decreased with the increase of culture time. This might be due to the combined application of high concentrations of OTC and Pb, which in turn changed the microbial population, inhibited their growth and reproduction, and led to lower expression of ARGs and MGEs. Moreover, Pei, et al. found that sul ARGs were higher than tet ARGs . These findings demonstrate that the choice of antibiotic has a long-term influence with regard to ARGs. On the whole, the expression of ARGs was inhibited in different degrees by adding different concentrations of Pb or OTC in the soil. This might be due to the fact that the type of pollutants applied is related to the higher concentration, which has a toxic effect on microorganisms and hinders the spread of ARGs. 3.3. The Soil Microbial Community Evolution Through 16 S rRNA high-throughput sequencing analysis, it was found that the application of Pb and OTC changed the soil microbial community structure. The soil rarefaction curve directly reflected the difference in microbial species diversity and species abundance among different soil samples ( a). The bubble plot shows genus-level species annotation information and the relative abundance of bacterial species for different sample groups, as well as species annotation information with corresponding phylum ( b). The bubble plot also shows a relatively high abundance of Actinomadura, Isoptericola, Streptomyces, Bacteroidetes, Galbibacter, and Luteimonas genera in each experimental group, indicating that they were the dominant bacteria under different treatment conditions. It was noted that Pseudomonas only had high relative abundance in the C100P160 group, indicating that Pseudomonas aeruginosa had strong adaptability in this environment under high concentrations of Pb and OTC treatments, and was one of the dominant bacteria in this treatment group. After adding Pb and OTC to the soil, the abundance of Isoptericola and Streptomyces was lower than that of the CK group, and this phenomenon became more obvious with the increase of pollutant concentration, which indicated that Pb and OTC had toxic effects on Isoptericola and Streptomyces genus. Compared with Pb, the application of OTC has a more obvious effect on Bacteroidetes. A low concentration of OTC increased the abundance of Bacteroidetes, while a high concentration of OTC inhibited the abundance of Bacteroidetes. a shows the relative abundance of different bacteria in different samples at the phylum level. Among them, the most dominant bacteria were Actinobacteria (19.59~63.24%), Proteobacteria (14.34~55.33%), Bacteroidetes (0.00~33.37%), Firmicutes (0.65~23.53%), and Planctomycetes (0.00~11.33%). a represents 89.03~98.46% of the total bacteria, which is similar to the conclusion of Sardar et al. . a also shows that when Pb or OTC was applied alone, high concentrations of Pb or OTC promoted the expression of Actinobacteria, but inhibited the expression of Proteobacteria. On the contrary, when Pb and OTC were applied combined, a high concentration of Pb or OTC inhibited the expression of Actinobacteria, but promoted the expression of Proteobacteria. The results showed that after OTC or Pb treatment alone, the relative abundance of Actinobacteria increased with the increase of added pollutant concentration. However, when OTC and Pb were applied together, the relative abundance of Actinobacteria decreased with the increase of added pollutant concentration. This indicates that the treatment of a single pollutant may produce resistant strains in microbial communities and enhances the expression of resistant strains. When OTC and Pb are combined, they have toxic effects on bacteria and reduce their metabolism and reproduction, thus affecting their abundance . In addition, it was noted that OTC and Pb treatment had a profound impact on Firmicutes. When OTC or Pb was applied to the soil alone, the abundance of Firmicutes decreased with increasing pollutant concentration, while when OTC and Pb were applied in combination, the abundance of Firmicutes increased with increasing pollutant concentration. The effect of Pb and OTC treatments on the soil microbial community was analyzed by sequencing. Venn diagrams showed 487, 1409, 1496, 1435, and 873 unique operational taxonomic units (OTUs) in the CK, C30, C100, P80, and P160 groups, respectively. Furthermore, 541 OTUs were found to be unique by the five experimental groups ( b). The results also showed that adding Pb and OTC changed the soil microbial community structure. Principal coordinates analysis (PCoA) was carried out to estimate the distance matrix and find the best eigenvalues, which can truly reflect the relationship between samples ( ). The different color dots in the PCoA diagram represent different treatment groups, and the distance between the dots indicates the similarity of microbial communities among different samples. PCoA showed a p -value of 0.985, as indicated in , indicating that the PCoA diagram had high reliability for the interpretation and geometric structures of the samples under different treatment groups. It can be seen from that there were differences in bacterial communities among the samples under different treatment conditions. For example, the purple dot in the square represents the C100P80 treatment group, and the pink dot represents the C100P160 treatment group, which almost coincide with each other, indicating that there was little difference in the microbial community structure between the two treatment groups. On the contrary, the orange dot in the oval represents the C30 treatment group, the blue dot represents the C30P160 treatment group, and the two points are far away, indicating that there is a great difference in the microbial community structure between the two treatment groups. The relative abundance of Galbibacter, Actinomadura, Isoptericola, and Alphaproteobacteria that were detected was significantly increased at the genus level after alone and combined treatment of Pb and OTC. In contrast, the relative abundance of Actinobacteria, Brachybacterium, and Pseudomonas decreased ( a). To explain the relevant relationship between the abundance of bacteria and ARGs in the soil samples treated with Pb and OTC, a network analysis was carried out. Network analysis showed a positive relationship among ARGs, MGEs, and potential host bacteria in the soil ( b). The data shown in b includes MGEs, ARGs, and the top 30 bacterial genera. The top 30 bacterial genera were summarized into eight bacterial phylum clades because sul1 and sul2 have little correlation with other bacterial species and it was difficult to demonstrate them in the same clade. The network analysis showed that tetB was positively correlated with Deinococcota, Firmicutes, Actinobacteriota, Proteobacteria, and Actinobacteria. TetQ was positively associated with Bacteroidota, Actinobacteriota, Firmicutes, Proteobacteria, and Actinobacteria. Sul3 had a positive correlation with Bacteroidota and Actinobacteria. These phylum-level bacteria might be potential repositories of ARGs. Through network analysis, it was found that there were six bacterial genera related to intl1 and intl2, respectively. These bacterial genera were potential MGEs repositories. At the same time, intl1 and intl2 might have the same potential host bacteria; they were Bacteroidota, Actinobacteriota, Proteobacteria, and Actinobacteria. Furthermore, among treatment groups such as C30 and C100 treated with OTC alone, a higher abundance of Alphaproteobacteria, Rhodospirillalesd, and Actinobacteria was found due to the increase of ARGs and MGEs. It was suggested that the presence of OTC in the soil would exert pressure on ARGs and resistant bacteria. However, among the other treatment groups, such as P80 and P160, a higher abundance of Actinobacteria, Alphaproteobacteria, Rhodospirillalesd, Gemmatimonadetes, and Isoptericola was also due to the increase of ARGs and MGEs. However, the abundance of Luteimonas did not change much in the P80 and P160 treated groups, which might be due to Luteimonas bacteria not being sensitive to Pb. Proteobacteria accounted for a high proportion among all bacterial genera, with the highest abundance noted in the C100P160 treatment group. From b, it was noted that Proteobacteria was positively correlated with intl1 , intl2 , tetB , and tetQ genes. However, high concentrations of Pb and OTC changed the structure of the microbial community, which affected the abundance of ARGs by affecting the expression of MGEs. Network analysis showed that MGEs is not only positively correlated with microbial flora, but also positively correlated with most ARGs ( p < 0.05). This phenomenon indicated that Pb or OTC treatment might enhance the potential of horizontal transfer of ARGs. According to the network analysis diagram, as shown in b, intl1 and intl2 genes of bacteria showed a positive correlation with most microbial flora. Furthermore, Intl1 and intl2 showed varying degrees of positive correlation with ARGs, which displayed a direct relationship between ARGs and bacteria communities. Luteimonas was a major contributor to the Proteobacteria phylum. The relative abundance of Luteimonas was noted to be higher in C30, C30P160, and C100P160 treatment groups, while a little changed pattern was noted in C100, P80, P160, and C100P80 treatment groups, and decreased abundance was noted in the C30P80 treatment group. The changing trend of Luteimonas under different treatment groups was comparable to that of ARGs. These results reflect the different expression levels of ARGs under different treatment conditions. 3.4. Relationship between Physical and Chemical Properties of Soil, Microbial Community, ARGs and MGEs Soil organic content, EC, and pH affect the ARGs, soil microorganisms’ growth, and reproduction . Redundancy analysis (RDA) was used to analyze the relationships among bacterial community, environmental factors, MGEs, and ARGs. Environmental factors included pH, organic matter, and EC of the soil. The dominant bacterial communities in the samples were Actinobacteria, Proteobacteria, Bacteroidetes, Firmicutes, Planctomycetes, Chloroflex, Gemmatimonadetes, Deinococcus-Thermus, and Aacidobacteria ( ). RDA1 and RDA2 jointly explained 46.74% of ARGs changes. Through RDA analysis, it could be found that ARGs was affected by many factors, and MGEs greatly influenced ARGs. Among the microbial communities, Firmicutes, Acidobacteria, Proteobacteria, Bacterpoidetes, and Bacteroidetes had an inordinate influence on ARGs, suggesting that these microbial communities might be the potential reservoir of ARGs. Microbial communities were greatly influenced by soil organic matter and pH. Soil pH and organic matter content were positively correlated with the relative abundance of ARGs. When soil pH and organic matter content were low, the expression of ARGs was inhibited. The results of this study are consistent with the conclusions of Zhang et al. and Chen et al. . Soil acidity also affected the abundance of ARGs, which was mainly because lower pH increased the solubility of pollutants, and a higher concentration of pollutants affected the metabolism of bacteria, which has been confirmed by a previous study . In addition, soil pH and organic matter also contributed significantly to the relative abundance of intl1 and intl2 genes, and the contribution of pH and organic matter to the relative abundance of the intl1 gene was more significant than that of intl2 gene. Furthermore, EC was negatively correlated with the relative abundance of ARGs and MGEs. MGEs were positively correlated with the expression of tetB , tetQ , sul2 , and sul3 genes. The results of this experiment show that the spread of ARGs might be through the contribution of MGEs . It was also confirmed that there was a correlation between antibiotics and the direct selection pressure of ARGs. RDA analysis further showed that intl1 was positively correlated with all ARGs, while intl2 had a positive correlation with tetB , tetQ , sul2 , and sul3 , and intl2 genes were negatively associated with sul1 . To sum up, tetB , tetQ , sul2 , and sul3 genes could be transmitted not only by intl1, but also through intl2 . ARGs are not only affected by MGEs, but also affected by other environmental pollutants and factors . The application of OTC and Pb in this study resulted in a decrease in the number of Bacteroidetes, Proteobacteria, Acidobacteria, and Firmicutes. These bacteria were probably the reservoir bacteria of MGEs, which would further decrease the ARGs. The results of this study show that OTC and Pb inhibited the expression of MGEs, thereby reducing the transfer of most ARGs. MGEs mainly caused the horizontal gene transfer of ARGs, likewise, it was also affected by microbial communities and environmental factors.
In the whole experimental process, soil organic matter was decomposed and utilized by microbes ( p < 0.05). With the increase in culture time, the organic matter contents of soil in different treatment groups showed a downward trend. At the beginning of the experiment, the content of soil organic matter was 80 g/kg, and at the end of the experiment, the content of soil organic matter decreased to 60~65 g/kg ( a). With the increase of culture days, the pH of each experimental group dropped sharply from 7.7~7.9 to 6.8~6.9 from the 1st to 20th day of the experiment. During the subsequent culture period, the pH decreased slowly to 6.4~6.5 until the end of the experiment ( b) ( p < 0.05). Shi et al. suggested that it might be due to the increase of soil base cations release and the decrease of soil exchangeable base cations and cation exchange capacity, resulting in the decrease of soil pH . With the progress of the experiment, the EC of soil increased significantly. From the 1st to the 50th day of the experiment, the EC increased rapidly from 2.3~2.7 ms/cm to 4.0~4.5 ms/cm. In the subsequent culture process, the rising rate of EC slowed down, and the value did not fluctuate significantly ( p < 0.05). The reason for the increase of EC in the soil might be that the sample contains a lot of inorganic ions . To sum up, the content of soil organic matter and pH decreased continuously during the whole planting process, while the EC value showed the opposite trend. The experimental results verify the conclusion of Antil et al. .
The effects of OTC and Pb application in the soil on ARGs and MGEs were also investigated ( ). The abundance of ARGs can reflect the population of drug-resistant bacteria in the soil. The relative abundance of tetB , sul1 , and sul2 in all samples was found to be high, and the relative abundance order of target genes was as follows: sul2 > sul1 > tetB > intl1 > tetQ > intl2 > sul3 . The results of this study verified the conclusion by Zhu et al. , in which the pollutants added to the soil changed the expression of ARGs. More information about ARGs and MGEs can be found in . With the change of Pb concentrations added to soil, the abundance of ARGs in soil samples varied remarkably ( p < 0.05) ( ). For the P80 and P160 experimental groups, on the 90th day of the experiment, the relative abundance of tetB was noted to be 54.2% and 10.3% of the original abundance, respectively, as compared to the start of the experiment; tetQ was 12.6% and 10.7% of the original abundance, respectively; sul1 was 109.5% and 152.5%; sul2 was 13.7% and 4.7%; sul3 was 4.3% and 2.9%; intl1 was 1.5% and 1.1%; and intl2 was 77.6% and 98.6% of the original abundance, respectively. At the end of the experiment, the total abundance of MGEs ( intl1 and intl2 ) in the P80 and P160 experimental groups was only 98.6% of that at the beginning of the experiment. On the 90th day of the experiment, the relative abundance of ARGs in the P80 group and P160 group was increased by 0.699-fold and 0.697-fold than that in the CK group, respectively. Similarly, the relative abundance of MGEs in groups P80 and P160 was increased by 0.188-fold and 0.228-fold than that in CK group. In this study, the addition of Pb inhibited the expression of some ARGs, which led to lower enrichment of ARGs. This phenomenon was probably because higher heavy metal content than MIC inhibits the further expression of ARGs . Therefore, excessively high concentrations of Pb in the soil may inhibit the growth and reproduction of microorganisms or even kill them, thereby reducing the expression and transfer of ARGs in the environment. The relative abundance of ARGs also changed significantly with the addition of OTC in the soil ( ). On the 90th day of the experiment, the relative abundance of tetQ in C30 and C100 groups was 12.1% and 4.2%; tetB in the was 15.2% and 52.2%; sul1 was 101.8% and 213.8%; sul2 was 169.4% and 1516.2%; sul3 was 1.5% and 1.2%; and intl1 was 0.8% and 2.3% of the original relative abundance, respectively. On the 90th day of the experiment, the total relative abundance of ARGs in the C30 and C100 experimental groups was 82.2% and 84.0% of the CK group, while the total relative abundance of MGEs was 18.8% and 41.0% of the CK group. However, the total relative abundance of intl2 in the C30 and C100 experimental groups was 20.9% and 25.1% of the CK group, respectively. The addition of OTC in the soil inhibited the spread of tetQ , sul3 , intl1 , and intl2 genes and promoted the expression of sul1 and sul2 genes. These findings were inconsistent with the previous research studies, which might be due to the fact that the concentration and properties of OTC used in the study are not exactly the same as those used in other experiments [ , , ]. However, related research studies revealed that adding high concentrations of antibiotics in the soil would adversely affect the structure of the drug-resistant microbial populations; that is, the abundance of soil microorganisms decreases significantly with the increase of OTC concentration . Furthermore, related studies have also demonstrated that an increase in antibiotic concentration gradually decreases antibiotic resistance in corresponding bacterial species . A decrease in the relative abundance of ARGs in the soil cultured with OTC was also observed during the current study, which may also be due to the increased concentration of OTC, which has a toxic effect on the drug-resistant bacteria and hinders the expression of ARGs. Under the OTC application in soil, Pb treatment further changed the relative abundance of ARGs and MGEs in the soil. On the 1st day of the experiment, the relative abundance of total ARGs in the C30P80 and C30P160 groups were 6.97 times and 4.42 times higher in comparison to group C30, respectively. Further, the total relative abundance of MGEs in the C30P80 and C30P160 groups was 1.31 times and 0.53 times higher than that of group C30, respectively. On the 90th day of the experiment, the relative abundance of total ARGs was 0.99 times and 0.97 times higher than that of the C30 group, respectively. However, the total relative abundance of MGEs on the 90th day of the experiment was 13.80 times and 1.25 times higher than that of the C30 group, respectively. The relative abundance of total ARGs in the C100P80 and C100P160 groups on 1st day of the experiment was 10.56 times and 13.22 times higher than that of the C100 group. The total relative abundance of MGEs was 1.01 and 1.78 times higher than that of the C100 group, respectively. On the 90th day of the experiment, the relative abundance of total ARGs was 1.00 times and 1.55 times higher than that of the C100 group, respectively. The total relative abundance of MGEs was 0.39 and 5.0 times higher than that of the C100 group, respectively. The total relative abundance of ARGs was positively correlated with Pb concentration under OTC-added to the soil. This might be because Pb exposure significantly increased the abundance of MGEs (integrons and inserts) and the overall relative abundance of ARGs . In contrast, this study found that lower concentrations of Pb did not contribute to enhancing the expression of MGEs, resulting in a lower overall relative abundance of ARGs. In addition, the total relative abundance of ARGs and MGEs decreased with the increase of culture time. This might be due to the combined application of high concentrations of OTC and Pb, which in turn changed the microbial population, inhibited their growth and reproduction, and led to lower expression of ARGs and MGEs. Moreover, Pei, et al. found that sul ARGs were higher than tet ARGs . These findings demonstrate that the choice of antibiotic has a long-term influence with regard to ARGs. On the whole, the expression of ARGs was inhibited in different degrees by adding different concentrations of Pb or OTC in the soil. This might be due to the fact that the type of pollutants applied is related to the higher concentration, which has a toxic effect on microorganisms and hinders the spread of ARGs.
Through 16 S rRNA high-throughput sequencing analysis, it was found that the application of Pb and OTC changed the soil microbial community structure. The soil rarefaction curve directly reflected the difference in microbial species diversity and species abundance among different soil samples ( a). The bubble plot shows genus-level species annotation information and the relative abundance of bacterial species for different sample groups, as well as species annotation information with corresponding phylum ( b). The bubble plot also shows a relatively high abundance of Actinomadura, Isoptericola, Streptomyces, Bacteroidetes, Galbibacter, and Luteimonas genera in each experimental group, indicating that they were the dominant bacteria under different treatment conditions. It was noted that Pseudomonas only had high relative abundance in the C100P160 group, indicating that Pseudomonas aeruginosa had strong adaptability in this environment under high concentrations of Pb and OTC treatments, and was one of the dominant bacteria in this treatment group. After adding Pb and OTC to the soil, the abundance of Isoptericola and Streptomyces was lower than that of the CK group, and this phenomenon became more obvious with the increase of pollutant concentration, which indicated that Pb and OTC had toxic effects on Isoptericola and Streptomyces genus. Compared with Pb, the application of OTC has a more obvious effect on Bacteroidetes. A low concentration of OTC increased the abundance of Bacteroidetes, while a high concentration of OTC inhibited the abundance of Bacteroidetes. a shows the relative abundance of different bacteria in different samples at the phylum level. Among them, the most dominant bacteria were Actinobacteria (19.59~63.24%), Proteobacteria (14.34~55.33%), Bacteroidetes (0.00~33.37%), Firmicutes (0.65~23.53%), and Planctomycetes (0.00~11.33%). a represents 89.03~98.46% of the total bacteria, which is similar to the conclusion of Sardar et al. . a also shows that when Pb or OTC was applied alone, high concentrations of Pb or OTC promoted the expression of Actinobacteria, but inhibited the expression of Proteobacteria. On the contrary, when Pb and OTC were applied combined, a high concentration of Pb or OTC inhibited the expression of Actinobacteria, but promoted the expression of Proteobacteria. The results showed that after OTC or Pb treatment alone, the relative abundance of Actinobacteria increased with the increase of added pollutant concentration. However, when OTC and Pb were applied together, the relative abundance of Actinobacteria decreased with the increase of added pollutant concentration. This indicates that the treatment of a single pollutant may produce resistant strains in microbial communities and enhances the expression of resistant strains. When OTC and Pb are combined, they have toxic effects on bacteria and reduce their metabolism and reproduction, thus affecting their abundance . In addition, it was noted that OTC and Pb treatment had a profound impact on Firmicutes. When OTC or Pb was applied to the soil alone, the abundance of Firmicutes decreased with increasing pollutant concentration, while when OTC and Pb were applied in combination, the abundance of Firmicutes increased with increasing pollutant concentration. The effect of Pb and OTC treatments on the soil microbial community was analyzed by sequencing. Venn diagrams showed 487, 1409, 1496, 1435, and 873 unique operational taxonomic units (OTUs) in the CK, C30, C100, P80, and P160 groups, respectively. Furthermore, 541 OTUs were found to be unique by the five experimental groups ( b). The results also showed that adding Pb and OTC changed the soil microbial community structure. Principal coordinates analysis (PCoA) was carried out to estimate the distance matrix and find the best eigenvalues, which can truly reflect the relationship between samples ( ). The different color dots in the PCoA diagram represent different treatment groups, and the distance between the dots indicates the similarity of microbial communities among different samples. PCoA showed a p -value of 0.985, as indicated in , indicating that the PCoA diagram had high reliability for the interpretation and geometric structures of the samples under different treatment groups. It can be seen from that there were differences in bacterial communities among the samples under different treatment conditions. For example, the purple dot in the square represents the C100P80 treatment group, and the pink dot represents the C100P160 treatment group, which almost coincide with each other, indicating that there was little difference in the microbial community structure between the two treatment groups. On the contrary, the orange dot in the oval represents the C30 treatment group, the blue dot represents the C30P160 treatment group, and the two points are far away, indicating that there is a great difference in the microbial community structure between the two treatment groups. The relative abundance of Galbibacter, Actinomadura, Isoptericola, and Alphaproteobacteria that were detected was significantly increased at the genus level after alone and combined treatment of Pb and OTC. In contrast, the relative abundance of Actinobacteria, Brachybacterium, and Pseudomonas decreased ( a). To explain the relevant relationship between the abundance of bacteria and ARGs in the soil samples treated with Pb and OTC, a network analysis was carried out. Network analysis showed a positive relationship among ARGs, MGEs, and potential host bacteria in the soil ( b). The data shown in b includes MGEs, ARGs, and the top 30 bacterial genera. The top 30 bacterial genera were summarized into eight bacterial phylum clades because sul1 and sul2 have little correlation with other bacterial species and it was difficult to demonstrate them in the same clade. The network analysis showed that tetB was positively correlated with Deinococcota, Firmicutes, Actinobacteriota, Proteobacteria, and Actinobacteria. TetQ was positively associated with Bacteroidota, Actinobacteriota, Firmicutes, Proteobacteria, and Actinobacteria. Sul3 had a positive correlation with Bacteroidota and Actinobacteria. These phylum-level bacteria might be potential repositories of ARGs. Through network analysis, it was found that there were six bacterial genera related to intl1 and intl2, respectively. These bacterial genera were potential MGEs repositories. At the same time, intl1 and intl2 might have the same potential host bacteria; they were Bacteroidota, Actinobacteriota, Proteobacteria, and Actinobacteria. Furthermore, among treatment groups such as C30 and C100 treated with OTC alone, a higher abundance of Alphaproteobacteria, Rhodospirillalesd, and Actinobacteria was found due to the increase of ARGs and MGEs. It was suggested that the presence of OTC in the soil would exert pressure on ARGs and resistant bacteria. However, among the other treatment groups, such as P80 and P160, a higher abundance of Actinobacteria, Alphaproteobacteria, Rhodospirillalesd, Gemmatimonadetes, and Isoptericola was also due to the increase of ARGs and MGEs. However, the abundance of Luteimonas did not change much in the P80 and P160 treated groups, which might be due to Luteimonas bacteria not being sensitive to Pb. Proteobacteria accounted for a high proportion among all bacterial genera, with the highest abundance noted in the C100P160 treatment group. From b, it was noted that Proteobacteria was positively correlated with intl1 , intl2 , tetB , and tetQ genes. However, high concentrations of Pb and OTC changed the structure of the microbial community, which affected the abundance of ARGs by affecting the expression of MGEs. Network analysis showed that MGEs is not only positively correlated with microbial flora, but also positively correlated with most ARGs ( p < 0.05). This phenomenon indicated that Pb or OTC treatment might enhance the potential of horizontal transfer of ARGs. According to the network analysis diagram, as shown in b, intl1 and intl2 genes of bacteria showed a positive correlation with most microbial flora. Furthermore, Intl1 and intl2 showed varying degrees of positive correlation with ARGs, which displayed a direct relationship between ARGs and bacteria communities. Luteimonas was a major contributor to the Proteobacteria phylum. The relative abundance of Luteimonas was noted to be higher in C30, C30P160, and C100P160 treatment groups, while a little changed pattern was noted in C100, P80, P160, and C100P80 treatment groups, and decreased abundance was noted in the C30P80 treatment group. The changing trend of Luteimonas under different treatment groups was comparable to that of ARGs. These results reflect the different expression levels of ARGs under different treatment conditions.
Soil organic content, EC, and pH affect the ARGs, soil microorganisms’ growth, and reproduction . Redundancy analysis (RDA) was used to analyze the relationships among bacterial community, environmental factors, MGEs, and ARGs. Environmental factors included pH, organic matter, and EC of the soil. The dominant bacterial communities in the samples were Actinobacteria, Proteobacteria, Bacteroidetes, Firmicutes, Planctomycetes, Chloroflex, Gemmatimonadetes, Deinococcus-Thermus, and Aacidobacteria ( ). RDA1 and RDA2 jointly explained 46.74% of ARGs changes. Through RDA analysis, it could be found that ARGs was affected by many factors, and MGEs greatly influenced ARGs. Among the microbial communities, Firmicutes, Acidobacteria, Proteobacteria, Bacterpoidetes, and Bacteroidetes had an inordinate influence on ARGs, suggesting that these microbial communities might be the potential reservoir of ARGs. Microbial communities were greatly influenced by soil organic matter and pH. Soil pH and organic matter content were positively correlated with the relative abundance of ARGs. When soil pH and organic matter content were low, the expression of ARGs was inhibited. The results of this study are consistent with the conclusions of Zhang et al. and Chen et al. . Soil acidity also affected the abundance of ARGs, which was mainly because lower pH increased the solubility of pollutants, and a higher concentration of pollutants affected the metabolism of bacteria, which has been confirmed by a previous study . In addition, soil pH and organic matter also contributed significantly to the relative abundance of intl1 and intl2 genes, and the contribution of pH and organic matter to the relative abundance of the intl1 gene was more significant than that of intl2 gene. Furthermore, EC was negatively correlated with the relative abundance of ARGs and MGEs. MGEs were positively correlated with the expression of tetB , tetQ , sul2 , and sul3 genes. The results of this experiment show that the spread of ARGs might be through the contribution of MGEs . It was also confirmed that there was a correlation between antibiotics and the direct selection pressure of ARGs. RDA analysis further showed that intl1 was positively correlated with all ARGs, while intl2 had a positive correlation with tetB , tetQ , sul2 , and sul3 , and intl2 genes were negatively associated with sul1 . To sum up, tetB , tetQ , sul2 , and sul3 genes could be transmitted not only by intl1, but also through intl2 . ARGs are not only affected by MGEs, but also affected by other environmental pollutants and factors . The application of OTC and Pb in this study resulted in a decrease in the number of Bacteroidetes, Proteobacteria, Acidobacteria, and Firmicutes. These bacteria were probably the reservoir bacteria of MGEs, which would further decrease the ARGs. The results of this study show that OTC and Pb inhibited the expression of MGEs, thereby reducing the transfer of most ARGs. MGEs mainly caused the horizontal gene transfer of ARGs, likewise, it was also affected by microbial communities and environmental factors.
The addition of Pb or OTC resulted in changes in the microbial community structure and a decrease in the relative abundance of Bacteroidetes, Proteobacteria, Acidobacteria, Firmicutes, and total ARGs and MGEs. However, when OTC and Pb were applied in combination, the inhibitory effect of a low concentration of Pb on ARGs was stronger than that of a high concentration of Pb, resulting in a lower abundance of ARGs. Further, network analysis showed that the abundance of ARGs was mainly affected by Actinobacteria, Proteobacteria, and Bacteroidetes. RDA analysis indicated that pH, organic matter, and MGEs significantly contributed to the expression of ARGs. Organic matter content and pH were positively correlated with ARGs abundance, while EC was negatively associated with ARGs and MGEs. Low organic matter content and low pH inhibited the expression of ARGs and MGEs. These conclusions can provide a reference for the transfer of ARGs in the environment, which is the basis of horizontal gene transfer mechanisms.
|
Land conversion to agriculture induces taxonomic homogenization of soil microbial communities globally | 8848ca44-1188-4e6e-9044-8e39ab02cff5 | 11058813 | Microbiology[mh] | Due to increasing human activities and agricultural intensification, an emerging body of research suggests that ecological communities are undergoing fundamental changes across various spatial dimensions . Most studies investigating the consequences of land-use changes and agricultural expansion on ecological communities have focused on local species diversity – due to its ease of measurement and monitoring . Such studies are relevant to highlight the loss of global biodiversity loss and species extinction – . However, in addition to reducing local species diversity, agricultural conversion also caused biotic homogenization at larger spatial scales – , posing a significant concern for ecosystem services and conservation. Biotic homogenization refers to the increase in taxonomic or functional similarities among ecological communities distributed spatially over time . Biotic homogenization can be quantified by a decrease in β -diversity, e.g., a decrease in compositional dissimilarity between sites. Biotic homogenization can occur due to the establishment of exotic species (increasing similarity between communities), the loss of native species specific for a limited set of locations (reducing similarity) or most likely a combination of both , . Indeed, both natural pressures and anthropogenic activities, such as climate change, agricultural expansion, urbanization and habitat homogenization, could cause biotic homogenization , – . So far, the impact of land use and agricultural conversion on biotic homogenization mainly focused on aboveground habitats , with limited attention given to belowground communities. The information about agriculture-induced biotic homogenization of belowground communities is essential for regional biodiversity planning and conservation purposes. Land-use change and agricultural conversion can alter community assembly processes, community composition and species diversity concurrently , – . These changes are underpinned by species extinction, colonization and uneven shifts in relative abundance among different geographic regions. Intense agriculture can contribute to soil compaction, salinization, acidification, metal accumulation, organic matter loss and nutrient imbalance . These related environmental stressors generally induce structural shifts in microbial taxonomic and functional composition , , such as the retention of acid-tolerant taxa and the loss of specific functional traits for pathogen suppression or crop fitness , . Consequently, these shifts create ecological feedbacks that further influences soil functions critical for maintaining soil health and agricultural productivity. Despite numerous studies examining the responses of microbiome composition and function to agricultural conversion – , these observations are predominantly site-specific and limited to a local scale , making it challenging to infer whether shifts in specific microbial taxa are relevant to the diverse range of soils worldwide . Currently, we still lack a generalizable and consistent understanding of how soil microbial taxonomic and functional profiles respond to agricultural conversion and which microbial lineages and functions are mostly impacted across a wide range of soil and climate types. This knowledge gap hinders our comprehensive understanding of the global decline in biodiversity and associated ecosystem functions. In the present study, we address two major questions: (1) whether agricultural effects lead to taxonomic and functional biotic homogenization of soil microbiomes at large spatial scales? (2) how land-use changes alter soil microbial community composition and functions across a wide range of soil and climate types, and which microbial lineages and functions most strongly impacted? We combined a continental soil survey and a global-scale meta-analysis to address these questions. For the first question, we conducted a continental soil survey of 1185 samples from agricultural fields and the adjacent natural ecosystems (covering forest, grassland, wetland; Fig. ) across China to provide large-scale evidence of agriculture-induced taxonomic and functional homogenization of soil microbiomes. To gain a global perspective on agricultural-induced biotic homogenization, and to complement the continental scale soil survey, we also collected 16S rRNA amplicon-based sequencing data from soil samples of global agricultural-natural ecosystem pairs from all available gene banks (Fig. ). We hypothesized that agricultural conversion causes taxonomic and functional homogenization of soil microbiomes. For the second question, we used the continental survey dataset to explore general patterns of soil microbiome taxonomic and functional responses to agricultural conversion across a wide range of soil and climate types. We also determined how these responses vary among ecosystem types and different microbial lineages. Our results demonstrate that land-use change for agricultural purposes reduces taxonomic diversity in soil bacterial communities.
Agriculture causes biotic homogenization of taxonomic and functional profiles Our continental soil survey dataset (Fig. and Supplementary Tables , ) revealed that β -diversity of both microbial taxonomic and functional composition (identified by KEGG and COG) was significantly lower in cropland than in natural soil (Fig. d, h and Supplementary Figs. , ) demonstrating that cropland soils are more similar than paired natural ecosystem soils. For example, the β -diversity of taxonomic composition was significantly lower in cropland than in forest ( F 1,91504 = 6.429, slope = 0.0016, p < 0.05), in grassland ( F 1,75348 = 1532, slope = 0.0276, p < 0.001), and in wetland ( F 1,77004 = 6450, slope = 0.0532, p < 0.001; Fig. ). The β -diversity of functional composition was significantly lower in cropland than in grassland ( F 1,88 = 9.021, slope = 0.0885, p < 0.01), and in wetland ( F 1,88 = 6.886, slope = 0.0527, p < 0.05; Fig. ). When considering β -diversity at the site-level and keeping the same number of samples across sites, significant lower value was also detected in croplands compared to forest, grassland and wetland (Supplementary Fig. ). The global meta-analysis based on more than 2400 soil samples across six continents (Fig. ) further showed that microbial communities from croplands were significantly different from forest soils in taxonomic composition (PERMANOVA; R 2 = 0.026, p < 0.001), and β -diversity was significantly lower in croplands than forest soils at global scale (wilcoxon test: p < 0.001; Fig. ). These results jointly provide large-scale, e.g., continental and global scale, evidence for biotic homogenization of soil microbiome under agricultural conversion. Moreover, we found that the phylotypes, that are present in both croplands and natural ecosystems, were found in significantly more samples of croplands than in natural ecosystems (wilcoxon test: p < 0.001; Supplementary Fig. ), indicating an increase in the geographic ranges of existing taxa in croplands. The phylotypes unique to natural ecosystems occurred in significantly fewer samples than other shared phylotypes that present in both croplands and natural ecosystems (wilcoxon test: p < 0.001; Supplementary Fig. ), implying a possible loss of these habitat-specific taxa after agricultural conversion. Given that microbial composition is critical for maintenance and resilience of soil functions, e.g., nutrient supply, litter decomposition and water regulation , , agricultural-induced biotic homogenization could cause ecosystem service degradation and threaten sustainable management. Thus, even though a few studies have assessed the impacts of agricultural land-use change on microbial diversity and composition, biotic homogenization along with the reduction of regional community heterogeneity at large spatial scales should be taken into full consideration as a significant consequence of agricultural conversion. Agricultural effects on specific bacterial phylotypes and functions To provide a general insight into agricultural-induced shifts in microbial phylotypes and functions originated from multiple natural ecosystems, we compared differences in community structure between cropland and surrounding forests, grasslands, and wetlands, respectively. Microbial communities of cropland soils were significantly different from those of natural forest, grassland and wetland soils in taxonomic composition (Fig. and Supplementary Fig. ). These differences were evident at both the phylum, class, order (Supplementary Fig. ) and phylotypes levels (Fig. ). Notably, the largest differences in taxonomic composition were found between croplands and wetlands (PERMANOVA; R 2 = 0.060, p < 0.001), followed by the comparison of croplands and forests (PERMANOVA; R 2 = 0.052, p < 0.001), and between croplands and grasslands (PERMANOVA; R 2 = 0.038, p < 0.001). Specifically, agricultural impacts significantly altered microbial composition (PERMANOVA; p < 0.05) in almost all of the locations, except for 2 of 37 sites in croplands and grasslands (Supplementary Table ). On average, agricultural effects significantly altered the abundance of nearly half of the phylotypes (44% for forests, 41% for grasslands, and 45% for wetlands; Supplementary Fig. ). Approximately 20% of the ASVs were lost from natural ecosystems upon conversion to agriculture, while approximately 23% of the ASVs increase in abundance. Specifically, the relative abundance of Chloroflexi, Gemmatimonadota, Planctomycetota, Myxcoccota and Latescibacterota increased in croplands compared with all three natural ecosystems (Fig. and Supplementary Fig. ), indicating that these taxa exhibited consistent responses to agricultural conversion across a broad range of habitat types. In addition, changes in the abundance of dominant phylotypes were mainly related to soil pH and moisture between ecosystems (Fig. and Supplementary Fig. ). Interestingly, the effects of agricultural conversion were much lower when focusing on the functional composition identified by KEGG (Fig. ) and there were no significant differences between agricultural and natural ecosystems. Moreover, the functional composition identified using COGs exhibited significant but minor differences between cropland and natural ecosystems (Supplementary Fig. ). Only less than 10% of functional groups identified by KEGG and COGs were affected by agricultural conversion (10%, 3% and 8% of KOs, and 5%, 1%, and 15% of COGs when comparing cropland with forest, grassland and wetland, respectively; Supplementary Fig. ). In terms of functional composition, agriculture significantly decreased the abundance of bacterial taxa specialized in nutrient cycling (for example, nitrogen fixation, phototrophy, and aromatic degradation) as classified by FAPROTAX compared with natural ecosystems (Fig. and Supplementary Fig. ) . Specific functional shifts were also observed in the metagenomic dataset (Fig. and Supplementary Fig. ). In total, three categories showed a consistent change in direction compared with other three natural ecosystems when aggregating over level 3 functional categories through COG annotations (Supplementary Fig. ). The functional categories “translation, ribosomal structure and biogenesis”, and “cytoskeleton” increased while “defense mechanisms” diminished in croplands. However, specific carbon-degrading genes exhibited inconsistent effects upon agricultural conversion (some genes were enriched or deleted), while significant differences in the overall carbon metabolism were not detected under agricultural land-use (Fig. ). This is most likely due to the high redundancy of broadly distributed functions, thereby buffering against taxonomic changes induced by agricultural land-use. Indeed, broad functions such as respiration, overall carbon catabolism and anabolism often seem more stable to shifts in microbial taxonomic composition than narrow metabolic functions such as the degradation of specific substrate – . Agriculture significantly altered a number of functionally important for N cycling, P utilization and sulfur metabolism genes. First, agriculture appeared to increase nitrification and denitrification processes, as indicated by increased nirK , narG , amoB and hao genes and it decreased the abundance of nitrogen fixation ( nifH ) (Fig. ), which could be due to the application of fertilizers and/or the loss of leguminous plant taxa found in natural ecosystems . These results are in agreement with the increase of N 2 O production and the decrease of nitrogen fixation upon land-use change , . The abundance of key genes for organic P mineralization and transportation (for example, phn and ugp ) were decreased in cropland (Fig. ). Opposite to this, dissimilatory sulfate reduction genes ( apr and dsr ) had higher abundance in croplands than in forests and grasslands but lower than in wetlands (Fig. ). Mechanisms underlying changed bacterial communities A set of specific microbial traits associated with microbial dormancy and dispersal would regulate their ability to survive in land-use change associated with resource-based and disturbance-based scenarios . For example, the abundance of Firmicutes and Actinobacteria with spore-forming ability was lower in croplands compared to three other natural ecosystems (see Fig. ), which was closely linked to a decrease in community-aggregated dormancy strategies (Supplementary Fig. ). We also observed that resuscitation-promoting gene was increased in cropland (Supplementary Fig. ), which are associated with long-term persistence of viable bacterial populations , indicating that the resuscitation after disturbance can allow for the proliferation of dormant taxa and accelerate increases in species richness . Moreover, homogeneous selection (HoS; selection under homogeneous abiotic and biotic conditions in space and time) dominated microbial community assembly (as calculated using β NTI ( β -nearest taxon index) and Raup–Crick based on Bray–Curtis dissimilarity (RC bray ) analysis) in croplands, with relative importance of 94.6% (Supplementary Fig. ). At the same time, agriculture, acting as an environmental filter, continues to enhance homogeneous selection on microbial assembly processes (Fig. ), as crop management result in homogeneous abiotic and biotic conditions across space. Our results suggest that both microbial traits and environmental filtering could play prominent roles in regulating agricultural-induced microbial composition shifts. Biotic interactions and abiotic environmental conditions also affect microbial composition under land-use change (Fig. ). Taxonomic composition showed significant correlations with environmental filtering of soil pH, moisture, and NH 4 -N content, the heterogeneity of soil pH and NH 4 -N content, and soil saprotrophic and pathogenic fungi. Functional composition was highly correlated with environmental filtering and heterogeneity of soil pH and NH 4 -N content. To disentangle direct and indirect impacts of land-use change and environmental drivers on microbial composition, we performed structural equation modeling (SEM; Supplementary Fig. ) using the most important soil and biotic explanatory variables, such as saprotrophic and pathogenic fungi, which were not collinear among them. Fungal saprotrophic and pathogenic composition, which was also affected by agricultural land-use, were significantly and directly correlated with bacterial taxonomic composition (Fig. ). Soil pH filtering played the strongest role in shaping taxonomic and functional composition (Fig. ). Moreover, the association of fungal and bacterial communities suggest an important role for biotic interactions in mediating agricultural-induced microbial composition changes. Although these variables could explain 85% of the variations in taxonomic composition, only 20% of the variations in functional composition were explained due to functional redundancy. More in-depth studies are necessary to determine the main drivers of changes in microbial functional composition. Links between bacterial communities and soil functions Soil enzyme functions involved in carbon, nitrogen, and phosphorus cycling differed between cropland and natural ecosystems (Fig. ). Interestingly, we did not observe the relationship between microbial functional composition and soil enzyme functions. We also found that the association of soil enzyme functions with microbial composition varied among different microbial lineages (Fig. ). The relative abundance of Bacteroidota was positively correlated with soil functions and activities of four of the five enzymes while the relative abundance of Gemmatimonadota were positively correlated with β −1,4-glucosidase (BG) and β -D-cellobiosidase (CBH) and negatively correlated with β −1,4-acetylglucosaminidase (NAG) and alkaline phosphatase (APP). In all, these results indicate significant linkages between soil functions and microbial taxonomic composition but not functional composition.
Our continental soil survey dataset (Fig. and Supplementary Tables , ) revealed that β -diversity of both microbial taxonomic and functional composition (identified by KEGG and COG) was significantly lower in cropland than in natural soil (Fig. d, h and Supplementary Figs. , ) demonstrating that cropland soils are more similar than paired natural ecosystem soils. For example, the β -diversity of taxonomic composition was significantly lower in cropland than in forest ( F 1,91504 = 6.429, slope = 0.0016, p < 0.05), in grassland ( F 1,75348 = 1532, slope = 0.0276, p < 0.001), and in wetland ( F 1,77004 = 6450, slope = 0.0532, p < 0.001; Fig. ). The β -diversity of functional composition was significantly lower in cropland than in grassland ( F 1,88 = 9.021, slope = 0.0885, p < 0.01), and in wetland ( F 1,88 = 6.886, slope = 0.0527, p < 0.05; Fig. ). When considering β -diversity at the site-level and keeping the same number of samples across sites, significant lower value was also detected in croplands compared to forest, grassland and wetland (Supplementary Fig. ). The global meta-analysis based on more than 2400 soil samples across six continents (Fig. ) further showed that microbial communities from croplands were significantly different from forest soils in taxonomic composition (PERMANOVA; R 2 = 0.026, p < 0.001), and β -diversity was significantly lower in croplands than forest soils at global scale (wilcoxon test: p < 0.001; Fig. ). These results jointly provide large-scale, e.g., continental and global scale, evidence for biotic homogenization of soil microbiome under agricultural conversion. Moreover, we found that the phylotypes, that are present in both croplands and natural ecosystems, were found in significantly more samples of croplands than in natural ecosystems (wilcoxon test: p < 0.001; Supplementary Fig. ), indicating an increase in the geographic ranges of existing taxa in croplands. The phylotypes unique to natural ecosystems occurred in significantly fewer samples than other shared phylotypes that present in both croplands and natural ecosystems (wilcoxon test: p < 0.001; Supplementary Fig. ), implying a possible loss of these habitat-specific taxa after agricultural conversion. Given that microbial composition is critical for maintenance and resilience of soil functions, e.g., nutrient supply, litter decomposition and water regulation , , agricultural-induced biotic homogenization could cause ecosystem service degradation and threaten sustainable management. Thus, even though a few studies have assessed the impacts of agricultural land-use change on microbial diversity and composition, biotic homogenization along with the reduction of regional community heterogeneity at large spatial scales should be taken into full consideration as a significant consequence of agricultural conversion.
To provide a general insight into agricultural-induced shifts in microbial phylotypes and functions originated from multiple natural ecosystems, we compared differences in community structure between cropland and surrounding forests, grasslands, and wetlands, respectively. Microbial communities of cropland soils were significantly different from those of natural forest, grassland and wetland soils in taxonomic composition (Fig. and Supplementary Fig. ). These differences were evident at both the phylum, class, order (Supplementary Fig. ) and phylotypes levels (Fig. ). Notably, the largest differences in taxonomic composition were found between croplands and wetlands (PERMANOVA; R 2 = 0.060, p < 0.001), followed by the comparison of croplands and forests (PERMANOVA; R 2 = 0.052, p < 0.001), and between croplands and grasslands (PERMANOVA; R 2 = 0.038, p < 0.001). Specifically, agricultural impacts significantly altered microbial composition (PERMANOVA; p < 0.05) in almost all of the locations, except for 2 of 37 sites in croplands and grasslands (Supplementary Table ). On average, agricultural effects significantly altered the abundance of nearly half of the phylotypes (44% for forests, 41% for grasslands, and 45% for wetlands; Supplementary Fig. ). Approximately 20% of the ASVs were lost from natural ecosystems upon conversion to agriculture, while approximately 23% of the ASVs increase in abundance. Specifically, the relative abundance of Chloroflexi, Gemmatimonadota, Planctomycetota, Myxcoccota and Latescibacterota increased in croplands compared with all three natural ecosystems (Fig. and Supplementary Fig. ), indicating that these taxa exhibited consistent responses to agricultural conversion across a broad range of habitat types. In addition, changes in the abundance of dominant phylotypes were mainly related to soil pH and moisture between ecosystems (Fig. and Supplementary Fig. ). Interestingly, the effects of agricultural conversion were much lower when focusing on the functional composition identified by KEGG (Fig. ) and there were no significant differences between agricultural and natural ecosystems. Moreover, the functional composition identified using COGs exhibited significant but minor differences between cropland and natural ecosystems (Supplementary Fig. ). Only less than 10% of functional groups identified by KEGG and COGs were affected by agricultural conversion (10%, 3% and 8% of KOs, and 5%, 1%, and 15% of COGs when comparing cropland with forest, grassland and wetland, respectively; Supplementary Fig. ). In terms of functional composition, agriculture significantly decreased the abundance of bacterial taxa specialized in nutrient cycling (for example, nitrogen fixation, phototrophy, and aromatic degradation) as classified by FAPROTAX compared with natural ecosystems (Fig. and Supplementary Fig. ) . Specific functional shifts were also observed in the metagenomic dataset (Fig. and Supplementary Fig. ). In total, three categories showed a consistent change in direction compared with other three natural ecosystems when aggregating over level 3 functional categories through COG annotations (Supplementary Fig. ). The functional categories “translation, ribosomal structure and biogenesis”, and “cytoskeleton” increased while “defense mechanisms” diminished in croplands. However, specific carbon-degrading genes exhibited inconsistent effects upon agricultural conversion (some genes were enriched or deleted), while significant differences in the overall carbon metabolism were not detected under agricultural land-use (Fig. ). This is most likely due to the high redundancy of broadly distributed functions, thereby buffering against taxonomic changes induced by agricultural land-use. Indeed, broad functions such as respiration, overall carbon catabolism and anabolism often seem more stable to shifts in microbial taxonomic composition than narrow metabolic functions such as the degradation of specific substrate – . Agriculture significantly altered a number of functionally important for N cycling, P utilization and sulfur metabolism genes. First, agriculture appeared to increase nitrification and denitrification processes, as indicated by increased nirK , narG , amoB and hao genes and it decreased the abundance of nitrogen fixation ( nifH ) (Fig. ), which could be due to the application of fertilizers and/or the loss of leguminous plant taxa found in natural ecosystems . These results are in agreement with the increase of N 2 O production and the decrease of nitrogen fixation upon land-use change , . The abundance of key genes for organic P mineralization and transportation (for example, phn and ugp ) were decreased in cropland (Fig. ). Opposite to this, dissimilatory sulfate reduction genes ( apr and dsr ) had higher abundance in croplands than in forests and grasslands but lower than in wetlands (Fig. ).
A set of specific microbial traits associated with microbial dormancy and dispersal would regulate their ability to survive in land-use change associated with resource-based and disturbance-based scenarios . For example, the abundance of Firmicutes and Actinobacteria with spore-forming ability was lower in croplands compared to three other natural ecosystems (see Fig. ), which was closely linked to a decrease in community-aggregated dormancy strategies (Supplementary Fig. ). We also observed that resuscitation-promoting gene was increased in cropland (Supplementary Fig. ), which are associated with long-term persistence of viable bacterial populations , indicating that the resuscitation after disturbance can allow for the proliferation of dormant taxa and accelerate increases in species richness . Moreover, homogeneous selection (HoS; selection under homogeneous abiotic and biotic conditions in space and time) dominated microbial community assembly (as calculated using β NTI ( β -nearest taxon index) and Raup–Crick based on Bray–Curtis dissimilarity (RC bray ) analysis) in croplands, with relative importance of 94.6% (Supplementary Fig. ). At the same time, agriculture, acting as an environmental filter, continues to enhance homogeneous selection on microbial assembly processes (Fig. ), as crop management result in homogeneous abiotic and biotic conditions across space. Our results suggest that both microbial traits and environmental filtering could play prominent roles in regulating agricultural-induced microbial composition shifts. Biotic interactions and abiotic environmental conditions also affect microbial composition under land-use change (Fig. ). Taxonomic composition showed significant correlations with environmental filtering of soil pH, moisture, and NH 4 -N content, the heterogeneity of soil pH and NH 4 -N content, and soil saprotrophic and pathogenic fungi. Functional composition was highly correlated with environmental filtering and heterogeneity of soil pH and NH 4 -N content. To disentangle direct and indirect impacts of land-use change and environmental drivers on microbial composition, we performed structural equation modeling (SEM; Supplementary Fig. ) using the most important soil and biotic explanatory variables, such as saprotrophic and pathogenic fungi, which were not collinear among them. Fungal saprotrophic and pathogenic composition, which was also affected by agricultural land-use, were significantly and directly correlated with bacterial taxonomic composition (Fig. ). Soil pH filtering played the strongest role in shaping taxonomic and functional composition (Fig. ). Moreover, the association of fungal and bacterial communities suggest an important role for biotic interactions in mediating agricultural-induced microbial composition changes. Although these variables could explain 85% of the variations in taxonomic composition, only 20% of the variations in functional composition were explained due to functional redundancy. More in-depth studies are necessary to determine the main drivers of changes in microbial functional composition.
Soil enzyme functions involved in carbon, nitrogen, and phosphorus cycling differed between cropland and natural ecosystems (Fig. ). Interestingly, we did not observe the relationship between microbial functional composition and soil enzyme functions. We also found that the association of soil enzyme functions with microbial composition varied among different microbial lineages (Fig. ). The relative abundance of Bacteroidota was positively correlated with soil functions and activities of four of the five enzymes while the relative abundance of Gemmatimonadota were positively correlated with β −1,4-glucosidase (BG) and β -D-cellobiosidase (CBH) and negatively correlated with β −1,4-acetylglucosaminidase (NAG) and alkaline phosphatase (APP). In all, these results indicate significant linkages between soil functions and microbial taxonomic composition but not functional composition.
Agricultural land-use change has exerted profound effects on above- and belowground biodiversity , , and the effects are likely to accelerate in the coming decades . While a number of studies showed that agricultural conversion led to biotic homogenization of aboveground communities, still very few studies investigated the belowground consequences. In the present study, we summarized the generalized effects of land-use conversion on belowground microbial communities and functions, encompassing multiple ecosystems. Our study provides large-scale evidence of taxonomic and, to a lesser degree, functional homogenization of soil microbiomes following agricultural conversion in terrestrial ecosystems at global and continental scales. The taxonomic variation across sites (Beta-diversity) was significantly lower in croplands than in grasslands, wetlands, and forests, pointing to biotic homogenization in croplands. Although land-use changes and agricultural conversion have been proven to be major drivers of biodiversity loss , , positive impacts of agriculture on biodiversity have been observed at regional and local scales in some studies – . One facet of these trends is that although local or alpha diversity may increase, this is typically at the expense of beta diversity . Previous studies have demonstrated that increases in local land use intensity led to biotic homogenization of microbial, plant, and animal groups both above- and below-ground , . Biotic homogenization is largely independent of changes in alpha diversity; land use intensity reduced local alpha-diversity in aboveground groups, but increased the α-diversity in belowground groups . Our study further extends these earlier observations at a continental and global scale and now provides widespread evidence that agricultural conversion results in biotic homogenization of the soil microbiome. Although taxonomic homogenization in cropland versus natural ecosystems was stronger and more significant in many cases, we observed very important microbial functional shifts under croplands, including functional homogenization , . This was evident when we calculated the beta-diversity across sites based on functional gene composition. Since the functional components of biodiversity are fundamental parts of ecosystem functions and services , , functional homogenization is the most direct evidence for the potential loss of ecosystem functions caused by agricultural conversion , . Our findings extend taxonomic-level results in Amazonian Forest and European grasslands that focus on the impact of agricultural management on belowground taxonomic homogenization in local-scale, to the large-scale functional homogenization. Overall, our study provides a comprehensive insight that agricultural land-use change cause biotic homogenization in taxonomic and functional composition, and suggests halting reclamation and developing ecological restoration for cropland to conserve landscape-scale biodiversity and ecosystem service provision , . Biotic homogenization in response to agricultural impacts is a multifaceted process that involves considering the invasion and extinction of species, as well as the heterogeneity of landscapes. In agricultural systems, it is generally believed that the biomes are a subset of the regional species pool, which is composed of surrounding natural ecosystems . This highlights the selective effects of agricultural conversion, which could cause pressure and force on soil communities from natural ecosystems. For example, the destruction of soil structure and aggregates, as well as alterations and homogenization in soil environmental conditions caused by agricultural conversion can result in the trait-based filtering out of certain species, leading to the loss of existing species and the dominance of microorganisms that are better adapted to agricultural management. Moreover, geographic range size is a major determinant of species’ extinction risk, and rare species therefore are vulnerable to land use change and are at greater risk of extinction . The establishment of agricultural systems through intensive management can facilitate the spread of colonizing species that are abundant and prevalent due to the characteristics of broad environmental adaptation, while rare or specialized species may decrease in their abundance and occupancy over time , , which led to a homogenization of community composition across space. Land-use change is proposed to affect turnover in community composition via its effect on stress tolerance, resource acquisition, and dispersal ability. Stronger stress-tolerant, broader resource-flexibility cosmopolitan species with unlimited dispersal capacity are more stable to land-use change because of increasing adaptive potential and/or extensive ability to exploit soil resource availability , . Frequently disturbed soil environments can promote the gains and proliferation of novel species and the gradual replacement of locally distinct communities by cosmopolitan communities via altered competitive and coexistence dynamics , homogenizing assemblage composition. On the other hand, the influence of agricultural conversion on biotic homogenization might be attributed to the reduction in environmental heterogeneity in monoculture-dominated landscapes . Landscape heterogeneity is central to the spatial organization of ecological communities . Variations in vegetation structural and soil conditions influence beta diversity and turnover of soil fauna, bacteria, and fungi. Monoculture-dominated croplands have lower environmental heterogeneity compared with vegetation structural complexity in natural ecosystems, where heterogeneous habitats contribute to increased beta diversity across spatial scales. Our findings, supported by the estimation of ecological processes based on β NTI and RC Bray (Fig. and Supplementary Fig. ), illustrate that the role of homogeneous selection was stronger for community assembly in croplands, suggesting the consequence of agricultural conversion on homogeneous abiotic and biotic conditions across space. The impact of agriculture on biotic homogenization might vary at different scales. In contrast to our results, a regional survey on the conversion of steppe to cropland demonstrated that agriculture increased spatial heterogeneity of soil functional genes . The lower functional turnover in steppe may be attributable to stable and similar soil environments across the region. Diverse in local but functionally homogeneous sward in regional natural steppe ecosystem exerts a stabilizing effect on the soil environment and soil ecosystem processes, reducing the impact of spatial and temporal variation in climate, soil texture and topography . Differently, agricultural management such as seasonal planting, crop types, and fallow cycles actually contribute to greater temporal and spatial variability that selects for greater heterogeneity across the region. Given the complexity of the soil environment, more attention needs to be paid to the biotic homogenization caused by agricultural conversion of the soil microbiome at various spatial scales. Our results showed that land use change had a greater impact on taxonomic composition than on functional composition, highlighting the functional redundancy of soil microbiomes , , . Soil microorganisms represent the most biologically and phylogenetically diverse community on Earth . Although the taxonomic composition of soil microbiome varies tremendously across soil, microbial gene composition or functional capacity remains highly conserved , , with lots of phylogenetically unrelated taxa carrying similar genes and performing similar functions . For example, lignin substrate can be degraded by gram-negative bacteria Comamonadaceae and Caulobacteraceae , and the genus Asticcacaulis and Caulobacter (members of Caulobacteraceae ) could degrade both hemicellulose and cellulose and all three lignocellulosic polymers, respectively . Numerous microorganisms with the ability to participate in carbon degradation can coexist on the surface of plant residues . Agricultural conversion, however, had minimal impact on overall carbon degradation and fixation, but did reduce nitrogen fixation and phosphorus mineralization and transportation potential (Fig. ), suggesting the functional redundancy for carbon metabolism in soils. The fact that the potential for nitrogen fixation and phosphorus mineralization is reduced, indicates that croplands rely less on these processes due to the breakdown of nutrient cycling plant-microbial symbioses under agricultural fertilization. Taken together, our results indicated that agricultural land-use change significantly altered microbial taxonomic composition while the gene content remains relatively conserved, especially in relation to carbon metabolism. More realistic functional gene expression studies the functional divergences, redundancies, and complementarities in the different land use scenarios, e.g. metatranscriptomics or quantitative stable-isotope probing (qSIP) , that correlate with the observed taxonomic shifts after agricultural conversion, needs to be further revealed in the future. Changes in soil microbial communities across space are often strongly correlated with differences in soil abiotic and biotic conditions . Similar to previous study , we observed soil pH is a major driver of the diversity and composition of soil bacterial communities across land-use types. More importantly, we found that fungal communities, particularly pathogens and saprotrophs, were strongly associated with changes in soil bacterial communities. Interactions between fungi and bacteria could partly drive the bacterial community shifts along a steep gradient of fungal community change , . For example, manipulating fungal richness can immediately mediate assembly processes of bacterial community . The fungal hyphae could provide soil bacteria with ecological opportunities in severely carbon-limited soils by releasing carbonaceous compounds and providing a colonizable surface for the creation of new bacterial niches , . In addition to the effect of external conditions (e.g., biotic interactions and abiotic environmental conditions), our results also emphasize the important roles of microbial traits in regulating the response of microbial composition to agricultural conversion. The dormancy potential strategy changed from sporulation and toxin–antitoxin systems to resuscitation-promoting factors . The sporulation trait affects species composition, with the abundance of phyla Firmicutes and Actinobacteria with spore-forming ability increasing in croplands. The impact of regional species pools on cropland bacterial diversity is modulated by sporulation trait . Many taxa with spore-forming ability had a higher species pool effect, indicating their survival and competitive advantage under environmental stress, as well as their retention during land use changes or their greater likelihood of spreading from natural ecosystems due to their adaptive capabilities. Our findings provide a valuable insight for predicting ecological consequences of land-use change and agricultural management. The links between microbial composition and ecosystem function suggest that biotic homogenization have previously unrecognized and negative consequences for agricultural sustainability and service. Although the functional redundancy with C metabolism of soil microbiomes supports the stability and resilience of ecosystem functioning in response to perturbations , increased agricultural intensification gives rise to large uncertainty in predicting the loss of ecosystem function. It is also important to note the ways observations at different spatial scales can impact the interpretation of broad soil microbiome responses. Although our study covered a global scale, study sites and sequencing data were not evenly distributed. Most observations focus on forest-cropland ecosystem contrasts and are subject to methodological limitations arising from comparisons of sequencing methods and sampling schemes. Overall, our study suggests that biotic homogenization of the belowground microbiome across large spatial scale should be taken into account when evaluating the sustainability and soil health of agricultural management practices.
Continental survey and sampling We conducted a continental field survey in croplands and adjacent natural ecosystems from 44 regions across China (Fig. and Supplementary Table ). Adjacent natural ecosystems were ~2 km from croplands and were selected to represent the most common and relatively undisturbed ecosystems, including forests, grasslands and wetlands. The distance between cropland and adjacent natural ecosystems is about 2 km in order to maintain a consistent climate and soil type. Among natural ecosystems of the 44 study regions, 30 regions include forests, grasslands, and wetlands, five regions include forests and wetlands, four regions include grasslands and wetlands, three regions include grasslands and wetlands, and two regions only include forests (Fig. and Supplementary Table ). The study survey represents a wide range of climate and soil gradients of climate, soil, and vegetation types (from tropical to boreal zones). For instance, mean annual precipitation and mean annual temperature in these regions are from 78 to 1775 mm and −2.8 to 24.4 °C, respectively. Soil pH ranged from 4.63 to 10.18 and soil organic matter ranged from 4.64 to 60.22 g·kg −1 across all of the survey regions, representing broad environmental conditions. To reduce variation between regions as much as possible, we focused on fields planted with maize ( Zea mays ) to represent agricultural systems since maize is widely cultivated throughout China and the world, with a total production exceeding that of wheat or rice . In each region, we collected 4 to 10 plots of each ecosystem type. Composite surface soil samples (top ~20 cm depth) were collected at each plot in July and August 2019, during the crop growing season. Soil samples at each site pair were collected within one day to minimize the impact of sampling times. Each plot has a size of 2 × 2 m 2 and is the same across sites and ecosystems. We focused on surface soils because (1) topsoil is most affected by land use change; (2) agricultural management practices also primarily deal with topsoil, such as conventional tillage and crop root growth, which shape the tillage layer. In brief, soil samples were mixed by taking three soil cores with a 5-cm-diameter auger for each plot in the surface layer. After sampling, we thoroughly rinsed the soil auger using clean water. To ensure disinfection and sterilization, we then applied a 75% alcohol solution to its surface. Afterward, we placed the auger bit into a sterile bag for safe-keeping until the subsequent sampling event. These soil samples were sieved through a 2.0-mm mesh to remove plant roots, litter, rocks, and other debris. A total of 1185 soil samples were collected representing 856 paired soils, with 303 forest-cropland pairs, 275 grassland-cropland pairs, and 278 wetland-cropland pairs obtained (Supplementary Table ). Each soil sample was divided into two subsamples where one set was frozen at −80 °C for DNA extraction and microbial analysis and the other set was air dried for measurement of soil physical and chemical properties. Global-scale meta-analysis We conducted an extensive literature survey from 2013 to February 2023 using the Web of Science database ( https://www.webofscience.com/ ). The format of the keywords used for the literature search includes (bacteri*) AND (land use change OR land cover change OR land use/cover change OR LULCC OR LUCC OR cropland OR farmland OR arable). After downloading the literature based on the keywords above, we obtained a total of 297 publications (Supplementary Fig. ). Following the criteria below, we conducted the initial selection of the studies: (1) studies with a one-to-one correspondence of sequencing data between agricultural land and natural ecosystems were included; (2) articles for which sequencing metadata were not available from public repositories or upon request from individual study authors were excluded. After the initial selection, 75 studies were left, over 6000 sample sequencing data. In these studies, high-throughput sequencing of bacterial communities was conducted using Illumina, Ion S5, and 454 pyrosequencing platforms. Twenty-three primer pairs were identified from the research metadata, and the most used primers in the sample were 515F and 907R (18/75), 515F and 806R (13/75), and 338F and 806R (16/75). After downloading the raw data corresponding to the data availability provided in the articles, the raw sequences were processed using QIIME 2 and annotated using the USEARCH tool. A final ASV dataset comprising 3482 samples was remained for subsequent analysis after excluding low-reads (<10,000 reads) and low-quality samples. We utilized the -fastq_filter command in the vsearch tool for sequence quality control, with the parameter -fastq_maxee set to 1. This implies that the maximum expected errors threshold for low-quality bases in all sequences is set to 1. Only sequences with an expected error count less than or equal to 1 are retained, while sequences exceeding this threshold are filtered out. In addition to sequencing data, we also collected the following parameters: ecosystem type, plant type, location (i.e., latitude and longitude). Taking into account sample size and coverage, we selected forest to represent natural ecosystem because forest soils included more than 1300 samples and covered six continents. Other ecosystems with only a few sites or low distribution range lacked representation for large-scale evidence (Supplementary Fig. ), and were excluded from further analyses. In total, 2403 samples were included in the global-scale meta-analysis. Soil environmental variables We evaluated soil chemistry and nutrients to gauge changes across agricultural land-use change and to consider the implications of those variables on microbial communities. Here, we selected the most important six soil variables, i.e., soil pH, organic matter (OM), soil moisture (Mo), available phosphorus (AP), and available nitrogen (NO 3 –N and NH 4 –N). These indicators were recognized as the main soil variables influencing bacterial diversity patterns at global and regional scales – . Soil pH was assessed in a 1:5 suspension (soil to distilled water) using a pH meter. Organic matter was determined calorimetrically following oxidation with a combination of potassium dichromate and sulfuric acid. Soil moisture was measured by the gravimetric method after samples were oven-dried at 100 °C for 24 h. NO 3 -N and NH 4 -N concentrations were measured using 1 M KCl solution with Continuous-Flow AutoAnalyzer. Available phosphorus concentrations were extracted by NaHCO 3 and measured by molybdenum blue colorimetry. We measured soil physicochemical properties for each plot. Local soil filtering was calculated as the average of all plots within each ecosystem for each soil variable and local soil heterogeneity was calculated as the within-ecosystem standard deviation of each soil variable. Soil enzyme activities The activities of soil extracellular enzymes involved in C, N, and P acquisition were determined using the microplate-scale fluorometric method . We used a 200 μM solution of substrates labeled with 4-methylumbelliferone or 7-amino-4-methylcoumarin. The C-acquisition enzymes analyzed included β −1,4-glucosidase (BG), 1,4- β -Dcellobiohydrolase (CBH) and β -xylosidase (BX). The N-acquisition enzymes analyzed were β −1,4-N-acetylglucosaminidase (NAG) and L-leucine aminopeptidase (LAP), while the P-acquisition enzyme analyzed was alkaline phosphatase (APP). After incubation at 35 °C, plates were centrifuged, and the supernatant was transferred to black, flat-bottom 96-well plates. Fluorescence was measured using a microplate reader with 365 nm excitation and 450 nm emission filters. Soil enzyme activities were expressed as nmol g −1 dry soil h −1 . DNA extraction, amplicon sequencing, and data preprocessing Genomic DNA was extracted from 0.5 g of the soils using the MP FastDNA spin kit for soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer’s instructions. The diversity of soil bacteria and fungi was measured by 16 S rRNA gene and nuclear ribosomal ITS amplicon sequencing using an Illumina MiSeq PE250 platform. For the bacterial community, 16 S rRNA genes were amplified using primer set 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 907 R (5′-CCGTCAATTCCTTTGAGTTT-3′), targeting the V4-V5 region of the 16 S rRNA gene. For the fungal community, the first nuclear ribosomal ITS sequences were amplified using primers ITS5-1737F (5′-GGAAGTAAAAGTCGTAACAAGG-3′) and ITS2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′), targeting the ITS1-5F region. PCR amplification was performed in a 50 μl volume: 25 μl 2x Premix Taq (Takara Biotechnology, Dalian Co. Ltd., China), 1 μl each primer (10 μM) and 3 μl DNA (20 ng/μl) template. The PCR thermal cycling conditions were performed by thermocycling: 5 min at 94 °C for initialization, followed by 30 cycles of 30 s denaturation at 94 °C, 30 s annealing at 52 °C, 30 s extension at 72 °C, and 10 min final elongation at 72 °C. The length and concentration of the PCR product were detected by 1% agarose gel electrophoresis. Sequencing libraries were generated using NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® (New England Biolabs, MA, USA) following the manufacturer’s recommendations and index codes were added. Bioinformatic processing, including filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads, was performed using the Divisive Amplicon Denoising Algorithm 2 (DADA2) package in R . In brief, the plotQualityProfile command was run to detect the quality of the amplified sequences. We imposed a minimum length of 100 bp to remove any small fragments at the filtering stage, at which, the error in the maxEE argument was 2 as this optimized the retention of reads throughout the pipeline. Error rates were subsequently calculated by the DADA2 algorithm before dereplication and merging of paired end sequences. Chimeras were removed using the removeBimeraDenovo command with method = “consensus” . Finally, the taxonomical annotation of the representative sequences of amplicon sequence variants (ASVs) was performed with a naïve Bayesian classifier using the Silva v. 138 (for bacteria) and the UNITE v. 7 (for fungi) database , . It should be noted that although the ITS region is by far the best option as a general DNA (meta) barcoding marker for fungi, there are inherent limitations associated with the use of a ITS region for enabling in-depth characterization of fungal communities. We were not concerned with changes at the fungal species level, so ITS region sequencing should have limited impact on our results. The sequence number in each sample was rarefied to the same depth for the 16 S rRNA gene (15000 reads) or ITS sequences (21921 reads), leaving a total of 31,402 bacterial ASVs and 77,962 fungal ASVs for further analyses. Shotgun metagenome sequencing A subset of 40 samples from 10 regions covering cropland, forest, grassland and wetland soils were selected for metagenomic sequencing to analyze changes in microbial community functional potential ( n = 10 per ecosystem type; Supplementary Fig. ). Metagenomic libraries for 40 samples were prepared according to the product instructions of ALFA-SEQ DNA Library Prep Kit (Findrop, Guangzhou, China) and index code was added. Initial quantification of the library concentration was performed using Qubit 3.0 fluorometer (Life Technologies, Carlsbad, CA, USA) and the library was diluted to 1 ng/µL. Agilent 2100 Bioanalyzer System (Agilent Technologies, CA, USA) was used to detect the integrity of library fragments and the length of insert size. Then, the library was sequenced on Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA) to generate 150 bp paired-end reads at Guangdong Magigene Biotechnology Co., Ltd. In total, 1.71 × 10 9 raw reads were sequenced across all samples, which yielded 512.3 Gbp of total sequence information with an average data volume of 12.8 Gbp per sample. Raw data were quality checked with FastQC (v0.11.9) and processed using Trimmomatic v.0.39 (leading: 3, trailing: 3, slidingwindow: 4:15, minlen:36) to trim adapters and discard bases with a quality score <15 and length <36 bp. After that, 12.2 Gbp clean data per sample were obtained. Clean reads were annotated for functional analysis of the microbiome using HUMAnN v3.7 (based on DIAMOND (version 2.1.6) and Bowtie2 (version 2.5.1) ) with ChocoPhlAn database (version “mpa_vJan21_CHOCOPhlAnSGB_202103”) and UniRef90 (version “uniref90_201901b”) protein database to quantify relative abundance of functional genes and metabolic pathways . The annotation results were organized according to Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologues (KOs), Clusters of Orthologous Group of proteins (COG) functional categories and MetaCyc functional pathways using “humann3_regroup_table” script. The abundance of functional gene was expressed as Transcripts per million. Estimation of ecological processes The estimation of ecological processes was performed according to Stegen et al. . The aim of framework is to quantitatively estimate the degree to which spatial turnover in community composition is influenced by selection, drift acting alone, dispersal limitation acting in concert with drift and homogenizing dispersal. The estimation of ecological processes followed a two-step procedure. First, we quantified β NTI ( β -nearest taxon index) for all pairwise community comparisons. A value of | β NTI| > 2 indicates that observed turnover between a pair of communities is governed primarily by selection. A value of | β NTI| < 2 indicates that observed turnover between a pair of communities is governed by drift, dispersal limitation and homogenizing dispersal. β NTI < − 2 indicates significantly less phylogenetic turnover than expected (i.e., homogeneous selection) while β NTI > 2 indicates significantly more phylogenetic turnover than expected (i.e., variable selection). Second, we quantified Raup–Crick (RC bray ) for pairwise community comparisons that were not governed by selection (that is, those with | β NTI| < 2). The relative influence of homogenizing dispersal was quantified as the fraction of pairwise comparisons with | β NT | < 2 and RC Bray < –0.95. Dispersal limitation was quantified as the fraction of pairwise comparisons with | β NTI| < 2 and RC Bray > 0.95. The fractions of all pairwise comparisons with | β NTI| < 2 and |RC Bray | < 0.95 were used to estimate influence of “undominated” assembly, which mostly consists of weak selection, weak dispersal, diversification, and/or drift . β NTI and RC Bray could differentiate the relative importance of five assembly processes to the whole community. The five assembly processes were assessed for their relative importance in governing community variations under agricultural land-use change. Statistical analyses All statistical analyses were conducted in the statistical platform R (V4.2.1; http://www.r-project.org/ ; Supplementary Table ). Large-scale microbial homogenization was reflected by a decrease in community turnover rate (decreased β -diversity in space). To analyse the response of β -diversity to agricultural conversion, we calculated taxonomic (16S) and functional (KEGG and COG module level) community dissimilarity between sites using Bray–Curtis index. We tested the effects of agricultural impacts on the relative abundance of microbial taxonomic and functional groups using linear mixed-effects model (LMM), in which sites were termed as random intercept effects. Microbial functional groups were predicted by the Functional Annotation of Prokaryotic Taxa (FAPROTAX) and PICRUSt2 . Analysis of LMM was conducted in lme4 R packages . To characterize how microbial communities differ, Principal coordinate analyses (PCoA) were conducted on Bray–Curtis index to examine dissimilarities among taxonomic and functional composition between croplands and natural ecosystems. PERMANOVA was utilized to test the statistical significance of dissimilarity among ecosystem types. To link soil environmental and fungal variables to microbial communities, the correlations between soil filtering and heterogeneity and fungal functional groups were tested by Mantel correlations. Fungal phylotypes were assigned into three functional groups—soil saprotrophs, litter saprotrophs and plant pathogens using FungalTraits . To assess changes in functional genes with agricultural conversion, we calculated log2-fold changes in croplands relative to natural ecosystems (forests, grasslands, and wetlands) using DESeq2 with the apeglm shrinkage algorithm. We also used DESeq2 to identify microbial phylotypes, and functional gene annotation assigned to COG and KEGG that significantly increased, decreased and unchanged under agricultural impacts relative to natural ecosystems. To discern the direct and indirect effects of agricultural impacts on microbial composition and soil functions, a structural equation model was conducted to assess the causal relationships among agricultural land-use change, soil environmental variables, fungal communities, and microbial composition and soil functions. We first considered a hypothesized conceptual model (Supplementary Fig. ) that included all reasonable pathways. Then, we sequentially eliminated non-significant pathways unless the pathways were biologically informative or added pathways on the basis of the residual correlations . Three metrics were used to quantify the goodness of fit of SEM models: the χ 2 test, the root mean square error of approximation (RMSEA), and the Comparative Fit Index (CFI). Specifically, the closer to 1 CFI value, closer to 0 RMSEA values, and the higher χ 2 and RMSEA P values, the better model performs. With a good model fit, we were able to interpret the path coefficients of the model and their associated P values. A path coefficient is analogous to the partial correlation coefficient, and describes the strength and sign of the relationship between two variables. Microbial taxonomic composition (16S) and functional (KEGG) composition were represented by the principal coordinate analyses 1, the first component of PCoA analysis. SEM were conducted using 40 site samples in the “lavaan” package in R environment . Reporting summary Further information on research design is available in the linked to this article.
We conducted a continental field survey in croplands and adjacent natural ecosystems from 44 regions across China (Fig. and Supplementary Table ). Adjacent natural ecosystems were ~2 km from croplands and were selected to represent the most common and relatively undisturbed ecosystems, including forests, grasslands and wetlands. The distance between cropland and adjacent natural ecosystems is about 2 km in order to maintain a consistent climate and soil type. Among natural ecosystems of the 44 study regions, 30 regions include forests, grasslands, and wetlands, five regions include forests and wetlands, four regions include grasslands and wetlands, three regions include grasslands and wetlands, and two regions only include forests (Fig. and Supplementary Table ). The study survey represents a wide range of climate and soil gradients of climate, soil, and vegetation types (from tropical to boreal zones). For instance, mean annual precipitation and mean annual temperature in these regions are from 78 to 1775 mm and −2.8 to 24.4 °C, respectively. Soil pH ranged from 4.63 to 10.18 and soil organic matter ranged from 4.64 to 60.22 g·kg −1 across all of the survey regions, representing broad environmental conditions. To reduce variation between regions as much as possible, we focused on fields planted with maize ( Zea mays ) to represent agricultural systems since maize is widely cultivated throughout China and the world, with a total production exceeding that of wheat or rice . In each region, we collected 4 to 10 plots of each ecosystem type. Composite surface soil samples (top ~20 cm depth) were collected at each plot in July and August 2019, during the crop growing season. Soil samples at each site pair were collected within one day to minimize the impact of sampling times. Each plot has a size of 2 × 2 m 2 and is the same across sites and ecosystems. We focused on surface soils because (1) topsoil is most affected by land use change; (2) agricultural management practices also primarily deal with topsoil, such as conventional tillage and crop root growth, which shape the tillage layer. In brief, soil samples were mixed by taking three soil cores with a 5-cm-diameter auger for each plot in the surface layer. After sampling, we thoroughly rinsed the soil auger using clean water. To ensure disinfection and sterilization, we then applied a 75% alcohol solution to its surface. Afterward, we placed the auger bit into a sterile bag for safe-keeping until the subsequent sampling event. These soil samples were sieved through a 2.0-mm mesh to remove plant roots, litter, rocks, and other debris. A total of 1185 soil samples were collected representing 856 paired soils, with 303 forest-cropland pairs, 275 grassland-cropland pairs, and 278 wetland-cropland pairs obtained (Supplementary Table ). Each soil sample was divided into two subsamples where one set was frozen at −80 °C for DNA extraction and microbial analysis and the other set was air dried for measurement of soil physical and chemical properties.
We conducted an extensive literature survey from 2013 to February 2023 using the Web of Science database ( https://www.webofscience.com/ ). The format of the keywords used for the literature search includes (bacteri*) AND (land use change OR land cover change OR land use/cover change OR LULCC OR LUCC OR cropland OR farmland OR arable). After downloading the literature based on the keywords above, we obtained a total of 297 publications (Supplementary Fig. ). Following the criteria below, we conducted the initial selection of the studies: (1) studies with a one-to-one correspondence of sequencing data between agricultural land and natural ecosystems were included; (2) articles for which sequencing metadata were not available from public repositories or upon request from individual study authors were excluded. After the initial selection, 75 studies were left, over 6000 sample sequencing data. In these studies, high-throughput sequencing of bacterial communities was conducted using Illumina, Ion S5, and 454 pyrosequencing platforms. Twenty-three primer pairs were identified from the research metadata, and the most used primers in the sample were 515F and 907R (18/75), 515F and 806R (13/75), and 338F and 806R (16/75). After downloading the raw data corresponding to the data availability provided in the articles, the raw sequences were processed using QIIME 2 and annotated using the USEARCH tool. A final ASV dataset comprising 3482 samples was remained for subsequent analysis after excluding low-reads (<10,000 reads) and low-quality samples. We utilized the -fastq_filter command in the vsearch tool for sequence quality control, with the parameter -fastq_maxee set to 1. This implies that the maximum expected errors threshold for low-quality bases in all sequences is set to 1. Only sequences with an expected error count less than or equal to 1 are retained, while sequences exceeding this threshold are filtered out. In addition to sequencing data, we also collected the following parameters: ecosystem type, plant type, location (i.e., latitude and longitude). Taking into account sample size and coverage, we selected forest to represent natural ecosystem because forest soils included more than 1300 samples and covered six continents. Other ecosystems with only a few sites or low distribution range lacked representation for large-scale evidence (Supplementary Fig. ), and were excluded from further analyses. In total, 2403 samples were included in the global-scale meta-analysis.
We evaluated soil chemistry and nutrients to gauge changes across agricultural land-use change and to consider the implications of those variables on microbial communities. Here, we selected the most important six soil variables, i.e., soil pH, organic matter (OM), soil moisture (Mo), available phosphorus (AP), and available nitrogen (NO 3 –N and NH 4 –N). These indicators were recognized as the main soil variables influencing bacterial diversity patterns at global and regional scales – . Soil pH was assessed in a 1:5 suspension (soil to distilled water) using a pH meter. Organic matter was determined calorimetrically following oxidation with a combination of potassium dichromate and sulfuric acid. Soil moisture was measured by the gravimetric method after samples were oven-dried at 100 °C for 24 h. NO 3 -N and NH 4 -N concentrations were measured using 1 M KCl solution with Continuous-Flow AutoAnalyzer. Available phosphorus concentrations were extracted by NaHCO 3 and measured by molybdenum blue colorimetry. We measured soil physicochemical properties for each plot. Local soil filtering was calculated as the average of all plots within each ecosystem for each soil variable and local soil heterogeneity was calculated as the within-ecosystem standard deviation of each soil variable.
The activities of soil extracellular enzymes involved in C, N, and P acquisition were determined using the microplate-scale fluorometric method . We used a 200 μM solution of substrates labeled with 4-methylumbelliferone or 7-amino-4-methylcoumarin. The C-acquisition enzymes analyzed included β −1,4-glucosidase (BG), 1,4- β -Dcellobiohydrolase (CBH) and β -xylosidase (BX). The N-acquisition enzymes analyzed were β −1,4-N-acetylglucosaminidase (NAG) and L-leucine aminopeptidase (LAP), while the P-acquisition enzyme analyzed was alkaline phosphatase (APP). After incubation at 35 °C, plates were centrifuged, and the supernatant was transferred to black, flat-bottom 96-well plates. Fluorescence was measured using a microplate reader with 365 nm excitation and 450 nm emission filters. Soil enzyme activities were expressed as nmol g −1 dry soil h −1 .
Genomic DNA was extracted from 0.5 g of the soils using the MP FastDNA spin kit for soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer’s instructions. The diversity of soil bacteria and fungi was measured by 16 S rRNA gene and nuclear ribosomal ITS amplicon sequencing using an Illumina MiSeq PE250 platform. For the bacterial community, 16 S rRNA genes were amplified using primer set 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 907 R (5′-CCGTCAATTCCTTTGAGTTT-3′), targeting the V4-V5 region of the 16 S rRNA gene. For the fungal community, the first nuclear ribosomal ITS sequences were amplified using primers ITS5-1737F (5′-GGAAGTAAAAGTCGTAACAAGG-3′) and ITS2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′), targeting the ITS1-5F region. PCR amplification was performed in a 50 μl volume: 25 μl 2x Premix Taq (Takara Biotechnology, Dalian Co. Ltd., China), 1 μl each primer (10 μM) and 3 μl DNA (20 ng/μl) template. The PCR thermal cycling conditions were performed by thermocycling: 5 min at 94 °C for initialization, followed by 30 cycles of 30 s denaturation at 94 °C, 30 s annealing at 52 °C, 30 s extension at 72 °C, and 10 min final elongation at 72 °C. The length and concentration of the PCR product were detected by 1% agarose gel electrophoresis. Sequencing libraries were generated using NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® (New England Biolabs, MA, USA) following the manufacturer’s recommendations and index codes were added. Bioinformatic processing, including filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads, was performed using the Divisive Amplicon Denoising Algorithm 2 (DADA2) package in R . In brief, the plotQualityProfile command was run to detect the quality of the amplified sequences. We imposed a minimum length of 100 bp to remove any small fragments at the filtering stage, at which, the error in the maxEE argument was 2 as this optimized the retention of reads throughout the pipeline. Error rates were subsequently calculated by the DADA2 algorithm before dereplication and merging of paired end sequences. Chimeras were removed using the removeBimeraDenovo command with method = “consensus” . Finally, the taxonomical annotation of the representative sequences of amplicon sequence variants (ASVs) was performed with a naïve Bayesian classifier using the Silva v. 138 (for bacteria) and the UNITE v. 7 (for fungi) database , . It should be noted that although the ITS region is by far the best option as a general DNA (meta) barcoding marker for fungi, there are inherent limitations associated with the use of a ITS region for enabling in-depth characterization of fungal communities. We were not concerned with changes at the fungal species level, so ITS region sequencing should have limited impact on our results. The sequence number in each sample was rarefied to the same depth for the 16 S rRNA gene (15000 reads) or ITS sequences (21921 reads), leaving a total of 31,402 bacterial ASVs and 77,962 fungal ASVs for further analyses.
A subset of 40 samples from 10 regions covering cropland, forest, grassland and wetland soils were selected for metagenomic sequencing to analyze changes in microbial community functional potential ( n = 10 per ecosystem type; Supplementary Fig. ). Metagenomic libraries for 40 samples were prepared according to the product instructions of ALFA-SEQ DNA Library Prep Kit (Findrop, Guangzhou, China) and index code was added. Initial quantification of the library concentration was performed using Qubit 3.0 fluorometer (Life Technologies, Carlsbad, CA, USA) and the library was diluted to 1 ng/µL. Agilent 2100 Bioanalyzer System (Agilent Technologies, CA, USA) was used to detect the integrity of library fragments and the length of insert size. Then, the library was sequenced on Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA) to generate 150 bp paired-end reads at Guangdong Magigene Biotechnology Co., Ltd. In total, 1.71 × 10 9 raw reads were sequenced across all samples, which yielded 512.3 Gbp of total sequence information with an average data volume of 12.8 Gbp per sample. Raw data were quality checked with FastQC (v0.11.9) and processed using Trimmomatic v.0.39 (leading: 3, trailing: 3, slidingwindow: 4:15, minlen:36) to trim adapters and discard bases with a quality score <15 and length <36 bp. After that, 12.2 Gbp clean data per sample were obtained. Clean reads were annotated for functional analysis of the microbiome using HUMAnN v3.7 (based on DIAMOND (version 2.1.6) and Bowtie2 (version 2.5.1) ) with ChocoPhlAn database (version “mpa_vJan21_CHOCOPhlAnSGB_202103”) and UniRef90 (version “uniref90_201901b”) protein database to quantify relative abundance of functional genes and metabolic pathways . The annotation results were organized according to Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologues (KOs), Clusters of Orthologous Group of proteins (COG) functional categories and MetaCyc functional pathways using “humann3_regroup_table” script. The abundance of functional gene was expressed as Transcripts per million.
The estimation of ecological processes was performed according to Stegen et al. . The aim of framework is to quantitatively estimate the degree to which spatial turnover in community composition is influenced by selection, drift acting alone, dispersal limitation acting in concert with drift and homogenizing dispersal. The estimation of ecological processes followed a two-step procedure. First, we quantified β NTI ( β -nearest taxon index) for all pairwise community comparisons. A value of | β NTI| > 2 indicates that observed turnover between a pair of communities is governed primarily by selection. A value of | β NTI| < 2 indicates that observed turnover between a pair of communities is governed by drift, dispersal limitation and homogenizing dispersal. β NTI < − 2 indicates significantly less phylogenetic turnover than expected (i.e., homogeneous selection) while β NTI > 2 indicates significantly more phylogenetic turnover than expected (i.e., variable selection). Second, we quantified Raup–Crick (RC bray ) for pairwise community comparisons that were not governed by selection (that is, those with | β NTI| < 2). The relative influence of homogenizing dispersal was quantified as the fraction of pairwise comparisons with | β NT | < 2 and RC Bray < –0.95. Dispersal limitation was quantified as the fraction of pairwise comparisons with | β NTI| < 2 and RC Bray > 0.95. The fractions of all pairwise comparisons with | β NTI| < 2 and |RC Bray | < 0.95 were used to estimate influence of “undominated” assembly, which mostly consists of weak selection, weak dispersal, diversification, and/or drift . β NTI and RC Bray could differentiate the relative importance of five assembly processes to the whole community. The five assembly processes were assessed for their relative importance in governing community variations under agricultural land-use change.
All statistical analyses were conducted in the statistical platform R (V4.2.1; http://www.r-project.org/ ; Supplementary Table ). Large-scale microbial homogenization was reflected by a decrease in community turnover rate (decreased β -diversity in space). To analyse the response of β -diversity to agricultural conversion, we calculated taxonomic (16S) and functional (KEGG and COG module level) community dissimilarity between sites using Bray–Curtis index. We tested the effects of agricultural impacts on the relative abundance of microbial taxonomic and functional groups using linear mixed-effects model (LMM), in which sites were termed as random intercept effects. Microbial functional groups were predicted by the Functional Annotation of Prokaryotic Taxa (FAPROTAX) and PICRUSt2 . Analysis of LMM was conducted in lme4 R packages . To characterize how microbial communities differ, Principal coordinate analyses (PCoA) were conducted on Bray–Curtis index to examine dissimilarities among taxonomic and functional composition between croplands and natural ecosystems. PERMANOVA was utilized to test the statistical significance of dissimilarity among ecosystem types. To link soil environmental and fungal variables to microbial communities, the correlations between soil filtering and heterogeneity and fungal functional groups were tested by Mantel correlations. Fungal phylotypes were assigned into three functional groups—soil saprotrophs, litter saprotrophs and plant pathogens using FungalTraits . To assess changes in functional genes with agricultural conversion, we calculated log2-fold changes in croplands relative to natural ecosystems (forests, grasslands, and wetlands) using DESeq2 with the apeglm shrinkage algorithm. We also used DESeq2 to identify microbial phylotypes, and functional gene annotation assigned to COG and KEGG that significantly increased, decreased and unchanged under agricultural impacts relative to natural ecosystems. To discern the direct and indirect effects of agricultural impacts on microbial composition and soil functions, a structural equation model was conducted to assess the causal relationships among agricultural land-use change, soil environmental variables, fungal communities, and microbial composition and soil functions. We first considered a hypothesized conceptual model (Supplementary Fig. ) that included all reasonable pathways. Then, we sequentially eliminated non-significant pathways unless the pathways were biologically informative or added pathways on the basis of the residual correlations . Three metrics were used to quantify the goodness of fit of SEM models: the χ 2 test, the root mean square error of approximation (RMSEA), and the Comparative Fit Index (CFI). Specifically, the closer to 1 CFI value, closer to 0 RMSEA values, and the higher χ 2 and RMSEA P values, the better model performs. With a good model fit, we were able to interpret the path coefficients of the model and their associated P values. A path coefficient is analogous to the partial correlation coefficient, and describes the strength and sign of the relationship between two variables. Microbial taxonomic composition (16S) and functional (KEGG) composition were represented by the principal coordinate analyses 1, the first component of PCoA analysis. SEM were conducted using 40 site samples in the “lavaan” package in R environment .
Further information on research design is available in the linked to this article.
Supplementary Information Reporting Summary Peer Review File
Source Data
|
Estimating Adverse Events Associated With Herbal Medicines Using Pharmacovigilance Databases: Systematic Review and Meta-Analysis | fa1bb200-ad40-4e89-9a2f-5cda83a2b63f | 11393504 | Pharmacology[mh] | The World Health Organization (WHO) defined herbal medicines (HMs) as substances that “[…]contain active ingredient parts of plants or other plant materials or combinations thereof” . These substances have been used as part of traditional (folk) medicine over the millennia, and they are becoming increasingly popular in recent years . More than 80% of the population worldwide relies on traditional herbal treatment, and those estimates differ by country, ethnicity, age group, gender, or clinical condition . There are many reasons for this high consumption, including escalating costs of health care, barriers to physician consultations, personal preferences, and perceived safety and health benefits of HMs. Most consumers/patients assume that HMs are of natural origin and believe them to be harmless . Contrary to this fallacy, there have been reports of serious adverse events (AEs) associated with HMs, including hepatotoxicity, renal failure, allergic reactions, colon perforation, carcinoma, coma, and even death . These AEs can be attributed to overdosing, adulteration, or contamination of HMs, herb-drug interactions, and herb-herb interactions . Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding, and prevention of the adverse effects of drugs or any other possible drug-related problems . Essentially, a PV system aims to avert AEs resulting from medication use and implement measures to minimize the consequences of potential adverse effects . Over recent decades, the assessment of medication safety and benefits has been significantly transformed by the development of large databases and statistical programs, enhancing the use and rapid analysis of data . Collecting and analyzing individual reports on AEs remains the most cost-effective and straightforward method for drug safety assessment and new signal detection . Individuals reporting AEs have been used as sources of data on the safety of medicinal products. These reporters may include health care professionals (HCPs), such as physicians, dentists, pharmacists, and nurses, who possess medical qualifications. Additionally, consumers (non-HCPs), such as patients, patients’ relatives, and caregivers, are now recognized as valuable sources of safety information about medicinal products . In light of increasing safety concerns, many researchers advocate for the integration of herbal products into the existing PV system and the use of a single reporting form . Recently, the PV of HMs, known as phytovigilance, has garnered attention , with spontaneous reporting identified as the primary method for monitoring these products . To the best of our knowledge, no similar systematic review and meta-analysis evaluating the reporting rate of the AEs of HMs using the data acquired from PV databases has been published. In addition, little is known about the quality of information provided within the AE reports through a spontaneous reporting system. Therefore, the aim of this systematic review and meta-analysis was to estimate the reporting rate of the AEs of HMs using data from PV databases and to assess the detailed information provided in AE reports. The study also aimed to scrutinize the characteristics of these AEs, including their severity, causality, and outcomes. Search Strategy This systematic review and meta-analysis was registered with PROSPERO (International Prospective Register of Systematic Reviews; ID CRD42021276492) and conducted by adhering to the Cochrane Handbook for Systematic Reviews of Interventions and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The MEDLINE/PubMed, EMBASE, CINAHL, and SCOPUS databases were searched from their inception until December 2023 by 2 researchers (authors DTAP and PJ). Additional relevant papers were also obtained through a manual search by checking each included study’s references. The gray literature, such as abstracts, conference proceedings, and editorials, was excluded. The keywords and their synonyms for the search strategy were as follows: (“pharmacovigilance” OR “post-marketing surveillance” OR “adverse event reporting” OR “adverse event reporting system” OR “adverse event reports” OR “self-reporting”) AND (“herbal medicine” OR “herbal remedies” OR “medicinal herb” OR “herbal drugs” OR “herbal products” OR “botanical medicines” OR “phytomedicine” OR “phytotherapy” OR “traditional medicine”). In this study, the term “herbal medicines” was defined as “herbs, herbal materials, herbal preparations and finished herbal products. Herbs include crude plant material (leaves, flowers, fruit, seeds, stems, wood, bark, roots, rhizomes or other plant parts, which may be entire, fragmented or powdered). Herbal materials include, alongside herbs, fresh juices, gums, fixed oils, essential oils, resins and dry powders of herbs. Herbal preparations include comminuted or powdered herbal materials, or extracts, tinctures and fatty oils of herbal materials. Finished herbal products consist of herbal preparations made from one or more herbs” . Study Selection Primary studies were included in this research if they were original studies reporting AEs associated with HMs through a voluntary reporting scheme, with no restriction of language. Studies were excluded if they (1) were reviews or systematic reviews, case reports, conference abstracts, or editorials; (2) were studies reporting AEs on animals; (3) did not provide AEs in detail; or (4) were studies reporting AEs associated with complementary and alternative medicines. The primary outcome was the reporting rate of the AEs of HMs, calculated as the number of reports referred to AEs associated with HMs (numerator) divided by the total number of reports of AEs associated with all medicines (denominator). Studies that could not be included in the meta-analysis because of incomplete data were reviewed narratively. Endnote 20 (Clarivate Analytics) was used as a reference manager to import citations and remove duplicate publications. Data Screening and Extraction Titles and abstracts were screened independently by 2 researchers (DTAP and PJ). The full text was then examined in detail by the same 2 researchers. Non-English papers were translated using Google Translate . The following data were independently double-extracted into evidence tables by 2 researchers (authors CK and DTAP): general characteristics of the studies (eg, country of study, source of databases, study period) and outcome data (eg, reporting rate of AEs of HMs, severity of AEs, causality assessment, affected body parts or systems most involved in AEs). Risk-of-Bias Assessment Two independent investigators (DTAP and PJ) assessed the risk of bias (ROB) using Crombie’s checklist for cross-sectional studies . The following items were appraised: (1) appropriate design, (2) adequate description of data, (3) reported response rates, (4) adequate representation of the total sample, (5) clearly stated aims and the likelihood of reliable and valid measurements, (6) statistical significance, and (7) adequate description of analyses. Each item scored 1 point for yes, 0.5 points for unclear, and 0 for no. The total score was modified as high ROB (0-4), moderate ROB (>4 and <6), and low ROB (6-7). Any discrepancies during data extraction and ROB assessment were resolved by consensus between these investigators and the principal investigator (CK). The κ statistic was used to evaluate the degree of agreement between investigators. A negative κ indicated a lack of agreement, while the following ranges were used to interpret the level of agreement: 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement . Data Synthesis and Statistical Analysis Pooled effect estimates for the reporting rate of the AEs of HMs across the included studies with corresponding 95% CIs were calculated as a percentage using the DerSimonian-Laird random effects model . To assess the heterogeneity among studies, standard χ 2 tests and the I 2 statistic were used . Where high heterogeneity was indicated ( I 2 ≥75%), the results across studies were summarized using the median reporting rate and the IQR. To explore the possible sources of heterogeneity, subgroup analyses were performed by continent (North America, Europe, Asia, Africa), the source of the reporter (consumer, HCPs, or all stakeholders), source of databases (poison control center, national PV center, or regional PV center), and the ROB. Sensitivity analysis was performed to examine the influence of studies with a high ROB and the source of the reporter to the pooled estimate of the reporting rate of AEs associated with HMs. A univariate random effects meta-regression was also used to investigate heterogeneity. Egger’s asymmetry test was conducted to look for signs of publication bias . If significant publication bias existed, the trim-and-fill method was performed to adjust the publication bias . Statistical tests were 2-sided with a significance of P <.05. All statistical analyses were performed with STATA 17.0 (Stata Corp LLC). This systematic review and meta-analysis was registered with PROSPERO (International Prospective Register of Systematic Reviews; ID CRD42021276492) and conducted by adhering to the Cochrane Handbook for Systematic Reviews of Interventions and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The MEDLINE/PubMed, EMBASE, CINAHL, and SCOPUS databases were searched from their inception until December 2023 by 2 researchers (authors DTAP and PJ). Additional relevant papers were also obtained through a manual search by checking each included study’s references. The gray literature, such as abstracts, conference proceedings, and editorials, was excluded. The keywords and their synonyms for the search strategy were as follows: (“pharmacovigilance” OR “post-marketing surveillance” OR “adverse event reporting” OR “adverse event reporting system” OR “adverse event reports” OR “self-reporting”) AND (“herbal medicine” OR “herbal remedies” OR “medicinal herb” OR “herbal drugs” OR “herbal products” OR “botanical medicines” OR “phytomedicine” OR “phytotherapy” OR “traditional medicine”). In this study, the term “herbal medicines” was defined as “herbs, herbal materials, herbal preparations and finished herbal products. Herbs include crude plant material (leaves, flowers, fruit, seeds, stems, wood, bark, roots, rhizomes or other plant parts, which may be entire, fragmented or powdered). Herbal materials include, alongside herbs, fresh juices, gums, fixed oils, essential oils, resins and dry powders of herbs. Herbal preparations include comminuted or powdered herbal materials, or extracts, tinctures and fatty oils of herbal materials. Finished herbal products consist of herbal preparations made from one or more herbs” . Primary studies were included in this research if they were original studies reporting AEs associated with HMs through a voluntary reporting scheme, with no restriction of language. Studies were excluded if they (1) were reviews or systematic reviews, case reports, conference abstracts, or editorials; (2) were studies reporting AEs on animals; (3) did not provide AEs in detail; or (4) were studies reporting AEs associated with complementary and alternative medicines. The primary outcome was the reporting rate of the AEs of HMs, calculated as the number of reports referred to AEs associated with HMs (numerator) divided by the total number of reports of AEs associated with all medicines (denominator). Studies that could not be included in the meta-analysis because of incomplete data were reviewed narratively. Endnote 20 (Clarivate Analytics) was used as a reference manager to import citations and remove duplicate publications. Titles and abstracts were screened independently by 2 researchers (DTAP and PJ). The full text was then examined in detail by the same 2 researchers. Non-English papers were translated using Google Translate . The following data were independently double-extracted into evidence tables by 2 researchers (authors CK and DTAP): general characteristics of the studies (eg, country of study, source of databases, study period) and outcome data (eg, reporting rate of AEs of HMs, severity of AEs, causality assessment, affected body parts or systems most involved in AEs). Two independent investigators (DTAP and PJ) assessed the risk of bias (ROB) using Crombie’s checklist for cross-sectional studies . The following items were appraised: (1) appropriate design, (2) adequate description of data, (3) reported response rates, (4) adequate representation of the total sample, (5) clearly stated aims and the likelihood of reliable and valid measurements, (6) statistical significance, and (7) adequate description of analyses. Each item scored 1 point for yes, 0.5 points for unclear, and 0 for no. The total score was modified as high ROB (0-4), moderate ROB (>4 and <6), and low ROB (6-7). Any discrepancies during data extraction and ROB assessment were resolved by consensus between these investigators and the principal investigator (CK). The κ statistic was used to evaluate the degree of agreement between investigators. A negative κ indicated a lack of agreement, while the following ranges were used to interpret the level of agreement: 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement . Pooled effect estimates for the reporting rate of the AEs of HMs across the included studies with corresponding 95% CIs were calculated as a percentage using the DerSimonian-Laird random effects model . To assess the heterogeneity among studies, standard χ 2 tests and the I 2 statistic were used . Where high heterogeneity was indicated ( I 2 ≥75%), the results across studies were summarized using the median reporting rate and the IQR. To explore the possible sources of heterogeneity, subgroup analyses were performed by continent (North America, Europe, Asia, Africa), the source of the reporter (consumer, HCPs, or all stakeholders), source of databases (poison control center, national PV center, or regional PV center), and the ROB. Sensitivity analysis was performed to examine the influence of studies with a high ROB and the source of the reporter to the pooled estimate of the reporting rate of AEs associated with HMs. A univariate random effects meta-regression was also used to investigate heterogeneity. Egger’s asymmetry test was conducted to look for signs of publication bias . If significant publication bias existed, the trim-and-fill method was performed to adjust the publication bias . Statistical tests were 2-sided with a significance of P <.05. All statistical analyses were performed with STATA 17.0 (Stata Corp LLC). Search Outcomes There were 3864 papers identified from the databases (n=3857, 99.8%, papers) and manual searching (n=7, 0.2%, papers). After removing duplicates, 3145 (81.4%) papers were screened against the eligibility criteria. Screening of titles or abstracts resulted in 3058 (97.2%) papers being excluded. A total of 87 (2.8%) papers remained for full-text screening. Of those, 61 (70.1%) studies were excluded because of AEs not collected through voluntary reporting schemes (n=33, 54.1%, papers), detailed AEs not provided (n=14, 23%, papers), duplicated data sources included from other reports (n=2, 3.3%, papers), and information about complementary and alternative medicines provided (n=12, 19.6%, papers). See for the PRISMA flowchart of the study selection process. Characteristics of Included Studies In total, 26 studies were included in the systematic review . These studies were published between 2005 and 2021, with 20 (76.9%) studies in English 3 (11.5%) in Chinese 2 (7.7%) in Spanish , and 1 (3.8%) in Dutch . Most of the studies were conducted in Asia (n=12, 46.2%) , followed by Europe (n=7, 26.9%) , North America (n=5, 19.2%) , and 1 (3.8%) each in Africa and Australia . In terms of the source of the reporter, AEs were reported only by HCPs in 4 (15.4%) studies , including physicians, pharmacists, nurses, or herbal practitioners; by HCPs, consumers, and other stakeholders (pharmaceutical companies, manufacturers) in 18 (69.2%) studies; and by only consumers in 2 (7.7%) studies ; the remaining 2 (7.7%) studies had no information on reporters. Data of AEs were acquired from the spontaneous reporting system of the national PV center (n=21, 80.8%) , the regional PV center (n=3, 11.5%) , and the poison control center (n=2, 7.7%) . Severity, Affected Body Systems, and Outcomes Involving AEs of HMs Of the 26 studies reviewed, 24 (92.3%) provided information about the severity of AEs but only 9 (34.6%) studies specifically mentioned the assessment scale. Among these, the WHO scale was the most commonly used, mentioned in 4 (15.4%) studies , while 1 (3.8%) study used a modified Hartwig scale . In contrast, 4 (15.4%) studies defined their own severity classification, distinguishing between nonserious and serious events or categorizing them as minor, moderate, or major. The most frequently affected systems as per the median reporting rate were (1) the skin and appendage (median 22.3%, IQR 13.3%-35.1%), with AEs such as rash, itching, erythema, urticaria, and pruritus ; (2) the gastrointestinal system (median 17.5%, IQR 9.0%-36.9%), with AEs such as abdominal pain, nausea, and vomiting ; (3) the central nervous system (median 12.5%, IQR 6.9%-17.9%), with AEs such as dizziness, fatigue, and headache ; and (4) the cardiovascular system (median 5.0%, IQR 1.8%-19.0%), with AEs such as hypertension, heart flutter, and tachycardia . One study reported AEs pertaining to the hepatobiliary system, including hepatitis (66.7%), hepatic necrosis (16.7%), and increasing hepatic enzymes (33.3%). Of the 26 studies, 11 (42.3%) reported AE outcomes. The reported outcomes after treatment were “not recovered” (median 0.6%, IQR 0.3%-11.8%) in 5 (45.5%) studies , “recovered” (median 46.4%, IQR 12.5%-66.8%) in 10 (90.9%) studies , “cured” (median 54.2%, IQR 23.8%-63.8%) in 5 (45.5%) studies , and “fatal/death” (median 1.5%, IQR 0.7%-6.6%) in 6 (54.5%) studies . Table S1 in shows a detailed summary. Causality Assessment Between AEs and HMs Of the 26 studies, 16 (61.5%) reported the causality assessment scale used, where the WHO Uppsala Monitoring Centre (UMC) was the most common in 8 (50%) studies , followed by the Naranjo algorithm in 2 (12.5%) studies ; the Karch and Lasagna criteria in 1 (6.3%) study ; a combination of the WHO-UMC, the Naranjo algorithm, and the local Thai algorithm in 1 (6.3%) study ; and a predefined scale (definite, probable, possible, doubtful) in 1 (6.3%) study . In addition, 3 (18.8%) of the 16 studies conducted in China applied data mining methods, including the proportional reporting ratio (PRR) and the Bayesian confidence propagation neural network (BCPNN), for signal monitoring to detect the correlation between AEs and HMs (Table S1 in ). The causality assessment ranged mostly from “possible” to “probable” (Table S1 in ). Notably, by using data mining methods (the PRR and the BCPNN), the 3 (18.8%) studies conducted in China could clarify the warning signals for specified HMs: Dengzhan Xixin (headache, dizziness, itching, chills, heart palpitations, fever, flushing) , Guizhi Fuling (gastric dysfunction, abdominal pain , and Shujinjianyao (rash, nausea, abdominal pain, headache, vomiting) . Quality of Information Provided in AE Reports None of included studies provided full details of herbal products, with deficiency noted in the omission of batch numbers, identified as crucial elements . Among the reviewed studies, a minority (n=11, 42.3%) mentioned specific brand names , 1 (3.8%) study identified the manufacturer , and 9 (34.6%) detailed the pharmaceutical form . Furthermore, there was a notable absence of data pertaining to the quality assessment of herbal medicinal products, including information about testing for contamination, the presence of adulterants, or purity levels. Risk-of-Bias Assessment and Publication Bias Applying Crombie’s checklist for ROB assessment, 3 (11.5%) of the 26 included studies fulfilled most of the criteria and were classified as showing low ROB (6-7 points), 8 (30.8%) studies showed a moderate ROB (>4 to <6 points), and 15 (57.5%) studies were classified as showing a high ROB (≤4 points). For ROB assessments, the interrater agreement was high (Cohen κ=0.94). The evidence of publication bias was detected by performing the Egger test ( P =.01). The trim-and-fill method of calibrating the publication bias identified 3 (11.5%) imputed studies . Quantitative Synthesis A total of 14 (53.8%) of the 26 studies providing sufficient data for calculating the reporting rate of AEs were submitted for meta-analyses. Pooled Reporting Rate of AEs of HMs Using PV Databases The meta-analysis of the 14 (53.8%) studies showed a pooled reporting rate of the AEs of HMs at a median of 1.42% (IQR 1.12%-1.71%). There was significant heterogeneity ( χ 2 13 =33,650.09, P <.001, I 2 =99.96%) as the reporting rates ranged considerably from 0.03% to 29.84% . Subgroup Analysis A subgroup analysis by continent showed that there was a significant difference between Asia and Europe , with median estimates of 1.12% (IQR 0.74%-1.50%) and 0.83% (IQR 0.28%-1.37%), respectively ( P <.001). A subgroup analysis by ROB showed that “moderate ROB” studies detected a higher reporting rate compared to “high ROB” studies , with median estimates of 0.95% (IQR 0.33%-1.57%) and 0.41% (IQR 0.25%-0.57%), respectively ( P <.001). Meta-Regression Model We conducted meta-regression analysis to identify potential sources of heterogeneity in the reporting rate of the AEs of HMs. The covariates considered included the continent, the source of the reporter, source of databases, and the ROB. From the model, we found that the source of the reporter was associated with the pooled reporting rate of the AEs of HMs ( P =.01), whereas there was no relationship between the location, databases, or ROB and the reporting rate of AEs . Sensitivity Analysis There was little difference in the pooled reporting rate when studies with a high ROB were added to the analysis. The reporting rate slightly decreased from 1.58% to 1.42%. In contrast, of the 14 (53.8%) studies included in the analysis, 1 (7.1%) with only consumer reports impacted the pooled reporting rate. When this study was added, the pooled reporting rate rose significantly from 1.09% to 1.42% . There were 3864 papers identified from the databases (n=3857, 99.8%, papers) and manual searching (n=7, 0.2%, papers). After removing duplicates, 3145 (81.4%) papers were screened against the eligibility criteria. Screening of titles or abstracts resulted in 3058 (97.2%) papers being excluded. A total of 87 (2.8%) papers remained for full-text screening. Of those, 61 (70.1%) studies were excluded because of AEs not collected through voluntary reporting schemes (n=33, 54.1%, papers), detailed AEs not provided (n=14, 23%, papers), duplicated data sources included from other reports (n=2, 3.3%, papers), and information about complementary and alternative medicines provided (n=12, 19.6%, papers). See for the PRISMA flowchart of the study selection process. In total, 26 studies were included in the systematic review . These studies were published between 2005 and 2021, with 20 (76.9%) studies in English 3 (11.5%) in Chinese 2 (7.7%) in Spanish , and 1 (3.8%) in Dutch . Most of the studies were conducted in Asia (n=12, 46.2%) , followed by Europe (n=7, 26.9%) , North America (n=5, 19.2%) , and 1 (3.8%) each in Africa and Australia . In terms of the source of the reporter, AEs were reported only by HCPs in 4 (15.4%) studies , including physicians, pharmacists, nurses, or herbal practitioners; by HCPs, consumers, and other stakeholders (pharmaceutical companies, manufacturers) in 18 (69.2%) studies; and by only consumers in 2 (7.7%) studies ; the remaining 2 (7.7%) studies had no information on reporters. Data of AEs were acquired from the spontaneous reporting system of the national PV center (n=21, 80.8%) , the regional PV center (n=3, 11.5%) , and the poison control center (n=2, 7.7%) . Of the 26 studies reviewed, 24 (92.3%) provided information about the severity of AEs but only 9 (34.6%) studies specifically mentioned the assessment scale. Among these, the WHO scale was the most commonly used, mentioned in 4 (15.4%) studies , while 1 (3.8%) study used a modified Hartwig scale . In contrast, 4 (15.4%) studies defined their own severity classification, distinguishing between nonserious and serious events or categorizing them as minor, moderate, or major. The most frequently affected systems as per the median reporting rate were (1) the skin and appendage (median 22.3%, IQR 13.3%-35.1%), with AEs such as rash, itching, erythema, urticaria, and pruritus ; (2) the gastrointestinal system (median 17.5%, IQR 9.0%-36.9%), with AEs such as abdominal pain, nausea, and vomiting ; (3) the central nervous system (median 12.5%, IQR 6.9%-17.9%), with AEs such as dizziness, fatigue, and headache ; and (4) the cardiovascular system (median 5.0%, IQR 1.8%-19.0%), with AEs such as hypertension, heart flutter, and tachycardia . One study reported AEs pertaining to the hepatobiliary system, including hepatitis (66.7%), hepatic necrosis (16.7%), and increasing hepatic enzymes (33.3%). Of the 26 studies, 11 (42.3%) reported AE outcomes. The reported outcomes after treatment were “not recovered” (median 0.6%, IQR 0.3%-11.8%) in 5 (45.5%) studies , “recovered” (median 46.4%, IQR 12.5%-66.8%) in 10 (90.9%) studies , “cured” (median 54.2%, IQR 23.8%-63.8%) in 5 (45.5%) studies , and “fatal/death” (median 1.5%, IQR 0.7%-6.6%) in 6 (54.5%) studies . Table S1 in shows a detailed summary. Of the 26 studies, 16 (61.5%) reported the causality assessment scale used, where the WHO Uppsala Monitoring Centre (UMC) was the most common in 8 (50%) studies , followed by the Naranjo algorithm in 2 (12.5%) studies ; the Karch and Lasagna criteria in 1 (6.3%) study ; a combination of the WHO-UMC, the Naranjo algorithm, and the local Thai algorithm in 1 (6.3%) study ; and a predefined scale (definite, probable, possible, doubtful) in 1 (6.3%) study . In addition, 3 (18.8%) of the 16 studies conducted in China applied data mining methods, including the proportional reporting ratio (PRR) and the Bayesian confidence propagation neural network (BCPNN), for signal monitoring to detect the correlation between AEs and HMs (Table S1 in ). The causality assessment ranged mostly from “possible” to “probable” (Table S1 in ). Notably, by using data mining methods (the PRR and the BCPNN), the 3 (18.8%) studies conducted in China could clarify the warning signals for specified HMs: Dengzhan Xixin (headache, dizziness, itching, chills, heart palpitations, fever, flushing) , Guizhi Fuling (gastric dysfunction, abdominal pain , and Shujinjianyao (rash, nausea, abdominal pain, headache, vomiting) . None of included studies provided full details of herbal products, with deficiency noted in the omission of batch numbers, identified as crucial elements . Among the reviewed studies, a minority (n=11, 42.3%) mentioned specific brand names , 1 (3.8%) study identified the manufacturer , and 9 (34.6%) detailed the pharmaceutical form . Furthermore, there was a notable absence of data pertaining to the quality assessment of herbal medicinal products, including information about testing for contamination, the presence of adulterants, or purity levels. Applying Crombie’s checklist for ROB assessment, 3 (11.5%) of the 26 included studies fulfilled most of the criteria and were classified as showing low ROB (6-7 points), 8 (30.8%) studies showed a moderate ROB (>4 to <6 points), and 15 (57.5%) studies were classified as showing a high ROB (≤4 points). For ROB assessments, the interrater agreement was high (Cohen κ=0.94). The evidence of publication bias was detected by performing the Egger test ( P =.01). The trim-and-fill method of calibrating the publication bias identified 3 (11.5%) imputed studies . A total of 14 (53.8%) of the 26 studies providing sufficient data for calculating the reporting rate of AEs were submitted for meta-analyses. Pooled Reporting Rate of AEs of HMs Using PV Databases The meta-analysis of the 14 (53.8%) studies showed a pooled reporting rate of the AEs of HMs at a median of 1.42% (IQR 1.12%-1.71%). There was significant heterogeneity ( χ 2 13 =33,650.09, P <.001, I 2 =99.96%) as the reporting rates ranged considerably from 0.03% to 29.84% . Subgroup Analysis A subgroup analysis by continent showed that there was a significant difference between Asia and Europe , with median estimates of 1.12% (IQR 0.74%-1.50%) and 0.83% (IQR 0.28%-1.37%), respectively ( P <.001). A subgroup analysis by ROB showed that “moderate ROB” studies detected a higher reporting rate compared to “high ROB” studies , with median estimates of 0.95% (IQR 0.33%-1.57%) and 0.41% (IQR 0.25%-0.57%), respectively ( P <.001). Meta-Regression Model We conducted meta-regression analysis to identify potential sources of heterogeneity in the reporting rate of the AEs of HMs. The covariates considered included the continent, the source of the reporter, source of databases, and the ROB. From the model, we found that the source of the reporter was associated with the pooled reporting rate of the AEs of HMs ( P =.01), whereas there was no relationship between the location, databases, or ROB and the reporting rate of AEs . Sensitivity Analysis There was little difference in the pooled reporting rate when studies with a high ROB were added to the analysis. The reporting rate slightly decreased from 1.58% to 1.42%. In contrast, of the 14 (53.8%) studies included in the analysis, 1 (7.1%) with only consumer reports impacted the pooled reporting rate. When this study was added, the pooled reporting rate rose significantly from 1.09% to 1.42% . The meta-analysis of the 14 (53.8%) studies showed a pooled reporting rate of the AEs of HMs at a median of 1.42% (IQR 1.12%-1.71%). There was significant heterogeneity ( χ 2 13 =33,650.09, P <.001, I 2 =99.96%) as the reporting rates ranged considerably from 0.03% to 29.84% . A subgroup analysis by continent showed that there was a significant difference between Asia and Europe , with median estimates of 1.12% (IQR 0.74%-1.50%) and 0.83% (IQR 0.28%-1.37%), respectively ( P <.001). A subgroup analysis by ROB showed that “moderate ROB” studies detected a higher reporting rate compared to “high ROB” studies , with median estimates of 0.95% (IQR 0.33%-1.57%) and 0.41% (IQR 0.25%-0.57%), respectively ( P <.001). We conducted meta-regression analysis to identify potential sources of heterogeneity in the reporting rate of the AEs of HMs. The covariates considered included the continent, the source of the reporter, source of databases, and the ROB. From the model, we found that the source of the reporter was associated with the pooled reporting rate of the AEs of HMs ( P =.01), whereas there was no relationship between the location, databases, or ROB and the reporting rate of AEs . There was little difference in the pooled reporting rate when studies with a high ROB were added to the analysis. The reporting rate slightly decreased from 1.58% to 1.42%. In contrast, of the 14 (53.8%) studies included in the analysis, 1 (7.1%) with only consumer reports impacted the pooled reporting rate. When this study was added, the pooled reporting rate rose significantly from 1.09% to 1.42% . Principal Findings The increasing worldwide trend of HM usage reflects the growing number of people placing faith in the efficacy of HMs, emphasizing the urgent need to address concerns about the risks and benefits of HMs. This systematic review and meta-analysis critically assessed the reporting rate of AEs associated with HMs using data sourced from PV databases. Our findings underscore the presence of potential risks, alongside benefits, prompting continued concerns regarding the safety of HMs in clinical applications. The reported median rate of the AEs of HMs was calculated as 1.42%, with the highest AE reported rate of 29.84% and the reported outcomes of AEs being as serious as death (1.5%). With the increased trend in the consumption of HMs, an effective ongoing process to detect, assess, and prevent adverse effects from HMs is essential, allowing us to understand more about their benefits and risks. This understanding is crucial for promoting informed decision-making among health care providers and consumers, ensuring safer use of these products. Additionally, it facilitates the development of evidence-based guidelines and recommendations for integrating HMs into health care practices, while addressing gaps in knowledge regarding their pharmacological interactions and long-term effects. The meta-regression and sensitivity analysis revealed a significant impact, with the highest reporting rate (median 29.84%, IQR 27.30%-32.51%) observed for AEs reports submitted by consumers. This highlights the importance of direct consumer reporting in identifying the adverse effects of HMs and underscores the necessity of integrating such reporting into the PV system . Since the 20th century, numerous countries have established consumer reporting systems, with Canada pioneering this initiative in 1965 . Various measures have been undertaken to motivate consumers to report, including providing feedback to reporters and promoting the reporting system through media, social media, and health care providers . In addition, consumer organizations play a crucial role in bridging the gap between consumers and national PV centers, thereby fostering the use of these reporting systems . However, since only medical professionals can accurately establish causality, relying solely on information provided by consumers does not validate the occurrence of an AE caused by a specific product . Hence, effort is needed to develop and enhance more effective processes for collecting, detecting, and assessing the quality of consumer reports. First, the reporting form should be simplified for consumers to complete by using layperson language and including consumer-specific questions, especially other aspects of medicine use, such as experiences of ineffectiveness . Their experiences might provide important insight, emphasizing aspects consumers are unable to communicate to their doctors . Since there has been no standardized consumer reporting form for the AEs of HMs, the promotion and emphasis of international guidelines for a standard patient reporting form are essential . Second, promotion and education of using the spontaneous reporting system for consumers are required, even though consumers may be aware of the self-reporting system and prepared to use it . Third, a follow-up strategy should be implemented to obtain more medical confirmations, which can aid in analyzing the cause-and-effect relationship and ensuring the data elements of reports are as complete and accurate as possible . A significant challenge in summarizing data in this systematic review and meta-analysis was the lack of comprehensive information about the HMs cited in the AE reports. Only the main ingredients were disclosed, while other potentially harmful components were not listed. Clearly stating all herbal components in AE reports is essential for ensuring accurate and comprehensive documentation of the potential risks associated with HMs . Understanding specific botanical ingredients helps HCPs and researchers analyze adverse reactions more effectively. Identifying these components also aids in assessing interactions between herbs and medications, improving comprehension of AE mechanisms . This knowledge supports creating clearer guidelines and warnings, enhancing transparency and reliability in reporting AEs, and promoting safer use and informed decision-making in the herbal product industry . AE reports for HMs should include the brand name, manufacturer, pharmaceutical form, extract amount per dose, ingredients, excipients, regulatory status, and test results for contamination, adulterants, or purity . Although numerous HMs and AEs were screened in this systematic review and meta-analysis, this is deemed insufficient, as many unregulated or self-prescribed HMs remain unaccounted for by existing PV systems . Challenges in HM monitoring include diversity in the classification of HMs across nations, a lack of rigorous quality management, and insufficient collaboration among stakeholders. Therefore, a PV system for HMs should raise awareness of PV activities for HMs in the public domain. The involvement of all relevant stakeholders, including HCPs, consumers/patients, manufacturers, complementary practitioners, and sellers/distributors, is crucial for the effective engagement of the system in actively monitoring and reporting AEs . Strengths and Limitations This study has many strengths, including a comprehensive literature search without language restrictions, double-data extraction, and ROB assessments. Our systematic review and meta-analysis also has some limitations. First, due to limited available publications, only 1 study targeting consumer reports was included in the quantitative analysis as a representative of the AE-reporting rate from consumer reports. The result therefore needs to be interpreted with caution. Additional studies gathering data on consumer reports should be conducted to obtain more accurate estimates of the reporting rate. Second, this systematic review and meta-analysis excluded studies on prevalence or incidence rates that did not collect AE reports from PV databases. Future studies should examine the prevalence rate of AEs associated with HMs in various settings (eg, hospitals, community pharmacies) to provide an overview of real-world data on these AEs. Conclusion This systematic review and meta-analysis highlighted HM risks with a wide range of AE-reporting rates, depending on the source of the reporter, and revealed deficiencies in detailed HM component information provided within AE reports. Continuous efforts are necessary to standardize consumer reporting systems in terms of the reporting form, education, and follow-up strategy to improve data quality assurance measures, aiming to enhance the reliability and utility of PV data for monitoring the safety of HMs. Achieving effective monitoring and reporting of these AEs necessitates collaborative efforts from diverse stakeholders, including patients/consumers, manufacturers, physicians, complementary practitioners, sellers/distributors, and health authorities. The increasing worldwide trend of HM usage reflects the growing number of people placing faith in the efficacy of HMs, emphasizing the urgent need to address concerns about the risks and benefits of HMs. This systematic review and meta-analysis critically assessed the reporting rate of AEs associated with HMs using data sourced from PV databases. Our findings underscore the presence of potential risks, alongside benefits, prompting continued concerns regarding the safety of HMs in clinical applications. The reported median rate of the AEs of HMs was calculated as 1.42%, with the highest AE reported rate of 29.84% and the reported outcomes of AEs being as serious as death (1.5%). With the increased trend in the consumption of HMs, an effective ongoing process to detect, assess, and prevent adverse effects from HMs is essential, allowing us to understand more about their benefits and risks. This understanding is crucial for promoting informed decision-making among health care providers and consumers, ensuring safer use of these products. Additionally, it facilitates the development of evidence-based guidelines and recommendations for integrating HMs into health care practices, while addressing gaps in knowledge regarding their pharmacological interactions and long-term effects. The meta-regression and sensitivity analysis revealed a significant impact, with the highest reporting rate (median 29.84%, IQR 27.30%-32.51%) observed for AEs reports submitted by consumers. This highlights the importance of direct consumer reporting in identifying the adverse effects of HMs and underscores the necessity of integrating such reporting into the PV system . Since the 20th century, numerous countries have established consumer reporting systems, with Canada pioneering this initiative in 1965 . Various measures have been undertaken to motivate consumers to report, including providing feedback to reporters and promoting the reporting system through media, social media, and health care providers . In addition, consumer organizations play a crucial role in bridging the gap between consumers and national PV centers, thereby fostering the use of these reporting systems . However, since only medical professionals can accurately establish causality, relying solely on information provided by consumers does not validate the occurrence of an AE caused by a specific product . Hence, effort is needed to develop and enhance more effective processes for collecting, detecting, and assessing the quality of consumer reports. First, the reporting form should be simplified for consumers to complete by using layperson language and including consumer-specific questions, especially other aspects of medicine use, such as experiences of ineffectiveness . Their experiences might provide important insight, emphasizing aspects consumers are unable to communicate to their doctors . Since there has been no standardized consumer reporting form for the AEs of HMs, the promotion and emphasis of international guidelines for a standard patient reporting form are essential . Second, promotion and education of using the spontaneous reporting system for consumers are required, even though consumers may be aware of the self-reporting system and prepared to use it . Third, a follow-up strategy should be implemented to obtain more medical confirmations, which can aid in analyzing the cause-and-effect relationship and ensuring the data elements of reports are as complete and accurate as possible . A significant challenge in summarizing data in this systematic review and meta-analysis was the lack of comprehensive information about the HMs cited in the AE reports. Only the main ingredients were disclosed, while other potentially harmful components were not listed. Clearly stating all herbal components in AE reports is essential for ensuring accurate and comprehensive documentation of the potential risks associated with HMs . Understanding specific botanical ingredients helps HCPs and researchers analyze adverse reactions more effectively. Identifying these components also aids in assessing interactions between herbs and medications, improving comprehension of AE mechanisms . This knowledge supports creating clearer guidelines and warnings, enhancing transparency and reliability in reporting AEs, and promoting safer use and informed decision-making in the herbal product industry . AE reports for HMs should include the brand name, manufacturer, pharmaceutical form, extract amount per dose, ingredients, excipients, regulatory status, and test results for contamination, adulterants, or purity . Although numerous HMs and AEs were screened in this systematic review and meta-analysis, this is deemed insufficient, as many unregulated or self-prescribed HMs remain unaccounted for by existing PV systems . Challenges in HM monitoring include diversity in the classification of HMs across nations, a lack of rigorous quality management, and insufficient collaboration among stakeholders. Therefore, a PV system for HMs should raise awareness of PV activities for HMs in the public domain. The involvement of all relevant stakeholders, including HCPs, consumers/patients, manufacturers, complementary practitioners, and sellers/distributors, is crucial for the effective engagement of the system in actively monitoring and reporting AEs . This study has many strengths, including a comprehensive literature search without language restrictions, double-data extraction, and ROB assessments. Our systematic review and meta-analysis also has some limitations. First, due to limited available publications, only 1 study targeting consumer reports was included in the quantitative analysis as a representative of the AE-reporting rate from consumer reports. The result therefore needs to be interpreted with caution. Additional studies gathering data on consumer reports should be conducted to obtain more accurate estimates of the reporting rate. Second, this systematic review and meta-analysis excluded studies on prevalence or incidence rates that did not collect AE reports from PV databases. Future studies should examine the prevalence rate of AEs associated with HMs in various settings (eg, hospitals, community pharmacies) to provide an overview of real-world data on these AEs. This systematic review and meta-analysis highlighted HM risks with a wide range of AE-reporting rates, depending on the source of the reporter, and revealed deficiencies in detailed HM component information provided within AE reports. Continuous efforts are necessary to standardize consumer reporting systems in terms of the reporting form, education, and follow-up strategy to improve data quality assurance measures, aiming to enhance the reliability and utility of PV data for monitoring the safety of HMs. Achieving effective monitoring and reporting of these AEs necessitates collaborative efforts from diverse stakeholders, including patients/consumers, manufacturers, physicians, complementary practitioners, sellers/distributors, and health authorities. |
Ophthalmic Simulated Surgical Competency Assessment Rubric for manual small-incision cataract surgery | 79a876f1-dee7-48c4-a867-346b3841cc19 | 6727782 | Ophthalmology[mh] | Sim-OSSCAR Content Revision and Development The ICO OSCAR for SICS was developed by experts at the ICO using a modified Dreyfus scale (novice, beginner, advanced beginner, and competent). , The “proficient” and “expert” steps of the scale were excluded. In this study, the original ICO-OSCAR was modified to develop an assessment and training tool for simulated ophthalmic surgical education in SICS surgery. The ICO-OSCAR was initially edited to remove content not appropriate for simulation-based surgical training. The OSCAR was further adapted to a modified three-stage Dreyfus scale (novice, advanced beginner, competent). The draft of the Sim-OSSCAR was sent to a panel of 8 international content experts for further amendments to the content and structure of the Sim-OSSCAR. These people were selected for their experience and expertise in performing and teaching SICS. Responses were collated and synthesized into a final version of the rubric, which was distributed for further review. Face and Content Validity Assessment Face and content validity were assessed using a standardized closed question evaluation on a 5-point Likert scale. This was done by a group of 12 international expert SICS cataract surgeons remotely via email, half of whom had been involved in the initial revision process. These SICS surgeons were selected based on their expertise and to ensure international representation. They teach and perform SICS surgery in Angola, Argentina, Ghana, Haiti, India, Malawi, Nepal, New Zealand, United Kingdom, and the United States. Surgeons were asked, “Do you think the Sim-OSSCAR represents the surgical techniques and skills upon which trainees should be assessed?” and “Would you change any of the cells/content? (If so, please include specific details).” Surgeons were also asked, “Do you think the Sim-OSSCAR (used with the artificial eye) is an appropriate way to assess trainees' surgical skill?” Responses on the 5-point Likert scale were given a numerical value and entered onto an Excel spreadsheet (Microsoft Corp.) before calculating the means ± SD. After the initial face and content validation round, three further minor amendments were made to the Sim-OSSCAR, and this validation process was repeated. Interobserver Reliability Assessment To assess interobserver Sim-OSSCAR grading reliability, 8 simulated SICS procedures, which were performed by 8 separate cataract surgeons, were recorded. Four of the surgeons were novice trainee surgeons and 4 were experienced ophthalmologists (who had performed more than 100 SICS procedures). The procedures were performed on the SICS-specific artificial eye, made by Phillips Studio, and recorded using a Stemi 305 microscope with AxioCam ERc5s camera and Labscope digital classroom (all Carl Zeiss Meditec AG). The videos were anonymized so that the people doing the scoring were masked to the level of the trainee. The recordings were independently graded by 4 expert SICS surgeons who currently or had previously worked in high-volume training ophthalmology units in Ethiopia, India, Malawi, the Western Pacific region, and Sierra Leone. Each surgeon independently scored the videos of 8 simulation SICS procedures using the Sim-OSSCAR. Analysis Data were managed in Excel and analyzed with Stata software (version 15.1, StataCorp, LLC). Krippendorff α was selected as the inter-rater agreement coefficient because there were multiple raters providing nonbinary ordinal scores. This was calculated separately for each of the 20 steps of the Sim-OSSCAR on a three-point ordinal point scale (0, 1, or 2). A value of 0.60 was deemed acceptable for a newly developed rubric. , A Wilcoxon rank-sum test was performed using the ranks for mean scores for novice and competent surgeons. The validation study was approved by the Medicine Education Ethics Committee, Faculty Education Office (Medicine), Imperial College, London (MEEC1415-12), and the London School of Hygiene & Tropical Medicine ethics committee (11795).
The ICO OSCAR for SICS was developed by experts at the ICO using a modified Dreyfus scale (novice, beginner, advanced beginner, and competent). , The “proficient” and “expert” steps of the scale were excluded. In this study, the original ICO-OSCAR was modified to develop an assessment and training tool for simulated ophthalmic surgical education in SICS surgery. The ICO-OSCAR was initially edited to remove content not appropriate for simulation-based surgical training. The OSCAR was further adapted to a modified three-stage Dreyfus scale (novice, advanced beginner, competent). The draft of the Sim-OSSCAR was sent to a panel of 8 international content experts for further amendments to the content and structure of the Sim-OSSCAR. These people were selected for their experience and expertise in performing and teaching SICS. Responses were collated and synthesized into a final version of the rubric, which was distributed for further review.
Face and content validity were assessed using a standardized closed question evaluation on a 5-point Likert scale. This was done by a group of 12 international expert SICS cataract surgeons remotely via email, half of whom had been involved in the initial revision process. These SICS surgeons were selected based on their expertise and to ensure international representation. They teach and perform SICS surgery in Angola, Argentina, Ghana, Haiti, India, Malawi, Nepal, New Zealand, United Kingdom, and the United States. Surgeons were asked, “Do you think the Sim-OSSCAR represents the surgical techniques and skills upon which trainees should be assessed?” and “Would you change any of the cells/content? (If so, please include specific details).” Surgeons were also asked, “Do you think the Sim-OSSCAR (used with the artificial eye) is an appropriate way to assess trainees' surgical skill?” Responses on the 5-point Likert scale were given a numerical value and entered onto an Excel spreadsheet (Microsoft Corp.) before calculating the means ± SD. After the initial face and content validation round, three further minor amendments were made to the Sim-OSSCAR, and this validation process was repeated.
To assess interobserver Sim-OSSCAR grading reliability, 8 simulated SICS procedures, which were performed by 8 separate cataract surgeons, were recorded. Four of the surgeons were novice trainee surgeons and 4 were experienced ophthalmologists (who had performed more than 100 SICS procedures). The procedures were performed on the SICS-specific artificial eye, made by Phillips Studio, and recorded using a Stemi 305 microscope with AxioCam ERc5s camera and Labscope digital classroom (all Carl Zeiss Meditec AG). The videos were anonymized so that the people doing the scoring were masked to the level of the trainee. The recordings were independently graded by 4 expert SICS surgeons who currently or had previously worked in high-volume training ophthalmology units in Ethiopia, India, Malawi, the Western Pacific region, and Sierra Leone. Each surgeon independently scored the videos of 8 simulation SICS procedures using the Sim-OSSCAR.
Data were managed in Excel and analyzed with Stata software (version 15.1, StataCorp, LLC). Krippendorff α was selected as the inter-rater agreement coefficient because there were multiple raters providing nonbinary ordinal scores. This was calculated separately for each of the 20 steps of the Sim-OSSCAR on a three-point ordinal point scale (0, 1, or 2). A value of 0.60 was deemed acceptable for a newly developed rubric. , A Wilcoxon rank-sum test was performed using the ranks for mean scores for novice and competent surgeons. The validation study was approved by the Medicine Education Ethics Committee, Faculty Education Office (Medicine), Imperial College, London (MEEC1415-12), and the London School of Hygiene & Tropical Medicine ethics committee (11795).
Sim-OSSCAR Content Revision and Development An international reference group of 8 surgeons from 6 countries contributed to the initial development of the SICS Sim-OSSCAR. shows the changes that arose from the editing of the ICO-OSCAR. The steps of draping, cauterization, irrigation/aspiration, and iris protection were removed. This group provided feedback on the content of the SICS Sim-OSSCAR. The discussion focused on anesthesia; preparation of the ocular surface; sterilizing the surgical field with povidone–iodine; conjunctival incision with flap, cautery, or hemostasis; decreasing pupil size; iris prolapse; and irrigation/aspiration clearance of cortical lens material. Comments regarding the global indices content also included adequacy of anesthesia and preparation. Consensus was reached that these content suggestions ( ) could be excluded from the Sim-OSSCAR because they largely related to live surgery and could not be simulated either by the artificial eyes or animal eye models. The initial Sim-OSSCAR was approved by the panel. Face and Content Validity The face and content validity were independently assessed by a group of 12 surgeons (6 of whom were in the initial reference group of 8). In response to the Face Validity question, “Do you think the Sim-OSSCAR (used with the artificial eye) is an appropriate way to assess trainees' surgical skill?,” all 12 of the respondents either agreed or strongly agreed. Overall, face validity was rated as 4.60 ± 0.52 out of 5 as a mean summation of 12 separate scores. In response to the Content Validity question, “Do you think the Sim-OSSCAR represents the surgical techniques and skills upon which trainees should be assessed?,” all 12 respondents either agreed or strongly agreed. The content was finally agreed upon by the panel of experts, and the content validity was rated as 4.5 (out of 5). Interobserver Reliability Interobserver reliability was assessed by an international panel of 4 experts in SICS. Eight separate masked video recordings of simulation SICS were sent to each expert surgeon for scoring using the Sim-OSSCAR. The recorded procedures represented a range of surgeon skills from complete novice to competent. The mean score for “novices” was 1.7 ± 1.0, and the mean score for “competent” SICS surgeons was 31.0 ± 2.7, out of a maximum score of 40. To assess the interobserver agreement on the specific items in the Sim-OSSCAR, Krippendorff α coefficients were calculated. shows the results for all 20 items in the Sim-OSSCAR, of which 17 exhibited an inter-rater agreement coefficient of Krippendorff α greater than 0.60. Three items had a lower Krippendorff α coefficient: “capsulotomy/capsulorhexis start,” “eye positioned centrally,” and “overall fluidity of the procedure.” Construct Validity Construct validity is an assessment of the “sharpness” of a tool: can it discriminate between two distinct groups? For this study, these groups are the novice and competent surgeons. shows the total score for each separate grader for all 8 videos. Novice surgeons were graded with a mean score range of 0.50 to 3.25 (out of 40), with standard deviations varying between graders' scores of 0.50 to 2.06. Competent surgeons were graded with a mean score range of 21.5 to 36.5 (with standard deviations varying from 0.58 to 4.51). A Wilcoxon rank-sum test showed that competent surgeons perform better than novices ( P = .02).
An international reference group of 8 surgeons from 6 countries contributed to the initial development of the SICS Sim-OSSCAR. shows the changes that arose from the editing of the ICO-OSCAR. The steps of draping, cauterization, irrigation/aspiration, and iris protection were removed. This group provided feedback on the content of the SICS Sim-OSSCAR. The discussion focused on anesthesia; preparation of the ocular surface; sterilizing the surgical field with povidone–iodine; conjunctival incision with flap, cautery, or hemostasis; decreasing pupil size; iris prolapse; and irrigation/aspiration clearance of cortical lens material. Comments regarding the global indices content also included adequacy of anesthesia and preparation. Consensus was reached that these content suggestions ( ) could be excluded from the Sim-OSSCAR because they largely related to live surgery and could not be simulated either by the artificial eyes or animal eye models. The initial Sim-OSSCAR was approved by the panel.
The face and content validity were independently assessed by a group of 12 surgeons (6 of whom were in the initial reference group of 8). In response to the Face Validity question, “Do you think the Sim-OSSCAR (used with the artificial eye) is an appropriate way to assess trainees' surgical skill?,” all 12 of the respondents either agreed or strongly agreed. Overall, face validity was rated as 4.60 ± 0.52 out of 5 as a mean summation of 12 separate scores. In response to the Content Validity question, “Do you think the Sim-OSSCAR represents the surgical techniques and skills upon which trainees should be assessed?,” all 12 respondents either agreed or strongly agreed. The content was finally agreed upon by the panel of experts, and the content validity was rated as 4.5 (out of 5).
Interobserver reliability was assessed by an international panel of 4 experts in SICS. Eight separate masked video recordings of simulation SICS were sent to each expert surgeon for scoring using the Sim-OSSCAR. The recorded procedures represented a range of surgeon skills from complete novice to competent. The mean score for “novices” was 1.7 ± 1.0, and the mean score for “competent” SICS surgeons was 31.0 ± 2.7, out of a maximum score of 40. To assess the interobserver agreement on the specific items in the Sim-OSSCAR, Krippendorff α coefficients were calculated. shows the results for all 20 items in the Sim-OSSCAR, of which 17 exhibited an inter-rater agreement coefficient of Krippendorff α greater than 0.60. Three items had a lower Krippendorff α coefficient: “capsulotomy/capsulorhexis start,” “eye positioned centrally,” and “overall fluidity of the procedure.”
Construct validity is an assessment of the “sharpness” of a tool: can it discriminate between two distinct groups? For this study, these groups are the novice and competent surgeons. shows the total score for each separate grader for all 8 videos. Novice surgeons were graded with a mean score range of 0.50 to 3.25 (out of 40), with standard deviations varying between graders' scores of 0.50 to 2.06. Competent surgeons were graded with a mean score range of 21.5 to 36.5 (with standard deviations varying from 0.58 to 4.51). A Wilcoxon rank-sum test showed that competent surgeons perform better than novices ( P = .02).
Globally, 65.2-million people are blind or moderate/severely vision impaired because of cataract. Twenty-eight percent of countries have less than 4 ophthalmologists per one-million people. By subregion, the lowest mean ratio is 2.7 ophthalmologists per one million in Sub-Saharan Africa. There is a disproportionately high prevalence rate of cataract blindness in regions with the fewest ophthalmologists and cataract surgeons. There is a huge need for an increased number of well-trained ophthalmic surgeons, both ophthalmologist and nonphysician cataract surgeons to tackle this burden. There is a growing appreciation of the role of simulation in surgical education, especially in the initial acquisition of competence. The SICS Sim-OSSCAR ( ) was developed to provide a formative assessment tool for initial cataract surgical training. The Sim-OSSCAR for SICS has good face and content validity as well as interobserver reliability and construct validity. It is important to note that face and content validity were quantified using closed-ended questions. Although open-ended comments were invited, we accept that this is a potential source of response bias. Fidelity is important in simulation-based surgical education. Animal eyes have been used for training; however, the tissue feel in terms of rigidity or elasticity is different than human eyes. Animal eyes have a small window of fidelity before they disintegrate, cannot be used as a “standardized” training model, and often need preparation with formalin (aqueous solution of formaldehyde). , Artificial eyes offer standardization, and overall fidelity was rated as “high” or “very high” by 79% of the trainees on SICS courses (manuscript in preparation). Fidelity of scleral tunnel formation and capsulorhexis steps of SICS were rated “high” or “very high” by 100% of the trainees. The OSACSS (Objective Structured Assessment of Cataract Surgical Skill) was developed as an objective performance-rating tool. The grading system contained global as well as phacoemulsification cataract surgery task-specific elements. Significant improvements in live surgical procedures have been shown after virtual reality cataract surgery training, as assessed by OSACSS. The OASIS (Objective Assessment of Skills in Intraocular Surgery) was also developed for phacoemulsification cataract surgery as an objective ophthalmic surgical evaluation protocol to assess surgical competency. The SPESA (Subjective Phacoemulsification Skills Assessment) assesses trainee performance in cataract surgery by combining a global approach, assessing detailed stage-specific criteria of critical components of cataract surgery. The ICO-OSCARs were originally based on the OSACSS; however, they were expanded upon by creating a set of behaviorally anchored scoring matrices that explicitly and precisely define what is expected for each step. The rubric was based on a modified Dreyfus model ; however, the final “expert” category was omitted because trainees were not expected to become experts during training. The ICO-OSCAR, as well as all other valuation tools described above, are aimed at assessment of surgical competence in the live operating theater setting. This currently validated Sim-OSSCAR is for use with SICS rather than phacoemulsification surgery, and it is aimed for use in a simulation surgical skill's center before live surgical training has commenced. It can be used during initial instruction, whereby the trainee SICS surgeon uses it as a clear list of the steps of the procedure. It can be used as a guide of what exactly is expected for each step to be deemed “competent.” Although models have been available for modern phacoemulsification cataract surgery for over a decade, no artificial eyes had been previously developed for SICS. A full-immersion computerized SICS simulator is in the final stages of development; however, it is not yet widely available. The primary aim of the SICS Sim-OSSCAR is to provide a formative assessment tool. It could be used as a summative assessment tool upon which to progress the successful trainee to live supervised surgical training in SICS. It may be left to the trainer or training institution to benchmark appropriately, depending on the setting and educational goals. An example might be to require a mean of 75% score (30/40) over three cases, and no “zero” scores in any of the 20 steps. Kappa measures (such as Krippendorff α) correct for chance agreement as the coefficients tend to punish variables with strongly skewed distributions. This explains the higher percentage agreements in . Three steps of the SICS Sim-OSSCAR had a lower interobserver reliability, with a Krippendorff α less than 0.60. These three steps were the starting of the capsulotomy, centration, and fluidity. First, separate techniques for starting a capsulotomy or capsulorhexis exist in conventional cataract surgery: a continuous curvilinear capsulorhexis, linear (or envelope) capsulotomy, and a can-opener technique. Different cataract surgeons will themselves have subtle variations within these. Second, a limitation of the Stemi 305 microscope and Labscope App is the high zoom when recording, relative to what the surgeon sees through the binocular eyepieces. Finally, “fluidity” is by definition a subjective term and description. We hope that the use of the newly developed Sim-OSSCAR will assist eye surgeon trainees in gaining competence and confidence within simulation-based surgical education, before then progressing to supervised live surgery. We present a newly validated learning and assessment tool for simulation-based surgical education in cataract surgery. Its aim is ultimately to guide and assess initial simulation surgical training in SICS, to then give trainees the green lights to progress to live supervised surgery. What Was Known • Ophthalmology surgical competency assessment tools exist for live cataract surgical evaluation. What This Paper Adds • Surgical competency can be reliably measured for simulated cataract surgery.
|
Éthique et pratiques du pharmacien | f57aadeb-bb42-4fa1-9e0b-5d257f0723ec | 11668154 | Psychiatry[mh] | Au cours de sa pratique, le pharmacien est confronté à des situations qui pourraient
poser un problème éthique, que ce soit en pharmacie clinique ou biologique, à
l'officine, en industrie pharmaceutique, au sein des compagnie d’assurance ou en tant
que décideur politique ( ). En effet, le pharmacien fournit non seulement des médicaments mais aussi des conseils. Il a un role et des responsabilités différentes de celles des autres professionnels de
la santé et des relations plus centrées sur le patient ( ) Il doit alors prendre des décisions conformément à des connaissances scientifiques mais
aussi aux principes de la bioéthique. Ces principes ont émergé dans la déclaration de Genève de 1948, après la seconde guerre
mondiale et sont: La bienfaisance, incluant le principe de la confidentialité,
l’autonomie, la non malfaisance et la justice ( ). Le pharmacien doit connaitre ces principes afin de présenter au patient une solution,
dans les situations critiques, conforme à l’étique. Au cours du cursus d'étude pharmaceutique en Tunisie, l'éthique médicale est enseignée
en première (20h) et cinquième année (40h). ( ) Cet enseignement permet-il au pharmacien d'avoir des attitudes éthiquement correctes
face à certains dilemmes auxquels il est confronté dans son travail au quotidien?
D’après notre recherche sur Pubmed, aucune étude en Tunisie ne s’est intéressée aux
attitudes des pharmaciens face à des situations éthiques critiques. C’est dans cette optique que nous envisageons d'évaluer les attitudes des pharmaciens
face à des dilemmes éthiques particuliers
Type de l’étude Etude prospective d’évaluation d’attitude éthique des pharmaciens exerçant en
Tunisie. Nous avons inclus dans notre étude les internes en pharmacie, résidents en pharmacie
et pharmaciens ayant suivi ces études dans la faculté de pharmacie de Monastir. Néanmoins, nous avons exclus les résponses reçues après cloture de collecte des
données Recueil des données Nous avons travaillé avec un questionnaire validé en anglais ( ). Ce questionnaire comportait 9 situations avec dilemme éthique à noter par le
participant selon une échelle de Likert à 5 point allant de 1= Tout à fait d’accord à
5= Pas du tout d’accord. Le questionnaire a inclut 4 dimensions de l’éthique: la confidentialité, l’autonomie,
la justice, non malfaisance. La répartition des situations selon les dimensions de l’éthique était la suivante;
théme 1 (Confidentialité): Scenarios 1, 3,7 , théme 2 (Autonomie): Scenarios 2, 5, 6;
théme 3 (Non malfaisance): Scenarios 4, 9; et théme 4 (Justice): Scenario 8. Pour toutes les situations, la réponse “" pas du tout d’accord” était considérée
comme la plus éthique. Nous avons considéré le score de 1 pour les réponses "" d'accord" et "" tout
à fait d'accord", le score de 2 pour les réponses neutres et le score de 3 pour les
réponses en désaccord et fortement en désaccord. Ainsi, le score total du questionnaire était de 27. Un score de connaissance suffisant était défini comme supérieur à 90 %. Si le score était inférieur à 90 %, il était considéré comme insuffisant. Nous avons ajouté une section visant à recueillir les caractéristiques
sociodémographiques de notre population d’étude, incluant le genre, l'âge, le secteur
d'activité, le nombre d'années d'expérience, la formation complémentaire en éthique,
ainsi que le gouvernorat d'exercice. Ce questionnaire a été créé sur Google Forms, puis distribué aux participants via: - Le groupe Facebook de l’association Tunisienne des Pharmaciens Hospitaliers - Le groupe Facebook des enseignants de la faculté de pharmacie de Monastir - Le groupe Facebook entre pharmaciens et futurs pharmaciens Tunisiens - Des groupes professionnels de pharmacien sur messenger et Whats'app Le recueil des données s’est fait, de façon anonyme et volontaire, du 13 Avril 2024
au 20 avril 2024 Analyse statistique Le traitement des données de consommation a été réalisé à l’aide du logiciel
Microsoft Excel et les tests statistiques ont été réalisés à l’aide du logiciel SPSS
version 23. Une analyse descriptive a été effectuée et selon le type de variable, nous avons
exprimé les résultats en moyenne, écart type pour les variables quantitatives et en
effectif(pourcentage) pour les variables qualitatives. L’existence d’une éventuelle corrélation entre les attitudes éthiques et les
caractéristiques socio-démographiques de notre population d’étude a été recherchée
par le test de Khi2, ANNOVA ou Student. Les variables qui avaient une valeur p ≤ 0,05 ont été considérées comme
statistiquement significatives.
Etude prospective d’évaluation d’attitude éthique des pharmaciens exerçant en
Tunisie. Nous avons inclus dans notre étude les internes en pharmacie, résidents en pharmacie
et pharmaciens ayant suivi ces études dans la faculté de pharmacie de Monastir. Néanmoins, nous avons exclus les résponses reçues après cloture de collecte des
données
Nous avons travaillé avec un questionnaire validé en anglais ( ). Ce questionnaire comportait 9 situations avec dilemme éthique à noter par le
participant selon une échelle de Likert à 5 point allant de 1= Tout à fait d’accord à
5= Pas du tout d’accord. Le questionnaire a inclut 4 dimensions de l’éthique: la confidentialité, l’autonomie,
la justice, non malfaisance. La répartition des situations selon les dimensions de l’éthique était la suivante;
théme 1 (Confidentialité): Scenarios 1, 3,7 , théme 2 (Autonomie): Scenarios 2, 5, 6;
théme 3 (Non malfaisance): Scenarios 4, 9; et théme 4 (Justice): Scenario 8. Pour toutes les situations, la réponse “" pas du tout d’accord” était considérée
comme la plus éthique. Nous avons considéré le score de 1 pour les réponses "" d'accord" et "" tout
à fait d'accord", le score de 2 pour les réponses neutres et le score de 3 pour les
réponses en désaccord et fortement en désaccord. Ainsi, le score total du questionnaire était de 27. Un score de connaissance suffisant était défini comme supérieur à 90 %. Si le score était inférieur à 90 %, il était considéré comme insuffisant. Nous avons ajouté une section visant à recueillir les caractéristiques
sociodémographiques de notre population d’étude, incluant le genre, l'âge, le secteur
d'activité, le nombre d'années d'expérience, la formation complémentaire en éthique,
ainsi que le gouvernorat d'exercice. Ce questionnaire a été créé sur Google Forms, puis distribué aux participants via: - Le groupe Facebook de l’association Tunisienne des Pharmaciens Hospitaliers - Le groupe Facebook des enseignants de la faculté de pharmacie de Monastir - Le groupe Facebook entre pharmaciens et futurs pharmaciens Tunisiens - Des groupes professionnels de pharmacien sur messenger et Whats'app Le recueil des données s’est fait, de façon anonyme et volontaire, du 13 Avril 2024
au 20 avril 2024
Le traitement des données de consommation a été réalisé à l’aide du logiciel
Microsoft Excel et les tests statistiques ont été réalisés à l’aide du logiciel SPSS
version 23. Une analyse descriptive a été effectuée et selon le type de variable, nous avons
exprimé les résultats en moyenne, écart type pour les variables quantitatives et en
effectif(pourcentage) pour les variables qualitatives. L’existence d’une éventuelle corrélation entre les attitudes éthiques et les
caractéristiques socio-démographiques de notre population d’étude a été recherchée
par le test de Khi2, ANNOVA ou Student. Les variables qui avaient une valeur p ≤ 0,05 ont été considérées comme
statistiquement significatives.
Au total 154 participants ont accepté de répondre à notre questionnaire en ligne
repartis. Nous avons retenu 122 participants correspondants à nos critères d’inclusion. Les caractéristiques de notre population d’étude sont représentées dans le tableau 1. La moyenne d’année d’expérience dans la fonction était de 5,49 ans avec un écart type de
± 6,26 ans et un minimum de 1 an et un maximum de 35 ans. Le score total du questionnaire était de 27. La moyenne des scores obtenus par nos participants était 19 (écart type±2,96). Ce qui représente 70% du score total. La répartition des réponses de nos participants selon le scenario est représentée dans
le tableau 2. Nous avons calculé la médiane de score total par dimension pour notre population
représente dans le tableau 3. L’analyse bi-varie a trouvé une corrélation positive significative entre le score obtenu
pour la dimension confidentialité et la non-malfaisance avec la formation complémentaire
en éthique. L’analyse bivariée est représentée dans le tableau 4.
Au cours de son exercice, le pharmacien est souvent confronté à des dilemmes éthiques. Il doit trouver des solutions appropriées et conformes aux principes qui régissent la
profession médicale. Pour cela, il est essentiel qu'il soit formé à l'éthique médicale. Nous considérons que notre étude est unique en son genre en Tunisie. Au cours de notre recherche, nous n’avons trouvé aucune publication s'intéressant à
l’étude des attitudes des pharmaciens face à des dilemmes éthiques. Notre étude s’est intéressée à évaluer les attitudes des pharmaciens face à des dilemmes
éthiques particuliers. Nous avons constaté que, face à la situation de nonmalfaisance suivante : « Dispensation
d'amphétamines à un étudiant en médecine », les pharmaciens ont adopté
l'attitude la plus éthique comparée aux autres situations étudiées. En ce qui concerne les dimensions en général, la confidentialité a obtenu le plus grand
nombre de réponses "" pas d'accord", tandis que l'attitude la moins éthique
concernait la dimension de la justice. Nous avons également trouvé une corrélation significative entre la formation
complémentaire en éthique et les dimensions de la confidentialité et de la
non-malfaisance. Les participants de notre étude ont obtenu 70 % du score total pour toutes les
situations rencontrées. Ce score semble être insuffisant. Dans une étude portant sur une population de pharmaciens iraniens, Sharif PS et al. ont
trouvé un résultat similaire de 65 % par rapport au score total ( ). Ce score relativement bas pourrait s'expliquer par le manque de connaissances dans le
domaine de l'éthique médicale. Il serait peut-être primordial de renforcer l'enseignement de l'éthique médicale chez
nos pharmaciens ou d'implémenter des programmes de formation complémentaire en éthique
médicale. En effet, Ali et al. ont mis au point une formation d'une heure basée sur la discussion
de sujets d'éthique médicale clinique. Par la suite, ils ont évalué les connaissances, attitudes et compétences des
professionnels de la santé avant et après cette formation. Finalement, ils ont conclu à une amélioration significative dans tous les paramètres
étudiés se rapportant à l'éthique médicale( ). Cette méthode de discussion autour de l’éthique médicale serait une bonne alternative
pour aider à adopter la meilleure attitude éthique. En effet, dans des situations posant un problème éthique, il n’y a pas de solution
claire. Veatch et al. présentent le dilemme éthique comme un problème sans solution unique et
claire, sans bonne ou mauvaise réponse sur laquelle tout le monde peut se mettre
d’accord ( ). Dans la pratique professionnelle, la prise de décision en matière d’éthique se baserait
davantage sur l’éthique procédurale que sur l’éthique normative ( ). Dans leur étude portant sur une évaluation qualitative des dilemmes éthiques auxquels
sont confrontés les pharmaciens dans leur profession en Arabie Saoudite, Orayj et al.
ont trouvé que la plupart des défis éthiques dans l’exercice du métier de pharmacien
portent sur les thèmes de la bienfaisance et de la nonmalfaisance ( ). Notamment, la bienfaisance inclut le principe de la confidentialité ( ). Dans notre étude, nous avons trouvé une médiane de score concernant la confidentialité
de 8 sur un score total de 9, avec 91 % de notre population ayant une attitude
éthiquement correcte. Par ailleurs, dans l’étude de Gharaibeh et al., qui évalue les pratiques et
connaissances actuelles des pharmaciens concernant la confidentialité des données, les
auteurs ont constaté que les pharmaciens étaient conscients de l’importance de la
confidentialité, mais ont identifié certaines lacunes ( ). Les pharmaciens ont obtenu un score médian de 3,5 sur 4 concernant les pratiques liées à
la confidentialité des données. Parmi eux, 78,4 % traitaient les informations médicales des patients avec une grande
confidentialité, et 85,5 % accordaient la plus haute confidentialité aux informations
concernant les maladies sexuellement transmissibles des patients ( ). Dans l’étude de Sharif et al., dont notre questionnaire a été extrait, le score total de
confidentialité des participants était de 4,15 sur 9, et 89,9 % des participants avaient
obtenu un score inférieur à 6. D'après ce score, la majorité des pharmaciens de cette population seraient d'accord pour
partager les informations des patients avec un proche sans leur autorisation ( ). La confidentialité est un pilier de l’éthique médicale. Il semble nécessaire de sensibiliser les pharmaciens aux conséquences de la divulgation
des informations des patients sans leur consentement. Cependant, il faut garder à l’esprit que la confidentialité n’est pas absolue et peut
être contournée dans certaines situations. En effet, elle peut être surmontée par la loi et les exigences en matière de santé
publique et de sécurité ( ). Par exemple, durant la pandémie de Covid-19, le processus de notification par les
établissements médicaux était essentiel pour le suivi et la maîtrise de la propagation
de la maladie. Toutefois, les systèmes de notification doivent être établis de manière à garantir un
équilibre entre les avantages pour les individus et ceux pour la société ( ) . Nos résultats ont montré que le genre, l’âge, la fonction et le Gouvernorat d’exercice
n’avaient pas d’influence sur l’attitude éthique du pharmacien, que ce soit sur le score
général ou le score selon la dimension. En revanche, la formation supplémentaire en éthique était significativement corrélée
avec un score élevé en rapport avec une attitude éthique concernant les dimensions de la
confidentialité et de la non-malfaisance. Cela renforce l’importance des formations complémentaires dans l’acquisition et le
déploiement des concepts de l’éthique dans le domaine de la santé. Dans leur étude, Sharif et al. n’ont également pas trouvé de différence dans les
attitudes à l’égard des questions éthiques concernant la confidentialité, l'autonomie,
la justice et la non-malfaisance entre les genres masculin et féminin ( ). En revanche, les auteurs rapportent que les populations âgées ont obtenu un score total
plus bas concernant les dimensions de la justice, de l’autonomie et de la
confidentialité, tandis que l’attitude concernant la dimension de la non-malfaisance n’a
pas été influencée par l’âge des pharmaciens. Ils en ont déduit qu’il y avait un accord général parmi la plupart des pharmaciens sur
le devoir prima facie de non-malfaisance ( ). Notre étude, étant la première en Tunisie à s’intéresser à l’attitude des pharmaciens
face à des dilemmes éthiques, nous a permis d’identifier certaines lacunes concernant
l’attitude éthique des pharmaciens, se rapportant essentiellement au domaine de la
justice, mais également une maîtrise de la dimension de la confidentialité par notre
population de pharmaciens. Cela nous encourage à mener d’autres études afin d’obtenir davantage d’informations sur
la compréhension et l’engagement éthique de cette entité du personnel de santé. Ces études permettront d’orienter les formations complémentaires en éthique afin de
cibler au mieux les lacunes constatées. Cette conclusion est renforcée par les résultats d'une étude menée à la faculté de
médecine de Monastir, où des séances d'apprentissage du raisonnement éthique ont été
mises en œuvre pour des étudiants de 5éme année en psychiatrie ( ). Cette étude a démontré une amélioration significative des compétences éthiques des
étudiants après avoir participé à des sessions de formation structurées, mettant en
lumière l'efficacité des programmes de formation en éthique pour améliorer les attitudes
professionnelles face aux dilemmes éthiques. Notre étude ne nous a pas permis de déceler les facteurs qui influencent les attitudes
éthiques de notre population d’étude. Ceci est un facteur limitant, mais il pourrait nous pousser à élargir la population
étudiée et à rechercher d’autres facteurs susceptibles d’influencer ces attitudes, tels
que le cadre réglementaire, les pressions économiques, les valeurs personnelles et les
croyances religieuses.
Les pharmaciens, comme tout professionnel de la santé, sont confrontés à des dilemmes
éthiques nécessitant des décisions conformes aux principes de l’éthique médicale. Notre étude, première du genre en Tunisie, a mis en évidence la nécessité de renforcer
les formations en éthique médicale chez les pharmaciens. Des programmes de formation centrés sur des échanges autour de l’éthique médicale
peuvent s’avérer bénéfiques pour améliorer les attitudes éthiques face à des dilemmes,
comme démontré dans des recherches antérieures. Nous avons identifié certaines faiblesses dans l’attitude éthique des pharmaciens,
notamment concernant la dimension de la justice, mais aussi une maîtrise satisfaisante
de la dimension de la confidentialité. Ceci souligne l’importance de poursuivre les recherches pour mieux comprendre et
améliorer l’engagement éthique des pharmaciens. Bien que notre étude n’ait pas pu identifier tous les facteurs influençant les attitudes
éthiques des pharmaciens, elle ouvre la voie à des recherches futures visant à explorer
d’autres déterminants potentiels, tels que le cadre réglementaire, les pressions
économiques et les valeurs personnelles.
|
3D exoscopic | 2594db92-4c30-4c7e-b626-d49493bcde91 | 11161429 | Microsurgery[mh] | The most commonly performed direct revascularization for Moyamoya disease (MMD), is anastomosis of the superficial temporal artery (STA) to distal M3 or M4 branches of the middle cerebral artery (MCA) (STA-MCA bypass) [ , , ]. The aim of direct revascularization is to improve cerebral blood flow (CBF) and prevent either transient ischemic attacks (TIAs) or cerebral infarction . Based on expert consensus, primary treatment of symptomatic MMD via surgical revascularization should be considered . Visualization in cerebrovascular surgery has - since the dawn of microneurosurgery - consisted out of the counterbalanced binocular operating microscope . Three-dimensional (3D) exoscopes offer an alternative for surgical magnification and illumination, with specific advantages related to image quality, ergonomics and training possibilities . First generation exoscopes offer image quality that challenges conventional optics with the additional benefits of higher magnification, live image processing ameliorating tissue differentiation, and a larger depth of field . In an experimental cerebral bypass setting, comparable procedural quality was found for the microscope and the 3D exoscope . Exoscope systems have been assessed in respect to their performance in cerebral revascularization surgery , but not in direct comparison with the operating microscope. The aim of this study was to provide a comparative analysis of the surgical performance of 3D exoscopes in STA-MCA bypass surgery exclusively carried out for MMD versus microscope-aided vascular anastomosis. Here, we hypothesized that exoscope use can lead to comparable results regarding overall duration of surgery, duration of end-to-side anastomosis (ischemia time) and bypass patency rates.
Patient population and study design All patients with MMD in Finland, for which bypass surgery is considered, are assessed at the Helsinki university hospital (HUS). Moyamoya angiopathy and disease are diagnosed based on international criteria and recommendation . The decision to recommend flow-augmentation surgery is made on an individual basis, in interdisciplinary consensus among pediatricians, neurologists and neuroradiologists. Treatment options are considered based on clinical presentation, course of the disease, angiographic severity and evidently, patient age and opinion. If bypass surgery is considered an option, patients undergo six-vessel cerebral angiography to estimate anatomical feasibility (incl. donor size). Since 2015, all EC-IC procedures in Helsinki, and thereby Finland, were performed by the senior author (ML) with few exceptions i.e. during the Helsinki live courses. Since 2020 all surgeries have been performed with a 3D digital exoscope (ORBEYE ® n = 1; AEOS ® n = 9). In this study, we aim to compare all STA-MCA bypass procedures for MMD using the surgical microscope (pre-2020) versus those procedures using the exoscope (2020–2023). It should be noted that the senior author had extensive prior experience in EC-IC bypass surgery as well as exoscopic microneurosurgery in form of skull base and neurovascular procedures . This article is written in accordance with the Preferred Reporting of Case Series in Surgery (PROCESS) statement for reporting observational studies/case series. Surgical treatment Bypass surgery is subjected to numerous nuances and there exists high variability in surgical technique between surgeons and MMD treating centers. Though the focus of this study does not lie on surgical technicalities, we provide an overview of the STA-MCA procedure as currently practices at HUS. Whenever surgically feasible, based on donor size and anticipated recipient caliber, a direct in situ bypass is preferred over an indirect bypass (i.e. encoephao-duro-arterio-myo-synangiosis or EDMS). The STA-MCA anastomoses are sometimes combined with EDMS in patients under 25 years of age, but purely indirect bypass procedures are reserved for young children only. The incision is planned during graft (STA) identification by palpating and Doppler sonography. Careful preoperative study of external carotid artery angiograms helps in assessing the location of the STA bifurcation and the size and usefulness of its branches. The incision is planned on top of the main STA trunk (usually the parietal branch) and it’s terminal branches, but may vary depending on anatomy and what is needed. The entire procedure from skin incision to closure is performed under either the microscope or exoscope. In exoscopic bypass surgery, the device is positioned next to the patient with the camera head coming between scrub nurse and surgeon, to float above the surgical field. A high-resolution screen is positioned at the foot-end of the operating table in direct line of sight of the surgeon. The operating room setup is demonstrated in Fig. . Dissection of the STA is begun distally so that if the artery is accidentally injured, the procedure can still continue. The skin is retracted upwards using fishhooks over rolled-up swabs functioning as pulleys elevating and everting skin edges (Fig. A). The artery is dissected within the loose areolar tissue and small branches are coagulated and interrupted by a plucking motion using short straight bipolar forceps. The dissection continues proximally and the STA bifurcation is identified. Care is taken to preserve all major proximal side branches not used as a main graft, in order not to compromise further spontaneous collateralization. The main trunk is dissected free to allow mobilization of the vessel. A second dissection run is performed along the vessel, this time from proximal to distal, disconnecting the deep surface of the donor vessel from its bed. The donor vessel is than wrapped in surgical patties soaked with papaverine and pulled gently away with a large piece of latex free surgical glove. The incision of the temporal muscle is tailored to individual anatomy and whether EDMS is planned or not. The muscle is usually split in a craniocaudal fashion, and the edges are reflected laterally with fishhooks. A single burr hole is made at the cranial border of the planned craniotomy after which a circular craniotomy is completed. Care is taken to protect the graft at all times. The durotomy is placed in such a fashion, that as few dural vessels are transected or coagulated. All potential candidate recipients within the exposed portion of the sylvian fissure are carefully inspected. The recipient vessel is chosen based on size, location and orientation. After opening the arachnoid and dissection of the recipient, the donor is clamped and distally disconnected. The STA is cut to length allowing it to be sutured into the recipient’s wall without traction, twisting or looping. The artery is rinsed with heparinized saline and the tip is colored with a water-based surgical marker. The vessel’s end is brought to the anastomosis site and a fish mouth cut is made at the appropriate position. The length of the planned arteriotomy is marked on the recipient vessel wall. The heel- and toe-stiches are preloaded into the donor to shorten ischemia time. Two mini temporary clips are placed at both ends of the recipient starting ischemia time. A straight arteriotomy is made and the donor is firstly fixated with heel- and toe-stiches. Interrupted 10 − 0 nylon stiches are placed to fixate the wings of the fish mouthed donor’s end, pointing away from the surgeon. The wall facing away is sutured first to allow inspection into the anastomosis. The anastomosis is completed by closing the section closest to the surgeon beginning with the wing of the fish mouth. Temporary clips are opened and the anastomosis is inspected for leakage. Some oozing is tolerated and observed under continuous irrigation and augmentation with Surgicel™. In case of relevant leakage, additional stiches are added. During closure, attention is paid not to kink or twist the donor vessel. The bone flap is trimmed down in size, allowing basal intracranial entry of the STA. Meticulous subcuticular closure aims not to stretch or injure the underlying donor vessel. Radiological and clinical evaluation All data were collected retrospectively by screening of electronic hospital records extracting demographic data, symptoms leading to initial MMD diagnosis, comorbidities on admission and occurrence of perioperative complications. Pre- and postoperative imaging was assessed by two independent assessors (MV, VN) including donor diameter (mm.), Suzuki staging , direct post-operative bypass patency in CTA imaging and bypass patency at last available follow-up imaging (either CTA or conventional cerebral angiography) (Fig. ). Analysis of surgical procedures and outcome assessment All surgical procedures were digitally recorded and video material was independently analyzed by two neurosurgeons (MV and RH). Total length of surgery (min.) was chosen as the primary outcome parameter, starting from skin incision until dural closure. Skin closure was not included in total duration of surgery because this was not recorded for all case. Additionally, the total duration (min.) of end-to-side anastomosis was measured (ischemia time) between placing and removing temporary clips on the donor vessel. In one microscopic case, a double-barrel bypass using both main STA branches was performed. In order to include this case, the duration of the first bypass anastomosis was used for analysis of ischemia time and the duration of the second anastomosis was subtracted from the total duration of surgery. Secondary endpoints were defined as: functional outcome measured by the modified Rankin scale (mRS) at discharge and last follow-up, number of stiches placed per anastomosis, number of added stiches after leakage test (as an indirect early measure of bypass quality), bypass patency on direct post-operative imaging and bypass patency on last available imaging. Statistical analysis All descriptive data are presented as mean and (±) standard deviation for normally and as median and interquartile range (Q 1 to Q 3 ) for non-normally distributed continuous variables. Categorical data are presented as proportions. Data was tested for normality via the Shapiro-Wilk test after which the appropriate statistical test was selected. Categorical data were tested by means of the χ 2 test and continuous data via the unpaired t-test or Mann-Whitney U-test. All statistical analyses were performed using IBM SPSS Statistics 29 (SPSS Inc., Chicago, IL, USA). Statistical significance was defined as a two-sided p < 0.05.
All patients with MMD in Finland, for which bypass surgery is considered, are assessed at the Helsinki university hospital (HUS). Moyamoya angiopathy and disease are diagnosed based on international criteria and recommendation . The decision to recommend flow-augmentation surgery is made on an individual basis, in interdisciplinary consensus among pediatricians, neurologists and neuroradiologists. Treatment options are considered based on clinical presentation, course of the disease, angiographic severity and evidently, patient age and opinion. If bypass surgery is considered an option, patients undergo six-vessel cerebral angiography to estimate anatomical feasibility (incl. donor size). Since 2015, all EC-IC procedures in Helsinki, and thereby Finland, were performed by the senior author (ML) with few exceptions i.e. during the Helsinki live courses. Since 2020 all surgeries have been performed with a 3D digital exoscope (ORBEYE ® n = 1; AEOS ® n = 9). In this study, we aim to compare all STA-MCA bypass procedures for MMD using the surgical microscope (pre-2020) versus those procedures using the exoscope (2020–2023). It should be noted that the senior author had extensive prior experience in EC-IC bypass surgery as well as exoscopic microneurosurgery in form of skull base and neurovascular procedures . This article is written in accordance with the Preferred Reporting of Case Series in Surgery (PROCESS) statement for reporting observational studies/case series.
Bypass surgery is subjected to numerous nuances and there exists high variability in surgical technique between surgeons and MMD treating centers. Though the focus of this study does not lie on surgical technicalities, we provide an overview of the STA-MCA procedure as currently practices at HUS. Whenever surgically feasible, based on donor size and anticipated recipient caliber, a direct in situ bypass is preferred over an indirect bypass (i.e. encoephao-duro-arterio-myo-synangiosis or EDMS). The STA-MCA anastomoses are sometimes combined with EDMS in patients under 25 years of age, but purely indirect bypass procedures are reserved for young children only. The incision is planned during graft (STA) identification by palpating and Doppler sonography. Careful preoperative study of external carotid artery angiograms helps in assessing the location of the STA bifurcation and the size and usefulness of its branches. The incision is planned on top of the main STA trunk (usually the parietal branch) and it’s terminal branches, but may vary depending on anatomy and what is needed. The entire procedure from skin incision to closure is performed under either the microscope or exoscope. In exoscopic bypass surgery, the device is positioned next to the patient with the camera head coming between scrub nurse and surgeon, to float above the surgical field. A high-resolution screen is positioned at the foot-end of the operating table in direct line of sight of the surgeon. The operating room setup is demonstrated in Fig. . Dissection of the STA is begun distally so that if the artery is accidentally injured, the procedure can still continue. The skin is retracted upwards using fishhooks over rolled-up swabs functioning as pulleys elevating and everting skin edges (Fig. A). The artery is dissected within the loose areolar tissue and small branches are coagulated and interrupted by a plucking motion using short straight bipolar forceps. The dissection continues proximally and the STA bifurcation is identified. Care is taken to preserve all major proximal side branches not used as a main graft, in order not to compromise further spontaneous collateralization. The main trunk is dissected free to allow mobilization of the vessel. A second dissection run is performed along the vessel, this time from proximal to distal, disconnecting the deep surface of the donor vessel from its bed. The donor vessel is than wrapped in surgical patties soaked with papaverine and pulled gently away with a large piece of latex free surgical glove. The incision of the temporal muscle is tailored to individual anatomy and whether EDMS is planned or not. The muscle is usually split in a craniocaudal fashion, and the edges are reflected laterally with fishhooks. A single burr hole is made at the cranial border of the planned craniotomy after which a circular craniotomy is completed. Care is taken to protect the graft at all times. The durotomy is placed in such a fashion, that as few dural vessels are transected or coagulated. All potential candidate recipients within the exposed portion of the sylvian fissure are carefully inspected. The recipient vessel is chosen based on size, location and orientation. After opening the arachnoid and dissection of the recipient, the donor is clamped and distally disconnected. The STA is cut to length allowing it to be sutured into the recipient’s wall without traction, twisting or looping. The artery is rinsed with heparinized saline and the tip is colored with a water-based surgical marker. The vessel’s end is brought to the anastomosis site and a fish mouth cut is made at the appropriate position. The length of the planned arteriotomy is marked on the recipient vessel wall. The heel- and toe-stiches are preloaded into the donor to shorten ischemia time. Two mini temporary clips are placed at both ends of the recipient starting ischemia time. A straight arteriotomy is made and the donor is firstly fixated with heel- and toe-stiches. Interrupted 10 − 0 nylon stiches are placed to fixate the wings of the fish mouthed donor’s end, pointing away from the surgeon. The wall facing away is sutured first to allow inspection into the anastomosis. The anastomosis is completed by closing the section closest to the surgeon beginning with the wing of the fish mouth. Temporary clips are opened and the anastomosis is inspected for leakage. Some oozing is tolerated and observed under continuous irrigation and augmentation with Surgicel™. In case of relevant leakage, additional stiches are added. During closure, attention is paid not to kink or twist the donor vessel. The bone flap is trimmed down in size, allowing basal intracranial entry of the STA. Meticulous subcuticular closure aims not to stretch or injure the underlying donor vessel.
All data were collected retrospectively by screening of electronic hospital records extracting demographic data, symptoms leading to initial MMD diagnosis, comorbidities on admission and occurrence of perioperative complications. Pre- and postoperative imaging was assessed by two independent assessors (MV, VN) including donor diameter (mm.), Suzuki staging , direct post-operative bypass patency in CTA imaging and bypass patency at last available follow-up imaging (either CTA or conventional cerebral angiography) (Fig. ).
All surgical procedures were digitally recorded and video material was independently analyzed by two neurosurgeons (MV and RH). Total length of surgery (min.) was chosen as the primary outcome parameter, starting from skin incision until dural closure. Skin closure was not included in total duration of surgery because this was not recorded for all case. Additionally, the total duration (min.) of end-to-side anastomosis was measured (ischemia time) between placing and removing temporary clips on the donor vessel. In one microscopic case, a double-barrel bypass using both main STA branches was performed. In order to include this case, the duration of the first bypass anastomosis was used for analysis of ischemia time and the duration of the second anastomosis was subtracted from the total duration of surgery. Secondary endpoints were defined as: functional outcome measured by the modified Rankin scale (mRS) at discharge and last follow-up, number of stiches placed per anastomosis, number of added stiches after leakage test (as an indirect early measure of bypass quality), bypass patency on direct post-operative imaging and bypass patency on last available imaging.
All descriptive data are presented as mean and (±) standard deviation for normally and as median and interquartile range (Q 1 to Q 3 ) for non-normally distributed continuous variables. Categorical data are presented as proportions. Data was tested for normality via the Shapiro-Wilk test after which the appropriate statistical test was selected. Categorical data were tested by means of the χ 2 test and continuous data via the unpaired t-test or Mann-Whitney U-test. All statistical analyses were performed using IBM SPSS Statistics 29 (SPSS Inc., Chicago, IL, USA). Statistical significance was defined as a two-sided p < 0.05.
Patient population and presenting symptoms In total, 16 consecutive moyamoya patients were included who underwent 21 STA-MCA bypass procedures. The average age of treated patients was 34 ± 11 years (range: 14–55) and proved comparable between microscopic and exoscopic patients. Five patients were treated with staged bilateral bypass surgery. One pediatric patient was included (14 years old) who underwent a combined left sided direct bypass and EDMS, and right direct bypass surgery during a second surgery. Of all included 16 patients, 6 (37.5%) were operated under the operating microscope and 10 (62.5%) using an exoscopic system (ORBEYE® n = 1; AEOS® n = 9). The left hemisphere was more commonly and more severely affected in all but two included patients showing left MCA stenosis to some extent. Baseline characteristics and angiographic MMD severity (affected vessels and Suzuki stage) were comparable between microscopic and exoscopic treated patients (see Table ). Duration of surgery and STA-MCA anastomosis Total duration of surgery was comparable between microscopic and exoscopic bypass procedures (313 min. ± 116 vs. 279 min. ± 42; p = 0.647), ischemia time also proved similar (microscope: 43 min. ± 19 vs. exoscope 41 min. ± 7; p = 0.701). The number of individual stiches per anastomosis did not differ between visualization devices (microscope: 17 ± 4 vs. exoscope: 17 ± 2; 0.887). In contrast, more additional stiches were needed in microscopic anastomoses after leakage testing of the bypass ( p = 0.035). An overview of procedure specific characteristics is presented in Table . The duration of surgery and anastomosis is graphically plotted for individual procedures in Fig. . Clinical and radiological outcome Nineteen out of 21 bypasses were patent on early post-operative imaging with one occlusion in both the microscopic and exoscopic group ( p = 0.943) (see Table ). No post-operative ischemia was observed after any of the 21 bypass procedures. No patients experienced a drop in mRS after surgery. During follow-up of a median six months (2.0 to 27.8), no additional bypasses occluded. Caused by the natural progression of the disease, five patients suffered new TIAs during follow-up, of which three in the microscopic and two in the exoscopic group ( p = 0.210). One cerebral infarction occurred in an patient in the exoscope group ( p = 0.424). Functional outcome, neither at discharge nor at the latest time point of follow-up, differed between both groups (see Table ).
In total, 16 consecutive moyamoya patients were included who underwent 21 STA-MCA bypass procedures. The average age of treated patients was 34 ± 11 years (range: 14–55) and proved comparable between microscopic and exoscopic patients. Five patients were treated with staged bilateral bypass surgery. One pediatric patient was included (14 years old) who underwent a combined left sided direct bypass and EDMS, and right direct bypass surgery during a second surgery. Of all included 16 patients, 6 (37.5%) were operated under the operating microscope and 10 (62.5%) using an exoscopic system (ORBEYE® n = 1; AEOS® n = 9). The left hemisphere was more commonly and more severely affected in all but two included patients showing left MCA stenosis to some extent. Baseline characteristics and angiographic MMD severity (affected vessels and Suzuki stage) were comparable between microscopic and exoscopic treated patients (see Table ).
Total duration of surgery was comparable between microscopic and exoscopic bypass procedures (313 min. ± 116 vs. 279 min. ± 42; p = 0.647), ischemia time also proved similar (microscope: 43 min. ± 19 vs. exoscope 41 min. ± 7; p = 0.701). The number of individual stiches per anastomosis did not differ between visualization devices (microscope: 17 ± 4 vs. exoscope: 17 ± 2; 0.887). In contrast, more additional stiches were needed in microscopic anastomoses after leakage testing of the bypass ( p = 0.035). An overview of procedure specific characteristics is presented in Table . The duration of surgery and anastomosis is graphically plotted for individual procedures in Fig. .
Nineteen out of 21 bypasses were patent on early post-operative imaging with one occlusion in both the microscopic and exoscopic group ( p = 0.943) (see Table ). No post-operative ischemia was observed after any of the 21 bypass procedures. No patients experienced a drop in mRS after surgery. During follow-up of a median six months (2.0 to 27.8), no additional bypasses occluded. Caused by the natural progression of the disease, five patients suffered new TIAs during follow-up, of which three in the microscopic and two in the exoscopic group ( p = 0.210). One cerebral infarction occurred in an patient in the exoscope group ( p = 0.424). Functional outcome, neither at discharge nor at the latest time point of follow-up, differed between both groups (see Table ).
With this study we aimed to document the performances of a foot switch-operated robotic 3D exoscope system, compared with the conventional operative microscope, for STA-MCA bypass surgery. In this single-surgeon series neither overall duration of surgery nor ischemia time were affected by the visualization device used. A significant lower number of added stiches after leakage testing after exoscopic anastomosis, might suggest the exoscope allows for a better 360 degree assessment during suture placement and better final quality of anastomosis. Although the senior author had bypass experience prior to this series, a learning curve effect herein cannot be excluded. To the best of our knowledge, this is the third report of exoscopic EC-IC bypass surgery and the first offering a control group. Nossek et al. describe their experience using the ORBEYE Exoscope (Sony Olympus Medical Solutions Inc, Tokyo, Japan) for STA-MCA bypass in five patients, four of which suffered MMD and one patient with a dissecting MCA aneurysm . No exoscope related problems or complications were reported. In addition, Patel et al. reported the use of a 3-dimensional exoscope to perform an internal maxillary to middle cerebral artery high-flow bypass . All exoscope systems on the market today have their advantages and disadvantages. Although the ORBEYE system allows foot switch control of focus, zoom and translational movements, rotational viewing angle adjustments need to be done manually, which can be challenging under high magnification . In an experimental bypass setting, we have previously documented similar duration and quality of anastomosis between microscopic and exoscopic visualization devices . However, the exoscope offers a better 3-dimensional view with increased focal depth alongside the ability to share the surgeons view with others for teaching purposes. Microvascular intracranial anastomosis in the context of MMD is microneurosurgery par excellence and requires optimal magnification and illumination. Unconvincing results of randomized EC-IC bypass trials for atherosclerotic disease have reduced the bypass case load and exposure of neurovascular surgeons to the procedure . In addition, endovascular techniques have reduced the need for revascularization procedures for complex aneurysms . Direct bypass surgery for those remaining MMD cases are oftentimes challenging due to brittle vessels and small pediatric anatomy . We believe surgical exoscopes as microsurgical magnification and illumination devices, might offer better teaching platforms for aspiring bypass surgeons because assistants / residents and OR observers all share the same image as the surgeon. Exoscopes could contribute in tackling the educational paradox of decreasing case load and increasing case complexity . Microvascular bypass surgery requires high magnification and different viewing angles, especially during the actual anastomosing of vessels. The exoscope offers the benefit of additional digital zoom and precise robotic changes of viewing angles under high magnification around a central focus point. Camera adjustments can be performed using a foot-switch, eliminating the need of the surgeon to remove a hand out of the surgical field. Although, foot-switch controlled motorized translational movements along the XY-axes are possible in operating microscopes, hands free rotational movements are not. In combination with increased focal depth, these advantages of exoscope surgery have the potential to shorten the duration of longer procedures that require multiple viewing angle adjustments . Many reports of exoscope use in neurosurgery have remained descriptive and focus on the perception of image quality and improved ergonomics . Here, we aimed at quantifying the transition process from STA-MCA bypass surgery using the binocular operative microscope to the use of a 3D exoscope system. Although there is still much to improve upon, we believe the first generation of exoscopes is capable of allowing complex neurosurgical producers such as neurovascular bypass surgery. These results our congruous to previous reports of exoscope cerebrovascular bypass surgery . Limitations As this is a single surgeon consecutive series, the external validity of our results might be limited. On the other hand by comparing results of a single neurosurgeon, smaller incremental changes and improvements in surgical technique as part of a lifelong continuous learning curve aside, other potential sources of biases related to indication, and patient selection, have stayed fairly constant throughout this series. From this point of view, the validity of our results can been graded as good for the purpose of comparing both visualization devices. Although numbers of included patients are low, to the best of our knowledge, this constitutes the largest series of exoscopic bypass procedures published to date. Bypass patency was, however, not routinely assessed by conventional cerebral angiography (but with CTA) as is common in some institutions. Duration of follow-up varies widely and is relatively short. Also, because exoscopic patients were the last to be operated on, the exoscopic group has a shorter duration of follow-up. This series documents the transition from microscopic to exclusively exoscopic bypass surgery. Although the senior author had extensive prior experience with bypass procedures, a potential learning curve effect cannot be excluded. Other critical microneurosurgical steps such as graft preparation, could have been compared in time and quality, between series. We, however, focused on duration of the anastomosis as this is a fairly standardized part of the surgery and the duration of this step (ischemia time) has relevant clinical implications.
As this is a single surgeon consecutive series, the external validity of our results might be limited. On the other hand by comparing results of a single neurosurgeon, smaller incremental changes and improvements in surgical technique as part of a lifelong continuous learning curve aside, other potential sources of biases related to indication, and patient selection, have stayed fairly constant throughout this series. From this point of view, the validity of our results can been graded as good for the purpose of comparing both visualization devices. Although numbers of included patients are low, to the best of our knowledge, this constitutes the largest series of exoscopic bypass procedures published to date. Bypass patency was, however, not routinely assessed by conventional cerebral angiography (but with CTA) as is common in some institutions. Duration of follow-up varies widely and is relatively short. Also, because exoscopic patients were the last to be operated on, the exoscopic group has a shorter duration of follow-up. This series documents the transition from microscopic to exclusively exoscopic bypass surgery. Although the senior author had extensive prior experience with bypass procedures, a potential learning curve effect cannot be excluded. Other critical microneurosurgical steps such as graft preparation, could have been compared in time and quality, between series. We, however, focused on duration of the anastomosis as this is a fairly standardized part of the surgery and the duration of this step (ischemia time) has relevant clinical implications.
End-to-side bypass procedures for moyamoya angiopathy using a foot switch-operated 3D exoscope proved safe and leads to comparable clinical and radiological results as classical microscopic bypass surgery. Hands free camera mobility of the exoscope allows the surgeon to keeps both hands in the surgical field during the anastomosis whilst adjusting viewing angles, which might offer the potential to reduce ischemia time.
Below is the link to the electronic supplementary material. Supplementary file1 (MP4 390 MB)
|
A Systematic Review of Parkinson’s Disease Pharmacogenomics: Is There Time for Translation into the Clinics? | 501653ea-8ba6-4907-983b-a32a86395495 | 8268929 | Pharmacology[mh] | Parkinson’s disease (PD) is the second most common neurodegenerative disease present today. The incidence and prevalence are highest in the population aged ≥65 years old, making the disease a significant public health burden in the elderly . The clinical course of the disease is progressive and is defined by motor symptoms such as resting tremor, bradykinesia and rigidity, along with a wide variety of non-motor symptoms such as autonomic dysfunction, sleep disorders, cognitive deficits and behavioural changes . The first symptoms appear several years before the classic motor symptoms during the prodromal PD, which is marked by non-specific symptoms like constipation and insomnia . Our understanding of underlying mechanisms in PD has significantly increased over recent years. The main postulated pathological mechanisms in PD include the intracellular aggregation of α-synuclein, which form Lewy bodies , as well as the loss of dopaminergic neurons, which first happens in the substantia nigra but later becomes more widespread as the disease progresses . The landmark paper published by Braak et al. describes a gradually evolving pathological severity, starting from the lower brainstem, with a progression to the limbic and neocortical brain regions in the later stages of PD . The variation of clinical states between patients can be significant, even though the underlying mechanisms are similar. Efforts have been made to categorize the disease into varying subtypes. Seyed-Mohammad et al. propose three subtypes based predominantly on clinical characteristics: the mild motor, intermediate and diffuse malignant subtypes. Importantly, findings from the study indicated that neuroimaging correlated better with the subtypes than genetic information, even after incorporating a single “genetic risk score” that encompassed 30 specific PD-related mutations. However, this could also be a consequence of a lack of patients with particular variations in the population they studied . The need to categorize the disease comes from its variability in presentation, response to treatment and incidence of side-effects. Current treatment options for PD are plentiful, at least in comparison to other neurodegenerative diseases, and offer PD patients extended control of symptom severity as well as an improved quality of life. Unfortunately, no treatment halts the pathological mechanisms that drive disease progression, with most treatment being focused on replacing or enhancing dopamine availability. The golden standard in pharmacologic therapy is dopamine replacement therapy, mainly levodopa, used in synergy with dopamine receptor agonists, monoamine oxidase (MAO) inhibitors or catechol-O-methyltransferase (COMT) inhibitors . The challenge that stems from this type of therapy is the delicate balance between the beneficial and harmful effects that can arise . There is a significant variation in therapy response and side-effect incidence in treating PD, which can be linked to the varied subtypes mentioned earlier, along with increasing evidence of complex environmental and genetic factor interaction . The consequence of this is the need to fine-tune and personalize the therapy to each patient to account for the variability in drug response . As most treatment is focused on L-dopa, understanding the key players in its metabolism has put the research focus in pharmacogenomics on genes that influence the enzymes and receptors in this pathway . The general principles and goals of pharmacogenomics are to identify the genetic factors behind the varied drug response in individuals, thereby predicting response and paving the way for personalised medicine . The two main areas where the variability of drug response is studied are known as pharmacokinetics and pharmacodynamics. Pharmacokinetics incorporates all processes that affect drug absorption, distribution and metabolism in the body as well as its excretion, while pharmacodynamics focuses on the target actions of the drug. Current evidence suggests that genetic variability and its effects on drug characteristics are concentrated in three major steps: the initial pharmacokinetic processes that ultimately affect the plasma concentration, the capability of drugs in passing the blood-brain barrier (BBB) and finally, the modification of target pharmacodynamic properties of the drug . Expanding the knowledge of the variations that affect these three factors will pave the way for predicting drug response, thus furthering the benefit of a personalized medicine approach in all diseases. Unfortunately, there are currently no clinical guidelines regarding the use of pharmacogenomics in the clinical practice of treating PD, with sparse clinical annotations on relevant databases . Therefore, our aim is to assess the current state of knowledge in this field and the possibility of translation into the clinics.
Current treatment in PD is focused on alleviating the symptoms and does little to slow down the pathophysiological progression of the disease. As such, the therapy goal is to increase the amount of dopamine to compensate for the loss of dopaminergic neurons. The therapeutic of choice for this is levodopa (L-dopa), which relieves the motor symptoms by increasing the availability of dopamine in the central nervous system (CNS) . All the current pharmaceutical treatment options centre around the dopamine metabolic pathway, which encompasses many genetic pathways. However, there are specific pharmacogenomic properties for different treatment options, as well as differences in pharmacogenomic properties in genotype driven PD. 2.1. Drug Specific Pharmacogenomic Properties 2.1.1. Pharmacogenomics of the Therapeutic Response to L-dopa Clinically, L-dopa is always combined with dopa decarboxylase (DDC) inhibitors, which causes a switch in L-dopa metabolism to the COMT pathway, thereby increasing the bioavailability of L-dopa in the CNS . The genetic variability of several genes has been implicated in the varied response to L-dopa. COMT gene is a protein-coding gene that provides instructions for creating the COMT enzyme, and its polymorphisms are involved in the varied response to numerous CNS diseases and treatments . The most studied polymorphism of the COMT gene is rs4680 (G>A), which results in a valine to methionine substitution at codon 158 (Val158Met). Single nucleotide polymorphisms of the COMT gene form haplotypes that result in lower (A_C_C_G), medium (A_T_C_A) and higher (G_C_G_G) enzyme activity, which, in the case of higher activity, had an impact on the required dosage compared to noncarriers . Studies have shown that the higher dosage is required during chronic administration in patients with greater COMT activity, while acute L-dopa administration was unchanged . Similar changes were observed in a recent study by Sampaio et al., where higher COMT enzyme activity was linked to higher doses of L-dopa required, while no significant changes in dosage were found in lower COMT enzymatic activity compared to the control . Common characteristics of patients that required the higher L-dopa dosage in multiple studies were advanced PD and earlier onset. A contradicting result was published in patients of Korean origins, with no significant association between the rs4680 polymorphism and the response to L-dopa; however, the study population did not have a considerable number of patients with advanced PD . Higher L-dopa doses were needed for patients with Solute Carrier Family 22 Member 1 (SLC22A1) gene rs622342A>C polymorphism that encodes the Organic Cation Transporter 1, along with the patients having higher mortality than the control population . On the other hand, lower required doses of L-dopa were found in patients with Synaptic vesicle glycoprotein 2C (SV2C) rs30196 polymorphism, as well as in Solute Carrier Family 6 Member 3 (SLC6A3) polymorphism after multivariate analysis . 2.1.2. Pharmacogenomics of the Side-Effects to L-dopa Increased incidence of adverse events in L-dopa treatment has been linked with various gene polymorphisms. Although the variations in COMT enzymatic activity on the onset of adverse events is still under debate, several studies have linked the lower COMT enzymatic activity to the increased incidence of motor complications such as dyskinesia, especially in advanced PD . Hypothetically, more moderate COMT enzymatic activity could lead to inadequate dopamine inactivation and the accumulation of dopamine in the synaptic cleft, thereby causing the dyskinesias. The same result was not replicated in studies by Watanabe et al. and Contin et al. . There is some evidence that the activation of the Mechanistic target of rapamycin (mTOR) signaling pathway contributes to L-dopa induced dyskinesia. It was indicative of earlier animal studies that the inhibition of mTOR pathways reduces the L-dopa related dyskinesia, most likely due to impaired metabolic homeostasis . These findings were corroborated in a recent human study, by Martin-Flores et al., that found significant associations with several SNPs affecting the mTOR pathway, indicating that the mTOR pathway contributes genetically to L-dopa induced dyskinesia susceptibility . Similarly, a functional Brain derived neurotrophic factor (BDNF) Val66Met polymorphism can lead to aberrant synaptic plasticity, which has been associated with L-dopa induced dyskinesia in a single study by Foltynie et al. . Limited evidence has been found in favour of a protective function of the Dopamine receptor 1 (DRD1) (rs4532) SNP, shown in a single study by Dos Santos et al. . The effect of Dopamine receptor 2 (DRD2) SNP’s on dyskinesia is a point of contention in current literature, as some studies indicate an increased risk of developing dyskinesia , while others revealed a protective effect on the incidence of dyskinesia . Interestingly, both studies that show reduced dyskinesias were conducted in the Italian population with the polymorphism DRD2 CAn-STR. Increased risk for developing L-dopa induced dyskinesia was seen in the Dopamine receptor 3 (DRD3) rs6280 polymorphism in a Korean population . However, opposing results were found by three research groups, with no evidence of correlation between DRD3 genetic polymorphisms and incidence of dyskinesias . Lower risk of L-dopa-associated dyskinesias was found in patients with Homer protein homolog 1 (HOMER1 ) rs4704560 G allele polymorphism . Finally, incidence of L-dopa induced dyskinesias was studied for the dopamine transporter gene (DAT) , where the presence of two genotypes 10R/10R (rs28363170) and A carrier (rs393795) led to a reduced risk of dyskinesias in an Italian population . Hyperhomocysteinemia is a known complication of L-dopa treatment in PD. The potential dangers of elevated plasma homocysteine are systemic, and include cardiovascular risk, increased risk for dementia and impaired bone health . A SNP C667T (rs1801133) in the MTHFR gene is consistently being linked to hyperhomocysteinemia due to L-dopa treatment in several studies. The result of this mutation is a temperature-labile MTHFR enzyme, which ultimately leads to hyperhomocysteinemia . In addition, a study by Gorgone et al. showed that elevated homocysteine levels lead to systemic oxidative stress in patients with this polymorphism . A recent study by Yuan et al. further adds to the claim that homocysteine levels are affected by L-dopa administration, especially in 677C/T and T/T genotypes . A possible option for homocysteine level reduction and alleviation of systemic oxidative stress is the addition of COMT inhibitors to the therapy, which presents a clear possibility for translation of this knowledge into the treatment of patients . There is contradicting evidence regarding whether COMT polymorphisms can influence the incidence of daytime sleepiness in PD patients, with differing results of the pilot and follow-up studies conducted by the same authors . Two additional studies by the same primary author revealed an association between sudden-sleep onset and the polymorphisms in hypocretin and DRD2, which was unrelated to a specific drug . Furthermore, increased risk of sleep attacks was found in Dopamine receptor 4 (DRD4) 48-bp VNTR polymorphism in a German population . The L-dopa adverse effects affecting emetic activity are not uncommon in PD treatment. DRD2 and DRD3 polymorphisms both showed an association with an increased risk of developing gastrointestinal adverse effects that do not respond well to therapy in a Brazilian population . However, that has not been reproduced in a recent study in a Slovenian population by Redenšek et al. . Mental and cognitive adverse effects of L-dopa are common due to the shared physiological dopaminergic pathways. A significant interaction was found between L-dopa and the COMT gene polymorphism in causing a detrimental effect on the activity in task-specific regions of the pre-frontal cortex due to altered availability of dopamine . Interestingly, carriers of at least one COMT rs165815 C allele had a decreased risk of developing visual hallucinations . In the same study carriers of the DRD3 rs6280 C allele had higher odds of developing visual hallucinations , which is in line with a previous study published by Goetz et al. . Increased risk of developing hallucinations is seen in patients with polymorphisms in the DRD2 gene , cholecystokinin gene and HOMER1 rs4704559 A allele , which encodes a protein that possesses a vital function for synaptic plasticity and glutamate signaling. On the other hand, the HOMER 1 rs4704559 G allele appears to decrease the risk of visual hallucinations . Furthermore, several studies link BDN F Val66Met polymorphism to impaired cognitive functioning in PD, but it appears to be irrespective of dopamine replacement therapy and is a genotype-specific trait . Impulse control disorder (ICD) is a well-known complication that can occur in some PD patients after initiating dopamine replacement therapy by either L-dopa or dopamine agonists . Heritability of ICD in a cohort of PD patients has been estimated at 57%, particularly for Opioid Receptor Kappa 1 (OPRK1) , 5-Hydroxytryptamine Receptor 2A (HTR2a) and Dopa decarboxylase (DDC) genotypes . A recent study found a suggestive association for developing ICD in variants of the opioid receptor gene OPRM1 and the DAT gene . Furthermore, there is evidence that polymorphisms in DRD1 (rs4857798, rs4532, rs265981), DRD2/ANKK1 (rs1800497) and glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) (rs7301328) bear an increased risk of developing ICD . The DRD3 (rs6280) mutation has also been linked with increased incidence of ICD with L-dopa therapy in studies by Lee et al. and Castro-Martinez et al. . On the other hand, there was no significant association found in COMT Val158Met and DRD2 Taq1A polymorphisms . Even though current data suggest high heritability for developing ICD after initiating dopamine replacement therapy, it should be noted that the effects of individual genes are small, and the development is most likely multigenic. 2.1.3. Dopamine Receptor Agonists Dopamine receptor agonists (DAs) are often the first therapies initiated in PD patients and are the main alternative to L-dopa . The effectiveness of DAs is lower than L-dopa, and most patients discontinue treatment within three years. Some significance has been found in polymorphisms of the DRD2 and DRD3 genes that could influence drug effectiveness and tolerability. A retrospective study by Arbouw et al. revealed that a DRD2 (CA)n-repeat polymorphism is linked with a decreased discontinuation of non-ergoline DA treatment, although the sample size in this study was small . A pilot study that included Chinese PD patients revealed that the DRD3 Ser9Gly (rs6280) polymorphism is associated with a varied response to pramipexole , which has since been confirmed in a recent study by Xu et al. . Interestingly, the same polymorphism has also been linked with depression severity in PD, indicating that in DRD3 Ser9Gly patients with Ser/Gly and Gly/Gly genotypes more care should be given to adjusting therapy and caring for non-motor complications . Furthermore, there is evidence from the aforementioned studies that DRD2 Taq1A polymorphism does not play a significant role in response to DA treatment . On the other hand, certain Taq1A polymorphisms (rs1800497) have been associated with differences in critical cognitive control processes depending on allele expression . As mentioned earlier, another crucial pharmacogenomic characteristic of DA to bear in mind when administering therapy is the possibility of genotype driven impulse control disorders, which is a problem, especially in de-novo PD patients starting DA therapy . Genetic model of polymorphisms in DRD1 (rs5326), OPRK1 (rs702764), OPRM1 (rs677830) and COMT (rs4646318) genes had a high prediction of ICD in patients of DA therapy (AUC of 0.70 (95% CI: 0.61–0.79) . 2.1.4. COMT Inhibitors COMT inhibitors are potent drugs that increase the bioavailability of L-dopa by stopping the physiological O-methylation of levodopa to its metabolite 3-O-methyldopa, and can work in tandem with DDC inhibitors . Similar to L-dopa, the presence of the previously mentioned rs4608 COMT gene polymorphism modified the motor response to COMT inhibitors entacapone in a small-sample study . Patients with higher COMT enzyme activity had greater response compared to patients with lower COMT enzyme activity during the acute challenge with entacapone . Subsequent studies have not found clinically significance in repeated administration of either entacapone or tolcapone , with the impact on opicapone still unknown, meriting further study. Increased doses of carbidopa combined with levodopa and entacapone can improve “off” times, which was shown in a recent randomized trial by Trenkwalder et al., with an even more pronounced effect in patients that had higher COMT enzymatic activity due to COMT gene polymorphisms . Pharmacokinetic studies have shown that COMT inhibitors are metabolized in the liver by glucuronidation, in particular by UDP-glucuronyltransferase UGT1A and UGT1A9 enzymes . Hepatotoxicity is a known rare side-effect of tolcapone , with only sparse reports of entacapone hepatotoxicity . Several studies indicate that SNPs in the UGT1A and UGT1A9 are responsible for these adverse events, which can cause inadequate metabolism and subsequent damage to the liver by the drugs . Interestingly, opicapone has not demonstrated evident hepatotoxicity related adverse events, while in-vitro studies show a favorable effect on hepatocytes when compared to entacapone and tolcapone . 2.1.5. MAO Inhibitors MAO inhibitors are used with L-dopa to extend its duration due to reduced degradation in the CNS. Most MAO inhibitors used today in PD treatment (e.g., selegiline, rasagiline) are focused on blocking the MAO-B enzyme that is the main isoform responsible for the degradation of dopamine . There have not been many studies performed to assess MAO inhibitor pharmacogenetic properties. Early clinical studies with rasagiline did reveal an inter-individual variation in the quality of response that could not be adequately explained at that time . Masellisi et al. conducted an extensive study using the ADAGIO study data to identify possible genetic determinants that can alter the response to rasagiline. They identified two SNPs on the DRD2 gene that were associated with statistically significant improvement of both motor and mental functions after 12 weeks of treatment . 2.2. Genotype Specific Treatment and Pharmacogenomic Properties Gene variations that influence pharmacogenomic properties and treatment in PD are not only focused on the metabolic and activity pathways of the drugs. There is a wide number of genes that are linked to monogenic PD, but only some had their association proven continuously in various research studies. Mutations in the genes coding α-Synuclein (SNCA) , Leucine-rich repeat kinase 2 (LRRK2) , vacuolar protein sorting-associated protein 35 (VPS35) , parkin RBR E3 ubiquitin-protein ligase (PRKN) , PTEN-induced putative kinase 1 (PINK1) , glucocerebrosidase (GBA) and oncogene DJ-1 have mostly been found before the onset of genome-wide association studies, while many candidate genes found after are yet to be definitively proven to cause a significant risk for PD. Importantly, the currently known candidate genes can explain only a small fraction of cases where there is a known higher familial incidence of PD . It is remarkable, however, that assessing polygenic risk scores and combining those with specific clinical parameters can yield impressive sensitivity of 83.4% and specificity of 90% . The unfortunate consequence of the rapid expansion of knowledge in the field and amount of target genes is that the studies assessing pharmacogenomics of these gene variants are not keeping up. 2.2.1. LRRK2 Current evidence, albeit limited, points to differences in treatment response between various genotypes of monogenic PD. Mutations in the LRRK2 gene are known to cause familial PD, especially in North African and Ashkenazi Jew populations . LRRK2 protein has a variety of physiological functions in intracellular trafficking and cytoskeleton dynamics, along with a substantial role in the cells of innate immunity. It is yet unclear how mutations in LRRK2 influence the pathogenesis of PD, but there is numerous evidence that links it to a disorder in cellular homeostasis and subsequent α-synuclein aggregation . Results in in vitro and in vivo animal model studies for inhibition of mutant LRRK2 are promising, and in most cases, confirm a reduced degeneration of dopaminergic neurons . The biggest challenge of human trials has been creating an LRRK2 inhibitor that can pass the blood-brain barrier, which was overcome by Denali Therapeutics, and the phase-1b trial for their novel LRRK2 inhibitor has been completed and is awaiting official results . Furthermore, LRRK2 -associated PD has a similar response to L-dopa compared to sporadic PD, with conflicting results for the possible earlier development of motor symptoms . Pharmacogenomics in LRRK2 associated PD are linked to specific genotype variants. G2019S and G2385R variants in LRRK2 have been linked as predictors of motor complications due to L-dopa treatment, along with requiring higher doses during treatment . On the other hand, G2019S carrier status did not influence the prevalence of L-dopa induced dyskinesias in a study by Yahalom et al. . Furthermore, a study covering the pharmacogenetics of Atremorine, a novel bioproduct with neuroprotective effects of dopaminergic neurons, found that LRRK2 associated PD patients had a more robust response to the compound, along with several genes that cover metabolic and detoxification pathways . 2.2.2. SNCA SNCA gene encodes the protein α-synuclein, now considered a central player in the pathogenesis of PD due to its aggregation into Lewy-bodies. SNP’s in the SNC A gene are consistently linked to an increased risk of developing PD in GWAS studies in both familial and even sporadic PD . In cases of autosomal dominant mutations, there is a solid L-dopa and classical PD treatment response, albeit with early cognitive and mental problems, akin to GBA mutations . There are several planned therapeutic approaches suited for SNCA polymorphism genotypes which include: targeted monoclonal antibody immunotherapy of α-synuclein , downregulation of SNCA expression by targeted DNA editing and RNA interference of SNCA . Roche Pharmaceuticals has developed an anti-α-synuclein monoclonal antibody which is in a currently ongoing phase two of clinical trials . Two other methods are still in preclinical testing, and their development shows promise for the future. 2.2.3. GBA Glucocerebrosidase mutations represent a known risk factor for developing PD. GBA mutation associated PD is characterized by the earlier onset of the disease, followed by a more pronounced cognitive deficit and a significantly higher risk of dementia . Gaucher’s disease (GD) is an autosomal recessive genetic disorder that also arises from mutations in the GBA gene. The current enzyme replacement and chaperone treatment options for systemic manifestations of GD are not effective enough in treating the neurological manifestations of the disease as they are not able to reach the CNS . Three genotype-specific therapies to address the cognitive decline are currently being tested with promising early results, with two focusing on the chaperones ambroxol and LTI-291 to increase glucocerebrosidase activity and the third focusing on reducing the levels of glucocerebrosidase with ibiglustat . There is growing evidence that GBA associated PD is often marked by rapid progression with many hallmarks of advanced PD, such as higher L-dopa daily dose required to control motor symptoms . However, current research does not show a significant influence of GBA mutations on L-dopa response properties with adequate motor symptom control . A single study by Lesage et al. in a population of European origin linked a higher incidence of L-dopa induced dyskinesias in GBA-PD patients , but that has not been replicated in a more recent study by Zhang et al. in a population of Chinese origin . 2.2.4. PRKN/PINK1/DJ1 Mutations in the PRKN gene can lead to early onset PD, characterized by a clinically typical form of PD that is often associated with dystonia and dyskinesia . Patients with PRKN mutations generally have excellent and sustained responses to L-dopa, even in lower doses than in sporadic PD . Dyskinesias can occur early on in the course of the disease with very low doses of L-dopa , while dystonia in these patients was not found to be linked to L-dopa treatment . Furthermore, patients with PINK1 mutations have a similar disease course as PRKN mutation carriers, with a good response to L-dopa treatment, but early dystonia and L-dopa induced dyskinesias . Pharmacogenomic properties and genotype-specific treatment of several other gene mutations in PD such as VPS35 and DJ1 have not yet been characterized fully due to the rarity of cases and are currently a focus of several studies that as of writing do not have preliminary results available .
2.1.1. Pharmacogenomics of the Therapeutic Response to L-dopa Clinically, L-dopa is always combined with dopa decarboxylase (DDC) inhibitors, which causes a switch in L-dopa metabolism to the COMT pathway, thereby increasing the bioavailability of L-dopa in the CNS . The genetic variability of several genes has been implicated in the varied response to L-dopa. COMT gene is a protein-coding gene that provides instructions for creating the COMT enzyme, and its polymorphisms are involved in the varied response to numerous CNS diseases and treatments . The most studied polymorphism of the COMT gene is rs4680 (G>A), which results in a valine to methionine substitution at codon 158 (Val158Met). Single nucleotide polymorphisms of the COMT gene form haplotypes that result in lower (A_C_C_G), medium (A_T_C_A) and higher (G_C_G_G) enzyme activity, which, in the case of higher activity, had an impact on the required dosage compared to noncarriers . Studies have shown that the higher dosage is required during chronic administration in patients with greater COMT activity, while acute L-dopa administration was unchanged . Similar changes were observed in a recent study by Sampaio et al., where higher COMT enzyme activity was linked to higher doses of L-dopa required, while no significant changes in dosage were found in lower COMT enzymatic activity compared to the control . Common characteristics of patients that required the higher L-dopa dosage in multiple studies were advanced PD and earlier onset. A contradicting result was published in patients of Korean origins, with no significant association between the rs4680 polymorphism and the response to L-dopa; however, the study population did not have a considerable number of patients with advanced PD . Higher L-dopa doses were needed for patients with Solute Carrier Family 22 Member 1 (SLC22A1) gene rs622342A>C polymorphism that encodes the Organic Cation Transporter 1, along with the patients having higher mortality than the control population . On the other hand, lower required doses of L-dopa were found in patients with Synaptic vesicle glycoprotein 2C (SV2C) rs30196 polymorphism, as well as in Solute Carrier Family 6 Member 3 (SLC6A3) polymorphism after multivariate analysis . 2.1.2. Pharmacogenomics of the Side-Effects to L-dopa Increased incidence of adverse events in L-dopa treatment has been linked with various gene polymorphisms. Although the variations in COMT enzymatic activity on the onset of adverse events is still under debate, several studies have linked the lower COMT enzymatic activity to the increased incidence of motor complications such as dyskinesia, especially in advanced PD . Hypothetically, more moderate COMT enzymatic activity could lead to inadequate dopamine inactivation and the accumulation of dopamine in the synaptic cleft, thereby causing the dyskinesias. The same result was not replicated in studies by Watanabe et al. and Contin et al. . There is some evidence that the activation of the Mechanistic target of rapamycin (mTOR) signaling pathway contributes to L-dopa induced dyskinesia. It was indicative of earlier animal studies that the inhibition of mTOR pathways reduces the L-dopa related dyskinesia, most likely due to impaired metabolic homeostasis . These findings were corroborated in a recent human study, by Martin-Flores et al., that found significant associations with several SNPs affecting the mTOR pathway, indicating that the mTOR pathway contributes genetically to L-dopa induced dyskinesia susceptibility . Similarly, a functional Brain derived neurotrophic factor (BDNF) Val66Met polymorphism can lead to aberrant synaptic plasticity, which has been associated with L-dopa induced dyskinesia in a single study by Foltynie et al. . Limited evidence has been found in favour of a protective function of the Dopamine receptor 1 (DRD1) (rs4532) SNP, shown in a single study by Dos Santos et al. . The effect of Dopamine receptor 2 (DRD2) SNP’s on dyskinesia is a point of contention in current literature, as some studies indicate an increased risk of developing dyskinesia , while others revealed a protective effect on the incidence of dyskinesia . Interestingly, both studies that show reduced dyskinesias were conducted in the Italian population with the polymorphism DRD2 CAn-STR. Increased risk for developing L-dopa induced dyskinesia was seen in the Dopamine receptor 3 (DRD3) rs6280 polymorphism in a Korean population . However, opposing results were found by three research groups, with no evidence of correlation between DRD3 genetic polymorphisms and incidence of dyskinesias . Lower risk of L-dopa-associated dyskinesias was found in patients with Homer protein homolog 1 (HOMER1 ) rs4704560 G allele polymorphism . Finally, incidence of L-dopa induced dyskinesias was studied for the dopamine transporter gene (DAT) , where the presence of two genotypes 10R/10R (rs28363170) and A carrier (rs393795) led to a reduced risk of dyskinesias in an Italian population . Hyperhomocysteinemia is a known complication of L-dopa treatment in PD. The potential dangers of elevated plasma homocysteine are systemic, and include cardiovascular risk, increased risk for dementia and impaired bone health . A SNP C667T (rs1801133) in the MTHFR gene is consistently being linked to hyperhomocysteinemia due to L-dopa treatment in several studies. The result of this mutation is a temperature-labile MTHFR enzyme, which ultimately leads to hyperhomocysteinemia . In addition, a study by Gorgone et al. showed that elevated homocysteine levels lead to systemic oxidative stress in patients with this polymorphism . A recent study by Yuan et al. further adds to the claim that homocysteine levels are affected by L-dopa administration, especially in 677C/T and T/T genotypes . A possible option for homocysteine level reduction and alleviation of systemic oxidative stress is the addition of COMT inhibitors to the therapy, which presents a clear possibility for translation of this knowledge into the treatment of patients . There is contradicting evidence regarding whether COMT polymorphisms can influence the incidence of daytime sleepiness in PD patients, with differing results of the pilot and follow-up studies conducted by the same authors . Two additional studies by the same primary author revealed an association between sudden-sleep onset and the polymorphisms in hypocretin and DRD2, which was unrelated to a specific drug . Furthermore, increased risk of sleep attacks was found in Dopamine receptor 4 (DRD4) 48-bp VNTR polymorphism in a German population . The L-dopa adverse effects affecting emetic activity are not uncommon in PD treatment. DRD2 and DRD3 polymorphisms both showed an association with an increased risk of developing gastrointestinal adverse effects that do not respond well to therapy in a Brazilian population . However, that has not been reproduced in a recent study in a Slovenian population by Redenšek et al. . Mental and cognitive adverse effects of L-dopa are common due to the shared physiological dopaminergic pathways. A significant interaction was found between L-dopa and the COMT gene polymorphism in causing a detrimental effect on the activity in task-specific regions of the pre-frontal cortex due to altered availability of dopamine . Interestingly, carriers of at least one COMT rs165815 C allele had a decreased risk of developing visual hallucinations . In the same study carriers of the DRD3 rs6280 C allele had higher odds of developing visual hallucinations , which is in line with a previous study published by Goetz et al. . Increased risk of developing hallucinations is seen in patients with polymorphisms in the DRD2 gene , cholecystokinin gene and HOMER1 rs4704559 A allele , which encodes a protein that possesses a vital function for synaptic plasticity and glutamate signaling. On the other hand, the HOMER 1 rs4704559 G allele appears to decrease the risk of visual hallucinations . Furthermore, several studies link BDN F Val66Met polymorphism to impaired cognitive functioning in PD, but it appears to be irrespective of dopamine replacement therapy and is a genotype-specific trait . Impulse control disorder (ICD) is a well-known complication that can occur in some PD patients after initiating dopamine replacement therapy by either L-dopa or dopamine agonists . Heritability of ICD in a cohort of PD patients has been estimated at 57%, particularly for Opioid Receptor Kappa 1 (OPRK1) , 5-Hydroxytryptamine Receptor 2A (HTR2a) and Dopa decarboxylase (DDC) genotypes . A recent study found a suggestive association for developing ICD in variants of the opioid receptor gene OPRM1 and the DAT gene . Furthermore, there is evidence that polymorphisms in DRD1 (rs4857798, rs4532, rs265981), DRD2/ANKK1 (rs1800497) and glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) (rs7301328) bear an increased risk of developing ICD . The DRD3 (rs6280) mutation has also been linked with increased incidence of ICD with L-dopa therapy in studies by Lee et al. and Castro-Martinez et al. . On the other hand, there was no significant association found in COMT Val158Met and DRD2 Taq1A polymorphisms . Even though current data suggest high heritability for developing ICD after initiating dopamine replacement therapy, it should be noted that the effects of individual genes are small, and the development is most likely multigenic. 2.1.3. Dopamine Receptor Agonists Dopamine receptor agonists (DAs) are often the first therapies initiated in PD patients and are the main alternative to L-dopa . The effectiveness of DAs is lower than L-dopa, and most patients discontinue treatment within three years. Some significance has been found in polymorphisms of the DRD2 and DRD3 genes that could influence drug effectiveness and tolerability. A retrospective study by Arbouw et al. revealed that a DRD2 (CA)n-repeat polymorphism is linked with a decreased discontinuation of non-ergoline DA treatment, although the sample size in this study was small . A pilot study that included Chinese PD patients revealed that the DRD3 Ser9Gly (rs6280) polymorphism is associated with a varied response to pramipexole , which has since been confirmed in a recent study by Xu et al. . Interestingly, the same polymorphism has also been linked with depression severity in PD, indicating that in DRD3 Ser9Gly patients with Ser/Gly and Gly/Gly genotypes more care should be given to adjusting therapy and caring for non-motor complications . Furthermore, there is evidence from the aforementioned studies that DRD2 Taq1A polymorphism does not play a significant role in response to DA treatment . On the other hand, certain Taq1A polymorphisms (rs1800497) have been associated with differences in critical cognitive control processes depending on allele expression . As mentioned earlier, another crucial pharmacogenomic characteristic of DA to bear in mind when administering therapy is the possibility of genotype driven impulse control disorders, which is a problem, especially in de-novo PD patients starting DA therapy . Genetic model of polymorphisms in DRD1 (rs5326), OPRK1 (rs702764), OPRM1 (rs677830) and COMT (rs4646318) genes had a high prediction of ICD in patients of DA therapy (AUC of 0.70 (95% CI: 0.61–0.79) . 2.1.4. COMT Inhibitors COMT inhibitors are potent drugs that increase the bioavailability of L-dopa by stopping the physiological O-methylation of levodopa to its metabolite 3-O-methyldopa, and can work in tandem with DDC inhibitors . Similar to L-dopa, the presence of the previously mentioned rs4608 COMT gene polymorphism modified the motor response to COMT inhibitors entacapone in a small-sample study . Patients with higher COMT enzyme activity had greater response compared to patients with lower COMT enzyme activity during the acute challenge with entacapone . Subsequent studies have not found clinically significance in repeated administration of either entacapone or tolcapone , with the impact on opicapone still unknown, meriting further study. Increased doses of carbidopa combined with levodopa and entacapone can improve “off” times, which was shown in a recent randomized trial by Trenkwalder et al., with an even more pronounced effect in patients that had higher COMT enzymatic activity due to COMT gene polymorphisms . Pharmacokinetic studies have shown that COMT inhibitors are metabolized in the liver by glucuronidation, in particular by UDP-glucuronyltransferase UGT1A and UGT1A9 enzymes . Hepatotoxicity is a known rare side-effect of tolcapone , with only sparse reports of entacapone hepatotoxicity . Several studies indicate that SNPs in the UGT1A and UGT1A9 are responsible for these adverse events, which can cause inadequate metabolism and subsequent damage to the liver by the drugs . Interestingly, opicapone has not demonstrated evident hepatotoxicity related adverse events, while in-vitro studies show a favorable effect on hepatocytes when compared to entacapone and tolcapone . 2.1.5. MAO Inhibitors MAO inhibitors are used with L-dopa to extend its duration due to reduced degradation in the CNS. Most MAO inhibitors used today in PD treatment (e.g., selegiline, rasagiline) are focused on blocking the MAO-B enzyme that is the main isoform responsible for the degradation of dopamine . There have not been many studies performed to assess MAO inhibitor pharmacogenetic properties. Early clinical studies with rasagiline did reveal an inter-individual variation in the quality of response that could not be adequately explained at that time . Masellisi et al. conducted an extensive study using the ADAGIO study data to identify possible genetic determinants that can alter the response to rasagiline. They identified two SNPs on the DRD2 gene that were associated with statistically significant improvement of both motor and mental functions after 12 weeks of treatment .
Clinically, L-dopa is always combined with dopa decarboxylase (DDC) inhibitors, which causes a switch in L-dopa metabolism to the COMT pathway, thereby increasing the bioavailability of L-dopa in the CNS . The genetic variability of several genes has been implicated in the varied response to L-dopa. COMT gene is a protein-coding gene that provides instructions for creating the COMT enzyme, and its polymorphisms are involved in the varied response to numerous CNS diseases and treatments . The most studied polymorphism of the COMT gene is rs4680 (G>A), which results in a valine to methionine substitution at codon 158 (Val158Met). Single nucleotide polymorphisms of the COMT gene form haplotypes that result in lower (A_C_C_G), medium (A_T_C_A) and higher (G_C_G_G) enzyme activity, which, in the case of higher activity, had an impact on the required dosage compared to noncarriers . Studies have shown that the higher dosage is required during chronic administration in patients with greater COMT activity, while acute L-dopa administration was unchanged . Similar changes were observed in a recent study by Sampaio et al., where higher COMT enzyme activity was linked to higher doses of L-dopa required, while no significant changes in dosage were found in lower COMT enzymatic activity compared to the control . Common characteristics of patients that required the higher L-dopa dosage in multiple studies were advanced PD and earlier onset. A contradicting result was published in patients of Korean origins, with no significant association between the rs4680 polymorphism and the response to L-dopa; however, the study population did not have a considerable number of patients with advanced PD . Higher L-dopa doses were needed for patients with Solute Carrier Family 22 Member 1 (SLC22A1) gene rs622342A>C polymorphism that encodes the Organic Cation Transporter 1, along with the patients having higher mortality than the control population . On the other hand, lower required doses of L-dopa were found in patients with Synaptic vesicle glycoprotein 2C (SV2C) rs30196 polymorphism, as well as in Solute Carrier Family 6 Member 3 (SLC6A3) polymorphism after multivariate analysis .
Increased incidence of adverse events in L-dopa treatment has been linked with various gene polymorphisms. Although the variations in COMT enzymatic activity on the onset of adverse events is still under debate, several studies have linked the lower COMT enzymatic activity to the increased incidence of motor complications such as dyskinesia, especially in advanced PD . Hypothetically, more moderate COMT enzymatic activity could lead to inadequate dopamine inactivation and the accumulation of dopamine in the synaptic cleft, thereby causing the dyskinesias. The same result was not replicated in studies by Watanabe et al. and Contin et al. . There is some evidence that the activation of the Mechanistic target of rapamycin (mTOR) signaling pathway contributes to L-dopa induced dyskinesia. It was indicative of earlier animal studies that the inhibition of mTOR pathways reduces the L-dopa related dyskinesia, most likely due to impaired metabolic homeostasis . These findings were corroborated in a recent human study, by Martin-Flores et al., that found significant associations with several SNPs affecting the mTOR pathway, indicating that the mTOR pathway contributes genetically to L-dopa induced dyskinesia susceptibility . Similarly, a functional Brain derived neurotrophic factor (BDNF) Val66Met polymorphism can lead to aberrant synaptic plasticity, which has been associated with L-dopa induced dyskinesia in a single study by Foltynie et al. . Limited evidence has been found in favour of a protective function of the Dopamine receptor 1 (DRD1) (rs4532) SNP, shown in a single study by Dos Santos et al. . The effect of Dopamine receptor 2 (DRD2) SNP’s on dyskinesia is a point of contention in current literature, as some studies indicate an increased risk of developing dyskinesia , while others revealed a protective effect on the incidence of dyskinesia . Interestingly, both studies that show reduced dyskinesias were conducted in the Italian population with the polymorphism DRD2 CAn-STR. Increased risk for developing L-dopa induced dyskinesia was seen in the Dopamine receptor 3 (DRD3) rs6280 polymorphism in a Korean population . However, opposing results were found by three research groups, with no evidence of correlation between DRD3 genetic polymorphisms and incidence of dyskinesias . Lower risk of L-dopa-associated dyskinesias was found in patients with Homer protein homolog 1 (HOMER1 ) rs4704560 G allele polymorphism . Finally, incidence of L-dopa induced dyskinesias was studied for the dopamine transporter gene (DAT) , where the presence of two genotypes 10R/10R (rs28363170) and A carrier (rs393795) led to a reduced risk of dyskinesias in an Italian population . Hyperhomocysteinemia is a known complication of L-dopa treatment in PD. The potential dangers of elevated plasma homocysteine are systemic, and include cardiovascular risk, increased risk for dementia and impaired bone health . A SNP C667T (rs1801133) in the MTHFR gene is consistently being linked to hyperhomocysteinemia due to L-dopa treatment in several studies. The result of this mutation is a temperature-labile MTHFR enzyme, which ultimately leads to hyperhomocysteinemia . In addition, a study by Gorgone et al. showed that elevated homocysteine levels lead to systemic oxidative stress in patients with this polymorphism . A recent study by Yuan et al. further adds to the claim that homocysteine levels are affected by L-dopa administration, especially in 677C/T and T/T genotypes . A possible option for homocysteine level reduction and alleviation of systemic oxidative stress is the addition of COMT inhibitors to the therapy, which presents a clear possibility for translation of this knowledge into the treatment of patients . There is contradicting evidence regarding whether COMT polymorphisms can influence the incidence of daytime sleepiness in PD patients, with differing results of the pilot and follow-up studies conducted by the same authors . Two additional studies by the same primary author revealed an association between sudden-sleep onset and the polymorphisms in hypocretin and DRD2, which was unrelated to a specific drug . Furthermore, increased risk of sleep attacks was found in Dopamine receptor 4 (DRD4) 48-bp VNTR polymorphism in a German population . The L-dopa adverse effects affecting emetic activity are not uncommon in PD treatment. DRD2 and DRD3 polymorphisms both showed an association with an increased risk of developing gastrointestinal adverse effects that do not respond well to therapy in a Brazilian population . However, that has not been reproduced in a recent study in a Slovenian population by Redenšek et al. . Mental and cognitive adverse effects of L-dopa are common due to the shared physiological dopaminergic pathways. A significant interaction was found between L-dopa and the COMT gene polymorphism in causing a detrimental effect on the activity in task-specific regions of the pre-frontal cortex due to altered availability of dopamine . Interestingly, carriers of at least one COMT rs165815 C allele had a decreased risk of developing visual hallucinations . In the same study carriers of the DRD3 rs6280 C allele had higher odds of developing visual hallucinations , which is in line with a previous study published by Goetz et al. . Increased risk of developing hallucinations is seen in patients with polymorphisms in the DRD2 gene , cholecystokinin gene and HOMER1 rs4704559 A allele , which encodes a protein that possesses a vital function for synaptic plasticity and glutamate signaling. On the other hand, the HOMER 1 rs4704559 G allele appears to decrease the risk of visual hallucinations . Furthermore, several studies link BDN F Val66Met polymorphism to impaired cognitive functioning in PD, but it appears to be irrespective of dopamine replacement therapy and is a genotype-specific trait . Impulse control disorder (ICD) is a well-known complication that can occur in some PD patients after initiating dopamine replacement therapy by either L-dopa or dopamine agonists . Heritability of ICD in a cohort of PD patients has been estimated at 57%, particularly for Opioid Receptor Kappa 1 (OPRK1) , 5-Hydroxytryptamine Receptor 2A (HTR2a) and Dopa decarboxylase (DDC) genotypes . A recent study found a suggestive association for developing ICD in variants of the opioid receptor gene OPRM1 and the DAT gene . Furthermore, there is evidence that polymorphisms in DRD1 (rs4857798, rs4532, rs265981), DRD2/ANKK1 (rs1800497) and glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) (rs7301328) bear an increased risk of developing ICD . The DRD3 (rs6280) mutation has also been linked with increased incidence of ICD with L-dopa therapy in studies by Lee et al. and Castro-Martinez et al. . On the other hand, there was no significant association found in COMT Val158Met and DRD2 Taq1A polymorphisms . Even though current data suggest high heritability for developing ICD after initiating dopamine replacement therapy, it should be noted that the effects of individual genes are small, and the development is most likely multigenic.
Dopamine receptor agonists (DAs) are often the first therapies initiated in PD patients and are the main alternative to L-dopa . The effectiveness of DAs is lower than L-dopa, and most patients discontinue treatment within three years. Some significance has been found in polymorphisms of the DRD2 and DRD3 genes that could influence drug effectiveness and tolerability. A retrospective study by Arbouw et al. revealed that a DRD2 (CA)n-repeat polymorphism is linked with a decreased discontinuation of non-ergoline DA treatment, although the sample size in this study was small . A pilot study that included Chinese PD patients revealed that the DRD3 Ser9Gly (rs6280) polymorphism is associated with a varied response to pramipexole , which has since been confirmed in a recent study by Xu et al. . Interestingly, the same polymorphism has also been linked with depression severity in PD, indicating that in DRD3 Ser9Gly patients with Ser/Gly and Gly/Gly genotypes more care should be given to adjusting therapy and caring for non-motor complications . Furthermore, there is evidence from the aforementioned studies that DRD2 Taq1A polymorphism does not play a significant role in response to DA treatment . On the other hand, certain Taq1A polymorphisms (rs1800497) have been associated with differences in critical cognitive control processes depending on allele expression . As mentioned earlier, another crucial pharmacogenomic characteristic of DA to bear in mind when administering therapy is the possibility of genotype driven impulse control disorders, which is a problem, especially in de-novo PD patients starting DA therapy . Genetic model of polymorphisms in DRD1 (rs5326), OPRK1 (rs702764), OPRM1 (rs677830) and COMT (rs4646318) genes had a high prediction of ICD in patients of DA therapy (AUC of 0.70 (95% CI: 0.61–0.79) .
COMT inhibitors are potent drugs that increase the bioavailability of L-dopa by stopping the physiological O-methylation of levodopa to its metabolite 3-O-methyldopa, and can work in tandem with DDC inhibitors . Similar to L-dopa, the presence of the previously mentioned rs4608 COMT gene polymorphism modified the motor response to COMT inhibitors entacapone in a small-sample study . Patients with higher COMT enzyme activity had greater response compared to patients with lower COMT enzyme activity during the acute challenge with entacapone . Subsequent studies have not found clinically significance in repeated administration of either entacapone or tolcapone , with the impact on opicapone still unknown, meriting further study. Increased doses of carbidopa combined with levodopa and entacapone can improve “off” times, which was shown in a recent randomized trial by Trenkwalder et al., with an even more pronounced effect in patients that had higher COMT enzymatic activity due to COMT gene polymorphisms . Pharmacokinetic studies have shown that COMT inhibitors are metabolized in the liver by glucuronidation, in particular by UDP-glucuronyltransferase UGT1A and UGT1A9 enzymes . Hepatotoxicity is a known rare side-effect of tolcapone , with only sparse reports of entacapone hepatotoxicity . Several studies indicate that SNPs in the UGT1A and UGT1A9 are responsible for these adverse events, which can cause inadequate metabolism and subsequent damage to the liver by the drugs . Interestingly, opicapone has not demonstrated evident hepatotoxicity related adverse events, while in-vitro studies show a favorable effect on hepatocytes when compared to entacapone and tolcapone .
MAO inhibitors are used with L-dopa to extend its duration due to reduced degradation in the CNS. Most MAO inhibitors used today in PD treatment (e.g., selegiline, rasagiline) are focused on blocking the MAO-B enzyme that is the main isoform responsible for the degradation of dopamine . There have not been many studies performed to assess MAO inhibitor pharmacogenetic properties. Early clinical studies with rasagiline did reveal an inter-individual variation in the quality of response that could not be adequately explained at that time . Masellisi et al. conducted an extensive study using the ADAGIO study data to identify possible genetic determinants that can alter the response to rasagiline. They identified two SNPs on the DRD2 gene that were associated with statistically significant improvement of both motor and mental functions after 12 weeks of treatment .
Gene variations that influence pharmacogenomic properties and treatment in PD are not only focused on the metabolic and activity pathways of the drugs. There is a wide number of genes that are linked to monogenic PD, but only some had their association proven continuously in various research studies. Mutations in the genes coding α-Synuclein (SNCA) , Leucine-rich repeat kinase 2 (LRRK2) , vacuolar protein sorting-associated protein 35 (VPS35) , parkin RBR E3 ubiquitin-protein ligase (PRKN) , PTEN-induced putative kinase 1 (PINK1) , glucocerebrosidase (GBA) and oncogene DJ-1 have mostly been found before the onset of genome-wide association studies, while many candidate genes found after are yet to be definitively proven to cause a significant risk for PD. Importantly, the currently known candidate genes can explain only a small fraction of cases where there is a known higher familial incidence of PD . It is remarkable, however, that assessing polygenic risk scores and combining those with specific clinical parameters can yield impressive sensitivity of 83.4% and specificity of 90% . The unfortunate consequence of the rapid expansion of knowledge in the field and amount of target genes is that the studies assessing pharmacogenomics of these gene variants are not keeping up. 2.2.1. LRRK2 Current evidence, albeit limited, points to differences in treatment response between various genotypes of monogenic PD. Mutations in the LRRK2 gene are known to cause familial PD, especially in North African and Ashkenazi Jew populations . LRRK2 protein has a variety of physiological functions in intracellular trafficking and cytoskeleton dynamics, along with a substantial role in the cells of innate immunity. It is yet unclear how mutations in LRRK2 influence the pathogenesis of PD, but there is numerous evidence that links it to a disorder in cellular homeostasis and subsequent α-synuclein aggregation . Results in in vitro and in vivo animal model studies for inhibition of mutant LRRK2 are promising, and in most cases, confirm a reduced degeneration of dopaminergic neurons . The biggest challenge of human trials has been creating an LRRK2 inhibitor that can pass the blood-brain barrier, which was overcome by Denali Therapeutics, and the phase-1b trial for their novel LRRK2 inhibitor has been completed and is awaiting official results . Furthermore, LRRK2 -associated PD has a similar response to L-dopa compared to sporadic PD, with conflicting results for the possible earlier development of motor symptoms . Pharmacogenomics in LRRK2 associated PD are linked to specific genotype variants. G2019S and G2385R variants in LRRK2 have been linked as predictors of motor complications due to L-dopa treatment, along with requiring higher doses during treatment . On the other hand, G2019S carrier status did not influence the prevalence of L-dopa induced dyskinesias in a study by Yahalom et al. . Furthermore, a study covering the pharmacogenetics of Atremorine, a novel bioproduct with neuroprotective effects of dopaminergic neurons, found that LRRK2 associated PD patients had a more robust response to the compound, along with several genes that cover metabolic and detoxification pathways . 2.2.2. SNCA SNCA gene encodes the protein α-synuclein, now considered a central player in the pathogenesis of PD due to its aggregation into Lewy-bodies. SNP’s in the SNC A gene are consistently linked to an increased risk of developing PD in GWAS studies in both familial and even sporadic PD . In cases of autosomal dominant mutations, there is a solid L-dopa and classical PD treatment response, albeit with early cognitive and mental problems, akin to GBA mutations . There are several planned therapeutic approaches suited for SNCA polymorphism genotypes which include: targeted monoclonal antibody immunotherapy of α-synuclein , downregulation of SNCA expression by targeted DNA editing and RNA interference of SNCA . Roche Pharmaceuticals has developed an anti-α-synuclein monoclonal antibody which is in a currently ongoing phase two of clinical trials . Two other methods are still in preclinical testing, and their development shows promise for the future. 2.2.3. GBA Glucocerebrosidase mutations represent a known risk factor for developing PD. GBA mutation associated PD is characterized by the earlier onset of the disease, followed by a more pronounced cognitive deficit and a significantly higher risk of dementia . Gaucher’s disease (GD) is an autosomal recessive genetic disorder that also arises from mutations in the GBA gene. The current enzyme replacement and chaperone treatment options for systemic manifestations of GD are not effective enough in treating the neurological manifestations of the disease as they are not able to reach the CNS . Three genotype-specific therapies to address the cognitive decline are currently being tested with promising early results, with two focusing on the chaperones ambroxol and LTI-291 to increase glucocerebrosidase activity and the third focusing on reducing the levels of glucocerebrosidase with ibiglustat . There is growing evidence that GBA associated PD is often marked by rapid progression with many hallmarks of advanced PD, such as higher L-dopa daily dose required to control motor symptoms . However, current research does not show a significant influence of GBA mutations on L-dopa response properties with adequate motor symptom control . A single study by Lesage et al. in a population of European origin linked a higher incidence of L-dopa induced dyskinesias in GBA-PD patients , but that has not been replicated in a more recent study by Zhang et al. in a population of Chinese origin . 2.2.4. PRKN/PINK1/DJ1 Mutations in the PRKN gene can lead to early onset PD, characterized by a clinically typical form of PD that is often associated with dystonia and dyskinesia . Patients with PRKN mutations generally have excellent and sustained responses to L-dopa, even in lower doses than in sporadic PD . Dyskinesias can occur early on in the course of the disease with very low doses of L-dopa , while dystonia in these patients was not found to be linked to L-dopa treatment . Furthermore, patients with PINK1 mutations have a similar disease course as PRKN mutation carriers, with a good response to L-dopa treatment, but early dystonia and L-dopa induced dyskinesias . Pharmacogenomic properties and genotype-specific treatment of several other gene mutations in PD such as VPS35 and DJ1 have not yet been characterized fully due to the rarity of cases and are currently a focus of several studies that as of writing do not have preliminary results available .
Current evidence, albeit limited, points to differences in treatment response between various genotypes of monogenic PD. Mutations in the LRRK2 gene are known to cause familial PD, especially in North African and Ashkenazi Jew populations . LRRK2 protein has a variety of physiological functions in intracellular trafficking and cytoskeleton dynamics, along with a substantial role in the cells of innate immunity. It is yet unclear how mutations in LRRK2 influence the pathogenesis of PD, but there is numerous evidence that links it to a disorder in cellular homeostasis and subsequent α-synuclein aggregation . Results in in vitro and in vivo animal model studies for inhibition of mutant LRRK2 are promising, and in most cases, confirm a reduced degeneration of dopaminergic neurons . The biggest challenge of human trials has been creating an LRRK2 inhibitor that can pass the blood-brain barrier, which was overcome by Denali Therapeutics, and the phase-1b trial for their novel LRRK2 inhibitor has been completed and is awaiting official results . Furthermore, LRRK2 -associated PD has a similar response to L-dopa compared to sporadic PD, with conflicting results for the possible earlier development of motor symptoms . Pharmacogenomics in LRRK2 associated PD are linked to specific genotype variants. G2019S and G2385R variants in LRRK2 have been linked as predictors of motor complications due to L-dopa treatment, along with requiring higher doses during treatment . On the other hand, G2019S carrier status did not influence the prevalence of L-dopa induced dyskinesias in a study by Yahalom et al. . Furthermore, a study covering the pharmacogenetics of Atremorine, a novel bioproduct with neuroprotective effects of dopaminergic neurons, found that LRRK2 associated PD patients had a more robust response to the compound, along with several genes that cover metabolic and detoxification pathways .
SNCA gene encodes the protein α-synuclein, now considered a central player in the pathogenesis of PD due to its aggregation into Lewy-bodies. SNP’s in the SNC A gene are consistently linked to an increased risk of developing PD in GWAS studies in both familial and even sporadic PD . In cases of autosomal dominant mutations, there is a solid L-dopa and classical PD treatment response, albeit with early cognitive and mental problems, akin to GBA mutations . There are several planned therapeutic approaches suited for SNCA polymorphism genotypes which include: targeted monoclonal antibody immunotherapy of α-synuclein , downregulation of SNCA expression by targeted DNA editing and RNA interference of SNCA . Roche Pharmaceuticals has developed an anti-α-synuclein monoclonal antibody which is in a currently ongoing phase two of clinical trials . Two other methods are still in preclinical testing, and their development shows promise for the future.
Glucocerebrosidase mutations represent a known risk factor for developing PD. GBA mutation associated PD is characterized by the earlier onset of the disease, followed by a more pronounced cognitive deficit and a significantly higher risk of dementia . Gaucher’s disease (GD) is an autosomal recessive genetic disorder that also arises from mutations in the GBA gene. The current enzyme replacement and chaperone treatment options for systemic manifestations of GD are not effective enough in treating the neurological manifestations of the disease as they are not able to reach the CNS . Three genotype-specific therapies to address the cognitive decline are currently being tested with promising early results, with two focusing on the chaperones ambroxol and LTI-291 to increase glucocerebrosidase activity and the third focusing on reducing the levels of glucocerebrosidase with ibiglustat . There is growing evidence that GBA associated PD is often marked by rapid progression with many hallmarks of advanced PD, such as higher L-dopa daily dose required to control motor symptoms . However, current research does not show a significant influence of GBA mutations on L-dopa response properties with adequate motor symptom control . A single study by Lesage et al. in a population of European origin linked a higher incidence of L-dopa induced dyskinesias in GBA-PD patients , but that has not been replicated in a more recent study by Zhang et al. in a population of Chinese origin .
Mutations in the PRKN gene can lead to early onset PD, characterized by a clinically typical form of PD that is often associated with dystonia and dyskinesia . Patients with PRKN mutations generally have excellent and sustained responses to L-dopa, even in lower doses than in sporadic PD . Dyskinesias can occur early on in the course of the disease with very low doses of L-dopa , while dystonia in these patients was not found to be linked to L-dopa treatment . Furthermore, patients with PINK1 mutations have a similar disease course as PRKN mutation carriers, with a good response to L-dopa treatment, but early dystonia and L-dopa induced dyskinesias . Pharmacogenomic properties and genotype-specific treatment of several other gene mutations in PD such as VPS35 and DJ1 have not yet been characterized fully due to the rarity of cases and are currently a focus of several studies that as of writing do not have preliminary results available .
There has been considerable progress in the field of pharmacogenomics in Parkinson’s disease. The main question in the field is whether we can use the current knowledge in clinical practice to benefit the patients. The data on Parkinson’s disease in PharmGKB, a pharmacogenomics database, are sparse, with only ten clinical annotations with most being supported by a rather low level of evidence, which is clear from this systematic review as well . Most of the pharmacogenomic studies that focus on antiparkinsonian drugs are highly centered on L-dopa and its metabolism. The current evidence on the pharmacogenomics of therapeutic response to L-dopa is contradictory, with most studies focusing on the COMT gene polymorphisms. The differences between studies limit the potential for clinical use. However, there is potential to clarify the effects of COMT gene polymorphisms by further studies analyzing the enzymatic activity in various genotypes and the L-dopa dosage and therapeutic response. More robust evidence is present for the pharmacogenomics of side-effects in L-dopa or dopaminergic therapy. The most studied motor complication of L-dopa therapy is treatment-induced dyskinesias. Looking at the evidence, we can see that there are numerous reports focusing on various genes, although often with contradictory results in COMT , DRD2 and DRD3 genes. On the other hand, SNPs in the mTOR pathway genes, BDNF , HOMER1 and DAT have been implicated in either increased or reduced risk for dyskinesias, but with single studies that are yet to be corroborated in larger cohorts. Other side-effects such as cognitive decline, visual hallucinations and daytime sleepiness have been implicated in various polymorphisms of the COMT, DRD2, DRD3, HOMER1 and BDNF genes, but lack consistency in the results to consider current clinical implementations. Hyperhomocysteinemia and ICD are known complications of dopaminergic therapy, and both have been consistently linked with genetic factors . Specifically, mutations in the MTHFR gene can increase the incidence of hyperhomocysteinemia, which could be ameliorated by the addition of COMT inhibitors to therapy, presenting a possibility for clinical interventions based on pharmacogenomic testing. The same can be said about ICD, where genetic models are gaining accuracy with each new study in the field . Potential for clinical use can especially be seen in younger patients which are only starting dopamine agonist therapy, as polymorphisms in DRD1 (rs5326), OPRK1 (rs702764), OPRM1 (rs677830) and COMT (rs4646318) genes showed a high prediction rate of ICD . There is evidence that polymorphisms in DRD2 and DRD3 gene could also cause these side-effects, leading to earlier discontinuation of DA therapy in patients. There is clear potential for clinical implementation in this area, and future goal should be to establish studies with larger cohorts in order to improve the genetic prediction models. There is lacking evidence regarding the pharmacogenomic properties of other drugs used in PD, such as COMT and MAO inhibitors. However, there is some evidence that mutations leading to varied COMT enzyme activity could have an influence on the potency of COMT inhibitors, but the results are not consistent . More consistent results have been found regarding entacapone hepatotoxicity, with several studies indicating that SNP’s in the UGT1A and UGT1A9 could lead to this adverse effect . MAO inhibitors are known to have inter-individual variation, which is still not explained in current studies, with a single study reporting improved motor and mental functions in DRD2 gene SNP. Taken together, the pharmacogenomic data regarding COMT and MAO inhibitors are still not strong enough to make any recommendations for clinical implementation. Finally, pharmacogenomics in PD also encompasses changes that occur in specific differences in genotype-associated PD. Three of the most studied single gene mutations are the LRRK2, GBA and SNCA gene mutations. Published studies covering L-dopa treatment with these mutations have contradicting results depending on the populations studied, which makes it difficult to give any firm recommendations regarding treatment optimization . The current evidence for PRKN, PINK1 and DJ1 point to a sustained L-dopa response with lower doses, albeit with early motor complications that include dyskinesias and dystonia . Therefore, this clinical phenotype can raise suspicions of these mutations and lead to earlier genetic testing and treatment optimization. However, the number of cases analyzed is low due to the rarity of these mutations, and further studies are required to confirm these early findings.
We have done a systematic search of articles indexed in Medline and Embase from its inception to July of 2020 focused on the pharmacogenomics in Parkinson’s disease using a strategy similar to what was described by Corvol et al. . The search terms included: Genetic Variation (MeSH), Genotype (MeSH), Genes (MeSH), Polymorphism, Allele, Mutation, Treatment outcome (MeSH), Therapeutics (MeSH), Pharmacogenomic (MeSH), Pharmacogenetics (MeSH), Adverse effects (MeSH Subheading), Toxicogenetics (MeSH) and Parkinson’s disease (MeSH). The articles included in the search were clinical trials, meta-analysis, and randomized controlled trial, with excluding case reports and reviews, with additional filters of human studies and English language. We included studies that had a clear methodology regarding study population and main findings. Exclusion criteria were articles not written in English, lacking study population information and findings not relevant to the theme of pharmacogenomics in PD. Several reviews were added into the overall analyzed papers using manual searches through websites and citation searching. PharmGKB database was accessed as well using the search parameter “Parkinson’s disease” to view current clinical annotations present for PD . The systematic literature search in Medline and Embase revealed 15,778 potential publications, which were first automatically and then manually filtered to exclude studies that do not fit the inclusion criteria . We included 75 studies, with the final count being 116 after adding publications found through manual search that include reviews covering this topic, along with studies focused on genotype specific PD forms .
Most pharmacogenomic data for PD treatment present today are still not consistent enough to be entered into clinical practice, and further studies are required to enable a more personalized approach to therapy for each patient. The main findings can be summarized as follows: Most evidence from the analyzed studies is found via secondary endpoints, which limits their power, with small sample size also being a diminishing factor. Conflicting reports between varied populations could be a consequence of low sample sizes and unaccounted interactions, which ultimately leads to low confidence in the data currently available. The most promising avenues for clinical implementation of pharmacogenetics lie in the current findings of impulse control disorders and hyperhomocysteinemia, where the available data are more consistent. Most of the studies focus on L-dopa and DA, and greater focus should also be given to other PD treatment options such as MAO-B and COMT inhibitors. Even though the wealth of knowledge is rapidly increasing, there are still not enough consistent data to make quality choices in the clinical treatment of patients. Studies that have a clear focus on pharmacogenomic properties of antiparkinsonian drugs are key for consolidating the current information and for the translation into clinical practice.
|
Integrative Lipid Pseudotargeted Metabolomics and Amino Acids Targeted Metabolomics Unravel the Therapeutic Mechanism of Rhizoma Paridis Saponins on Experimental Colitis of Damp-Heat Type | 53970464-cb54-41d1-9f3f-5551f247bda5 | 11568858 | Biochemistry[mh] | The main forms of inflammatory bowel disease (IBD) are Crohn’s disease (CD) and Ulcerative colitis (UC). These diseases are becoming more common and more prevalent all over the world. However, the pathophysiology and etiology are still poorly understood. According to current theories, abnormalities in intestinal homeostasis originate from the interaction of genetic, environmental, and microbial factors, which weaken intestinal barrier function and produce a disorganized immune response. This requires further research on IBD to identify not only therapeutic targets but also diagnostic and prognostic markers to improve disease management. Metabolomics is a powerful tool for examining the pathophysiology of disease and locating potential biomarkers, utilizing biofluids (such as plasma and/or urine), tissues, or other biological extracts. By measuring hundreds of metabolites in biological samples, metabolomics enables the identification of each patient’s unique metabolic profile. These profiles could advance metabolomic personalized therapy by enhancing our comprehension of this complicated disease and potentially enhancing the diagnosis and treatment of IBD. Researchers use metabolomics to distinguish between CD and UC, as well as the many IBD subclassifications, and explore metabolomic variations based on disease activity and relapse predictors. Moreover, the link between IBD metabolome dynamics and treatment response has been further shown by recent studies, however, more research is still needed in this area. Researches have been done to identify biomarkers for IBD diagnosis and to demonstrate the illness mechanism, which showed that various lipid species, important substances for energy metabolism, and amino acids were noticeably altered in patients with IBD. Studies show that the etiopathogenesis of IBD may be significantly influenced by changes in the lipid profile. Primary bile acid biosynthesis, arachidonic acid metabolism, sphingolipid metabolism, fatty acid elongation, and glycerophospholipid metabolism were all connected to IBD dysregulation, according to lipid profile research in individuals with IBD. , In the past few years, an increasing number of studies have employed lipidomics to identify particular biomarkers for different diseases by detecting changes in lipid species. Also, studies find disruption of amino acid metabolism in IBD. Some studies have shown that leucine, lysine, and valine, as well as the semi-essential amino acids arginine and glutamine, and the non-essential amino acid serine, were all reduced in CD patients compared to control subjects and UC patients had decreased amounts of creatine, proline, and tryptophan. This irregularity in amino acid metabolism may have important effects on the prognosis of IBD. Similar to this result, our previous studies have shown that a number of aromatic acid and lipid derivative metabolites may serve as potential biomarkers to distinguish IBD from healthy controls. When compared to healthy controls, phosphatidylcholine (PC), phosphatidylethanolamines (PE) are differential metabolites in our IBD patients, which mostly functioned as components of distinct glycerophospholipid metabolites. Aside from that, acylcarnitine can also distinguish between IBD and healthy controls. From the perspective of traditional Chinese medicine, the consensus from the Chinese Association of Traditional Chinese Medicine and the previous retrospective analysis showed that damp-heat syndrome is one of the main subtypes of active IBD. , Studies have shown that damp-heat syndrome could lead to disorders of lipid metabolism and amino acid metabolism. , , Consistent with this, some studies have shown that there are differential metabolic pathways between patients in the active stage of UC with syndrome of damp-heat and syndrome of non-damp-heat, which are mainly concentrated in tryptophan metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and pyrimidine metabolism. In summary, we can conclude that both our previous studies and other studies have found that patients with damp-heat syndrome with IBD have shown metabolic changes clinically, but the specific mechanism is still unclear. Therefore, we aim to explore the metabolic characteristics of rats’ experimental colitis of damp-heat type, so as to analyze the possible mechanism of such metabolic changes. Based on these results, we focused on lipids and amino acids metabolism in experimental colitis of damp-heat type and therapeutic interventions, such as medical plants treatment. At the same time, there is evidence that herbs and herbal formulations are effective in treating IBD. Professor Xu Jingfan, the great Traditional Chinese Medicine (TCM) master, used Rhizoma Paridis to treat refractory IBD. The ZaoXiu (Chinese synonym of Rhizoma Paridis) formulation has been a representative prescription according to the collection and studies of his therapeutic experiences. Zaoxiu formulation extracts showed promising effects on protecting the animals from 2,4,6-trinitrobenzene sulfonic acid (TNBS) -induced colitis. However, the specific mechanism and major pharmacological active ingredient still need further studies. Rhizoma Paridis is a well-known Chinese herbal medicine, which has effects of clearing heat and detoxification and is often used to treat pustulosis, sore throat, snakebite envenoming, traumatic injury and other diseases. What’s more, it has been applied to treat IBD in recent years. Rhizoma paridis total saponins (RPTS) are the main active components of Rhizoma Paridis, which have various pharmacological effects, such as anti-tumor, anti-bacterial, anti-viral, hemostatic and immune regulation. The RPTS is a mixture of various saponins extracted from the Rhizoma Paridis, including a variety of saponins with different structures, among which the main saponins are pennogenin and diosgenin, while pennogenin (PN) includes Polyphyllin IV and Polyphyllin VII. Based on these background, we conducted the following study. The primary goal of this investigation was to identify significant metabolites related to TNBS-induced colitis with damp-heat type and explore the role of Rhizoma Paridis active substances in regulating its metabolic profiles. We used lipid pseudotargeted metabolomics and amino acids targeted metabolomics to observe changes of lipids and amino acids in rat model. Meanwhile, we described the dysregulated metabolic pathways and certain cytokine levels in the IBD rat model of damp-heat type in this study and explored the correlations between circulating metabolites and cytokines. Furthermore, we explored the mechanism of Rhizoma Paridis in treating experimental colitis of damp-heat type.
Chemicals and Reagents 5% TNBS (2,4,6-Trinitrobenzenesulfonic acid) (P2297-5X, 10 mL, SLCG2348, Sigma Corporation, USA); Rhizoma Paridis Total Saponins (RPTS) (MUST-20203001), Polyphyllin VI (PPVI) (MUST-21090811, ≥ 98%) and Polyphyllin VII (PPVII) (MUST-21040610, ≥ 97%) were purchased from Chengdu Manstead Biotechnology Co. Ltd, China; 5-ASA (RH110815, Shanghai Macklin Biochemical Co.,Ltd). RPTS, PPVI, PPVII, and 5-ASA were prepared into 0.5% carboxymethylcellulose sodium (CMC-Na) (C104987, Shanghai Aladdin Bio-Chem Technology Co., LTD) solution respectively when used. All reagents used in the HE staining were purchased from Zhuhai Baso Medical Device Co., Ltd. Methanol, acetonitrile, and formic acid were purchased from Fisher Scientific (USA). Ultra-pure water was generated employing a Milli-Q Integral Water Purification System from Merck Millipore (Merck Millipore, USA). All standards of metabolites were purchased from Sigma Corporation. All enzyme-linked immunosorbent assay (ELISA) kits were purchased from Jianglai Industrial Company, Shanghai, China. Animal Experiments Sprague-Dawley (SD) rats (male, 6–8 weeks, weighting 180–220 g) were obtained from SPF (Beijing) Biotechnology Co., Ltd. Animals were housed in cages, controlled temperature (22°C ± 2°C), humidity (45% ± 5%) and illumination (12 h light / dark cycle). Rats were given sterile food and water libitum, and were adapted to the facility for one week before experiments. After one week of adaptive feeding, rats were randomly divided into 8 groups: control (RNC) (n=7), control with damp-heat type (DNC) (n=7), TNBS-treated with damp-heat type (DAT) (n=13), low RPTS dose-treated (50 mg/kg) (DRL) (n=9), high RPTS dose-treated (100 mg/kg) (DRH) (n=9), low PN dose-treated (20 mg/kg) (DPL) (n=9), high PN dose-treated (40 mg/kg) (DPH) (n=9), 5-ASA-treated group (100 mg/kg) (DAS) (n=9). RPTS and PN were suspended in 0.5% CMC-Na solution and supplemented to rats by gavage for 14 days. The experiments strictly followed the Guide for the Care and Use of Laboratory Animals by the National Institutes of Health, and the animal study was approved by the Animal Welfare Committee of the Affiliated Drum Tower Hospital of Nanjing University Medical School. Six samples were randomly selected from each group for the following experiments, including lipid pseudotargeted metabolomics and amino acids targeted metabolomics, ELISA, and RT–qPCR. Each repeat of the experiments was performed as a separate, independent experiment. Establishment of Experimental Colitis Model with Damp-Heat Type Before modeling experimental colitis, we need to construct the rat model of large intestine damp-heat type. Using the procedure described in the paper, except for the RNC group, the other 7 groups of rats were all housed in an environmental simulation room under an ambient temperature of 28°C ± 2°C at a relative humidity of 60–80% for 9 h every day for 14 days. The 7 groups living in the damp-heat area were given drinking water containing honey (200 g/L) and a high-sugar and high-fat diet (HSHFD) throughout the whole process. From day 1 to day 14, RPTS, PN, and 5-ASA were administered by gavage once a day. The normal group was fed a normal diet and lived in an environment with a relative humidity of 45% ± 5% and an ambient temperature of 22°C ± 2°C. The experimental colitis model was constructed under damp-heat environment. On the 11th day of feeding, experimental colitis model manipulation was performed. Rats were fasted for 24 h with freely drinking before modeling. Rats were anesthetized with isoflurane. The rats in RNC and DNC groups were given 1 mL of 0.9% saline by rectal administration, the other groups were given 1mL TNBS solution (100 mg/kg) (5% TNBS dissolved in 50% ethanol) by rectal administration. The mixed solution was slowly injected into 4–6 cm of the proximal end of the descending colon, and the rats were kept in the vertical position for 60s. The severity of colitis was recorded daily, including weight changes, diarrhea, and bloody stools. All rats were sacrificed on day 14 and the colon tissues and plasma were removed for further analysis. Indicators Related to Colitis Status checks on laboratory animals were done every day. The weight was recorded. Each lab animal was graded every day for the Disease Activity Index (DAI) including body weight loss, blood in the stool, and stool consistency. The rats were anesthetized and sacrificed at day 14, and the pertinent samples were gathered. The Colon length and Colon weight / length ratio were recorded. The gross morphological changes linked to TNBS-induced colitis were documented. For histology evaluation, segments of colonic tissues were fixed in 10% formalin. Based on the tissue damage seen under the optical microscope, hematoxylin and eosin (HE) staining was done to evaluate histopathological alterations. The evaluation scale was used to get the damage score. Enzyme-Linked Immunosorbent Assay (ELISA) According to the manufacturer’s instructions, ELISA kits (Jianglaibio, Shanghai, China) were used to detect the serum levels of tumor necrosis factor (TNF)-α (JL13202-96T, 081901006132020826), interleukin (IL)-6 (JL20896-96T, 081901006208960826), interleukin (IL)-1B (JL20884-96T, 081901006208840826), interferon (IFN)-γ (JL13241-96T, 081901006132410826), IL-10 (JL13427-96T, 081901006134270826), IL-13 (JL20877-96T, 081901006208770826), IL-18 (JL20882-96T, 081901006208820826), IL-4 (JL20894-96T, 081901006208940826), S100A8 (JL15633-96T, 081901006156330826), S100A9 (JL24667-96T, 081901006246670826), sPLA2 (JL15703-96T, 081901006211580826), cPLA2 (JL15759-96T, 081901006157590826), Arg-1 (JL21104-96T, 081901006211040826) and TGF-β (JL13643-96T, 081901006136430826). Real-Time Quantitative PCR Analysis (RT–qPCR) With the use of Trizol (R401-01, Vazyme, China), total RNA from the colon was extracted, and an RT SuperMix kit (R323-01, Vazyme, China) was then utilized for the reverse transcription procedure. Real-time PCR analysis was performed using an Applied Biosystems™ QuantStudio™ (thermofisher) and a SYBR Green qPCR Master Mix kit (Q711-02, Vazyme, China). Table S1 in the supporting information lists the primer sequences. Using the comparative CT approach and GAPDH as a correction, the expressions of pertinent genes were measured and quantified. Metabolomic Plasma Sample Handling A. Lipid Pseudotargeted Metabolomics Plasma samples (100 µL) were homogenized in a 400 µL 75% methanol, including internal standard substances ( Table S2 ). After homogenization, 1 mL MTBE was added. The mixture was vortexed for 2 min and stood at a low temperature for 1 h. The mixture homogenized in 250 µL water was vortexed for 2 min and stood at low temperature for 10 min, then centrifuged at 14,000 rpm at 4°C for 15 min. 400 µL of supernatant was absorbed and evaporated, then stored at −20°C. The extract samples were redissolved in 120 µL of Acetonitrile / isopropanol / water (65:30:5). Until complete dissolution, the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. The blank sample used in the experiment was acetonitrile / isopropanol / water (65:30:5). QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples. B. Amino Acids Targeted Metabolomics A total of 35 amino acids were detected by targeted metabolomics analysis ( Table S3 ). Plasma samples (100 µL) were homogenized in 400 µL 50% methylene acid water. The mixture was vortexed and mixed for 10 min, and ultrasound for 20 min. Then centrifuged at 20,000 rpm at 4°C for 10 min. The supernatant was removed, and the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. All standards were purchased from Sigma Corporation. The standards were dissolved in methanol and diluted in 80% methanol to 1 ppb, 10 ppb, 50 ppb, 100 ppb, 500 ppb, 500 ppb, and 1000 ppb. The blank sample was a 20% methanol/aqueous solution. QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples. Chromatography-Mass Spectrometry Acquisition Conditions A. Lipid Pseudotargeted Metabolomics Chromatography Analysis Conditions The chromatography was performed on the BEH C8 column (2.1×100 mm × 1.7 μm, Waters, Milford, MA, United States). The flow rate was 0.2 mL/min. The injection volume was 5 μL. The mobile phase was composed of solvent A (water with 60% acetonitrile and 10 mm ammonium acetate) and solvent B (isopropanol: acetonitrile = 9:1 (v / v) and 10 mm ammonium acetate). The detailed LC method is summarized in ( Table S4-1 ). Mass Spectrometry Conditions Under positive-ion mode ( Table S4-2 ), AB 5500 (SCIEX, USA) was used. The Spray Voltage was +3.5 kV in positive ionization mode and −3.0 kV in negative ionization mode. The other mass spectrometry conditions under negative-ion mode are the same as positive-ion mode ( Table S4-2 ). The Capillary temperature was maintained at 300°C. The Curtain Gas (CUR) was 35 Arb. The Collision Gas (CAD) was Medium. Ion Source Gas1 (GS1) and Ion Source Gas1 (GS2) was 60 Arb. Entrance Potential (EP) was 10. CXP was 13. The Mass Spectrometry conditions are the same as in positive-ion mode. B. Amino Acids Targeted Metabolomics Chromatography Analysis Conditions The chromatography was performed on the Kinetex PFP C18 (250*4.6 mm, 5 μm). The flow rate was 0.50 mL / min. The injection volume was 2 μL. The mobile phase was composed of solvent A (water with 0.1% acetic acid) and solvent B (methyl alcohol with 0.1% acetic acid). The detailed LC method is summarized in ( Table S5-1 ). Under negative-ion mode, the chromatographic conditions are the same as in positive-ion mode. Mass Spectrometry Conditions The Thermo TSQ Quautiva was used. The IS Pos: 3500.0 V, Neg: 2500.0 V.TEM was 550.0°C. The Sheath GAS was 40.0 psi. Aux GAS was 10 psi. The Ion Transfer TUBE Temp was 350°C. The vaporizer Tempwas 350°C ( Table S5-2 ). Data Acquisition Instructions We use the DRMS / HRMS data quality online real-time monitoring software to realize the process monitoring of data quality. Data Processing and Statistical Analysis All measurement data are collected by Xcalibur data acquisition software. The “80% rule” was used as a pretreatment on the data to lessen the entry of missing values. For multi-dimensional statistical analysis using principal component analysis, partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA), the raw data were imported into the One-MAP / PTO software. With the variable importance projection (VIP) > 1.0 as the standard to identify potential difference variables. Two sample unpaired t-tests are used in metabolomics research to demonstrate which metabolites have the ability to distinguish between the various groups in the data set, p < 0.05 was used as a threshold. The fold change (FC) was calculated relative to a given reference sample based on averaged raw signal intensities and serves as a metric for the relative change in a given concentration of the metabolite in the various settings under examination. A criterion of FC > 3/2 or < 2/3 was established. The possible biomarkers were screened using the two-sample unpaired t -test, fold change (FC) analysis, and variable importance projection (VIP) from the peak intensity. Finally, it was decided that data with VIP > 1 and p < 0.05, FC > 1.5, or < 2/3 were differential metabolites. The One-MAP platform ( www.5omics.com ) was used to map the above already obtained metabolites, to metabolic pathways. One-step Metabolomics was used to analyze all the data. For lipidomics, we used the LIPEA website to get the already obtained qualitative annotation analysis, mapping onto the metabolic pathways. Statistical Analysis We used two software programs for statistical analysis: GraphPad Prism 8 and IBM SPSS Statistics software (version 22.0). The Shapiro–Wilk normality test was performed to determine the data distribution. To assess differences between the two groups, the Student’s t -test was applied and to assess differences among multiple groups, a one-way ANOVA was applied for data following the normal distribution. Non-normally distributed continuous variables were compared by using the Kruskal–Wallis test. Using Pearson Correlation, the relationships between cytokines and metabolites were examined. p < 0.05 was considered significant and used for subsequent analysis. Related images were drawn by the bioinformatics platform ( https://www.bioinformatics.com.cn/ ).
5% TNBS (2,4,6-Trinitrobenzenesulfonic acid) (P2297-5X, 10 mL, SLCG2348, Sigma Corporation, USA); Rhizoma Paridis Total Saponins (RPTS) (MUST-20203001), Polyphyllin VI (PPVI) (MUST-21090811, ≥ 98%) and Polyphyllin VII (PPVII) (MUST-21040610, ≥ 97%) were purchased from Chengdu Manstead Biotechnology Co. Ltd, China; 5-ASA (RH110815, Shanghai Macklin Biochemical Co.,Ltd). RPTS, PPVI, PPVII, and 5-ASA were prepared into 0.5% carboxymethylcellulose sodium (CMC-Na) (C104987, Shanghai Aladdin Bio-Chem Technology Co., LTD) solution respectively when used. All reagents used in the HE staining were purchased from Zhuhai Baso Medical Device Co., Ltd. Methanol, acetonitrile, and formic acid were purchased from Fisher Scientific (USA). Ultra-pure water was generated employing a Milli-Q Integral Water Purification System from Merck Millipore (Merck Millipore, USA). All standards of metabolites were purchased from Sigma Corporation. All enzyme-linked immunosorbent assay (ELISA) kits were purchased from Jianglai Industrial Company, Shanghai, China.
Sprague-Dawley (SD) rats (male, 6–8 weeks, weighting 180–220 g) were obtained from SPF (Beijing) Biotechnology Co., Ltd. Animals were housed in cages, controlled temperature (22°C ± 2°C), humidity (45% ± 5%) and illumination (12 h light / dark cycle). Rats were given sterile food and water libitum, and were adapted to the facility for one week before experiments. After one week of adaptive feeding, rats were randomly divided into 8 groups: control (RNC) (n=7), control with damp-heat type (DNC) (n=7), TNBS-treated with damp-heat type (DAT) (n=13), low RPTS dose-treated (50 mg/kg) (DRL) (n=9), high RPTS dose-treated (100 mg/kg) (DRH) (n=9), low PN dose-treated (20 mg/kg) (DPL) (n=9), high PN dose-treated (40 mg/kg) (DPH) (n=9), 5-ASA-treated group (100 mg/kg) (DAS) (n=9). RPTS and PN were suspended in 0.5% CMC-Na solution and supplemented to rats by gavage for 14 days. The experiments strictly followed the Guide for the Care and Use of Laboratory Animals by the National Institutes of Health, and the animal study was approved by the Animal Welfare Committee of the Affiliated Drum Tower Hospital of Nanjing University Medical School. Six samples were randomly selected from each group for the following experiments, including lipid pseudotargeted metabolomics and amino acids targeted metabolomics, ELISA, and RT–qPCR. Each repeat of the experiments was performed as a separate, independent experiment. Establishment of Experimental Colitis Model with Damp-Heat Type Before modeling experimental colitis, we need to construct the rat model of large intestine damp-heat type. Using the procedure described in the paper, except for the RNC group, the other 7 groups of rats were all housed in an environmental simulation room under an ambient temperature of 28°C ± 2°C at a relative humidity of 60–80% for 9 h every day for 14 days. The 7 groups living in the damp-heat area were given drinking water containing honey (200 g/L) and a high-sugar and high-fat diet (HSHFD) throughout the whole process. From day 1 to day 14, RPTS, PN, and 5-ASA were administered by gavage once a day. The normal group was fed a normal diet and lived in an environment with a relative humidity of 45% ± 5% and an ambient temperature of 22°C ± 2°C. The experimental colitis model was constructed under damp-heat environment. On the 11th day of feeding, experimental colitis model manipulation was performed. Rats were fasted for 24 h with freely drinking before modeling. Rats were anesthetized with isoflurane. The rats in RNC and DNC groups were given 1 mL of 0.9% saline by rectal administration, the other groups were given 1mL TNBS solution (100 mg/kg) (5% TNBS dissolved in 50% ethanol) by rectal administration. The mixed solution was slowly injected into 4–6 cm of the proximal end of the descending colon, and the rats were kept in the vertical position for 60s. The severity of colitis was recorded daily, including weight changes, diarrhea, and bloody stools. All rats were sacrificed on day 14 and the colon tissues and plasma were removed for further analysis. Indicators Related to Colitis Status checks on laboratory animals were done every day. The weight was recorded. Each lab animal was graded every day for the Disease Activity Index (DAI) including body weight loss, blood in the stool, and stool consistency. The rats were anesthetized and sacrificed at day 14, and the pertinent samples were gathered. The Colon length and Colon weight / length ratio were recorded. The gross morphological changes linked to TNBS-induced colitis were documented. For histology evaluation, segments of colonic tissues were fixed in 10% formalin. Based on the tissue damage seen under the optical microscope, hematoxylin and eosin (HE) staining was done to evaluate histopathological alterations. The evaluation scale was used to get the damage score.
Before modeling experimental colitis, we need to construct the rat model of large intestine damp-heat type. Using the procedure described in the paper, except for the RNC group, the other 7 groups of rats were all housed in an environmental simulation room under an ambient temperature of 28°C ± 2°C at a relative humidity of 60–80% for 9 h every day for 14 days. The 7 groups living in the damp-heat area were given drinking water containing honey (200 g/L) and a high-sugar and high-fat diet (HSHFD) throughout the whole process. From day 1 to day 14, RPTS, PN, and 5-ASA were administered by gavage once a day. The normal group was fed a normal diet and lived in an environment with a relative humidity of 45% ± 5% and an ambient temperature of 22°C ± 2°C. The experimental colitis model was constructed under damp-heat environment. On the 11th day of feeding, experimental colitis model manipulation was performed. Rats were fasted for 24 h with freely drinking before modeling. Rats were anesthetized with isoflurane. The rats in RNC and DNC groups were given 1 mL of 0.9% saline by rectal administration, the other groups were given 1mL TNBS solution (100 mg/kg) (5% TNBS dissolved in 50% ethanol) by rectal administration. The mixed solution was slowly injected into 4–6 cm of the proximal end of the descending colon, and the rats were kept in the vertical position for 60s. The severity of colitis was recorded daily, including weight changes, diarrhea, and bloody stools. All rats were sacrificed on day 14 and the colon tissues and plasma were removed for further analysis.
Status checks on laboratory animals were done every day. The weight was recorded. Each lab animal was graded every day for the Disease Activity Index (DAI) including body weight loss, blood in the stool, and stool consistency. The rats were anesthetized and sacrificed at day 14, and the pertinent samples were gathered. The Colon length and Colon weight / length ratio were recorded. The gross morphological changes linked to TNBS-induced colitis were documented. For histology evaluation, segments of colonic tissues were fixed in 10% formalin. Based on the tissue damage seen under the optical microscope, hematoxylin and eosin (HE) staining was done to evaluate histopathological alterations. The evaluation scale was used to get the damage score.
According to the manufacturer’s instructions, ELISA kits (Jianglaibio, Shanghai, China) were used to detect the serum levels of tumor necrosis factor (TNF)-α (JL13202-96T, 081901006132020826), interleukin (IL)-6 (JL20896-96T, 081901006208960826), interleukin (IL)-1B (JL20884-96T, 081901006208840826), interferon (IFN)-γ (JL13241-96T, 081901006132410826), IL-10 (JL13427-96T, 081901006134270826), IL-13 (JL20877-96T, 081901006208770826), IL-18 (JL20882-96T, 081901006208820826), IL-4 (JL20894-96T, 081901006208940826), S100A8 (JL15633-96T, 081901006156330826), S100A9 (JL24667-96T, 081901006246670826), sPLA2 (JL15703-96T, 081901006211580826), cPLA2 (JL15759-96T, 081901006157590826), Arg-1 (JL21104-96T, 081901006211040826) and TGF-β (JL13643-96T, 081901006136430826).
With the use of Trizol (R401-01, Vazyme, China), total RNA from the colon was extracted, and an RT SuperMix kit (R323-01, Vazyme, China) was then utilized for the reverse transcription procedure. Real-time PCR analysis was performed using an Applied Biosystems™ QuantStudio™ (thermofisher) and a SYBR Green qPCR Master Mix kit (Q711-02, Vazyme, China). Table S1 in the supporting information lists the primer sequences. Using the comparative CT approach and GAPDH as a correction, the expressions of pertinent genes were measured and quantified.
Plasma Sample Handling A. Lipid Pseudotargeted Metabolomics Plasma samples (100 µL) were homogenized in a 400 µL 75% methanol, including internal standard substances ( Table S2 ). After homogenization, 1 mL MTBE was added. The mixture was vortexed for 2 min and stood at a low temperature for 1 h. The mixture homogenized in 250 µL water was vortexed for 2 min and stood at low temperature for 10 min, then centrifuged at 14,000 rpm at 4°C for 15 min. 400 µL of supernatant was absorbed and evaporated, then stored at −20°C. The extract samples were redissolved in 120 µL of Acetonitrile / isopropanol / water (65:30:5). Until complete dissolution, the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. The blank sample used in the experiment was acetonitrile / isopropanol / water (65:30:5). QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples. B. Amino Acids Targeted Metabolomics A total of 35 amino acids were detected by targeted metabolomics analysis ( Table S3 ). Plasma samples (100 µL) were homogenized in 400 µL 50% methylene acid water. The mixture was vortexed and mixed for 10 min, and ultrasound for 20 min. Then centrifuged at 20,000 rpm at 4°C for 10 min. The supernatant was removed, and the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. All standards were purchased from Sigma Corporation. The standards were dissolved in methanol and diluted in 80% methanol to 1 ppb, 10 ppb, 50 ppb, 100 ppb, 500 ppb, 500 ppb, and 1000 ppb. The blank sample was a 20% methanol/aqueous solution. QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples. Chromatography-Mass Spectrometry Acquisition Conditions A. Lipid Pseudotargeted Metabolomics Chromatography Analysis Conditions The chromatography was performed on the BEH C8 column (2.1×100 mm × 1.7 μm, Waters, Milford, MA, United States). The flow rate was 0.2 mL/min. The injection volume was 5 μL. The mobile phase was composed of solvent A (water with 60% acetonitrile and 10 mm ammonium acetate) and solvent B (isopropanol: acetonitrile = 9:1 (v / v) and 10 mm ammonium acetate). The detailed LC method is summarized in ( Table S4-1 ). Mass Spectrometry Conditions Under positive-ion mode ( Table S4-2 ), AB 5500 (SCIEX, USA) was used. The Spray Voltage was +3.5 kV in positive ionization mode and −3.0 kV in negative ionization mode. The other mass spectrometry conditions under negative-ion mode are the same as positive-ion mode ( Table S4-2 ). The Capillary temperature was maintained at 300°C. The Curtain Gas (CUR) was 35 Arb. The Collision Gas (CAD) was Medium. Ion Source Gas1 (GS1) and Ion Source Gas1 (GS2) was 60 Arb. Entrance Potential (EP) was 10. CXP was 13. The Mass Spectrometry conditions are the same as in positive-ion mode. B. Amino Acids Targeted Metabolomics Chromatography Analysis Conditions The chromatography was performed on the Kinetex PFP C18 (250*4.6 mm, 5 μm). The flow rate was 0.50 mL / min. The injection volume was 2 μL. The mobile phase was composed of solvent A (water with 0.1% acetic acid) and solvent B (methyl alcohol with 0.1% acetic acid). The detailed LC method is summarized in ( Table S5-1 ). Under negative-ion mode, the chromatographic conditions are the same as in positive-ion mode. Mass Spectrometry Conditions The Thermo TSQ Quautiva was used. The IS Pos: 3500.0 V, Neg: 2500.0 V.TEM was 550.0°C. The Sheath GAS was 40.0 psi. Aux GAS was 10 psi. The Ion Transfer TUBE Temp was 350°C. The vaporizer Tempwas 350°C ( Table S5-2 ). Data Acquisition Instructions We use the DRMS / HRMS data quality online real-time monitoring software to realize the process monitoring of data quality. Data Processing and Statistical Analysis All measurement data are collected by Xcalibur data acquisition software. The “80% rule” was used as a pretreatment on the data to lessen the entry of missing values. For multi-dimensional statistical analysis using principal component analysis, partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA), the raw data were imported into the One-MAP / PTO software. With the variable importance projection (VIP) > 1.0 as the standard to identify potential difference variables. Two sample unpaired t-tests are used in metabolomics research to demonstrate which metabolites have the ability to distinguish between the various groups in the data set, p < 0.05 was used as a threshold. The fold change (FC) was calculated relative to a given reference sample based on averaged raw signal intensities and serves as a metric for the relative change in a given concentration of the metabolite in the various settings under examination. A criterion of FC > 3/2 or < 2/3 was established. The possible biomarkers were screened using the two-sample unpaired t -test, fold change (FC) analysis, and variable importance projection (VIP) from the peak intensity. Finally, it was decided that data with VIP > 1 and p < 0.05, FC > 1.5, or < 2/3 were differential metabolites. The One-MAP platform ( www.5omics.com ) was used to map the above already obtained metabolites, to metabolic pathways. One-step Metabolomics was used to analyze all the data. For lipidomics, we used the LIPEA website to get the already obtained qualitative annotation analysis, mapping onto the metabolic pathways.
A. Lipid Pseudotargeted Metabolomics Plasma samples (100 µL) were homogenized in a 400 µL 75% methanol, including internal standard substances ( Table S2 ). After homogenization, 1 mL MTBE was added. The mixture was vortexed for 2 min and stood at a low temperature for 1 h. The mixture homogenized in 250 µL water was vortexed for 2 min and stood at low temperature for 10 min, then centrifuged at 14,000 rpm at 4°C for 15 min. 400 µL of supernatant was absorbed and evaporated, then stored at −20°C. The extract samples were redissolved in 120 µL of Acetonitrile / isopropanol / water (65:30:5). Until complete dissolution, the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. The blank sample used in the experiment was acetonitrile / isopropanol / water (65:30:5). QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples. B. Amino Acids Targeted Metabolomics A total of 35 amino acids were detected by targeted metabolomics analysis ( Table S3 ). Plasma samples (100 µL) were homogenized in 400 µL 50% methylene acid water. The mixture was vortexed and mixed for 10 min, and ultrasound for 20 min. Then centrifuged at 20,000 rpm at 4°C for 10 min. The supernatant was removed, and the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. All standards were purchased from Sigma Corporation. The standards were dissolved in methanol and diluted in 80% methanol to 1 ppb, 10 ppb, 50 ppb, 100 ppb, 500 ppb, 500 ppb, and 1000 ppb. The blank sample was a 20% methanol/aqueous solution. QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples.
Plasma samples (100 µL) were homogenized in a 400 µL 75% methanol, including internal standard substances ( Table S2 ). After homogenization, 1 mL MTBE was added. The mixture was vortexed for 2 min and stood at a low temperature for 1 h. The mixture homogenized in 250 µL water was vortexed for 2 min and stood at low temperature for 10 min, then centrifuged at 14,000 rpm at 4°C for 15 min. 400 µL of supernatant was absorbed and evaporated, then stored at −20°C. The extract samples were redissolved in 120 µL of Acetonitrile / isopropanol / water (65:30:5). Until complete dissolution, the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. The blank sample used in the experiment was acetonitrile / isopropanol / water (65:30:5). QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples.
A total of 35 amino acids were detected by targeted metabolomics analysis ( Table S3 ). Plasma samples (100 µL) were homogenized in 400 µL 50% methylene acid water. The mixture was vortexed and mixed for 10 min, and ultrasound for 20 min. Then centrifuged at 20,000 rpm at 4°C for 10 min. The supernatant was removed, and the supernatant was taken by oscillation and centrifugation for UHPLC-TQMS analysis. All standards were purchased from Sigma Corporation. The standards were dissolved in methanol and diluted in 80% methanol to 1 ppb, 10 ppb, 50 ppb, 100 ppb, 500 ppb, 500 ppb, and 1000 ppb. The blank sample was a 20% methanol/aqueous solution. QC samples were prepared by mixing a portion of all actual samples and using the same data acquisition method as the actual samples. The pretreatment of QC samples and blank samples was the same as that of analyzed samples.
A. Lipid Pseudotargeted Metabolomics Chromatography Analysis Conditions The chromatography was performed on the BEH C8 column (2.1×100 mm × 1.7 μm, Waters, Milford, MA, United States). The flow rate was 0.2 mL/min. The injection volume was 5 μL. The mobile phase was composed of solvent A (water with 60% acetonitrile and 10 mm ammonium acetate) and solvent B (isopropanol: acetonitrile = 9:1 (v / v) and 10 mm ammonium acetate). The detailed LC method is summarized in ( Table S4-1 ). Mass Spectrometry Conditions Under positive-ion mode ( Table S4-2 ), AB 5500 (SCIEX, USA) was used. The Spray Voltage was +3.5 kV in positive ionization mode and −3.0 kV in negative ionization mode. The other mass spectrometry conditions under negative-ion mode are the same as positive-ion mode ( Table S4-2 ). The Capillary temperature was maintained at 300°C. The Curtain Gas (CUR) was 35 Arb. The Collision Gas (CAD) was Medium. Ion Source Gas1 (GS1) and Ion Source Gas1 (GS2) was 60 Arb. Entrance Potential (EP) was 10. CXP was 13. The Mass Spectrometry conditions are the same as in positive-ion mode. B. Amino Acids Targeted Metabolomics Chromatography Analysis Conditions The chromatography was performed on the Kinetex PFP C18 (250*4.6 mm, 5 μm). The flow rate was 0.50 mL / min. The injection volume was 2 μL. The mobile phase was composed of solvent A (water with 0.1% acetic acid) and solvent B (methyl alcohol with 0.1% acetic acid). The detailed LC method is summarized in ( Table S5-1 ). Under negative-ion mode, the chromatographic conditions are the same as in positive-ion mode. Mass Spectrometry Conditions The Thermo TSQ Quautiva was used. The IS Pos: 3500.0 V, Neg: 2500.0 V.TEM was 550.0°C. The Sheath GAS was 40.0 psi. Aux GAS was 10 psi. The Ion Transfer TUBE Temp was 350°C. The vaporizer Tempwas 350°C ( Table S5-2 ).
Chromatography Analysis Conditions The chromatography was performed on the BEH C8 column (2.1×100 mm × 1.7 μm, Waters, Milford, MA, United States). The flow rate was 0.2 mL/min. The injection volume was 5 μL. The mobile phase was composed of solvent A (water with 60% acetonitrile and 10 mm ammonium acetate) and solvent B (isopropanol: acetonitrile = 9:1 (v / v) and 10 mm ammonium acetate). The detailed LC method is summarized in ( Table S4-1 ). Mass Spectrometry Conditions Under positive-ion mode ( Table S4-2 ), AB 5500 (SCIEX, USA) was used. The Spray Voltage was +3.5 kV in positive ionization mode and −3.0 kV in negative ionization mode. The other mass spectrometry conditions under negative-ion mode are the same as positive-ion mode ( Table S4-2 ). The Capillary temperature was maintained at 300°C. The Curtain Gas (CUR) was 35 Arb. The Collision Gas (CAD) was Medium. Ion Source Gas1 (GS1) and Ion Source Gas1 (GS2) was 60 Arb. Entrance Potential (EP) was 10. CXP was 13. The Mass Spectrometry conditions are the same as in positive-ion mode.
The chromatography was performed on the BEH C8 column (2.1×100 mm × 1.7 μm, Waters, Milford, MA, United States). The flow rate was 0.2 mL/min. The injection volume was 5 μL. The mobile phase was composed of solvent A (water with 60% acetonitrile and 10 mm ammonium acetate) and solvent B (isopropanol: acetonitrile = 9:1 (v / v) and 10 mm ammonium acetate). The detailed LC method is summarized in ( Table S4-1 ).
Under positive-ion mode ( Table S4-2 ), AB 5500 (SCIEX, USA) was used. The Spray Voltage was +3.5 kV in positive ionization mode and −3.0 kV in negative ionization mode. The other mass spectrometry conditions under negative-ion mode are the same as positive-ion mode ( Table S4-2 ). The Capillary temperature was maintained at 300°C. The Curtain Gas (CUR) was 35 Arb. The Collision Gas (CAD) was Medium. Ion Source Gas1 (GS1) and Ion Source Gas1 (GS2) was 60 Arb. Entrance Potential (EP) was 10. CXP was 13. The Mass Spectrometry conditions are the same as in positive-ion mode.
Chromatography Analysis Conditions The chromatography was performed on the Kinetex PFP C18 (250*4.6 mm, 5 μm). The flow rate was 0.50 mL / min. The injection volume was 2 μL. The mobile phase was composed of solvent A (water with 0.1% acetic acid) and solvent B (methyl alcohol with 0.1% acetic acid). The detailed LC method is summarized in ( Table S5-1 ). Under negative-ion mode, the chromatographic conditions are the same as in positive-ion mode. Mass Spectrometry Conditions The Thermo TSQ Quautiva was used. The IS Pos: 3500.0 V, Neg: 2500.0 V.TEM was 550.0°C. The Sheath GAS was 40.0 psi. Aux GAS was 10 psi. The Ion Transfer TUBE Temp was 350°C. The vaporizer Tempwas 350°C ( Table S5-2 ).
The chromatography was performed on the Kinetex PFP C18 (250*4.6 mm, 5 μm). The flow rate was 0.50 mL / min. The injection volume was 2 μL. The mobile phase was composed of solvent A (water with 0.1% acetic acid) and solvent B (methyl alcohol with 0.1% acetic acid). The detailed LC method is summarized in ( Table S5-1 ). Under negative-ion mode, the chromatographic conditions are the same as in positive-ion mode.
The Thermo TSQ Quautiva was used. The IS Pos: 3500.0 V, Neg: 2500.0 V.TEM was 550.0°C. The Sheath GAS was 40.0 psi. Aux GAS was 10 psi. The Ion Transfer TUBE Temp was 350°C. The vaporizer Tempwas 350°C ( Table S5-2 ).
We use the DRMS / HRMS data quality online real-time monitoring software to realize the process monitoring of data quality.
All measurement data are collected by Xcalibur data acquisition software. The “80% rule” was used as a pretreatment on the data to lessen the entry of missing values. For multi-dimensional statistical analysis using principal component analysis, partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA), the raw data were imported into the One-MAP / PTO software. With the variable importance projection (VIP) > 1.0 as the standard to identify potential difference variables. Two sample unpaired t-tests are used in metabolomics research to demonstrate which metabolites have the ability to distinguish between the various groups in the data set, p < 0.05 was used as a threshold. The fold change (FC) was calculated relative to a given reference sample based on averaged raw signal intensities and serves as a metric for the relative change in a given concentration of the metabolite in the various settings under examination. A criterion of FC > 3/2 or < 2/3 was established. The possible biomarkers were screened using the two-sample unpaired t -test, fold change (FC) analysis, and variable importance projection (VIP) from the peak intensity. Finally, it was decided that data with VIP > 1 and p < 0.05, FC > 1.5, or < 2/3 were differential metabolites. The One-MAP platform ( www.5omics.com ) was used to map the above already obtained metabolites, to metabolic pathways. One-step Metabolomics was used to analyze all the data. For lipidomics, we used the LIPEA website to get the already obtained qualitative annotation analysis, mapping onto the metabolic pathways.
We used two software programs for statistical analysis: GraphPad Prism 8 and IBM SPSS Statistics software (version 22.0). The Shapiro–Wilk normality test was performed to determine the data distribution. To assess differences between the two groups, the Student’s t -test was applied and to assess differences among multiple groups, a one-way ANOVA was applied for data following the normal distribution. Non-normally distributed continuous variables were compared by using the Kruskal–Wallis test. Using Pearson Correlation, the relationships between cytokines and metabolites were examined. p < 0.05 was considered significant and used for subsequent analysis. Related images were drawn by the bioinformatics platform ( https://www.bioinformatics.com.cn/ ).
Low-Dose RPTS Treatment and PN Treatment Ameliorated TNBS-Induced Colitis The rat colitis model was induced by TNBS. The administration of TNBS dramatically reduced food intake, and loss of weight was seen. The animals of the DAT group, DRL group, and DPL group suffered a drastic weight loss compared with the RNC group ( ). While the RNC groups retained their normal structure, TNBS caused damage to the intestinal mucosa of the rats, including transmural involvement, ulcerations, edema, and bowel wall thickening. Colonic tissue responded better to the RPTS therapy (50 mg/kg), with improvements in the damage score, degree of damaged areas, and colon weight / length ratio, leading to an increase in colon length ( and ). Compared with the DAT group, 50 mg/kg RPTS significantly reduced DAI score ( p < 0.05), alleviated colonic shortening, and improved colonic mucosal bleeding ( ). Histologically, the DAT group, in contrast to the RNC groups, displayed an intensive transmural interruption, significant ulceration, inflammation, edema, and huge infiltration of neutrophils, primarily in the mucosa ( ). Mild interstitial neutrophil and lymphocyte infiltration were observed in the DNC group when compared to the RNC group. The DAS, DRL, DRH, DPL, and DPH groups showed various degrees of colonic mucosal erosion remission, but discontinuous intestinal epithelial lesions with granulomatous hyperplasia, mild to moderate interstitial neutrophil and lymphocyte infiltration, and structural changes in the crypt were still visible. Compared with the RNC group and DNC group, the histology score was significantly increased in the DAT group ( p < 0.001). Compared with the DAT group, the histology score of the DPL and DAS groups decreased significantly ( p < 0.01) ( ). DRL reduced histology scores, but the difference was not statistically significant. In conclusion, low-dose RPTS and PN ameliorated the severity of TNBS-induced colitis. The therapeutic effect of low doses of RPTS and PN was more effective than that of high doses of RPTS and PN for the relief of colitis. Based on this, we continued to focus on the therapeutic effects of low-dose RPTS and PN in the treatment of TNBS-induced experimental colitis and investigate the mechanism of action. Low-Dose RPTS and PN Regulated the Levels of S100A8/9, Arg-1 and Cytokines in TNBS-Induced Colitis Abnormal activation of the immune response plays a direct role in the development and progression of IBD. Cytokines and cytokine-producing immune cells play a key role in the pathogenesis of IBD. We studied the effects of RPTS and PN on the levels of S100A8/9, Arg-1 and cytokines. illustrates the variations in TNF-α, IL-1B, IL-18, IFN-γ, IL-6, IL-4, TGF-β, IL-10, IL-13, S100A8, S100A9, and Arg-1 levels in the plasma of the rats in each group. In the DAT group, plasma levels of pro-inflammatory cytokines (TNF-α ( p < 0.01), IL-1B ( p < 0.05), IFN-γ ( p < 0.001), and IL-6 ( p < 0.01)) were significantly increased, and anti-inflammatory cytokines (IL-4 ( p < 0.01), TGF-β ( p < 0.01), IL-10 ( p < 0.01), IL-13 ( p < 0.05)) significantly decreased, and IL-18 did not change significantly. Meanwhile, the levels of S100A8 ( p < 0.001), S100A9 ( p < 0.001), and Arg-1 ( p < 0.01) were significantly increased in the DAT group. Low-dose PN significantly decreased the levels of pro-inflammatory cytokines TNF-α ( p < 0.01), IL-1B ( p < 0.01), IFN-γ ( p < 0.001) and IL-6 ( p < 0.01) and elevated the levels of anti-inflammatory cytokines IL-4 ( p < 0.01), TGF-β ( p < 0.001), IL-10 ( p < 0.01) and IL-13 ( p < 0.05) while decreasing levels of S100A8, S100A9, and Arg-1 ( p < 0.01). Compared with the DAT group, low-dose RPTS significantly reduced the levels of pro-inflammatory cytokines TNF-α ( p < 0.05), IFN-γ ( p < 0.01), and IL-1B ( p < 0.05) and could downregulate the levels of IL-6, S100A8, S100A9, Arg-1, and increased the levels of IL-4, TGF-β, IL-10, IL-13, but the difference was not significant. The Unbalance of Lipid Metabolism is Closely Associated with Experimental Colitis with Damp-Heat Type As previously mentioned, patients with IBD have abnormal lipid metabolism profiles. To investigate the different lipids closely associated with the illness process in experimental colitis of damp-heat type and to get ready for the subsequent study on the mechanism of the anti-IBD effect of RPTS and PN, lipid Pseudotargeted metabolomics technologies based on UHPLC-TQ/MS systems were undertaken. After data preprocessing and metabolite identification, we discovered 2696 lipids characteristics from the raw data recorded in positive- and negative-ionization modes, including 701 triacylglycerols (TGs), 254 Diacylglycerol (DGs), 25 Cardiolipin (CLs), 50 Lysophosphatidylcholine (LPCs), 37 Lysophosphatidylethanolamine(LPEs), 152 Phosphatidic acids (PAs), 261 phosphatidylcholines (PCs), 268 phosphatidylethanolamines (PEs), 109 Phosphatidylglycerols (PGs), 142 phosphoinositols (PIs), 114 Phosphatidylserine (PSs), 82 ceramides (Cers), 88 monohexosylceramide(Hexcer), 78 dihexosylceramide (Hex2cer), 80 Sphingomyelins (SMs), 31 Cholesterol ester (CEs), 27 other sterol lipids, 39 Acyl carnitine (CARs), 43 fatty acids (FAs), 83 unsaturated fatty acids (USFA) and others ( ). According to a thorough investigation, glycerophospholipids, which made up 42.95% of the overall lipid abundance, had the highest relative abundance when compared to sphingolipids, glycerolipids, and fatty Acyls. Glycerolipids had the second largest relative abundance, followed by sphingolipids and fatty Acyls ( ). To further analyze the effects of different factors on rats, we first performed a comparative analysis of RNC and DAT two groups. The RNC group and DAT group were distinct from each other using the PCA approach, which was focused on discriminating against the tested groups in an unsupervised pattern ( Figure S1a and b ). To demonstrate that the DAT group was significantly separated from the control group, OPLS-DA was a more effective application ( Figure S1c – f ). In the positive and negative modes, 245 and 49 important differential metabolites with significant effects on the modeling analysis were obtained. Furthermore, based on VIP > 1, p < 0.05, and FC > 1.5 or < 2/3, 294 differentiated metabolite ions were identified. 209 ions were upregulated and 85 ions were downregulated in the DAT group ( Table S6 ). These metabolites are mainly composed of 4 major classes. Through cluster analysis (heatmap) ( Figure S1g and h ) and volcano plot ( Figure S1i and j ), we could observe a distinct metabolic profile between DAT and RNC in plasma samples. Compared with the RNC group, the rats in the DAT group showed predominantly glycerophospholipid metabolic changes accompanied by changes in glycerolipids and sphingolipids abundance, and these differential lipid abundance were markedly elevated. Using the same statistical approach, compared with the DNC group, we find that the plasma lipid profiles of rats in the DAT group ( Table S7 ) still showed changes in glycerophospholipid metabolism, accompanied by changes in the abundance of glycerolipids and sphingolipids, and the abundance of these different lipids was significantly higher ( Figure S2 ). However, the trends of changes in the types and abundance of differentiated lipids in the DAT and DNC groups were different from those in the DAT and RNC groups. In the same way, comparing the DNC group with the RNC group ( Table S8 ), we found that the differential lipids in the DNC group were dominated by PC, PI, and TG, with a significant decrease in partial glycerophospholipid abundance compared with the RNC group, which was the main change of lipid metabolism in damp-heat type ( Figure S3 ). To better screen the specific lipids for experimental colitis with damp-heat type, we performed a comprehensive analysis of the three groups of differential metabolites. Analysis of variance (ANOVA) was performed on the screened metabolites from the three groups. We found that 146 differentiated metabolite ions of the DAT group were identified compared to RNC and DNC ( Table S9 ). PCA and particularly OPSL-DA suggest differences in metabolite profiles of the three groups ( ). A heatmap depicting the top 25 differentially abundant metabolites between DAT, DNC, and RNC ( and ) Among them, glycerophospholipids (47.95%), sphingolipids (26%), carnitine (15%), and glycerolipids (7.5%) were the four most perturbed lipid classes in DAT groups’ plasma samples. LPC 11:0, PC 43:6, PE 32:4, PE 32:5, PI 35:4, and PI 44:2 were significantly downregulated and others were upregulated. These metabolites were specific targets of DAT ( ). RPTS and PN Modulated Lipid Profile The results of animal studies showed that low doses of RPTS and PN were more effective in relieving colitis. A lipidomic analysis was also performed to determine the lipid profile transformation in DRL and DPL in comparison with DAT. First, we screened the differential metabolites between the DPL and DAT groups to explore the changes in lipid metabolic profiles after using PN. The treatment group and model group were distinct from each other using the PCA approach. Furthermore, based on VIP > 1, p < 0.05, and FC > 1.5 or < 2/3 in OPLS-DA, 68 differentiated metabolite ions were identified. 28 ions were upregulated and 40 ions were downregulated in the DPL group. Through cluster analysis, we could observe a distinct metabolic profile between DPL and DAT in plasma samples. PGE2, PGF22, PGD2, 8(9)-EpETrE, 14.15-DiHETrE, TG 64:16-2-a, TG 61:7, Hex2Cer 39:2–1, Hex2Cer 37:1–5 and Dehydroepiandrosterone were upregulated. PG 40:7–2, PS 38:5–2, PS 35:2, PS 37:6, PE O-33:0, PE O-35:0, PE 34:0, CL (72:6_18:2) −2H, DG 40:1–8 and so on were downregulated. Compared with the DNC and RNC groups, it is also noteworthy that PE 34:0, PE O-33:0, PE O-37:0, HexCer 36:2–6, HexCer 40:7–1, TG(e)54:2 were all significantly upregulated in DAT groups, which were then reversed by PN treatments. At the same time, when compared to DAT, RPTS could regulate disorders of lipid metabolism. Among them, PGE2, PGD2, PGF22, 8(9)-EpETrE, PS 44:8–1, PE 48:0, trenbolone, HexCer 38:3–7, HexCer 34:0–1, DG 37:2–7 and others were upregulated. PG 43:6, PI 32:0, PS 38:5–2, PE O-33:0, PE 34:1, LPE 20:5, DG 40:7–4, HexCer 39:1–4, and so on were downregulated. The considerable upregulation of PI 32:0, PG 42:6, PE 34:0, HexCer 40:7–1, LPE 20:3, LPE 22:5, PE 38:6, PE 40:8, and TG(e) 54:2 was reversed by RPTS treatments. Both RPTS and PN significantly downregulated the contents of PE 34:0, PE O-33:0, and TG(e)54:2. However, PG 42:6, PI 32:0, LPE 20:3, LPE 22:5, PE 38:6, and PE 40:8 were only moderately downregulated by RPTS. This suggested that these lipids were specific targets of RPTS but not PN. On the other hand, Cer 38:2–5, HexCer 36:2–6, and PE O-37:0 were specific targets of PN. Since RPTS and PN had differing preferences for modifying lipid profiles, the underlying processes of effect on TNBS-induced experimental colitis were diverse ( ). Experimental Colitis with Damp-Heat Type is Closely Related to Amino Acid Disorder In addition to the changes in lipid profile, the amino acid metabolic profile of IBD patients was also disturbed. Therefore, we conducted amino acids targeted metabolomics to determine the amino acid metabolism characteristics of experimental colitis with damp-heat type. To evaluate the repeatability and reliability of the method, QC samples were found clustered in the PCA scores plot. Then, the DAT and DNC groups were also well separated from the RNC group in the PCA and OPLS-DA analyses, with good model parameters respectively. A total of 35 amino acids were detected. It indicated that significant amino acid metabolic changes occurred in the DAT and RNC groups ( Table S10 ). Compared with the RNC group, the levels of serine, histidine, N-acetylneuraminic acid, and sulfocysteine were found significantly increased in the DAT group ( p < 0.05). Kynurenine and taurine were found significantly decreased in the DAT group ( p < 0.05). Amino acid metabolism was also altered between incident DAT and DNC groups ( Table S11 ). Proline and tryptophan were down-regulated and histidine and N-acetylneuraminic acid were up-regulated in the DAT, compared with the DNC ( p < 0.05). In addition, there were also significant differences between DNC and RNC groups ( Table S12 ). The levels of serine, N-acetylneuraminic acid, glycine, and sulfocysteine were found to increase in DNC, compared with the RNC ( p < 0.05), while kynurenine was decreased ( p < 0.05). Compared with the RNC group, the levels of N-acetylneuraminic acid were found to significantly increase both in the DAT and DNC groups ( p < 0.05), and Kynurenine was found to significantly decrease ( p <0.05) ( ). Different from their effects on lipid metabolism, RPTS, and PN did not regulate abnormal amino acid metabolism effectively. The levels of serine and sulfocysteine were found to increase in the DPL group, compared with the DAT group ( p < 0.05) ( Table S13 ). Serine was found to increase in the DRL group, compared with the DAT group ( p < 0.05), while arginine was decreased in the DRL group ( p < 0.05) ( Table S14 ). There was a tendency for taurine, tryptophan, and proline to elevate in the DPL group, but it was not statistically significant, whereas these amino acids were decreased in the DAT group. Furthermore, we found that the abundance of leucine in the DRL group tends to increase, but without significant difference. The lipids and amino acids involved in the findings are shown in . Regulation of Lipid-Related Metabolic Enzymes by RPTS and PN We found that the main altered lipids in the DAT group were glycerophospholipids dominated by PE, PC, and LPE. Moreover, we found that RPTS and PN regulate disorders of glycerophospholipid metabolism. Thus, the therapeutic effect of RPTS and PN on experimental colitis with damp-heat type might be related to its ability to regulate disorders of glycerophospholipid metabolism. In order to better understand how RPTS and PN work, we chose to look into the regulatory impact of RPTS and PN on glycerophospholipid metabolism-related enzymes, such as cytosolic phospholipase A2 (cPLA2), Secreted phospholipases A2 (sPLA2), phosphatidylethanolamine N-methyltransferase (PEMT). Compared with the RNC group, the colonic expression levels of sPLA2 and cPLA2 in the DAT group were significantly increased, and low-dose RPTS and PN treatment could lower the expression of sPLA2 and cPLA2 ( and ). In addition, compared with the RNC group, the colonic expression of PEMT was markedly decreased in the DAT group, while low-dose RPTS and PN administration had an influence on the level of PEMT ( ). Meanwhile, the levels of sPLA2 and cPLA2 in the plasma were significantly increased in the DAT group. Low-dose PN significantly decreased the levels of sPLA2 and cPLA2 ( p < 0.05). Compared with the DAT group, low-dose RPTS reduced the plasma levels of sPLA2 and cPLA2, but the differences were not statistically significant ( and ). Correlation Analysis of Serum Metabolites and Cytokines In our study, DAT group-specific metabolites were correlated with cytokines ( ). Among them, PE O-37:0 and Cer 41:1–2 were highly positively correlated with IL-1B, TNF-α and IL-6 (r > 0.8, p < 0.001). CAR 22:1, CAR 20:1, and LPE 18:0 were highly positively correlated with IFN-γ (r > 0.8, p < 0.001). CAR 22:1 and LPE 18:0 were moderately positively correlated with IL-1B, TNF-α, and IL-6 (r > 0.5, p < 0.001). CAR 16:3, Cer 42:1–4, Cer 42:2–3, HexCer 40:7–1, LPE 16:0, CAR 18:0, TG 56:4–1 and TG 57:9–2 were moderately positively correlated with IL-1B, TNF-α, IL-6 and IFN-γ (r > 0.5, p < 0.05). In addition, PE O-37:0 was highly negatively correlated with TGF-β, IL4, IL-10 and IL-13 (|r| > 0.8, p < 0.001). N-acetylneuraminic acid was moderately correlated with IL-1B, TNF-α, IL-6, and IFN-γ (r > 0.5, p < 0.01). Meanwhile, N-acetylneuraminic acid had a highly negative correlation for TGF-β, IL4, IL-10, and IL-13 (|r| > 0.8, p < 0.001). Histidine was moderately correlated with IL-1B, TNF-α, IL-6, and IFN-γ (r > 0.5, p < 0.05).
The rat colitis model was induced by TNBS. The administration of TNBS dramatically reduced food intake, and loss of weight was seen. The animals of the DAT group, DRL group, and DPL group suffered a drastic weight loss compared with the RNC group ( ). While the RNC groups retained their normal structure, TNBS caused damage to the intestinal mucosa of the rats, including transmural involvement, ulcerations, edema, and bowel wall thickening. Colonic tissue responded better to the RPTS therapy (50 mg/kg), with improvements in the damage score, degree of damaged areas, and colon weight / length ratio, leading to an increase in colon length ( and ). Compared with the DAT group, 50 mg/kg RPTS significantly reduced DAI score ( p < 0.05), alleviated colonic shortening, and improved colonic mucosal bleeding ( ). Histologically, the DAT group, in contrast to the RNC groups, displayed an intensive transmural interruption, significant ulceration, inflammation, edema, and huge infiltration of neutrophils, primarily in the mucosa ( ). Mild interstitial neutrophil and lymphocyte infiltration were observed in the DNC group when compared to the RNC group. The DAS, DRL, DRH, DPL, and DPH groups showed various degrees of colonic mucosal erosion remission, but discontinuous intestinal epithelial lesions with granulomatous hyperplasia, mild to moderate interstitial neutrophil and lymphocyte infiltration, and structural changes in the crypt were still visible. Compared with the RNC group and DNC group, the histology score was significantly increased in the DAT group ( p < 0.001). Compared with the DAT group, the histology score of the DPL and DAS groups decreased significantly ( p < 0.01) ( ). DRL reduced histology scores, but the difference was not statistically significant. In conclusion, low-dose RPTS and PN ameliorated the severity of TNBS-induced colitis. The therapeutic effect of low doses of RPTS and PN was more effective than that of high doses of RPTS and PN for the relief of colitis. Based on this, we continued to focus on the therapeutic effects of low-dose RPTS and PN in the treatment of TNBS-induced experimental colitis and investigate the mechanism of action.
Abnormal activation of the immune response plays a direct role in the development and progression of IBD. Cytokines and cytokine-producing immune cells play a key role in the pathogenesis of IBD. We studied the effects of RPTS and PN on the levels of S100A8/9, Arg-1 and cytokines. illustrates the variations in TNF-α, IL-1B, IL-18, IFN-γ, IL-6, IL-4, TGF-β, IL-10, IL-13, S100A8, S100A9, and Arg-1 levels in the plasma of the rats in each group. In the DAT group, plasma levels of pro-inflammatory cytokines (TNF-α ( p < 0.01), IL-1B ( p < 0.05), IFN-γ ( p < 0.001), and IL-6 ( p < 0.01)) were significantly increased, and anti-inflammatory cytokines (IL-4 ( p < 0.01), TGF-β ( p < 0.01), IL-10 ( p < 0.01), IL-13 ( p < 0.05)) significantly decreased, and IL-18 did not change significantly. Meanwhile, the levels of S100A8 ( p < 0.001), S100A9 ( p < 0.001), and Arg-1 ( p < 0.01) were significantly increased in the DAT group. Low-dose PN significantly decreased the levels of pro-inflammatory cytokines TNF-α ( p < 0.01), IL-1B ( p < 0.01), IFN-γ ( p < 0.001) and IL-6 ( p < 0.01) and elevated the levels of anti-inflammatory cytokines IL-4 ( p < 0.01), TGF-β ( p < 0.001), IL-10 ( p < 0.01) and IL-13 ( p < 0.05) while decreasing levels of S100A8, S100A9, and Arg-1 ( p < 0.01). Compared with the DAT group, low-dose RPTS significantly reduced the levels of pro-inflammatory cytokines TNF-α ( p < 0.05), IFN-γ ( p < 0.01), and IL-1B ( p < 0.05) and could downregulate the levels of IL-6, S100A8, S100A9, Arg-1, and increased the levels of IL-4, TGF-β, IL-10, IL-13, but the difference was not significant.
As previously mentioned, patients with IBD have abnormal lipid metabolism profiles. To investigate the different lipids closely associated with the illness process in experimental colitis of damp-heat type and to get ready for the subsequent study on the mechanism of the anti-IBD effect of RPTS and PN, lipid Pseudotargeted metabolomics technologies based on UHPLC-TQ/MS systems were undertaken. After data preprocessing and metabolite identification, we discovered 2696 lipids characteristics from the raw data recorded in positive- and negative-ionization modes, including 701 triacylglycerols (TGs), 254 Diacylglycerol (DGs), 25 Cardiolipin (CLs), 50 Lysophosphatidylcholine (LPCs), 37 Lysophosphatidylethanolamine(LPEs), 152 Phosphatidic acids (PAs), 261 phosphatidylcholines (PCs), 268 phosphatidylethanolamines (PEs), 109 Phosphatidylglycerols (PGs), 142 phosphoinositols (PIs), 114 Phosphatidylserine (PSs), 82 ceramides (Cers), 88 monohexosylceramide(Hexcer), 78 dihexosylceramide (Hex2cer), 80 Sphingomyelins (SMs), 31 Cholesterol ester (CEs), 27 other sterol lipids, 39 Acyl carnitine (CARs), 43 fatty acids (FAs), 83 unsaturated fatty acids (USFA) and others ( ). According to a thorough investigation, glycerophospholipids, which made up 42.95% of the overall lipid abundance, had the highest relative abundance when compared to sphingolipids, glycerolipids, and fatty Acyls. Glycerolipids had the second largest relative abundance, followed by sphingolipids and fatty Acyls ( ). To further analyze the effects of different factors on rats, we first performed a comparative analysis of RNC and DAT two groups. The RNC group and DAT group were distinct from each other using the PCA approach, which was focused on discriminating against the tested groups in an unsupervised pattern ( Figure S1a and b ). To demonstrate that the DAT group was significantly separated from the control group, OPLS-DA was a more effective application ( Figure S1c – f ). In the positive and negative modes, 245 and 49 important differential metabolites with significant effects on the modeling analysis were obtained. Furthermore, based on VIP > 1, p < 0.05, and FC > 1.5 or < 2/3, 294 differentiated metabolite ions were identified. 209 ions were upregulated and 85 ions were downregulated in the DAT group ( Table S6 ). These metabolites are mainly composed of 4 major classes. Through cluster analysis (heatmap) ( Figure S1g and h ) and volcano plot ( Figure S1i and j ), we could observe a distinct metabolic profile between DAT and RNC in plasma samples. Compared with the RNC group, the rats in the DAT group showed predominantly glycerophospholipid metabolic changes accompanied by changes in glycerolipids and sphingolipids abundance, and these differential lipid abundance were markedly elevated. Using the same statistical approach, compared with the DNC group, we find that the plasma lipid profiles of rats in the DAT group ( Table S7 ) still showed changes in glycerophospholipid metabolism, accompanied by changes in the abundance of glycerolipids and sphingolipids, and the abundance of these different lipids was significantly higher ( Figure S2 ). However, the trends of changes in the types and abundance of differentiated lipids in the DAT and DNC groups were different from those in the DAT and RNC groups. In the same way, comparing the DNC group with the RNC group ( Table S8 ), we found that the differential lipids in the DNC group were dominated by PC, PI, and TG, with a significant decrease in partial glycerophospholipid abundance compared with the RNC group, which was the main change of lipid metabolism in damp-heat type ( Figure S3 ). To better screen the specific lipids for experimental colitis with damp-heat type, we performed a comprehensive analysis of the three groups of differential metabolites. Analysis of variance (ANOVA) was performed on the screened metabolites from the three groups. We found that 146 differentiated metabolite ions of the DAT group were identified compared to RNC and DNC ( Table S9 ). PCA and particularly OPSL-DA suggest differences in metabolite profiles of the three groups ( ). A heatmap depicting the top 25 differentially abundant metabolites between DAT, DNC, and RNC ( and ) Among them, glycerophospholipids (47.95%), sphingolipids (26%), carnitine (15%), and glycerolipids (7.5%) were the four most perturbed lipid classes in DAT groups’ plasma samples. LPC 11:0, PC 43:6, PE 32:4, PE 32:5, PI 35:4, and PI 44:2 were significantly downregulated and others were upregulated. These metabolites were specific targets of DAT ( ).
The results of animal studies showed that low doses of RPTS and PN were more effective in relieving colitis. A lipidomic analysis was also performed to determine the lipid profile transformation in DRL and DPL in comparison with DAT. First, we screened the differential metabolites between the DPL and DAT groups to explore the changes in lipid metabolic profiles after using PN. The treatment group and model group were distinct from each other using the PCA approach. Furthermore, based on VIP > 1, p < 0.05, and FC > 1.5 or < 2/3 in OPLS-DA, 68 differentiated metabolite ions were identified. 28 ions were upregulated and 40 ions were downregulated in the DPL group. Through cluster analysis, we could observe a distinct metabolic profile between DPL and DAT in plasma samples. PGE2, PGF22, PGD2, 8(9)-EpETrE, 14.15-DiHETrE, TG 64:16-2-a, TG 61:7, Hex2Cer 39:2–1, Hex2Cer 37:1–5 and Dehydroepiandrosterone were upregulated. PG 40:7–2, PS 38:5–2, PS 35:2, PS 37:6, PE O-33:0, PE O-35:0, PE 34:0, CL (72:6_18:2) −2H, DG 40:1–8 and so on were downregulated. Compared with the DNC and RNC groups, it is also noteworthy that PE 34:0, PE O-33:0, PE O-37:0, HexCer 36:2–6, HexCer 40:7–1, TG(e)54:2 were all significantly upregulated in DAT groups, which were then reversed by PN treatments. At the same time, when compared to DAT, RPTS could regulate disorders of lipid metabolism. Among them, PGE2, PGD2, PGF22, 8(9)-EpETrE, PS 44:8–1, PE 48:0, trenbolone, HexCer 38:3–7, HexCer 34:0–1, DG 37:2–7 and others were upregulated. PG 43:6, PI 32:0, PS 38:5–2, PE O-33:0, PE 34:1, LPE 20:5, DG 40:7–4, HexCer 39:1–4, and so on were downregulated. The considerable upregulation of PI 32:0, PG 42:6, PE 34:0, HexCer 40:7–1, LPE 20:3, LPE 22:5, PE 38:6, PE 40:8, and TG(e) 54:2 was reversed by RPTS treatments. Both RPTS and PN significantly downregulated the contents of PE 34:0, PE O-33:0, and TG(e)54:2. However, PG 42:6, PI 32:0, LPE 20:3, LPE 22:5, PE 38:6, and PE 40:8 were only moderately downregulated by RPTS. This suggested that these lipids were specific targets of RPTS but not PN. On the other hand, Cer 38:2–5, HexCer 36:2–6, and PE O-37:0 were specific targets of PN. Since RPTS and PN had differing preferences for modifying lipid profiles, the underlying processes of effect on TNBS-induced experimental colitis were diverse ( ).
In addition to the changes in lipid profile, the amino acid metabolic profile of IBD patients was also disturbed. Therefore, we conducted amino acids targeted metabolomics to determine the amino acid metabolism characteristics of experimental colitis with damp-heat type. To evaluate the repeatability and reliability of the method, QC samples were found clustered in the PCA scores plot. Then, the DAT and DNC groups were also well separated from the RNC group in the PCA and OPLS-DA analyses, with good model parameters respectively. A total of 35 amino acids were detected. It indicated that significant amino acid metabolic changes occurred in the DAT and RNC groups ( Table S10 ). Compared with the RNC group, the levels of serine, histidine, N-acetylneuraminic acid, and sulfocysteine were found significantly increased in the DAT group ( p < 0.05). Kynurenine and taurine were found significantly decreased in the DAT group ( p < 0.05). Amino acid metabolism was also altered between incident DAT and DNC groups ( Table S11 ). Proline and tryptophan were down-regulated and histidine and N-acetylneuraminic acid were up-regulated in the DAT, compared with the DNC ( p < 0.05). In addition, there were also significant differences between DNC and RNC groups ( Table S12 ). The levels of serine, N-acetylneuraminic acid, glycine, and sulfocysteine were found to increase in DNC, compared with the RNC ( p < 0.05), while kynurenine was decreased ( p < 0.05). Compared with the RNC group, the levels of N-acetylneuraminic acid were found to significantly increase both in the DAT and DNC groups ( p < 0.05), and Kynurenine was found to significantly decrease ( p <0.05) ( ). Different from their effects on lipid metabolism, RPTS, and PN did not regulate abnormal amino acid metabolism effectively. The levels of serine and sulfocysteine were found to increase in the DPL group, compared with the DAT group ( p < 0.05) ( Table S13 ). Serine was found to increase in the DRL group, compared with the DAT group ( p < 0.05), while arginine was decreased in the DRL group ( p < 0.05) ( Table S14 ). There was a tendency for taurine, tryptophan, and proline to elevate in the DPL group, but it was not statistically significant, whereas these amino acids were decreased in the DAT group. Furthermore, we found that the abundance of leucine in the DRL group tends to increase, but without significant difference. The lipids and amino acids involved in the findings are shown in .
We found that the main altered lipids in the DAT group were glycerophospholipids dominated by PE, PC, and LPE. Moreover, we found that RPTS and PN regulate disorders of glycerophospholipid metabolism. Thus, the therapeutic effect of RPTS and PN on experimental colitis with damp-heat type might be related to its ability to regulate disorders of glycerophospholipid metabolism. In order to better understand how RPTS and PN work, we chose to look into the regulatory impact of RPTS and PN on glycerophospholipid metabolism-related enzymes, such as cytosolic phospholipase A2 (cPLA2), Secreted phospholipases A2 (sPLA2), phosphatidylethanolamine N-methyltransferase (PEMT). Compared with the RNC group, the colonic expression levels of sPLA2 and cPLA2 in the DAT group were significantly increased, and low-dose RPTS and PN treatment could lower the expression of sPLA2 and cPLA2 ( and ). In addition, compared with the RNC group, the colonic expression of PEMT was markedly decreased in the DAT group, while low-dose RPTS and PN administration had an influence on the level of PEMT ( ). Meanwhile, the levels of sPLA2 and cPLA2 in the plasma were significantly increased in the DAT group. Low-dose PN significantly decreased the levels of sPLA2 and cPLA2 ( p < 0.05). Compared with the DAT group, low-dose RPTS reduced the plasma levels of sPLA2 and cPLA2, but the differences were not statistically significant ( and ).
In our study, DAT group-specific metabolites were correlated with cytokines ( ). Among them, PE O-37:0 and Cer 41:1–2 were highly positively correlated with IL-1B, TNF-α and IL-6 (r > 0.8, p < 0.001). CAR 22:1, CAR 20:1, and LPE 18:0 were highly positively correlated with IFN-γ (r > 0.8, p < 0.001). CAR 22:1 and LPE 18:0 were moderately positively correlated with IL-1B, TNF-α, and IL-6 (r > 0.5, p < 0.001). CAR 16:3, Cer 42:1–4, Cer 42:2–3, HexCer 40:7–1, LPE 16:0, CAR 18:0, TG 56:4–1 and TG 57:9–2 were moderately positively correlated with IL-1B, TNF-α, IL-6 and IFN-γ (r > 0.5, p < 0.05). In addition, PE O-37:0 was highly negatively correlated with TGF-β, IL4, IL-10 and IL-13 (|r| > 0.8, p < 0.001). N-acetylneuraminic acid was moderately correlated with IL-1B, TNF-α, IL-6, and IFN-γ (r > 0.5, p < 0.01). Meanwhile, N-acetylneuraminic acid had a highly negative correlation for TGF-β, IL4, IL-10, and IL-13 (|r| > 0.8, p < 0.001). Histidine was moderately correlated with IL-1B, TNF-α, IL-6, and IFN-γ (r > 0.5, p < 0.05).
IBD is a chronic inflammatory disease and is generally thought to be caused by a variety of factors, including genetics, the environment, and the gut microbiome etc. Nowadays, the incidence of IBD is on the rise in the worldwide scale, and some anti-inflammatory drugs and immunosuppressants have been used to treat IBD. However, current therapeutic strategies have inherent drawbacks and are unable to reach the ultimate goal with long-term remission. Many Chinese herbs have shown clinical efficacy against IBD and are expected to be candidates for the treatment of IBD. Among the herbs, Rhizoma Paridis has been reported to have antioxidant, anti-infection, anti-inflammatory, anti-allergy effects. To the best of our knowledge, the therapeutic effect of Rhizoma Paridis on IBD has not been reported. In this paper, the therapeutic effects of its main extracts, RPTS or PN on experimental colitis of damp-heat type was studied, and some new insights were provided for the basic research and clinical application of Rhizoma Paridis. In the current study, we established TNBS-induced colitis with the damp-heat type and explored its lipid and amino acid metabolic profile, as well as demonstrated that both RPTS and PN improved the disease. Our data showed that low-dose RPTS and PN significantly downregulate circulating cytokines in experimental colitis with damp-heat type. Furthermore, we intensively assessed alterations in lipid and amino acid metabolism in rats with TNBS-induced colitis with damp-heat type by using integrative pseudotargeted lipidomic and amino acids targeted metabolomics analyses. Pseudotargeted lipidomic metabolomics revealed that glycerophospholipids, sphingolipids, carnitine, and glycerolipids were the four most perturbed lipid classes in plasma samples of DAT groups. Amino acids targeted metabolomics revealed that, in the DAT group, serine, N-acetylneuraminic acid, histidine, proline, taurine, and kynurenine changed significantly compared with the RNC group. Among these differential lipids and amino acids, some of Cer, SM, CAR, LPE, PE, TG, and N-acetylneuraminic acid were highly positively correlated with pro-inflammatory cytokines, suggesting that these metabolites are closely related to inflammation and immunity. Meanwhile, we assessed the effects of low dosages of RPTS and PN on the lipid metabolic spectrum and amino acid metabolism in plasma. Interestingly, the effects of RPTS and PN on the dysregulation of the lipid profile were surprisingly different, although RPTS and PN both regulate glycerophospholipid metabolism and sphingolipid metabolism, especially the abundance of PE and LPE. RPTS mainly regulates PE, PG, and PS, and significantly reduced TG accumulation. However, PN mainly regulated metabolites related to sphingolipid metabolism. These results revealed that, although both RPTS and PN affected TNBS-induced experimental colitis, their underlying mechanisms might be distinct. However, both of them did not have a significant effect on amino acid modulation. Based on the results of drug efficacy of RPTS and PN on damp-heat type with experimental colitis in rats, we found that low doses of RPTS and PN were more effective in relieving colitis than high doses group. Low-dose RPTS could increase body weight, decrease DAI score, decrease histopathological score and significantly increase colon length. Low dose of PN could increase colon length, decrease colon weight / length ratio and significantly reduce the histopathologic score. However, the effect of high-dose RPTS and PN groups was poor. Studies have shown that low intake of Rhizoma Paridis may have therapeutic effects on disease, but long-term excessive intake could cause irreversible damage. , In order to explore the metabolic regulation of RPTS and PN on experimental colitis of damp-heat type, the study has focused on the effect of the low-dose group on the damp-heat type with experimental colitis. Based on our previous clinical findings, the IBD and IBD with damp-heat syndrome (IBD-DH) groups showed changes in plasma glycerophospholipid metabolic pathways, but the types of glycerophospholipid metabolites were different. However, there are few studies on the metabolic characteristics of IBD-DH patients and animal models. In this investigation, we described a comprehensive plasma lipidome of experimental colitis of damp-heat type. To be more specific, we showed that the plasma lipidomic profiles in the DAT group were significantly altered when compared to the RNC group. In the DAT group, there were noticeable variations in the glycerophospholipid metabolism, sphingolipid metabolism, glycerolipid metabolism, and carnitine metabolism. Similar to clinical study, in animal experiments, glycerophospholipids (PLs) metabolism disorder were also found in the DAT group. Glycerophospholipids serve as binding sites for both internal and extracellular proteins and are the primary elements of cell membranes. PC, PE, PG, PI, and PS are the five groups that are divided based on polar headgroup. Several interrelated enzyme pathways, including the Kennedy pathway (PC and PE synthesis), the cytidine diphosphate diacylglycerol (CDP-DAG) pathway (synthesis of PS and PI from PA), and the Lands cycle, are used to generate and modify PLs (FA removal and reattachment to PLs). Previous studies have confirmed that IBD patients and experimental colitis animals both have altered glycerophospholipids. , In previous studies, PC and LPC were the metabolites that changed significantly. For instance, research has shown that disturbed PC catabolism and LPCs had considerably higher plasma abundance in the TNBS-induced acute colitis rats model. In the DSS-induced colitis mice, Wang et al discovered altered LPC expressions, including decreased LPC (18:1) and LPC (18:2), and elevated LPC (18:0). Reduced LPC (18:1) was also found in pediatric CD patients who had recently received a diagnosis. Similarly, our data showed that some LPCs and PCs have risen and some have fallen in the DAT group. Moreover, PC is hydrolyzed by phospholipase (PLA) to create LysoPC (LPC). LPC is mainly derived from the turnover of phosphatidylcholine (PC) in the circulation by phospholipase A2 (PLA2). Changes in PC and LPC may be closely related to PLA2. Patients with IBD have significantly higher activity and expression levels of PLA2 in their inflamed intestinal regions. Low-dose PN reduced sPLA2 and cPLA2 levels measured by ELISA in our study. We, therefore, hypothesized that PN is acting on PLA2 and thus regulating PC and LPC. The most noticeable differences in PE and LPE were observed between the healthy control group and the IBD group. These phenomena are consistent with what we have observed. In our results, significant changes in PE and LPE in the DAT group were found. Almost all cell membranes of organisms contain the two lipid groups, PE, and LPE, which are involved in cellular signaling, division, death, and inflammation. Mammalian cells can produce PE by four different processes, including the base-exchange pathway, the CDP-ethanolamine pathway, the PSD pathway, and the acylation of LPE. We therefore speculated that elevated PE may be related to multiple pathways and other phospholipid transformations. PS is delivered to the mitochondria for decarboxylation into PE by PS decarboxylase (PSD). PS and PE levels are closely related to mitochondrial function. Research has shown that UC patients had a considerably higher abundance of LPS and PS, and patients with CD had significantly greater concentrations of PSacyl (PSa) 40:3 and PSa 38:3 and also had a positive correlation with Crohn’s Disease Activity Index. Our data also showed that in the DAT group, the abundance of PS increased. Apart from PS, PC is closely related to PE. An essential enzyme in the formation of hepatic phosphatidylcholine (PC) is phosphatidylethanolamine N-methyltransferase (PEMT). During three consecutive methylation processes, PEMT transforms phosphatidylethanolamine (PE) into PC. Excessively high or low cellular PC / PE molar ratios influence the energy metabolism of different tissues, which have also been connected to the development of disease. In addition, in CD patients significant changes were also found in PI and PA. , In addition to serving as a substrate for lipid kinases and phosphatases, which can produce phosphoinositide derivatives, phosphatidylinositol (PI) is a crucial structural phospholipid (PIP). According to in vivo studies, phospholipids play a protective role in barrier function and have a significant impact on cytokine release. Our results showed a decline in the abundance of PI in DAT group. Both RPTS and PN regulate PE and LPE and PE-related glycerophospholipids. In addition to regulating PLA2, RPTS and PN may regulate other glycerophospholipid-related enzymes, such as PEMT. Similar to PLs, sphingolipids (SLs) were significantly altered in the DAT group. Representative membrane lipids known as SLs are crucial for the maintenance and balance of the gastrointestinal (GI) immune system. Studies have specifically examined the role of SLs in IBD. Ceramides increase in the current data in accordance with the UC condition, from remission to active inflammation, its trends are consistent with the results of our study. Ceramides and their derivatives, including lactosylceramide, sphingomyelin, and sphingosine 1-phosphate, have been shown to change in IBD patients in several investigations. Ceramide is regarded as the center of SL metabolism as well as the structural foundation of other SLs. Ceramides can be produced de novo, from the remedial pathway, or from the hydrolysis of sphingolipids (SM) or other complex SLs, which play a crucial role in inflammation, immunology, and inflammatory diseases. Ceramides can also be produced by additional routes, which may involve certain bacteria and cytokines. The abundance of ceramide in macrophage cell lines was previously demonstrated to increase quickly in response to LPS, TNF, and IL-1. In our research, there was a strong correlation between circulating levels of inflammatory cytokines such as IL-6, IL-1B, and TNF and Cer42:2:3, Cer41:1:2. Thus, it became crucial to concentrate on more complex issues, such as which specific SL(s), at what dose, and via what mechanism mediates cytokine signaling (s). Furthermore, the addition of ceramide may initiate PLC activation and subsequently potentiate the sequential PKC-MAPK cascade-cPLA2 pathway in order to cause arachidonic acid (AA) release and cPLA2 activation. Ceramide 1-phosphate (Cer-1-P) activates cPLA2 possibly in response to IL-1B stimulation. Ceramide was also discovered to mediate sPLA2 activation and the expression of TNF-induced COX-2. We should also focus on the effect of ceramide on PLA2 in response to proinflammatory cytokine stimulation. Consistent with the change in Ceramides, LacCer may be a promising biomarker for IBD patients. On granulocytes, monocytes, and platelets, LacCer, also known as CD17, is expressed. It is involved in cell-cell communication, intracellular signaling, nitric oxide production, and phagocytosis. TNF-α, a proinflammatory cytokine that is a well-known target in the treatment of IBD, mediates LacCer production. Moreover, LacCer causes the release of arachidonic acid (AA), an inflammatory mediator, and the activation of phospholipase A2 (PLA2). In addition, SM and Hexcer also changed significantly in the DAT group. PN tends to regulate sphingolipids more than RPTS. We speculate that this tendency may be related to low-dose PN modulating pro-inflammatory factors better. Unlike ceramide, which is highly correlated with IL-6, CAR22:1, and CAR20:1 are highly correlated with IFN. Total medium-chain acylcarnitines and long-chain carnitine esters were evidently present in larger amounts in UC patients than in control persons. Carnitine transports and carries long-chain fatty acids (LCFAs). As a result, the interaction of different lengths of fatty acid chains attached to acylcarnitines may play a role in the pathogenesis of UC. Additionally, L-carnitine is crucial for energy metabolism, since it allows the transfer of activated long-chain fatty acids (LCFA) as carnitine esters through the inner mitochondrial membrane. Since the availability of acylcarnitines is the rate-limiting step in the -oxidation of fatty acids, this fact along with a higher abundance of acylcarnitine derivatives suggests an enhanced oxidation of fatty acids in IBD patients. Indeed, higher energy demanded to mobilize immune cells to combat inflammation may be the cause of increased oxidation in IBD patients. The immunological response could become more pronounced and pro-inflammatory factors produced. Consistent with this, we found that CARs were highly correlated with IFN. However, we did not find evidence for RPTS and PN in regulating CAR in the lipid profiles. Our metabolomic profiling also discovered changes in certain amino acid metabolism in the DAT group. Of these, serine is closely related to lipid metabolism. Serine is involved in ceramide synthesis and PS synthesis. Several studies have shown that non-essentialamino acids (serine) were decreased in IBD subjects compared to control subjects. , However, our results indicated an increase in serine in the DAT group compared to the RNC group, but no significant difference in serine of the DAT group compared to the DNC group. More experiments are needed to verify the exact mechanism. Additionally, tryptophan and its metabolites have been identified as significantly altered in the blood of patients with IBD compared to controls. A vital amino acid called tryptophan can be found in meals high in protein. The kynurenine pathway is responsible for the metabolism of more than 90% of the dietary tryptophan. The initial and rate-limiting step in converting TRP to KYN is carried out by the enzyme indoleamine 2.3-dioxygenase (IDO) 1. Blood levels of IBD patients have lower level of tryptophan. Our results showed that tryptophan and kynurenine were significantly lower in the DAT group. Since we noted that kynurenine was also decreased in the DNC group, we speculate that the difference may be related to damp-heat type. Specific mechanisms need to be explored further. Despite the large number of metabolites that have been discovered, there is still a significant gap between the identification of these substances and the understanding of how they specifically contribute to IBD. This research might reveal novel molecular pathways and biomarkers implicated in the etiology of IBD. Future research should incorporate the metabolomic profiles with additional findings obtained from other omics. Analytical procedure variations could potentially contribute to the conflict between studies. Future studies with larger sample sizes should further validate changes in the lipidomic profiles provided here when utilizing a quantitative targeted approach.
Low dose of RPTS and PN from Rhizoma Paridis could alleviate experimental colitis of damp-heat type. Lipidomic and targeted quantitative amino acid metabolomics revealed disorders of plasma lipid and amino acid metabolism in rats caused by combined modeling of damp-heat type and TNBS. Most of these differential metabolites were significantly associated with pro-inflammatory cytokines. Low doses of RPTS and PN could regulate glycerophospholipid metabolism. The mechanism may be related to reducing pro-inflammatory cytokines, increasing anti-inflammatory cytokines, and regulation of lipid metabolism-related enzymes, such as sPLA2, cPLA2, and PEMT. But it may not be related to amino acid metabolism.
|
88d37217-8f82-4836-82cd-435f3fc18588 | 11096282 | Anatomy[mh] | Pancreatic ductal adenocarcinoma (PDAC) represents 90% of pancreatic cancers . It is a highly malignant disease with a 5‐year survival rate of ~10% and is projected to become the second leading cause of cancer death before 2030 . Because of its nonspecific symptoms and aggressive biological behaviors, the majority of PDAC patients are diagnosed at an advanced stage and have a median survival of only 10–12 months . Frequent recurrence after surgery and a lack of effective treatment options strongly contribute to the poor outcome of affected patients. Despite continuous efforts over the past years, novel therapeutic methods have turned out to have minimal effect in improving the prognosis of PDAC patients and predictive biomarkers are still lacking. Following the path of treatment in other cancer entities, one way to move forward is the development of molecularly based patient stratification strategies and to identify different types of PDAC with differing therapy responses. In this scenario, molecular alterations of pancreatic cancer need urgently to be further clarified to identify vulnerabilities for individualized molecularly guided patient treatments. Over decades, PDAC had been perceived as a molecularly ‘boring’ disease, homogenously driven by KRAS and TP53 mutations. However, research conducted in the last decade indicates that pancreatic cancer has a more complex makeup than previously considered with broad intra‐ and intertumoral heterogeneity . By RNA sequencing and DNA translocation profiling, the heterogeneity could be elucidated and different molecular subtypes of PDAC were defined, initially as the classical, exocrine‐like, and quasi‐mesenchymal types by Collisson et al . Through further investigations, these three subtypes were summarized into two subtypes by other groups, differentiating only between a classical/pancreatic progenitor subtype, which is enriched in GNAS mutations and includes the former classical and exocrine‐like subtypes ; and the basal‐like/squamous subtype, with highly abundant TP53 mutations, consistent with the former quasi‐mesenchymal subtype . Possible contributions to better selection of treatment for individual patients or refined definition of patient‐specific prognosis has been discussed for some of these new molecular classifiers . Although the novel subtyping methods could stratify tumors into prognosis‐related and potentially treatment relevant groups, the technical requirements as well as high per case costs continue to limit the wide clinical application of individualized RNA expression profiling for a clinically relevant subtype classification of PDAC patients. Therefore, a cheap, widely available immunohistochemical alternative, which can be used in clinical practice, is desperately needed. In our previous study, we suggested hepatocyte nuclear factor 1A (HNF1A) and cytokeratin‐81 (KRT81) as two surrogate protein markers to categorize the subtypes with innate molecular features . By evaluating the expression levels of these proteins by immunohistochemistry (IHC), we proposed three subtypes described as KRT81‐positive, HNF1A‐positive, and double‐negative; and concordance between our IHC‐based subtypes and those defined by Collisson et al was shown. The KRT81‐positive group recaptured the main features of the quasi‐mesenchymal subtype, while the HNF1A‐positive group showed an association with the exocrine‐like subtype and the double‐negative group with the classical subtype . The biological and prognostic relevance of the IHC‐based subtypes has been further confirmed in our subsequent studies . The subtyping method was applied in two independent primary resected PDAC cohorts, and we could identify robustly different prognostic PDAC subtypes: the KRT81‐positive subtype with the worst survival, the HNF1A‐positive subtype with the best survival, and the double‐negative subtype with an intermediate survival. However, so far, a certain number of double‐positive cases, which we always encountered, had to be excluded as not definitely classifiable, as they represented the smallest subtype group and patients showed variable biological behavior. In the study presented here, we expand our previously investigated cohort from 130 to 269 cases, reinvestigate the prognostic value of our IHC‐based subtyping and revise our subtyping method to avoid the exclusion of double‐positive cases. Furthermore, we validate the revised subtyping model in the tissue cohort of a controlled, prospectively documented, and randomized phase III trial (CONKO‐005), which investigated the efficacy of erlotinib in the adjuvant treatment of resectable PDAC. Patient cohorts Two cohorts, one retrospectively assembled from our archives, the second prospectively recruited in the context of the prospective CONKO‐005 trial, were investigated in this study. The primary resected cohort (cohort 1, Munich), consisting of 269 individuals who received an elective pancreatic resection for PDAC, between 2007 and 2016 at the Department of Surgery, Klinikum rechts der Isar, TU Munich, Germany. Grading and staging followed the World Health Organization (WHO) recommendations at the time of cohort generation (TNM classification of the seventh edition). Clinical data and follow‐up were obtained from a patient database, by reviewing the medical charts and directly contacting the patients and/or their physicians. The observation period for each patient started with the surgical resection. The study was approved by the Institutional Review Board (ethics committee) of the TU Munich, Germany (documents no. 1926/2007 and 126/2016 S). Second, a sub‐cohort of the CONKO‐005 was investigated (cohort 2, Berlin) consisting of 286 patients with resectable PDAC after R0 resection between 2008 and 2013. The tissue cohort was prospectively generated accompanying this previous published controlled, open‐label, multicenter, randomized phase III trial. Patients received six cycles adjuvant gemcitabine (1,000 mg/m 2 intravenously; days 1, 8, 15; every 4 weeks) with or without erlotinib (100 mg orally once per day; days 1–28; every 4 weeks). The primary endpoint was to improve disease‐free survival (DFS) by adding erlotinib to gemcitabine, which achieved a statistical power of 80% from 14 to 18 months. The protocol required patients to have R0 resection with histologically tumor‐free surgical margins as defined by International Union Against Cancer criteria (sixth edition, 2002). Overall, 436 patients were randomly assigned at 57 study centers between April 2008 and July 2013. With a median follow‐up of 54 months, no difference in median DFS (GemErlo 11.4 months; Gem 11.4 months) or median overall survival (GemErlo 24.5 months; Gem 26.5 months) was detected. There was a trend toward long‐term survival in favor of GemErlo (estimated survival after 1, 2, and 5 years for GemErlo was 77%, 53%, and 25% versus 79%, 54%, and 20% for Gem, respectively) . Histological examinations and assessments were conducted in the pathology department of the recruiting centers under the WHO recommendations (TNM classification of the seventh edition). Follow‐up examinations were performed every 3 months for 2 years or until disease recurrence, and then every 6 months for up to 5 years or until death. The start point of the observation was the surgical resection. Use of patient data was approved by the Central Ethics Committee of Berlin (EudraCT2007‐003813‐15). The study has been registered in the German Clinical Trials Register (DRKS) with the registration number DRKS00000247. Detailed study outcome data have been published elsewhere . Patient and public involvement All patient data were collected after informed consent and retrospectively analyzed in accordance with the ethics committee vote. Patients had the right to withdraw their permission of data and material usage at all stages of the study. Because of the retrospective nature of the study, patients were not directly involved in the study design or plans to disseminate study results. Nevertheless, patient interests were considered at all points of the study, as the avoidance of unnecessary side effects due to better tumor stratification and therapy selection is of self‐evident value to future patients. This study was performed in accordance with the Declaration of Helsinki. Written consent of subjects was obtained. Immunohistochemistry Tissue microarrays of primary tumors were constructed and used for immunohistochemical staining. In both cohorts, two to three tissue cores with a diameter of 1‐mm tumor area were taken from each FFPE block and inserted into the blank tissue microarray block. The tumor areas were previously marked by a board‐certified pathologist (AM). TMAs were made using a tissue microarrayer (Beecher Instruments, Tartu, Estonia). For cohort 1 (Munich), IHC staining of cases from 2007 to 2011 was performed by hand; more details of the staining method were shown in our previous published paper . Staining of cases from 2012 to 2016 was performed on a Ventana Benchmark XT (Ventana Medical Systems, Tucson, AZ, USA). For cohort 2 (Berlin), IHC staining of all the cases was performed on a Ventana Benchmark XT (Ventana Medical Systems). Epitopes were unmasked by boiling slides in citrate buffered distilled water (pH 6) for 15 min in a pressure cooker and allowing a 30‐min cool‐down period. The Dako REAL Peroxidase Detection System kit (Dako, Jena, Germany) was used according to the manufacturers' specifications, including the ready‐to‐use anti‐rabbit/mouse secondary antibody (catalog no. K5003). Primary antibodies used were rabbit polyclonal anti‐HNF‐1A antibody (catalog no. sc‐8986) at a dilution of 1:100, and mouse monoclonal anti‐keratin 81 antibody (catalog no. sc‐100929) at a dilution of 1:500, both from Santa Cruz Biotechnology Inc (Dallas, TX, USA). Primary antibodies were incubated for 2 h at room temperature. Slides were digitalized using a Leica Aperio AT2 slide scanner (Leica, Wetzlar, Germany) and examined with an Olympus CX31 light microscope (Olympus Corporation, Tokyo, Japan). The images were obtained using Leica Aperio ImageScope software (version 12.4.6.5003, Leica). IHC assessment The IHC staining of all slides was reviewed and evaluated by an experienced, board‐certified pathologist (AM). The staining intensity scores of HNF1A were graded as follows: 0, no staining; 1, ‘weak’ indicated by barely discernable light brown nuclear staining only visible at high magnifications (at least ×100); 2, ‘medium’ identified by heterogeneous nuclear staining of varying shades of medium to dark brown; and 3, ‘strong’ where there was homogeneous dark brown staining. Tumor cells with medium to strong nuclear staining were classified as HNF1A‐positive, regardless of the percentage of stained tumor cells. Normal small intestine was used as positive control, due to its moderate to strong positivity for HNF1A in epithelial cells. KRT81 staining was evaluated using a threshold of ≤30% positive tumor cells as KRT81‐negative, and >30% positive tumor cells as KRT81‐positive, regardless of staining intensity. The proportion of KRT81positive tumor cells was visually estimated, as it is hard to recognize the cellular borders and manually count the exact numbers of single tumor cells under an interferential background of strong cytoplasmic staining of KRT81. Thirty percentage was selected as a cutoff value because it was high enough to avoid possible overinterpretation caused by KRT81‐positive budding tumor cells while maintaining the necessary detection sensitivity. Most cases in our cohorts could be clearly classified as above or below the threshold resulting in a minimal number of borderline cases. Samples of normal skin served as positive control as KRT81 is strongly expressed in hair follicles. In the previously used four‐tier classification system, tumors negative for both HNF1A and KRT81 were classified as double‐negative, and tumors with expression of both markers above the thresholds double‐positive. In the three‐tier classification system proposed in this study, tumors with expression of both markers were included in the new HNF1A‐positive subtype due to their comparable clinical behavior. Statistical analysis All statistical analyses were carried out using SPSS software (IBM Corp., Armonk, NY, USA, version 27.0). The Kaplan–Meier analysis was performed to estimate and compare survival curves between different subgroups followed by log‐rank test. Associations of clinicopathological features with IHC‐based subtypes were examined using chi‐square test (or Fisher's exact test when appropriate) for categorical variables, Spearman correlation coefficient for two interval variables, and one‐way ANOVA for interval variable with nominal/ordinal variables. The independent prognostic factors in PDAC were determined by univariate Cox regression and multivariate Cox regression. p values lower than 0.05 were considered as statistically significant. For multivariable survival analysis, tumor grade and UICC (Union for International Cancer Control) stage were considered as prognostically relevant factors. Additionally, in cohort 1 the resection status after surgery by histopathological examination was considered. In cohort 2, the CA19.9 value in blood was tested after surgery as an indicator of residual tumor. The cases were divided into three groups according to their postoperative CA19.9 values (low: ≤100 U/ml; medium: >100 U/ml and ≤500 U/ml; high: >500 U/ml). Two cohorts, one retrospectively assembled from our archives, the second prospectively recruited in the context of the prospective CONKO‐005 trial, were investigated in this study. The primary resected cohort (cohort 1, Munich), consisting of 269 individuals who received an elective pancreatic resection for PDAC, between 2007 and 2016 at the Department of Surgery, Klinikum rechts der Isar, TU Munich, Germany. Grading and staging followed the World Health Organization (WHO) recommendations at the time of cohort generation (TNM classification of the seventh edition). Clinical data and follow‐up were obtained from a patient database, by reviewing the medical charts and directly contacting the patients and/or their physicians. The observation period for each patient started with the surgical resection. The study was approved by the Institutional Review Board (ethics committee) of the TU Munich, Germany (documents no. 1926/2007 and 126/2016 S). Second, a sub‐cohort of the CONKO‐005 was investigated (cohort 2, Berlin) consisting of 286 patients with resectable PDAC after R0 resection between 2008 and 2013. The tissue cohort was prospectively generated accompanying this previous published controlled, open‐label, multicenter, randomized phase III trial. Patients received six cycles adjuvant gemcitabine (1,000 mg/m 2 intravenously; days 1, 8, 15; every 4 weeks) with or without erlotinib (100 mg orally once per day; days 1–28; every 4 weeks). The primary endpoint was to improve disease‐free survival (DFS) by adding erlotinib to gemcitabine, which achieved a statistical power of 80% from 14 to 18 months. The protocol required patients to have R0 resection with histologically tumor‐free surgical margins as defined by International Union Against Cancer criteria (sixth edition, 2002). Overall, 436 patients were randomly assigned at 57 study centers between April 2008 and July 2013. With a median follow‐up of 54 months, no difference in median DFS (GemErlo 11.4 months; Gem 11.4 months) or median overall survival (GemErlo 24.5 months; Gem 26.5 months) was detected. There was a trend toward long‐term survival in favor of GemErlo (estimated survival after 1, 2, and 5 years for GemErlo was 77%, 53%, and 25% versus 79%, 54%, and 20% for Gem, respectively) . Histological examinations and assessments were conducted in the pathology department of the recruiting centers under the WHO recommendations (TNM classification of the seventh edition). Follow‐up examinations were performed every 3 months for 2 years or until disease recurrence, and then every 6 months for up to 5 years or until death. The start point of the observation was the surgical resection. Use of patient data was approved by the Central Ethics Committee of Berlin (EudraCT2007‐003813‐15). The study has been registered in the German Clinical Trials Register (DRKS) with the registration number DRKS00000247. Detailed study outcome data have been published elsewhere . All patient data were collected after informed consent and retrospectively analyzed in accordance with the ethics committee vote. Patients had the right to withdraw their permission of data and material usage at all stages of the study. Because of the retrospective nature of the study, patients were not directly involved in the study design or plans to disseminate study results. Nevertheless, patient interests were considered at all points of the study, as the avoidance of unnecessary side effects due to better tumor stratification and therapy selection is of self‐evident value to future patients. This study was performed in accordance with the Declaration of Helsinki. Written consent of subjects was obtained. Tissue microarrays of primary tumors were constructed and used for immunohistochemical staining. In both cohorts, two to three tissue cores with a diameter of 1‐mm tumor area were taken from each FFPE block and inserted into the blank tissue microarray block. The tumor areas were previously marked by a board‐certified pathologist (AM). TMAs were made using a tissue microarrayer (Beecher Instruments, Tartu, Estonia). For cohort 1 (Munich), IHC staining of cases from 2007 to 2011 was performed by hand; more details of the staining method were shown in our previous published paper . Staining of cases from 2012 to 2016 was performed on a Ventana Benchmark XT (Ventana Medical Systems, Tucson, AZ, USA). For cohort 2 (Berlin), IHC staining of all the cases was performed on a Ventana Benchmark XT (Ventana Medical Systems). Epitopes were unmasked by boiling slides in citrate buffered distilled water (pH 6) for 15 min in a pressure cooker and allowing a 30‐min cool‐down period. The Dako REAL Peroxidase Detection System kit (Dako, Jena, Germany) was used according to the manufacturers' specifications, including the ready‐to‐use anti‐rabbit/mouse secondary antibody (catalog no. K5003). Primary antibodies used were rabbit polyclonal anti‐HNF‐1A antibody (catalog no. sc‐8986) at a dilution of 1:100, and mouse monoclonal anti‐keratin 81 antibody (catalog no. sc‐100929) at a dilution of 1:500, both from Santa Cruz Biotechnology Inc (Dallas, TX, USA). Primary antibodies were incubated for 2 h at room temperature. Slides were digitalized using a Leica Aperio AT2 slide scanner (Leica, Wetzlar, Germany) and examined with an Olympus CX31 light microscope (Olympus Corporation, Tokyo, Japan). The images were obtained using Leica Aperio ImageScope software (version 12.4.6.5003, Leica). assessment The IHC staining of all slides was reviewed and evaluated by an experienced, board‐certified pathologist (AM). The staining intensity scores of HNF1A were graded as follows: 0, no staining; 1, ‘weak’ indicated by barely discernable light brown nuclear staining only visible at high magnifications (at least ×100); 2, ‘medium’ identified by heterogeneous nuclear staining of varying shades of medium to dark brown; and 3, ‘strong’ where there was homogeneous dark brown staining. Tumor cells with medium to strong nuclear staining were classified as HNF1A‐positive, regardless of the percentage of stained tumor cells. Normal small intestine was used as positive control, due to its moderate to strong positivity for HNF1A in epithelial cells. KRT81 staining was evaluated using a threshold of ≤30% positive tumor cells as KRT81‐negative, and >30% positive tumor cells as KRT81‐positive, regardless of staining intensity. The proportion of KRT81positive tumor cells was visually estimated, as it is hard to recognize the cellular borders and manually count the exact numbers of single tumor cells under an interferential background of strong cytoplasmic staining of KRT81. Thirty percentage was selected as a cutoff value because it was high enough to avoid possible overinterpretation caused by KRT81‐positive budding tumor cells while maintaining the necessary detection sensitivity. Most cases in our cohorts could be clearly classified as above or below the threshold resulting in a minimal number of borderline cases. Samples of normal skin served as positive control as KRT81 is strongly expressed in hair follicles. In the previously used four‐tier classification system, tumors negative for both HNF1A and KRT81 were classified as double‐negative, and tumors with expression of both markers above the thresholds double‐positive. In the three‐tier classification system proposed in this study, tumors with expression of both markers were included in the new HNF1A‐positive subtype due to their comparable clinical behavior. All statistical analyses were carried out using SPSS software (IBM Corp., Armonk, NY, USA, version 27.0). The Kaplan–Meier analysis was performed to estimate and compare survival curves between different subgroups followed by log‐rank test. Associations of clinicopathological features with IHC‐based subtypes were examined using chi‐square test (or Fisher's exact test when appropriate) for categorical variables, Spearman correlation coefficient for two interval variables, and one‐way ANOVA for interval variable with nominal/ordinal variables. The independent prognostic factors in PDAC were determined by univariate Cox regression and multivariate Cox regression. p values lower than 0.05 were considered as statistically significant. For multivariable survival analysis, tumor grade and UICC (Union for International Cancer Control) stage were considered as prognostically relevant factors. Additionally, in cohort 1 the resection status after surgery by histopathological examination was considered. In cohort 2, the CA19.9 value in blood was tested after surgery as an indicator of residual tumor. The cases were divided into three groups according to their postoperative CA19.9 values (low: ≤100 U/ml; medium: >100 U/ml and ≤500 U/ml; high: >500 U/ml). Frequency distribution and clinicopathologic patient characteristics By using the revised algorithm, we could assign every case in both cohorts to one of the defined groups. For the staining of a double‐positive case, see Figure . For example images of HNF1A and KRT81 staining, see supplementary material, Figure . In cohort 1, the most prevalent subtype was double‐negative with 113 cases (42.0%), followed by the HNF1A‐positive subtype with 84 cases (31.2%), the KRT81‐positive subtype with 51 cases (19.0%), and the double‐positive subtype 21 cases (7.8%). In cohort 2, the double‐negative subtype remained the most numerous with 143 cases (50.0%) as in the first cohort, followed by the KRT81‐positive subtype (77cases, 27.0%) and the HNF1A‐positive subtype (47 cases, 16.4%).The double‐positive subtype was still the least common with 19 cases (6.6%) (supplementary material, Figure ). An overview table of the clinicopathologic characteristics of the investigated patient cohorts is provided as supplementary material, Table . An overview table of the clinicopathologic characteristics according to IHC‐based subtypes is provided as supplementary material, Table . In this study, we further analyzed the association between the revised IHC subtypes and clinicopathologic characteristics. Sex was found to have a significant correlation with subtypes in a previous cohort ; however, the distribution did not differ in both cohorts investigated in this study (cohort 1: p = 0.227, cohort 2: p = 0.346). Additionally, no associations were observed between subtype and age or other prognostic variables, namely grade, UICC stage, nodal status, and surgical resection status (cohort 1) or postoperative CA19.9 levels (cohort 2) (supplementary material, Table ). Prognostic value of IHC ‐based subtype in PDAC cohorts In a direct comparison between the previously used four‐tier and the three‐tier classification system proposed in this study, we found that the previously unclassifiable double‐positive cases showed relatively good survival, similar to HNF1A‐positive cases, allowing us to integrate cases of both subtypes into a combined new HNF1A‐positive subtype proposed here. Kaplan–Meier survival analysis for each subtype in cohort 1 indicated that the new ‘combined’ HNF1A‐positive subtype was associated with the best survival (median 619 days), followed by the double‐negative subtype with an intermediate survival (median 600 days). The KRT81‐positive subtype was associated with the worst survival (median 413 days; p < 0.001). For the clinical study cohort 2, similar survival curves for the different subtypes were observed; the new ‘combined’ HNF1A‐positive group was associated with the best outcome with a median survival time of 1,067 days, followed by the double‐negative subtype (median 821 days) and the KRT81‐positive subtype (median 531 days; p < 0.001) (Figure ). Multivariate analysis in both cohorts showed that IHC‐based subtypes were an independent prognostic factor ( p = 0.001 in cohort 1; p = 0.003 in cohort 2). As expected, other independent prognostic factors were tumor stage ( p < 0.001 in cohort 1; p = 0.002 in cohort 2), incomplete resection status in cohort 1 ( p = 0.002) and postoperative CA19.9 level in cohort 2 ( p < 0.001). Tumor grade in cohort 2 was found to significantly relate to survival (cohort 2 p = 0.008; cohort 1 p = 0.049). For an overview of multivariate analysis, see Table . Predictive value of IHC subtyping in cohort 2 Multivariate analysis in cohort 2 showed that patients with the new HNF1A‐positive subtype tended to benefit more from a cotreatment of gemcitabine with erlotinib than the double‐negative subtype or the KRT81‐positive subtype despite failing to reach the threshold of statistical significance ( p = 0.064, supplementary material, Figure ). In the cotreatment group, the new HNF1A‐positive subtype achieved the best overall survival and the KRT81‐positive subtype the worst ( p = 0.007, Figure ). However, in the gemcitabine‐only treatment group there was no significant difference in overall survival between the three IHC‐based subtypes ( p = 0.134, Figure ). By using the revised algorithm, we could assign every case in both cohorts to one of the defined groups. For the staining of a double‐positive case, see Figure . For example images of HNF1A and KRT81 staining, see supplementary material, Figure . In cohort 1, the most prevalent subtype was double‐negative with 113 cases (42.0%), followed by the HNF1A‐positive subtype with 84 cases (31.2%), the KRT81‐positive subtype with 51 cases (19.0%), and the double‐positive subtype 21 cases (7.8%). In cohort 2, the double‐negative subtype remained the most numerous with 143 cases (50.0%) as in the first cohort, followed by the KRT81‐positive subtype (77cases, 27.0%) and the HNF1A‐positive subtype (47 cases, 16.4%).The double‐positive subtype was still the least common with 19 cases (6.6%) (supplementary material, Figure ). An overview table of the clinicopathologic characteristics of the investigated patient cohorts is provided as supplementary material, Table . An overview table of the clinicopathologic characteristics according to IHC‐based subtypes is provided as supplementary material, Table . In this study, we further analyzed the association between the revised IHC subtypes and clinicopathologic characteristics. Sex was found to have a significant correlation with subtypes in a previous cohort ; however, the distribution did not differ in both cohorts investigated in this study (cohort 1: p = 0.227, cohort 2: p = 0.346). Additionally, no associations were observed between subtype and age or other prognostic variables, namely grade, UICC stage, nodal status, and surgical resection status (cohort 1) or postoperative CA19.9 levels (cohort 2) (supplementary material, Table ). IHC ‐based subtype in PDAC cohorts In a direct comparison between the previously used four‐tier and the three‐tier classification system proposed in this study, we found that the previously unclassifiable double‐positive cases showed relatively good survival, similar to HNF1A‐positive cases, allowing us to integrate cases of both subtypes into a combined new HNF1A‐positive subtype proposed here. Kaplan–Meier survival analysis for each subtype in cohort 1 indicated that the new ‘combined’ HNF1A‐positive subtype was associated with the best survival (median 619 days), followed by the double‐negative subtype with an intermediate survival (median 600 days). The KRT81‐positive subtype was associated with the worst survival (median 413 days; p < 0.001). For the clinical study cohort 2, similar survival curves for the different subtypes were observed; the new ‘combined’ HNF1A‐positive group was associated with the best outcome with a median survival time of 1,067 days, followed by the double‐negative subtype (median 821 days) and the KRT81‐positive subtype (median 531 days; p < 0.001) (Figure ). Multivariate analysis in both cohorts showed that IHC‐based subtypes were an independent prognostic factor ( p = 0.001 in cohort 1; p = 0.003 in cohort 2). As expected, other independent prognostic factors were tumor stage ( p < 0.001 in cohort 1; p = 0.002 in cohort 2), incomplete resection status in cohort 1 ( p = 0.002) and postoperative CA19.9 level in cohort 2 ( p < 0.001). Tumor grade in cohort 2 was found to significantly relate to survival (cohort 2 p = 0.008; cohort 1 p = 0.049). For an overview of multivariate analysis, see Table . IHC subtyping in cohort 2 Multivariate analysis in cohort 2 showed that patients with the new HNF1A‐positive subtype tended to benefit more from a cotreatment of gemcitabine with erlotinib than the double‐negative subtype or the KRT81‐positive subtype despite failing to reach the threshold of statistical significance ( p = 0.064, supplementary material, Figure ). In the cotreatment group, the new HNF1A‐positive subtype achieved the best overall survival and the KRT81‐positive subtype the worst ( p = 0.007, Figure ). However, in the gemcitabine‐only treatment group there was no significant difference in overall survival between the three IHC‐based subtypes ( p = 0.134, Figure ). In a wide range of solid tumors, a clinically relevant correlation between molecular subtyping and prognosis has been observed, and the understanding of molecular processes underlying different patient outcomes has led to more precise treatment and improved survival . Studies in resectable PDAC patients proposed RNA expression‐based molecular subclassification systems based on the characterization of activated/suppressed pathways regulated by epigenic modifications and posttranscriptional mechanisms . Although the proposed subtyping methods were somewhat different, certain kinds of distinct subtypes could be identified by most research groups, showed a certain degree of overlap and were confirmed to have a significant relevance for treatment responses and overall survival . It was found that currently used postoperative chemotherapy may produce very different results for PDAC patients even within the same histopathological type of tumor . However, there are inherent difficulties for subtyping methods based on gene expression analysis hindering its implementation in routine diagnostics. In our previous research , we attempted to discover potential IHC markers based on the established RNA classification systems, which could help in building a clinically implementable novel classification system. The IHC classifier was proposed to recapitulate the major functions of the molecular subtyping method used by Collisson et al . while being easy to use, cheap, and widely available. We identified two surrogate IHC markers, KRT81 and HNF1A, which we used to define three subtypes. KRT81 was initially characterized in hair follicle formation but was later found to have associations with several kinds of cancer such as non‐small cell lung cancer or breast cancer . It has been reported that KRT81 plays a predominant role in cancer progression by activating genes related to tumor invasion and migration, such as matrix metallopeptidase 9 and lipocalin 2 . KRT81 may also function as a regulator of inflammatory cytokine interleukin‐8 and be involved in malignant transformation in melanoma . The transcription factor HNF1A was originally identified as an important factor in the regulation of glucose metabolism and plays a role in the development of diabetes , and also in B‐cell differentiation . HNF1A has been reported to have conflicting functions in tumor progression and drug resistance among different cancer types. According to Fujino et al and Wang et al , HNF1A inhibition led to a significant reduction of proliferation and anticancer drug resistance of non‐small cell lung cancer and colorectal cancer via glucose metabolism . However, another recent study suggested that upregulation of HNF1A induces remarkable inhibition of tumor growth and platinum‐based chemotherapy resistance in PDAC through the activation of p53‐binding protein 1 . In a cohort including 217 patients, the HNF1A‐positive subgroup (exocrine‐like) ranked best for overall survival, while the KRT81‐positive subgroup (basal‐like) had the worst survival, and double‐negative (classical) cases showed an intermediate survival. This was in line with survival data reported for the RNA‐based classifications . To validate the feasibility of the IHC‐based subtyping method, a subsequent study investigating three independent PDAC cohorts was conducted by us . In two primarily resected cohorts, we found a significant association between the identified subtypes and patient outcome. Again, the KRT81‐positive subtype had the worst prognosis, while the HNF1A‐positive subtype had the best, and the double‐negative subtype showed intermediate survival. In a cohort of advanced stage, nonresectable patients receiving primary chemotherapy, the KRT81‐positive subtype also showed dismal survival compared to the other two subtypes, while no significant difference in survival between the HNF1A‐positive and double‐negative subgroup could be found. Additionally, the KRT81‐positive patients did not seem to profit from intensified chemotherapy with FOLFIRINOX, whereas patients with HNF1A‐positive tumors seemed to have the biggest benefit. In the adjuvant treatment of PDAC patients, gemcitabine‐based chemotherapy or mFOLFIRINOX are standards of care resulting in 5‐year survival rates from 20% to almost 50% . So far, the choice of these different regimens is only done with regard to the general condition. Especially in patients with good performance status, predictive biomarkers are urgently needed to identify the best treatment option with the lowest side effects. This is even more relevant in the ongoing discussion about neoadjuvant and perioperative treatment strategies . In our previous research, a few double‐positive cases (3/262, 18/130, 5/125) were defined as ‘unclassifiable’ as the survival curves of this small patient subgroup seemed to cross with the curves of the other three subtypes due to small sample size, providing limited prognostic value. In the study presented here, we expanded one of the previously investigated cohorts from 130 to 269 patients and introduced a new, independent primary resected cohort. Now, in our enlarged cohort, these double‐positive cases showed relatively good survival, similar to the HNF1A‐positive subtype. Therefore, a revised stratifying method was generated in which we aggregated the double‐positive and HNF1A‐positive cases into a combined new HNF1A‐positive group. Compared with the classifiers used in our previous research, the modified classification system remains essentially the same but allows us now to avoid exclusion of any individuals of the cohort, thus enabling the IHC‐based subtyping of all PDAC patients. In both cohorts, the double‐negative subtype remained the largest subgroup and double‐positive subtype the smallest. The number of HNF1A‐positive cases was more than that of KRT81‐positive cases in cohort 1 but the opposite was observed in cohort 2. The reason for the variance of subtype frequencies might be due to the fact that the two cohorts investigated in our research adopted different criteria to select patients. Compared to the retrospective cohort 1, cohort 2 was generated in the context of a phase‐3 clinical trial, which demand significantly stricter inclusion criteria which might have induced a selection bias. We could validate again that there was a marked survival difference between the IHC‐based subtypes. In short, the new HNF1A‐positive subtype was associated with the best overall survival at a median survival of 619 days in cohort 1 and 1,067 days in cohort 2, the KRT81‐positive the worst (413 days in cohort 1, 531 days in cohort 2) with the double‐negative subtype in between (600 days in cohort 1 and 821 days in cohort 2). The revised three‐tier classifier including the double‐positive cases in the HNF1A‐positive subgroup reliably retained the statistically significant difference in overall survival across the three subtypes. From a pathogenetic point of view, the underlying mechanism of different survival between IHC‐based as well as molecular subgroups is not yet fully understood. In the previous studies, the molecular subtypes proposed by different research groups and used as references in our research are almost entirely based on transcriptomic data, which only reflects substantial epigenetic changes in PDAC . As described in those studies, the differences in the genomic landscape between these subtypes appeared to be minor. Except the four predominate driver genes ( KRAS , TP53 , SMAD4 , and CDKN2A ), just a handful of genes mutated at 5–10% prevalence in PDAC cohorts were detected, which could not be well linked with the histological characteristics, transcriptomic data, or prognosis of the tumors . This finding is also in line with the unpublished results of the present study. It was suggested by Collison et al based on the data of Singh et al that the classical subtype, which is recapitulated to a certain extent by our double‐negative subtype might be more dependent on the common KRAS mutations than the other subtypes . However, none of the authors suggested possible differences in carcinogenesis between the subtypes as it still remains unclear whether they represent differences in tumor generation and progression or different equilibriums of tumor–host interaction. Therefore, the interpretation of biological relevance and oncogenesis between subtypes at a genomic level needs further investigation and more convincing evidence. Apart from its prognostic value, the potential predictive value of the IHC‐based subtype was also investigated. In cohort 2, patients with the new HNF1A‐positive subtype showed a propensity to profit from cotreatment with gemcitabine and erlotinib in contrast to those with double‐negative and KRT81‐positive subtypes ( p = 0.064). Besides, in the multivariate analysis of cotreatment group, patients with the new HNF1A‐positive subtype achieved the best survival ( p = 0.007), while in the gemcitabine‐only treatment group no significant difference in survival was found among the three subtypes. What merits proper attention is that the revised IHC‐based subtype is not a simple recapitulation of the expression‐based subtype identified by Collisson et al , but incorporates its own subtyping characteristics, especially when the double‐positive cases are taken into account. This subtype, which was expected to relate to worse survival in previous research , appears instead to have a more favorable prognosis. This seems to indicate that biological shift caused by HNF1A expression dominates if KRT81 is coexpressed. In summary, our study again confirms that, by evaluating the IHC expression level of KRT81 and HNF1A as partial but not full surrogates for RNA‐defined PDAC subtypes, PDAC patients could be classified into three distinct subgroups with prognostic implications, which is highly consistent with previous findings. Through an improvement of the classifier, we could include all cases therefore providing more reliable information without patient dropouts, potentially contributing to better clinical decision‐making in the future. The straightforward, easy to use evaluation criteria, and the wide availability of these immunohistochemical assays make the subtyping method practicable and easy to implement into pathological routine diagnostics. The classifier based on our data could not only be investigated in retrospective cohorts, but also included in future clinical trials with an instructive perspective. We believe that research into biologically relevant subtypes of PDAC patients may bring new opportunities for better patient management by individualizing patient treatments based on prognosis and expected treatment response. Overall, our results warrant further research to elucidate the role of KRT81 and HNF1A in PDAC and other tumor entities. JR carried out experiments, performed statistical analysis and visualization and wrote the original draft of the paper. MS conceived and designed the study, reviewed and revised the paper and provided material support. UP, HR, HO, IED, HF and CJ provided material support, curated data, and reviewed and revised the paper. KS provided material support, project administration, and reviewed and revised the paper. AM conceived and designed the study, developed methodology, validated data, and reviewed and revised the paper. All authors were involved in writing the paper and had final approval of the submitted and published versions. Figure S1. Example images of HNF1A and KRT81 staining of different intensities and percentages Figure S2. Frequency distributions of four‐tier and three‐tier subtypes in cohort 1 and cohort 2 Figure S3. Multivariate analysis of survival effects of chemotherapeutic regimens in patient groups with different subtypes Table S1. Overview table of the clinicopathologic characteristics of investigated patient cohorts Table S2. Overview table of the clinicopathologic characteristics of IHC‐based subtypes Table S3. Correlation analysis between IHC‐based subtypes and other clinicopathologic characteristics |
|
Low-frequency magnetic field therapy for glioblastoma: Current advances, mechanisms, challenges and future perspectives | 5bb468f0-201a-443d-9b89-5b18f7460f00 | 11954840 | Cardiovascular System[mh] | Glioblastoma (GBM) is the most common malignant brain tumor, and surgical resection combined with radiotherapy and chemotherapy is the standard treatment for GBM. Nevertheless, the 5-year survival rate of GBM is less than 10%, with a mean survival time of less than 2 years . Despite incremental advances in the therapeutic approach to GBM, including the use of targeted therapy and immunotherapy, there have been no breakthroughs. Currently, the treatment regimens approved by the United States Food and Drug Administration (FDA) for GBM include physical therapies such as tumor-treating fields (TTF) therapy, in addition to radiotherapy and chemotherapy. However, the efficacy remains limited ; thus, it is urgent to explore new methods for the treatment of GBM. Two main reasons for the high failure rate of drug therapy for GBM are the blood–brain barrier limiting drug entry and the high heterogeneity of tumors . High heterogeneity makes monotherapy ineffective or causes short-term drug resistance , but patients treated with multiple drugs have to endure drug-related side effects . Therefore, it is necessary to develop new therapies to address these disadvantages. Noninvasive approaches like light, electrical field, ultrasound, and magnetic fields(MFs) therapies are being explored as alternatives, with MFs therapy showing promise due to its strong penetration, multitarget effects, and minimal side effects ; thus, it can overcome the clinical challenges associated with the above drugs , , , . MFs are classified into static MFs and dynamic MFs based on intensity and direction. MFs are classified into weak MFs (<1 mT), medium-intensity MFs (1 mT − 1 T) and high-intensity MFs (>1 T) based on intensity . According to the frequency, MFs can be divided into low-frequency MFs (<30 kHz), radiofrequency MFs (30–300 kHz), medium-frequency MFs (300 kHz-3 MHz) and higher-frequency MFs (>3 MHz) . MFs therapy involves two main mechanisms: thermal effect (ionizing radiation) and non-thermal effect (non-ionizing radiation). Higher-frequency MFs, which include gamma rays, X-rays, and higher ultraviolet, directly cause DNA damage, while low- frequency MFs mainly affect biochemical reactions . However, few risk factors are known for brain tumors, except for ionizing radiation . In 2002 and 2011, the International Agency for Research on Cancer (IARC) classified extremely low-frequency electromagnetic fields (ELF-EMFs) and radiofrequency electromagnetic fields (RF-EMFs) as “possibly carcinogenic to humans” based on epidemiological studies , . However, in 2012, the International Commission on Non-Ionizing Radiation Protection (ICNIRP) found no evidence of long-term health effects from low-frequency electromagnetic field exposure . Interestingly, several studies have shown that LF-EMFs have anticancer effects, with potential benefits in reducing the risk of certain tumors and improving outcomes for cancer patients ( , ). Eventually, MFs may be developed as a strategy for cancer treatment ( ). This paper aims to conduct a systematic review and analysis of the findings from preclinical studies and clinical trials pertaining to the use of LF-MFs in the treatment of GBM. The focus is on elucidating the potential mechanisms, challenges, future application value of LF-MFs therapy for GBM.
Search strategy A systematic search of the literature was conducted to identify published preclinical and clinical trials that reported studies related to LF-MFs and GBM as of May 1, 2023. The key words “MF or EMF (<30 kHz)”, “ultra-low radio frequency energy”, “glioma” and “GBM” were used to search the literature in the PubMed, Embase, Web of Science, China Knowledge Network (CNKI) and Wanfang databases, and the references of published trials and reviews were also searched for more qualified studies. Studies on the effects of LF-MFs on other organs and systems were excluded from the literature. The search identified 751 unique studies, and we included 41 studies (preclinical studies and clinical trials) that examined the effects of LF-MFs on glioma growth and patient outcomes after screening of title and abstract followed by screening of the full text ( ). Data extraction The following data were extracted from the included preclinical studies: MF type, cell line type, MF parameters, treatment time, effect on tumour, and references. The following parameters were extracted from the included clinical studies: MF type, number and classification of patients, MF parameters, treatment time, effect on tumour, and references.
LF-MFs change the structure of GBM cells Experimental studies have found that exposure of tumour cells, such as GBM cells, to LF-MFs not only causes changes in the overall structure of the tumour cells, such as cell process degeneration, cell elongation, and cell swelling , , , , , but also changes the subcellular structure of the tumour cells, for example, causing disorder of mitochondrial structure and enlargement of the endoplasmic reticulum and decreasing nuclear chromatin density , , , . These changes may be related to the rearrangement of the cytoskeleton and the changes in plasma membrane structure induced by MFs , , , which ultimately disturb the biological functions of tumour cells . At present, it is suggested that the plasma membrane, mitochondria and microtubule spindles are the main targets of LF-MFs, which affect the proliferation, differentiation, migration and apoptosis of tumor cells , , , . LF-MFs induce Ca 2+ influx into GBM cells Abundant evidence has validated that effects of MFs on ion channels in the tumour cell membrane produce subsequent biological effects mainly through three mechanisms. First, ion channel proteins in cell membranes respond to changes in MFs . Second, the phospholipid bilayer structure of the cell membrane is altered by MFs , . Third, the Lorentz force, that is the force of MFs on the moving charges, changes the permeability of the cell membrane to charged particles, thus affecting the depolarization of the membranes , , . Among them, the calcium channel and the calcium signaling pathway may be the first step in the coupling of MFs and living organisms , , , , , . When calcium channel blockers are administered, the magnetobiological effect is significantly decreased , . The specific mechanism of LF-MF therapy may be that it can exert corresponding biological effects by inducing extracellular Ca 2+ influx into GBM cells , , , while changes of the Ca 2+ signalling pathway have been widely proven to affect the proliferation, differentiation, apoptosis, angiogenesis and gene transcription of tumour cells , ( ). In addition, the increase of cytoplasmic Ca 2+ concentration will lead to the increase of reactive oxygen species(ROS) and lipid peroxidation, while the increase of ROS will in turn stimulate the increase of intracellular Ca 2+ concentration, the interaction between ROS and Ca 2+ signaling pathway is bi-directional. ROS can regulate the Ca 2+ signalling pathway, and the Ca 2+ signalling pathway is very important for ROS production . Therefore, the production of ROS and activation of the Ca 2+ signalling pathway may be initial inducible effects induced by LF-MFs in living organisms , . Possible mechanisms underlying LF-MF effects on the regulation of the GBM cell cycle The cell cycle is divided into the G1, S, G2 and M phases, which are closely linked with cell differentiation, growth and death. Abnormal expression of cyclins can accelerate the DNA replication of tumour cells . Multiple studies have confirmed that MFs regulate G1/S and G2/M phase checkpoints and the cyclin-CDK-CKI signalling network of GBM cells by affecting the Rb-E2F and p53 signalling pathways, causing tumour cell death by damaging their DNA and inducing cell cycle arrest or decreasing GBM cell migration , , , , , . Studies have shown that the p53 signalling pathway induces cell apoptosis by arresting cell cycle progression via mediating cyclins , and different LF-MF parameters can activate the p53 signalling pathway in GBM cells , , . Moreover, MFs are also capable of mediating the balance between cell cycle progression and apoptosis by activating the p38-MAPK signalling pathway , , ( ). Potential mechanisms of LF-MFs in regulating the apoptosis of GBM cells Apoptosis is a process of programmed cell death that is vital in tumour treatment . It is initiated by either the mitochondrial pathway (intrinsic pathway) or the death receptor pathway (extrinsic pathway). The former is mainly regulated by the Bcl-2 family. Through the change of mitochondrial permeability, various proapoptotic and antiapoptotic proteins are released to activate caspases and induce apoptosis . Previous studies have suggested the role of LF-MFs in inducing tumor cell apoptosis via multiple ways , , , , , ( ). LF-MFs increase ROS levels in GBM cells , , , , , which further induces apoptosis via the mitochondrial pathway . In addition, as a tumour suppressor gene, p53 is of great significance in cell apoptosis, and LF-MFs not only trigger the apoptosis of GBM cells by upregulating p53 and activating the mitochondrial pathway , , but also sensitize GBM cells to temozolomide (TMZ) and inhibit migration by inducing p53-mediated MGMT inhibition , . In contrast, LF-MFs have also been reported to promote tumour cell proliferation and inhibit apoptosis , , and protect GBM against oxidative stress . The controversial findings regarding the role of LF-MFs in regulating cell apoptosis may be a result of the differences in the frequency, amplitude, exposure time, and cell and/or tissue types used in the experiments . Potential mechanisms of LF-MFs in regulating the ferroptosis of GBM cells Ferroptosis is an iron-dependent type of programmed cell death that is characterized by lipid peroxidation and the accumulation of ROS . Studies have shown that the proliferation of GBM cells can be inhibited by various drugs via inducing ferroptosis, and the erastin, a ferroptosis activator, sensitizes GBM cells to TMZ , . Erastin can also induce cell death through the Ras-RAF-MEK-ERK pathway , , while alternating MFs can regulate cell death by reducing ERK phosphorylation in glioma cells , it is indicated that MFs are able to determine the fate of GBM cells through regulating ferroptosis. A previous study revealed that differentially expressed proteins of tumor cells exposing to LF-MFs were mainly enriched in the p53 signaling pathway . The p53 gene not only regulates the cell cycle, DNA repair, senescence, and apoptosis , but also affects ferroptosis in tumour cells by regulating SLC7A11 or iPLA2β , . It has been suggested that the p53 gene can be used not only as a drug target but also as a target of LF-MFs , , , , and activation of the p53 signalling pathway is observed in GBM cells exposed to different frequencies or amplitudes of LF-MFs , . Therefore, LF-MFs may cause p53-induced ferroptosis to function as effective treatment for GBM ( ). Furthermore, LF-MFs can induce an increase in ROS levels in GBM cells , , , , , and the accumulation of ROS is a crucial indicator involved in ferroptosis , while NAC (ROS scavenger) can inhibit ferroptosis induced by H 2 O 2 in GBM . Additionally, previous research has revealed that MFs, composed of periodical SMF and ELF-MF modulations with time-averaged intensity, induce apoptosis and ferroptosis in other tumor cells through ROS-mediated DNA damage . Therefore, we speculate that ROS accumulation plays a significant role in LF-MFs-induced ferroptosis in glioblastoma, but the specific mechanisms require further exploration. The synergistic effect of LF-MF therapy and chemotherapy in GBM A growing amount of evidence has validated that LF-MFs combined with chemotherapeutic drugs have a synergistic effect in the treatment of GBM; the combination promotes not only apoptosis by increasing the cytoplasmic Ca 2+ concentration and regulating redox balance but also alleviates the aggravation of GBM by promoting cell differentiation and ultimately remarkably reduces the incidences of chemotherapy-induced adverse events and drug resistance , , , , . Moreover, the combination inhibits the migration of GBM cells and enhances their sensitivity to chemotherapeutic drugs by regulating p53, cyclin D1 and MGMT . Previous research reports that the interaction between the Raf/MEK/ERK and PI3K/AKT signalling pathways enhances the proliferation of tumour cells and their ability to avoid apoptosis . The induction of protective autophagy in cells by inhibiting the Akt/mTOR signaling pathway may be one of the mechanisms by which GBM evades apoptosis and, simultaneously, could be a crucial mechanism in the development of drug resistance , . But it is noteworthy that MFs can inhibit drug-induced protective autophagy and enhance drug cytotoxicity by downregulating phosphorylated ERK ( ). Therefore, as a type of adjuvant chemotherapy or radiotherapy, LF-MF therapy enhances sensitivity to chemotherapy drugs and reduces the required dose of antitumour drugs, thus reducing the incidence of adverse events. However, one study showed that exposure to LF-MFs alone inhibited the proliferation of U87 cells, while LF-MF therapy combined with carboplatin downregulated caspase-3 by regulating redox mechanism .
A large number of studies on the biological effects of LF-MFs on glioma cell lines have shown that LF-MFs can inhibit the proliferation of glioma cells (especially GBM cell lines) through a variety of molecular mechanisms and have synergistic or sensitizing effects when applied with tumour chemotherapeutic drugs ( ). These results provide support for the use of LF-MFs in the treatment of gliomas, and some researchers have come up with some surprising results ( ). For example, researchers have discovered that LF-MFs can improve the quality of life of patients with recurrent GBM, and alleviate the peritumoral edema in the surrounding areas of recurrent glioma , . Simultaneously, a patient with recurrent anaplastic astrocytoma underwent pulsed magnetic field. Over the course of 6–36 months of treatment, the tumor gradually reduced, and the clinical symptoms of the patient were alleviated . Additionally, oscillating MF was applied to treat a patient with recurrent glioblastoma, resulting in a 31% reduction in tumor volume by the 31st day, with no apparent side effects . The therapeutic efficacy of LF-MFs in GBM has been widely reported, although the underlying mechanisms remain largely unclear. The latest research has found that the specific ultra-low radio frequency energy ( u /RFE®) signal of a molecule (e.g., chemotherapy drug or siRNA) can be recorded by the superconducting quantum interference device (SQUID) , , , . The specific u /RFE® signal can be amplified and converted into MF energy, which can exert similar effects on GBM cells as anticancer drugs . Based on this discovery, two randomized controlled clinical trials were conducted to treat 26 patients with recurrent GBM using two unique cognates (16 treated with A1A, a u /RFE® cognate that mimics the action of paclitaxel by inhibiting microtubule function , ; and 10 treated with A2HU, a u /RFE® cognate that was derived from siRNA sequences known to inhibit the expression of CTLA-4 and PD-1 ). The fact that 30%–50% of patients in two clinical trials were alive 12 months after starting therapy is encouraging and confirmed that u /RFE® signal-based devices are efficacious in the treatment of GBM and have almost no treatment-related side effects. However, the mechanisms have not been deeply studied, so it is not clear whether LF-MFs can simulate the effects of chemotherapeutic drugs.
The mechanisms underlying the biological effect of MFs have not been fully elucidated due to the complexity of relevant parameters and the diversity of magnetic influences. At present, the following hypotheses have been proposed for explaining the non-thermal biological effects of MFs( ). First, electromagnetic induction theory believes that an induced current and electric field are produced by MFs around living organisms. Cells are considered closed circuits in conductors due to the differences in the electromagnetic characteristics of tissues and organs caused by various charged particles. An additional voltage is produced on the cell membrane because of the intracellular induced electric field following the addition of external MFs . The potential change further causes the opening or closing of voltage-sensitive ion channels such as voltage-gated Ca 2+ channels (VGCCs) , thereafter influencing potential biological effects . In addition, compared with non tumor cells, cancer cells have a larger volume and a larger magnetic flux passing through them, resulting in a larger induced current and induced electric field, and thus, there is a greater impact on cancer cells. Second, the Lorentz force is produced in the moving charged particles of living organisms by MFs. This force influences ion permeability by altering the cell membrane permeability to charged particles, thus regulating biological functions. For instance, the Lorentz force contributes to inducing cancer cell apoptosis by mediating the influx of Ca 2+ , , . Moreover, the Lorentz force induces conformational changes by altering the charge distribution of molecules or proteins, which will affect their biological activity . For example, such changes can affect the charge distribution of tubulin, which modulates mitosis by enhancing the bond between the monomer and dimer, as well as tubulin polymerization . Third, the magnetic susceptibility and magnetic anisotropy of biological samples determine the effectiveness of MF therapy. For example, the orientation of microtubules and DNA with magnetic anisotropy would change in the MF . The diamagnetic anisotropy of cell membrane lipid molecules after an external MF changes the physical properties of the lipid bilayer, thereby regulating ion channels or receptors on the membrane, such as mechanosensitive ion channels , or transmembrane signaling of receptor , . Fourth, MFs exert biological effects by affecting the pairing mechanism of free radicals in tumour cells. Quantum theory posits that paired free radicals or ionic radicals can generate electron spins and magnetic moments, and magnetic interactions can induce changes in the electron spin state and magnetic moment to control biochemical reactions. In addition, as a spin nanoreactor, free radical pairs serve as common chemical keys for magnetism and very low-frequency signals by receiving MFs . By fixing the magnetic moments and reducing electron spins, an additional MF can inhibit or catalyse biochemical reactions by interfering with the conversion of radical pairs from the singlet state to the triplet state . Fifth, magnetobiology is achieved through narrow-band resonance. This theory holds that organisms are selective to MF signals. Tumour cells are believed to be selective to low-frequency alternating MF frequencies, and tumours have tumour-specific frequencies . Such frequency signals can affect the rate of quantum mechanical state transition in biochemical reactions in biological systems. That is, a resonance effect is induced under the same frequency of reactions and magnetic field signals, thus presenting significant biological effects , .
The clinical use of LF-MFs for management of GBM is still in the early stages despite the plethora of preclinical studies in this field ( ). Firstly, MF-induced cell biological effects are the result of the interactions of MFs and cells and are closely related to the parameters of both. However, both biological systems and MFs are very complex, this complexity contributes to the intricate nature of the study of magnetic field biology. Secondly, Electrical characteristics vary greatly in different tissues and cells, resulting in contrary magnetic bioeffects. For instance, MFs can either promote the proliferation of GBM cells , or inhibit it , . Thirdly, tumor cells exhibit a obvious window effect on the response to LF-MFs. Reportedly, CT2A mouse glioma cells, originating from murine glioma (astrocytoma) and recapitulating several features of human high-grade glioma , , are sensitive to the frequency of 33 Hz compared to other frequencies, exposure to which reduced cell activity by 40% , , . Therefore, the frequency close to 30 Hz may produce athermo-biological effect, that is, a window effect . However, at the same frequency, the intensity of MFs determines the cellular outcomes, but it is not simply a linear proportional relationship. It is known as the effect of intensity window , , , , , , . Additionally, LF-MFs can enhance the sensitivity and anticancer capacity of tumor cells to traditional anticancer drugs , , , , , , , . However, they may also inhibit the anticancer efficacy of these drugs , . Therefore, in summary, it is extremely challenging to find the optimal magnetic field parameters(MF type, intensity, frequency, uniformity, direction and treatment time) for different tumor cells, which also makes their practical use more challenging. More importantly, the same MF parameters can inhibit the proliferation of tumour cells, but there are contradictions and controversies about their curative effects in tumour-bearing rats , , . The reasons for the above may be that 2D cell cultures cannot simulate the actual tumor microenvironment and cellular interactions . As an alternative in vitro cell culture technique, 3D tumor spheroids can simulate various aspects of real glioblastoma, bridging the gap between in vitro and in vivo studies of anti-tumor effects . Similarly, animal models used for in vivo assessment of anti-tumor effects, such as humanized animal models and animal models of species with immune systems more closely resembling humans, should be considered as alternative approaches for in vivo evaluation of magnetic field in GBM treatment.
Up to now, the primary reasons for the poor efficacy of GBM treatment are still the blood–brain barrier and tumor heterogeneity. These clinical challenges drove the research towards the development of more efficient therapeutic solutions for GBM. LF-MFs have gradually garnered researchers' attention and have the potential to become one of the most promising therapeutic approaches for treating GBM, owing to several intrinsic characteristic features such as strong penetration and few side effects. Critical analysis of important literature revealed that LF-MFs have been proven to inhibit the proliferation of tumour cells and induce apoptosis, while enhancing sensitivity to anticancer agents. At the same time, the clinical trials have also validated the excellent therapeutic efficacy of LF-MFs in prolonging OS and improving quality of life in GBM patients. In cancer treatments involving different magnetic fields, besides the common LF-MFs, the specific magnetic fields detected by various techniques appear to have strong application potential due to the avoidance of spending a significant amount of time on blindly screening effective anti-tumor magnetic field parameters in the future ( ). Firstly, “Tumor-specific amplitude-modulated electromagnetic fields” refers to the tumor-specific frequencies present in tumors, which can be measured using non-invasive biofeedback techniques , , , . And Sambad Sharma et al. successfully extracted the tumor-specific amplitude-modulated electromagnetic fields from patients with breast cancer, significantly inhibiting tumor growth and preventing metastasis to the brain . Secondly, “The specific u/RFE® signal” refers to the specific ultra-low radiofrequency magnetic field energy signals that can be detected by SQUID in the chemotherapy drugs or anti-cancer biological preparations, which can produce therapeutic effects equivalent to those of anti-cancer drugs , . Thirdly, “Resonance generating fields™ (RGFIELDS™)” describes the utilization of this technology to generate ultra-low-intensity resonance frequencies from the oncogenic or mutated genes in tumors, which possess the capability to inhibit tumor proliferation . Although the reports on the above-mentioned techniques in the treatment of human tumors have been limited, further exploration of these specific frequencies can lead to successful application of these techniques for GBM treatment. Therefore, in the future, if it becomes possible to detect the GBM-specific-frequencies or the magnetic field signals of drugs effective against GBM, the use of a specialized LF-MFs generation device holds the potential for implementing synchronized multi-targeted therapy for GBM. Additionally, with the increased use of whole genome sequencing, proteomics techniques and single-cell sequencing, a variety of oncogenes, proteins or pathogenic tumour cell subsets can be easily identified in tissues of tumour patients, so as to help to identify the optimal ultra-low intensity resonance frequencies needed for customized, personalized therapies that are precise. Furthermore, such strategies can be used to treat animals and patients immediately, which greatly shortens the time it takes for research to move from in vivo animal experiments to clinical trials. However, this therapy is accompanied by some associated side effects of magnetic field treatment, such as headaches, seizures, amnesia, and aphasia , . Therefore, proactive preparation for preventing complications is crucial, such as early administration of oral antiepileptic drugs. Additionally, the limited number of cases included in clinical trials restricts the statistical assessment of the treatment's effectiveness and complications. Therefore, it is necessary to further expand the sample size through prospective controlled studies. Simultaneously, selecting appropriate glioblastoma multiforme (GBM) patients based on statistical results, considering factors like tumor size, spatial location, and peritumoral edema, is essential for reducing the probability of complications associated with MFs therapy. Finally, the effectiveness of LF-MFs therapy may be related to the duration of MFs treatment, and prolonging the treatment time may lead to better therapeutic outcomes. This aspect may require further research and exploration. Although the studies discussed in this review provide a good understanding of the effects of magnetic fields on glioblastoma (GBM), further research is needed to confirm their efficacy in a clinical setting. Due to the strong dependence of treatment outcomes on magnetic field parameters and differences in experimental conditions, comparing results from different literature sources can be extremely challenging. Therefore, detailed and systematic research on the effects of LF-MFs on GBM is necessary in the future. Moreover, the in vitro tests using 2D cell cultures to assess the effectiveness of LF-MFs are limited. GBM patient-derived organoid (PDO) models will be a key direction for future research in investigating the efficacy of LF-MFs.
This article does not contain any studies with human or animal subjects.
Yinlong Liu: Conceptualization, Methodology, Resources, Writing–original draft, Writing – review & editing. Qisheng Tang: Methodology, Datacuration. Quan Tao Validation, Formal analysis, Datacuration. Hui Dong: Validation, Formal analysis, Datacuration. Zhifeng Shi: Validation, Formal analysis, Writing – review & editing. Liangfu Zhou: Validation, Methodology, Writing – review & editing.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
|
Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones | d9eb4c11-dc8f-4c83-9578-ca5219d6c1a3 | 11941470 | Cytology[mh] | Single-cell genomics is key to study genetic heterogeneity in cells of developing and ageing organisms in health and disease. Compared to conventional bulk DNA sequencing, these methods offer enhanced resolution to characterize the mutational processes operative in tissues and organs . Leveraging the detected somatic mutations, the phylogeny of the cells and tissues can be reconstructed , increasing our understanding of the developmental and evolutionary mechanisms of (diseased) tissues as well as the aetiological role of the acquired mutations in the phenotype . The majority of single-cell genome sequencing methods require whole-genome amplification (WGA) to preamplify the genomic DNA (gDNA) and obtain enough material for preparing a sequencing library. In general, WGA methods apply either polymerase chain reaction (PCR, e.g. DOP-PCR, LM-PCR, Ampli1) , multiple displacement amplification (MDA; e.g. RepliG, GenomiPhi) , or a combination of both (e.g. MALBAC, picoPlex) . Amplification, however, introduces coverage bias and nucleotide errors due to buffer and polymerase artifacts. Additionally, the process of pre-amplification followed by library preparation is low throughput, labour intensive, and costly. Recently, tagmentation-based methods were reported, preparing sequencing libraries from single-cell gDNA without upfront WGA , and providing a more uniform representation of the cell's genome, while enabling a higher throughput. Single-cell multi-omics technologies have been developed to simultaneously assess the genome, epigenome, transcriptome, and/or selected proteins of single cells by either joint or parallel processing of the molecular hierarchy or by data analysis methodologies . Methods for the joint processing of gDNA and polyadenylated RNA of single cells that are individually isolated and lysed in a reaction container either use a single-tube gDNA-and-RNA preamplification (DR-seq and scONE-seq) or rely on physical separation of gDNA and RNA before their parallel amplification (G&T-seq and DNTR-seq) [ , , , ]. DR-seq preamplifies gDNA and RNA simultaneously, minimizing nucleic acid loss, and then splits the reaction to complete gDNA and RNA sequencing libraries separately. However, its data analysis and interpretation are complicated due to the presence of RNA-derived reads in the gDNA-seq data. While scONE-seq has the advantage of a one-pot reaction up to differentially barcoded gDNA- and RNA-sequencing libraries, both the gDNA and RNA must be sequenced together, making it impossible to re-sequence only the gDNA or RNA at higher depth, and the method is also less flexible for selecting subsets of gDNA or RNA for sequencing. Physical separation of RNA and gDNA, as in genome-and-transcriptome sequencing (G&T-seq) and DNTR-seq yields a more flexible protocol, allowing the RNA- and gDNA-seq protocols to be adapted to the researcher's needs. This is important because protocols for both RNA-seq and gDNA-seq differ significantly in their performance for detecting (full-length) transcripts and classes of genetic variation across cells, respectively. Up to now, gDNA sequencing in G&T-seq necessitates WGA techniques, making large-scale G&T-seq cost-prohibitive and time-consuming. Also, methods where single cells are not individually tubed but rather processed in pools using principles of combinatorial indexing for joint gDNA and polyadenylated RNA analysis have been developed as in sci-L3-RNA/DNA . Other groups have studied the interplay between genome and transcriptome by either inferring copy number alterations (CNA) from gene expression profiles or integrating separate single-cell gDNA and RNA datasets . While these approaches have led to new insights into tumour heterogeneity, tumour progression, as well as therapeutic strategies, only direct multi-omics approaches allow genotype–phenotype relations to be unambiguously ascertained . Here, we present Gtag&T-sequencing, a method that enables genome-and-transcriptome sequencing of single cells without upfront WGA, enhancing throughput while minimizing coverage bias, amplification noise, and cost. Gtag&T-seq of a patient-derived xenograft (PDX) melanoma model highlights the advantages of genome-based over transcriptome-based CNA inference and the transcriptional effects of complex genomic alterations. We construct a gDNA-based cell lineage tree annotated with RNA-based cell type and state information from the same cells, providing unique insights in the role of genetic and non-genetic factors as well as their interplay during tumour evolution under therapeutic pressure.
PDX model The MEL006 cutaneous melanoma PDX model is part of the Trace collection ( https://gbiomed.kuleuven.be/english/research/50488876/54502087/Trace/PDX-repository ) and was established using lesions derived from a patient undergoing surgery as part of standard treatment at UZ Leuven. Written informed consent was obtained and all procedures were approved by the UZ Leuven/KU Leuven Medical Ethical Committee (S63799, S57760, S58277) and performed in accordance with the principles of the Declaration of Helsinki and with GDPR regulations. The experiments were approved by the KU Leuven animal ethical committee (P164-2019) and performed in accordance with the internal, national, and European guidelines of animal care and use. Single-cell suspensions were implanted subcutaneously in the interscapular fat pad of female NMRI nude BomTac: NMRI-Foxn1nu, 4-week-old females (Taconic Biosciences). Mice were maintained in a pathogen-free facility under standard housing conditions with continuous access to food and water. The health and welfare of the animals was supervised by a designated veterinarian. The KU Leuven animal facilities comply with all appropriate standards (cages, space per animal, temperature [22°C], light, humidity, food, and water), and all cages are enriched with materials that allow the animals to exert their natural behaviour. Mice used in the study were maintained on a diurnal 12-h light/dark cycle. MEL006 was derived from a female, drug-naïve melanoma patient. When the tumour reached 1000 mm 3 , the mice were randomly assigned to the different experimental groups. Mice were treated daily by oral gavage with a capped dose of 600 μg dabrafenib and 6 μg trametinib (DT) in 250 μl total volume. Sample preparation for single-cell sorting HCC38 breast cancer cells and HCC38-BL lymphoblastoid cells were cultured in Dulbecco's Modified Eagle Medium (DMEM/F12) containing 10% fetal bovine serum (FBS) at 37°C in a 5% CO2 incubator . Trypsinized HCC38 cells and HCC38-BL cells were washed in fresh culture medium and then resuspended in FACS sorting buffer (DMEM/F12 supplemented with 5% FBS, 1 mM EDTA, and 1.5 μM DAPI). The BD FACS Melody sorter device was used for sorting single cells into 96-well plates (FrameStar®, 4TI-0960/C) containing 2.5 μl RLT plus buffer (Qiagen). Plates were then spun down at 1000 g for 1 min at 4°C, and finally stored at −80°C. MEL006 cells were retrieved as described before , resuspended in serum-free DMEM/F12 medium and sorted using the BD FACS Aria III into 96-well plates containing 2.5 μl RLT plus buffer. Gtag&T gDNA and RNA separation and DNA precipitation were performed as per the G&T protocol of Macaulay et al. on a Hamilton ® liquid handling robot. First, the plate containing the lysed cells is supplemented with RNA spike-ins [1 μl of a 1:1 600 000 dilution of ERCC spike-in mixture A (Life Technologies)], which is followed by the addition of oligo-dT conjugated to streptavidin beads. After incubation, the poly-adenylated RNAs are collected to the side of the well using a magnet (and processed further according to the G&T-seq protocol), while the DNA present in the supernatant is transferred to a new recipient DNA plate. The wash solution used to rinse the RNA-bead complexes is also added to the DNA plate. After mixing the solution containing the DNA with 0.7:1 ratio AMPure XP beads, the DNA is precipitated using a magnet. For Gtag, the precipitated gDNA was resuspended in 4.5 μl resuspension buffer (0.5X NEB4, 0.37% Igepal CA-630, 0.37% Tween-20). Next, tagmentation is performed in a total volume of 10 μl by adding 5.5 μl tagmentation master mix (5 μl Tagment DNA buffer, 0.1 μl Tagment DNA Enzyme, 0.4 μl nuclease free water) for 10 min at 55°C. The reaction was inactivated by adding 1 μl of 0.44% SDS to the sample and incubating 5 min at 55°C. Then we added 13 μl of Q5 Ultra II (NEB, 2x mastermix), 1 μl S5 primer, and 1 μl S7 primer to the sample. PCR amplification was performed with the following cycling program: 72°C for 3 min; 98°C for 30 s; 16 cycles of 98°C for 10 s, 60°C for 30 s; and 72°C for 30 s, 72°C for 5 min and held at 10°C. In between reaction steps, 96-well plates were placed in an Eppendorf thermomixer at room temperature to mix (1000 rpm) for one minute and briefly centrifuged using a tabletop centrifuge. Finally, the PCR products were pooled per plate and purified using AMPure XP beads (1x ratio). Sequencing library preparation DNA and cDNA quality of picoPlex and Smart-seq2 amplification reactions, respectively, was confirmed using the 2100 Bioanalyzer (high sensitivity chip, Agilent). Next, DNA and cDNA concentrations were determined using a Quantifluor ® Assay (Promega ® ). Samples were diluted to 200 or 100 pg/μl for Nextera XT (Illumina) library preparation in respectively one-fourth or one-tenth of the volume recommended by the manufacturer using manual or automated liquid handling. After library preparation, samples were pooled and purified using AMPure XP beads (0.6x ratio). Quality check of the DNA (picoPlex and Gtag) and cDNA library pools was performed using the 2100 Bioanalyzer (high sensitivity chip, Agilent) in combination with Qubit™ HS (High sensitivity) DNA Assay Kit (Invitrogen™) before diluting the pools to a concentration of 4 nM. We used the KAPA Library Quantification Kit for Illumina ® platforms (Roche, KK4854) on the LightCycler 480 and diluted the sequencing pools. HCC38 and HCC38-BL samples were diluted to 2 nM and sequenced 51-bp single end on a HiSeq2500 platform. The first batch of MEL006 samples was diluted to 1.5 nM and sequenced on a HiSeq4000 platform (51-bp single-end), while the second batch was diluted to 0.75 nM and sequenced on a NextSeq2000 (2 × 50-bp paired-end). Processing of genome data and DNA copy number analysis Single-end sequencing reads obtained were aligned to the GRCh37 human reference genome using BWA-MEM . Samtools was used to sort, index, and sample the mapped BAM files down to 400 000 reads. Our mapping statistics were obtained through samtools (version 1.11) and Picard (version 2.23.8) ( http://broadinstitute.github.io/picard/ ). PCR duplicates were removed with Picard. To create pseudo-bulk genomes, samtools merge was used to combine BAM files. DNA copy number (CN) analysis was performed as discussed in Macaulay et al. . Segmentation of the corrected logR values was done using piecewise constant fitting, with the penalty parameter (γ) set to 10 for the 500 k UMPs bin genomes and γ = 35 for the 10 k UMPs bin genomes. Integer DNA CNs were estimated as 2 logR *Ψ, with the average ploidy of the cell, Ψ, determined as the average CN value with the lowest penalty from a 1.2–6 grid with possible CN values, transformed from segmented LogR values. High penalty values are given to a possible average CN when the sum of squared differences between the unrounded and rounded CN is high. scDNA-seq quality filtering For quality filtering of MEL006 genomes, we only processed single cells with at least 400 000 reads before deduplication. HCC38 genomes were only processed if they had at least 100 000 raw reads before deduplication. We calculated the median absolute pairwise difference (MAPD) score for all samples by first measuring the absolute difference between two consecutive logR values, %GC-corrected and normalized, across the genome. Next, the median across all absolute differences is computed. For MAPD cut-offs, genomes only passed if their MAPD score was less than the 75 th percentile +1.5 times the interquartile range (HCC38, MAPD cut off = 0.64; MEL006, MAPD cut off = 0.76). An overview of the QC pass/fail samples can be found in and . Coverage uniformity calculations Lorenz curves were computed by taking the cumulative fraction of the covered genome against the cumulative fraction of the mapped bases. From the BAM files, duplicates were first removed, and all genomes were downsampled to 230 000 unique reads (with a quality of at least 20). Gini coefficients were calculated in R using the ineq package ( https://cran.r-project.org/web/packages/ineq/ ). Heatmaps and clustering Subclones in the MEL006 dataset were determined using the CopyKit R package (version 0.1.2). The segmented CN values were embedded in two dimensions using the uwot R package (version 0.1.14, min dist = 0, n neighbours = 25, seed = 13). Superclones were identified from the shared nearest neighbour ( K = 15) graph using the R package igraph (version 1.4.2). Subclones were subsequently identified from the UMAP embedding using the clustering algorithm hdbscan from the dbscan R package (version 1.1–11). Regions of focal amplifications were determined on the pseudo-bulk Gtag genomes on 10 k UMPs bins segmented by piecewise constant fitting ( γ = 35) using GISTIC2. For each region, 20 additional genomic bins were taken on each flank for visualization. All heatmaps were constructed using the ComplexHeatmap R package (version 2.10.0). Processing of single-cell RNA seq data After trimming the adaptor sequences with cutadapt (version 1.13), sequencing reads were aligned to the GRCh37 reference genome, including ERCC sequences using STAR with default parameters (version 2.5.2b). HTseq (version 0.6.0) with the GENCODE H19 transcript annotations were used to generate the count matrix. Analysis of single-cell RNA-seq: HCC38 and HCC38-BL Quality control was performed using the scater R package (version 1.28.0): cells with less than 100 000 counts, expression of less than 2000 unique genes, more than 30% counts assigned to mitochondrial sequences or 8% counts belonging to ERCC sequences were removed for downstream analysis. Genes with less than 32 counts across the complete dataset were excluded from downstream analysis. All data analysis was conducted in R version 4.3.0 (CRAN), while plots were created with the ggplot2 (version 3.4.4) R package. Analysis of single-cell RNA-seq: MEL006 Quality control was performed using the scater R package (version 1.28.0): cells with less than 100 000 counts, expression of less than 1000 unique genes, more than 25% counts assigned to mitochondrial sequences or 15% counts belonging to ERCC sequences were removed for downstream analysis . Genes with less than five counts across the complete dataset were excluded from downstream analysis. Expression value scaling and normalization, cell-cycle regression, batch correction, PCA and UMAP dimensionality reductions and clustering were performed using the Seurat R package (version 4.3.2) . Marker gene discovery was performed using the FindAllMarkers function of the Seurat package using the Wilcoxon Ranked Sum test. The R package presto (version 1.0.0) was used to perform a fast Wilcoxon rank sum test where the AUC value served as input for gene set enrichment analysis (GSEA) with FGSEA R package (version 1.26.0). Gene sets were accessed with msigdbr R package (version 7.5.1). GSEA was also performed with FGSEA using the correlation scores of all genes passing quality control and the CN of the 22q11.21 amplicon. Gene dosage plots were created for all genes located on focal amplifications when at least 5% of the single cells expressed the gene, the unsegmented CN was taken from the bin overlapping with the transcription start site for each gene. The CN of the bin closest to the middle of focal amplification was used as the overall CN of the amplicon. Minimal residual disease (MRD) states were assigned as described in Rambow et al. . All data analysis was conducted in Python version 3.9 (Python software foundation) or R version 4.3.0 (CRAN). Plots were created with the ggplot2 (version 3.4.4) and ggpubr (version 0.6.0) R packages. Benchmarking of inferCNV with G (tag)&T-seq data and classification of the Rambow et al. Smart-seq2 data Raw single-cell RNA-seq reads from 96 normal human melanocytes (ethics approval S63257), as well as the Rambow et al. (GEO: GSE116237) data were aligned to the GRCh37 reference genome including ERCC sequences using STAR with default parameters (version 2.5.2b). After creating a count matrix with HTseq (version 0.6.0) and the GENCODE H19 transcript annotations, the data were merged with the G (tag)&T transcriptome counts. The scater R package was used to discard low-quality cells, namely cells with less than 100 000 counts, less than 1000 unique genes expressed, more than 25% counts assigned to mitochondrial sequences or more than 15% counts belonging to ERCC sequences. R package InferCNV (version 1.16.0) was subsequently used to infer CN estimates from the scRNA-seq data using standard parameters for Smart-seq2 and the 6-state Hidden-Markov Model while using the transcriptome data of the normal melanocytes as reference. The mitochondrial and sex chromosomes were excluded from the analysis. The G (tag)&T data were used to benchmark the CN calls obtained with inferCNV, where the DNA-seq data were considered the ground truth. Segments were then classified as (i) true positive if both DNA and RNA CN calls indicated a gain or if both indicated a loss; (ii) true negative if both indicated a neutral CN state; (iii) false negative if the DNA data indicated a CN aberration and the RNA did not; (iv) false positive if the RNA indicated a CN aberration and the DNA did not. The CN calls and modified expression intensities obtained with inferCNV for the G (tag)&T transcriptome data were both used to train five machine learning algorithms (Linear Discriminant Analysis, Classification and Regression Trees, k-Nearest Neighbours, radial function support vector machine (SVM) and random forest) to classify a sample to the correct genomic subclone. Briefly, the caret R package (version 6.0–94) was used to split the data 80–20% after removing highly correlated features (cor > 0.8) and train the classifiers using 10-fold cross-validation. Accuracy was selected as the scoring metric to assess the performance. The best accuracy on the test data was observed for the radial function SVM trained on the modified expression intensities when combining subclones A and B. The model was then used to assign the Rambow et al. to either subclone AB or C. Data analysis was conducted in R version 4.3.0 (CRAN) while plots were created with the ggplot2 (version 3.4.4) and ggpubr (version 0.6.0) R packages. Data from scONE, sci-L3 & DNTR Single-cell DNA-seq data for human cells used in this study can be accessed in Sequence Read Archive under accession numbers PRJNA603321 (DNTR), PRJNA768428 (scONE), and PRJNA511715 (sci-L3). Sci-L3 fastq files were processed using the pipeline from Yin et al. . For the three assays, BAM files were down-sampled to 400 000 reads and processed in the same way as described above. Near-diploid cells processed with DNTR-seq, scONE-seq and sci-L3 with an average ploidy between 1.9 and 2.1 were chosen and compared with single-cell DNA samples of HCC38-BL DNA processed by Gtag and picoPlex using the Wilcoxon test. A comparison was made between the MAPD of logR values between consecutive genomic bins, analysed using 500 k UMPs genomic bins, the coverage uniformity and Lorenz curves showing the average coverage uniformity of single-cell genomes. Code availability Code is available through the following GitHub link: https://github.com/voetlab/Single_cell_GtagT_Manuscript
The MEL006 cutaneous melanoma PDX model is part of the Trace collection ( https://gbiomed.kuleuven.be/english/research/50488876/54502087/Trace/PDX-repository ) and was established using lesions derived from a patient undergoing surgery as part of standard treatment at UZ Leuven. Written informed consent was obtained and all procedures were approved by the UZ Leuven/KU Leuven Medical Ethical Committee (S63799, S57760, S58277) and performed in accordance with the principles of the Declaration of Helsinki and with GDPR regulations. The experiments were approved by the KU Leuven animal ethical committee (P164-2019) and performed in accordance with the internal, national, and European guidelines of animal care and use. Single-cell suspensions were implanted subcutaneously in the interscapular fat pad of female NMRI nude BomTac: NMRI-Foxn1nu, 4-week-old females (Taconic Biosciences). Mice were maintained in a pathogen-free facility under standard housing conditions with continuous access to food and water. The health and welfare of the animals was supervised by a designated veterinarian. The KU Leuven animal facilities comply with all appropriate standards (cages, space per animal, temperature [22°C], light, humidity, food, and water), and all cages are enriched with materials that allow the animals to exert their natural behaviour. Mice used in the study were maintained on a diurnal 12-h light/dark cycle. MEL006 was derived from a female, drug-naïve melanoma patient. When the tumour reached 1000 mm 3 , the mice were randomly assigned to the different experimental groups. Mice were treated daily by oral gavage with a capped dose of 600 μg dabrafenib and 6 μg trametinib (DT) in 250 μl total volume.
HCC38 breast cancer cells and HCC38-BL lymphoblastoid cells were cultured in Dulbecco's Modified Eagle Medium (DMEM/F12) containing 10% fetal bovine serum (FBS) at 37°C in a 5% CO2 incubator . Trypsinized HCC38 cells and HCC38-BL cells were washed in fresh culture medium and then resuspended in FACS sorting buffer (DMEM/F12 supplemented with 5% FBS, 1 mM EDTA, and 1.5 μM DAPI). The BD FACS Melody sorter device was used for sorting single cells into 96-well plates (FrameStar®, 4TI-0960/C) containing 2.5 μl RLT plus buffer (Qiagen). Plates were then spun down at 1000 g for 1 min at 4°C, and finally stored at −80°C. MEL006 cells were retrieved as described before , resuspended in serum-free DMEM/F12 medium and sorted using the BD FACS Aria III into 96-well plates containing 2.5 μl RLT plus buffer.
gDNA and RNA separation and DNA precipitation were performed as per the G&T protocol of Macaulay et al. on a Hamilton ® liquid handling robot. First, the plate containing the lysed cells is supplemented with RNA spike-ins [1 μl of a 1:1 600 000 dilution of ERCC spike-in mixture A (Life Technologies)], which is followed by the addition of oligo-dT conjugated to streptavidin beads. After incubation, the poly-adenylated RNAs are collected to the side of the well using a magnet (and processed further according to the G&T-seq protocol), while the DNA present in the supernatant is transferred to a new recipient DNA plate. The wash solution used to rinse the RNA-bead complexes is also added to the DNA plate. After mixing the solution containing the DNA with 0.7:1 ratio AMPure XP beads, the DNA is precipitated using a magnet. For Gtag, the precipitated gDNA was resuspended in 4.5 μl resuspension buffer (0.5X NEB4, 0.37% Igepal CA-630, 0.37% Tween-20). Next, tagmentation is performed in a total volume of 10 μl by adding 5.5 μl tagmentation master mix (5 μl Tagment DNA buffer, 0.1 μl Tagment DNA Enzyme, 0.4 μl nuclease free water) for 10 min at 55°C. The reaction was inactivated by adding 1 μl of 0.44% SDS to the sample and incubating 5 min at 55°C. Then we added 13 μl of Q5 Ultra II (NEB, 2x mastermix), 1 μl S5 primer, and 1 μl S7 primer to the sample. PCR amplification was performed with the following cycling program: 72°C for 3 min; 98°C for 30 s; 16 cycles of 98°C for 10 s, 60°C for 30 s; and 72°C for 30 s, 72°C for 5 min and held at 10°C. In between reaction steps, 96-well plates were placed in an Eppendorf thermomixer at room temperature to mix (1000 rpm) for one minute and briefly centrifuged using a tabletop centrifuge. Finally, the PCR products were pooled per plate and purified using AMPure XP beads (1x ratio).
DNA and cDNA quality of picoPlex and Smart-seq2 amplification reactions, respectively, was confirmed using the 2100 Bioanalyzer (high sensitivity chip, Agilent). Next, DNA and cDNA concentrations were determined using a Quantifluor ® Assay (Promega ® ). Samples were diluted to 200 or 100 pg/μl for Nextera XT (Illumina) library preparation in respectively one-fourth or one-tenth of the volume recommended by the manufacturer using manual or automated liquid handling. After library preparation, samples were pooled and purified using AMPure XP beads (0.6x ratio). Quality check of the DNA (picoPlex and Gtag) and cDNA library pools was performed using the 2100 Bioanalyzer (high sensitivity chip, Agilent) in combination with Qubit™ HS (High sensitivity) DNA Assay Kit (Invitrogen™) before diluting the pools to a concentration of 4 nM. We used the KAPA Library Quantification Kit for Illumina ® platforms (Roche, KK4854) on the LightCycler 480 and diluted the sequencing pools. HCC38 and HCC38-BL samples were diluted to 2 nM and sequenced 51-bp single end on a HiSeq2500 platform. The first batch of MEL006 samples was diluted to 1.5 nM and sequenced on a HiSeq4000 platform (51-bp single-end), while the second batch was diluted to 0.75 nM and sequenced on a NextSeq2000 (2 × 50-bp paired-end).
Single-end sequencing reads obtained were aligned to the GRCh37 human reference genome using BWA-MEM . Samtools was used to sort, index, and sample the mapped BAM files down to 400 000 reads. Our mapping statistics were obtained through samtools (version 1.11) and Picard (version 2.23.8) ( http://broadinstitute.github.io/picard/ ). PCR duplicates were removed with Picard. To create pseudo-bulk genomes, samtools merge was used to combine BAM files. DNA copy number (CN) analysis was performed as discussed in Macaulay et al. . Segmentation of the corrected logR values was done using piecewise constant fitting, with the penalty parameter (γ) set to 10 for the 500 k UMPs bin genomes and γ = 35 for the 10 k UMPs bin genomes. Integer DNA CNs were estimated as 2 logR *Ψ, with the average ploidy of the cell, Ψ, determined as the average CN value with the lowest penalty from a 1.2–6 grid with possible CN values, transformed from segmented LogR values. High penalty values are given to a possible average CN when the sum of squared differences between the unrounded and rounded CN is high.
For quality filtering of MEL006 genomes, we only processed single cells with at least 400 000 reads before deduplication. HCC38 genomes were only processed if they had at least 100 000 raw reads before deduplication. We calculated the median absolute pairwise difference (MAPD) score for all samples by first measuring the absolute difference between two consecutive logR values, %GC-corrected and normalized, across the genome. Next, the median across all absolute differences is computed. For MAPD cut-offs, genomes only passed if their MAPD score was less than the 75 th percentile +1.5 times the interquartile range (HCC38, MAPD cut off = 0.64; MEL006, MAPD cut off = 0.76). An overview of the QC pass/fail samples can be found in and .
Lorenz curves were computed by taking the cumulative fraction of the covered genome against the cumulative fraction of the mapped bases. From the BAM files, duplicates were first removed, and all genomes were downsampled to 230 000 unique reads (with a quality of at least 20). Gini coefficients were calculated in R using the ineq package ( https://cran.r-project.org/web/packages/ineq/ ).
Subclones in the MEL006 dataset were determined using the CopyKit R package (version 0.1.2). The segmented CN values were embedded in two dimensions using the uwot R package (version 0.1.14, min dist = 0, n neighbours = 25, seed = 13). Superclones were identified from the shared nearest neighbour ( K = 15) graph using the R package igraph (version 1.4.2). Subclones were subsequently identified from the UMAP embedding using the clustering algorithm hdbscan from the dbscan R package (version 1.1–11). Regions of focal amplifications were determined on the pseudo-bulk Gtag genomes on 10 k UMPs bins segmented by piecewise constant fitting ( γ = 35) using GISTIC2. For each region, 20 additional genomic bins were taken on each flank for visualization. All heatmaps were constructed using the ComplexHeatmap R package (version 2.10.0).
After trimming the adaptor sequences with cutadapt (version 1.13), sequencing reads were aligned to the GRCh37 reference genome, including ERCC sequences using STAR with default parameters (version 2.5.2b). HTseq (version 0.6.0) with the GENCODE H19 transcript annotations were used to generate the count matrix.
Quality control was performed using the scater R package (version 1.28.0): cells with less than 100 000 counts, expression of less than 2000 unique genes, more than 30% counts assigned to mitochondrial sequences or 8% counts belonging to ERCC sequences were removed for downstream analysis. Genes with less than 32 counts across the complete dataset were excluded from downstream analysis. All data analysis was conducted in R version 4.3.0 (CRAN), while plots were created with the ggplot2 (version 3.4.4) R package.
Quality control was performed using the scater R package (version 1.28.0): cells with less than 100 000 counts, expression of less than 1000 unique genes, more than 25% counts assigned to mitochondrial sequences or 15% counts belonging to ERCC sequences were removed for downstream analysis . Genes with less than five counts across the complete dataset were excluded from downstream analysis. Expression value scaling and normalization, cell-cycle regression, batch correction, PCA and UMAP dimensionality reductions and clustering were performed using the Seurat R package (version 4.3.2) . Marker gene discovery was performed using the FindAllMarkers function of the Seurat package using the Wilcoxon Ranked Sum test. The R package presto (version 1.0.0) was used to perform a fast Wilcoxon rank sum test where the AUC value served as input for gene set enrichment analysis (GSEA) with FGSEA R package (version 1.26.0). Gene sets were accessed with msigdbr R package (version 7.5.1). GSEA was also performed with FGSEA using the correlation scores of all genes passing quality control and the CN of the 22q11.21 amplicon. Gene dosage plots were created for all genes located on focal amplifications when at least 5% of the single cells expressed the gene, the unsegmented CN was taken from the bin overlapping with the transcription start site for each gene. The CN of the bin closest to the middle of focal amplification was used as the overall CN of the amplicon. Minimal residual disease (MRD) states were assigned as described in Rambow et al. . All data analysis was conducted in Python version 3.9 (Python software foundation) or R version 4.3.0 (CRAN). Plots were created with the ggplot2 (version 3.4.4) and ggpubr (version 0.6.0) R packages.
et al. Smart-seq2 data Raw single-cell RNA-seq reads from 96 normal human melanocytes (ethics approval S63257), as well as the Rambow et al. (GEO: GSE116237) data were aligned to the GRCh37 reference genome including ERCC sequences using STAR with default parameters (version 2.5.2b). After creating a count matrix with HTseq (version 0.6.0) and the GENCODE H19 transcript annotations, the data were merged with the G (tag)&T transcriptome counts. The scater R package was used to discard low-quality cells, namely cells with less than 100 000 counts, less than 1000 unique genes expressed, more than 25% counts assigned to mitochondrial sequences or more than 15% counts belonging to ERCC sequences. R package InferCNV (version 1.16.0) was subsequently used to infer CN estimates from the scRNA-seq data using standard parameters for Smart-seq2 and the 6-state Hidden-Markov Model while using the transcriptome data of the normal melanocytes as reference. The mitochondrial and sex chromosomes were excluded from the analysis. The G (tag)&T data were used to benchmark the CN calls obtained with inferCNV, where the DNA-seq data were considered the ground truth. Segments were then classified as (i) true positive if both DNA and RNA CN calls indicated a gain or if both indicated a loss; (ii) true negative if both indicated a neutral CN state; (iii) false negative if the DNA data indicated a CN aberration and the RNA did not; (iv) false positive if the RNA indicated a CN aberration and the DNA did not. The CN calls and modified expression intensities obtained with inferCNV for the G (tag)&T transcriptome data were both used to train five machine learning algorithms (Linear Discriminant Analysis, Classification and Regression Trees, k-Nearest Neighbours, radial function support vector machine (SVM) and random forest) to classify a sample to the correct genomic subclone. Briefly, the caret R package (version 6.0–94) was used to split the data 80–20% after removing highly correlated features (cor > 0.8) and train the classifiers using 10-fold cross-validation. Accuracy was selected as the scoring metric to assess the performance. The best accuracy on the test data was observed for the radial function SVM trained on the modified expression intensities when combining subclones A and B. The model was then used to assign the Rambow et al. to either subclone AB or C. Data analysis was conducted in R version 4.3.0 (CRAN) while plots were created with the ggplot2 (version 3.4.4) and ggpubr (version 0.6.0) R packages.
Single-cell DNA-seq data for human cells used in this study can be accessed in Sequence Read Archive under accession numbers PRJNA603321 (DNTR), PRJNA768428 (scONE), and PRJNA511715 (sci-L3). Sci-L3 fastq files were processed using the pipeline from Yin et al. . For the three assays, BAM files were down-sampled to 400 000 reads and processed in the same way as described above. Near-diploid cells processed with DNTR-seq, scONE-seq and sci-L3 with an average ploidy between 1.9 and 2.1 were chosen and compared with single-cell DNA samples of HCC38-BL DNA processed by Gtag and picoPlex using the Wilcoxon test. A comparison was made between the MAPD of logR values between consecutive genomic bins, analysed using 500 k UMPs genomic bins, the coverage uniformity and Lorenz curves showing the average coverage uniformity of single-cell genomes.
Code is available through the following GitHub link: https://github.com/voetlab/Single_cell_GtagT_Manuscript
WGA-free parallel genome and transcriptome sequencing of single cells Gtag&T-seq is based on genome-and-transcriptome (G&T) sequencing (Fig. ), developed by Macaulay et al. . Following physical separation of the gDNA and polyadenylated RNA of the same cell, Gtag&T-seq applies tagmentation to produce a gDNA sequencing library directly from the cell’s genome instead of pre-amplifying it with WGA (Fig. ). Following tagmentation, PCR adds cell-specific barcodes and sequencing adapters to enable multiplexed low coverage sequencing and cost-effective multi-modal analysis of single cells (Fig. ). The performance of Gtag&T-seq was evaluated against conventional G&T-seq , using picoPlex for WGA, for both the HCC38 cancer cell line and its matched normal cell line (HCC38-BL). PicoPlex was chosen for its proven reproducibility and high accuracy in detecting DNA CNs . At the RNA level, the datasets were comparable, highlighted by the high correlation of the mean expression per gene for both cell lines (HCC38-BL, R 2 = 0.83; HCC38, R 2 = 0.87; Fig. and and ). To compare Gtag also with other tagmentation-based methods in a DNA/RNA co-assay, we contrasted our HCC38-BL samples with near-diploid samples processed by DNTR-seq , scONE-seq , and sci-L3 (Fig and ). To allow for a fair comparison of the genome sequences, gDNA reads of each cell were downsampled to a maximum of 400 000 reads before duplicate removal (‘Materials and methods’, and and ). Reliable genetic variant detection from single-cell gDNA data largely depends on the noise and coverage uniformity attained by the method. When compared to picoPlex-based G&T-seq, genomic readouts were less noisy for Gtag (HCC38-BL, P ≤ 0.0001; HCC38, P ≤ 0.0001) as assessed by MAPD (Fig. ), but MAPD values were increased when compared to DNTR-seq, sci-L3-seq and scONE-seq (Fig. ). In addition, Gtag improved coverage uniformity in comparison to picoPlex, discernible from Lorenz curves (Fig. and ) and compared favourably to picoPlex, scONE and sci-L3 when using the Gini index (μ Gtag = 0.20 versus μ picoPlex = 0.27, μ DNTR = 0.17, μ scONE = 0.21 and μ sciL3 = 0.22) (Fig. ). Low-depth single-cell genomes can be pooled to derive pseudo-bulk genomes, refining genomic variant calling . To investigate differences in performance resulting from omitting WGA, we compared coverage breadth, uniformity, and noise after merging single-cell genomes (HCC38-BL) in silico for both G&T-based multi-omics methods. Plotting the theoretical versus the observed coverage breadth for increasing amounts of pooled single-cell genomes (Fig. ) showed a rapid saturation of coverage breadth for picoPlex, likely caused by limited random priming during WGA. Pseudo-bulks can be further leveraged to map DNA breakpoints more precisely, conditional on smaller bin sizes not exacerbating noise. We observed that picoPlex suffered from inflated MAPD scores compared to Gtag for pseudo-bulks analysed with smaller bin sizes (Fig. ). In addition, coverage was more uniform for the Gtag 20-cell pseudo-bulk HCC38-BL genomes, as evidenced by Gini indexes of 0.08 for Gtag pseudo-bulk and 0.15 for picoPlex pseudo-bulk. Single-cell and pseudo-bulk analysis of a human melanoma PDX model The development of resistance to targeted therapy presents a significant clinical challenge. Emerging evidence indicates that both genetic and non-genetic mechanisms conspire to drive resistance. A deeper understanding of the interplay between these mechanisms is essential for designing more effective, long-lasting combination treatments. The Gtag&T-seq method offers an approach to dissecting the respective contribution of these mechanisms at single-cell resolution. We processed 703 and 175 single cells from a melanoma PDX model using Gtag&T- and G&T-seq, respectively. The MEL006 model was established from a patient with a BRAF V600E mutant melanoma who had an almost complete response to combined dabrafenib (BRAF V600E inhibitor) and trametinib (MEK inhibitor) therapy . Single cells from the PDX model were collected before treatment (T0), during treatment (T04), and at MRD (T28), when most of the tumour cells are eradicated by the therapy. At this late time point, a small subset of drug-tolerant cancer cells persists, providing a substrate for relapse. After removing low-quality genomes (‘Materials and methods’, and ), 494 single-cell Gtag genomes were compared to 142 single-cell picoPlex genomes. DNA CN profiles were called using genomic bins of 500 k unique mappable positions (UMPs), revealing a highly rearranged tumour cell population (Fig. ) with an average ploidy of 3.5, indicative of an early whole-genome doubling event, coherent with previous bulk sequencing observations . Three distinct subclones (A–C; Fig. ) were identified using the density-based clustering strategy , which differed by the presence of an additional copy of chr2 in A and B and a loss of one copy of chr7 in subclone B. Within each subclone, single cells displayed similar CN profiles (mean pairwise R: A = 0.85, B = 0.87, C = 0.79), indicative of stable clonal expansions. To refine the CNA states and breakpoints, pseudo-bulk genomes were created per method for each subclone (A–C) at both time points. Similar to our observations with cell lines, WGA resulted in increased noise and decreased coverage uniformity in pseudo-bulk genomes ( ), leading to incorrect clustering of the picoPlex-derived pseudo-bulks (Fig. ) rather by time point rather than by subclone. In addition, discrepancies are noted in the CN calling for chr1, chr5, chr11, chr19, and chr20. As a consequence, Gtag pseudo-bulk genomes allowed for a more detailed dissection of focal CNAs (Fig. and ). Detection of subclone-specific focal amplifications at near base-pair resolution using Gtag Single-cell and pseudo-bulk CNA profiling with 500 k UMP bins (Fig. – ) indicated the existence of highly amplified loci on chr13 and chr22. The elevated amplification levels in these regions, combined with the lower noise and higher uniformity of Gtag genomes, allowed us to further refine the breakpoints using 10 k UMP bins. In total, 11 focal amplifications ranging in size from 0.12 to 1.20 Mb were found: 10 on chr13 (q13.3A, q13.3B, q14.2–3, q14.3, q21.2, q21.33, q22.3, q31.1, q31.3, and q32.3) and 1 on chr22 (q11.21). GISTIC2 analysis of 367 skin cutaneous melanoma samples revealed that 13q and 22q arms are frequently amplified . Moreover, a 22q11.21 focal amplification is found in 10% of the CCLE skin cancer cell lines ( e.g . Hs294-T and COLO679 BRAF (V600) mutant cell lines), 22q11.21 was also identified as recurrently amplified in a pan-cancer analysis , and recently associated with inferior survival in acral melanoma . Subclonal differences in the presence, size and CN were observed for the majority of the focal amplifications on chr13 (Fig. ). Subclone C was characterised by the presence of 13q31.1 and 13q32.3 amplicons, as well as different breakpoints for 4 focal amplifications (e.g. 13q13.3A, 13q14.3, 13q21.2, and 13q21.33; Fig. ). Furthermore, a small number of cells belonging to subclone A and B showed additional breakpoints in 13q14.2–3 and 13q21.33, resulting in multiple smaller amplicons (Fig. ) as well as small alterations in 22q11.21 that were only detected in Gtag genomes and not in picoPlex genomes, but are confirmed using pseudo-bulk genomes as well as transcriptomic analysis ( ). Among subclones, most breakpoints remained consistent, resulting in a higher similarity within subclones compared to those outside, as determined by Euclidean distance ( t-test, P < 2.2e-16 ). The CN of the majority of chr13 amplicons was correlated with the amplification level of 22q11.21, suggesting that these amplicons are co-amplified ( ). The absence of correlation for amplicon 13q22.3 is explained by its absence in half of the cells, irrespective of clonal context. All aforementioned subclonal differences were also detected in pseudo-bulk genomes and remained after downsampling to the same depth (Fig. and and ). Furthermore, these pseudo-bulk genomes allowed us to pinpoint the breakpoint of the focal amplifications to near base-pair resolution (Fig. ). Although full reconstruction of the amplicon structure is challenging from single-end sequencing data, we were able to make two key observations. First, the subclonal organization of these focal amplifications supports the existence of at least three major genomic subpopulations in this melanoma lesion, with subclones A and B closely related. Second, the heterogeneity in the CN of the amplicons indicates that these are dynamic instead of static DNA entities. Transcriptome-based DNA CN inference has limited accuracy and fails to detect focal amplifications To address the added value of direct genome measurements along with transcriptome profiling, we first set out to identify the three major genomic subclones from the single-cell transcriptome data. Of the G (tag)&T single-cell transcriptomes, 636 passed our quality thresholds (‘Materials and methods’ and ) and were used for downstream analysis. We inferred CNAs using averaged gene expression patterns with normal human melanocytes as a reference using inferCNV . Comparison to the matching gDNA-derived DNA CN profiles of the same cells, revealed an average sensitivity and specificity of 52% and 91%, respectively (‘Materials and methods’). InferCNV failed to detect the CN of the whole-chromosome gains for chromosomes 4, 6, 8, and 18 for all cells, as well as chr2 for cells belonging to subclones A and B (Fig. ). Instead, we obtained the correct CN for smaller regions of these chromosomes (Fig. ), suggesting that not all genes are affected to the same degree by genomic imbalances. Furthermore, the focal amplifications on chromosomes 13 and 22 and the p-ter amplification of chr12 were not detected. These shortcomings notwithstanding, we trained a SVM model on our single-cell genome-and-transcriptome data that achieved a mean classification accuracy of 0.71 (95% CI confidence interval, CI 95% = [0.63, 0.79]. Although we identified the three subclones to a degree, we found frequent misclassification of subclone B to A. Combining subclones A and B increased the accuracy of the model to 0.86 ( CI 95% = [0.79, 0.91]. This highlights the need for direct multi-omics to accurately dissect both genomic evolution and transcriptome plasticity in full. Differential expression analysis reveals subtle effects of subclonal chromosomal alterations Single-cell RNA-seq of the MEL006 PDX melanoma model exposed to BRAFi/MEKi treatment identified four drug-tolerant cell states: a ‘starved’ (starved-like melanoma cell, SMC) state, a differentiated and ‘pigmented’ state, a ‘neural crest stem cell (NCSC)-like’ state and a de-differentiated state also referred to as ‘invasive/mesenchymal-like’ state . To investigate the effects of the genomic alterations on the cell's transcriptome and the aforementioned drug-tolerant states, we performed pairwise differential gene expression analysis between the three genomic subclones at each timepoint. In total, only 100 differentially expressed genes were identified (FDR < 0.05) (Fig. and ). No differentially expressed genes were identified between any of the subclones at T04, most likely due to the lower number of cells processed ( n = 121). At respectively T0 and T28, we found 39 and 72 differentially expressed genes, of which 11 genes were found to be differentially expressed at both timepoints, including THAP7, LZTR1 , and GPC5 . This observation indicates that during treatment, the transcriptomic differences between subclones become greater. Between subclones A and B, no differentially expressed genes were found, except for H2AFJ , indicating that the loss of one copy of chr7 has only subtle effects on the transcriptomes of cells belonging to subclone B ( ). This observation agrees with the difficulties of the SVM model to correctly classify subclone B based on the CNAs obtained with inferCNV. In contrast, we found that the loss of chromosome 2 in subclone C resulted in lower expression of TMSB10, ARPC2, ATP5G3, PCBP1 , and OST4 compared to subclones A and B. Meanwhile, PTMA and GYPC were upregulated only in subclone A, and RTN4, BIN1 , and EIF5B were upregulated only in subclone B. At T28, we observed a shift in the differentially expressed genes towards markers of the MRD states (indicated with symbols on Fig. ), as well as genes located on the 22q11.21 focal region and chromosome 7 (indicated with orange and red colours, respectively, on Fig. ). TMSB10 was the only gene upregulated in both subclones at both timepoints, while OST4 showed higher expression in subclone B at both timepoints. Additionally, COL5A2 and SEPT2 were upregulated in subclones A and B compared to C at T28. The invasive state marker IGFBP5 and SPTBN1 were highly expressed in subclone A, while subclone B showed higher expression of RHOB . At T28, AQP1 (chr7), GFRA3 (chr5), MPZ (chr1), NGFR (chr17), RSPO3 (chr6), SLC22A17 (chr14), L1CAM (chrX), GFRA2 (chr8), and PRIMA1 (chr14), all markers of the NCSC state, showed increased expression in subclone A and/or B, while TFA2B (chr6) was higher expressed in subclone A at T0. CD36 (chr7), a marker for the SMC state, was expressed at higher levels in subclone C compared to subclone A at T28 and the SMC marker PRELP (chr1) at T0. Nine differentially expressed genes were located on the focal amplicons of chr13 and chr22. At both T0 and T28, THAP7 (22q11.21) and LZTR1 (22q11.21) were expressed at higher levels in subclone C in both T0 and T28, while KLHL22 (22q11.21) was not differentially expressed compared to subclone B at T0. PI4KA (22q11.21), TUBA3FP (22q11.21), P2RX6 (22q11.21) and TMEM191A (22q11.21) only appear to be differentially expressed in T28 and TM9SF2 (13q31.3) only in T0. GPC5 (13q31.3) had a higher expression in cells belonging to subclone A and B at both T0 and T28. These observations indicate subclonal differences in expression levels of genes located on the focal amplifications or, alternatively, differences in the CN of the amplicons influencing the gene expression between subclones A, B, and C. Focal amplifications influence subclone-specific gene expression To assess the effect of ongoing amplicon evolution on the phenotype of the subclones, we calculated the correlation between the DNA CN of genes located on each focal amplification and their normalized expression (Fig. and and ). For chr13, we found nine genes with a gene-dosage effect across all the subclones (e.g. UFM1 and CLN5 , Fig. ), 8 genes with a subclone-specific effect (e.g. TM9SF2 ; Fig. ) and finally, four genes that did not show a dosage effect ( e.g. MAB21L1; Fig. ). All 18 expressed genes located on the 22q11.21 amplicon showed clear gene-dosage effects (e.g. CRKL ; Fig. ) in at least one of the subclones. Eight of these genes showed subclone-specific differences in gene expression when controlling for amplicon CN (e.g. LZTR1 , THAP7, PI4KA ; and Fig. ). For LZTR1 , a tumour suppressor in many cancers although recently also suggested as a key oncogene in acral melanoma , and THAP7 , the disparity is elucidated by additional breakpoints in subclones A and B, resulting in a small region of lower CN ( ). The difference in PI4KA expression, on the other hand, did not appear to be caused by subclone-specific breakpoints. Taken together these observations suggest concomitant gene-dosage effects and epigenomic regulation modulating differential expression of genes between subclones. Amplicon CN is associated with drug-tolerant cell state plasticity We next investigated whether the amplicons are associated with specific drug-tolerant cell states. In contrast to the other states, the NCSC state generally exhibits lower CNs for all chr13 focal amplicons containing expressed genes, except for 13q13.3B and 13q31.3. Conversely, the SMC state consistently displays the highest CNs, independently or in conjunction with the pigmented and/or invasive states ( and ). Out of the 21 expressed genes examined, disparities in gene expression among the drug-tolerant cell states were evident in only 11 genes. The majority adhered to a pattern where genes exhibited the lowest expression in the NCSC state and the highest in the SMC state. However, exceptions were noted for CCDC169 , LINC00401 , LINC00383 , and GPC5 , all displaying lower expression levels in the pigmented state. Furthermore, it was observed that genes within the same amplicon demonstrated distinct expression patterns across the states, as exemplified by DLCK1 and CCDC169 . For the 22q11.21 amplicon, we found that the expression of MLANA , a marker for the pigmented state, was inversely correlated with the CN (Fig. ). In addition, GSEA revealed enrichment of gene ontology terms related to pigmentation (e.g. generalized developmental pigmentation, cellular pigmentation, ) among genes negatively correlated with the CN of this amplicon (e.g. DCT , TYR, PMEL ). These findings might indicate that cells with a lower 22q11.21 CN are more prone to engage the differentiated pigmented cell state. Indeed, we found enrichment for a high 22q11.21 CN in all other states compared to the pigmented state, with the highest enrichment in respectively the mesenchymal-like, SMC and NCSC states ( ). At the gene level, we identified 12 out of 18 genes exhibiting expression differences among the drug-tolerant cell states, all displayed lower expression in the NCSC state, with the most pronounced disparities observed in comparison to the SMC state. Additionally, we noted that CRKL was also expressed at lower levels in cells associated with the pigmented state ( ). In summary, while none of the previously described MRD-specific markers or known melanoma-specific transcription factors are located on the 22q11.21 and chromosome 13 amplicons, our data indicate a correlation between these amplicons and the MRD states available to the subclones. Cellular plasticity and phenotypic cell-state diversity within and between different genetic subclones on treatment G (tag)&T-seq allow the construction, with single-cell resolution, of a phylogenetic tree throughout therapy, annotated with the aforementioned drug-tolerant states. We found that while all genetic subclones were observed at both timepoints, their relative abundance shifted. T0 was enriched for subclones A and B (52% and 24% of cells, respectively), while subclone C was the most abundant at T04 and T28, increasing from 24% at T0 to 58% in T04 and to 65% in T28 ( X 2 -test, P < 2.2e-16; Fig. ). When we compare T0 with T04 we observed a decrease from the mesenchymal-like state and an increase in SMC state at T04 in all three subclones. Strikingly, we found the NCSC state to be statistically enriched in subclones A and B at T28 ( X 2 -test, P = 0.0001 ), while the SMC and pigmented states were present in all genetic subclones. Both observations were validated using inferCNV and our trained SVM classifier on the Smart-seq2 data from Rambow et al. (Fig. ). Differential expression analysis indeed revealed higher expression of the SMC-marker CD36 in subclone C, and increased expression of NCSC markers AQP1, GFRA3, MPZ, NGFR, RSPO3, SLC22A17, GFRA2, PRIMA1 and L1CAM in subclones A and B (Fig. ). Furthermore, GSEA revealed enrichment of genes related to epithelial-mesenchymal transition (EMT) at T28 in both subclone A and B, but not in subclone C (A, P = 0.0005; B, P = 0.0005; , ). Moreover, we found that at T28, the CN of the 22q11.21 amplicon in subclone C had increased compared to T0 and T04 (Fig. ), and to A and B at T28 (Fig. ). Between T0 and T04, we observed a decrease in the 22q11.21 amplicon CN for subclone C. In summary, we conclude that subclone C likely has a higher prevalence at MRD, whereas the NCSC state is primarily found in subclones A and B. This indicates that all genomic subclones have the potential to adopt various drug-tolerant states and survive therapy. However, their genetic makeup influences their ability to engage these states and successfully endure the treatment.
Gtag&T-seq is based on genome-and-transcriptome (G&T) sequencing (Fig. ), developed by Macaulay et al. . Following physical separation of the gDNA and polyadenylated RNA of the same cell, Gtag&T-seq applies tagmentation to produce a gDNA sequencing library directly from the cell’s genome instead of pre-amplifying it with WGA (Fig. ). Following tagmentation, PCR adds cell-specific barcodes and sequencing adapters to enable multiplexed low coverage sequencing and cost-effective multi-modal analysis of single cells (Fig. ). The performance of Gtag&T-seq was evaluated against conventional G&T-seq , using picoPlex for WGA, for both the HCC38 cancer cell line and its matched normal cell line (HCC38-BL). PicoPlex was chosen for its proven reproducibility and high accuracy in detecting DNA CNs . At the RNA level, the datasets were comparable, highlighted by the high correlation of the mean expression per gene for both cell lines (HCC38-BL, R 2 = 0.83; HCC38, R 2 = 0.87; Fig. and and ). To compare Gtag also with other tagmentation-based methods in a DNA/RNA co-assay, we contrasted our HCC38-BL samples with near-diploid samples processed by DNTR-seq , scONE-seq , and sci-L3 (Fig and ). To allow for a fair comparison of the genome sequences, gDNA reads of each cell were downsampled to a maximum of 400 000 reads before duplicate removal (‘Materials and methods’, and and ). Reliable genetic variant detection from single-cell gDNA data largely depends on the noise and coverage uniformity attained by the method. When compared to picoPlex-based G&T-seq, genomic readouts were less noisy for Gtag (HCC38-BL, P ≤ 0.0001; HCC38, P ≤ 0.0001) as assessed by MAPD (Fig. ), but MAPD values were increased when compared to DNTR-seq, sci-L3-seq and scONE-seq (Fig. ). In addition, Gtag improved coverage uniformity in comparison to picoPlex, discernible from Lorenz curves (Fig. and ) and compared favourably to picoPlex, scONE and sci-L3 when using the Gini index (μ Gtag = 0.20 versus μ picoPlex = 0.27, μ DNTR = 0.17, μ scONE = 0.21 and μ sciL3 = 0.22) (Fig. ). Low-depth single-cell genomes can be pooled to derive pseudo-bulk genomes, refining genomic variant calling . To investigate differences in performance resulting from omitting WGA, we compared coverage breadth, uniformity, and noise after merging single-cell genomes (HCC38-BL) in silico for both G&T-based multi-omics methods. Plotting the theoretical versus the observed coverage breadth for increasing amounts of pooled single-cell genomes (Fig. ) showed a rapid saturation of coverage breadth for picoPlex, likely caused by limited random priming during WGA. Pseudo-bulks can be further leveraged to map DNA breakpoints more precisely, conditional on smaller bin sizes not exacerbating noise. We observed that picoPlex suffered from inflated MAPD scores compared to Gtag for pseudo-bulks analysed with smaller bin sizes (Fig. ). In addition, coverage was more uniform for the Gtag 20-cell pseudo-bulk HCC38-BL genomes, as evidenced by Gini indexes of 0.08 for Gtag pseudo-bulk and 0.15 for picoPlex pseudo-bulk.
The development of resistance to targeted therapy presents a significant clinical challenge. Emerging evidence indicates that both genetic and non-genetic mechanisms conspire to drive resistance. A deeper understanding of the interplay between these mechanisms is essential for designing more effective, long-lasting combination treatments. The Gtag&T-seq method offers an approach to dissecting the respective contribution of these mechanisms at single-cell resolution. We processed 703 and 175 single cells from a melanoma PDX model using Gtag&T- and G&T-seq, respectively. The MEL006 model was established from a patient with a BRAF V600E mutant melanoma who had an almost complete response to combined dabrafenib (BRAF V600E inhibitor) and trametinib (MEK inhibitor) therapy . Single cells from the PDX model were collected before treatment (T0), during treatment (T04), and at MRD (T28), when most of the tumour cells are eradicated by the therapy. At this late time point, a small subset of drug-tolerant cancer cells persists, providing a substrate for relapse. After removing low-quality genomes (‘Materials and methods’, and ), 494 single-cell Gtag genomes were compared to 142 single-cell picoPlex genomes. DNA CN profiles were called using genomic bins of 500 k unique mappable positions (UMPs), revealing a highly rearranged tumour cell population (Fig. ) with an average ploidy of 3.5, indicative of an early whole-genome doubling event, coherent with previous bulk sequencing observations . Three distinct subclones (A–C; Fig. ) were identified using the density-based clustering strategy , which differed by the presence of an additional copy of chr2 in A and B and a loss of one copy of chr7 in subclone B. Within each subclone, single cells displayed similar CN profiles (mean pairwise R: A = 0.85, B = 0.87, C = 0.79), indicative of stable clonal expansions. To refine the CNA states and breakpoints, pseudo-bulk genomes were created per method for each subclone (A–C) at both time points. Similar to our observations with cell lines, WGA resulted in increased noise and decreased coverage uniformity in pseudo-bulk genomes ( ), leading to incorrect clustering of the picoPlex-derived pseudo-bulks (Fig. ) rather by time point rather than by subclone. In addition, discrepancies are noted in the CN calling for chr1, chr5, chr11, chr19, and chr20. As a consequence, Gtag pseudo-bulk genomes allowed for a more detailed dissection of focal CNAs (Fig. and ).
Single-cell and pseudo-bulk CNA profiling with 500 k UMP bins (Fig. – ) indicated the existence of highly amplified loci on chr13 and chr22. The elevated amplification levels in these regions, combined with the lower noise and higher uniformity of Gtag genomes, allowed us to further refine the breakpoints using 10 k UMP bins. In total, 11 focal amplifications ranging in size from 0.12 to 1.20 Mb were found: 10 on chr13 (q13.3A, q13.3B, q14.2–3, q14.3, q21.2, q21.33, q22.3, q31.1, q31.3, and q32.3) and 1 on chr22 (q11.21). GISTIC2 analysis of 367 skin cutaneous melanoma samples revealed that 13q and 22q arms are frequently amplified . Moreover, a 22q11.21 focal amplification is found in 10% of the CCLE skin cancer cell lines ( e.g . Hs294-T and COLO679 BRAF (V600) mutant cell lines), 22q11.21 was also identified as recurrently amplified in a pan-cancer analysis , and recently associated with inferior survival in acral melanoma . Subclonal differences in the presence, size and CN were observed for the majority of the focal amplifications on chr13 (Fig. ). Subclone C was characterised by the presence of 13q31.1 and 13q32.3 amplicons, as well as different breakpoints for 4 focal amplifications (e.g. 13q13.3A, 13q14.3, 13q21.2, and 13q21.33; Fig. ). Furthermore, a small number of cells belonging to subclone A and B showed additional breakpoints in 13q14.2–3 and 13q21.33, resulting in multiple smaller amplicons (Fig. ) as well as small alterations in 22q11.21 that were only detected in Gtag genomes and not in picoPlex genomes, but are confirmed using pseudo-bulk genomes as well as transcriptomic analysis ( ). Among subclones, most breakpoints remained consistent, resulting in a higher similarity within subclones compared to those outside, as determined by Euclidean distance ( t-test, P < 2.2e-16 ). The CN of the majority of chr13 amplicons was correlated with the amplification level of 22q11.21, suggesting that these amplicons are co-amplified ( ). The absence of correlation for amplicon 13q22.3 is explained by its absence in half of the cells, irrespective of clonal context. All aforementioned subclonal differences were also detected in pseudo-bulk genomes and remained after downsampling to the same depth (Fig. and and ). Furthermore, these pseudo-bulk genomes allowed us to pinpoint the breakpoint of the focal amplifications to near base-pair resolution (Fig. ). Although full reconstruction of the amplicon structure is challenging from single-end sequencing data, we were able to make two key observations. First, the subclonal organization of these focal amplifications supports the existence of at least three major genomic subpopulations in this melanoma lesion, with subclones A and B closely related. Second, the heterogeneity in the CN of the amplicons indicates that these are dynamic instead of static DNA entities.
To address the added value of direct genome measurements along with transcriptome profiling, we first set out to identify the three major genomic subclones from the single-cell transcriptome data. Of the G (tag)&T single-cell transcriptomes, 636 passed our quality thresholds (‘Materials and methods’ and ) and were used for downstream analysis. We inferred CNAs using averaged gene expression patterns with normal human melanocytes as a reference using inferCNV . Comparison to the matching gDNA-derived DNA CN profiles of the same cells, revealed an average sensitivity and specificity of 52% and 91%, respectively (‘Materials and methods’). InferCNV failed to detect the CN of the whole-chromosome gains for chromosomes 4, 6, 8, and 18 for all cells, as well as chr2 for cells belonging to subclones A and B (Fig. ). Instead, we obtained the correct CN for smaller regions of these chromosomes (Fig. ), suggesting that not all genes are affected to the same degree by genomic imbalances. Furthermore, the focal amplifications on chromosomes 13 and 22 and the p-ter amplification of chr12 were not detected. These shortcomings notwithstanding, we trained a SVM model on our single-cell genome-and-transcriptome data that achieved a mean classification accuracy of 0.71 (95% CI confidence interval, CI 95% = [0.63, 0.79]. Although we identified the three subclones to a degree, we found frequent misclassification of subclone B to A. Combining subclones A and B increased the accuracy of the model to 0.86 ( CI 95% = [0.79, 0.91]. This highlights the need for direct multi-omics to accurately dissect both genomic evolution and transcriptome plasticity in full.
Single-cell RNA-seq of the MEL006 PDX melanoma model exposed to BRAFi/MEKi treatment identified four drug-tolerant cell states: a ‘starved’ (starved-like melanoma cell, SMC) state, a differentiated and ‘pigmented’ state, a ‘neural crest stem cell (NCSC)-like’ state and a de-differentiated state also referred to as ‘invasive/mesenchymal-like’ state . To investigate the effects of the genomic alterations on the cell's transcriptome and the aforementioned drug-tolerant states, we performed pairwise differential gene expression analysis between the three genomic subclones at each timepoint. In total, only 100 differentially expressed genes were identified (FDR < 0.05) (Fig. and ). No differentially expressed genes were identified between any of the subclones at T04, most likely due to the lower number of cells processed ( n = 121). At respectively T0 and T28, we found 39 and 72 differentially expressed genes, of which 11 genes were found to be differentially expressed at both timepoints, including THAP7, LZTR1 , and GPC5 . This observation indicates that during treatment, the transcriptomic differences between subclones become greater. Between subclones A and B, no differentially expressed genes were found, except for H2AFJ , indicating that the loss of one copy of chr7 has only subtle effects on the transcriptomes of cells belonging to subclone B ( ). This observation agrees with the difficulties of the SVM model to correctly classify subclone B based on the CNAs obtained with inferCNV. In contrast, we found that the loss of chromosome 2 in subclone C resulted in lower expression of TMSB10, ARPC2, ATP5G3, PCBP1 , and OST4 compared to subclones A and B. Meanwhile, PTMA and GYPC were upregulated only in subclone A, and RTN4, BIN1 , and EIF5B were upregulated only in subclone B. At T28, we observed a shift in the differentially expressed genes towards markers of the MRD states (indicated with symbols on Fig. ), as well as genes located on the 22q11.21 focal region and chromosome 7 (indicated with orange and red colours, respectively, on Fig. ). TMSB10 was the only gene upregulated in both subclones at both timepoints, while OST4 showed higher expression in subclone B at both timepoints. Additionally, COL5A2 and SEPT2 were upregulated in subclones A and B compared to C at T28. The invasive state marker IGFBP5 and SPTBN1 were highly expressed in subclone A, while subclone B showed higher expression of RHOB . At T28, AQP1 (chr7), GFRA3 (chr5), MPZ (chr1), NGFR (chr17), RSPO3 (chr6), SLC22A17 (chr14), L1CAM (chrX), GFRA2 (chr8), and PRIMA1 (chr14), all markers of the NCSC state, showed increased expression in subclone A and/or B, while TFA2B (chr6) was higher expressed in subclone A at T0. CD36 (chr7), a marker for the SMC state, was expressed at higher levels in subclone C compared to subclone A at T28 and the SMC marker PRELP (chr1) at T0. Nine differentially expressed genes were located on the focal amplicons of chr13 and chr22. At both T0 and T28, THAP7 (22q11.21) and LZTR1 (22q11.21) were expressed at higher levels in subclone C in both T0 and T28, while KLHL22 (22q11.21) was not differentially expressed compared to subclone B at T0. PI4KA (22q11.21), TUBA3FP (22q11.21), P2RX6 (22q11.21) and TMEM191A (22q11.21) only appear to be differentially expressed in T28 and TM9SF2 (13q31.3) only in T0. GPC5 (13q31.3) had a higher expression in cells belonging to subclone A and B at both T0 and T28. These observations indicate subclonal differences in expression levels of genes located on the focal amplifications or, alternatively, differences in the CN of the amplicons influencing the gene expression between subclones A, B, and C.
To assess the effect of ongoing amplicon evolution on the phenotype of the subclones, we calculated the correlation between the DNA CN of genes located on each focal amplification and their normalized expression (Fig. and and ). For chr13, we found nine genes with a gene-dosage effect across all the subclones (e.g. UFM1 and CLN5 , Fig. ), 8 genes with a subclone-specific effect (e.g. TM9SF2 ; Fig. ) and finally, four genes that did not show a dosage effect ( e.g. MAB21L1; Fig. ). All 18 expressed genes located on the 22q11.21 amplicon showed clear gene-dosage effects (e.g. CRKL ; Fig. ) in at least one of the subclones. Eight of these genes showed subclone-specific differences in gene expression when controlling for amplicon CN (e.g. LZTR1 , THAP7, PI4KA ; and Fig. ). For LZTR1 , a tumour suppressor in many cancers although recently also suggested as a key oncogene in acral melanoma , and THAP7 , the disparity is elucidated by additional breakpoints in subclones A and B, resulting in a small region of lower CN ( ). The difference in PI4KA expression, on the other hand, did not appear to be caused by subclone-specific breakpoints. Taken together these observations suggest concomitant gene-dosage effects and epigenomic regulation modulating differential expression of genes between subclones.
We next investigated whether the amplicons are associated with specific drug-tolerant cell states. In contrast to the other states, the NCSC state generally exhibits lower CNs for all chr13 focal amplicons containing expressed genes, except for 13q13.3B and 13q31.3. Conversely, the SMC state consistently displays the highest CNs, independently or in conjunction with the pigmented and/or invasive states ( and ). Out of the 21 expressed genes examined, disparities in gene expression among the drug-tolerant cell states were evident in only 11 genes. The majority adhered to a pattern where genes exhibited the lowest expression in the NCSC state and the highest in the SMC state. However, exceptions were noted for CCDC169 , LINC00401 , LINC00383 , and GPC5 , all displaying lower expression levels in the pigmented state. Furthermore, it was observed that genes within the same amplicon demonstrated distinct expression patterns across the states, as exemplified by DLCK1 and CCDC169 . For the 22q11.21 amplicon, we found that the expression of MLANA , a marker for the pigmented state, was inversely correlated with the CN (Fig. ). In addition, GSEA revealed enrichment of gene ontology terms related to pigmentation (e.g. generalized developmental pigmentation, cellular pigmentation, ) among genes negatively correlated with the CN of this amplicon (e.g. DCT , TYR, PMEL ). These findings might indicate that cells with a lower 22q11.21 CN are more prone to engage the differentiated pigmented cell state. Indeed, we found enrichment for a high 22q11.21 CN in all other states compared to the pigmented state, with the highest enrichment in respectively the mesenchymal-like, SMC and NCSC states ( ). At the gene level, we identified 12 out of 18 genes exhibiting expression differences among the drug-tolerant cell states, all displayed lower expression in the NCSC state, with the most pronounced disparities observed in comparison to the SMC state. Additionally, we noted that CRKL was also expressed at lower levels in cells associated with the pigmented state ( ). In summary, while none of the previously described MRD-specific markers or known melanoma-specific transcription factors are located on the 22q11.21 and chromosome 13 amplicons, our data indicate a correlation between these amplicons and the MRD states available to the subclones.
G (tag)&T-seq allow the construction, with single-cell resolution, of a phylogenetic tree throughout therapy, annotated with the aforementioned drug-tolerant states. We found that while all genetic subclones were observed at both timepoints, their relative abundance shifted. T0 was enriched for subclones A and B (52% and 24% of cells, respectively), while subclone C was the most abundant at T04 and T28, increasing from 24% at T0 to 58% in T04 and to 65% in T28 ( X 2 -test, P < 2.2e-16; Fig. ). When we compare T0 with T04 we observed a decrease from the mesenchymal-like state and an increase in SMC state at T04 in all three subclones. Strikingly, we found the NCSC state to be statistically enriched in subclones A and B at T28 ( X 2 -test, P = 0.0001 ), while the SMC and pigmented states were present in all genetic subclones. Both observations were validated using inferCNV and our trained SVM classifier on the Smart-seq2 data from Rambow et al. (Fig. ). Differential expression analysis indeed revealed higher expression of the SMC-marker CD36 in subclone C, and increased expression of NCSC markers AQP1, GFRA3, MPZ, NGFR, RSPO3, SLC22A17, GFRA2, PRIMA1 and L1CAM in subclones A and B (Fig. ). Furthermore, GSEA revealed enrichment of genes related to epithelial-mesenchymal transition (EMT) at T28 in both subclone A and B, but not in subclone C (A, P = 0.0005; B, P = 0.0005; , ). Moreover, we found that at T28, the CN of the 22q11.21 amplicon in subclone C had increased compared to T0 and T04 (Fig. ), and to A and B at T28 (Fig. ). Between T0 and T04, we observed a decrease in the 22q11.21 amplicon CN for subclone C. In summary, we conclude that subclone C likely has a higher prevalence at MRD, whereas the NCSC state is primarily found in subclones A and B. This indicates that all genomic subclones have the potential to adopt various drug-tolerant states and survive therapy. However, their genetic makeup influences their ability to engage these states and successfully endure the treatment.
We developed Gtag&T-seq, a genome-and-transcriptome sequencing protocol of the same single cell that omits WGA using direct genomic tagmentation. Like G&T-seq, Gtag&T-seq is applicable to both whole cells as well as nuclei from fresh frozen tissue. Samples manually picked or sorted in the lysis buffer can be stored for months and even years at −80°C ( ). Following physical separation of the gDNA and polyadenylated RNA of the same cell or nucleus, usually, the RNA is processed first to assess the quality of the samples and the efficiency of the sort, while the gDNA can be stored for later analyses. Because the gDNA and RNA are processed separately, either the gDNA or RNA of a subset of cells can be re-sequenced at a higher depth if needed or sequenced with a different technology (e.g. long-read sequencing), if required, as we demonstrated before . Compared to G&T-seq using picoPlex, Gtag&T-seq is characterised by improved coverage breadth and uniformity in both single-cell and pseudo-bulk genomes, which allows for more precise detection of genomic alterations. This is in line with previous reports of other research groups that used tagmentation-based library preparation without WGA . Furthermore, Gtag&T-seq requires less processing time and significantly reduces the cost of G&T-seq. Moreover, it proved to be superior in the profiling of small focal amplifications (0.12–1.20 Mb), allowing breakpoints to be pinpointed to near base-pair resolution in pseudo-bulk genomes. In addition, Gtag&T allowed us to confirm the resulting gene expression dosage effects using the RNA of the same cell. Importantly, we highlight the need for direct multi-omics approaches to accurately dissect both genomic evolution and transcriptome plasticity in full, as opposed to inferring CNAs from single-cell transcriptomes. Besides failing to detect small focal amplifications, several whole-chromosome gains were found as smaller regions of amplification, suggesting that not all genes are affected to the same degree by genomic imbalances. Furthermore, this approach had difficulties in obtaining the correct CN, potentially underestimating the heterogeneity present in the sample when subclones have shared breakpoints. We applied Gtag&T-seq to a human PDX melanoma model to study the interplay of genomic and transcriptomic alterations in the context of tumour evolution and therapy resistance. Previously, this model was used to identify four drug-tolerant cell states—SMC, NCSC, pigmented and mesenchymal-like states—and revealed limited genomic heterogeneity . It remains unclear whether the cell fate decision to engage one versus another drug-tolerant cell fate is dictated by intrinsic mechanisms and underlying genetic underpinnings and/or exposure to specific environmental cues. Here, we discerned three major genomic subclones and constructed a longitudinal cell lineage tree annotated with the drug-tolerant states throughout therapy. We found subclonal differences with regard to treatment response and transcriptome plasticity: subclone C, which lost a copy of chr2, appeared better suited to survive the initial treatment but had a significant lack of cells with the NCSC state. In addition, we suggest a potential role for the chromosome 13 and 22q11.21 amplifications in determining phenotypic differences between the subclones as well as the drug-tolerant states. One hypothesis is that this effect is mediated by THAP7 , which is expressed higher in both subclone C and the SMC state and is known to promote cell proliferation in lung adenocarcinoma . Another key gene to explain the subclonal differences could be LZTR1 , which is highly expressed in clone C cells with a high CN of 22q11.21 and is located on a breakpoint in clones A and B. Although LZTR1 is generally considered a tumour suppressor, a recent study identified LZTR1 , as well as CRKL , as drivers of tumour development and metastasis in acral melanoma . Furthermore, we identified an inverse correlation between the 22q11.21 amplicon CN and pigmentation-related gene expression, such as MLANA , suggesting that cells with lower CNs may have an increased propensity to differentiate into pigmented cells to withstand drug pressure. Conversely, cells with higher CNs may have a growth advantage under BRAFi/MEKi therapy. Functional validation is necessary to confirm if this amplification and associated genes drive tumour evolution and therapy resistance. In the melanoma model we observed extensive heterogeneity regarding the presence, size, and dosage of focal amplicons that would be difficult to resolve using bulk sequencing and was also not always detected by picoPlex. Similar levels of amplicon heterogeneity were recently also observed in breast cancer by single-nucleus sequencing . It should be further investigated whether focal amplifications, as found in this melanoma model, are drivers of subclonal differences regarding tumour evolution and therapy resistance or whether they are passenger events. Nevertheless, recent studies have shown the importance of profiling driver amplicons as they proved to be predictors for survival as well as actionable targets for cancer therapy . In this work, we show that Gtag&T is a suitable method to profile focal amplifications at near base-pair resolution in single cells. A recent publication introduced Single-cell Extrachromosomal Circular DNA and Transcriptome sequencing (scEC&T-seq), a method enabling parallel sequencing of circular DNAs and full-length RNA from individual cells . While this technique facilitates a detailed characterization of ecDNA content differences and their transcriptional effects, it does so at the expense of overlooking the CN landscape of non-circular DNA structures. In the past years, several multi-omics techniques that interrogate the genome and transcriptome have been developed. The separation principle of transcriptogenomics is similar to that of G&T-seq, but Li et al. only performed exome sequencing. Other techniques based on gDNA-and-RNA separation disunite the nucleus from the cytoplasm following cellular lysis and, therefore have several limitations, including loss of nuclear RNAs in the transcriptome analysis, loss of mitochondrial DNA molecules in the genome analysis, inability of the characterization of mitotic cells in phases where the nuclear membrane has disassembled, and the necessity of intact cells prohibiting the use of the technology on archived fresh-frozen tissue [ , , ]. In DR-seq , gDNA and RNA are first preamplified before splitting the reaction, which minimizes the risk of losing nucleic acids during the separation process but increases the risk of contaminating for example, the gDNA readout with RNA-derived amplicons. Similar advantages and disadvantages are also inherent to scONE-seq , though based on gDNA-versus-RNA specific barcoding and sequencing library preparation in a single-pot reaction, it is additionally limited in sequencing gDNA and RNA libraries separately to optimal depths. Furthermore, methods such as DR-seq and scONE-seq that process gDNA and RNA in a single reaction have limited flexibility, as the joint gDNA-and-RNA pre-amplification protocol needs to be suitable for both molecular analytes. Separating the RNA and gDNA prior to amplification enables more flexibility in choosing which assay is used downstream. For example, in Gtag&T, Smart-seq2 can be replaced with recently developed alternatives, like Smart-seq3 or 3′ RNA-seq technologies to further reduce costs. While the methods above have low-to-medium throughput because (deoxy)ribonucleic acids are processed in a tube per cell, the recently developed sci-L3-RNA/DNA co-assay enables at least 10 000s of single nuclei to be profiled per 2-day experiment. Yin et al. show that this combinatorial indexing-based co-assay can distinguish female HEK293T cells from male BJ cells based on Y chromosome presence. Importantly, we emphasize the need for both high-throughput, like sci-L3, as well as low-to-medium throughput methods when studying rare cells that can be isolated or when only smaller populations of cells are available, for example, when studying genomic instability during preimplantation embryo development . Taken together, Gtag&T will enable researchers to study the interplay of genome and transcriptome in unprecedented detail. We anticipate that our method will be broadly applicable to characterise the role of somatic variation in health and disease in fields such as oncology, neurology, and embryology.
gkaf173_Supplemental_Files
|
Emerging multiscale insights on microbial carbon use efficiency in the land carbon cycle | 0898f714-981b-4d01-bb49-10bf2e145e1e | 11399347 | Microbiology[mh] | Earth System Models (ESMs) are indispensable tools for predicting the planetary response to climate change . The accuracy and reliability of ESMs are crucial for informing climate projections that guide policy decisions. Soils store more carbon (C) than plants, the surface ocean or the atmosphere, and thus are critical for the functioning of the Earth system . While ESMs are becoming increasingly complex, their predictions of soil organic C (SOC) stocks have improved only marginally in recent decades , . Microbial communities process most of the C entering the soil, thereby shaping its fate , . Microbes metabolize multiple C sources, including detritus, root exudates, and microbial metabolites . The energy needed to acquire C depends on whether the compounds can be taken up directly or require prior enzymatic degradation . Additionally, microbial community composition and functioning are influenced by prevailing climatic conditions – . The general omission of microbial community structure and related processes in C cycle models has been suggested as one of the causes for their poor performance in predicting SOC stocks and their responses to climate change , . Recognizing the impracticality of representing every conceivable microbial metabolic pathway, many models combine a spectrum of microbial processes into a single metric referred to as microbial C use efficiency (CUE) , . CUE, as a model parameter or as a system property emerging from multiple co-occurring processes, represents the fraction of C uptake allocated to the production of new microbial biomass . Using this definition, CUE declines as more C is used for respiration to generate energy (for substrate uptake, cellular maintenance, enzyme production) or for exudation (extracellular enzymes, polysaccharides) , . This pragmatic approach streamlines the modeling of soil C cycling by incorporating the diverse fates of microbial C, including biomass production, respiration, and exudation, thereby providing a more comprehensive understanding of microbially-mediated C-pathways. However, accurately integrating the spatial or temporal dynamics of microbial CUE into soil C models remains a significant challenge. Most of the current C cycle models either lack explicit representation of CUE or treat it as a constant value , despite our understanding that CUE varies under different environmental conditions. For example, observations indicate significant variability in CUE at the global scale , which may be partially attributed to inconsistencies among measurement techniques (Fig. ). Moreover, comparisons across ecosystems reveal that CUE is generally higher in grasslands than in croplands, with forests consistently showing the lowest CUE values, regardless of the measurement approaches used , (Fig. ). CUEs derived from data assimilation are also lower than those from more direct measurement approaches (Fig. ). Several attempts have been made to reflect or incorporate CUE variations into models of litter or soil organic matter , decomposition with the aim of assessing the implications for soil C cycling. For example, incorporating an empirically-derived negative relationship between microbial CUE and temperature into a microbial-explicit SOC model improved the simulation of contemporary soil C stocks . Zhang et al. introduced the effects of substrate quality and soil fertility on microbial respiration, highlighting the joint control of litter quality and quantity on the steady-state SOC stocks. Wieder et al. enhanced the understanding of CUE variation by including two types of decomposers with differing substrate preferences and CUE (Fig. ). These examples suggest that more realistic representations of microbial C transformations have the scope for improving model predictions of soil C , . However, these predictions were poorly constrained by observational data, calling their reliability into question , , . In this Perspective, we synthesize our understanding of CUE regulatory factors and databases for constraining numerical models, with the aim of clarifying complexities, addressing controversies, and providing a holistic perspective on pathways to adequately reflect CUE variations in C cycle models and their consequences for simulated soil C stocks.
Terminology and definitions of microbial CUE The concept of microbial CUE, the fraction of C uptake that is used to produce microbial biomass – , is intuitively straightforward, but CUE definitions vary depending on the ecological processes involved, measurement methods, and scales of biological organization (e.g., population, community and ecosystem) , . Therefore, CUE can be regarded as an emergent parameter, encapsulating multiple processes within a single metric. It is useful in modeling as the number of processes that can be modeled is constrained by practical limitations (e.g., availability of data for calibration). Consequently, ecosystem models often simplify microbial process complexity, which in reality, escalates from the genomic to the ecosystem level (Fig. ). CUE is quantitatively expressed as the ratio of microbial growth (μ) to C uptake (U) , , that is, CUE = μ/U. This ratio encapsulates the efficiency with which microorganisms convert assimilated C into biomass. Microbial uptake involves C assimilation for growth (μ), respiration (R), and the secretion of extracellular enzymes and metabolites (EX). Geyer et al. introduced a nested conceptual framework for understanding CUE across different biological organization levels: population (CUE P ), community (CUE C ), and ecosystem (CUE E ). This framework is useful for integrating C fluxes mediated by soil microbes into models at various ecological scales (Fig. ). CUE P reflects the species-specific functioning of microbial taxa (e.g., biosynthesis rate, exudate production) and thermodynamics of C substrate metabolism that limits the proportion of C uptake used for biosynthesis versus C lost from the cell (e.g., mineralized or exuded as metabolites). Typically measured in cultured populations, the CUE P formula adjusts for respiration (R) and exudation (EX) losses from the uptake, expressed as CUE P = [12pt]{minimal}
$$}{U}$$ U − R − E X U . CUE C incorporates additional environmental and community factors influencing microbial metabolism in natural communities consisting of multiple populations. It focuses on gross microbial production prior to the recursive substrate recycling of necromass and exudates, capturing the metabolic response of microbial communities to substrates over short durations (hours), and is similarly expressed as CUE C = [12pt]{minimal}
$$}{U}$$ U − R − E X U . CUE E considers C retention as net microbial growth over longer time scales (days to months), taking into account the drivers of CUE P and CUE C as well as microbial biomass turnover. On these time scales, a significant proportion of microbial biomass is converted to necromass following microbial death (MD) such that CUE E = [12pt]{minimal}
$$-{MD}}{U}$$ U − R − E X − M D U , encompassing all aspects of microbial C processing, including death and recycling processes. Methods for measuring microbial CUE Multiple approaches can be used to quantify CUE, such as isotopically labeling substrates , , stoichiometric modeling , and others . These methods rely on different assumptions and capture distinct microbial processes, which can explain the variability in CUE estimates across methods , , (Fig. ), including differences in the response of CUE to environmental changes , and the relationship between CUE and SOC (Fig. ). The most common approach for measuring CUE is the tracking of isotopically labeled compounds ( 14 C, 13 C labeled substrate, or 18 O water) introduced to the system. Carbon isotopes in microbial substrates enable the differentiation between C allocated to microbial biomass and that released through respiration. Although this labeling technique is widely used, its results can be influenced by the choice and combination of substrates , as well as the incubation period , . A significant limitation of this approach is that measured CUE reflects only the efficiency of those microbes that use the introduced substrates, not the entire microbial community. Furthermore, the variation in incubation times and temperatures across different studies (Fig. ) presents a substantial obstacle to standardizing CUE measurements. The method using 18 O-labeled water is based on the incorporation of the 18 O-atom into microbial DNA as a measure of growth as compared to catabolic C losses as CO 2 , . This method has higher accuracy than the C labeling method as it is not substrate specific, does not perturb microbial metabolism like methods involving substrate addition, and exhibits comparatively less variability over time . Nonetheless, this method faces limitations such as higher cost and demanding technical procedures. Concerns also arise regarding the method’s foundational assumptions, e.g., the presumption that water is the sole oxygen source for microbial DNA synthesis and the hypothesis that all microbial cells maintain a consistent DNA to biomass C ratio . Furthermore, its applicability in dry soils is challenging . Stoichiometric modeling is a common method for indirectly estimating CUE, which is based on the assumption that microbes growing on plant detritus allocate C to produce enzymes and other necessary components to acquire nutrients in the appropriate elemental ratios at the whole-community scale , . This approach offers the advantage of requiring only a limited number of parameters, such as the activities of enzymes targeting C versus nitrogen (N) or phosphorus (P) acquisition and the C:N:P composition of the substrate and microbial biomass, which can be constrained by existing observations. However, it relies on highly simplified assumptions regarding elemental ratios and C allocation . This approach inherently suggests lower CUE in soils with high SOC due to its focus on the metabolic costs of nutrient acquisition under conditions where nutrients are scarce relative to C. This outcome (Fig. ) starkly contrasts with the positive correlation between CUE and SOC observed using isotopic labeling techniques (Fig. ), which are commonly considered to provide a more realistic insight into the relationship between CUE and SOC. The isotope labeling method estimates microbial growth and CUE by tracking the incorporation of labeled atoms into biomass or DNA, reflecting intracellular biochemical transformations. In contrast, the stoichiometry model method estimates CUE by analyzing the activities of extracellular enzymes and the stoichiometric balance between organic matter and microbial biomass, focusing on extracellular metabolic processes . Therefore, caution is advised when comparing results obtained from these two methods, even though they use the same term (CUE). We do not yet know the extent to which the stoichiometric and isotope methods are comparable. Until we understand which patterns can be accurately captured by the simpler stoichiometric method, we should rely on the more robust 18 O method for measuring actual CUE and the 13 C method for CUE associated with specific substrates. In addition to the methods mentioned above, there are other less commonly used approaches, including the use of 18 O in water vapor to minimize impact on soil moisture , metabolic flux analysis , and calorespirometry . Each method offers unique advantages and faces specific limitations, grounded in their underlying assumptions and theoretical bases – . These limitations not only affect the accuracy of these methods but also introduce significant comparability issues. Consequently, there is an urgent need to improve current methodologies and integrate innovative techniques to more accurately assess soil microbial CUE. Data gap Given the methodological challenges in measuring CUE in situ, field assessments of microbial CUE are rare. The vast majority of existing CUE observations have been obtained from lab incubations. Yet, these CUE observations remain scarce at the global scale, a situation which is exacerbated by the lack of harmonization of observations from different measurement approaches. For some ecosystems, observations are few or even nonexistent, including ecosystems that play a critical role in the global C cycle, such as tropical rainforests, wetlands, and peatlands , . Existing CUE measurements mostly come from studies of the litter and surface mineral soil . Thus, our understanding of microbial CUE in subsurface soil remains limited, which is problematic as large amounts of C are stored in subsoils globally, and especially those of wetlands and peatlands. The few existing studies indicate that microbial CUE decreases with soil depth , and that subsurface CUE may be less sensitive to warming but more sensitive to nutrient variations . Moreover, data on temporal variations in CUE are lacking. A commonly overlooked factor that may contribute significantly to CUE variability in soil ecosystems, regardless of methodology, is seasonality in CUE. Seasonal changes are associated with significant variations in substrate availability, temperature and moisture, all of which may have a substantial impact on the growth and respiration of soil microorganisms, thereby altering microbial CUE . For example, CUE estimated using the 18 O incorporation method ranged from 0.1 to 0.7 in soils from an agricultural field site and from 0.1 to 0.6 at a forest site within one year . It has also been reported that soil microbial CUE exhibits significant fluctuations within a short period (daily) after rewetting , . This temporal dynamic in CUE values could contribute to the significant variability observed in CUE measurements.
The concept of microbial CUE, the fraction of C uptake that is used to produce microbial biomass – , is intuitively straightforward, but CUE definitions vary depending on the ecological processes involved, measurement methods, and scales of biological organization (e.g., population, community and ecosystem) , . Therefore, CUE can be regarded as an emergent parameter, encapsulating multiple processes within a single metric. It is useful in modeling as the number of processes that can be modeled is constrained by practical limitations (e.g., availability of data for calibration). Consequently, ecosystem models often simplify microbial process complexity, which in reality, escalates from the genomic to the ecosystem level (Fig. ). CUE is quantitatively expressed as the ratio of microbial growth (μ) to C uptake (U) , , that is, CUE = μ/U. This ratio encapsulates the efficiency with which microorganisms convert assimilated C into biomass. Microbial uptake involves C assimilation for growth (μ), respiration (R), and the secretion of extracellular enzymes and metabolites (EX). Geyer et al. introduced a nested conceptual framework for understanding CUE across different biological organization levels: population (CUE P ), community (CUE C ), and ecosystem (CUE E ). This framework is useful for integrating C fluxes mediated by soil microbes into models at various ecological scales (Fig. ). CUE P reflects the species-specific functioning of microbial taxa (e.g., biosynthesis rate, exudate production) and thermodynamics of C substrate metabolism that limits the proportion of C uptake used for biosynthesis versus C lost from the cell (e.g., mineralized or exuded as metabolites). Typically measured in cultured populations, the CUE P formula adjusts for respiration (R) and exudation (EX) losses from the uptake, expressed as CUE P = [12pt]{minimal}
$$}{U}$$ U − R − E X U . CUE C incorporates additional environmental and community factors influencing microbial metabolism in natural communities consisting of multiple populations. It focuses on gross microbial production prior to the recursive substrate recycling of necromass and exudates, capturing the metabolic response of microbial communities to substrates over short durations (hours), and is similarly expressed as CUE C = [12pt]{minimal}
$$}{U}$$ U − R − E X U . CUE E considers C retention as net microbial growth over longer time scales (days to months), taking into account the drivers of CUE P and CUE C as well as microbial biomass turnover. On these time scales, a significant proportion of microbial biomass is converted to necromass following microbial death (MD) such that CUE E = [12pt]{minimal}
$$-{MD}}{U}$$ U − R − E X − M D U , encompassing all aspects of microbial C processing, including death and recycling processes.
Multiple approaches can be used to quantify CUE, such as isotopically labeling substrates , , stoichiometric modeling , and others . These methods rely on different assumptions and capture distinct microbial processes, which can explain the variability in CUE estimates across methods , , (Fig. ), including differences in the response of CUE to environmental changes , and the relationship between CUE and SOC (Fig. ). The most common approach for measuring CUE is the tracking of isotopically labeled compounds ( 14 C, 13 C labeled substrate, or 18 O water) introduced to the system. Carbon isotopes in microbial substrates enable the differentiation between C allocated to microbial biomass and that released through respiration. Although this labeling technique is widely used, its results can be influenced by the choice and combination of substrates , as well as the incubation period , . A significant limitation of this approach is that measured CUE reflects only the efficiency of those microbes that use the introduced substrates, not the entire microbial community. Furthermore, the variation in incubation times and temperatures across different studies (Fig. ) presents a substantial obstacle to standardizing CUE measurements. The method using 18 O-labeled water is based on the incorporation of the 18 O-atom into microbial DNA as a measure of growth as compared to catabolic C losses as CO 2 , . This method has higher accuracy than the C labeling method as it is not substrate specific, does not perturb microbial metabolism like methods involving substrate addition, and exhibits comparatively less variability over time . Nonetheless, this method faces limitations such as higher cost and demanding technical procedures. Concerns also arise regarding the method’s foundational assumptions, e.g., the presumption that water is the sole oxygen source for microbial DNA synthesis and the hypothesis that all microbial cells maintain a consistent DNA to biomass C ratio . Furthermore, its applicability in dry soils is challenging . Stoichiometric modeling is a common method for indirectly estimating CUE, which is based on the assumption that microbes growing on plant detritus allocate C to produce enzymes and other necessary components to acquire nutrients in the appropriate elemental ratios at the whole-community scale , . This approach offers the advantage of requiring only a limited number of parameters, such as the activities of enzymes targeting C versus nitrogen (N) or phosphorus (P) acquisition and the C:N:P composition of the substrate and microbial biomass, which can be constrained by existing observations. However, it relies on highly simplified assumptions regarding elemental ratios and C allocation . This approach inherently suggests lower CUE in soils with high SOC due to its focus on the metabolic costs of nutrient acquisition under conditions where nutrients are scarce relative to C. This outcome (Fig. ) starkly contrasts with the positive correlation between CUE and SOC observed using isotopic labeling techniques (Fig. ), which are commonly considered to provide a more realistic insight into the relationship between CUE and SOC. The isotope labeling method estimates microbial growth and CUE by tracking the incorporation of labeled atoms into biomass or DNA, reflecting intracellular biochemical transformations. In contrast, the stoichiometry model method estimates CUE by analyzing the activities of extracellular enzymes and the stoichiometric balance between organic matter and microbial biomass, focusing on extracellular metabolic processes . Therefore, caution is advised when comparing results obtained from these two methods, even though they use the same term (CUE). We do not yet know the extent to which the stoichiometric and isotope methods are comparable. Until we understand which patterns can be accurately captured by the simpler stoichiometric method, we should rely on the more robust 18 O method for measuring actual CUE and the 13 C method for CUE associated with specific substrates. In addition to the methods mentioned above, there are other less commonly used approaches, including the use of 18 O in water vapor to minimize impact on soil moisture , metabolic flux analysis , and calorespirometry . Each method offers unique advantages and faces specific limitations, grounded in their underlying assumptions and theoretical bases – . These limitations not only affect the accuracy of these methods but also introduce significant comparability issues. Consequently, there is an urgent need to improve current methodologies and integrate innovative techniques to more accurately assess soil microbial CUE.
Given the methodological challenges in measuring CUE in situ, field assessments of microbial CUE are rare. The vast majority of existing CUE observations have been obtained from lab incubations. Yet, these CUE observations remain scarce at the global scale, a situation which is exacerbated by the lack of harmonization of observations from different measurement approaches. For some ecosystems, observations are few or even nonexistent, including ecosystems that play a critical role in the global C cycle, such as tropical rainforests, wetlands, and peatlands , . Existing CUE measurements mostly come from studies of the litter and surface mineral soil . Thus, our understanding of microbial CUE in subsurface soil remains limited, which is problematic as large amounts of C are stored in subsoils globally, and especially those of wetlands and peatlands. The few existing studies indicate that microbial CUE decreases with soil depth , and that subsurface CUE may be less sensitive to warming but more sensitive to nutrient variations . Moreover, data on temporal variations in CUE are lacking. A commonly overlooked factor that may contribute significantly to CUE variability in soil ecosystems, regardless of methodology, is seasonality in CUE. Seasonal changes are associated with significant variations in substrate availability, temperature and moisture, all of which may have a substantial impact on the growth and respiration of soil microorganisms, thereby altering microbial CUE . For example, CUE estimated using the 18 O incorporation method ranged from 0.1 to 0.7 in soils from an agricultural field site and from 0.1 to 0.6 at a forest site within one year . It has also been reported that soil microbial CUE exhibits significant fluctuations within a short period (daily) after rewetting , . This temporal dynamic in CUE values could contribute to the significant variability observed in CUE measurements.
The incorporation of soil microbial CUE dynamics into process-based models necessitates a comprehensive understanding of a range of regulatory factors influencing CUE (Fig. ). CUE at a specific biological level is influenced by features of both the microbial community itself (biological controls) and its external environment (abiotic controls). These factors frequently interact, particularly at the community and ecosystem levels: abiotic controls can modify CUE C or CUE E by regulating biological controls, while biological controls may induce adaptation to abiotic factors, thereby influencing the impact of abiotic controls. Biological controls Microbial physiological state Microbial CUE reflects the physiological state of microorganisms. Under natural conditions, only a small proportion (values vary from 1% to >20% in different studies , ) of soil microbial cells are metabolically active, and soil respiration primarily originates from these metabolically active cells . Nonetheless, a high fraction of microbial cells in the soil are in a potentially active state (10 to 60% of the total microbial biomass), meaning that they are ready to start using available substrates within a few hours after easily available substrate is added. The shifts in physiological states of these microbial cells, resulting from changes in temperature, moisture, or substrate availability, significantly impact CUE . Consequently, CUE P or CUE C measurement methods relying on substrate addition may overestimate CUE , and shifts in physiological state can lead to seasonal variations in CUE . Microbial community diversity and composition Increased microbial diversity enriches the spectrum of metabolic functions within a community, potentially leading to greater microbial growth and CUE C by facilitating more efficient use of varied C sources , . The composition of microbial communities, notably the ratio of fungal to bacterial biomass (F:B), plays a critical role in determining CUE C . Communities dominated by fungi can show higher CUE C , attributed to their higher biomass C to N) ratios (C:N) and their proficiency in decomposing complex organic materials , or lower CUE due to the high costs associated with resource acquisition by decomposer fungi . Therefore, this contrasting evidence from plant litter studies indicates that the relationship between F:B ratio and CUE is context-dependent , . Alternatively, an approach categorizing microorganisms into copiotrophs ( r -strategists with low CUE) versus oligotrophs ( K -strategists with high CUE) has been promising for estimating CUE . For example, shifts from r -strategists to K -strategists explain increased CUE C along a successional gradient in the southeastern Tibetan Plateau . Changes in community composition may also enable microbial communities to alter their CUE in response to environmental changes or fluctuations , . For instance, long-term warming experiments indicate a decline in the temperature sensitivity of CUE C , suggesting that shifts in microbial composition can maintain CUE C despite changes in temperature and substrate quality . Similarly, modeling studies suggest that changing microbial community composition can reduce the sensitivity of CUE C to substrate quality and soil moisture fluctuations . Biotic interactions In the soil food web, biotic interactions such as mutualism, facilitation, competition, and predation can shape CUE C . Interspecific microbial competition drives accelerated growth rates, accompanied by the release of secondary metabolites that can negatively affect CUE C . Antagonistic interactions may trigger stress responses, further diminishing CUE C . Conversely, facilitation enhances CUE C by broadening species-realized niches, alleviating environmental stress, and reducing extracellular enzyme production costs . Biotic interactions at higher trophic levels, such as predation, can variably affect CUE C by altering microbial density and influencing the outcomes of interspecific competition , . Abiotic controls Temperature Temperature significantly affects soil microbial CUE, with respiration often increasing more than growth in short-term incubations, resulting in a decrease in CUE P , , . The impact on CUE C and CUE E is less clear , likely due to varied responses among microbial taxa , and interactive effects with other environmental factors , , , . Temperature shifts can lead to changes in community traits or select for taxa with distinct life strategies, known as trait modification and trait filtering, respectively , . However, limited research on how CUE P varies among different taxa in response to temperature impairs our ability to accurately predict changes in CUE C – . The interplay between direct and indirect temperature effects on soil microbial CUE C and CUE E complicates our understanding of the impact of warming on CUE. Warming can intensify C-nutrient imbalances, potentially diminishing microbial CUE , but it can also improve the efficiency of substrate utilization, thereby enhancing CUE , . Expected reductions in soil moisture due to increased evapotranspiration under warming conditions add another layer of complexity, with the combined impacts of temperature and moisture on microbial CUE remaining inadequately explored , . Some soil C models, including Millennial and MIMICS have begun to account for the temperature dependency of CUE C , indicating a growing recognition of the importance of including the dynamic response of microbial CUE to fluctuations in temperature. Soil water availability Increased soil moisture promotes microbial growth and CUE by improving substrate diffusivity and accessibility, and lowering investment in osmolyte synthesis, as long as conditions remain oxic , , . Prolonged water stress reduces soil substrate accessibility and increases the need to synthesize osmolytes to survive during dry periods, leading to lower CUE C , even though the taxa that remain active in dry conditions can maintain relatively high growth rates . Furthermore, drought reduces plant C inputs to the soil , thus potentially leaving microbes with fewer lower resources, resulting in lower CUE. The intricate interplay of drought-induced changes in microbial respiration and growth may leave CUE unchanged if the affected processes balance each other . High levels of soil moisture may also reduce microbial CUE. As soil pores fill with water, air spaces and oxygen diffusivity decline, potentially leading to anaerobic conditions if saturation occurs. Under O 2 limitation, soil microbes shift from aerobic to anaerobic respiration or fermentation, significantly reducing energy yield and leading to decreased microbial growth and CUE while having little impact on CO 2 production rate due to upregulated biochemical rates . Microbial responses to rewetting of a dry soil also cause rapid changes in CUE, as shown in modeling studies and confirmed by empirical evidence . Upon rewetting, respiration increases while growth lags behind, especially when the soil has been dry for a long period . As a result, just after rewetting, CUE is low and then increases as growth recovers during the first days after rewetting. However, after this initial pulse of microbial activity, CUE peaks and decreases again as substrates released during rewetting are consumed . Nutrient availability The availability of nutrients such as N and P significantly affects microbial growth and respiration according to the concept of stoichiometric homeostasis which assumes constrained biomass C:N:P ratios of microbial cells , . Consequently, CUE decreases with increasing substrate C-to-nutrient ratios and increases with nutrient amendment when organic substrates are nutrient-poor , . Several C cycle models, such as the one proposed by Manzoni et al. and its later implementation , have integrated CUE dynamics as a function of stoichiometry. In contrast to the homeostasis concept, recent findings highlight the capability of microbes to store and use nutrients dynamically, contributing to a stable CUE across different environments by separating growth and respiration processes from immediate nutrient availability . This resilience to nutrient stress suggests that future C modeling should incorporate microbial nutrient storage dynamics for enhanced predictive accuracy. Soil pH Soil pH influences microbial CUE C and CUE E by affecting the bacterial community composition and acting as a potential stressor . It also impacts CUE by altering microbial community composition , nutrient solubility , and metal toxicity (e.g., aluminum ). Habitats with neutral pH generally have higher bacterial diversity and biomass compared to acidic or alkaline soils . The response of community composition to a shift in soil pH from acidic to neutral corresponded with a significant increase in CUE C , . However, recent research indicates a complex interplay between soil pH, microbial community composition, and CUE dynamics, evidenced by both negative correlations and a U-shaped response curve, pinpointing a critical threshold at pH 6.4 , although the calculations to document this are complex and may necessitate refinement. Soil texture and structure Microbial growth is intricately linked to substrate accessibility, which is influenced by soil environmental conditions like texture and soil structure. Approximately 40–70% of soil bacteria are associated with microaggregates and clay particles . The structural complexity of the soil environment also plays a crucial role in shaping the community structure and function of soil microorganisms at the ecosystem level . Heterogeneity of soil structure and composition creates diverse microhabitats that influence microbial interactions, diversity, distributions, and activity, as well as ecosystem processes like nutrient cycling and organic matter decomposition . Still, limited information exists on the relationship between soil texture or structure and microbial CUE. A recent meta-analysis found a significant positive link between microbial CUE C or CUE E for glucose and soil clay content , which was attributed to increased clay content enhancing substrate adsorption , thereby limiting substrate availability to microbes , and resulting in higher microbial CUE C or CUE E . Substrate quality Substrate quality, defined by the chemical characteristics of organic matter that influence its decomposability, such as the C:N ratio and molecular composition, significantly impacts soil microbial CUE . A “high-quality” substrate typically has a lower C:N ratio, indicating a balanced N content relative to C, and a lower content of recalcitrant compounds, which generally leads to faster decomposition and higher CUE by providing C and nutrients that microbes require for growth and metabolism . Compounds requiring multiple enzymatic steps for degradation can lead to reduced efficiency in building biomass. Polymeric substrates like lignin and cellulose need depolymerization before cellular uptake, whereas smaller substrates readily diffuse across membranes . Takriti et al. (2018) found a positive association between soil CUE C and ratios of cellulase to phenol oxidase enzyme activity potential, which was considered to be indicative of soil organic matter (SOM) substrate quality . Different substrates necessitate distinct metabolic pathways, resulting in different respiration rates per unit C assimilated , . Frey et al. (2013) observed lower microbial CUE C when soils were amended with oxalic acid or phenolic compounds compared to glucose, despite similar molecular sizes . Microbial CUE increases with the chemical energy per mole of C in the substrate, highlighting the importance of substrate chemistry for microbial CUE variability in soil . This relationship is akin to the concept of energetic imbalance , which parallels the idea of stoichiometric imbalance. The energy content of soil microbial biomass and substrate can be quantified by the degree of reduction (γ), which refers to the average number of electrons available per C atom for biochemical reactions, indicating the energy density of the substrate or biomass . The degree of reduction of soil microbial biomass (γ B ) is typically around 4.2, while that of substrate (γ S ) usually varies between 1 (e.g., for oxalate) and 8 (methane) . Most of the substrates used by soil microorganisms have a γ S of 3 (e.g., various organic acids), 4 (e.g., glucose and other carbohydrates), and rarely 5 or higher (e.g., leucine, polyhydroxyalkanoates or lipids) . When γ S is lower than γ B , the substrate’s energy content is insufficient to meet microbial demand, necessitating the oxidation of more substrate per unit of C assimilated, thereby reducing CUE . These insights form the basis of the stoichiometric modeling for indirect CUE estimates.
Microbial physiological state Microbial CUE reflects the physiological state of microorganisms. Under natural conditions, only a small proportion (values vary from 1% to >20% in different studies , ) of soil microbial cells are metabolically active, and soil respiration primarily originates from these metabolically active cells . Nonetheless, a high fraction of microbial cells in the soil are in a potentially active state (10 to 60% of the total microbial biomass), meaning that they are ready to start using available substrates within a few hours after easily available substrate is added. The shifts in physiological states of these microbial cells, resulting from changes in temperature, moisture, or substrate availability, significantly impact CUE . Consequently, CUE P or CUE C measurement methods relying on substrate addition may overestimate CUE , and shifts in physiological state can lead to seasonal variations in CUE .
Microbial CUE reflects the physiological state of microorganisms. Under natural conditions, only a small proportion (values vary from 1% to >20% in different studies , ) of soil microbial cells are metabolically active, and soil respiration primarily originates from these metabolically active cells . Nonetheless, a high fraction of microbial cells in the soil are in a potentially active state (10 to 60% of the total microbial biomass), meaning that they are ready to start using available substrates within a few hours after easily available substrate is added. The shifts in physiological states of these microbial cells, resulting from changes in temperature, moisture, or substrate availability, significantly impact CUE . Consequently, CUE P or CUE C measurement methods relying on substrate addition may overestimate CUE , and shifts in physiological state can lead to seasonal variations in CUE .
Increased microbial diversity enriches the spectrum of metabolic functions within a community, potentially leading to greater microbial growth and CUE C by facilitating more efficient use of varied C sources , . The composition of microbial communities, notably the ratio of fungal to bacterial biomass (F:B), plays a critical role in determining CUE C . Communities dominated by fungi can show higher CUE C , attributed to their higher biomass C to N) ratios (C:N) and their proficiency in decomposing complex organic materials , or lower CUE due to the high costs associated with resource acquisition by decomposer fungi . Therefore, this contrasting evidence from plant litter studies indicates that the relationship between F:B ratio and CUE is context-dependent , . Alternatively, an approach categorizing microorganisms into copiotrophs ( r -strategists with low CUE) versus oligotrophs ( K -strategists with high CUE) has been promising for estimating CUE . For example, shifts from r -strategists to K -strategists explain increased CUE C along a successional gradient in the southeastern Tibetan Plateau . Changes in community composition may also enable microbial communities to alter their CUE in response to environmental changes or fluctuations , . For instance, long-term warming experiments indicate a decline in the temperature sensitivity of CUE C , suggesting that shifts in microbial composition can maintain CUE C despite changes in temperature and substrate quality . Similarly, modeling studies suggest that changing microbial community composition can reduce the sensitivity of CUE C to substrate quality and soil moisture fluctuations .
In the soil food web, biotic interactions such as mutualism, facilitation, competition, and predation can shape CUE C . Interspecific microbial competition drives accelerated growth rates, accompanied by the release of secondary metabolites that can negatively affect CUE C . Antagonistic interactions may trigger stress responses, further diminishing CUE C . Conversely, facilitation enhances CUE C by broadening species-realized niches, alleviating environmental stress, and reducing extracellular enzyme production costs . Biotic interactions at higher trophic levels, such as predation, can variably affect CUE C by altering microbial density and influencing the outcomes of interspecific competition , .
Temperature Temperature significantly affects soil microbial CUE, with respiration often increasing more than growth in short-term incubations, resulting in a decrease in CUE P , , . The impact on CUE C and CUE E is less clear , likely due to varied responses among microbial taxa , and interactive effects with other environmental factors , , , . Temperature shifts can lead to changes in community traits or select for taxa with distinct life strategies, known as trait modification and trait filtering, respectively , . However, limited research on how CUE P varies among different taxa in response to temperature impairs our ability to accurately predict changes in CUE C – . The interplay between direct and indirect temperature effects on soil microbial CUE C and CUE E complicates our understanding of the impact of warming on CUE. Warming can intensify C-nutrient imbalances, potentially diminishing microbial CUE , but it can also improve the efficiency of substrate utilization, thereby enhancing CUE , . Expected reductions in soil moisture due to increased evapotranspiration under warming conditions add another layer of complexity, with the combined impacts of temperature and moisture on microbial CUE remaining inadequately explored , . Some soil C models, including Millennial and MIMICS have begun to account for the temperature dependency of CUE C , indicating a growing recognition of the importance of including the dynamic response of microbial CUE to fluctuations in temperature.
Temperature significantly affects soil microbial CUE, with respiration often increasing more than growth in short-term incubations, resulting in a decrease in CUE P , , . The impact on CUE C and CUE E is less clear , likely due to varied responses among microbial taxa , and interactive effects with other environmental factors , , , . Temperature shifts can lead to changes in community traits or select for taxa with distinct life strategies, known as trait modification and trait filtering, respectively , . However, limited research on how CUE P varies among different taxa in response to temperature impairs our ability to accurately predict changes in CUE C – . The interplay between direct and indirect temperature effects on soil microbial CUE C and CUE E complicates our understanding of the impact of warming on CUE. Warming can intensify C-nutrient imbalances, potentially diminishing microbial CUE , but it can also improve the efficiency of substrate utilization, thereby enhancing CUE , . Expected reductions in soil moisture due to increased evapotranspiration under warming conditions add another layer of complexity, with the combined impacts of temperature and moisture on microbial CUE remaining inadequately explored , . Some soil C models, including Millennial and MIMICS have begun to account for the temperature dependency of CUE C , indicating a growing recognition of the importance of including the dynamic response of microbial CUE to fluctuations in temperature.
Increased soil moisture promotes microbial growth and CUE by improving substrate diffusivity and accessibility, and lowering investment in osmolyte synthesis, as long as conditions remain oxic , , . Prolonged water stress reduces soil substrate accessibility and increases the need to synthesize osmolytes to survive during dry periods, leading to lower CUE C , even though the taxa that remain active in dry conditions can maintain relatively high growth rates . Furthermore, drought reduces plant C inputs to the soil , thus potentially leaving microbes with fewer lower resources, resulting in lower CUE. The intricate interplay of drought-induced changes in microbial respiration and growth may leave CUE unchanged if the affected processes balance each other . High levels of soil moisture may also reduce microbial CUE. As soil pores fill with water, air spaces and oxygen diffusivity decline, potentially leading to anaerobic conditions if saturation occurs. Under O 2 limitation, soil microbes shift from aerobic to anaerobic respiration or fermentation, significantly reducing energy yield and leading to decreased microbial growth and CUE while having little impact on CO 2 production rate due to upregulated biochemical rates . Microbial responses to rewetting of a dry soil also cause rapid changes in CUE, as shown in modeling studies and confirmed by empirical evidence . Upon rewetting, respiration increases while growth lags behind, especially when the soil has been dry for a long period . As a result, just after rewetting, CUE is low and then increases as growth recovers during the first days after rewetting. However, after this initial pulse of microbial activity, CUE peaks and decreases again as substrates released during rewetting are consumed .
The availability of nutrients such as N and P significantly affects microbial growth and respiration according to the concept of stoichiometric homeostasis which assumes constrained biomass C:N:P ratios of microbial cells , . Consequently, CUE decreases with increasing substrate C-to-nutrient ratios and increases with nutrient amendment when organic substrates are nutrient-poor , . Several C cycle models, such as the one proposed by Manzoni et al. and its later implementation , have integrated CUE dynamics as a function of stoichiometry. In contrast to the homeostasis concept, recent findings highlight the capability of microbes to store and use nutrients dynamically, contributing to a stable CUE across different environments by separating growth and respiration processes from immediate nutrient availability . This resilience to nutrient stress suggests that future C modeling should incorporate microbial nutrient storage dynamics for enhanced predictive accuracy.
Soil pH influences microbial CUE C and CUE E by affecting the bacterial community composition and acting as a potential stressor . It also impacts CUE by altering microbial community composition , nutrient solubility , and metal toxicity (e.g., aluminum ). Habitats with neutral pH generally have higher bacterial diversity and biomass compared to acidic or alkaline soils . The response of community composition to a shift in soil pH from acidic to neutral corresponded with a significant increase in CUE C , . However, recent research indicates a complex interplay between soil pH, microbial community composition, and CUE dynamics, evidenced by both negative correlations and a U-shaped response curve, pinpointing a critical threshold at pH 6.4 , although the calculations to document this are complex and may necessitate refinement.
Microbial growth is intricately linked to substrate accessibility, which is influenced by soil environmental conditions like texture and soil structure. Approximately 40–70% of soil bacteria are associated with microaggregates and clay particles . The structural complexity of the soil environment also plays a crucial role in shaping the community structure and function of soil microorganisms at the ecosystem level . Heterogeneity of soil structure and composition creates diverse microhabitats that influence microbial interactions, diversity, distributions, and activity, as well as ecosystem processes like nutrient cycling and organic matter decomposition . Still, limited information exists on the relationship between soil texture or structure and microbial CUE. A recent meta-analysis found a significant positive link between microbial CUE C or CUE E for glucose and soil clay content , which was attributed to increased clay content enhancing substrate adsorption , thereby limiting substrate availability to microbes , and resulting in higher microbial CUE C or CUE E .
Substrate quality, defined by the chemical characteristics of organic matter that influence its decomposability, such as the C:N ratio and molecular composition, significantly impacts soil microbial CUE . A “high-quality” substrate typically has a lower C:N ratio, indicating a balanced N content relative to C, and a lower content of recalcitrant compounds, which generally leads to faster decomposition and higher CUE by providing C and nutrients that microbes require for growth and metabolism . Compounds requiring multiple enzymatic steps for degradation can lead to reduced efficiency in building biomass. Polymeric substrates like lignin and cellulose need depolymerization before cellular uptake, whereas smaller substrates readily diffuse across membranes . Takriti et al. (2018) found a positive association between soil CUE C and ratios of cellulase to phenol oxidase enzyme activity potential, which was considered to be indicative of soil organic matter (SOM) substrate quality . Different substrates necessitate distinct metabolic pathways, resulting in different respiration rates per unit C assimilated , . Frey et al. (2013) observed lower microbial CUE C when soils were amended with oxalic acid or phenolic compounds compared to glucose, despite similar molecular sizes . Microbial CUE increases with the chemical energy per mole of C in the substrate, highlighting the importance of substrate chemistry for microbial CUE variability in soil . This relationship is akin to the concept of energetic imbalance , which parallels the idea of stoichiometric imbalance. The energy content of soil microbial biomass and substrate can be quantified by the degree of reduction (γ), which refers to the average number of electrons available per C atom for biochemical reactions, indicating the energy density of the substrate or biomass . The degree of reduction of soil microbial biomass (γ B ) is typically around 4.2, while that of substrate (γ S ) usually varies between 1 (e.g., for oxalate) and 8 (methane) . Most of the substrates used by soil microorganisms have a γ S of 3 (e.g., various organic acids), 4 (e.g., glucose and other carbohydrates), and rarely 5 or higher (e.g., leucine, polyhydroxyalkanoates or lipids) . When γ S is lower than γ B , the substrate’s energy content is insufficient to meet microbial demand, necessitating the oxidation of more substrate per unit of C assimilated, thereby reducing CUE . These insights form the basis of the stoichiometric modeling for indirect CUE estimates.
The relationship between CUE and SOC concentration at the ecosystem level can be positive, negative, or non-existent, depending on the interactions among multiple processes , , , – . Higher CUE can lead to increased SOC through biosynthesis and accumulation of microbial by-products—facilitating SOC formation via the entombing effect , , — or conversely, trigger SOC decline through the priming effect by ramping up microbial biomass and enzyme activity . While some studies suggest a negative correlation between CUE and SOC , , , the majority of research supports a positive relationship , , , , indicating that higher CUE is often linked to increased SOC levels. In a recent study, Tao et al. employed observational data and data assimilation algorithms and found that, on a global scale, CUE is positively correlated with SOC concentration, arguing for CUE as the major determinant for SOC formation. However, subsequent arguments have raised methodological concerns which might have obscured the importance of microbial community dynamics and SOC stabilization processes . Indeed, the link between microbial CUE and SOC is contingent upon the stabilization of microbial necromass within soil aggregates or its association with minerals , , . This stabilization process, pivotal for enhancing SOC, is significantly influenced by physico-chemical soil properties, which vary greatly and determine the potential for necromass protection , . Positive SOC-CUE relationships could be anticipated in soils with high physicochemical C stabilization potential and microbial communities that convert simple chemical substrates into necromass . Conversely, when soil microbes face environmental stress, the relationship between CUE and SOC becomes less predictable. Particularly under conditions where nutrients are limited relative to carbon, the increased microbial respiration required to maintain stoichiometric balance leads to a decreased CUE , . Further reductions in CUE may be driven by environmental challenges such as low oxygen or pH , , as well as the physiological costs of microbial competition . However, these stressors on microbial activity may differently affect SOC, potentially leading to either a negative or negligible correlation between CUE and SOC . It’s worth noting that in organic-rich soils, such as peat, C stabilization relies more on the accumulation of undecomposed plant material than on necromass formation , making the link between CUE and SOC less direct. Therefore, the CUE-SOC relationship in organic soils is expected to differ from mineral soils where C is mainly stabilized by mineral associations. Additionally, it is important to recognize the distinct sensitivities of microbial CUE and SOC to environmental changes, as their responses are not synchronized. Microbial CUE can adjust rapidly, from days to months, in contrast to SOC, which may take years or even decades to respond to a measurable extent , . Data from two meta-analyses highlight this disparity, showing that although fertilization positively affects both CUE C and SOC , , the response ratios of CUE C were not significantly correlated with the response ratios of SOC, or even microbial biomass C content (Fig. ). Here, the “response ratio” is calculated as the ratio of the measured value in the treatment to the value in the control. Furthermore, the response ratios of microbial CUE C were not significantly related to treatment duration (within ten years of treatment) (Fig. ), whereas the response ratios of SOC increased significantly with experiment duration (Fig. ). Therefore, SOC gradually approaches a new equilibrium over several decades, whereas CUE achieves equilibrium almost immediately. This discrepancy underscores the importance of considering the state (SOC and microbial biomass) dynamics of an ecosystem when evaluating the interplay between microbial CUE and SOC dynamics. Using models and data across scales to clarify the microbial role in C cycling Integrating genomic data with CUE and C models With the rise of high throughput sequencing technology, the use of genomic datasets to help calibrate or validate C models has become both feasible and affordable. This capacity is especially valuable when predicting CUE . As genomic data related to microbial traits becomes more readily available at both the population and community levels through metagenomics , there is a growing need to effectively integrate this data into C cycle models. This integration requires models that can handle complex microbial interactions, from individual populations to entire communities (Fig. ). One way to integrate genomic data is by converting the genetic sequences of microbes into information on metabolic pathways (e.g., cellulose degradation, lignin degradation, nitrogen reduction, and fermentation) using genome-scale metabolic models (GEMs) . GEMs take into account the microbe’s environment, such as substrate availability, and predict the transformation of metabolites within a cell based on its genomic information. This process allows for the calculation of CUE at the population level by analyzing substrate use and CO 2 production . For community-level CUE, GEMs can be combined into microbial community models that simulate interactions between different microbial taxa: The ‘computation of microbial ecosystems in time and space metabolic modeling platform’ (COMETS) extends GEMs to include dynamics of microbial growth and interactions, providing a tool for predicting CUE C under various environmental conditions . An alternative modeling approach at the community level is based on traits (e.g., quantity of cellulase produced, maximum rate of reaction (V max ) of cellulose decay by cellulase, V max of cellulose-monomer uptake, and turnover rate), such as the DEMENT model, which uses data on microbial traits to simulate substrate use and CO 2 production . This model can predict both CUE P and CUE C under different environmental conditions and over time. However, translating genomic data into traits remains challenging . Genomic datasets typically indicate the presence or absence of certain genes or pathways, but additional information, such as that from GEMs or experimental data, is necessary to accurately map these genes to functional traits in the models. Validating genomic and trait-based models is crucial and can be achieved using community-level genomic datasets, which offer insights into microbial strategies that affect CUE, such as nutrient recycling and stress tolerance , . Combining these models with traditional CUE measurements and omics data allows for the creation of detailed maps of community-level CUE, offering new insights into C cycling dynamics and providing input information for C cycle models. A major challenge in this field is the high computational demand of integrating omic data into complex models. One solution is the development of computational emulators that can simulate the dynamics of microbial models more efficiently, bridging the gap between detailed, small-scale models and broader applications in C cycle studies . This approach promises to improve our understanding of microbial contributions to C cycling, leveraging the power of genomic data to inform and validate complex ESMs. Harmonization of CUE measurements and aligning measured and modeled CUE Harmonizing soil microbial CUE measurements across different methods, i.e., aligning results from different methodologies, poses a challenge due to the differences across measurement techniques. While adopting a universal protocol for CUE measurement—a single, standardized measurement method— would be ideal, it may not be feasible given the complexities of CUE. Therefore, a more practical approach involves providing a clear and comprehensive description of the methodologies used in different studies. This detailed reporting should include information on the physiological processes considered, such as maintenance, enzyme production, biomass generation, and mortality rates. This level of detail helps in understanding and comparing results across studies, as well as in selecting appropriate data for model calibration . In contemporary soil C models that explicitly incorporate microbial processes , , the CUE is close to empirically measured CUE C . To achieve a uniform approach to CUE measurement, microbial models that resolve key processes influencing CUE, such as uptake, respiration, exudation, and microbial death could be used . Such models can generate CUE metrics that align with different measurement methodologies by incorporating a complete or partial set of these processes into their calculations. Furthermore, these models can be adapted to conduct numerical experiments with specific substrates or to incorporate isotopic tracers (e.g., 13 C, 14 C, 18 O) to simulate outcomes from labeling experiments. This adaptability allows for the exploration of hypotheses regarding discrepancies in measurements under diverse conditions by modifying model boundary conditions. Additionally, microbial models serve as foundational tools for integrating microbial metabolism into broader global C models, potentially enhanced by machine learning emulators for improved scalability and applicability. Constraining CUE using model-data fusion Data assimilation encompasses a collection of techniques, including Bayesian inference, that refine biogeochemical models by integrating observational data. This process not only updates model parameters to reflect the most likely values based on available data but also quantifies their uncertainties, thus bridging the gap between empirical observations and theoretical models . This approach is particularly valuable for parameters like microbial CUE, which are challenging to measure directly in the field due to technical limitations. An innovative application of data assimilation is demonstrated by Tao et al. , who developed the PROcess-guided deep learning and DAta-driven (PRODA) approach , . This method integrates global-scale SOC data with a microbially explicit model to produce a global map of microbial CUE. PRODA employs traditional Bayesian data assimilation to estimate parameters at specific sites and then uses deep learning to extrapolate these site-specific parameter estimates to a global scale. The result is a set of parameters that optimally align with observed data, offering a detailed view of microbial CUE and SOC storage patterns worldwide, along with other soil C cycle dynamics such as decomposition rates, environmental impacts on soil respiration, and vertical C transport . Despite the potential of approaches like PRODA to harness large datasets for enhancing our understanding of the soil C cycle, their computational intensity—stemming from the extensive data sampling required by Bayesian inference—may limit their application in models with complex structures. The next wave of data assimilation techniques will likely integrate process-based models with deep learning algorithms more seamlessly . Such advancements could offer quicker parameter optimization and facilitate comparisons across different models, paving the way for more accurate and comprehensive assessments of microbial CUE and C cycle dynamics on a global scale. Long-term SOC records and ecosystem manipulation experiments Ecosystem manipulation experiments and observations of natural gradients offer invaluable insights into how microbial communities and CUE adapt to global change factors. Especially insightful are field experiments (or studies leveraging natural gradients) that alter environmental factors such as soil temperature, precipitation patterns, or nutrient levels , over long durations. These experiments provide critical data on the enduring effects of global change drivers on CUE, while simultaneously highlighting the limitations of current models and enhancing our comprehension of ecological processes. Integrating the results from these experiments with model simulations, supported by proven site modeling protocols and extra observational data, is crucial for steadily enhancing the accuracy and complexity of models . Incorporating radiocarbon ( 14 C) data and long-term SOC records into models is also vital for refining CUE forecasts across longer (decadal to centennial) time scales. This temporal information is essential for capturing the dynamics of CUE over time, thereby improving the precision of models in depicting spatial and temporal fluctuations . Diagnosing CUE from existing models or simulation archives In global C modeling, approaches to quantify the environmental impact on organic matter decomposition and stabilization differ significantly. An effective method for estimating microbial CUE at the ecosystem level as emerging from model simulations involves the calculation of the ratio between soil heterotrophic respiration (R) and gross decomposition (D) within these models. Gross decomposition refers to the sum of all C fluxes transferred between the modeled soil C pools that are mediated by microbial processes, excluding physically mediated transfers (e.g., sorption, aggregation, or leaching). This includes all C removed from organic matter pools, whether it is lost as CO 2 or transferred to another pool (SI-Text 1). This ratio effectively quantifies microbial-mediated C losses from SOC pools, integrating both growth (anabolic processes) and respiration (catabolic processes). Under steady-state conditions, it is assumed that heterotrophic respiration aligns with microbial C uptake, resulting in the formula: CUE = 1 - R/D. The steady-state assumption implies that microbial communities and SOC stock are stable in time (i.e., in equilibrium with boundary conditions). This is an approximation of real systems where SOC varies due to anthropogenic and natural changes (e.g., Holocene climatic variations). This diagnosed CUE, emerging as a property inherent to the model, is not susceptible to the equifinality issues that can affect the underlying intrinsic model parameters (like CUE C ), and it does not necessitate the incorporation of explicitly microbial models, offering a simplified yet insightful metric. These model-based CUE estimates, derived from long-term flux averages (e.g., 20 years), represent stable C stocks. In contrast, measurement-based estimates, taken over shorter periods, are more susceptible to significant CUE variations due to asynchronous fluctuations in components such as respiration and degradation, potentially introducing estimation inaccuracies. This timescale discrepancy likely accounts for the greater variability observed in measurement-based CUE compared to model-based CUE. We propose this “model-diagnosed CUE” as a novel metric, designed to estimate microbial CUE from model outputs without direct measurements of microbial uptake. Analyzing diagnosed CUE and its relationship with SOC across various models, such as those evaluated in the Trends in the land carbon cycle (TRENDY) model intercomparison project , facilitates the identification of differences attributable to unique model structures and assumptions. For example, warming-induced CO 2 emissions should be higher in models with low diagnosed CUE compared to high CUE as the warming-induced stimulation of microbial activity will result in relatively more C being respired than cycled within the soil systems. This approach further allows the benchmarking and subsequent refinement of diagnosed CUE estimates using observed CUE E data. For instance, we derived CUE estimates from simulations conducted with two different versions of the Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model , which differ in the SOC model deployed. The CENTURY SOC model (Fig. ), which is widely used but does not resolve microbial processes, uses first-order decay, while the MIMICS model (Fig. ) resolves microbial physiology, providing a more mechanistic understanding of microbial processes. The resulting global CUE maps (the average of simulation results over 20 consecutive years) revealed significant spatial variability (Fig. ). While the two maps showed a good correlation (Fig. ), the CUE values diagnosed from the MIMICS model were higher than those from the CENTURY model (Fig. ). These findings underscore the importance of incorporating observational data into model calibration efforts to enhance the accuracy and reliability of SOC predictions by realistically resolving CUE. In conclusion, the inherent structure of a model significantly shapes its outcomes, making the integration of empirical data with data-constrained models a fundamental step toward realistic predictions , . Precisely delineating the spatial and temporal dynamics of CUE in models that specifically address microbial activities is crucial for the reliability of their predictions of SOC status and dynamics. Moreover, future soil C models must navigate the intricate balance between the complex regulatory mechanisms of CUE, other processes governing SOC formation and stabilization, and the practicality of model use to promote more precise projections of CUE responses under diverse environmental scenarios. This Perspective underscores the importance of combining different data sources with sophisticated modeling techniques to refine global CUE predictions. By incorporating genomic data, standardizing measurement protocols, applying data assimilation practices and critically evaluating CUE within existing frameworks, our comprehension of the global dynamics of microbial CUE can be markedly improved. This Perspective provides a roadmap for establishing an effective modeling approach to accurately represent global soil microbial CUE and its interactions with other biological and abiotic processes that regulate SOC dynamics.
Integrating genomic data with CUE and C models With the rise of high throughput sequencing technology, the use of genomic datasets to help calibrate or validate C models has become both feasible and affordable. This capacity is especially valuable when predicting CUE . As genomic data related to microbial traits becomes more readily available at both the population and community levels through metagenomics , there is a growing need to effectively integrate this data into C cycle models. This integration requires models that can handle complex microbial interactions, from individual populations to entire communities (Fig. ). One way to integrate genomic data is by converting the genetic sequences of microbes into information on metabolic pathways (e.g., cellulose degradation, lignin degradation, nitrogen reduction, and fermentation) using genome-scale metabolic models (GEMs) . GEMs take into account the microbe’s environment, such as substrate availability, and predict the transformation of metabolites within a cell based on its genomic information. This process allows for the calculation of CUE at the population level by analyzing substrate use and CO 2 production . For community-level CUE, GEMs can be combined into microbial community models that simulate interactions between different microbial taxa: The ‘computation of microbial ecosystems in time and space metabolic modeling platform’ (COMETS) extends GEMs to include dynamics of microbial growth and interactions, providing a tool for predicting CUE C under various environmental conditions . An alternative modeling approach at the community level is based on traits (e.g., quantity of cellulase produced, maximum rate of reaction (V max ) of cellulose decay by cellulase, V max of cellulose-monomer uptake, and turnover rate), such as the DEMENT model, which uses data on microbial traits to simulate substrate use and CO 2 production . This model can predict both CUE P and CUE C under different environmental conditions and over time. However, translating genomic data into traits remains challenging . Genomic datasets typically indicate the presence or absence of certain genes or pathways, but additional information, such as that from GEMs or experimental data, is necessary to accurately map these genes to functional traits in the models. Validating genomic and trait-based models is crucial and can be achieved using community-level genomic datasets, which offer insights into microbial strategies that affect CUE, such as nutrient recycling and stress tolerance , . Combining these models with traditional CUE measurements and omics data allows for the creation of detailed maps of community-level CUE, offering new insights into C cycling dynamics and providing input information for C cycle models. A major challenge in this field is the high computational demand of integrating omic data into complex models. One solution is the development of computational emulators that can simulate the dynamics of microbial models more efficiently, bridging the gap between detailed, small-scale models and broader applications in C cycle studies . This approach promises to improve our understanding of microbial contributions to C cycling, leveraging the power of genomic data to inform and validate complex ESMs.
With the rise of high throughput sequencing technology, the use of genomic datasets to help calibrate or validate C models has become both feasible and affordable. This capacity is especially valuable when predicting CUE . As genomic data related to microbial traits becomes more readily available at both the population and community levels through metagenomics , there is a growing need to effectively integrate this data into C cycle models. This integration requires models that can handle complex microbial interactions, from individual populations to entire communities (Fig. ). One way to integrate genomic data is by converting the genetic sequences of microbes into information on metabolic pathways (e.g., cellulose degradation, lignin degradation, nitrogen reduction, and fermentation) using genome-scale metabolic models (GEMs) . GEMs take into account the microbe’s environment, such as substrate availability, and predict the transformation of metabolites within a cell based on its genomic information. This process allows for the calculation of CUE at the population level by analyzing substrate use and CO 2 production . For community-level CUE, GEMs can be combined into microbial community models that simulate interactions between different microbial taxa: The ‘computation of microbial ecosystems in time and space metabolic modeling platform’ (COMETS) extends GEMs to include dynamics of microbial growth and interactions, providing a tool for predicting CUE C under various environmental conditions . An alternative modeling approach at the community level is based on traits (e.g., quantity of cellulase produced, maximum rate of reaction (V max ) of cellulose decay by cellulase, V max of cellulose-monomer uptake, and turnover rate), such as the DEMENT model, which uses data on microbial traits to simulate substrate use and CO 2 production . This model can predict both CUE P and CUE C under different environmental conditions and over time. However, translating genomic data into traits remains challenging . Genomic datasets typically indicate the presence or absence of certain genes or pathways, but additional information, such as that from GEMs or experimental data, is necessary to accurately map these genes to functional traits in the models. Validating genomic and trait-based models is crucial and can be achieved using community-level genomic datasets, which offer insights into microbial strategies that affect CUE, such as nutrient recycling and stress tolerance , . Combining these models with traditional CUE measurements and omics data allows for the creation of detailed maps of community-level CUE, offering new insights into C cycling dynamics and providing input information for C cycle models. A major challenge in this field is the high computational demand of integrating omic data into complex models. One solution is the development of computational emulators that can simulate the dynamics of microbial models more efficiently, bridging the gap between detailed, small-scale models and broader applications in C cycle studies . This approach promises to improve our understanding of microbial contributions to C cycling, leveraging the power of genomic data to inform and validate complex ESMs.
Harmonizing soil microbial CUE measurements across different methods, i.e., aligning results from different methodologies, poses a challenge due to the differences across measurement techniques. While adopting a universal protocol for CUE measurement—a single, standardized measurement method— would be ideal, it may not be feasible given the complexities of CUE. Therefore, a more practical approach involves providing a clear and comprehensive description of the methodologies used in different studies. This detailed reporting should include information on the physiological processes considered, such as maintenance, enzyme production, biomass generation, and mortality rates. This level of detail helps in understanding and comparing results across studies, as well as in selecting appropriate data for model calibration . In contemporary soil C models that explicitly incorporate microbial processes , , the CUE is close to empirically measured CUE C . To achieve a uniform approach to CUE measurement, microbial models that resolve key processes influencing CUE, such as uptake, respiration, exudation, and microbial death could be used . Such models can generate CUE metrics that align with different measurement methodologies by incorporating a complete or partial set of these processes into their calculations. Furthermore, these models can be adapted to conduct numerical experiments with specific substrates or to incorporate isotopic tracers (e.g., 13 C, 14 C, 18 O) to simulate outcomes from labeling experiments. This adaptability allows for the exploration of hypotheses regarding discrepancies in measurements under diverse conditions by modifying model boundary conditions. Additionally, microbial models serve as foundational tools for integrating microbial metabolism into broader global C models, potentially enhanced by machine learning emulators for improved scalability and applicability.
Data assimilation encompasses a collection of techniques, including Bayesian inference, that refine biogeochemical models by integrating observational data. This process not only updates model parameters to reflect the most likely values based on available data but also quantifies their uncertainties, thus bridging the gap between empirical observations and theoretical models . This approach is particularly valuable for parameters like microbial CUE, which are challenging to measure directly in the field due to technical limitations. An innovative application of data assimilation is demonstrated by Tao et al. , who developed the PROcess-guided deep learning and DAta-driven (PRODA) approach , . This method integrates global-scale SOC data with a microbially explicit model to produce a global map of microbial CUE. PRODA employs traditional Bayesian data assimilation to estimate parameters at specific sites and then uses deep learning to extrapolate these site-specific parameter estimates to a global scale. The result is a set of parameters that optimally align with observed data, offering a detailed view of microbial CUE and SOC storage patterns worldwide, along with other soil C cycle dynamics such as decomposition rates, environmental impacts on soil respiration, and vertical C transport . Despite the potential of approaches like PRODA to harness large datasets for enhancing our understanding of the soil C cycle, their computational intensity—stemming from the extensive data sampling required by Bayesian inference—may limit their application in models with complex structures. The next wave of data assimilation techniques will likely integrate process-based models with deep learning algorithms more seamlessly . Such advancements could offer quicker parameter optimization and facilitate comparisons across different models, paving the way for more accurate and comprehensive assessments of microbial CUE and C cycle dynamics on a global scale.
Ecosystem manipulation experiments and observations of natural gradients offer invaluable insights into how microbial communities and CUE adapt to global change factors. Especially insightful are field experiments (or studies leveraging natural gradients) that alter environmental factors such as soil temperature, precipitation patterns, or nutrient levels , over long durations. These experiments provide critical data on the enduring effects of global change drivers on CUE, while simultaneously highlighting the limitations of current models and enhancing our comprehension of ecological processes. Integrating the results from these experiments with model simulations, supported by proven site modeling protocols and extra observational data, is crucial for steadily enhancing the accuracy and complexity of models . Incorporating radiocarbon ( 14 C) data and long-term SOC records into models is also vital for refining CUE forecasts across longer (decadal to centennial) time scales. This temporal information is essential for capturing the dynamics of CUE over time, thereby improving the precision of models in depicting spatial and temporal fluctuations .
In global C modeling, approaches to quantify the environmental impact on organic matter decomposition and stabilization differ significantly. An effective method for estimating microbial CUE at the ecosystem level as emerging from model simulations involves the calculation of the ratio between soil heterotrophic respiration (R) and gross decomposition (D) within these models. Gross decomposition refers to the sum of all C fluxes transferred between the modeled soil C pools that are mediated by microbial processes, excluding physically mediated transfers (e.g., sorption, aggregation, or leaching). This includes all C removed from organic matter pools, whether it is lost as CO 2 or transferred to another pool (SI-Text 1). This ratio effectively quantifies microbial-mediated C losses from SOC pools, integrating both growth (anabolic processes) and respiration (catabolic processes). Under steady-state conditions, it is assumed that heterotrophic respiration aligns with microbial C uptake, resulting in the formula: CUE = 1 - R/D. The steady-state assumption implies that microbial communities and SOC stock are stable in time (i.e., in equilibrium with boundary conditions). This is an approximation of real systems where SOC varies due to anthropogenic and natural changes (e.g., Holocene climatic variations). This diagnosed CUE, emerging as a property inherent to the model, is not susceptible to the equifinality issues that can affect the underlying intrinsic model parameters (like CUE C ), and it does not necessitate the incorporation of explicitly microbial models, offering a simplified yet insightful metric. These model-based CUE estimates, derived from long-term flux averages (e.g., 20 years), represent stable C stocks. In contrast, measurement-based estimates, taken over shorter periods, are more susceptible to significant CUE variations due to asynchronous fluctuations in components such as respiration and degradation, potentially introducing estimation inaccuracies. This timescale discrepancy likely accounts for the greater variability observed in measurement-based CUE compared to model-based CUE. We propose this “model-diagnosed CUE” as a novel metric, designed to estimate microbial CUE from model outputs without direct measurements of microbial uptake. Analyzing diagnosed CUE and its relationship with SOC across various models, such as those evaluated in the Trends in the land carbon cycle (TRENDY) model intercomparison project , facilitates the identification of differences attributable to unique model structures and assumptions. For example, warming-induced CO 2 emissions should be higher in models with low diagnosed CUE compared to high CUE as the warming-induced stimulation of microbial activity will result in relatively more C being respired than cycled within the soil systems. This approach further allows the benchmarking and subsequent refinement of diagnosed CUE estimates using observed CUE E data. For instance, we derived CUE estimates from simulations conducted with two different versions of the Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model , which differ in the SOC model deployed. The CENTURY SOC model (Fig. ), which is widely used but does not resolve microbial processes, uses first-order decay, while the MIMICS model (Fig. ) resolves microbial physiology, providing a more mechanistic understanding of microbial processes. The resulting global CUE maps (the average of simulation results over 20 consecutive years) revealed significant spatial variability (Fig. ). While the two maps showed a good correlation (Fig. ), the CUE values diagnosed from the MIMICS model were higher than those from the CENTURY model (Fig. ). These findings underscore the importance of incorporating observational data into model calibration efforts to enhance the accuracy and reliability of SOC predictions by realistically resolving CUE. In conclusion, the inherent structure of a model significantly shapes its outcomes, making the integration of empirical data with data-constrained models a fundamental step toward realistic predictions , . Precisely delineating the spatial and temporal dynamics of CUE in models that specifically address microbial activities is crucial for the reliability of their predictions of SOC status and dynamics. Moreover, future soil C models must navigate the intricate balance between the complex regulatory mechanisms of CUE, other processes governing SOC formation and stabilization, and the practicality of model use to promote more precise projections of CUE responses under diverse environmental scenarios. This Perspective underscores the importance of combining different data sources with sophisticated modeling techniques to refine global CUE predictions. By incorporating genomic data, standardizing measurement protocols, applying data assimilation practices and critically evaluating CUE within existing frameworks, our comprehension of the global dynamics of microbial CUE can be markedly improved. This Perspective provides a roadmap for establishing an effective modeling approach to accurately represent global soil microbial CUE and its interactions with other biological and abiotic processes that regulate SOC dynamics.
Supplementary information
|
Storylines of family medicine IX: people and places—diverse populations and locations of care | cf3038c5-6b97-419b-81d2-07dc6d1a1abf | 11029404 | Family Medicine[mh] | The ecology of care is an important consideration on both sides of the stethoscope. On one hand, ecology of care describes where patients present to and receive their medical care—remember, most people neither seek help from nor make their way to academic institutions. On the other hand, ecology of care also describes where physicians generally practice and the kind of services they provide. The essays below elaborate on some of the distinct patient populations that family physicians commonly care for, physical spaces they occupy when providing care and nature of the care they deliver. Emad Abou-Arab We must broaden our understanding of gender and sexuality to improve our clinical judgement. A growing number of people in the USA identify as LGBTQIA+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexual and gender minorities). Close to 6% of US adults identify as LGBTQIA+, and one in six adults in Generation Z considers themselves part of the LGBTQIA+ community. Additionally, the number of people identifying as transgender has also increased. Members of the LGBTQIA+ community belong to almost every race, ethnicity, religion, age and socioeconomic group, and the health needs of sexual and gender minorities span the entire spectrum of family medicine. Though family physicians may not be aware of their patients’ sexual orientation and gender identities, most family physicians encounter LGBTQIA+ individuals in their practices. Sexual orientation is the emotional and sexual attraction one feels for others. Sexual orientation can range from exclusively gay/lesbian (ie, same-sex attraction) to exclusively straight (ie, attraction to different sex only). However, there is a broad spectrum of sexual orientations that can vary depending on the gender identity of the person and that person’s various attractions, which may include attractions to certain sexes, gender identities, gender expressions and combinations thereof. Gender identity differs from gender expression, but both are clinically important. Gender identity refers to a person’s internalised concept of self as a particular gender regardless of physical appearance. Since gender identity is internally defined, it is separate from a person’s physical anatomy. To the extent that someone’s gender identity is non-congruent with their sex assigned at birth, they may identify as transgender or non-binary. Gender expression is the way people communicate their gender to the world through clothing, speech and mannerisms. A person may express a particular gender at any given time without changing their gender identity. While societal acceptance of diverse patient populations has improved, people who identify as LGBTQIA+ continue to encounter stigma and biases against them. These factors have led to significant health disparities that include higher rates of sexually transmitted infections, substance use, mental health disorders, obesity and certain cancers. These poor health outcomes are worse among minoritised communities. To offer high-quality, culturally competent care to our LGBTQIA+ patients, we as family physicians must continue to grow in knowledge and understanding. To meet the needs of the LGBTQIA+ community, it is important we develop a basic understanding of sexual orientation and gender identity. It is essential we understand the healthcare risks our LGBTQIA+ patients face. Both our clinical judgement and our abilities to address patient needs improve as we consider the context of gender and sexuality in our diagnostic and therapeutic planning. As family physicians, we must also understand how interpersonal and structural factors affect our patients. Understanding the different dimensions and manifestations of sexual orientation and gender identity can help us build better therapeutic relationships with the LGBTQIA+ community. This means avoiding assumptions, staying open to cultural differences, being culturally compassionate and creating inclusive workplace environments. The unique healthcare needs and experiences of the LGBTQIA+ population deserve no less . Readings National LGBTQIA+ Health Education Center. Glossary of LGBTQIA+ terms for health care teams. 03 February 2020. Available: https://www.lgbtqiahealtheducation.org/publication/lgbtqia-glossary-of-terms-for-health-care-teams (Accessed 31 January 2024). Hatzenbuehler ML, Pachankis JE. Stigma and minority stress as social determinants of health among lesbian, gay, bisexual, and transgender youth: research evidence and clinical implications. Pediatr Clin North Am 2016;63:985–97. doi: 10.1016/j.pcl.2016.07.003 Hafeez H, Zeshan M, Tahir MA, Jahan N, Naveed S. Health care disparities among lesbian, gay, bisexual, and transgender youth: a literature review. Cureus 2017;9:e1184. doi:10.7759/cureus.1184 National LGBTQIA+ Health Education Center. Glossary of LGBTQIA+ terms for health care teams. 03 February 2020. Available: https://www.lgbtqiahealtheducation.org/publication/lgbtqia-glossary-of-terms-for-health-care-teams (Accessed 31 January 2024). Hatzenbuehler ML, Pachankis JE. Stigma and minority stress as social determinants of health among lesbian, gay, bisexual, and transgender youth: research evidence and clinical implications. Pediatr Clin North Am 2016;63:985–97. doi: 10.1016/j.pcl.2016.07.003 Hafeez H, Zeshan M, Tahir MA, Jahan N, Naveed S. Health care disparities among lesbian, gay, bisexual, and transgender youth: a literature review. Cureus 2017;9:e1184. doi:10.7759/cureus.1184 Julio Meza Family physicians play a vital role in working with patients dealing with substance abuse. Substance abuse continues to be a significant public health concern, affecting individuals of all ages, backgrounds and socioeconomic statuses. As society grapples with the devastating consequences of substance abuse, it is increasingly clear that addressing this issue requires a comprehensive and holistic approach. The holistic approach of family medicine— Family medicine focuses on providing comprehensive healthcare to individuals of all ages while also considering the impact of health and illness on the family unit. It emphasises a holistic approach that considers the physical, mental and social aspects of health. Substance abuse, a complex issue with multifaceted causes and consequences, necessitates a similarly comprehensive approach. Understanding family dynami cs — Family physicians are well positioned to address substance abuse because of their relationships with patients and resulting abilities to gain valuable insight into the family dynamics of their patients. They can assess the family’s role in both contributing to and mitigating substance abuse. By considering the family context, family physicians can provide more effective interventions and support systems for patients dealing with substance abuse. Early identification and intervention —One of the significant advantages of a family medicine approach is the ability to identify and intervene early in cases of substance abuse. Family physicians are trained to recognise subtle signs and symptoms of substance abuse and are skilled in conducting thorough assessments. With this expertise, family physicians can identify individuals at risk or in the early stages of substance abuse and offer appropriate interventions. Early identification increases the likelihood of successful treatment outcomes and reduces the potential for long-term harm. Collaborative and supportive relationships— Such relationships are key to helping patients who struggle with substance use disorders. Family physicians can provide a non-judgemental environment where patients feel safe discussing their substance use concerns. This therapeutic alliance facilitates open communication, leading to a more accurate understanding of patients’ needs and treatment preferences. By involving patients in the decision-making process, family physicians can tailor treatment plans that are acceptable to patients. This commitment to patients increases the likelihood of treatment adherence and success; it also helps physicians be present and attentive during periods of non-adherence or relapse. Coordinating multidisciplinary care— Although medicinal interventions in primary care practice are gaining acceptance, substance abuse often requires a multidisciplinary approach that involves various healthcare professionals and support services. Family physicians are crucial to coordinating this care. They collaborate with addiction specialists, mental health professionals, counsellors and social workers to develop comprehensive treatment plans. By facilitating effective communication among these professionals, family physicians act as a kind of glue that holds together the treatment team. The bottom line— Substance abuse poses significant challenges to individuals, families and communities. By adopting a holistic approach, family physicians can make a substantial impact on the prevention, early identification and management of substance abuse. Their position vis-à-vis patients allows them to address the complex interplay between individuals, families and communities, and provide comprehensive care that considers the physical, mental and social aspects of health. Through collaboration and support, family physicians can work alongside patients to overcome the devastating effects of substance abuse and guide them toward a healthier and more fulfilling life . Readings Practical approach to substance use disorders for the family physician. College of Family Physicians of Canada. 2021. Available: https://www.cfpc.ca/CFPC/media/PDF/MIGS-2021-Addiction-Medicine-ENG-Final.pdf (Accessed 31 January 2024). Edelman EJ, Oldfield BJ, Tetrault JM. Office-based addiction treatment in primary care: approaches that work. Med Clin 2018;102:635–52. doi: 10.1016/j.mcna.2018.02.007 Shapiro B, Coffa D, McCance-Katz EF. A primary care approach to substance misuse. Am Fam Physician 2013;88:113–21. Practical approach to substance use disorders for the family physician. College of Family Physicians of Canada. 2021. Available: https://www.cfpc.ca/CFPC/media/PDF/MIGS-2021-Addiction-Medicine-ENG-Final.pdf (Accessed 31 January 2024). Edelman EJ, Oldfield BJ, Tetrault JM. Office-based addiction treatment in primary care: approaches that work. Med Clin 2018;102:635–52. doi: 10.1016/j.mcna.2018.02.007 Shapiro B, Coffa D, McCance-Katz EF. A primary care approach to substance misuse. Am Fam Physician 2013;88:113–21. Dave Buck and Jerome Crowder How to create therapeutic relationships free of shame. Working with people who are experiencing homelessness is challenging because of the negative experiences they have repeatedly suffered in their lives. We must meet them where they are, realise our work is not about ‘saving’ them (the modern remnant of a colonial mentality), and recognise their autonomy, authority, and power in the patient–practitioner relationship. To successfully provide care to homeless and other vulnerable populations, we must understand the power structures in which we all live and work. Shameless medicine places patients’ values and agency squarely within the clinical dialogues of medical care. Why shameless medicine ? Our job as clinicians is to understand patients’ values and keep health challenges from getting in the way of those values. We must also build a caring relationship free of shame. Whatever patients do—use drugs, eat fast food or engage in prostitution—should not hurt that relationship. Our objective is to figure out how to make the system of healthcare work better for them. Unlike many of the other relationships in homeless patients’ lives, the partnership between physician and patient must establish an environment in which patients cannot fail. You must first establish ground rules: ‘My role is to be the best doctor I can be, and this means always being the doctor you need.’ Note that the needs of the patients should not conflict with our efforts to be the best doctors we can be. Agree to disagree with patients, if necessary, but focus on the positive: ‘Are there behaviours that could help you feel better?’ The idea is to help patients realise they can succeed by working with us. Consider everything they try a success. When something does go wrong, that is not their fault—we hold our patients’ shame at the same time we advocate, facilitate and help them do what they can to feel better. Often, like many other people, homeless patients see immediate gains as good for them—doing crack, telling someone off and fighting are good examples. Nonetheless, what helps is being consistent about how we approach them, what we can do for them and what they can expect from us. Consistency grounds confidence. In working with homeless patients, our job is to frame the care we provide in terms of their values, respecting what they see as a desired ‘endpoint’ of care. Work to agree on such endpoints, addressing what patients want from us and what we can do to help them achieve it. Commonly, homeless patients blame themselves for failures in life: ‘I screw up everything!’ Many of them have heard this refrain since childhood; someone told them they were worthless or simply did nothing right. Counter this belief by saying, ‘We all make mistakes, and that’s part of being human.’ Like all of us, when homeless and other vulnerable patients realise their human nature, they become honest and authentic in interactions with others. It is our job to reinforce this honest engagement, encourage success in daily life and help our patients discover for themselves what things work for them . Readings Lazare A. Shame and humiliation in the medical encounter. Arch Intern Med 1987;147:1653–8. Usatine RP, Gelberg L, Smith MH, Lesser J. Health care for the homeless: a family medicine perspective. Am Fam Physician 1994;49:139–46. Pottie K, Kendall CE, Aubry T, et al . Clinical guideline for homeless and vulnerably housed people, and people with lived homelessness experience. CMAJ 2020;192:E240–54. doi: 10.1503/cmaj.190777 Lazare A. Shame and humiliation in the medical encounter. Arch Intern Med 1987;147:1653–8. Usatine RP, Gelberg L, Smith MH, Lesser J. Health care for the homeless: a family medicine perspective. Am Fam Physician 1994;49:139–46. Pottie K, Kendall CE, Aubry T, et al . Clinical guideline for homeless and vulnerably housed people, and people with lived homelessness experience. CMAJ 2020;192:E240–54. doi: 10.1503/cmaj.190777 Jennifer Edgoose Learning to work successfully with so-called difficult patients means listening to their stories—everyone’s experience as a human being is unique. It also means exploring our own human responses to patients’ expressions of distress. Practising clinical medicine is an enormously rewarding profession that is sometimes creative and fun, and often meaningful and poignant. Remarkably, this depth and breadth of experience may encompass a single morning, especially in generalist disciplines. In the space of clinical encounters, however, there are also patients who may evoke a sense of heartsink and dread among the physicians who attend to them. All physicians will invariably attend to ‘difficult’ patients. In fact, because of how physical and emotional stressors adversely affect people, such patients are very common. Unfortunately, clinicians who believe they are caring for many difficult patients are more likely to feel burned out; mitigating this situation is critical for fulfilment as a physician. Experienced physicians who successfully work with difficult patients do so through collaboration, the judicious application of power and the use of empathy; they do not employ oppositional behaviours, misuse power or express compassion fatigue. Still, how do they put these strategies into practice? They use patient-centred care and self-reflection. Patient-centred care: reaching out— One important strategy is to seek to understand who the patient is. Listening to patients’ stories and gathering medical histories are critical, as the practice of good medicine is all about context. While family physicians should embrace evidence-based principles and practices, they must also consider the circumstances of each individual and apply those principles and practices of evidence-based medicine accordingly. When family physicians try to better understand their patients, they not only discover their unique needs but also their unique strengths, attributes and resources. They learn about the people and pets who love and need them and who the patients love and need in return; this approach is called ‘mining for gold’. By appreciating and incorporating this approach into practice, the application of knowledge and skills becomes far more nuanced, acceptable and effective. At the same time, doctor–patient relationships become more engaging and enjoyable. Self-reflection: looking inward— It is important to consider one’s own presence vis-à-vis patients. This entails developing an attitude of equanimity and incorporating aspects of mindfulness. Mindful practice includes attending to the ordinary, observing ourselves as much as our patients, being curious and welcoming uncertainty. This means family physicians should reflect upon their own agendas and consider why they label patients as difficult. It also means family physicians should explore what biases they bring to professional relationships with patients. Using the structured BREATHE OUT approach can help family physicians achieve these goals. Before seeing a difficult patient, try asking the BREATHE questions before walking into the examination room. Take three slow breaths before you open the door. Then, immediately after leaving the examination room and before moving on to the next task, answer the OUT questions. These prompts only take a couple extra minutes and have been shown to improve physicians’ experiences with heartsink patients . Readings Levinson, W. Mining for gold. J Gen Intern Med 1993;8:172. Epstein R. Mindful practice. JAMA 1999;282:833–9. doi: 10.1001/jama.282.9.833 Edgoose JY, Regner CJ, Zakletskaia LI. BREATHE OUT: a randomized controlled trial of a structured intervention to improve clinician satisfaction with ‘difficult’ visits. J Am Board Fam Med 2015;28:13–20. doi: 10.3122/jabfm.2015.01.130323 Levinson, W. Mining for gold. J Gen Intern Med 1993;8:172. Epstein R. Mindful practice. JAMA 1999;282:833–9. doi: 10.1001/jama.282.9.833 Edgoose JY, Regner CJ, Zakletskaia LI. BREATHE OUT: a randomized controlled trial of a structured intervention to improve clinician satisfaction with ‘difficult’ visits. J Am Board Fam Med 2015;28:13–20. doi: 10.3122/jabfm.2015.01.130323 Alex Brown and Ellie Plumb What is the secret to working with patients with medically unexplained symptoms? If the story is the pathology, listening is the intervention. A 68-year-old woman presented to our family medicine office with a list of problems: cervical radiculopathy, joint pain, colitis, depression and anxiety. She would be seen by a second-year family medicine resident who was working in our morning clinic with the two of us (AB and EJP) jointly attending. In our pre-visit huddle, the resident was uncertain whether she was treating the patient effectively, as the patient had not responded optimally to trial interventions. Indeed, the resident was not confident that the patient had been accurately diagnosed. We encouraged her to collect more of the story and reminded her that ‘sometimes, the story is the pathology’. By simply listening, the resident learnt that the patient had been routinely abused by her late husband, who she strongly suspected had sexually abused their daughter with whom she now had a fractured relationship. The patient had finally prepared herself to leave her husband later in life, only to find herself bound to his bedside as she cared for him as he slowly advanced toward death. Today, she is burdened by crippling debt and social isolation; her meaningful relationships have withered and disappeared. This patient’s story was painfully similar to the stories of so many patients who experience medically unexplained symptoms (MUS). Although the patient’s lifetime of accumulated trauma may not have directly caused her physical ailments, it was certainly making her symptoms worse. MUS is a term that has become synonymous in certain corners of healthcare with psychosomatics, malingering and histrionics. Even as the importance of holistic, biopsychosocial medicine is widely recognised and neuroscience confirms the important link between chronic stress and inflammatory diseases, clinicians commonly meet patients with MUS, directly or indirectly, with frustration and disregard. The most obvious reason for this is that such patients’ symptoms fail to fit within the strictly biomedical models of illness. Clinicians routinely feel ill-equipped to treat these patients and have difficulty relying on one important principle: if the story is the pathology, listening is the intervention. After speaking with the patient, the resident felt she had done more to help her patient in 20 min of attentive listening than in all their previous visits, in which she had focused on disease diagnosis and specialist consultations. In bearing witness to the patient’s story, the resident was able to accomplish two clinically relevant tasks. First, she began to establish the trust needed to develop the secure attachment necessary for true trauma-informed care. Second, she began to reinforce that the patient’s physical experience is linked directly to her emotional experience and social history, helping to create a new context for the patient to understand her illnesses. Caring for patients with MUS requires patience. That morning, the resident learnt an important lesson: often, it is not medicine, but the clinician, that is the therapeutic agent. Deeply listening helps ground both patients and clinicians in the shared understanding that just meeting is doing something and that sometimes saying ‘thank you for sharing’ is the only effective intervention available . Readings McWhinney IR, Epstein RM, Freeman TR. Rethinking somatization. Ann Intern Med 1997;126:747–50. doi: 10.7326/0003-4819-126-9-199705010-00037 Oyama O, Paltoo C, Greengold J. Somatoform disorders. Am Fam Physician 2007;76:1333–8. Ventres W. PRESSS: a new patient-centered name for an old problem. J Am Board Fam Med 2021;34:1030–2. doi: 10.3122/jabfm.2021.05.200647 McWhinney IR, Epstein RM, Freeman TR. Rethinking somatization. Ann Intern Med 1997;126:747–50. doi: 10.7326/0003-4819-126-9-199705010-00037 Oyama O, Paltoo C, Greengold J. Somatoform disorders. Am Fam Physician 2007;76:1333–8. Ventres W. PRESSS: a new patient-centered name for an old problem. J Am Board Fam Med 2021;34:1030–2. doi: 10.3122/jabfm.2021.05.200647 Amber Norris Home visits are family medicine in its purest form. When first asked to conduct home visits for my institution, I was looking for a break from clinical responsibilities. I had begun to feel a bit like a cog in a wheel, and I wanted more individuality, creativity and independence in my work. In the interview with my medical director, she explained the basics of house calls and some of the minimum criteria for home visits: severe illness, dementia and repeated difficulty with transportation, to name a few. Even then, it was evident to me that the house calls programme could easily become a ‘dumping ground’ for the sickest and highest utilisers in our system. However, I focused on the silver lining—the maximum number of patients I would see in a day was eight. At the time, I was drowning in chart notes and patient messages. I was willing to take on just about anything to ease my workload, so I said, ’Sign me up!’ I remember my first home visit. Betty, a middle-aged woman with Down syndrome who was bedbound due to a stroke. I spent two hours in her home—not because I was going through her medical history, which was extensive, but because I was talking with her and her family, getting to know them on a personal level. We talked about her favourite cartoons. Her family taught me how to check her blood pressure the way she liked. When I left Betty’s home, I was smiling. I felt encouraged. I had helped someone. I was doing good work. Why did I feel this way? Simply put, the visit was not about me, but rather the patient. Some may argue that all visits are about the patient, but those of us in this profession know that is a lie. Visits cannot be just about patients when doctors are 45 minutes late, push a prescribed agenda (one patients often do not want to have pushed on them) and leave without addressing patients’ issues because, ‘We don’t prescribe ”X“ or ”Y”.’ Going into a patient’s home changes the entire dynamic of patient encounters. I am still in a position of service but also one of vulnerability—I am on someone else’s turf, immersed in someone else’s space. I can no longer hide behind my computer. Amazingly, the more I become vulnerable, the more my patients do too. I have always understood health issues, but on home visits, patients cannot hide their dirty dishes, agoraphobia or poverty. My patients and I sit beside one another rather than across from each other. I am no longer positioned above my patients on a pedestal; I am down on the ground, in the muck of life, with the folks I care for. By visiting patients’ homes, I can build relationships, trust and respect at a faster rate than in the clinic with its time limits and restrictions. With everything out in the open, we—patients, families and I—make decisions as a unit, as a team. I am no longer agenda-building for myself. Instead, I plan for my patients, creating goals that are realistic and helpful to their current state of life. Home visits have saved my belief in the profession of medicine and my chosen career, family medicine . Readings Clair MCS, Sundberg G, Kram JJF. Incorporating home visits in a primary care residency clinic: the patient and physician experience. J Patient Cent Res Rev 2019;6:203–9. doi: 10.17294/2330–0698.1701 Unwin BK, Jerant AF. The home visit. Am Fam Physician 1999;60:1481–8. Yang M, Thomas J, Zimmer R, Cleveland M, Hayashi JL, Colburn JL. Ten things every geriatrician should know about house calls. J Am Geriatr Soc 2019;67:139–44. doi: 10.1111/jgs.15670 Clair MCS, Sundberg G, Kram JJF. Incorporating home visits in a primary care residency clinic: the patient and physician experience. J Patient Cent Res Rev 2019;6:203–9. doi: 10.17294/2330–0698.1701 Unwin BK, Jerant AF. The home visit. Am Fam Physician 1999;60:1481–8. Yang M, Thomas J, Zimmer R, Cleveland M, Hayashi JL, Colburn JL. Ten things every geriatrician should know about house calls. J Am Geriatr Soc 2019;67:139–44. doi: 10.1111/jgs.15670 Jay Allen, Lauren Giammar and John Wood Never expect a farmer to drive three hours away to get a procedure. He’ll say, ‘Fine, Doc.’ Then he’ll drive home and do it himself. Services provided in primary care settings can be largely organised into three categories: disease prevention, medical management and therapeutic procedures. Although disease prevention and medical management remain core tenets of every primary care specialty, only family medicine continues to place a significant emphasis on the development of procedural skills in residency training and their application in practice. Why? Procedural care contributes to patients’ trust in their family physicians: it nurtures the physician–patient relationship and breaks down barriers to healthcare access. Common procedures in family medicine— What procedures do we as family physicians do? Because every medical ecosystem is different, as is every physician, there is no ‘one-size-fits-all’ list of procedures. As family physicians, we may do any of the following procedures: Remove things, including ingrown toenails, skin lesions or other tissues, skin tags, fishhooks placed by overzealous anglers and other foreign bodies. Shave, punch, excise, fulgurate, desiccate, cauterise, suture and freeze. Aspirate and inject, poke needles into joints and other spaces, often accompanied by point-of-care ultrasound. Drain abscesses. Splint and cast common fractures. Insert and remove long-acting reversible contraceptives, along with a variety of other procedures related to women’s and reproductive health needs. Procedures and physician well-being— The inclusion of procedural care in clinical practice positively affects many measured outcomes of clinician well-being. Physicians who do procedures are more likely to have greater job satisfaction, and they are less likely to experience burnout. When they maintain a broader scope of practice that includes procedures, both rural and urban physicians score higher on American Board of Family Medicine recertification examinations than their colleagues who exclude procedures from their skill sets. Procedures are a way for family physicians to improve their physician well-being and career longevity. Patient satisfaction and access to healthcare— ‘Can’t you just do it, Doc?’ is a question we hear repeatedly. Many patients want their procedure done today. As well, perhaps it needs to be done today. Family physicians offer not only the convenience of timelier procedures but also the comfort of having it done by an individual who patients trust. Patients are more likely to use services when they are performed by someone with whom they feel comfortable. Patients whose family physicians provide more services have lower healthcare costs. With the nearly one month average wait time to see specialists and the current overwhelming burden on emergency departments, it is easy to imagine the improvements in efficiency and satisfaction with offering these procedures—and more—in primary care offices. Family medicine practices devoid of procedures can certainly run efficiently and properly. Only it is not efficiency, but our own patients, that lie at the heart of our profession. To care for our patients well, with the added benefit of improved physician well-being, we ought to offer care for all facets of robust primary care, procedural care included . Readings Kahn NB Jr. Redesigning family medicine training to meet the emerging health care needs of patients and communities: be the change we wish to see. Fam Med 2021;53:499–505. doi: 10.22454/FamMed.2021.897904 Nothnagle M, Sicilia JM, Forman S, et al ; STFM Group on Hospital Medicine and Procedural Training. Required procedural training in family medicine residency: a consensus statement. Fam Med 2008;40:248–52. Wearne S. Teaching procedural skills in general practice. Aust Fam Physician 2011;40:63–7. Kahn NB Jr. Redesigning family medicine training to meet the emerging health care needs of patients and communities: be the change we wish to see. Fam Med 2021;53:499–505. doi: 10.22454/FamMed.2021.897904 Nothnagle M, Sicilia JM, Forman S, et al ; STFM Group on Hospital Medicine and Procedural Training. Required procedural training in family medicine residency: a consensus statement. Fam Med 2008;40:248–52. Wearne S. Teaching procedural skills in general practice. Aust Fam Physician 2011;40:63–7. Scott Dickson and Leslie Stone Rural communities face daunting challenges that jeopardise the well-being of their residents. Family physicians are essential resources for these communities and well qualified to help improve the lives of all who call these communities home. Rural America is struggling. Over the last generation, rural small businesses have been crowded out of their markets by larger corporate entities, and small family farms have similarly been bought out or replaced by larger producers. As a result, limited educational and economic opportunities are contributing to the dramatic rise in what some call deaths of despair—overdoses of drugs and alcohol, suicides and alcoholic liver diseases. While family medicine cannot rectify all the ills that rural communities in the USA or elsewhere face, it can be part of the solution. This requires that family medicine be seen as a foundational element of a fully functioning healthcare system rather than as an appendage to subspecialty and hospital care. Robust rural family medicine includes the following : Provides medical care that is socially accountable and broadly centred on whole patients, families and communities rather than biomedical care that is narrowly focused and disease centred. Offers geographically decentralised care that is local and accessible rather than institutionally centralised care in distant and often poorly accessible medical centres. Relies on the democratisation of knowledge and the equitable allocation of financial, educational and technological resources. Encourages family physicians to exhibit clinical courage —expanding the limits of the care they provide through broad training. Certainly, there are barriers to such robust care in rural areas. These include geographical isolation and often limited means of transportation, extensive poverty, poor resource availability and few financial or social incentives to attract and retain a motivated professional workforce. On the flip side of the equation, rural medical facilities—community hospitals and local clinics—are often drivers of community development; they routinely spur the presence of other social, educational and healthcare services, and promote economic growth. To meet the needs of people living in geographically distant areas, a conceptual shift is critical: rather than simply viewed as a means by which patients can be funnelled toward urban specialty services and hospitals, rural primary care clinics must be valued by highly centralised institutions and supported as decentralised centres of excellence in and of themselves. Rural family physicians and distant healthcare institutions can partner together to provide dynamic clinician-to-clinician consultation services and link accessible broad-scope family-centred and community-centred care with up-to-date subspecialty expertise. Such partnerships help rural family physicians provide comprehensive care, often across generations. They empower rural healthcare systems to address presenting problems both big and small, creating a sense of accountability among those who live and work on the periphery of the consciousness of urban-centred healthcare institutions . Readings Colwill JM, Cultice JM. The future supply of family physicians: implications for rural America. Health Aff (Millwood ) 2003;22:190–8. doi: 10.1377/hlthaff.22.1.190 Rosenblatt RA. A view from the periphery - health care in rural America. N Engl J Med 2004;351:1049–51. doi: 10.1056/NEJMp048073 Bingham JL. A prayer for deliverance. Life in rural family practice. Can Fam Physician 2004;50:701–2. Colwill JM, Cultice JM. The future supply of family physicians: implications for rural America. Health Aff (Millwood ) 2003;22:190–8. doi: 10.1377/hlthaff.22.1.190 Rosenblatt RA. A view from the periphery - health care in rural America. N Engl J Med 2004;351:1049–51. doi: 10.1056/NEJMp048073 Bingham JL. A prayer for deliverance. Life in rural family practice. Can Fam Physician 2004;50:701–2. Austin Brown Full-spectrum family medicine takes on different meanings in different contexts. Nonetheless, it is a foundational feature of the discipline. Spend anytime in or around the discipline of family medicine, and you will likely come across the phrase ‘full spectrum’. Although frequently used and overheard, accurate definitions of this phrase are seemingly difficult to come by. ‘Full spectrum’ is often used idealistically and with a hint of nostalgia to refer to family doctors who provide prenatal and intrapartum maternity care, including operative obstetrics; maintain an inpatient practice; and perform a variety of procedures. That is only one definition, and ‘full spectrum’ can represent a ‘spectrum’ of care. Nascent family physicians—interns and residents—come to residency having inherited from their medical school experience a disease model of care that is nearly, if not entirely, grounded in the evaluation, diagnosis and treatment of pathological conditions. Their legacy upon graduation is one heavily influenced by a curriculum in the preclinical years that prioritises a reductionist interpretation of biomedical science and a model of clinical preparation that emphasises subspecialty practice in tertiary-level and quaternary-level hospitals. They have also undoubtedly encountered the informal and hidden curricula of medical schools that routinely disparage primary care. Historically, family medicine has rejected this model and these influences, though not entirely, of course, as family physicians still use conventional diagnostic evaluations and treatment interventions to attend to the physical complaints that concern patients. Rather, family medicine has promoted a different way of looking at patients and their problems and a different conception as to the central focus of medical practice. Patients are not ‘dirty windows’ through which clinicians look to ascertain correct diagnoses. Rather, family physicians frame pathology in context of individuals living and working in communities, their lives and their illnesses, shaped by such factors as faith, gender, class and relationships. What emerges from this understanding of family medicine is whole-person care : treating people as whole people who have a wide variety of needs—psychological, social, biological and existential—that commonly present in clinical encounters as inter-related admixtures of distressing signs and symptoms. In this regard, ‘full-spectrum’ family medicine means tailoring medical practice to meet as many of those needs as possible, all the while acknowledging patients—people—as objects of primary attention. ‘Full spectrum’ does not suggest a style of family medicine, neither does it imply what any one family physician does. Depending on resources, geography and local needs, what family medicine ultimately looks like will differ in different communities. Still—along with the drive to learn new skills, to widen the scope of practice as is required and to teach these characteristics to new generations of physicians—‘full-spectrum’ care is a key component of family physicians’ professional identity. It is at the heart of why family physicians exist . Readings Borins M. Holistic medicine in family practice. Can Fam Physician 1984;30:101–6. Risdon C, Edey L. Human doctoring: bringing authenticity to our care. Acad Med 1999;74:896–9. doi: 10.1097/00001888-199908000-00013 Stange KC. A science of connectedness. Ann Fam Med 2009;7:387–95. doi: 10.1370/afm.990. Borins M. Holistic medicine in family practice. Can Fam Physician 1984;30:101–6. Risdon C, Edey L. Human doctoring: bringing authenticity to our care. Acad Med 1999;74:896–9. doi: 10.1097/00001888-199908000-00013 Stange KC. A science of connectedness. Ann Fam Med 2009;7:387–95. doi: 10.1370/afm.990. |
Implementation of Cystic Fibrosis Responsibility, Independence, Self‐Care, Education Program Enhances Cystic Fibrosis Knowledge in Limited Resource Country: Results From a Randomized Controlled Trial | a57cda8c-2683-4753-a712-ae3c0cb2e948 | 11789547 | Patient Education as Topic[mh] | Introduction Cystic fibrosis (CF) is an autosomal recessive inherited disease caused by mutations in the cystic fibrosis transmembrane regulator (CFTR) gene, with an incidence of 1/3000–1/6000 live births . In addition to significant improvements in medical treatment and standards of care, highly effective modulator treatment has led to a gradual increase in the life expectancy of people with CF (pwCF) . More than half of the CF population in Europe, Canada, and North America is over 18 years old, and there is a notable rise in individuals with CF transitioning from pediatric to adult care . For adolescents with chronic diseases including CF, the transition phase can be challenging and lead to negative health outcomes . It is important for patients to fully understand their illness to ensure a successful transition and improve their self‐confidence and independence. Therefore, comprehensive transition programs and clinics are becoming increasingly prevalent in many countries . Lack of knowledge is one of the most significant factors that complicate this transition. Structured transition programs are important for addressing these challenges and have the potential to increase patient satisfaction, reduce anxiety, and develop greater self‐confidence . The CF R.I.S.E. (Responsibility, Independence, Self‐Care, Education) program is a structured transition program that has been successfully implemented in the United States since 2015 . It includes 13 Knowledge Assessment Questionnaires (KAQ) and six Responsibility Checklists (RCL) to assess and enhance CF knowledge . The topics covered ranged from lung and liver health to nutrition and social security . Patients completed these assessments independently, and based on the results, the CF care team created personalized plans to fill knowledge gaps. Progress was monitored through assessments every 6–12 months . The program exhibits significant potential in addressing transition‐related gaps and garnered positive feedback from CF healthcare providers during its implementation . The Marmara University (MU) Selim Coremen CF Center, the largest in Turkey, currently cares for 424 individuals with CF, of whom 103 are adults, and this number continues to grow. Although the adult CF population has steadily increased over the years, our center still lacks a structured transition program. The MU CF team comprises five pediatric pulmonologists, six pediatric pulmonology fellows, two CF nurses, one dietitian, and one physiotherapist. Furthermore, as the number of adults with CF under our care increases, our center lacks specialized personnel such as a social worker or psychologist who could directly address the challenges adults with CF face, including changes in social rights, financial and college issues, sexual health and fertility concerns, and psychological challenges. Additionally, there is currently no structured adult CF center in our hospital, and there are no dedicated CF exam rooms, CF nurses, dietitians, or physiotherapists for adult pwCF. There are a total of 103 pwCF aged 18 and over. Among them, 90 are followed exclusively by our CF center, and 13 are followed by both adult and pediatric teams. There are no pwCF who are followed solely by the adult clinic. Joint visits with adult pulmonologists have been initiated on a monthly basis as part of the CF S.O.B.E. transition program. Although our center has a multidisciplinary team, most hospitals in the country do not have a CF center. Additionally, the availability of high‐efficacy modulator therapies (HEMT) is extremely limited due to the lack of coverage by the Social Security Institution (SSI), preventing the majority of pwCF in our country from accessing these life‐changing medications . To address the gap in transition readiness, we translated and adapted the CF R.I.S.E. program into Turkish during the Cystic Fibrosis Foundation (CFF) Virtual Improvement Program‐F7 (VIP‐F7). We named our transition program CF S.O.B.E., which is derived from the Turkish initials for responsibility, self‐care, independence, and education . The translation and implementation of the CF S.O.B.E. protocol in our center were completed within a 6‐month period . In this study, the CF S.O.B.E. program was applied as the transition project, and educational interventions were provided to pwCF involved in the program. Our primary aim was to compare the effect of the modified CF S.O.B.E. program versus the standard CF S.O.B.E. program on the knowledge improvement of pwCF. Our secondary aim was to assess caregiver/participant satisfaction with the modified CF S.O.B.E. program to better understand its acceptability and practical implementation. Methods 2.1 Setting and Design This prospective, randomized 18‐month study was conducted between March 2022 and August 2023 at the Marmara University Selim Çöremen Cystic Fibrosis Centre. Marmara University CF Center (MUCFC), the largest CF center in Turkey, followed by 424 individuals with CF, 103 of whom (24.3%) were adults. This study focused on the individuals between the ages of 16 and 25. Our transition team included five pediatric pulmonologists, six pediatric pulmonology fellows, two CF nurses, one dietitian, one physical therapy specialist, and one patient representative. As part of the CFF QI project, the CF R.I.S.E program was translated and adapted with permission from the CFF. All members of the transition team actively participated in weekly VIP‐7 meetings with two coaches from October 2021 to October 2022. Due to the absence of adequate social workers and psychologists at our CF center, they were not included in the team. The CF S.O.B.E transition program for adults was introduced to 83 patients aged 16–25 years, who are followed by our center, during a Zoom meeting attended by pwCF, their caregivers, and all CF team members. Participants were informed that some would receive face‐to‐face education while others would receive modified CF S.O.B.E. education. After this information was provided, written consent was obtained from the participants and the parents of those under 18 years old. Participants were then assigned to one of two groups through a simple sequential randomization process. Prior to baseline testing, a team member assigned anonymous unique identification numbers (ranging from 1 to 83) to all participants using Microsoft Excel. This ensured participant anonymity and impartiality throughout the randomization process. The randomization was performed by a blinded team member who was completely unaware of participant identities and was not involved in any part of the intervention process. Using these unique identification numbers, the blinded team member applied simple sequential randomization to allocate participants into two groups: Group 1 ( n = 41) and Group 2 ( n = 42). Two participants in Group 1 were excluded from the study after indicating that they did not wish to participate regularly in face‐to‐face education (Figure ). Initially, the participants were asked to respond to 11 KAQs via an online survey . Results were scored as the percentage of correct answers per question for each topic . Group 1 ( n = 39), the standard CF S.O.B.E group, received face‐to‐face feedback on 11 topics every 3 months in the outpatient clinic as part of the project. Standardized feedback was provided by a clinician based on questions that could not be answered correctly on the KAQ. Group 2 ( n = 42), the modified CF S.O.B.E group, continued their routine clinical visits every 3 months without receiving face‐to‐face feedback (Figure ). Both groups were invited to participate in online training webinars, which were organized specifically for each topic every 3 months. Free access was provided to online webinars. During their routine visits every 3 months, participants in both groups received a set of baseline survey tests they had initially completed, with their correct and incorrect answers marked. Along with this, they were given a printed answer sheet containing the correct responses to the questions. Additionally, printed patient leaflets covering all relevant topics were provided to both groups to help address any knowledge gaps. Leaflets and educational videos, such as cleaning and disinfection of medical equipment and chest physiotherapy, were available for both groups on the CF S.O.B.E project website created by the Cystic Fibrosis Patient and Family Association of Turkey (KIFDER) . The training clinician maintained this time during each training session. Posttests on the relevant topic were administered via online Survey Monkey questionnaires to both groups 6 months after each training topic. The demographic data of the patients were obtained from their medical records. Participants' and their parents' experiences regarding the project were also evaluated using another survey. The survey focused on the project's overall benefits and limitations. All participants were asked about their participation in online webinars and their opinions on the online training materials prepared for the project. The survey questions were answered with “Yes,” “No,” and “Undecided.” At the end of the survey, open‐ended questions were asked to families, participants, and the CF team for their suggestions on improving the program. The modified CF S.O.B.E. group was asked if they wanted to receive face‐to‐face education as part of the standard CF S.O.B.E. project. The MU CF team of 12 healthcare professionals was also evaluated using the CF S.O.B.E program with a questionnaire. This study was approved by the Ethics Committee of Marmara University (Project No.: 07.10.2022.1359). Written consent was obtained from both the participants and their families. 2.2 Statistical Analysis The analysis of data was conducted using the Statistical Program for Social Sciences (SPSS, version 22.0). Descriptive statistics were used to provide numerical summaries, including counts, means, standard deviations (SDs), and interquartile ranges (IQR), depending on the distribution of continuous variables. Categorical variables were compared using Pearson's Chi‐square and Fisher's exact tests, while continuous variables for the two groups were compared using the Mann – Whitney U test. The Wilcoxon test was performed for nonparametric analysis of continuous variables across two dependent groups, with the 95% confidence interval (CI) presented for each measurement. To calculate the score differences between each participant's posttest and pretest, delta calculations were performed using the formula: Delta = (Posttest result − pretest result)/pretest result × 100 for each participant. This study evaluated the effect of liver disease, pancreatic insufficiency, and diabetes on patients' baseline knowledge levels regarding these conditions, as well as the influence of sexual health knowledge by gender, using the Mann–Whitney U test. Statistical significance was set at p < 0.05. Setting and Design This prospective, randomized 18‐month study was conducted between March 2022 and August 2023 at the Marmara University Selim Çöremen Cystic Fibrosis Centre. Marmara University CF Center (MUCFC), the largest CF center in Turkey, followed by 424 individuals with CF, 103 of whom (24.3%) were adults. This study focused on the individuals between the ages of 16 and 25. Our transition team included five pediatric pulmonologists, six pediatric pulmonology fellows, two CF nurses, one dietitian, one physical therapy specialist, and one patient representative. As part of the CFF QI project, the CF R.I.S.E program was translated and adapted with permission from the CFF. All members of the transition team actively participated in weekly VIP‐7 meetings with two coaches from October 2021 to October 2022. Due to the absence of adequate social workers and psychologists at our CF center, they were not included in the team. The CF S.O.B.E transition program for adults was introduced to 83 patients aged 16–25 years, who are followed by our center, during a Zoom meeting attended by pwCF, their caregivers, and all CF team members. Participants were informed that some would receive face‐to‐face education while others would receive modified CF S.O.B.E. education. After this information was provided, written consent was obtained from the participants and the parents of those under 18 years old. Participants were then assigned to one of two groups through a simple sequential randomization process. Prior to baseline testing, a team member assigned anonymous unique identification numbers (ranging from 1 to 83) to all participants using Microsoft Excel. This ensured participant anonymity and impartiality throughout the randomization process. The randomization was performed by a blinded team member who was completely unaware of participant identities and was not involved in any part of the intervention process. Using these unique identification numbers, the blinded team member applied simple sequential randomization to allocate participants into two groups: Group 1 ( n = 41) and Group 2 ( n = 42). Two participants in Group 1 were excluded from the study after indicating that they did not wish to participate regularly in face‐to‐face education (Figure ). Initially, the participants were asked to respond to 11 KAQs via an online survey . Results were scored as the percentage of correct answers per question for each topic . Group 1 ( n = 39), the standard CF S.O.B.E group, received face‐to‐face feedback on 11 topics every 3 months in the outpatient clinic as part of the project. Standardized feedback was provided by a clinician based on questions that could not be answered correctly on the KAQ. Group 2 ( n = 42), the modified CF S.O.B.E group, continued their routine clinical visits every 3 months without receiving face‐to‐face feedback (Figure ). Both groups were invited to participate in online training webinars, which were organized specifically for each topic every 3 months. Free access was provided to online webinars. During their routine visits every 3 months, participants in both groups received a set of baseline survey tests they had initially completed, with their correct and incorrect answers marked. Along with this, they were given a printed answer sheet containing the correct responses to the questions. Additionally, printed patient leaflets covering all relevant topics were provided to both groups to help address any knowledge gaps. Leaflets and educational videos, such as cleaning and disinfection of medical equipment and chest physiotherapy, were available for both groups on the CF S.O.B.E project website created by the Cystic Fibrosis Patient and Family Association of Turkey (KIFDER) . The training clinician maintained this time during each training session. Posttests on the relevant topic were administered via online Survey Monkey questionnaires to both groups 6 months after each training topic. The demographic data of the patients were obtained from their medical records. Participants' and their parents' experiences regarding the project were also evaluated using another survey. The survey focused on the project's overall benefits and limitations. All participants were asked about their participation in online webinars and their opinions on the online training materials prepared for the project. The survey questions were answered with “Yes,” “No,” and “Undecided.” At the end of the survey, open‐ended questions were asked to families, participants, and the CF team for their suggestions on improving the program. The modified CF S.O.B.E. group was asked if they wanted to receive face‐to‐face education as part of the standard CF S.O.B.E. project. The MU CF team of 12 healthcare professionals was also evaluated using the CF S.O.B.E program with a questionnaire. This study was approved by the Ethics Committee of Marmara University (Project No.: 07.10.2022.1359). Written consent was obtained from both the participants and their families. Statistical Analysis The analysis of data was conducted using the Statistical Program for Social Sciences (SPSS, version 22.0). Descriptive statistics were used to provide numerical summaries, including counts, means, standard deviations (SDs), and interquartile ranges (IQR), depending on the distribution of continuous variables. Categorical variables were compared using Pearson's Chi‐square and Fisher's exact tests, while continuous variables for the two groups were compared using the Mann – Whitney U test. The Wilcoxon test was performed for nonparametric analysis of continuous variables across two dependent groups, with the 95% confidence interval (CI) presented for each measurement. To calculate the score differences between each participant's posttest and pretest, delta calculations were performed using the formula: Delta = (Posttest result − pretest result)/pretest result × 100 for each participant. This study evaluated the effect of liver disease, pancreatic insufficiency, and diabetes on patients' baseline knowledge levels regarding these conditions, as well as the influence of sexual health knowledge by gender, using the Mann–Whitney U test. Statistical significance was set at p < 0.05. Results 3.1 Characteristics The study included 81 patients, divided into two groups: Group 1 (Standard CF S.O.B.E group) with 39 patients (48.1%), and Group 2 (Modified CF S.O.B.E group) with 42 patients (51.8%). The median age of the participants was 18.5 years (IQR: 16.9–21.2) and 52.0% of the patients were female ( n = 42). The demographic characteristics of both the groups were similar (Table ). 3.2 Assessment of Knowledge Levels The overall response rate for the pretest questionnaires was 95.1%. The overall response rate for the 9963 questions included in the questionnaires across all groups was 94.2% (total number of unanswered questions: 577). For the posttests, the overall response rate for the questionnaires was 100%. The overall response rate for 9963 questions included in the questionnaires across all groups was 96.2% (total number of unanswered questions: 374). No significant differences were observed in the pretest results between the groups. In the standard CF S.O.B.E group, the median scores on the posttest were higher for all topics except for Male Sexual Health , where the pre‐ and postscores were similar (Table ). For all topics, except for CF‐related Liver Disease and Male Sexual Health , these differences were statistically significant (Table ). In the modified CF S.O.B.E group, statistically higher posttest results were observed for all KAQs except Lung Health and Airway Clearance, CF‐related Liver Disease, Pancreatic Insufficiency and Nutrition , and Male Sexual Health (Table ). No significant gender‐based difference was found in the level of knowledge of sexual health in both groups ( p = 0.33 and 0.83 for men and women, respectively). Similarly, the presence of CF‐related Liver Disease did not have a significant effect on the participants' knowledge levels ( p = 0.62). Regarding Pancreatic Insufficiency and Nutrition and CF‐related Diabetes , individuals with these conditions demonstrated higher pretest scores than those without these conditions ( p = 0.01 and 0.002, respectively). In the analysis comparing the pre‐ and posttest results for both groups using delta analysis, it was observed that the standard CF S.O.B.E group showed a more significant improvement in the posttest scores, particularly in the areas of “ Lung Health and Airway Clearance ” and “ Equipment Maintenance and Infection Control , ” compared to the modified group. This improvement was statistically significant, with p values of 0.014 and 0.002, respectively (Table ). 3.3 Surveys A survey was administered to all the patients and their parents to gather their opinions on the training sessions attended. This questionnaire evaluated the perceived usefulness of the standard/modified CF S.O.B.E project. The results showed that 80% of patients in the standard CF S.O.B.E group and 90% of their parents, along with 60% of patients in the modified CF S.O.B.E group and 75% of their parents, found the program beneficial. While both groups reported a similar increase in disease knowledge, a higher proportion of patients in the standard CF S.O.B.E. group reported gains in self‐confidence and disease management skills. Additional results are presented in Table . The CF S.O.B.E program evaluated by the MU CF team of doctors, nurses, and dietitians (12 participants in total) showed strong support among health care providers. The team agreed that the program would benefit patients. While 90% the team believed the program was feasible in a routine setting, there were concerns about ‘time’ in the busy environment of the clinic. Positive feedback highlighted the contribution of the program toward increasing patients' knowledge, self‐care, and responsibility. The median visiting time for face‐to‐face training sessions was 20 min (IQR: 15–19 min) (the shortest session lasted 10 min and the longest 50 min). Characteristics The study included 81 patients, divided into two groups: Group 1 (Standard CF S.O.B.E group) with 39 patients (48.1%), and Group 2 (Modified CF S.O.B.E group) with 42 patients (51.8%). The median age of the participants was 18.5 years (IQR: 16.9–21.2) and 52.0% of the patients were female ( n = 42). The demographic characteristics of both the groups were similar (Table ). Assessment of Knowledge Levels The overall response rate for the pretest questionnaires was 95.1%. The overall response rate for the 9963 questions included in the questionnaires across all groups was 94.2% (total number of unanswered questions: 577). For the posttests, the overall response rate for the questionnaires was 100%. The overall response rate for 9963 questions included in the questionnaires across all groups was 96.2% (total number of unanswered questions: 374). No significant differences were observed in the pretest results between the groups. In the standard CF S.O.B.E group, the median scores on the posttest were higher for all topics except for Male Sexual Health , where the pre‐ and postscores were similar (Table ). For all topics, except for CF‐related Liver Disease and Male Sexual Health , these differences were statistically significant (Table ). In the modified CF S.O.B.E group, statistically higher posttest results were observed for all KAQs except Lung Health and Airway Clearance, CF‐related Liver Disease, Pancreatic Insufficiency and Nutrition , and Male Sexual Health (Table ). No significant gender‐based difference was found in the level of knowledge of sexual health in both groups ( p = 0.33 and 0.83 for men and women, respectively). Similarly, the presence of CF‐related Liver Disease did not have a significant effect on the participants' knowledge levels ( p = 0.62). Regarding Pancreatic Insufficiency and Nutrition and CF‐related Diabetes , individuals with these conditions demonstrated higher pretest scores than those without these conditions ( p = 0.01 and 0.002, respectively). In the analysis comparing the pre‐ and posttest results for both groups using delta analysis, it was observed that the standard CF S.O.B.E group showed a more significant improvement in the posttest scores, particularly in the areas of “ Lung Health and Airway Clearance ” and “ Equipment Maintenance and Infection Control , ” compared to the modified group. This improvement was statistically significant, with p values of 0.014 and 0.002, respectively (Table ). Surveys A survey was administered to all the patients and their parents to gather their opinions on the training sessions attended. This questionnaire evaluated the perceived usefulness of the standard/modified CF S.O.B.E project. The results showed that 80% of patients in the standard CF S.O.B.E group and 90% of their parents, along with 60% of patients in the modified CF S.O.B.E group and 75% of their parents, found the program beneficial. While both groups reported a similar increase in disease knowledge, a higher proportion of patients in the standard CF S.O.B.E. group reported gains in self‐confidence and disease management skills. Additional results are presented in Table . The CF S.O.B.E program evaluated by the MU CF team of doctors, nurses, and dietitians (12 participants in total) showed strong support among health care providers. The team agreed that the program would benefit patients. While 90% the team believed the program was feasible in a routine setting, there were concerns about ‘time’ in the busy environment of the clinic. Positive feedback highlighted the contribution of the program toward increasing patients' knowledge, self‐care, and responsibility. The median visiting time for face‐to‐face training sessions was 20 min (IQR: 15–19 min) (the shortest session lasted 10 min and the longest 50 min). Discussion The current study revealed that implementation of the CF standard transition program for young adults with CF resulted in significant improvement in knowledge levels, disease management skills, and self‐confidence. CF is a medical condition that imposes a significant treatment burden on the patients. To effectively manage their disease, pwCF must first acquire necessary knowledge and abilities before transitioning to adult care. Knowledge of the disease state has proven to be a significant barrier to a successful transition. In a study by Faint et al., investigators noted that most adolescent CF patients had poor knowledge of lung disease and nutrition . Baker et al. reported the results of the implementation of CF R.I.S.E transition program at CF care centers across the United States in 2015 . Providers reported that the CF R.I.S.E facilitated communication with the family, particularly knowledge and skills assessments. Although all providers rated the program as valuable and 95% felt that the program could become a sustainable part of the clinic, only a few prospective studies have evaluated the effect of the CF R.I.S.E program on patients knowledge level . Perez et al. reported that implementation of targeted education for adolescent CF patients on pancreatic insufficiency and nutrition domain of CF R.I.S.E resulted in increases in knowledge assessment scores between initial and follow‐up assessments . In the current study, we also had the opportunity to compare the changes in the knowledge levels of the patients in the standard CF S.O.B.E group with those in the modified CF S.O.B.E group who were provided only with educational materials. Although the knowledge level of patients in most domains increased significantly in both groups, the level of knowledge on Lung Health and Airway Clearance and Pancreatic Insufficiency and Nutrition increased significantly in the standard CF S.O.B.E group. Lung Health and Airway Clearance and Pancreatic Insufficiency and Nutrition are the most important issues in the care of patients with pwCF, and improvement in the standard CF S.O.B.E group was much better than expected. The significant differences observed in self‐confidence and disease management skills between the standard CF S.O.B.E. group and the modified CF S.O.B.E. group underscore the potential impact of personalized education, including face‐to‐face feedback. However, the improvement in the knowledge of domains in the modified CF R.I.S.E group was promising, which is important for countries with limited resources, such as ours. Although many studies highlight the need for a dedicated transition coordinator during the transition from pediatric to adult care our CF center lacks a project coordinator or social worker . We believe that the relatively higher knowledge assessment scores regarding the Equipment Maintenance and Infection Control domain in both groups are related to the standard education and training materials previously provided to all patients in our center, resulting in a significant increase in the rate of correct practices for nebulizer hygiene . The current study revealed that although pwCFs understand many aspects of their disease, there are important knowledge gaps in the area of reproductive and sexual health (RSH). Studies have shown that healthcare professionals in CF clinics inform patients about RSH later than preferred. Several factors, such as emotional barriers, cultural issues, and personal preferences, play an important role in communication with RSH . Similar to our results in the study by Siklosi et al., while the level of knowledge about nutrition and lung health was relatively high, knowledge scores about reproduction and genetics were lower, which was unrelated to the patient's age or socioeconomic status . A lack of adequate information about fertility and reproductive health can result in suboptimal choices for family planning and increase the risk of contracting sexually transmitted infections . In our study, although there was a significant increase in knowledge scores in the female sex health domain in both groups, there was no significant improvement in the male sexual health domain. As overall health improves with HEMT, a greater proportion of pwCF are considering fertility, making appropriately timed conversations and providing appropriate resources about RSH become even more important . To improve the knowledge level of our patients, we aimed to create multiple sources of information about RSH, including up‐to‐date written information and videos, and provide necessary consultations from specialists as required. When each domain of knowledge was analyzed separately, scores for liver disease, CF‐related diabetes, school, work, and insurance were also lower than all other domains, indicating the importance of training in these domains prior to transfer to adult units. It is likely that these issues are not routinely addressed during the clinical visits. Our patients' baseline scores in the school, work, and health insurance domains were low. The patient association prepared detailed educational materials on these subjects, which were provided to all patients, and standard training resulted in a significant increase in the knowledge level of the patients. Many patients began to benefit from rights that they could not benefit from. A systematic review by Coyne et al. revealed the benefits of structured transition programs, including better patient satisfaction, self‐care, and reduced anxiety without worsening health outcomes . In our study, almost 80% of patients in the standard CF S.O.B.E. group reported increased self‐confidence and management skills, and 90% of patients and parents found the program helpful. However, self‐confidence and management skills improved in only 25% of the patients in the modified CF S.O.B.E. group, and this rate was significantly lower than that in the standard CF S.O.B.E. group. The observed significant differences in self‐confidence and disease management skills between the Standard CF S.O.B.E. group and the modified CF S.O.B.E. group underscore the importance of tailored interventions. Feedback from healthcare providers on the MU CF team was highly supportive of the program and noted an improvement in the quality of patient care. Concerns about the integration of programs into clinics, especially due to time constraints, were the most important barriers noted by the team. Baker et al. reported the result of process evaluation of CF R.I.S.E implementation based on the Consolidated Framework of Implementation Research at 10 CF centers US. In their study, time was most commonly listed as the biggest barrier to implementing the program. Providers also noted coordination between clinic staff and clinic organization/planning as barriers . Lack of time was also reported as a barrier to transition clinics in the Netherlands by Peeters et al.'s study . These organizational challenges underscore the need for systemic support at multiple levels from the care team to hospital management. This perspective is particularly important when considering the integration of transition programs into routine clinical practice. Our study had several limitations. This was a single‐center study and was completed in one pediatric CF clinic; it may not be entirely representative of the other clinic sites in our country. Additional large‐scale studies are needed to evaluate the efficacy of the CF S.O.B.E program and the areas that require further improvement. Our study also indicated an unmet need for education, particularly on the topics of RSH. When the transition and implementation of CF R.I.S.E to CF S.O.B.E began in March 2022, CF R.I.S.E Resources did not include the CFTR Modulators topic. Considering the increasing number of patients receiving modulator treatments, despite the legal challenges in accessing these therapies in Turkey, we are contemplating the inclusion of the CFTR Modulators resource in the CF S.O.B.E project. Another limitation is the lack of a dedicated project coordinator on our team. Since there was no project coordinator and no additional staff was provided during the implementation of the CF S.O.B.E program, the clinicians and CF nurses in the team had to undertake extra duties in addition to their regular patient care responsibilities. Moreover, time constraints have emerged as a significant challenge in implementing transition programs and integrating them into routine clinical settings. These limitations highlight areas for improvement and underscore the need for more comprehensive and sustained approaches to transitional care for pwCF. One additional limitation was the survey design for gathering feedback on the CF S.O.B.E. program. To keep it brief and manageable, questions were limited to “Yes,” “No,” and “Undecided” responses instead of a Likert scale. While this approach addressed participant fatigue due to multiple surveys, it may have limited the depth of feedback collected on participant experiences and satisfaction. Conclusion In conclusion, our study confirms the effectiveness of the CF S.O.B.E program in improving knowledge, disease management skills, and self‐confidence among pwCF. We also observed the benefits of structured transition programs in improving patient satisfaction. Improvement in modified CF S.O.B.E patients also revealed the importance of multiple sources of information as a part of transition programs. Although modified S.O.B.E is not as effective as standard CF S.O.B.E in terms of increasing knowledge levels, it can be utilized in centers with limited resources and time constraints. However, since self‐confidence and disease management skills were found to be significantly low in the modified CF S.O.B.E group, the primary aim should be to eliminate deficiencies in the centers and implement the standard CF S.O.B.E. Our study lays the groundwork for future research in this area. We plan to make the CF S.O.B.E program a routine practice in our center and follow up these patients in the long term to evaluate the impact of the CF S.O.B.E program on clinical outcomes. Merve Selcuk Balcı: conceptualization, investigation, funding acquisition, writing–original draft, methodology, validation, visualization, writing–review and editing, software, formal analysis, project administration, data curation, supervision, resources. Yasemin Gökdemir: supervision, investigation, conceptualization, funding acquisition, writing–original draft, methodology, validation, visualization, writing–review and editing, software, formal analysis, project administration, data curation, resources. Ela Erdem Eralp: data curation, supervision, resources, software, formal analysis, project administration, writing–review and editing, visualization, validation, methodology, conceptualization, investigation, funding acquisition, writing–original draft. Almala Pınar Ergenekon: conceptualization, investigation, validation, methodology, software, supervision. Cansu Yılmaz Yegit: supervision, conceptualization, investigation, writing–original draft, visualization, validation, methodology, project administration. Mürüvvet Yanaz: conceptualization, investigation, methodology, validation, software, data curation, resources. Aynur Gulieva: software, formal analysis, data curation, writing–review and editing, visualization, writing–original draft, funding acquisition, conceptualization. Mine Kalyoncu: conceptualization, investigation, methodology, validation, funding acquisition, software, formal analysis, data curation. Seyda Karabulut: conceptualization, investigation, funding acquisition, methodology, validation, visualization, software, data curation, formal analysis, resources, writing–review and editing. Neval Metin Cakar: conceptualization, investigation, funding acquisition, methodology, validation, visualization, data curation, software. Burcu Uzunoglu: data curation, resources, project administration, software, methodology, validation, visualization, funding acquisition, investigation, conceptualization. Gamze Tastan: conceptualization, investigation, methodology, data curation, Validation. Damla Kocaman: conceptualization, investigation. Ozge Kenis Coskun: conceptualization, investigation, methodology, data curation. Ilknur Gorgun: conceptualization, data curation, methodology, formal analysis, funding acquisition. Randall Messier: supervision, writing–original draft, writing–review and editing. Pamela Mertz: writing–original draft, writing–review and editing, supervision. Fazilet Karakoc: data curation, supervision, resources, software, formal analysis, project administration, writing–review and editing, visualization, methodology, conceptualization, investigation, funding acquisition, writing–original draft, validation. Bülent Karadag: conceptualization, investigation, funding acquisition, writing–original draft, methodology, validation, visualization, writing–review and editing, project administration, formal analysis, software, data curation, supervision, resources. The authors declare no conflicts of interest. |
Towards better digital pathology workflows: programming libraries for high-speed sharpness assessment of Whole Slide Images | c0dc367f-dc2e-43aa-8384-5676c84d6a96 | 4305973 | Pathology[mh] | Since microscopic slides can now be automatically digitized and integrated in the clinical workflow, quality assessment of these Whole Slide Images (WSI) has become a crucial issue. Until now, the quality of a WSI has been verified a posteriori by a technician or by a pathologist. There is however a significant amount of WSI that are too insufficient in quality (blurred, bad colors, poor contrast) to be used for diagnoses. These slides have then to be scanned again with delay thus slowing down the diagnostic workflow. To address this problem, we chose to design a method of quality assessment followed by reacquisition, as opposed to a process of enhancement or restoration . Such process indeed too frequently results in the degradation of image quality, a key factor in medical diagnosis. The quality of a flat image can be defined by several quantifiable parameters such as color, brightness, and contrast. One of the most important parameters, yet difficult to assess, is the focus sharpness (i.e. the level of focus blur) ). Quality assessment of WSI is much more complex than that of flat images because of their intrinsic structure made of multiple magnification levels (pyramidal structure) and resolutions above the gigapixel. One study has shown the possibility of comparing the tiles' contrast and entropy in two WSI obtained with two different scanners digitizing the same slide. Another work assessed the focus sharpness of the tiles of a WSI with the generation of a focus assessment map of the WSI at a given magnification level. However, both these methods still require a human eye to assess if the WSI must be accepted or discarded after the scan .The method we designed to automatically assess the quality of a WSI without any sort of comparison (no-reference assessment) has been patented and thoroughly tested in the last four years. It is currently being implemented in our university-hospital Saint-Louis - Assistance Publique - Hôpitaux de Paris (APHP) - Université Paris Diderot - Paris 7, in Paris, France. It is also part of the FlexMIm project , which aims to improve the global workflow of digital pathology. This project, funded by an R&D grant of the French government for highly innovative technologies, also involves universities Paris 6 (LIP6 and IPAL laboratories) and Paris 7 (LIAFA laboratory) and industrial partners Orange Healthcare, Pertimm and TRIBVN as well as 27 anatomo-pathological centers in Paris and its suburbs. For these projects, we have developed two programming libraries, in Java and Python, that can be integrated in various types of WSI and image handling applications.
The development has been carried out on a MacBook Pro Intel Core i7 2.6GHz, 16GB RAM, 512GB SSD, and the tests were carried out in University Paris Diderot Paris 7, with the following configuration: 2 Intel Xeon E5-2680 2.70GHz, 20M Cache, 8.0GT/s QPI, 24GB RDIMM, 1333MHz FBD RAM, 146GB SAS 6Gbps 15k RAID 1, 5 2TB SAS 6Gbps 7.2k RAID 5. The tiles of each magnification level of the WSI need to be accessible to perform the analysis. Many open-source programs as well as proprietary ones can be used to extract WSI files from different formats (3dHistech, Aperio, Hamamatsu, Olympus) into series of tiles at different magnification levels. Any WSI can be converted, at a given magnification level, into a series of tiles or strips (wider tiles) indexed by their (x,y) coordinates. Once the tiles of each magnification level are extracted, the saturation of each of them is computed. In every system, many "blank tiles" are stored because they contain visual artifacts detected as regions of interest but do not contain any specimen. As these blank tiles have saturation values close to zero, our system discards them from the set of images to analyze, saving from 5% (when the sample takes most of the WSI) to 90% (in blank WSI, containing no sample at all) of the time required to complete an analysis of a virtual slide at maximum magnification. The remaining tiles are then analyzed with different tests such as blurriness, contrast, brightness and color. More tests can be integrated as plug-ins in the program. For the blurriness assessment we used our fast reference-free method designed to compute accurately the amount of blur in a single tile based on an edge brightness ratio . Other tests such as contrast, brightness and color assessment are a result of computations made on the tile's pixels values, compared with their respective thresholds. For instance, one test could be to check if more than 90% of the pixels color values inside a tile were contained in three ranges of color. Each tile receives quantitative and qualitative scores for each of the analyzed parameters and are compared to their respective thresholds. Note that the tiles can be virtually split to add granularity and refine the final assessment. For instance, at a 2× magnification, if more than 90% of the tiles are considered sharp, the complete 2× layer of the WSI is considered as sharp. If more than 70% of the 10× magnification is considered sharp, the 10× layer of the WSI is considered as sharp. The analysis can be limited to the lower magnification levels of a WSI for a quicker result or extended to the highest magnification level for a more comprehensive quality assessment. Once the tile analysis is done, if the WSI passed the quality assessment tests at each processed layer of magnification, the WSI is suitable for further use. In order to test and validate the method, we analyzed a series of 100 WSI made of a mix of WSI with optimal focus and of WSI with various blurred areas, some of them being obviously totally blurred. We compared the computer assessment of these WSI to the human assessment in two settings: - We first presented the 100 WSI in a random order to two observers from our research team. - We then conducted a web survey among 22 trained pathologists, asking them whether the overall quality of each WSI seemed sufficient for a clinical use. The human assessment was distributed among three possible answers: Poor; Fair; Good. The computer assessment represented the computed highest acceptable magnification for a WSI, higher magnifications being therefore considered by the computer as of insufficient quality for diagnosis. The libraries implementing the blur assessment method we designed have been developed in Java, Python, PHP5 and MySQL5 using Eclipse IDE, Apache HTTP Server. For web usage, JavaScript, Ajax, JSON and/or Sockets were used for multithreaded interactions between the web application hosted on one server, the java or Python services hosted on the same server, or a different (decentralized) one and the files stored on the same server or on a decentralized storage server. We also used the Google Maps API, as demonstrated in the NYUVM (NYU's virtual microscope, developed by NYU school of medicine) . Native reading of NDPI files was carried out using a modified version of Matthias Baldauf's NDPI to OME-TIFF Converter . Aperio SVS files were converted into the Google Maps format using VIPS and Openslide libraries .
In the following, we use the blur assessment method described in the method section as an example to describe any other quantifiable criterion in an image, to be used a fortiori to assess the quality of WSI. The complete quality assessment method is a logical intersection of independent tests, marking a WSI as of insufficient quality if at least one of the tests fails. We applied the quality analysis routine with the blur assessment parameter on hundreds of WSI. An example of automatic blur assessment is shown in Figure . On a collection of 100 WSI, two observers could easily assess the overall level of quality they observed and they visually verified that the thresholds we set were highly predictive of the global sharpness or blurriness of the WSI. For the web survey, the results obtained after the visual analysis on 100 WSI by 22 pathologists are shown in Figure . The results found by our algorithms are fully consistent with the pathologists' answers to the survey: the mean computer assessment is 1.25× with a standard deviation of 2.37× in the "poor" human assessment category, increasing to 2.90× with a standard deviation of 2.51× in the "fair" category and to 6.35× with a standard deviation of 5.57× in the "good" category. However, the survey showed that the human assessment do not entirely correspond to the computer assessment, due to the fact that some diagnoses do not need high magnification for human eyes to be done. Indeed, a high computer quality at low magnification was sometimes enough to give a correct diagnosis (blue disks on the lower right part of Figure ), but a high-level computer assessment (computed high quality at high magnification) always corresponded to a high level human assessment (blue disks on the upper right part of Figure ). As further improvements of our method, we will contextualize the assessment by refining the thresholds depending on staining and lesion. In terms of computing speed, Zerbe et al. showed a distributed computing model to assess the focus sharpness of a WSI, generating a focus assessment map of the WSI at a given magnification level in around 6 minutes per gigapixel per computer. We analyzed on our testing server 8 complete 1.73 gigapixel digital slides in 400 seconds as eight distinct threads, equivalent to 34 Megapixels per second or 2 gigapixels per minute, per computer. Already 12 times faster than the previous method, we are currently optimizing the program into a multi-thread, multi-node parallel processing system using C++ with OpenMP and OpenMPI libraries to scale it up to match demanding industry requirements. The WSI sharpness analysis Java library we designed is a Service Provider Interface (SPI): an Application Programming Interface (API) aimed at being extendable by third parties. The full library (JAR file) weighs 12 KB and is fully operational for sharpness analysis of single images (tiles), and for array of images such as the WSI in Figure . The speed of analysis is in average 3 billion pixels by minute using our development environment with the JAI (Java Advanced Imaging) API. The Python mono-threaded interface was tested with an average rate of 1 billion pixels by minute. We designed 4 sharpness assessment programs based on our Java multithreaded library: One java program using any regular image file (JPEG, PNG, TIFF, GIF, BMP...) or array of image files, and returning a list of values as described in our paper, with text-only results. One java program using WSI in the Hamamatsu NDPI file format, and returning global results for the slide sharpness at each magnification, as well as a sharpness map of the WSI summarizing the results with colors relative to the sharpness assessed (green for sharp regions, yellow for partially sharp regions, red for blurred regions). Implementation is shown in Figure . One java program using JPEG files structured as required by the Google Maps format: a tree structure containing folders numbered as such (starting with 0 and incrementing as required): Magnification-index/Y-position/X-position.jpg and returning similar results (text and image, as described above). One web application using JPEG files structured as required by the Google Maps format, to be viewed with the NYUVM. We connected our Java library to NYUVM by adding Ajax functions, triggering socket connections with PHP to receive a JSON array containing the results of the sharpness analysis for each visible tile, and display the sharpness results of each tile in real time. The sharpness analysis of the tiles are computed and sent concurrently and faster than the images are displayed, with no slow-down compared to the original NYUVM viewer, thereby in real-time . Implementation is shown in Figure . Programs 1., 3. and 4. also have Python implementations. Our Python implementations were 3-times slower in average than our Java implementations as we haven't yet used Python's multithreading capabilities. We are also currently developing multithreaded Python and Open MPI C++ implementation. Tests were made on 5000 single images, 200 WSI in Hamamatsu formats, 100 WSI in Aperio SVS format converted to the Google Maps format. It is currently being implemented in the French national project FlexMIm and additional results should be provided in the last quarter of 2014. In this perspective, we think that integrating these programs in the WSI acquisition systems can tremendously increase the quality of each scanned WSI without significantly slowing down the acquisition workflow. It will also most definitely speed up the quality assurance process, currently done manually after the WSI has been acquired, and by a subjective visual-only assessment. Implementing these libraries, coupled with regions-of-interest detection algorithms, may enhance intelligent image transfer protocols by sending and displaying the WSI's regions marked as being of interest and of highest quality before other regions. On another matter, image compression algorithms could be designed to favor sharp regions, by requiring lossless methods, and, on the contrary, accept lossy methods to be used on blurred regions. Should such quality assessment scores become part of the WSI's metadata, they may help standardize image quality requirements for digital pathology.
As quality assurance is crucial in a context of daily use in diagnostic pathology, we have developed a fast and reliable no-reference quality assessment library for WSI and digital images in general. The proof of concept for this no-reference and high-speed quality assessment tool for virtual slide was developed in 2010, thoroughly tested and described in 2012. Development of Service Providing Interfaces and Application Programming Interfaces has been carried out in 2012-2014, and implementation started in French national projects in 2013. Applications based on these libraries can be used upstream, as calibration and quality control tool for the WSI acquisition systems, or as tools to reacquire tiles while the WSI is being scanned. They can also be used downstream to reacquire the whole slides that are below the quality threshold for surgical pathology analysis. We think that implementing these libraries could be used as an intelligent accelerator to viewing WSI by sending and displaying the regions marked as being of highest quality before other regions. Such quality assessment scores could be integrated as WSI's metadata shared in clinical, research or teaching contexts, for a more efficient medical informatics workflow.
WSI: Whole Slide Images; NYUVM: New York University's Virtual Microscope; JAI: Java Advanced Imaging.
Financial competing interests David Ameisen is a recipient of a postdoctoral fellowship grant from the FlexMIm project (2013-2014), and was a recipient of a doctoral fellowship grant from Aurora Interactive (2008-2011); Olympus provided him travel reimbursements for one scientific meeting presentation in 2008. None of these organizations are financing this manuscript. David Ameisen and Philippe Bertheau have published one patent (WO2012080643A1) relating to the content of this manuscript. They are receiving salaries from Université Paris Diderot that has applied for this patent. SATT idfinnov is currently funding this work (2014). No other author has financial competing interests. Non-financial competing interests None
David Ameisen is a recipient of a postdoctoral fellowship grant from the FlexMIm project (2013-2014), and was a recipient of a doctoral fellowship grant from Aurora Interactive (2008-2011); Olympus provided him travel reimbursements for one scientific meeting presentation in 2008. None of these organizations are financing this manuscript. David Ameisen and Philippe Bertheau have published one patent (WO2012080643A1) relating to the content of this manuscript. They are receiving salaries from Université Paris Diderot that has applied for this patent. SATT idfinnov is currently funding this work (2014). No other author has financial competing interests.
None
DA participated in the design of the study, the development of the libraries, and drafted the manuscript, CD carried out the Hamamatsu tiles extraction and participated in the design of the study, VP participated in the design of the study, FB participated in the statistical analysis, MB participated in the statistical analysis, LL participated in the statistical analysis, AJ participated in the design of the study, PB participated in the design of the study, and drafted the manuscript, JBY participated in the design of the study, the development of the libraries, and drafted the manuscript. PB and JBY have contributed equally to the work. All authors read and approved the final manuscript.
|
Analysis of data and common mutations encountered during routine parentage testing in Zimbabwe | fac02791-dbca-446d-a30b-2ba80d7c83b4 | 10791675 | Forensic Medicine[mh] | Short tandem repeat (STR) markers are a powerful tool in forensic genetic analysis, parentage testing, kinship analysis and population genetic studies. They exhibit high allelic variability due to high rate of germline mutations. STR markers are stably inherited from parents to children despite being highly variable making them effective for human identification. Twenty CODIS Core loci are currently recommended by the FBI for forensic DNA and human identification analysis and testing. These include CSF1PO, D3S1358, D5S818, D7S820, D8S1179, D13S317, D16S539, D18S51, D21S11, FGA, TH01, TPOX, vWA, D1S1656, D2S441, D2S1338, D10S1248, D12S391, D19S433 and D22S1045. These loci are commonly used because of their high heterozygosity, discriminatory power, clearly defined repetitive units and simple amplification and detection using commercial kits. Testing for parentage can be differentiated into a variety of cases including the direct paternity and maternity test, as well as the indirect tests (kinship) such as grandparentage, siblingship and avuncular tests. These tests have been applied to solve peace of mind (regular), legal and human identification cases. The legal tests are conducted to meet the needs of justice, hence are conducted under strict conditions. Regular cases are conducted at the request of private individuals, but under less strict conditions compared to the legal cases. Paternity cases are the most conducted tests especially in private laboratories and requests for motherless paternity are often made. The testing in all these cases is based on genetic polymorphism, associated with differences between individuals. The informativeness of genetic markers is traditionally measured through finding inconsistences in parent–child Mendelian rules of transmission in randomly chosen individuals. Parentage testing follows Mendelian inheritance law where the child receives one allele from each parent. There are however instances where spontaneous mutations can lead to mismatches, complicating maternity, or paternity cases . Several mechanisms of STR mutation have been described and strand slippage have been identified as the main pattern of STR mutation . Single step mutations are the most widely reported in routine parentage testing while multi-step mutations rarely occur , . These mutations complicate analysis of parentage cases as they affect the paternity or maternity index, and in such cases it becomes important to use mutation rates instead of the routine allele frequencies for calculation . It is therefore important to account for the possibility of gene mutations when considering parentage exclusions. Parentage calculations can also be influence by several genotyping irregularities which include the null alleles and triallelic patterns. Triallelic patterns are three peak profiles observed at a single locus and like mutations occur in rare cases. Two identical peaks are expected per locus with homozygotes presenting a single peak while heterozygotes present 2 peaks. Triallelic patterns present as either three imbalanced peaks (type 1) or three peaks of balanced peaks (type 2) . The type 2 pattern can either be a 1:1:1 pattern or 2:1 pattern. Therefore, the investigation of tri-allelic patterns can help characterize the somatic and germline mutation of genetic makers and facilitate the statistical interpretation of STR loci during forensic DNA analysis. This paper provides an overview and analysis of parentage case types, mutation rates and triallelic patterns observed during routine parentage testing over a period of ten years. We determined the frequency of mutation rates and triallelic patterns in the Zimbabwean population. These have been shown to vary in populations affecting interpretation of results in parentage and forensic analysis. We also investigated the frequency of STR markers involved in the parentage exclusion. This assists in the choice of appropriate selection of STR typing kits.
Parentage case types We collected 1303 cases during a 10-year period (2013–2023). Cases included paternity, maternity, sibling, avuncular and grandparentage tests (Table ). These were used to solve various cases including parentage disputes, criminal cases, disaster victim identification and inheritance disputes. Paternity was the most common type of tests (87.37%) followed by kinship (7.01%) and maternity (5.62%). Kinship included all tests where an alleged biological relative was tested to determine biological relationship in the absence of the alleged father. Motherless paternity (duo) was the most common paternity test, both for regular peace of mind and court ordered tests. Maternity tests were the least ordered test. It was however the most common performed tests for identification of disaster victims with 34 out of 73 cases (46.57%). Sibling test accounted for 54.94% indirect paternity tests followed by avuncular (37.36%) and grandparentage tests (7.69%). Of the total tests conducted 59.75% of the disputed offspring were male while 40.25% were female. Parentage exclusion rates Exclusion rates differed per parentage and case type. We observed 367 paternity exclusions from 1135 cases performed, giving an overall paternity exclusion rate of 32.33%. Paternity cases for resolving crime having the highest exclusion rate followed by court ordered duo cases (Table ). Regular trio cases had the lowest paternity exclusion rate (28.42%). The exclusion rate for regular cases was lower compared to the court ordered tests for both duo and trio cases. This pattern was the same in the regular cases where lower exclusion rates were observed in trio cases compared to the duos. Maternity cases had the lowest exclusion rate for all case types (8.33%). The exclusion rate was however high for criminal cases where the exclusion rate was 33.33% (4 out of 12 cases). High exclusion rates were also observed for the indirect parentage tests with the sibling test having the highest exclusion rate (44.90%). The overall parentage exclusion rate was 31.16%. There were no inconclusive maternity or paternity results. We however observed 6 inconclusive results out of 91 indirect parentage tests. The number of mismatched STR loci for all cases ranged from 2–12 for duo cases and 4–18 for the trio cases. The analysis was only done for the direct parentage tests. The data was normally distributed as per Shapiro-Wilks test with α = 0.05 and p = 0.3265 and 0.0800 for the 16 and 21 marker test respectively. The mean of the distribution was 7 for the 16-marker test and 10.65 for the 21-marker test (Fig. ). FGA and D2S1338 were the most frequent autosomal markers in parentage discrepancy (Table ). FGA and D18S51 were the most frequent markers for duo cases while SE33 and D21S338 were most frequent for trio cases. D12S391 and D2S441 were the least informative markers in terms of exclusions in this population. Triallelic patterns We only observed triallelic patterns at the TPOX locus at a frequency of 0.0414. The three peaks encountered in all the cases were approximately of equal height confirming to the Clayton type 2 pattern except for one which was type 1 exhibiting all three uneven peaks. The majority of triallelic individuals had allele 10 (51 out of 54) except for 3 cases where the extra allele was 11. The observed triallelic genotypes are summarized in Table . The [8, 10, 11], [8, 9, 10] and [9, 10, 11] were the most frequent triallelic genotypes with frequencies of 0.1707, 0.1463 and 0.1707 respectively (Table ). The rest of the genotypes were observed in relatively low frequencies. These were genotypes observed in all individuals with triallelic pattern regardless of whether they were related on not. A total of 60 unrelated individuals out of a total of 1089 were triallelic at the TPOX locus, translating to a frequency of 0.047. Of these 44 (73.33%) were type 2A while 16 (26.67%) were type 2B. The pattern was observed in 54 cases, where 52 were paternity, 1 maternity and 1 grandparentage case. Of the paternity cases 18 were exclusions and 34 inclusions. Both the maternity and grandparentage cases were inclusions. The inheritance pattern of the extra allele could not be conclusively determined since most of the cases were duo’s with either a missing mother or father. However, in 3 clear paternity inclusion cases, the fathers transmitted the extra allele to their daughters. Mothers on the other hand could transmit the extra allele to either their sons or daughters. Mutation rates A total of 30 mutations were observed in the analyzed 837 cases (Table ). Paternal mutations were more common as compared to the maternal mutations. Of the cases, 9 were mother–child pairs while 17 were paternal, where 11 were father-child duos and 6 mother-father-child trios. Mutations were observed in 12 of the 23 loci. Twenty-three of the cases (76.67%) were single step mutation events, while 2 step accounted for 6 cases (6.67%). The only 6 cases of the cases involved the loss of repeats while the remaining 24 were gain of repeats. Higher mutation rates were observed in loci with longer uninterrupted repeats for example FGA, D5S818 and SE33. The shorter ones D10S1248, CSFPO, D1S1656 and D2S244 had the lowest rates. The paternal mutation rate was 0.0021, while the maternal rates was 0.0011. The average mutation rate estimated across all loci was 0.0036. Mutations were not observed for Penta D, PentaE, TPOX, TH01, D16S539, D8S1179, D22S1045, D13S317, D7S820, D12S391 and D6S1043.
We collected 1303 cases during a 10-year period (2013–2023). Cases included paternity, maternity, sibling, avuncular and grandparentage tests (Table ). These were used to solve various cases including parentage disputes, criminal cases, disaster victim identification and inheritance disputes. Paternity was the most common type of tests (87.37%) followed by kinship (7.01%) and maternity (5.62%). Kinship included all tests where an alleged biological relative was tested to determine biological relationship in the absence of the alleged father. Motherless paternity (duo) was the most common paternity test, both for regular peace of mind and court ordered tests. Maternity tests were the least ordered test. It was however the most common performed tests for identification of disaster victims with 34 out of 73 cases (46.57%). Sibling test accounted for 54.94% indirect paternity tests followed by avuncular (37.36%) and grandparentage tests (7.69%). Of the total tests conducted 59.75% of the disputed offspring were male while 40.25% were female.
Exclusion rates differed per parentage and case type. We observed 367 paternity exclusions from 1135 cases performed, giving an overall paternity exclusion rate of 32.33%. Paternity cases for resolving crime having the highest exclusion rate followed by court ordered duo cases (Table ). Regular trio cases had the lowest paternity exclusion rate (28.42%). The exclusion rate for regular cases was lower compared to the court ordered tests for both duo and trio cases. This pattern was the same in the regular cases where lower exclusion rates were observed in trio cases compared to the duos. Maternity cases had the lowest exclusion rate for all case types (8.33%). The exclusion rate was however high for criminal cases where the exclusion rate was 33.33% (4 out of 12 cases). High exclusion rates were also observed for the indirect parentage tests with the sibling test having the highest exclusion rate (44.90%). The overall parentage exclusion rate was 31.16%. There were no inconclusive maternity or paternity results. We however observed 6 inconclusive results out of 91 indirect parentage tests. The number of mismatched STR loci for all cases ranged from 2–12 for duo cases and 4–18 for the trio cases. The analysis was only done for the direct parentage tests. The data was normally distributed as per Shapiro-Wilks test with α = 0.05 and p = 0.3265 and 0.0800 for the 16 and 21 marker test respectively. The mean of the distribution was 7 for the 16-marker test and 10.65 for the 21-marker test (Fig. ). FGA and D2S1338 were the most frequent autosomal markers in parentage discrepancy (Table ). FGA and D18S51 were the most frequent markers for duo cases while SE33 and D21S338 were most frequent for trio cases. D12S391 and D2S441 were the least informative markers in terms of exclusions in this population.
We only observed triallelic patterns at the TPOX locus at a frequency of 0.0414. The three peaks encountered in all the cases were approximately of equal height confirming to the Clayton type 2 pattern except for one which was type 1 exhibiting all three uneven peaks. The majority of triallelic individuals had allele 10 (51 out of 54) except for 3 cases where the extra allele was 11. The observed triallelic genotypes are summarized in Table . The [8, 10, 11], [8, 9, 10] and [9, 10, 11] were the most frequent triallelic genotypes with frequencies of 0.1707, 0.1463 and 0.1707 respectively (Table ). The rest of the genotypes were observed in relatively low frequencies. These were genotypes observed in all individuals with triallelic pattern regardless of whether they were related on not. A total of 60 unrelated individuals out of a total of 1089 were triallelic at the TPOX locus, translating to a frequency of 0.047. Of these 44 (73.33%) were type 2A while 16 (26.67%) were type 2B. The pattern was observed in 54 cases, where 52 were paternity, 1 maternity and 1 grandparentage case. Of the paternity cases 18 were exclusions and 34 inclusions. Both the maternity and grandparentage cases were inclusions. The inheritance pattern of the extra allele could not be conclusively determined since most of the cases were duo’s with either a missing mother or father. However, in 3 clear paternity inclusion cases, the fathers transmitted the extra allele to their daughters. Mothers on the other hand could transmit the extra allele to either their sons or daughters.
A total of 30 mutations were observed in the analyzed 837 cases (Table ). Paternal mutations were more common as compared to the maternal mutations. Of the cases, 9 were mother–child pairs while 17 were paternal, where 11 were father-child duos and 6 mother-father-child trios. Mutations were observed in 12 of the 23 loci. Twenty-three of the cases (76.67%) were single step mutation events, while 2 step accounted for 6 cases (6.67%). The only 6 cases of the cases involved the loss of repeats while the remaining 24 were gain of repeats. Higher mutation rates were observed in loci with longer uninterrupted repeats for example FGA, D5S818 and SE33. The shorter ones D10S1248, CSFPO, D1S1656 and D2S244 had the lowest rates. The paternal mutation rate was 0.0021, while the maternal rates was 0.0011. The average mutation rate estimated across all loci was 0.0036. Mutations were not observed for Penta D, PentaE, TPOX, TH01, D16S539, D8S1179, D22S1045, D13S317, D7S820, D12S391 and D6S1043.
This is, to our knowledge, the first comprehensive report describing cases analyzed in a Zimbabwean paternity testing laboratory. We analyzed data from 1303 parentage cases in the over a 10-year period from 2013 to 2023. The cases were divided into paternity, maternity, grandparentage, sibling and avuncular tests. Paternity tests were the most popular test with the indirect tests being the least. The number of mismatched loci in excluded cases ranged from 2–18 depending on the testing kit with FGA and D2S1338 were the most frequent autosomal markers in parentage discrepancy. Parentage testing plays an important role in determining biological relatedness, and the results have an impact in social, medical, judicial and immigration decisions. Paternity remains the most popular parentage tests and, in this study, it accounted for 87.02% of the requested tests. Most of the requested tests were for peace of mind (regular). The motherless paternity cases were the most frequently requested and conducted case despite its lower statistical weight as compared to the trio. Some of the reasons why it remains a popular test include affordability, privacy where the father does not want the mother to know of the tests and the fact that many of the cases were regular (non-legal) tests where the participation of the mother was optional. Some countries however discourage the motherless paternity tests to reduce the risk of false inclusions especially in cases where fewer STR markers are used, and the case background is not known – . As expected, maternity tests were not popular for solving parentage disputes. They were however the most requested tests for disaster victim identification as it is always assumed mother of a child is known especially where no criminal activity is suspected. We observed an overall parentage exclusion rate of 31.16%. The paternity exclusion rate was 32.33 which is comparable to our previous findings but slightly higher than other reported rates , – . This could be because exclusion rates were estimated from cases that arose from doubts regarding biological parenthood. Variations in exclusion rates are expected as they are influenced by factors such as sample size, the choice and number of STR markers used, societal factors and the population analyzed. There was a difference in the exclusion rate between the regular and court directed paternity cases (Table ), with court cases having a higher exclusion rate. This could be explained by the fact that fathers who contest child support in courts are more confident of an exclusion result. Zimbabwean courts consider the welfare of the child first and is often in favor of granting child support. This therefore becomes an encouraging factor for paternity testing especially where the alleged father strongly believes the child is not theirs. The same is true for criminal cases where in many instances parentage testing was used as supporting evidence in kidnapping or late reported sexual assault cases. Exclusion rates were also high for indirect parentage tests where sibling tests had a rate of 44.90% while the rate for sibling tests was 42.85%. Most of the cases were used to settle inheritance disputes and involved children who were introduced to families after the death of one or both parents. This could be an indication of inheritance fraud or may be due to parenthood uncertainties especially in children born out of wedlock. The usefulness of a genetic marker is measured by its power of exclusion, that is its ability to exclude the random man . The average number of STR markers which determined the exclusion of paternity was 7 and 10.65 for the 16- and 21 marker tests respectively (Fig. ). The most informative markers for exclusion across all cases were D21S338 (67.03%), FGA (62.91), SE33 (57.05%) and D18S51 (59.89%). In addition, D10S1248 was an important marker for exclusions in duo cases. This is consistent with reported power of exclusion in the Zimbabwean population where the same markers had the highest power of exclusion with D21S338, SE33, D10S1248, D1S1656 and Penta E having a power of exclusion of 0.7976, 0.8683, 0.7323, 0.7452 and 0.8064 respectively . We could not determine the power of exclusion for Penta E in this study because of the small sample size. Of the total tests conducted 59.75% of the disputed offspring were male while 40.25% were female. This is comparable to our previous report and could further indicate stronger interest by families to claim or ascertain paternity of a male child . Given that the male to female ratio at birth is 1.02 we therefore expect the number to be skewed towards more female children being tested. This ratio also declines with age, favoring the females, therefore there are more females in any group. Zimbabwe is a patrilineal society where descent is traced through the male line. Male children are therefore still considered superior to female children as they ensure the continuity of the family legacy and name . We only observed the triallelic genotype at the TPOX locus (Table ). A threshold of 300 rfu was set to ensure the observations were not due to stochastic effects such as allele drop in, sister allele imbalance and elevated stutter. In addition, the data was generated from single source samples with adequate DNA concentrations to minimize the stochastic effects. Triallelic genotypes are rare but are expected to be encountered at all traditional regions used in forensic DNA analysis . This has implications for paternity index calculations, with new methods being formulated according to the generation and genetic transmission of tri-allelic pattern . All the triallelic genotypes observed in this study except for 1 were type 2 suggesting chromosomal duplication or aneuploidy where multiple alleles with peaks of equal height are produced . Only one case had a type I pattern where all the three peaks were of unequal height. Type 1 mutation at the TPOX locus is rare and suggests somatic mutation at a heterozygous locus during development causing mosaicism, where some alleles have the mutant allele while others have the original allele . The bias towards the type 2 allelic pattern was excepted and has been previously reported as biased toward type 2 , . High frequency of triallelic patterns at the TPOX locus have also been previously reported , with variation between populations and is highest among Africans . Triallelic genotypes at other loci, for example TH01 and CSFPO have been reported but at very low frequencies , . We observed an TPOX triallele frequency of 0.0414 which compares well with 0.024 in the South African population . Frequencies ranging between 0.004 to 0.045 have been also reported in African populations while non-African regions have reported frequencies below 0.006 , , . The extra TPOX allele was allele 10 in most of the cases (51 out of 54) except for 3 cases where the extra allele was 11 (Table ). The extra allele has been hypothesized as being a translocation of allele 10 onto chromosome X , . This agrees with our observations where fathers transmitted the extra allele to their daughters only. Mothers on the other hand could transmit the extra allele to either their sons or daughters. The presence of the allele however does not determine the alleles it is transmitted together with as seen from the variety of observed triallelic genotype combinations (Table ). The high frequency of TPOX triallelic genotype was also expected as it has been previous reported to occur at high frequencies in African populations with allele 10 being transmitted as the extra allele , . The extra allele 11 is mainly found in Chinese and Korean populations and its presence in our populations could be explained by either population admixture or mutations. Paternal mutations were more common when compared to maternal mutations and this is consistent with published literature. The paternal mutation rate was 0.0021, while the maternal rates was 0.0011. The average mutation rate estimated across all loci was 0.0036. Our mutation rates were higher compared to other populations – . Higher mutation rates were observed in the loci which are longer and have uninterrupted repeats for example FGA and SE33 (Table ). This has been previously reported , . The mutation rates tended to differ between loci. This could possibly be due to replication slippages and stalling which tend to be more pronounced in longer repetitive sequences . This however generates a high variety of polymorphisms resulting from gain or loss of alleles . Mutations were not observed for Penta D, PentaE, TPOX, TH01, D16S539, D8S1179, D22S1045, D13S317, D7S820, D12S391 and D6S1043. This could be because of the small sample size as mutations rates for some STR loci have been reported to be low , , . The mutation rates were corrected for the possibility of null alleles. We used a simple intuitive method where p was based on the count of null alleles inferred from parentage analysis where there was a mismatch between parent and child homozygous genotypes at the focal locus only. The frequency was then estimated as the number of inferred null heterozygotes divided by 2N assuming the null allele in a sample of N diploid individuals is rare. This was then used to correct the mutation frequencies.
This study provides useful insights and data on parentage in Zimbabwe. Parentage testing is slowly gaining popularity in Zimbabwe. The number of tests carried our per year in the country is still very low compared to other countries. Some of the data and rates in this report are approximations and preliminary due to the small sample size. This is however the first comprehensive report on parentage testing data for Zimbabwe and provides useful information for further studies.
Information dataset The information dataset was obtained from cases that were performed over a 10-year period at the African Institute of Biomedical Science and Technology DNA testing center. All methods were performed in accordance with the relevant guidelines and regulations as set by the Health Professions Authority of Zimbabwe and the Medical Laboratory and Clinical Scientist Council of Zimbabwe (MLSCZ). Written consent to collect samples and conduct testing was obtained from the participants through an approved client identification form. The study experimental protocols were approved by the Research council of Zimbabwe (MRCZ/B/1323). The study was conducted following the ethical principles outlined in the Declaration of Helsinki. We created a database in excel where personal information of individuals involved in the paternity cases was not shared according to legal regulations (personal data protection, Zimbabwe). The laboratory conducts quality control proficiency annually organized by the English working group of the International Society for Forensic Genetics (ISFG). Genotyping DNA from blood, buccal swabs or spotted FTA cards was extracted using either Prep-n-Go™ Buffer (Applied Biosystems by Life Technologies, UK) or standard extraction kits as per manufacturer’s instructions. PCR amplification was conducted using the following commercial HID kits as per manufacturer’s instructions: AmpFlSTR ® Identifiler ® , GlobalFiler™ PCR Amplification Kit and VeriFiler™ Direct PCR Amplification Kit (Applied Biosystems by Life Technologies, UK) and SureID ® PanGlobal Human DNA Identification Kit (Health gene technologies). The kits were gradually introduced into the lab for paternity testing resolution. The amplified PCR product was separated by capillary electrophoresis on the 3500 Genetic analyzer and data collected using Data Collection v2 Software (Applied Biosystems). The Genemapper ® v 1.4 and corresponding allelic ladders were used for allele calling. Parentage analysis We performed parentage analysis using The Mass Fatality Identification System (M-FISys) (GeneCodes, Michigan, USA). Paternity was called following published guidelines . At least 15 loci were genotyped. Paternity was calculated at each STR locus as a likelihood ratio. This was generated by comparing the probability that the alleged father contributed the obligate allele with probability that the randomly chosen man contributed the allele. The combined paternity index (CPI) was calculated by multiplying the PI values at each locus. The probability of paternity (PP) was calculated using the formula PP = CPI/(CPI + 1). Cases showing 4 or more excluding loci and a CPI of less than 10,000 were excluded. PI computation in the presence of isolated mutations were used with a corresponding mutation rate (μ) and power of exclusion (PE) as recommended by the American Association of Blood Banks (AABB) (PI = μ/PE). The stepwise model was considered where more than two mutation steps occurred. Relatives such as aunt, uncle, grandparents, or siblings were used to determine biological kinship in cases where the alleged father was missing. Triallelic patterns Data was collected from unrelated individuals with triallelic patterns in the tested locus. All samples containing a triallelic pattern were confirmed by re-extraction and amplification with a different STR typing kit. Data and physical counting were done in excel where number of cases and allelic combinations were recorded. The transmission of the extra allele was investigated from family cases with true biological relationship at loci that exhibited the triallelic pattern. The pattern type was allocated based on the observed intensities on the electropherogram, with triallelic variants categorized as described by Clayton and co-workers . Peak intensities were used to identify the pattern type where alleles with 3 imbalanced peaks were identified as type 1 and those with equal intensity being identified as type 2. Mutation rates Mutations were investigated in all parentage non excluded cases by investigating paternal and maternal allelic transmissions (meiosis). A total of 837 cases consisting of 532 father-child (paternity duos), 238 mother-father-child (paternity trios) and 67 mother–child (maternity duos) transfers were included in the analysis. Cases favoring a biological parent–child relationship (LR (write in full when using for first time) > 1000) were chosen. Mutations were considered where there was evidence of biological parent–child relationship, but one or two loci failed to match. Mutations were confirmed by typing with a different kit following recommendations and guidelines . The number of allelic transmissions used to calculate the mutation rate were specified for each marker since we used different commercial kits for the genotyping. Biostatistical analysis was carried out in M-Fysis. Mutation rates at each STR locus were calculated using the relationship: [12pt]{minimal}
$$Locus\, Mutation\, rate= 100$$ L o c u s M u t a t i o n r a t e = n u m b e r o f m u t a t i o n s d e t e c t e d a t e a c h l o c u s t o t a l n u m b e r o f m e i o s i s o b e s e r v e d a t t h e l o c u s × 100
The information dataset was obtained from cases that were performed over a 10-year period at the African Institute of Biomedical Science and Technology DNA testing center. All methods were performed in accordance with the relevant guidelines and regulations as set by the Health Professions Authority of Zimbabwe and the Medical Laboratory and Clinical Scientist Council of Zimbabwe (MLSCZ). Written consent to collect samples and conduct testing was obtained from the participants through an approved client identification form. The study experimental protocols were approved by the Research council of Zimbabwe (MRCZ/B/1323). The study was conducted following the ethical principles outlined in the Declaration of Helsinki. We created a database in excel where personal information of individuals involved in the paternity cases was not shared according to legal regulations (personal data protection, Zimbabwe). The laboratory conducts quality control proficiency annually organized by the English working group of the International Society for Forensic Genetics (ISFG).
DNA from blood, buccal swabs or spotted FTA cards was extracted using either Prep-n-Go™ Buffer (Applied Biosystems by Life Technologies, UK) or standard extraction kits as per manufacturer’s instructions. PCR amplification was conducted using the following commercial HID kits as per manufacturer’s instructions: AmpFlSTR ® Identifiler ® , GlobalFiler™ PCR Amplification Kit and VeriFiler™ Direct PCR Amplification Kit (Applied Biosystems by Life Technologies, UK) and SureID ® PanGlobal Human DNA Identification Kit (Health gene technologies). The kits were gradually introduced into the lab for paternity testing resolution. The amplified PCR product was separated by capillary electrophoresis on the 3500 Genetic analyzer and data collected using Data Collection v2 Software (Applied Biosystems). The Genemapper ® v 1.4 and corresponding allelic ladders were used for allele calling.
We performed parentage analysis using The Mass Fatality Identification System (M-FISys) (GeneCodes, Michigan, USA). Paternity was called following published guidelines . At least 15 loci were genotyped. Paternity was calculated at each STR locus as a likelihood ratio. This was generated by comparing the probability that the alleged father contributed the obligate allele with probability that the randomly chosen man contributed the allele. The combined paternity index (CPI) was calculated by multiplying the PI values at each locus. The probability of paternity (PP) was calculated using the formula PP = CPI/(CPI + 1). Cases showing 4 or more excluding loci and a CPI of less than 10,000 were excluded. PI computation in the presence of isolated mutations were used with a corresponding mutation rate (μ) and power of exclusion (PE) as recommended by the American Association of Blood Banks (AABB) (PI = μ/PE). The stepwise model was considered where more than two mutation steps occurred. Relatives such as aunt, uncle, grandparents, or siblings were used to determine biological kinship in cases where the alleged father was missing.
Data was collected from unrelated individuals with triallelic patterns in the tested locus. All samples containing a triallelic pattern were confirmed by re-extraction and amplification with a different STR typing kit. Data and physical counting were done in excel where number of cases and allelic combinations were recorded. The transmission of the extra allele was investigated from family cases with true biological relationship at loci that exhibited the triallelic pattern. The pattern type was allocated based on the observed intensities on the electropherogram, with triallelic variants categorized as described by Clayton and co-workers . Peak intensities were used to identify the pattern type where alleles with 3 imbalanced peaks were identified as type 1 and those with equal intensity being identified as type 2.
Mutations were investigated in all parentage non excluded cases by investigating paternal and maternal allelic transmissions (meiosis). A total of 837 cases consisting of 532 father-child (paternity duos), 238 mother-father-child (paternity trios) and 67 mother–child (maternity duos) transfers were included in the analysis. Cases favoring a biological parent–child relationship (LR (write in full when using for first time) > 1000) were chosen. Mutations were considered where there was evidence of biological parent–child relationship, but one or two loci failed to match. Mutations were confirmed by typing with a different kit following recommendations and guidelines . The number of allelic transmissions used to calculate the mutation rate were specified for each marker since we used different commercial kits for the genotyping. Biostatistical analysis was carried out in M-Fysis. Mutation rates at each STR locus were calculated using the relationship: [12pt]{minimal}
$$Locus\, Mutation\, rate= 100$$ L o c u s M u t a t i o n r a t e = n u m b e r o f m u t a t i o n s d e t e c t e d a t e a c h l o c u s t o t a l n u m b e r o f m e i o s i s o b e s e r v e d a t t h e l o c u s × 100
|
An Innovative Management in the Diagnosis of Mediastinal Masses | 177a226d-ffaf-4872-a14c-0843af021515 | 11922675 | Surgical Procedures, Operative[mh] | Introduction The mediastinum is a complex anatomical region that contains vital structures, including the great vessels, heart, esophagus, and trachea. Mediastinal masses constitute a broad spectrum of lesions that can be malignant or benign. Anterior mediastinal masses (AMM) are heterogeneous in etiology and origin, including epithelial, mesenchymal, hematopoietic, lymphoid, and metastatic neoplasms [ , , , ]. Some typically benign lesions do not require treatment, while patients with clinical symptoms or suspected malignant AMM should be treated promptly [ , , ]. Treatment strategies include surgery, chemotherapy, and/or radiotherapy based on the specific pathological diagnosis and clinical staging . Therefore, a precise and accurate histopathological pre‐treatment diagnosis is essential for patients. There are several strategies for taking samples from mediastinal lesions for histopathological diagnosis, such asmediastinotomy, mediastinoscopy, bronchoscopy, open surgical biopsy, video‐assisted thoracoscopic surgery (VATS) and CT‐guided or ultrasound‐guided transthoracic biopsy [ , , ]. Each technique has advantages and disadvantages in terms of accuracy, invasiveness, cost, and risk . Although transparietal needle aspiration is a valid tool for the diagnosis of AMM, thanks to the minimally invasive approach, it is not considered the first choice by recent guidelines; therefore, a surgical biopsy is often preferred to obtain an adequate amount of tissue for diagnosis [ , , ]. The role of VATS is fundamental because it combines low invasiveness with the possibility of obtaining a histological sample . Our study aimed to evaluate whether the combination of intraoperative ultrasound (IUS) and VATS can allow obtaining an adequate, correct, and safer diagnosis in patients with a mediastinal mass, especially in severely ill patients affected by heart failure, renal failure, advanced oncological stage, and respiratory failure.
Materials and Methods This single‐center, retrospective observational study includes all consecutive patients evaluated between March 2018 and December 2024; all consecutive patients admitted to the Department of Thoracic Surgery of the Vanvitelli University of Naples underwent biopsies of mediastinal masses in VATS and with IUS. Patients underwent general anesthesia and endotracheal intubation with a double‐lumen tube and contralateral single‐lung ventilation; the correct position of the orotracheal tube was confirmed by fiberoptic bronchoscopy with unilateral pulmonary ventilation. After being anesthetized, the patient was positioned in the lateral decubitus position. The uniportal incision was made at the fifth intercostal space, anterior to the latissimus dorsi and posterior to the pectoralis major. The size of the uniportal access incision ranged from 2 to 4 cm (mean 3 cm). Chest exploration revealed the presence or absence of pleural adhesions and pleural nodules. The probe was inserted into the chest through the uniportal incision, and the AMMs were explored using ultrasound. The AMMs were subsequently labeled, and their characteristics were classified under ultrasound guidance. The ultrasound processor used for the examination of the AMM was the BK 5000, and through uniportal access, a sterile intracavitary laparoscopic probe was introduced, 38 cm long and 10 mm in diameter, with a flexible tip, equipped with a convex matrix transducer with frequencies between 4 and 12 MHz (Figure ). The lung under examination was collapsed to facilitate the movements of the instruments and the ultrasound probe inside the pleural cavity; by applying pressure on the visceral pleura using the probe, a desufflation of the lung was obtained to eliminate the air around the affected lung during the exam. The probe was positioned perpendicular to the mediastinal tissue containing the mass. A warm sterile saline solution allows for the best ultrasound transmission. No changes in standardized surgical procedures were determined by the use of the probe. IUS prolonged the operative time by approximately 11 min (range 8–12 with 95% confidence interval [CI] [9.57–10.19]) compared to surgery without IUS. The shape of the tumor was assessed with a small ultrasound probe introduced through the trocar, equipped with a function that allows a 180° rotation from right to left and from top to bottom, thanks to a handle that acts as a grip and controls its movement, allowing visualization of the internal mediastinal mass. AMMs were classified based on their ultrasound characteristics related to shape, echogenicity (heterogeneous or homogeneous), short axis diameter, margin (distinct or indistinct), increased color Doppler flow, absence or presence of the sign of coagulative necrosis, absence or presence of calcification, nodal conglomeration, and vascularization of the lesion (Figure ). The ultrasound characteristics of mediastinal masses were large hypoechoic lesions, inhomogeneous echostructure, increased vascularization in the intra‐ and perinodular color box, presence of hyperechoic spots, and contextual anechoic colliquated areas (Figures and ). Using IUS, we identified the exact site and the solid part of the lesion where to perform the biopsies, indicating the depth and direction of the forceps (Figure ). Furthermore, with the use of IUS, we identified in real time the main intrathoracic blood vessels and the internal thoracic arteries, as well as any major collateral veins that could have formed in the event of obstruction of the superior vena cava and therefore, we avoided injuring blood vessels during the biopsy, also avoiding sampling necrotic material that is inadequate for diagnosis [ , , ]. In the group of patients undergoing IUS, we selected the optimal site to perform a biopsy; after opening the mediastinal pleura by forming a small incision, the neoplasm was then sampled with biopsy forceps and sent to pathological anatomy, ensuring an adequate quantity of tissue also for immunohistochemistry. Finally, careful hemostasis was performed before closing the chest and positioning the endopleural drainage tube. The patient was returned to the ward after a short period of observation in the operating room, and a chest x‐ray was performed to exclude a pneumothorax. Postoperative pain was also quantified using a visual analog scale (VAS) with levels ranging from 0 to 10, immediately after surgery. To achieve optimal postoperative pain control, an ultrasound‐guided Erector Spinae Plane (ESP) block was performed with a bolus of ropivacaine 1% and dexamethasone. Patients did not experience significant complications such as bleeding and/or pneumothorax .
Statistics Categorial data was expressed as percentages. Continuous data with normal distribution was expressed as mean ± standard deviation; otherwise, it was expressed as median. The difference in lesion size between a group with IUs and a group without IUS was analyzed via the Independent‐Samples T test. A significant difference in diagnostic accuracy, specificity, and sensitivity was found between the group that used IUS versus the group in which no IUS was used (100%vs. 93%, 99.8% vs. 94%, 98.5% vs. 90.5% respect) (Figure ).
Results This is a single‐center retrospective observational study including 298 consecutive patients with mediastinal mass, evaluated between March 2018 and December 2023 at the Thoracic Surgery Unit of the Vanvitelli University of Naples. Inclusion criteria: patients with mediastinal lesions larger than 35 mm, detected by contrasted chest CT and showing increased uptake on FDG‐PET/CT (SUVmax [mean ± SD] 7.4 ± 4.2); patients without histological diagnosis after bronchoscopic biopsy and transbronchial needle aspiration (FNA); normal coagulation. Exclusion criteria: uncorrectable coagulation abnormalities, INR > 1.4; platelets < 50 000/mL; aPTT > 1.5 times higher than the reference range. Of the total number of selected patients, 113 were operated on in the operating block not equipped with an IUS probe and were therefore used as a control group (NO [IUS] GROUP). The remaining 185 patients all underwent VATS and IUS for mediastinal mass biopsy ([IUS] GROUP). In the NO [IUS] GROUP, 68 patients were male (60.1%) and 45 were female (39.9%). In this group, 8 patients (7%) were observed with an incidental finding of AMM without symptoms during a routine physical examination, while the others showed symptoms such as 48 patients with cough (42.5%), 52 patients with chest pain (46%), 8 patients with fever (7%), and 3 patients with dyspnea (2.7%) (Table ). In the [IUS] GROUP, 110 patients were male (59.4%) and 75 were female (40.5%). In this group, 7 patients (4%) were observed with incidental discovery of AMM without symptoms during routine physical examination, while the others showed symptoms such as 83 patients with cough (46.6%), 86 patients with chest pain (48.6%), 28 patients with fever (15.6%), 16 patients with dyspnea (9.3%), 7 patients with swelling of the head and face (4%), 4 patients with hoarseness of the voice (2.5%), 5 patients with dysphagia (2.8%) and 3 patients with haemoptysis (1.5%) (Table ). Ultrasound visualization of mediastinal lesions located in the anterosuperior compartment was successful in all patients (100%) (Figure ). Preoperative evaluation included: cardiac examination, electrocardiogram (ECG) and echocardiography; functional respiratory tests, standard spirometry, and arterial blood gas analysis. IUS was performed in all patients. Procedures were performed under direct IUS guidance. The IUS allowed real‐time visualization of the exact and appropriate site where to perform the biopsy; Color Doppler allowed the large vessels, collaterals, and tumor vessels to be clearly and easily highlighted, avoiding their laceration during the biopsy. The size of the lesion was large in both the short axis (median [IQR] mm = 61 [46.3–108.8]) and long axis (median [IQR] mm = 108.5 [92–135.3]). Based on the final diagnosis, lymphoma 108 (58.6%) was the most common entity, followed by thymic carcinoma 43 (23.5%) and germ cell tumor 25 (13.5%). The percentage (185/185) 100% of patients who underwent biopsies via VATS with IUS obtained a specific diagnosis with sufficient information for therapy. The ultrasound characteristics of the lesions: hypoechoic ovoid shape, regular margins (78.7%), irregular margins (69%), inhomogeneous structure (71%), colliquated areas (43%), floating echoes (55%), thin walls (43%), thickened walls (26%), internal calcifications (73%), increased intralesional color box signal (33%) and perilesional (41%) (Table ). The mean operative time was 25.4 ± 5.2 min. Careful hemostasis was then performed before chest closure. All patients were returned to the ward after a short period of observation in the operating room of approximately 20 minutes, and a chest x‐ray was performed to exclude a pneumothorax. Then, patients were referred for appropriate chemotherapy and/or radiotherapy within 30 days of biopsy. The group of patients in whom ultrasound was not used (NO [IUS] GROUP) includes 113 patients, of which 6 patients whose samples contained a large necrotic area could not obtain a diagnosis; therefore, they underwent a new biopsy (two squamous cell carcinoma of the thymus and four lymphomas) for a therapeutic program. The other 10 (20.8%) cases were accepted as diagnostic biopsy failures based on the final diagnosis. Patients showed no symptomatic complications such as bleeding, pneumothorax, or hemoptysis after biopsy. A significant difference in diagnostic accuracy, specificity, and sensibility was found between the group that used ultrasound versus the group in which no ultrasound was used (95%vs. 77%, 96% vs. 61%, 98% vs. 80% respect).
Discussion AMMs include multiple diseases: primary and secondary, neoplastic and infectious, malignant and benign. Early diagnosis and treatment are crucial in the management of patients with malignant AMM. Saito et al. two decades performed ultrasound‐assisted biopsies of mediastinal masses for the first time [ , , ]. Nowadays, B‐mode ultrasound is rarely used in the study of the mediastinum, although this method is a valid diagnostic tool complementary to other techniques such as chest x‐ray, chest CT, and chest MRI. However, ultrasound of the mediastinum is much more sensitive compared to standard chest radiography in the diagnosis of mediastinal tumors . Considering chest CT as a reference method, ultrasound, compared to radiography, shows greater sensitivity for each site: paratracheal region 89% versus 69%, aorta‐pulmonary window 81% versus 62%, prevascular region 92% versus 46%, subcarinal region 69% versus 31%, versus 67%, supra‐aortic region 98% versus 67%, and pericardial region 100% versus 67% . Obviously, ultrasound of the mediastinum is less effective than CT in detecting pericardial, supra‐aortic, and perivascular lesions (sensitivity 98%–100%) and some sites, such as the posterior mediastinal and paravertebral regions, can only be evaluated by MRI and CT The use of ultrasound, thanks to the possibility of performing it in different contexts, from the radiology department to the patient's bed and to the operating room. In the diagnosis of mediastinal lesions, it is very widespread . IUS can provide useful information in the complex evaluation of mediastinal masses located in the anterior (prevascular) and posterior compartments of the mediastinum. It also offers the possibility to guide biopsies in real time in such clinical scenarios, with several advantages over CT. IUS guaranteed rapid localization of the mass in the operating room, it does not use ionizing radiation to the benefit of the patient and color Doppler allowed the vessels and vascularized tumor tissue to be correctly identified to avoid intraoperative hemorrhages . An accurate histological diagnosis is essential for a correct therapeutic plan and also for preoperative neoadjuvant therapy, an inconclusive diagnosis delays specific therapy . The most common cause of diagnostic failure is tumor necrosis, sampling error, or inadequate sampling . IUS shows non‐liquefied necrosis, effectively assessing tissue necrosis and viability and identifying perfused areas resulting in increased diagnostic accuracy. In the present study, the visualization and identification of areas of internal necrosis significantly improved by 100% after the use of IUS. However, IUS guidance allows effective real‐time monitoring of the exact site where the biopsy should be performed, providing precise indications regarding the exact angle and depth of the forces; Color Doppler also allows you to evaluate the vascularity of the lesion and the vascular structures, preventing possible bleeding, this method therefore allows critical and advanced cancer patients to be subjected to a mediastinal biopsy, thus avoiding complications and possible re‐operations . Surgical procedures have an accuracy of up to 100% and in some cases could simultaneously establish the diagnosis and provide treatment. All surgical procedures in VATS with IUS; require general anesthesia and short hospitalization, no complications are observed in all patients [ , , ]. The success of a mediastinal biopsy depends on many factors including the clinical circumstances, the size and location of the lesion, the presence of comorbidities, the operator's experience in IUS, and the institution's willingness to allow performing this diagnostic method [ , , ]. However, this study has several limitations, being a retrospective observational single‐center study; the application of the IUS was not random and therefore was based on the specialist's recommendation and the patient's agreement.
Conclusion In the era of minimally invasive approaches, VATS together with IUS allows the correct, safe, and precise identification of masses and vessels; this method can be performed quickly and with no perioperative complications, with a diagnostic yield of 100% thanks to the satisfactory quality of tissue obtained and the possibility of evaluating the malignancy of the pathology, thus allowing a timely and appropriate treatment of this oncohematological pathology. Therefore, IUS is considered the “Gold Standard” for procedure guidance if the target lesion can be adequately imaged; however, further studies are necessary to strengthen the results we obtained.
Author Contributions All authors contributed to design of the study. G. Messina and D. G. Pica contributed to the conception of the study. G. Vicario, V. Di Filippo, and F. Capasso contributed to the drafting of the article. N. M. Giorgiano, F. Panini D'Alba, R. Vinciguerra and B. Leonardi contributed to data collection and imaging analysis. R. Mirra, M. A. Puca, M. Grande, M. Messinó and M. Marvulli participated in data analysis and interpretation and led the revision of the article. M. Ciaravola, L. Ferrante, G. Vicidomini and A. Fiorelli supervised the study. All authors reviewed and approved the final manuscript.
The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was led in compliance with the principles of the Declaration of Helsinki; written informed consent was obtained from all participants during preoperative communication, and the protocol was approved by the Ethics Committee of the University of ‘Luigi Vanvitelli’ of Naples (32655/2021).
The authors declare no conflicts of interest.
|
Application and limitation of a biological clock-based method for estimating time of death in forensic practices | cc6f5f71-2500-4afe-92f4-5a8c9567b69e | 10102023 | Forensic Medicine[mh] | Estimating the time of death, which is often extremely difficult, is one of the most important tasks in forensic practice. To date, numerous methods for estimating the time of death have been developed , . Over the last decade, various innovative techniques, such as tissue nano mechanics , mass spectrometry-based quantitative proteomics , analysis of oral microbiota community and micro-RNA analysis , have been introduced to estimate the postmortem interval, bringing substantial progress into this field. However, most of these methods estimate the time since death, but not estimate the time of death. The current method for estimating the time of death remains unsatisfactory. Advances in chronobiology have brought about great impacts and progress in various medical fields, such as chronopharmacology, chronotherapy and sleep disorder therapy – . Chronobiology can contribute to forensic medicine, especially in the estimation of the time of death. However, the forensic application of chronobiology is quite limited. To our knowledge, there is currently only one report of the application of chronobiology to forensic investigation, in which the time of death was estimated based on the melatonin concentration in pineal body, serum and urine . Therefore, we tried to apply the biological clock to the estimation of the time of death. In 2011, we reported the first forensic application of chronobiology in the estimation of the time of death using a mouse model and applied the method to a few autopsy cases . In our previous report, we used two main oscillator genes, brain and muscle aryl hydrocarbon receptor nuclear translocator-like 1 ( BMAL1 or ARNTL ) and nuclear receptor subfamily 1 group D member 1 (Rev-Erbα, NR1D1 ), in the circadian clock system to read the biological clock in the kidneys, livers and hearts. Since these two clock genes oscillate in opposite phases , , the NR1D1 / BMAL1 ratio amplifies the circadian oscillation of each gene expression . We demonstrated the applicability of our method in forensic practice, but we could not clarify the reliability and limitations of the method, because only a limited number of autopsy cases were examined. Since its development, we have applied the method to our routine practice of estimating the time of death in autopsy cases. In this study, we evaluated our method based on the results of its application to 318 autopsy cases with known times of death in our department. We show the practical applicability and limitations of our method, which estimates the time of death based on the biological clock.
The pattern of clock gene expression in the hearts of autopsy cases The NR1D1/BMAL1 ( N/B ) and BMAL1/NR1D1 ( B/N ) ratios were plotted against the time of death, resulting in clear peaks around 6:00 and 18:00, respectively (Fig. a and b), indicating that clock gene expression can be precisely detected even in dead bodies. Figure c and d show the mean values of the N/B and B/N ratio in the four-time domains (morning, 3:00–8:59, noon, 9:00–14:59, evening, 15:00–20:59 and night, 21:00–2:59). The N/B and B/N ratios were significantly higher in the morning and evening than in the other time domains, respectively, which confirms that these ratios are suitable parameters for estimating the time of death. However, in some autopsy cases, the N/B and B/N ratios exhibited very low values in the morning and evening, respectively (Fig. a and b), suggesting that some factors affected these parameters. Evaluation of the factors affecting the biological clock in the deceased We next examined the factors affecting the ratios in the deceased. First, we examined gender differences in the temporal pattern of the ratios (male, n = 224; female, n = 94). Both genders showed a similar temporal pattern of the N/B (Fig. a) and B/N (Fig. b) ratios. The N /B (Fig. c) and B/N (Fig. d) ratios in deceased males were significantly higher in the morning and evening, respectively, which was similar to the results of total cases (Fig. c and d). On the other hand, the N/B ratio in deceased females (Fig. c) was significantly higher in the morning, which is similar to the results in deceased males, whereas the B/N ratio was higher in the evening than in other time domains, but the difference was not statistically significant. (Fig. d). We divided the cases into three age groups (≤ 19 years, n = 13; 20–69 years, n = 200; ≥ 70 years, n = 105). All age groups showed similar temporal patterns (Fig. a–d). The N/B ratio in the morning was significantly higher than those in the three other time domains in the 20–69 and ≥ 70 years groups (Fig. c). The B/N ratio in the evening was higher than those in the three other time domains only in the 20–69 years group (Fig. d). In contrast, the temporal pattern of the N/B ratio in the morning (3:00–8:59) and that of the B/N ratio in the evening (15:00–20:59) did not significantly differ from those in other time domains in the ≤ 19 years group (Fig. c and d). The N/B ratio in the morning and the B/N ratio in the evening were plotted against age; the results showed that the N/B and B/N ratios are independent of age (Fig. e and f). However, the case number in young and high-age groups was small. Therefore, more cases must be used for statistical analysis of these groups. Finally, we examined the effect of post-mortem intervals on the ratios. We divided the cases into two groups, < 30 h postmortem interval (n = 250) and > 30 h postmortem interval (n = 68). The N/B and B/N ratios in both groups showed peaks in the morning and evening, respectively, indicating that the post-mortem interval had virtually no effect on them (Fig. a–f). However, there was no significant difference in the B/N ratio between the evening and noon time domains in the > 30 h post-mortem interval group (Fig. d). This is likely due to the small number of cases (n = 9) in the noon time domain of the > 30 h post-mortem interval group. The N/B ratio in the morning and the B/N ratio in the evening were plotted against the postmortem interval; the results indicated that the ratios are independent of the postmortem interval (Fig. e and f). Evaluation of the cause of death affecting the biological clock We next examined the differences in the temporal pattern of the N/B and B/N ratios between intrinsic (n = 73) and extrinsic (n = 245) death groups. As shown in Fig. a and b, there were no significant differences between the groups. In the extrinsic death cases, the N/B ratio in the morning and the B/N ratio in the evening were significantly higher than those in other time domains (Fig. c and d). However, in the intrinsic death cases, the N/B ratio in the morning was significantly higher than those in other time domains, but the B/N ratio in the evening did not significantly differ from those in other time domains (Fig. c and d). We also examined the effect of specific causes of death on the ratios. The most common causes of death (Table ), including hemorrhagic and traumatic shock, aortic rupture, drowning, burn, asphyxia, intoxication, and ischemic heart failure, except brain injury, did not seem to have a significant effect on the ratios (data not shown). Of note, brain injury, especially, chronic brain injury with cerebral edema, cerebral hernia, and cerebral hypoxia seemed to strongly affect the ratios in the hearts of the deceased. As shown in Fig. a and b, the morning peak of the N/B ratio and the evening peak of the B/N ratio did not take place in cases of delayed death due to chronic brain injury (n = 15), whereas the peaks of the N/B and B/N ratios were observed in acute death cases with severe brain injury (n = 35). The cases of delayed death due to chronic brain injury did not show an oscillation in the N/B and B/N ratios (Fig. c and d). The N/B ratio in the morning significantly differs from that in the evening in cases of acute death with severe brain injury (Fig. c). However, these findings are from a limited small number of cases, and the loss of oscillation of N/B and B/N ratios due to chronic brain injury needs to be confirmed in more cases. Applicability of our method to forensic practice Our method reads the biological clock in the deceased; however, there are only two-time domains (morning, around 6:00; evening, around 18:00) in the clock. The N/B ratio is suitable for reading at 6:00 and the B/N ratio is suitable for reading at 18:00. All cases where the N/B ratio was > 25 were deaths occurring from 1:00 to 10:00 (n = 40), and those where the ratio was > 40 were deaths occurring from 3:00 to 9:00 (n = 23) (Fig. a). On the other hand, all cases where the B/N ratio was > 1.5 were deaths occurring from 14:00 to 22:00 (n = 39), and those where the ratio was > 4 were deaths occurring from 15:00 to 20:00 (n = 11) (Fig. b). However, only 24.8% (79/318) of morning and evening deaths were predicted by our method, and low values of N/B and B/N ratios do not exclude morning and evening deaths. Therefore, although this method is not effective in all cases, it is still important in forensic practice because it complements conventional methods from a completely different perspective.
The NR1D1/BMAL1 ( N/B ) and BMAL1/NR1D1 ( B/N ) ratios were plotted against the time of death, resulting in clear peaks around 6:00 and 18:00, respectively (Fig. a and b), indicating that clock gene expression can be precisely detected even in dead bodies. Figure c and d show the mean values of the N/B and B/N ratio in the four-time domains (morning, 3:00–8:59, noon, 9:00–14:59, evening, 15:00–20:59 and night, 21:00–2:59). The N/B and B/N ratios were significantly higher in the morning and evening than in the other time domains, respectively, which confirms that these ratios are suitable parameters for estimating the time of death. However, in some autopsy cases, the N/B and B/N ratios exhibited very low values in the morning and evening, respectively (Fig. a and b), suggesting that some factors affected these parameters.
We next examined the factors affecting the ratios in the deceased. First, we examined gender differences in the temporal pattern of the ratios (male, n = 224; female, n = 94). Both genders showed a similar temporal pattern of the N/B (Fig. a) and B/N (Fig. b) ratios. The N /B (Fig. c) and B/N (Fig. d) ratios in deceased males were significantly higher in the morning and evening, respectively, which was similar to the results of total cases (Fig. c and d). On the other hand, the N/B ratio in deceased females (Fig. c) was significantly higher in the morning, which is similar to the results in deceased males, whereas the B/N ratio was higher in the evening than in other time domains, but the difference was not statistically significant. (Fig. d). We divided the cases into three age groups (≤ 19 years, n = 13; 20–69 years, n = 200; ≥ 70 years, n = 105). All age groups showed similar temporal patterns (Fig. a–d). The N/B ratio in the morning was significantly higher than those in the three other time domains in the 20–69 and ≥ 70 years groups (Fig. c). The B/N ratio in the evening was higher than those in the three other time domains only in the 20–69 years group (Fig. d). In contrast, the temporal pattern of the N/B ratio in the morning (3:00–8:59) and that of the B/N ratio in the evening (15:00–20:59) did not significantly differ from those in other time domains in the ≤ 19 years group (Fig. c and d). The N/B ratio in the morning and the B/N ratio in the evening were plotted against age; the results showed that the N/B and B/N ratios are independent of age (Fig. e and f). However, the case number in young and high-age groups was small. Therefore, more cases must be used for statistical analysis of these groups. Finally, we examined the effect of post-mortem intervals on the ratios. We divided the cases into two groups, < 30 h postmortem interval (n = 250) and > 30 h postmortem interval (n = 68). The N/B and B/N ratios in both groups showed peaks in the morning and evening, respectively, indicating that the post-mortem interval had virtually no effect on them (Fig. a–f). However, there was no significant difference in the B/N ratio between the evening and noon time domains in the > 30 h post-mortem interval group (Fig. d). This is likely due to the small number of cases (n = 9) in the noon time domain of the > 30 h post-mortem interval group. The N/B ratio in the morning and the B/N ratio in the evening were plotted against the postmortem interval; the results indicated that the ratios are independent of the postmortem interval (Fig. e and f).
We next examined the differences in the temporal pattern of the N/B and B/N ratios between intrinsic (n = 73) and extrinsic (n = 245) death groups. As shown in Fig. a and b, there were no significant differences between the groups. In the extrinsic death cases, the N/B ratio in the morning and the B/N ratio in the evening were significantly higher than those in other time domains (Fig. c and d). However, in the intrinsic death cases, the N/B ratio in the morning was significantly higher than those in other time domains, but the B/N ratio in the evening did not significantly differ from those in other time domains (Fig. c and d). We also examined the effect of specific causes of death on the ratios. The most common causes of death (Table ), including hemorrhagic and traumatic shock, aortic rupture, drowning, burn, asphyxia, intoxication, and ischemic heart failure, except brain injury, did not seem to have a significant effect on the ratios (data not shown). Of note, brain injury, especially, chronic brain injury with cerebral edema, cerebral hernia, and cerebral hypoxia seemed to strongly affect the ratios in the hearts of the deceased. As shown in Fig. a and b, the morning peak of the N/B ratio and the evening peak of the B/N ratio did not take place in cases of delayed death due to chronic brain injury (n = 15), whereas the peaks of the N/B and B/N ratios were observed in acute death cases with severe brain injury (n = 35). The cases of delayed death due to chronic brain injury did not show an oscillation in the N/B and B/N ratios (Fig. c and d). The N/B ratio in the morning significantly differs from that in the evening in cases of acute death with severe brain injury (Fig. c). However, these findings are from a limited small number of cases, and the loss of oscillation of N/B and B/N ratios due to chronic brain injury needs to be confirmed in more cases.
Our method reads the biological clock in the deceased; however, there are only two-time domains (morning, around 6:00; evening, around 18:00) in the clock. The N/B ratio is suitable for reading at 6:00 and the B/N ratio is suitable for reading at 18:00. All cases where the N/B ratio was > 25 were deaths occurring from 1:00 to 10:00 (n = 40), and those where the ratio was > 40 were deaths occurring from 3:00 to 9:00 (n = 23) (Fig. a). On the other hand, all cases where the B/N ratio was > 1.5 were deaths occurring from 14:00 to 22:00 (n = 39), and those where the ratio was > 4 were deaths occurring from 15:00 to 20:00 (n = 11) (Fig. b). However, only 24.8% (79/318) of morning and evening deaths were predicted by our method, and low values of N/B and B/N ratios do not exclude morning and evening deaths. Therefore, although this method is not effective in all cases, it is still important in forensic practice because it complements conventional methods from a completely different perspective.
To date, most methods for estimating the time of death estimate the time since death and are affected by internal, external, antemortem, and postmortem conditions. We hypothesized that the biological clock stops at the time of death, and developed a method to read this stopped biological clock . Therefore, our method estimates the time of death, not the time since death, and appears to be independent of environmental factors; however, it can be influenced by internal factors such as age, gender, cause of death, and lifestyle of the deceased. The reliability and limitations of the practical application of newly developed methods must be evaluated. Thus, we examined our method in increased number of cases with a defined time of death. The N/B ratio showed a peak around 6:00, indicating that the method can give a stable result. Furthermore, we examined a novel reverse parameter, the B/N ratio, which showed a peak around 18:00. The N/B ratio was high in the morning, while the B/N ratio was high in the evening; therefore, we can determine whether death occurred in the morning or evening with this method. However, low N/B and B/N values were often found in cases of death at around 6:00 and 18:00, respectively. Such irregular values were not seen in the animal experiments because mice had a uniform genetic background and were bred in a strictly controlled environment . Furthermore, all mice were sacrificed quickly by cervical dislocation under deep anesthesia. On the other hand, humans have different genetic backgrounds and live in various time patterns (e.g., shift workers), which might affect the expression pattern of biological clock genes , . In the present study, we demonstrated that gender, age, and postmortem interval (within 96 h after death) did not significantly affect the N/B and B/N ratios. However, the youngest (< 1 year old, n = 5), and oldest (> 90 years old, n = 14) cases as well as those with long postmortem intervals (> 48 h, n = 11) were examined in a limited number. It is known that circadian rhythms such as body temperature and nocturnal sleep onset appear within 60 days after birth . Moreover, the circadian oscillation of clock gene expression in the SNC (suprachiasmatic nucleus) and some peripheral tissues has been confirmed in nonhuman primate fetuses , suggesting that clock gene expression in the heart of human infants may also show circadian oscillation. Therefore, the biological clock-based estimation of the time of death seems to be applicable to infant cases. However, maternal melatonin affects clock gene expression in nonhuman primate fetuses , indicating that the breastfeeding pattern might affect the circadian clock in infants. Therefore, differences in clock gene expression patterns between the infant's and adult's heart may be found in future research. On the other hand, it has been reported that aging significantly affects the circadian pattern of gene expression in the human prefrontal cortex, which might bring about changes in the circadian rhythm in old age . Different circadian rhythms in older individuals, especially the feeding pattern, can affect biological clock gene expression , . Since the biological clock in the peripheral tissues is also under adrenergic control , age-related changes in the beta-adrenergic neuroeffector system might alter the clock gene expression pattern in the heart of older adults . Based on the above-mentioned facts, our method should be applied carefully to infants and older adults. Longer postmortem intervals might cause RNA deterioration , which increases the uncertainty of the results. Since the number of cases in children, the elderly, and cases with a long postmortem interval is small, a study using an increased number of cases is necessary for a statistically meaningful discussion. The cause of death seemed to affect the N/B and B/N ratios. However, there were no significant differences in the temporal patterns between intrinsic and extrinsic death cases. Moreover, most causes of death did not significantly affect the ratios. Exceptionally, the peaks of both ratios almost disappeared in the cases of death with cerebral edema, cerebral hernia, or cerebral hypoxia. We also found an alteration of the N/B ratio in the iliopsoas muscle tissue of cases with chronic brain injury (not shown), suggesting that chronic brain injury-induced SCN damage brings about a systemic alteration of peripheral clock gene expression. Disturbances in circadian rhythms due to brain trauma have been reported , , . Recently, traumatic brain injury-induced alteration of clock gene expression in the SCN and hippocampus was reported in a rat model . Our preliminary result in mouse model of water intoxication showed that cerebral edema induced alteration of biological clock in the heart ( ). Therefore, biological clock-based estimation of the time of death should be applied with caution to cases of severe brain injury or intrinsic death with diseases affecting brain function such as severe hepatic encephalopathy. We analyzed 318 cases in the present study. However, there was bias in the number of cases with regards to gender, age, cause of death, and other factors. The number of cases in some groups, such as females, was less than 100, and some of these cases did not show statistical significance in the N/B and B/N ratios between morning and evening time domains compared to other time domains. Therefore, our method should be further validated with studies using a larger number of cases. Multifacility research may be necessary to conduct an analysis with a sufficient number of cases. Recently, an analysis of human transcriptional rhythms using a cyclic ordering algorithm called Cyclops was reported . The Cyclops algorithm enables the estimation of the circadian phase of a sample from high-throughput data that lack temporal information, and is expected to be an innovative approach to estimating the time of death in forensic practice. As Cyclops is an algorithm for the temporary reconstruction of population-based human organ data, its usefulness as a method for estimating the time of death for individual autopsy samples in forensic practice is uncertain. The usefulness and problems of Cyclops will be clarified by verifying it in forensic practice. Another problem is that high-throughput analysis is currently expensive for forensics. In the present study, our method was able to predict only 79 cases of morning or evening deaths out of a total of 318 cases (about 25%). This indicates that our method only works in limited cases. However, all classical methods for estimating time of death have uncertainties, and are based on postmortem changes that begin at death and are influenced by various environmental factors. In contrast, our method directly estimates the death time based on the circadian clock, which stops at death and is unaffected by factors that influence postmortem changes. For example, after a deceased person's body temperature reaches ambient temperature, it is difficult to estimate time since death based on body temperature. In the case of burn death, many classical estimation methods, such as body temperature, corneal opacity and rigor mortis cannot be used. Therefore, all classical estimation methods have limitations in their applicability. Our method complements conventional methods from a completely different perspective and can be used where conventional methods are not applicable. In conclusion, our method makes it possible to estimate the morning and evening deaths by reading the N/B and B/N ratios in the heart of the deceased, regardless of gender, age, postmortem interval, and cause of death. Although the N/B and B/N ratios cannot exclude the possibility of death occurring in the morning or evening, our method is still valuable in forensic practice because it can complement the classical methods that are dependent on postmortem changes. However, since severe brain injury profoundly affects the peripheral circadian clock, our method may not apply to cases of severe brain injury. Additionally, the applicability of the method to infants and older adults needs to be evaluated in more cases.
Autopsy samples Heart samples were obtained from 318 forensic autopsy cases with known times of death (224 men and 94 women). The age of autopsied subjects ranged from 2 months to 97 years (average: 58.7 years), and postmortem intervals in all cases were less than 96 h (average: 22.3 h). The causes of death of the subjects were shown in Table . Tissue samples were taken during autopsy, immediately frozen in liquid nitrogen and stored at − 80 °C until use. Clock gene expression is routinely analyzed in all autopsy cases at our Institute as part of the process for estimating the time of death. Extraction of total RNA and real-time RT-PCR Total RNA was extracted from tissue samples (about 100 mg) and applied to Maxwell System with Maxwell RSC simplyRNA Tissue Kit (Promega Corporation, Madison, WI) according to the manufacturer’s instructions. Then 1 μg of total RNA was reverse-transcribed into cDNA by using a PrimeScript RT reagent Kit (TAKARA BIO INC., Otsu, Japan) with six random primers (TAKARA BIO INC.). Thereafter, generated cDNA was subjected to qPCR analysis using a SYBR ® Premix Ex Taq ™ II kit (TAKARA BIO INC.) with specific primer sets (Table ). Amplification and detection of mRNA were performed using Thermal Cycler Dice ® Real Time System (TP800, TAKARA BIO INC). Statistical analysis Data were expressed as the mean ± standard error of the mean. Unpaired Student t -test and Scheffe’s F test were performed to compare the values between two groups and for multiple comparisons, respectively. Statistical significance was set at p < 0.05. Ethical approval Our study was approved by the Research Ethics Committee of Wakayama Medical University (No. 3177). All procedures were carried out in accordance with the principles of the Declaration of Helsinki. In addition, this study was conducted using past autopsy records and heart tissues; we were unable to obtain informed consent from the bereaved family for the use of the records and the heart tissues. In accordance with the "Ethical Guidelines for Medical Research Involving Human Subjects (enacted by the Ministry of Health, Labor and Welfare in Japan)," Sect. 12–1 (2) (a) (c), the review board of the Research Ethics Committee of Wakayama Medical University waived the need for written informed consent from relatives of the individuals studied because this was a de-identified retrospective study of archived autopsy-derived tissues.
Heart samples were obtained from 318 forensic autopsy cases with known times of death (224 men and 94 women). The age of autopsied subjects ranged from 2 months to 97 years (average: 58.7 years), and postmortem intervals in all cases were less than 96 h (average: 22.3 h). The causes of death of the subjects were shown in Table . Tissue samples were taken during autopsy, immediately frozen in liquid nitrogen and stored at − 80 °C until use. Clock gene expression is routinely analyzed in all autopsy cases at our Institute as part of the process for estimating the time of death.
Total RNA was extracted from tissue samples (about 100 mg) and applied to Maxwell System with Maxwell RSC simplyRNA Tissue Kit (Promega Corporation, Madison, WI) according to the manufacturer’s instructions. Then 1 μg of total RNA was reverse-transcribed into cDNA by using a PrimeScript RT reagent Kit (TAKARA BIO INC., Otsu, Japan) with six random primers (TAKARA BIO INC.). Thereafter, generated cDNA was subjected to qPCR analysis using a SYBR ® Premix Ex Taq ™ II kit (TAKARA BIO INC.) with specific primer sets (Table ). Amplification and detection of mRNA were performed using Thermal Cycler Dice ® Real Time System (TP800, TAKARA BIO INC).
Data were expressed as the mean ± standard error of the mean. Unpaired Student t -test and Scheffe’s F test were performed to compare the values between two groups and for multiple comparisons, respectively. Statistical significance was set at p < 0.05.
Our study was approved by the Research Ethics Committee of Wakayama Medical University (No. 3177). All procedures were carried out in accordance with the principles of the Declaration of Helsinki. In addition, this study was conducted using past autopsy records and heart tissues; we were unable to obtain informed consent from the bereaved family for the use of the records and the heart tissues. In accordance with the "Ethical Guidelines for Medical Research Involving Human Subjects (enacted by the Ministry of Health, Labor and Welfare in Japan)," Sect. 12–1 (2) (a) (c), the review board of the Research Ethics Committee of Wakayama Medical University waived the need for written informed consent from relatives of the individuals studied because this was a de-identified retrospective study of archived autopsy-derived tissues.
Supplementary Information.
|
Evolving drug regulatory landscape in China: A clinical pharmacology perspective | d75d7c30-5166-4661-bb7a-dd5b11967eb4 | 8301550 | Pharmacology[mh] | In order to encourage innovation to address unmet Chinese medical needs, China has changed its drug regulatory landscape to speed up access to new drugs. Publication of “State Council Circular No. 44” on August of 2015 marked the beginning of China drug regulatory reform. In the following years, new guidances have been published and old guidances have been updated, especially after China joined the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) as its regulatory member in 2017. Starting from the beginning of 2020 to the end of September of 2020, more than 60 draft guidances have been published on the National Medical Products Administration (NMPA) website ( http://www.cde.org.cn ) to solicit public comments and opinion, indicating the rapid process of drug regulation standardization in China. All guidances published in NMPA websites are Chinese and there are no official English versions. In order to evaluate the fast changing landscape and to enable us to better plan drug development programs and study designs in China, we reviewed published clinical pharmacology‐related guidances (draft and final) by the NMPA, compared them with reference guidances from the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the ICH, to understand the similarities and differences, especially any China‐specific requirements, such as ethnic sensitivity analysis. Along with drug regulatory reform, the China drug regulatory agency name has been changed several times, State Drug Administration (SDA), 1998–2003, State Food and Drug Administration (SFDA), 2003–2013, China Food and Drug Administration (CFDA), 2013–2018, and NMPA, 2018–present. To be consistent in this analysis, as the guidances we selected to review cover the period from SDA to NMPA, we will use the latest name, NMPA, in this publication when describing China’s regulatory agency for drugs.
A group of AstraZeneca Clinical Pharmacologists and Pharmacometricians who are fluent in both English and Chinese reviewed the website of the NMPA Center for Drug Evaluation ( http://www.cde.org.cn ), guidance page. Key guidances related to clinical pharmacology topics, including first time in man, pharmacokinetics (PK) and pharmacodynamics (PD), drug‐drug interaction (DDI), special population PK, bioavailability (BA) and bioequivalence (BE), etc. are selected to include in the analysis. The guidances that were reviewed are listed in Table . Each guidance was reviewed by two assessors separately. The information in each guidance was compared with the comparable guidances published by the FDA, the EMA, or the ICH to identify major differences in principles, especially the China‐specific requirements. As China guidances usually cover both chemical drugs and Chinese medicine, this review is focused on chemical drugs. Ethnic sensitivity is usually assessed by PK comparison between Chinese versus non‐Chinese participants to support clinical trial conduct and registration in China. The importance of ethnic sensitivity is highlighted and discussed in two recently NMPA published guidances, about how to evaluate overseas clinical data and how to evaluate drugs that are approved overseas but not in China yet. Therefore, ethnic sensitivity analysis for China is discussed and summarized in this paper without comparison to guidances of other regions.
The Technical Guideline of New Drug Phase I Clinical Study Application was published by the NMPA in 2018. It referenced FDA guidance (1995): Content and Format of Investigational New Drug (IND) applications for Phase I Studies of Drugs, Including Well‐Characterized, Therapeutic, Biotechnology‐derived Products, and Questions and Answers published in 2000. This NMPA guidance is very similar to the FDA guidance in overall content. Both guidances describe what should be included in submission. In addition, the NMPA guidance has sections to cover “biologics” and “overseas data,” and states that the format and content of IND can directly reference ICH common technical document for preparation. EMA guidance (2017): guideline on strategies to identify and mitigate risks for first‐in‐human and early clinical trials with investigational medicinal products, had a markedly different focus and it was not fully reflected in this NMPA guidance. Estimating the Maximum Recommended Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers was published by the NMPA in 2012. It outlines approaches to calculate maximum recommended starting dose (MRSD) for first‐in‐human clinical trials of new molecular entities in adult healthy volunteers, and recommends a standardized process by which the MRSD can be selected. This NMPA guidance is very similar to the FDA guidance (2005): Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers. In addition, the NMPA guidance discussed two additional approaches to estimate MRSD, (1) estimation of MRSD based on systemic exposure level and allometric scaling approach, and (2) estimation of MRSD based on minimum anticipated biological effect level. The constant ( K m ) that is used to convert dose in mg/kg to dose in mg/m is slightly different between the NMPA guidance and the FDA guidance. For example, for a human adult, the K m was 36.88 and 37 in the NMPA and the FDA guidance, respectively. For child with 20 kg body weight, the K m was 26.47 and 25 in the NMPA and the FDA guidance, respectively. Therefore, the calculated human equivalent doses may be slightly different when using different K m values from the two guidances. The clinical impact of the differences is expected to be minimum but should be evaluated on a case‐by‐case basis. Technical Guideline of Clinical Pharmacokinetic Study for Chemical Drugs was published by the NMPA in 2005. The NMPA guidance includes two parts, (1) bioanalytical method establishment and validation and (2) PK study. For bioanalytical methods, the NMPA guidance focused on chromatographic assays and has only one paragraph to summarize microbiology and immunology method validation. The FDA guidance on bioanalysis issued in 2001 was also primarily focusing on chemical assay, with some discussion on microbiologic and ligand binding assays. The FDA guidance was updated in 2018 and has extended the scope of ligand binding assays and includes the discussion for the analysis of endogenous compounds, biomarker analysis, and for the application of diagnostic kits and new technologies. This NMPA guidance is the earliest PK guidance to discuss PK study details. It covers studies of healthy volunteers, patients, special populations, and pediatrics. Later on, the NMPA published more population‐specific guidances, that will be discussed later. The FDA and the EMA do not have a single equivalent guidance to cover all of the above topics, but have population‐specific and subject‐specific PK guidances, including hepatic and renal impairment PK studies, drug interaction, pediatric PK, and population PK. These were referenced by the NMPA when publishing this guidance. Technical Guideline on Pharmacokinetics and Pharmacodynamics Study in the Development of Antibacterial Drugs was published by the NMPA in 2015. The objective of this guidance is to provide technical standards for PK and PD studies for the development of antibacterial agents. It focuses on the use of PK/PD analyses to identify potentially efficacious dose regimens. Even though this guidance was for antibacterial drugs, this guidance can also be referenced when developing antifungal agents. The main reference guidance is EMA guidance (2016): Guideline on The Use of Pharmacokinetics and Pharmacodynamics in The Development of Antibacterial Medicinal Products. The contents of this NMPA guidance are concordant with the EMA guidance but with different structure. In addition, the NMPA guidance provides more discussion on certain topics (e.g., post antibiotic effects, animal PK/PD studies, traditional PK study, population PK study, metabolites PK/PD study, point estimate method, and Monte Carlo Simulation in the development of antibacterial agents. Technical Guideline of Pharmacokinetics in Patients with Impaired Hepatic Function was published by the NMPA in 2012. It references FDA guidance (2003): Pharmacokinetics in Patients with Impaired Hepatic Function: Study Design, Data Analysis, and Impact on Dosing and Labeling. The NMPA guidance was basically translated from the FDA guidance with identical content and structure. The purpose of this guidance is to aid sponsors and applicants in determining whether an adjustment of the dosage would be indicated in patients with hepatic impairment. It covers full and reduced PK study designs, data analysis, and recommendation of labeling statements. The Technical Guideline of Pharmacokinetics in Patients with Impaired Renal Function was published by the NMPA in 2012. It references FDA guidance (2010): Pharmacokinetics in Patients with Impaired Renal Function — Study Design, Data Analysis, and Impact on Dosing and Labeling. The NMPA guidance was basically translated from FDA 2010 guidance, with similar contents and structure. It covers study design (full or reduced PK study), data analysis, and label language instruction. The FDA guidance was updated in 2020. The NMPA guidance is not updated accordingly, but presenting data from a global renal impairment study following updated FDA guidance is expected to be acceptable by the NMPA. Technical Guideline of Drug Interaction Studies (draft) was published by the NMPA in September 2020. It covers in vitro DDI studies, clinical DDI studies, and recommendation for labeling language. It also includes decision trees for in vitro DDI studies, model based DDI prediction and determination, in vitro test systems and details, and probe drugs commonly used in DDI studies. The FDA published two final DDI guidances in January 2020, one covering in vitro DDI studies and one covering clinical DDI studies as follows: In Vitro Drug Interaction Studies — Cytochrome P450 Enzyme‐ and Transporter‐Mediated Drug Interactions. 2020 Clinical Drug Interaction Studies — Cytochrome P450 Enzyme‐ and Transporter‐Mediated Drug Interactions 2020 The NMPA DDI guidance was compared with the FDA DDI guidances. For in vitro DDI studies, the NMPA guidance is very similar to the FDA guidance in contents and structure, including aspects related to model‐based DDI prediction and in vitro test systems. For clinical DDI studies, the NMPA guidance is similar to the FDA guidance but structured slightly different. In general, the FDA guidances provide more background and explanations. The NMPA guidance includes in vitro DDI study decision trees and list of probe drugs recommended to use in in vitro and clinical DDI studies in the appendices, but FDA guidances do not include them. In August 2020, the FDA published a draft guidance: Drug‐Drug Interaction Assessment for Therapeutic Proteins. The content of this FDA guidance is not covered in the NMPA DDI guidance. Technical Guideline of Safety Testing of Drug Metabolites was published by the NMPA in 2012. It references FDA guidance (2008): Safety Testing of Drug Metabolites. The FDA guidance was finalized in 2016. The contents are almost identical between the NMPA and the FDA guidances (2008). Both guidances indicate human metabolites can raise a safety concern for those formed at greater than 10% of total drug‐related systemic exposure at steady‐state. The systemic exposure to metabolite is generally quantitated using area under the curve (AUC), but sometimes it may be more appropriate to use maximum plasma concentration (C max ). Technical Guideline of Bioavailability and Bioequivalence Studies was published by the NMPA in 2005. It summarized the concept of BA and BE and its application scope, and clarified the requirements for BA and BE studies in ordinary and specific formulations. It references FDA guidance (2003): Bioavailability and Bioequivalence Studies for Orally Administered Drug Products‐General Considerations. Later on, the FDA updated the BA and BE guidance in 2014: Bioavailability and Bioequivalence Studies Submitted in New Drug Applications or Investigational New Drugs — General Considerations. In 2019, the FDA issued a draft guidance, Bioavailability Studies Submitted in New Drug Applications or Investigational New Drugs — General Considerations. After finalization, the 2019 guidance will replace the 2014 guidance. Recently, the NMPA was asking sponsors from other regions to conduct BA or BE studies in China to demonstrate BA or BE in the Chinese population, in addition to the BA or BE data generated from other regions. Therefore, in addition to referencing the NMPA guidance, it is reasonable for the sponsor to reference the latest version of BA or BE guidance from the FDA. Technical Guideline for Human Bioequivalence Studies with Pharmacokinetic Endpoints for Chemical Drug Generics was published by the NMPA in 2015. It references FDA guidance (2013): Bioequivalence Studies with Pharmacokinetic Endpoints for Drugs Submitted Under an Abbreviated New Drug Application. The NMPA guidance covers topics about overall designs, population, PK parameters for evaluating BE, recommendations for different dosage forms, special considerations, general principles for study design, and data treatment in BE studies. The contents of the NMPA guidance, in principle, are similar to that in the FDA guidance, but structured slightly different. The FDA guidance provides more explanation and examples in certain sections. In addition, the FDA guidance discusses “Sprinkle Bioequivalence Studies,” “Bioequivalence Studies of Products Administered in Specific Beverages,” and “Drug Products with Complex Mixtures as the Active Ingredients,” but these topics are omitted from the NMPA guidance. Guideline of Waiver of In Vivo Bioequivalence Studies was published by the NMPA in 2016. It references FDA draft guidance (2015): Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate‐Release Solid Oral Dosage Forms Based on a Biopharmaceutics Classification System. This FDA guidance was finalized in 2017. In general, the NMPA guidance is very similar to the FDA guidance. EMA guidance (2010): Guideline on the Investigation of Bioequivalence, has a short appendix “BCS‐based biowaiver.” The contents in this appendix are covered by the NMPA guidance. In the NMPA and the FDA guidances, both class 1 (high solubility and high permeability) and class 3 (high solubility and low permeability) can apply biowaiver, but class 3 has tighter requirements. For fixed dose combination (FDC), if both drugs are class 1 drugs, the FDA can consider a biowaiver if there are DDIs between the two drugs, but the NMPA will not consider a biowaiver. If both drugs belong to Biopharmaceutics Classification System (BCS) class 3 or a combination of class 1 and 3, and there is a DDI between the two drugs, the FDA can consider BCS‐based biowaiver for immediate release FDC if excipients fulfill the considerations outlined in the guidance, however, the NMPA will not consider a biowaiver in this situation. As the NMPA is asking sponsors to conduct BE studies in the Chinese population now even with BE data available from other regions, the sponsor should share the China BE study design and engage biowaiver strategy discussion with NMPA as early as possible. Guideline of Statistical Approaches to Establishing Bioequivalence was published by the NMPA in 2016 first, then updated in 2018. It references FDA guidance (2001): Statistical Approaches to Establishing Bioequivalence. The EMA does not have an equivalent guidance. The FDA guidance is very comprehensive, covers multiple statistical models (average BE, population BE, individual BE, etc.), study design, statistical analysis, and miscellaneous issues. The NMPA guidance is more focused on study design, statistical model based on average BE, and relevant data analysis, which are similar in contents to corresponding sections in the FDA guidance. In the NMPA guidance, it defines that “BE set” includes subjects with at least one evaluable PK parameter in at least one period, which means that a subject who has only one PK parameter from one period (test or reference) will be included for BE calculation. That is different from common practice that only subjects who have at least one evaluable PK parameter from both periods (test and reference) will be included in BE calculation, as stated in EMA guidance 2010: Guideline on The Investigation of Bioequivalence, “Ideally, all treated subjects should be included in the statistical analysis. However, subjects in a crossover trial who do not provide evaluable data for both of the test and reference products (or who fail to provide evaluable data for the single period in a parallel group trial) should not be included.” The differences in the “BE set” definition may mean larger sample size in the China BE study. With regard to sample size, both the NMPA and the FDA guidances recommend that sample size should be estimated appropriately for study design if average BE approach is the selection. The FDA guidance further recommends that a minimum number of 12 evaluable subjects should be included in any BE study. Technical Guideline of Bioequivalence of Highly Variable Drugs published by the NMPA in 2018 comprehensively discussed BE study design, statistical analysis, and data analysis for highly variable drugs (HVDs). In comparison, both the FDA and the EMA only provided a short discussion as part of their BE guidances (FDA 2014: Bioavailability and Bioequivalence Studies Submitted in NDAs or INDs — General Considerations ; EMA 2010: Guideline on the Investigation of Bioequivalence ). HVD is defined as one or more major PK parameter’s coefficient of variation percentage (CV%) is greater than or equal to 30%. The NMPA guidance states that wider BE criteria can be considered under the condition of good tolerability and large safety margin. However, it does not further discuss how wide the criteria can be. The EMA guidance (2010) specified that the BE criteria window is based on the variability of the drug, for example, if CV% is greater than or equal to 50%, the BE range can be widened to 69.84% to 143.19%. The possibility to widen the acceptance of BE criteria does not apply to AUC where the acceptance range should remain at 80.00% to 125.00%, regardless of variability. NMPA Guidance Flexibilities: Guidance, in general, provides recommendations from regulators. For example, the FDA states in the first page of guidance “Clinical Drug Interaction Studies — Cytochrome P450 Enzyme‐ and Transporter‐Mediated Drug Interactions that “This guidance represents the current thinking of the Food and Drug Administration (FDA or Agency) on this topic. It does not establish any rights for any person and is not binding on FDA or the public. You can use an alternative approach if it satisfies the requirements of the applicable statutes and regulations. To discuss an alternative approach, contact the FDA office responsible for this guidance as listed on the title page.” Similar language is included in all FDA guidances. The EMA is in the similar position to the FDA. For example, the EMA states in “Guideline on The Use of Pharmacokinetics and Pharmacodynamics in The Development of Antibacterial Medicinal Products” that “This Guideline has been developed to outline the regulatory expectations for application dossiers and reflects both the scientific advances and the regulatory experience” and “it is recognised that sponsors may propose alternative strategies to those outlined in this Guideline, in which case discussion with EU Competent Authorities would be appropriate.” Similar flexibility was not stated in NMPA guidances included in this analysis that were published in 2018 and before. However, in “Technical Guideline of Drug Interaction Studies (draft)” that was published in 2020, it is stated that “This guideline only represents the current views and understandings of NMPA, it serves as a references only for sponsors and is not legal binding. As scientific research progresses, the relevant content in this guideline will be continuously improved and updated” and “If needed, methods other than those described in this guideline can also be used.” By searching the NMPA guidance website ( http://www.cde.org.cn ), it was noted that a similar flexibility statement is included in most of guidances published in 2020, indicating that the NMPA is adapting flexibility into their newly published guidances, which gives the sponsor more flexibility to apply new methods that were not included in the guidance to achieve the objective of the guidance. Overall, by reviewing these clinical pharmacology related NMPA guidances, it is clear that the NMPA guidances are very similar to the FDA, the EMA, or ICH guidances. There is no difference in major principles, but some differences in structure, contents, and focus were found. NMPA referenced guidances from the FDA, the EMA, or the ICH when preparing their guidances even before China joined the ICH in 2017. The harmonization of guidance does not only help sponsors from overseas to bring their drugs to Chinese patients faster, but also helps China’s domestic companies to bring their drugs to international markets. When referencing old NMPA guidances that were published early or referenced old version of FDA, EMA, or ICH guidances, it is reasonable to reference the latest version of FDA, EMA, or ICH guidances and engage early conversation with the NMPA as they are open for scientific discussions.
The ICH published “E5: Ethnic Factors in the Acceptability of Foreign Clinical Data” in 1998. It describes factors to consider when extrapolating and facilitating acceptance of foreign clinical data in a new region, and describes development strategies for ethnic sensitivity analysis. Ethnic sensitivity factors defined in ICH E5 include not only internal factors, such as genetics and physiology, but also social culture, living environment and other external factors (Table ). Based on these factors, drug ethnic sensitivity can be defined (see Table ). In 2017, the ICH published another guidance, “E17: General Principles for Planning and Design of Multi‐Regional Clinical Trials,” where ethnic sensitivity was further discussed. Using clinical data generated from overseas and bringing innovative drugs from other countries to China, one of the key factors to consider is ethnic sensitivity. In July 2018, the NMPA published “Technical Guidelines for Accepting Data from Overseas Clinical Trials of Drugs.” For clinical trials conducted in overseas, when applying for registration in China, the sponsor needs to fully analyze the ethnic sensitivity of the Chinese population versus the non‐Chinese population to support the bridging of overseas clinical data to the Chinese population (Table ). In 2020, the NMPA published “Clinical Requirement for Drugs Approved on Overseas but not in China” highlights the importance of ethnic sensitivity analysis on approvability of innovative drugs or generic drugs that are approved overseas (Table ). The NMPA clinical pharmacology reviewers summarized their views about conducting PK ethnic differences analyses in China in a Chinese language publication. To address PK ethnic differences, traditional PK comparison with intensive PK samples and/or population PK (PopPK) analysis can be used. The results of the PK ethnic difference analysis will not only impact the decision of acceptability or approvability, but also the usage and dosage adjustment based on ethnicity. It is recommended to use the above two methods to evaluate ethnic differences separately, and to combine the evaluation results of the two methods to comprehensively evaluate the drug PK ethnic differences. Under special circumstances, if it is not possible to collect intensive PK samples from the Chinese population, then only the PopPK method can be considered for the evaluation of PK ethnic differences. If the PK ethnic comparison analysis shows a certain PK difference, the clinical impact of the PK ethnic differences on safety and effectiveness will have to be evaluated. If the PK ethnic differences are considered as clinically meaningful, more studies or data analysis, including dose adjustment, will have to be considered. Chinese people and Asian people, such as Japanese, Koreans, Indians, and Southeast Asians, have large differences in living environment and eating habits, sometimes in terms of background treatment, medical practice, and availability. Therefore, the ethnic sensitivity analysis generally needs to be done by comparing Chinese versus non‐Chinese populations. The non‐Chinese can be other Asians, Whites, and Blacks. The necessary clinical studies in the Chinese population are the basis for the evaluation of PK ethnic differences. For drugs that have not been studied clinically in the Chinese population, in view of the unknown ethnic sensitivity issues that may exist, based on risk considerations, it is generally recommended that the sponsors first evaluate their tolerability and safety in the Chinese population and PK ethnic differences. After ensuring that the drug is tolerated in the Chinese population and PK ethnic differences are assessed, follow‐up clinical studies can be conducted accordingly. In summary, for the sponsors from overseas seeking to conduct clinical studies or bring their drugs to China market, ethnic sensitivity analysis has to be implemented in the drug development plan early. The NMPA encourages the sponsors to conduct early clinical trials in China or include China in multiregional clinical trials early, to obtain safety, efficacy, and PK data for ethnic sensitivity analysis. Depending on the stage of the development, ethnic sensitivity analysis can be conducted based on in vitro or literature data, based on Asian clinical data, or based on Chinese data. As there are many questions that are not addressed in the NMPA guidances, for example, for an ethnic insensitive drug (such as monoclonal antibody), is a PK study sufficient to support China joining global phase III? What is the likelihood of waiving a phase I study in China if joining phase II and generate PopPK data? If we have sufficient data to support ethnic justification in adults, can we extrapolate it to the pediatric population? What is the minimum number of Chinese subjects for PK ethnic difference analysis? Can we use Chinese data collected from Chinese subjects living overseas? An early consultation with the NMPA will help to plan studies and strategies of drug development in China.
Fifteen clinical pharmacology‐related NMPA guidances have been reviewed and compared with reference guidances from the FDA, the EMA, or the ICH. There is no difference in the major principles, but some difference in structure, content, and focus were found between the NMPA guidance and reference guidances. Ethnic sensitivity analysis in Chinese populations has to be implemented in drug development plans early.
The authors declare no conflict of interest.
|
Factors associated with compassion fatigue and compassion satisfaction in obstetrics and gynaecology nurses: A cross‐sectional study | 1126a3cb-3164-4a33-af0e-2202a3632652 | 10333879 | Gynaecology[mh] | INTRODUCTION Compassion is the understanding and sharing of the emotional state of others. Compassion has long played a positive and active role in both academic research and social life. Research has found that compassion promotes pro‐social behaviour (Singer & Klimecki, ), improves interpersonal relationships and increases an individual's level of well‐being (Saunders, ). According to the International Council of Nurses Code of Ethics for nurses, compassion is one of the eight professional values required of nurses (ICN, ). Compassion is both an essential quality and one of the required competencies for nurses. However, beneath these positive auras, compassion may also have certain negative effects. Due to the high workload of prolonged contact with illness, disability and death, nurses' compassion is highly susceptible to compassion fatigue. Studies have shown that approximately two in five clinical nurses surveyed suffer from compassion fatigue (Duarte & Pinto‐Gouveia, ), which has adverse physical, psychological, emotional and cognitive effects (Alharbi et al., ). It is also known as the ‘cost of care’. In order to scientifically and effectively reduce the generation of compassion fatigue and improve the compassion satisfaction of nurses, it is crucial to identify the influencing factors that induce compassion fatigue and compassion satisfaction generation in nurses. Several studies have been conducted in clinical departments. For example, a study in an intensive care unit showed that female nurses aged between 18 and 25 years, with a bachelor's degree and 1–3 years of service had higher levels of compassion fatigue (İlter et al., ); for oncology and palliative care nurses, long patient stays and high mortality rates trigger compassion fatigue while decreasing compassion satisfaction (Frey et al., ; Jarrad & Hammad, ); women and the experience of traumatic events in their lives are exacerbating compassion fatigue, while poor work environment, poor colleague relationships and irregular working hours are influencing factors of low compassion satisfaction (Kartsonaki et al., ). However, no studies have focused on compassion fatigue and compassion satisfaction among obstetrics and gynaecology nurses. In recent years, with the opening of the three‐child policy, an ageing population and the promotion of assisted reproductive technologies, the incidence of obstetric and gynaecological diseases has increased, nursing workload has increased and nurses are under correspondingly greater stress (Favrod et al., ). Obstetrics and gynaecology nurses are prone to compassion fatigue and low compassion satisfaction as they serve vulnerable groups such as women or children for long periods of time. Persistent compassion fatigue leads to decreased productivity, increases the incidence of adverse care events and directly reduces the quality of care and patient satisfaction (Labrague & de Los Santos, ). Therefore, this study aimed to investigate the current status of compassion fatigue among obstetrics and gynaecology nurses and analyse its influencing factors. And based on conservation of resources theory, it further explored the influence of lack of professional efficacy on compassion fatigue and the role of social support as a bridge between the two. Thus, prevention and intervention targets were identified to improve the quality of obstetrical and gynaecological care. 1.1 Background The term ‘compassion fatigue’ was originally described by Joinson to refer to the emotional, physical and psychological exhaustion of healthcare workers as a result of work‐related stress. Compassion fatigue was prevalent among nurses, and it not only decreased work efficiency, but also increased the incidence of adverse nursing events, which directly reduced the quality of care and patient satisfaction (Ondrejková & Halamová, ). Therefore, compassion fatigue was also named ‘cost of caring’ (Figley, ). Compassion fatigue caused some physical symptoms and mental symptoms (Alharbi et al., ). The current state of compassion fatigue among nurses cannot be ignored. Compassion fatigue has been studied in various contexts and was found in several areas of health care: intensive care (İlter et al., ), emergency (Yu & Gui, ) and paediatrics (Kartsonaki et al., ). We all know that obstetrics and gynaecology is a special unit in hospitals because the majority of patients are women during pregnancy, childbirth, postpartum and illness. Nurses often witness women's most stressful moments, trauma and pain, and may absorb patients' pain and suffering while experiencing traumatic distress (Berger & Gelkopf, ). Chronic compassion fatigue led to decreased quality of care, reduced job satisfaction for nurses and increased turnover (Labrague & de Los Santos, ). The causes of compassion fatigue are not yet clear. Conservation of resources theory was based on the concept that individuals have a tendency to preserve, protect and acquire resources (Hobfoll, ). In 2017, conservation of resources theory was applied to the study of compassion fatigue in nursing (Coetzee & Laschinger, ). When the resources available to caregiver are adequate, caregiver will provide caring and compassionate resources that will help alleviate the patient's suffering. However, once the nurse experienced the lack of understanding from the patients and the lack of support from the hospital leadership, her resources would be consumed more than replenished, that is, a loss of resources occurred, resulting in compassion fatigue for the nurse (Coetzee & Laschinger, ). According to conservation of resources theory, nurses' emotional labour is an important resource and an influencing factor on compassion fatigue. Emotional labour was defined as the management of feelings to create a publicly observable facial and bodily display (Hochschild, ). Hospitals, in order for patients to feel that they are being cared for appropriately and safely, require nurses to work without reflecting the negative emotions they experience to patients, families and colleagues, which greatly increases the level of emotional labour in nursing (Hwang et al., ). Emotional labour leads to emotional dysregulation, which manifests as a conflict between the underlying emotion and the actual emotion expressed. A study found that the level of emotional labour in nurses was strongly associated with the occurrence of compassion fatigue (Barnett et al., ). In addition, a Korean study found that 117 nurses had moderate to high levels of emotional labour, which correlated strongly with compassion fatigue. And 23% of the nurses had medical errors in the past 6 months and had a desire to leave nursing (Kwon et al., ). Prolonged emotional labour can drain nurses, make them feel fatigued and will lead to burnout (Morris & Feldman, ). Burnout was also known as a syndrome of emotional exhaustion, cynicism and lack of professional efficacy (Maslach & Jackson, ). When burnout occurs, the nurse's resources are depleted. Nurses feel that patient poses a threat to their resources and exhibits cynicism and lack of professional efficacy. Some studies have shown that the occurrence of burnout was positively correlated with compassion fatigue (Ruiz‐Fernández et al., ). Frequent emotional labour can lead to burnout or compassion fatigue and reduce the quality of life and work‐related care of nurses (Kwak et al., ). According to conservation of resources theory, when individuals have insufficient internal resources, they look for supportive resources in the work environment to supplement the lost internal resources. Individuals' social support is a typical supportive resource, it helps individuals to regulate the relationship between stress and physical and mental health, thus helping to alleviate compassion fatigue. Social support was defined as the level of helpful social interaction in the workplace from both co‐workers and supervisors (Karasek et al., ). In a study of paediatric oncology nurses, it was found that when nurses felt more social support, it would reduce nurses' compassion fatigue, thus increasing their productivity and well‐being (Sullivan et al., ). In addition, in a study of critical care, nurses who received leadership and administrative support had lower levels of compassion fatigue (Alharbi et al., ). Therefore, social support is an important influential factor in reducing compassion fatigue. In summary, many factors influence the occurrence of compassion fatigue in obstetrics and gynaecology nurses. Most studies focused on the effect of a single factor on compassion fatigue, while ignoring the combined results of multiple factors. Therefore, the purpose of this study was to understand the levels of nurses' compassion fatigue and compassion satisfaction and to examine their relationships with multiple variables. To this end, the research questions for this study were as follows. What are the levels of compassion fatigue and compassion satisfaction among obstetrics and gynaecology nurses? What are the influencing factors of compassion fatigue and compassion satisfaction among obstetrics and gynaecology nurses? Are there any associations between these influencing factors? Background The term ‘compassion fatigue’ was originally described by Joinson to refer to the emotional, physical and psychological exhaustion of healthcare workers as a result of work‐related stress. Compassion fatigue was prevalent among nurses, and it not only decreased work efficiency, but also increased the incidence of adverse nursing events, which directly reduced the quality of care and patient satisfaction (Ondrejková & Halamová, ). Therefore, compassion fatigue was also named ‘cost of caring’ (Figley, ). Compassion fatigue caused some physical symptoms and mental symptoms (Alharbi et al., ). The current state of compassion fatigue among nurses cannot be ignored. Compassion fatigue has been studied in various contexts and was found in several areas of health care: intensive care (İlter et al., ), emergency (Yu & Gui, ) and paediatrics (Kartsonaki et al., ). We all know that obstetrics and gynaecology is a special unit in hospitals because the majority of patients are women during pregnancy, childbirth, postpartum and illness. Nurses often witness women's most stressful moments, trauma and pain, and may absorb patients' pain and suffering while experiencing traumatic distress (Berger & Gelkopf, ). Chronic compassion fatigue led to decreased quality of care, reduced job satisfaction for nurses and increased turnover (Labrague & de Los Santos, ). The causes of compassion fatigue are not yet clear. Conservation of resources theory was based on the concept that individuals have a tendency to preserve, protect and acquire resources (Hobfoll, ). In 2017, conservation of resources theory was applied to the study of compassion fatigue in nursing (Coetzee & Laschinger, ). When the resources available to caregiver are adequate, caregiver will provide caring and compassionate resources that will help alleviate the patient's suffering. However, once the nurse experienced the lack of understanding from the patients and the lack of support from the hospital leadership, her resources would be consumed more than replenished, that is, a loss of resources occurred, resulting in compassion fatigue for the nurse (Coetzee & Laschinger, ). According to conservation of resources theory, nurses' emotional labour is an important resource and an influencing factor on compassion fatigue. Emotional labour was defined as the management of feelings to create a publicly observable facial and bodily display (Hochschild, ). Hospitals, in order for patients to feel that they are being cared for appropriately and safely, require nurses to work without reflecting the negative emotions they experience to patients, families and colleagues, which greatly increases the level of emotional labour in nursing (Hwang et al., ). Emotional labour leads to emotional dysregulation, which manifests as a conflict between the underlying emotion and the actual emotion expressed. A study found that the level of emotional labour in nurses was strongly associated with the occurrence of compassion fatigue (Barnett et al., ). In addition, a Korean study found that 117 nurses had moderate to high levels of emotional labour, which correlated strongly with compassion fatigue. And 23% of the nurses had medical errors in the past 6 months and had a desire to leave nursing (Kwon et al., ). Prolonged emotional labour can drain nurses, make them feel fatigued and will lead to burnout (Morris & Feldman, ). Burnout was also known as a syndrome of emotional exhaustion, cynicism and lack of professional efficacy (Maslach & Jackson, ). When burnout occurs, the nurse's resources are depleted. Nurses feel that patient poses a threat to their resources and exhibits cynicism and lack of professional efficacy. Some studies have shown that the occurrence of burnout was positively correlated with compassion fatigue (Ruiz‐Fernández et al., ). Frequent emotional labour can lead to burnout or compassion fatigue and reduce the quality of life and work‐related care of nurses (Kwak et al., ). According to conservation of resources theory, when individuals have insufficient internal resources, they look for supportive resources in the work environment to supplement the lost internal resources. Individuals' social support is a typical supportive resource, it helps individuals to regulate the relationship between stress and physical and mental health, thus helping to alleviate compassion fatigue. Social support was defined as the level of helpful social interaction in the workplace from both co‐workers and supervisors (Karasek et al., ). In a study of paediatric oncology nurses, it was found that when nurses felt more social support, it would reduce nurses' compassion fatigue, thus increasing their productivity and well‐being (Sullivan et al., ). In addition, in a study of critical care, nurses who received leadership and administrative support had lower levels of compassion fatigue (Alharbi et al., ). Therefore, social support is an important influential factor in reducing compassion fatigue. In summary, many factors influence the occurrence of compassion fatigue in obstetrics and gynaecology nurses. Most studies focused on the effect of a single factor on compassion fatigue, while ignoring the combined results of multiple factors. Therefore, the purpose of this study was to understand the levels of nurses' compassion fatigue and compassion satisfaction and to examine their relationships with multiple variables. To this end, the research questions for this study were as follows. What are the levels of compassion fatigue and compassion satisfaction among obstetrics and gynaecology nurses? What are the influencing factors of compassion fatigue and compassion satisfaction among obstetrics and gynaecology nurses? Are there any associations between these influencing factors? METHODS 2.1 Design An online cross‐sectional study was conducted. 2.2 Instrument with validity and reliability The questionnaires used in this study included socio‐demographic characteristics, the Chinese version of the Compassion Fatigue Scale, the Maslach Burnout Inventory General Survey (MBI‐GS), the Emotional Labour Scale and the Social Support Rate Scale (SSRS). All questionnaires were reviewed by five professors (three professors in the field of obstetrics and gynaecology, one professor in psychological care and one professor in care management) in the field and then used. 2.2.1 Socio‐demographic characteristics Self‐designed after a pre‐review of the literature. This includes age, marital status, the only‐child, number of children, education level, work experience, professional title, employment status, night shift, average weekly hours and physical condition. 2.2.2 Chinese version of the Compassion Fatigue Scale The Professional Quality of Life Scale (ProQOL) was revised by Stamm to form the Chinese version of the Compassion Fatigue Scale, which was used in this study. The scale includes three dimensions: compassion satisfaction, burnout and secondary traumatic stress, each with 10 entries, for a total of 30 entries. The scale is based on Likert 5‐point scale, with the frequency of occurrence ranging from ‘none’ to ‘always’, and the reverse scoring method is used for items 14, 15, 17 and 29. The total score for each of the three dimensions is 50, and the threshold values are <37, >27 and >17 respectively. The total score of one dimension exceeded the threshold value for mild empathy fatigue, two dimensions exceeded the threshold value for moderate compassion fatigue and all three dimensions exceeded the threshold value for high compassion fatigue. In this study, the sum of the scores of the two dimensions was used as the compassion fatigue score. The total Cronbach's alpha coefficient of the scale in this study was 0.821, the Cronbach's alpha coefficient of compassion fatigue was 0.820 and the Cronbach's alpha coefficient of compassion satisfaction was 0.882. 2.2.3 Maslach Burnout Inventory General Survey The MBI‐GS scale formulated was used, which includes 15 items (Maslach et al., ). Scores range from ‘never (0)’ to ‘very frequently (6)’. The scale is divided into three dimensions: cynicism, emotional exhaustion and lack of professional efficacy. Cynicism and emotional exhaustion are positive scores, that is, the higher the score, the more serious the degree of job burnout. However, the lack of professional efficacy dimension is scored in reverse, that is, the lower the score, the more obvious the lack of professional efficacy. And the sum of the scores of the two dimensions of cynicism and emotional exhaustion was used as the job burnout score. The total Cronbach's alpha coefficient of the scale in this study was 0.885, the Cronbach's alpha coefficient of the total score of the two dimensions was 0.943 and the Cronbach's alpha coefficient of the low achievement dimension was 0.902. 2.2.4 Emotional Labour Scale The Chinese Emotional Labour Scale for Nurses compiled by Grandey was used, which has sub‐categories for surface acting (seven items), emotional expression requirements (four items) and deep acting (three items). Each item was measured using a 6‐point Likert scale from 1 point (‘strongly disagree’) to 6 points (‘strongly agree’). The total score ranges from 14 to 84, with higher scores indicating higher levels of emotional labour. In this study, the total Cronbach's α coefficient of the scale was 0.870. 2.2.5 Social Support Rate Scale The SSRS was originally developed by Xiao Shuiyuan (Yu et al., ), including subjective support, objective support and support utilization, with a total of 10 entries, of which entries 1–4 and 8–10 are single‐choice questions, each entry has four options, and the first, second, third and fourth answers are scored 1, 2, 3 and 4 respectively; entry 5 has five options, A, B, C, D and E, and each option is scored from ‘none’ to ‘fully support’. Each option from ‘none’ to ‘fully support’ will be scored from 1 to 4 points, and the score of the entry will be the sum of the scores of each option; entries 6 and 7 will be scored 0 points if you answer ‘no source’, and 0 points if you choose from ‘the following sources’. If you choose from the ‘following sources’, you will be given several points. The total score of the scale ranged from 12 to 66, and the higher the total score, the more social support was received. The total Cronbach's alpha coefficient for the scale in this study was 0.815. 2.3 Sampling and recruitment This is a cross‐sectional study, using convenience sampling, in which obstetrics and gynaecology nurses from January to February 2022 from five tertiary care hospitals in ‘XX’ were selected for recruitment. We collected data through a mobile phone questionnaire star mini programme. After the questionnaire was created, the mini programme generated a two‐dimensional code, and the investigators asked participants to carefully review the informed consent form and then fill out the questionnaire anonymously. 2.4 Sample size and power Sample size calculation formula: N = [( t α/2 + t β ) S / δ ] 2 . Interpretation: α = 0.05, β = 0.10, power (1− β ) = 0.90. t α,∞ = t 0.05, ∞ = 1.96; t β,∞ = t 0.10, ∞ = 1.645. S is the standard deviation obtained from the pre‐experiment. δ is the allowable error, which is set by 0.25 times or 0.50 times the standard deviation according to the literature for cases where the allowable error level is not given in a professional sense. N is the sample size, and 208 samples are obtained by calculation. If a 20% error rate is set, 250 are obtained. 2.5 Quality appraisal Design: The study participants were selected according to the inclusion and exclusion criteria, exclusion bias was controlled, the purpose of the study was informed and consent was sought from the study participants to ensure the quality of the survey. Implementation: A uniform guideline was used to inform the survey about the entries of the questionnaire and the precautions for filling it out, so that the study participants could obtain cooperation. If there are any questions, the researcher or investigator promptly answers them and provides objective guidance to fill them out, requiring the survey participants to fill them out anonymously and independently, so as to control confounding bias. Data collation and analysis: After data collection, the investigator checked and accepted the returned questionnaires one by one, eliminating invalid questionnaires such as missing items ≥5%, misfilled, regular responses and identical questionnaires. The data entry was done by two‐person double‐computer entry method, and the data were compared item by item to ensure the accuracy of the data before statistical analysis. According to the nature of the variables and the purpose of the study, appropriate statistical analysis methods were selected to ensure the reliability of the study results. 2.6 Population and sample There are 11 public hospitals in ‘XX’, of which five tertiary hospitals containing obstetrics and gynaecology departments (including four grade A hospitals and one grade B hospital), with an estimated overall number of nurses 444. In this study, obstetrics and gynaecology nurses in public tertiary hospitals containing obstetrics and gynaecology departments in ‘XX’ area were studied as a whole, and a total of 329 cases were investigated, with a valid sample of 311 cases. A convenience sampling method was used, and according to the formula, the minimum sample size was 250, so this sample of 311 cases could represent the obstetrics and gynaecology nurses in the whole ‘XX’ tertiary hospitals. 2.7 Inclusion and/or exclusion criteria The inclusion criteria were as follows: (1) working registered nurses (midwives should hold a maternal and child health certificate); (2) more than 1 year of work experience. Intern nurses, nurses who were studying, nurses on rotation or nurses who were on leave for various reasons during the survey period were excluded from the study. 2.8 Data analysis The data were checked by Excel 2019 and analysed by SPSS 24.0. Categorical variables were expressed as frequency and percentage, continuous variables were described by mean ± standard deviation. Demographic data were analysed by univariate analysis, including independent samples T test, one‐way ANOVA test and Kruskal–Wallis test. Pearson's correlation analysis was used to access the relationships between the two variables and Spearman's correlation analysis was used when the data did not conform to normal distribution. The influencing factors of variables were evaluated by stepwise multiple linear regression analysis. Harman's single factor analysis was performed to test the degree of variation. Meanwhile, model 4 and model 8 in the Process macro of SPSS software were conducted to analyse the mediating effect, a level of p < 0.05 was accepted as statistically significant difference. Bootstrap procedure (5000 duplicate samples) was performed to test the significance of the mediating effect and 95% confidence interval (CI) without zero indicates a significant indirect effect. 2.9 Ethical considerations This study was approved by the Ethics Committee of ‘XX’ (REDACTED). All participants provided informed consent and voluntarily participated in the study, which was conducted anonymously. Their information was confidential. All information collected was kept by the investigator, and only the investigator had access to the survey information. All methods used in this study were in accordance with the principles of the Institutional Research Committee and the Declaration of Helsinki. Design An online cross‐sectional study was conducted. Instrument with validity and reliability The questionnaires used in this study included socio‐demographic characteristics, the Chinese version of the Compassion Fatigue Scale, the Maslach Burnout Inventory General Survey (MBI‐GS), the Emotional Labour Scale and the Social Support Rate Scale (SSRS). All questionnaires were reviewed by five professors (three professors in the field of obstetrics and gynaecology, one professor in psychological care and one professor in care management) in the field and then used. 2.2.1 Socio‐demographic characteristics Self‐designed after a pre‐review of the literature. This includes age, marital status, the only‐child, number of children, education level, work experience, professional title, employment status, night shift, average weekly hours and physical condition. 2.2.2 Chinese version of the Compassion Fatigue Scale The Professional Quality of Life Scale (ProQOL) was revised by Stamm to form the Chinese version of the Compassion Fatigue Scale, which was used in this study. The scale includes three dimensions: compassion satisfaction, burnout and secondary traumatic stress, each with 10 entries, for a total of 30 entries. The scale is based on Likert 5‐point scale, with the frequency of occurrence ranging from ‘none’ to ‘always’, and the reverse scoring method is used for items 14, 15, 17 and 29. The total score for each of the three dimensions is 50, and the threshold values are <37, >27 and >17 respectively. The total score of one dimension exceeded the threshold value for mild empathy fatigue, two dimensions exceeded the threshold value for moderate compassion fatigue and all three dimensions exceeded the threshold value for high compassion fatigue. In this study, the sum of the scores of the two dimensions was used as the compassion fatigue score. The total Cronbach's alpha coefficient of the scale in this study was 0.821, the Cronbach's alpha coefficient of compassion fatigue was 0.820 and the Cronbach's alpha coefficient of compassion satisfaction was 0.882. 2.2.3 Maslach Burnout Inventory General Survey The MBI‐GS scale formulated was used, which includes 15 items (Maslach et al., ). Scores range from ‘never (0)’ to ‘very frequently (6)’. The scale is divided into three dimensions: cynicism, emotional exhaustion and lack of professional efficacy. Cynicism and emotional exhaustion are positive scores, that is, the higher the score, the more serious the degree of job burnout. However, the lack of professional efficacy dimension is scored in reverse, that is, the lower the score, the more obvious the lack of professional efficacy. And the sum of the scores of the two dimensions of cynicism and emotional exhaustion was used as the job burnout score. The total Cronbach's alpha coefficient of the scale in this study was 0.885, the Cronbach's alpha coefficient of the total score of the two dimensions was 0.943 and the Cronbach's alpha coefficient of the low achievement dimension was 0.902. 2.2.4 Emotional Labour Scale The Chinese Emotional Labour Scale for Nurses compiled by Grandey was used, which has sub‐categories for surface acting (seven items), emotional expression requirements (four items) and deep acting (three items). Each item was measured using a 6‐point Likert scale from 1 point (‘strongly disagree’) to 6 points (‘strongly agree’). The total score ranges from 14 to 84, with higher scores indicating higher levels of emotional labour. In this study, the total Cronbach's α coefficient of the scale was 0.870. 2.2.5 Social Support Rate Scale The SSRS was originally developed by Xiao Shuiyuan (Yu et al., ), including subjective support, objective support and support utilization, with a total of 10 entries, of which entries 1–4 and 8–10 are single‐choice questions, each entry has four options, and the first, second, third and fourth answers are scored 1, 2, 3 and 4 respectively; entry 5 has five options, A, B, C, D and E, and each option is scored from ‘none’ to ‘fully support’. Each option from ‘none’ to ‘fully support’ will be scored from 1 to 4 points, and the score of the entry will be the sum of the scores of each option; entries 6 and 7 will be scored 0 points if you answer ‘no source’, and 0 points if you choose from ‘the following sources’. If you choose from the ‘following sources’, you will be given several points. The total score of the scale ranged from 12 to 66, and the higher the total score, the more social support was received. The total Cronbach's alpha coefficient for the scale in this study was 0.815. Socio‐demographic characteristics Self‐designed after a pre‐review of the literature. This includes age, marital status, the only‐child, number of children, education level, work experience, professional title, employment status, night shift, average weekly hours and physical condition. Chinese version of the Compassion Fatigue Scale The Professional Quality of Life Scale (ProQOL) was revised by Stamm to form the Chinese version of the Compassion Fatigue Scale, which was used in this study. The scale includes three dimensions: compassion satisfaction, burnout and secondary traumatic stress, each with 10 entries, for a total of 30 entries. The scale is based on Likert 5‐point scale, with the frequency of occurrence ranging from ‘none’ to ‘always’, and the reverse scoring method is used for items 14, 15, 17 and 29. The total score for each of the three dimensions is 50, and the threshold values are <37, >27 and >17 respectively. The total score of one dimension exceeded the threshold value for mild empathy fatigue, two dimensions exceeded the threshold value for moderate compassion fatigue and all three dimensions exceeded the threshold value for high compassion fatigue. In this study, the sum of the scores of the two dimensions was used as the compassion fatigue score. The total Cronbach's alpha coefficient of the scale in this study was 0.821, the Cronbach's alpha coefficient of compassion fatigue was 0.820 and the Cronbach's alpha coefficient of compassion satisfaction was 0.882. Maslach Burnout Inventory General Survey The MBI‐GS scale formulated was used, which includes 15 items (Maslach et al., ). Scores range from ‘never (0)’ to ‘very frequently (6)’. The scale is divided into three dimensions: cynicism, emotional exhaustion and lack of professional efficacy. Cynicism and emotional exhaustion are positive scores, that is, the higher the score, the more serious the degree of job burnout. However, the lack of professional efficacy dimension is scored in reverse, that is, the lower the score, the more obvious the lack of professional efficacy. And the sum of the scores of the two dimensions of cynicism and emotional exhaustion was used as the job burnout score. The total Cronbach's alpha coefficient of the scale in this study was 0.885, the Cronbach's alpha coefficient of the total score of the two dimensions was 0.943 and the Cronbach's alpha coefficient of the low achievement dimension was 0.902. Emotional Labour Scale The Chinese Emotional Labour Scale for Nurses compiled by Grandey was used, which has sub‐categories for surface acting (seven items), emotional expression requirements (four items) and deep acting (three items). Each item was measured using a 6‐point Likert scale from 1 point (‘strongly disagree’) to 6 points (‘strongly agree’). The total score ranges from 14 to 84, with higher scores indicating higher levels of emotional labour. In this study, the total Cronbach's α coefficient of the scale was 0.870. Social Support Rate Scale The SSRS was originally developed by Xiao Shuiyuan (Yu et al., ), including subjective support, objective support and support utilization, with a total of 10 entries, of which entries 1–4 and 8–10 are single‐choice questions, each entry has four options, and the first, second, third and fourth answers are scored 1, 2, 3 and 4 respectively; entry 5 has five options, A, B, C, D and E, and each option is scored from ‘none’ to ‘fully support’. Each option from ‘none’ to ‘fully support’ will be scored from 1 to 4 points, and the score of the entry will be the sum of the scores of each option; entries 6 and 7 will be scored 0 points if you answer ‘no source’, and 0 points if you choose from ‘the following sources’. If you choose from the ‘following sources’, you will be given several points. The total score of the scale ranged from 12 to 66, and the higher the total score, the more social support was received. The total Cronbach's alpha coefficient for the scale in this study was 0.815. Sampling and recruitment This is a cross‐sectional study, using convenience sampling, in which obstetrics and gynaecology nurses from January to February 2022 from five tertiary care hospitals in ‘XX’ were selected for recruitment. We collected data through a mobile phone questionnaire star mini programme. After the questionnaire was created, the mini programme generated a two‐dimensional code, and the investigators asked participants to carefully review the informed consent form and then fill out the questionnaire anonymously. Sample size and power Sample size calculation formula: N = [( t α/2 + t β ) S / δ ] 2 . Interpretation: α = 0.05, β = 0.10, power (1− β ) = 0.90. t α,∞ = t 0.05, ∞ = 1.96; t β,∞ = t 0.10, ∞ = 1.645. S is the standard deviation obtained from the pre‐experiment. δ is the allowable error, which is set by 0.25 times or 0.50 times the standard deviation according to the literature for cases where the allowable error level is not given in a professional sense. N is the sample size, and 208 samples are obtained by calculation. If a 20% error rate is set, 250 are obtained. Quality appraisal Design: The study participants were selected according to the inclusion and exclusion criteria, exclusion bias was controlled, the purpose of the study was informed and consent was sought from the study participants to ensure the quality of the survey. Implementation: A uniform guideline was used to inform the survey about the entries of the questionnaire and the precautions for filling it out, so that the study participants could obtain cooperation. If there are any questions, the researcher or investigator promptly answers them and provides objective guidance to fill them out, requiring the survey participants to fill them out anonymously and independently, so as to control confounding bias. Data collation and analysis: After data collection, the investigator checked and accepted the returned questionnaires one by one, eliminating invalid questionnaires such as missing items ≥5%, misfilled, regular responses and identical questionnaires. The data entry was done by two‐person double‐computer entry method, and the data were compared item by item to ensure the accuracy of the data before statistical analysis. According to the nature of the variables and the purpose of the study, appropriate statistical analysis methods were selected to ensure the reliability of the study results. Population and sample There are 11 public hospitals in ‘XX’, of which five tertiary hospitals containing obstetrics and gynaecology departments (including four grade A hospitals and one grade B hospital), with an estimated overall number of nurses 444. In this study, obstetrics and gynaecology nurses in public tertiary hospitals containing obstetrics and gynaecology departments in ‘XX’ area were studied as a whole, and a total of 329 cases were investigated, with a valid sample of 311 cases. A convenience sampling method was used, and according to the formula, the minimum sample size was 250, so this sample of 311 cases could represent the obstetrics and gynaecology nurses in the whole ‘XX’ tertiary hospitals. Inclusion and/or exclusion criteria The inclusion criteria were as follows: (1) working registered nurses (midwives should hold a maternal and child health certificate); (2) more than 1 year of work experience. Intern nurses, nurses who were studying, nurses on rotation or nurses who were on leave for various reasons during the survey period were excluded from the study. Data analysis The data were checked by Excel 2019 and analysed by SPSS 24.0. Categorical variables were expressed as frequency and percentage, continuous variables were described by mean ± standard deviation. Demographic data were analysed by univariate analysis, including independent samples T test, one‐way ANOVA test and Kruskal–Wallis test. Pearson's correlation analysis was used to access the relationships between the two variables and Spearman's correlation analysis was used when the data did not conform to normal distribution. The influencing factors of variables were evaluated by stepwise multiple linear regression analysis. Harman's single factor analysis was performed to test the degree of variation. Meanwhile, model 4 and model 8 in the Process macro of SPSS software were conducted to analyse the mediating effect, a level of p < 0.05 was accepted as statistically significant difference. Bootstrap procedure (5000 duplicate samples) was performed to test the significance of the mediating effect and 95% confidence interval (CI) without zero indicates a significant indirect effect. Ethical considerations This study was approved by the Ethics Committee of ‘XX’ (REDACTED). All participants provided informed consent and voluntarily participated in the study, which was conducted anonymously. Their information was confidential. All information collected was kept by the investigator, and only the investigator had access to the survey information. All methods used in this study were in accordance with the principles of the Institutional Research Committee and the Declaration of Helsinki. RESULTS 3.1 Current situation of compassion fatigue and compassion satisfaction in gynaecology and obstetrics nurses with different characteristics In this study, 311 valid questionnaires were returned, with an effective rate of 94.5%. There were 75 (24.12%) normal and mild compassion fatigue, 148 (47.59%) moderate and 88 (28.30%) high compassion fatigue among obstetrics and gynaecology nurses; there were 42 (13.50%) low compassion satisfaction, 248 (79.74%) moderate and 21 (6.75%) high compassion satisfaction among obstetrics and gynaecology nurses. The analysis of general information of obstetrics and gynaecology nurses is shown in Table . 3.2 Survey respondents' scores on each scale Table showed that obstetrics and gynaecology nurses had moderate to high levels of compassion fatigue and moderate level of compassion satisfaction. And of the three dimensions of emotional labour, the surface acting played the highest score and dominates. 3.3 Correlational analysis From Table , compassion satisfaction was negatively associated with compassion fatigue ( p < 0.01); emotional exhaustion, cynicism, lack of professional efficacy and emotional labour were positively associated with compassion fatigue ( p < 0.01) and social support was negatively associated with compassion fatigue ( p < 0.01). 3.4 Stepwise multiple linear regression analysis of compassion fatigue and compassion satisfaction of nurses in obstetrics and gynaecology A stepwise multiple linear regression analysis was conducted with compassion satisfaction and compassion fatigue as dependent variables, and meaningful general demographic data in univariate analysis, cynicism score, emotional exhaustion score, lack of professional efficacy score, emotional labour score and social support score as independent variables. According to the results (Table ), significant predictors of compassion satisfaction were lack of professional efficacy, cynicism, social support, work experience, employment status and night shift ( p < 0.01); significant predictors of compassion fatigue were physical condition, number of children, emotional labour, lack of professional efficacy, emotional exhaustion and the none‐only‐child ( p < 0.05). 3.5 Common method deviation test Since the data for this study were obtained from self‐report, common method bias may exist. We used Harman's single factor method to test deviation. Results showed 15 factors with characteristic root greater than ‘1’ and the variance contribution rate of the first factor without rotation was 22.86%, indicating that there was no serious common method deviation in this study. 3.6 Mediating effect analysis According to conservation of resources theory, when individuals have insufficient internal resources, they will look for supportive resources in the work environment to supplement the lost internal resources. The social support perceived by individuals is a typical supportive resource, which helps individuals regulate the relationship between stress and physical and mental health, and it has a facilitating effect on the formation of psychological resources, thus helping to alleviate compassion fatigue. Therefore, this study used social support as a mediating variable and confirmed the mediating role of social support between lack of professional efficacy and compassion fatigue using Model 4 in the Process macro. Results of the mediation effect analysis have been presented in Table and Figure . The total effect of lack of professional efficacy on compassion fatigue was significant ( ß = 0.147, 95% CI [0.042, 0.252]); the direct effect of lack of professional efficacy on social support and social support on compassion fatigue were also significant. Furthermore, the direct effect of lack of professional efficacy on compassion fatigue was significant after adjusting for social support ( ß = 0.112, 95% CI [0.006, 0.219]), suggesting that social support partially mediates the relationship between lack of professional efficacy and compassion fatigue. That is, social support can effectively mitigate the exacerbation of lack of professional efficacy on compassion fatigue. Current situation of compassion fatigue and compassion satisfaction in gynaecology and obstetrics nurses with different characteristics In this study, 311 valid questionnaires were returned, with an effective rate of 94.5%. There were 75 (24.12%) normal and mild compassion fatigue, 148 (47.59%) moderate and 88 (28.30%) high compassion fatigue among obstetrics and gynaecology nurses; there were 42 (13.50%) low compassion satisfaction, 248 (79.74%) moderate and 21 (6.75%) high compassion satisfaction among obstetrics and gynaecology nurses. The analysis of general information of obstetrics and gynaecology nurses is shown in Table . Survey respondents' scores on each scale Table showed that obstetrics and gynaecology nurses had moderate to high levels of compassion fatigue and moderate level of compassion satisfaction. And of the three dimensions of emotional labour, the surface acting played the highest score and dominates. Correlational analysis From Table , compassion satisfaction was negatively associated with compassion fatigue ( p < 0.01); emotional exhaustion, cynicism, lack of professional efficacy and emotional labour were positively associated with compassion fatigue ( p < 0.01) and social support was negatively associated with compassion fatigue ( p < 0.01). Stepwise multiple linear regression analysis of compassion fatigue and compassion satisfaction of nurses in obstetrics and gynaecology A stepwise multiple linear regression analysis was conducted with compassion satisfaction and compassion fatigue as dependent variables, and meaningful general demographic data in univariate analysis, cynicism score, emotional exhaustion score, lack of professional efficacy score, emotional labour score and social support score as independent variables. According to the results (Table ), significant predictors of compassion satisfaction were lack of professional efficacy, cynicism, social support, work experience, employment status and night shift ( p < 0.01); significant predictors of compassion fatigue were physical condition, number of children, emotional labour, lack of professional efficacy, emotional exhaustion and the none‐only‐child ( p < 0.05). Common method deviation test Since the data for this study were obtained from self‐report, common method bias may exist. We used Harman's single factor method to test deviation. Results showed 15 factors with characteristic root greater than ‘1’ and the variance contribution rate of the first factor without rotation was 22.86%, indicating that there was no serious common method deviation in this study. Mediating effect analysis According to conservation of resources theory, when individuals have insufficient internal resources, they will look for supportive resources in the work environment to supplement the lost internal resources. The social support perceived by individuals is a typical supportive resource, which helps individuals regulate the relationship between stress and physical and mental health, and it has a facilitating effect on the formation of psychological resources, thus helping to alleviate compassion fatigue. Therefore, this study used social support as a mediating variable and confirmed the mediating role of social support between lack of professional efficacy and compassion fatigue using Model 4 in the Process macro. Results of the mediation effect analysis have been presented in Table and Figure . The total effect of lack of professional efficacy on compassion fatigue was significant ( ß = 0.147, 95% CI [0.042, 0.252]); the direct effect of lack of professional efficacy on social support and social support on compassion fatigue were also significant. Furthermore, the direct effect of lack of professional efficacy on compassion fatigue was significant after adjusting for social support ( ß = 0.112, 95% CI [0.006, 0.219]), suggesting that social support partially mediates the relationship between lack of professional efficacy and compassion fatigue. That is, social support can effectively mitigate the exacerbation of lack of professional efficacy on compassion fatigue. DISCUSSION In our study, we surveyed obstetrics and gynaecology nurses in different tertiary hospitals in ‘XX’ to find out the compassion fatigue and compassion satisfaction of obstetrics and gynaecology nurses. Also, compassion fatigue was determined by job burnout and secondary traumatic stress, as these are the variables used in the survey instrument. According to our data, 75.88% of obstetrics and gynaecology nurses reported moderate to high levels of compassion fatigue. Only 6.75% of obstetrics and gynaecology nurses reported high levels of compassion satisfaction. Compared to oncology nurses (Xie et al., ), emergency nurses (O'Callaghan et al., ) and haematology cancer nurses (Chen et al., ), obstetrics and gynaecology nurses in this study had lower levels of compassion satisfaction. And the level of compassion fatigue among nurses in the context of maternal and perinatal deaths was comparable to this study (Mashego et al., ). All of the above studies used ProQOL Version 5, as did our study. These differences may be related to differences in personal environment and work environment (Stamm, ). According to our findings, the nurse's personal environment is a factor that influences compassion fatigue, including physical condition and the number of children. This article showed that the poorer the physical condition of nurses, the higher the level of compassion fatigue, which was consistent with the previous study (Qu et al., ). The body is the source of energy, and when individuals are in poor health, their resource balance is disrupted, their compassion decreases and compassion fatigue occurs in severe cases (Hobfoll & Wells, ). In addition, the number of children were associated with compassion fatigue. The number of children of nurses is an important factor affecting their quality of life and work (Jarrad & Hammad, ). In this study, 87.5% of the nurses were in their young adulthood, taking on various roles as mothers, daughters and wives in their lives, making family–work conflicts inevitable. Given this, we hypothesized that when nurses are faced with a larger number of people to care for, work and family are prone to conflict, which constitutes a risk factor for compassion fatigue. The nurse's work environment is a factor that influences compassion satisfaction, including work experience, and night shift. First, compassion satisfaction was higher among nurses with <4 years of experience and more than 16 years of experience. Nurses with <4 years of experience are new to the profession, have light family responsibilities and full work ambitions; nurses with more than 16 years of experience are more competent and mature in their thinking (Alharbi et al., ). In contrast, the lower compassion satisfaction of nurses with 4–16 years may be related to their inability to reconcile family and work. What is more, night shift work was associated with high levels of burnout and secondary traumatic stress. A study of Chinese midwives working in the delivery room showed that night shift work increase their level of compassion fatigue (Qu et al., ). Other study found that night shift work resulted in lower levels of physical and mental health in obstetrics and gynaecology nurses (Coetzee & Klopper, ). Night shift work has an irregular schedule, leading to the onset of lower compassion satisfaction. In response to the above factors, nursing managers should use flexible scheduling and pay more attention to the emotional status of nurses with 4–16 years of experience. As revealed in our study, compassion fatigue was higher in nurses with high emotional labour and compassion satisfaction was higher in nurses with high social support. Emotional labour is work that requires individuals to control their emotions in order to achieve desired outcomes, and is usually associated with negative outcomes (Hwang et al., ). Continuous and regular emotional labour can lead to burnout or compassion fatigue and reduce the quality of life and work‐related care of nurses (Kwak et al., ). The results of our study also showed such results. The negative effects of nurses' emotional labour are an important factor affecting patient service delivery (Kim, ). Similar to the results of some studies (Hunsaker et al., ; Yu et al., ), we found that social support was a protective factor for compassion satisfaction. Social support facilitates physical and mental health, and promotes the formation of psychological resources, thus contributing to the improvement of compassion satisfaction (Park et al., ). Studies have shown that social support can reduce the occurrence of compassion fatigue in nurses and that recognition and support from leaders and colleagues were the main sources of social support that can effectively improve nurses' compassion (Kelly & Lefton, ). Alternatively, a good work environment (e.g. peer or social support, recognition of professional values, manageable workload) increased nurses' job satisfaction, which led them to be more proactive in their work and increased compassion satisfaction (Qu et al., ). In addition, lack of professional efficacy was a predictor of both compassion satisfaction and compassion fatigue. It has been found that lack of professional efficacy led to high compassion fatigue and low compassion satisfaction, and can also affect an individual's productivity and sense of accomplishment at work (Fan & Lin, ; Koutra et al., ). Specifically, individuals who lack professional efficacy have a lower recognition of themselves and are always in a negative state, resulting in lower compassion ability. We were surprised to find that lack of professional efficacy can influence compassion fatigue and compassion satisfaction through social support. Studies have demonstrated that lack of professional efficacy negatively predicted social support, while social support was a protective factor for compassion satisfaction against compassion fatigue, supporting existing theoretical perspectives and empirical studies (Hunsaker et al., ; Ye et al., ). For individuals, social support was an important form of resource that provided nurses with emotional support and affirmation of self‐worth (Park et al., ). According to our mediation analysis, social support is a critical intermediary between lack of professional efficacy and compassion fatigue/compassion satisfaction. Social support can buffer and compensate for the loss of resources due to lack of professional efficacy, and reduce the incidence of compassion fatigue, and increase nurses' compassion satisfaction. In summary, social support acted as a ‘bridge’ between lack of professional efficacy and compassion fatigue/compassion satisfaction. Therefore, nursing managers can provide an external resource (e.g. social support) for obstetrics and gynaecology nurses to better retain a compassionate and dedicated obstetrics and gynaecology nurse workforce based on the findings. 4.1 Strength and limitations of the work The research topic is relatively new. Compassion fatigue among obstetrics and gynaecology nurses in ‘XX’ provincial tertiary hospitals is hardly a focus; the impact of ‘XX’ comprehensive two‐child policy on compassion fatigue among obstetrics and gynaecology nurses also opens up a new area of research; this may raise concerns about the occupational health of obstetrics and gynaecology nurses in ‘XX’ and motivate the government to increase the training of related professionals. Limitations of this study include the cross‐sectional survey was conducted in XX and most participants were from tertiary care hospitals, which may limit the generalizability of the results; this subject group captured the views of participants at a specific time without follow‐up and the results only reflect what participants really thought at that time; self‐report bias is an inherent limitation of the study design. Finally, due to the lack of research in this area, this article was only a preliminary study of the current situation, with the hope of conducting more in‐depth research, such as interviews and consultations with professionals. 4.2 Recommendations for further research It is recommended that subsequent studies will focus on obstetrics and gynaecology nurses who have been working for 4–16 years and may incorporate semi‐structure interviews to further explore in depth more factors influencing compassion fatigue in obstetrics and gynaecology nurses; this study presents only a simple mediating model with moderation, and there are more potential mediators and moderators between these two variables that are worth exploring; appropriate interventions may also be developed based on the results obtained in this study. Strength and limitations of the work The research topic is relatively new. Compassion fatigue among obstetrics and gynaecology nurses in ‘XX’ provincial tertiary hospitals is hardly a focus; the impact of ‘XX’ comprehensive two‐child policy on compassion fatigue among obstetrics and gynaecology nurses also opens up a new area of research; this may raise concerns about the occupational health of obstetrics and gynaecology nurses in ‘XX’ and motivate the government to increase the training of related professionals. Limitations of this study include the cross‐sectional survey was conducted in XX and most participants were from tertiary care hospitals, which may limit the generalizability of the results; this subject group captured the views of participants at a specific time without follow‐up and the results only reflect what participants really thought at that time; self‐report bias is an inherent limitation of the study design. Finally, due to the lack of research in this area, this article was only a preliminary study of the current situation, with the hope of conducting more in‐depth research, such as interviews and consultations with professionals. Recommendations for further research It is recommended that subsequent studies will focus on obstetrics and gynaecology nurses who have been working for 4–16 years and may incorporate semi‐structure interviews to further explore in depth more factors influencing compassion fatigue in obstetrics and gynaecology nurses; this study presents only a simple mediating model with moderation, and there are more potential mediators and moderators between these two variables that are worth exploring; appropriate interventions may also be developed based on the results obtained in this study. CONCLUSION The study found that 75.88% of obstetrics and gynaecology nurses had moderate to high levels of compassion fatigue. Based on the results, it was found that among the personal factors of obstetrics and gynaecology nurses, physical condition and the number of children raised were influential factors closely related to compassion fatigue. Secondly, nurses with 4–16 years of work experience among the work environment factors were more likely to experience low satisfaction. What is more, nurses who lacked professional efficacy were more likely to experience compassion fatigue, and the mediated analysis revealed that compassion fatigue could be effectively reduced by obtaining social support. In response to these findings, nursing managers are advised to focus on caring for obstetrics and gynaecology nurses who are in poor health or have more children; to provide appropriate interventions to reduce the incidence of compassion fatigue for the nurses who have worked for 4–16 years and to provide more social support for nurses to achieve more satisfaction and happiness in their work. In this study, after identifying the influencing factors of compassion fatigue, we will develop appropriate interventions, such as positive stress reduction therapy, reflective debriefing and group drawing, to effectively prevent and reduce compassion fatigue among obstetrics and gynaecology nurses. Jia Wang and Mei Su contributed to the conceptualization of the study, performed the analysis, wrote the manuscript; Wenzhong Chang, Yuchong Hu and Peijuan Tang contributed significantly to investigation and project administration; Yujia Ma assisted with data curation; Jiaxin Sun contributed to the conceptualization of the study and reviewed the manuscript. All authors have read and approved the manuscript. This study was supported by the Inner Mongolia Science and Technology Planning Project Fund (2020GG011). The authors declare that they have no conflict of interests. |
Loss of the Novel Myelin Protein CMTM5 in Multiple Sclerosis Lesions and Its Involvement in Oligodendroglial Stress Responses | 5c4dbfa4-356a-45dd-a00b-bb7ad3defc5b | 10453064 | Forensic Medicine[mh] | Multiple sclerosis (MS) is one of the most common demyelinating disorders affecting the central nervous system (CNS). Demyelination and axonal degeneration are pathological hallmarks found in MS lesions, leading to a heterogeneous degree of neurological deficits in individual patients . At the pathophysiological level, demyelination (i.e., loss of myelin) may be triggered by inflammatory attacks on myelin by recruited peripheral immune cells as well as resident microglia. At the lesion sites, demyelinated axons are exposed to cytotoxic immune cells, among other factors, which may induce axonal damage. Non-inflammatory aspects such as the release of reactive oxygen species and glutamate, disruption of mitochondrial function, and accumulation of calcium may also contribute to the cytodegeneration of oligodendrocytes and progressive axonal degeneration . Although it is known that an intact myelin structure has a predominant role in maintaining axon–myelin unit homeostasis, detailed characteristics of myelin components and their respective roles in maintaining axon–myelin function are still incompletely understood. In recent years, members of the chemokine-like factor-like MARVEL-transmembrane domain-containing family proteins (CMTM) were found to be expressed as novel myelin proteins in the central and peripheral nervous system . Under physiological conditions, CMTM6 is expressed on the adaxonal surface of Schwann cells and regulates axon diameters in the peripheral nervous system . CMTM5, another member of the CMTM protein family, is highly enriched in mature oligodendrocytes of the CNS, and its depletion leads to impaired axonal integrity without affecting myelin biogenesis per se . The expression and function of CMTM5 under pathological conditions such as in MS is, as yet, unclear. In this study, we investigated the expression of CMTM5 in postmortem MS tissues as well as in different MS-related animal models. Functional studies using CRISPR interference were performed to study the relevance of Cmtm5 expression in relation to cell responsiveness to endoplasmic reticulum (ER) stress.
2.1. Animals For this study, 10-week-old C57BL/6 female and male mice were purchased from Janvier Labs (Le Genest-Saint-Isle, France). All experimental procedures were approved by the review boards for the care of animal subjects of the district government (District government of Mecklenburg-Western Pomerania, reference number 7221.3-1-001/19; District government of Bavaria, reference number 55.2-154-2532-73-15, Germany). At maximum, five animals were housed per cage (435 cm 2 area). Animals were kept under standard laboratory conditions (12 h light/dark cycle, controlled temperature 23 °C ± 2 °C and 50% ± 5% humidity) with access to food and water ad libitum. The mice were allowed to acclimatize to the environment for at least one week prior to the beginning of the experiment. Body weights were controlled weekly. The mice were randomly assigned to the experimental group or control group, respectively (see ). 2.2. Cuprizone Intoxication In short, mice in the cuprizone experimental group were fed a diet containing 0.25% cuprizone (bis(cyclohexanone) oxaldihydrazone; Sigma-Aldrich, St. Louis, MO, USA) mixed into standard rodent chow (V1530–0; Ssniff, Soest, Germany). Cuprizone intoxication was performed as previously established by our group . The cuprizone was weighed using precision scales and then mechanically mixed into ground standard rodent chow using a blender kitchen machine (Kult X, WMF Group, Geislingen an der Steige, Germany). The chow was mixed at low speed and under manual agitation of the entire machine for 1 min and was provided daily in two separate plastic Petri dishes per cage. The following exclusion criteria, which were checked daily, were applied to the mice in the animal experiment: severe weight loss (>10% within 24 h), severe behavioral disturbances (decreased locomotion, stupor, convulsions), or infections. During this study, no animal met the exclusion criteria. 2.3. Experimental Autoimmune Encephalomyelitis Induction and Disease Scoring For the induction of experimental autoimmune encephalomyelitis (EAE), 10–12-week-old C57BL/6 female mice were subcutaneously immunized at two sites (0.1 mL/site, upper and lower back) with an emulsion of 1 mg/mL myelin oligodendrocyte glycoprotein (MOG 35–55 , peptide sequence MEVGWYRSPFSRVVHLYRNGK) dissolved in complete Freund’s adjuvant. After MOG 35–55 immunization, each mouse was intraperitoneally injected with 300 ng pertussis toxin in 0.1 mL PBS twice, first on the day of immunization and then again the following day (Hooke Laboratories, Inc., Lawrence, KS, USA) as published previously . To reduce stress after injection, mice were kept in their “home cage” without excessive noise or vibration. The severity of EAE development was scored as follows: 1: the entire tail falls over the observer’s finger when the mouse is picked up by the base of the tail; 2: the legs are not spread but are held close together when the mouse is picked up by the base of the tail, and the mice show a clearly visible wobbly gait; 3: the tail is flaccid, and the mice show complete paralysis of the hind legs (a score of 3.5 is assigned if the mouse is unable to raise itself when placed on its side); 4: the tail is flaccid, and the mice show complete paralysis of the hind legs and partial paralysis of the front legs, and the mouse barely moves in the cage but appears to be awake and eating; 5: the mouse is euthanized due to severe paralysis. Endpoints of EAE-mice were defined as a score of 4 for two consecutive days. Additional handlings/treatments were adopted according to the guideline for better implementation of the “three Rs” (replacement, reduction and refinement) to reduce suffering in EAE experiments . When a mouse reached a score of 2.5, both control and EAE cages were supplied with food pellets and HydroGel (ClearH2O, Westbrook, ME, USA) on the floor of each cage for easier access. After an animal reached a score of 3, it received a subcutaneous injection of Ringer’s solution for fluidic supplement. In the presented EAE cohort, mice were sacrificed at 17 days post-immunization after the first mouse developed a score 4 and two mice developed a score of 3 (see ). 2.4. Tissue Preparation Mice were deeply anaesthetized with ketamine (100 mg/kg i.p.) and xylazine (10 mg/kg i.p.) and then transcardially perfused with 20 mL of ice-cold PBS followed by a 3.7% formaldehyde solution (pH = 7.4). All immunohistochemical analyses were performed using paraffin-embedded 5 µm-thick coronal brain sections. For gene expression studies, the corpus callosum was manually dissected after perfusion with PBS only, immediately frozen and kept in liquid nitrogen until further processing. 2.5. Multiple Sclerosis Tissues Paraffin-embedded post-mortem brain tissues were obtained via a rapid autopsy protocol from donors with progressive MS in collaboration with the Netherlands Brain Bank, Amsterdam. The study was approved by the Medical Ethical Committee of the Amsterdam UMC, the autopsy regimen and the ethical and legal declaration of the Netherlands Brain Bank were followed (coordinator Prof. I. Huitiga, https://www.brainbank.nl/about-us/ accessed on 14 July 2023), and all donors or their relatives gave written informed consent for the use of brain tissue and clinical information for research purposes. In total, 3 chronically active lesions from 3 donors and 4 non-MS control patients were included in this study (see ). The mean age of the patients at death was 64.4 ± 18.3 years (mean ± standard deviation). The mean post-mortem delay was 7.8 ± 2.2 h. Lesions were classified via consecutive immunostaining for myelin proteolipid protein (PLP) and human leukocyte antigen [HLA]-DR (clone LN3), as previously reported . 2.6. Histological and Immunohistochemical Evaluation For the histological evaluation, luxol fast blue (LFB)/periodic acid-Schiff (PAS) stains were performed to evaluate inflammatory demyelination within the white matter following standard protocols . For the immunohistochemical studies, sections were deparaffinized, rehydrated, and if necessary, antigens were unmasked by cooking in Citrate (pH 6.0) buffer or tris(hydroxymethyl)aminomethane/ethylenediamine tetraacetic acid (Tris/EDTA) buffer (pH 9.0). After washing in PBS, unspecific binding sites were blocked by incubating the slides in the serum of the species in which the secondary antibody was raised for 1 h. Then, the sections were incubated overnight (at 4 °C) with the primary antibodies (see ) diluted in 5% normal serum. Slides were then exposed to a PBS solution containing 0.35% hydrogen peroxide for 30 min in order to saturate endogenous peroxidase activities. After washing in PBS, the sections were incubated with biotinylated secondary antibodies for 1 h and then with a peroxidase-coupled avidin–biotin complex (ABC kit; Vector Laboratories, Peterborough, UK). The antigenic sites were then detected by incubation with 3,3′-diaminobenzidine (DAKO, Hamburg, Germany) for 10 min. Appropriate negative controls (omission of primary antibodies) were performed in parallel to ensure the specificity of the staining. Staining intensities were quantified via densitometrical analyses. All histological analyses were performed with coronal sections using a Leica DM6 B microscope equipped with the Leica DMC 6200 camera. Therefore, binary-converted images were evaluated within the ROI using ImageJ (NIH, Bethesda, MD ). A value of 100% represents maximum and 0% represents minimum staining intensity. The results are shown as staining intensity in (%) area of the entire ROI. 2.7. Generation of Cmtm5 Knockdown Oli-Neu CRISPRi Cell Line An Oli-neu cell line was lentivirally integrated with the plasmid cassette pMH0006 (pHR-SFFV-dCas9-BFP-KRAB) to generate the Oli-neu CRIPSRi cell line for further genetic engineering. pMH0006 was a gift from Martin Kampmann and Jonathan Weissman (Addgene plasmid # 135448; http://n2t.net/addgene:135448 accessed on 14 July 2023) . The Oli-neu CRISPRi cell line was purified using fluorescence-activated cell sorting of Blue Fluorescent Protein (BFP). To generate a stable Cmtm5 knockdown cell line, sgRNA targeting Cmtm5 (sgRNA sequence: GAGCTGGGTGAAGCCCATCC) was cloned into the CRISPRia-v2 plasmid. CRISPRia-v2 was a gift from Jonathan Weissman (Addgene plasmid # 84832; http://n2t.net/addgene:84832 accessed on 14 July 2023) . 2.8. Real-Time RT-PCR The primer sequences and individual annealing temperatures are given in . Relative quantifications of gene expression were performed for each sample using an internal standard curve generated by pooling cDNA from all samples. In this study, 18S expression levels were used as an internal reference. Gel electrophoresis and melting curves of the PCR products were routinely performed to determine the specificity of the PCR reactions. To exclude contamination of the reagents with either RNA or DNA, appropriate negative controls were performed (i.e., omission of RNA or cDNA; melting curve analyses, gel electrophoresis of the PCR products). 2.9. Single-Cell RNA Sequencing Analysis Single-cell RNA (scRNA) sequencing data were obtained from public databases. Data from Wheeler et al. were accessed at GSE129609 . Data from Jäkel et al. were accessed at GSE118257 . We also downloaded the annotation data and UMAP coordination information from UCSC Cell Browser for Human scRNA sequencing analysis . The analysis pipelines were detailed and described in a previous study . Briefly, using Seurat , the expression matrices of samples were log-normalized, and doublets were removed. Canonical correlation analysis was performed to correct for batch effects and integrate different samples within each dataset. After integrating the data, principal component analysis was used to perform dimension reduction and clustering analysis. In all cases, the first 15 PCs were used. The cells were clustered using Louvain algorithm with a resolution parameter of 0.5. The MAST algorithm was used to conduct differential expression analysis for each cluster compared to all other cells. TSNE was used to visualize the data. 2.10. Statistical Analysis All data are given as the arithmetic means ± Standard error of the mean (SEM). Differences between groups were statistically analyzed using the software GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA). The Shapiro–Wilk test was applied to test for normal data distribution. The definite statistical tests applied for the different analyses are provided in the respective figure legends. p -values of ≤ 0.05 were considered to be statistically significant. The following symbols are used to indicate the level of significance: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, ns = not significant.
For this study, 10-week-old C57BL/6 female and male mice were purchased from Janvier Labs (Le Genest-Saint-Isle, France). All experimental procedures were approved by the review boards for the care of animal subjects of the district government (District government of Mecklenburg-Western Pomerania, reference number 7221.3-1-001/19; District government of Bavaria, reference number 55.2-154-2532-73-15, Germany). At maximum, five animals were housed per cage (435 cm 2 area). Animals were kept under standard laboratory conditions (12 h light/dark cycle, controlled temperature 23 °C ± 2 °C and 50% ± 5% humidity) with access to food and water ad libitum. The mice were allowed to acclimatize to the environment for at least one week prior to the beginning of the experiment. Body weights were controlled weekly. The mice were randomly assigned to the experimental group or control group, respectively (see ).
In short, mice in the cuprizone experimental group were fed a diet containing 0.25% cuprizone (bis(cyclohexanone) oxaldihydrazone; Sigma-Aldrich, St. Louis, MO, USA) mixed into standard rodent chow (V1530–0; Ssniff, Soest, Germany). Cuprizone intoxication was performed as previously established by our group . The cuprizone was weighed using precision scales and then mechanically mixed into ground standard rodent chow using a blender kitchen machine (Kult X, WMF Group, Geislingen an der Steige, Germany). The chow was mixed at low speed and under manual agitation of the entire machine for 1 min and was provided daily in two separate plastic Petri dishes per cage. The following exclusion criteria, which were checked daily, were applied to the mice in the animal experiment: severe weight loss (>10% within 24 h), severe behavioral disturbances (decreased locomotion, stupor, convulsions), or infections. During this study, no animal met the exclusion criteria.
For the induction of experimental autoimmune encephalomyelitis (EAE), 10–12-week-old C57BL/6 female mice were subcutaneously immunized at two sites (0.1 mL/site, upper and lower back) with an emulsion of 1 mg/mL myelin oligodendrocyte glycoprotein (MOG 35–55 , peptide sequence MEVGWYRSPFSRVVHLYRNGK) dissolved in complete Freund’s adjuvant. After MOG 35–55 immunization, each mouse was intraperitoneally injected with 300 ng pertussis toxin in 0.1 mL PBS twice, first on the day of immunization and then again the following day (Hooke Laboratories, Inc., Lawrence, KS, USA) as published previously . To reduce stress after injection, mice were kept in their “home cage” without excessive noise or vibration. The severity of EAE development was scored as follows: 1: the entire tail falls over the observer’s finger when the mouse is picked up by the base of the tail; 2: the legs are not spread but are held close together when the mouse is picked up by the base of the tail, and the mice show a clearly visible wobbly gait; 3: the tail is flaccid, and the mice show complete paralysis of the hind legs (a score of 3.5 is assigned if the mouse is unable to raise itself when placed on its side); 4: the tail is flaccid, and the mice show complete paralysis of the hind legs and partial paralysis of the front legs, and the mouse barely moves in the cage but appears to be awake and eating; 5: the mouse is euthanized due to severe paralysis. Endpoints of EAE-mice were defined as a score of 4 for two consecutive days. Additional handlings/treatments were adopted according to the guideline for better implementation of the “three Rs” (replacement, reduction and refinement) to reduce suffering in EAE experiments . When a mouse reached a score of 2.5, both control and EAE cages were supplied with food pellets and HydroGel (ClearH2O, Westbrook, ME, USA) on the floor of each cage for easier access. After an animal reached a score of 3, it received a subcutaneous injection of Ringer’s solution for fluidic supplement. In the presented EAE cohort, mice were sacrificed at 17 days post-immunization after the first mouse developed a score 4 and two mice developed a score of 3 (see ).
Mice were deeply anaesthetized with ketamine (100 mg/kg i.p.) and xylazine (10 mg/kg i.p.) and then transcardially perfused with 20 mL of ice-cold PBS followed by a 3.7% formaldehyde solution (pH = 7.4). All immunohistochemical analyses were performed using paraffin-embedded 5 µm-thick coronal brain sections. For gene expression studies, the corpus callosum was manually dissected after perfusion with PBS only, immediately frozen and kept in liquid nitrogen until further processing.
Paraffin-embedded post-mortem brain tissues were obtained via a rapid autopsy protocol from donors with progressive MS in collaboration with the Netherlands Brain Bank, Amsterdam. The study was approved by the Medical Ethical Committee of the Amsterdam UMC, the autopsy regimen and the ethical and legal declaration of the Netherlands Brain Bank were followed (coordinator Prof. I. Huitiga, https://www.brainbank.nl/about-us/ accessed on 14 July 2023), and all donors or their relatives gave written informed consent for the use of brain tissue and clinical information for research purposes. In total, 3 chronically active lesions from 3 donors and 4 non-MS control patients were included in this study (see ). The mean age of the patients at death was 64.4 ± 18.3 years (mean ± standard deviation). The mean post-mortem delay was 7.8 ± 2.2 h. Lesions were classified via consecutive immunostaining for myelin proteolipid protein (PLP) and human leukocyte antigen [HLA]-DR (clone LN3), as previously reported .
For the histological evaluation, luxol fast blue (LFB)/periodic acid-Schiff (PAS) stains were performed to evaluate inflammatory demyelination within the white matter following standard protocols . For the immunohistochemical studies, sections were deparaffinized, rehydrated, and if necessary, antigens were unmasked by cooking in Citrate (pH 6.0) buffer or tris(hydroxymethyl)aminomethane/ethylenediamine tetraacetic acid (Tris/EDTA) buffer (pH 9.0). After washing in PBS, unspecific binding sites were blocked by incubating the slides in the serum of the species in which the secondary antibody was raised for 1 h. Then, the sections were incubated overnight (at 4 °C) with the primary antibodies (see ) diluted in 5% normal serum. Slides were then exposed to a PBS solution containing 0.35% hydrogen peroxide for 30 min in order to saturate endogenous peroxidase activities. After washing in PBS, the sections were incubated with biotinylated secondary antibodies for 1 h and then with a peroxidase-coupled avidin–biotin complex (ABC kit; Vector Laboratories, Peterborough, UK). The antigenic sites were then detected by incubation with 3,3′-diaminobenzidine (DAKO, Hamburg, Germany) for 10 min. Appropriate negative controls (omission of primary antibodies) were performed in parallel to ensure the specificity of the staining. Staining intensities were quantified via densitometrical analyses. All histological analyses were performed with coronal sections using a Leica DM6 B microscope equipped with the Leica DMC 6200 camera. Therefore, binary-converted images were evaluated within the ROI using ImageJ (NIH, Bethesda, MD ). A value of 100% represents maximum and 0% represents minimum staining intensity. The results are shown as staining intensity in (%) area of the entire ROI.
An Oli-neu cell line was lentivirally integrated with the plasmid cassette pMH0006 (pHR-SFFV-dCas9-BFP-KRAB) to generate the Oli-neu CRIPSRi cell line for further genetic engineering. pMH0006 was a gift from Martin Kampmann and Jonathan Weissman (Addgene plasmid # 135448; http://n2t.net/addgene:135448 accessed on 14 July 2023) . The Oli-neu CRISPRi cell line was purified using fluorescence-activated cell sorting of Blue Fluorescent Protein (BFP). To generate a stable Cmtm5 knockdown cell line, sgRNA targeting Cmtm5 (sgRNA sequence: GAGCTGGGTGAAGCCCATCC) was cloned into the CRISPRia-v2 plasmid. CRISPRia-v2 was a gift from Jonathan Weissman (Addgene plasmid # 84832; http://n2t.net/addgene:84832 accessed on 14 July 2023) .
The primer sequences and individual annealing temperatures are given in . Relative quantifications of gene expression were performed for each sample using an internal standard curve generated by pooling cDNA from all samples. In this study, 18S expression levels were used as an internal reference. Gel electrophoresis and melting curves of the PCR products were routinely performed to determine the specificity of the PCR reactions. To exclude contamination of the reagents with either RNA or DNA, appropriate negative controls were performed (i.e., omission of RNA or cDNA; melting curve analyses, gel electrophoresis of the PCR products).
Single-cell RNA (scRNA) sequencing data were obtained from public databases. Data from Wheeler et al. were accessed at GSE129609 . Data from Jäkel et al. were accessed at GSE118257 . We also downloaded the annotation data and UMAP coordination information from UCSC Cell Browser for Human scRNA sequencing analysis . The analysis pipelines were detailed and described in a previous study . Briefly, using Seurat , the expression matrices of samples were log-normalized, and doublets were removed. Canonical correlation analysis was performed to correct for batch effects and integrate different samples within each dataset. After integrating the data, principal component analysis was used to perform dimension reduction and clustering analysis. In all cases, the first 15 PCs were used. The cells were clustered using Louvain algorithm with a resolution parameter of 0.5. The MAST algorithm was used to conduct differential expression analysis for each cluster compared to all other cells. TSNE was used to visualize the data.
All data are given as the arithmetic means ± Standard error of the mean (SEM). Differences between groups were statistically analyzed using the software GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA). The Shapiro–Wilk test was applied to test for normal data distribution. The definite statistical tests applied for the different analyses are provided in the respective figure legends. p -values of ≤ 0.05 were considered to be statistically significant. The following symbols are used to indicate the level of significance: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, ns = not significant.
3.1. Decreased Expression of CMTM5 Expression in the Cuprizone-Induced Animal Model of Demyelination It has been suggested that CMTM5 is highly enriched in oligodendrocytes during maturation . As a first step, we aimed to verify the expression of CMTM5 in an animal model of toxin-induced oligodendrocyte injury followed by demyelination. To this end, mice were continuously intoxicated with cuprizone for 1 week (1 wk), 3 weeks (3 wks) or 5 weeks (5 wks). Controls (Ctrl) were fed a normal diet throughout the entire experiment (i.e., animal cohort “Cup IHC/IF”, as described in the Materials and Methods Section). To verify the successful demyelination in the cuprizone model, coronal sections were processed for the analyses of anti-PLP labelling intensities. As shown in a, profound demyelination of the medial corpus callosum was observed after 5 weeks of cuprizone intoxication,( a: anti-PLP 81.28% ± 2.14% in Ctrl, 86.05% ± 2.01% at 1 wk Cup, 82.54% ± 2.30% at 3 wks Cup and 4.71% ± 0.67% at 5 wks Cup). Similar to the decrease in anti-PLP staining intensities during the course of cuprizone intoxication, the anti-CMTM5 staining intensities were found to be significantly reduced after 5 weeks in the cuprizone model ( a: anti-CMTM5 89.01% ± 0.76% in Ctrl, 84.65% ± 2.98% at 1 wk Cup, 83.33% ± 0.55% at 3 wks Cup and 34.15% ± 3.52% at 5 wks Cup). Of note, apart from widespread CMTM5-immunoreactivity morphologically resembling myelin sheaths, CMTM5-positive spheroids were observed in the medial corpus callosum of mice after 5 weeks of cuprizone intoxication ( ). To systematically evaluate the expression of CMTM proteins in the toxin-induced demyelination model, we analyzed the transcriptional expression of CMTM family genes using the RNA-seq dataset from control groups vs. 3 wks Cuprizone intoxicated groups (GSE213374 ). As shown in the heatmap ( b), only the expression of Cmtm5, but not other CMTM members, was reduced during the curprizone-induced demyelination process. To further confirm the transcriptome results, we analyzed the expression of Plp and Cmtm5 by real-time RT-PCR ( c: Plp, Control 100.00 ± 11.84% vs. 4 d Cup 6.817 ± 0.7011%; Cmtm5, Control 100.00 ± 7.068% vs. 4 d Cup 14.15 ± 1.630%; d: Plp, Control 100.00 ± 6.909% vs. 3 wks Cup 11.88 ± 2.144%; Cmtm5, Control 99.99 ± 6.980% vs. 4 d Cup 65.30 ± 6.252%) using a set of independent biological samples (i.e., animal cohort “4 d Cup qPCR”, “3 wks Cup qPCR” as described in the Materials and Methods Section). Combined, these data demonstrate that CMTM5, as a novel myelin protein, is reduced after toxin-induced demyelination at both the mRNA and protein level. 3.2. CMTM5-Expression Is Reduced in the Inflammatory EAE Animal Model The EAE model, in which encephalitogenic T helper 1 (Th1) and 17 (Th17) cells are induced to trigger autoimmune myelin attacks, is commonly used to study focal inflammatory demyelination, as also observed in acute MS lesions. To investigate the expression of CMTM5 in the inflammatory EAE model, acute MOG 35–55 -EAE was induced in C57BL/6 mice (i.e., animal cohort “EAE” as described in the Materials and Methods Section). As shown in a, mice were sacrificed at the peak of disease for the following analyses: To first validate inflammatory demyelination in the animal model, we performed real-time PCR on the acutely dissected cerebellum of EAE mice and age-matched controls. A significant induction of inflammatory chemokine Cxcl10 expression together with a reduction in Plp expression was observed in EAE animals ( b: Cxcl10, Control 100.00 ± 17.09% vs. EAE 2669 ± 877.5%; c: Plp, Control 100.00 ± 9.756% vs. EAE 35.30 ± 6.652%). We then analyzed the expression of Cmtm5 in the EAE model. At the transcriptional level, a trend toward a decrease in Cmtm5 expression was detected in the cerebellum of EAE mice. ( d: Cmtm5, Control 100.00 ± 6.918% vs. EAE 63.55 ± 10.06%). Via bioinformatic analysis of the single-cell RNA sequencing (scRNA-seq) dataset from Wheeler et al. , we found that Cmtm5 was mainly enriched in cells of the oligodendrocyte lineage (see h–m) and was reduced in the priming and acute phase of EAE (see g). At the protein level, CMTM5 was found to be widely expressed in the white matter of the murine spinal cord (see ). A profound reduction in CMTM5 was observed in the white matter surrounding the inflammatory perivascular cuffs in the spinal cord of EAE mice (see e,f). In addition, CMTM5-positive spheroids were present in the inflammatory foci of EAE spinal cord (see ). At the cellular level, CMTM5-positive spheroids in an advanced EAE-model were found near/within IBA1-positive microglia/macrophages ( ) but not OLIG2-positive oligodendrocyte lineage cells ( ). 3.3. A Cmtm5 Knockdown Does Not Affect Oli-Neu Cell Responsiveness to ER Stress To further elucidate the function of CMTM5 in oligodendrocytes, we first confirmed endogenous expression of Cmtm5 in the mouse oligodendroglial Oli-neu cell line. The Oli-neu cell line was characterized via immunofluorescence staining with the oligodendrocyte marker anti-SOX10 (see a). To perform loss-of-function manipulation of endogenic genes in the Oli-neu cell line, we lentivirally integrated the plasmid cassette (pHR-SFFV-dCas9-BFP-KRAB) into the Oli-neu cell line (i.e., Oli-neu CRISPRi). Thus, catalytically dead Cas9 (dCas9) fused with a KRAB transcriptional repression domain that blocks the transcription start sites in the genome, thereby inhibiting gene transcription . sgRNAs targeting Cmtm5 were introduced into Oli-neu CRISPRi cell line using lentivirus, and the knockdown of Cmtm5 was confirmed using real-time PCR ( b: Cmtm5, non-targeting control 100.00 ± 2.497% vs. Cmtm5 sgRNA 10.63 ± 1.883%; Sox10, non-targeting control 100.00 ± 4.892% vs. Cmtm5 sgRNA 107.6 ± 3.210%). As recently shown by our group, ER-triggered cell death is involved in the cytodegeneration process of oligodendrocytes, and it is critical to maintain a dynamic stress response to ensure oligodendroglia survival under stress . Since CMTM5 expression was found to be reduced in demyelination models (see and ), we asked whether knocking down Cmtm5 per se has an effect on the responsiveness of oligodendroglia to ER stress. To this end, we treated the Cmtm5 knockdown Oli-neu cell line with thapsigargin, an ER stress inducer that inhibits the sarco-endoplasmic reticulum Ca 2+ ATPase. Our results show that the knockdown of Cmtm5 has no effect on the induction of Atf4, an activator of cytoprotective responses under cellular stress conditions ( c: Cmtm5, non-targeting control DMSO treated 96.68 ± 4.840% vs. Cmtm5 sgRNA DMSO treated 13.78 ± 2.602%, non-targeting control thapsigargin treated 104.6 ± 2.564% vs. Cmtm5 sgRNA thapsigargin treated 11.17 ± 0.9641%; Atf4, non-targeting control DMSO treated 98.79 ± 6.265% vs. non-targeting control thapsigargin treated 414.1 ± 48.36%, Cmtm5 sgRNA DMSO treated 80.20 ± 4.478% vs. Cmtm5 sgRNA thapsigargin treated 547.1 ± 31.15%). In addition, no obvious morphological changes were observed in the Cmtm5 knockdown cell line when treated with thapsigargin. Similar to our findings, Buscham et al. reported that conditional knockout of Cmtm5 in mature oligodendrocytes does not affect oligodendroglial function, such as myelin biogenesis but functionally appears to contribute to progressive axonopathy, among other effects . Overall, these results suggest that further investigations are warranted to understand the function of CMTM5 in the context of neuron–glia interaction. 3.4. CMTM5 Protein Expression Is Reduced in Demyelinated Post Mortem MS Lesions As we have previously shown, CMTM5 expression was reduced in animal models recapitulating specific histopathological features of MS such as intrinsic oligodendrocyte degeneration (i.e., Cuprizone model) and inflammatory demyelination (i.e., EAE model) . Therefore, in a translational approach, we next investigated whether the reduction in CMTM5 expression observed in animal models also occurs in lesions of progressive MS patients. To this end, brain sections from three progressive MS patients, together with four non-MS control patients, were processed for anti-PLP and anti-CMTM5 immunohistochemistry (see ). Similar to the expression of PLP in human brains, CMTM5 was found to be enriched in the white matter and sparsely distributed in the cortex (see b and ). In addition to the CMTM5-positive myelin sheath morphology in white matter and cortex, CMTM5-positive spheroids were observed in human brain white matter (see , arrowheads). CMTM5, similar to PLP, was markedly reduced in the center of the lesion compared with normal-looking white matter near the lesion ( d,e: anti-PLP, 65.31% ± 3.79% in normal-appearing white matter (NAWM), 25.43% ± 0.19% in cortex, 23.92% ± 8.09% in the MS lesion center; anti-CMTM5, 41.73% ± 2.19% NAWM, 9.84% ± 4.38% in cortex, 14.44% ± 7.98% in the MS lesion center). To further identify the expressed cell type of CMTM5 in humans, we performed bioinformatic analysis using the published single nuclei RNA-sequencing dataset for MS . Consistent with the cellular expression pattern in mouse brains, CMTM5 was predominately expressed in oligodendrocyte-lineage cells (see j).
It has been suggested that CMTM5 is highly enriched in oligodendrocytes during maturation . As a first step, we aimed to verify the expression of CMTM5 in an animal model of toxin-induced oligodendrocyte injury followed by demyelination. To this end, mice were continuously intoxicated with cuprizone for 1 week (1 wk), 3 weeks (3 wks) or 5 weeks (5 wks). Controls (Ctrl) were fed a normal diet throughout the entire experiment (i.e., animal cohort “Cup IHC/IF”, as described in the Materials and Methods Section). To verify the successful demyelination in the cuprizone model, coronal sections were processed for the analyses of anti-PLP labelling intensities. As shown in a, profound demyelination of the medial corpus callosum was observed after 5 weeks of cuprizone intoxication,( a: anti-PLP 81.28% ± 2.14% in Ctrl, 86.05% ± 2.01% at 1 wk Cup, 82.54% ± 2.30% at 3 wks Cup and 4.71% ± 0.67% at 5 wks Cup). Similar to the decrease in anti-PLP staining intensities during the course of cuprizone intoxication, the anti-CMTM5 staining intensities were found to be significantly reduced after 5 weeks in the cuprizone model ( a: anti-CMTM5 89.01% ± 0.76% in Ctrl, 84.65% ± 2.98% at 1 wk Cup, 83.33% ± 0.55% at 3 wks Cup and 34.15% ± 3.52% at 5 wks Cup). Of note, apart from widespread CMTM5-immunoreactivity morphologically resembling myelin sheaths, CMTM5-positive spheroids were observed in the medial corpus callosum of mice after 5 weeks of cuprizone intoxication ( ). To systematically evaluate the expression of CMTM proteins in the toxin-induced demyelination model, we analyzed the transcriptional expression of CMTM family genes using the RNA-seq dataset from control groups vs. 3 wks Cuprizone intoxicated groups (GSE213374 ). As shown in the heatmap ( b), only the expression of Cmtm5, but not other CMTM members, was reduced during the curprizone-induced demyelination process. To further confirm the transcriptome results, we analyzed the expression of Plp and Cmtm5 by real-time RT-PCR ( c: Plp, Control 100.00 ± 11.84% vs. 4 d Cup 6.817 ± 0.7011%; Cmtm5, Control 100.00 ± 7.068% vs. 4 d Cup 14.15 ± 1.630%; d: Plp, Control 100.00 ± 6.909% vs. 3 wks Cup 11.88 ± 2.144%; Cmtm5, Control 99.99 ± 6.980% vs. 4 d Cup 65.30 ± 6.252%) using a set of independent biological samples (i.e., animal cohort “4 d Cup qPCR”, “3 wks Cup qPCR” as described in the Materials and Methods Section). Combined, these data demonstrate that CMTM5, as a novel myelin protein, is reduced after toxin-induced demyelination at both the mRNA and protein level.
The EAE model, in which encephalitogenic T helper 1 (Th1) and 17 (Th17) cells are induced to trigger autoimmune myelin attacks, is commonly used to study focal inflammatory demyelination, as also observed in acute MS lesions. To investigate the expression of CMTM5 in the inflammatory EAE model, acute MOG 35–55 -EAE was induced in C57BL/6 mice (i.e., animal cohort “EAE” as described in the Materials and Methods Section). As shown in a, mice were sacrificed at the peak of disease for the following analyses: To first validate inflammatory demyelination in the animal model, we performed real-time PCR on the acutely dissected cerebellum of EAE mice and age-matched controls. A significant induction of inflammatory chemokine Cxcl10 expression together with a reduction in Plp expression was observed in EAE animals ( b: Cxcl10, Control 100.00 ± 17.09% vs. EAE 2669 ± 877.5%; c: Plp, Control 100.00 ± 9.756% vs. EAE 35.30 ± 6.652%). We then analyzed the expression of Cmtm5 in the EAE model. At the transcriptional level, a trend toward a decrease in Cmtm5 expression was detected in the cerebellum of EAE mice. ( d: Cmtm5, Control 100.00 ± 6.918% vs. EAE 63.55 ± 10.06%). Via bioinformatic analysis of the single-cell RNA sequencing (scRNA-seq) dataset from Wheeler et al. , we found that Cmtm5 was mainly enriched in cells of the oligodendrocyte lineage (see h–m) and was reduced in the priming and acute phase of EAE (see g). At the protein level, CMTM5 was found to be widely expressed in the white matter of the murine spinal cord (see ). A profound reduction in CMTM5 was observed in the white matter surrounding the inflammatory perivascular cuffs in the spinal cord of EAE mice (see e,f). In addition, CMTM5-positive spheroids were present in the inflammatory foci of EAE spinal cord (see ). At the cellular level, CMTM5-positive spheroids in an advanced EAE-model were found near/within IBA1-positive microglia/macrophages ( ) but not OLIG2-positive oligodendrocyte lineage cells ( ).
To further elucidate the function of CMTM5 in oligodendrocytes, we first confirmed endogenous expression of Cmtm5 in the mouse oligodendroglial Oli-neu cell line. The Oli-neu cell line was characterized via immunofluorescence staining with the oligodendrocyte marker anti-SOX10 (see a). To perform loss-of-function manipulation of endogenic genes in the Oli-neu cell line, we lentivirally integrated the plasmid cassette (pHR-SFFV-dCas9-BFP-KRAB) into the Oli-neu cell line (i.e., Oli-neu CRISPRi). Thus, catalytically dead Cas9 (dCas9) fused with a KRAB transcriptional repression domain that blocks the transcription start sites in the genome, thereby inhibiting gene transcription . sgRNAs targeting Cmtm5 were introduced into Oli-neu CRISPRi cell line using lentivirus, and the knockdown of Cmtm5 was confirmed using real-time PCR ( b: Cmtm5, non-targeting control 100.00 ± 2.497% vs. Cmtm5 sgRNA 10.63 ± 1.883%; Sox10, non-targeting control 100.00 ± 4.892% vs. Cmtm5 sgRNA 107.6 ± 3.210%). As recently shown by our group, ER-triggered cell death is involved in the cytodegeneration process of oligodendrocytes, and it is critical to maintain a dynamic stress response to ensure oligodendroglia survival under stress . Since CMTM5 expression was found to be reduced in demyelination models (see and ), we asked whether knocking down Cmtm5 per se has an effect on the responsiveness of oligodendroglia to ER stress. To this end, we treated the Cmtm5 knockdown Oli-neu cell line with thapsigargin, an ER stress inducer that inhibits the sarco-endoplasmic reticulum Ca 2+ ATPase. Our results show that the knockdown of Cmtm5 has no effect on the induction of Atf4, an activator of cytoprotective responses under cellular stress conditions ( c: Cmtm5, non-targeting control DMSO treated 96.68 ± 4.840% vs. Cmtm5 sgRNA DMSO treated 13.78 ± 2.602%, non-targeting control thapsigargin treated 104.6 ± 2.564% vs. Cmtm5 sgRNA thapsigargin treated 11.17 ± 0.9641%; Atf4, non-targeting control DMSO treated 98.79 ± 6.265% vs. non-targeting control thapsigargin treated 414.1 ± 48.36%, Cmtm5 sgRNA DMSO treated 80.20 ± 4.478% vs. Cmtm5 sgRNA thapsigargin treated 547.1 ± 31.15%). In addition, no obvious morphological changes were observed in the Cmtm5 knockdown cell line when treated with thapsigargin. Similar to our findings, Buscham et al. reported that conditional knockout of Cmtm5 in mature oligodendrocytes does not affect oligodendroglial function, such as myelin biogenesis but functionally appears to contribute to progressive axonopathy, among other effects . Overall, these results suggest that further investigations are warranted to understand the function of CMTM5 in the context of neuron–glia interaction.
As we have previously shown, CMTM5 expression was reduced in animal models recapitulating specific histopathological features of MS such as intrinsic oligodendrocyte degeneration (i.e., Cuprizone model) and inflammatory demyelination (i.e., EAE model) . Therefore, in a translational approach, we next investigated whether the reduction in CMTM5 expression observed in animal models also occurs in lesions of progressive MS patients. To this end, brain sections from three progressive MS patients, together with four non-MS control patients, were processed for anti-PLP and anti-CMTM5 immunohistochemistry (see ). Similar to the expression of PLP in human brains, CMTM5 was found to be enriched in the white matter and sparsely distributed in the cortex (see b and ). In addition to the CMTM5-positive myelin sheath morphology in white matter and cortex, CMTM5-positive spheroids were observed in human brain white matter (see , arrowheads). CMTM5, similar to PLP, was markedly reduced in the center of the lesion compared with normal-looking white matter near the lesion ( d,e: anti-PLP, 65.31% ± 3.79% in normal-appearing white matter (NAWM), 25.43% ± 0.19% in cortex, 23.92% ± 8.09% in the MS lesion center; anti-CMTM5, 41.73% ± 2.19% NAWM, 9.84% ± 4.38% in cortex, 14.44% ± 7.98% in the MS lesion center). To further identify the expressed cell type of CMTM5 in humans, we performed bioinformatic analysis using the published single nuclei RNA-sequencing dataset for MS . Consistent with the cellular expression pattern in mouse brains, CMTM5 was predominately expressed in oligodendrocyte-lineage cells (see j).
In the present project, we were able to demonstrate that (i) CMTM5 expression is reduced in toxin-induced and inflammatory demyelination animal models; (ii) based on our in vitro experiments, knockdown of Cmtm5 does not seem to affect cell responsiveness to ER stress, which is an early event during oligodendrocyte cytodegeneration; and (iii) that CMTM5 protein expression is reduced in post-mortem MS lesions. As a first step, we investigated the expression of CMTM5 under the pathological condition of demyelination, taking into account its enrichment in mature oligodendrocytes at both the mRNA and protein level, as previously reported [ , , ]. The high correlation of CMTM5 expression with PLP strongly suggests its property as a myelin protein in the central nervous system (see and ). Interestingly, the reduction in CMTM5 expression both at the protein level (about 40% left after 5 weeks of cuprizone intoxication) and at the mRNA level (about 60% left after 3 weeks of cuprizone intoxication) is less pronounced than for PLP, which is almost completely lost in the demyelinated regions (less than 10% left, see c,d). This could be related to the CMTM5-positive spheroids detected in the demyelinated areas (see ). Interestingly, in the cuprizone model, we found a unique reduction in CMTM5 expression in oligodendrocytes that is absent in other CMTM members (see b). There are several reasons why CMTM5 specifically should be studied in the context of demyelination: (i) CMTM5 is independently localized on chromosome 14q11.2 compared to other CMTM members. Previous CMTM5-related studies have focused on the expression and function under pathological conditions such as tumorigenesis in particular . However, little attention has been paid to the fact that CMTM5 is more abundant in the central nervous system. (ii) Other members of the CMTM family, such as CMTM6, which has been reported to be a myelin protein of the peripheral nervous system , were expressed at much lower levels and were not affected in CNS demyelination processes (see b). Considering that CMTM5 is also expressed in Schwann cells and enriched in PNS myelin [ , , ], studies on CMTM5 may elucidate novel functions of myelin proteins in both PNS and CNS. (iii) Compared to other CMTM members, CMTM5 does not contain an apparent chemokine-like structure. Therefore, it remains interesting to investigate how CMTM5 regulates neuron–glia interaction, particularly in the axon myelin unit, at the functional level. Further studies are necessary to investigate the dynamic regulation of CMTM5 expression, particularly its function in subsequent events such as activation and proliferation of oligodendrocyte progenitor cells and phagocytosis of myelin debris by microglia/macrophages in demyelinated lesions . Structurally, CMTM5 belongs to the tetraspan–transmembrane proteins with small intracellular N- and C-terminal regions . Other myelin tetraspan proteins, such as PLP , CD9 and CD81 , have been shown to play pivotal roles in cell migration, vesicle trafficking and membrane adhesion . Although lacking the N-terminal signal peptide, the secretory form of CMTM5 was found in the prostate lumen, secreted via a nonclassical secretory pathway . Similarly, other CMTM members (e.g., CMTM-3, -4, -6, -7) have also been shown to regulate vesicle trafficking and stability of membrane proteins such as EGFR, VE-cadherin and PD-L1 [ , , , , ]. Considering the enrichment of CMTM5 in the white matter, it remains intriguing to investigate whether CMTM5 is secreted into the cerebrospinal fluid and whether this results in properties as a biomarker or potential therapeutic approach. Of note, in addition to the widespread expression of CMTM5 in the white matter, we found CMTM5-positive spheroids to be co-localized with markers for axonal damage (see ). In the context of our observation, it is interesting to note that Buscham et al. reported that Cmtm5 deficiency induces progressive axonopathy . Although oligodendrocytes are well known to provide trophic support to neurons in the axon–myelin unit, the specific mechanisms underlying axonopathy (e.g., interrupted axonal transport) due to myelin protein deficiency are still not entirely clear . Frühbeis et al. showed that deficiency of different myelin proteins, such as PLP and CNP, impairs extracellular vesicle secretion . Future studies need to investigate whether the absence of CMTM5 in the regulation of axonal integrity affects extracellular vesicle transport between oligodendrocytes and neurons. At the expressional level, the abundance of CMTM5 gradually increases during oligodendrocyte maturation . Interestingly, we observed that Cmtm5 is, compared to controls, reduced to around 10% after one week of cuprizone intoxication and recovered back to around 60% after 3 weeks of continuous cuprizone intoxication (see c,d). Interestingly, the reduction in CMTM5 and PLP at the protein level became apparent only after 5 weeks of cuprizone intoxication (see a). However, a reduction in mRNA expression of Cmtm5 and Plp was already observed in mice intoxicated with cuprizone for only 4 days (see c). The observed early reduction in Cmtm5 and Plp expression in the cuprizone model may be due to regulated Ire1-dependent mRNA decay (RIDD), which is a selective decay of ER-bound mRNAs in response to ER stress . According to the animal model characterization performed previously, cuprizone as a copper chelator first induces oxidative and ER stress in oligodendrocytes (e.g., within one week of cuprizone intoxication), which eventually leads to oligodendrocyte apoptosis . Other degenerative processes, such as glial cell activation and axonal injury, follow with continuous intoxication (e.g., 3 weeks of cuprizone intoxication) . These results imply that CMTM5, in addition to its role as a myelin component, might be involved in other reactive or degenerative cascades. Based on our and other studies, CMTM5 does not seem to be essential for myelin biogenesis and OPC responsiveness to ER stress (see c,d). Future experiments, for instance, using primary oligodendrocyte or co-culture with neurons, would be required to investigate the function of CMTM5 during oligodendrocyte maturation. This study is not without limitations. First, our studies using the Oli-neu cell line examined the function of Cmtm5 in oligodendroglial responses to ER stress induced by thapsigargin. However, the Oli-neu cell line was artificially produced as an immortalized mouse oligodendroglial cell line . It is necessary to cross-compare the results with other oligodendroglial cell lines, such as OLN-93 and primary oligodendrocyte cultures. Second, the expression of ATF4 should be examined not only at the transcriptional level but also at the translational level to better assess the cellular responses under ER stress . Third, although mice were randomly assigned to the experimental groups (i.e., Cup IHC/IF, 4 d Cup qPCR, 3 wks Cup qPCR, EAE), sex differences could be a potential confounding factor in the experimental setting. In addition, experimenters were not blinded to treatment/intoxication. However, the acquisition of IHC/IF results was always analyzed in a blinded manner by two independent evaluators and subsequently compared. In the cup IHC/IF cohort, mice were intoxicated with cuprizone for 1 week, 3 weeks, and 5 weeks, respectively. Control mice were sacrificed along with the mice that were intoxicated for 1 week. By adding multiple age-matched control groups, an even higher degree of comparability could be achieved.
|
Confusing Histopathological Features and HPV Testing Results in Vulvar Squamous Cell Carcinoma Arising in a Young Woman: A Case Solved Using Next-Generation Sequencing | f3da1694-b604-4093-aaf7-5e649459c572 | 11801420 | Anatomy[mh] | A 21-year-old woman, gravida 0, was referred to our hospital with an erythematous and itching vulvar lesion. She had a history of type 1 diabetes mellitus, diagnosed at the age of 10. She had received the complete scheme of HPV vaccination (3 doses of Gardasil-4, MSD) at the age of 12, and had started sexual activity at the age of 20. Topical treatment with Centella asiatica extract was prescribed. Due to the persistence of the lesion and the progressively increasing itching, the patient presented again at the outpatient clinic 5 months later with a 3×2 cm erythematous-keratotic lesion in the left labia minora, close to the vaginal introitus. No palpable inguinal lymph nodes were identified. A biopsy of the vulvar lesion revealed an invasive VSCC. The patient underwent a left hemi-vulvectomy (Fig. ) and bilateral inguinal sentinel lymph node selective biopsy. Histologic examination confirmed the diagnosis of invasive VSCC (11 mm in diameter and 3 mm of invasive depth) and also revealed an intraepithelial lesion in the adjacent skin, which was in contact with the surgical margin. The invasive tumor showed keratinizing histology (Fig. A), whereas the adjacent intraepithelial lesion showed warty features as well as the presence of large cells with clear cytoplasm (Fig. B), morphologically fitting into the diagnosis of HSIL. No inflammation reaction was identified in the adjacent non-affected skin. p16 IHC showed intense and continuous positivity in the invasive component (Fig. C), whereas the intraepithelial lesion showed a patchy p16 staining (Fig. D). p53 IHC showed diffuse overexpression in the 2 components, infiltrative and intraepithelial (Fig. E and F). HPV DNA testing (INNO-LiPA HPV Genotyping Extra II kit) revealed a low-risk HPV type (HPV67) in the invasive tumor and in the adjacent intraepithelial lesion. The RNA in-situ hybridization for high-risk HPV (RNAscope, USA) was negative in the 2 components. The case was reported as VSCC not otherwise specified (NOS), due to the puzzling histologic, IHC and HPV testing findings, nonconclusive to establish an HPV relationship. The biopsy of the left inguinal sentinel lymph node revealed the presence of isolated tumor cells (ITCs). The tumor was staged as Ib (2021 FIGO staging). Due to the presence of ITCs, the patient received adjuvant radiotherapy to the left inguinal region with a dose of 50 Gy (normo-fractionated at 2Gy/session). One year later, a recurrent 0.5 cm nodular, elevated, and indurated lesion located on the inner side of the left labia majora (left lateral introitus) was noted on routine clinical examination. A wide excision of the lesion confirmed the diagnosis of VSCC (Fig. A’). A left hemi-vulvectomy was subsequently performed, which showed no residual invasive tumor, but an extensive intraepithelial lesion, histologically identical to the previous lesion (Fig. B’) with clear extension to the surgical margins. Inflammatory changes in the adjacent skin were absent. p16 IHC was negative in the invasive tumor (Fig. C’) and the intraepithelial lesion (Fig. D’), whereas p53 IHC showed diffuse overexpression in both components (Fig. E’ and F’). HPV DNA testing revealed a high-risk HPV type (HPV66), which was detected in the tumor and in the adjacent lesion. However, RNA in-situ hybridization for high-risk HPV was negative. The tumor was again reported as VSCC NOS, nonconclusive for HPV association due to conflicting results between p16 IHC and HPV-PCR testing, in similarity to the primary tumor (Table ). Written informed consent was obtained from the patient for the molecular analysis and the publication of the case. The molecular analyses were performed on the formalin-fixed, paraffin-embedded tissue from the primary invasive tumor: DNA sequencing (panel Oncomine™ Comprehensive Assay v3 GX, ThermoFisher Scientific, USA) and RNA expression analysis (HTG-EdgeSeq system, whole transcriptome HTP panel), which was also performed on a control (normal) tissue (nonmetastatic lymph node). The DNA sequencing revealed a missense TP53 mutation codifying for T125M protein, of unknown pathogenic significance, with an allele frequency of 50.4%, accompanied by a TP53 overexpression by RNA sequencing (log2FoldChange=3.68). Other point mutational variants included POLE S1896L mutation (of unknown significance), also with overexpression on RNA sequencing, as well as MYC S161L (likely pathogenic) and SMARCA4 G48E (of unknown significance). RNA sequencing additionally revealed overexpression of cell cycle genes, especially of CCND1 , and of genes coding for collagen family proteins, most of which have recently been shown upregulated in HPV-I VSCC (Table ). We also identified overexpression of genes involved in G1/S transition and mini chromosome maintenance and in genes involved in DNA damage repair, most of which have been shown as upregulated in HPV-A VSCC (Table ). When comparing the mean log2FoldChange of cell cycle regulators and collagen of extracellular matrix genes (which have been shown more frequently altered in HPV-I VSCC) with that of genes involved in G1/S transition, mini chromosome maintenance and DNA damage repair (which have been shown more frequently altered in HPV-A VSCC) , we identified significantly higher values of expression in the former genes (mean log2FoldChange of 40.07 (SD=42,85) vs. mean of 2.84 (SD=2.37), t(11)=2.71, P =0.011). Finally, we have not identified any differential expression in other genes previously reported as altered in HPV-A VSCC, including E2F2 , EYA2 , FCGBP , and TMN1 . , This case illustrates the challenges in terms of HPV status attribution, and consequently, in terms of 2020 WHO classification, that the pathologist may face in the routine diagnosis of a VSCC. Indeed, several clinical (age, absence of clinical history of vulvar dermatosis) and pathologic features (warty features of the intraepithelial lesion, absence of inflammatory changes in the adjacent skin, p16 positive staining of the invasive primary tumor, detection of HPV DNA sequences in the primary and recurrent tumor) strongly favored the diagnosis of an HPV-A lesion. In contrast, other features (keratinizing histology of the invasive carcinoma, consistently negative p16 IHC of the intraepithelial lesion, consistently negative RNA in-situ hybridization for high-risk HPV in all lesions, diffuse p53 IHC overexpression in the tumor and the intraepithelial lesion) strongly supported an HPV-I lesion. Finally, some of the features were controversial or puzzling and even raised doubts regarding the quality of the technical process (vanishing p16 IHC staining from the primary to the recurrent invasive tumor, different HPV types identified in the primary and the recurrent lesion, with low-risk HPV detected in the primary lesion). The HPV testing results were a disturbing feature in this case. Indeed, a low-risk HPV type was detected in the primary tumor, which shifted to a high-risk HPV type in the recurrent lesion. These striking changes strongly suggest that PCR-based HPV DNA tests may provide false positive or nonspecific results, as suggested in a previous study by our group . The high sensitivity of the test used in our case, as well as the extremely short size (65 base pairs) of the SPF10 amplimers, may be associated with these nonspecific results. The consistent negative results of in-situ hybridization for HPV RNA were also in support of the false positive results of the PCR testing. The HPV-I nature of the primary tumor, with p16 and p53 co-overexpression, is also in accordance with a recently described algorithm for VSCC despite the apparent evidence of a positive p16 IHC staining favoring an HPV-A lesion. In this recent report, Yang et al identified 4 of such p16-p53 “double-positive” cases with diffuse p53 overexpression, which tested negative for in-situ hybridization for high-risk HPV and showed TP53 mutations in the next-generation sequencing. Another remarkable finding, in this case, was the warty features of the adjacent intraepithelial lesion, morphologically suggestive of vulvar HSIL. However, HPV-I HSIL-like lesions , also described as basaloid/warty dVIN, have been described in the literature. A recent report by our group has shown that invasive carcinoma arising on this type of lesion is highly recurrent, which might explain, in addition to the presence of positive surgical margin, the early recurrence in this patient. Another similarity with recently described tumors arising on HSIL-like lesions is the small tumor size and the exophytic, superficially invasive growth of the associated invasive tumor. However, HSIL-like lesions are more common in postmenopausal women and are frequently associated with inflammatory lesions. , Notably, recent evidence suggests that such lesions are highly oncogenic, as is conventional dVIN. The molecular analysis provided relevant data that helped to solve the diagnostic dilemma. Firstly, a TP53 mutation with high allele frequency was detected, accompanied by a TP53 overexpression, both of which are a hallmark of HPV-I VSCC. Secondly, we found significantly higher expression values in genes previously reported as upregulated in HPV-I VSCC rather than in those shown as upregulated in HPV-A VSCC. Another strong evidence in favor of HPV-I status is the identification of CCND1 overexpression in our tumor, typically found in HPV-I VSCC. , The diagnosis of an HPV-I VSCC in a young woman raises some doubts, especially in the absence of inflammatory lesions in the adjacent skin, which are typically involved in the pathogenesis of this type of tumor. Indeed, no clinical history of vulvar dermatosis was reported in this patient. Although there are a few reports of HPV-I VSCC in young women, in most of them, inflammatory lesions and/or dVIN have been documented. , Remarkably, diabetes has been reported as one of the main risk factors for VSCC in young women, probably due to the immunosuppression associated with this disease. Finally, occasional examples of HPV-I VSCC have been reported in young women without any risk factors and with no evidence of premalignant lesions. In conclusion, the molecular analysis helped to correctly classify a challenging VSCC, showing puzzling clinical, morphologic, and IHC characteristics. |
A virtual COVID-19 ophthalmology rotation | cfddc9f6-dfc1-4a0f-b3ea-627c2861faab | 7550053 | Ophthalmology[mh] | Introduction Currently, the undergraduate medical education community faces unprecedented challenges with the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the infectious disease, COVID-19. The COVID-19 pandemic, responsible for millions of cases and hundreds of thousands of deaths nationwide, has required unprecedented changes in the way that undergraduate medical education departments deliver clinical instruction in order to comply with social distancing efforts aimed at prioritizing safety and slowing the spread of the virus. In most medical schools, ophthalmology clinical rotations are typically considered surgical electives that students may take in their third or fourth year. Traditionally, these electives are comprised of in-person patient care experiences, teaching sessions and conferences, and scholarly projects that result in a holistic exposure to the specialty. Away rotations are electives completed at a medical school outside a student's home institution, expanding the opportunity for faculty interaction and subsequent letters of recommendation for residency applications. These away rotations are particularly important for students who do not have an ophthalmology department with a residency at their medical school, often providing the only exposure to the field. On March 17, 2020, the Association of American Medical Colleges recommended a temporary suspension of clinical experiences in an effort to preserve personal protective equipment and ensure student safety. This pause in rotations, while necessary, disrupted the opportunity for students to explore and experience specialty fields, such as ophthalmology. There has been a lasting effect on away rotation experiences, which at the time of submission of this article are still suspended. Although the full impact on undergraduate medical education by COVID-19 remains to be seen, one outcome has been the supplementation of a traditional in-person curricula with virtual content. We describe a novel design of a completely virtual elective in neuro-ophthalmology for medical students that enables clinical exposure, instruction, faculty mentorship, and research experience while still complying with social distancing measures and restrictions to away rotation experiences. To our knowledge, this COVID-19 virtual neuro-ophthalmology medical student rotation curriculum is the first of its kind to be described in the English language literature.
Consequences of limited clinical ophthalmology exposure for medical students A survey of United Kingdom medical students showed 59% of students felt less prepared for their future careers as a result of suspension of clinical experiences. Surgical specialties in particular have been affected by the cancellation of elective procedures with limited personal protective equipment and testing available to students, exacerbating an already low medical student exposure to ophthalmology. Inclusion of ophthalmology education in clinical curriculum had already been on a steadily waning path. The proportion of medical schools requiring an ophthalmology rotation dwindled from 68% to 30% from 2000 to 2004, creating potential barriers for medical students to explore and consider the specialty. Specifically, the suspension and/or limitation of in-person clinical rotations has impaired the ability of medical students to receive clinical exposure, mentorship, letters of recommendation, and research experience required for application to residency. A number of medical specialties, including allergy, rheumatology, surgery, and dermatology, have found some success with residents currently employing technologies such as video conferencing to remotely interact with the patient in obtaining a medical history and presentation. Though resourceful, this setup critically lacks a physical examination component. Additionally, the logistics of performing a complete, remote eye exam also have yet to be elucidated by ophthalmologists. The clinical rotation suspensions have impaired the ability of prospective ophthalmologists to attain a meaningful letter of recommendation. A survey of program directors, chairpersons, and members of the resident selection committee for ophthalmology residency programs indicated that 83% felt that letters of recommendation were among the most important factors considered in the resident selection process, with 70% believing that the most important letter is from an ophthalmologist. Despite the pandemic, a statement released by the Association of University of Professors of Ophthalmology states that three letters of recommendation are required for the ophthalmology match application, with at least one being from an ophthalmologist. These negative implications are subject to regional variability. Those enrolled in medical schools with a traditional two-year preclinical curriculum will have had 6 months less of clinical experience in comparison to their colleagues at medical schools with shortened preclinical schedules. The impact of COVID-19 on the ability of medical schools to provide faculty interaction and thus meaningful letters of recommendation has already been shown to be widely disparate between some medical schools and others. If neglected as a confounding variable in residency application review, medical students applying from an area more severely impaired by COVID-19 or without a home ophthalmology program may be disadvantaged. While the effect on students applying for ophthalmology is obvious, the limited exposure may also impact students who do not choose ophthalmology as a career. The long-term consequences of limited medical student exposure to ophthalmology have been well studied. , Medical student graduates report lower confidence and poorer understanding of common ophthalmic disorders, which can contribute to over-referral of simple eye disorders. , Ultimately, diminished time for ophthalmic education adversely affects patient care as graduating physicians have less experience with the ophthalmic examination and diagnosing, managing, and referring patients with ophthalmic conditions. Overall, limited exposure to a specialty in medical school has been shown to increase the likelihood of future medical mismanagement and misdiagnoses. An additional component of the residency application is research experience. A probability model created using the San Francisco Residency and Fellowship Matching Data found that over 99% of medical students applying for residency in ophthalmology had published clinical research. Additionally, research often bolsters applicants with lower examination scores, providing another way to showcase strengths outside of the traditional curriculum. Clinical research has also been impacted by COVID-19, with only essential research being permitted in most medical centers. From a sampling perspective, there may be an expected decrease in follow-up and participation due to fear of contracting the virus. Additionally, diagnostics used through telemedicine may not be sufficient for clinical trials. Institutions and research protocols will likely need to revise their guidelines to accommodate clinical research during a pandemic. Therefore, there is evident strain on medical students who wish to become involved in clinical research during the COVID-19 outbreak.
Innovative learning modalities used to continue clinical medical education during COVID-19 While the COVID-19 pandemic has negatively impacted ophthalmic undergraduate medical education globally, it has also provided an opportunity to modernize educational approaches by adopting novel digital curricula. As the pandemic continues, online and virtual teaching resources are set to play a larger role of medical student education in general. The COVID-19 outbreak has disrupted the educational experience of medical students worldwide. Typically, medical schools are comprised of 12–24 months of a basic science curriculum, followed by 18 months of clinical rotations, clinical electives, subinternships, and scholarly projects. Generally, the preclinical stage can be more easily transitioned into an online format. The basic science didactic lectures have been administered using online video formats in place of lecture halls, and examinations have also been conducted using an online format. Clinical skills sessions may also occur in online virtual instructional formats, along with the implementation of dissection videos and online simulations in place of traditional anatomy laboratory dissections. , Restructuring the clinical aspect of medical school presents more challenges in the age of COVID-19. A medical student functions as part of the clinical team as a learner, and authentic patient experiences are essential for their education. Medical schools across the world have quickly developed innovative strategies to create experiences for students who were suspended from their clinical rotations. Didactic meetings such as academic lectures, departmental grand rounds, and seminars have been converted to online video conferences using platforms such as Cisco WebEx and Zoom. , , , , The virtual format of these meetings may be more convenient in some situations and can allow for increased availability of national and international speakers at a lower cost; however, the virtual format results in less opportunity for medical students and trainees to interact and network with faculty members. Social media formats such as Twitter and Facebook may also be used to disseminate medical educational material and may help foster community between medical students, trainees, and specialized physicians. Murdock and coworkers describe a multi-institutional virtual morning report format that they developed to continue education for trainees via Zoom. The morning report consisted of a clinical-educational facilitator and two groups of residents and medical students as either audiovisual “active participants” or nonaudiovisual “passive” participants. The passive participants were engaged in the case using the Zoom chat function, and a facilitator could filter comments and create synchronous communication between active and passive participants. Overall, they found this format to be of value to both active and passive participants as all learners were able to appreciate the clinical reasoning, differential diagnoses, and problem representations of the case. Similarly, Almarzooq and coworkers describe the use of Microsoft Teams to facilitate virtual didactics. They utilize different channels in Microsoft Teams for different purposes, including morning report, different fellowship committees, and teaching conferences. To simulate clinical teaching with live patients, some schools have transitioned to using online case-based learning tutorials and videos of patient encounters. , Some schools may consider restructuring the academic calendar to defer clinical rotations to a later date in exchange for other scholarly projects. , Another option is to involve students in the telemedicine environment. Chandra and coworkers describe an emergency medicine clerkship they developed with medical students using Zoom to conduct virtual patient encounters. Medical students conducted virtual follow-up visits with COVID-19 positive patients who were discharged from the emergency department and with patients who presented to the emergency department with general medical complaints that were discharged within the last 48 hours. All encounters were supervised by a faculty preceptor, allowing for rapid assessment of student performance. This format was successful in that it allowed medical students to lead a patient interaction and obtain faculty feedback in a virtual manner.
Ophthalmology-specific virtual teaching resources In ophthalmology, telemedicine can be useful for screening and basic visits, but the learning experience is limited for students because of the inherent limitations of the ophthalmic physical examination in an indirect environment. Shih and coworkers attempted to address this issue by replacing in-person tutorials of ophthalmic clinical skills with a virtual approach using written materials and Zoom. Students first self-studied with written information and recorded videos. Next, they were placed in small groups of 30 for a 60-minute tutorial with a clinical preceptor on Zoom to discuss key points and questions. Finally, an objective, structured clinical examination was used for assessment. This was an effective learning experience for medical students overall; however, a limitation encountered was difficulty in effectively teaching direct ophthalmoscopy online. Successful direct ophthalmoscopy requires an understanding of the angle of approaching the patient and the physical adjustments required to view the fundus. In-person tutorials are a superior method for teaching this particular clinical skill. For teaching direct ophthalmoscopy, Borgersen and coworkers systematically evaluated the effectiveness of instructional YouTube videos on direct ophthalmoscopy by evaluating their content and approach to visualization. Their group provided the following suggestions for videos instructing in direct ophthalmoscopy: (1) illustrate the key themes and points essential for performing direct ophthalmoscopy; (2) consider how to illustrate the key concepts so that the learner sees what he/she should expect to see; (3) put emphasis on how to examine the fundus and interpret findings; (4) consider omitting irrelevant details, highlighting essential information, and presenting words and pictures in combination. In addition to instructional videos, the direct ophthalmoscope examination can be taught using virtual reality tools. For example, the Eyesi Direct Ophthalmoscope (VRmagic, Mannheim, Germany) is a simulator tool composed of an ophthalmoscope, mannequin head with eye sensors, and a computer monitor. The student can perform the funduscopic examination on virtual patients with varying case presentations, and the computer monitor will display the fundus image and provide feedback to students on their performance. Other virtual reality tools for the direct ophthalmoscope examination include the Digital Eye Examination/Retinopathy Trainer (Nasco, Wisconsin, USA), OphthoSim™ Ophthalmoscopy Training & Simulation System (OtoSim, Toronto, Canada), and the EYE Examination Simulator (Kyoto Kagaku Co. Ltd., Kyoto, Japan). While these tools are useful for students to learn the direct ophthalmoscope examination technique without direct patient contact, they are expensive and would require in person sessions for students to use the equipment. The Virtual Ophthalmology Clinic is an innovative web-based program which can be used during COVID-19. It is designed to enhance teaching by allowing medical students to sharpen their clinical reasoning skills by formulating a diagnosis and treatment plan on virtual patients with simulated eye conditions on off-site locations. The application of Virtual Ophthalmology Clinic resulted in increased academic performance and sustained retention over traditional teaching alone. Other e-Learning modules which can be used are summarized in and . Developing learning environments during pandemics such as COVID-19 that emulate in-person experiences may require the application of simulations, digital curricula with remote mentoring, and feedback. The Association of University of Professors of Ophthalmology Medical Student Educators Council recently developed a modified list of ophthalmology objectives for graduating medical students. These objectives can be used by any medical school, regardless of whether they have a department of ophthalmology, as they can be incorporated into the curricula of other medical specialties such as neurology, family practice, internal medicine, and pediatrics. The International Council on Ophthalmology, United States Medical Licensing Exam (USMLE), and the Royal College of Ophthalmologists offer further curricular recommendations and guidelines which can be followed when developing new ophthalmic modules for medical student teaching.
Implementation of a novel, neuro-ophthalmology elective during COVID-19 A virtual curriculum will need to address the lack of clinical experiences in ophthalmology and should include observation time, the support of mentorship, and optimization of technology to simulate real clinical hours. We created a novel virtual neuro-ophthalmology elective designed for medical students rotating at Methodist Hospital in Houston, Texas. Through implementation of this novel virtual elective, we will provide medical students quality exposure to ophthalmology despite challenges posed by COVID-19. Although we created a curriculum for a neuro-ophthalmology specific elective, we envision that the core components can be used as a template for other ophthalmology subspecialties in the future. The elective includes a virtual curriculum that teaches the core anatomy, diseases and concepts of neuro-ophthalmology; opportunities to study unique cases through morning reports, research opportunities, and grand rounds presentations; clinical experience via patient encounters; and assessments in the form of oral and written examinations. 5.1 Clinical knowledge The virtual curriculum is the foundation of our neuro-ophthalmology elective, developed to cover most of the level 1 topics established by the North American Neuro-ophthalmology Society (NANOS) Curriculum on the Neuro-Ophthalmology Virtual Education Library (NOVEL) website. These level 1 topics are recommended by NANOS for medical students and cover a variety of foundational topics in neuro-ophthalmology. The core components of the virtual elective include: morning report, grand rounds, research experience, patient encounters, and oral examination. The level 1 topics are divided across the twenty days of the elective in a progressive manner, so that concepts build upon each other. Students will learn relevant physiology and anatomy in week 1, followed by clinical signs and symptoms in week 2. In week 3 and 4, students learn a variety of diseases relevant to neuro-ophthalmology (see for a detailed schedule). In addition to the core components of the elective, students are provided links to EyeWiki articles and YouTube didactic videos by an expert source (AGL) corresponding to each topic. , Finally, a brief multiple-choice exam (10–15 questions) will be administered at the end of each week to assess students' mastery of the coursework and to provide feedback for course improvements. 5.2 Morning report Students are expected to attend and participate in a virtual neuro-ophthalmology morning report daily during the elective. Morning reports, like Grand Rounds, are relatively well-suited to a virtual format and can be transitioned to a video conference platform. Through involvement in a virtual morning report, students receive similar value as attending in-person rounds, such as improving their clinical and basic science knowledge, developing clinical decision-making, and learning how to present cases. We have implemented a number of strategies to facilitate student participation in virtual morning reports. Students are expected to respond to questions throughout the report from the attending physician. If there are many medical students in the elective, a select number may be assigned each day as “active” participants who are designated to answer the morning's questions. Alternatively, students may alternate in a queue to answer questions. To further aid student engagement, at each morning report, one student is expected to send via email the night before and then give a very brief presentation on a relevant topic. This “med-student minute” is an effective way to reinforce material from the virtual curriculum while adding clinical context. Additionally, a weekly student-run morning report session is facilitated by a medical student, providing an opportunity to lead and present a case to an audience. Students can evaluate the effectiveness of each of these methods via a postelective survey. We anticipate that video-based morning reports and conferences could be used in the future. The virtual nature of these conferences allows for increased medical student attendance due to decreased travel constraints and the ability for students at a different institution to attend. 5.3 Grand rounds An enrolled student is required to present at least one case during the elective at the institution's Neuro-Ophthalmology Grand Rounds via video conference. The student should confer with fellows and/or the attending physician to find an appropriate case to present, cumulating in a formal slide deck presentation. The presentation should include an introduction slide with a “focused stem” summary, relevant past medical and ocular history, physical examination including images where possible, representative imaging studies and pathology (if relevant), diagnostic procedures, and a “take-home message” summary slide. 5.4 Research experience The elective also facilitates student exploration of neuro-ophthalmology research. Students are expected to compose an article to be published on the American Academy of Ophthalmology's EyeWiki website, write a case report based on a unique patient encounter during the elective, and/or contribute to a neuro-ophthalmology book chapter. The research component of the elective is well-suited to a virtual format. 5.5 Patient encounters Patient encounters are a challenging component of a course to conduct virtually, and where possible, in-person encounters are preferred. In this elective some students may still participate in in-person patient encounters, in which they accompany and assist an attending physician during a history and physical examination. The patient examination room is situated in a manner that follows social distancing guidelines by using floor “X” stickers to mark appropriate safe distances from others. When these accommodations are not possible, such as for students enrolling in the elective as an away rotation, virtual encounters will be provided via video chat on tablets mounted on rolling stands. Through this methodology, students will observe the entirety of the patient encounter and may be taught and questioned by the attending in a similar manner to in-person students. Though virtual students cannot carry out any of the physical examination, they may be asked to observe and evaluate signs which the attending physician elicits. Virtual students may also interact with the patient by asking follow-up questions after the history. Both virtual and in-person students may be asked to construct a differential diagnosis, describe a management plan, or write notes following these patient encounters. 5.6 Oral examination An oral examination will be administered virtually during the last week of the elective by the course director. The purpose of the oral examination is to assess the student's ability to communicate their acquired knowledge in neuro-ophthalmology in a well-organized, succinct manner. The oral examination will serve as an opportunity for the student to demonstrate their ability to develop a differential diagnosis and make clinical decisions about diagnostic testing and treatments for common neuro-ophthalmic conditions. A typical oral examination will involve a case selected by the course director. The case will be presented to the student by the course director, followed by prompted questions by the course director to facilitate discussion. The student will be expected to answer questions relating to patient history, examination findings, differential diagnosis, and treatments. The student will be graded based on their ability to demonstrate proper clinical approach and reasoning, rather than their memorization skills. Our oral examination format is modeled after the American Board of Ophthalmology's oral examination for graduating ophthalmology residents seeking board certification.
Clinical knowledge The virtual curriculum is the foundation of our neuro-ophthalmology elective, developed to cover most of the level 1 topics established by the North American Neuro-ophthalmology Society (NANOS) Curriculum on the Neuro-Ophthalmology Virtual Education Library (NOVEL) website. These level 1 topics are recommended by NANOS for medical students and cover a variety of foundational topics in neuro-ophthalmology. The core components of the virtual elective include: morning report, grand rounds, research experience, patient encounters, and oral examination. The level 1 topics are divided across the twenty days of the elective in a progressive manner, so that concepts build upon each other. Students will learn relevant physiology and anatomy in week 1, followed by clinical signs and symptoms in week 2. In week 3 and 4, students learn a variety of diseases relevant to neuro-ophthalmology (see for a detailed schedule). In addition to the core components of the elective, students are provided links to EyeWiki articles and YouTube didactic videos by an expert source (AGL) corresponding to each topic. , Finally, a brief multiple-choice exam (10–15 questions) will be administered at the end of each week to assess students' mastery of the coursework and to provide feedback for course improvements.
Morning report Students are expected to attend and participate in a virtual neuro-ophthalmology morning report daily during the elective. Morning reports, like Grand Rounds, are relatively well-suited to a virtual format and can be transitioned to a video conference platform. Through involvement in a virtual morning report, students receive similar value as attending in-person rounds, such as improving their clinical and basic science knowledge, developing clinical decision-making, and learning how to present cases. We have implemented a number of strategies to facilitate student participation in virtual morning reports. Students are expected to respond to questions throughout the report from the attending physician. If there are many medical students in the elective, a select number may be assigned each day as “active” participants who are designated to answer the morning's questions. Alternatively, students may alternate in a queue to answer questions. To further aid student engagement, at each morning report, one student is expected to send via email the night before and then give a very brief presentation on a relevant topic. This “med-student minute” is an effective way to reinforce material from the virtual curriculum while adding clinical context. Additionally, a weekly student-run morning report session is facilitated by a medical student, providing an opportunity to lead and present a case to an audience. Students can evaluate the effectiveness of each of these methods via a postelective survey. We anticipate that video-based morning reports and conferences could be used in the future. The virtual nature of these conferences allows for increased medical student attendance due to decreased travel constraints and the ability for students at a different institution to attend.
Grand rounds An enrolled student is required to present at least one case during the elective at the institution's Neuro-Ophthalmology Grand Rounds via video conference. The student should confer with fellows and/or the attending physician to find an appropriate case to present, cumulating in a formal slide deck presentation. The presentation should include an introduction slide with a “focused stem” summary, relevant past medical and ocular history, physical examination including images where possible, representative imaging studies and pathology (if relevant), diagnostic procedures, and a “take-home message” summary slide.
Research experience The elective also facilitates student exploration of neuro-ophthalmology research. Students are expected to compose an article to be published on the American Academy of Ophthalmology's EyeWiki website, write a case report based on a unique patient encounter during the elective, and/or contribute to a neuro-ophthalmology book chapter. The research component of the elective is well-suited to a virtual format.
Patient encounters Patient encounters are a challenging component of a course to conduct virtually, and where possible, in-person encounters are preferred. In this elective some students may still participate in in-person patient encounters, in which they accompany and assist an attending physician during a history and physical examination. The patient examination room is situated in a manner that follows social distancing guidelines by using floor “X” stickers to mark appropriate safe distances from others. When these accommodations are not possible, such as for students enrolling in the elective as an away rotation, virtual encounters will be provided via video chat on tablets mounted on rolling stands. Through this methodology, students will observe the entirety of the patient encounter and may be taught and questioned by the attending in a similar manner to in-person students. Though virtual students cannot carry out any of the physical examination, they may be asked to observe and evaluate signs which the attending physician elicits. Virtual students may also interact with the patient by asking follow-up questions after the history. Both virtual and in-person students may be asked to construct a differential diagnosis, describe a management plan, or write notes following these patient encounters.
Oral examination An oral examination will be administered virtually during the last week of the elective by the course director. The purpose of the oral examination is to assess the student's ability to communicate their acquired knowledge in neuro-ophthalmology in a well-organized, succinct manner. The oral examination will serve as an opportunity for the student to demonstrate their ability to develop a differential diagnosis and make clinical decisions about diagnostic testing and treatments for common neuro-ophthalmic conditions. A typical oral examination will involve a case selected by the course director. The case will be presented to the student by the course director, followed by prompted questions by the course director to facilitate discussion. The student will be expected to answer questions relating to patient history, examination findings, differential diagnosis, and treatments. The student will be graded based on their ability to demonstrate proper clinical approach and reasoning, rather than their memorization skills. Our oral examination format is modeled after the American Board of Ophthalmology's oral examination for graduating ophthalmology residents seeking board certification.
Conclusion The COVID-19 pandemic has presented numerous challenges for medical students aspiring to apply into ophthalmology. With uncertainty regarding the duration of the pandemic and the possibility of additional quarantine periods in the future, educational innovation is needed to ensure continued medical student immersion in the clinical environment. The virtual neuro-ophthalmology elective described herein is a novel solution to expose students to a relevant, accessible ophthalmology curriculum while providing research and presentation opportunities, ultimately providing them an opportunity to obtain a meaningful letter of recommendation for their residency application. Although pandemic restrictions may eventually be lifted allowing students to return to the clinical environment, this virtual elective approach still provides value to learners. Virtual teaching conferences, didactics, and clinical experiences are innovative tools that are transforming medical education and will likely play a larger role in the future as educational technologies continue to develop. The virtual nature of these experiences is useful to foster community between students and faculty at different academic institutions. This elective format is easily adaptable to other ophthalmology subspecialties and provides utility to other institutions navigating this new virtual environment. Future research will be needed to demonstrate the effectiveness of this elective.
Literature search A MEDLINE/PubMed database search was conducted to review the literature on the impact of COVID-19 on the clinical aspect of undergraduate medical education. We included articles from all years, but the review is based mainly on articles published from 2019 to June 2020, after the rise of COVID-19. All study designs were included due to the recency of the pandemic and paucity of the current literature. The search was conducted using various combinations of the following search terms: medical student learning, undergraduate medical education, ophthalmology medical student teaching, ophthalmology medical curricula, COVID-19, Coronavirus, SARS-CoV-2, Virtual Learning. Articles were reviewed and included if the information therein was pertinent to undergraduate medical curriculum during the COVID-19 pandemic. Articles were excluded if they focused solely on postgraduate education and continuing medical education. English language abstracts were screened for relevance and the full texts of articles that met the inclusion criteria were obtained. A further hand search of reference lists for articles were reviewed for other publications of significance. English abstracts for articles written in another language were reviewed and included if the inclusion criteria were met.
Disclosure The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.
|
An interpretative phenomenological study about maternal perceptions of cesarean birth | e3e4ef3c-b50c-4f39-a81a-061d2022e021 | 11801696 | Surgical Procedures, Operative[mh] | In Jordan, maternity care encompasses both public and private healthcare services, supporting women through pregnancy, childbirth, and the postpartum period. While many women utilize public health facilities due to their accessibility and affordability, a significant number choose private healthcare for its perceived higher quality and personalized care, despite the associated higher costs. Public hospitals often remain the primary choice for complex pregnancies or labor due to their comprehensive services . Maternity care services are provided by obstetricians, nurses, and midwives across both healthcare sectors. Notably, 96% of women prefer to receive antenatal care from a doctor, with only 3% selecting for midwives or nurses, indicating a strong preference for physician-led services. Antenatal visits typically include essential check-ups, screenings, nutritional advice, and health education. The experience can vary based on the chosen healthcare setting: private facilities often provide longer consultations, which many women find more reassuring, while public services are favored for their affordability and availability . The global rise in Cesarean Section (CS) rates has become a concern to healthcare providers, policymakers, and expectant mothers alike. The World Health Organization (WHO) states that an optimal CS rate falls between 10% and 15% of all births . In Jordan, recent years have seen a rate of approximately 25% . Data collected from different sectors of Jordan’s healthcare system reported the following rates for 2015–2017: 40.4% (±2.6) in university hospitals, 39.1% (±1.8) in private hospitals, 36.1% (±0.2) in military hospitals, and 27.4% (±0.7) in governmental hospitals . These statistics have prompted researchers to investigate the factors contributing to the prevalence of CS deliveries and to understand the perceptions of CS among pregnant Jordanian women, specifically those who are experiencing their first birth (primipara) or have previously given birth once (para 1). Numerous factors influence a woman’s choice to undergo a CS delivery. These factors include medical indications, cultural norms, socioeconomic status, and individual preferences. Salem (2021) pointed to previous CS (33.6%), abnormal presentation (20.3%), and patient request (16%) as common indications for CS in Jordan. CS, when necessary, is undoubtedly a life-saving intervention. However, when performed without medical justification, it can put both mother and baby at risk, leading to increased rates of maternal morbidity and mortality, neonatal respiratory complications, and longer recovery times . Therefore, we need to understand how Jordanian primipara and para 1 women perceive CS delivery to promote birthing practices and achieve optimal maternal and neonatal outcomes. Women’s perceptions and decisions about CS are shaped by personal beliefs, cultural influences, and healthcare setting. Some women view CS as a necessary medical intervention to ensure the safety of both mother and baby, perceiving it as a modern advancement that offers controlled and planned delivery while potentially reducing the risks associated with normal vaginal delivery (NVD). However, this perception is often influenced by factors such as trust in doctors, advice from healthcare providers, and familial or cultural expectations, particularly in private hospitals where personalized care is emphasized . Previous research on CS delivery has focused on healthcare professionals’ perspectives rather than those of pregnant women. This qualitative study seeks to explore this population’s perceptions, beliefs, and decision-making processes surrounding CS. This will enable healthcare providers to tailor care to serve expectant mothers better, ultimately promoting evidence-based, woman-centered maternity care in Jordan. This qualitative study investigated the perceptions of Jordanian primipara and para 1 women regarding CS delivery. Focus group interviews were conducted to reveal participants’ attitudes and knowledge about CS, its risks and benefits, and their reasons for opting for or rejecting this mode of delivery. The findings serve to inform clinical practice, policy development, and future research endeavors to enhance maternity care practices and outcomes in Jordan and similar contexts. Study design An interpretative phenomenological approach was adopted because it allows participants to respond to in-depth, probing questions, express their opinions, and expand upon their experiences . Setting and participants This Jordanian study recruited its participants from two public hospitals and two health centers. Three focus groups were held with primiparous women (2nd and third trimesters) at hospital antenatal care (ANC) clinics. Four other focus groups were conducted with para 1 women (14–50 days postpartum) who brought their infants to the centers for vaccination and neonatal screening. Purposive sampling was employed, utilizing a maximum variation strategy . Based on age, education level, and family income. In total, 41 women who met the criteria and agreed to participate were divided into seven groups of 5–7, the sample size was determined based on data saturation . Data collection and procedures Focus groups interviews were used for data collection as they are effective approaches for exploring perceptions and lived experiences . Participants expressed themselves freely, and the researchers posed questions uncovering in-depth data. To ensure comprehensive coverage of relevant topics . The research team developed an interview guide, as shown in . The lead author conducted the interviews, each lasting 60–70 minutes. The participants’ recruitment commenced after obtaining IRB approval and took place from February 1 to March 30, 2024. Data analysis Upon completion of each interview, the recorded data underwent a verbatim transcription to Arabic and then was translated into English. For data organization and categorization, the English transcripts were entered into the NVivo 12 software (QSR International Pty Ltd, Doncaster, Victoria, Australia), which labels each paragraph with phrases representing specific ideas. The first author analyzed data using the interpretative phenomenological analysis framework . The NVivo-generated categorized data was manually assigned as single phrases to groups of linked sub-phrases, which were then listed under a significant overarching idea. The research team held comprehensive discussions to identify themes predominantly zeroing in on extracted quotes from the interviews, which were then entered into the final thematic list. Ethical consideration Ethical clearance was obtained from the Institutional Review Boards of the Ministry of Health and the Royal Medical Services. The women were told that participation was voluntary and that their identities would not be disclosed. Informed consent was obtained from all participants before commencing the interviews. Trustworthiness of the study The investigation adhered to the criteria of dependability, credibility, confirmability, and transferability to ensure its rigor . The first author discussed the emerging themes and their interpretations with the research team, taking their feedback into account. During the study, several participants from the primipara focus groups 2 and 3 and the para 1 focus groups 2 and 4 expressed interest in reviewing the final list of themes derived from the interviews. To facilitate this, the thematic summaries were shared via WhatsApp with these participants, enabling them to provide feedback and ensure the accuracy of the findings with their experiences. This process ensured that participants’ opinions, experiences, and needs were accurately represented. An interpretative phenomenological approach was adopted because it allows participants to respond to in-depth, probing questions, express their opinions, and expand upon their experiences . This Jordanian study recruited its participants from two public hospitals and two health centers. Three focus groups were held with primiparous women (2nd and third trimesters) at hospital antenatal care (ANC) clinics. Four other focus groups were conducted with para 1 women (14–50 days postpartum) who brought their infants to the centers for vaccination and neonatal screening. Purposive sampling was employed, utilizing a maximum variation strategy . Based on age, education level, and family income. In total, 41 women who met the criteria and agreed to participate were divided into seven groups of 5–7, the sample size was determined based on data saturation . Focus groups interviews were used for data collection as they are effective approaches for exploring perceptions and lived experiences . Participants expressed themselves freely, and the researchers posed questions uncovering in-depth data. To ensure comprehensive coverage of relevant topics . The research team developed an interview guide, as shown in . The lead author conducted the interviews, each lasting 60–70 minutes. The participants’ recruitment commenced after obtaining IRB approval and took place from February 1 to March 30, 2024. Upon completion of each interview, the recorded data underwent a verbatim transcription to Arabic and then was translated into English. For data organization and categorization, the English transcripts were entered into the NVivo 12 software (QSR International Pty Ltd, Doncaster, Victoria, Australia), which labels each paragraph with phrases representing specific ideas. The first author analyzed data using the interpretative phenomenological analysis framework . The NVivo-generated categorized data was manually assigned as single phrases to groups of linked sub-phrases, which were then listed under a significant overarching idea. The research team held comprehensive discussions to identify themes predominantly zeroing in on extracted quotes from the interviews, which were then entered into the final thematic list. Ethical clearance was obtained from the Institutional Review Boards of the Ministry of Health and the Royal Medical Services. The women were told that participation was voluntary and that their identities would not be disclosed. Informed consent was obtained from all participants before commencing the interviews. The investigation adhered to the criteria of dependability, credibility, confirmability, and transferability to ensure its rigor . The first author discussed the emerging themes and their interpretations with the research team, taking their feedback into account. During the study, several participants from the primipara focus groups 2 and 3 and the para 1 focus groups 2 and 4 expressed interest in reviewing the final list of themes derived from the interviews. To facilitate this, the thematic summaries were shared via WhatsApp with these participants, enabling them to provide feedback and ensure the accuracy of the findings with their experiences. This process ensured that participants’ opinions, experiences, and needs were accurately represented. Forty-one women were interviewed within seven focus groups. The women fell into the 19–49 age range. 17 women were primipara, and 24 were para 1, of whom 15 had delivered by cesarean section. 16 women were employed, and 25 were housewives. All the husbands were employed. 19 received ANC in public health facilities, 10 in private, and 12 mixed between public and private. The thematic analysis of the Jordanian women’s perceptions regarding CS is shown in . The foundation of women’s knowledge and cognitive structures This theme explains how the woman’s view of CS is formed and crystallized based on the knowledge she possesses and, ultimately, her feelings regarding her preferred mode of delivery. This theme consists of several sub-themes, as follows: Perceived meaning of CS Some participants concurred that a CS does not represent the typical course of labor. Conversely, a ‘normal’ vaginal delivery (NVD) indicates an absence of complications and a smooth progression of labor. One participant explained: “NVD means a natural stage that the woman goes through at the end of her pregnancy. CS means there is a problem. Risk for the mother or the baby leads to having CS.” (Para 1, FG4) Some women reported that a CS represented a bearable pain compared to the intense pain of NVD. “NVD means unbearable pain for me. Cesarean section is painful, but with strong painkillers, the pain is not comparable to the pain of labor.” (Para 1, FG 1) Perceived benefits of CS Some participants said that they opted for CS because it meant that they would not be exposed to suffering and pain before seeing the baby. The pain after the CS could be tolerated as it would be alleviated by the presence of the newborn: “I prefer a CS because I will not suffer before I see my babies. I want to go to sleep and wake up to find them next to me. I feel that looking at them will ease my pain.” (Primipara, FG 3) One participant added: “CS is easier and better for me. A CS is more comfortable, as the lady said. You sleep and wake up to find the baby next to you.” (Para 1, FG1) Some women view CS as the preferable mode of birth because it does not entail waiting for the unknown. They appreciate the scheduled nature of the procedure, allowing for clear expectations regarding the date and mode of delivery. As one participant articulated: “I gave birth to CS. For sure, you are in pain after giving birth, but it is enough for you to enter the hospital without pain and go directly to the theatre; then, you and the baby will come out safely. You will suffer after giving birth, but God will make it easy.” (Para 1, FG2) The factor of close and continuous monitoring of the mother during childbirth was a top priority among primipara women. They explained that one of the benefits of CS is that the doctor remains present from the beginning of the operation to its end, next to the woman giving birth. While in NVD, the doctor will only be present intermittently, coming at different times to examine her: “I intend to have a CS. The doctor is by your side. The anaesthesiologists are taking care of you and are paying attention. In NVD, it is normal for them to put you on the bed and forget about you. There is no care like that, I hear.” (Primipara, FG3). Some women also felt that a woman who delivers by CS is given extra care because she has been through surgery and needs care until the wound heals. “A cesarean section means pampering and attention. You will stay asleep and comfortable... Oh, you will relax and be pampered for the first period.” (Primipara, FG2) Some of the women confirmed their fears that NVD would alter the shape of the vulva and size of the vagina in a way that is unattractive, and this may affect the marital relationship, even leading to possible infidelity: “I know that the shape and size of the area will be changed after NVD. I heard the sight will be disgusting, and your husband may become disgusted to sleep with you, and he may cheat on you.” (Primipara, FG2) This idea was reiterated and confirmed by other participants, who brought to light how their husbands directly requested a CS delivery, prompted by their concerns about the change in the shape of the vulva area and expansion of the vagina. “My husband went to the doctor and asked him for a CS. He discussed with the doctor the deformity of the area, and he couldn’t bear to see this, and the doctor agreed to perform a cesarean.” (Para 1, FG4) Perceived concerns about CS Some participants explained that the nature and duration of the pain were uppermost in their thoughts: “Honestly, both are painful, but I prefer NVD to CS because it will only hurt for hours, while CS will cause pain for weeks, and I will not be able to take care of my baby.” (Para 1, FG3) One para 1 added that she was afraid of infection in the CS wound and was concerned about how to care for it: “I was afraid of infection from the operation and infection of the wound. I had no other fears.” (Para 1, FG3) On the other hand, some women were aware of the dangers of CS and some complications that the mother may be exposed to, such as wound pain and possible infection, as well as long-term complications of adhesions. As for the fetus, he would be vulnerable to lack of oxygen: “A CS means adhesions, more bleeding, and a risk to the baby of low oxygen levels, requiring the baby to enter an incubator. CS means pain at the wound site, postpartum infections, lack of movement, and back needles.” (Para 1, FG2) Some participants were excited about the CS but worried about their post-surgery condition and the resulting pain. They expressed concerns about being able to move about and care for the baby independently without help. They also thought about the future, their body shape, and the fear of sagging in the abdominal area, as one of the participants explained: “The thing I am most afraid of is coughing or sneezing after CS because they are very painful. Also, the first walk after the operation. Also, after I go home, when I wake up for the baby at night, I will sleep on my back; how will I get up? It will be a lot of suffering.” (Primipara, FG1) Previous experiences Previous experiences greatly influenced the participants’ perceptions regarding CS. It was clear that participants all knew about bad experiences, whether they were her own or those of a sister or friend. The trace of the experience remains engraved in the memory, often overshadowing many successful experiences. There are many women who were induced and went on to have an NVD, but what remains in the women’s collective memory are the occasions when birth began with induction and ended, for mostly medical reasons, with a CS. In these cases, the women were exposed to the pain of normal delivery, added to the pain of a CS delivery: “When I was delivered, I lived through a week of terror, induction, and internal examination, opposite to what I expected. To be honest, I have been living in terror until now, from what I suffered.” (Para 1, FG 2) Another participant added: “I preferred CS, to be honest; after what happened to my sister, I didn’t want to give birth normally. My sister was admitted for labor, and a trainee doctor came and ruptured her membrane and the baby suffocated in her abdomen and died. I will tell them that I want a CS. I learned from the experience of my sister.” (Primipara, FG 1) Some women narrated how, at the hospital, they witnessed other women who suffered during natural childbirth. They saw that there was negligence on the part of the medical staff and a lack of respect for the woman’s privacy, so they felt discouraged about NVD and favoured CS: “From the front of the operating room, I could see a normal delivery room where a woman giving birth. She was bleeding, and no one paid attention to her. I tried to call the doctors and they said nothing was wrong. They did not come and see her. There was a lot of neglect and no care. The patient was also exposed in front of passers-by. This is unacceptable. In a CS, there is privacy, cleanliness, and care. Within a quarter of an hour, you are finished, and everything is fine, thank God.” (Para 1, FG1) Some women told stories of relatives who were handled badly at birth and ongoingly experienced the consequences. A woman told us that when her husband was born, his shoulder was dislocated, and he had spent all his life unable to use his hand normally, so she preferred CS. “I am afraid of NVD. My husband had a normal birth, but when he was born, he had a dislocation in his shoulder. This thing affected him, and his hand is not normal until now. This thing scares me a lot. I don’t want to give birth normally.” (Para 1, FG2). The parties influential in shaping women’s perceptions This theme discusses the parties who are influential in shaping women’s opinions regarding CS delivery in both positive and negative ways. Trust in the doctor It was evident that most of the participants trusted their doctor’s opinion. The paternalistic concept was very clear during the participants’ interviews. That is, the woman firmly believes that the doctor knows what is best for her condition and can make the appropriate decision: “He explained it to me exactly, from the beginning. He told me that I would have complications. So, I felt that he knew about my condition, and it was better to trust him.” (Primipara, FG2) “If the doctor had tried with me, I would have given birth normally. The decision is in the doctor’s hands.” (Para 1, FG2) Influence of the husband and gender equity Most women had discussed the preferred method of delivery with their husbands. Most husbands respected the doctor’s opinion about the mode of delivery depending on the wife’s health condition. Some husbands leave the decision to the wife, allowing her to do what is appropriate and comfortable for her: “He said the decision is in your hands and consult the doctor. No one feels the pain except you.” (Primipara, FG3) “My husband told me to do what I wanted. He said, “The important thing is that they save you.” (Primipara, FG1) “I used to talk to my husband a lot about the subject, and he encouraged me to deliver normally.” (Para 1, FG3) Role of culture Cultural beliefs and norms surrounding childbirth heavily influence women’s perceptions of CS. Jordanian culture sees vaginal birth as more natural or traditional. On the other hand, CS may be viewed as a safer option, entailing less suffering. Cultural attitudes towards pain, childbirth, and medical interventions can all shape how women perceive CS and influence their decision-making. Our participants differed about whether the prevailing culture encourages NVD or CS, but ultimately, they agreed that the old generation and some of the new generation encourage NVD: “My mother and mother-in-law encourage NVD, while my friends encourage CS.” (Para 1, FG 1) Many participants felt that most of the new generation of mothers encourages CS: “I am Primipara, but I worked as a maternity nurse. A week ago, I swear, 3 pregnant women asked for a CS. The doctor put them in for labor induction. They said that they didn’t want to, so the doctor told them to sign a paper and performed a CS for them.” (Primipara, FG 1) Several women expressed the belief that some doctors actively promote CS delivery: “I feel that women are not very enthusiastic about NVD, and doctors encourage women to have CS because of the financial factor involved in the matter. I feel that most new mothers have had cesarean births at present. My friend was pregnant with her fourth child. She had excellent conditions for a natural birth, but they referred her to have a CS without knowing the reason.” (Para 1, FG 1). Maternal health condition A woman’s health status and any potential complications during pregnancy can also impact her perception of CS. Pre-existing health conditions or pregnancy-related complications may increase the risk of a difficult NVD, thus encouraging the mother to consider a planned CS as a safer option for herself and her baby. “I had no dilatation and no contractions. The cervix was closed. At the end, the baby’s pulse began to decrease because the fluids were very light, because of this factor. The only reason I chose CS was because there was a threat to the life of the fetus.” (Para 1, FG 4) “I conceived through IVF. What made me give birth by CS was that I gave birth to her in the eighth month. My water broke, and I had to go to the hospital. There, they started telling me to wait, but I told them that I was not ready to take the risk, not even 1%. I did not agree with the NVD because I told them that I had waited all these years to see the baby. That’s why I requested CS .” (Para 1, FG2) Transforming perception into action Perceptions of CS delivery, therefore, are built on both preconceived notions (stemming from personal or familial experience) and objective open-mindedness (allowing physician intake). We understand how knowledge was formed among the participants and the factors that led to the formation of women’s perception of CS, thus the main theme remains on transforming these perceptions into reality. Decision on mode of delivery Some women asserted their autonomy in the decision-making process, and their husbands’ sole concern was the safety of mother and baby. Their words echoed the sentiment of empowerment and safety, highlighting their trust in their decision and the support of their partner. “The decision to choose the method of delivery is up to me. My husband tells me to do what I feel comfortable about. The important thing is that you and the baby are safe.” (Primipara, FG 2) Similarly, some women showed a sense of independence and self-assurance in expressing their desire for a C-section, committed to their own beliefs and unaffected by external pressures or opinions: “I want to give birth by CS. I don’t feel that those around me are influencing me. I don’t listen to their opinions. I told you; I only do what I am convinced of.” (Primipara, FG 3) Other women see the doctor as pivotal in the decision-making process, and they are prepared to change their decisions based on medical advice. “The doctor is first in line. He knows my condition and that of the fetus. I mean, I like NVD. If he tells me that a CS is better, I will respond .” (Primipara, FG 1). Recommendations for future mothers When asked what recommendations they would give to women about giving birth, most of the participants were aware of the impact of their advice but chose not to express their opinion. They emphasized the importance of informed decision-making guided by medical expertise based on each mother’s particular circumstances. They avoided imposing their own experiences on other expectant mothers, honoring their autonomy to make their own decisions. “I will not express my opinion. She may take my words seriously. I will tell her, “See what suits you and do it. See what the doctor tells you.” (Primipara, FG2) “ According to her doctor and his advice. According to her condition and that of her fetus. I won’t tell her about my experience.” (Para 1, FG3) This theme explains how the woman’s view of CS is formed and crystallized based on the knowledge she possesses and, ultimately, her feelings regarding her preferred mode of delivery. This theme consists of several sub-themes, as follows: Some participants concurred that a CS does not represent the typical course of labor. Conversely, a ‘normal’ vaginal delivery (NVD) indicates an absence of complications and a smooth progression of labor. One participant explained: “NVD means a natural stage that the woman goes through at the end of her pregnancy. CS means there is a problem. Risk for the mother or the baby leads to having CS.” (Para 1, FG4) Some women reported that a CS represented a bearable pain compared to the intense pain of NVD. “NVD means unbearable pain for me. Cesarean section is painful, but with strong painkillers, the pain is not comparable to the pain of labor.” (Para 1, FG 1) Some participants said that they opted for CS because it meant that they would not be exposed to suffering and pain before seeing the baby. The pain after the CS could be tolerated as it would be alleviated by the presence of the newborn: “I prefer a CS because I will not suffer before I see my babies. I want to go to sleep and wake up to find them next to me. I feel that looking at them will ease my pain.” (Primipara, FG 3) One participant added: “CS is easier and better for me. A CS is more comfortable, as the lady said. You sleep and wake up to find the baby next to you.” (Para 1, FG1) Some women view CS as the preferable mode of birth because it does not entail waiting for the unknown. They appreciate the scheduled nature of the procedure, allowing for clear expectations regarding the date and mode of delivery. As one participant articulated: “I gave birth to CS. For sure, you are in pain after giving birth, but it is enough for you to enter the hospital without pain and go directly to the theatre; then, you and the baby will come out safely. You will suffer after giving birth, but God will make it easy.” (Para 1, FG2) The factor of close and continuous monitoring of the mother during childbirth was a top priority among primipara women. They explained that one of the benefits of CS is that the doctor remains present from the beginning of the operation to its end, next to the woman giving birth. While in NVD, the doctor will only be present intermittently, coming at different times to examine her: “I intend to have a CS. The doctor is by your side. The anaesthesiologists are taking care of you and are paying attention. In NVD, it is normal for them to put you on the bed and forget about you. There is no care like that, I hear.” (Primipara, FG3). Some women also felt that a woman who delivers by CS is given extra care because she has been through surgery and needs care until the wound heals. “A cesarean section means pampering and attention. You will stay asleep and comfortable... Oh, you will relax and be pampered for the first period.” (Primipara, FG2) Some of the women confirmed their fears that NVD would alter the shape of the vulva and size of the vagina in a way that is unattractive, and this may affect the marital relationship, even leading to possible infidelity: “I know that the shape and size of the area will be changed after NVD. I heard the sight will be disgusting, and your husband may become disgusted to sleep with you, and he may cheat on you.” (Primipara, FG2) This idea was reiterated and confirmed by other participants, who brought to light how their husbands directly requested a CS delivery, prompted by their concerns about the change in the shape of the vulva area and expansion of the vagina. “My husband went to the doctor and asked him for a CS. He discussed with the doctor the deformity of the area, and he couldn’t bear to see this, and the doctor agreed to perform a cesarean.” (Para 1, FG4) Some participants explained that the nature and duration of the pain were uppermost in their thoughts: “Honestly, both are painful, but I prefer NVD to CS because it will only hurt for hours, while CS will cause pain for weeks, and I will not be able to take care of my baby.” (Para 1, FG3) One para 1 added that she was afraid of infection in the CS wound and was concerned about how to care for it: “I was afraid of infection from the operation and infection of the wound. I had no other fears.” (Para 1, FG3) On the other hand, some women were aware of the dangers of CS and some complications that the mother may be exposed to, such as wound pain and possible infection, as well as long-term complications of adhesions. As for the fetus, he would be vulnerable to lack of oxygen: “A CS means adhesions, more bleeding, and a risk to the baby of low oxygen levels, requiring the baby to enter an incubator. CS means pain at the wound site, postpartum infections, lack of movement, and back needles.” (Para 1, FG2) Some participants were excited about the CS but worried about their post-surgery condition and the resulting pain. They expressed concerns about being able to move about and care for the baby independently without help. They also thought about the future, their body shape, and the fear of sagging in the abdominal area, as one of the participants explained: “The thing I am most afraid of is coughing or sneezing after CS because they are very painful. Also, the first walk after the operation. Also, after I go home, when I wake up for the baby at night, I will sleep on my back; how will I get up? It will be a lot of suffering.” (Primipara, FG1) Previous experiences greatly influenced the participants’ perceptions regarding CS. It was clear that participants all knew about bad experiences, whether they were her own or those of a sister or friend. The trace of the experience remains engraved in the memory, often overshadowing many successful experiences. There are many women who were induced and went on to have an NVD, but what remains in the women’s collective memory are the occasions when birth began with induction and ended, for mostly medical reasons, with a CS. In these cases, the women were exposed to the pain of normal delivery, added to the pain of a CS delivery: “When I was delivered, I lived through a week of terror, induction, and internal examination, opposite to what I expected. To be honest, I have been living in terror until now, from what I suffered.” (Para 1, FG 2) Another participant added: “I preferred CS, to be honest; after what happened to my sister, I didn’t want to give birth normally. My sister was admitted for labor, and a trainee doctor came and ruptured her membrane and the baby suffocated in her abdomen and died. I will tell them that I want a CS. I learned from the experience of my sister.” (Primipara, FG 1) Some women narrated how, at the hospital, they witnessed other women who suffered during natural childbirth. They saw that there was negligence on the part of the medical staff and a lack of respect for the woman’s privacy, so they felt discouraged about NVD and favoured CS: “From the front of the operating room, I could see a normal delivery room where a woman giving birth. She was bleeding, and no one paid attention to her. I tried to call the doctors and they said nothing was wrong. They did not come and see her. There was a lot of neglect and no care. The patient was also exposed in front of passers-by. This is unacceptable. In a CS, there is privacy, cleanliness, and care. Within a quarter of an hour, you are finished, and everything is fine, thank God.” (Para 1, FG1) Some women told stories of relatives who were handled badly at birth and ongoingly experienced the consequences. A woman told us that when her husband was born, his shoulder was dislocated, and he had spent all his life unable to use his hand normally, so she preferred CS. “I am afraid of NVD. My husband had a normal birth, but when he was born, he had a dislocation in his shoulder. This thing affected him, and his hand is not normal until now. This thing scares me a lot. I don’t want to give birth normally.” (Para 1, FG2). This theme discusses the parties who are influential in shaping women’s opinions regarding CS delivery in both positive and negative ways. It was evident that most of the participants trusted their doctor’s opinion. The paternalistic concept was very clear during the participants’ interviews. That is, the woman firmly believes that the doctor knows what is best for her condition and can make the appropriate decision: “He explained it to me exactly, from the beginning. He told me that I would have complications. So, I felt that he knew about my condition, and it was better to trust him.” (Primipara, FG2) “If the doctor had tried with me, I would have given birth normally. The decision is in the doctor’s hands.” (Para 1, FG2) Most women had discussed the preferred method of delivery with their husbands. Most husbands respected the doctor’s opinion about the mode of delivery depending on the wife’s health condition. Some husbands leave the decision to the wife, allowing her to do what is appropriate and comfortable for her: “He said the decision is in your hands and consult the doctor. No one feels the pain except you.” (Primipara, FG3) “My husband told me to do what I wanted. He said, “The important thing is that they save you.” (Primipara, FG1) “I used to talk to my husband a lot about the subject, and he encouraged me to deliver normally.” (Para 1, FG3) Cultural beliefs and norms surrounding childbirth heavily influence women’s perceptions of CS. Jordanian culture sees vaginal birth as more natural or traditional. On the other hand, CS may be viewed as a safer option, entailing less suffering. Cultural attitudes towards pain, childbirth, and medical interventions can all shape how women perceive CS and influence their decision-making. Our participants differed about whether the prevailing culture encourages NVD or CS, but ultimately, they agreed that the old generation and some of the new generation encourage NVD: “My mother and mother-in-law encourage NVD, while my friends encourage CS.” (Para 1, FG 1) Many participants felt that most of the new generation of mothers encourages CS: “I am Primipara, but I worked as a maternity nurse. A week ago, I swear, 3 pregnant women asked for a CS. The doctor put them in for labor induction. They said that they didn’t want to, so the doctor told them to sign a paper and performed a CS for them.” (Primipara, FG 1) Several women expressed the belief that some doctors actively promote CS delivery: “I feel that women are not very enthusiastic about NVD, and doctors encourage women to have CS because of the financial factor involved in the matter. I feel that most new mothers have had cesarean births at present. My friend was pregnant with her fourth child. She had excellent conditions for a natural birth, but they referred her to have a CS without knowing the reason.” (Para 1, FG 1). A woman’s health status and any potential complications during pregnancy can also impact her perception of CS. Pre-existing health conditions or pregnancy-related complications may increase the risk of a difficult NVD, thus encouraging the mother to consider a planned CS as a safer option for herself and her baby. “I had no dilatation and no contractions. The cervix was closed. At the end, the baby’s pulse began to decrease because the fluids were very light, because of this factor. The only reason I chose CS was because there was a threat to the life of the fetus.” (Para 1, FG 4) “I conceived through IVF. What made me give birth by CS was that I gave birth to her in the eighth month. My water broke, and I had to go to the hospital. There, they started telling me to wait, but I told them that I was not ready to take the risk, not even 1%. I did not agree with the NVD because I told them that I had waited all these years to see the baby. That’s why I requested CS .” (Para 1, FG2) Perceptions of CS delivery, therefore, are built on both preconceived notions (stemming from personal or familial experience) and objective open-mindedness (allowing physician intake). We understand how knowledge was formed among the participants and the factors that led to the formation of women’s perception of CS, thus the main theme remains on transforming these perceptions into reality. Some women asserted their autonomy in the decision-making process, and their husbands’ sole concern was the safety of mother and baby. Their words echoed the sentiment of empowerment and safety, highlighting their trust in their decision and the support of their partner. “The decision to choose the method of delivery is up to me. My husband tells me to do what I feel comfortable about. The important thing is that you and the baby are safe.” (Primipara, FG 2) Similarly, some women showed a sense of independence and self-assurance in expressing their desire for a C-section, committed to their own beliefs and unaffected by external pressures or opinions: “I want to give birth by CS. I don’t feel that those around me are influencing me. I don’t listen to their opinions. I told you; I only do what I am convinced of.” (Primipara, FG 3) Other women see the doctor as pivotal in the decision-making process, and they are prepared to change their decisions based on medical advice. “The doctor is first in line. He knows my condition and that of the fetus. I mean, I like NVD. If he tells me that a CS is better, I will respond .” (Primipara, FG 1). When asked what recommendations they would give to women about giving birth, most of the participants were aware of the impact of their advice but chose not to express their opinion. They emphasized the importance of informed decision-making guided by medical expertise based on each mother’s particular circumstances. They avoided imposing their own experiences on other expectant mothers, honoring their autonomy to make their own decisions. “I will not express my opinion. She may take my words seriously. I will tell her, “See what suits you and do it. See what the doctor tells you.” (Primipara, FG2) “ According to her doctor and his advice. According to her condition and that of her fetus. I won’t tell her about my experience.” (Para 1, FG3) This study’s findings show that cultural, social, and individual factors influence women’s perceptions of CS. Some women perceived CS as a necessary medical intervention to ensure the safety of both mother and baby, viewing it as a modern advancement that offers a controlled and planned delivery while potentially reducing the risks associated with NVD, this finding was incongruent with the results of Longo et al. . However, this perception is not always grounded in medical necessity but may stem from fear, misinformation, or cultural beliefs. It is critical for healthcare providers to engage in mutual, open communication with expectant mothers, ensuring that decisions for CS are based on accurate medical indications rather than convenience or societal perceptions. In cultures where NVD is traditionally preferred, CS often carries negative associations, representing intervention and a deviation from the normal process of childbirth . Healthcare providers must acknowledge and address these diverse perspectives to provide tailored support, education, and informed decision-making to expectant mothers. Most of our participants perceive CS as a safer and less painful alternative to NVD. A study conducted in Pakistan reported that women chose CS because they feared the pain associated with NVD, and they preferred to schedule the delivery . In our study, women self-reported that their obstetricians and their recommendations were a major influence. Despite some women relying on specific knowledge areas and personal or family experiences to choose their mode of delivery, a power hierarchy exists between them and obstetricians. This often results in medical professionals making the final decision. Women and their husbands frequently expressed trust in the doctors, placing the final decision in their hands. As observed in , although pregnant women expressed a preference for NVD, their wishes are often overlooked due to the dominance of CS preference in obstetrics, which can stem from institutional protocols, practitioner convenience, or liability concerns. This underscores the need for healthcare providers to prioritize patient-centered care, ensuring that women’s preferences for NVD are respected and supported whenever medically appropriate. Moreover, the narrative literature review by Sys et al. (2021) asserted that communication with medical professionals is the key to making an informed decision regarding the mode of delivery. These results can help obstetricians identify and acknowledge their role as crucial members in the decision-making process for CS deliveries within their institutions. They can also form the basis for the development of an intervention that targets women and their husbands during the antenatal period, raising awareness of the benefits of NVD and the risks of CS. Obstetricians and other maternity healthcare providers have a duty to try to change women’s negative perceptions of natural childbirth, so they don’t opt for medically unjustified CS deliveries. Krychman (2016) opined that vaginal laxity can decrease sexual satisfaction, which can affect a woman’s sense of sexual self-esteem and her relationship with her sexual partner. The women in the current study revealed that the fear of vaginal laxity following NVD is a significant concern for them and their husbands, causing them to opt for CS over NVD. They worry about a loss of vaginal tightness, which they believe could negatively affect sexual satisfaction for both themselves and their husbands. This belief can lead women to reject natural childbirth due to the potential consequences of vaginal delivery, ignoring the higher medical risks and longer recovery associated with CS. It also underscores the mix of psychological and social factors that influence childbirth decisions. A holistic approach is needed to address childbirth’s full range of medical, emotional, and relational dimensions. Comprehensive education is called for so that healthcare providers can support women in making informed decisions while reducing the health risks of unnecessary medical procedures. Limitations of the study It should be noted that the perceptions of our participants are not representative of all primipara and para 1 Jordanian women, whose experiences may differ. The analysis presented in this study emerged from the experiences of the women in our sample, which included varying opinions regarding cesarean sections (CS), with some in favor and others against. We found that women generally followed an individualized decision-making process regarding CS, shaped by their knowledge, personal context, and several influencing factors while placing significant trust in their doctors’ recommendations and engaging in a collaborative decision-making process. However, specific contexts and cultural settings may limit the generalizability of these findings. This limitation, inherent to qualitative research, is due to its focus on in-depth exploration of experiences within a specific group rather than statistical representation. As such, the viewpoints of this subset of Jordanian primiparous and para 1 women may not be universally applicable. Future research could benefit from broader sampling and mixed-method approaches to enhance the generalizability and applicability of these findings It should be noted that the perceptions of our participants are not representative of all primipara and para 1 Jordanian women, whose experiences may differ. The analysis presented in this study emerged from the experiences of the women in our sample, which included varying opinions regarding cesarean sections (CS), with some in favor and others against. We found that women generally followed an individualized decision-making process regarding CS, shaped by their knowledge, personal context, and several influencing factors while placing significant trust in their doctors’ recommendations and engaging in a collaborative decision-making process. However, specific contexts and cultural settings may limit the generalizability of these findings. This limitation, inherent to qualitative research, is due to its focus on in-depth exploration of experiences within a specific group rather than statistical representation. As such, the viewpoints of this subset of Jordanian primiparous and para 1 women may not be universally applicable. Future research could benefit from broader sampling and mixed-method approaches to enhance the generalizability and applicability of these findings In conclusion, women hold a complex blend of ideas and perceptions when it comes to modes of birth. Although they have their own perceptions regarding the meaning, benefits, and disadvantages of delivery by Cesarean section, the major influence on their decision-making was ultimately found to be the advice they received from their doctor. A full understanding of the diverse perspectives and preferences of expectant women when it comes to the mode of delivery can potentially inform patient-centered care approaches where women are encouraged to make informed decisions while receiving psychological support and holistic antenatal care. Furthermore, the findings of this study highlight the importance of integrating women’s perspectives into policy development to enhance patient-centered maternity care. Policies should prioritize shared decision-making between women and healthcare providers, emphasizing the importance of informed consent and providing clear, evidence-based information about cesarean delivery and its implications. Additionally, the study underscores the need for policies that support education and counseling programs to address non-medical requests for cesarean sections, ensuring that maternal and neonatal health outcomes are optimized. These findings can guide the development of maternity care policies in Jordan and similar contexts, promoting better alignment between women’s preferences and evidence-based clinical practices |
Research investigating patient and carer psychoeducation needs regarding post-stroke cognition: a scoping review | 8e5b1159-f4e0-4185-afdf-7e4dd0b0dba2 | 11751859 | Patient Education as Topic[mh] | The majority of stroke survivors experience cognitive impairment affecting at least one domain in the first weeks after stroke, although exact prevalence estimates vary depending on the nature of assessments used and sample characteristics. In the months after stroke, cognitive trajectories vary but post-stroke cognitive impairment persists in a substantial proportion of cases and stroke survivors are at a significantly increased risk of developing vascular and mixed dementia. Furthermore, stroke survivors consistently report cognitive problems as one of their greatest concerns and unmet needs. Clinical guidelines recommend cognitive screening as soon as possible after stroke to identify any cognitive impairments and recent evidence suggests early screening may also be helpful for predicting longer-term outcomes. Specifically, while there is currently no way to predict long-term post-stroke cognitive outcomes reliably on an individual level, a recent systematic review and meta-analysis identified baseline cognitive impairment as the strongest risk factor for longer-term cognitive impairment after stroke. This highlights the importance of acute cognitive screening to flag and support patients at risk of poor long-term outcomes. After initial cognitive screening, psychoeducation and adjustment often become the focus of cognitive rehabilitation as there is currently no strong evidence to support interventions that directly improve cognitive outcomes after stroke. Providing information through psychoeducation supports patients (and their family members) to understand and cope with diagnoses and previous research has found a beneficial impact of psychoeducation on self-efficacy and knowledge among those with minor stroke. Nevertheless, stroke survivors and their family members have reported substantial unmet psychoeducation needs, including about cognition. Furthermore, although clinical guidelines highlight the importance of psychoeducation generally, it remains unclear exactly what information should be provided about cognition. Without clear guidance, healthcare professionals face a substantial challenge in providing cognition-related information, as post-stroke cognitive impairment is a complex syndrome that affects various domains, including memory, language, attention, executive function, number processing and praxis. Furthermore, despite overall high prevalence of post-stroke cognitive impairment over the long term, the underlying aetiologies and longer-term trajectories of domain-specific impairments vary substantially. In addition, information about post-stroke cognition presents risks as well as benefits to patient well-being—for example, discussing increased dementia risk may help some individuals prepare for the future, but others may find the information highly anxiety-provoking. Successfully navigating this complexity requires a clearer understanding of what stroke survivors and their family members want to know about cognition and when the need for cognition-related psychoeducation arises and peaks, as stroke survivors and their family members are likely to benefit most if psychoeducation is provided when they are psychologically ready to receive it and able to process it appropriately. The aim of this scoping review was therefore to map and identify gaps within existing peer-reviewed articles describing cognition-related psychoeducation needs of stroke survivors and family members. Alongside other primary research, the ultimate goal of the research is to inform the design of a complex intervention focused on monitoring and psychoeducation to support cognition after stroke. The specific questions addressed by this review are: What research methods and designs have been used in previous studies describing stroke survivor and family member information needs regarding cognition? What timepoints after stroke have been investigated in previous studies? What are the characteristics of stroke survivors and family members included in previous studies? What psychoeducation needs related to post-stroke cognition have been reported in previous studies? What factors have been suggested to impact psychoeducation needs in previous studies? What key gaps exist within the current evidence base?
Review protocol The review was conducted in accordance with the Johanna Briggs Institute (JBI) methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. The protocol for this review underwent a rigorous peer-review process for publication and has been made openly available ( https://osf.io/fmz9t ). Any divergences from the protocol are outlined in . Patient and public involvement Patients were involved in developing the funding proposal for the fellowship of which this research forms a part. Patients were consulted on the importance of the research question and the approach through a survey with the Stroke Association’s Voices in Research (43 respondents) and three smaller focus groups. Of particular relevance to the present study, they emphasised the importance of including family member/carer perspectives where possible. Search strategy The search strategy was developed in consultation with an expert librarian at the University of Oxford. The final approved search strategy was adapted to search additional electronic databases using the Polyglot tool provided by Systematic Review Accelerator software ( https://sr-accelerator.com/ ). Systematic searches were conducted in five electronic databases on 25 August 2023: MEDLINE (PubMed), PsycINFO (Ovid), Embase (Elsevier), CINAHL (Ebsco) and Scopus (Elsevier). Search strategies developed using the Polyglot tool are presented in . Grey literature was not included; as the aim of the review was to inform an evidence-based intervention, we included articles that had been through rigorous peer-review only. The search strategy was limited to English, but it was not limited by year. Inclusion criteria The inclusion criteria were based on the JBI Population/Concept/Context (PCC) framework. Articles were eligible for inclusion in this review if they met the following criteria: Participants Stroke survivors and/or family members of stroke survivors. Stroke survivors were defined as someone who has experienced a clinically diagnosed stroke of any type. Family members were defined as someone who identifies as related to a stroke survivor by blood, marriage or with other familial involvement. Stroke survivors and/or family members of stroke survivors comprising at least 50% of the study population, in line with cut-offs used in previous scoping reviews. Stroke survivors and family members aged 18 years and over. Concept Self-reported information needs regarding post-stroke cognition. Information needs were defined as a desire to obtain information to satisfy a conscious (or unconscious) need. Cognition was defined as thinking skills related to any of the following domains: memory, language, attention, executive function, praxis and number processing. Context Studies conducted in the UK and other high-income countries, defined using the most recent World Bank country classifications (2022). Participants based either in a clinical setting or the community. Types of sources We included published peer-reviewed articles that used quantitative, qualitative or mixed methods designs. Review articles, peer-reviewed commentaries and opinion pieces were excluded. Study selection process Identified records were collated and uploaded into EndNote v.X9 (Clarivate Analytics, PA, USA). SR-Accelerator Deduplicator was used to remove duplicates. Two members of the research team (GH and FT) independently screened records against eligibility criteria by title, abstract and then full-text after conducting a pilot screening round. They recorded reasons for exclusion for articles excluded at the full-text stage. Differences in inclusion/exclusion decisions were settled by discussion among the research team. Reference lists of the included articles were hand searched to identify further relevant records. Data extraction A data extraction tool was developed prior to extracting data and refined iteratively throughout the process. One researcher (GH) used the final version of the tool to extract data from the included articles. Another researcher (FT) reviewed extracted data for accuracy. Synthesis Extracted data were synthesised using quantitative and qualitative methods. Descriptive frequency counts were used to characterise the included articles, in terms of key article characteristics (year of publication, location) and factors relevant to the research questions (research methods/designs, characteristics of study population, post-stroke timepoint). A pragmatic inductive approach to thematic analysis resembling template analysis was used to identify specific cognition-related psychoeducation needs and factors potentially impacting them. First, one member of the research team (GH) familiarised themselves with the data by reading and rereading the included articles. Then, they developed candidate themes and integrated them into an initial template, which was used to code relevant text from included articles (ie, text describing psychoeducation needs and factors impacting them) at a semantic level. The template was revised iteratively throughout the coding process to ensure themes were firmly rooted in the data (ie, inductive analysis). The research team discussed and agreed on the final template, then one member of the research team applied it to all articles to ensure it adequately captured the data. Any themes or subthemes mentioned within the included articles and corresponding codes were recorded using the data extraction table in the ‘relevant findings’ section and a second member of the research team verified these against the original source articles. To ensure the analysis remained at the descriptive level, as recommended in JBI guidance, themes resembled domain summaries rather than broader interpretive units of meaning. In line with the critical realist positioning of the analysis, the aim was to generate a situated theme structure with translational value rather than a reliable and reproducible one. Indeed, the research team recognised that the final themes would inevitably be shaped by their own expertise (ie, clinical neuropsychology, clinical psychology), experiences (eg, working on hyperacute stroke units and in community brain injury rehabilitation settings) and values (eg, importance of addressing cognitive changes after stroke during rehabilitation). Rather than seeing these factors as threats to the reliability of the analysis, however, they were considered an asset that would mitigate the risk of relevant findings from included articles being overlooked.
The review was conducted in accordance with the Johanna Briggs Institute (JBI) methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. The protocol for this review underwent a rigorous peer-review process for publication and has been made openly available ( https://osf.io/fmz9t ). Any divergences from the protocol are outlined in .
Patients were involved in developing the funding proposal for the fellowship of which this research forms a part. Patients were consulted on the importance of the research question and the approach through a survey with the Stroke Association’s Voices in Research (43 respondents) and three smaller focus groups. Of particular relevance to the present study, they emphasised the importance of including family member/carer perspectives where possible.
The search strategy was developed in consultation with an expert librarian at the University of Oxford. The final approved search strategy was adapted to search additional electronic databases using the Polyglot tool provided by Systematic Review Accelerator software ( https://sr-accelerator.com/ ). Systematic searches were conducted in five electronic databases on 25 August 2023: MEDLINE (PubMed), PsycINFO (Ovid), Embase (Elsevier), CINAHL (Ebsco) and Scopus (Elsevier). Search strategies developed using the Polyglot tool are presented in . Grey literature was not included; as the aim of the review was to inform an evidence-based intervention, we included articles that had been through rigorous peer-review only. The search strategy was limited to English, but it was not limited by year.
The inclusion criteria were based on the JBI Population/Concept/Context (PCC) framework. Articles were eligible for inclusion in this review if they met the following criteria: Participants Stroke survivors and/or family members of stroke survivors. Stroke survivors were defined as someone who has experienced a clinically diagnosed stroke of any type. Family members were defined as someone who identifies as related to a stroke survivor by blood, marriage or with other familial involvement. Stroke survivors and/or family members of stroke survivors comprising at least 50% of the study population, in line with cut-offs used in previous scoping reviews. Stroke survivors and family members aged 18 years and over. Concept Self-reported information needs regarding post-stroke cognition. Information needs were defined as a desire to obtain information to satisfy a conscious (or unconscious) need. Cognition was defined as thinking skills related to any of the following domains: memory, language, attention, executive function, praxis and number processing. Context Studies conducted in the UK and other high-income countries, defined using the most recent World Bank country classifications (2022). Participants based either in a clinical setting or the community. Types of sources We included published peer-reviewed articles that used quantitative, qualitative or mixed methods designs. Review articles, peer-reviewed commentaries and opinion pieces were excluded.
Stroke survivors and/or family members of stroke survivors. Stroke survivors were defined as someone who has experienced a clinically diagnosed stroke of any type. Family members were defined as someone who identifies as related to a stroke survivor by blood, marriage or with other familial involvement. Stroke survivors and/or family members of stroke survivors comprising at least 50% of the study population, in line with cut-offs used in previous scoping reviews. Stroke survivors and family members aged 18 years and over.
Self-reported information needs regarding post-stroke cognition. Information needs were defined as a desire to obtain information to satisfy a conscious (or unconscious) need. Cognition was defined as thinking skills related to any of the following domains: memory, language, attention, executive function, praxis and number processing.
Studies conducted in the UK and other high-income countries, defined using the most recent World Bank country classifications (2022). Participants based either in a clinical setting or the community.
We included published peer-reviewed articles that used quantitative, qualitative or mixed methods designs. Review articles, peer-reviewed commentaries and opinion pieces were excluded.
Identified records were collated and uploaded into EndNote v.X9 (Clarivate Analytics, PA, USA). SR-Accelerator Deduplicator was used to remove duplicates. Two members of the research team (GH and FT) independently screened records against eligibility criteria by title, abstract and then full-text after conducting a pilot screening round. They recorded reasons for exclusion for articles excluded at the full-text stage. Differences in inclusion/exclusion decisions were settled by discussion among the research team. Reference lists of the included articles were hand searched to identify further relevant records.
A data extraction tool was developed prior to extracting data and refined iteratively throughout the process. One researcher (GH) used the final version of the tool to extract data from the included articles. Another researcher (FT) reviewed extracted data for accuracy.
Extracted data were synthesised using quantitative and qualitative methods. Descriptive frequency counts were used to characterise the included articles, in terms of key article characteristics (year of publication, location) and factors relevant to the research questions (research methods/designs, characteristics of study population, post-stroke timepoint). A pragmatic inductive approach to thematic analysis resembling template analysis was used to identify specific cognition-related psychoeducation needs and factors potentially impacting them. First, one member of the research team (GH) familiarised themselves with the data by reading and rereading the included articles. Then, they developed candidate themes and integrated them into an initial template, which was used to code relevant text from included articles (ie, text describing psychoeducation needs and factors impacting them) at a semantic level. The template was revised iteratively throughout the coding process to ensure themes were firmly rooted in the data (ie, inductive analysis). The research team discussed and agreed on the final template, then one member of the research team applied it to all articles to ensure it adequately captured the data. Any themes or subthemes mentioned within the included articles and corresponding codes were recorded using the data extraction table in the ‘relevant findings’ section and a second member of the research team verified these against the original source articles. To ensure the analysis remained at the descriptive level, as recommended in JBI guidance, themes resembled domain summaries rather than broader interpretive units of meaning. In line with the critical realist positioning of the analysis, the aim was to generate a situated theme structure with translational value rather than a reliable and reproducible one. Indeed, the research team recognised that the final themes would inevitably be shaped by their own expertise (ie, clinical neuropsychology, clinical psychology), experiences (eg, working on hyperacute stroke units and in community brain injury rehabilitation settings) and values (eg, importance of addressing cognitive changes after stroke during rehabilitation). Rather than seeing these factors as threats to the reliability of the analysis, however, they were considered an asset that would mitigate the risk of relevant findings from included articles being overlooked.
Selection of evidence sources The database searches retrieved 8112 records. This was reduced to 6726 records after deduplication. 27 records were selected for inclusion after screening. A further 3 records were identified from reference lists. This resulted in a total of 30 articles being selected for inclusion in the scoping review. documents the selection process. The included articles were published between 1996 and 2023. Most studies (n=20) were published between 2001 and 2020, with 14 published in the last 10 years (2013–2023). Studies were conducted in Australia (n=7), USA (n=6), UK (n=5), Canada (n=3), New Zealand (n=3), Ireland (n=2), the Netherlands (n=2), South Korea (n=1) and Sweden (n=1). Each included article was numbered to facilitate concise reporting. A summary of extracted data and numbers corresponding to each article are presented in . What research methods have been used? 21 articles used an exclusively qualitative approach to data collection and analysis and six further articles used qualitative methods combined with quantitative methods. Most studies that used a qualitative data collection approach conducted semi-structured interviews but five studies conducted focus groups. Participant sample sizes in qualitative studies varied substantially. Two articles presented case studies involving a single family member. The maximum sample size among the articles using exclusively qualitative methods was 50 participants with aphasia. Focus group sizes varied between 2–4 participants and 6–10 participants. Articles that used qualitative data collection methods employed different analytic approaches and frameworks. Eight articles used a version of thematic analysis, eight articles used a version of content analysis and two used the constant comparative method. Other approaches were narrative analysis and a modified referenced five-step process. One article described an approach that resembled thematic analysis but did not label it as such. Two articles did not describe how semi-structured interview data were analysed. Most studies that used qualitative methods did not mention how they dealt with important qualitative concepts, such as positionality, in their data collection and analysis processes. Five of the included articles used surveys or questionnaires. All of these articles used custom measures rather than validated standard questionnaires. Questionnaires were administered remotely in three studies and face-to-face in the other two studies. Face-to-face administrations were audio-recorded and analysed qualitatively to complement quantitative questionnaire data. What timepoints after stroke have been investigated? 10 articles explicitly stated their investigation pertained to the first 6 months after stroke (acute/subacute stage). Specific timepoints investigated included stroke onset/first days after stroke, first week after stroke, 2 weeks after stroke, first month after stroke, first 3 months after stroke and 4 months after stroke. Three articles explicitly stated their investigation pertained to the period at least 6 months after stroke (chronic stage). Specific timepoints investigated were 6 months, 7 months, 11 months, 12 months, more than 12 months, and 2 years after stroke. Eight articles investigated information needs at multiple timepoints after stroke. However, 18 articles did not specify the timepoint under investigation and some articles used ambiguous terminology. Temporally ambiguous terms used to describe the timepoint under investigation included initial rehabilitation, rehabilitation, up to 1 month after discharge, starting to recover, preparing to leave hospital, just returned home, settled at home and chronic phase (defined as stroke survivor’s return home). What are the characteristics of participants? Seven studies recruited stroke survivors only and 11 articles included both stroke survivors and family members. Four studies did not report the mean age of stroke survivor participants and 10 studies did not report the mean time since stroke. The mean age of stroke survivors was less than 70 years in the 12 studies that reported this variable. The mean time since stroke for stroke survivor participants was between 11 months and 7 years but these studies did not describe how stroke date was established (eg, self-report, medical records). With regard to the cognitive status of stroke survivors, 18 articles focused on stroke survivors with or family members of stroke survivors with aphasia. Other cognitive impairment (including dementia) was listed as part of the inclusion/exclusion criteria in eight studies, while the other 10 articles did not report whether stroke survivors had cognitive impairments affecting domains other than language. One article described participants as affected by ‘mild physical, cognitive, and/or psychosocial disabilities’ (p.2) but did not specify the precise nature of these difficulties. One article assessed cognitive functioning in non-language domains using Raven’s Coloured Progressive Matrices (Raven, Court, & Raven, 1995). Only one article reported in detail the cognitive status of stroke survivors in domains other than language. Family members reported that their relative with stroke experienced problems with memory (n=4/4), executive function (n=4/4), attention (n=3/4) and neglect (n=2/4). 12 studies recruited family members but not stroke survivors. Two of these articles were case studies involving only one family member. Most family members were described as spouses/partners/significant others (n=134 across these 12 articles). Some articles also included offspring caring for the stroke survivor (n=37), parents (n=6) and siblings (n=6). Other family members were relatives-in-law (n=4), aunts/uncles (n=3) and grandchildren (n=2). Two studies included one friend alongside other family member participants and one study included three friends. One study included ex-family members (n=3) as well as current family members (n=45). We note that different terminology was used to describe stroke survivors in the articles, including stroke survivors, patients, individuals/people/participants with aphasia due to stroke, and individuals with communication-debilitating illness or injury due to stroke. The following terms were used to refer to family member participants: family members, significant others, carers, caregivers, informal carers, relatives, communication partners and care partners. What psychoeducation needs have been reported? Participants across the included studies reported psychoeducation needs regarding cognitive difficulties after stroke. Psychoeducation needs mentioned within the articles were most often described in the context of aphasia but participants also described a need for psychoeducation about memory problems, concentration problems and general cognitive changes. When describing psychoeducation needs related to aphasia, participants reported a desire for general information, including definitions and information about symptoms, and participants in two studies wanted information about psychological comorbidities. With regard to recovering from aphasia, participants wanted information about what to expect in the future, treatments for aphasia and their efficacy, as well as ways to maximise recovery. The following information about living with aphasia was also sought: compensatory strategies, maximising communicative effectiveness, available support and services, psychosocial support and counselling, support for family members, support groups, employment, financial aid, and information to help maintain hope and optimism. Though far fewer studies considered non-language cognitive impairments, participants in these studies similarly described a need for general information about symptoms and definitions. Some participants also wanted information about recovering, including what to expect in the future, treatments and rehabilitation available, and information to track recovery progress. Finally, in terms of living with cognitive impairments, some participants wanted information about compensatory strategies, support for family members, and information to help maintain hope and optimism. Themes and subthemes are summarised in . What factors impact psychoeducation needs? Cognition-related psychoeducation needs were reported in articles investigating both the acute/subacute stage (ie, less than 6 months since stroke) and chronic stage after stroke (ie, more than or equal to 6 months after stroke), but the prevalence and content of these information needs varied depending on the timepoint under investigation. Two of the articles that investigated cognition-related information needs at multiple timepoints found that prevalence increased over time. Hanger et al reported that only 4 out of 60 (7%) participants asked questions about poor memory/concentration in the first 2 weeks after stroke; whereas, 25 out of 111 (32%) asked these questions 2 years after stroke. Similarly, whereas 3 out of 60 (5%) participants asked questions about communication difficulties in the first 2 weeks after stroke, 7 out of 72 (10%) participants asked these questions 2 years after stroke. Rose et al similarly found that only 9% of stroke survivors considered it helpful to receive written stroke and aphasia information on the day of admission but 91% of participants considered this information helpful more than 12 months after stroke. Results from Rose et al suggest that information needs around aphasia may peak before this, however, as 97% of stroke survivors considered it helpful to receive written stroke and aphasia information 6 months after stroke. Only one article provided insight into how the content of cognition-related information needs evolves over time. Family members in this study considered some information more useful to receive in the first days after stroke and other information more useful once they were settled at home. For example, 93.8% considered it useful to receive information about what aphasia is in the first days after stroke, compared with 75% who considered this information useful once settled at home. On the other hand, only 52.3% of participants considered it useful to receive information about support groups for people with aphasia in the first days after stroke but 90.4% considered this information useful once settled at home. There were no obvious differences in the information needs reported in articles that included stroke survivors only vs family members only but results from one article tentatively suggest that information needs may vary depending on the specific relationship of the family member to the stroke survivor. Cheng et al reported that non-partners tended to want information about aphasia prognosis, regardless of whether the prognosis was ‘good or bad’. However, partners tended to favour information about rehabilitation over prognostic information and they felt that the delivery of prognostic information should be dictated by the preference of the stroke survivor. What key gaps exist across the included articles? The majority of articles focused on stroke survivors with or family members of stroke survivors with aphasia. Psychoeducation needs related to other cognitive domains (eg, memory, attention, executive function) were rarely mentioned. shows the number of times cognitive terms featured in the search strategy were used in included articles. Furthermore, most studies investigating aphasia did not report cognitive status in other domains, making it difficult to determine whether non-language cognitive impairments were also present within the sample. Relatively few studies considered psychoeducation needs at multiple timepoints after stroke and only two of these articles investigated how the prevalence and content of cognition-related information needs evolve over time.
The database searches retrieved 8112 records. This was reduced to 6726 records after deduplication. 27 records were selected for inclusion after screening. A further 3 records were identified from reference lists. This resulted in a total of 30 articles being selected for inclusion in the scoping review. documents the selection process. The included articles were published between 1996 and 2023. Most studies (n=20) were published between 2001 and 2020, with 14 published in the last 10 years (2013–2023). Studies were conducted in Australia (n=7), USA (n=6), UK (n=5), Canada (n=3), New Zealand (n=3), Ireland (n=2), the Netherlands (n=2), South Korea (n=1) and Sweden (n=1). Each included article was numbered to facilitate concise reporting. A summary of extracted data and numbers corresponding to each article are presented in .
21 articles used an exclusively qualitative approach to data collection and analysis and six further articles used qualitative methods combined with quantitative methods. Most studies that used a qualitative data collection approach conducted semi-structured interviews but five studies conducted focus groups. Participant sample sizes in qualitative studies varied substantially. Two articles presented case studies involving a single family member. The maximum sample size among the articles using exclusively qualitative methods was 50 participants with aphasia. Focus group sizes varied between 2–4 participants and 6–10 participants. Articles that used qualitative data collection methods employed different analytic approaches and frameworks. Eight articles used a version of thematic analysis, eight articles used a version of content analysis and two used the constant comparative method. Other approaches were narrative analysis and a modified referenced five-step process. One article described an approach that resembled thematic analysis but did not label it as such. Two articles did not describe how semi-structured interview data were analysed. Most studies that used qualitative methods did not mention how they dealt with important qualitative concepts, such as positionality, in their data collection and analysis processes. Five of the included articles used surveys or questionnaires. All of these articles used custom measures rather than validated standard questionnaires. Questionnaires were administered remotely in three studies and face-to-face in the other two studies. Face-to-face administrations were audio-recorded and analysed qualitatively to complement quantitative questionnaire data.
10 articles explicitly stated their investigation pertained to the first 6 months after stroke (acute/subacute stage). Specific timepoints investigated included stroke onset/first days after stroke, first week after stroke, 2 weeks after stroke, first month after stroke, first 3 months after stroke and 4 months after stroke. Three articles explicitly stated their investigation pertained to the period at least 6 months after stroke (chronic stage). Specific timepoints investigated were 6 months, 7 months, 11 months, 12 months, more than 12 months, and 2 years after stroke. Eight articles investigated information needs at multiple timepoints after stroke. However, 18 articles did not specify the timepoint under investigation and some articles used ambiguous terminology. Temporally ambiguous terms used to describe the timepoint under investigation included initial rehabilitation, rehabilitation, up to 1 month after discharge, starting to recover, preparing to leave hospital, just returned home, settled at home and chronic phase (defined as stroke survivor’s return home).
Seven studies recruited stroke survivors only and 11 articles included both stroke survivors and family members. Four studies did not report the mean age of stroke survivor participants and 10 studies did not report the mean time since stroke. The mean age of stroke survivors was less than 70 years in the 12 studies that reported this variable. The mean time since stroke for stroke survivor participants was between 11 months and 7 years but these studies did not describe how stroke date was established (eg, self-report, medical records). With regard to the cognitive status of stroke survivors, 18 articles focused on stroke survivors with or family members of stroke survivors with aphasia. Other cognitive impairment (including dementia) was listed as part of the inclusion/exclusion criteria in eight studies, while the other 10 articles did not report whether stroke survivors had cognitive impairments affecting domains other than language. One article described participants as affected by ‘mild physical, cognitive, and/or psychosocial disabilities’ (p.2) but did not specify the precise nature of these difficulties. One article assessed cognitive functioning in non-language domains using Raven’s Coloured Progressive Matrices (Raven, Court, & Raven, 1995). Only one article reported in detail the cognitive status of stroke survivors in domains other than language. Family members reported that their relative with stroke experienced problems with memory (n=4/4), executive function (n=4/4), attention (n=3/4) and neglect (n=2/4). 12 studies recruited family members but not stroke survivors. Two of these articles were case studies involving only one family member. Most family members were described as spouses/partners/significant others (n=134 across these 12 articles). Some articles also included offspring caring for the stroke survivor (n=37), parents (n=6) and siblings (n=6). Other family members were relatives-in-law (n=4), aunts/uncles (n=3) and grandchildren (n=2). Two studies included one friend alongside other family member participants and one study included three friends. One study included ex-family members (n=3) as well as current family members (n=45). We note that different terminology was used to describe stroke survivors in the articles, including stroke survivors, patients, individuals/people/participants with aphasia due to stroke, and individuals with communication-debilitating illness or injury due to stroke. The following terms were used to refer to family member participants: family members, significant others, carers, caregivers, informal carers, relatives, communication partners and care partners.
Participants across the included studies reported psychoeducation needs regarding cognitive difficulties after stroke. Psychoeducation needs mentioned within the articles were most often described in the context of aphasia but participants also described a need for psychoeducation about memory problems, concentration problems and general cognitive changes. When describing psychoeducation needs related to aphasia, participants reported a desire for general information, including definitions and information about symptoms, and participants in two studies wanted information about psychological comorbidities. With regard to recovering from aphasia, participants wanted information about what to expect in the future, treatments for aphasia and their efficacy, as well as ways to maximise recovery. The following information about living with aphasia was also sought: compensatory strategies, maximising communicative effectiveness, available support and services, psychosocial support and counselling, support for family members, support groups, employment, financial aid, and information to help maintain hope and optimism. Though far fewer studies considered non-language cognitive impairments, participants in these studies similarly described a need for general information about symptoms and definitions. Some participants also wanted information about recovering, including what to expect in the future, treatments and rehabilitation available, and information to track recovery progress. Finally, in terms of living with cognitive impairments, some participants wanted information about compensatory strategies, support for family members, and information to help maintain hope and optimism. Themes and subthemes are summarised in .
Cognition-related psychoeducation needs were reported in articles investigating both the acute/subacute stage (ie, less than 6 months since stroke) and chronic stage after stroke (ie, more than or equal to 6 months after stroke), but the prevalence and content of these information needs varied depending on the timepoint under investigation. Two of the articles that investigated cognition-related information needs at multiple timepoints found that prevalence increased over time. Hanger et al reported that only 4 out of 60 (7%) participants asked questions about poor memory/concentration in the first 2 weeks after stroke; whereas, 25 out of 111 (32%) asked these questions 2 years after stroke. Similarly, whereas 3 out of 60 (5%) participants asked questions about communication difficulties in the first 2 weeks after stroke, 7 out of 72 (10%) participants asked these questions 2 years after stroke. Rose et al similarly found that only 9% of stroke survivors considered it helpful to receive written stroke and aphasia information on the day of admission but 91% of participants considered this information helpful more than 12 months after stroke. Results from Rose et al suggest that information needs around aphasia may peak before this, however, as 97% of stroke survivors considered it helpful to receive written stroke and aphasia information 6 months after stroke. Only one article provided insight into how the content of cognition-related information needs evolves over time. Family members in this study considered some information more useful to receive in the first days after stroke and other information more useful once they were settled at home. For example, 93.8% considered it useful to receive information about what aphasia is in the first days after stroke, compared with 75% who considered this information useful once settled at home. On the other hand, only 52.3% of participants considered it useful to receive information about support groups for people with aphasia in the first days after stroke but 90.4% considered this information useful once settled at home. There were no obvious differences in the information needs reported in articles that included stroke survivors only vs family members only but results from one article tentatively suggest that information needs may vary depending on the specific relationship of the family member to the stroke survivor. Cheng et al reported that non-partners tended to want information about aphasia prognosis, regardless of whether the prognosis was ‘good or bad’. However, partners tended to favour information about rehabilitation over prognostic information and they felt that the delivery of prognostic information should be dictated by the preference of the stroke survivor.
The majority of articles focused on stroke survivors with or family members of stroke survivors with aphasia. Psychoeducation needs related to other cognitive domains (eg, memory, attention, executive function) were rarely mentioned. shows the number of times cognitive terms featured in the search strategy were used in included articles. Furthermore, most studies investigating aphasia did not report cognitive status in other domains, making it difficult to determine whether non-language cognitive impairments were also present within the sample. Relatively few studies considered psychoeducation needs at multiple timepoints after stroke and only two of these articles investigated how the prevalence and content of cognition-related information needs evolve over time.
This study mapped and identified gaps in 30 published articles investigating self-reported psychoeducation needs of stroke survivors and family members regarding cognition. Both stroke survivors and family members reported cognition-related psychoeducation needs and these were present at all timepoints investigated, although the prevalence and specific content varied in some articles over time. Participants wanted information about expected cognitive recovery, treatment/therapy options, services/resources available, and hopeful information. Time since stroke and family member relationship may affect prevalence and content of cognition-related psychoeducation needs, but very few studies explicitly described how psychoeducation needs vary at different timepoints and across different types of relationships. Furthermore, very few articles addressed non-language cognitive domains commonly affected by stroke (eg, memory, attention, executive function, number processing, praxis). Stroke survivors and family members in the included articles expressed a need for information about cognitive impairment diagnosis, prognosis, treatment and available services. While these needs were apparent throughout the post-stroke period, two articles found cognition-related psychoeducation needs became more prevalent over time, which may reflect the early focus on medical management and physical recovery after stroke and emergence of cognitive concerns later in the post-stroke recovery period. Clinical reviews are recommended by the UK clinical guidelines at 6 months, 12 months and then annually and these reviews are crucial to ensure cognition-related psychoeducation needs are identified and addressed. However, data from the Stroke Sentinel National Audit Programme (SSNAP) suggest completion of these reviews is currently inadequate, with 6-month reviews received by only 36.9% of stroke survivors in 2022/2023, a reduction from 2021/2022 when reviews were received by 40.7%. Improving cognitive monitoring and psychoeducation may help to address the substantial long-term unmet needs surrounding cognition after stroke. We identified key gaps in the existing literature. In particular, more than half of the included articles focused exclusively on aphasia, with very few articles considering other commonly affected cognitive domains (eg, memory, attention, executive function, number processing, praxis) and only one study reporting the prevalence of non-language cognitive impairments in their stroke survivor sample. Understanding psychoeducation needs related to other domains is crucial as non-language impairments may be even more prevalent than language impairments and domain-specific impairments vary substantially in their underlying aetiologies and likely trajectories. Future research should also aim to include stroke survivor samples with cognitive profiles that better reflect the clinical reality (ie, patients with impairments across different cognitive domains) to ensure any psychoeducational materials are tailored appropriately. This scoping review has several potential limitations. First, there was a possible selection bias due to the exclusion of unpublished grey literature. Because this scoping review sits alongside a broader body of qualitative research aiming to develop an evidence-based complex intervention providing psychological support after stroke, we were keen to focus on articles that had been through a rigorous peer-review process. Nevertheless, this decision may have led to omission of informative sources. Second, our decision to include studies with a sample comprising at least 50% stroke survivors or family members may have led to exclusion of additional potentially informative literature—for example, research investigating psychoeducation needs from the perspective of healthcare professionals. By focusing on self-reported needs of stroke survivors and their family members, we restricted our review to generate a patient-centred picture. Overall, as stroke mortality rates continue to decline and the number of stroke survivors experiencing cognitive impairment correspondingly rises, it is critical to consider how to prepare stroke survivors and their family members to cope with cognitive changes and - ultimately - to integrate this insight into a cognitive care pathway for stroke. This scoping review demonstrates that stroke survivors and their family members are generally keen to receive psychoeducation about cognition throughout the post-stroke care continuum, but further research is required to strengthen our understanding of these psychoeducation needs and how best to meet them in clinical practice.
10.1136/bmjopen-2024-084681 online supplemental file 1
|
Advances in Clinical Cardiology 2022: A Summary of Key Clinical Trials | 492e37a9-a73a-4605-8bdd-9d6d9efb3644 | 10100625 | Internal Medicine[mh] |
In 2022, multiple clinical trials with the potential to influence current practice and future guidelines were presented at major international meetings including the American College of Cardiology (ACC), European Association for Percutaneous Cardiovascular Interventions (EuroPCR), European Society of Cardiology (ESC), Transcatheter Cardiovascular Therapeutics (TCT), American Heart Association (AHA), European Heart Rhythm Association (EHRA), Society for Cardiovascular Angiography and Interventions (SCAI), TVT-The Heart Summit (TVT) and Cardiovascular Research Technologies (CRT). In this article, we review key studies across the spectrum of cardiovascular subspecialties including acute coronary syndromes (ACS), interventional and structural, electrophysiology and atrial fibrillation, heart failure and preventative cardiology.
The results of clinical trials presented at major international cardiology meetings in 2022 were reviewed. In addition to this, a literature search of PubMed, Medline, Cochrane library and Embase was completed, including the terms “acute coronary syndrome”, “atrial fibrillation”, “coronary prevention”, “electrophysiology”, “heart failure” and “interventional cardiology”. Trials were selected based on their relevance to the cardiology community and the potential to change future clinical guidelines or guide further phase 3 research. This article is based on previously completed work and does not involve any new studies of human or animal subjects performed by any of the authors. Advances in Percutaneous Coronary Intervention Several practice changing trials in Percutaneous Coronary Intervention (PCI) have been published this year (Table ). Historically, PCI has been used to treat ischaemic cardiomyopathy, despite limited supporting evidence . In the REVascularisation for Ischaemic VEntricular Dysfunction (REVIVED-BCIS2) trial , 700 patients with left ventricular ejection fraction (LVEF) ≤ 35% and extensive coronary artery disease (CAD), as defined by the British Cardiovascular Intervention Society (BCIS) jeopardy score, were randomised to PCI or optimal medical therapy (OMT). Over a median follow-up time of 3.4 years, PCI versus OMT alone did not result reduction in the primary composite outcome of death or hospitalization for heart failure [37.2% vs. 38.0%; HR 0.99; 95% confidence interval (CI), 0.78–1.27; P = 0.96] . The optimal treatment for left main (LM) and multivessel CAD remains hotly debated. New observational data from the Swedish Coronary Angiography and Angioplasty Registry (SCAAR) compared outcomes among 10,254 such patients undergoing PCI (52.6%) versus coronary artery bypass grafting (CABG) (47.4%). PCI was associated with a 59% increased risk of death versus CABG after 7 years of follow-up ( P = 0.011). Despite the limitations of observational data, findings are in keeping with the NOBLE study , supporting use of CABG where clinically appropriate in LM patients with additional multivessel CAD. In contrast, a meta-analysis of 2913 patients from four RCTs (SYNTAXES, PRECOMBAT, LE MANS, and MASS II) undergoing PCI versus CABG for LM or multivessel CAD did not report any significant difference in 10-year survival (RR 1.05; 95% CI 0.86–1.28), nor significant difference in the subgroup with LM disease alone or multivessel disease alone. This may reflect a lower extent of non-LM disease complexity in the four trials. Of note, a new analysis from the SYNergy Between PCI With TAXUS and Cardiac Surgery Extended Study (SYNTAXES) evaluated mortality according to presence or absence of bifurcation lesions . In the PCI group, those undergoing stenting of ≥ 1 bifurcation lesions versus no bifurcation stenting, had a higher risk of death at 10 years (30.1% vs. 19.8%; P < 0.001). Furthermore, a 2 versus 1 stent bifurcation strategy was associated with a higher risk of death at 10 years (HR 1.51; 95% CI 1.06–2.14). Conversely, in the CABG, the presence or absence of bifurcation lesions had no impact on mortality. As this was a post hoc analysis, results can only be considered hypothesis-generating, but are in keeping with previous data highlighting the complexity of bifurcations and the preference for a simple rather than a complex strategy where possible. Female sex has been associated with worse outcomes following PCI related to smaller vessel disease. However, previous LM have been unclear and, given that LM has a larger diameter, more equivalent results. A substudy of the NOBLE trial showed no difference in outcomes for male versus female, with both showing an excess of major adverse cardiovascular and cerebrovascular events (MACCE) with PCI at 5 years, although no difference in all-cause mortality. For those undergoing PCI for LM disease, the IDEAL-LM (Individualizing Dual Antiplatelet Therapy After Percutaneous Coronary Intervention in patients with left main stem disease) study reported that a strategy of short 4-month DAPT (dual-antiplatelet therapy) plus a biodegradable polymer platinum-chromium everolimus-eluting stent was non-inferior to a strategy of conventional 12-month DAPT plus durable polymer cobalt-chromium everolimus-eluting stent (DP-CoCr-EES), with respect to a composite of death, MI or target vessel revascularisation at 2 years. However, the shorter DAPT strategy did not show any reduction in bleeding events. The Complete Revascularization with Multivessel PCI for Myocardial Infarction (COMPLETE) trial previously reported that complete versus culprit-only PCI had lower risk of cardiovascular (CV) death/myocardial infarction (MI) over 3 years of follow-up. In a new pre-specified analysis , complete versus culprit-only PCI was associated with a greater absence of residual angina (87.5% vs. 84.3%; P = 0.013) and improved quality of life, as assessed via the 19-item Seattle Angina Questionnaire, including reduced physical limitation. Improving PCI outcomes in patients with diabetes remains a focus of several trials. The Second-generation drUg-elutinG Stents in diAbetes: a Randomized Trial (SUGAR trial), which randomised 1175 patients with diabetes and CAD to an amphilimus-eluting stent (Cre8 EVO) vs. conventional Resolute Onyx stent, previously reported that the Cre8 stent met non-inferiority and was associated with a possible 35% reduction in Target Lesion Failure (TLF) at 12 months . However, by 2 years , the difference in TLF was no longer significant (10.4% vs. 12.1%; HR 0.84; 95% CI 0.60–1.19) with numerical but non-significant differences in the individual components of cardiac death (3.1% vs. 3.4%), target vessel MI (6.6% vs. 7.6%), and target lesion revascularization (4.3% vs. 4.6%). While these 2-year results were disappointing, we await results of further studies of new stents in this clinical setting, including the ABILITY trial (NCT04236609) comparing an Abluminus DES + sirolimus-eluting stent system versus Xience. Quantitative flow ratio (QFR), an angiography-based approach to estimate the fractional flow reserve, previously reported superiority versus conventional angiography guidance at 1 year in the FAVOR III (Comparison of Quantitative Flow Ratio Guided and Angiography-Guided Percutaneous InterVention in Patients With cORonary Artery Disease) trial . New data report that the benefit with the QFR-guided strategy was sustained at 2 years, associated with a 34% reduction in the composite of death, MI or ischaemia-driven revascularization [8.5% vs. 12.5%; HR 0.66 (95% CI 0.54–0.81)] . The degree of outcome improvement was greatest amongst those patients in whom the pre-planned PCI strategy was modified by QFR. Current ESC guidelines give post-PCI surveillance with stress testing with a Class IIb recommendation. The POST-PCI (Routine Functional Testing or Standard Care in High-Risk Patients after PCI) trial randomised 1706 patients at 1 year after PCI to routine functional testing (nuclear stress testing, exercise electrocardiography, or stress echocardiography) versus standard care . Use of routine functional testing failed to show any reduction in the primary outcome of death MI, or hospitalization for unstable angina at 2 years (5.5% vs. 6.0%; HR, 0.90; 95% CI 0.61–1.35; P = 0.62), supporting standard care in these patients. Procedural time in graft-angiography studies may be much longer than a non-graft cases. The Randomised Controlled Trial to Assess Whether Computed Tomography Cardiac Angiography Can Improve Invasive Coronary Angiography in Bypass Surgery Patients (BYPASS CTCA), randomised 688 prior CABG patients to CTCA prior to coronary angiography versus standard care. Those who underwent prior CTCA had a shorter procedure duration (mean 17.4 vs. 39.5 min; OR − 22.12; 95% CI − 24.68 to − 19.56), less contrast during the invasive angiogram (mean 77.4 vs. 173 mls), less contrast-induced nephropathy (3.2% vs. 27.9%; P < 0.0001) and 40% greater patient satisfaction . BYPASS CTCA thus supports consideration of prior CTCA particularly with more complex or uncertain graft location or patients at greater renal risk. The 2018 ESC guidelines recommend radial access for PCI unless overriding procedural considerations. A new patient-level meta-analysis of 7 trials, incorporating 21,700 patients reported that, at 30 days, transradial versus transfemoral access was associated with a 23% reduction in all-cause mortality (1.6% vs. 2.1%; P = 0 .012) and 45% reduction in major bleeding (1.5% vs. 2.7%; P < 0.001) . However, transradial access is not without complications, the commonest of which is radial artery occlusion. In the RIVARAD (Prevention of Radial Artery Occlusion With Rivaroxaban After Transradial Coronary Procedures) trial, 538 patients were randomised following coronary angiography to rivaroxaban 10 mg once daily for 7 days versus standard care (no rivaroxaban) . At 30 days, use of rivaroxaban was associated with a 50% reduction in radial artery occlusion as defined by ultrasound (6.9% vs. 13.0%; OR 0.50; 95% CI 0.27–0.91). Bleeding Academic Research Consortium (BARC)-defined bleeding events were numerically but not significantly higher in the rivaroxaban group (2.7% vs. 1.9%; OR 1.4; 95% CI 0.4–4.5). To assess whether distal radial artery puncture might reduce occlusion rates, the Distal Versus Conventional Radial Access DISCO-RADIAL) trial randomised 1,307 patients to distal versus conventional radial access . Distal access was associated with shorter median hemostasis time (153 vs. 180; P < 0.001), but radial artery spasm was more common (5.4% vs. 2.7%; P = 0.015), crossover rates were higher (7.4% vs. 3.5%; P = 0.002) and no difference in the primary endpoint of occlusion on vascular ultrasound was noted at discharge (0.31% vs. 0.91%; P = 0.29). While radial access now considered preferable, transfemoral access is still required in certain cases. As transfemoral operator skills may potentially decline through reduction in volume or lack of experience, ultrasound-guided access techniques are increasingly being used. The UNIVERSAL (Routine Ultrasound Guidance for Vascular Access for Cardiac Procedures) trial randomised 621 patients to femoral access with ultrasound guidance and fluoroscopy versus fluoroscopy alone . Interestingly, and in contrast with previous trials, ultrasound guidance was not associated with significant reduction in the composite of BARC 2, 3, and 5 bleeding or major vascular complication at 30 days (12.9% vs. 16.1%; p = 0.25). The strategy of multi-arterial CABG is endorsed by surgical guidelines but takes longer, is more technically demanding and can be associated with increased complications, such as deep sternal wound infections. An observational single-centre study by Momin et al. of 2979 patients undergoing isolated CABG (from 1999 to 2020) reported those receiving total arterial revascularization had the longest mean survival (18.7 years) versus single internal mammary artery (SIMA) plus vein grafts 16.1 years; P < 0.00001) versus vein grafts only (10.4 years; P < 0.00001). Interestingly, survival with total arterial revascularization was not significantly different to SIMA plus radial artery ± vein grafting (18.60 years). This study supports the durability of arterial grafting, although conclusions are limited by its non-randomised design. Conversely, Saadat et al. stratified 241,548 patients from the Society of Thoracic Surgeons (STS) database undergoing isolated CABG in 2017 into 3 groups: single arterial (86%), bilateral internal thoracic artery-multi-arterial (BITA-MABG; 5.6%), and radial artery multiarterial (RA-MABG; 8.5%). After risk adjustment, the observed to expected event (O/E) ratios showed no significant difference in mortality between the three strategies (1.00 vs. 0.98 vs. 0.96) and the risk of deep sternal wound infection was highest in the BITA-MABG group (1.91 vs. 0.90 vs. 0.96). Given the ongoing data uncertainty, results from the prospective randomised ROMA trial are eagerly awaited (NCT03217006). Structural: Aortic Valve Interventions There has been a dramatic expansion in transcatheter aortic valve interventions over the past decade . A recent analysis of US registry data conducted by Sharma et al. reported a near doubling in transcatheter aortic valve replacement (TAVR) volume overall between 2015 and 2021 (44.9% vs. 2021, 88%, P < 0.01), including a 2.7 fold increase in those < 65 years (now similar to surgical aortic valve replacement (SAVR) (47.5% TAVR vs. 52.5% SAVR, P = ns) particularly in younger patients with heart failure (HF) (OR 3.84; 95% CI 3.56–4.13; P < 0.0001), or prior CABG (OR, 3.49; 95% CI, 2.98–4.08; P < 0.001) . These numbers may further increase across all risk categories with the early long-term data from the seminal PARTNER (Placement of AoRTic TraNscathetER Valve Trial) trials awaited. Emerging evidence from trials such as AVATAR (Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis) and RECOVERY (Early Surgery Versus Conventional Treatment in Very Severe Aortic Stenosis) suggests that early intervention for severe aortic stenosis (AS), before patients develop symptoms, may be of benefit . In a pooled analysis of key trials (PARTNER2A, 2B &3) involving 1974 patients (mean age 81 years; 45% women), Généreux et al. evaluated the relationship between cardiac damage at baseline and prognosis in patients with severe symptomatic AS who underwent AVR (40% SAVR, 60% TAVI) . Baseline cardiac damage was defined using a 0–4 scoring system (0 = no damage and 4 = biventricular failure). Baseline damage correlating strongly with 2-year mortality (HR 1.51 per higher stage; 95% CI 1.32–1.72) with each increase in stage conferred a 24% increase in mortality ( P = 0.001) (from stage 0 = 2.5% to stage 4 = 28.2%) suggesting a role for earlier intervention. Several ongoing trials, such as EARLY TAVR (Evaluation of TAVR Compared to Surveillance for Patients With Asymptomatic Severe Aortic Stenosis), TAVR UNLOAD (Transcatheter Aortic Valve Replacement to UNload the Left Ventricle in Patients With ADvanced Heart Failure) and PROGRESS (Management of Moderate Aortic Stenosis by Clinical Surveillance or TAVR), aim to answer these questions directly. Valve in valve (VIV) TAVR is being increasing utilised in patients with failed AVR; however, it remains unclear whether these patients do better with or without balloon valve fracture (BVF). In a registry analysis of 2975 patients undergoing VIV-TAVR (with balloon-expandable SAPIEN 3 or SAPIEN 3 Ultra) between December 2020 and March 2022, Garcia et al. reported that BVF versus no BVF led to larger mean valve area (1.6 vs. 1.4 cm2; P < 0.01) and lower mean valve gradient (18.2 vs. 22.0 mm Hg; P < 0.01) but also to higher rates of death or life-threatening bleeding (OR 2.55; 95% CI 1.44–4.50) and vascular complications (OR 2.06; 95% CI 0.95–4.44). However, sub-analysis suggested the increase in mortality was mainly if BVF undertaken before VIV-TAVR (OR 2.90; 95% CI 1.21–6.94), whereas no difference was noted if undertaken after VIV-TAVR. This suggests that VIV-BVF should only be performed once the operator has a new TAVR in place. While designed primarily for AS, conventional TAVR devices have sometimes utilised for the treatment of severe aortic regurgitation (AR). The novel trilogy heart valve system, specifically developed for AR, and was evaluated in 45 patients (mean age 77, 40% female, mean Euroscore 7.1%) with moderate to severe AR by Tamm et al. . The primary endpoint, a reduction in ≥ 1 AR grade, was met in 100% of cases. There were no episodes of stroke, death, or conversion to open surgery, but 9 patients (23%) required permanent pacing. Subclinical leaflet thrombosis (SLT) is a relatively common complication of TAVR; however, the optimal treatment strategies, whether with anticoagulation or antiplatelets, remain contested. The multicentre ADAPT TAVR (Edoxaban vs. DAPT in reducing subclinical leaflet thrombosis and Cerebral Thromboembolism After TAVR) randomised 229 patients (mean age 80.1 years; 41.9% men) undergoing TAVR for symptomatic severe AS, and without other indication for OAC, to edoxaban 60 mg or 30 mg once daily versus DAPT with aspirin and clopidogrel . At 6 months, Edoxaban, by intention to treat analysis, was associated with a trend to reduced SLT as assessed by cardiac CT (9.8% vs. 18.4%; P = 0.076) and, in contrast to prior trials with DOAC post-TAVR, there was no difference in bleeding rates (11.7% vs. 12.7%; P = ns). Interestingly, a secondary per-protocol analysis focusing on patients with high compliance did reach statistical significance (19.1% vs. 9.1%; risk ratio 0.48; 95% CI 0.23–0.99). However, despite the use of serial brain MRI, there was no difference in the presence/number of cerebral lesions and no difference in neurocognitive outcomes including stroke at 6 months. Giustino et al. reported a new secondary analysis from the GALILEO trial (Rivaroxaban-based Antithrombotic Strategy to an Antiplatelet-based Strategy After TAVR to Optimize Clinical Outcomes) which, as described previously , had randomised 1644 patients post-TAVR without an indication for oral anticoagulation (OAC) to rivaroxaban 10 mg plus aspirin versus DAPT with aspirin plus clopidogrel for 90 days, but was stopped early due to higher thromboembolic bleeding and mortality events in the Rivaroxaban group . In the new analysis, thromboembolic events appeared to be associated with higher risk of mortality (HR 8.41; 95% CI 5.10–13.87) versus BARC 3 bleeding (HR 4.34; 95% CI 2.31–8.15). Furthermore, this mortality risk appeared higher than that conferred by known risk factors such as age (adjusted HR 1.04; 95% CI 1.01–1.08) and chronic obstructive pulmonary disease (COPD) (adjusted HR 2.11; 95% CI 1.30–3.41). These findings along with previous data from ALANTIS (AntiThrombotic Strategy After Trans-Aortic Valve Implantation for Aortic Stenosis) and ENVISAGE-TAVI AF (Edoxaban Compared to Standard Care After Heart Valve Replacement Using a Catheter in Patients With Atrial Fibrillation) show how the role of DOACs post-TAVI remains uncertain . However, given the devastating impact of thromboembolic events in this patient group, ongoing research is warranted. The absence of a bleeding signal with DOAC in ADAPT TAVR, in which most received lower dose Edoxaban, suggests that lower dose DOAC for a short duration while the valve is endothelialising may improve the risk/benefit ratio. Another area of current contention is the use of cerebral embolic protection (CEP) to reduce risk of stroke. While current guidance does not mandate use, some operators use in high-risk cases . Kaur et al. conducted a meta-analysis of 1,016 patients (mean age 81.3 years) from several randomised trials (DEFLECT III, MISTRAL-C, CLEAN-TAVI, SENTINEL, and REFLECT I and II) evaluating the TriGuard (Keystone Heart) and Sentinel devices versus standard care. At 30 days, CEP was not associated with a reduction in the primary outcome of all-cause stroke (RR 0.93; 95% CI 0.57–1.53), nor a reduction in mortality. Subsequently, the PROTECTED TAVR (Stroke PROTECTion With SEntinel During Transcatheter Aortic Valve Replacement) trial randomised 300 patients (mean age 72 years, 40% female) to CEP with a Sentinel device versus standard care . Again, no significant difference in primary outcome of stroke at 72 h was noted (2.4% vs. 2.9%, P = 0.30), although numbers were relatively small. The results of BHF PROTECT TAVI (British Heart Foundation Randomised Clinical Trial of Cerebral Embolic Protection in Transcatheter Aortic Valve Implantation) plans to enrol 7000 patients and findings are eagerly awaited. Stuctural: Mitral and Tricuspid Valve interventions The favourable findings in COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation [MR) helped lead to device approval . However, it has been suggested the reason COAPT was favourable was the strict eligibility criteria, mandating LVEF ≥ 20% to ≤ 50%, left ventricular end-systolic dimension (LVESD) ≤ 70 mm and failure of aggressive medical therapy . EXPAND (A Contemporary, Prospective Study Evaluating Real-world Experience of Performance and Safety for the Next Generation of MitraClip Devices) was a prospective multicentre registry of 1,041 patients with site-reported MR 3 + /4 + were enrolled and received the MitraClip. A recent analysis compared 125 “COAPT-like” patients meeting COAPT inclusion criteria versus 128 “non-COAPT” patients. At 1 year, COAPT-like patients did not show any difference in the primary outcome of all-cause mortality (22.6% vs. 19.6%, P = 0.37) or heart failure hospitalisation (32.6% vs. 25%, P = 0.08). In keeping with their lower baseline MR severity, more non-COAPT patients achieved reduction in MR to mild or less (≤ 1 +) (97.2% vs. 86.5%), suggesting that Mitraclip may benefit patients beyond the strict COAPT criteria, but prospective randomised data are needed, such as the ongoing EVOLVE-MR (MitraClip for the Treatment of Moderate Functional Mitral Regurgitation). Previous data from CLASP (Edwards PASCAL TrAnScatheter Mitral Valve RePair System Study) and CLASPII have validated the safety and efficacy of the Edwards PASCAL™ transcatheter valve repair system . CLASP IID randomised 180 patients with severe degenerative symptomatic MR not eligible for surgery (mean age 81 years, 67% male, median STS 5.9%) to transcatheter Edge-to-Edge Repair (TEER) with the Pascal device (Edwards Lifesciences) vs. MitraClip (Abbott) device . At 30 days, the Pascal device met criteria for non-inferiority with respect to the composite endpoint of CV death, stroke, MI, renal replacement therapy, severe bleeding and re-intervention (3.4% vs. 4.8%; P for noninferiority < 0.05). Of interest, the proportion of patients with MR ≤ 1 + was durable in the PASCAL group (87.2% discharge vs. 83.7% at 6 months; P = 0.317); whereas MitraClip outcomes showed some loss of efficacy (88.5% discharge vs. 71.2% at 6 months; P = 0.003). Although only interim data, this hints that the Pascal device may have superior durability. ViV-transcatheter mitral valve replacement (ViV-TMRV) may be utilised in very high-risk patients without a surgical option on a case-by-case basis despite paucity of real-world outcome data. Bresica et al. retrospectively compared outcomes of 48 patients with bioprosthetic mitral valve (MV) failure undergoing ViV-TMRV (mean age 65 years, 63% female, mean STS 7.9%) versus 36 patients undergoing re-do MV surgery (mean age 58, 72% female, mean STS 7.1%) . ViV-TMVR was not associated with improvement in 1-year survival (90% vs. 80%, P = 0.33) and was associated with higher average postprocedural gradient (8.9 vs. 5.7 mm Hg; P < 0.001). Thus, ViV-TMRV is a good option for high-risk patients, but in less comorbid patients may not provide as good a long-term benefit as surgery, particularly in those with smaller original surgical valves. Data to come from the ongoing PARTNER 3 (Mitral Valve-in-Valve trial) will be useful to help guide decision-making in such patients. Several seminal trials, such as TRILUMMINATE (Abbott Transcatheter lip Repair System in Patients With Moderate or Greater TR), Triband (TranscatheterRepair of Tricuspid Regurgitation With Edwards Cardioband TR System Post-Market Study) and TRISCEND (Investigation of Safety and Clinical Efficacy After Replacement of Tricuspid Valve With Transcatheter Device), have led to a much greater focus on transcatheter tricuspid interventions . CLASP TR (Edwards PASCAL Transcatheter Valve Repair System Pivotal Clinical Trial), a prospective single-arm multicentre study, evaluated 1-year outcomes of the PASCAL transcatheter valve repair system in 65 patients (mean age 77 ± 9 years, 55% female, mean STS 7.7%) with severe tricuspid regurgitation (TR) . In keeping with the high baseline comorbidity, major adverse event rate was 16.9% ( n = 11) with all-cause mortality 10.8% ( n = 7) and 18.5% ( n = 12) re-admitted with heart failure. Paired analysis demonstrated significant improvements in New-York Heart Association (NYHA) grade ( P < 0.001), KCCQ score ( P < 0.001) and 6-min walk test (6MWT) ( P = 0.014). Importantly, the reduction in TR severity noted at 30 days ( P < 0.001) was maintained at 1 year (100% had ≥ 1 grade reduction and 75% had ≥ 2 grade reduction, P < 0.001). TRICLASP (Transcatheter Repair of Tricuspid Regurgitation With Edwards PASCAL Transcatheter Valve Repair System), a prospective, single-arm multicentre trial, evaluated 30-day outcomes in 67 of 74 patients (mean age 80 years, 58% female, mean STS 9%) undergoing the Pascal Ace transcatheter repair system for severe symptomatic inoperable TR (Fig. ). The primary composite outcome of major adverse events was 3% with 88% achieving ≤ 1 grade reduction in TR vs. baseline; P < 0.001), along with significant improvements in NYHA, KCCQ score, and 6MWT ( P < 0.001). Longer term follow-up data are awaited. TriClip-Bright (An Observational Real-world Study Evaluating Severe Tricuspid Regurgitation Patients Treated With the Abbott TriClip™ Device) study , a multicentre, prospective study reported 30-day outcomes for 300 patients (78 ± 7.6 years) undergoing the Triclip Transcatheter valve repair system (Fig. ). The primary endpoint of procedural success (survival to discharge) was met in 91%. Significant reductions in both NYHA and KCCQ score were noted at ( P < 0.001). The trial is still actively recruiting, with a planned follow-up duration of 1 year. Structural: Catheter Based Left Atrial Appendage and Patent Foramen Ovale Closure While definitive studies to guide patent foramen ovale (PFO) closure practice are still lacking, a multidisciplinary consensus statement by SCAI was published this year recommending closure in patients aged 18–60 with a PFO-associated stroke, platypnoea-orthodeoxia syndrome with no other cause, and systemic embolism with no other cause. Of note in the absence of PFO-associated stroke, the guidance does not recommend PFO closure in transient ischaemic attack, AF with ischaemic stroke, migraine, decompression illness or thrombophilia. Several left atrial appendage closure (LAAC) devices have been approved in recent years with favourable long-term data published last year for the Watchman LAAC device (Boston Scientific). The AMULET IDE trial (Amplatzer Amulet Left Atrial Appendage Occluder Versus Watchman Device for Stroke Prophylaxis) trial randomised patients with non-valvular atrial fibrillation (AF), not suitable as anticoagulation to LAAC with an Amulet device ( n = 934) versus Watchman device ( n = 944). At 3 years, there was no difference in the primary composite endpoint of CV mortality, ischaemic stroke or systemic embolism (11.1% vs. 12.7%, P = 0.31) all-cause mortality (14.6% vs. 17.9%; P = 0.07) or major bleeding (16.1% vs. 14.7%; P = 0.46). Similarly, updated data from the US LAAC registry, comparing the Watchman FLX to its previous iteration, the Watchman 2.5, was published this year by Freeman et al. who reported US LAAC registry outcomes from 54,206 patients (mean age 76 years; 59% men) undergoing LAAC with the new Watchman FLX ( n = 27,103) versus previous Watchman 2.5 ( n = 27,103). In-hospital major adverse events were significantly lower with the new Watchman FLX (1.35% vs. 2.4%, OR 0.57: 95% CI 0.50–0.65) driven by reductions in pericardial effusion requiring intervention (0.42% vs. 1.23%), device embolization (0.02 vs. 0.06%) and major bleeding (1.08% vs. 2.05%). Longer follow-up will help clarify if technical aspects between devices confer long-term clinical outcome advantages. Despite the evolution of device technology for LAAC, key clinical questions, such as anticoagulation strategy, remain. Freeman et al. conducted a US LAAC registry analysis of 31,994 patients who underwent Watchman LAAC between 2016 and 2018. Only 12.2% of patients received the full anticoagulation protocol mandated by clinical trials (Fig. ). In contrast to previous European reports from EWOLUTION (Registry on WATCHMAN Outcomes in Real-Life Utilization), the 45-day adjusted adverse event rate was longer if discharged on warfarin alone (HR 0.692; 95% CI 0.569–0.841) or DOAC alone (HR 0.731; 95% CI 0.574–0.930) versus warfarin plus aspirin, suggesting that further research is needed to guide the optimal antithrombotic strategy post-LAAC. Acute Coronary Syndromes The ISCHAEMIA trial (Initial Invasive or Conservative Strategy for Stable Coronary Disease) was a previously reported that routine invasive therapy versus optimal medical therapy (OMT) in stable patients with moderate ischaemia did not reduce major adverse events (MAE), but the possibility of excess events over longer follow-up was queried. The ISCHAEMIA-EXTEND study (median follow-up 5.7 years) reported that while there was still no difference in all-cause mortality in routine invasive versus medical therapy (12.7% vs. 13.4%, P = 0.74), after 2 years the survival curves for cardiovascular (CV) death started to diverge and by 7 years were significantly lower in the routine invasive group (6.4% vs. 8.6% HR 0.78; 95% CI 0.63, 0.96). Conversely, there was an increase in non-CV death in the routine invasive group (5.5% vs. 4.4%, HR 1.44; 95% CI 1.08–1.91). On balance, this still supports an initial OMT strategy but highlights the utility of understanding anatomy to risk stratify and perhaps identify those patients who will benefit the most from CV risk reduction (Fig. ) . Ten-year follow-up data will prove informative. New onset, stable chest pain remains a substantial burden on healthcare systems. SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) and PROMISE (PROspective Multicenter Imaging Study for Evaluation of Chest Pain) previously reported benefit in early computed tomography coronary angiogram (CTCA) for the evaluation of stable chest pain. FFR-CT may further improve CT diagnosis. PRECISE (Prospective Randomized Trial of the Optimal Evaluation of Cardiac Symptoms and Revascularization) randomised 2103 patients (mean age 58 years, 50% women) with suspected CAD to a risk scoring algorithm (with low-risk patients deferred and high-risk patients undergoing FFR-CT) versus standard care. At a median follow-up of 11.8 months, algorithm-guided use of FFR-CT resulted in markedly lower MACE (4.2% vs. 11.3%; adjusted HR 0.29; 95% CI 0.20–0.41), driven by a lower rate of catheterisation (4.2% vs. 11.3%; adjusted HR 0.29; 95% CI 0.20–0.41). There was no difference in all-cause death. A subsequent cost-effectiveness analysis is ongoing. Despite current advances in ACS detection, prediction of recurrent events remains difficult. Batra et al. assessed the predictive valve of biomarker modelling (with hs-TNT, CRP, DGF-15, cystatin C, NT-proBNP) from 14,221 patients enrolled in PLATO (A Comparison of Ticagrelor and Clopidogrel in Patients With Acute Coronary Syndrome) and TRACER (Trial to Assess the Effects of Vorapaxar (SCH 530,348; MK-5348) in Preventing Heart Attack and Stroke in Participants With Acute Coronary trials. An outcome model termed “ABC-ACS Ischaemia” predicted 1-year risk of CV death/MI with C-indices of 0.71 and 0.72 in the development and validation cohorts, respectively. While encouraging, such models likely need to be integrated with additional individual patient characteristics in improve risk prediction. Optical Coherence Tomography (OCT) has demonstrable utility in assessing plaque morphology and so may be useful in delineating between different aetiologies of ACS. The Tokyo, Kanagawa, Chiba, Shizuoka, and Ibaraki active OCT applications for ACS (TACTICS) registry, evaluated plaque morphology in 702 ACS patients undergoing OCT-guided PCI and reported rupture was the commonest aetiology (59%), followed by plaque erosion (26%), and then calcification (4%) ( Fig. ) . However, at 12 months, calcified nodules conferred the worst outcome with a 32.1% MACE rate compared to 12.4% and 6.2% amongst ruptures or erosions, respectively. Antiplatelet therapy Strategies to shorten DAPT duration post-PCI in high bleeding risk patients continue to be evaluated. Longer-term follow-up at 15 months of the MASTER DAPT (Management of High Bleeding Risk Patients Post-Bioresorbable Polymer Coated Stent Implantation With an Abbreviated Versus Prolonged DAPT Regimen) confirmed initial results , with the incidence of the composite endpoint (death, MI, stroke, major bleeding) remaining non-inferiority for shortened DAPT versus standard care (HR 0.92, 95% CI 0.76–1.12; P = 0.40), but a significantly lower rate of major bleeding in the short DAPT group (HR 0.68, 95% CI 0.56–0.83; P = 0.001). These data, although important, were applied in the context of contemporary stent design such as the biodegradable-polymer sirolimus-eluting Ultimaster stent (Terumo) as used in MASTER DAPT. Effective reversal of antiplatelets could be helpful when active bleeding risk outweighs ischaemic risk, particularly in elderly patients. No formal antiplatelets reversal agents are currently licensed; however, an interesting drug under investigation is Bentracimab—a recombinant IgG1 monoclonal antibody antigen-binding fragment that binds with high affinity to ticagrelor and its active metabolite. Bhatt et al., in a phase IIb trial, randomised 205 patients (mean age 61 years, female 50%) already treated with DAPT for 30 days to Bentracimab ( n = 154) versus placebo ( n = 51). Use of Bentracimab was associated with a significant reduction in the primary endpoint of percentage inhibition of P2Y12 reaction units at 4 h ( P < 0.0001) without any excess of thrombotic events or deaths . Further larger-scale phase III trials are eagerly awaited. In patients with an indication for antiplatelet monotherapy, previous studies have suggested a possible benefit for clopidogrel versus aspirin at least in certain patient subgroups. PANTHER (P2Y12 inhibitor vs. aspirin monotherapy in patients with coronary artery disease) was a meta-analysis of several large, randomised trials totalling 24,325 patients with established coronary artery disease (mean age 64 years, 22% women) which compared P2Y12 inhibition (62% clopidogrel, 38% ticagrelor) versus aspirin . Use of P2Y12 inhibition was associated with a 12% reduction in the primary composite outcome of CV death, MI or stroke at 18 months (5.5% vs. 6.3%; HR 0.88; 95% CI 0.79–0.97) driven by a lower risk of MI (HR 0.77; 95% CI 0.66–0.90), but with no difference in stroke (HR 0.85; 95% CI 0.70–1.02) or bleeding (6.4% vs. 7.2%; HR 0.89; 95% CI 0.81–0.98). While firm conclusions are difficult due to the inclusion of 2 different P2Y12 inhibitors, it suggested P2Y12 inhibitor may be warranted instead of aspirin for long-term secondary prevention in patients with coronary artery disease. Indobufen is a reversible COX inhibitor with similar anti-thrombotic effects to aspirin but less gastrointestinal side effects and potentially lower risk of bleeding . The OPTION (the Efficacy and Safety of Indobufen and Low-dose Aspirin in Different Regimens of Antiplatelet Therapy) trial randomised 4,551 patients (mean age 61 years; 65% male) without acute troponin rise, undergoing PCI with DES to 1 year of DAPT (indobufen 100 mg BD plus clopidogrel 75 mg; n = 2258 vs. aspirin plus clopidogrel 100 mg OD; n = 2293). At 1 year, use of indobufen versus aspirin meet non-inferiority with respect to the primary composite outcome (CV death, MI, stroke, ISR and BARC type 2,3 or 5 bleeding) (4.47% vs. 6.11%; HR 0.73; 95% CI 0.56–0.94; P < 0.001 for noninferiority). The secondary safety endpoint of BARC 2, 3 or 5 bleeding was lower with indobufen (2.97% vs. 4.71%; HR 0.63; 95% CI 0.46–0.85), driven by a reduction in BARC 2 bleeding (1.68% vs. 3.49%; P < 0.001). These intriguing data suggest a potential new treatment option particularly for patients with gastrointestinal bleeding or aspirin allergy. Full dose anticoagulation plus antiplatelet therapy significantly increases bleeding risk but the role of low-dose anticoagulation for vascular prevention continues to be studied. Asundexian is a novel oral activated factor XI inhibitor which may lower thromboembolic events but with lower bleeding risk . In the phase II PACIFIC-AMI trial (Study to Gather Information About the Proper Dosing and Safety of the Oral FXIa Inhibitor BAY 2,433,334 in Patients Following an Acute Heart Attack), 1601 patients (median age 68 years, 23% women) with recent acute MI were randomised to asundexian (10 mg, 20 mg or 50 mg) versus placebo in addition to standard DAPT. At 4 weeks, asundexian was not associated with a significant increase in the pre-specified safety outcome of BARC2 bleeding versus placebo 0.98 (90% CI, 0.71–1.35), although there was a numerical increase in bleeding with higher asundexian doses. Based on this trial, asundexian 50 mg daily is being considered for a phase III cardiovascular outcomes trial in acute MI. Asundexian was also evaluated in the phase IIb PACIFIC-STROKE trial (Study to Gather Information About the Proper Dosing and Safety of the Oral FXIa Inhibitor BAY 2,433,334 in Patients Following an Acute Stroke) which randomised 1808 patients with non-embolic ischaemic stroke to asundexian (10 mg, 20 mg or 50 mg) versus placebo in addition to standard care including antiplatelet therapy . Asundexian (whether by pooled or individual dose analysis) was not associated with reduction in the primary efficacy outcome of ischemic stroke or overt stroke at 6 months, although the primary safety outcome of major significant bleeding was not significantly different [asundexian pooled vs. placebo HR1·57 (90% CI 0·91–2·71)]. It thus remains unclear if asundexian has a useful role in ischaemic stroke. In current PPCI guidelines, Bivalirudin (Class IIa) was replaced by unfractionated heparin (UFH) (Class I) as previous studies reported equipoise in clinical outcomes but more difficult drug administration with Bivalirudin. BRIGHT-4 (Bivalirudin With Prolonged Full Dose Infusion Versus Heparin Alone During Emergency PCI) randomised 6,016 PPCI patients from 63 Chinese centres in open-label fashion to Bivalirudin bolus plus infusion for a median of 3 h versus UFH bolus . Patients underwent predominantly radial PPCI (93%) without any prior thrombolytic, anticoagulant or glycoprotein inhibitor treatment. At 30 days, Bivalirudin was associated with a 31% reduction in the primary outcome of all-cause or BARC 3–5 bleeding (HR 0.69; 95% CI 0.53–0.91, P = 0.007), reduced BARC 3–5 bleeding (HR 0.21; 95% CI 0.08–0.54), reduced all-cause mortality (3.0% vs. 3.6%, P = 0.04), and reduced stent thrombosis (0.4% vs. 1.1%, P = 0.0015). Despite these favourable data, given the inherent difficulties in bivalirudin delivery and moderate increase in cost versus UFH, it is unclear if BRIGHT-4 findings will change practice, although a stronger guideline recommendation would be expected. Tongxinluo (TXL) is a traditional Chinese medicine, approved in China for the treatment of stroke and angina . CTS-AMI (China Tongxinluo Study for Myocardial Protection in Patients With Acute Myocardial Infarction) was a randomised trial of 3755 patients with STEMI undergoing PPCI at 124 Chinese centres to TXL versus placebo (in addition to standard therapy). Use of TXL was associated with a 36% reduction in the primary composite outcome of CV death, revascularisation, MI and stroke at 30 days (3.39% vs. 5.25%; RR 0.64; 95% CI 0.47–0.88) and a 30% reduction in cardiac death (2.97% vs. 4.24%; RR 0.70; 95% CI: 0.50–0.99). While the findings are dramatic, further work is necessary to understand the mechanism of action of this novel drug and further randomised multicentre trials to confirm efficacy. Electrophysiology and Devices Following on from the HIS-Alternative trial (His Pacing Versus Biventricular Pacing in Symptomatic HF With Left Bundle Branch Block) , which reported similar outcomes with His-Bundle CRT (His-CRT) versus conventional biventricular CRT (BiV-CRT), the LBBP-RESYNC (Left Bundle Branch Versus Biventricular Pacing For Cardiac Resynchronization Therapy) trial randomised 40 patients with non-ischaemic cardiomyopathy, LBBB and an indication for resynchronisation to left bundle branch CRT (LBB-CRT) versus standard BiV-CRT pacing . LBB-CRT was associated with a larger improvement in LVEF at 6 months (21.1% vs. 15.6%; P = 0.039, 95% CI 0.3–10.9), greater reduction in LV end systolic volumes and greater reduction NT-proBNP ( Fig. ). Vijayaraman et al. presented a retrospective analysis of 477 patients comparing those who underwent conduction pacing (LBB pacing or His-bundle) versus conventional BiV-CRT. Conduction pacing was associated with a lower incidence of the primary composite of death or heart failure hospitalisation (28.3% vs. 38.4%; P = 0.013), mainly driven by a reduction in HF hospitalisations. Vijayaraman et al. also presented a retrospective analysis of 212 patients with rescue LBB pacing who met indications for CRT but had coronary venous lead failure or were non-responders to BiV-CRT . LBB pacing (successful in 94%) was associated with improvement in LVEF from 29% at baseline to 40% at follow-up ( P < 0.001) ( Fig. ). The MELOS (Multicentre European Left Bundle Branch Area Pacing Outcomes Study) registry evaluated 2533 patients from 14 European centres undergoing transseptal left bundle branch area pacing (LBBAP), 27.5% for heart failure and 72.5% for bradycardia . LB fascicular capture was most common (69.5%) followed by LV septal capture (21.5%) then proximal LBB capture (9%). Overall complication rate was 11.7%, including ventricular trans-septal complications in 8.3%. Overall, these trials collectively support the efficacy and safety of conduction system pacing as a suitable alternative to conventional BiV-CRT, although larger randomised trials are required to formally test superiority. Infections related to cardiac implanted electronic devices (CIEDs) have high mortality and morbidity, and the European heart rhythm association (EHRA) consensus advises prompt extraction . Pokornery et al. analysed a Medicare database of 11,619 patients admitted with a CIED infection of whom only 2,109 (28.2%) had device extraction within 30 days. Device extraction versus no extraction was associated with reduction in 1-year mortality (HR 0.79, 95% CI 0.70–0.81) and early device extraction within 6 days versus no extraction was associated with a 41% reduction in 1-year mortality ( P < 0.001). Subcutaneous ICDs (S-ICDs) have been evaluated in previous trials including PRAETORIAN and UNTOUCHED as an alternative to transvenous systems for patients at risk of lead complications or infections. The ATLAS -ICD (Avoid Transvenous Leads in Appropriate Subjects) trial randomised 593 patients with an indication for ICD to SC-ICD versus transvenous ICD (TV-ICD) implantation . SC-ICD was associated with a 92% reduction in perioperative lead complications at 6 months (0.4% vs. 4.8%; OR 0.08; 95% CI 0.00–0.55), although the composite safety outcome (including the primary outcomes plus device-related infection requiring surgical revision, significant wound hematoma requiring evacuation or interruption of oral anticoagulation, MI, stroke/TIA, or death) was similar (4.4% vs. 5.6%; OR 0.78, 95% CI 0.35–1.75) and inappropriate shocks were non-significantly more common (2.7% vs. 1.7%; HR2.37, 95% CI 0.98–5.77). In heart failure patients, there is contradictory evidence whether defibrillator capability improves prognosis in patients receiving CRT. RESET-CRT (Re-evaluation of Optimal Re-synchronization Therapy in Patients with Chronic Heart Failure) retrospectively compared outcomes in 847 CRT-P versus 2722 CRT-D patients undergoing CRT (of whom 27% had a non-ischaemic aetiology and exclusion criteria included recent ACS, revascularisation, or any indication for secondary prevention ICD). The primary endpoint of all-cause mortality at 2.35 years follow-up (adjusted for age and entropy balance) was non-inferior for CRT-P versus CRT-D (HR 0.99, 95% CI 0.81–1.20), suggesting no mortality benefit with defibrillator capability in this population. Atkas et al. compared propensity matched outcomes of 535 patients with ICD versus 535 patients without ICD from the Empagliflozin arm of the Emperor-Reduced trial . Those with ICD versus no ICD had non-significantly lower mortality (HR 0.74, 95% CI 0.51–1.07, P = 0.114) and sudden cardiac death (HR 0.59, 95% CI 0.31–1.15, P = 0.122). However, despite propensity matching, the results were confounded by differences in medical therapy between groups, with more ICD patients receiving B-blockers and ARNIs but fewer receiving ACE-I/ARBs and MRAs. Ventricular Arrhythmias and SCD The VANISH (Ventricular Tachycardia Ablation versus Escalation of Antiarrhythmic Drugs) trial previously demonstrated superiority with regards to mortality, VT storm and appropriate ICD shocks of catheter ablation versus escalated AAD therapy in patients with previous MI and VT . A new sub-analysis compared shock-treated VT events and appropriate shock burden between the 2 groups. Catheter ablation was associated with a significant reduction in shock-treated VT events (39.07 vs. 64.60 per 100 person-years; HR 0.60; 95% CI 0.38–0.95) and total shock burden (48.35 vs. 78.23; HR 0.61; 95% CI 0.37–0.96). Prediction risk of sudden cardiac death (SCD) after MI has typically guided by LVEF < 35%, but many patients with LVEF < 35% who receive ICD never require it, whereas some with higher LVEF are still at risk of SCD. The additional predictive value of CMRI, in particular core scar size and grey zone size, for the PROFID risk prediction model was investigators in 2,049 patients imaged > 40 days post-MI . In the subgroup without ICD, use of CMRI data versus no CMRI data significantly improved prediction of SCD [area under curve (AUC) of model 0.753 vs. AUC 0.618]. In the subgroup with ICD, addition of CMRI data did not significantly improve prediction of SCD (AUC 0.598 vs. 0.535). This suggests CMRI may be useful to risk stratify post-MI and guide ICD use but further prospective studies are required. The SMART-MI-ICM trial previously reported that, in post-MI patients with EF 35–50%, implantable cardiac monitor (ICM) use versus control was associated with higher rates of arrhythmia detection although the clinical significance was unclear. The BIOGUARD-MI (BIO monitorinG in Patients With Preserved Left ventricUlar Function AfteR Diagnosed Myocardial Infarction ) trial aimed to assess the clinical value of arrhythmia detection on ICM, by randomising 804 patients with NSTEMI/STEMI to ICM versus standard care. Use of ICM was not associated with an overall significant reduction in the primary composite endpoint of CV death or hospitalisation at 2.5 years (HR 0.84, P = 0.21, 95% CI 0.64–1.10), although a reduction was noted in the NSTEMI subgroup (HR 0.69, 95% CI 0.49–0.98). This subgroup observation can only be hypothesis generating but is plausible given the more complex and co-morbid nature of a NSTEMI population. Atrial Fibrillation While smartwatches may improve detection of atrial fibrillation (AF), including asymptomatic AF, previous studies have reported high false positive rates. The mAF-App II trial, which used Huawei smartwatch photoplethysmography, reported data from 2.8 million people in China who downloaded the app . During 4 years follow-up, 12,244 (0.4%) people received a query AF notification, 5,227 attended for clinical evaluation with ECG and 24-h Holter monitoring and, within this group, AF was confirmed in 93.8%. This suggests much better specificity than previous studies, although the notification rate was lower than some studies, reflecting the relatively young population, and clinical data were not available for the 7017 people who received a notification but did not attend for evaluation. Unlike previous Apple, Fitbit and Huawei studies, E-Brave used the Preventicus smartphone app and invited 67,488 policyholders of a German health insurance scheme to participate, of whom 5,551 met inclusion criteria and agreed to enroll (AF naïve, median age 65 years; 31% female; median CHA2DS2-VASc of 3) and were randomised to active AF screening (photoplethysmogram [PPG] for 1 min twice per day for 2 weeks then twice weekly for 6 months, plus 2-week loop recorder if abnormal PPG) versus standard care. At 6 months, those in the active arm had double the rate of AF detection requiring OAC treatment (1.33 vs. 0.63%; OR 2.12; 95% CI 1.19–3.76). After 6 months, those without a new AF diagnosis were invited to cross-over to the opposite study arm, and, after a further 6 months, active screening with the app again doubled the detection and treatment of AF (1.38% vs. 0.51%; OR 2.75; 95% CI 1.42–5.34). Given the widespread availability of smartphones particularly in higher-risk populations, this may be a useful public health intervention, although further prospective studies are required to evaluate clinical outcomes of treating AF detected in this fashion. AF has been widely associated with increased risk of dementia and better control of AF may reduce this risk. Zeitler et al. using the Optum Clinformatics database, evaluated the propensity-matched risk of dementia in 19,088 patients following catheter ablation versus 19,088 patents treated with antiarrhythmic drugs (AAD) for AF . Catheter ablation was associated with a 41% reduction in risk of dementia (HR 0.59; 95% CI 0.51–0.68; P < 0.0001) and a 49% reduction in the secondary endpoint of mortality (HR 0.51, 95% CI 0.46–0.55, P < 0.001), supporting the value of effective AF treatment in this population. The Augustus trial previously reported the benefit of apixaban instead of vitamin-K antagonist (VKA) and ongoing P2Y12i monotherapy rather than DAPT for patients with AF and ACS/PCI . Harskamp et al. undertook a new analysis of 4,386 patients from Augustus to assess if benefits varied depending on baseline HASBLED (≤ 2 vs. ≥ 3) and CHAD 2 S 2 VASc (≤ 2 vs. ≥ 3) scores . Apixaban was associated with lower bleeding versus VKA irrespective of baseline risk [HR: 0.57 (HAS-BLED ≤ 2), HR 0.72 (HAS-BLED ≥ 3); interaction P = 0.23] and lower risk of death or hospitalization (HR 0.92 (CHA 2 DS 2 -VASc ≤ 2); HR 0.82 (CHA 2 DS 2 -VASc ≥ 3); interaction P = 0.53]. Aspirin versus placebo increased bleeding irrespective of baseline risk [HR: 1.86 (HAS-BLED ≤ 2); HR: 1.81 (HAS-BLED ≥ 3); interaction P = 0.88] with no significant difference in death or hospitalization [HR: 1.09 (CHA 2 DS 2 -VASc ≤ 2); HR: 1.07 (CHA 2 DS 2 -VASc ≥ 3); interaction P = 0.90]. The INVICTUS (Investigation of Rheumatic AF Treatment Using Vitamin K Antagonists, Rivaroxaban or Aspirin Studies) trial , randomised 4565 patients with rheumatic mitral valve and at high risk (CHAD 2 S 2 VASc ≥ 2, mitral valve area ≤ 2cm 2 , left atrial spontaneous contrast or thrombus) to Rivaroxaban versus VKA. Rivaroxaban was associated with increased incidence of the primary composite endpoint of stroke, systemic embolus, MI, or death from vascular/unknown cause (560 vs. 446 events; HR 1.25, 95% CI 1.10–1.41) despite suboptimal VKA control (only 33.2% having at appropriate INR enrolment, and the time in therapeutic range (TTR) being only 56–65% during follow-up). Rivaroxaban was also associated with a 37% increased risk of stroke and 23% increased risk. Thus, for AF and rheumatic mitral valve disease, VKA remains preferable to rivaroxaban. Previous studies reported that high-power, short duration (HPSD) versus conventional radiofrequency ablation (RFA) for AF was more effective with similar safety . The POWER FAST III (High Radiofrequency Power for Faster and Safer Pulmonary Vein Ablation) trial randomised 267 patients with AF to HPSD versus conventional RFA . HPSD was associated with a reduced ablation time but no difference in the primary efficacy outcome of freedom of atrial arrhythmia (99.2% vs. 98.4% in right pulmonary veins, 100% vs. 100% in left pulmonary veins) or the primary safety outcome of oesophageal lesions at endoscopy (7.5% vs. 6.5%; P = 0.94). Both conventional RFA and cryoablation for pulmonary vein isolation induce injury to neurocardiac structures (nerves and ganglia) which may be detected may release of S100b levels and post-procedure rise in heart rate . The technique of pulsed field ablation (PFA) may reduce neurocardiac trauma. Lemoine et al. randomised 56 patients to PFA versus cryoablation for AF. In those treated with PFA versus cryoablation, troponin I levels were 3 times higher ( P < 0.01), indicating more myocardial injury, but S100b levels were 2.9 times lower ( P < 0.001), and there was no increase in post-procedural heart rate (vs. marked increase with cryoablation; P < 0.01), indicating less neurocardiac damage with PFA. In addition, procedural success and durability of PFA appears encouraging. Keffer et al. evaluated 41 patients undergoing pulmonary vein PFA . The primary outcome of AF > 30 s or atrial tachycardia after a 30-day blanking period detected on 7-day Holter monitoring at 3 and 6 months occurred in 5 patients, of whom 3 underwent redo ablation during which all pulmonary veins were found to be still isolated. EAST-AFNET 4 previously reported a benefit of early rhythm control versus standard care in patients with AF , but there has been a paucity of data regarding initial ablation in such patients. In PROGRESSIVE-AF (a 3-year follow-up of the EARLY-AF trial), 303 patients with newly diagnosed symptomatic paroxysmal AF were randomised to upfront ablation versus AAD . Ablation was associated with a 75% reduction in the primary outcome of progression to persistent AF/flutter/tachycardia requiring cardioversion (1.9% vs. 7.4%; HR 0.25; 95% CI 0.09–0.70), a 49% reduction in any atrial arrhythmia > 30 s (56.5% vs. 77.2%; HR 0.51; 95% CI 0.38–0.67), a 69% reduction in hospitalisations (5.2% vs. 16.8%; RR 0.31; 95% CI 0.14–0.66) and 53% reduction in adverse effects (11% vs. 23.5%; RR 0.47; 95% CI 0.28–0.79). Use of botulinum toxin A to reduce AF was assessed in the NOVA (NeurOtoxin for the PreVention of Post-Operative Atrial Fibrillation) study which randomised 323 patients undergoing cardiac (bypass and/or valve) surgery to epicardial botulinum toxin A (125 units or 250 units) versus placebo . Overall, botulinum 125 units or 250 units versus placebo was not associated with a reduction in the primary outcome of AF > 30 s at 30 days (RR 0.80; 95% CI 0.58–1.10 and RR 1.04; 95% CI 0.79–1.37), respectively, although in the patient subgroup > 65 years, botulinum 125 units was associated with AF reduction (RR 0.64; 95% CI 0.43–0.94) which may be considered hypothesis-generating and warrant further study. Etripamil is a novel non-dihydropyridine calcium channel blocker, which may be given as a nasal spray, for acute treatment of patients with paroxysmal supraventricular tachycardia (PSVT) or AF. The RAPID (Efficacy and Safety of Etripamil for the Termination of Spontaneous PSVT) study screened 706 patients with PSVT ultimately assigning in random fashion 135 patients to etripamil versus 120 to placebo. Etripamil was associated with more than double the primary outcome of conversion to sinus rhythm within 30 min (64.3% vs. 31.2%; HR 2.62; 95% CI 1.66–4.15) and a median time to conversion of 17 min (almost 3 times quicker than placebo). Heart Failure Previous studies have shown the selective cardiac myosin activator Omecamtiv Mecarbilon may improve CV outcomes in HFrEF patients . To assess functional impact, the METEORIC-HF (Effect of Omecamtiv Mecarbil on Exercise Capacity in Chronic Heart Failure With Reduced Ejection Fraction) trial randomised 276 patients with LVEF ≤ 35%; NYHA II-III (in 2:1 fashion) to Omecamtiv Mecarbilon versus placebo for 20 weeks, in addition to standard therapy. Surprisingly, despite good tolerability and the previous favourable CV outcome data, Omecamtiv Mecarbilon was not found to improve exercise capacity (assessed by peak oxygen uptake on cardiopulmonary exercise stress testing). A major stumbling block in optimising HF medications can be hyperkalaemia. Patiromer, a non-absorbed sodium-free potassium-binding polymer increases faecal potassium excretion. The DIAMOND (Patiromer for the Management of Hyperkalemia in Subjects Receiving RAASi for HFrEF) trial randomised 1642 patients with HFrEF and renin–angiotensin–aldosterone system inhibitor (RAASi)-related hyperkalaemia to Patiromer versus placebo. Over a period of 13–42 (mean 27) weeks, Patiromer was associated with less increase in potassium (adjusted mean change + 0.03 vs. + 0.13 mmol/l; 95% CI –0.13 to 0.07; P < 0.001). The risk of hyperkalamia and need for reduction of MRA dose were numerically (although not statistically) lower. These important findings support Patiromer being incorporated in local HF protocols. Implementation of HF guidelines can be hampered by many factors. PROMPT-HF (PRagmatic trial of Messaging to Providers about Treatment of Heart Failure) randomised 1310 patients with HFrEF, not already taking all four pillars of therapy to a strategy of targeted, tailored electronic healthcare record alerts to optimise guideline-directed medical therapy (GDMT) versus standard care. The electronic alert strategy was associated with a significant increase in the number of drug classes prescribed at 30 days (26% vs. 19%; adjusted RR 1.41; 95% CI: 1.03–1.93; P = 0.03; number needed to alert = 14). In an impressive attempt to improve secondary prevention therapy delivery, the SECURE (Secondary Prevention of Cardiovascular Disease in the Elderly Trial) trial randomised 2499 patients with MI ≤ 6 months to an open label polypill, comprising aspirin 100 mg, ramipril (2.5, 5 or 10 mg) and atorvastatin ( or mg), versus standard care. At 3-year follow-up, use of the polypill was associated with a 24% reduction in the primary endpoint of CV death, type 1 MI or ischaemic stroke (9.5% vs. 12.7%; HR 0.76, 95% CI: 0.6–0.96; P = 0.02). Sodium-glucose cotransporter-2 inhibitors (SGLT2i) trials continue to dominate HF research. A meta-analysis of 13 SGLT2i trials involving 90,413 participants (82 reported a 37% reduction in risk of progressive renal dysfunction 37% (RR 0·63, 95% CI 0·58–0·69) and a 23% reduction in risk of CV death or HF hospitalisation (RR 0·77; 0·74–0·81). Effects were similar in diabetics versus non-diabetics and regardless of baseline renal function (Fig. ). When first introduced and before reno-protective properties became clear, SGLT2i use was restricted to patients with eGFR > 60 to optimise glycaemic control. EMPA-KIDNEY (Study of Heart and Kidney Protection With Empagliflozin) randomised 6609 patients with impaired renal function (eGFR 20 to < 45, or eGFR 45 to < 90 plus urinary albumin-to-creatinine ratio > 200) to empagliflozin versus placebo. At 2 years, empagliflozin was associated with a 28% reduction in the primary endpoint of progression of kidney disease (defined as end-stage kidney disease, eGFR < 10, decrease in eGFR ≥ 40% from baseline, death from renal causes) or CV death (13.1% vs. 16.9% of the control group (HR 0.72; 95% CI: 0.64–0.82; P < 0.001). The EMPULSE (Empagliflozin in Patients Hospitalized for Acute Heart Failure) trial randomised 530 acutely decompensated patients hospitalised with HF, regardless of ejection fraction or diabetic status to Empagliflozin versus placebo. Those with IV vasodilators, IV inotropes, requiring increasing IV diuretic doses, cardiogenic shock or recent ACS were excluded. Empagliflozin versus placebo was more frequently associated clinical benefit in the primary composite endpoint of death, number of HF events, time to first HF event, and change in Kansas City Cardiomyopathy Questionnaire-Total Symptom Score at 90 days (stratified win ratio 1.36; 95% CI 1.09–1.68; P = 0.0054) ( Fig. ). The DELIVER (Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction) study randomised 6263 hospitalised or recently hospitalised patients with HF and LVEF > 40% to dapagliflozin versus placebo. Dapagliflozin was associated with an 18% reduction in the primary endpoint of death or worsening HF (16.4% vs. 19.5%; HR 0.82, 95% CI 0.73–0.92; P < 0.001). Acetazolamide, a carbonic anhydrase inhibitor, through reduction of proximal tubular sodium reabsorption, may improve the efficiency of loop diuretics, potentially leading to faster decongestion in patients with acute decompensated heart failure. The ADVOR (Acetazolamide in Decompensated Heart Failure with Volume Overload) study randomised 519 patients with decompensated HF patients to IV acetazolamide (500 mg daily) versus placebo in addition to IV loop diuretics (at twice the oral maintenance dose) examining the role. Acetazolamide was associated with a 46% improvement in attaining the primary endpoint of absence of signs of fluid overload at 3 days (42.2% vs. 30.5%; RR 1.46, 95% CI 1.17–1.82; P < 0.001) with higher urine output and natriuresis but without an excess of acute kidney injury, hypokalaemia, or hypotension. While the importance of optimised dosing of HF treatment is well established, since HF therapies may be associated with hypotension and renal decline, the ideal rate of titration is less clear. The STRONG-HF (Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP Testing, of Heart Failure Therapies) trial randomised 1078 patients admitted to hospital with acute HF to rapid up-titration (achieving full recommended doses within 2 weeks of discharge) versus usual care. Rapid up-titration was associated with a significantly lower rate of readmission for HF or all-cause death (15.2% vs. 23.3%; 95% CI 2.9–13.2; P = 0.0021), approximately a 10% increase in adverse events, but a similar rate for serious adverse events. IV iron has a Class IIa recommendation for patients with HF and anaemia. Most trials have used ferric carboxymaltose. IRONMAN (Intravenous ferric derisomaltose in patients with heart failure and iron deficiency in the UK) randomised 1,137 patients with chronic HF and iron deficiency (LVEF < 45%, with Transferrin saturation < 20% or ferritin < 100 µg/l) to ferric derisomaltose (which can be given as a rapid, high-dose infusion) versus usual care. At a median fgollow up of 2.7 years, ferric derisomaltose showed a trend to reduction in the primary composite endpoint of HF hospitalisation and CV death (336 vs. 411 events; RR 0.82, 95% CI 0.66–1.02; P = 0.07) and a significant reduction in HF hospitalisations. Since study outcomes may have been confounded by the COVID-19 pandemic, a pre-specified analysis censoring follow-up on September 30, 2020 was undertaken which reported a significant reduction in the primary endpoint (210 vs. 280 events; RR 0·76 [95% CI 0·58 to 1·00]; P = 0·047). Myosin inhibition using mavacamten in patients with obstructive hypertrophic cardiomyopathy was examined in the VALOR-HCM (Mavacamten in Adults With Symptomatic Obstructive HCM Who Are Eligible for Septal Reduction Therapy) trial which randomised 112 patients eligible for septal reduction therapy (SRT) to mavacamten (starting at 5 mg and titrating using LVEF and LVOT gradient) versus placebo. After 16 weeks follow-up, mavacamten was associated with marked reduction in obstructive parameters with only 17.9% still meeting guideline criteria for SRT (vs. 76.8% of placebo patients; 95% CI: 0.44–0.74; P < 0.001). Prevention Lipoprotein[Lp] (a) is highly genetically determined and higher levels are associated with an increased risk of CV disease. Statins have minimal effect and PCSK9i only modest effect but Olpasiran, a small interfering RNA (siRNA) may enable significant Lp(a) reduction. In the OCEAN(a)-DOSE TIMI 67 trial , 281 patients with elevated Lp(a) > 150 nmol/L were randomised to 1 of 4 olpasiran doses (10 mg, 75 mg, or 225 mg every 12 weeks, or 225 mg every 24 weeks) versus placebo. By 36 weeks, the 4 doses of olpasiran were associated with placebo-adjusted percent reductions in Lp(a) concentration of 70.5%, 97.4%, 101.1%, and 100.5%, respectively, along with useful reductions in low-density lipoprotein (LDL) cholesterol and apolipoprotein B. In addition to Olpasiran, other siRNA drugs are in development including SLN360, and pelacarsen, an mRNA-based antisense oligonucleotide targeting the Lp(a) gene being studied in the 8000-patient outcomes study, Lp(a)HORIZON which will hopefully clarify if reduction of Lp(a) is of benefit . Perceived myalgia remains an important limitation for statin adherence. The Cholesterol Treatment Trialists’ Collaboration evaluated incidence of myalgia in a meta-analysis of 19 double-blind trials of statin versus placebo ( n = 123,940) and four double-blind trials of more versus less intensive statin regimen ( n = 30,724). For the 19 placebo-controlled trials, statin use was associated with a 3% increase in reported muscle pain or weakness at a median 4·3 years follow-up (27.1% vs. 26.6%; RR 1.03, CI 95% 1.01–1.06), but the excess was mainly during the first year, when statin use was associated with an absolute excess of 11 events per 1000 person-years. Similarly, a small increase in reported muscle pain or weakness was seen with higher versus lower intensity statin groups, (36.1% vs. 34.8%; RR 1.05, CI 95% 1.01–1.09). In summary, while statin therapy can cause myalgia, most (> 90%) reports of muscle symptoms by participants allocated statin therapy were not due to the statin. The FOURIER-OLE (Fourier Open-label Extension Study in Subjects With Clinically Evident Cardiovascular Disease in Selected European Countries) evaluated the long-term follow-up of the FOURIER study in 6635 patients randomised to the PCSK9 inhibitor Evolocumab versus placebo. At a median of 5 years, Evolocumab was associated with resulted in a 20% reduction in CV death, MI or stroke (HR 0.8, 95% CI 0.68–0.93; P = 0.003) with low risk of adverse events. Elevated uric acid is recognised as an independent risk factor for CV events. The ALL-HEART (Allopurinol versus usual care in UK patients with ischaemic heart disease) study randomised 5721 patients > 60 years with ischaemic heart disease but no history of gout to allopurinol (up-titrated to maximum of 600 mg) versus placebo. However, over a mean of 4.8 years follow-up, allopurinol was not associated with reduction in the primary endpoint of CV death, MI or stroke (11% vs. 11.3%; P = 0.65). The endothelin pathway has been implicated in the pathogenesis of hypertension, but is currently not targeted therapeutically, leaving this pathway unopposed with currently available drugs. The global PRECISION (Dual endothelin antagonist aprocitentan for resistant hypertension) trial randomised 730 patients with hypertension resistant to at least 3 antihypertensives to the dual endothelin receptor antagonist aprocitentan aprocitentan 12·5 mg or 25 mg versus placebo in a 1:1:1 fashion. At 4 weeks, aprocitentan was associated with met the primary endpoint with greater systolic blood pressure reduction (mean change for aprocitentan 12.5 mg of − 15.3 mmHg and for aprocitentan 25 mg of − 15.2 mmHg vs. placebo − 11.5 mg; P < 0.005 for both treatment doses). Delivering healthcare in rural environments can be challenging. In China, non-physician village doctors may initiate and titrate antihypertensive medications according to a standard protocol with supervision from primary care physicians, and undertake health coaching on home blood pressure monitoring, lifestyle changes, and medication adherence. The China Rural Hypertension Control Project randomised 33,995 patients from 326 villages to village doctor-led multifaceted intervention versus usual care . By 36 months, the intervention group reported a drop in mean systolic pressure from 157 to 126.1 mmHg, whereas the usual-care group only dropped from 155.4 mmHg to 146.7 mmHg and a significant reduction in the primary composite CV endpoint (1.98% vs. 2.85% per year; HR 0.69, CI 95% 0.63–0.76) with 33% fewer strokes ( P < 0.0001), 39% fewer cases of HF ( P = 0.005), 24% fewer CV deaths ( P = 0.0004), and 15% fewer all-cause deaths ( P = 0.009). Previous trial data suggested a protective effect for nocturnal dosing of anti-hypertensive therapies on cardiovascular events, although the trial methodology was subsequently questioned . The TIME (Treatment in Morning versus Evening) trial randomised 21,104 patients (mean age 65 years, female 43%) to evening versus morning dosing of their regular antihypertensive agent . After 5 years, the primary outcome (composite of vascular death, MI or stroke) occurred in 3.4% of the evening dosing group versus 3.7% of the morning group ( P = 0.53). There was no difference in rates of stroke between groups (1.2% vs. 1.3%, P = 0.54); however, there was a modestly higher rate of falls in the morning dosing group (22.2% vs. 21.1%, P = 0.048). This informative trial demonstrates no difference in cardiovascular outcomes with respect to timing of anti-hypertensive dosing albeit a slightly reduced risk of falls with evening dosing.
Several practice changing trials in Percutaneous Coronary Intervention (PCI) have been published this year (Table ). Historically, PCI has been used to treat ischaemic cardiomyopathy, despite limited supporting evidence . In the REVascularisation for Ischaemic VEntricular Dysfunction (REVIVED-BCIS2) trial , 700 patients with left ventricular ejection fraction (LVEF) ≤ 35% and extensive coronary artery disease (CAD), as defined by the British Cardiovascular Intervention Society (BCIS) jeopardy score, were randomised to PCI or optimal medical therapy (OMT). Over a median follow-up time of 3.4 years, PCI versus OMT alone did not result reduction in the primary composite outcome of death or hospitalization for heart failure [37.2% vs. 38.0%; HR 0.99; 95% confidence interval (CI), 0.78–1.27; P = 0.96] . The optimal treatment for left main (LM) and multivessel CAD remains hotly debated. New observational data from the Swedish Coronary Angiography and Angioplasty Registry (SCAAR) compared outcomes among 10,254 such patients undergoing PCI (52.6%) versus coronary artery bypass grafting (CABG) (47.4%). PCI was associated with a 59% increased risk of death versus CABG after 7 years of follow-up ( P = 0.011). Despite the limitations of observational data, findings are in keeping with the NOBLE study , supporting use of CABG where clinically appropriate in LM patients with additional multivessel CAD. In contrast, a meta-analysis of 2913 patients from four RCTs (SYNTAXES, PRECOMBAT, LE MANS, and MASS II) undergoing PCI versus CABG for LM or multivessel CAD did not report any significant difference in 10-year survival (RR 1.05; 95% CI 0.86–1.28), nor significant difference in the subgroup with LM disease alone or multivessel disease alone. This may reflect a lower extent of non-LM disease complexity in the four trials. Of note, a new analysis from the SYNergy Between PCI With TAXUS and Cardiac Surgery Extended Study (SYNTAXES) evaluated mortality according to presence or absence of bifurcation lesions . In the PCI group, those undergoing stenting of ≥ 1 bifurcation lesions versus no bifurcation stenting, had a higher risk of death at 10 years (30.1% vs. 19.8%; P < 0.001). Furthermore, a 2 versus 1 stent bifurcation strategy was associated with a higher risk of death at 10 years (HR 1.51; 95% CI 1.06–2.14). Conversely, in the CABG, the presence or absence of bifurcation lesions had no impact on mortality. As this was a post hoc analysis, results can only be considered hypothesis-generating, but are in keeping with previous data highlighting the complexity of bifurcations and the preference for a simple rather than a complex strategy where possible. Female sex has been associated with worse outcomes following PCI related to smaller vessel disease. However, previous LM have been unclear and, given that LM has a larger diameter, more equivalent results. A substudy of the NOBLE trial showed no difference in outcomes for male versus female, with both showing an excess of major adverse cardiovascular and cerebrovascular events (MACCE) with PCI at 5 years, although no difference in all-cause mortality. For those undergoing PCI for LM disease, the IDEAL-LM (Individualizing Dual Antiplatelet Therapy After Percutaneous Coronary Intervention in patients with left main stem disease) study reported that a strategy of short 4-month DAPT (dual-antiplatelet therapy) plus a biodegradable polymer platinum-chromium everolimus-eluting stent was non-inferior to a strategy of conventional 12-month DAPT plus durable polymer cobalt-chromium everolimus-eluting stent (DP-CoCr-EES), with respect to a composite of death, MI or target vessel revascularisation at 2 years. However, the shorter DAPT strategy did not show any reduction in bleeding events. The Complete Revascularization with Multivessel PCI for Myocardial Infarction (COMPLETE) trial previously reported that complete versus culprit-only PCI had lower risk of cardiovascular (CV) death/myocardial infarction (MI) over 3 years of follow-up. In a new pre-specified analysis , complete versus culprit-only PCI was associated with a greater absence of residual angina (87.5% vs. 84.3%; P = 0.013) and improved quality of life, as assessed via the 19-item Seattle Angina Questionnaire, including reduced physical limitation. Improving PCI outcomes in patients with diabetes remains a focus of several trials. The Second-generation drUg-elutinG Stents in diAbetes: a Randomized Trial (SUGAR trial), which randomised 1175 patients with diabetes and CAD to an amphilimus-eluting stent (Cre8 EVO) vs. conventional Resolute Onyx stent, previously reported that the Cre8 stent met non-inferiority and was associated with a possible 35% reduction in Target Lesion Failure (TLF) at 12 months . However, by 2 years , the difference in TLF was no longer significant (10.4% vs. 12.1%; HR 0.84; 95% CI 0.60–1.19) with numerical but non-significant differences in the individual components of cardiac death (3.1% vs. 3.4%), target vessel MI (6.6% vs. 7.6%), and target lesion revascularization (4.3% vs. 4.6%). While these 2-year results were disappointing, we await results of further studies of new stents in this clinical setting, including the ABILITY trial (NCT04236609) comparing an Abluminus DES + sirolimus-eluting stent system versus Xience. Quantitative flow ratio (QFR), an angiography-based approach to estimate the fractional flow reserve, previously reported superiority versus conventional angiography guidance at 1 year in the FAVOR III (Comparison of Quantitative Flow Ratio Guided and Angiography-Guided Percutaneous InterVention in Patients With cORonary Artery Disease) trial . New data report that the benefit with the QFR-guided strategy was sustained at 2 years, associated with a 34% reduction in the composite of death, MI or ischaemia-driven revascularization [8.5% vs. 12.5%; HR 0.66 (95% CI 0.54–0.81)] . The degree of outcome improvement was greatest amongst those patients in whom the pre-planned PCI strategy was modified by QFR. Current ESC guidelines give post-PCI surveillance with stress testing with a Class IIb recommendation. The POST-PCI (Routine Functional Testing or Standard Care in High-Risk Patients after PCI) trial randomised 1706 patients at 1 year after PCI to routine functional testing (nuclear stress testing, exercise electrocardiography, or stress echocardiography) versus standard care . Use of routine functional testing failed to show any reduction in the primary outcome of death MI, or hospitalization for unstable angina at 2 years (5.5% vs. 6.0%; HR, 0.90; 95% CI 0.61–1.35; P = 0.62), supporting standard care in these patients. Procedural time in graft-angiography studies may be much longer than a non-graft cases. The Randomised Controlled Trial to Assess Whether Computed Tomography Cardiac Angiography Can Improve Invasive Coronary Angiography in Bypass Surgery Patients (BYPASS CTCA), randomised 688 prior CABG patients to CTCA prior to coronary angiography versus standard care. Those who underwent prior CTCA had a shorter procedure duration (mean 17.4 vs. 39.5 min; OR − 22.12; 95% CI − 24.68 to − 19.56), less contrast during the invasive angiogram (mean 77.4 vs. 173 mls), less contrast-induced nephropathy (3.2% vs. 27.9%; P < 0.0001) and 40% greater patient satisfaction . BYPASS CTCA thus supports consideration of prior CTCA particularly with more complex or uncertain graft location or patients at greater renal risk. The 2018 ESC guidelines recommend radial access for PCI unless overriding procedural considerations. A new patient-level meta-analysis of 7 trials, incorporating 21,700 patients reported that, at 30 days, transradial versus transfemoral access was associated with a 23% reduction in all-cause mortality (1.6% vs. 2.1%; P = 0 .012) and 45% reduction in major bleeding (1.5% vs. 2.7%; P < 0.001) . However, transradial access is not without complications, the commonest of which is radial artery occlusion. In the RIVARAD (Prevention of Radial Artery Occlusion With Rivaroxaban After Transradial Coronary Procedures) trial, 538 patients were randomised following coronary angiography to rivaroxaban 10 mg once daily for 7 days versus standard care (no rivaroxaban) . At 30 days, use of rivaroxaban was associated with a 50% reduction in radial artery occlusion as defined by ultrasound (6.9% vs. 13.0%; OR 0.50; 95% CI 0.27–0.91). Bleeding Academic Research Consortium (BARC)-defined bleeding events were numerically but not significantly higher in the rivaroxaban group (2.7% vs. 1.9%; OR 1.4; 95% CI 0.4–4.5). To assess whether distal radial artery puncture might reduce occlusion rates, the Distal Versus Conventional Radial Access DISCO-RADIAL) trial randomised 1,307 patients to distal versus conventional radial access . Distal access was associated with shorter median hemostasis time (153 vs. 180; P < 0.001), but radial artery spasm was more common (5.4% vs. 2.7%; P = 0.015), crossover rates were higher (7.4% vs. 3.5%; P = 0.002) and no difference in the primary endpoint of occlusion on vascular ultrasound was noted at discharge (0.31% vs. 0.91%; P = 0.29). While radial access now considered preferable, transfemoral access is still required in certain cases. As transfemoral operator skills may potentially decline through reduction in volume or lack of experience, ultrasound-guided access techniques are increasingly being used. The UNIVERSAL (Routine Ultrasound Guidance for Vascular Access for Cardiac Procedures) trial randomised 621 patients to femoral access with ultrasound guidance and fluoroscopy versus fluoroscopy alone . Interestingly, and in contrast with previous trials, ultrasound guidance was not associated with significant reduction in the composite of BARC 2, 3, and 5 bleeding or major vascular complication at 30 days (12.9% vs. 16.1%; p = 0.25). The strategy of multi-arterial CABG is endorsed by surgical guidelines but takes longer, is more technically demanding and can be associated with increased complications, such as deep sternal wound infections. An observational single-centre study by Momin et al. of 2979 patients undergoing isolated CABG (from 1999 to 2020) reported those receiving total arterial revascularization had the longest mean survival (18.7 years) versus single internal mammary artery (SIMA) plus vein grafts 16.1 years; P < 0.00001) versus vein grafts only (10.4 years; P < 0.00001). Interestingly, survival with total arterial revascularization was not significantly different to SIMA plus radial artery ± vein grafting (18.60 years). This study supports the durability of arterial grafting, although conclusions are limited by its non-randomised design. Conversely, Saadat et al. stratified 241,548 patients from the Society of Thoracic Surgeons (STS) database undergoing isolated CABG in 2017 into 3 groups: single arterial (86%), bilateral internal thoracic artery-multi-arterial (BITA-MABG; 5.6%), and radial artery multiarterial (RA-MABG; 8.5%). After risk adjustment, the observed to expected event (O/E) ratios showed no significant difference in mortality between the three strategies (1.00 vs. 0.98 vs. 0.96) and the risk of deep sternal wound infection was highest in the BITA-MABG group (1.91 vs. 0.90 vs. 0.96). Given the ongoing data uncertainty, results from the prospective randomised ROMA trial are eagerly awaited (NCT03217006). Structural: Aortic Valve Interventions There has been a dramatic expansion in transcatheter aortic valve interventions over the past decade . A recent analysis of US registry data conducted by Sharma et al. reported a near doubling in transcatheter aortic valve replacement (TAVR) volume overall between 2015 and 2021 (44.9% vs. 2021, 88%, P < 0.01), including a 2.7 fold increase in those < 65 years (now similar to surgical aortic valve replacement (SAVR) (47.5% TAVR vs. 52.5% SAVR, P = ns) particularly in younger patients with heart failure (HF) (OR 3.84; 95% CI 3.56–4.13; P < 0.0001), or prior CABG (OR, 3.49; 95% CI, 2.98–4.08; P < 0.001) . These numbers may further increase across all risk categories with the early long-term data from the seminal PARTNER (Placement of AoRTic TraNscathetER Valve Trial) trials awaited. Emerging evidence from trials such as AVATAR (Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis) and RECOVERY (Early Surgery Versus Conventional Treatment in Very Severe Aortic Stenosis) suggests that early intervention for severe aortic stenosis (AS), before patients develop symptoms, may be of benefit . In a pooled analysis of key trials (PARTNER2A, 2B &3) involving 1974 patients (mean age 81 years; 45% women), Généreux et al. evaluated the relationship between cardiac damage at baseline and prognosis in patients with severe symptomatic AS who underwent AVR (40% SAVR, 60% TAVI) . Baseline cardiac damage was defined using a 0–4 scoring system (0 = no damage and 4 = biventricular failure). Baseline damage correlating strongly with 2-year mortality (HR 1.51 per higher stage; 95% CI 1.32–1.72) with each increase in stage conferred a 24% increase in mortality ( P = 0.001) (from stage 0 = 2.5% to stage 4 = 28.2%) suggesting a role for earlier intervention. Several ongoing trials, such as EARLY TAVR (Evaluation of TAVR Compared to Surveillance for Patients With Asymptomatic Severe Aortic Stenosis), TAVR UNLOAD (Transcatheter Aortic Valve Replacement to UNload the Left Ventricle in Patients With ADvanced Heart Failure) and PROGRESS (Management of Moderate Aortic Stenosis by Clinical Surveillance or TAVR), aim to answer these questions directly. Valve in valve (VIV) TAVR is being increasing utilised in patients with failed AVR; however, it remains unclear whether these patients do better with or without balloon valve fracture (BVF). In a registry analysis of 2975 patients undergoing VIV-TAVR (with balloon-expandable SAPIEN 3 or SAPIEN 3 Ultra) between December 2020 and March 2022, Garcia et al. reported that BVF versus no BVF led to larger mean valve area (1.6 vs. 1.4 cm2; P < 0.01) and lower mean valve gradient (18.2 vs. 22.0 mm Hg; P < 0.01) but also to higher rates of death or life-threatening bleeding (OR 2.55; 95% CI 1.44–4.50) and vascular complications (OR 2.06; 95% CI 0.95–4.44). However, sub-analysis suggested the increase in mortality was mainly if BVF undertaken before VIV-TAVR (OR 2.90; 95% CI 1.21–6.94), whereas no difference was noted if undertaken after VIV-TAVR. This suggests that VIV-BVF should only be performed once the operator has a new TAVR in place. While designed primarily for AS, conventional TAVR devices have sometimes utilised for the treatment of severe aortic regurgitation (AR). The novel trilogy heart valve system, specifically developed for AR, and was evaluated in 45 patients (mean age 77, 40% female, mean Euroscore 7.1%) with moderate to severe AR by Tamm et al. . The primary endpoint, a reduction in ≥ 1 AR grade, was met in 100% of cases. There were no episodes of stroke, death, or conversion to open surgery, but 9 patients (23%) required permanent pacing. Subclinical leaflet thrombosis (SLT) is a relatively common complication of TAVR; however, the optimal treatment strategies, whether with anticoagulation or antiplatelets, remain contested. The multicentre ADAPT TAVR (Edoxaban vs. DAPT in reducing subclinical leaflet thrombosis and Cerebral Thromboembolism After TAVR) randomised 229 patients (mean age 80.1 years; 41.9% men) undergoing TAVR for symptomatic severe AS, and without other indication for OAC, to edoxaban 60 mg or 30 mg once daily versus DAPT with aspirin and clopidogrel . At 6 months, Edoxaban, by intention to treat analysis, was associated with a trend to reduced SLT as assessed by cardiac CT (9.8% vs. 18.4%; P = 0.076) and, in contrast to prior trials with DOAC post-TAVR, there was no difference in bleeding rates (11.7% vs. 12.7%; P = ns). Interestingly, a secondary per-protocol analysis focusing on patients with high compliance did reach statistical significance (19.1% vs. 9.1%; risk ratio 0.48; 95% CI 0.23–0.99). However, despite the use of serial brain MRI, there was no difference in the presence/number of cerebral lesions and no difference in neurocognitive outcomes including stroke at 6 months. Giustino et al. reported a new secondary analysis from the GALILEO trial (Rivaroxaban-based Antithrombotic Strategy to an Antiplatelet-based Strategy After TAVR to Optimize Clinical Outcomes) which, as described previously , had randomised 1644 patients post-TAVR without an indication for oral anticoagulation (OAC) to rivaroxaban 10 mg plus aspirin versus DAPT with aspirin plus clopidogrel for 90 days, but was stopped early due to higher thromboembolic bleeding and mortality events in the Rivaroxaban group . In the new analysis, thromboembolic events appeared to be associated with higher risk of mortality (HR 8.41; 95% CI 5.10–13.87) versus BARC 3 bleeding (HR 4.34; 95% CI 2.31–8.15). Furthermore, this mortality risk appeared higher than that conferred by known risk factors such as age (adjusted HR 1.04; 95% CI 1.01–1.08) and chronic obstructive pulmonary disease (COPD) (adjusted HR 2.11; 95% CI 1.30–3.41). These findings along with previous data from ALANTIS (AntiThrombotic Strategy After Trans-Aortic Valve Implantation for Aortic Stenosis) and ENVISAGE-TAVI AF (Edoxaban Compared to Standard Care After Heart Valve Replacement Using a Catheter in Patients With Atrial Fibrillation) show how the role of DOACs post-TAVI remains uncertain . However, given the devastating impact of thromboembolic events in this patient group, ongoing research is warranted. The absence of a bleeding signal with DOAC in ADAPT TAVR, in which most received lower dose Edoxaban, suggests that lower dose DOAC for a short duration while the valve is endothelialising may improve the risk/benefit ratio. Another area of current contention is the use of cerebral embolic protection (CEP) to reduce risk of stroke. While current guidance does not mandate use, some operators use in high-risk cases . Kaur et al. conducted a meta-analysis of 1,016 patients (mean age 81.3 years) from several randomised trials (DEFLECT III, MISTRAL-C, CLEAN-TAVI, SENTINEL, and REFLECT I and II) evaluating the TriGuard (Keystone Heart) and Sentinel devices versus standard care. At 30 days, CEP was not associated with a reduction in the primary outcome of all-cause stroke (RR 0.93; 95% CI 0.57–1.53), nor a reduction in mortality. Subsequently, the PROTECTED TAVR (Stroke PROTECTion With SEntinel During Transcatheter Aortic Valve Replacement) trial randomised 300 patients (mean age 72 years, 40% female) to CEP with a Sentinel device versus standard care . Again, no significant difference in primary outcome of stroke at 72 h was noted (2.4% vs. 2.9%, P = 0.30), although numbers were relatively small. The results of BHF PROTECT TAVI (British Heart Foundation Randomised Clinical Trial of Cerebral Embolic Protection in Transcatheter Aortic Valve Implantation) plans to enrol 7000 patients and findings are eagerly awaited. Stuctural: Mitral and Tricuspid Valve interventions The favourable findings in COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation [MR) helped lead to device approval . However, it has been suggested the reason COAPT was favourable was the strict eligibility criteria, mandating LVEF ≥ 20% to ≤ 50%, left ventricular end-systolic dimension (LVESD) ≤ 70 mm and failure of aggressive medical therapy . EXPAND (A Contemporary, Prospective Study Evaluating Real-world Experience of Performance and Safety for the Next Generation of MitraClip Devices) was a prospective multicentre registry of 1,041 patients with site-reported MR 3 + /4 + were enrolled and received the MitraClip. A recent analysis compared 125 “COAPT-like” patients meeting COAPT inclusion criteria versus 128 “non-COAPT” patients. At 1 year, COAPT-like patients did not show any difference in the primary outcome of all-cause mortality (22.6% vs. 19.6%, P = 0.37) or heart failure hospitalisation (32.6% vs. 25%, P = 0.08). In keeping with their lower baseline MR severity, more non-COAPT patients achieved reduction in MR to mild or less (≤ 1 +) (97.2% vs. 86.5%), suggesting that Mitraclip may benefit patients beyond the strict COAPT criteria, but prospective randomised data are needed, such as the ongoing EVOLVE-MR (MitraClip for the Treatment of Moderate Functional Mitral Regurgitation). Previous data from CLASP (Edwards PASCAL TrAnScatheter Mitral Valve RePair System Study) and CLASPII have validated the safety and efficacy of the Edwards PASCAL™ transcatheter valve repair system . CLASP IID randomised 180 patients with severe degenerative symptomatic MR not eligible for surgery (mean age 81 years, 67% male, median STS 5.9%) to transcatheter Edge-to-Edge Repair (TEER) with the Pascal device (Edwards Lifesciences) vs. MitraClip (Abbott) device . At 30 days, the Pascal device met criteria for non-inferiority with respect to the composite endpoint of CV death, stroke, MI, renal replacement therapy, severe bleeding and re-intervention (3.4% vs. 4.8%; P for noninferiority < 0.05). Of interest, the proportion of patients with MR ≤ 1 + was durable in the PASCAL group (87.2% discharge vs. 83.7% at 6 months; P = 0.317); whereas MitraClip outcomes showed some loss of efficacy (88.5% discharge vs. 71.2% at 6 months; P = 0.003). Although only interim data, this hints that the Pascal device may have superior durability. ViV-transcatheter mitral valve replacement (ViV-TMRV) may be utilised in very high-risk patients without a surgical option on a case-by-case basis despite paucity of real-world outcome data. Bresica et al. retrospectively compared outcomes of 48 patients with bioprosthetic mitral valve (MV) failure undergoing ViV-TMRV (mean age 65 years, 63% female, mean STS 7.9%) versus 36 patients undergoing re-do MV surgery (mean age 58, 72% female, mean STS 7.1%) . ViV-TMVR was not associated with improvement in 1-year survival (90% vs. 80%, P = 0.33) and was associated with higher average postprocedural gradient (8.9 vs. 5.7 mm Hg; P < 0.001). Thus, ViV-TMRV is a good option for high-risk patients, but in less comorbid patients may not provide as good a long-term benefit as surgery, particularly in those with smaller original surgical valves. Data to come from the ongoing PARTNER 3 (Mitral Valve-in-Valve trial) will be useful to help guide decision-making in such patients. Several seminal trials, such as TRILUMMINATE (Abbott Transcatheter lip Repair System in Patients With Moderate or Greater TR), Triband (TranscatheterRepair of Tricuspid Regurgitation With Edwards Cardioband TR System Post-Market Study) and TRISCEND (Investigation of Safety and Clinical Efficacy After Replacement of Tricuspid Valve With Transcatheter Device), have led to a much greater focus on transcatheter tricuspid interventions . CLASP TR (Edwards PASCAL Transcatheter Valve Repair System Pivotal Clinical Trial), a prospective single-arm multicentre study, evaluated 1-year outcomes of the PASCAL transcatheter valve repair system in 65 patients (mean age 77 ± 9 years, 55% female, mean STS 7.7%) with severe tricuspid regurgitation (TR) . In keeping with the high baseline comorbidity, major adverse event rate was 16.9% ( n = 11) with all-cause mortality 10.8% ( n = 7) and 18.5% ( n = 12) re-admitted with heart failure. Paired analysis demonstrated significant improvements in New-York Heart Association (NYHA) grade ( P < 0.001), KCCQ score ( P < 0.001) and 6-min walk test (6MWT) ( P = 0.014). Importantly, the reduction in TR severity noted at 30 days ( P < 0.001) was maintained at 1 year (100% had ≥ 1 grade reduction and 75% had ≥ 2 grade reduction, P < 0.001). TRICLASP (Transcatheter Repair of Tricuspid Regurgitation With Edwards PASCAL Transcatheter Valve Repair System), a prospective, single-arm multicentre trial, evaluated 30-day outcomes in 67 of 74 patients (mean age 80 years, 58% female, mean STS 9%) undergoing the Pascal Ace transcatheter repair system for severe symptomatic inoperable TR (Fig. ). The primary composite outcome of major adverse events was 3% with 88% achieving ≤ 1 grade reduction in TR vs. baseline; P < 0.001), along with significant improvements in NYHA, KCCQ score, and 6MWT ( P < 0.001). Longer term follow-up data are awaited. TriClip-Bright (An Observational Real-world Study Evaluating Severe Tricuspid Regurgitation Patients Treated With the Abbott TriClip™ Device) study , a multicentre, prospective study reported 30-day outcomes for 300 patients (78 ± 7.6 years) undergoing the Triclip Transcatheter valve repair system (Fig. ). The primary endpoint of procedural success (survival to discharge) was met in 91%. Significant reductions in both NYHA and KCCQ score were noted at ( P < 0.001). The trial is still actively recruiting, with a planned follow-up duration of 1 year. Structural: Catheter Based Left Atrial Appendage and Patent Foramen Ovale Closure While definitive studies to guide patent foramen ovale (PFO) closure practice are still lacking, a multidisciplinary consensus statement by SCAI was published this year recommending closure in patients aged 18–60 with a PFO-associated stroke, platypnoea-orthodeoxia syndrome with no other cause, and systemic embolism with no other cause. Of note in the absence of PFO-associated stroke, the guidance does not recommend PFO closure in transient ischaemic attack, AF with ischaemic stroke, migraine, decompression illness or thrombophilia. Several left atrial appendage closure (LAAC) devices have been approved in recent years with favourable long-term data published last year for the Watchman LAAC device (Boston Scientific). The AMULET IDE trial (Amplatzer Amulet Left Atrial Appendage Occluder Versus Watchman Device for Stroke Prophylaxis) trial randomised patients with non-valvular atrial fibrillation (AF), not suitable as anticoagulation to LAAC with an Amulet device ( n = 934) versus Watchman device ( n = 944). At 3 years, there was no difference in the primary composite endpoint of CV mortality, ischaemic stroke or systemic embolism (11.1% vs. 12.7%, P = 0.31) all-cause mortality (14.6% vs. 17.9%; P = 0.07) or major bleeding (16.1% vs. 14.7%; P = 0.46). Similarly, updated data from the US LAAC registry, comparing the Watchman FLX to its previous iteration, the Watchman 2.5, was published this year by Freeman et al. who reported US LAAC registry outcomes from 54,206 patients (mean age 76 years; 59% men) undergoing LAAC with the new Watchman FLX ( n = 27,103) versus previous Watchman 2.5 ( n = 27,103). In-hospital major adverse events were significantly lower with the new Watchman FLX (1.35% vs. 2.4%, OR 0.57: 95% CI 0.50–0.65) driven by reductions in pericardial effusion requiring intervention (0.42% vs. 1.23%), device embolization (0.02 vs. 0.06%) and major bleeding (1.08% vs. 2.05%). Longer follow-up will help clarify if technical aspects between devices confer long-term clinical outcome advantages. Despite the evolution of device technology for LAAC, key clinical questions, such as anticoagulation strategy, remain. Freeman et al. conducted a US LAAC registry analysis of 31,994 patients who underwent Watchman LAAC between 2016 and 2018. Only 12.2% of patients received the full anticoagulation protocol mandated by clinical trials (Fig. ). In contrast to previous European reports from EWOLUTION (Registry on WATCHMAN Outcomes in Real-Life Utilization), the 45-day adjusted adverse event rate was longer if discharged on warfarin alone (HR 0.692; 95% CI 0.569–0.841) or DOAC alone (HR 0.731; 95% CI 0.574–0.930) versus warfarin plus aspirin, suggesting that further research is needed to guide the optimal antithrombotic strategy post-LAAC.
There has been a dramatic expansion in transcatheter aortic valve interventions over the past decade . A recent analysis of US registry data conducted by Sharma et al. reported a near doubling in transcatheter aortic valve replacement (TAVR) volume overall between 2015 and 2021 (44.9% vs. 2021, 88%, P < 0.01), including a 2.7 fold increase in those < 65 years (now similar to surgical aortic valve replacement (SAVR) (47.5% TAVR vs. 52.5% SAVR, P = ns) particularly in younger patients with heart failure (HF) (OR 3.84; 95% CI 3.56–4.13; P < 0.0001), or prior CABG (OR, 3.49; 95% CI, 2.98–4.08; P < 0.001) . These numbers may further increase across all risk categories with the early long-term data from the seminal PARTNER (Placement of AoRTic TraNscathetER Valve Trial) trials awaited. Emerging evidence from trials such as AVATAR (Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis) and RECOVERY (Early Surgery Versus Conventional Treatment in Very Severe Aortic Stenosis) suggests that early intervention for severe aortic stenosis (AS), before patients develop symptoms, may be of benefit . In a pooled analysis of key trials (PARTNER2A, 2B &3) involving 1974 patients (mean age 81 years; 45% women), Généreux et al. evaluated the relationship between cardiac damage at baseline and prognosis in patients with severe symptomatic AS who underwent AVR (40% SAVR, 60% TAVI) . Baseline cardiac damage was defined using a 0–4 scoring system (0 = no damage and 4 = biventricular failure). Baseline damage correlating strongly with 2-year mortality (HR 1.51 per higher stage; 95% CI 1.32–1.72) with each increase in stage conferred a 24% increase in mortality ( P = 0.001) (from stage 0 = 2.5% to stage 4 = 28.2%) suggesting a role for earlier intervention. Several ongoing trials, such as EARLY TAVR (Evaluation of TAVR Compared to Surveillance for Patients With Asymptomatic Severe Aortic Stenosis), TAVR UNLOAD (Transcatheter Aortic Valve Replacement to UNload the Left Ventricle in Patients With ADvanced Heart Failure) and PROGRESS (Management of Moderate Aortic Stenosis by Clinical Surveillance or TAVR), aim to answer these questions directly. Valve in valve (VIV) TAVR is being increasing utilised in patients with failed AVR; however, it remains unclear whether these patients do better with or without balloon valve fracture (BVF). In a registry analysis of 2975 patients undergoing VIV-TAVR (with balloon-expandable SAPIEN 3 or SAPIEN 3 Ultra) between December 2020 and March 2022, Garcia et al. reported that BVF versus no BVF led to larger mean valve area (1.6 vs. 1.4 cm2; P < 0.01) and lower mean valve gradient (18.2 vs. 22.0 mm Hg; P < 0.01) but also to higher rates of death or life-threatening bleeding (OR 2.55; 95% CI 1.44–4.50) and vascular complications (OR 2.06; 95% CI 0.95–4.44). However, sub-analysis suggested the increase in mortality was mainly if BVF undertaken before VIV-TAVR (OR 2.90; 95% CI 1.21–6.94), whereas no difference was noted if undertaken after VIV-TAVR. This suggests that VIV-BVF should only be performed once the operator has a new TAVR in place. While designed primarily for AS, conventional TAVR devices have sometimes utilised for the treatment of severe aortic regurgitation (AR). The novel trilogy heart valve system, specifically developed for AR, and was evaluated in 45 patients (mean age 77, 40% female, mean Euroscore 7.1%) with moderate to severe AR by Tamm et al. . The primary endpoint, a reduction in ≥ 1 AR grade, was met in 100% of cases. There were no episodes of stroke, death, or conversion to open surgery, but 9 patients (23%) required permanent pacing. Subclinical leaflet thrombosis (SLT) is a relatively common complication of TAVR; however, the optimal treatment strategies, whether with anticoagulation or antiplatelets, remain contested. The multicentre ADAPT TAVR (Edoxaban vs. DAPT in reducing subclinical leaflet thrombosis and Cerebral Thromboembolism After TAVR) randomised 229 patients (mean age 80.1 years; 41.9% men) undergoing TAVR for symptomatic severe AS, and without other indication for OAC, to edoxaban 60 mg or 30 mg once daily versus DAPT with aspirin and clopidogrel . At 6 months, Edoxaban, by intention to treat analysis, was associated with a trend to reduced SLT as assessed by cardiac CT (9.8% vs. 18.4%; P = 0.076) and, in contrast to prior trials with DOAC post-TAVR, there was no difference in bleeding rates (11.7% vs. 12.7%; P = ns). Interestingly, a secondary per-protocol analysis focusing on patients with high compliance did reach statistical significance (19.1% vs. 9.1%; risk ratio 0.48; 95% CI 0.23–0.99). However, despite the use of serial brain MRI, there was no difference in the presence/number of cerebral lesions and no difference in neurocognitive outcomes including stroke at 6 months. Giustino et al. reported a new secondary analysis from the GALILEO trial (Rivaroxaban-based Antithrombotic Strategy to an Antiplatelet-based Strategy After TAVR to Optimize Clinical Outcomes) which, as described previously , had randomised 1644 patients post-TAVR without an indication for oral anticoagulation (OAC) to rivaroxaban 10 mg plus aspirin versus DAPT with aspirin plus clopidogrel for 90 days, but was stopped early due to higher thromboembolic bleeding and mortality events in the Rivaroxaban group . In the new analysis, thromboembolic events appeared to be associated with higher risk of mortality (HR 8.41; 95% CI 5.10–13.87) versus BARC 3 bleeding (HR 4.34; 95% CI 2.31–8.15). Furthermore, this mortality risk appeared higher than that conferred by known risk factors such as age (adjusted HR 1.04; 95% CI 1.01–1.08) and chronic obstructive pulmonary disease (COPD) (adjusted HR 2.11; 95% CI 1.30–3.41). These findings along with previous data from ALANTIS (AntiThrombotic Strategy After Trans-Aortic Valve Implantation for Aortic Stenosis) and ENVISAGE-TAVI AF (Edoxaban Compared to Standard Care After Heart Valve Replacement Using a Catheter in Patients With Atrial Fibrillation) show how the role of DOACs post-TAVI remains uncertain . However, given the devastating impact of thromboembolic events in this patient group, ongoing research is warranted. The absence of a bleeding signal with DOAC in ADAPT TAVR, in which most received lower dose Edoxaban, suggests that lower dose DOAC for a short duration while the valve is endothelialising may improve the risk/benefit ratio. Another area of current contention is the use of cerebral embolic protection (CEP) to reduce risk of stroke. While current guidance does not mandate use, some operators use in high-risk cases . Kaur et al. conducted a meta-analysis of 1,016 patients (mean age 81.3 years) from several randomised trials (DEFLECT III, MISTRAL-C, CLEAN-TAVI, SENTINEL, and REFLECT I and II) evaluating the TriGuard (Keystone Heart) and Sentinel devices versus standard care. At 30 days, CEP was not associated with a reduction in the primary outcome of all-cause stroke (RR 0.93; 95% CI 0.57–1.53), nor a reduction in mortality. Subsequently, the PROTECTED TAVR (Stroke PROTECTion With SEntinel During Transcatheter Aortic Valve Replacement) trial randomised 300 patients (mean age 72 years, 40% female) to CEP with a Sentinel device versus standard care . Again, no significant difference in primary outcome of stroke at 72 h was noted (2.4% vs. 2.9%, P = 0.30), although numbers were relatively small. The results of BHF PROTECT TAVI (British Heart Foundation Randomised Clinical Trial of Cerebral Embolic Protection in Transcatheter Aortic Valve Implantation) plans to enrol 7000 patients and findings are eagerly awaited.
The favourable findings in COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation [MR) helped lead to device approval . However, it has been suggested the reason COAPT was favourable was the strict eligibility criteria, mandating LVEF ≥ 20% to ≤ 50%, left ventricular end-systolic dimension (LVESD) ≤ 70 mm and failure of aggressive medical therapy . EXPAND (A Contemporary, Prospective Study Evaluating Real-world Experience of Performance and Safety for the Next Generation of MitraClip Devices) was a prospective multicentre registry of 1,041 patients with site-reported MR 3 + /4 + were enrolled and received the MitraClip. A recent analysis compared 125 “COAPT-like” patients meeting COAPT inclusion criteria versus 128 “non-COAPT” patients. At 1 year, COAPT-like patients did not show any difference in the primary outcome of all-cause mortality (22.6% vs. 19.6%, P = 0.37) or heart failure hospitalisation (32.6% vs. 25%, P = 0.08). In keeping with their lower baseline MR severity, more non-COAPT patients achieved reduction in MR to mild or less (≤ 1 +) (97.2% vs. 86.5%), suggesting that Mitraclip may benefit patients beyond the strict COAPT criteria, but prospective randomised data are needed, such as the ongoing EVOLVE-MR (MitraClip for the Treatment of Moderate Functional Mitral Regurgitation). Previous data from CLASP (Edwards PASCAL TrAnScatheter Mitral Valve RePair System Study) and CLASPII have validated the safety and efficacy of the Edwards PASCAL™ transcatheter valve repair system . CLASP IID randomised 180 patients with severe degenerative symptomatic MR not eligible for surgery (mean age 81 years, 67% male, median STS 5.9%) to transcatheter Edge-to-Edge Repair (TEER) with the Pascal device (Edwards Lifesciences) vs. MitraClip (Abbott) device . At 30 days, the Pascal device met criteria for non-inferiority with respect to the composite endpoint of CV death, stroke, MI, renal replacement therapy, severe bleeding and re-intervention (3.4% vs. 4.8%; P for noninferiority < 0.05). Of interest, the proportion of patients with MR ≤ 1 + was durable in the PASCAL group (87.2% discharge vs. 83.7% at 6 months; P = 0.317); whereas MitraClip outcomes showed some loss of efficacy (88.5% discharge vs. 71.2% at 6 months; P = 0.003). Although only interim data, this hints that the Pascal device may have superior durability. ViV-transcatheter mitral valve replacement (ViV-TMRV) may be utilised in very high-risk patients without a surgical option on a case-by-case basis despite paucity of real-world outcome data. Bresica et al. retrospectively compared outcomes of 48 patients with bioprosthetic mitral valve (MV) failure undergoing ViV-TMRV (mean age 65 years, 63% female, mean STS 7.9%) versus 36 patients undergoing re-do MV surgery (mean age 58, 72% female, mean STS 7.1%) . ViV-TMVR was not associated with improvement in 1-year survival (90% vs. 80%, P = 0.33) and was associated with higher average postprocedural gradient (8.9 vs. 5.7 mm Hg; P < 0.001). Thus, ViV-TMRV is a good option for high-risk patients, but in less comorbid patients may not provide as good a long-term benefit as surgery, particularly in those with smaller original surgical valves. Data to come from the ongoing PARTNER 3 (Mitral Valve-in-Valve trial) will be useful to help guide decision-making in such patients. Several seminal trials, such as TRILUMMINATE (Abbott Transcatheter lip Repair System in Patients With Moderate or Greater TR), Triband (TranscatheterRepair of Tricuspid Regurgitation With Edwards Cardioband TR System Post-Market Study) and TRISCEND (Investigation of Safety and Clinical Efficacy After Replacement of Tricuspid Valve With Transcatheter Device), have led to a much greater focus on transcatheter tricuspid interventions . CLASP TR (Edwards PASCAL Transcatheter Valve Repair System Pivotal Clinical Trial), a prospective single-arm multicentre study, evaluated 1-year outcomes of the PASCAL transcatheter valve repair system in 65 patients (mean age 77 ± 9 years, 55% female, mean STS 7.7%) with severe tricuspid regurgitation (TR) . In keeping with the high baseline comorbidity, major adverse event rate was 16.9% ( n = 11) with all-cause mortality 10.8% ( n = 7) and 18.5% ( n = 12) re-admitted with heart failure. Paired analysis demonstrated significant improvements in New-York Heart Association (NYHA) grade ( P < 0.001), KCCQ score ( P < 0.001) and 6-min walk test (6MWT) ( P = 0.014). Importantly, the reduction in TR severity noted at 30 days ( P < 0.001) was maintained at 1 year (100% had ≥ 1 grade reduction and 75% had ≥ 2 grade reduction, P < 0.001). TRICLASP (Transcatheter Repair of Tricuspid Regurgitation With Edwards PASCAL Transcatheter Valve Repair System), a prospective, single-arm multicentre trial, evaluated 30-day outcomes in 67 of 74 patients (mean age 80 years, 58% female, mean STS 9%) undergoing the Pascal Ace transcatheter repair system for severe symptomatic inoperable TR (Fig. ). The primary composite outcome of major adverse events was 3% with 88% achieving ≤ 1 grade reduction in TR vs. baseline; P < 0.001), along with significant improvements in NYHA, KCCQ score, and 6MWT ( P < 0.001). Longer term follow-up data are awaited. TriClip-Bright (An Observational Real-world Study Evaluating Severe Tricuspid Regurgitation Patients Treated With the Abbott TriClip™ Device) study , a multicentre, prospective study reported 30-day outcomes for 300 patients (78 ± 7.6 years) undergoing the Triclip Transcatheter valve repair system (Fig. ). The primary endpoint of procedural success (survival to discharge) was met in 91%. Significant reductions in both NYHA and KCCQ score were noted at ( P < 0.001). The trial is still actively recruiting, with a planned follow-up duration of 1 year.
While definitive studies to guide patent foramen ovale (PFO) closure practice are still lacking, a multidisciplinary consensus statement by SCAI was published this year recommending closure in patients aged 18–60 with a PFO-associated stroke, platypnoea-orthodeoxia syndrome with no other cause, and systemic embolism with no other cause. Of note in the absence of PFO-associated stroke, the guidance does not recommend PFO closure in transient ischaemic attack, AF with ischaemic stroke, migraine, decompression illness or thrombophilia. Several left atrial appendage closure (LAAC) devices have been approved in recent years with favourable long-term data published last year for the Watchman LAAC device (Boston Scientific). The AMULET IDE trial (Amplatzer Amulet Left Atrial Appendage Occluder Versus Watchman Device for Stroke Prophylaxis) trial randomised patients with non-valvular atrial fibrillation (AF), not suitable as anticoagulation to LAAC with an Amulet device ( n = 934) versus Watchman device ( n = 944). At 3 years, there was no difference in the primary composite endpoint of CV mortality, ischaemic stroke or systemic embolism (11.1% vs. 12.7%, P = 0.31) all-cause mortality (14.6% vs. 17.9%; P = 0.07) or major bleeding (16.1% vs. 14.7%; P = 0.46). Similarly, updated data from the US LAAC registry, comparing the Watchman FLX to its previous iteration, the Watchman 2.5, was published this year by Freeman et al. who reported US LAAC registry outcomes from 54,206 patients (mean age 76 years; 59% men) undergoing LAAC with the new Watchman FLX ( n = 27,103) versus previous Watchman 2.5 ( n = 27,103). In-hospital major adverse events were significantly lower with the new Watchman FLX (1.35% vs. 2.4%, OR 0.57: 95% CI 0.50–0.65) driven by reductions in pericardial effusion requiring intervention (0.42% vs. 1.23%), device embolization (0.02 vs. 0.06%) and major bleeding (1.08% vs. 2.05%). Longer follow-up will help clarify if technical aspects between devices confer long-term clinical outcome advantages. Despite the evolution of device technology for LAAC, key clinical questions, such as anticoagulation strategy, remain. Freeman et al. conducted a US LAAC registry analysis of 31,994 patients who underwent Watchman LAAC between 2016 and 2018. Only 12.2% of patients received the full anticoagulation protocol mandated by clinical trials (Fig. ). In contrast to previous European reports from EWOLUTION (Registry on WATCHMAN Outcomes in Real-Life Utilization), the 45-day adjusted adverse event rate was longer if discharged on warfarin alone (HR 0.692; 95% CI 0.569–0.841) or DOAC alone (HR 0.731; 95% CI 0.574–0.930) versus warfarin plus aspirin, suggesting that further research is needed to guide the optimal antithrombotic strategy post-LAAC.
The ISCHAEMIA trial (Initial Invasive or Conservative Strategy for Stable Coronary Disease) was a previously reported that routine invasive therapy versus optimal medical therapy (OMT) in stable patients with moderate ischaemia did not reduce major adverse events (MAE), but the possibility of excess events over longer follow-up was queried. The ISCHAEMIA-EXTEND study (median follow-up 5.7 years) reported that while there was still no difference in all-cause mortality in routine invasive versus medical therapy (12.7% vs. 13.4%, P = 0.74), after 2 years the survival curves for cardiovascular (CV) death started to diverge and by 7 years were significantly lower in the routine invasive group (6.4% vs. 8.6% HR 0.78; 95% CI 0.63, 0.96). Conversely, there was an increase in non-CV death in the routine invasive group (5.5% vs. 4.4%, HR 1.44; 95% CI 1.08–1.91). On balance, this still supports an initial OMT strategy but highlights the utility of understanding anatomy to risk stratify and perhaps identify those patients who will benefit the most from CV risk reduction (Fig. ) . Ten-year follow-up data will prove informative. New onset, stable chest pain remains a substantial burden on healthcare systems. SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) and PROMISE (PROspective Multicenter Imaging Study for Evaluation of Chest Pain) previously reported benefit in early computed tomography coronary angiogram (CTCA) for the evaluation of stable chest pain. FFR-CT may further improve CT diagnosis. PRECISE (Prospective Randomized Trial of the Optimal Evaluation of Cardiac Symptoms and Revascularization) randomised 2103 patients (mean age 58 years, 50% women) with suspected CAD to a risk scoring algorithm (with low-risk patients deferred and high-risk patients undergoing FFR-CT) versus standard care. At a median follow-up of 11.8 months, algorithm-guided use of FFR-CT resulted in markedly lower MACE (4.2% vs. 11.3%; adjusted HR 0.29; 95% CI 0.20–0.41), driven by a lower rate of catheterisation (4.2% vs. 11.3%; adjusted HR 0.29; 95% CI 0.20–0.41). There was no difference in all-cause death. A subsequent cost-effectiveness analysis is ongoing. Despite current advances in ACS detection, prediction of recurrent events remains difficult. Batra et al. assessed the predictive valve of biomarker modelling (with hs-TNT, CRP, DGF-15, cystatin C, NT-proBNP) from 14,221 patients enrolled in PLATO (A Comparison of Ticagrelor and Clopidogrel in Patients With Acute Coronary Syndrome) and TRACER (Trial to Assess the Effects of Vorapaxar (SCH 530,348; MK-5348) in Preventing Heart Attack and Stroke in Participants With Acute Coronary trials. An outcome model termed “ABC-ACS Ischaemia” predicted 1-year risk of CV death/MI with C-indices of 0.71 and 0.72 in the development and validation cohorts, respectively. While encouraging, such models likely need to be integrated with additional individual patient characteristics in improve risk prediction. Optical Coherence Tomography (OCT) has demonstrable utility in assessing plaque morphology and so may be useful in delineating between different aetiologies of ACS. The Tokyo, Kanagawa, Chiba, Shizuoka, and Ibaraki active OCT applications for ACS (TACTICS) registry, evaluated plaque morphology in 702 ACS patients undergoing OCT-guided PCI and reported rupture was the commonest aetiology (59%), followed by plaque erosion (26%), and then calcification (4%) ( Fig. ) . However, at 12 months, calcified nodules conferred the worst outcome with a 32.1% MACE rate compared to 12.4% and 6.2% amongst ruptures or erosions, respectively.
Strategies to shorten DAPT duration post-PCI in high bleeding risk patients continue to be evaluated. Longer-term follow-up at 15 months of the MASTER DAPT (Management of High Bleeding Risk Patients Post-Bioresorbable Polymer Coated Stent Implantation With an Abbreviated Versus Prolonged DAPT Regimen) confirmed initial results , with the incidence of the composite endpoint (death, MI, stroke, major bleeding) remaining non-inferiority for shortened DAPT versus standard care (HR 0.92, 95% CI 0.76–1.12; P = 0.40), but a significantly lower rate of major bleeding in the short DAPT group (HR 0.68, 95% CI 0.56–0.83; P = 0.001). These data, although important, were applied in the context of contemporary stent design such as the biodegradable-polymer sirolimus-eluting Ultimaster stent (Terumo) as used in MASTER DAPT. Effective reversal of antiplatelets could be helpful when active bleeding risk outweighs ischaemic risk, particularly in elderly patients. No formal antiplatelets reversal agents are currently licensed; however, an interesting drug under investigation is Bentracimab—a recombinant IgG1 monoclonal antibody antigen-binding fragment that binds with high affinity to ticagrelor and its active metabolite. Bhatt et al., in a phase IIb trial, randomised 205 patients (mean age 61 years, female 50%) already treated with DAPT for 30 days to Bentracimab ( n = 154) versus placebo ( n = 51). Use of Bentracimab was associated with a significant reduction in the primary endpoint of percentage inhibition of P2Y12 reaction units at 4 h ( P < 0.0001) without any excess of thrombotic events or deaths . Further larger-scale phase III trials are eagerly awaited. In patients with an indication for antiplatelet monotherapy, previous studies have suggested a possible benefit for clopidogrel versus aspirin at least in certain patient subgroups. PANTHER (P2Y12 inhibitor vs. aspirin monotherapy in patients with coronary artery disease) was a meta-analysis of several large, randomised trials totalling 24,325 patients with established coronary artery disease (mean age 64 years, 22% women) which compared P2Y12 inhibition (62% clopidogrel, 38% ticagrelor) versus aspirin . Use of P2Y12 inhibition was associated with a 12% reduction in the primary composite outcome of CV death, MI or stroke at 18 months (5.5% vs. 6.3%; HR 0.88; 95% CI 0.79–0.97) driven by a lower risk of MI (HR 0.77; 95% CI 0.66–0.90), but with no difference in stroke (HR 0.85; 95% CI 0.70–1.02) or bleeding (6.4% vs. 7.2%; HR 0.89; 95% CI 0.81–0.98). While firm conclusions are difficult due to the inclusion of 2 different P2Y12 inhibitors, it suggested P2Y12 inhibitor may be warranted instead of aspirin for long-term secondary prevention in patients with coronary artery disease. Indobufen is a reversible COX inhibitor with similar anti-thrombotic effects to aspirin but less gastrointestinal side effects and potentially lower risk of bleeding . The OPTION (the Efficacy and Safety of Indobufen and Low-dose Aspirin in Different Regimens of Antiplatelet Therapy) trial randomised 4,551 patients (mean age 61 years; 65% male) without acute troponin rise, undergoing PCI with DES to 1 year of DAPT (indobufen 100 mg BD plus clopidogrel 75 mg; n = 2258 vs. aspirin plus clopidogrel 100 mg OD; n = 2293). At 1 year, use of indobufen versus aspirin meet non-inferiority with respect to the primary composite outcome (CV death, MI, stroke, ISR and BARC type 2,3 or 5 bleeding) (4.47% vs. 6.11%; HR 0.73; 95% CI 0.56–0.94; P < 0.001 for noninferiority). The secondary safety endpoint of BARC 2, 3 or 5 bleeding was lower with indobufen (2.97% vs. 4.71%; HR 0.63; 95% CI 0.46–0.85), driven by a reduction in BARC 2 bleeding (1.68% vs. 3.49%; P < 0.001). These intriguing data suggest a potential new treatment option particularly for patients with gastrointestinal bleeding or aspirin allergy. Full dose anticoagulation plus antiplatelet therapy significantly increases bleeding risk but the role of low-dose anticoagulation for vascular prevention continues to be studied. Asundexian is a novel oral activated factor XI inhibitor which may lower thromboembolic events but with lower bleeding risk . In the phase II PACIFIC-AMI trial (Study to Gather Information About the Proper Dosing and Safety of the Oral FXIa Inhibitor BAY 2,433,334 in Patients Following an Acute Heart Attack), 1601 patients (median age 68 years, 23% women) with recent acute MI were randomised to asundexian (10 mg, 20 mg or 50 mg) versus placebo in addition to standard DAPT. At 4 weeks, asundexian was not associated with a significant increase in the pre-specified safety outcome of BARC2 bleeding versus placebo 0.98 (90% CI, 0.71–1.35), although there was a numerical increase in bleeding with higher asundexian doses. Based on this trial, asundexian 50 mg daily is being considered for a phase III cardiovascular outcomes trial in acute MI. Asundexian was also evaluated in the phase IIb PACIFIC-STROKE trial (Study to Gather Information About the Proper Dosing and Safety of the Oral FXIa Inhibitor BAY 2,433,334 in Patients Following an Acute Stroke) which randomised 1808 patients with non-embolic ischaemic stroke to asundexian (10 mg, 20 mg or 50 mg) versus placebo in addition to standard care including antiplatelet therapy . Asundexian (whether by pooled or individual dose analysis) was not associated with reduction in the primary efficacy outcome of ischemic stroke or overt stroke at 6 months, although the primary safety outcome of major significant bleeding was not significantly different [asundexian pooled vs. placebo HR1·57 (90% CI 0·91–2·71)]. It thus remains unclear if asundexian has a useful role in ischaemic stroke. In current PPCI guidelines, Bivalirudin (Class IIa) was replaced by unfractionated heparin (UFH) (Class I) as previous studies reported equipoise in clinical outcomes but more difficult drug administration with Bivalirudin. BRIGHT-4 (Bivalirudin With Prolonged Full Dose Infusion Versus Heparin Alone During Emergency PCI) randomised 6,016 PPCI patients from 63 Chinese centres in open-label fashion to Bivalirudin bolus plus infusion for a median of 3 h versus UFH bolus . Patients underwent predominantly radial PPCI (93%) without any prior thrombolytic, anticoagulant or glycoprotein inhibitor treatment. At 30 days, Bivalirudin was associated with a 31% reduction in the primary outcome of all-cause or BARC 3–5 bleeding (HR 0.69; 95% CI 0.53–0.91, P = 0.007), reduced BARC 3–5 bleeding (HR 0.21; 95% CI 0.08–0.54), reduced all-cause mortality (3.0% vs. 3.6%, P = 0.04), and reduced stent thrombosis (0.4% vs. 1.1%, P = 0.0015). Despite these favourable data, given the inherent difficulties in bivalirudin delivery and moderate increase in cost versus UFH, it is unclear if BRIGHT-4 findings will change practice, although a stronger guideline recommendation would be expected. Tongxinluo (TXL) is a traditional Chinese medicine, approved in China for the treatment of stroke and angina . CTS-AMI (China Tongxinluo Study for Myocardial Protection in Patients With Acute Myocardial Infarction) was a randomised trial of 3755 patients with STEMI undergoing PPCI at 124 Chinese centres to TXL versus placebo (in addition to standard therapy). Use of TXL was associated with a 36% reduction in the primary composite outcome of CV death, revascularisation, MI and stroke at 30 days (3.39% vs. 5.25%; RR 0.64; 95% CI 0.47–0.88) and a 30% reduction in cardiac death (2.97% vs. 4.24%; RR 0.70; 95% CI: 0.50–0.99). While the findings are dramatic, further work is necessary to understand the mechanism of action of this novel drug and further randomised multicentre trials to confirm efficacy.
Following on from the HIS-Alternative trial (His Pacing Versus Biventricular Pacing in Symptomatic HF With Left Bundle Branch Block) , which reported similar outcomes with His-Bundle CRT (His-CRT) versus conventional biventricular CRT (BiV-CRT), the LBBP-RESYNC (Left Bundle Branch Versus Biventricular Pacing For Cardiac Resynchronization Therapy) trial randomised 40 patients with non-ischaemic cardiomyopathy, LBBB and an indication for resynchronisation to left bundle branch CRT (LBB-CRT) versus standard BiV-CRT pacing . LBB-CRT was associated with a larger improvement in LVEF at 6 months (21.1% vs. 15.6%; P = 0.039, 95% CI 0.3–10.9), greater reduction in LV end systolic volumes and greater reduction NT-proBNP ( Fig. ). Vijayaraman et al. presented a retrospective analysis of 477 patients comparing those who underwent conduction pacing (LBB pacing or His-bundle) versus conventional BiV-CRT. Conduction pacing was associated with a lower incidence of the primary composite of death or heart failure hospitalisation (28.3% vs. 38.4%; P = 0.013), mainly driven by a reduction in HF hospitalisations. Vijayaraman et al. also presented a retrospective analysis of 212 patients with rescue LBB pacing who met indications for CRT but had coronary venous lead failure or were non-responders to BiV-CRT . LBB pacing (successful in 94%) was associated with improvement in LVEF from 29% at baseline to 40% at follow-up ( P < 0.001) ( Fig. ). The MELOS (Multicentre European Left Bundle Branch Area Pacing Outcomes Study) registry evaluated 2533 patients from 14 European centres undergoing transseptal left bundle branch area pacing (LBBAP), 27.5% for heart failure and 72.5% for bradycardia . LB fascicular capture was most common (69.5%) followed by LV septal capture (21.5%) then proximal LBB capture (9%). Overall complication rate was 11.7%, including ventricular trans-septal complications in 8.3%. Overall, these trials collectively support the efficacy and safety of conduction system pacing as a suitable alternative to conventional BiV-CRT, although larger randomised trials are required to formally test superiority. Infections related to cardiac implanted electronic devices (CIEDs) have high mortality and morbidity, and the European heart rhythm association (EHRA) consensus advises prompt extraction . Pokornery et al. analysed a Medicare database of 11,619 patients admitted with a CIED infection of whom only 2,109 (28.2%) had device extraction within 30 days. Device extraction versus no extraction was associated with reduction in 1-year mortality (HR 0.79, 95% CI 0.70–0.81) and early device extraction within 6 days versus no extraction was associated with a 41% reduction in 1-year mortality ( P < 0.001). Subcutaneous ICDs (S-ICDs) have been evaluated in previous trials including PRAETORIAN and UNTOUCHED as an alternative to transvenous systems for patients at risk of lead complications or infections. The ATLAS -ICD (Avoid Transvenous Leads in Appropriate Subjects) trial randomised 593 patients with an indication for ICD to SC-ICD versus transvenous ICD (TV-ICD) implantation . SC-ICD was associated with a 92% reduction in perioperative lead complications at 6 months (0.4% vs. 4.8%; OR 0.08; 95% CI 0.00–0.55), although the composite safety outcome (including the primary outcomes plus device-related infection requiring surgical revision, significant wound hematoma requiring evacuation or interruption of oral anticoagulation, MI, stroke/TIA, or death) was similar (4.4% vs. 5.6%; OR 0.78, 95% CI 0.35–1.75) and inappropriate shocks were non-significantly more common (2.7% vs. 1.7%; HR2.37, 95% CI 0.98–5.77). In heart failure patients, there is contradictory evidence whether defibrillator capability improves prognosis in patients receiving CRT. RESET-CRT (Re-evaluation of Optimal Re-synchronization Therapy in Patients with Chronic Heart Failure) retrospectively compared outcomes in 847 CRT-P versus 2722 CRT-D patients undergoing CRT (of whom 27% had a non-ischaemic aetiology and exclusion criteria included recent ACS, revascularisation, or any indication for secondary prevention ICD). The primary endpoint of all-cause mortality at 2.35 years follow-up (adjusted for age and entropy balance) was non-inferior for CRT-P versus CRT-D (HR 0.99, 95% CI 0.81–1.20), suggesting no mortality benefit with defibrillator capability in this population. Atkas et al. compared propensity matched outcomes of 535 patients with ICD versus 535 patients without ICD from the Empagliflozin arm of the Emperor-Reduced trial . Those with ICD versus no ICD had non-significantly lower mortality (HR 0.74, 95% CI 0.51–1.07, P = 0.114) and sudden cardiac death (HR 0.59, 95% CI 0.31–1.15, P = 0.122). However, despite propensity matching, the results were confounded by differences in medical therapy between groups, with more ICD patients receiving B-blockers and ARNIs but fewer receiving ACE-I/ARBs and MRAs.
The VANISH (Ventricular Tachycardia Ablation versus Escalation of Antiarrhythmic Drugs) trial previously demonstrated superiority with regards to mortality, VT storm and appropriate ICD shocks of catheter ablation versus escalated AAD therapy in patients with previous MI and VT . A new sub-analysis compared shock-treated VT events and appropriate shock burden between the 2 groups. Catheter ablation was associated with a significant reduction in shock-treated VT events (39.07 vs. 64.60 per 100 person-years; HR 0.60; 95% CI 0.38–0.95) and total shock burden (48.35 vs. 78.23; HR 0.61; 95% CI 0.37–0.96). Prediction risk of sudden cardiac death (SCD) after MI has typically guided by LVEF < 35%, but many patients with LVEF < 35% who receive ICD never require it, whereas some with higher LVEF are still at risk of SCD. The additional predictive value of CMRI, in particular core scar size and grey zone size, for the PROFID risk prediction model was investigators in 2,049 patients imaged > 40 days post-MI . In the subgroup without ICD, use of CMRI data versus no CMRI data significantly improved prediction of SCD [area under curve (AUC) of model 0.753 vs. AUC 0.618]. In the subgroup with ICD, addition of CMRI data did not significantly improve prediction of SCD (AUC 0.598 vs. 0.535). This suggests CMRI may be useful to risk stratify post-MI and guide ICD use but further prospective studies are required. The SMART-MI-ICM trial previously reported that, in post-MI patients with EF 35–50%, implantable cardiac monitor (ICM) use versus control was associated with higher rates of arrhythmia detection although the clinical significance was unclear. The BIOGUARD-MI (BIO monitorinG in Patients With Preserved Left ventricUlar Function AfteR Diagnosed Myocardial Infarction ) trial aimed to assess the clinical value of arrhythmia detection on ICM, by randomising 804 patients with NSTEMI/STEMI to ICM versus standard care. Use of ICM was not associated with an overall significant reduction in the primary composite endpoint of CV death or hospitalisation at 2.5 years (HR 0.84, P = 0.21, 95% CI 0.64–1.10), although a reduction was noted in the NSTEMI subgroup (HR 0.69, 95% CI 0.49–0.98). This subgroup observation can only be hypothesis generating but is plausible given the more complex and co-morbid nature of a NSTEMI population.
While smartwatches may improve detection of atrial fibrillation (AF), including asymptomatic AF, previous studies have reported high false positive rates. The mAF-App II trial, which used Huawei smartwatch photoplethysmography, reported data from 2.8 million people in China who downloaded the app . During 4 years follow-up, 12,244 (0.4%) people received a query AF notification, 5,227 attended for clinical evaluation with ECG and 24-h Holter monitoring and, within this group, AF was confirmed in 93.8%. This suggests much better specificity than previous studies, although the notification rate was lower than some studies, reflecting the relatively young population, and clinical data were not available for the 7017 people who received a notification but did not attend for evaluation. Unlike previous Apple, Fitbit and Huawei studies, E-Brave used the Preventicus smartphone app and invited 67,488 policyholders of a German health insurance scheme to participate, of whom 5,551 met inclusion criteria and agreed to enroll (AF naïve, median age 65 years; 31% female; median CHA2DS2-VASc of 3) and were randomised to active AF screening (photoplethysmogram [PPG] for 1 min twice per day for 2 weeks then twice weekly for 6 months, plus 2-week loop recorder if abnormal PPG) versus standard care. At 6 months, those in the active arm had double the rate of AF detection requiring OAC treatment (1.33 vs. 0.63%; OR 2.12; 95% CI 1.19–3.76). After 6 months, those without a new AF diagnosis were invited to cross-over to the opposite study arm, and, after a further 6 months, active screening with the app again doubled the detection and treatment of AF (1.38% vs. 0.51%; OR 2.75; 95% CI 1.42–5.34). Given the widespread availability of smartphones particularly in higher-risk populations, this may be a useful public health intervention, although further prospective studies are required to evaluate clinical outcomes of treating AF detected in this fashion. AF has been widely associated with increased risk of dementia and better control of AF may reduce this risk. Zeitler et al. using the Optum Clinformatics database, evaluated the propensity-matched risk of dementia in 19,088 patients following catheter ablation versus 19,088 patents treated with antiarrhythmic drugs (AAD) for AF . Catheter ablation was associated with a 41% reduction in risk of dementia (HR 0.59; 95% CI 0.51–0.68; P < 0.0001) and a 49% reduction in the secondary endpoint of mortality (HR 0.51, 95% CI 0.46–0.55, P < 0.001), supporting the value of effective AF treatment in this population. The Augustus trial previously reported the benefit of apixaban instead of vitamin-K antagonist (VKA) and ongoing P2Y12i monotherapy rather than DAPT for patients with AF and ACS/PCI . Harskamp et al. undertook a new analysis of 4,386 patients from Augustus to assess if benefits varied depending on baseline HASBLED (≤ 2 vs. ≥ 3) and CHAD 2 S 2 VASc (≤ 2 vs. ≥ 3) scores . Apixaban was associated with lower bleeding versus VKA irrespective of baseline risk [HR: 0.57 (HAS-BLED ≤ 2), HR 0.72 (HAS-BLED ≥ 3); interaction P = 0.23] and lower risk of death or hospitalization (HR 0.92 (CHA 2 DS 2 -VASc ≤ 2); HR 0.82 (CHA 2 DS 2 -VASc ≥ 3); interaction P = 0.53]. Aspirin versus placebo increased bleeding irrespective of baseline risk [HR: 1.86 (HAS-BLED ≤ 2); HR: 1.81 (HAS-BLED ≥ 3); interaction P = 0.88] with no significant difference in death or hospitalization [HR: 1.09 (CHA 2 DS 2 -VASc ≤ 2); HR: 1.07 (CHA 2 DS 2 -VASc ≥ 3); interaction P = 0.90]. The INVICTUS (Investigation of Rheumatic AF Treatment Using Vitamin K Antagonists, Rivaroxaban or Aspirin Studies) trial , randomised 4565 patients with rheumatic mitral valve and at high risk (CHAD 2 S 2 VASc ≥ 2, mitral valve area ≤ 2cm 2 , left atrial spontaneous contrast or thrombus) to Rivaroxaban versus VKA. Rivaroxaban was associated with increased incidence of the primary composite endpoint of stroke, systemic embolus, MI, or death from vascular/unknown cause (560 vs. 446 events; HR 1.25, 95% CI 1.10–1.41) despite suboptimal VKA control (only 33.2% having at appropriate INR enrolment, and the time in therapeutic range (TTR) being only 56–65% during follow-up). Rivaroxaban was also associated with a 37% increased risk of stroke and 23% increased risk. Thus, for AF and rheumatic mitral valve disease, VKA remains preferable to rivaroxaban. Previous studies reported that high-power, short duration (HPSD) versus conventional radiofrequency ablation (RFA) for AF was more effective with similar safety . The POWER FAST III (High Radiofrequency Power for Faster and Safer Pulmonary Vein Ablation) trial randomised 267 patients with AF to HPSD versus conventional RFA . HPSD was associated with a reduced ablation time but no difference in the primary efficacy outcome of freedom of atrial arrhythmia (99.2% vs. 98.4% in right pulmonary veins, 100% vs. 100% in left pulmonary veins) or the primary safety outcome of oesophageal lesions at endoscopy (7.5% vs. 6.5%; P = 0.94). Both conventional RFA and cryoablation for pulmonary vein isolation induce injury to neurocardiac structures (nerves and ganglia) which may be detected may release of S100b levels and post-procedure rise in heart rate . The technique of pulsed field ablation (PFA) may reduce neurocardiac trauma. Lemoine et al. randomised 56 patients to PFA versus cryoablation for AF. In those treated with PFA versus cryoablation, troponin I levels were 3 times higher ( P < 0.01), indicating more myocardial injury, but S100b levels were 2.9 times lower ( P < 0.001), and there was no increase in post-procedural heart rate (vs. marked increase with cryoablation; P < 0.01), indicating less neurocardiac damage with PFA. In addition, procedural success and durability of PFA appears encouraging. Keffer et al. evaluated 41 patients undergoing pulmonary vein PFA . The primary outcome of AF > 30 s or atrial tachycardia after a 30-day blanking period detected on 7-day Holter monitoring at 3 and 6 months occurred in 5 patients, of whom 3 underwent redo ablation during which all pulmonary veins were found to be still isolated. EAST-AFNET 4 previously reported a benefit of early rhythm control versus standard care in patients with AF , but there has been a paucity of data regarding initial ablation in such patients. In PROGRESSIVE-AF (a 3-year follow-up of the EARLY-AF trial), 303 patients with newly diagnosed symptomatic paroxysmal AF were randomised to upfront ablation versus AAD . Ablation was associated with a 75% reduction in the primary outcome of progression to persistent AF/flutter/tachycardia requiring cardioversion (1.9% vs. 7.4%; HR 0.25; 95% CI 0.09–0.70), a 49% reduction in any atrial arrhythmia > 30 s (56.5% vs. 77.2%; HR 0.51; 95% CI 0.38–0.67), a 69% reduction in hospitalisations (5.2% vs. 16.8%; RR 0.31; 95% CI 0.14–0.66) and 53% reduction in adverse effects (11% vs. 23.5%; RR 0.47; 95% CI 0.28–0.79). Use of botulinum toxin A to reduce AF was assessed in the NOVA (NeurOtoxin for the PreVention of Post-Operative Atrial Fibrillation) study which randomised 323 patients undergoing cardiac (bypass and/or valve) surgery to epicardial botulinum toxin A (125 units or 250 units) versus placebo . Overall, botulinum 125 units or 250 units versus placebo was not associated with a reduction in the primary outcome of AF > 30 s at 30 days (RR 0.80; 95% CI 0.58–1.10 and RR 1.04; 95% CI 0.79–1.37), respectively, although in the patient subgroup > 65 years, botulinum 125 units was associated with AF reduction (RR 0.64; 95% CI 0.43–0.94) which may be considered hypothesis-generating and warrant further study. Etripamil is a novel non-dihydropyridine calcium channel blocker, which may be given as a nasal spray, for acute treatment of patients with paroxysmal supraventricular tachycardia (PSVT) or AF. The RAPID (Efficacy and Safety of Etripamil for the Termination of Spontaneous PSVT) study screened 706 patients with PSVT ultimately assigning in random fashion 135 patients to etripamil versus 120 to placebo. Etripamil was associated with more than double the primary outcome of conversion to sinus rhythm within 30 min (64.3% vs. 31.2%; HR 2.62; 95% CI 1.66–4.15) and a median time to conversion of 17 min (almost 3 times quicker than placebo).
Previous studies have shown the selective cardiac myosin activator Omecamtiv Mecarbilon may improve CV outcomes in HFrEF patients . To assess functional impact, the METEORIC-HF (Effect of Omecamtiv Mecarbil on Exercise Capacity in Chronic Heart Failure With Reduced Ejection Fraction) trial randomised 276 patients with LVEF ≤ 35%; NYHA II-III (in 2:1 fashion) to Omecamtiv Mecarbilon versus placebo for 20 weeks, in addition to standard therapy. Surprisingly, despite good tolerability and the previous favourable CV outcome data, Omecamtiv Mecarbilon was not found to improve exercise capacity (assessed by peak oxygen uptake on cardiopulmonary exercise stress testing). A major stumbling block in optimising HF medications can be hyperkalaemia. Patiromer, a non-absorbed sodium-free potassium-binding polymer increases faecal potassium excretion. The DIAMOND (Patiromer for the Management of Hyperkalemia in Subjects Receiving RAASi for HFrEF) trial randomised 1642 patients with HFrEF and renin–angiotensin–aldosterone system inhibitor (RAASi)-related hyperkalaemia to Patiromer versus placebo. Over a period of 13–42 (mean 27) weeks, Patiromer was associated with less increase in potassium (adjusted mean change + 0.03 vs. + 0.13 mmol/l; 95% CI –0.13 to 0.07; P < 0.001). The risk of hyperkalamia and need for reduction of MRA dose were numerically (although not statistically) lower. These important findings support Patiromer being incorporated in local HF protocols. Implementation of HF guidelines can be hampered by many factors. PROMPT-HF (PRagmatic trial of Messaging to Providers about Treatment of Heart Failure) randomised 1310 patients with HFrEF, not already taking all four pillars of therapy to a strategy of targeted, tailored electronic healthcare record alerts to optimise guideline-directed medical therapy (GDMT) versus standard care. The electronic alert strategy was associated with a significant increase in the number of drug classes prescribed at 30 days (26% vs. 19%; adjusted RR 1.41; 95% CI: 1.03–1.93; P = 0.03; number needed to alert = 14). In an impressive attempt to improve secondary prevention therapy delivery, the SECURE (Secondary Prevention of Cardiovascular Disease in the Elderly Trial) trial randomised 2499 patients with MI ≤ 6 months to an open label polypill, comprising aspirin 100 mg, ramipril (2.5, 5 or 10 mg) and atorvastatin ( or mg), versus standard care. At 3-year follow-up, use of the polypill was associated with a 24% reduction in the primary endpoint of CV death, type 1 MI or ischaemic stroke (9.5% vs. 12.7%; HR 0.76, 95% CI: 0.6–0.96; P = 0.02). Sodium-glucose cotransporter-2 inhibitors (SGLT2i) trials continue to dominate HF research. A meta-analysis of 13 SGLT2i trials involving 90,413 participants (82 reported a 37% reduction in risk of progressive renal dysfunction 37% (RR 0·63, 95% CI 0·58–0·69) and a 23% reduction in risk of CV death or HF hospitalisation (RR 0·77; 0·74–0·81). Effects were similar in diabetics versus non-diabetics and regardless of baseline renal function (Fig. ). When first introduced and before reno-protective properties became clear, SGLT2i use was restricted to patients with eGFR > 60 to optimise glycaemic control. EMPA-KIDNEY (Study of Heart and Kidney Protection With Empagliflozin) randomised 6609 patients with impaired renal function (eGFR 20 to < 45, or eGFR 45 to < 90 plus urinary albumin-to-creatinine ratio > 200) to empagliflozin versus placebo. At 2 years, empagliflozin was associated with a 28% reduction in the primary endpoint of progression of kidney disease (defined as end-stage kidney disease, eGFR < 10, decrease in eGFR ≥ 40% from baseline, death from renal causes) or CV death (13.1% vs. 16.9% of the control group (HR 0.72; 95% CI: 0.64–0.82; P < 0.001). The EMPULSE (Empagliflozin in Patients Hospitalized for Acute Heart Failure) trial randomised 530 acutely decompensated patients hospitalised with HF, regardless of ejection fraction or diabetic status to Empagliflozin versus placebo. Those with IV vasodilators, IV inotropes, requiring increasing IV diuretic doses, cardiogenic shock or recent ACS were excluded. Empagliflozin versus placebo was more frequently associated clinical benefit in the primary composite endpoint of death, number of HF events, time to first HF event, and change in Kansas City Cardiomyopathy Questionnaire-Total Symptom Score at 90 days (stratified win ratio 1.36; 95% CI 1.09–1.68; P = 0.0054) ( Fig. ). The DELIVER (Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction) study randomised 6263 hospitalised or recently hospitalised patients with HF and LVEF > 40% to dapagliflozin versus placebo. Dapagliflozin was associated with an 18% reduction in the primary endpoint of death or worsening HF (16.4% vs. 19.5%; HR 0.82, 95% CI 0.73–0.92; P < 0.001). Acetazolamide, a carbonic anhydrase inhibitor, through reduction of proximal tubular sodium reabsorption, may improve the efficiency of loop diuretics, potentially leading to faster decongestion in patients with acute decompensated heart failure. The ADVOR (Acetazolamide in Decompensated Heart Failure with Volume Overload) study randomised 519 patients with decompensated HF patients to IV acetazolamide (500 mg daily) versus placebo in addition to IV loop diuretics (at twice the oral maintenance dose) examining the role. Acetazolamide was associated with a 46% improvement in attaining the primary endpoint of absence of signs of fluid overload at 3 days (42.2% vs. 30.5%; RR 1.46, 95% CI 1.17–1.82; P < 0.001) with higher urine output and natriuresis but without an excess of acute kidney injury, hypokalaemia, or hypotension. While the importance of optimised dosing of HF treatment is well established, since HF therapies may be associated with hypotension and renal decline, the ideal rate of titration is less clear. The STRONG-HF (Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP Testing, of Heart Failure Therapies) trial randomised 1078 patients admitted to hospital with acute HF to rapid up-titration (achieving full recommended doses within 2 weeks of discharge) versus usual care. Rapid up-titration was associated with a significantly lower rate of readmission for HF or all-cause death (15.2% vs. 23.3%; 95% CI 2.9–13.2; P = 0.0021), approximately a 10% increase in adverse events, but a similar rate for serious adverse events. IV iron has a Class IIa recommendation for patients with HF and anaemia. Most trials have used ferric carboxymaltose. IRONMAN (Intravenous ferric derisomaltose in patients with heart failure and iron deficiency in the UK) randomised 1,137 patients with chronic HF and iron deficiency (LVEF < 45%, with Transferrin saturation < 20% or ferritin < 100 µg/l) to ferric derisomaltose (which can be given as a rapid, high-dose infusion) versus usual care. At a median fgollow up of 2.7 years, ferric derisomaltose showed a trend to reduction in the primary composite endpoint of HF hospitalisation and CV death (336 vs. 411 events; RR 0.82, 95% CI 0.66–1.02; P = 0.07) and a significant reduction in HF hospitalisations. Since study outcomes may have been confounded by the COVID-19 pandemic, a pre-specified analysis censoring follow-up on September 30, 2020 was undertaken which reported a significant reduction in the primary endpoint (210 vs. 280 events; RR 0·76 [95% CI 0·58 to 1·00]; P = 0·047). Myosin inhibition using mavacamten in patients with obstructive hypertrophic cardiomyopathy was examined in the VALOR-HCM (Mavacamten in Adults With Symptomatic Obstructive HCM Who Are Eligible for Septal Reduction Therapy) trial which randomised 112 patients eligible for septal reduction therapy (SRT) to mavacamten (starting at 5 mg and titrating using LVEF and LVOT gradient) versus placebo. After 16 weeks follow-up, mavacamten was associated with marked reduction in obstructive parameters with only 17.9% still meeting guideline criteria for SRT (vs. 76.8% of placebo patients; 95% CI: 0.44–0.74; P < 0.001).
Lipoprotein[Lp] (a) is highly genetically determined and higher levels are associated with an increased risk of CV disease. Statins have minimal effect and PCSK9i only modest effect but Olpasiran, a small interfering RNA (siRNA) may enable significant Lp(a) reduction. In the OCEAN(a)-DOSE TIMI 67 trial , 281 patients with elevated Lp(a) > 150 nmol/L were randomised to 1 of 4 olpasiran doses (10 mg, 75 mg, or 225 mg every 12 weeks, or 225 mg every 24 weeks) versus placebo. By 36 weeks, the 4 doses of olpasiran were associated with placebo-adjusted percent reductions in Lp(a) concentration of 70.5%, 97.4%, 101.1%, and 100.5%, respectively, along with useful reductions in low-density lipoprotein (LDL) cholesterol and apolipoprotein B. In addition to Olpasiran, other siRNA drugs are in development including SLN360, and pelacarsen, an mRNA-based antisense oligonucleotide targeting the Lp(a) gene being studied in the 8000-patient outcomes study, Lp(a)HORIZON which will hopefully clarify if reduction of Lp(a) is of benefit . Perceived myalgia remains an important limitation for statin adherence. The Cholesterol Treatment Trialists’ Collaboration evaluated incidence of myalgia in a meta-analysis of 19 double-blind trials of statin versus placebo ( n = 123,940) and four double-blind trials of more versus less intensive statin regimen ( n = 30,724). For the 19 placebo-controlled trials, statin use was associated with a 3% increase in reported muscle pain or weakness at a median 4·3 years follow-up (27.1% vs. 26.6%; RR 1.03, CI 95% 1.01–1.06), but the excess was mainly during the first year, when statin use was associated with an absolute excess of 11 events per 1000 person-years. Similarly, a small increase in reported muscle pain or weakness was seen with higher versus lower intensity statin groups, (36.1% vs. 34.8%; RR 1.05, CI 95% 1.01–1.09). In summary, while statin therapy can cause myalgia, most (> 90%) reports of muscle symptoms by participants allocated statin therapy were not due to the statin. The FOURIER-OLE (Fourier Open-label Extension Study in Subjects With Clinically Evident Cardiovascular Disease in Selected European Countries) evaluated the long-term follow-up of the FOURIER study in 6635 patients randomised to the PCSK9 inhibitor Evolocumab versus placebo. At a median of 5 years, Evolocumab was associated with resulted in a 20% reduction in CV death, MI or stroke (HR 0.8, 95% CI 0.68–0.93; P = 0.003) with low risk of adverse events. Elevated uric acid is recognised as an independent risk factor for CV events. The ALL-HEART (Allopurinol versus usual care in UK patients with ischaemic heart disease) study randomised 5721 patients > 60 years with ischaemic heart disease but no history of gout to allopurinol (up-titrated to maximum of 600 mg) versus placebo. However, over a mean of 4.8 years follow-up, allopurinol was not associated with reduction in the primary endpoint of CV death, MI or stroke (11% vs. 11.3%; P = 0.65). The endothelin pathway has been implicated in the pathogenesis of hypertension, but is currently not targeted therapeutically, leaving this pathway unopposed with currently available drugs. The global PRECISION (Dual endothelin antagonist aprocitentan for resistant hypertension) trial randomised 730 patients with hypertension resistant to at least 3 antihypertensives to the dual endothelin receptor antagonist aprocitentan aprocitentan 12·5 mg or 25 mg versus placebo in a 1:1:1 fashion. At 4 weeks, aprocitentan was associated with met the primary endpoint with greater systolic blood pressure reduction (mean change for aprocitentan 12.5 mg of − 15.3 mmHg and for aprocitentan 25 mg of − 15.2 mmHg vs. placebo − 11.5 mg; P < 0.005 for both treatment doses). Delivering healthcare in rural environments can be challenging. In China, non-physician village doctors may initiate and titrate antihypertensive medications according to a standard protocol with supervision from primary care physicians, and undertake health coaching on home blood pressure monitoring, lifestyle changes, and medication adherence. The China Rural Hypertension Control Project randomised 33,995 patients from 326 villages to village doctor-led multifaceted intervention versus usual care . By 36 months, the intervention group reported a drop in mean systolic pressure from 157 to 126.1 mmHg, whereas the usual-care group only dropped from 155.4 mmHg to 146.7 mmHg and a significant reduction in the primary composite CV endpoint (1.98% vs. 2.85% per year; HR 0.69, CI 95% 0.63–0.76) with 33% fewer strokes ( P < 0.0001), 39% fewer cases of HF ( P = 0.005), 24% fewer CV deaths ( P = 0.0004), and 15% fewer all-cause deaths ( P = 0.009). Previous trial data suggested a protective effect for nocturnal dosing of anti-hypertensive therapies on cardiovascular events, although the trial methodology was subsequently questioned . The TIME (Treatment in Morning versus Evening) trial randomised 21,104 patients (mean age 65 years, female 43%) to evening versus morning dosing of their regular antihypertensive agent . After 5 years, the primary outcome (composite of vascular death, MI or stroke) occurred in 3.4% of the evening dosing group versus 3.7% of the morning group ( P = 0.53). There was no difference in rates of stroke between groups (1.2% vs. 1.3%, P = 0.54); however, there was a modestly higher rate of falls in the morning dosing group (22.2% vs. 21.1%, P = 0.048). This informative trial demonstrates no difference in cardiovascular outcomes with respect to timing of anti-hypertensive dosing albeit a slightly reduced risk of falls with evening dosing.
While all summarised trials have been presented at major cardiology conferences in 2022, not all trials have been published as yet in peer-reviewed journals.
This paper has highlighted and summarised the key cardiology trials that were published and presented during 2022. Many will guide clinical practice and influence guideline development. Others have shown encouraging early data which will guide future study.
|
The implications of a cost-of-living crisis for oral health and dental care | fb53b8a8-9d60-4774-8758-e4300d38ad11 | 10103663 | Dental[mh] | ' Please sir, my brother went to stay with my dad last night and he took the toothbrush' . This explanation was offered by a young girl to an author of this paper (IGC) as an explanation as to why her oral hygiene was less than ideal, in spite of a long discussion on the topic at a previous dental visit. The child was being totally honest. It said everything about her family and personal circumstances. This incident occurred more than 30 years ago but its impact was such that it has been used when teaching successive cohorts of dental students in the time since. It is a stark reminder that many who we care for do not live in the same circumstances as us. To think that there might only be one, or no, toothbrushes in a home comes as a shock to many dental students. Dental caries can be a disease of poverty and poor oral health is significantly related to social and economic disadvantage. Much has been written in the pages of this journal, , , and its sister publications in recent months, , , about the impact of the current economy on the dental profession and dentistry, but have we thought sufficiently about how the current cost-of-living crisis is impacting society and the patients that we are here to care for? The basics of securing oral health are: Brush your teeth twice a day with a fluoride-containing toothpaste Reduce both the amount and frequency of free-sugar consumption Visit your dentist regularly. These simple actions are currently in peril for many people. This article discusses the cost-of-living crisis from the perspective of people living in poverty and the impact that it is likely to have on their access to dental care and their oral health.
The term 'hygiene poverty' has received attention in relation to menstrual health , but what about those who cannot afford the personal products needed to maintain their oral health? What if your personal circumstances and disposable income are such that you cannot afford to buy toothbrushes and toothpaste for your children, or that the toothbrush has to be shared? The Hygiene Bank defines hygiene poverty as: 'not being able to afford many of the everyday hygiene and personal grooming products most of us take for granted'. Hygiene poverty occurs when a person's household income is such that they face a choice between paying the rent, heating their home, eating, or keeping themselves clean. Charities are reporting families asking for toiletries such as toothbrushes. A recent survey conducted by YouGov estimated that 3,150,000 adults in the UK - 6.5% of the population - are currently experiencing hygiene poverty. Of a sample of 2,006 people experiencing hygiene poverty, 28% said that they had gone without toothpaste, toothbrushes or essential dental products. Hygiene essentials were reported as being bottom of the list when budgets were tight. As is often the case, vulnerable groups are disproportionately affected by hygiene poverty - individuals from minority ethnic groups and those with a disability or long-term health condition are more likely to report hygiene poverty. There is some evidence that providing families with young children with toothbrushes and fluoride toothpaste (within a multicomponent programme) result in overall cost savings to a healthcare service. , Oral health improvement programmes, such as Designed to Smile (Wales) and Childsmile (Scotland), recognise that implementing supervised toothbrushing in schools will not achieve their full potential if children do not have access to the wherewithal to brush their teeth at home. For this reason, packs containing toothbrushes and toothpaste for home use are delivered as part of these schemes. We know that the improvements that have been seen in oral health in the UK over the past four decades are in large part due to twice daily use of fluoridated toothpaste. If those who are most susceptible to dental caries can no longer afford a toothbrush and toothpaste, then inequalities in oral health can only widen.
The Food and Agriculture Organisation of the United Nations defines food insecurity as a 'lack of regular access to enough safe and nutritious food for normal growth and development and an active and healthy life'. While food insecurity is mostly associated with the developing world, moderate or severe food insecurity also exists in high-income countries. Approximately 8% of the population in North America and Northern Europe - around 88 million people - were food insecure in 2017-2019. Unsurprisingly, higher rates of food insecurity are more prevalent in households of lower socioeconomic position, in disadvantaged communities, and among lower-income households. However, poverty (defined as 60% of the median equivalised net household income) and food insecurity are not synonymous. One-fifth of individuals in poverty are food insecure, compared to 4% of individuals not in poverty. Children in poverty are the most likely to be suffering from food insecurity and families consisting of single adults with children in poverty are particularly vulnerable. Comparatively, pensioners in poverty are the least likely to be food insecure. Lower-income households spend a higher percentage of their budget on food. The average UK household spends 11% of their weekly budget on food, while for the lowest 20% of households by equivalised income, this is closer to 15%. Living in poverty is expensive. Examples of the 'poverty premium' include the use of pre-paid utility meters, dearer insurance policies and more expensive credit. Food is also typically more affordable when bought in bulk, but what happens if you don't have the facilities to refrigerate or freeze food or can't afford to turn on your oven? The inability of low-income households to access the best deals for food and services exacerbates pre-existing inequalities in society. Food insecurity is not only about being able to afford enough food, but also being able to afford food that is nutritious. The dietary quality of food purchased by food-insecure households is lower than that of food-secure households. There is a consistent inverse association between food insecurity and intake of nutrient-rich foods, such as fruit and vegetables. Similarly, consumption of energy-dense foods, such as high-fat dairy products, salty snacks, and sugar-sweetened beverages, is higher among food-insecure households. , There is also evidence of a strong, consistent and dose-response relationship of food insecurity, with lower vegetable intake among children aged 1-5 years, and strong and consistent evidence of higher added sugar intake among food-insecure children aged 6-11 years, compared with food-secure children. Of specific relevance at the present time, analysis of food bank parcels distributed in Oxfordshire found that they exceeded energy requirements and provided disproportionately high sugar and carbohydrates compared to UK guidelines. Foodbanks, which act to alleviate food insecurity, are now a feature of most communities in the UK, and while they play an important role in preventing people going hungry, evidence suggests that they will not make reducing dietary sugar intake and compliance with nutritional guidelines any easier. The increasing prevalence of food insecurity leading to poorer health outcomes becomes a stubborn cycle leading to chronic disease and adverse quality of life.
Access to NHS dental care and the difficulties therein have in recent months received endless attention in the broadcast, print, social and specialist dental media. In the latter, the attention has most commonly focused on the difficulties facing dental providers as they struggle with the aftermath of the COVID-19 pandemic and the shortcomings of NHS funding and contracting arrangements. Less attention has been paid to how the cost-of-living crisis has impacted on patients. Dentistry can offer more than the State can afford to pay for - dental implants and tooth whitening being just two examples. As a result, a two-tier system in the provision of dental care has existed for a long time. It is simply a fact of life that not everyone can be provided with, or afford, 'high-end' treatments. Patients not being able to afford what they would ideally like from dental care, or anxiety about finding out how much dental care would cost in advance of attending, has long been an issue. However, the current cost-of-living crisis means we are now experiencing an era where more patients may not be able to afford even basic NHS dental care. While there is no patient charge for those who are in receipt of certain state benefits, and an NHS low-income scheme that will assist some low earners, as always in any means tested system, it is those who just fail to qualify that are likely to be worst affected. In recent months, the press has been rife with stories of those who have resorted to do-it-yourself dentistry, , sometimes attributed to the inability to find a dentist, or inability to pay for care. A case headlined by the BBC - 'I had to choose between heating or my teeth' - reported on a patient opting to pay £50 to have her tooth extracted rather than paying £1,000 for a root-filling and crown to save the tooth due to the energy crisis. The Money and Pensions Service, an arm's-length body sponsored by the Department of Work and Pensions, recently commissioned a survey which claimed that one in six adults in the UK - nine million people - have no savings. Another five million have less than £100 in savings. Consider these findings in light of the cost of dental care. Even if provided via the NHS, it is easy to see the dilemma that those most likely to experience a dental emergency are likely to find themselves in. The establishment of urgent treatment centres may go some way to alleviating access issues, but if these are a distance from people's homes, can they afford the costs to travel there, whether reliant on public transport or needing to buy fuel to travel by car? 'Visiting your dentist regularly' is an unaffordable expense for many of those in our society who would most benefit from such a visit.
A charity has the tagline: 'the opposite of poverty is not wealth, the opposite of poverty is enough' . This leads to one final consideration in relation to the present cost-of-living crisis; this time, it is not concern for patients, but for staff. Dental nurses are essential to the success of a dental practice, yet Sellars, commenting on the largest sector of the dental workforce, said dental nurses feel 'overworked, undervalued and underpaid'. Perhaps it is not only the person in your dental chair that is struggling to heat their home or feed their children. It may also be true of the person sitting on the other side of the chair. Being in work is no longer a defence against poverty. In a recent publication, the highly regarded Joseph Rowntree Foundation stated that around two-thirds (68%) of working-age adults in poverty live in a household where at least one adult is in work. Since 2011/12, the employment sector which has seen the greatest increase in poverty for those in work is the human health and social care sector. As of November 2022, the UK national living wage (for those aged 23 and over) is £9.50 per hour. This equates to an annual full-time salary of between £17,290-23,712, depending on the exact hours worked. However, it is argued that the national living wage provides insufficient resource to facilitate the opportunities and choices necessary to participate in society. Instead, the Joseph Rowntree Foundation propose a minimum income standard; a public consensus on the financial resource that households need in order not just to survive, but to live with dignity. For a single person in 2022, this was £25,500, and for a single parent with two young children, £38,400. In contrast, the most recent salary review by the British Association for Dental Nurses reported that 73% of dental nurses earned under £20,000 per annum. Two-thirds of dental nurses responding worked full-time. The majority live with partners/spouses and their children and 31% claimed to be the primary earner in the household. Further, 16% of dental nurses said that they had a second job and just under half of those reported that their second job was necessary to meet basic needs. One response to disparity between dental nurses' salaries and cost-of-living may be the trend towards agency nursing or self-employment. Of the 65% of dental nurses responding to a 2020 survey who reported considering leaving the profession, pay was the most commonly cited factor. When training, registration, indemnity and continuing professional development costs are also considered, it's perhaps not surprising why alternative employment opportunities outside dentistry are a rational financial decision for some dental nurses and their families. It is beyond the experience of the authors of this article to discuss the complexities of practice ownership and employee pay, particularly within the fixed financial envelope of NHS practice. This is, however, an opportunity to call for wider recognition of our lowest-paid colleagues and to highlight the moral responsibility we have to ensure that those employed in dentistry have the means by which to live in dignity and fully participate in society.
As everyday costs continue to rise, many of our patients and the communities which we serve are likely to experience difficulties securing the basics to achieve good oral health. This impact will not be felt equally. Targeted support is needed for those most at risk of experiencing food insecurity, hygiene poverty and financial barriers to dental care. However, in an already over-stretched health and social care system and fragmented state-benefits structure, it seems more likely than ever that these individuals will fall through the gaps. Short-term government assistance and the services of third-sector organisations can only go so far in off-setting rising prices for some of the most vulnerable households and does nothing to improve the forecast for those currently struggling to get by day-to-day.
|
Small extracellular vesicles from young adipose-derived stem cells ameliorate age-related changes in the heart of old mice | 124a957c-93fe-4a38-ac7c-4dfe01303b69 | 11907833 | Musculoskeletal System[mh] | Aging is accompanied by a decline in tissue regenerative capacity, increasing susceptibility to various age-related diseases. While stem cell therapies have shown promise in regenerative medicine, recent studies indicate that their therapeutic effects are primarily mediated by paracrine mechanisms rather than direct tissue integration. Extracellular vesicles (EVs), including small extracellular vesicles (sEVs), have emerged as crucial mediators of intercellular communication, carrying a diverse range of proteins, nucleic acids, and lipids. Traditionally, EVs were classified based on their biogenesis pathways, with microsvesicles budding directly from the plasmatic membrane, and exosomes being formed through the endosomal pathway and contained in multivesicular bodies . While these differences in origin exist, EVs are now broadly categorized based on their size as current isolation methods usually rely on vesicle size to separate vesicle populations. Large extracellular vesicles (lEVs) range from approximately 200 nm to 1 μm in diameter and sEVs typically range from 30 to 200 nm . Mesenchymal stem cells (MSCs) have been extensively investigated for their regenerative potential due to their ease of isolation, culture, and low immunogenicity. However, the use of MSCs is complicated by their limited survival and integration into host tissues. In contrast, the secretome of MSCs, particularly EVs, offers advantages such as stability, ease of dosage, and lower immunogenicity, with several ongoing clinical trials for various conditions. EVs are also being studied as regulators of aging-related processes, participating in cellular senescence, oxidative stress, telomere dysfunction, inflammation, and metabolic dysregulation. The average human lifespan is increasing, leading to a significant rise in the population aged 65 and older, a trend expected to continue over the next two decades. Within this demographic, cardiovascular disease remains the primary cause of mortality, coupled with rising treatment costs. Aging, an inherent aspect of life, is the predominant risk factor for cardiovascular diseases. Therefore, the relationship between cardiovascular disease and the molecular and cellular aspects of aging is evident. Heart tissue undergoes several age-associated changes, such as fibrosis, valvular degeneration, calcification, cardiomyocyte hypertrophy, degeneration of the conduction system, and cellular senescence-related alterations. These changes contribute to the development of several age-related diseases, whose prevalence and incidence are increasing, such as heart failure with preserved ejection fraction (HFpEF) and aortic stenosis. The present study focuses on the therapeutic potential of ADSC-sEVs in mitigating age-related changes in the heart. We demonstrate that ADSC-sEVs exert anti-aging effects in the heart of old mice by modulating cellular senescence, oxidative stress, inflammation, and metabolism.
Mouse model This investigation adhered rigorously to all relevant federal and institutional guidelines. The University of Valencia Animal Ethics Committee, following European Union (EU) regulations on animal research, approved the protocol under identification number A1508582840889 (“Efecto de la administración de vesículas extracelulares de células madre de tejido adiposo derivadas de ratones jóvenes en ratones envejecidos”, approved on 31-01-2018). Mice were housed in the animal center of the Central Unit for Research in Medicine (UCIM) of the Faculty of Medicine and Odontology of the University of Valencia. Animals were kept in the facility at 22 ± 2˚C, with a relative humidity of 60% under 12–12 h of dark-light cycles and with access to food and water ad libitum . The mice in the study were of a C57BL/6J background, randomly selected to treatment with EVs or with PBS as a control. Mouse ages for the present study were chosen based on recommendations from the American Federation of Aging Research (Principles of Animal Use for Gerontological Research). The average age of the mice that received sEVs was 21.925 months, while those that received PBS had an average age of 21.975 months. Both genders were included, and, when possible, littermates of the same sex were used in different groups. The sample size was determined using G*Power, and random assignment to experimental groups was conducted. Each mouse received two doses separated by 7 days, comprising either 20 µg of sEV protein or PBS as a control. The doses were administered intravenously to mice via the tail vein in a total volume of 100 µl diluted in PBS. The protocol was replicated across several batches of old mice treated with sEVs versus PBS as a control. Mouse weight was annotated during the experimental procedure as a surrogate marker of toxicity. Upon euthanasia by cervical dislocation 30 days post-treatment with sEVs/PBS, the heart was collected, weighed, and stored at -80 °C for further processing. Please see the Major Resources Table in the Supplemental Materials. The work has been reported in line with the ARRIVE guidelines 2.0. Stem cell isolation and culture Stem cell culture involved obtaining mesenchymal stem cells (MSCs) from inguinal fat pads of mice aged 3 to 6 months (ADSCs) according to a previously described protocol . In brief, inguinal fat pads were extracted under sterile conditions, and adipose tissue was subjected to collagenase I digestion (0.1% type I collagenase and 1% BSA in PBS supplemented with 2 mM CaCl2). The digested tissue was centrifuged twice at 300 g to remove floating adipocytes, and the stromal vascular fraction was seeded. Some endothelial cells were observed in passage 0 but were lost after passage 1. Cells in passages 2 or 3 were used, cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM) with 10% FBS and 1% penicillin/streptomycin. Culture conditions included 37 °C, 5% CO2, and 3% O2. ADSCs’ characterization As referenced in our previous work, ADSCs’ characterization was conducted using flow cytometry . We assessed the presence of MSC markers CD29 and CD44, using CD45 and CD31 as negative controls to identify potential contaminants in the culture. Once the cells reached 80% confluency, they were trypsinized and resuspended in fluorescence-activated cell sorting (FACS) buffer that contained 10% FBS and 1% sodium azide in PBS. For each condition, 100,000 cells were stained with antibodies at 1 µg/µl and incubated for 30 min at 4 °C. The antibodies used, all obtained from BioLegend, were as follows: CD29 R-phycoerythrin/Cyanine7 (PE/Cy7), catalog number 10,222; CD31 PE, catalog number 102,507; CD44 Alexa Fluor 488, catalog number 103,015; and CD45 PE, catalog number 103,106. Isolation of ADSC-derived sEVs The isolation of ADSC-derived small extracellular vesicles (sEVs) involved culturing ADSCs in high-glucose DMEM supplemented with 2% exosome-depleted FBS and 1% penicillin/streptomycin (Gibco, A2720803) for 48 h. The conditioned medium was then collected, and sEVs were isolated through differential ultracentrifugation. The culture medium was centrifuged at 2000 g for 10 min and centrifuged at 20,000 g for 30 min to remove whole cells, cell debris, and larger extracellular vesicles (EVs). The resulting supernatant was subjected to ultracentrifugation at 100,000 g for 70 min. The pelleted vesicles were re-suspended in PBS and underwent another round of ultracentrifugation at 100,000 g for 70 min for thorough washing, followed by re-suspension in PBS. sEVs isolated from the conditioned medium were stored at 4 °C and used for treatment within 24 h. The dosage of sEVs was determined by protein quantification using the Lowry method. Transmission electron microscopy and Immunogold labeling of sEVs The isolated sEVs were fixed in a 2% paraformaldehyde (PFA) solution in 0.1 M PBS for 30 min. A glow discharge technique (30 s, 7.2 V) using a Bal-Tec MED 020 Coating System was applied to carbon-coated copper grids. Subsequently, these treated grids were placed over the sample drops for 15 min. The grids with attached sEVs were then immersed in a 0.1 M PBS solution for washing, followed by an additional fixation step in 1% glutaraldehyde for 5 min. After thorough washing in distilled water, the grids were contrasted with 1% uranyl acetate and embedded in methylcellulose. Excess fluid was carefully removed, and the samples were allowed to dry before examination using a transmission electron microscope, FEI Tecnai G2 Spirit (Thermo Fisher Scientific, OR, USA). Image acquisition was performed using a digital camera, Morada (EMSIS GmbH, Münster, Germany). For immunogold labeling, 8 µl of isolated sEVs were fixed in 2% PFA in 0.1 M PBS for 30 min. Carbon-coated nickel grids were then positioned over these sEV drops for 15 min. After washing in 0.1 M PBS, the grids were blocked in a solution of 0.1 M glycine and 0.3% BSA for 10 min. The grids were subsequently incubated with the primary antibody anti-CD63 (MBL, D263-3) at a dilution 1:100 for 1 h. Following another blocking step for 10 min, the grids were exposed to Gold 6 nm–conjugated goat anti-rat secondary antibody (Abcam, ab105300) at a dilution of 1:1000 for 1 h. Lastly, a standard negative staining procedure was applied after thorough washing, and the samples were observed under a transmission electron microscope, as previously described. PKH26 staining For in vivo tracking of sEVs, we obtained sEVs from the conditioned medium of young ADSCs. Subsequently, these sEVs were labeled with 5 µM PKH26 (Sigma-Aldrich, MINI26-1KT) following the first ultracentrifugation. Following the labeling, the sEVs were resuspended in PBS and subjected to an additional round of washing through ultracentrifugation. A total of 20 µg of PKH26-labeled sEVs in 100 µL of PBS were injected into aged mice (20 months), and control mice were injected with 100 µL of a PBS solution containing 5 µM PKH26. The mice were euthanized 24 h later, and heart tissue was harvested, fixed in 4% PFA for 24 h, and cryoprotected in 30% sucrose for another 24 h. Ten-micrometer cryostat slices were mounted on slides. Sections were blocked with 10% normal goat serum (Invitrogen) in PBS containing 0.05% Tween 20 (PBS-t) and incubated overnight at 4 °C with a primary antibody against Desmin (Cell Signaling, 5332; 1:500 dilution) and with Alexa Fluor 488 anti-rabbit (Abcam, ab150077; 1:2000 dilution) for 2 h at room temperature as the secondary antibody. Sections were counterstained with DAPI (Invitrogen) at a 1:1000 dilution for 30 min at room temperature and then mounted on coverslips using an aqueous mounting medium sealed with nail polish. Imaging was performed using an Olympus FV1000 confocal laser scanning biological microscope. Images were processed in ImageJ while maintaining equal ratios. Echocardiography assessment in mice Anesthesia was induced in a chamber with 2–3% isoflurane delivered in a mixture of oxygen (1–2 L/min). Once anesthetized, mice were transferred to a nose cone for continuous isoflurane inhalation at a 1–2% maintenance concentration. Anesthesia depth was monitored throughout the procedure by assessing the respiratory rate and pedal withdrawal reflex. Echocardiography was performed using an imaging system (GE Versana Active Veterinary Ultrasound Scanner) equipped with a 12-MHz transducer for small animals. Mice were positioned in a supine position with external heating to maintain body temperature. The chest area was shaved, and ultrasound gel was applied for optimal acoustic coupling. Two-dimensional (2D) M-mode and pulsed Doppler images were acquired in various views, including parasternal long-axis, short-axis, and apical views. For data analysis, images were analyzed using the same ultrasound scanner for acquisition. Standard echocardiographic parameters shown in the results section were measured for fractional shortening (FS) calculation, M-mode images were used to measure left ventricle end-diastolic diameter (LVEDD), and end-systolic diameter (LVESD), FS = LVEDD– LVESD / LVEDD x 100. Treadmill physical endurance test The animals underwent a progressive intensity treadmill test (Treadmill Control LE 8710 Panlab, Harvard Apparatus) to assess their endurance, measured by maximum time running. Following a warm-up period, the treadmill belt velocity was incrementally elevated until the animals reached exhaustion, indicating their inability to continue running. The test commenced with an initial 4-minute session at 10 cm/s, followed by successive increments of 4 cm/s every 2 min. Exhaustion was defined as the point at which a mouse remained on the shock grid for 5 s instead of actively running. Histology Heart tissue was freshly frozen in liquid nitrogen and 10-micron slices were obtained using a cryostat for histological staining and immunofluorescence. Hematoxylin and eosin staining (Sigma-Aldrich, MHS32, and E4009, respectively) or Sirius red staining (Sigma-Aldrich, 365548) was performed on the sections, followed by mounting and sealing for morphometric analysis. Images were captured using an optical microscope (Leica), and three images from different areas of each slice were obtained. Morphometric analysis of heart sections was conducted using ImageJ. Immunofluorescence imaging Ten-micrometer tissue slices were mounted on slides and fixed with ice-cold acetone for 20 min. For permeabilization, slices were incubated in 1% Triton X-100 in PBS for 10 min. Sections were blocked with 10% normal goat serum (Invitrogen) in PBS with 0.05% Tween 20 (PBS-t) and incubated with primary antibodies overnight at 4 °C in the same buffer. After primary antibody incubation, sections underwent three 10-minute PBS-t washes and were then incubated with secondary antibodies for 2 h at room temperature. Subsequent washes were performed, and tissues were counterstained with DAPI (Invitrogen; 1:1000 dilution) for 30 min at room temperature. Coverslips were mounted with an aqueous mounting medium and sealed with nail polish. Images were acquired using an Olympus FV1000 confocal laser scanning biological microscope. Image processing was carried out using CellProfiler with custom pipelines for automatic cell counting and analysis of CD3, LMNB1, and γH2AX positive cells. CD31 area was calculated using ImageJ, with uniform adjustments to levels. Three images from different areas of each slice were obtained. The following antibodies and concentrations were used: CD31 staining: Alexa Fluor 647 anti-mouse CD31 antibody (Biolegend, 102515, 1:50 dilution). LMNB1 staining: anti-LMNB1 (Proteintech, 12987-1-AP; 1:50 dilution) and Alexa Fluor 488 anti-rabbit (Abcam, ab150077; 1:2000 dilution); a minimum of 150 nuclei were analyzed per sample. γH2AX staining: anti-γH2AX (Cell Signaling Technology, 9718 S; 1:1000 dilution) and Alexa Fluor 488 anti-rabbit (Abcam, ab150077; 1:2000 dilution); a minimum of 150 were analyzed per sample. CD3 staining: anti-CD3 (Proteintech, 17617-1-AP; 1:1000 dilution) and Alexa Fluor 647 anti-rabbit (Abcam, ab150079, 1:2000 dilution). Quantification of lipid peroxidation by HPLC Heart tissue was lysed using a KPi-EDTA buffer [50 mM KPi and 1 mM EDTA (pH 7.4)], and the levels of lipid peroxidation were assessed by quantifying malondialdehyde (MDA) using high-performance liquid chromatography (HPLC). MDA was determined as an MDA–thiobarbituric acid (TBA) adduct following a previously established method. This approach relies on the hydrolysis of lipoperoxides and the subsequent formation of a TBA-MDA2 adduct, which was detected using reverse-phase HPLC and quantified at 532 nm. The chromatographic technique was carried out under isocratic conditions, with the mobile phase comprising a mixture of monopotassium phosphate at 50 mM (pH 6.8) and acetonitrile (70:30). The MDA levels in each sample were normalized to the protein concentration determined by the Lowry method. Protein oxidation quantification with Immunoblotting Total protein carbonylation was detected by immunoblotting using the OxyBlot Protein Oxidation Detection kit (Merck) following the manufacturer’s instructions. In brief, tissues were lysed in tris/SDS/glycerol buffer, and protein concentration was determined using the Lowry method. Subsequently, 20 µg of proteins were separated on SDS polyacrylamide gels and transferred onto nitrocellulose membranes. The membranes were blocked with 3% BSA in PBS-t for 60 min at room temperature and then incubated overnight at 4 °C with the primary antibody from the kit. After three washes (10 min each) with PBS-t, the membranes were incubated with the secondary antibody for 120 min at room temperature. Following three additional washes with PBS-t, the membranes were developed with Luminol (Sigma-Aldrich) using the ImageQuant LAS4000 system. Image analysis was performed in ImageJ, and Ponceau staining of the membranes served as the loading control. Interleukin quantification We utilized two commercially available enzyme-linked immunosorbent assay (ELISA) kits for the quantitative analysis of IL-6 (Abcam, ab100713) and IL-8 (Abcam, ab234567), adhering to the manufacturer’s provided instructions. In brief, tissues underwent lysis using specific buffers included in each kit, protein concentration was determined using the Lowry method, and samples were appropriately diluted at ratios of 1:5. Absorbance at 450 nm was quantified using the Molecular Devices SPECTRAmax Plus 384. All samples were subjected to duplicate assays. Metabolomics Metabolomics was performed as previously described, with minor adjustments . A 75 µL mixture of the following internal standards in water was added to approximately 3 mg of freeze-dried heart tissue: adenosine- 15 N 5 -monophosphate (100 µM), adenosine- 15 N 5 -triphosphate (1 mM), D 4 -alanine (100 µM), D 7 -arginine (100 µM), D 3 -aspartic acid (100 µM), D 3 -carnitine (100 µM), D 4 -citric acid (100 µM), 13 C 1 -citrulline (100 µM), 13 C 6 -fructose-1,6-diphosphate (100 µM), guanosine-15N 5 -monophosphate (100 µM), guanosine- 15 N 5 -triphosphate (1 mM), 13 C 6 -glucose (1 mM), 13 C 6 -glucose-6-phosphate (100 µM), D 3 -glutamic acid (100 µM), D 5 -glutamine (100 µM), 13 C 6 -isoleucine (100 µM), D 3 -leucine (100 µM), D 4 -lysine (100 µM), D 3 -methionine (100 µM), D 6 -ornithine (100 µM), D 5 -phenylalanine (100 µM), D 7 -proline (100 µM), 13 C 3 -pyruvate (100 µM), D 3 -serine (100 µM), D 5 -tryptophan (100 µM), D 4 -tyrosine (100 µM), D 8 -valine (100 µM). Subsequently, 425 µL water, 500 µL methanol, and 1 mL chloroform were added to the same 2 mL tube before thorough mixing and centrifugation for 10 min at 14.000 rpm. The top layer, containing the polar phase, was transferred to a new 1.5 mL tube and dried using a vacuum concentrator at 60 °C. Dried samples were reconstituted in 100 µL methanol/water (6/4; v/v). Metabolites were analyzed using a Waters Acquity ultra-high-performance liquid chromatography system coupled to a Bruker Impact II™ Ultra-High Resolution Qq-Time-Of-Flight mass spectrometer. Samples were kept at 12 °C during analysis and 5 µL of each sample was injected. Chromatographic separation was achieved using a Merck Millipore SeQuant ZIC-cHILIC column (PEEK 100 × 2.1 mm, 3 μm particle size). Column temperature was held at 30 °C. The mobile phase consisted of (A) 1:9 acetonitrile: water and (B) 9:1 acetonitrile: water, both containing 5 mM ammonium acetate. Using a flow rate of 0.25 mL/min, the LC gradient consisted of: Dwell at 100% Solvent B, 0–2 min; Ramp to 54% Solvent B at 13.5 min; Ramp to 0% Solvent B at 13.51 min; Dwell at 0% Solvent B, 13.51–19 min; Ramp to 100% B at 19.01 min; Dwell at 100% Solvent B, 19.01–19.5 min. Equilibrate the column using a 0.4 mL/min flow at 100% B from 19.5 to 21 min. MS data were acquired using negative and positive ionization in full scan mode over the range of m/z 50-1200. Data were analysed using Bruker TASQ software version 2021b (2021.1.2.452). All reported metabolite intensities were normalized to dry tissue weight, as well as to internal standards with comparable retention times and response in the MS. Metabolite identification was based on a combination of accurate mass, (relative) retention times, and fragmentation spectra, compared with the analysis of a library of standards. Statistical analysis and visualization of the acquired data were done in an R environment using the ggplot2, ropls, and mixOmics packages . Statistical analysis Ratios depicting the comparison of physical test values after treatment against baseline were calculated and presented as percentages over baseline, with the baseline defined as 0%. Outliers were assessed in all groups using the ROUT method (Q = 2%), with no exclusion of any data point. The normality of each group was assessed using the Shapiro-Wilk test. For pairwise comparisons, either the Unpaired Student’s t-test or the Mann-Whitney test was employed, depending on the data distribution. In the case of multiple comparisons, ANOVA was applied, with Tukey’s multiple comparisons serving as a post hoc test. Alternatively, for nonparametric data, the Kruskal-Wallis test was utilized, with Dunn’s multiple comparisons as the post hoc test. Each data point presented in the manuscript represents a biological replicate. GraphPad Prism 9.0 software was utilized for both analysis and graphical design if not otherwise stated.
This investigation adhered rigorously to all relevant federal and institutional guidelines. The University of Valencia Animal Ethics Committee, following European Union (EU) regulations on animal research, approved the protocol under identification number A1508582840889 (“Efecto de la administración de vesículas extracelulares de células madre de tejido adiposo derivadas de ratones jóvenes en ratones envejecidos”, approved on 31-01-2018). Mice were housed in the animal center of the Central Unit for Research in Medicine (UCIM) of the Faculty of Medicine and Odontology of the University of Valencia. Animals were kept in the facility at 22 ± 2˚C, with a relative humidity of 60% under 12–12 h of dark-light cycles and with access to food and water ad libitum . The mice in the study were of a C57BL/6J background, randomly selected to treatment with EVs or with PBS as a control. Mouse ages for the present study were chosen based on recommendations from the American Federation of Aging Research (Principles of Animal Use for Gerontological Research). The average age of the mice that received sEVs was 21.925 months, while those that received PBS had an average age of 21.975 months. Both genders were included, and, when possible, littermates of the same sex were used in different groups. The sample size was determined using G*Power, and random assignment to experimental groups was conducted. Each mouse received two doses separated by 7 days, comprising either 20 µg of sEV protein or PBS as a control. The doses were administered intravenously to mice via the tail vein in a total volume of 100 µl diluted in PBS. The protocol was replicated across several batches of old mice treated with sEVs versus PBS as a control. Mouse weight was annotated during the experimental procedure as a surrogate marker of toxicity. Upon euthanasia by cervical dislocation 30 days post-treatment with sEVs/PBS, the heart was collected, weighed, and stored at -80 °C for further processing. Please see the Major Resources Table in the Supplemental Materials. The work has been reported in line with the ARRIVE guidelines 2.0.
Stem cell culture involved obtaining mesenchymal stem cells (MSCs) from inguinal fat pads of mice aged 3 to 6 months (ADSCs) according to a previously described protocol . In brief, inguinal fat pads were extracted under sterile conditions, and adipose tissue was subjected to collagenase I digestion (0.1% type I collagenase and 1% BSA in PBS supplemented with 2 mM CaCl2). The digested tissue was centrifuged twice at 300 g to remove floating adipocytes, and the stromal vascular fraction was seeded. Some endothelial cells were observed in passage 0 but were lost after passage 1. Cells in passages 2 or 3 were used, cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM) with 10% FBS and 1% penicillin/streptomycin. Culture conditions included 37 °C, 5% CO2, and 3% O2.
As referenced in our previous work, ADSCs’ characterization was conducted using flow cytometry . We assessed the presence of MSC markers CD29 and CD44, using CD45 and CD31 as negative controls to identify potential contaminants in the culture. Once the cells reached 80% confluency, they were trypsinized and resuspended in fluorescence-activated cell sorting (FACS) buffer that contained 10% FBS and 1% sodium azide in PBS. For each condition, 100,000 cells were stained with antibodies at 1 µg/µl and incubated for 30 min at 4 °C. The antibodies used, all obtained from BioLegend, were as follows: CD29 R-phycoerythrin/Cyanine7 (PE/Cy7), catalog number 10,222; CD31 PE, catalog number 102,507; CD44 Alexa Fluor 488, catalog number 103,015; and CD45 PE, catalog number 103,106.
The isolation of ADSC-derived small extracellular vesicles (sEVs) involved culturing ADSCs in high-glucose DMEM supplemented with 2% exosome-depleted FBS and 1% penicillin/streptomycin (Gibco, A2720803) for 48 h. The conditioned medium was then collected, and sEVs were isolated through differential ultracentrifugation. The culture medium was centrifuged at 2000 g for 10 min and centrifuged at 20,000 g for 30 min to remove whole cells, cell debris, and larger extracellular vesicles (EVs). The resulting supernatant was subjected to ultracentrifugation at 100,000 g for 70 min. The pelleted vesicles were re-suspended in PBS and underwent another round of ultracentrifugation at 100,000 g for 70 min for thorough washing, followed by re-suspension in PBS. sEVs isolated from the conditioned medium were stored at 4 °C and used for treatment within 24 h. The dosage of sEVs was determined by protein quantification using the Lowry method.
The isolated sEVs were fixed in a 2% paraformaldehyde (PFA) solution in 0.1 M PBS for 30 min. A glow discharge technique (30 s, 7.2 V) using a Bal-Tec MED 020 Coating System was applied to carbon-coated copper grids. Subsequently, these treated grids were placed over the sample drops for 15 min. The grids with attached sEVs were then immersed in a 0.1 M PBS solution for washing, followed by an additional fixation step in 1% glutaraldehyde for 5 min. After thorough washing in distilled water, the grids were contrasted with 1% uranyl acetate and embedded in methylcellulose. Excess fluid was carefully removed, and the samples were allowed to dry before examination using a transmission electron microscope, FEI Tecnai G2 Spirit (Thermo Fisher Scientific, OR, USA). Image acquisition was performed using a digital camera, Morada (EMSIS GmbH, Münster, Germany). For immunogold labeling, 8 µl of isolated sEVs were fixed in 2% PFA in 0.1 M PBS for 30 min. Carbon-coated nickel grids were then positioned over these sEV drops for 15 min. After washing in 0.1 M PBS, the grids were blocked in a solution of 0.1 M glycine and 0.3% BSA for 10 min. The grids were subsequently incubated with the primary antibody anti-CD63 (MBL, D263-3) at a dilution 1:100 for 1 h. Following another blocking step for 10 min, the grids were exposed to Gold 6 nm–conjugated goat anti-rat secondary antibody (Abcam, ab105300) at a dilution of 1:1000 for 1 h. Lastly, a standard negative staining procedure was applied after thorough washing, and the samples were observed under a transmission electron microscope, as previously described.
For in vivo tracking of sEVs, we obtained sEVs from the conditioned medium of young ADSCs. Subsequently, these sEVs were labeled with 5 µM PKH26 (Sigma-Aldrich, MINI26-1KT) following the first ultracentrifugation. Following the labeling, the sEVs were resuspended in PBS and subjected to an additional round of washing through ultracentrifugation. A total of 20 µg of PKH26-labeled sEVs in 100 µL of PBS were injected into aged mice (20 months), and control mice were injected with 100 µL of a PBS solution containing 5 µM PKH26. The mice were euthanized 24 h later, and heart tissue was harvested, fixed in 4% PFA for 24 h, and cryoprotected in 30% sucrose for another 24 h. Ten-micrometer cryostat slices were mounted on slides. Sections were blocked with 10% normal goat serum (Invitrogen) in PBS containing 0.05% Tween 20 (PBS-t) and incubated overnight at 4 °C with a primary antibody against Desmin (Cell Signaling, 5332; 1:500 dilution) and with Alexa Fluor 488 anti-rabbit (Abcam, ab150077; 1:2000 dilution) for 2 h at room temperature as the secondary antibody. Sections were counterstained with DAPI (Invitrogen) at a 1:1000 dilution for 30 min at room temperature and then mounted on coverslips using an aqueous mounting medium sealed with nail polish. Imaging was performed using an Olympus FV1000 confocal laser scanning biological microscope. Images were processed in ImageJ while maintaining equal ratios.
Anesthesia was induced in a chamber with 2–3% isoflurane delivered in a mixture of oxygen (1–2 L/min). Once anesthetized, mice were transferred to a nose cone for continuous isoflurane inhalation at a 1–2% maintenance concentration. Anesthesia depth was monitored throughout the procedure by assessing the respiratory rate and pedal withdrawal reflex. Echocardiography was performed using an imaging system (GE Versana Active Veterinary Ultrasound Scanner) equipped with a 12-MHz transducer for small animals. Mice were positioned in a supine position with external heating to maintain body temperature. The chest area was shaved, and ultrasound gel was applied for optimal acoustic coupling. Two-dimensional (2D) M-mode and pulsed Doppler images were acquired in various views, including parasternal long-axis, short-axis, and apical views. For data analysis, images were analyzed using the same ultrasound scanner for acquisition. Standard echocardiographic parameters shown in the results section were measured for fractional shortening (FS) calculation, M-mode images were used to measure left ventricle end-diastolic diameter (LVEDD), and end-systolic diameter (LVESD), FS = LVEDD– LVESD / LVEDD x 100.
The animals underwent a progressive intensity treadmill test (Treadmill Control LE 8710 Panlab, Harvard Apparatus) to assess their endurance, measured by maximum time running. Following a warm-up period, the treadmill belt velocity was incrementally elevated until the animals reached exhaustion, indicating their inability to continue running. The test commenced with an initial 4-minute session at 10 cm/s, followed by successive increments of 4 cm/s every 2 min. Exhaustion was defined as the point at which a mouse remained on the shock grid for 5 s instead of actively running.
Heart tissue was freshly frozen in liquid nitrogen and 10-micron slices were obtained using a cryostat for histological staining and immunofluorescence. Hematoxylin and eosin staining (Sigma-Aldrich, MHS32, and E4009, respectively) or Sirius red staining (Sigma-Aldrich, 365548) was performed on the sections, followed by mounting and sealing for morphometric analysis. Images were captured using an optical microscope (Leica), and three images from different areas of each slice were obtained. Morphometric analysis of heart sections was conducted using ImageJ.
Ten-micrometer tissue slices were mounted on slides and fixed with ice-cold acetone for 20 min. For permeabilization, slices were incubated in 1% Triton X-100 in PBS for 10 min. Sections were blocked with 10% normal goat serum (Invitrogen) in PBS with 0.05% Tween 20 (PBS-t) and incubated with primary antibodies overnight at 4 °C in the same buffer. After primary antibody incubation, sections underwent three 10-minute PBS-t washes and were then incubated with secondary antibodies for 2 h at room temperature. Subsequent washes were performed, and tissues were counterstained with DAPI (Invitrogen; 1:1000 dilution) for 30 min at room temperature. Coverslips were mounted with an aqueous mounting medium and sealed with nail polish. Images were acquired using an Olympus FV1000 confocal laser scanning biological microscope. Image processing was carried out using CellProfiler with custom pipelines for automatic cell counting and analysis of CD3, LMNB1, and γH2AX positive cells. CD31 area was calculated using ImageJ, with uniform adjustments to levels. Three images from different areas of each slice were obtained. The following antibodies and concentrations were used: CD31 staining: Alexa Fluor 647 anti-mouse CD31 antibody (Biolegend, 102515, 1:50 dilution). LMNB1 staining: anti-LMNB1 (Proteintech, 12987-1-AP; 1:50 dilution) and Alexa Fluor 488 anti-rabbit (Abcam, ab150077; 1:2000 dilution); a minimum of 150 nuclei were analyzed per sample. γH2AX staining: anti-γH2AX (Cell Signaling Technology, 9718 S; 1:1000 dilution) and Alexa Fluor 488 anti-rabbit (Abcam, ab150077; 1:2000 dilution); a minimum of 150 were analyzed per sample. CD3 staining: anti-CD3 (Proteintech, 17617-1-AP; 1:1000 dilution) and Alexa Fluor 647 anti-rabbit (Abcam, ab150079, 1:2000 dilution).
Heart tissue was lysed using a KPi-EDTA buffer [50 mM KPi and 1 mM EDTA (pH 7.4)], and the levels of lipid peroxidation were assessed by quantifying malondialdehyde (MDA) using high-performance liquid chromatography (HPLC). MDA was determined as an MDA–thiobarbituric acid (TBA) adduct following a previously established method. This approach relies on the hydrolysis of lipoperoxides and the subsequent formation of a TBA-MDA2 adduct, which was detected using reverse-phase HPLC and quantified at 532 nm. The chromatographic technique was carried out under isocratic conditions, with the mobile phase comprising a mixture of monopotassium phosphate at 50 mM (pH 6.8) and acetonitrile (70:30). The MDA levels in each sample were normalized to the protein concentration determined by the Lowry method.
Total protein carbonylation was detected by immunoblotting using the OxyBlot Protein Oxidation Detection kit (Merck) following the manufacturer’s instructions. In brief, tissues were lysed in tris/SDS/glycerol buffer, and protein concentration was determined using the Lowry method. Subsequently, 20 µg of proteins were separated on SDS polyacrylamide gels and transferred onto nitrocellulose membranes. The membranes were blocked with 3% BSA in PBS-t for 60 min at room temperature and then incubated overnight at 4 °C with the primary antibody from the kit. After three washes (10 min each) with PBS-t, the membranes were incubated with the secondary antibody for 120 min at room temperature. Following three additional washes with PBS-t, the membranes were developed with Luminol (Sigma-Aldrich) using the ImageQuant LAS4000 system. Image analysis was performed in ImageJ, and Ponceau staining of the membranes served as the loading control.
We utilized two commercially available enzyme-linked immunosorbent assay (ELISA) kits for the quantitative analysis of IL-6 (Abcam, ab100713) and IL-8 (Abcam, ab234567), adhering to the manufacturer’s provided instructions. In brief, tissues underwent lysis using specific buffers included in each kit, protein concentration was determined using the Lowry method, and samples were appropriately diluted at ratios of 1:5. Absorbance at 450 nm was quantified using the Molecular Devices SPECTRAmax Plus 384. All samples were subjected to duplicate assays.
Metabolomics was performed as previously described, with minor adjustments . A 75 µL mixture of the following internal standards in water was added to approximately 3 mg of freeze-dried heart tissue: adenosine- 15 N 5 -monophosphate (100 µM), adenosine- 15 N 5 -triphosphate (1 mM), D 4 -alanine (100 µM), D 7 -arginine (100 µM), D 3 -aspartic acid (100 µM), D 3 -carnitine (100 µM), D 4 -citric acid (100 µM), 13 C 1 -citrulline (100 µM), 13 C 6 -fructose-1,6-diphosphate (100 µM), guanosine-15N 5 -monophosphate (100 µM), guanosine- 15 N 5 -triphosphate (1 mM), 13 C 6 -glucose (1 mM), 13 C 6 -glucose-6-phosphate (100 µM), D 3 -glutamic acid (100 µM), D 5 -glutamine (100 µM), 13 C 6 -isoleucine (100 µM), D 3 -leucine (100 µM), D 4 -lysine (100 µM), D 3 -methionine (100 µM), D 6 -ornithine (100 µM), D 5 -phenylalanine (100 µM), D 7 -proline (100 µM), 13 C 3 -pyruvate (100 µM), D 3 -serine (100 µM), D 5 -tryptophan (100 µM), D 4 -tyrosine (100 µM), D 8 -valine (100 µM). Subsequently, 425 µL water, 500 µL methanol, and 1 mL chloroform were added to the same 2 mL tube before thorough mixing and centrifugation for 10 min at 14.000 rpm. The top layer, containing the polar phase, was transferred to a new 1.5 mL tube and dried using a vacuum concentrator at 60 °C. Dried samples were reconstituted in 100 µL methanol/water (6/4; v/v). Metabolites were analyzed using a Waters Acquity ultra-high-performance liquid chromatography system coupled to a Bruker Impact II™ Ultra-High Resolution Qq-Time-Of-Flight mass spectrometer. Samples were kept at 12 °C during analysis and 5 µL of each sample was injected. Chromatographic separation was achieved using a Merck Millipore SeQuant ZIC-cHILIC column (PEEK 100 × 2.1 mm, 3 μm particle size). Column temperature was held at 30 °C. The mobile phase consisted of (A) 1:9 acetonitrile: water and (B) 9:1 acetonitrile: water, both containing 5 mM ammonium acetate. Using a flow rate of 0.25 mL/min, the LC gradient consisted of: Dwell at 100% Solvent B, 0–2 min; Ramp to 54% Solvent B at 13.5 min; Ramp to 0% Solvent B at 13.51 min; Dwell at 0% Solvent B, 13.51–19 min; Ramp to 100% B at 19.01 min; Dwell at 100% Solvent B, 19.01–19.5 min. Equilibrate the column using a 0.4 mL/min flow at 100% B from 19.5 to 21 min. MS data were acquired using negative and positive ionization in full scan mode over the range of m/z 50-1200. Data were analysed using Bruker TASQ software version 2021b (2021.1.2.452). All reported metabolite intensities were normalized to dry tissue weight, as well as to internal standards with comparable retention times and response in the MS. Metabolite identification was based on a combination of accurate mass, (relative) retention times, and fragmentation spectra, compared with the analysis of a library of standards. Statistical analysis and visualization of the acquired data were done in an R environment using the ggplot2, ropls, and mixOmics packages .
Ratios depicting the comparison of physical test values after treatment against baseline were calculated and presented as percentages over baseline, with the baseline defined as 0%. Outliers were assessed in all groups using the ROUT method (Q = 2%), with no exclusion of any data point. The normality of each group was assessed using the Shapiro-Wilk test. For pairwise comparisons, either the Unpaired Student’s t-test or the Mann-Whitney test was employed, depending on the data distribution. In the case of multiple comparisons, ANOVA was applied, with Tukey’s multiple comparisons serving as a post hoc test. Alternatively, for nonparametric data, the Kruskal-Wallis test was utilized, with Dunn’s multiple comparisons as the post hoc test. Each data point presented in the manuscript represents a biological replicate. GraphPad Prism 9.0 software was utilized for both analysis and graphical design if not otherwise stated.
Intravenously delivered sEVs from young ADSCs effectively reach the heart of old mice Firstly, we conducted a characterization of the vesicles isolated from the cell culture supernatants of young ADSCs (Fig. A-B), according to the minimal information for studies of extracellular vesicles (MISEV) recommendations for the characterization and functional studies of sEVs . We utilized transmission electron microscopy (TEM) to evaluate the size and morphology of the isolated vesicles, revealing round-shaped vesicles ranging from 50 to 200 nm in diameter (Fig. C). Additionally, the presence of CD63, a classical marker of sEVs, in the vesicle membrane was corroborated through immunogold labeling (Fig. C). sEVs are delivered to several tissues after intravenous injection, with a preference for the liver . To demonstrate the delivery and uptake of sEVs in the heart tissue, we labeled the sEVs with a lipophilic dye (PKH26) and administered them into the bloodstream of old mice through tail vein injection. Subsequent histological study with fluorescence microscopy of the heart tissue revealed the dye uptake in this organ, colocalizing with cardiomyocyte fibers (Fig. D). ADSC-sEVs improve functional parameters associated with aging in the heart of old mice We then assessed the impact of ADSC-sEVs on functional parameters associated with cardiac aging in old mice. To this end, we performed transthoracic echocardiography with a transducer designed for small animals in young (3–6 months) and old C57BL/6J mice (20–24 months), to obtain which parameters are altered with age in this mouse strain. To better characterize the effect of sEVs on echocardiographic parameters in old mice, we performed echocardiography before treatment (Day 0) and 30 days after treatment (Day 30) with PBS or sEVs (Fig. A). Heart rate exhibited no significant changes from young to old mice, and this pattern persisted in old mice following sEVs treatment (Fig. B-C). Fractional shortening remained unaltered across age groups and treatment status, indicating little changes in systolic function during mouse aging, in line with previous studies (Fig. D-E). Peak aortic velocity tended to increase in old mice, probably suggesting a propensity to develop aortic valve calcification in these mice, as previously noted . sEVs treatment didn’t significantly reduce this trend (Fig. F-G). The most prominent functional changes associated with aging in the heart, both in mice and humans, are the increased left ventricle (LV) mass and reduced diastolic function . Indeed, we found that LV posterior wall thickness exhibited an age-related increase in old mice compared to the young group; however, treatment with sEVs reversed this trend, reducing wall thickness (Fig. H-I). Regarding the pulsed Doppler of the LV filling, peak E wave velocity increased with age in mice; conversely, peak A wave velocity decreased with age, leading to an increased E/A relationship (> 2) in the old mice. This indicates an altered diastolic function with a restrictive pattern, where LV filling depends mainly on the proto-diastolic wave. Treatment with sEVs in old mice partially reversed this pattern, with a significant reduction of the peak E wave velocity and a non-significant trend showing reduced E/A ratios (Fig. J-O). To assess overall physical endurance and potential indirect effects on cardiac function, we conducted a treadmill test 30 days after treatment (Day 30) with PBS or sEVs. Although treadmill performance isn’t specific to cardiac function, it offers a comprehensive evaluation of multiple systems, including the cardiovascular, respiratory, and musculoskeletal systems. Young mice showed higher performance levels in the treadmill test than old mice (Fig. P). When splitting by sex, female mice treated with sEVs showed an increased endurance in this test compared to old female control mice (Fig. Q), which was not significant in male mice (Fig. R). This indicates a sex-dependent effect of the treatment on this parameter, which we did not observe in other measurements, a finding commonly seen in various anti-aging interventions . Collectively, these findings show the potential of ADSC-sEVs in ameliorating age-associated alterations in cardiac functional parameters. ADSCs-sEVs alleviate age-related histological alterations of the mouse heart To further correlate the effect of sEVs on functional parameters, we explored structural alterations in heart tissue at the histological level. Some of these changes include an increase in LV mass, myocardial fibrosis, and altered vascularization, all of which contribute to functional decline, especially the diastolic dysfunction observed during aging [ , , ]. First, we measured the heart weight normalized by the mice’s total body weight (TBW) as an indicator of total heart mass. Old mice treated with sEVs showed a lower heart weight ratio when compared to controls (Fig. A), indicating a lower heart mass; the comparison between young and old mice was non-significant, probably because TBW is much lower in young mice. Histological analysis with Sirius red staining demonstrated an increase of fibrotic tissue in the hearts of old mice compared to young ones. Treatment with ADSCs-sEVs significantly decreased fibrotic tissue (Fig. B and D), suggesting a protective effect against aging-associated cardiac remodeling and fibrosis. Furthermore, assessment of vascularization through CD31 immunostaining unveiled a significant reduction of the CD31 + area in the aged heart. At the same time, we observed an increase in the CD31 + area in heart slices from mice treated with ADSC-sEVs (Fig. C and E), indicating a pro-angiogenic effect of sEVs. This EV-dependent enhancement in vascularization has also been observed previously in several tissue damage models . These results show the role of ADSC-sEVs in counteracting age-related cardiac structural changes in aged mice. Cellular and molecular markers of aging are partially reversed by young ADSCs-sEVs in the heart of old mice Some of the molecular and cellular markers of aging are common to the different species and tissues, such as oxidative damage, pro-inflammatory factors accumulation, and cellular senescence . Here, we investigated the impact of young ADSCs-sEVs on these markers in heart tissue. Oxidative damage is commonly viewed as a contributor to the aging process, and the heart is particularly sensitive to oxidative damage . We measured MDA as a marker of lipid peroxidation and protein carbonylation as a marker of protein oxidation , heart tissue of old mice exhibited higher levels of lipid peroxidation and protein carbonylation when compared to their young counterparts, indicative of increased oxidative damage in the aged heart (Fig. A-B). Remarkably, treatment with sEVs reduced these markers, suggesting a mitigating effect on oxidative stress-induced damage (Fig. A-B). Regarding the pro-inflammatory landscape that usually accompanies aging , we measured the levels of two factors that are usually increased in aged tissues, interleukin-6 (IL-6) and interleukin-8 (IL-8), pro-inflammatory cytokines that are also tightly associated with the senescence-associated secretory phenotype (SASP) and have been proposed as mediators of age-related changes in cardiac tissue, including hypertrophy . Both factors displayed an age-related increase in the heart, notably attenuated following sEVs treatment (Fig. C-D). As another tissue inflammatory, we measured the infiltration of T cells (CD3+) outside of the blood vessels in the heart tissue using immunofluorescence, which was almost non-existent in the young tissues and followed a similar increasing age-related pattern. Again, treatment with sEVs partially reversed this trend (Fig. E-F). We then measured two closely related markers of cellular senescence and DNA damage. We employed Lamin B1 (LMNB1) immunofluorescence to quantify cellular senescence; the loss of LMNB1 is widely used in tissues to measure the levels of senescent cells . We found a reduced proportion of LMNB1 + cells in the heart with aging, while the treatment with ADSCs-sEVs showed a non-significant increase in this marker (Fig. G-H). The γH2AX marker of DNA damage exhibited an age-related increase in the heart, along with the results regarding oxidative damage to proteins and lipids; the treatment with ADSCs-sEVs reduced the levels of this marker (Fig. I-J). These findings suggest that treatment with young ADSCs-sEVs ameliorates cellular and molecular markers associated with aging in the heart, reducing oxidative and DNA damage and counteracting the age-related pro-inflammatory environment. Treatment with ADSC-sEVs switches the heart metabolome of old mice to a youthful state Although a significant body of evidence shows that impaired metabolism in the heart accompanies the aging process and contributes to different age-associated alterations [ – ], little is known about specific changes in different metabolites with age and with targeted aging interventions. We conducted a comprehensive metabolomic analysis of the heart to substantiate further our findings on the beneficial effects of ADSC-sEVs on aged heart tissue. This allowed us to explore the alterations in the metabolic landscape of the heart that accompany aging and investigate how ADSC-sEV treatment influences these changes. Firstly, we utilized unsupervised UMAP representation on the whole metabolite set and observed a remarkable similarity between young and old ADSC-sEVs treated mice compared to the old control mice (Fig. A, Supplementary data 1). When comparing young and old hearts, we discovered that old mice tend to accumulate a high number of metabolites implicated in several metabolic pathways (62 upregulated and 14 downregulated in Old-PBS mice), such as Acetyl-CoA and related metabolites, GMP, AMP, CMP, or UMP, and short-chain acylcarnitines. On the contrary, young hearts showed an increased concentration of anserine, carnosine, and long-chain acylcarnitines (Fig. B). This pattern may indicate a dysregulation of mitochondrial metabolism and fatty acid oxidation, a common finding during aging . Anserine is a natural derivative of carnosine, and both have been implied in cardiac health and aging, as they are essential scavengers of lipid peroxidation products [ – ]. The increased acetyl-CoA levels in old mice could indicate a lower utilization or an increased production; notably, lower cytosolic levels of acetyl-CoA have been linked to increased autophagy and the beneficial effects of caloric restriction during aging . When comparing the heart metabolome of young and old mice treated with ADSC-sEVs, we observed a similar but attenuated pattern, with a tendency to accumulate several metabolites in the old-treated mice but less pronounced (30 upregulated and 8 downregulated in Old-sEVs mice), once again, young mice had higher levels of carnosine, anserine and long-chain acylcarnitines (Fig. C). Interestingly, when comparing Old-PBS and Old-sEVs mice, we found an analogous pattern, where Old-sEVs treated mice showed an increased concentration of long-chain acylcarnitines and a downregulation of several metabolites such as acetyl-CoA related (41 downregulated and 4 upregulated in Old-sEVs mice) (Fig. D). To better visualize the changes associated with the treatment in old hearts, we performed a multiple comparison analysis and represented a heatmap of the statistically significant metabolites between the three groups (Fig. E). Although it should be the task of future research, old hearts show an increased concentration of short-chain and a reduced concentration of long-chain acylcarnitines, which may indicate that aging influences the heart’s preference or ability to utilize different energy substrates. An increase in short-chain acylcarnitines and a decrease in long-chain acylcarnitines might suggest a shift towards using medium or short-chain fatty acids for energy production.
Firstly, we conducted a characterization of the vesicles isolated from the cell culture supernatants of young ADSCs (Fig. A-B), according to the minimal information for studies of extracellular vesicles (MISEV) recommendations for the characterization and functional studies of sEVs . We utilized transmission electron microscopy (TEM) to evaluate the size and morphology of the isolated vesicles, revealing round-shaped vesicles ranging from 50 to 200 nm in diameter (Fig. C). Additionally, the presence of CD63, a classical marker of sEVs, in the vesicle membrane was corroborated through immunogold labeling (Fig. C). sEVs are delivered to several tissues after intravenous injection, with a preference for the liver . To demonstrate the delivery and uptake of sEVs in the heart tissue, we labeled the sEVs with a lipophilic dye (PKH26) and administered them into the bloodstream of old mice through tail vein injection. Subsequent histological study with fluorescence microscopy of the heart tissue revealed the dye uptake in this organ, colocalizing with cardiomyocyte fibers (Fig. D).
We then assessed the impact of ADSC-sEVs on functional parameters associated with cardiac aging in old mice. To this end, we performed transthoracic echocardiography with a transducer designed for small animals in young (3–6 months) and old C57BL/6J mice (20–24 months), to obtain which parameters are altered with age in this mouse strain. To better characterize the effect of sEVs on echocardiographic parameters in old mice, we performed echocardiography before treatment (Day 0) and 30 days after treatment (Day 30) with PBS or sEVs (Fig. A). Heart rate exhibited no significant changes from young to old mice, and this pattern persisted in old mice following sEVs treatment (Fig. B-C). Fractional shortening remained unaltered across age groups and treatment status, indicating little changes in systolic function during mouse aging, in line with previous studies (Fig. D-E). Peak aortic velocity tended to increase in old mice, probably suggesting a propensity to develop aortic valve calcification in these mice, as previously noted . sEVs treatment didn’t significantly reduce this trend (Fig. F-G). The most prominent functional changes associated with aging in the heart, both in mice and humans, are the increased left ventricle (LV) mass and reduced diastolic function . Indeed, we found that LV posterior wall thickness exhibited an age-related increase in old mice compared to the young group; however, treatment with sEVs reversed this trend, reducing wall thickness (Fig. H-I). Regarding the pulsed Doppler of the LV filling, peak E wave velocity increased with age in mice; conversely, peak A wave velocity decreased with age, leading to an increased E/A relationship (> 2) in the old mice. This indicates an altered diastolic function with a restrictive pattern, where LV filling depends mainly on the proto-diastolic wave. Treatment with sEVs in old mice partially reversed this pattern, with a significant reduction of the peak E wave velocity and a non-significant trend showing reduced E/A ratios (Fig. J-O). To assess overall physical endurance and potential indirect effects on cardiac function, we conducted a treadmill test 30 days after treatment (Day 30) with PBS or sEVs. Although treadmill performance isn’t specific to cardiac function, it offers a comprehensive evaluation of multiple systems, including the cardiovascular, respiratory, and musculoskeletal systems. Young mice showed higher performance levels in the treadmill test than old mice (Fig. P). When splitting by sex, female mice treated with sEVs showed an increased endurance in this test compared to old female control mice (Fig. Q), which was not significant in male mice (Fig. R). This indicates a sex-dependent effect of the treatment on this parameter, which we did not observe in other measurements, a finding commonly seen in various anti-aging interventions . Collectively, these findings show the potential of ADSC-sEVs in ameliorating age-associated alterations in cardiac functional parameters.
To further correlate the effect of sEVs on functional parameters, we explored structural alterations in heart tissue at the histological level. Some of these changes include an increase in LV mass, myocardial fibrosis, and altered vascularization, all of which contribute to functional decline, especially the diastolic dysfunction observed during aging [ , , ]. First, we measured the heart weight normalized by the mice’s total body weight (TBW) as an indicator of total heart mass. Old mice treated with sEVs showed a lower heart weight ratio when compared to controls (Fig. A), indicating a lower heart mass; the comparison between young and old mice was non-significant, probably because TBW is much lower in young mice. Histological analysis with Sirius red staining demonstrated an increase of fibrotic tissue in the hearts of old mice compared to young ones. Treatment with ADSCs-sEVs significantly decreased fibrotic tissue (Fig. B and D), suggesting a protective effect against aging-associated cardiac remodeling and fibrosis. Furthermore, assessment of vascularization through CD31 immunostaining unveiled a significant reduction of the CD31 + area in the aged heart. At the same time, we observed an increase in the CD31 + area in heart slices from mice treated with ADSC-sEVs (Fig. C and E), indicating a pro-angiogenic effect of sEVs. This EV-dependent enhancement in vascularization has also been observed previously in several tissue damage models . These results show the role of ADSC-sEVs in counteracting age-related cardiac structural changes in aged mice.
Some of the molecular and cellular markers of aging are common to the different species and tissues, such as oxidative damage, pro-inflammatory factors accumulation, and cellular senescence . Here, we investigated the impact of young ADSCs-sEVs on these markers in heart tissue. Oxidative damage is commonly viewed as a contributor to the aging process, and the heart is particularly sensitive to oxidative damage . We measured MDA as a marker of lipid peroxidation and protein carbonylation as a marker of protein oxidation , heart tissue of old mice exhibited higher levels of lipid peroxidation and protein carbonylation when compared to their young counterparts, indicative of increased oxidative damage in the aged heart (Fig. A-B). Remarkably, treatment with sEVs reduced these markers, suggesting a mitigating effect on oxidative stress-induced damage (Fig. A-B). Regarding the pro-inflammatory landscape that usually accompanies aging , we measured the levels of two factors that are usually increased in aged tissues, interleukin-6 (IL-6) and interleukin-8 (IL-8), pro-inflammatory cytokines that are also tightly associated with the senescence-associated secretory phenotype (SASP) and have been proposed as mediators of age-related changes in cardiac tissue, including hypertrophy . Both factors displayed an age-related increase in the heart, notably attenuated following sEVs treatment (Fig. C-D). As another tissue inflammatory, we measured the infiltration of T cells (CD3+) outside of the blood vessels in the heart tissue using immunofluorescence, which was almost non-existent in the young tissues and followed a similar increasing age-related pattern. Again, treatment with sEVs partially reversed this trend (Fig. E-F). We then measured two closely related markers of cellular senescence and DNA damage. We employed Lamin B1 (LMNB1) immunofluorescence to quantify cellular senescence; the loss of LMNB1 is widely used in tissues to measure the levels of senescent cells . We found a reduced proportion of LMNB1 + cells in the heart with aging, while the treatment with ADSCs-sEVs showed a non-significant increase in this marker (Fig. G-H). The γH2AX marker of DNA damage exhibited an age-related increase in the heart, along with the results regarding oxidative damage to proteins and lipids; the treatment with ADSCs-sEVs reduced the levels of this marker (Fig. I-J). These findings suggest that treatment with young ADSCs-sEVs ameliorates cellular and molecular markers associated with aging in the heart, reducing oxidative and DNA damage and counteracting the age-related pro-inflammatory environment.
Although a significant body of evidence shows that impaired metabolism in the heart accompanies the aging process and contributes to different age-associated alterations [ – ], little is known about specific changes in different metabolites with age and with targeted aging interventions. We conducted a comprehensive metabolomic analysis of the heart to substantiate further our findings on the beneficial effects of ADSC-sEVs on aged heart tissue. This allowed us to explore the alterations in the metabolic landscape of the heart that accompany aging and investigate how ADSC-sEV treatment influences these changes. Firstly, we utilized unsupervised UMAP representation on the whole metabolite set and observed a remarkable similarity between young and old ADSC-sEVs treated mice compared to the old control mice (Fig. A, Supplementary data 1). When comparing young and old hearts, we discovered that old mice tend to accumulate a high number of metabolites implicated in several metabolic pathways (62 upregulated and 14 downregulated in Old-PBS mice), such as Acetyl-CoA and related metabolites, GMP, AMP, CMP, or UMP, and short-chain acylcarnitines. On the contrary, young hearts showed an increased concentration of anserine, carnosine, and long-chain acylcarnitines (Fig. B). This pattern may indicate a dysregulation of mitochondrial metabolism and fatty acid oxidation, a common finding during aging . Anserine is a natural derivative of carnosine, and both have been implied in cardiac health and aging, as they are essential scavengers of lipid peroxidation products [ – ]. The increased acetyl-CoA levels in old mice could indicate a lower utilization or an increased production; notably, lower cytosolic levels of acetyl-CoA have been linked to increased autophagy and the beneficial effects of caloric restriction during aging . When comparing the heart metabolome of young and old mice treated with ADSC-sEVs, we observed a similar but attenuated pattern, with a tendency to accumulate several metabolites in the old-treated mice but less pronounced (30 upregulated and 8 downregulated in Old-sEVs mice), once again, young mice had higher levels of carnosine, anserine and long-chain acylcarnitines (Fig. C). Interestingly, when comparing Old-PBS and Old-sEVs mice, we found an analogous pattern, where Old-sEVs treated mice showed an increased concentration of long-chain acylcarnitines and a downregulation of several metabolites such as acetyl-CoA related (41 downregulated and 4 upregulated in Old-sEVs mice) (Fig. D). To better visualize the changes associated with the treatment in old hearts, we performed a multiple comparison analysis and represented a heatmap of the statistically significant metabolites between the three groups (Fig. E). Although it should be the task of future research, old hearts show an increased concentration of short-chain and a reduced concentration of long-chain acylcarnitines, which may indicate that aging influences the heart’s preference or ability to utilize different energy substrates. An increase in short-chain acylcarnitines and a decrease in long-chain acylcarnitines might suggest a shift towards using medium or short-chain fatty acids for energy production.
The aging process is intricately linked to the decline in tissue function, and the cardiovascular system is one of the most affected. Cardiovascular diseases are the main contributors to late-life mortality in the developed world; therefore, addressing the degenerative changes associated with aging in this system is of great importance. Age-associated alterations of the heart tissues, including fibrosis, valvular degeneration, or cardiomyocyte hypertrophy, are thought to be tightly related to molecular and cellular aspects of aging, such as inflammation, oxidative damage, or cellular senescence . The field of EV research has grown exponentially in the last two decades due to its ability to transport various molecules and factors between cells, making them essential players in intercellular communication. In the area of aging, EVs have shown promise as anti-aging agents. We and other research groups have demonstrated some beneficial effects of EVs from different cell types in specific tissues, such as the kidneys, skeletal muscle, lungs, and the heart. Here, we present a comprehensive study on the effects of ADSC-derived sEVs on the functional, structural, molecular, and cellular parameters that change with aging in the hearts of physiologically aged mice For the functional assessment of heart function, we used echocardiography, as some established changes occur during aging in these animals, which are also common to humans, mainly an increased LV mass and reduced diastolic function, while systolic function is usually not altered . ADSCs-sEVs treatment partially reversed the age-associated increase in LV wall thickness and the altered diastolic function reflected in the E/A wave ratio, in line with previous studies . Additionally, the treadmill physical endurance test revealed beneficial sex-dependent effects of sEVs treatment, emphasizing the need for considering gender-specific responses in anti-aging interventions. Histological analyses unveiled the impact of ADSC-sEVs on structural changes in the aged heart. Reductions in heart weight related to body weight, fibrotic tissue content, and the enhancement of vascularization collectively suggest a potential protective role of ADSC-sEVs against age-related cardiac remodeling, fibrosis, and impaired vascularization. These results support a growing body of evidence showing the potential of EVs in the modulation of fibrosis and angiogenesis . We also provide insights into the cellular and molecular markers associated with aging in the heart. Notably, treatment with ADSC-sEVs was able to reverse the pattern of increased oxidative and DNA damage, inflammation, and cellular senescence present during aging in the mouse heart. All these processes are firmly linked, as different types of damage to the cellular integrity predispose to the acquisition of the senescent phenotype. Interestingly, cellular senescence has traditionally been viewed as a property of proliferating cells; however, the heart, as a tissue whose primary cells are post-mitotic, also shows characteristics of cellular senescence, a trait termed amitosenescence . The senescent phenotype also contributes to changes in the extracellular environment that lead to the accumulation of pro-inflammatory factors during aging . This SASP, in turn, leads to increased cellular damage and paracrine senescence, a feedback loop thought to contribute substantially to tissue dysfunction during aging and a target of many anti-aging interventions [ – ]. EVs, in part due to their anti-inflammatory properties , are believed to counteract this loop in a senomorphic manner, shifting the extracellular environment to a non-senescent phenotype and thus reducing paracrine senescence . Finally, we demonstrated specific changes in a subset of metabolites that are altered in the aged heart and the influence of ADSC-sEVs on them through metabolomic profiling of heart tissue. These changes include a tendency to accumulate acyl-CoA-related metabolites, possibly because of an inability to use different substrates. Interestingly, we showed that old hearts are prone to accumulate short-chain acylcarnitines and have lower levels of long-chain acylcarnitines. In previous research, acylcarnitine levels in plasma have been associated with cardiovascular events in humans ; together with our results, this may indicate a change in fatty acid metabolism in the aged heart. We have also demonstrated that ADSC-sEVs can induce a metabolic shift in the hearts of aged mice, resulting in a metabolomic profile that closely resembles that of younger mice. miRNAs are suggested to play a role mediating the therapeutic effects of EVs, among these, the let-7 family has been extensively studied for their regulatory functions in metabolism as it targets the PI3K/AKT/insulin pathway and it has been shown that this family regulates fatty acid metabolism in cultured cardiomyocytes . Notably, let-7c shows reduced circulating levels in patients with coronary artery disease and the let-7 family is enriched in stem cell-derived EVs , suggesting a potential mechanism through which ADSC-sEVs exert their beneficial effects. While these findings are compelling, the specific molecular components within ADSC-sEVs responsible for these changes remain unclear. It is possible that a combination of bioactive molecules, such as miRNAs, lipids, and proteins, contributes to this effect. Further studies are needed to identify these key molecules and to determine whether the observed metabolic changes are a cause or consequence of the broader rejuvenation effects on aging cellular and molecular markers. Notably, while the observed effects in the heart are significant, we have only analyzed the effects one month after treatment, the transient nature of sEVs impact remains unclear, highlighting the need for follow-up studies to assess long-term benefits and determine whether repeated or sustained interventions are necessary. It is also essential to acknowledge that sEVs were delivered systemically, and their effects are not necessarily confined to cardiac tissue. ADSC-sEVs likely exert a systemic rejuvenation effect by targeting multiple tissues and cellular pathways, collectively contributing to the observed changes. Future investigations should explore the interactions between different tissues in mediating these effects and the underlying mechanisms and translational potential of ADSC-sEVs or other MSC-derived sEVs in combating age-related cardiovascular disease.
In this study, we demonstrate that systemically delivered small extracellular vesicles derived from young adipose-derived stem cells (ADSCs-sEVs) enhance functional parameters related to aging and HFpEF in aged mice. ADSCs-sEVs improve structural and molecular parameters linked to aging in the heart, including fibrosis, vascularization, cellular senescence, inflammation, and oxidative stress. Furthermore, ADSCs-sEVs treatment shifts the heart’s metabolome to a younger state. Our findings provide further evidence of the therapeutic potential of EVs in addressing aging and cardiovascular disease, serving as a strategy to mitigate age-related cardiac tissue changes.
Below is the link to the electronic supplementary material. Supplementary Material 1
|
Frequency and Reasons for Fixation Hardware Removal After Orthognathic Surgery in Patients Treated in One Center | f3365e75-763a-4720-9bc0-7a2139c6d792 | 11943589 | Surgery[mh] | Over the years, the methods and surgical techniques used to treat skeletal deformities have changed, but there is no doubt that three procedures have become firmly established in the canon of orthognathic surgery: sagittal osteotomy of the mandible (BSSO) using the Obwegeser technique with subsequent modifications, Le Fort I maxillary osteotomy, and chin plastic surgery or genioplasty . Just as the methods themselves evolved, although the fundamentals remained the same, so too did the techniques and methods of immobilizing bone fragments after osteotomy. External immobilization in the form of intermaxillary wiring was originally used, but various types of osteosynthesis are now the standard. Despite the introduction of osteosynthesis of bone fragments using plates and screws into orthognathic surgery in the 1970s, a consensus on the optimal approach to fixation hardware remains elusive. In practice, there is no shortage of support for their removal [ , , , , ] and for their retention after the primary operation [ , , ]. In the event of clinical symptoms associated with fixation hardware, the treatment of choice is their removal. For fixation hardware that does not cause symptoms, the decision usually depends on the patient, the surgical team, or the experience of the clinical unit. The Strasbourg Osteosynthesis Research Group (SORG) made the following recommendation in 1991: “A plate which is intended to assist the healing of bone becomes a non-functional implant once this role is completed. It may then be regarded as a foreign body. While there is no clear evidence to date that a plate causes actual harm, our knowledge remains incomplete. It is therefore not possible to state with certainty that an otherwise symptomless plate, left in situ, is harmless. The removal of a non-functioning plate is desirable provided that the procedure does not cause undue risk to the patient.” . At the time of writing, a number of osteosynthesis kits are available for use in orthognathic surgery. Manufacturers are continually developing new solutions that are more effective than those already on the market. Modifications are being made to the shape and stiffness of the plates, as well as the diameter of the screws. Preformed plates are also available for a specific type of treatment. There are both screws that require pre-drilling and self-drilling screws, which only need to be screwed in without prior drilling. The gold standard is fixation hardware made of a titanium alloy, although in recent years there have been many attempts to use resorbable materials that would not require another procedure for their removal. The advent of three-dimensional planning technology and the printing of operating templates has led to the emergence of patient-specific osteosynthesis plates tailored to individual anatomical characteristics and the specifics of the planned surgical procedure. Despite these advancements, there is still no universally accepted approach to the management of fixation hardware after bone healing. Some clinicians recommend routine removal due to potential complications such as infection, foreign body reactions, or long-term discomfort, while others emphasize the stability and biocompatibility of modern implants, arguing against unnecessary surgical interventions. In the absence of standardized guidelines, the decision regarding fixation hardware removal is often made on an individual basis, influenced by clinical experience, patient preferences, and institutional policies. Therefore, this study aims to analyze the frequency and underlying reasons for fixation hardware removal in a single-center cohort, providing insights that may contribute to the ongoing discussion about optimal post-surgical management strategies .
The present research study was conducted in accordance with the regulations of the Bioethics Committee University of Zielona Góra, which stipulate that scientific research involving the retrospective study of medical records does not require the opinion of the Bioethics Committee provided that the findings of such research do not influence the routine management of patients. Therefore, while we acknowledge the importance of ethical considerations in research, we followed the established guidelines of our institution for this particular type of study. This study complies with the World Medical Association Declaration of Helsinki on medical research protocols and ethics. Our retrospective study examined the medical records of patients treated surgically for skeletal deformities at the Department and Clinic of Otolaryngology and Maxillofacial Surgery of Collegium Medicum (formerly the Otolaryngology Department of the Provincial Hospital in Zielona Góra) from 2015 to 2020. From January 2015 to December 2020, 124 orthognathic procedures were performed, including 56 one-jaw operations (BSSO or Le Fort I maxillary osteotomy), 2 one-jaw operations with genioplasty, 55 bimaxillary operations (BSSO + Le Fort I maxillary osteotomy), 6 bimaxillary operations with genioplasty, and 5 isolated genioplasty procedures. The reason for treatment was a class III deformity in 80 cases, a class II deformity in 40 cases, an open bite in 3 cases, and a facial asymmetry in 1 case. The necessity to remove fixation hardware after orthognathic surgery performed by the same team was adopted as the basic criterion for qualifying patients for the study group. Only cases where the primary indication for hardware removal was explicitly documented in the medical records were considered. Exclusion criteria included patients who sought hardware removal for reasons unrelated to orthognathic surgery, cases with incomplete medical records lacking documentation on the reason for removal, and patients with systemic conditions that could affect wound healing. Additionally, patients who underwent orthognathic surgery outside of our institution but later presented for hardware removal were excluded from the study. The fixation hardware removal procedures were performed by two experienced maxillofacial surgeons, and the technique of their application was similar. After analyzing the medical records, it was found that between 2016 and 2022, the fixation hardware removal procedure was performed in 77 cases. Medical data were obtained from the operation protocols as well as the patients’ case histories. All fixation hardware used in orthognathic procedures was made of titanium ( ). In the case of sagittal mandibular ramus osteotomy, the typical way to fix the osteotomy fragments was to use one bicortical screw as well as a single plate and monocortical screws on both the right and left side. In a few selected cases, only three bicortical screws were used without a plate, the use of a bicortical screw was abandoned and the fragments were provided fixation using only one plate, and monocortical screws or more than one bicortical screw and a plate were used. Each time it was dictated by individual anatomical and mid-operative conditions. All fixation hardware in the mandible was made with screws that required drilling and were made through an intraoral approach. In Le Fort I maxillary osteotomy, the osteotomy fragments were typically fixed using 4 plates (most commonly in the shape of the letter “L”; for maxillary segmentation “Y” plates were used) and an appropriate number of monocortical screws. Screws that do not require drilling, i.e., self-drilling screws, were used. In selected situations dictated by anatomical conditions, customized solutions were used in the form of an additional plate, a non-standard shaped plate, or a reduced number of screws. For genioplasty, dedicated plates with a bridge of appropriate length or two plates bent by the operator according to the planned bone movement were used together with monocortical screws. In each case, regardless of the reason for fixation hardware removal, patients were informed of the technique, the risks associated with the procedure, and the recovery period and gave their formal written consent to the procedure. The surgical procedure was performed under general anesthesia with nasal–tracheal intubation or oral–tracheal intubation and local anesthesia (mepivacainum 0.5% 10 mL) and perioperative antibiotic prophylaxis (2 g cefazoline i.v.). Mid-operatively, patients received 8 mg dexamethasone, and in justified cases, due to prolonged surgery or concomitant infectious complications, antibiotic therapy was continued until 5 days after surgery (amoxicillinum + acidum clavulanicum 1 g post-surgery or cefuroxime 0.5 g post-surgery). Patients were discharged from the hospital on the first day after surgery. The data obtained from the records were entered into Numbers (Apple, Cupertino, CA, USA) to create a database and then subjected to statistical analysis using R version 4.4.2 (The R Foundation, Vienna, Austria). The analysis of quantitative variables (i.e., expressed in numbers) was carried out by calculating descriptive statistics such as mean, standard deviation, median, quartiles, and minimum and maximum. The analysis of qualitative (i.e., not expressed in numbers) variables was carried out by calculating the absolute and percentage rates of occurrence of all the values that these variables could take. Comparisons of values for qualitative variables between the groups were made using the chi-squared test (with Yates correction for 2 × 2 tables) or the Fisher’s exact test when the chi-squared test assumptions regarding expected values were not met. The comparison of quantitative variables in more than 2 groups was performed using the Kruskal–Wallis test and, when statistically significant differences between the groups were detected, the post hoc Dunn test was used. The study used a significance level of 0.05, so all p values below 0.05 were interpreted as indicating significant relationships. The age, sex of patients, type of orthognathic procedure, type of skeletal deformity, and reasons for fixation hardware removal in the groups of patients were analyzed.
A total of 124 orthognathic procedures using osteosynthesis were performed and fixation hardware was removed in 77 patients, including 57 women and 20 men. This represents 62.10% of 124 procedures performed. The distribution of age and sex is presented in . The reasons for osteosynthesis removal were divided into three groups. The first group included complications such as the occurrence of inflammation, infection, and fistula in the fixation area, swelling, or fixation exposure ( ). This occurred in 17 cases, representing 22.08% of 77 fixation hardware removal procedures and 11.97% of 124 orthognathic procedures performed. In instances of inflammatory complications or infections, the affected area in 11 cases was the maxilla and in 6 cases it was the mandible. In 4 of 17 cases where complications were the reason for osteosynthesis removal, only the hardware that caused the problem was removed, and in the remaining 13 cases, all fixation hardware was removed at the same time. shows the location where the complication occurred—inflammation/infection associated with fixation hardware. The second group of reasons included all subjective feelings of the patient related to the presence of fixation hardware. These feelings included the sensation of a foreign body, the sensation of cold when the ambient temperature changed, discomfort when touching the fixation site, and the sensation of mobility in the fixation area. These sensations were not accompanied by any clinical symptoms of inflammation or any other pathological processes. Due to subjective feelings of patients, fixation hardware was removed in 23 cases (29.87% of 77 fixation hardware removal procedures, which is 18.54% of 124 orthognathic procedures performed). The third group included circumstances in which fixation hardware was removed at the explicit request of the patient without any objective and subjective clinical reasons. These were the most prevalent, accounting for 37 cases (48.05% and 29.83%, respectively). presents the reasons for osteosynthesis removal in the group of 77 patients who underwent such surgery classified according to sex, type of primary operation, and skeletal class. Using the p chi-squared or Fischer’s exact test, no statistically significant differences were found in either of these groups. However, taking into account the number of originally performed orthognathic procedures ( n = 124) classified according to the sex of patients (74 women and 50 men), statistically significant differences with regard to sex were found. Fixation hardware was statistically more often removed in women than in men for each of the listed reasons. shows the reasons for removing fixation hardware according to sex. It was also found that fixation hardware was statistically more frequently removed in patients with skeletal class II: out of 40 patients, fixation hardware was removed in 31 cases (77.5%). In 80 patients with skeletal class III, fixation hardware was removed in 45 cases (56.25%). In both skeletal classes, the percentage of inflammatory reasons for fixation hardware removal was similar: 12.5% in class II and 15% in class III. For subjective reasons, fixation hardware was removed in 10 cases in class II (25%) and 13 cases in class III (16.25%), while there were clearly more cases of removal at the patient’s request in class II (40%) than in class III (25%). The duration of the procedure for osteosynthesis removal was mainly dependent on the extent of the primary operation. As could be expected, procedures for removing osteosyntheses after one-jaw operations were shorter than after bimaxillary operations. It took less time to remove fixation hardware after Le Fort I jaw osteotomy than after sagittal/split mandibular ramus osteotomy (BSSO) ( , ). The length of the surgical procedure was also influenced by the fact that in some cases, the fixation hardware was covered with a layer of bone tissue of different thickness and it was necessary to clean the screw slots, which required additional time and, as a result, prolonged the time of surgery ( ). This situation was recorded in 15 cases. In 11 cases, it was related to osteosynthesis in the mandible. shows the time from orthognathic surgery to fixation hardware removal. This is consistent with the experience of other centers , as 44 procedures were performed between 6 and 12 months. At the earliest, we had to remove fixation hardware after 7 weeks because of persistent acute inflammation, and at the latest, 59 months after the primary operation. In four cases, septoplasty was performed simultaneously with osteosynthesis removal. In two cases, septoplasty was performed, and in one case, mandibular body reconstruction was performed.
Before Hugo Obwegeser pioneered modern orthognathic surgery in the 1950s, numerous surgical techniques were employed to cut the deformed bone and reposition the bone fragments. The common feature of these procedures was the use of external immobilization either in the form of rigid maxillary wiring or with the use of various orthopedic devices. The concept of direct bone fragment immobilization was first developed in the field of maxillofacial traumatology, which in turn originated in orthopedics. The basic principle of treating fractures is to position them correctly and to use internal or external immobilization to hold them in the position obtained until bone adhesions are formed. Various types of external immobilization have been used for long bone fractures since antiquity, and the concept of open treatment using the technique of direct fixation of bone fragments only emerged in the 19th century. Similarly, until the 19th century, external immobilization with bandages, wire ligatures, or various types of chin straps was the usual method of treating craniofacial injuries. The first successful fixation of long bone fragments using silver wire was performed by the New York surgeon Rodgers in 1827, while the first known surgical treatment of a mandible fracture was the placement of circumferential wiring by the French military surgeon Baudens in 1840 . The author of the term “osteosynthesis” was Albin Lambotte, who also introduced a set of instruments and aluminum plates to immobilize mandibular fragments. Sir Arbuthnot William Lane is also considered to have pioneered the use of direct fixation in the treatment of fractures . According to some sources, Hansmann first described the use of a metal plate to treat jaw fractures in 1886 , and other sources report that the first plate fixation was performed by the German surgeon Soerensen in 1917, using a wedding ring . A real revolution in the field of providing fixation to bone fragments in the skull occurred in the 1970s with the introduction of treatment using miniaturized plates and the knowledge of the most advantageous sites for their placement in the bone . It was not long before these techniques were used in orthognathic surgery. For the first time, bicortical screws were used by Spiessl in 1974 to join mandibular bones after osteotomy. Although these techniques made it possible to eliminate the need for intermaxillary wiring after surgery, thereby facilitating greater control over the airways, they were initially met with criticism from the surgical community dealing with skeletal deformities, with Hugo Obwegeser himself being a prominent opponent. However, clinical observations of fixation using miniplates and screws provided a more stable adhesion of bone fragments than previously used techniques, which meant that nothing stood in the way of further development of osteosynthesis. The first osteosynthesis kits were made from various alloys containing steel, chromium, and cobalt. In the 1980s, titanium and its alloys were introduced as the main material for the production of plates and screws. Due to its properties such as biocompatibility, osteoinduction, and osteoconduction; elasticity comparable to that of compact bone; high mechanical strength; no electrical or thermal conductivity; no magnetic field influence; low weight; and low corrosivity, titanium seems to be an ideal material for bone fixation . The compatibility of titanium with human tissues has been demonstrated in both in vivo and in vitro studies. However, concerns have been raised regarding the long-term effects of local and general titanium retention in the human body. The existing literature reveals that there are reports indicating that pure titanium does not exert any influence on carcinogenesis, inflammation, or allergic reactions . Nevertheless, there are also reports indicating a correlation between chronic fibrous tissue inflammation and the presence of titanium. The presence of titanium molecules in the lymph nodes was also observed . In the absence of clear guidelines from opinion leaders and scientific societies on how to approach postoperative osteosynthesis, the decision to leave or remove fixation hardware is made individually by treatment centers. In the case of fixation hardware that is causing inflammation or other objective discomfort, it is clear that it must be removed. However, in the case of fixation hardware that causes no discomfort, the decision is based on experience. Some centers advocate for the routine removal of plates . This is corroborated by the potential for complications to emerge due to the presence of specific bacterial flora in the oral cavity and the influence of masticatory forces. The presence of teeth and potential odontogenic foci also increases the risk of inflammatory complications in the event of infection. Other authors recommend observation and action in the event of clinical symptoms [ , , , ]. In the course of our study, we removed fixation hardware in 77 cases out of 124 orthognathic procedures performed, which represents 62.10% of the total number of cases. This is a high percentage compared to the data available in the literature [ , , , , , , , , , , , , ], where the percentage varies from 2% to 55%. This may be due in part to the fact that patients are routinely informed of the possibility of fixation hardware removal prior to orthognathic surgery, and there is still a lack of consensus in the medical community on how to approach osteosynthesis after healing. Patients prepared for orthognathic surgery in our team are informed about the risks associated with the removal of fixation hardware and the fact that the asymptomatic course in the initial period after orthognathic surgery does not guarantee that fixation hardware will not cause problems in the long term. We also inform patients that the optimal time for surgery is 6–12 months after the primary operation and that the planned osteosynthesis removal procedure is performed under general anesthesia. However, it is important to bear in mind the growing awareness of patients in the age of information sharing enabled by the Internet. In our study, almost 30% of fixation hardware was removed at the explicit request of the patient, without any clinical symptoms or subjective feelings of the patient, and the main argument for surgery was to avoid future complications. Sukegawa et al. reported that fixation hardware was removed at the patient’s request after orthognathic procedures in 19.6% of all cases, while Park et al. also reported the patient’s request as the main reason for osteosynthesis removal. The second most common reason for fixation hardware removal was the presence of discomfort in the absence of evident inflammatory symptoms or infection. In this case, it was not considered a complication but a subjective feeling of the patient. In our group, this cause was the reason for 18.54% of all interventions. For this reason, Parasher et al. , in their study on a group of 352 patients, removed fixation hardware in 24 cases, or 6.81% of all patients. The third reason, inflammation, infection, or fixation exposure in our material, occurred in 17 cases, or 11.97%. It is therefore widely accepted that osteosynthesis removal is a necessary step in the treatment of this complication. According to reports in the literature, the percentage of cases in which it is necessary to remove fixation hardware for inflammatory and/or infectious reasons ranges from 2.8% to 22.2% . There is a tendency for fewer inflammatory complications when bicortical screws are used to provide fixation for osteotomy fragments in the mandible than when plates are used [ , , , , , ]. There have also been reports of potential bacterial colonization of osteosynthesis materials by blood, which, despite the lack of clinical symptoms in the initial postoperative period, may be the reason for the need for intervention in the future . In a meta-analysis of fixation hardware removal in orthognathic surgery, Gomez-Barrachina et al. demonstrated that indications for re-intervention were present in 13.4% of all cases. Furthermore, they identified female sex as a risk factor, in addition to the two aforementioned factors. In our material, complications related to fixation hardware in the maxilla were more frequent, which differs from the above reports, while in our patients, complications occurring in the fixation area in the mandible were much more clinically serious than those occurring in the maxilla. Statistically, we removed fixation hardware more frequently in women (77.03% vs. 40%, ) both for inflammatory/infectious reasons and at the patient’s request. This is consistent with previous research suggesting that female patients may be more likely to request hardware removal due to subjective factors such as sensitivity, pain, palpability, or psychological discomfort . Similarly, Falter et al. reported a significantly higher rate of hardware removal in female patients (31.7%) compared to male patients (20.3%) ( p = 0.0091), with differences in the indications for removal. While the rates of removal due to infection were comparable between sexes, female patients were significantly more likely to undergo removal due to clinical irritation (15.2% vs. 5.3%), further supporting the hypothesis that subjective factors may contribute to their decision . Furthermore, our findings align with Gomez-Barrachina’s observations that the female sex shows a greater predisposition to inflammatory complications associated with osteosynthesis . However, the influence of factors such as differences in pain perception, heightened body awareness, or sociocultural expectations regarding medical interventions requires further investigation. Among the complications after the procedure of fixation hardware removal, the authors mentioned intraoperative and postoperative bleeding and sensory disorders, which occurred in 23 cases (9.87%). In three of our patients, healing was complicated by the formation of hematoma and associated significant swelling, which caused discomfort to the patients in the postoperative period and required prolongation of antibiotic therapy as well as the use of non-steroidal anti-inflammatory drugs. Many authors agree that fixation hardware removal after maxillofacial surgery should take place within 1 year of the primary operation . In the study group, the most frequent removal of fixation hardware occurred between 6 and 12 months after orthognathic surgery, although some patients decided to undergo elective surgery after more than 12 months. In six cases, the need to remove osteosyntheses before 6 months after surgery was due to the clinical situation—developing inflammation. Resorbable plates have been introduced as an alternative to titanium fixation hardware to eliminate the need for a second operation for plate removal [ , , ]. While the use of resorbable materials appears to offer certain advantages, such as reducing the long-term presence of foreign bodies and avoiding additional surgical interventions, concerns remain regarding their stability, degradation process, and potential complications. A multi-center randomized controlled trial by van Bakelen et al. found that the risk of plate removal in patients treated with biodegradable fixation systems was 2.2 times higher than in those treated with titanium plates, with abscess formation being the most common reason for removal . Similarly, a review comparing resorbable and titanium plates reported a higher removal rate for resorbable plates (3.63%) compared to titanium (1.53%), with the main complications being infection, plate exposure, and sinus tract formation . Given these findings, while resorbable plates offer theoretical advantages, their higher removal rate and increased risk of complications, particularly in mandibular osteotomies, raise concerns about their routine use in orthognathic surgery. A major limitation of this study is its retrospective design, which inherently introduces potential biases, particularly in patient-reported outcomes. Since data were obtained from medical records rather than prospective patient follow-up, factors influencing the decision to remove fixation hardware may not have been fully captured. Additionally, this study was conducted at a single center, which limits the generalizability of the findings to other populations and institutions with different surgical protocols or healthcare policies. Another limitation is the lack of a comparative follow-up for patients who retained their fixation hardware. Without long-term outcome data for patients who did not undergo hardware removal, it remains unclear whether their long-term satisfaction or complication rates differ significantly from those who opted for removal. Future prospective studies, ideally randomized trials, are needed to assess patient satisfaction and long-term outcomes in both groups. Furthermore, while we analyzed sex differences in hardware removal rates, we did not account for other potential confounding variables, such as patient comorbidities, socioeconomic factors, or surgical variations, which could have influenced the decision for hardware removal. Addressing these factors in future multi-center studies could provide a more comprehensive understanding of the determinants influencing fixation hardware management.
The results of this study indicate that the percentage of fixation hardware removal in our cohort was higher than what has been reported in the literature, with patient preference being the most common reason for removal. Additionally, women were more likely to undergo hardware removal than men, which may be associated with increased sensitivity to discomfort or greater awareness of potential long-term complications. These findings highlight the variability in current clinical practice and underscore the need for clearer guidelines on post-surgical fixation hardware management. From a clinical standpoint, the decision to remove fixation hardware should be individualized, taking into account the presence of symptoms, risk factors for complications, and patient preferences. Given the higher removal rates observed in women, particular attention should be paid to understanding the factors influencing their decision making. Further research is needed to evaluate long-term outcomes in patients who retain their fixation hardware compared to those who undergo removal. Prospective, multi-center studies, ideally including randomized controlled trials, could provide stronger evidence to guide clinical decision making. Additionally, further advancements in biodegradable fixation materials should be explored to determine whether they can serve as a viable alternative to titanium implants and potentially reduce the need for secondary procedures.
|
Consequences of the lack of clinical forensic medicine in emergency departments | 8cb6d509-4b8d-447b-b96e-2cfcf747d82a | 10772006 | Forensic Medicine[mh] | Interpersonal violence refers to violence between individuals and is divided into family and intimate partner violence and community violence, according to WHO . Of course, this definition includes the most common types of violence, such as domestic violence, sexual violence, child abuse, and elder abuse. Interpersonal violence globally kills more than 1.6 millions of people every year, but the impact of non-fatal violence is also enormous and has serious long-term physical, psychological, economic, and social consequences . Violence is quite common in today’s society, but in many cases it is a hidden phenomenon, leading to underestimation and inadequate resources to properly address the problem [ – ]. Despite the importance of properly recognizing and treating violence from a medical point of view, health professionals, who have a crucial role in the detection and prevention of violence, seem to be currently not yet prepared to treat this kind of “disease” appropriately. The few existing publications on this issue highlight the inadequate preparation of health professionals in hospitals in this sense. Recently, Vieira et al. , through a series of questionnaires, demonstrated that in Brazilian hospitals most of the doctors and nurses knew less than 50% of the procedures required for the proper documentation, collection, and preservation of forensic evidence . Specialist centers for domestic, sexual, and child abuse exist; however, these are usually located in large cities and scarcely distributed in single regions and countries. For this reason, all medical centers should have personnel capable of detecting and responding to clinical signs of violence given that reading through medical science crime or abuse on a body is functional to preserving health and life. Hence, in emergency wards especially the documentation and correct interpretation of voluntary injuries upon the medical and/or surgical intervention are crucial to protect the patients’ health in its broadest sense; yet this does not seem to occur. For this reason, we decided to carry out an examination of all the cases admitted to the emergency department (ED) of the Policlinico of Milan, one of the most important hospitals in Lombardy (North Italy), in a pre-pandemic 1-year activity, to assess how the cases labeled as “violence by others” were treated from a descriptive and diagnostic point of view. The aim was to analyze, for the first time, the performance of emergency room physicians in the management of victims of violence, evaluating the set of assessments carried out by physicians during the examination of patients, the activities performed, and, finally, the data actually reported in the patients’ medical records. Particular emphasis was placed on monitoring the quantity and quality of samples taken for forensic purposes. We therefore present the results and discuss their implications and critical aspects from a medicolegal and clinical perspective, presenting a flowchart for forensic precautions that we believe should be part of the cultural heritage of all clinical practitioners in EDs and more generally in hospitals.
The medical records of all patients who were admitted to the emergency department of the Policlinico Hospital of Milan in 2017 were reviewed. All the medical records that had been labeled by health professionals as “patient victim of violence by others” were extrapolated completely anonymously. This may have involved non-sexual physical assault as well as sexual assault: the study assessed only the ability of clinicians at the ED to describe, evaluate, and treat violence, regardless of the fact that some of these victims might later be referred to a specialized sexual violence center. Subsequently, the study of the enrolled cases was implemented by extracting a series of common data, which are relevant both in the clinical and forensic field. As a result, 16 parameters were analyzed for each selected medical record, including epidemiological data, information about the characteristics of the aggression, and the clinical exams performed, as well as the description of findings and sampling carried out, as listed below: General patient information: gender (male; female) and age (divided into subgroups of 10 years each: 0–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, 90–99, or not known). The division into age decades corresponded only to the need to divide the population into homogeneous age groups. In Italy, the age of majority is reached at 18 years of age, and children and adolescents up to this age are admitted to the pediatric ED, yet given that the databases between the pediatric ED and the adult one are merged, we kept the adolescent limit at 19 since adolescence is traditionally considered complete at 19 years of age approximately . Time of transport and arrival at the emergency department: elapsed time between event and patient arrival at the ED (< 2 h, 2–4 h, 4–6 h, 6–11h, 11–16 h, 16–24 h, 24–48 h, > 48h, or not known). Information on injuries: both type of trauma claimed by the patient and diagnosed by the clinician (blunt force trauma, sharp force trauma, firearm, thermal, electric, asphyxia, intoxication/poisoning, or not known); type of injuries diagnosed by the clinician based on their morphological features (e.g., bruises, abrasions, skin lacerations, fractures, dislocations, scars, etc., or not known); was the injury suffered by the patient described by the health care provider? (yes, no, or not known); if yes, which information had been reported? In detail: injury’s characteristics (number of lesions, size, color, and shape) and body location. In the case of multiple coexisting injuries in the same individual, we considered the best descriptive performance of the health professional for the purposes of the descriptive statistics of this study. Information collected and specimens sampled at the ED: any instrumental examinations performed to better interpret the injury (e.g., X-ray or computerized tomography); were any photographs of the injuries taken? (yes, no, or not known); was the victim’s clothing sampled (yes, no, or not known). Clinical examination result: was the patient admitted to a hospital ward? (yes, no, or not known); was the Judicial Authority involved? (yes, no, or not known); what was the patient’s prognosis? (no prognosis, 2 days, 3–5 days, 6–10 days, 11–20 days, > 20 days, reserved prognosis, or not known). Prognosis is the number of days the physician estimates it will take for the patient’s injuries to heal.
There were a total of 80,934 hospitalizations at the ED of Policlinico of Milan in the year examined, of which 58,598 were registered at the adult ED and 22,336 at the pediatric ED. Out of the total admissions, 991 (1.22%) were patients that had been victim of violence configuring crimes against persons according to the Italian criminal code. In detail, in 761 cases (76.8%), it was general physical violence by others (i.e., assaults, fights, quarrels), and in the remaining 230 cases (23.2%), it was sexual violence. Overall, 519 (52.4%) victims referred to male, and 472 (47.6%) to female patients. The breakdown of the victims’ gender by the type of violence suffered showed that in the case of non-sexual physical violence, 470 were men (61.7%) and 291 were women (38.3%); in contrast, in the case of sexual violence, more than three-quarters of the victims were women (181, 78.7%). The number of admissions of patients at the ED varied according to the age group, as shown in Fig. . In a very small number of cases (13 cases, 1.3%), the victim’s age was unknown since the patient had left the ED before the clinical examination. However, it was found that among male victims of non-sexual physical violence, the age range was from 5 to 81 years, with an average age of 28.6 ± 17 years; among female victims, the age range was from 10 to 95 years, with an average age of 33.7 ± 15 years. The age of male victims of sexual violence ranged from 12 to 37 years, with an average age of 24.3 ± 6 years; for female victims, the age ranged from 4 to 68 years, with an average age of 30.5 ± 11 years. Globally, a total of 52 minors (under 18 years of age) suffered some type of physical violence (5.3% of 978 patients with known age), distributed as follows: for non-sexual violence, there were 18 (58%) males and 13 (42%) females; for sexual violence, there were 4 (19%) males and 17 (81%) females. Among adults, the total number of victims of physical violence was 926 (93.4% of all patients with known age), distributed as follows: for non-sexual violence, 448 (62%) men and 274 (38%) women; for sexual violence, 45 (22%) men and 159 (78%) women. After analyzing the time of arrival of patients to the ED (which was reported in 618 cases, 62.4% out of the total), we observed that 337 (54.5%) victims arrived at the ED within 2 h after having suffered the violence. After that, 124 (20%) victims arrived between 2 and 4 h, 43 (7%) between 4 and 6 h, 33 (5.4%) between 6 and 11 h, 28 (4.5%) between 11 and 16 h, and 16 (2.6%) between 16 and 24 h. Admissions were also registered after more than 24 h from the violence, in detail: 29 (4.7%) between 24 and 48 h and 8 (1.3%) after 48 h. Specific data broken down by the time of arrival at the ED, type of violence suffered (non-sexual and sexual), and age of victims are reported in Table . Unfortunately, in 373 cases (37.6% out of the total), involving both types of violence and both adults and minors, it was not possible to assess the elapsed time before the patient’s arrival at the ED, since the time of the violence by others had not been reported in medical records. Considering the reported type of trauma by the patients, out of the total we observed that 845 (85.3%) claimed to have suffered from blunt force trauma, 59 (6%) from sharp force trauma, 26 (2.6%) from asphyxial maneuvers (ligature or manual strangulation of the neck), 8 (0.8%) from intoxication/poisoning, and 2 (0.2%) from thermal trauma. In 51 cases (5.1%), the reported type of trauma by the patients had not been noted in medical records (in 13 cases, patients had left the ED before the clinical evaluation). Finally, out of the total, 59 patients claimed to have been victim of multiple types of injury (Fig. ) and the most common associations were blunt force trauma and asphyxial maneuvers, followed by blunt force trauma and sharp force trauma. Following the medical examination, the diagnoses made by clinicians were as follows: blunt force trauma in 799 patients (80.6%), sharp force trauma in 34 patients (3.4%), asphyxial maneuvers in 5 patients (0.5%), intoxication/poisoning in 5 patients (0.5%), and thermal trauma in 1 patient (0.1%) (Fig. ). In 147 cases (14.9%), the diagnosis was absent in medical records. Specific data broken down by the reported type of trauma by the patients and diagnosed by the clinicians, type of violence suffered (non-sexual and sexual), and age of victims are reported in Table . For the sake of completeness, it should be mentioned that blood samples are taken from all victims who claim to have suffered sexual violence in order to perform toxicological screening; however, this is not done routinely for victims of non-sexual physical violence. Focusing specifically on injury description and diagnosis made by clinicians, we observed that bruises had been reported in 519 cases, abrasions in 246 cases, skin lacerations in 211 cases, and bone fractures in 112 cases. Bruising and skin abrasions occurred in all types of patients, in 296 cases (57%) and 115 cases (46.8%), respectively, in adult victims of non-sexual violence, in 169 cases (32.6%) and 74 cases (30%) in adult victims of sexual violence, in 35 cases (6.8%) and 33 cases (13.5%) in minor victims of non-sexual violence, and in 19 cases (3.6%) and 24 cases (9.7%) in minor victims of sexual violence, respectively. In contrast, skin lacerations and bone fractures were predominantly found in young adult victims of non-sexual physical violence in 137 cases (65%) and 74 cases (66%), respectively. Skin reddening, dislocations, scars, and petechiae were reported way less frequently in all victim types. In 110 cases of the total clinically examined victims (11%), the type of reported injury, or lack of objective lesions, had not been noted on the medical records (Fig. ). Of course, different types of injuries could coexist in the same patients overall, and the most common associations were bruises and abrasions, as well as bruises and skin lacerations. We also investigated how in-depth the injuries had been described on medical reports by clinicians. Of the total number of patients who underwent clinical evaluation, in 933 cases (95.3%) at least one of the description parameters taken into consideration was reported, whereas in 44 cases (4.5%) there was no injury description. In 1 case (0.2%), it was not possible to ascertain whether the description had been made or not. The size of the injury was reported in 95 cases (10.2%), the color in 59 cases (6.3%), the shape in 61 cases (6.5%), and the location in 718 cases (77%). The total number of lesions was noted in 76 cases. Specific data broken down by the type of violence suffered (non-sexual and sexual) and age of victims are reported in Table . In the case of multiple coexisting injuries in the same individual, the best descriptive performance was considered. Body location of the reported injuries was the following: head in 235 patients (32.8%), face in 384 patients (53.5%), neck in 19 cases (2.6%), chest in 88 patients (12.3%), abdomen in 38 patients (5.3%), upper limbs in 118 patients (26.1%), lower limbs in 115 patients (16.1%), back in 47 patients (6.5%), genitalia in 10 patients (1.3%), and buttocks in 22 patients (3.0%). Details are shown in Table . We note that the percentages given refer to the totality of injuries for which anatomical location was reported in the description, since in some cases the same individual had multiple injuries in different anatomical areas. However, of the total 978 subjects who underwent clinical evaluation, only 718 had at least one lesion for which the affected anatomic site was noted; in 260 cases (26.6%), this information was missing. As for tests that had been prescribed by clinicians, X-ray examinations were performed in 416 cases (42.5%) and computed tomography scans in 357 cases (36.5%). These instrumental examinations were predominantly performed in adult victims of non-sexual physical violence in 305 cases (73.3%) and 259 cases (72.6%), respectively, followed by adult victims of sexual violence in 83 cases (20%) and 93 cases (26%), minor victims of non-sexual violence in 20 cases (4.7%) and 5 cases (1.4%), and minor victims of sexual violence (X-ray in 8 cases, 2%). Ultrasound and echo-Doppler examinations were also reported in a few cases (exclusively in adults). No instrumental examinations were performed in 198 cases (20.2%). We observed that in 984 cases (99.3%) no clothes belonging to the victim or the aggressor were kept; only in 5 cases (0.5%), all relating to non-sexual physical violence in adults, some clothes were seized, and in the remaining 2 cases (0.2%), this information was not available (Fig. ). Furthermore, in 961 cases (97.0%), no photos of the reported injuries were taken by the health professionals; in 28 cases (2.8%), photos were taken; and in the remaining 2 cases (0.2%), this information was not available (Fig. ). Specifically, photographs of injuries were taken in 17 cases (60.7) of adult victims of non-sexual violence, in 5 cases (17.8%) of adult victims of sexual violence, in 2 cases (7.1%) of minor victims of non-sexual violence, and in 4 cases (14.4) of minor victims of sexual violence. The report to the judicial authority was made in 667 cases (67.3%) when the crimes could be prosecuted ex officio and there was an obligation to report. Finally, after admission at the ED, 59 patients (6%) were hospitalized, while in the remaining 919 cases (94%), they were discharged (it is recalled that 13 patients left before the medical examination). For 225 patients (23%), there was no prognosis. It was 2 days in 20 patients (2%), 3–5 days in 162 patients (16.6%), 6–10 days in 363 patients (37.2%), 11–20 days in 108 patients (11%), and > 20 days in 92 patients (9.4%); it was reserved in 8 patients (0.8%). Adult victims of non-sexual physical violence reported a longer prognosis, 7 days on average.
Victims of violence very often visit hospital emergency wards to receive medical assistance. This is a fact that should induce the implementation of thorough medicolegal and forensic procedures for the appropriate treatment of these patients and for prevention. This is especially true given that violence is a pervasive phenomenon in today’s society. However, recent findings in the literature clearly show that emergency clinicians do not exercise due diligence in documenting, collecting, and preserving forensic evidence . This is an excellent missed opportunity, however, as a patient’s entry into the ED immediately after a violent crime or even suspicious trauma would be the best time to properly diagnose and intercept violence in order to safeguard the health and life not only of that patient but of society, and provide preventive measures. Indeed, the forensic examinations that can be performed at the ED are several (descriptions, photographs, interpretations, and collection of biological specimens) and, by their nature, require little or no delay as the injured biological substrate of a living individual is subject to contamination or change until final recovery . Therefore, timely examinations usually provide more accurate information than delayed assessments and may also prove to be critical in the differential diagnosis between an accidental and a non-accidental injury. These are considerations that may seem trivial, but data from the literature seems to indicate that these seemingly familiar concepts are not successfully applied in daily practice in the clinical settings . Perhaps often not considered is that a patient who has suffered violence may also face legal proceedings at the end of the clinical process. Therefore, if the patient is injured, early implementation of forensic measures could allow for the collection of evidence necessary for future legal consequences, as well as taking measures to protect that particular individual. Ultimately, this would be the best way to protect the health of the victim in the broadest sense. Given the importance of forensic medical intervention in emergency wards, we decided to carry out an examination of all the cases admitted to the emergency department of the Policlinico of Milan, one of the most important hospitals in Lombardy (North Italy), in a standard (pre-COVID) 1-year activity, to assess how the cases labeled as “violence by others” were dealt with from a descriptive and diagnostic point of view. Particular emphasis was placed on assessing the implementation of medicolegal measures by emergency clinicians in the management of patients who arrive classified as victims of violence. A total of 991 medical records were examined. In our experience, slightly less than 80% of patients were victims of general physical violence by others, such as assaults and fights. The remaining 20% approximately, mostly composed of women, was victim of sexual violence. Overall, there was no significant difference between male and female gender, but the breakdown of results showed that more men were victims of non-sexual physical violence and more women were victims of sexual violence. This was true for both minors (under 18 years in Italy) and adults. Overall, the age group most frequently affected by violence was between 19 and 49 years, and the average age for both types of violence (non-sexual and sexual) was around 30 years for both men and women. It is noteworthy that the average age of male victims of sexual violence was lower than that of female victims. This is consistent with data from the literature showing that older women are still at risk of becoming victims of sexual violence . On the whole, all these findings are in line with the data provided by ISTAT (Italian National Statistics Institute) about the victims of physical and sexual violence . Indeed, younger subjects may be more frequently victim of violence, since they are more socially active and prone to alcohol and illicit drug assumption. Such substances may often lead to a violent behavior toward other individuals , triggering violence based on discriminating factors such as disability, ethnicity, religion, and sexuality . Nevertheless, this information cannot be considered a representation of violence and victims in the city as many cases of more subtle violence may have been missed or misdiagnosed by clinicians and therefore not classified as violence upon arrival and hence not included in this study. Through the careful analysis of the events that occurred, it was observed that 54% of patients arrived at the ED within 2 h from the event, and 27% within 2 and 6 h. This means very quick access for most patients and confirms how effective the adoption of medicolegal measures could be in evaluating injuries. However, a significant number of patients (20% approximately) went to the ED even after 6 h, which confirms that the request for help after violence can also be delayed in time , especially in cases of sexual violence, where the percentage rises to almost 60% for adults and 100% for minors. This is not surprising given the intimate nature of the violence experienced and the possible inner conflict when seeking help or telling parents what happened. Of great concern, however, was that in approximately 37% of cases (373 patients), the timing of the violence was not reported in the medical records. In the vast majority of cases, patients reported being victims of blunt force trauma (85% approximately), regardless of the type of violence (non-sexual or sexual) and age. It was followed by sharp force trauma in all victims, with the only exception of minor victims of sexual violence, where asphyxial maneuvers took second place in terms of frequency. If in about 5% of the cases the injury claimed by the patient was not indicated in the medical record, the fact that in about 15% of the cases the medical diagnosis of the injury suffered by the patient was completely missing is even more worrying. To be realistic, we assume that any clinical physician is capable of diagnosing a blunt or sharp force trauma but perhaps asphyxial injuries are more challenging to interpret correctly as detecting signs such as petechiae may be trickier for inexperienced personnel. Consistently, the study found that the most commonly diagnosed injuries across all types of violence victims were bruises and abrasions. They were followed by skin lacerations and bone fractures that were most common among young adult victims of non-sexual physical violence. In contrast, all other types of injuries were reported in very few cases, especially petechiae in 3 cases only. In general, according to the literature , the head and face were the most commonly affected body sites (about 32% and 53%, respectively), followed by the upper limbs. In particular, the upper and lower limbs were most commonly affected in cases of sexual violence, both in adults and minors. Since from a medicolegal point of view it is crucial to properly describe the injuries, we assessed if the emergency room clinicians noted all the relevant characteristics, such as size, color, shape, location, and number of lesions. The site of the lesion was quite often described (77% of cases), unlike all the other parameters, which were almost always absent or incomplete. Indeed, the injury size was reported only in 10.2% of cases, the color in 6.3% of cases, and the shape in 6.5% of cases. This deficiency in lesion description affected all types of patients, but the breakdown of the results showed that all the parameters of injury description were better reported in victims of sexual violence than in victims of non-sexual physical violence, with the sole exception of injury localization. This seems to indicate greater (perhaps unconscious) attention by clinicians when dealing with cases of sexual violence injuries. However, almost all of the reports assessed in this study were uninformative from a medicolegal perspective and of little relevance in a forensic setting. Indeed, a poor description does not allow at a later stage to retrospectively assess the injury severity, timing of onset, and evolution, possibly causing an underestimation and not allowing a correct assessment of the consistency between history and lesions suffered [ , , ]. Furthermore behind a poor description may lie poor interpretation and consequently a lesser inclination to a more thorough investigation of wound nature and timing. In almost 80% of cases, emergency physicians requested imaging tests, and almost exclusively X-rays and computed tomography scans, primarily for adult victims of non-sexual physical violence. For other victims, far fewer instrumental examinations were performed. However, this approach not only allows for a more comprehensive clinical examination that ensures a higher level of care, but may also provide elements that can be used later from a medicolegal perspective, such as detecting signs of physical abuse (in the case of multiple bone calluses) or assessing the correspondence between the reported bone fracture and the type of trauma that occurred . Adult victims of non-sexual physical violence reported a longer prognosis, 7 days on average, and this inevitably also draws attention to the economic and social costs of violence. However, in addition to the poor descriptions of injuries, we also observed more critical issues in other procedures that have a purely forensic purpose, but should never be overlooked [ – ]. Photographs of injuries were taken only in 28 patients (2.8% of cases), with very low numbers in all victim types. However, the acquisition of photographs is of fundamental importance since evidence rapidly changes . Indeed, since photos can document the initial aspect of lesions, they may have a crucial role in court. Also, the preservation of clothing belonging to the victim can be pivotal for investigative purposes, since they may be subjected to forensic genetic examination to search for perpetrator’s biological traces. However, only in 5 patients (0.5%), all relating to non-sexual physical violence in adults, was the victim’s clothing kept. Again, these are seemingly commonplace considerations, but reality shows that while these concepts are widely known, their practical application is completely neglected. Of course, the primary purpose of healthcare professionals in the emergency department is to identify life-threatening conditions and address them. However, when this task has been accomplished, or if the conditions were not life-threatening, care for the patient’s health in its broadest sense should also take over. This also involves understanding whether that patient has been a victim of violence and how to protect him or her. Therefore, the role of physicians, surgeons, or nurses should consist also in assessing if that injury may have been intentional or not. To do that, it is critical to observe, describe, document, test, and then verify nature, timing, and mode of production of injuries. It is expected that a patient arriving at the ED with chest pain will have at least a thorough medical history, blood tests, and electrocardiogram (ECG) . The same approach should apply to a child with a broken arm, for example, which has been attributed to an accidental fall. Tests, X-rays, or even chest scans or MRIs, and whatever else is necessary, should be performed to see if type and pattern of injury as well as timing are consistent with the history. If violence is not hypothesized and maltreatment not suspected, nothing will be done to protect the patient’s health and life . Therefore, violence should begin to be considered on the same level as any other disease and, if not diagnosed and addressed, can lead to health deterioration or even have lethal outcomes, as in the case of feminicides . Whether this should be the role of all first responders or, more reasonably, whether forensic physicians should be present for these specific tasks in all hospitals is a political decision. But there is no doubt that this clinical service should be professionally dispensed in all hospitals, as it has been introduced in France through reforms and investments . Therefore, it is crucial that even in hospitals where there is no forensic staff, at least the clinicians who work in the emergency department are properly trained and prepared. Since they are rightly primarily concerned with the purely clinical aspects, a good compromise might be to adopt and correctly apply some essential medicolegal measures that can be applied to all the cases of violence in general, which have been briefly summarized in the flowchart in Fig. . Overall, it is, first and foremost, a health and public health issue. The steps taken to correctly interpret and then diagnose violence are critical to protect health and life through medical intervention. This is the main reason why it is crucial that the culture of diagnosing violence is ingrained in all physicians, just like cancer and heart disease [ , , ]. A good start would be to equip emergency rooms with a kit containing an assigned camera for this purpose, and a metric reference to measure lesions and to include in the photographs, as well as swabs and vials. This should be flanked by the introduction of courses in which all these aspects can be dealt with and emergency room clinicians (both physicians and nurses as first responders) made aware of the importance of not neglecting measures and precautions that might seem exclusively of forensic interest. While this may seem directly interpreted as a service to justice (and indeed it is, which is why diatribes sometimes arise as to whether the justice system or the National Health System should pay for these expenses), in the end, it is always related to the mission of protecting health. Moreover, the collection of data and evidence is closely related to medical acts, such as swabbing genitals or other anatomical areas, taking intimate and anatomically clear photographs, describing a bruise appropriately, and collecting traces from a wound [ , – ]. By and large, proper training would provide health professionals with those indispensable but invaluable skills that would enable them to better deal with victims of violence. Moreover, since EDs are privileged observers of the impact of violence on the population, a better capacity to detect and intercept this phenomenon could have enormous consequences on the epidemiology of violence and prevention especially in the most fragile population groups such as children and elderly people. Of course, the enormous number of submerged cases of violence could also decrease. In this context, it is very likely that there were patients in our study who were victims of violence and were not recognized as such by the treating physicians. However, this should be considered a limitation of the current organization at higher levels rather than a limitation of the study. Indeed, we focused on assessing the performance of emergency clinicians in medicolegal interventions when dealing with claimed cases of violence. The reported numbers, besides describing a cross-section of the phenomenon of violence in a society over the course of 1 year, have brought to light the current critical problems we have discussed. The issues do not end there, of course, because it would be necessary, for example, for the forensic samples taken to actually be stored and analyzed, which is not happening . However, the introduction of forensic experts or at least skills into the EDs would be a major step forward. To date, this lack may be the equivalent of not having someone to take care of cardiological or neurological issues given the high frequency of violence. Of course, this does apply not only to Italy but also to other countries. In our opinion, given the large quantity of cases in EDs, and hence the fact that clinicians in EDs are busy with non-forensic clinical tasks (and rightly so), it should be ensured that there is specific forensic clinical personnel .
The correct fulfillment of a medicolegal protocol during the medical examination in the emergency rooms, at the first contact with a patient victim of violence, is essential not only for the correct future legal development but especially to guarantee the patient’s overall health. Lack of detailed history, descriptions, and photographs as well as of the sampling of evidence is probably due to the lack of time, resources, and specialized personnel in the emergency ward. Currently, proper medical and forensic interventions are necessary for the improvement and maintenance of the mental and physical health of the victims of violence.
|
The Stability of Synthetic Cathinones and the Study of Potential Intake Biomarkers in the Biological Material from a Case of 3-CMC Poisoning | 0848f195-a5e4-4972-9273-452f9f642ea8 | 10373627 | Forensic Medicine[mh] | New psychoactive substances (NPSs) are constantly popular among drug users. Each year, dozens of NPSs appear in the European Union’s Early Warning System (EWS) of the European Monitoring Center for Drugs and Drug Addiction (EMCDDA). By the end of 2021, the EMCDDA was already monitoring ∼880 NPSs, 52 of which were reported in Europe for the first time in 2021 ( ). NPSs are currently one of the most serious toxicological threats, affecting many areas of society, including legislation, forensic medicine and public health, but they are also a significant clinical problem. NPSs are a complex and diverse group of substances. They are analogs of existing controlled drugs and pharmaceutical products, which mimic their effects through similar interactions with the central nervous system ( ). Considering the psychoactive effects they produce, NPSs can be divided into six groups: opioids (e.g., furanylfentanyl), cannabimimetics (e.g., AMB-FUBINACA), dissociatives (e.g., deschloroketamine), hallucinogens (e.g., 1p-LSD), sedatives (e.g., etizolam) and stimulants (e.g., dimethylcathinone) ( ). While many NPSs often disappear from the drug market rather quickly, some are still found due to their high popularity among users. Among these substances, synthetic cathinones (SCs) are one of the most widespread classes of NPSs that have become popular due to their psychostimulant and hallucinogenic effects being similar to cocaine, 3,4-methylenedioxymethamphetamine (MDMA) and amphetamines ( ). SCs also represent the second largest category of NPSs monitored by the European Union’s EWS. At the end of 2021, the EMCDDA was monitoring 162 SCs ( ). The high structural variability of SCs, combined with the absence of knowledge of their metabolic transformations, creates a great challenge for toxicological analysis. An important problem, from the point of view of toxicological poisoning evaluation, is also their stability in the biological material. Understanding the stability of analyzed xenobiotics in biological samples is crucial for the proper interpretation of the analytical results. Loss of analytes can occur due to chemical degradation, enzymatic metabolism or the presence of interfering substances due to degradation of the biological matrix, including (but not limited to) inappropriate sample storage, handling and/or transport conditions ( ). In view of the low stability of SCs, it may be crucial to search for potential biomarkers of admission that can demonstrate greater stability in the biological material. 3-Chloromethcathinone (3-CMC, 1-(3-chlorophenyl)-2-(methylamino)propan-1-one) was for the first time identified on the European drug market in September 2014, following a police seizure in Sweden ( ). In addition, the first mentions on Polish internet forums, posted by NPS users, about taking 3-CMC appeared in April 2014 ( ). The substance is a halogenated (in the aromatic ring) and N -alkylated derivative of cathinone. It has a center of chirality, so there can be two enantiomers: ( R )-3-CMC and ( S )-3-CMC. 3-CMC is similar in both structure and induced stimulant effects to methcathinone (ephedrone) and 4-chloromethcathinone (4-CMC, clephedrone). 3-CMC has two constitutional isomers: 2-chloromethcathinone (2-CMC) and 4-CMC. Distinguishing 3-CMC from 2- and 4-CMC requires appropriate analytical techniques ( ). The purpose of the present study was to identify the metabolites of SC, 3-CMC, and to select a potential biomarker for the intake of this substance based on the analysis of blood and urine samples from a case of fatal poisoning with this substance. We also evaluated the stability of 3-CMC and its potential biomarker, the dihydro-3-CMC metabolite, depending on the elapsed time since the autopsy as well as the storage conditions of the biological material. The experiment was based on an untypical case of a man, in which circumstances of finding a corpse allowed the detection of 3-CMC and the appropriate preservation of biological material for the stability experiment on the same day of the autopsy.
A 42-year-old male was found with no signs of life on the floor in a church apartment. The body was in a lying position and was naked. The man had only a pair of wireless headphones on his ears. In the area of his left ankle, he had a peripheral intravenous cannula (Venflon) inserted and taped in place with a plaster that was unrelated to any medical procedures. Numerous bruises and injection marks were visible on both lower extremities. Several pieces of physical evidence were revealed next to the body and in the room, including empty peripheral intravenous cannula packages, used cannulas, paper towels contaminated with a red-colored substance, intimate gel packages, empty syringes and needles. In addition, two ampules of “Biostimine 1 mL do not inject” and a glass jar filled with a white crystalline powder with a sticker reading “Vit C sodium ascorbate” were revealed. The medicolegal autopsy was performed at the Department of Forensic Medicine in Kraków 2 days after the discovery of the body. No additional external changes were found during the inspection of the body. The internal examination revealed no injuries related to death as a result of mechanical trauma or pathological changes that would unequivocally explain the cause of death. Pulmonary edema, hyperemia of internal organs, enlargement of the heart cavities, slight atherosclerosis of the coronary arteries, signs of hepatic steatosis and scars in the kidneys cortex were reported, which were further confirmed by a microscopic examination. Moreover, a histopathological examination showed fine myocardial fibrosis and diffuse necrosis of single hepatocytes; subcutaneous tissue specimens from the area of the lesions of the left lower leg presented chronic reactive (inflammatory) changes relevant to chronic injections. Samples of blood and urine were collected during the autopsy for a toxicological examination, including ethyl alcohol evaluation. The peripheral intravenous cannula was also secured. A further search of the decedent’s apartment revealed additional 106 pieces of fragments of string bags with traces of white powder, which were also shipped for toxicological testing.
Biological and non-biological materials Blood and urine samples were collected during the autopsy of the deceased. Blank whole blood samples used for the development and validation of the analytical method were purchased from the Regional Blood Donation and Blood Treatment Center in Kraków ( n = 3). Fluid from the Venflon was secured during the autopsy of the deceased. Standards and chemicals The standard solution of 3-CMC was purchased from Cayman Chemical (USA) and 4-chloroethcathinone-d 5 (4-CEC-d 5 ), used as an internal standard (IS), was from Cerilliant (USA). Bond Elut solid-phase extraction (SPE) columns filled with a 500-mg bed of non-polar silica gel modified with the octadecyl (C18-EC) phase were sourced from Agilent Technologies (USA). The acetonitrile, acetic acid, ammonium formate, formic acid, methanol and acetonitrile for high-performance liquid chromatography coupled to mass spectrometry were obtained from Merck (Poland).
Blood and urine samples were collected during the autopsy of the deceased. Blank whole blood samples used for the development and validation of the analytical method were purchased from the Regional Blood Donation and Blood Treatment Center in Kraków ( n = 3). Fluid from the Venflon was secured during the autopsy of the deceased.
The standard solution of 3-CMC was purchased from Cayman Chemical (USA) and 4-chloroethcathinone-d 5 (4-CEC-d 5 ), used as an internal standard (IS), was from Cerilliant (USA). Bond Elut solid-phase extraction (SPE) columns filled with a 500-mg bed of non-polar silica gel modified with the octadecyl (C18-EC) phase were sourced from Agilent Technologies (USA). The acetonitrile, acetic acid, ammonium formate, formic acid, methanol and acetonitrile for high-performance liquid chromatography coupled to mass spectrometry were obtained from Merck (Poland).
The fluid from the Venflon was acetylated and then analyzed by gas chromatography coupled to mass spectrometry (GC--MS), as described by Synowiec et al. ( ). Screening analysis was performed using a library developed by Pfleger, Maurer and Weber ( ), which contains a broad group of compounds, including acetyl or trimethylsilyl derivatives, as well as the authors’ library of SCs ( ). The analysis was conducted on the day of the autopsy, which was 2 days after the body was discovered. Following preliminary analytical results indicating the presence of chloromethcathinone (CMC), the liquid from the Venflon further underwent a trimethylsilylation process ( ) to distinguish the constitutional isomer of CMC. The same procedure was carried out in the urine sample after SPE, according to the methods described in the authors’ earlier works ( , ).
Due to the positive result of the identification analysis of the fluid from the Venflon, the biological material (blood and urine) was properly preserved on the same day. In the case of the urine sample, the pH was first measured using a universal indicator paper (pH = 6.0). The blood sample was preserved in a tube with sodium fluoride (NaF) and potassium oxalate (KOx). The biological material was divided into three parts, each with a volume of 1 mL. The first part was stored at 4°C. The second part was acidified with 1 M HCl: 100 µL of HCl was added to 1 mL of the blood sample to obtain pH <7, and 50 µL of HCl was added to 1 mL of the urine sample to obtain pH = 4.2. The third part of the blood and urine samples was stored at −30°C.
Screening tests included the analysis of the man’s blood samples using enzyme-linked immunosorbent assay (ELISA) kits from Neogen (UK) for the presence of opiates, cocaine, amphetamine and its derivatives, methamphetamine/MDMA, cannabinoids, benzodiazepines, barbiturates and tricyclic antidepressants. In addition, the blood and urine samples were analyzed for the presence of alkaline, neutral and acidic pharmacological agents using the high performance liquid chromatography with photodiode array detection method in the Merck Tox Screening System developed by Merck (Germany).
The 200 µL of blank, calibration and quality control (QC) blood samples and the man’s blood and urine samples were enriched with the addition of methanolic IS 4-CEC-d 5 . For the blood sample, the final IS concentration was 100 ng/mL, and for the urine sample, it was 1,000 ng/mL. A homogenizing element in the form of a 5-mm-diameter stainless steel bead was added to the Eppendorf Tubes containing the samples. The samples were then deproteinized with frozen acetonitrile (400 µL). The samples were then centrifuged, and the supernatant was evaporated in a stream of nitrogen in a 40°C sand bath. The dry residue was dissolved in 100 µL of a mixture of phase A and B solution in a volume ratio of 60:40 ( v/v ). Mobile phase A is a mixture of 2 mM ammonium formate and 0.2% formic acid solution in water ( v/v ), and phase B is a mixture of 2 mM ammonium formate and 0.2% formic acid solution in acetonitrile ( v/v ).
An Agilent 1200 liquid chromatograph (Agilent) equipped with a binary pump (G1312 A) and an autosampler (G1329 A) was used. The chromatographic separation was performed with a Poroshell 120 EC-C18 column (3.0 × 100 mm, particle size 2.7 μm, Agilent). The column was thermostated at 40°C. The flow of the mobile phases through the chromatographic column occurred at a programmed gradient of mobile phase composition and flow rate (initially, 95% of phase A and 5% of phase B at a flow rate of 0.5 mL/min; then, the proportion of phases was increased linearly to 90% of phase B at a flow rate of 1.0 mL/min at 10 minutes, and the condition was maintained for 2 min). Subsequently, the proportion of phases was increased linearly to 95% of phase B at a flow rate of 1.0 mL/min at 15 minutes. The volume of the samples injected into the chromatographic column was 15 μL. A 6410 triple-quadrupole mass spectrometer (Agilent) with an electrospray ionization (ESI) source, operated under a positive mode, was used. The operational parameters of the ESI source were as follows: vaporizing temperature 350˚C, pressure of the nebulizing gas 40 psi, flow of the drying gas 9 L/min and capillary potential 3.5 kV. The multiple reaction monitoring (MRM) was employed. The fragmentation parameters of the analyzed compounds are listed in . For the analysis of the selected potential biomarker, dihydro-3-CMC, the total ion current scanning mode was used in the m / z range of 50–650 amu.
To assess the selectivity of the method, blank blood samples ( n = 3) without added analyte and IS were subjected to extraction and analysis. Potential interferences from the biological matrix were evaluated in the 3-CMC or IS elution areas. The specificity of the method was assessed by analyzing a mixture of 78 compounds, including SCs, amphetamine and its derivatives, at a concentration of 1,000 ng/mL each. The calibration curve was generated based on the analysis of blank blood samples containing a known amount of 3-CMC. The blood samples were spiked with 3-CMC at the concentrations of 50, 100, 250, 500, 1,000 and 2,500 ng/mL. Each calibration sample was prepared in triplicate. The calibration samples were deproteinized with frozen acetonitrile according to the previously described procedure. Intra-day and inter-day accuracy and precision were evaluated for two 3-CMC concentrations: 125 ng/mL (QC1) and 1,250 ng/mL (QC2). Intra-day accuracy and precision were determined for one batch, in three replicates, for QC1 and QC2. Inter-day accuracy and precision were determined for three separate batches, in three replicates, for QC1 and QC2. The accuracy of the method was presented as relative error (%). The precision of the method was expressed as the relative standard deviation (% RSD). The limit of detection (LOD) of the method was determined as the lowest concentration at which the signal-to-noise (S/N) ratio, calculated as the height of the peak obtained for the defined MRM pair with a lower intensity (198.2→180.1), was ≥10. The limit of quantification (LOQ) of the method was adopted as the lowest concentrations for which the LOD criteria were fulfilled; the accuracy of the method was in the range of 80–120% of the actual concentration, and the precision of the method was ≤20% of RSD.
While planning the experiment, the limitation was the amount of the biological material secured during the autopsy, as it came from an authentic case. The reference point (day 1) was the extraction of the biological material stored at 4°C for 24 hours using the procedure described earlier. The deproteination was performed the day after the autopsy was conducted, and the biological material was preserved. Subsequently, all the adequately preserved biological material was examined similarly after 2, 4 and 12 months. These intervals were chosen on the basis of the time that elapses between the autopsy and the intervals on which toxicological examinations are most often ordered to the Toxicology Laboratory of the Department of Forensic Medicine in Krakow by the Prosecutor’s Office. A period of 12 months is an extreme interval, but it also does occur in the Toxicology Laboratory. In addition to the parent substance, the metabolite dihydro-3-CMC, selected on the basis of two complementary methods (GC--MS and HPLC--ESI-MS), was also analyzed during stability studies in order to assess its usefulness as a potential intake biomarker of 3-CMC.
To study the selectivity and specificity of the method for the determination of 3-CMC in blood samples, no significant interferences were found in the analyte elution or IS areas. However, because the constitutional isomers of CMC have identical mass spectra and retention times, they will therefore result in the same analytical signal. Complementary methods are needed to differentiate them. The parameters of the calibration curve and LOD and LOQ are shown in . The accuracy and precision results for 3-CMC were within the ranges of −19.9 to 7.3 and 7–10% RSD, respectively. The blood samples collected during the autopsy of the deceased did not show the presence of ethyl alcohol. The results obtained from the screening of the man’s blood samples using ELISA tests were positive for compounds of the amphetamine and methamphetamine/MDMA groups. The unusual circumstances in revealing the body and the presence of a Venflon in the deceased, which had not been attached during the medical procedures performed, prompted the authors to analyze the fluid from the Venflon immediately after the autopsy. The preliminary analysis using the GC--MS method revealed the presence of one of the constitutional isomers of CMC. Following literature reports and the authors’ own experience regarding its stability, an experiment was planned to identify the metabolites, to select a potential intake biomarker of this substance and to examine the stability of CMC, along with its biomarker, in authentic biological material. Acetylation of the fluid from the Venflon was performed for screening analysis. Then, after a positive analysis for CMC, a trimethylsilylation process was performed to discriminate the constitutional isomer of CMC. An analogous procedure was carried out in the man’s urine sample. Acetyl derivatives of the constitutional isomers of CMC do not allow for their identification, especially in the case of biological material, since the retention times of the individual CMC isomers are very similar. However, after the acetylation process, we obtain chromatograms that are more transparent and easier to interpret but most importantly can be compared with the published literature data of SCs, as well as their metabolites. In the case of trimethylsilyl derivatives, it is possible to separate the constitutional isomers of CMC. The analysis result of the fluid from the Venflon after the trimethylsilylation process clearly showed the presence of 3-CMC in the investigated evidence. After performing the analogous analysis on the urine sample, the presence of 3-CMC was also confirmed. Moreover, in the urine sample tested, the parent substance was present in trace amounts, and the dihydro metabolite was mainly present then. The metabolite corresponded to the two peaks in the chromatogram, which came from its two diastereoisomers. Since dihydro-3-CMC has two stereogenic centers, it, therefore, exists in the form of two enantiomers and two diastereoisomers. Diastereoisomers, unlike enantiomers, show differences in their physical properties, so two peaks can be observed. Differentiation of the enantiomers would require the use of an enantioselective column, where an adequate peak separation could be achieved. The liquid from the Venflon contained a trace amount of blood, which may explain the presence on the chromatogram of small peaks originating from the dihydro metabolite in the evidence tested ( ). In the performed stability experiment, the parent substance was analyzed by HPLC--ESI-MS-MS, and its potential intake biomarker, the dihydro metabolite, was analyzed by HPLC--ESI-MS using the total ion current scanning option. This metabolite appears in the form of a characteristic double peak because, as noted earlier, it exists in the form of two diastereoisomers. The mass spectrum shows a pattern of isotopic peaks that is the characteristic for the presence of a chlorine atom (disturbed in the case of the parent substance due to the coelution of the dihydro metabolite), as well as the mass of the pseudomolecular ion differing by two units from the mass of the pseudomolecular ion of the parent compound ( ). The GC--MS analysis of the urine sample after acetylation showed the presence of 3-CMC as well as its metabolites N -desmethyl-3-CMC-AC and N -desmethyl-dihydro-3-CMC-2AC (two diastereoisomers), whereas mainly the dihydro-3-CMC-2AC metabolite (two diastereoisomers) was present. The metabolites were identified by comparing the obtained mass spectra with the published mass spectra of other halogen derivatives of SC ( ). Details along with the characteristic molecular fragments of 3-CMC and its metabolites that were obtained using the “MassSpec Scissors” command from a free software for drawing chemical structures (ChemSketch, ACD/Labs) are shown in . Based on the results obtained from the metabolite analysis, shows the proposed metabolism of 3-CMC. The metabolite dihydro-3-CMC was selected as a potential intake biomarker of 3-CMC, and a stability experiment was conducted for this metabolite as well. In the stability experiments, the point of reference was the biological material, which was stored at 4°C after the autopsy. Its extraction was performed the day after the autopsy and the preservation of the biological material. This was due to the limited amount of biological material that was secured, as we were unable to determine how many time points could be determined at the beginning of the experiment. Thus, in some cases as shown in and , where the conditions, such as acidification and/or freezing, helped the stability of the test substance, a significant increase in the amount of the tested analyte can be observed. This demonstrates, therefore, how unstable the examined 3-CMC is and how significant decomposition of the substance occurs after just 24 hours of storage at 4°C. In the case of the urine sample, this increase is not as intensive as in the case of the blood sample. As such, it is clear that pH = 6.0 of the urine sample slightly protects the parent substance when compared to the blood sample. When the blood sample was stored at ∼4°C, 3-CMC was no longer detected after 2 months. Under the same temperature conditions, in an acidified blood sample, it was possible to detect the parent substance for 4 months. At −30°C, it was possible to detect it even after 12 months. In the graph for storage temperature at −30°C ( ), it can be observed that after 12 months, under these conditions, there is a decrease in the concentration similar to that observed after storage at 4°C for 24 hours. In the urine sample that had a pH of ∼6, it was also impossible to detect 3-CMC after 2 months. After acidification and freezing, the substance was stable throughout the investigated time period ( ). The metabolite dihydro-3-CMC, on the other hand, showed high stability under any given conditions for 12 months in both blood and urine samples ( and ). Accordingly, it represents a relatively potential intake biomarker of 3-CMC. Unfortunately, the lack of commercially available standards for 3-CMC metabolites prevented the authors from quantifying dihydro-3-CMC. While comparing the obtained concentrations of 3-CMC in the man’s blood depending on the time elapsed after the autopsy ( ), it can be seen that these concentrations will significantly differ depending on the storage conditions. Moreover, the autopsy was conducted 2 days after the body was found. Thus, the initial concentrations of 3-CMC could have been entirely different. The concentration increase from 880 ng/mL to >2,000 ng/mL, when stored at −30°C, is a result of the short half-life of 3-CMC. When the material was frozen immediately, we obtained a high stability of 3-CMC, while the analyte had already decomposed significantly when it was stored for 24 hours at 4°C before the determination. In the present case, toxicological tests were ordered to the Toxycology Laboratory at the same time as the shipment of physical evidence, in the form of string bags with traces of white powder (analysis result—3/4-CMC). This happened 2 months after the autopsy was performed. Actually, if the blood sample was stored at 4°C at the time the toxicological examination was ordered, the result of the analysis for the presence of the parent compound would be negative. Thus, the recommended solution is to freeze the biological material if there is any suspicion of poisoning due to NPSs, especially SC. Unfortunately, this type of information is often received very late. The results obtained in the present study also support the suggestions made in a letter to the editor by Gerostamoulos et al. ( ). The authors point out that in the case of substances from the NPS groups, a qualitative study of these substances, preferably together with their metabolites, seems more important. Although there are some exceptions, we have limited data on the pharmacology, pharmacokinetics and pharmacodynamics of these substances. Furthermore, the lack of data on the tolerance, routes of administration, dosage and sudden withdrawal syndrome of these substances makes reliable and accurate interpretation of NPS concentrations impossible. Moreover, many poisoning case reports involve polydrug intoxications, so it would be necessary to consider the issue of interactions between them as well. Thus, the toxicologist cannot comment much on the presence of NPSs beyond confirming or not confirming its occurrence. In addition, there is the stability parameter discussed in this paper and the need for studies of NPS metabolism to provide intake biomarkers of NPSs. However, the quantitative determinations of NPSs are still a very valuable source of information because knowing the reference concentration values allows us to understand NPSs and learn about the nature of these substances. The problem of SC stability in the biological material is already rather well understood. It is influenced by a number of factors, such as the type of biological matrix, sample storage temperature, storage duration, the pH of the sample, the type of stabilizing additives used and the chemical structure of the analyte to be determined ( ). According to the published research data, in general, these substances are more stable in urine samples than in blood samples ( ). This was also confirmed by the experiment conducted in the present study. However, there was no correlation between the concentration of SCs and their stability ( , ). Moreover, the higher the storage temperature of the samples, the faster the degradation process occurs. SC halogen derivatives are highly sensitive to storage temperature ( ). Thus, for blood samples containing 4-CMC, which is the constitutional isomer of 3-CMC, the estimated time for the complete degradation of this substance when stored at −26°C was 11.6 months, at 5°C ∼4 months and at room temperature 22 days. In the case of the urine sample, the time was >3 years at −26°C, 4.4 months at 5°C and 24 days at 24°C. The half-life of 4-CMC in the blood sample stored at 5°C was estimated to be 1 day, and in the sample of urine with pH = 5, it was estimated to be ∼2.5 months ( ). The stability of SCs is strongly dependent on the pH of the sample. As Sørensen ( ) showed, the stability of SCs decreases significantly for pH >5.5. For pH in the range of 2.5–3.5, no significant degradation of analytes was observed after 7 days of storage at 20°C. This may explain the increased stability for urine samples. After all, in the case of urine sample, the pH can be in the range of 4.5–8. Thus, in the case of acidic urine samples, the substances will be relatively stable. However, as seen in the experiment by Adamowicz and Malczyk ( ), the pH of the urine sample can change over time depending on the storage conditions of the sample. When stored at 5°C, the pH of the urine sample increased from 5.0 to 7 after 6 months, and at 24°C, it increased to 8. Adding an antimicrobial agent, such as sodium azide, to the urine sample can prevent the growth of bacteria and the subsequent alkalization of the sample. As an additive in the role of anticoagulant to stabilize blood samples, both in clinical and forensic practice, the usual agents used are ethylenediaminetetraacetic acid (EDTA) salts, heparin, citrate buffer (citrate) and KOx. Some anticoagulants are often combined with a preservative, such as NaF, to inhibit blood glucose glycolysis by affecting enolase. In addition, NaF can inhibit the activity of many other enzymes, such as urease and cholinesterase, as well as bacterial proliferation, thereby reducing the loss of nitroaromatic compounds. The addition of NaF to various biological samples (e.g., urine, bile and vitreous humor) is a common practice in forensic medicine as it helps protect various xenobiotics from degradation ( ). However, as Sørensen ( ) showed in his study, the stability is affected not only by the addition of stabilizing substances but also by their type. When a blood sample fortified with SCs was stored at room temperature with citrate as anticoagulant, the increased stability of these substances was observed when compared to the use of KOx as an anticoagulant. This is also most likely related to the pH that the reagent provides, and in the case of the sample protected by the addition of citrate, the pH was ∼5.9, and with KOx, the pH was 7.4. Busardò et al. ( ) studied the stability of mephedrone in blood samples collected intravenously and postmortem, under conditions without stabilizing additives, and with the use of EDTA as a stabilizer and also KOx with NaF. Greater stability was acquired for ante-mortem blood samples, and greater stability was obtained when KOx with NaF was used rather than EDTA as a stabilizer. The increased stability of SCs was also observed when antioxidants such as l -ascorbic acid and sodium sulfite were added to the sample ( ). The relationship between the chemical structure and the stability depends mostly on the amine’s order. The least stable are secondary amines, especially those containing a halogen atom in the aromatic ring. Tertiary amines are the most stable ( , ). Their high stability is probably due to the fact that tertiary amines will not undergo oxidative deamination. During this process, SCs—mephedrone degradation products, known from the literature, were formed ( ). An additional stabilizing effect of tertiary amines occurred when a methylenedioxy substituent was present in the aromatic ring ( , ). Interestingly, the studies conducted indicate that the stability is also strongly influenced by the position of the halogen substituent in the aromatic ring. For fluoromethcathinone, it was shown that the substitution at position 2 ( ortho ) of the aromatic ring is the least stable, the substitution at position 3 ( meta ) is more stable and the substitution at position 4 ( para ) is the most stable. The most common studies in the literature are those on the stability of the parent compound. In cases where it is not possible to detect the xenobiotic, it seems important to monitor compounds that are decomposition products or metabolites. These substances may differ significantly in their properties from the parent compound and therefore may exhibit much higher stability in the biological material. Very often, it is impossible to monitor them, not only due to the limited data on metabolism or degradation but also due to the lack of commercially available standards for such substances. Consequently, there are few papers that describe the monitoring of degradation products or metabolites of SCs. Soh and Elliott’s work showed that in the case of 4-MEC, the dihydro-4-MEC metabolite also appeared in trace amounts as a degradation product of the parent compound ( ). Tsujikawa et al. ( ) conducted a study on non-biological samples, in which they demonstrated probable degradation pathways of mephedrone in an alkaline environment. In addition, Maskell et al. ( ) identified a degradation product of mephedrone in a formalin environment. Czerwinska et al. ( ) studied the stability of mephedrone and its five phase I metabolites, including dihydro-mephedrone, dihydro-desmethyl-mephedrone, desmethyl-mephedrone, hydroxytolyl-mephedrone and 4-carboxy-mephedrone in blood samples. Blank blood sample was protected with the addition of NaF and KOx and then stored for 10 days at 4°C and −20°C. The study showed the stability of dihydro-mephedrone and dihydro-desmethyl-mephedrone under all given conditions. In their work, Concheiro et al. ( ) demonstrated the stability of the dihydro metabolites: 4-MEC, bufedrone and mephedrone in urine samples (pH = 7.6) for 24 hours at room temperature and for 72 hours at 4°C. The same metabolites were tested in the work by Alsenedi and Morrison ( ), where it was shown that these metabolites in urine samples were stable at −20°C. In contrast, they were less stable at 4°C and at room temperature, but it was still possible to detect them in samples after 201 days of storage. In two papers, Nowak et al. ( , ) studied the stability of 4-CMC on authentic biological material. However, they focused on evaluating the stability of the parent compound. In a study of a serum sample, Nowak et al. ( ) reported a 65% decrease in 4-CMC concentration when compared to the original extraction after just 3 days of storage at 4°C (11.5 ng/mL at day 0 to 4.0 ng/mL at day 3). In another paper, the same authors ( ) studied the stability of 4-CMC in the blood sample and vitreous humor. There was a 63% decrease in the concentration in the blood sample after 30 days of storage at 4°C and after 3 days by 54% at 23°C. The blood sample stored at −15°C was stable for the entire time of the experiment (90 days). No data have yet been found in the available literature on the stability of 3-CMC at this time. However, using the data for other halogen derivatives and the data for 4-CMC as a basis, it can be presumed that 3-CMC shows less stability than 4-CMC. There are also differences in the reported stability results for 4-CMC. The total decay time during refrigerated storage for the blank blood sample enriched with 4-CMC was ∼4 months ( ). In the case of the blood sample from an authentic case, the time was 90 days (∼3 months) ( ). By contrast, 113 days (∼3.7 months) after the original extraction, the concentration in the serum sample was below the LOQ, but there was a 65% decrease in concentration after just 3 days ( ). In Poland, the average time from a body’s discovery to the autopsy ranges from a few days to a few weeks ( ). In our case, the autopsy was conducted 2 days after the man’s death. Autopsies are performed in our department up to 48 hours after the delivery of the corpse. Most commonly, they are conducted on the same day or the very next day. However, the biological material to the Laboratory of Toxicology can be delivered several months after the autopsy or the incident. Therefore, the storage conditions of the biological material and subsequent transport are very important. According to the data provided by Adamowicz and Malczyk ( ) compiled on the basis of nearly 3,000 samples of the biological material (mainly blood), it was estimated that the average time between the collection of the material and its delivery to the Institute of Forensic Research (IFR) in Kraków is 26 days (with a median of 10 days). A longer time was observed for material collected during the autopsy than for blood samples collected from drivers. In addition, the time from shipping the material to delivery to the IFR was 7 days (with a median of 5 days). The authors also noted a lack of control over the conditions in which the biological material is stored (usually in a refrigerator) and transported. According to the data from the literature, and the stability experiment we conducted, we found that these factors can have a significant impact on the result of SC determination. Thus, in many cases, without the monitoring of possible degradation products or metabolites, the result obtained from the determination may not reflect the concentration of SCs at the time of the event or death and will most often be falsely negative.
SCs, especially their halogen derivatives like 3-CMC, demonstrate low stability in the biological material. Stability studies of 3-CMC have shown that acidification of the biological material or storage at low temperatures sustains its stability. It is crucial to monitor the presence of dihydro-3-CMC in the biological material during analysis, which showed high stability under all set storage conditions. SCs may not be detected in the biological material, and if they are, the determined concentrations at the time of testing might not correspond to the actual concentrations at the time of the event or death. Consequently, the interpretation of the results obtained for 3-CMC and dihydro-3-CMC in terms of assessing their toxicity and possible cause of death is difficult. The area of research into the search for other intake biomarkers of unstable halogenated SC derivatives remains open.
|
Immunohistochemical analysis of tumor budding in stage II colon cancer: exploring zero budding as a prognostic marker | 0e121645-4fba-42cd-ae2b-b50e619a6b76 | 11522105 | Anatomy[mh] | Tumor budding has proven to be a prognostic biomarker in various cancers. High-grade tumor budding indicates disease progression and unfavorable survival outcomes and is a consistent predictor of unfavorable prognosis and recurrence in stage II colon cancer . The International Tumor Budding Consensus Conference (ITBCC) established a consensus on standardized definitions, scoring methods, and cut-off values for tumor budding in 2016 . Since then, tumor budding has been incorporated as a histological prognostic factor in the 8th edition of the UICC TNM Classification , and the ITBCC recommendations have been validated in large cohorts of colorectal cancer . Although tumor budding is widely recognized among gastrointestinal pathologists, its significance as a prognostic marker is not universally accepted. The reluctance to report tumor budding scores stems from various factors, such as the extra time and effort needed for calculating the ITBCC score and limited clinical demand, as high-grade tumor budding in isolation is not a biomarker warranting immediate recommendation of adjuvant chemotherapy. A tendency toward a beneficial effect of adjuvant chemotherapy has been demonstrated in intermediate and high-grade budding tumors; however, the results did not reach statistical significance . Consequently, the lack of a convincing advantage of chemotherapy on survival in patients with a high grade of tumor budding remains a significant challenge in clinical practice. According to the American Society of Clinical Oncology (ASCO), high-grade tumor budding should be considered along with other high-risk factors in a shared decision-making process . The implementation of tumor budding is also faced with interobserver variation, which directly influences the prognostic value of tumor budding , and various studies have highlighted the presence of variability among pathologists in determining the tumor budding score. Pan-cytokeratin immunohistochemistry (IHC) has been proposed as a potential approach to mitigate interobserver variation and enhance the precision of tumor budding assessment , thereby improving its clinical applicability. The identification of tumor buds using routine hematoxylin and eosin (H&E) staining can be challenging due to inflammation and the presence of reactive inflammatory and stromal cells being misinterpreted as buds. The ITBCC recommends the use of a supporting cytokeratin in challenging cases to confirm that the counted cells are truly budding . However, the final bud count should be performed on H&E , which is also in line with daily diagnostic practice from the participants in the Delphi consensus study . Even so, additional cytokeratin staining has not demonstrated superiority over H&E alone , and therefore, more evidence is needed before considering IHC assessment of tumor budding in routine practice. The scoring criteria and cut-off values for high and low tumor budding would need to be defined independently from those based on H&E staining . The use of pan-cytokeratin staining for the identification of budding cells was initially introduced by Prall et al. , evaluating tumor budding by examining a field of vision measuring 0.785 mm 2 , also suggested by the ITBCC guidelines. This densest high power field (HPF) approach has been confirmed to be effective in IHC-based tumor budding evaluation and comparable to the 10 HPF scoring method , which involves assessing the average number of buds and is commonly used for IHC-based prognostic analysis . Zlobec et al. proposed a “zero-budding” category for colon cancer that appears to be less aggressive than tumors with any degree of budding . Some studies demonstrate that patients with zero budding have superior survival outcomes compared to those with even minimal budding . However, the zero-budding category has not previously been investigated or assessed using immunohistochemistry in stage II colon cancer patients. This study aimed to evaluate tumor budding in a contemporary stage II colon cancer cohort from a screened population using IHC. We followed guidelines established by ITBCC and correlated H&E and IHC-based tumor budding using a four-tiered scoring system that included a Bd0 category. We investigated the potential of a cut-off to differentiate between high and low-grade tumor budding. The prognostic significance of tumor budding was examined by comparing time to recurrence and/or death between tumor budding groups while controlling for clinicopathological factors. Ethical statement The reporting of this study follows the guidelines outlined in the Reporting Recommendations for Tumor MARKer prognostic studies (REMARK). The study adhered to the Declaration of Helsinki and received approval from The Regional Committees on Health Research Ethics for Southern Denmark (S-20190164), with dispensation from obtaining informed consent from the study patients. No patients were excluded based on registration in the Danish Registry of Tissue Utilization. Patients and tissue This population-based study included 493 patients who underwent curative surgical resection of UICC stage II colon cancer between 2014 and 2016 in the Region of Southern Denmark. The patients were sourced from a screened population across four hospitals identified using the Danish Colorectal Cancer Group database and the Danish Pathology System. None of the patients included received neoadjuvant chemotherapy, and they had no history of colon cancer or any malignant disease (except non-melanoma skin cancer) within the 10 years leading up to the diagnosis of colon cancer. Patients who received postoperative adjuvant chemotherapy were included in the study, comprising 69 individuals, corresponding to 14% of the cohort. Further information on patient selection is described in detail elsewhere . A retrospective histopathological characterization of the tumors was performed, coupled with a comprehensive review of medical records encompassing surgical details, follow-up information, and survival data. Details of the baseline clinicopathological characteristics have previously been presented . All archived formalin-fixed, paraffin-embedded tissue blocks and slides originally utilized for routine diagnostic purposes were obtained from the four pathology departments in the Region of Southern Denmark. The number of tumor-containing tissue blocks per patient varied from 2 to 48, with a mean of 7. Histologic sections of 4-um thickness were cut from the tumor block with the highest degree of H&E-based tumor budding, and consecutive sections were stained with H&E and pan-cytokeratin, respectively. Immunohistochemistry Immunohistochemical staining was performed automatically on a DAKO Autostainer Link 48 platform (DAKO, Glostrup, Denmark) as described elsewhere . In short, the primary antibody used was mouse monoclonal anti-cytokeratin (clone AE1AE3, code M3515, DAKO, Glostrup, Denmark) diluted at 1:250. Following deparaffination and rehydration, antigen retrieval was performed using Envision Target Retrieval Solution (DAKO, Glostrup, Denmark) at pH 9 and 97 °C for 20 min. Slides were treated with EnVision FLEX Peroxidase-Blocking Reagent (DAKO) for 5 min to inhibit endogenous peroxidase, followed by a 30-min incubation with primary antibody at room temperature. Amplification was achieved using Envision Flex + Mouse (Linker) (DAKO, Glostrup, Denmark) for 20 min. Detection of bound antibodies was carried out using Envision FLEX/HRP (DAKO, Glostrup, Denmark) and visualized with Envision FLEX DAB (DAKO, Glostrup, Denmark) and Chromogen. Hematoxylin served as the counterstain. Evaluation of tumor budding Tumor budding is defined as single tumor cells or clusters of up to four cells budding of the primary tumor . The evaluation of tumor budding was done following the ITBCC guidelines, and all diagnostic H&E slides were reviewed at low power to identify the tumor block with the highest degree of budding at the invasive front . Based on two consecutive sections from this tissue block, tumor budding was assessed using both H&E and IHC, following the same method: Ten individual fields were scanned at medium power, and tumor buds were counted in the hotspot area normalized to the field size of 0.785 mm 2 using a Leica HC microscope. The tumors were categorized based on the proposed categories by ITBCC, including a separate category for Bd0 tumors. Consequently, a four-tiered scoring system, as suggested by Zlobec et al. , was implemented, classifying them into Bd0 (zero) 0 buds, Bd1 (low) 1–4 buds, Bd2 (intermediate) 5–9 buds, and Bd3 (high) ≥ 10 buds. We enumerated up to 100 buds and assigned a count of 100 to tumors exceeding this number. The pan-cytokeratin-stained tumor budding cells were required to show cytoplasmatic positivity and a clearly defined hematoxylin-stained nucleus to distinguish the cells from apoptotic bodies and cellular debris. Caution was exercised when assessing tumor budding in regions exhibiting significant inflammation in order to differentiate true buds from mechanically fragmented glands and not erroneously count these so-called pseudo buds as genuine tumor buds. Intra- and interobserver agreement The assessment of tumor budding was conducted by one observer, MPK, while SKF contributed to the interobserver evaluation. The observers scored the tumors independently of each other and were blinded to former bud count, as well as clinical and histopathological information. The intra- and interobserver reproducibility was assessed on 50 randomly selected tumor slides from both T3 and T4 tumors. Statistics Summary statistics included mean and standard deviation (normal-distributed variables) or median and interquartile range (non-normal-distributed variables). Categorical variables are presented as numbers and percentages. Analyses of associations between tumor budding categories and clinicopathological characteristics used the chi-squared test or Fisher’s exact test, where appropriate. The Wilcoxon rank-sum test or Wilcoxon signed-rank test was employed for independent or matched continuous variables, respectively. Weighted kappa statistics were used to determine the intra- and interobserver agreement between the tumor budding categories. A comparison of the tumor budding categorization assessed by H&E or IHC was performed using descriptive statistics and visualized using a scatter plot and a Bland–Altman plot. For the prognostic evaluation, a receiver operating characteristic (ROC) curve analysis with either recurrence or death as an endpoint was performed to determine a clinically relevant cut-off score for IHC-evaluated tumor budding. Time to recurrence (TTR) was defined as the time from surgery to the date of local or distant recurrence of colon cancer or the date of death from colon cancer. Recurrence-free survival (RFS) was defined as the time from surgery to the date of local or distant recurrence or death from any cause, whichever occurred first. Overall survival (OS) was defined as the time from surgery to death from any cause or end of follow-up. If no events occurred, all records were censored either at the point of loss to follow-up ( n = 2) or at the end of the study period (May 15th, 2023). Events of metachronous cancer in the follow-up period were not considered a censoring event in the analyses . Kaplan–Meier curves and log-rank tests were used to test for differences in survival times by the tumor budding groups. Uni- and multivariable Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs). Bd0 was used as the reference group. The multivariable analysis was adjusted for potential confounders identified by a previously published causal-directed acyclic graph (DAG) and included the T category, mismatch repair (MMR) status, and histologic type. Multivariable analyses were conducted on complete cases ( n = 492) due to minimal missing data (MMR status not assessed in one tumor). Scaled Schoenfeld residuals checked the proportional hazard assumption for each regression analysis and did not violate it. All analyses were carried out using Stata software (version 18.0 BE). All data were recorded in a Research Electronic Data Capture (REDCap®) database with an automatically generated entry check via the Open Patient Data Explorative Network (OPEN) organization. P -values of < 0.05 were considered to be statistically significant. The reporting of this study follows the guidelines outlined in the Reporting Recommendations for Tumor MARKer prognostic studies (REMARK). The study adhered to the Declaration of Helsinki and received approval from The Regional Committees on Health Research Ethics for Southern Denmark (S-20190164), with dispensation from obtaining informed consent from the study patients. No patients were excluded based on registration in the Danish Registry of Tissue Utilization. This population-based study included 493 patients who underwent curative surgical resection of UICC stage II colon cancer between 2014 and 2016 in the Region of Southern Denmark. The patients were sourced from a screened population across four hospitals identified using the Danish Colorectal Cancer Group database and the Danish Pathology System. None of the patients included received neoadjuvant chemotherapy, and they had no history of colon cancer or any malignant disease (except non-melanoma skin cancer) within the 10 years leading up to the diagnosis of colon cancer. Patients who received postoperative adjuvant chemotherapy were included in the study, comprising 69 individuals, corresponding to 14% of the cohort. Further information on patient selection is described in detail elsewhere . A retrospective histopathological characterization of the tumors was performed, coupled with a comprehensive review of medical records encompassing surgical details, follow-up information, and survival data. Details of the baseline clinicopathological characteristics have previously been presented . All archived formalin-fixed, paraffin-embedded tissue blocks and slides originally utilized for routine diagnostic purposes were obtained from the four pathology departments in the Region of Southern Denmark. The number of tumor-containing tissue blocks per patient varied from 2 to 48, with a mean of 7. Histologic sections of 4-um thickness were cut from the tumor block with the highest degree of H&E-based tumor budding, and consecutive sections were stained with H&E and pan-cytokeratin, respectively. Immunohistochemical staining was performed automatically on a DAKO Autostainer Link 48 platform (DAKO, Glostrup, Denmark) as described elsewhere . In short, the primary antibody used was mouse monoclonal anti-cytokeratin (clone AE1AE3, code M3515, DAKO, Glostrup, Denmark) diluted at 1:250. Following deparaffination and rehydration, antigen retrieval was performed using Envision Target Retrieval Solution (DAKO, Glostrup, Denmark) at pH 9 and 97 °C for 20 min. Slides were treated with EnVision FLEX Peroxidase-Blocking Reagent (DAKO) for 5 min to inhibit endogenous peroxidase, followed by a 30-min incubation with primary antibody at room temperature. Amplification was achieved using Envision Flex + Mouse (Linker) (DAKO, Glostrup, Denmark) for 20 min. Detection of bound antibodies was carried out using Envision FLEX/HRP (DAKO, Glostrup, Denmark) and visualized with Envision FLEX DAB (DAKO, Glostrup, Denmark) and Chromogen. Hematoxylin served as the counterstain. Tumor budding is defined as single tumor cells or clusters of up to four cells budding of the primary tumor . The evaluation of tumor budding was done following the ITBCC guidelines, and all diagnostic H&E slides were reviewed at low power to identify the tumor block with the highest degree of budding at the invasive front . Based on two consecutive sections from this tissue block, tumor budding was assessed using both H&E and IHC, following the same method: Ten individual fields were scanned at medium power, and tumor buds were counted in the hotspot area normalized to the field size of 0.785 mm 2 using a Leica HC microscope. The tumors were categorized based on the proposed categories by ITBCC, including a separate category for Bd0 tumors. Consequently, a four-tiered scoring system, as suggested by Zlobec et al. , was implemented, classifying them into Bd0 (zero) 0 buds, Bd1 (low) 1–4 buds, Bd2 (intermediate) 5–9 buds, and Bd3 (high) ≥ 10 buds. We enumerated up to 100 buds and assigned a count of 100 to tumors exceeding this number. The pan-cytokeratin-stained tumor budding cells were required to show cytoplasmatic positivity and a clearly defined hematoxylin-stained nucleus to distinguish the cells from apoptotic bodies and cellular debris. Caution was exercised when assessing tumor budding in regions exhibiting significant inflammation in order to differentiate true buds from mechanically fragmented glands and not erroneously count these so-called pseudo buds as genuine tumor buds. The assessment of tumor budding was conducted by one observer, MPK, while SKF contributed to the interobserver evaluation. The observers scored the tumors independently of each other and were blinded to former bud count, as well as clinical and histopathological information. The intra- and interobserver reproducibility was assessed on 50 randomly selected tumor slides from both T3 and T4 tumors. Summary statistics included mean and standard deviation (normal-distributed variables) or median and interquartile range (non-normal-distributed variables). Categorical variables are presented as numbers and percentages. Analyses of associations between tumor budding categories and clinicopathological characteristics used the chi-squared test or Fisher’s exact test, where appropriate. The Wilcoxon rank-sum test or Wilcoxon signed-rank test was employed for independent or matched continuous variables, respectively. Weighted kappa statistics were used to determine the intra- and interobserver agreement between the tumor budding categories. A comparison of the tumor budding categorization assessed by H&E or IHC was performed using descriptive statistics and visualized using a scatter plot and a Bland–Altman plot. For the prognostic evaluation, a receiver operating characteristic (ROC) curve analysis with either recurrence or death as an endpoint was performed to determine a clinically relevant cut-off score for IHC-evaluated tumor budding. Time to recurrence (TTR) was defined as the time from surgery to the date of local or distant recurrence of colon cancer or the date of death from colon cancer. Recurrence-free survival (RFS) was defined as the time from surgery to the date of local or distant recurrence or death from any cause, whichever occurred first. Overall survival (OS) was defined as the time from surgery to death from any cause or end of follow-up. If no events occurred, all records were censored either at the point of loss to follow-up ( n = 2) or at the end of the study period (May 15th, 2023). Events of metachronous cancer in the follow-up period were not considered a censoring event in the analyses . Kaplan–Meier curves and log-rank tests were used to test for differences in survival times by the tumor budding groups. Uni- and multivariable Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs). Bd0 was used as the reference group. The multivariable analysis was adjusted for potential confounders identified by a previously published causal-directed acyclic graph (DAG) and included the T category, mismatch repair (MMR) status, and histologic type. Multivariable analyses were conducted on complete cases ( n = 492) due to minimal missing data (MMR status not assessed in one tumor). Scaled Schoenfeld residuals checked the proportional hazard assumption for each regression analysis and did not violate it. All analyses were carried out using Stata software (version 18.0 BE). All data were recorded in a Research Electronic Data Capture (REDCap®) database with an automatically generated entry check via the Open Patient Data Explorative Network (OPEN) organization. P -values of < 0.05 were considered to be statistically significant. Patient characteristics and clinicopathological data The study included 497 patients with complete clinicopathological data and available diagnostic slides. Four cases failed to complete the IHC evaluation due to technical reasons. The analyses were conducted on 493 cases with complete tumor budding evaluation by both H&E and IHC. Out of the 493 patients, 43 (9%) experienced a recurrence, and 175 patients died, of whom 27 died from colon cancer. The median follow-up time was 6.7 years (range 0.4–9.3 years). Tumor budding assessment by H&E and IHC The distribution of tumor budding in categories evaluated by H&E was as follows: 115 (23%) Bd0, 217 (44%) Bd1, 108 (22%) Bd2, and 53 (11%) Bd3, whereas assessment by IHC resulted in 21 (4%) Bd0, 104 (21%) Bd1, 111 (23%) Bd2, and 257 (52%) Bd3. Evaluation by IHC classified more tumors as Bd3 than by H&E (Fig. a). All tumors examined exhibited positive pan-cytokeratin immunohistochemistry. The tumor cells were prominently highlighted and readily discernible, thereby facilitating their assessment (Fig. b). The H&E-based evaluation resulted in a median of 4 buds (range 0–59) and involved a review of an average of 7.2 slides. As expected, the tumor bud count assessed by IHC was significantly higher ( p < 0.01) and showed a median of 17 (range 0–100) (Fig. c). The IHC tumor bud count was, on average, 16 buds higher and the disparity between the staining methods escalated with increasing bud count, as illustrated in the Bland–Altman plot in Fig. d. Intra- and interobserver variability in the four-tiered grading system Regarding tumor budding estimation, both intraobserver agreement using H&E staining (Kappa 0.76) and IHC-based evaluation (Kappa 0.76) demonstrated substantial agreement. Moreover, interobserver agreement showed improvement from H&E-based evaluation (Kappa 0.64) to IHC-based evaluation (Kappa 0.68), remaining substantial. Tumor budding cut-off determination The ROC-derived thresholds were investigated and revealed no clear cut-off for recurrence discrimination in the distribution of tumor budding counts within this cohort (Supplementary Fig. ). The area under the curve (AUC) was 0.52, indicating the method did not have significant discrimination capacity to differentiate between recurrence and non-recurrence, as it was not significantly higher than the value of 0.5 where the prediction ability would equal a random guess. Given the outcomes yielded from the ROC curve analysis, we have endeavored to identify an optimal threshold for evaluating tumor budding through the application of IHC. This pursuit aims to establish a suitable cut-off point for accurate classification and prognostic assessment based on IHC-based tumor budding measurements. This determination involved considering multiple factors, such as the traditional Youden index and Liu’s index, along with selecting a threshold based on existing literature. Using the Youden index, the resulting cut-off value was 38, yielding a sensitivity of 16% and a specificity of 96%. Considered together with Liu´s index as well as the previously published cut-off value of 25 buds/0.785mm 2 by Prall et al. , none had been identified as the optimal differentiating threshold (Supplementary Table ). The ROC-derived thresholds were also examined for their association with mortality as an endpoint, with the AUC yielding a similar result (0.52). Characteristics of the Bd0 tumors Among the 493 patients included in the study, 21 patients were identified as having a complete absence of tumor budding and categorized as Bd0 based on IHC. On the corresponding H&E slide, the Bd0 comprised 115 tumors. Twenty tumors classified as Bd0 on IHC were included in the H&E-based Bd0 group (Fig. a). The IHC-based Bd0 tumors differed significantly from tumors exhibiting budding concerning MMR status, histologic subtype, and postoperative adjuvant chemotherapy ( p = 0.04, p = 0.01, and p = 0.05, respectively) (Table ). Twenty tumors (95%) showed microsatellite stability (MSS). It appeared that IHC-based Bd0 tumors were frequently located in the left side of the colon (67%) and had a mucinous phenotype (38%), although not consistently. There was a higher prevalence of T4 tumors in the IHC-based Bd0 group compared to tumors with budding; however, this difference was not statistically significant. No lymphatic invasion was observed among the IHC-based Bd0 tumors. Despite a high degree (29%) of venous invasion among IHC-based Bd0 tumors, no recurrences were observed in the group. However, it is worth noting that six of the patients (29%) received postoperative adjuvant chemotherapy. This percentage is partly due to the high proportion of T4 tumors, as four out of five patients with T4 were treated. In comparison, the H&E-based Bd0 tumors demonstrated variances from tumors exhibiting budding solely in relation to the examination of lymph nodes, histological subtype, and perineural invasion. Our analysis revealed that a higher number of lymph nodes were examined in the H&E-based Bd0 tumors, which presents an unexpected finding. The H&E-based Bd0 tumors were more frequently characterized by mucinous phenotype and demonstrated a lower incidence of perineural invasion. This aligns with previously documented correlations between tumor budding, histological subtype, and perineural invasion . Apart from these discrepancies, the H&E-based Bd0 tumors closely resemble budding tumors, suggesting a less prominent distinction compared to the IHC-based approach. The other characteristics observed in the IHC-based Bd0 tumors were not observed in the H&E-based Bd0 tumors. The application of IHC revealed tumor buds that may not have been visible. Therefore, the IHC-based Bd0 group can be considered as true Bd0 tumors. IHC-based Bd0 adds prognostic value Survival analyses were performed on the cohort, grouping the patients based on whether there was tumor budding or not. The 5-year rate of RFS was 90% in the IHC-based Bd0 group compared to 78% in the tumors with budding, while the corresponding 5-year rate of OS was 90% and 82%, respectively (Fig. ). No recurrences occurred in the IHC-based Bd0 group in contrast to 43 (9%) in the tumor budding group. The two groups exhibited statistically significant differences in survival functions for RFS ( p = 0.01) and OS ( p = 0.02), while the difference did not reach statistical significance for TTR ( p = 0.15). The results from the uni- and multivariable Cox regression analyses are presented in Table . The presence of tumor budding was significantly associated with reduced RFS (HR = 4.95, 95% CI 1.23–19.96, p = 0.02) and OS (HR = 4.51, 95% CI 1.12–18.18, p = 0.03) compared to no tumor budding. The presence of tumor budding maintained a significant and adverse effect on survival outcomes RFS (HR = 5.19, 95% CI 1.27–21.16, p = 0.02) and OS (HR = 4.47, 95% CI 1.10–18.27, p = 0.04) when correcting for MMR status, T category, and histologic type. A subgroup analysis of the H&E-based categorization did not show any significant differences between patients with budding tumors and those without in terms of survival endpoints (Fig. ). No differences were observed in the uni- or multivariable Cox regression analysis, with almost identical hazard rates being achieved (Table ). The study included 497 patients with complete clinicopathological data and available diagnostic slides. Four cases failed to complete the IHC evaluation due to technical reasons. The analyses were conducted on 493 cases with complete tumor budding evaluation by both H&E and IHC. Out of the 493 patients, 43 (9%) experienced a recurrence, and 175 patients died, of whom 27 died from colon cancer. The median follow-up time was 6.7 years (range 0.4–9.3 years). The distribution of tumor budding in categories evaluated by H&E was as follows: 115 (23%) Bd0, 217 (44%) Bd1, 108 (22%) Bd2, and 53 (11%) Bd3, whereas assessment by IHC resulted in 21 (4%) Bd0, 104 (21%) Bd1, 111 (23%) Bd2, and 257 (52%) Bd3. Evaluation by IHC classified more tumors as Bd3 than by H&E (Fig. a). All tumors examined exhibited positive pan-cytokeratin immunohistochemistry. The tumor cells were prominently highlighted and readily discernible, thereby facilitating their assessment (Fig. b). The H&E-based evaluation resulted in a median of 4 buds (range 0–59) and involved a review of an average of 7.2 slides. As expected, the tumor bud count assessed by IHC was significantly higher ( p < 0.01) and showed a median of 17 (range 0–100) (Fig. c). The IHC tumor bud count was, on average, 16 buds higher and the disparity between the staining methods escalated with increasing bud count, as illustrated in the Bland–Altman plot in Fig. d. Regarding tumor budding estimation, both intraobserver agreement using H&E staining (Kappa 0.76) and IHC-based evaluation (Kappa 0.76) demonstrated substantial agreement. Moreover, interobserver agreement showed improvement from H&E-based evaluation (Kappa 0.64) to IHC-based evaluation (Kappa 0.68), remaining substantial. The ROC-derived thresholds were investigated and revealed no clear cut-off for recurrence discrimination in the distribution of tumor budding counts within this cohort (Supplementary Fig. ). The area under the curve (AUC) was 0.52, indicating the method did not have significant discrimination capacity to differentiate between recurrence and non-recurrence, as it was not significantly higher than the value of 0.5 where the prediction ability would equal a random guess. Given the outcomes yielded from the ROC curve analysis, we have endeavored to identify an optimal threshold for evaluating tumor budding through the application of IHC. This pursuit aims to establish a suitable cut-off point for accurate classification and prognostic assessment based on IHC-based tumor budding measurements. This determination involved considering multiple factors, such as the traditional Youden index and Liu’s index, along with selecting a threshold based on existing literature. Using the Youden index, the resulting cut-off value was 38, yielding a sensitivity of 16% and a specificity of 96%. Considered together with Liu´s index as well as the previously published cut-off value of 25 buds/0.785mm 2 by Prall et al. , none had been identified as the optimal differentiating threshold (Supplementary Table ). The ROC-derived thresholds were also examined for their association with mortality as an endpoint, with the AUC yielding a similar result (0.52). Among the 493 patients included in the study, 21 patients were identified as having a complete absence of tumor budding and categorized as Bd0 based on IHC. On the corresponding H&E slide, the Bd0 comprised 115 tumors. Twenty tumors classified as Bd0 on IHC were included in the H&E-based Bd0 group (Fig. a). The IHC-based Bd0 tumors differed significantly from tumors exhibiting budding concerning MMR status, histologic subtype, and postoperative adjuvant chemotherapy ( p = 0.04, p = 0.01, and p = 0.05, respectively) (Table ). Twenty tumors (95%) showed microsatellite stability (MSS). It appeared that IHC-based Bd0 tumors were frequently located in the left side of the colon (67%) and had a mucinous phenotype (38%), although not consistently. There was a higher prevalence of T4 tumors in the IHC-based Bd0 group compared to tumors with budding; however, this difference was not statistically significant. No lymphatic invasion was observed among the IHC-based Bd0 tumors. Despite a high degree (29%) of venous invasion among IHC-based Bd0 tumors, no recurrences were observed in the group. However, it is worth noting that six of the patients (29%) received postoperative adjuvant chemotherapy. This percentage is partly due to the high proportion of T4 tumors, as four out of five patients with T4 were treated. In comparison, the H&E-based Bd0 tumors demonstrated variances from tumors exhibiting budding solely in relation to the examination of lymph nodes, histological subtype, and perineural invasion. Our analysis revealed that a higher number of lymph nodes were examined in the H&E-based Bd0 tumors, which presents an unexpected finding. The H&E-based Bd0 tumors were more frequently characterized by mucinous phenotype and demonstrated a lower incidence of perineural invasion. This aligns with previously documented correlations between tumor budding, histological subtype, and perineural invasion . Apart from these discrepancies, the H&E-based Bd0 tumors closely resemble budding tumors, suggesting a less prominent distinction compared to the IHC-based approach. The other characteristics observed in the IHC-based Bd0 tumors were not observed in the H&E-based Bd0 tumors. The application of IHC revealed tumor buds that may not have been visible. Therefore, the IHC-based Bd0 group can be considered as true Bd0 tumors. Survival analyses were performed on the cohort, grouping the patients based on whether there was tumor budding or not. The 5-year rate of RFS was 90% in the IHC-based Bd0 group compared to 78% in the tumors with budding, while the corresponding 5-year rate of OS was 90% and 82%, respectively (Fig. ). No recurrences occurred in the IHC-based Bd0 group in contrast to 43 (9%) in the tumor budding group. The two groups exhibited statistically significant differences in survival functions for RFS ( p = 0.01) and OS ( p = 0.02), while the difference did not reach statistical significance for TTR ( p = 0.15). The results from the uni- and multivariable Cox regression analyses are presented in Table . The presence of tumor budding was significantly associated with reduced RFS (HR = 4.95, 95% CI 1.23–19.96, p = 0.02) and OS (HR = 4.51, 95% CI 1.12–18.18, p = 0.03) compared to no tumor budding. The presence of tumor budding maintained a significant and adverse effect on survival outcomes RFS (HR = 5.19, 95% CI 1.27–21.16, p = 0.02) and OS (HR = 4.47, 95% CI 1.10–18.27, p = 0.04) when correcting for MMR status, T category, and histologic type. A subgroup analysis of the H&E-based categorization did not show any significant differences between patients with budding tumors and those without in terms of survival endpoints (Fig. ). No differences were observed in the uni- or multivariable Cox regression analysis, with almost identical hazard rates being achieved (Table ). In this retrospective, population-based cohort study, we highlighted tumor budding in a contemporary stage II colon cancer cohort using IHC. The identification of tumor buds increased dramatically using IHC, with tumors categorized as Bd3 showing a five-fold increase. The average bud count was 16 cells higher with IHC, and the differences between the two approaches escalated as the bud count increased. In 21 tumors, a complete absence of tumor budding based on IHC was observed. Remarkably, during the follow-up period, none of these patients experienced recurrences and demonstrated a significantly increased RFS as well as OS. Finding a clinically applicable cut-off point for tumor budding in this study proved to be a significant challenge. Other studies have used different cut-off values to categorize IHC-based tumor budding. Prall et al. found a cut-off of 25 tumor buds and reported a strong association between high-grade tumor budding and poor prognosis in stage I/II colorectal cancer examining a field of view measuring 0.785 mm 2 , as suggested by the ITBCC guidelines while incorporating up to five cells within their definition of tumor budding. Karamitopoulou et al. determined a cut-off of 10 tumor buds for prognostic subgroups across 10 HPFs in colorectal cancer. Quantitative scoring methods with no cut-offs have also been used, with Horcic et al. showing an exponential effect on the risk of death with increasing numbers of tumor buds in stage II colon cancer. Rieger et al. found significant associations between continuous peritumoral tumor budding scores both in a hotspot and in 10 HPF and disease-free survival in all stages of colorectal cancer, but the association was lost when evaluated by pre-defined cut-off scores. The different approaches and cut-off values proposed in similar studies reflect that finding a cut-off point for IHC-based tumor budding is not a straightforward task. Our results contribute to this discussion. In such circumstances, translating a prognostic biomarker into clinical practice becomes challenging, as clear guidelines for a biomarker must be in place before it can be clinically applied. Our recent study demonstrated the prognostic significance of high-grade tumor budding, as assessed by H&E staining, in the same patient cohort. We used the ITBCC guidelines and their recommended three-tiered classification system. With the use of H&E staining, we were able to distinguish different prognostic outcomes based on the established cut-off points. However, it seemed that the determination of tumor budding using IHC did not show the same pattern. There is no established criteria for cut-off values, and the literature explores different approaches and thresholds. In this study, we attempted to determine a cut-off value, but we were unable to find one that seemed clinically relevant. Consequently, this discrepancy led to the exclusion of a comparable classification between H&E and IHC staining, and it required a broader analysis of IHC tumor budding, where the distinction between budding and non-budding was made. This revealed intriguing prognostic significance associated with IHC-based Bd0. Our findings indicate that the Bd0 subgroup is associated with a complete absence of recurrences, suggesting that Bd0 carries a 100% predictive value for the absence of recurrences. The unique feature of the Bd0 group is its composition of patients who do not align with the low-risk category based on established risk factors. When examining Table , a distinct morphological profile of the IHC-based Bd0 tumors is not readily apparent, although these tumors were more likely to be of a mucinous type and show pMMR status. In future studies, it would be prudent to investigate the presence of inflammation in these tumors, particularly along with their molecular characteristics. The level of agreement in categorizing tumor budding among different observers varies across studies . Kai et al. showed that more experienced pathologists tend to assign higher tumor budding grades. In our results, the less experienced observer had a higher bud count, regardless of the staining approach (data not shown). Despite this, the interobserver agreement was deemed acceptable regardless of the staining method. The implementation of IHC demonstrated a slight improvement in the interobserver agreement, although a significant advantage for IHC over H&E was not observed, which aligns with findings from other studies . Therefore, it is essential to recognize that there may still be variability among observers in the assessment of IHC, and this variability remains significant . The size of the field of view is an important consideration when evaluating tumor budding. The ITBCC recommendations include the possibility of normalizing the field of view to a standard area of 0.785 mm 2 . However, this normalization may result in an underestimation of the bud count if the budding cells are not evenly distributed across the field of view . This effect is expected to be more pronounced when using IHC, which typically yields higher bud counts. Furthermore, this presents challenges when comparing findings with other studies, as previous studies using the 1HPF method often employ smaller fields of view, such as 0.238 mm 2 and 0.49 mm 2 . Therefore, caution must be exercised when extrapolating bud counts from other studies. Our results demonstrate that the use of IHC in comparison to H&E-stained sections detects three to four times more buds. These findings must be interpreted with caution, not just assuming that the IHC-based approach simply just facilitates the visualization of budding cells. Utilizing immunohistochemistry (IHC) for assessing tumor budding presents challenges in interpreting morphology and avoiding potential pitfalls, including pseudobudding. Distinguishing true buds from mechanically fragmented glands is difficult. True tumor buds infiltrate the peritumoral stroma, while pseudobuds are surrounded by inflammatory cells and typically found near fragmented glands caused by reactive processes like inflammation and glandular disruption . The presence of pseudobudding can lead to misleading results when using IHC staining, as individual cytokeratin-positive cells may be mistakenly counted as true tumor buds, artificially inflating the bud count. Caution is advised when evaluating tumor budding in areas with significant inflammation. The use of H&E staining is essential in such cases and cannot be substituted by IHC. H&E and IHC must complement each other, and perhaps we should not place excessive emphasis on the transferability of the H&E method to IHC but rather explore the alternative possibilities inherent in IHC. In the current era where artificial intelligence has gained significant attention, there have been numerous efforts to develop semi-automated methods for assessing morphological characteristics, such as tumor budding . In this regard, utilizing IHC may serve as a viable substitute for H&E staining in constructing these applications. However, relying solely on IHC for these assessments may pose some challenges as moderate agreement between observers has been reported, with complete agreement observed for only 34% of 3000 tumor bud candidates in a recent study . Therefore, it is important to emphasize the synergy between IHC and H&E, as it offers a more comprehensive perspective. In conclusion, prognostic markers need to exhibit appropriate levels of sensitivity and specificity to ensure clinical relevance. In this retrospective study, using a contemporary stage II colon cancer cohort, we were not able to find such a meaningful cut-off based on IHC-evaluated tumor budding. The successful adoption of the Bd0 category using IHC is of prognostic significance and mandates the need for further independent studies to gather an adequate amount of data. Due to the limited number of tumors ( n = 21) not exhibiting budding, our ability to draw significant conclusions is constrained. Nevertheless, our research findings indicate that Bd0 tumors display a lower level of aggressiveness in colon cancer compared to tumors that exhibit any degree of budding, and this is significant in a clinical setting when making the decision regarding adjuvant chemotherapy. Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 1270 KB) Supplementary file2 (PDF 182 KB) |
Pediatric primary care in Ontario and Manitoba after the onset of the COVID-19 pandemic: a population-based study | e04c3b21-cb11-4e02-9ad9-c61266f0e301 | 8687490 | Pediatrics[mh] | Study design and setting We conducted a population-based repeated cross-sectional study of rates of primary care visits for all children and adolescents in Ontario and Manitoba between Jan. 1, 2017, and Nov. 28, 2020, using linked health and administrative data sets. In Ontario, the first “wave” occurred from March to July 2020 and the second wave from September 2020 to February 2021, whereas in Manitoba, these waves occurred in August 2020 and in October 2020 to February 2021, respectively. Population We included children and adolescents (age ≤ 17 yr) living in Ontario or Manitoba during the study period. We excluded children and youth not residing in Ontario or Manitoba on Jan. 1 of each year and those who were ineligible for provincial health insurance coverage within 90 days of Jan. 1. Newborns (age < 29 d) were included as a rolling cohort. Newborns were excluded if they did not reside in Ontario or Manitoba, were ineligible for provincial health insurance at birth or had less than 28 days of follow-up at the end of the accrual period. Data sources We used health and demographic databases housed and linked at ICES (Ontario) and the Manitoba Centre for Health Policy (Appendix 1, Table S1, available at www.cmajopen.ca/content/9/4/E1149/suppl/DC1 ). Individual-level records were linked by means of unique encoded identifiers derived from the health care numbers of people eligible for provincial health insurance coverage. We used demographic information (date of birth, sex and postal code) from provincial health insurance registries (Ontario’s Registered Persons Database, Manitoba Health Insurance Registry) and physician billings databases (Ontario Health Insurance Plan, Manitoba Medical Services) to ascertain outpatient physician visits to family physicians and pediatricians for primary care, both in person and virtually (Appendix 1, Table S2). ICES data have been shown to be valid for sociodemographic data and physician billing claims. Equity strata of interest measurable in available administrative data included neighbourhood material deprivation quintile from the Ontario Marginalization Index and the Canadian Marginalization Index for Manitoba; rural (community size < 10 000) versus urban residence based on the 2016 Canadian census; and the child’s immigrant status (refugee, nonrefugee or Canadian-born) based on presence of a record in the provincial portions of Immigration, Refugees and Citizenship Canada’s Permanent Resident Data set. The Permanent Resident Data set includes demographic information for all people who arrived in Ontario from Jan. 1, 1985, to May 31, 2017, and in Manitoba from Jan. 1, 1985, to Dec. 31, 2017. Linkage of Immigration, Refugees and Citizenship Canada data to population registries has been conducted and validated in each province, with a linkage rate of 86% in Ontario and 96.2% in Manitoba. , Those whose date of eligibility for provincial health insurance coverage was after May 31, 2017 (Ontario) or Dec. 31, 2017 (Manitoba) were not included in immigrant and refugee analyses as they may have represented interprovincial migrants. Immigrant analyses excluded all children less than 3 years of age owing to data availability. Primary outcome measures Our main outcome measures included overall rates of primary care visits (in person and virtual) to a pediatrician or family physician (Appendix 1, Table S2). We further examined visits by type, including well-child visits (periodic health visits with or without vaccinations) , and sick visits (all other non–well-child visits). Statistical analysis We conducted Ontario and Manitoba analyses separately because the large Ontario population (relative to Manitoba) would have obscured Manitoba’s findings had analyses been combined. We expressed visit rates as total visits per 1000 eligible population, computed overall and by subgroups of clinically relevant age groups, with age defined on the day of the visit , (< 28 d, 29–365 d, > 1 yr to 5 yr, 6–12 yr and 13–17 yr), material deprivation quintile, rurality and immigrant status. Individual newborns were followed for 28 days after birth. For nonnewborns, we aggregated daily visit counts to strata of age group, sex and week, and used the corresponding population on Jan. 1 of each year as the denominator for rates as it did not change substantially over the year. The exposure was the period of the implementation of COVID-19 restrictions to the end of complete data availability, defined as Mar. 1 to Nov. 30, 2020. We used Poisson generalized estimating equation models for clustered count data to model pre-COVID-19 trends and used these to predict expected trends in the 9 months after the onset of COVID-19 in the absence of restrictions. The unit of analysis was the age group–sex–week stratum. The dependent variable was the count of events to the population in the stratum; the offset was log of the stratum-specific population; and the working correlation structure was AR(1). The pre-COVID-19 model included age group–sex indicators, a linear term of weeks since Jan. 1, 2017, to estimate the general trend in visit rates through Mar. 1, 2020, and pre-COVID-19 month indicators to model seasonal variations, with April as the reference month. The 3 years of prepandemic data was to allow for sufficient stability in pre-COVID-19 visit rates and population denominators. We computed expected visit rates in the 9 months after the onset of pandemic restrictions (and 95% confidence intervals [CIs]) by applying the linear combination of pre-COVID-19 regression coefficients to the post-onset age–sex–month strata and exponentiating. We expressed the relative change in post-onset visit rates as an adjusted rate ratio of observed to expected rates by exponentiating the difference of observed and expected post-onset log rates and CIs. We used Poisson regression for newborn models, with individual newborn as the unit of analysis and similar model terms but without an autoregressive correlation term. Statistical analyses were conducted with SAS statistical software, version 9.4 (SAS Institute). Ethics approval The Research Ethics Board at The Hospital for Sick Children and the Health Research Ethics Board at the University of Manitoba approved this study.
We conducted a population-based repeated cross-sectional study of rates of primary care visits for all children and adolescents in Ontario and Manitoba between Jan. 1, 2017, and Nov. 28, 2020, using linked health and administrative data sets. In Ontario, the first “wave” occurred from March to July 2020 and the second wave from September 2020 to February 2021, whereas in Manitoba, these waves occurred in August 2020 and in October 2020 to February 2021, respectively.
We included children and adolescents (age ≤ 17 yr) living in Ontario or Manitoba during the study period. We excluded children and youth not residing in Ontario or Manitoba on Jan. 1 of each year and those who were ineligible for provincial health insurance coverage within 90 days of Jan. 1. Newborns (age < 29 d) were included as a rolling cohort. Newborns were excluded if they did not reside in Ontario or Manitoba, were ineligible for provincial health insurance at birth or had less than 28 days of follow-up at the end of the accrual period.
We used health and demographic databases housed and linked at ICES (Ontario) and the Manitoba Centre for Health Policy (Appendix 1, Table S1, available at www.cmajopen.ca/content/9/4/E1149/suppl/DC1 ). Individual-level records were linked by means of unique encoded identifiers derived from the health care numbers of people eligible for provincial health insurance coverage. We used demographic information (date of birth, sex and postal code) from provincial health insurance registries (Ontario’s Registered Persons Database, Manitoba Health Insurance Registry) and physician billings databases (Ontario Health Insurance Plan, Manitoba Medical Services) to ascertain outpatient physician visits to family physicians and pediatricians for primary care, both in person and virtually (Appendix 1, Table S2). ICES data have been shown to be valid for sociodemographic data and physician billing claims. Equity strata of interest measurable in available administrative data included neighbourhood material deprivation quintile from the Ontario Marginalization Index and the Canadian Marginalization Index for Manitoba; rural (community size < 10 000) versus urban residence based on the 2016 Canadian census; and the child’s immigrant status (refugee, nonrefugee or Canadian-born) based on presence of a record in the provincial portions of Immigration, Refugees and Citizenship Canada’s Permanent Resident Data set. The Permanent Resident Data set includes demographic information for all people who arrived in Ontario from Jan. 1, 1985, to May 31, 2017, and in Manitoba from Jan. 1, 1985, to Dec. 31, 2017. Linkage of Immigration, Refugees and Citizenship Canada data to population registries has been conducted and validated in each province, with a linkage rate of 86% in Ontario and 96.2% in Manitoba. , Those whose date of eligibility for provincial health insurance coverage was after May 31, 2017 (Ontario) or Dec. 31, 2017 (Manitoba) were not included in immigrant and refugee analyses as they may have represented interprovincial migrants. Immigrant analyses excluded all children less than 3 years of age owing to data availability.
Our main outcome measures included overall rates of primary care visits (in person and virtual) to a pediatrician or family physician (Appendix 1, Table S2). We further examined visits by type, including well-child visits (periodic health visits with or without vaccinations) , and sick visits (all other non–well-child visits).
We conducted Ontario and Manitoba analyses separately because the large Ontario population (relative to Manitoba) would have obscured Manitoba’s findings had analyses been combined. We expressed visit rates as total visits per 1000 eligible population, computed overall and by subgroups of clinically relevant age groups, with age defined on the day of the visit , (< 28 d, 29–365 d, > 1 yr to 5 yr, 6–12 yr and 13–17 yr), material deprivation quintile, rurality and immigrant status. Individual newborns were followed for 28 days after birth. For nonnewborns, we aggregated daily visit counts to strata of age group, sex and week, and used the corresponding population on Jan. 1 of each year as the denominator for rates as it did not change substantially over the year. The exposure was the period of the implementation of COVID-19 restrictions to the end of complete data availability, defined as Mar. 1 to Nov. 30, 2020. We used Poisson generalized estimating equation models for clustered count data to model pre-COVID-19 trends and used these to predict expected trends in the 9 months after the onset of COVID-19 in the absence of restrictions. The unit of analysis was the age group–sex–week stratum. The dependent variable was the count of events to the population in the stratum; the offset was log of the stratum-specific population; and the working correlation structure was AR(1). The pre-COVID-19 model included age group–sex indicators, a linear term of weeks since Jan. 1, 2017, to estimate the general trend in visit rates through Mar. 1, 2020, and pre-COVID-19 month indicators to model seasonal variations, with April as the reference month. The 3 years of prepandemic data was to allow for sufficient stability in pre-COVID-19 visit rates and population denominators. We computed expected visit rates in the 9 months after the onset of pandemic restrictions (and 95% confidence intervals [CIs]) by applying the linear combination of pre-COVID-19 regression coefficients to the post-onset age–sex–month strata and exponentiating. We expressed the relative change in post-onset visit rates as an adjusted rate ratio of observed to expected rates by exponentiating the difference of observed and expected post-onset log rates and CIs. We used Poisson regression for newborn models, with individual newborn as the unit of analysis and similar model terms but without an autoregressive correlation term. Statistical analyses were conducted with SAS statistical software, version 9.4 (SAS Institute).
The Research Ethics Board at The Hospital for Sick Children and the Health Research Ethics Board at the University of Manitoba approved this study.
Characteristics of Ontario children (almost 3 million) and Manitoba children (> 300 000) eligible for provincial health care in 2017, 2018, 2019 and 2020 are presented in (Appendix 1, Table S3). During the pre-COVID-19 period, overall weekly visit rates per 1000 population were 49.5 in Ontario and 46.7 in Manitoba . The corresponding rates for well-child visits were 12.2 and 11.2, and for sick visits, 37.4 and 36.5. In the 9 months after pandemic restrictions were imposed, primary care visit rates decreased overall ; rates were 0.80 (95% CI 0.77–0.82) of expected in Ontario and 0.82 (95% CI 0.79–0.84) of expected in Manitoba ( ; Appendix 1, Table S4). In this period, 53% of visits in Ontario and 29% of those in Manitoba took place virtually. Primary care visit rates reached a nadir in April 2020, after which they slowly increased, peaking in November 2020 (Appendix 1, Table S4). The extent of the decline was greatest for well-child visits in Ontario (adjusted rate ratio 0.73, 95% CI 0.66–0.80) and for sick visits in Manitoba (adjusted rate ratio 0.78, 95% CI 0.75–0.81). Age groups In Ontario, all age groups except newborns experienced a sharp immediate decrease in well-child visits after pandemic restrictions were imposed, with some recovery by November 2020 ( and ; Appendix 1, Tables S5 and S6). Among newborns, well-child visits were lower than and sick visits were higher than expected levels. For sick visits among those aged 29–365 days, visit rates were at or above expected levels, whereas for all children more than 1 year of age, they were well below expected levels (Appendix 1, Table S5). In Manitoba, newborn well-child visit rates were similar to expected, but sick visit rates were above expected levels (Appendix 1, Table S6). As in Ontario, Manitoba children aged more than 1 year had lower than expected rates of both well-child and sick visits, with some return toward baseline for well-child visits in the final 2 months of the study period. Material deprivation We found a small gradient in observed versus expected visit rates by neighbourhood material deprivation quintile. Those in the most deprived quintile had the lowest relative visit rates compared to expected in Ontario but not Manitoba ( ; Appendix 1, Figure S1, Tables S7 and S8). Uptake of virtual care was lowest in the most deprived quintile for Ontario but not Manitoba (Ontario: 54.6% in quintile 1 v. 50.1% in quintile 5; Manitoba: 27.9% in quintile 1 v. 32.0% in quintile 5) (Appendix 1, Table S13). Rurality The largest decreases in adjusted relative rates in overall primary care visits were observed for urban Ontarians (adjusted rate ratio 0.79, 95% CI 0.77–0.82) and rural Manitobans (adjusted rate ratio 0.78, 95% CI 0.75–0.80). These declines were most pronounced for sick visits in Ontario and well-child visits in Manitoba ( ; Appendix 1, Figure S2, Tables S9 and S10). Immigrant status Refugees and immigrants to Manitoba had similar rates of well-child visits as Canadian-born children after the onset of pandemic restrictions, with rates at or near expected ( ; Appendix 1, Figure S3, Tables S11 and S12). Sick visit rates were lower than expected among these groups, with Canadian-born children (adjusted rate ratio 0.80, 95% CI 0.78–0.83) experiencing a greater relative decrease than refugees (adjusted rate ratio 0.91, 95% CI 0.86–0.97). In contrast, Ontario well-child visits were well below expected across all groups, with lowest rates observed among nonrefugee immigrants (adjusted rate ratio 0.54, 95% CI 0.51–0.58). Sick visit rates were similarly low across groups. In Ontario, a smaller proportion of visits were virtual for refugees (49.5%) than for immigrants (61.2%) and Canadian-born children (52.7%). Uptake of virtual care was generally much lower in Manitoba than in Ontario, with lowest rates among refugees (22.1%) (Appendix 1, Table S13).
In Ontario, all age groups except newborns experienced a sharp immediate decrease in well-child visits after pandemic restrictions were imposed, with some recovery by November 2020 ( and ; Appendix 1, Tables S5 and S6). Among newborns, well-child visits were lower than and sick visits were higher than expected levels. For sick visits among those aged 29–365 days, visit rates were at or above expected levels, whereas for all children more than 1 year of age, they were well below expected levels (Appendix 1, Table S5). In Manitoba, newborn well-child visit rates were similar to expected, but sick visit rates were above expected levels (Appendix 1, Table S6). As in Ontario, Manitoba children aged more than 1 year had lower than expected rates of both well-child and sick visits, with some return toward baseline for well-child visits in the final 2 months of the study period.
We found a small gradient in observed versus expected visit rates by neighbourhood material deprivation quintile. Those in the most deprived quintile had the lowest relative visit rates compared to expected in Ontario but not Manitoba ( ; Appendix 1, Figure S1, Tables S7 and S8). Uptake of virtual care was lowest in the most deprived quintile for Ontario but not Manitoba (Ontario: 54.6% in quintile 1 v. 50.1% in quintile 5; Manitoba: 27.9% in quintile 1 v. 32.0% in quintile 5) (Appendix 1, Table S13).
The largest decreases in adjusted relative rates in overall primary care visits were observed for urban Ontarians (adjusted rate ratio 0.79, 95% CI 0.77–0.82) and rural Manitobans (adjusted rate ratio 0.78, 95% CI 0.75–0.80). These declines were most pronounced for sick visits in Ontario and well-child visits in Manitoba ( ; Appendix 1, Figure S2, Tables S9 and S10).
Refugees and immigrants to Manitoba had similar rates of well-child visits as Canadian-born children after the onset of pandemic restrictions, with rates at or near expected ( ; Appendix 1, Figure S3, Tables S11 and S12). Sick visit rates were lower than expected among these groups, with Canadian-born children (adjusted rate ratio 0.80, 95% CI 0.78–0.83) experiencing a greater relative decrease than refugees (adjusted rate ratio 0.91, 95% CI 0.86–0.97). In contrast, Ontario well-child visits were well below expected across all groups, with lowest rates observed among nonrefugee immigrants (adjusted rate ratio 0.54, 95% CI 0.51–0.58). Sick visit rates were similarly low across groups. In Ontario, a smaller proportion of visits were virtual for refugees (49.5%) than for immigrants (61.2%) and Canadian-born children (52.7%). Uptake of virtual care was generally much lower in Manitoba than in Ontario, with lowest rates among refugees (22.1%) (Appendix 1, Table S13).
In this population-based study of children and adolescents in 2 Canadian provinces, we found a large, rapid decrease in primary care use in the first 9 months after COVID-19 pandemic restrictions were imposed. Much of primary care for children was delivered virtually, especially in Ontario. Well-child visits for vaccinations and growth and development surveillance occurred at about three-quarters the rate in previous years in Ontario but at close to expected levels in Manitoba. Such interprovincial differences were unexpected. Importantly, we found small disparities in the extent of shifts in primary care in Ontario but not in Manitoba, with a disproportionate reduction in essential well-child care for children and adolescents from immigrant and refugee families, of low socioeconomic status and from urban neighbourhoods. Although delays and reductions in primary care were expected given the large disruptions to service delivery and decreased transmission of other infectious agents, the decline in primary care delivery persisted through the first 9 months of the pandemic, including during periods when little virus was circulating, personal protective equipment was more available and infection control measures were in place. In Ontario, Glazier and colleagues reported a 28% decrease in primary care visits in the first few months after the pandemic onset across all ages, with more pronounced effects among children. We also found a rapid decrease in observed visits rates in Ontario as well as Manitoba, but the extent of change, especially for well-child care, was less in Manitoba. Lower SARS-CoV-2 disease activity in Manitoba may explain this finding. In both provinces, the use of virtual care declined in the later months of 2020. The levels at which virtual care will be sustained, and the longer-term impact on child health, access to care and quality of care of this wide-spread shift to virtual care remain to be determined. More transient visit declines after the onset of the pandemic have been described elsewhere. In Chicago, well-child and vaccination visits decreased to half of prepandemic levels and then returned to more than 90% of the prior year within 8 weeks. In South Africa, there was a rapid decrease in pediatric primary care, followed by a rapid return to baseline within 3 months. In jurisdictions where telemedicine remuneration did not match that of in-person visits (e.g., Chicago), virtual care uptake was low (< 10%). It is possible that, in Ontario and Manitoba, adequate remuneration for virtual care may have facilitated access to care for some families and the observed interprovincial differences may have been fuelled by the volume of circulating virus (and consequent restrictions) within either province. In parallel with these observed changes in primary care, a substantial shift in caregiver and family health-seeking behaviour for acute care and after-hours ambulatory care was reported in Canada and elsewhere, with large, rapid declines in visits after the pandemic onset. , , Despite these changes in use of health care services, there has been no reported change in clinical severity or increase in severe harm. Although other investigators have documented the rapid decline in both primary and acute care use after the onset of the COVID-19 pandemic, few have reported on socioeconomic and demographic disparities of observed changes. , , The pandemic has magnified structural factors underpinning global health inequities, – and our findings show that, at least in Ontario, primary care for children may have also been affected. Schweiberger and colleagues reported that white non-Hispanic children in the United States were more likely to have a preventive or telemedicine visit than other racial groups. Our findings of particularly low well-child visit rates among those from more materially deprived neighbourhoods in Ontario may be explained by amplification of challenges accessing and navigating the health care system, virtual care literacy and access, and heightened fear of seeking care driven by high levels of infection in these communities. – Equitable primary care use observed in Manitoba may be related to more centralized delivery of pediatric primary care through hospital-based clinics that serve large proportions of urban, refugee and low-income children. The role providers had in contributing to these shifts in primary care delivery is unclear; possible factors include a lack of personal protective equipment, workforce redeployment, capacity for virtual care delivery and practice jurisdiction. Limitations One strength of this study is complete population coverage spanning the first 9 months after COVID-19 pandemic restrictions were imposed across 2 Canadian provinces with different SARS-CoV-2 disease activity. Limitations include that virtual care codes did not allow us to differentiate telephone and video visits, the latter of which may be better suited to clinical assessment of children. Similarly, fee codes have not been validated to distinguish well-child from sick visits, and these codes are commonly used in Ontario and Manitoba to measure primary care use. However, immunization codes have been validated to distinguish these visits, and vaccinations typically occur at well-child visits. We did not have individual measures of sociodemographic characteristics or family composition, although neighbourhood-level measures have been shown to have important associations with health outcomes. We did not assess provider-level characteristics, which may be important to understand drivers of inequities and reduced care access during the pandemic. Salaried physician care and some non-physician care (< 1% of population), including that provided by community health centres, nurse practitioners and social workers, were not included owing to data availability, but such providers care disproportionately for more marginalized populations. Conclusion We found large and rapid decreases in primary care visits for well-child care, vaccinations and sickness in the 9 months after the onset of the COVID-19 pandemic in Ontario and Manitoba, with a substantial proportion of care delivered virtually. Ontarian but not Manitoban children of low socioeconomic status and from urban neighbourhoods had slightly lower visit rates compared to expected. The pandemic, and measures instituted to lessen its impact, may have threatened essential elements of primary care, including mechanisms to mitigate spread of vaccine-preventable diseases, ensure early identification of developmental concerns and reduce health inequities. The longer-term impact on child development and health and vaccine coverage remains to be determined, and understanding health care provider factors contributing to the shifts warrants further study.
One strength of this study is complete population coverage spanning the first 9 months after COVID-19 pandemic restrictions were imposed across 2 Canadian provinces with different SARS-CoV-2 disease activity. Limitations include that virtual care codes did not allow us to differentiate telephone and video visits, the latter of which may be better suited to clinical assessment of children. Similarly, fee codes have not been validated to distinguish well-child from sick visits, and these codes are commonly used in Ontario and Manitoba to measure primary care use. However, immunization codes have been validated to distinguish these visits, and vaccinations typically occur at well-child visits. We did not have individual measures of sociodemographic characteristics or family composition, although neighbourhood-level measures have been shown to have important associations with health outcomes. We did not assess provider-level characteristics, which may be important to understand drivers of inequities and reduced care access during the pandemic. Salaried physician care and some non-physician care (< 1% of population), including that provided by community health centres, nurse practitioners and social workers, were not included owing to data availability, but such providers care disproportionately for more marginalized populations.
We found large and rapid decreases in primary care visits for well-child care, vaccinations and sickness in the 9 months after the onset of the COVID-19 pandemic in Ontario and Manitoba, with a substantial proportion of care delivered virtually. Ontarian but not Manitoban children of low socioeconomic status and from urban neighbourhoods had slightly lower visit rates compared to expected. The pandemic, and measures instituted to lessen its impact, may have threatened essential elements of primary care, including mechanisms to mitigate spread of vaccine-preventable diseases, ensure early identification of developmental concerns and reduce health inequities. The longer-term impact on child development and health and vaccine coverage remains to be determined, and understanding health care provider factors contributing to the shifts warrants further study.
Appendix 1: Supplemental material Reviewer comments Original submision RECORD statement
|
FHL1: A novel diagnostic marker for papillary thyroid carcinoma | 80b82a62-a413-4ea4-81aa-5aee559e80cd | 11551809 | Anatomy[mh] | Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, accounting for over 85% of all thyroid malignancies. Over recent decades, the global incidence of PTC has risen significantly. , , While PTC is typically indolent, it can occasionally exhibit aggressive behaviors, such as metastasis and local recurrence. , Accurate identification and diagnosis of thyroid nodules are crucial for distinguishing tumors with aggressive characteristics. Contemporary research on the molecular pathogenesis of PTC has primarily concentrated on genetic alterations that are highly expressed across various signaling pathways. PTC exhibits several variants characterized by mutually exclusive mutations that primarily affect the mitogen‐activated protein kinase pathway, essential for PTC progression. , the BRAF V600E mutation is the most common, present in 60% of cases, followed by RAS mutations (15%) and chromosomal rearrangements affecting genes like BRAF or receptor tyrosine kinases such as RET, NTRK, and ALK (12%). The remaining 13% of cases have no known driver mutations. Numerous studies have explored biomarkers critical for tumorigenesis and prognosis in PTC. Established markers include TTF‐1, involved in thyroid cell differentiation, and BRAF, whose mutations promote tumorigenesis and are associated with poor prognosis. Other markers such as PAX‐8, thyroperoxidase, CK19, galectin‐3, FN1, CD56, and HBME‐1 are upregulated in PTC compared to normal thyroid tissues. However, the diagnostic reliability of these markers is variable, and they lack specificity for PTC. Additionally, the expressions of CK19, TPO, HBME‐1, and galectin‐3 do not significantly aid in distinguishing PTC from other aggressive lesions. Therefore, in this study, we aimed to identify molecular biomarkers that can serve as reliable indicators specifically related to the oncogenesis and progression of PTC. Patients This study analyzed 30 archival paraffin‐embedded blocks histologically diagnosed as PTC, collected from January 2015 to December 2016. These cases were surgically treated at the Department of General or Head and Neck Surgery at Joint Logistic Support Force 900th Hospital, Fuzhou. Each patient underwent thyroidectomy, with surgical specimens fixed in 10% buffered formalin and diagnosed histopathologically by the Department of Pathology. Clinical and pathological data from these 30 patients were retrospectively reviewed (Table ), comprising 19 women (63.3%) and 11 men (36.7%), with an average age of 44.8 ± 14.7 years. Datasets and data preprocessing We investigated the gene expression profile of PTC samples by searching the NCBI Gene Expression Omnibus (GEO) ( http://www.ncbi.nlm.nih.gov/geo ). Two datasets based on GPL570, including the GSE3678 dataset from New York Medical School and the GSE33630 dataset from the Free University of Brussels, were downloaded for further research. These datasets were chosen based on their comprehensive inclusion of PTC and corresponding normal tissues‐essential for our analysis‐and their extensive citation in prior research, affirming their reliability and scientific relevance. We analyzed 7 PTC and adjacent normal samples from GSE3678, alongside 49 PTC and 45 normal thyroid samples from GSE33630. Differentially expressed genes (DEGs) were identified through fold‐change filtering, using a threshold of |log2FC| (Fold Change) ≥ 1.5 and an adjusted p ＜0.01 to ascertain significant differential expression. Pathway and Gene Ontology (GO) analysis were conducted to assess the biological functions of this subset of DEGs ( p ＜0.01). Simultaneously, we utilized BisoGenet to visualize the protein−protein interaction (PPI) network of DEGs, and filtered the network by calculating the value of all the nodes, which was carried out by Degree‐sorting. The hub genes are core genes with important physiological functions. DEGs between Low‐ and high four and a half LIM domains 1 (FHL1) expression groups were identified by using the DESeq. 2 R package. In this study, Gene Set Enrichment Analysis (GSEA) was performed using the TCGAplot R package to demonstrate the significant functions and pathways between the two groups. ONCOMINE analysis The ONCOMINE gene expression array datasets ( www.oncomine.org ) provide a platform for analyzing the mRNA levels of the hub genes. It is an online cancer microarray database that can facilitate genetic screening through genome‐wide expression analyses. In this study, the gene expression datasets of human malignant and normal thyroid tissues were compared using a Student's‐test to generate a p Value. The p Value was set at 0.05, and the fold change was defined as 2. The data type in the datasets was restricted to mRNA. Quantitative real‐time polymerase chain reaction (Q‐PCR) Total RNA was extracted from consecutive paraffin slices using AmoyDx® DNA and RNA Extraction Kits Silica‐based Spin Column Reagent (Amoy Diagnostics Co.). Subsequently, the RNA was converted to cDNA using the Primescript RT reagent kit (Takara Bio) following the manufacturer's protocol. Q‐PCR was performed using SYBR Premix Ex Taq (Takara Bio) in the Applied Biosystems StepOne Real‐Time PCR System (Applied Biosystems). The β‐actin gene was used as internal control. The comparative C t method was used to quantify gene expression. The target gene expression level was normalized to the expression of the housekeeping gene β‐actin within the same sample (−Δ C t ), where the relative expression of each gene was calculated with 10 3 × 2 − Δ C t . Histopathological examination All the hematoxylin and eosin stain (H&E) sections were assessed by two independent pathologists to confirm the diagnosis of PTC. All cases were classical forms of PTC without any histological variants. The histological diagnosis of PTC was based on the criteria of the 2017 WHO classification of tumours of endocrine organs. , These criteria include nuclear features such as: enlargement, crowding, and overlapping, irregularity of nuclear contours, nuclear grooves, ground‐glass nuclei characteristics, pseudoinclusions in the nucleus and psammoma bodies. In addition, the classical‐type PTC showed characteristic papillary architecture, either pure or mixed with follicles in varying percentages. Immunohistochemistry (IHC) The paraffin tissue blocks were sectioned into 4 μm‐thick slices and then transferred onto adhesive slides for IHC analysis. Tissue sections were deparaffinized in xylene three times for 5 min each, and then rehydrated in 100%, 95%, 80%, and 75% ethanol for 3 min respectively after overnight incubation at 60°C. Antigen retrieval was performed in a pressure cooker (Supor) for 3 min using x1 citrate buffer (pH 6.0) or EDTA buffer (PH9.0). Slides were immunostained with monoclonal antibodies against the BCL2 antigen (clone SP66, Maixin), CD117/C‐kit antigen (clone YR145, Maixin), and with monoclonal antibodies against the FHL1 (10991‐1‐AP, Proteintech Group, Inc.), PPARGC1A (Boster, China), and TLE1 antigen (Maixin). The IHC staining was performed using an Envision Plus System (Fuzhou Maixin Biotech Co., Ltd.) according to the manufacturer's protocol. Finally, all the slides were counterstained with hematoxylin. Appropriate positive and negative controls were prepared. The immunostaining scores were assigned by two pathologists using an Olympus BX‐51 light microscope (Olympus). Scoring was conducted semi‐quantitatively, based on the cytoplasmic staining for BCL2 and FHL1. The staining intensity was graded on a scale of 0 to 3, where 0, 1+, 2+, and 3+ indicate no staining, weak/slight staining, moderate staining, and intense staining respectively. The proportion of positive, immunoreactive tumor cells was interpreted as 1+ (<5% of cells), 2+ (5%−50% of cells), and 3+ (>50% of cells). , The proportional score and the intensity score were multiplied to obtain a total score, ranging from 0 to 9. Sections with total scores ≤ 2 were considered negative, while those with total scores ≥ 3 were considered positive. Written informed consent for the study using these samples was obtained from the patients and approved by the Ethics Committee of 900th Hospital of PLA. Statistical analysis Statistical analysis was conducted using SPSS version 20 software. The chi‐square test was used for immunohistochemical analysis of the clinical specimens. This study analyzed 30 archival paraffin‐embedded blocks histologically diagnosed as PTC, collected from January 2015 to December 2016. These cases were surgically treated at the Department of General or Head and Neck Surgery at Joint Logistic Support Force 900th Hospital, Fuzhou. Each patient underwent thyroidectomy, with surgical specimens fixed in 10% buffered formalin and diagnosed histopathologically by the Department of Pathology. Clinical and pathological data from these 30 patients were retrospectively reviewed (Table ), comprising 19 women (63.3%) and 11 men (36.7%), with an average age of 44.8 ± 14.7 years. We investigated the gene expression profile of PTC samples by searching the NCBI Gene Expression Omnibus (GEO) ( http://www.ncbi.nlm.nih.gov/geo ). Two datasets based on GPL570, including the GSE3678 dataset from New York Medical School and the GSE33630 dataset from the Free University of Brussels, were downloaded for further research. These datasets were chosen based on their comprehensive inclusion of PTC and corresponding normal tissues‐essential for our analysis‐and their extensive citation in prior research, affirming their reliability and scientific relevance. We analyzed 7 PTC and adjacent normal samples from GSE3678, alongside 49 PTC and 45 normal thyroid samples from GSE33630. Differentially expressed genes (DEGs) were identified through fold‐change filtering, using a threshold of |log2FC| (Fold Change) ≥ 1.5 and an adjusted p ＜0.01 to ascertain significant differential expression. Pathway and Gene Ontology (GO) analysis were conducted to assess the biological functions of this subset of DEGs ( p ＜0.01). Simultaneously, we utilized BisoGenet to visualize the protein−protein interaction (PPI) network of DEGs, and filtered the network by calculating the value of all the nodes, which was carried out by Degree‐sorting. The hub genes are core genes with important physiological functions. DEGs between Low‐ and high four and a half LIM domains 1 (FHL1) expression groups were identified by using the DESeq. 2 R package. In this study, Gene Set Enrichment Analysis (GSEA) was performed using the TCGAplot R package to demonstrate the significant functions and pathways between the two groups. The ONCOMINE gene expression array datasets ( www.oncomine.org ) provide a platform for analyzing the mRNA levels of the hub genes. It is an online cancer microarray database that can facilitate genetic screening through genome‐wide expression analyses. In this study, the gene expression datasets of human malignant and normal thyroid tissues were compared using a Student's‐test to generate a p Value. The p Value was set at 0.05, and the fold change was defined as 2. The data type in the datasets was restricted to mRNA. Total RNA was extracted from consecutive paraffin slices using AmoyDx® DNA and RNA Extraction Kits Silica‐based Spin Column Reagent (Amoy Diagnostics Co.). Subsequently, the RNA was converted to cDNA using the Primescript RT reagent kit (Takara Bio) following the manufacturer's protocol. Q‐PCR was performed using SYBR Premix Ex Taq (Takara Bio) in the Applied Biosystems StepOne Real‐Time PCR System (Applied Biosystems). The β‐actin gene was used as internal control. The comparative C t method was used to quantify gene expression. The target gene expression level was normalized to the expression of the housekeeping gene β‐actin within the same sample (−Δ C t ), where the relative expression of each gene was calculated with 10 3 × 2 − Δ C t . All the hematoxylin and eosin stain (H&E) sections were assessed by two independent pathologists to confirm the diagnosis of PTC. All cases were classical forms of PTC without any histological variants. The histological diagnosis of PTC was based on the criteria of the 2017 WHO classification of tumours of endocrine organs. , These criteria include nuclear features such as: enlargement, crowding, and overlapping, irregularity of nuclear contours, nuclear grooves, ground‐glass nuclei characteristics, pseudoinclusions in the nucleus and psammoma bodies. In addition, the classical‐type PTC showed characteristic papillary architecture, either pure or mixed with follicles in varying percentages. The paraffin tissue blocks were sectioned into 4 μm‐thick slices and then transferred onto adhesive slides for IHC analysis. Tissue sections were deparaffinized in xylene three times for 5 min each, and then rehydrated in 100%, 95%, 80%, and 75% ethanol for 3 min respectively after overnight incubation at 60°C. Antigen retrieval was performed in a pressure cooker (Supor) for 3 min using x1 citrate buffer (pH 6.0) or EDTA buffer (PH9.0). Slides were immunostained with monoclonal antibodies against the BCL2 antigen (clone SP66, Maixin), CD117/C‐kit antigen (clone YR145, Maixin), and with monoclonal antibodies against the FHL1 (10991‐1‐AP, Proteintech Group, Inc.), PPARGC1A (Boster, China), and TLE1 antigen (Maixin). The IHC staining was performed using an Envision Plus System (Fuzhou Maixin Biotech Co., Ltd.) according to the manufacturer's protocol. Finally, all the slides were counterstained with hematoxylin. Appropriate positive and negative controls were prepared. The immunostaining scores were assigned by two pathologists using an Olympus BX‐51 light microscope (Olympus). Scoring was conducted semi‐quantitatively, based on the cytoplasmic staining for BCL2 and FHL1. The staining intensity was graded on a scale of 0 to 3, where 0, 1+, 2+, and 3+ indicate no staining, weak/slight staining, moderate staining, and intense staining respectively. The proportion of positive, immunoreactive tumor cells was interpreted as 1+ (<5% of cells), 2+ (5%−50% of cells), and 3+ (>50% of cells). , The proportional score and the intensity score were multiplied to obtain a total score, ranging from 0 to 9. Sections with total scores ≤ 2 were considered negative, while those with total scores ≥ 3 were considered positive. Written informed consent for the study using these samples was obtained from the patients and approved by the Ethics Committee of 900th Hospital of PLA. Statistical analysis was conducted using SPSS version 20 software. The chi‐square test was used for immunohistochemical analysis of the clinical specimens. GO and pathway analysis for DEGs between PTC and normal thyroid tissues Differential expression analysis was conducted using Bayes t‐statistics within the linear models for microarray data (Limma), facilitated by the Bioconductor limma package. DEGs were deemed significant if they met both criteria: an adjusted p Value of <0.01 and a |log2FC| (fold change) ≥ 1.5. Upon merging datasets GSE3678 and GSE33630, we identified 429 genes that were significantly altered in PTC tissues compared to normal thyroid tissues. Hierarchical clustering analysis revealed 237 genes were upregulated and 192 genes were downregulated (Figure ). This study primarily focuses on the down‐regulated genes in PTC, which have been less frequently reported in existing literature. Significant pathway and GO analyses for these down‐regulated genes highlighted associations with thyroid hormone biosynthesis and amino acid metabolism between the PTC and normal thyroid tissue groups (Figure ). Biological functions and pathway analysis of FHL1 To further elucidate the biological functions of FHL1, we analyzed the DEGs between the low‐ and high‐expression FHL1 groups based on the median FHL1 expression value in The Cancer GenomeAtlas Program (TCGA) data thyroid cancer expression profile (Figure ). We also conducted a GSEA pathway analysis. The results showed that high FHL1 expression was mainly enriched in adaptive immune response, lymphocyte‐mediated immunity, T cell‐mediated immunity, and cell killing (Figure ). PPI network for the down‐regulated DEGs To further investigate the interactions among down‐regulated DEGs in PTC, we utilized BisoGenet software to construct a PPI network. This network elucidates direct or indirect connections among the proteins encoded by these genes. The network comprised 552 nodes and 952 edges, indicating a robust set of predicted functional associations among the proteins (Figure ). Additionally, we identified a core network centered around six proteins corresponding to six pivotal hub genes: TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A. Each of these proteins had more than twenty connections to other proteins in the network, with a node degree cutoff set at 25 (Figure ). This core network underscores the significant roles these hub genes play in modulating the tumor biology of PTC through their influence on down‐regulated DEGs. mRNA expression of six hub genes To validate the results of microarray data, six selected hub genes (TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A) were analyzed using ONCOMINE and quantitative real‐time PCR. The results are presented in (Figure ). All six genes showed downregulated in PTC compared with the normal thyroid follicles surrounding the tumour, confirming the results of the microarray analysis (Figure ). By contrast to other genes, however, GHR showed a less significant difference in our specimens (Figure ). IHC Results Quantitative real‐time PCR results prompted us to further explore the expression levels of TLE1, BCL2, FHL1, KIT, GHR, and PPARGC1A in human PTC tissues via immunohistochemical staining. The clinical, histopathological, and immunohistochemical profiles of the patients are detailed in Table . The cohort comprised 19 females (63.3%) and 11 males (36.7%) diagnosed with PTC, with an average age of 44.8 years (SD ± 14.7). Immunohistochemical analysis showed a strong nuclear and cytoplasmic presence of FHL1 in normal thyroid tissues adjacent to tumors, whereas it was notably absent in PTC tissues ( p ＜0.01) In contrast, BCL2 expression did not significantly differ between PTC and adjacent normal thyroid tissues ( p > 0.05) Intriguingly, TLE1, KIT, PPARGC1A, and GHR were undetectable in both tumor and normal tissues (Figure ). In addition, we have conducted IHC testing on three fine‐needle aspiration (FNA) specimens. Two of them showed weak positive expression of FHL1 (Figure ). Differential expression analysis was conducted using Bayes t‐statistics within the linear models for microarray data (Limma), facilitated by the Bioconductor limma package. DEGs were deemed significant if they met both criteria: an adjusted p Value of <0.01 and a |log2FC| (fold change) ≥ 1.5. Upon merging datasets GSE3678 and GSE33630, we identified 429 genes that were significantly altered in PTC tissues compared to normal thyroid tissues. Hierarchical clustering analysis revealed 237 genes were upregulated and 192 genes were downregulated (Figure ). This study primarily focuses on the down‐regulated genes in PTC, which have been less frequently reported in existing literature. Significant pathway and GO analyses for these down‐regulated genes highlighted associations with thyroid hormone biosynthesis and amino acid metabolism between the PTC and normal thyroid tissue groups (Figure ). To further elucidate the biological functions of FHL1, we analyzed the DEGs between the low‐ and high‐expression FHL1 groups based on the median FHL1 expression value in The Cancer GenomeAtlas Program (TCGA) data thyroid cancer expression profile (Figure ). We also conducted a GSEA pathway analysis. The results showed that high FHL1 expression was mainly enriched in adaptive immune response, lymphocyte‐mediated immunity, T cell‐mediated immunity, and cell killing (Figure ). To further investigate the interactions among down‐regulated DEGs in PTC, we utilized BisoGenet software to construct a PPI network. This network elucidates direct or indirect connections among the proteins encoded by these genes. The network comprised 552 nodes and 952 edges, indicating a robust set of predicted functional associations among the proteins (Figure ). Additionally, we identified a core network centered around six proteins corresponding to six pivotal hub genes: TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A. Each of these proteins had more than twenty connections to other proteins in the network, with a node degree cutoff set at 25 (Figure ). This core network underscores the significant roles these hub genes play in modulating the tumor biology of PTC through their influence on down‐regulated DEGs. To validate the results of microarray data, six selected hub genes (TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A) were analyzed using ONCOMINE and quantitative real‐time PCR. The results are presented in (Figure ). All six genes showed downregulated in PTC compared with the normal thyroid follicles surrounding the tumour, confirming the results of the microarray analysis (Figure ). By contrast to other genes, however, GHR showed a less significant difference in our specimens (Figure ). Quantitative real‐time PCR results prompted us to further explore the expression levels of TLE1, BCL2, FHL1, KIT, GHR, and PPARGC1A in human PTC tissues via immunohistochemical staining. The clinical, histopathological, and immunohistochemical profiles of the patients are detailed in Table . The cohort comprised 19 females (63.3%) and 11 males (36.7%) diagnosed with PTC, with an average age of 44.8 years (SD ± 14.7). Immunohistochemical analysis showed a strong nuclear and cytoplasmic presence of FHL1 in normal thyroid tissues adjacent to tumors, whereas it was notably absent in PTC tissues ( p ＜0.01) In contrast, BCL2 expression did not significantly differ between PTC and adjacent normal thyroid tissues ( p > 0.05) Intriguingly, TLE1, KIT, PPARGC1A, and GHR were undetectable in both tumor and normal tissues (Figure ). In addition, we have conducted IHC testing on three fine‐needle aspiration (FNA) specimens. Two of them showed weak positive expression of FHL1 (Figure ). While there are established morphological criteria for diagnosing PTC, distinguishing these features, particularly in fine needle aspiration and biopsy specimens, remains challenging. Immunohistochemical staining is valuable for differential diagnosis. In this research, we leveraged bioinformatics to identify several hub genes linked to PTC. PCR and IHC results indicated that FHL1 expression was down‐regulated in PTC tissues. This study undertook a comprehensive gene expression profile analysis to pinpoint candidate genes that could act as reliable molecular markers for diagnosing PTC. By integrating microarray data from 108 samples, including 56 PTC specimens and 52 normal thyroid tissues, we identified 429 DEGs‐237 were up‐regulated and 192 down‐regulated. Our focus was particularly on the down‐regulated genes given their potential role in PTC pathogenesis. The thyroid gland's critical role in regulating body metabolism is well‐documented. Accordingly, GO and pathway enrichment analyses revealed that down‐regulated DEGs were predominantly associated with thyroid hormone synthesis and protein metabolism, suggesting these pathways may be crucial in PTC oncogenesis. Further, we identified six hub genes‐TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A‐through data mining. The mRNA expressions of these genes were consistent with microarray data. Immunohistochemical staining was used to examine the expression of these genes in human PTC tissues. Our findings revealed strong nuclear and moderate cytoplasmic expression of FHL1 in normal thyroid tissues surrounding tumors, whereas it was absent in PTC tissues. Interestingly, BCL2 expression did not significantly differ between PTC and normal thyroid tissues (p > 0.05). The four other markers, TLE1, GHR, KIT, and PPARGC1A, were not detected in either PTC or normal thyroid tissues, indicating the need for further investigation into their roles. It is reported that the immunohistochemical markers associated with PTC, such as thyroperoxidase, CK19, galectin‐3, FN1, CD56, and HBME‐1, play a role in thyroid carcinomas and have been widely described. , , , , However, their diagnostic utility in PTC remains a subject of ongoing debate. The use of combined antibodies has shown improved diagnostic accuracy. Our findings indicate that immunohistochemical staining for FHL1 could be valuable in differentiating PTC and may be applicable to cytological specimens in future studies. FHL1 is a subfamily of proteins within the LIM‐only protein family. It is reported that the expression of FHL1 is found in different tissues and plays a role in various types of cancer, such as tongue squamous cell carcinoma, gastric cancer, esophageal squamous cell carcinoma, lung cancer, liver and colorectal tumors. To investigate FHL1's role in carcinogenesis further, we analyzed its expression across 37 human cancers using the TCGA database. As depicted in Figure , FHL1 showed significant differential expression in 15 cancer types relative to normal tissues, including BLCA, BRCA, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, STAD, thyroid carcinoma, and UCEC. This underscores FHL1 is potential as a biomarker across a broad spectrum of cancers. The diagnosis of PTC traditionally relies on distinctive nuclear features alongside papillary or solid/trabecular architecture, or infiltrative growth in follicular‐patterned tumors. However, the emphasis on nuclear features is increasingly being supplanted by molecular diagnostics. Molecular mechanisms in PTC are predominantly linked to mutations in oncogenes such as BRAF, RAS, RET, and TERT. These genetic alterations can result in varied biological behaviors and growth patterns of the tumor, underscoring the need for deeper exploration of PTC's molecular characteristics. FHL1, in particular, may serve as a promising biomarker for tumorigenesis. This study, however, is not without its limitations. Firstly, we have assessed FHL1 immunostaining on FNA samples in a limited number of cases, which will be further discussed in subsequent research. Secondly, the introduction of noninvasive follicular thyroid neoplasm with papillary‐like features (NIFTP) in 2016 as a noncancer entity poses challenges for thyroid FNA interpretation, particularly the cytological suspicion of NIFTP. Given the limited availability of NIFTP data in the GEO database and its minimal representation in our study, it was not extensively discussed. Future studies could focus on this aspect. Thirdly, while this study did not experimentally explore the FHL1 gene mechanism, we did analyze the correlation between FHL1 expression and GSEA pathway analysis, warranting further detailed investigation. In conclusion, this study pioneers the assertion that the dysregulation of FHL1 may be integral to the development and progression of PTC. FHL1 holds promise as a significant marker for accurate diagnosis and a prospective therapeutic target in PTC management. Nonetheless, the specific mechanisms underlying the dysregulation of FHL1 expression require further investigation. Future studies should explore the correlation between FHL1 expression levels and clinical variables, including age, gender, primary tumor size, disease stage, lymph node involvement, distant metastasis, and various histopathological types of thyroid cancer. Evaluating a larger cohort will enhance our understanding of thyroid cancer pathogenesis. Ultimately, our goal is to identify reliable biomarkers that not only clarify the pathogenic underpinnings of PTC but also facilitate precise diagnostics and personalized treatment approaches. Yeting Zeng and Dehua Zeng : Conceptualization, methodology, acquisition and analysis of data, writing‐original draft, writing‐review and editing, project administration. Xingfeng Qi and Hanxi Wang : Conceptualization, materials preparation and immunohistochemical experiments. Xuzhou Wang and Xiaodong Dai : Resources, validation. Yeting Zeng and Lijuan Qu : Conceptualization, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript. The authors declare no conflict of interest. Figure S1. Expression of FHL1 in FNA specimens of PTC. A The normal thyroid follicular epithelium exhibited robust positive immunoreactivity for FHL1(400×). B In PTC cells, only minimal cytoplasmic expression of FHL1 was observed(400×). |
Changes in experienced quality of oncological cancer care during the COVID-19 pandemic based on patient reported outcomes – a cross-sectional study | 774616cc-e1fc-4ada-aaf4-b81e43971219 | 11332494 | Internal Medicine[mh] | The COVID-19 pandemic exposed cancer patients to exceptional vulnerabilities due to the inherent life-threatening nature of their condition. These vulnerabilities stemmed from a convergence of factors, including pandemic-induced limitations on hospital visits, apprehensions about COVID-19 contagion, and concerns regarding timely detection and intervention for cancer progression or recurrence . In response to the pandemic, the European Organization for Research and Treatment of Cancer (EORTC) recommended changing oncological routines and suggested contingency plans for modifying cancer care if forced by the circumstances . Such changes could include temporarily deferring cancer screening, delaying or avoiding outpatient visits, and postponing elective surgery and systemic cancer treatment in patients . Such changes in cancer care can place an additional burden on patients, increasing the substantial toll of cancer diagnosis and treatment on psychological well-being and physical health . A recent meta-analysis, including 27,590 cancer patients during the COVID-19 pandemic, indicated that approximately one-third of the participants suffered from clinical levels of depression and anxiety and that almost two-thirds reported heightened fear of cancer progression or recurrence . To the best of our knowledge, no previous studies have explored how cancer patients experienced the changes in their cancer care during the pandemic. In this study, we examined data from a tax-funded, universal accessible healthcare system in the two most populated regions of Denmark. Specifically, we present patient-reported outcome (PRO) data on the changes experienced by patients during the pandemic in the quality of treatment, care, and follow-up, exploring the associations between experienced reductions in the quality of their treatment, fear of SARS-CoV-2 infection, and fear of cancer progression or recurrence. Patient selection Our survey study enrolled patients in active treatment or follow-up care at the two largest oncology departments in Denmark: The Department of Oncology, Aarhus University Hospital (AUH), and the Department of Oncology, Rigshospitalet (RH). Collectively, the two departments provide oncological care to more than 1/3 of all Danish residents. The Danish healthcare system is based on the principle of universal health coverage, and all Danish residents, irrespective of their socio-economic status, have equal access to healthcare at all levels, from general practitioners to highly specialised hospital departments, including cancer treatment. Patients were invited to participate in the study via a secure national electronic mail system linked to the Danish civil registration number, a unique personal 10-digit identifier assigned to all Danish residents since 1st April 1968. More than 90% of Danish adults have access to a secure electronic mail account, routinely utilised by public administration, including healthcare providers, for communication. At AUH/RH, study enrolment lasted from March 11, 2020 (the official lockdown date in Denmark) to May 27, 2020. Patients consenting to participate received a link to an electronic questionnaire. REDCap , a GDPR-compliant electronic data capture platform administered by the Aarhus University Clinical Trial Unit, was used for data acquisition. If patients did not answer the questionnaire, a reminder was sent. Patient-reported outcomes Participants were asked to complete a comprehensive questionnaire including between 107 and 122 items, depending on the responses provided, with an estimated completion time of 20–30 minutes after giving written consent to participate; no exclusion criteria were present in this study. The instruments and references to the questionnaires are listed in Appendix 1. In brief, the questionnaire included scales and individual items covering the following domains: (1) demographic information, for example, marital status, children living at home; (2) clinical details, including cancer diagnosis and type of treatment; (3) questions on any previous or suspected COVID-19 infections; (4) ad hoc questions on perceived changes in cancer treatment and care; (5) a six-point scale assessing physical activity together with single items on health behaviors, for example, smoking, nutrition, and alcohol. The patients were also asked to rate (6) physical health, (7) social distancing behaviors, (8) fear of SARS-CoV-2 infection, and (9) fear of cancer progression or recurrence, including a question about whether the current COVID-19 situation had worsened their fear of cancer progression or recurrence. Additional topics included (10) perceived stress, (11) sleep duration, sleep disturbance, and sleep quality, (12) social support and social isolation, (13) items on depression, anxiety, fatigue, and general quality of life (QoL), and, finally, (14) an open-ended question, allowing patients to report any additional aspects they found relevant. Data analysis To verify the cancer diagnosis for the responder and non-responder groups, we linked the self-reported data to clinical data from the patient’s medical records using the hospital electronic database, specifically for patients treated at AUH. Differences between responders and non-responders were analysed using t -tests or Chi 2 tests, as appropriate for the respective data types. Due to data privacy concerns, RH did not authorise access to data on non-responders, precluding a responder–non-responder analysis for this center. In the present report, we examine the patient perspective concerning treatment quality and focus on its correlations with two key factors: fear of SARS-CoV-2 infection and fear of cancer progression or recurrence. Fear of SARS-CoV-2 infection was assessed with a seven-item scale , with each item rated on a scale from 1 to 5. The total score ranged from 5 to 35, with higher scores indicating higher levels of fear. Based on the suggested cut-off of 16 , patients were dichotomised into a low (≤ 16) and high level of fear group (> 16). Fear of cancer recurrence or progression was measured with the 3-item version of the Concerns About Recurrence Questionnaire (CARQ-3) , with three 11-point (0–10) numerical rating scales yielding a total score from 0 to 30. Using the suggested cut-off, patients were categorised as either expressing minimal concern about their disease (≤ 10) or experiencing heightened worry (> 10). The remaining independent variables were analysed as continuous variables and included depression , emotional, informational, and instrumental social support , and social isolation . The questionnaire details, including internal consistencies (Cronbach’s alpha) calculated for the answers provided in this study, are shown in . The primary endpoint was patient-reported perceived change in treatment quality. The investigated predictors of perceived reductions in treatment quality were analysed with a hierarchical logistic regression analysis. The analysis involved six consecutive steps. Variables at each step reaching statistical significance ( p < 0.05) were carried forward and adjusted for at the next step, advancing from the more distal demographic background factors (step 1) over the clinical characteristics (step 2), to health behaviors and physical function (step 3), psychological and physical symptoms (step 4), and aspects of social support (step 5). Finally (step 6), all variables reaching statistical significance at the 5% level at the fifth step were entered together in a final model. All statistical analyses were performed using STATA 17.0 (StataCorp LLC, USA). Ethics and data protection The study was approved by the Danish Patient Safety Authority (Record no., 31-1521-376) and the Danish Data Protection Agency (Record no., 1-16-02-143-20). Our survey study enrolled patients in active treatment or follow-up care at the two largest oncology departments in Denmark: The Department of Oncology, Aarhus University Hospital (AUH), and the Department of Oncology, Rigshospitalet (RH). Collectively, the two departments provide oncological care to more than 1/3 of all Danish residents. The Danish healthcare system is based on the principle of universal health coverage, and all Danish residents, irrespective of their socio-economic status, have equal access to healthcare at all levels, from general practitioners to highly specialised hospital departments, including cancer treatment. Patients were invited to participate in the study via a secure national electronic mail system linked to the Danish civil registration number, a unique personal 10-digit identifier assigned to all Danish residents since 1st April 1968. More than 90% of Danish adults have access to a secure electronic mail account, routinely utilised by public administration, including healthcare providers, for communication. At AUH/RH, study enrolment lasted from March 11, 2020 (the official lockdown date in Denmark) to May 27, 2020. Patients consenting to participate received a link to an electronic questionnaire. REDCap , a GDPR-compliant electronic data capture platform administered by the Aarhus University Clinical Trial Unit, was used for data acquisition. If patients did not answer the questionnaire, a reminder was sent. Participants were asked to complete a comprehensive questionnaire including between 107 and 122 items, depending on the responses provided, with an estimated completion time of 20–30 minutes after giving written consent to participate; no exclusion criteria were present in this study. The instruments and references to the questionnaires are listed in Appendix 1. In brief, the questionnaire included scales and individual items covering the following domains: (1) demographic information, for example, marital status, children living at home; (2) clinical details, including cancer diagnosis and type of treatment; (3) questions on any previous or suspected COVID-19 infections; (4) ad hoc questions on perceived changes in cancer treatment and care; (5) a six-point scale assessing physical activity together with single items on health behaviors, for example, smoking, nutrition, and alcohol. The patients were also asked to rate (6) physical health, (7) social distancing behaviors, (8) fear of SARS-CoV-2 infection, and (9) fear of cancer progression or recurrence, including a question about whether the current COVID-19 situation had worsened their fear of cancer progression or recurrence. Additional topics included (10) perceived stress, (11) sleep duration, sleep disturbance, and sleep quality, (12) social support and social isolation, (13) items on depression, anxiety, fatigue, and general quality of life (QoL), and, finally, (14) an open-ended question, allowing patients to report any additional aspects they found relevant. To verify the cancer diagnosis for the responder and non-responder groups, we linked the self-reported data to clinical data from the patient’s medical records using the hospital electronic database, specifically for patients treated at AUH. Differences between responders and non-responders were analysed using t -tests or Chi 2 tests, as appropriate for the respective data types. Due to data privacy concerns, RH did not authorise access to data on non-responders, precluding a responder–non-responder analysis for this center. In the present report, we examine the patient perspective concerning treatment quality and focus on its correlations with two key factors: fear of SARS-CoV-2 infection and fear of cancer progression or recurrence. Fear of SARS-CoV-2 infection was assessed with a seven-item scale , with each item rated on a scale from 1 to 5. The total score ranged from 5 to 35, with higher scores indicating higher levels of fear. Based on the suggested cut-off of 16 , patients were dichotomised into a low (≤ 16) and high level of fear group (> 16). Fear of cancer recurrence or progression was measured with the 3-item version of the Concerns About Recurrence Questionnaire (CARQ-3) , with three 11-point (0–10) numerical rating scales yielding a total score from 0 to 30. Using the suggested cut-off, patients were categorised as either expressing minimal concern about their disease (≤ 10) or experiencing heightened worry (> 10). The remaining independent variables were analysed as continuous variables and included depression , emotional, informational, and instrumental social support , and social isolation . The questionnaire details, including internal consistencies (Cronbach’s alpha) calculated for the answers provided in this study, are shown in . The primary endpoint was patient-reported perceived change in treatment quality. The investigated predictors of perceived reductions in treatment quality were analysed with a hierarchical logistic regression analysis. The analysis involved six consecutive steps. Variables at each step reaching statistical significance ( p < 0.05) were carried forward and adjusted for at the next step, advancing from the more distal demographic background factors (step 1) over the clinical characteristics (step 2), to health behaviors and physical function (step 3), psychological and physical symptoms (step 4), and aspects of social support (step 5). Finally (step 6), all variables reaching statistical significance at the 5% level at the fifth step were entered together in a final model. All statistical analyses were performed using STATA 17.0 (StataCorp LLC, USA). The study was approved by the Danish Patient Safety Authority (Record no., 31-1521-376) and the Danish Data Protection Agency (Record no., 1-16-02-143-20). At AUH, a total of 3,587 patients responded to the questionnaire, corresponding to a response rate of 3,587/7,943 (45%). An additional 2,386 patients from RH responded. Participant recruitment and flow is illustrated in . A responder–non-responder analysis for patients at AUH is summarised in . Comparing characteristics of patients from AUH and RH showed that a higher percentage of the patients at RH were single or separated (marital status), had an intermediate or long higher education, had children living at home, and had a full-time position at work. The distributions of treatment modality and treatment intent between the centres were comparable . At the onset of the pandemic, 38% of patients experienced changes in treatment or follow-up program. Of these, a majority (56%) indicated a perceived reduction in treatment quality. Among patients who did not report changes in treatment, 421 still perceived a decline in treatment quality, yielding a total of 1,569 out of 5,372 (29%) patients who perceived the quality of their treatment to be reduced. Of these, 236/1,569 (15%) experienced an exceptionally high degree of reduction in the quality of treatment and care (See ). Concerns regarding the management of pandemic related challenges by the Department of Oncology were also raised, with 13% of the patients expressing dissatisfaction in this regard. Among those dissatisfied patients, 28% perceived a decline in treatment quality. illustrates a Venn diagram depicting the overlap between patients’ perceptions of reduced treatment quality, the department’s pandemic response, and alterations in their treatment plans. Patients who found that the department did not handle the situation well, were more likely to experience a reduced quality in their treatment (OR = 1.44; 95% CI: 1.15–1.80; p < 0.001). As seen in , experiencing a reduced treatment quality was associated with experienced changes in contact with the department or other health care providers. The available data did not enable us to determine whether the visits were unscheduled or scheduled. Higher scores on emotional, informative, or instrumental support correlated with greater treatment satisfaction. Conversely, patients reporting higher levels of depression, anxiety, pain, and fatigue were more likely to report reduced treatment quality and heightened fears of cancer progression or SARS-CoV-2 infection. Lack of trust in the handling of the pandemic by Department of Oncology increased the perception of reduced treatment quality. The final adjusted logistic regression model showed that female gender, younger age, active treatment, social isolation, pain, and fear of cancer recurrence/progression were all associated with reduced perceived quality of treatment . A total of 2,259/5,371 (42%) patients reported fear of being infected by SARS-CoV-2. There was no association between patients’ fear of being infected by SARS-CoV-2 and their satisfaction with the department’s pandemic response. Patients with high levels of fear of SARS-CoV-2 infection were also more likely to experience higher levels of fear of cancer progression or recurrence (1,466/2,259; 65%). A higher percentage of patients with COVID-19 symptoms were worried about cancer progression or recurrence than patients without COVID-19 symptoms (56% vs 47%, respectively). The results from our survey, to which all cancer patients at the two largest oncological treatment centres in Denmark were invited to participate, indicate that the COVID-19 pandemic affected the cancer patients’ experience of their treatment, with one-third of patients reporting changes in their treatment or follow-up program. Female sex, younger age, undergoing ,active cancer treatment, being socially isolated, being in pain, and experiencing high levels of fear of cancer progression or recurrence, were all factors that increased the probability of reporting reduced oncological treatment quality during the COVID-19 pandemic. While patients experiencing high levels of fear of SARS-CoV-2 infection also experienced reductions in the quality of their treatment, they were not more dissatisfied with how the oncology department had handled the pandemic. Our study provides a unique insight into the well-being of cancer patients during a pandemic. Particular strengths are the non-responder versus responder analyses and the comparison between RH and AUH, with RH serving as the capital of Denmark and AUH serving as not only the second-largest city but also smaller towns and villages. Our finding that more than one-third of the patients had experienced changes in their treatment or follow-up program at the start of the pandemic is consistent with results of other studies showing that 25–30% of patients reported such changes . A British study found that the pandemic significantly impacted radiation therapy, increasing the use of hypofractionated radiation therapy, but whether such changes may have influenced the outcomes of interest in our study is unclear. The contact with the health care providers changed for 9.1% of the patients in our study, a percentage similar to that found in a smaller study ( n = 366) of melanoma patients, in which 10.1% reported changes in their appointments due to the pandemic . Our results show that an altered frequency, both higher or lower, of contacts with either the Department of Oncology or the GP was associated with perception of reduced treatment quality. We found no associations between perceived reduced treatment quality, perceptions of how the oncology departments handled the pandemic, and the frequency of contacts with the healthcare system. This appears to indicate that perception of reduced treatment quality is associated with change in general, rather than changes in a specific direction. A meta-analysis of 40 studies conducted during the COVID-19 pandemic revealed that among cancer patients roughly 33% reported depression, 31% anxiety, and 67% fear cancer progression or recurrence. Our study observed lower rates of these outcomes, with approximately 11% showing signs of depression or anxiety and 47% expressing a fear of progression or recurrence. The difference between the results from the meta-analysis and our results could be explained by several factors. One explanation could be differences in the measures used. Another could be the overrepresentation of breast cancer patients in our study and the inclusion of cancer survivors in follow-up programs, who may experience fewer symptoms of depression and anxiety compared with those undergoing active treatment for their disease. Fear of cancer progression and recurrence has consistently been shown to be associated with higher levels of psychological distress and impaired quality of life . Our results indicated that patients with breast cancer and a high fear of cancer progression or recurrence were more likely to perceive poorer treatment quality, suggesting that changes to treatment and appointment schedules might be perceived as particularly threatening by some patient groups. Furthermore, patients who missed social interaction with others during the pandemic and who felt socially isolated also reported reduced treatment quality. This is consistent with previous studies showing that social isolation and loneliness can negatively impact cancer patients’ quality of life and psychological well-being . Providing appropriate support for cancer patients during the pandemic is essential to address their fears and concerns. Half the patients report being afraid of being infected by SARS-CoV-2; these patients were primarily women, lung cancer patients, those living alone, and patients undergoing curative-intended treatment. Lung cancer patients may have higher anxiety due to their perceived susceptibility to severe infection. Similarly, patients living alone may experience increased fear due to limited social support, while those undergoing curative-intended treatment may worry about the potential impact of infection on their treatment outcomes. The percentage of patients with cancer being afraid of SARS-CoV-2 infection is similar to the percentage of the general population. A German survey reported that 59% of participants were afraid of being infected with SARS-CoV-2 . The generalizability of our results might be questioned. The response rate among patients from AUH was as anticipated but relatively low at approximately 45%. The limited response, along with the variation between responders and non-responders, was elucidated though responder–non-responder analysis. Additionally, the absence of data regarding the number of patients invited at RH reduces the study’s interpretive scope. Another limitation is the potential selection bias, for example, related to socio-economic status (SES) . However, patients in Denmark have equal access to medical care in the public health care system; therefore, selection bias caused by socio-economic factors should not play a major role in the study results. Furthermore, cancer treatment in Denmark is guided by national guidelines that aim to ensure uniform, high-quality, evidence-based care for all patients. This does not exclude the possibility of selection bias due to excluding patients with missing data on crucial variables and patients who could not receive the questionnaire through the public electronic communication tool. A second potential limitation could be the relatively large number of variables included in the analyses. We have attempted to balance the risk of over- and underfitting the data by conducting a hierarchical multiple logistic regression analysis, selecting only statistically significant variables to be carried forward to the next step but choosing a reasonably liberal significance level (5%). Still, the regression analysis results should be interpreted with some caution, as some degree of multicollinearity between the variables does exist. Third, the study relied on self-reported data, which could have introduced misclassification insofar that patients might be unclear on their specific diagnosis, and some may be unaware of the aim of their treatment. Finally, although our findings generally appear to be consistent with the results of previous studies, some results may not be generalisable to other healthcare systems. In our study, we found that separating patients undergoing treatment from those in follow-up or treatment pause is not straightforward. This is mainly because distinguishing between patients in treatment pause with residual disease and those without any evidence of disease during follow-up is impossible. Additionally, patients in active treatment included both palliative patients and those receiving adjuvant treatment, who may have markedly different clinical profiles; therefore, this stratification was not made. In conclusion, our study highlights the fact that many patients experienced changes in treatment during the outbreak of the COVID-19 pandemic, with reduced quality in cancer treatment, particularly among patients with specific cancer types. Dissatisfaction with how the Department of Oncology handled the pandemic challenges was linked to the perception of reduced treatment quality. Additionally, the study showed how social support in patient satisfaction is essential, and the impact of psychological factors such as depression and anxiety are linked to the treatment experiences. The study reveals the COVID-19 pandemic’s significant impact on cancer patient’s treatment experiences, highlighting the need for proactive health services planning. Strategies should focus on effective communication, addressing psychological well-being, and promoting social support by prioritising patient-centred care, even during crises. Conceptualisation, L.J.T., N.M.T., J.T.D., K.L., S.B., C.J., R.Z., and NAP; methodology, R.Z., L.J.T, and N.A.P.; formal analysis, R.Z., L.J.T., and N.A.P.; investigation, L.J.T., N.M.T., J.T.D., K.L., S.B., C.J., R.Z., and N.A.P.; data curation, L.J.T., N.M.T., J.T.D., K.L., S.B., C.J., R.Z., and N.A.P.; writing—original draft preparation, L.J.T. and N.A.P.; writing—review and editing, L.J.T., N.M.T., J.T.D., K.L., S.B., C.J., R.Z., N.A.P.; visualisation, L.J.T. and N.A.P.. All authors have read and agreed to the published version of the manuscript. Changes in experienced quality of oncological cancer care during the COVID-19 pandemic based on patient reported outcomes – a cross-sectional study |
The optional apex sites for quad zygomatic implant placement in edentulous patients with severe alveolar bone resorption: a CBCT anatomical analysis | 3f986ea6-c306-4e4b-9452-411778834b6b | 11569599 | Dentistry[mh] | Edentulism is often a critical yet challenging case in prosthodontics. Numerous clinical studies have confirmed that implant-supported prostheses can provide substantially improved oral function rehabilitation . For patients with severe alveolar bone resorption, zygomatic implants (ZIs) offer a reliable and effective solution for oral rehabilitation. Studies indicate that in such patients, the success rate of zygomatic implants ranges from 94.2–100% . The concept of zygomatic implants was introduced by Branemark in 1990 . Subsequently, numerous enhancements and innovations in ZI techniques have been developed . For patients with severe maxillary alveolar bone loss, a single ZI on each side may not provide adequate stability for full-arch implant restoration and could increase potential risks. The currently recommended approach is to place two ZIs on each side, i.e., quad zygomatic implants. This can offer ample support for full-arch prosthesis in edentulous patients . Quad ZIs occupy a significant portion of the zygomatic bone and require precise placement. It is crucial to evaluate the zygoma’s structure and determine the suitable apex point for ZI insertion before proceeding with quad zygomatic approach. The success and durability of ZIs hinge on the extent of bone-implant contact (BIC) within the zygoma. Moreover, the implant should not harm adjacent maxillofacial structures, thereby preventing intra- and post-operative complications. The zygoma’s inner aspect is close to the infratemporal fossa, home to vital neurovascular elements like the pterygoid plexus, maxillary artery and its offshoots, and the maxillary and mandibular nerve branches. Injury to these structures can result in severe complications, including deep hematomas, sensory and motor impairments, and potentially fatal outcomes. Therefore, careful placement of quad ZIs is imperative to avoid encroaching on the infratemporal fossa and avert serious postoperative complications. Moreover, orbital cavity penetration has been reported as a intra-operative complication with the incidence of 5.9% . As a typical and the most serious ZI therapy complication, orbital penetration by ZI could lead to severe pain in the region of the orbit, persistent anesthesia, physiological abduction and elevation of the involved eye , extraocular muscle injury, diplopia , eye movement limitation , etc. Intranasinus technique may increase the risk of orbit invasion due to lack of vision and control of the drills during implant bed preparation . The alveolar ridge is categorized into six stages of resorption severity according to the Cawood and Howell classification . For patients exhibiting varying degrees of alveolar bone loss, the location for quad zygomatic implants may vary. Currently, few research has been conducted to compare the choices of implant sites among patients with different levels of alveolar bone resorption. Two studies assessed the zygomatic BIC of quad ZIs using Cone Beam Computed Tomography (CBCT) images . Hung et al. evaluated the zygomatic BIC at various implant sites and its association with the infratemporal fossa, proposing the most suitable implant location in the zygoma . This research focused on an Asian demographic. Conversely, Bertos et al. conducted their research on a European cohort, examining the influence of alveolar bone resorption on the BIC of quad ZIs, the volume of implant-engaged zygoma bone, and its correlation with the maxillary sinus. Nevertheless, there is still a lacking of research on how alveolar bone resorption affects the BIC and its relationship with the infratemporal fossa at individual ZI sites. This study aims to investigate the difference in BIC, the infratemporal fossa intrusion, and the relationship between ZIs and orbit among differenct apex sites for quad ZIs placement in edentulous patients with varying degrees of alveolar bone loss.
Patient selection This study received approval from the Institutional Review Board of the University (Ethical Approval No: PKUSSIRB-202162013). The clinical research is registered under the number ChiCTR2100044472 (18/03/2021). Edentulous patients in need of implant treatment who underwent CBCT scans at the University dental school clinic between October 2019 and August 2021, and who met the inclusion/exclusion criteria, were included in the study. All participants read and signed informed consents. Inclusion criteria: (1) Patient age over 18 years old; (2) Maxillary edentulism for a minimum of 3 months; (3) Require implant restoration, and undergo a CBCT examination; (4) Have read and signed the informed consent form; (5) Alveolar bone resorption classified as either Class IV or Class V/VI according to the Cawood & Howell classification. Exclusion criteria : (1) Anatomical abnormalities in the maxilla or zygoma; (2) Severe facial asymmetry; (3) Alveolar bone resorption classified from level I to III according to the Cawood & Howell classification; (4) Edentulism due to maxillofacial trauma or tumor resection surgery. Classification of alveolar bone resorption level The edentulous patients’ alveolar bone resorption was categorized using the Cawood & Howell classification as per Bertos et al. . A knife-edge ridge form of the residual ridge, insufficient in width and exceeding 5 mm in height, was classified as Class IV alveolar bone resorption. Conversely, a flat residual ridge, insufficient in width and less than 5 mm in height, was classified as Class V/VI alveolar bone resorption. The level of alveolar bone resorption was assessed individually on each side, utilizing both two-dimensional and three-dimensional CBCT reconstructed images. Virtual implant planning CBCT data was exported in the Digital Imaging and Communications in Medicine (DICOM) format and imported into the planning software (Nobel Clinician 2.10.1.3, Nobel Biocare). For each side of the maxilla and zygoma, two zygomatic implants (Branemark System Zygoma Tiunite RP) were virtually planned in accordance with protocols from previously published articles , namely one anterior zygomatic implant and one posterior implant, totaling four implants per case (Fig. ). Implant site selection Entery point on the alveolar ridge Points E1 and E2 are designated on the alveolar ridge (Fig. ). Point E1 is located 5 mm palatally from where the lowest point of the maxillary alveolar ridge intersects with a perpendicular line extending from the lateral margin of the nasal incisure . When viewing the maxilla from above, point E2 is situated 5 mm palatally from where The lowermost point of the alveolar crest was identified by taking a line at a tangent to the lateral margin of the infraorbital foramen. The degree of alveolar bone resorption is assessed individually at points E1 and E2 . Apex point in the zygoma Draw a horizontal line, IM, at the lower orbital margin and a vertical line, LM, parallel to the median plane, intersecting the lateral orbital margin. At their intersection lies point C. Construct an angular bisector between IM and LM to locate point O at their intersection with the orbital margin. Connect points C and O with line L1 (Fig. ). Shift L1 medially by 5 mm to create a parallel line L0; shift L1 laterally by 5 mm and 10 mm to form parallel lines L2 and L3, respectively. These lines extend from the orbital margin to the zygoma’s lower edge. The apex of the quad ZIs is positioned in the zygomatic bone between L0 and L3. Segment L0 and L3 into four equal parts to identify quarter points A0, B0, C0, and A3, B3, C3, from superior to inferior. Draw lines LA, LB, and LC by connecting A0 to A3, B0 to B3, and C0 to C3. The intersection points of LA, LB, LC with L1, L2 are labeled A1, B1, C1 and A2, B2, C2, respectively. The areas that the lines LA, LB, LC pass through is namely the upper, middle and lower section of the zygomatic. The 12 points thus identified represent the center of the 12 zygomatic segments . The ZI apex will be positioned at these 12 points as indicated in Fig. . According to the previous studies of Hung and colleagues , the apex of the anterior ZI is positioned at the upper section (points A series), with its entry at point E1. The apex of the posterior ZI is positioned at the middle and lower sections (points B and C series), with its entry at point E2. Measurements and data collection The patient’s CBCT was imported into the planning software (NobelClinician 2.10.1.3, Nobel Biocare, Zurich) for virtual planning of the quad ZI site. Implant simulations (Branemark System Zygoma Tiunite RP) were virtually designed. Measurements of the quad ZIs at 12 apex locations were taken, with the following detailed metrics: Measurements of Bone-Implant contact (BIC) This is a linear measurement protocol. The BIC was the average value of zygomatic BIC length on the facial and the temporal sides in the facial -temporal cross section (Fig. ) . The zygomatic bone-implant contact (zBIC) and alveolar bone-implant contact (aBIC) were assessed. The average contact length between the ZI and the zygomatic or alveolar bone on both the facial and temporal sides was calculated as zBIC or aBIC, respectively. The total bone-implant contact (tBIC) represents the combined measurement of zBIC and aBIC. Evaluation of ZI Intrusion The occurrence of ZI intrusion into the infratemporal fossa was examined, along with the depth and length of such intrusions (Fig. ). Meausrement of the distance between the orbit and the anterior implant The closest distance between the implant and patient’s orbit cavity was measured for anterior ZI at each apex point. Determination of Optional Zygomatic Implant position The possible location for ZI placement within the zygoma was identified by analyzing the implant’s BIC and the frequency of infratemporal fossa intrusion and orbital penetration. This process was then applied to determine the implant sites for patients with alveolar bone resorption Class IV and V/VI. The findings from these two subgroups were compared to the overall participant data to identify any differences. Statistical analysis All analyses were performed with SPSS (SPSS Statistics 27.0, IBM) as follows: A one way ANOVA was used to compare the zygomatic BIC (zBIC) and alveolar BIC (aBIC) among ZIs at various apex locations to determine the most suitable insertion points. Subsequently, BIC was assessed within each subgroup based on the degree of alveolar bone resorption, followed by a Chi-square test to compare BIC across subgroups and to ascertain the possible apex design for zygomatic implants. A Chi-square test was employed to evaluate the frequency of ZI penetration into the infratemporal fossa among subgroups and the entire cohort. Additionally, the extent and magnitude of ZI intrusion were compared using the same statistical method. When P is less than 0.05, it is considered that there is a significant difference.
This study received approval from the Institutional Review Board of the University (Ethical Approval No: PKUSSIRB-202162013). The clinical research is registered under the number ChiCTR2100044472 (18/03/2021). Edentulous patients in need of implant treatment who underwent CBCT scans at the University dental school clinic between October 2019 and August 2021, and who met the inclusion/exclusion criteria, were included in the study. All participants read and signed informed consents. Inclusion criteria: (1) Patient age over 18 years old; (2) Maxillary edentulism for a minimum of 3 months; (3) Require implant restoration, and undergo a CBCT examination; (4) Have read and signed the informed consent form; (5) Alveolar bone resorption classified as either Class IV or Class V/VI according to the Cawood & Howell classification. Exclusion criteria : (1) Anatomical abnormalities in the maxilla or zygoma; (2) Severe facial asymmetry; (3) Alveolar bone resorption classified from level I to III according to the Cawood & Howell classification; (4) Edentulism due to maxillofacial trauma or tumor resection surgery.
The edentulous patients’ alveolar bone resorption was categorized using the Cawood & Howell classification as per Bertos et al. . A knife-edge ridge form of the residual ridge, insufficient in width and exceeding 5 mm in height, was classified as Class IV alveolar bone resorption. Conversely, a flat residual ridge, insufficient in width and less than 5 mm in height, was classified as Class V/VI alveolar bone resorption. The level of alveolar bone resorption was assessed individually on each side, utilizing both two-dimensional and three-dimensional CBCT reconstructed images.
CBCT data was exported in the Digital Imaging and Communications in Medicine (DICOM) format and imported into the planning software (Nobel Clinician 2.10.1.3, Nobel Biocare). For each side of the maxilla and zygoma, two zygomatic implants (Branemark System Zygoma Tiunite RP) were virtually planned in accordance with protocols from previously published articles , namely one anterior zygomatic implant and one posterior implant, totaling four implants per case (Fig. ).
Entery point on the alveolar ridge Points E1 and E2 are designated on the alveolar ridge (Fig. ). Point E1 is located 5 mm palatally from where the lowest point of the maxillary alveolar ridge intersects with a perpendicular line extending from the lateral margin of the nasal incisure . When viewing the maxilla from above, point E2 is situated 5 mm palatally from where The lowermost point of the alveolar crest was identified by taking a line at a tangent to the lateral margin of the infraorbital foramen. The degree of alveolar bone resorption is assessed individually at points E1 and E2 . Apex point in the zygoma Draw a horizontal line, IM, at the lower orbital margin and a vertical line, LM, parallel to the median plane, intersecting the lateral orbital margin. At their intersection lies point C. Construct an angular bisector between IM and LM to locate point O at their intersection with the orbital margin. Connect points C and O with line L1 (Fig. ). Shift L1 medially by 5 mm to create a parallel line L0; shift L1 laterally by 5 mm and 10 mm to form parallel lines L2 and L3, respectively. These lines extend from the orbital margin to the zygoma’s lower edge. The apex of the quad ZIs is positioned in the zygomatic bone between L0 and L3. Segment L0 and L3 into four equal parts to identify quarter points A0, B0, C0, and A3, B3, C3, from superior to inferior. Draw lines LA, LB, and LC by connecting A0 to A3, B0 to B3, and C0 to C3. The intersection points of LA, LB, LC with L1, L2 are labeled A1, B1, C1 and A2, B2, C2, respectively. The areas that the lines LA, LB, LC pass through is namely the upper, middle and lower section of the zygomatic. The 12 points thus identified represent the center of the 12 zygomatic segments . The ZI apex will be positioned at these 12 points as indicated in Fig. . According to the previous studies of Hung and colleagues , the apex of the anterior ZI is positioned at the upper section (points A series), with its entry at point E1. The apex of the posterior ZI is positioned at the middle and lower sections (points B and C series), with its entry at point E2.
Points E1 and E2 are designated on the alveolar ridge (Fig. ). Point E1 is located 5 mm palatally from where the lowest point of the maxillary alveolar ridge intersects with a perpendicular line extending from the lateral margin of the nasal incisure . When viewing the maxilla from above, point E2 is situated 5 mm palatally from where The lowermost point of the alveolar crest was identified by taking a line at a tangent to the lateral margin of the infraorbital foramen. The degree of alveolar bone resorption is assessed individually at points E1 and E2 .
Draw a horizontal line, IM, at the lower orbital margin and a vertical line, LM, parallel to the median plane, intersecting the lateral orbital margin. At their intersection lies point C. Construct an angular bisector between IM and LM to locate point O at their intersection with the orbital margin. Connect points C and O with line L1 (Fig. ). Shift L1 medially by 5 mm to create a parallel line L0; shift L1 laterally by 5 mm and 10 mm to form parallel lines L2 and L3, respectively. These lines extend from the orbital margin to the zygoma’s lower edge. The apex of the quad ZIs is positioned in the zygomatic bone between L0 and L3. Segment L0 and L3 into four equal parts to identify quarter points A0, B0, C0, and A3, B3, C3, from superior to inferior. Draw lines LA, LB, and LC by connecting A0 to A3, B0 to B3, and C0 to C3. The intersection points of LA, LB, LC with L1, L2 are labeled A1, B1, C1 and A2, B2, C2, respectively. The areas that the lines LA, LB, LC pass through is namely the upper, middle and lower section of the zygomatic. The 12 points thus identified represent the center of the 12 zygomatic segments . The ZI apex will be positioned at these 12 points as indicated in Fig. . According to the previous studies of Hung and colleagues , the apex of the anterior ZI is positioned at the upper section (points A series), with its entry at point E1. The apex of the posterior ZI is positioned at the middle and lower sections (points B and C series), with its entry at point E2.
The patient’s CBCT was imported into the planning software (NobelClinician 2.10.1.3, Nobel Biocare, Zurich) for virtual planning of the quad ZI site. Implant simulations (Branemark System Zygoma Tiunite RP) were virtually designed. Measurements of the quad ZIs at 12 apex locations were taken, with the following detailed metrics: Measurements of Bone-Implant contact (BIC) This is a linear measurement protocol. The BIC was the average value of zygomatic BIC length on the facial and the temporal sides in the facial -temporal cross section (Fig. ) . The zygomatic bone-implant contact (zBIC) and alveolar bone-implant contact (aBIC) were assessed. The average contact length between the ZI and the zygomatic or alveolar bone on both the facial and temporal sides was calculated as zBIC or aBIC, respectively. The total bone-implant contact (tBIC) represents the combined measurement of zBIC and aBIC. Evaluation of ZI Intrusion The occurrence of ZI intrusion into the infratemporal fossa was examined, along with the depth and length of such intrusions (Fig. ). Meausrement of the distance between the orbit and the anterior implant The closest distance between the implant and patient’s orbit cavity was measured for anterior ZI at each apex point. Determination of Optional Zygomatic Implant position The possible location for ZI placement within the zygoma was identified by analyzing the implant’s BIC and the frequency of infratemporal fossa intrusion and orbital penetration. This process was then applied to determine the implant sites for patients with alveolar bone resorption Class IV and V/VI. The findings from these two subgroups were compared to the overall participant data to identify any differences.
This is a linear measurement protocol. The BIC was the average value of zygomatic BIC length on the facial and the temporal sides in the facial -temporal cross section (Fig. ) . The zygomatic bone-implant contact (zBIC) and alveolar bone-implant contact (aBIC) were assessed. The average contact length between the ZI and the zygomatic or alveolar bone on both the facial and temporal sides was calculated as zBIC or aBIC, respectively. The total bone-implant contact (tBIC) represents the combined measurement of zBIC and aBIC.
The occurrence of ZI intrusion into the infratemporal fossa was examined, along with the depth and length of such intrusions (Fig. ).
The closest distance between the implant and patient’s orbit cavity was measured for anterior ZI at each apex point.
The possible location for ZI placement within the zygoma was identified by analyzing the implant’s BIC and the frequency of infratemporal fossa intrusion and orbital penetration. This process was then applied to determine the implant sites for patients with alveolar bone resorption Class IV and V/VI. The findings from these two subgroups were compared to the overall participant data to identify any differences.
All analyses were performed with SPSS (SPSS Statistics 27.0, IBM) as follows: A one way ANOVA was used to compare the zygomatic BIC (zBIC) and alveolar BIC (aBIC) among ZIs at various apex locations to determine the most suitable insertion points. Subsequently, BIC was assessed within each subgroup based on the degree of alveolar bone resorption, followed by a Chi-square test to compare BIC across subgroups and to ascertain the possible apex design for zygomatic implants. A Chi-square test was employed to evaluate the frequency of ZI penetration into the infratemporal fossa among subgroups and the entire cohort. Additionally, the extent and magnitude of ZI intrusion were compared using the same statistical method. When P is less than 0.05, it is considered that there is a significant difference.
Demographics Maxillary edentulous patients who visited the University dental school clinic from March 2021 to October 2022 for implant-supported prosthesis treatment and underwent CBCT were evaluated. A total of 48 patients were collected. The edentulous arches were classified using the Cawood and Howell classification. Of these patients, eleven were excluded because their alveolar bone classification was categorized as level III. Nine patients were excluded due to inadequate CBCT images that precluded the virtual design of zygomatic implants. Ultimately, 28 participants were included in the study, comprising 12 males and 16 females, with an average age of 63.8 ± 12.6 years. The 28 edentulous maxillae were divided into 56 hemi-maxillae, with 39 of the 56 edentulous posterior residual ridges classified as Class IV and the remaining 17 as Class V/VI. In total, 112 implants were virtually planned. BIC of zygomatic implants BIC of the anterior zygomatic implant The apex points of the anterior ZIs were at A0, A1, A2, and A3. The total BIC (tBIC) and the zygomatic BIC (zBIC) of implants decreased significantly from A3 to A0 ( P < 0.01, Table ) while the alveolar BIC (aBIC) showed no significant change ( P = 0.769). The average zBIC at A3, A2, A1, and A0 was 18.3 ± 3.9 mm, 13.4 ± 3.7 mm, 7.4 ± 2.8 mm, and 4.2 ± 1.7 mm respectively. In the Class IV subgroup, tBIC and zBIC decreased from A3 to A0, with a significant difference observed among ZIs at all apex points ( P < 0.01), However, no significant difference was found in aBIC among implants at any apex points ( P = 0.849). In the Class V/VI subgroups, tBIC and zBIC also decreased from A3 to A0. A significant difference in zBIC was noted among implants at all apex points ( P < 0.01), while no significant difference was observed in tBIC between implants at A3 and A2 ( P = 0.217), or between those at A1 and A0 ( P = 0.132). The tBIC of implants at A3 and A2 was significantly higher than that of implants at A1 and A0 ( P < 0.05). No significant difference in aBIC was found among implants at any apex points ( P = 0.939). For implants at the four apex points of the upper zygoma, no significant difference in tBIC, zBIC, or aBIC was detected between the two subgroups ( P > 0.05). BIC of the posterior zygomatic implant The apex points of the posterior implants comprised eight locations: B0, B1, B2, and B3 at the middle zygoma, and C0, C1, C2, and C3 at the lower zygoma. Within the B series, the highest zBIC and tBIC were recorded at the B2 point (16.3 ± 5.3 mm; 22.1 ± 7.5 mm), while the C series showed the highest values at the C1 point (13.8 ± 5.0 mm; 19.5 ± 5.5 mm) as indicated in Table . The B2 point demonstrated the highest zBIC and tBIC among all eight apex sites, with a significant difference from the other points. No significant difference in zBIC and tBIC were observed among the B1, B3, C1, and C2 points (zBIC at B3: 13.4 ± 4.8 mm, at B1: 13.5 ± 5.5 mm, at C1: 13.8 ± 5.0 mm, at C2: 12.3 ± 5.8 mm), though these were significantly higher than those at the B0, C0, and C3 points (zBIC at B0: 8.9 ± 4.9 mm, at C0: 10.9 ± 4.1 mm, at C3: 8.5 ± 4.5 mm). Across all apex points, aBIC did not show significant variation ( P > 0.05) (Table ). In the Class IV and Class V/VI subgroups, the highest BIC was noted at the B2 point in the middle zygoma and at the C1 point in the lower zygoma. For Class IV subgroup, zBIC and tBIC at the B2 point were significantly higher compared to other apex points. Between the C1 and B1 apex points, no significant difference in zBIC and tBIC was detected. In the Class V/VI subgroup, zBIC and tBIC did not significantly differ between the B2 and B1 points, but a significant difference was found between the B2 point and other points, excluding B1 (Table ). No significant difference was found in aBIC and tBIC of implants at the same apex points between Class IV and V/VI subgroups. Relationship between zygomatic implants and the Infratemporal Fossa (Table ) Anterior zygomatic implants and infratemporal fossa For the anterior zygomatic implants, only one of the 56 implants (1.8%) at the A3 point intruded into the infratemporal fossa. The depth of the intrusion was 1.2 mm. No other anterior implants showed entry into the fossa. There was no significant difference in the rate of ZI intrusion among the four apex points (A series points) ( P = 0.390). The single implant that intruded into the infratemporal fossa was from the subgroup of class IV, although there was no significant difference in the rate of anterior ZI intrusion between the two subgroups. Posterior zygomatic implants and infratemporal fossa For the B series apex points (mid-zygoma), no implant intrusion into the infratemporal fossa was observed at B0. However, intrusion rates at B1, B2, and B3 were 1.8%, 42.9%, and 92.9%, respectively, with the average depth and length of intrusion into the infratemporal fossa increasing progressively. The average intrusion depth was 2.4 mm, 2.2 ± 1.0 mm, and 3.9 ± 1.6 mm at B1, B2, and B3 respectively. Significant differences in intrusion rates were noted between B3 and B0, and B2 and B0, but not between B1 and B0. For the C series apex points (lower zygoma), varying rates of ZI intrusion into the infratemporal fossa were recorded across the four sites. From C0 to C3, the intrusion rate, depth and length of ZI exposure in the infratemporal fossa increased (Table ). At the C3 apex point, all implants intruded into the infratemporal fossa, with an average intrusion depth of 7.8 ± 2.6 mm. Significant differences in intrusion rates were present among all C series sites. In Class IV subgroup, no ZI intrusion into infratemporal fossa occurred at B0 and B1 site, while 35.9% of the ZI at B2 site and 89.7% of the ZI at B3 site showed intrusion, with intrusion depth of 2.4 ± 1.0 mm (B2) and 3.9 ± 1.7 mm (B3). For the C series apex points, 15.4% of ZI at C0, 56.4% of ZI at C1 site, and over 90% of ZI at C2 and C3 sites entered the infratemporal fossa (Table ), with intrusion depths increasing from C0 to C3. Within the Class V/VI subgroup, there was no ZI intrusion into the infratemporal fossa at the B0 site, and only one ZI at the B1 site (5.9%) penetrated the infratemporal fossa with an intrusion depth of 2.4 mm. In contrast, 58.8% of ZIs at the B2 site and all ZIs at the B3 site (100%) intruded into the infratemporal fossa (Table ). Regarding the C series sites, 23.5% of ZIs at the C0 site, 64.7% of ZIs at the C1 site, and over 90% of ZIs at both the C2 and C3 sites entered the infratemporal fossa (Table ). Although the exact values for ZI intrusion rate, as well as the average depth and length of intrusion, were higher in the Class V/VI subgroup compared to Class IVsubgroup, there was no statistically significant difference in the rate, depth, or length of ZI intrusion at any of the middle and lower zygoma apex sites between the two subgroups ( P > 0.05). The risk of orbital cavity penetration No orbital cavity penetration was detected in all the cases at all apex points. The distance between the anterior ZI and the orbital cavity was shown in Table . The average distance between the anterior ZI and the orbit was 2.5 ± 1.0 mm, 3.2 ± 1.0 mm, 3.8 ± 1.0 mm, and 4.3 ± 0.9 mm at A3, A2, A1, and A0 apex points respectively. There was significant difference in ZI-orbital distance among the four apex groups ( P < 0.001). No significant difference in ZI-orbital distance was found between the Class IV and Class V/VI subgroups at all the A series points. Three anterior ZI at A3 point showed less than 1 mm distance from the orbital cavity.
Maxillary edentulous patients who visited the University dental school clinic from March 2021 to October 2022 for implant-supported prosthesis treatment and underwent CBCT were evaluated. A total of 48 patients were collected. The edentulous arches were classified using the Cawood and Howell classification. Of these patients, eleven were excluded because their alveolar bone classification was categorized as level III. Nine patients were excluded due to inadequate CBCT images that precluded the virtual design of zygomatic implants. Ultimately, 28 participants were included in the study, comprising 12 males and 16 females, with an average age of 63.8 ± 12.6 years. The 28 edentulous maxillae were divided into 56 hemi-maxillae, with 39 of the 56 edentulous posterior residual ridges classified as Class IV and the remaining 17 as Class V/VI. In total, 112 implants were virtually planned.
BIC of the anterior zygomatic implant The apex points of the anterior ZIs were at A0, A1, A2, and A3. The total BIC (tBIC) and the zygomatic BIC (zBIC) of implants decreased significantly from A3 to A0 ( P < 0.01, Table ) while the alveolar BIC (aBIC) showed no significant change ( P = 0.769). The average zBIC at A3, A2, A1, and A0 was 18.3 ± 3.9 mm, 13.4 ± 3.7 mm, 7.4 ± 2.8 mm, and 4.2 ± 1.7 mm respectively. In the Class IV subgroup, tBIC and zBIC decreased from A3 to A0, with a significant difference observed among ZIs at all apex points ( P < 0.01), However, no significant difference was found in aBIC among implants at any apex points ( P = 0.849). In the Class V/VI subgroups, tBIC and zBIC also decreased from A3 to A0. A significant difference in zBIC was noted among implants at all apex points ( P < 0.01), while no significant difference was observed in tBIC between implants at A3 and A2 ( P = 0.217), or between those at A1 and A0 ( P = 0.132). The tBIC of implants at A3 and A2 was significantly higher than that of implants at A1 and A0 ( P < 0.05). No significant difference in aBIC was found among implants at any apex points ( P = 0.939). For implants at the four apex points of the upper zygoma, no significant difference in tBIC, zBIC, or aBIC was detected between the two subgroups ( P > 0.05). BIC of the posterior zygomatic implant The apex points of the posterior implants comprised eight locations: B0, B1, B2, and B3 at the middle zygoma, and C0, C1, C2, and C3 at the lower zygoma. Within the B series, the highest zBIC and tBIC were recorded at the B2 point (16.3 ± 5.3 mm; 22.1 ± 7.5 mm), while the C series showed the highest values at the C1 point (13.8 ± 5.0 mm; 19.5 ± 5.5 mm) as indicated in Table . The B2 point demonstrated the highest zBIC and tBIC among all eight apex sites, with a significant difference from the other points. No significant difference in zBIC and tBIC were observed among the B1, B3, C1, and C2 points (zBIC at B3: 13.4 ± 4.8 mm, at B1: 13.5 ± 5.5 mm, at C1: 13.8 ± 5.0 mm, at C2: 12.3 ± 5.8 mm), though these were significantly higher than those at the B0, C0, and C3 points (zBIC at B0: 8.9 ± 4.9 mm, at C0: 10.9 ± 4.1 mm, at C3: 8.5 ± 4.5 mm). Across all apex points, aBIC did not show significant variation ( P > 0.05) (Table ). In the Class IV and Class V/VI subgroups, the highest BIC was noted at the B2 point in the middle zygoma and at the C1 point in the lower zygoma. For Class IV subgroup, zBIC and tBIC at the B2 point were significantly higher compared to other apex points. Between the C1 and B1 apex points, no significant difference in zBIC and tBIC was detected. In the Class V/VI subgroup, zBIC and tBIC did not significantly differ between the B2 and B1 points, but a significant difference was found between the B2 point and other points, excluding B1 (Table ). No significant difference was found in aBIC and tBIC of implants at the same apex points between Class IV and V/VI subgroups. Relationship between zygomatic implants and the Infratemporal Fossa (Table ) Anterior zygomatic implants and infratemporal fossa For the anterior zygomatic implants, only one of the 56 implants (1.8%) at the A3 point intruded into the infratemporal fossa. The depth of the intrusion was 1.2 mm. No other anterior implants showed entry into the fossa. There was no significant difference in the rate of ZI intrusion among the four apex points (A series points) ( P = 0.390). The single implant that intruded into the infratemporal fossa was from the subgroup of class IV, although there was no significant difference in the rate of anterior ZI intrusion between the two subgroups. Posterior zygomatic implants and infratemporal fossa For the B series apex points (mid-zygoma), no implant intrusion into the infratemporal fossa was observed at B0. However, intrusion rates at B1, B2, and B3 were 1.8%, 42.9%, and 92.9%, respectively, with the average depth and length of intrusion into the infratemporal fossa increasing progressively. The average intrusion depth was 2.4 mm, 2.2 ± 1.0 mm, and 3.9 ± 1.6 mm at B1, B2, and B3 respectively. Significant differences in intrusion rates were noted between B3 and B0, and B2 and B0, but not between B1 and B0. For the C series apex points (lower zygoma), varying rates of ZI intrusion into the infratemporal fossa were recorded across the four sites. From C0 to C3, the intrusion rate, depth and length of ZI exposure in the infratemporal fossa increased (Table ). At the C3 apex point, all implants intruded into the infratemporal fossa, with an average intrusion depth of 7.8 ± 2.6 mm. Significant differences in intrusion rates were present among all C series sites. In Class IV subgroup, no ZI intrusion into infratemporal fossa occurred at B0 and B1 site, while 35.9% of the ZI at B2 site and 89.7% of the ZI at B3 site showed intrusion, with intrusion depth of 2.4 ± 1.0 mm (B2) and 3.9 ± 1.7 mm (B3). For the C series apex points, 15.4% of ZI at C0, 56.4% of ZI at C1 site, and over 90% of ZI at C2 and C3 sites entered the infratemporal fossa (Table ), with intrusion depths increasing from C0 to C3. Within the Class V/VI subgroup, there was no ZI intrusion into the infratemporal fossa at the B0 site, and only one ZI at the B1 site (5.9%) penetrated the infratemporal fossa with an intrusion depth of 2.4 mm. In contrast, 58.8% of ZIs at the B2 site and all ZIs at the B3 site (100%) intruded into the infratemporal fossa (Table ). Regarding the C series sites, 23.5% of ZIs at the C0 site, 64.7% of ZIs at the C1 site, and over 90% of ZIs at both the C2 and C3 sites entered the infratemporal fossa (Table ). Although the exact values for ZI intrusion rate, as well as the average depth and length of intrusion, were higher in the Class V/VI subgroup compared to Class IVsubgroup, there was no statistically significant difference in the rate, depth, or length of ZI intrusion at any of the middle and lower zygoma apex sites between the two subgroups ( P > 0.05). The risk of orbital cavity penetration No orbital cavity penetration was detected in all the cases at all apex points. The distance between the anterior ZI and the orbital cavity was shown in Table . The average distance between the anterior ZI and the orbit was 2.5 ± 1.0 mm, 3.2 ± 1.0 mm, 3.8 ± 1.0 mm, and 4.3 ± 0.9 mm at A3, A2, A1, and A0 apex points respectively. There was significant difference in ZI-orbital distance among the four apex groups ( P < 0.001). No significant difference in ZI-orbital distance was found between the Class IV and Class V/VI subgroups at all the A series points. Three anterior ZI at A3 point showed less than 1 mm distance from the orbital cavity.
The apex points of the anterior ZIs were at A0, A1, A2, and A3. The total BIC (tBIC) and the zygomatic BIC (zBIC) of implants decreased significantly from A3 to A0 ( P < 0.01, Table ) while the alveolar BIC (aBIC) showed no significant change ( P = 0.769). The average zBIC at A3, A2, A1, and A0 was 18.3 ± 3.9 mm, 13.4 ± 3.7 mm, 7.4 ± 2.8 mm, and 4.2 ± 1.7 mm respectively. In the Class IV subgroup, tBIC and zBIC decreased from A3 to A0, with a significant difference observed among ZIs at all apex points ( P < 0.01), However, no significant difference was found in aBIC among implants at any apex points ( P = 0.849). In the Class V/VI subgroups, tBIC and zBIC also decreased from A3 to A0. A significant difference in zBIC was noted among implants at all apex points ( P < 0.01), while no significant difference was observed in tBIC between implants at A3 and A2 ( P = 0.217), or between those at A1 and A0 ( P = 0.132). The tBIC of implants at A3 and A2 was significantly higher than that of implants at A1 and A0 ( P < 0.05). No significant difference in aBIC was found among implants at any apex points ( P = 0.939). For implants at the four apex points of the upper zygoma, no significant difference in tBIC, zBIC, or aBIC was detected between the two subgroups ( P > 0.05).
The apex points of the posterior implants comprised eight locations: B0, B1, B2, and B3 at the middle zygoma, and C0, C1, C2, and C3 at the lower zygoma. Within the B series, the highest zBIC and tBIC were recorded at the B2 point (16.3 ± 5.3 mm; 22.1 ± 7.5 mm), while the C series showed the highest values at the C1 point (13.8 ± 5.0 mm; 19.5 ± 5.5 mm) as indicated in Table . The B2 point demonstrated the highest zBIC and tBIC among all eight apex sites, with a significant difference from the other points. No significant difference in zBIC and tBIC were observed among the B1, B3, C1, and C2 points (zBIC at B3: 13.4 ± 4.8 mm, at B1: 13.5 ± 5.5 mm, at C1: 13.8 ± 5.0 mm, at C2: 12.3 ± 5.8 mm), though these were significantly higher than those at the B0, C0, and C3 points (zBIC at B0: 8.9 ± 4.9 mm, at C0: 10.9 ± 4.1 mm, at C3: 8.5 ± 4.5 mm). Across all apex points, aBIC did not show significant variation ( P > 0.05) (Table ). In the Class IV and Class V/VI subgroups, the highest BIC was noted at the B2 point in the middle zygoma and at the C1 point in the lower zygoma. For Class IV subgroup, zBIC and tBIC at the B2 point were significantly higher compared to other apex points. Between the C1 and B1 apex points, no significant difference in zBIC and tBIC was detected. In the Class V/VI subgroup, zBIC and tBIC did not significantly differ between the B2 and B1 points, but a significant difference was found between the B2 point and other points, excluding B1 (Table ). No significant difference was found in aBIC and tBIC of implants at the same apex points between Class IV and V/VI subgroups.
) Anterior zygomatic implants and infratemporal fossa For the anterior zygomatic implants, only one of the 56 implants (1.8%) at the A3 point intruded into the infratemporal fossa. The depth of the intrusion was 1.2 mm. No other anterior implants showed entry into the fossa. There was no significant difference in the rate of ZI intrusion among the four apex points (A series points) ( P = 0.390). The single implant that intruded into the infratemporal fossa was from the subgroup of class IV, although there was no significant difference in the rate of anterior ZI intrusion between the two subgroups. Posterior zygomatic implants and infratemporal fossa For the B series apex points (mid-zygoma), no implant intrusion into the infratemporal fossa was observed at B0. However, intrusion rates at B1, B2, and B3 were 1.8%, 42.9%, and 92.9%, respectively, with the average depth and length of intrusion into the infratemporal fossa increasing progressively. The average intrusion depth was 2.4 mm, 2.2 ± 1.0 mm, and 3.9 ± 1.6 mm at B1, B2, and B3 respectively. Significant differences in intrusion rates were noted between B3 and B0, and B2 and B0, but not between B1 and B0. For the C series apex points (lower zygoma), varying rates of ZI intrusion into the infratemporal fossa were recorded across the four sites. From C0 to C3, the intrusion rate, depth and length of ZI exposure in the infratemporal fossa increased (Table ). At the C3 apex point, all implants intruded into the infratemporal fossa, with an average intrusion depth of 7.8 ± 2.6 mm. Significant differences in intrusion rates were present among all C series sites. In Class IV subgroup, no ZI intrusion into infratemporal fossa occurred at B0 and B1 site, while 35.9% of the ZI at B2 site and 89.7% of the ZI at B3 site showed intrusion, with intrusion depth of 2.4 ± 1.0 mm (B2) and 3.9 ± 1.7 mm (B3). For the C series apex points, 15.4% of ZI at C0, 56.4% of ZI at C1 site, and over 90% of ZI at C2 and C3 sites entered the infratemporal fossa (Table ), with intrusion depths increasing from C0 to C3. Within the Class V/VI subgroup, there was no ZI intrusion into the infratemporal fossa at the B0 site, and only one ZI at the B1 site (5.9%) penetrated the infratemporal fossa with an intrusion depth of 2.4 mm. In contrast, 58.8% of ZIs at the B2 site and all ZIs at the B3 site (100%) intruded into the infratemporal fossa (Table ). Regarding the C series sites, 23.5% of ZIs at the C0 site, 64.7% of ZIs at the C1 site, and over 90% of ZIs at both the C2 and C3 sites entered the infratemporal fossa (Table ). Although the exact values for ZI intrusion rate, as well as the average depth and length of intrusion, were higher in the Class V/VI subgroup compared to Class IVsubgroup, there was no statistically significant difference in the rate, depth, or length of ZI intrusion at any of the middle and lower zygoma apex sites between the two subgroups ( P > 0.05).
For the anterior zygomatic implants, only one of the 56 implants (1.8%) at the A3 point intruded into the infratemporal fossa. The depth of the intrusion was 1.2 mm. No other anterior implants showed entry into the fossa. There was no significant difference in the rate of ZI intrusion among the four apex points (A series points) ( P = 0.390). The single implant that intruded into the infratemporal fossa was from the subgroup of class IV, although there was no significant difference in the rate of anterior ZI intrusion between the two subgroups.
For the B series apex points (mid-zygoma), no implant intrusion into the infratemporal fossa was observed at B0. However, intrusion rates at B1, B2, and B3 were 1.8%, 42.9%, and 92.9%, respectively, with the average depth and length of intrusion into the infratemporal fossa increasing progressively. The average intrusion depth was 2.4 mm, 2.2 ± 1.0 mm, and 3.9 ± 1.6 mm at B1, B2, and B3 respectively. Significant differences in intrusion rates were noted between B3 and B0, and B2 and B0, but not between B1 and B0. For the C series apex points (lower zygoma), varying rates of ZI intrusion into the infratemporal fossa were recorded across the four sites. From C0 to C3, the intrusion rate, depth and length of ZI exposure in the infratemporal fossa increased (Table ). At the C3 apex point, all implants intruded into the infratemporal fossa, with an average intrusion depth of 7.8 ± 2.6 mm. Significant differences in intrusion rates were present among all C series sites. In Class IV subgroup, no ZI intrusion into infratemporal fossa occurred at B0 and B1 site, while 35.9% of the ZI at B2 site and 89.7% of the ZI at B3 site showed intrusion, with intrusion depth of 2.4 ± 1.0 mm (B2) and 3.9 ± 1.7 mm (B3). For the C series apex points, 15.4% of ZI at C0, 56.4% of ZI at C1 site, and over 90% of ZI at C2 and C3 sites entered the infratemporal fossa (Table ), with intrusion depths increasing from C0 to C3. Within the Class V/VI subgroup, there was no ZI intrusion into the infratemporal fossa at the B0 site, and only one ZI at the B1 site (5.9%) penetrated the infratemporal fossa with an intrusion depth of 2.4 mm. In contrast, 58.8% of ZIs at the B2 site and all ZIs at the B3 site (100%) intruded into the infratemporal fossa (Table ). Regarding the C series sites, 23.5% of ZIs at the C0 site, 64.7% of ZIs at the C1 site, and over 90% of ZIs at both the C2 and C3 sites entered the infratemporal fossa (Table ). Although the exact values for ZI intrusion rate, as well as the average depth and length of intrusion, were higher in the Class V/VI subgroup compared to Class IVsubgroup, there was no statistically significant difference in the rate, depth, or length of ZI intrusion at any of the middle and lower zygoma apex sites between the two subgroups ( P > 0.05).
No orbital cavity penetration was detected in all the cases at all apex points. The distance between the anterior ZI and the orbital cavity was shown in Table . The average distance between the anterior ZI and the orbit was 2.5 ± 1.0 mm, 3.2 ± 1.0 mm, 3.8 ± 1.0 mm, and 4.3 ± 0.9 mm at A3, A2, A1, and A0 apex points respectively. There was significant difference in ZI-orbital distance among the four apex groups ( P < 0.001). No significant difference in ZI-orbital distance was found between the Class IV and Class V/VI subgroups at all the A series points. Three anterior ZI at A3 point showed less than 1 mm distance from the orbital cavity.
Choice of apex site for quad zygomatic implants The anterior ZI of quad zygomatic implant exhibited the highest zygomatic BIC when the A3 apex point at the upper zygoma was selected. The posterior ZI demonstrated the highest zygomatic BIC at the B2 apex point in the middle zygoma and the C1 apex point in the lower zygoma. These findings align with the clinical study by Wu and colleagues 。. The zygomatic BIC of anterior ZI increased progressively as the apex point of ZI moved from A0 to A3, due to the elongating distance the ZI traversed through the zygoma. For the posterior ZI, the zygomatic BIC rose from B0 to B2, then diminished from B2 to B3, and similarly increased from C0 to C1 before decreasing from C1 to C3. This pattern is attributed to the portion of the ZI that is exposed in the infratemporal fossa. The more distal the apex point, the greater the percentage of ZI exposure in the infratemporal fossa. Among all middle and lower zygoma apex points, the highest average zBIC and tBIC can be achieved when ZI ended at the B2 point, The B1, B3, C1, and C2 apex points can also provide relatively high BIC for the ZI. Infratemporal fossa serves as the passage way of many important neurovascular structure, including the maxillary artery and its branches, the pterygoid venous piexus, mandibular nerve, and more. Given the zygomatic bone’s curvature and the invariably straight path of the ZI, there’s a significant risk of implants entering the infratemporal fossa when selecting distal apex points. Rossi et al. have recommended adjusting the ZI insertion angle to prevent its encroachment into the infratemporal fossa and to protect the vital neurovascular structures . For the anterior quad ZI, the selection of the A3 apex point resulted in only one instance of infratemporal fossa intrusion out of 56 ZIs. Statistical analysis indicated no significant difference in the rate of infratemporal fossa intrusion across all A series apex points, implying that A3 remains a secure and preferred choice. For the posterior quad ZI, despite the highest zBIC and tBIC at the B2 point, the rate of infratemporal fossa intrusion was 42.9%, with an average intrusion depth of 2.2 ± 1.0 mm, potentially harming the contents in infratemporal fossa. All apex points in the lower zygoma (C series) demonstrated a risk of ZI intrusion into the infratemporal fossa, particularly at C2 and C3. However, at the B1 site, only one of the 56 posterior quad ZIs intruded into the infratemporal fossa, mirroring the anterior ZI at the A3 apex point. Therefore, placing ZI at the B1 site is considered safe for achieving high BIC while avoiding damage to the content in infratemporal fossa. In summary, maximizing the zygomatic bone volume for optimal BIC, while ensuring patient safety, A3 and B1 emerged as the superior apex points for anterior and posterior quad ZI, respectively. The optimal sites for the apex points of quad ZIs were identified as the upper posterior and the anterior middle portions of the zygoma. Risk of ZI intrusion into the infratemporal fossa and difference between the two subgroups In this study the edentulous maxilla residual ridges were classified in to Class IV and Class V/VI according to the Cawood and Howell classification . Simillar to the entire cohort, the highest zygomatic and total BIC for the anterior ZI in both subgroups was achieved at the A3 apex point, with no significant difference in the rate of intrusion into the infratemporal fossa between the Class IV and Class V/VI subgroups at the A3 point. For the posterior ZI, the highest zBIC and tBIC were achieved when the ZI was placed at the B2 apex point. The second highest zBIC and tBIC point in the Class IV subgroup were observed at the C1, B3, and B1 points. In the Class V/VI subgroup, the B1 point was also a favorable choice for the apex since there was no significant difference in zBIC and tBIC between the B1 and B2 points. Considering the risk of intrusion into the infratemporal fossa, it was found that the rate of posterior ZI penetration into the infratemporal fossa was high in ZIs at B2, B3, and all C series points. Consequently, the B1 and B0 points were safer choices. Taking into account both BIC and infratemporal fossa risk factors, the optimal apex point for the posterior ZI was B1. One case of anterior ZI intrusion into the infratemporal fossa at the A3 apex point in the Class IV subgroup was noted, along with a posterior ZI intrusion at the B1 apex point in the Class V/VI subgroup. The zygomas in these cases exhibited greater curvature and were thinner than those in other cases, resulting in a shallower infratemporal fossa and an increased likelihood of ZI penetration into infratemporal fossa. No significant differences were found between Class IV and Class V/VI subgroups at any apex point sites, indicating that the risk of ZI intrusion is influenced by zygomatic anatomy rather than the classification of residual ridge resorption. Preoperative analysis of the zygomatic anatomy is crucial, particularly for patients with prominent zygomatic bones. Should ZI intrusion be detected, the anterior ZI apex point could be moved forward to the A2 point, and the posterior ZI apex point could be adjusted toward the B0 point. For the same apex point, no significant differences were observed in BIC or the rate, length, and depth of ZI intrusion into infratemporal fossa between Class IV and Class V/IV subgroups. These findings suggest that the classification of the edentulous residual ridge has minimal impact on the primary stability and safety of quad ZI, which can be safely applied in patients with severe maxillary residual ridge resorption. It should be noted that the infratemporal intrusion in this Chinese population may be different from those of the western demographic groups since the facial features of asian population are characterized by a relatively flatter facial profile and more prominent zygomatic bones. Risk of orbital cavity penetration Orbital cavity penetration is a severe complication with the incidence of 5.9%[21] in ZI placement. Care must be taken to avoid the bony orbit during ZI placement. In this study, three anterior ZI at A3 apex point showed less than 1 mm distance from the orbital cavity. Even with the computer assisted implant surgery (CAIS), the deviation of ZI placement remains above 2 mm for static and dynamic CAIS . The orbital penetration risk should be taken into account when A3 point was selected as the apex point for anterior ZI. The zygomatic anatomy-guided approach (ZAGA) concept that focus on interindividual anatomic differences can be considered to avoid this complication . Possible risk of interference between the anterior and posterior ZIs There are potential risk of interference between the anterior and posterior ZIs when this zygoma segementation protocol was used. When B series points were taken as the apex points for the posterior ZI, there are chances that the anterior ZI pass by in a distance of less than 1.6 mm at some point. There is a risk for the two ZIs coming into contact considering the deviations in ZI placement. Causions should be taken when the B series points were taken as the apex point for the posterior ZI. This study has some limitations. First, the study design and outcome are based solely on CBCT anatomical analysis and virtual implant planning, and the BIC measurement was base on linear instead of area measurement, and these may be different from actual clinical practice. Second, the classification of the edentulous residual ridge was based on ridge height measurements and clinical examination, suggesting that a more precise classification system is needed for future research. Third, the choice of apex points for the anterior ZI was set at the A series point, in actual clinical practice, points B series can also be selected as apical points for anterior ZIs. Risk of orbital injury need to be considered when placing ZIs at A series points, especially A3 apex point. More refined zygomatic bone segmentation is needed to avoid orbital risk. These needs further investigation in future studies.
The anterior ZI of quad zygomatic implant exhibited the highest zygomatic BIC when the A3 apex point at the upper zygoma was selected. The posterior ZI demonstrated the highest zygomatic BIC at the B2 apex point in the middle zygoma and the C1 apex point in the lower zygoma. These findings align with the clinical study by Wu and colleagues 。. The zygomatic BIC of anterior ZI increased progressively as the apex point of ZI moved from A0 to A3, due to the elongating distance the ZI traversed through the zygoma. For the posterior ZI, the zygomatic BIC rose from B0 to B2, then diminished from B2 to B3, and similarly increased from C0 to C1 before decreasing from C1 to C3. This pattern is attributed to the portion of the ZI that is exposed in the infratemporal fossa. The more distal the apex point, the greater the percentage of ZI exposure in the infratemporal fossa. Among all middle and lower zygoma apex points, the highest average zBIC and tBIC can be achieved when ZI ended at the B2 point, The B1, B3, C1, and C2 apex points can also provide relatively high BIC for the ZI. Infratemporal fossa serves as the passage way of many important neurovascular structure, including the maxillary artery and its branches, the pterygoid venous piexus, mandibular nerve, and more. Given the zygomatic bone’s curvature and the invariably straight path of the ZI, there’s a significant risk of implants entering the infratemporal fossa when selecting distal apex points. Rossi et al. have recommended adjusting the ZI insertion angle to prevent its encroachment into the infratemporal fossa and to protect the vital neurovascular structures . For the anterior quad ZI, the selection of the A3 apex point resulted in only one instance of infratemporal fossa intrusion out of 56 ZIs. Statistical analysis indicated no significant difference in the rate of infratemporal fossa intrusion across all A series apex points, implying that A3 remains a secure and preferred choice. For the posterior quad ZI, despite the highest zBIC and tBIC at the B2 point, the rate of infratemporal fossa intrusion was 42.9%, with an average intrusion depth of 2.2 ± 1.0 mm, potentially harming the contents in infratemporal fossa. All apex points in the lower zygoma (C series) demonstrated a risk of ZI intrusion into the infratemporal fossa, particularly at C2 and C3. However, at the B1 site, only one of the 56 posterior quad ZIs intruded into the infratemporal fossa, mirroring the anterior ZI at the A3 apex point. Therefore, placing ZI at the B1 site is considered safe for achieving high BIC while avoiding damage to the content in infratemporal fossa. In summary, maximizing the zygomatic bone volume for optimal BIC, while ensuring patient safety, A3 and B1 emerged as the superior apex points for anterior and posterior quad ZI, respectively. The optimal sites for the apex points of quad ZIs were identified as the upper posterior and the anterior middle portions of the zygoma.
In this study the edentulous maxilla residual ridges were classified in to Class IV and Class V/VI according to the Cawood and Howell classification . Simillar to the entire cohort, the highest zygomatic and total BIC for the anterior ZI in both subgroups was achieved at the A3 apex point, with no significant difference in the rate of intrusion into the infratemporal fossa between the Class IV and Class V/VI subgroups at the A3 point. For the posterior ZI, the highest zBIC and tBIC were achieved when the ZI was placed at the B2 apex point. The second highest zBIC and tBIC point in the Class IV subgroup were observed at the C1, B3, and B1 points. In the Class V/VI subgroup, the B1 point was also a favorable choice for the apex since there was no significant difference in zBIC and tBIC between the B1 and B2 points. Considering the risk of intrusion into the infratemporal fossa, it was found that the rate of posterior ZI penetration into the infratemporal fossa was high in ZIs at B2, B3, and all C series points. Consequently, the B1 and B0 points were safer choices. Taking into account both BIC and infratemporal fossa risk factors, the optimal apex point for the posterior ZI was B1. One case of anterior ZI intrusion into the infratemporal fossa at the A3 apex point in the Class IV subgroup was noted, along with a posterior ZI intrusion at the B1 apex point in the Class V/VI subgroup. The zygomas in these cases exhibited greater curvature and were thinner than those in other cases, resulting in a shallower infratemporal fossa and an increased likelihood of ZI penetration into infratemporal fossa. No significant differences were found between Class IV and Class V/VI subgroups at any apex point sites, indicating that the risk of ZI intrusion is influenced by zygomatic anatomy rather than the classification of residual ridge resorption. Preoperative analysis of the zygomatic anatomy is crucial, particularly for patients with prominent zygomatic bones. Should ZI intrusion be detected, the anterior ZI apex point could be moved forward to the A2 point, and the posterior ZI apex point could be adjusted toward the B0 point. For the same apex point, no significant differences were observed in BIC or the rate, length, and depth of ZI intrusion into infratemporal fossa between Class IV and Class V/IV subgroups. These findings suggest that the classification of the edentulous residual ridge has minimal impact on the primary stability and safety of quad ZI, which can be safely applied in patients with severe maxillary residual ridge resorption. It should be noted that the infratemporal intrusion in this Chinese population may be different from those of the western demographic groups since the facial features of asian population are characterized by a relatively flatter facial profile and more prominent zygomatic bones.
Orbital cavity penetration is a severe complication with the incidence of 5.9%[21] in ZI placement. Care must be taken to avoid the bony orbit during ZI placement. In this study, three anterior ZI at A3 apex point showed less than 1 mm distance from the orbital cavity. Even with the computer assisted implant surgery (CAIS), the deviation of ZI placement remains above 2 mm for static and dynamic CAIS . The orbital penetration risk should be taken into account when A3 point was selected as the apex point for anterior ZI. The zygomatic anatomy-guided approach (ZAGA) concept that focus on interindividual anatomic differences can be considered to avoid this complication .
There are potential risk of interference between the anterior and posterior ZIs when this zygoma segementation protocol was used. When B series points were taken as the apex points for the posterior ZI, there are chances that the anterior ZI pass by in a distance of less than 1.6 mm at some point. There is a risk for the two ZIs coming into contact considering the deviations in ZI placement. Causions should be taken when the B series points were taken as the apex point for the posterior ZI. This study has some limitations. First, the study design and outcome are based solely on CBCT anatomical analysis and virtual implant planning, and the BIC measurement was base on linear instead of area measurement, and these may be different from actual clinical practice. Second, the classification of the edentulous residual ridge was based on ridge height measurements and clinical examination, suggesting that a more precise classification system is needed for future research. Third, the choice of apex points for the anterior ZI was set at the A series point, in actual clinical practice, points B series can also be selected as apical points for anterior ZIs. Risk of orbital injury need to be considered when placing ZIs at A series points, especially A3 apex point. More refined zygomatic bone segmentation is needed to avoid orbital risk. These needs further investigation in future studies.
For the placement of quad zygomatic implants, the optional apex location for the anterior ZI is A3, while for the posterior ZI, it is B1. These apex points ensure favorable BIC and a reduced risk of infratemporal fossa invasion. Anterior ZI positioned at A3 point may present high risk for orbital penetration and may not be reccomended in a Quad ZI approach. The degree of residual alveolar bone resorption does not affect the BIC of quad zygomatic implants.
|
Genetic targeting of adult Renshaw cells using a | 783b06dc-e32e-42b7-8ef5-aa5a6dc8f2c7 | 8494874 | Anatomy[mh] | Renshaw cells (RCs) are inhibitory spinal interneurons that synapse onto motoneurons (MNs) and receive direct input from recurrent collaterals of motor axons as they exit the spinal cord – . This MN-RC recurrent circuit, discovered in 1946 , is the oldest known inhibitory circuit in the mammalian CNS and is presumed to exert rapid feedback control of MN activity. Despite its simplicity and longstanding literature presence, its exact function is still debated. Detailed analyses of RC connections, input/output properties and the dynamic behavior of RC synapses on MNs and Ia inhibitory interneurons led to several hypotheses about possible RC functions that were critically reviewed in 1996 . To this day, these hypotheses remain unchanged and largely untested , , in part due to the inability to specifically manipulate RCs and isolate their activity from other network elements during motor actions. Because of these limitations, several studies have instead opted to interrogate RCs through computational models – . However, these approaches necessarily rely on assumptions about unknown parameters, including the exact synaptic connectivity between RCs and motor pools and the full complement of RC inputs and outputs . To better understand the function of this circuit and validate computational models, it is necessary to perform experiments that selectively target RCs to precisely reveal their full connectome and alter their activity specifically during motor actions in vivo. Recent advances in molecular characterization of RCs suggest possible genetic approaches to specifically label and manipulate RCs. Renshaw cells uniquely receive strong cholinergic inputs and display high postsynaptic nicotinic sensitivity , mediated in part by expression of the α2 nicotinic receptor subunit ( Chrna2 ) . This property was used to develop a Chrna2-Cre bacterial artificial chromosome (BAC) transgenic line—the only mouse model for RC targeting published to date , . These mice were used to delete the vesicular inhibitory amino acid transporter (VIAAT), thereby preventing throughout development inhibitory neurotransmission from RCs and other Chrna2 expressing cells in brain and spinal cord . No significant effects were detected in motor function during in vitro fictive locomotion or whole-animal locomotion. The authors concluded that compensatory changes during development possibly obscured any functional deficits due to RC silencing. This highlights the need for novel strategies to target RCs after the maturation of spinal networks is completed. However, it should be also noted that analyses to confirm synaptic silencing of RCs or the recurrent inhibitory circuit were not performed in this study and therefore alternative explanations are also possible. Molecular and developmental studies of RCs suggest several alternative genetic strategies for manipulating RCs with greater temporal control. RCs are a type of V1 interneuron that express the transcription factors engrailed-1 ( En1, defining all V1 interneurons), musculoaponeurotic fibrosarcoma oncogene homolog A and B ( Mafa , Mafb ), as well as Oc1 , Oc2 , and Foxd3 – . In addition, all RCs express calbindin ( Calb1 ) and many express parvalbumin ( Pvalb ) and/or calretinin ( Calb2 ) . Each of these genes is developmentally regulated, offering temporal windows for RC targeting. For example, En1 and Mafb are expressed in many embryonic V1 interneurons, and while En1 is downregulated in postnatal RCs, these same RCs maintain Mafb expression – . Similarly, early widespread Calb1 expression is downregulated postnatally, but specifically maintained by mature/adult RCs within the ventral horn and the V1 group . In contrast, Pvalb expression in spinal interneurons, including RCs, only begins after P10 and becomes widespread later . None of these genes are unique to RCs, necessitating combinatorial approaches to increase targeting specificity. We thus explored the temporal dynamics and combinatorial expression of these genes to target RCs in their mature state. To bypass development, we took advantage of Calb1-2A-dgCre-D mice (JAX#023531, referred to as Calb1 -dgCre) that express a destabilized version of Cre (dgCre) dependent on Calb1 regulatory sequences . dgCre consists of an N-terminal fusion protein of Cre with the first 159 amino acids of the Escherichia coli dihydrofolate reductase (ecDHFR), directing Cre to proteosomal degradation and preventing nuclear translocation and recombination unless degradation is blocked with trimethoprim (TMP) . We analyzed optimal TMP dosage and timing to target RCs using the Calb1 -dgCre allele, validating labeling specificity using a combination of localization criteria and Mafb gene expression using a Mafb-GFP animal . To increase specificity, we vetted calretinin (CR, Calb2 gene) and parvalbumin (PV, Pvalb gene) against Calb1-dgCre mice. Intersectional designs combining Calb1-dgCre mice with a Pvalb-2A-FlpO-D animal (JAX#022730, referred to as Pvalb -Flpo) resulted in over 90% targeting of RCs and restricted targeting to after P10. However, a few dorsal horn cells, as well as significant groups of brain neurons were also targeted. To more specifically target the lumbar spinal cord, we show that dual conditional transgenes can be effectively introduced into RCs by local intraspinal delivery of AAV9 vectors. To restrict targeting to ventral inhibitory interneurons, we characterized a new En1 -Flpo knock-in animal to focus Calb1 -dgCre targeting to V1 interneurons. We found 60–70% RC targeting with this combination. These results describe the first models for in vivo adult RC targeting. We discuss the advantages and disadvantages of each model with the objective that these models, or variations thereof, can be adopted to accelerate discovery of RC functions.
Identification criteria for Renshaw cells and the Renshaw cell area To determine whether genetically targeted cells across different ages are Renshaw cells (RCs), we validated criteria for RC identification that are independent of age or spinal cord size (Fig. ). Analyses focused on lumbar 4 and 5 segments (L4, L5). We used En1- Cre / + :: Mafb- GFP / + :: Ai9 R26 lsl-tdT/ + animals (Fig. A) to define RCs as triple-labeled calbindin-immunoreactive (CB-IR) V1-MafB cells (Fig. B). At P5, P15, and 6 months of age, 6–7% of V1 interneurons ( En1 -tdT) are positive for Mafb- GFP and CB-IR. At all ages, 90–95% of these triple-labeled cells are found in a region we defined as the Renshaw cell area (RCA; Fig. B,C, see methods) and that occupies the bottom 45% of the ventral horn in these lumbar segments (Fig. C). In the RCA, 98–99% of CB-IR cells are V1-derived and express Mafb- GFP at P15 and 6 months of age; however, at P5 this percentage is only 74%. Thus, while there are significant numbers of CB-IR cells in the RCA at P5 that are not RCs, CB-IR cells in the RCA are almost exclusively RCs after P15. This is consistent with the progressive downregulation from P5 to P60 of CB-IR in ventral horn cells, other than RCs (Supplemental Fig. ), which we previously reported . Targeting Renshaw cells using Calb1- dgCre mice Using the above criteria, we analyzed the specificity and efficiency of RCs targeting at different developmental time points using Calb1- dgCre mice crossed to Ai9 R26-tdTomato reporter mice (Fig. ). In these animals, trimethoprim (TMP) administration sets the timing of Cre recombination and tdTomato (tdT) labeling in cells expressing Calb1 (Fig. A). Calb1 -tdT cells are visible throughout the spinal cord by 24 h after TMP injection with expression plateauing at 36 h (Supplemental Fig. ). To validate RC targeting efficiency we introduced the Mafb- GFP allele in these animals ( Calb1 -dgCre / + :: Mafb- GFP / + :: Ai9 R26 lsl-tdT/ + ; Fig. A), injected 100 mg/kg TMP or vehicle (DMSO/saline solution) at P15, and analyzed the results at P21 (Fig. B,C). Within the RCA, 87.8% of genetically labeled Calb1 -tdT cells expressed both CB-IR and Mafb- GFP and 89.1% of these Calb1 -tdT cells were CB-IR. A high proportion of RCs are labeled in this model; 88.1% of CB-IR cells and 90.7% of CB-IR/ Mafb- GFP cells in the RCA were Calb1 -tdT (Fig. C). To validate TMP dose, we injected Calb1 -dgCre / + :: Ai9 R26 lsl-tdT/ + mice with 50, 100, or 125 mg/kg TMP at P15. TMP dose had a modest but significant effect on the percentage of RCs expressing Calb1 -tdT (p = 0.0132, one-way ANOVA; Fig. D, Supplemental Table , Supplemental Fig. ). This percentage differed significantly only between 50 and 125 mg/kg (81.2% vs. 92.2%; p = 0.0126, post-hoc Bonferroni test). We used 100 mg/kg TMP in subsequent experiments as it was not significantly different from 125 mg/kg, which requires higher DMSO concentrations to administer. Next, we varied the time at which we induced Cre recombination with TMP (Fig. E, Supplemental Fig. , Supplemental Table ). Renshaw cell targeting was analyzed at P21 after TMP injections at P5, P10 or P15 or at two months after injections at P15 or P60. Genetic labeling after TMP injections at P5, P10 and P15 was similarly efficient, with 87.9% to 99.1% of RCs expressing Calb1 -tdT. In contrast, significantly fewer RCs expressed Calb1 -tdT when recombination was induced at P60 (p < 0.0001, post-hoc Bonferroni compared to all other time points). Comparing RCs at P21 or P60 after P15 TMP injections revealed no significant effect of age of analysis, suggesting stable labeling of RCs after TMP-induced Cre recombination. Many non-RCs throughout the spinal cord are also targeted in animals treated with TMP (Fig. B, top). This was expected given the widespread expression of Calb1 . Most Calb1- tdT cells were in the dorsal horn and many lacked CB-IR at the time of analysis. In the RCA, a small number of Calb1 -tdT cells without CB-IR were also found. Their number was larger following TMP at P5 and then progressively decreased with TMP injection age, although the trend did not reach significance (Fig. E grey bars, Supplemental Fig. , Supplemental Table ). CB-immunonegative Calb1 -tdT + cells are best explained by calbindin downregulation after TMP administration. In addition, we observed significant numbers of Calb1 -tdT cells in the absence of TMP, suggesting “leakiness” in the genetic system with some dgCre escaping effective degradation (Fig. B, bottom). Untimed TMP-independent labeling, which we refer to as spontaneous recombination, is variable between animals and spinal cord sides. It is also not uniform across cell types and is minimal in RCs until two months of age (Fig. C; Supplemental Table ). Calb1 -tdT cells in the absence of TMP increased with age, as expected if spontaneous recombination events accumulate with time. Before P21, TMP-independent Calb1 -tdT cells are mostly in the dorsal horn, but spread to the ventral horn at later ages. By 6 months, TMP-independent Calb1 -tdT labeling increases dramatically, with Calb1 -tdT neurons scattered throughout the dorsoventral axis of the spinal cord. Moreover, we observed tdT + sensory axons in dorsal roots and dorsal columns. These axons innervated spinal laminae III and IV, suggestive of cutaneous mechanoreceptors. Notably, spontaneous recombination in RCs is almost absent before P60: 0% of RCs at P5; 3.2% at P21, 16.2% at P60, and 55.6% at 6 months (Fig. E, Supplemental Fig. ; Supplemental Table ). The percentage of RCs labeled at 6 months and P60 was greater than at prior time points (p ≤ 0.0001, post-hoc Bonferroni tests). In summary, targeting RCs with the Calb1 -dgCre allele is highly efficient when timed in the first three weeks of life with TMP; however, it lacks specificity, with many other spinal interneurons targeted by either TMP-dependent (timed) or TMP-independent (untimed) activity of Calb1 -dgCre. Previous studies indicate that intersection of Calb1- dgCre with other calcium buffering proteins expressed by postnatal RCs might increase specificity while retaining accurate temporal control of RC targeting. Thus, we tested for expression of calretinin (CR) or parvalbumin (PV) in Calb1 -tdT RCs. Mice injected with TMP at P5, P10, or P15 were analyzed at P21 for CR or PV immunoreactivity. An additional P60 timepoint after TMP injection at P15 was included for CR. Across all conditions, 56–66% of Calb1 -tdT cells were CR-IR (Fig. F, ANOVA, p = 0.5443; Supplemental Fig. , Supplemental Table ) and 57–62% of Calb1 -tdT cells were PV-IR with no significant differences according to TMP injection age (Fig. G, ANOVA, p = 0.8490; Supplemental Fig. , Supplemental Table ). All Calb1 -tdT RCs with PV-IR were also CB-IR (Fig. G). Thus, co-expression of Calb1 with either Calb2 (CR gene) or Pvalb (PV gene) should target similar numbers of RCs at P21. The possibility of increased PV expression at later time points was not possible to characterize with our antibodies as PV-IR accumulates in the spinal cord postnatally and after P21 immunolabeling is rather diffuse and has limited cell resolution . Dual-conditional targeting of Renshaw cells using Calb1- dgCre :: Pvalb- Flpo animals To determine whether an intersectional genetic approach might target Renshaw cells with increased specificity, we took advantage of the availability of animals carrying a Pvalb1- Flpo allele to generate Calb1 dgCre / + :: Pvalb Flpo / + :: R26 RCE:dual-EGFP/ + animals, in which EGFP expression requires activity of both recombinases (Fig. A) . Compared to Calb1- dgCre animals, the number of genetically targeted cells in the absence of TMP is drastically reduced at P21, P60, and 6 months (Fig. B top, Fig. C, Supplemental Fig. , Supplemental Tables and ). This suggests that spontaneous Calb1 -dgCre recombination mostly occurs in cells that do not express Pvalb -Flpo and genetic intersection with Pvalb removes most of these cells. Next, we analyzed TMP-induced recombination in RCs at 2 months of age following TMP administration at P5, P10, or P15. We expected to target at least 50% of RCs based on PV-IR colocalization at P21 (Fig. G); however, only 20%-25% of RCs were Calb1/Pvalb- EGFP with no significant differences among injection dates, suggesting TMP stabilization of Cre in this context might be suboptimal (Fig. C, Supplemental Fig. , Supplemental Table ). We therefore explored whether two TMP doses separated by 48 h (P10) or 24 h (P15, P21, P30, or P60) could increase RC coverage. Double injections at P10 and P15 labeled 92.9% and 79.9% of RCs, respectively (Fig. B, bottom), with no significant difference between the two conditions (p > 0.9999, Bonferroni test; Supplemental Fig. , Supplemental Table ). Consistent with Calb1 -dgCre mice, the effectiveness of RC targeting decreased when TMP is administered at later ages: 15.8% of RCs were Calb1 -tdT after double injections at P21, 3.3% at P30, and 0.8% at P60 (p < 0.0001 for all pair-wise Bonferroni comparisons; Supplemental Table ). The higher specificity of the Calb1 / Pvalb intersection was best visualized in Calb1- dgCre :: Pvalb- Flpo animals with two different reporters in the R26 locus: one allele carrying the RCE:dual-EGFP , and the other the Ai9 Cre-dependent lsl-tdT (Supplemental Fig. ). In the Ai9 line, the tdT cassette is removed by Flpo recombination; thus, cells expressing both Calb1 and Pvalb are only EGFP-labeled, while cells that express only Calb1 are tdT-labeled. After a double injection of TMP at P10, tdT labeling at P60 was widespread, while EGFP labeling was restricted to RCs and a few dorsal horn neurons (Supplemental Fig. ). Surprisingly, some RCs were dual-labeled with tdT and EGFP. This might be explained by late activation of the Pvalb promoter and lingering tdT protein, which can be retained by neurons for at least two weeks after switching off expression . Together with our previous estimate that 57–62% of Calb1- tdT RCs were PV-IR at P21 (Fig. G), this suggests that late upregulation of Pvalb in some RCs might explain the larger than expected percentage of RCs targeted at P60 (89% to 97%). Consistent with this, only 72% of RCs were labeled at P21 in animals after similar dual TMP injections at P10 (n = 2 animals). Therefore, RCs continue to upregulate Pvalb between P21 and P60, increasing coverage of RCs with age of analysis under the dual condition of Calb1 and Pvalb expression. In all TMP injection/analysis dates analyzed, there were negligible numbers of Calb1/Pvalb- EGFP cells in the RCA that were not RCs (CB-IR negative; Fig. C, Supplemental Fig. ). The number of Calb1/Pvalb -EGFP cells outside the RCA was also dramatically reduced, but some dorsal horn cells were still labeled. Remarkably, there was a significant difference between P10 and P15 (26.8 ± 2.1 vs. 6.6 ± 2.3 dorsal horn cells per hemicord ± S.D.; p = 0.0156 Bonferroni multiple comparisons, Supplemental Table ), despite both targeting similar numbers of RCs. Double TMP injections at P15 therefore increased RC targeting specificity with no significant reduction in efficiency. In conclusion, Calb1- dgCre and Pvalb- Flpo intersection targets almost all RCs by P60 when two TMP doses are injected between P10 and P15, but the later dual injections result in greater specificity. Dual-conditional approach with Calb1- dgCre :: En1- Flpo animals Compared to Calb1 -dgCre alone, the Calb1 -dgCre and Pvalb -Flpo intersection dramatically increases RC targeting specificity, but a significant number of dorsal horn cells were also labeled with this strategy. To avoid this dorsal interneuron population, we restricted Calb1- dgCre targeting to ventral V1 interneurons using a novel En1- Flpo animal (Fig. A). We first confirmed that V1 interneurons were correctly targeted in En1 -Flpo mice using a Flp-dependent GFP reporter mouse (R26:: RCE-fsf-GFP ), resulting in GFP+ cells distributed in a manner characteristic of V1 interneurons (Fig. B). En1 immunoreactivity confirmed these cells were V1 interneurons, though not all GFP+ neurons were En1-IR due to rapid developmental downregulation of En1 expression in many V1 interneurons. We then generated Calb1- dgCre :: En1- Flpo animals and crossed them to R26 FLTG reporter mice (see Fig. A). In this model, En1 -Flpo labels V1 cells with tdT while TMP-timed Calb1 -dgCre activity removes tdT and replaces it with EGFP in Calb1 - expressing V1 cells (Fig. A,B). We injected Calb1- dgCre :: En1- Flpo mice with either one or two doses of TMP at P15, P21, and P30 to avoid non-RC V1 interneurons expressing Calb1 at earlier ages, followed by analysis at either P21 or P60. In contrast to the Calb1 -dgCre :: Pvalb -Flpo model, there were no significant differences in the number or percentage of RCs targeted according to age of analysis or one or two TMP doses (Fig. 5C1, 5C2, Supplemental Fig. , Supplemental Tables ). The percentage of RCs decreased with TMP injection age, similar to all previous results with Calb1 -dgCre. Two TMP doses at P30 labeled 24% of RCs at P60, whereas 73% and 57% of RCs were labeled when two doses of TMP were respectively injected at P15 or P21 (Supplemental Fig. ; Supplemental Table ). Notably, the Calb1- dgCre :: En1- Flpo model labeled fewer non-RCs compared to the Calb1- dgCre :: Pvalb- Flpo model, indicating an increased specificity of labeling. The number of En1 / Calb1 -EGFP non-RC V1 interneurons was negligible within the RCA, though we did observe a modest, somewhat variable number outside the RCA (averaging 0 to 7.4 cells per hemicord in different animals). The largest numbers of these cells were found after P15 TMP injections, but differences according to TMP injection dates were non-significant (Supplemental Fig. and Supplemental Table ). Most non-RC V1 interneurons were not CB-IR at the time of analysis (P60). The few adult V1 cells that expressed calbindin but are not RCs correspond to some sparse populations that were reported earlier , . Despite improved specificity, targeting efficiency was lower than expected. To investigate this, we took advantage of the dual fluorescent reporters in the R26 FLTG mouse and pooled together all RCs labeled with either EGFP or tdT. Although the proportions of RCs labeled with EGFP or tdT varied according to TMP injection, unlabeled RCs remained relatively constant: 29.8% ± 8.0 (± S.D.) of RCs lacked any fluorescent protein across all experiments. This suggests incomplete RC targeting by the En1- Flpo allele (Supplemental Fig. ; Supplemental Tables ). To confirm this, we generated one En1- Flpo R26 FLTG animal with all V1 interneurons labeled with tdT and observed that 33.8% of CB-IR cells in the RCA lacked tdT (Fig. D). Then we incorporated additional criteria for RC identification based on synaptic markers to exclude any influence of CB-IR non-RCs in the results. First, we identified RCs by CB-IR and the large gephyrin clusters on their cell bodies and proximal dendrites , (Fig. E, 5F). Consistently, 32.4% of lumbar 4/5 large-gephyrin/CB-IR RCs lacked genetic labeling in En1 -Flpo animals (n = 68 RCs). As further confirmation, we performed immunohistochemistry against the vesicular acetylcholine transporter (VAChT) to identify RCs by their distinctive high density cholinergic input on their dendrites , , (Fig. G): 27.4% of RCs such defined lacked tdT (n = 53 RCs; Figs. 5H 1 -H 2 and 5I 1 -I 2 ). We conclude that around 30% of RCs do not undergo Flp recombination in En1- Flpo animals. This differs from En1- Cre animals, in which 100% of RCs undergo genetic recombination (Fig. C). Therefore, Calb1- dgCre :: En1- Flpo mice display higher specificity but lower efficiency of RC targeting compared to the Calb1- dgCre :: Pvalb- Flpo model. Brain cells targeted in the intersection of Calb1- dgCre with either Pvalb- Flpo or En1- Flpo To determine the extent to which these genetic strategies also label neuronal populations in the brain, we performed an analysis of the distribution of lineage-traced neurons in the brains of Calb1 -dgCre/ Pvalb -Flpo or Calb1 -dgCre/ En1 -Flpo mice. Neurons in several brain areas were targeted by both intersections (Fig. A,B). We compared the brains of four Calb1/Pvalb animals at P60, two with 30% RC labeling following a single dose of TMP at P15, and two with 90% RC labeling following two doses of TMP at P10. Regardless of TMP dose, the same brain regions were genetically targeted, although sometimes at different labeling densities varying in the number of cells. We also analyzed the brains of two Calb1- dgCre :: En1-Flpo animals injected with two doses of TMP at P21 (55% RC labeling). In both Calb1/Pvalb and En1/Calb1 brains, every cerebellar Purkinje cell was similarly labeled (Fig. ). Both models also labeled all neurons in the main nucleus of the trapezoid body (MNTB) and many neurons in the superficial layers of the superior colliculus. Labeling in other brain regions differ between both models (Fig. C). Broadly speaking, Calb1/Pvalb neurons can be found throughout the brainstem, midbrain and forebrain, while En1/Calb1 cells are focused to the midbrain. Spatially restricted targeting of RCs via intraspinal AAV injection To restrict genetic manipulations to the spinal cord while avoiding neurons in the brain, we tested the efficiency of targeting RCs with dual conditional AAV9 vectors injected into the postnatal spinal cord. For this purpose we used only Calb1- dgCre :: Pvalb- Flpo animals because RCs in these mice maintain Cre and Flpo expression postnatally, whereas En1 is downregulated in RCs embryonically and appears to not maintain expression of recombinases at postnatal ages. We injected five animals (ages P5-P8) with AAV9 carrying a dual conditional eyfp gene under the control of the hSyn promoter (Fig. A) , targeting the dorsal midline with a rostral bias (upper lumbar injection, n = 2) or caudal bias (caudal lumbar injection, n = 3), followed by administration of TMP twice at P10 or P15 (Fig. B,C; see “ ). Serial sections were aligned according to cytoarchitectonic landmarks and quantified. All animals displayed EYFP bilaterally in both the dorsal and ventral horns (Fig. C-F), indicating adequate penetrance of AAV9 throughout the dorso-ventral extent of the spinal cord. The total number of cells labeled varied across animals, with approximately 3–25 EYFP + cells per hemicord from S1-T13 (Fig. C). In the L4/L5 region, this number increased in caudal bias animals (15.0–33.8 cells per hemicord) but not rostral bias animals (0.7–9.0 cells per hemicord) (Fig. F, left). While 14.7–34.7% of RCs throughout the lumbar region were EYFP labeled overall, the percentages increased depending on the lumbar segments analyzed and the location of the injection. For example, in L4/L5, 33.6–81.2% of RCs were labeled in caudal bias animals and only 1.7–30.9% in rostral bias animals (Fig. F, right). The high degree of inter-animal variability indicates that the rostro-caudal spread of AAV9 needs to be confirmed in each animal, but in all cases the majority of RCs were targeted around the injection site: 83.3—100% of RCs in L4/5 or L1/L2 segments after caudal or rostral bias injections, respectively. EYFP labeling of RCs included the cell body, dendrites, axons and synapses on motoneurons (Fig. G), suggesting its utility for examining synaptic connectivity. Together, the results show that intraspinal AAV9 transduction in Calb1 -dgCre :: Pvalb -Flpo animals targets a large percentage of RCs in a region comprising approximately 2 segments above and below the injection site.
To determine whether genetically targeted cells across different ages are Renshaw cells (RCs), we validated criteria for RC identification that are independent of age or spinal cord size (Fig. ). Analyses focused on lumbar 4 and 5 segments (L4, L5). We used En1- Cre / + :: Mafb- GFP / + :: Ai9 R26 lsl-tdT/ + animals (Fig. A) to define RCs as triple-labeled calbindin-immunoreactive (CB-IR) V1-MafB cells (Fig. B). At P5, P15, and 6 months of age, 6–7% of V1 interneurons ( En1 -tdT) are positive for Mafb- GFP and CB-IR. At all ages, 90–95% of these triple-labeled cells are found in a region we defined as the Renshaw cell area (RCA; Fig. B,C, see methods) and that occupies the bottom 45% of the ventral horn in these lumbar segments (Fig. C). In the RCA, 98–99% of CB-IR cells are V1-derived and express Mafb- GFP at P15 and 6 months of age; however, at P5 this percentage is only 74%. Thus, while there are significant numbers of CB-IR cells in the RCA at P5 that are not RCs, CB-IR cells in the RCA are almost exclusively RCs after P15. This is consistent with the progressive downregulation from P5 to P60 of CB-IR in ventral horn cells, other than RCs (Supplemental Fig. ), which we previously reported .
Calb1- dgCre mice Using the above criteria, we analyzed the specificity and efficiency of RCs targeting at different developmental time points using Calb1- dgCre mice crossed to Ai9 R26-tdTomato reporter mice (Fig. ). In these animals, trimethoprim (TMP) administration sets the timing of Cre recombination and tdTomato (tdT) labeling in cells expressing Calb1 (Fig. A). Calb1 -tdT cells are visible throughout the spinal cord by 24 h after TMP injection with expression plateauing at 36 h (Supplemental Fig. ). To validate RC targeting efficiency we introduced the Mafb- GFP allele in these animals ( Calb1 -dgCre / + :: Mafb- GFP / + :: Ai9 R26 lsl-tdT/ + ; Fig. A), injected 100 mg/kg TMP or vehicle (DMSO/saline solution) at P15, and analyzed the results at P21 (Fig. B,C). Within the RCA, 87.8% of genetically labeled Calb1 -tdT cells expressed both CB-IR and Mafb- GFP and 89.1% of these Calb1 -tdT cells were CB-IR. A high proportion of RCs are labeled in this model; 88.1% of CB-IR cells and 90.7% of CB-IR/ Mafb- GFP cells in the RCA were Calb1 -tdT (Fig. C). To validate TMP dose, we injected Calb1 -dgCre / + :: Ai9 R26 lsl-tdT/ + mice with 50, 100, or 125 mg/kg TMP at P15. TMP dose had a modest but significant effect on the percentage of RCs expressing Calb1 -tdT (p = 0.0132, one-way ANOVA; Fig. D, Supplemental Table , Supplemental Fig. ). This percentage differed significantly only between 50 and 125 mg/kg (81.2% vs. 92.2%; p = 0.0126, post-hoc Bonferroni test). We used 100 mg/kg TMP in subsequent experiments as it was not significantly different from 125 mg/kg, which requires higher DMSO concentrations to administer. Next, we varied the time at which we induced Cre recombination with TMP (Fig. E, Supplemental Fig. , Supplemental Table ). Renshaw cell targeting was analyzed at P21 after TMP injections at P5, P10 or P15 or at two months after injections at P15 or P60. Genetic labeling after TMP injections at P5, P10 and P15 was similarly efficient, with 87.9% to 99.1% of RCs expressing Calb1 -tdT. In contrast, significantly fewer RCs expressed Calb1 -tdT when recombination was induced at P60 (p < 0.0001, post-hoc Bonferroni compared to all other time points). Comparing RCs at P21 or P60 after P15 TMP injections revealed no significant effect of age of analysis, suggesting stable labeling of RCs after TMP-induced Cre recombination. Many non-RCs throughout the spinal cord are also targeted in animals treated with TMP (Fig. B, top). This was expected given the widespread expression of Calb1 . Most Calb1- tdT cells were in the dorsal horn and many lacked CB-IR at the time of analysis. In the RCA, a small number of Calb1 -tdT cells without CB-IR were also found. Their number was larger following TMP at P5 and then progressively decreased with TMP injection age, although the trend did not reach significance (Fig. E grey bars, Supplemental Fig. , Supplemental Table ). CB-immunonegative Calb1 -tdT + cells are best explained by calbindin downregulation after TMP administration. In addition, we observed significant numbers of Calb1 -tdT cells in the absence of TMP, suggesting “leakiness” in the genetic system with some dgCre escaping effective degradation (Fig. B, bottom). Untimed TMP-independent labeling, which we refer to as spontaneous recombination, is variable between animals and spinal cord sides. It is also not uniform across cell types and is minimal in RCs until two months of age (Fig. C; Supplemental Table ). Calb1 -tdT cells in the absence of TMP increased with age, as expected if spontaneous recombination events accumulate with time. Before P21, TMP-independent Calb1 -tdT cells are mostly in the dorsal horn, but spread to the ventral horn at later ages. By 6 months, TMP-independent Calb1 -tdT labeling increases dramatically, with Calb1 -tdT neurons scattered throughout the dorsoventral axis of the spinal cord. Moreover, we observed tdT + sensory axons in dorsal roots and dorsal columns. These axons innervated spinal laminae III and IV, suggestive of cutaneous mechanoreceptors. Notably, spontaneous recombination in RCs is almost absent before P60: 0% of RCs at P5; 3.2% at P21, 16.2% at P60, and 55.6% at 6 months (Fig. E, Supplemental Fig. ; Supplemental Table ). The percentage of RCs labeled at 6 months and P60 was greater than at prior time points (p ≤ 0.0001, post-hoc Bonferroni tests). In summary, targeting RCs with the Calb1 -dgCre allele is highly efficient when timed in the first three weeks of life with TMP; however, it lacks specificity, with many other spinal interneurons targeted by either TMP-dependent (timed) or TMP-independent (untimed) activity of Calb1 -dgCre. Previous studies indicate that intersection of Calb1- dgCre with other calcium buffering proteins expressed by postnatal RCs might increase specificity while retaining accurate temporal control of RC targeting. Thus, we tested for expression of calretinin (CR) or parvalbumin (PV) in Calb1 -tdT RCs. Mice injected with TMP at P5, P10, or P15 were analyzed at P21 for CR or PV immunoreactivity. An additional P60 timepoint after TMP injection at P15 was included for CR. Across all conditions, 56–66% of Calb1 -tdT cells were CR-IR (Fig. F, ANOVA, p = 0.5443; Supplemental Fig. , Supplemental Table ) and 57–62% of Calb1 -tdT cells were PV-IR with no significant differences according to TMP injection age (Fig. G, ANOVA, p = 0.8490; Supplemental Fig. , Supplemental Table ). All Calb1 -tdT RCs with PV-IR were also CB-IR (Fig. G). Thus, co-expression of Calb1 with either Calb2 (CR gene) or Pvalb (PV gene) should target similar numbers of RCs at P21. The possibility of increased PV expression at later time points was not possible to characterize with our antibodies as PV-IR accumulates in the spinal cord postnatally and after P21 immunolabeling is rather diffuse and has limited cell resolution .
Calb1- dgCre :: Pvalb- Flpo animals To determine whether an intersectional genetic approach might target Renshaw cells with increased specificity, we took advantage of the availability of animals carrying a Pvalb1- Flpo allele to generate Calb1 dgCre / + :: Pvalb Flpo / + :: R26 RCE:dual-EGFP/ + animals, in which EGFP expression requires activity of both recombinases (Fig. A) . Compared to Calb1- dgCre animals, the number of genetically targeted cells in the absence of TMP is drastically reduced at P21, P60, and 6 months (Fig. B top, Fig. C, Supplemental Fig. , Supplemental Tables and ). This suggests that spontaneous Calb1 -dgCre recombination mostly occurs in cells that do not express Pvalb -Flpo and genetic intersection with Pvalb removes most of these cells. Next, we analyzed TMP-induced recombination in RCs at 2 months of age following TMP administration at P5, P10, or P15. We expected to target at least 50% of RCs based on PV-IR colocalization at P21 (Fig. G); however, only 20%-25% of RCs were Calb1/Pvalb- EGFP with no significant differences among injection dates, suggesting TMP stabilization of Cre in this context might be suboptimal (Fig. C, Supplemental Fig. , Supplemental Table ). We therefore explored whether two TMP doses separated by 48 h (P10) or 24 h (P15, P21, P30, or P60) could increase RC coverage. Double injections at P10 and P15 labeled 92.9% and 79.9% of RCs, respectively (Fig. B, bottom), with no significant difference between the two conditions (p > 0.9999, Bonferroni test; Supplemental Fig. , Supplemental Table ). Consistent with Calb1 -dgCre mice, the effectiveness of RC targeting decreased when TMP is administered at later ages: 15.8% of RCs were Calb1 -tdT after double injections at P21, 3.3% at P30, and 0.8% at P60 (p < 0.0001 for all pair-wise Bonferroni comparisons; Supplemental Table ). The higher specificity of the Calb1 / Pvalb intersection was best visualized in Calb1- dgCre :: Pvalb- Flpo animals with two different reporters in the R26 locus: one allele carrying the RCE:dual-EGFP , and the other the Ai9 Cre-dependent lsl-tdT (Supplemental Fig. ). In the Ai9 line, the tdT cassette is removed by Flpo recombination; thus, cells expressing both Calb1 and Pvalb are only EGFP-labeled, while cells that express only Calb1 are tdT-labeled. After a double injection of TMP at P10, tdT labeling at P60 was widespread, while EGFP labeling was restricted to RCs and a few dorsal horn neurons (Supplemental Fig. ). Surprisingly, some RCs were dual-labeled with tdT and EGFP. This might be explained by late activation of the Pvalb promoter and lingering tdT protein, which can be retained by neurons for at least two weeks after switching off expression . Together with our previous estimate that 57–62% of Calb1- tdT RCs were PV-IR at P21 (Fig. G), this suggests that late upregulation of Pvalb in some RCs might explain the larger than expected percentage of RCs targeted at P60 (89% to 97%). Consistent with this, only 72% of RCs were labeled at P21 in animals after similar dual TMP injections at P10 (n = 2 animals). Therefore, RCs continue to upregulate Pvalb between P21 and P60, increasing coverage of RCs with age of analysis under the dual condition of Calb1 and Pvalb expression. In all TMP injection/analysis dates analyzed, there were negligible numbers of Calb1/Pvalb- EGFP cells in the RCA that were not RCs (CB-IR negative; Fig. C, Supplemental Fig. ). The number of Calb1/Pvalb -EGFP cells outside the RCA was also dramatically reduced, but some dorsal horn cells were still labeled. Remarkably, there was a significant difference between P10 and P15 (26.8 ± 2.1 vs. 6.6 ± 2.3 dorsal horn cells per hemicord ± S.D.; p = 0.0156 Bonferroni multiple comparisons, Supplemental Table ), despite both targeting similar numbers of RCs. Double TMP injections at P15 therefore increased RC targeting specificity with no significant reduction in efficiency. In conclusion, Calb1- dgCre and Pvalb- Flpo intersection targets almost all RCs by P60 when two TMP doses are injected between P10 and P15, but the later dual injections result in greater specificity.
Calb1- dgCre :: En1- Flpo animals Compared to Calb1 -dgCre alone, the Calb1 -dgCre and Pvalb -Flpo intersection dramatically increases RC targeting specificity, but a significant number of dorsal horn cells were also labeled with this strategy. To avoid this dorsal interneuron population, we restricted Calb1- dgCre targeting to ventral V1 interneurons using a novel En1- Flpo animal (Fig. A). We first confirmed that V1 interneurons were correctly targeted in En1 -Flpo mice using a Flp-dependent GFP reporter mouse (R26:: RCE-fsf-GFP ), resulting in GFP+ cells distributed in a manner characteristic of V1 interneurons (Fig. B). En1 immunoreactivity confirmed these cells were V1 interneurons, though not all GFP+ neurons were En1-IR due to rapid developmental downregulation of En1 expression in many V1 interneurons. We then generated Calb1- dgCre :: En1- Flpo animals and crossed them to R26 FLTG reporter mice (see Fig. A). In this model, En1 -Flpo labels V1 cells with tdT while TMP-timed Calb1 -dgCre activity removes tdT and replaces it with EGFP in Calb1 - expressing V1 cells (Fig. A,B). We injected Calb1- dgCre :: En1- Flpo mice with either one or two doses of TMP at P15, P21, and P30 to avoid non-RC V1 interneurons expressing Calb1 at earlier ages, followed by analysis at either P21 or P60. In contrast to the Calb1 -dgCre :: Pvalb -Flpo model, there were no significant differences in the number or percentage of RCs targeted according to age of analysis or one or two TMP doses (Fig. 5C1, 5C2, Supplemental Fig. , Supplemental Tables ). The percentage of RCs decreased with TMP injection age, similar to all previous results with Calb1 -dgCre. Two TMP doses at P30 labeled 24% of RCs at P60, whereas 73% and 57% of RCs were labeled when two doses of TMP were respectively injected at P15 or P21 (Supplemental Fig. ; Supplemental Table ). Notably, the Calb1- dgCre :: En1- Flpo model labeled fewer non-RCs compared to the Calb1- dgCre :: Pvalb- Flpo model, indicating an increased specificity of labeling. The number of En1 / Calb1 -EGFP non-RC V1 interneurons was negligible within the RCA, though we did observe a modest, somewhat variable number outside the RCA (averaging 0 to 7.4 cells per hemicord in different animals). The largest numbers of these cells were found after P15 TMP injections, but differences according to TMP injection dates were non-significant (Supplemental Fig. and Supplemental Table ). Most non-RC V1 interneurons were not CB-IR at the time of analysis (P60). The few adult V1 cells that expressed calbindin but are not RCs correspond to some sparse populations that were reported earlier , . Despite improved specificity, targeting efficiency was lower than expected. To investigate this, we took advantage of the dual fluorescent reporters in the R26 FLTG mouse and pooled together all RCs labeled with either EGFP or tdT. Although the proportions of RCs labeled with EGFP or tdT varied according to TMP injection, unlabeled RCs remained relatively constant: 29.8% ± 8.0 (± S.D.) of RCs lacked any fluorescent protein across all experiments. This suggests incomplete RC targeting by the En1- Flpo allele (Supplemental Fig. ; Supplemental Tables ). To confirm this, we generated one En1- Flpo R26 FLTG animal with all V1 interneurons labeled with tdT and observed that 33.8% of CB-IR cells in the RCA lacked tdT (Fig. D). Then we incorporated additional criteria for RC identification based on synaptic markers to exclude any influence of CB-IR non-RCs in the results. First, we identified RCs by CB-IR and the large gephyrin clusters on their cell bodies and proximal dendrites , (Fig. E, 5F). Consistently, 32.4% of lumbar 4/5 large-gephyrin/CB-IR RCs lacked genetic labeling in En1 -Flpo animals (n = 68 RCs). As further confirmation, we performed immunohistochemistry against the vesicular acetylcholine transporter (VAChT) to identify RCs by their distinctive high density cholinergic input on their dendrites , , (Fig. G): 27.4% of RCs such defined lacked tdT (n = 53 RCs; Figs. 5H 1 -H 2 and 5I 1 -I 2 ). We conclude that around 30% of RCs do not undergo Flp recombination in En1- Flpo animals. This differs from En1- Cre animals, in which 100% of RCs undergo genetic recombination (Fig. C). Therefore, Calb1- dgCre :: En1- Flpo mice display higher specificity but lower efficiency of RC targeting compared to the Calb1- dgCre :: Pvalb- Flpo model.
Calb1- dgCre with either Pvalb- Flpo or En1- Flpo To determine the extent to which these genetic strategies also label neuronal populations in the brain, we performed an analysis of the distribution of lineage-traced neurons in the brains of Calb1 -dgCre/ Pvalb -Flpo or Calb1 -dgCre/ En1 -Flpo mice. Neurons in several brain areas were targeted by both intersections (Fig. A,B). We compared the brains of four Calb1/Pvalb animals at P60, two with 30% RC labeling following a single dose of TMP at P15, and two with 90% RC labeling following two doses of TMP at P10. Regardless of TMP dose, the same brain regions were genetically targeted, although sometimes at different labeling densities varying in the number of cells. We also analyzed the brains of two Calb1- dgCre :: En1-Flpo animals injected with two doses of TMP at P21 (55% RC labeling). In both Calb1/Pvalb and En1/Calb1 brains, every cerebellar Purkinje cell was similarly labeled (Fig. ). Both models also labeled all neurons in the main nucleus of the trapezoid body (MNTB) and many neurons in the superficial layers of the superior colliculus. Labeling in other brain regions differ between both models (Fig. C). Broadly speaking, Calb1/Pvalb neurons can be found throughout the brainstem, midbrain and forebrain, while En1/Calb1 cells are focused to the midbrain.
To restrict genetic manipulations to the spinal cord while avoiding neurons in the brain, we tested the efficiency of targeting RCs with dual conditional AAV9 vectors injected into the postnatal spinal cord. For this purpose we used only Calb1- dgCre :: Pvalb- Flpo animals because RCs in these mice maintain Cre and Flpo expression postnatally, whereas En1 is downregulated in RCs embryonically and appears to not maintain expression of recombinases at postnatal ages. We injected five animals (ages P5-P8) with AAV9 carrying a dual conditional eyfp gene under the control of the hSyn promoter (Fig. A) , targeting the dorsal midline with a rostral bias (upper lumbar injection, n = 2) or caudal bias (caudal lumbar injection, n = 3), followed by administration of TMP twice at P10 or P15 (Fig. B,C; see “ ). Serial sections were aligned according to cytoarchitectonic landmarks and quantified. All animals displayed EYFP bilaterally in both the dorsal and ventral horns (Fig. C-F), indicating adequate penetrance of AAV9 throughout the dorso-ventral extent of the spinal cord. The total number of cells labeled varied across animals, with approximately 3–25 EYFP + cells per hemicord from S1-T13 (Fig. C). In the L4/L5 region, this number increased in caudal bias animals (15.0–33.8 cells per hemicord) but not rostral bias animals (0.7–9.0 cells per hemicord) (Fig. F, left). While 14.7–34.7% of RCs throughout the lumbar region were EYFP labeled overall, the percentages increased depending on the lumbar segments analyzed and the location of the injection. For example, in L4/L5, 33.6–81.2% of RCs were labeled in caudal bias animals and only 1.7–30.9% in rostral bias animals (Fig. F, right). The high degree of inter-animal variability indicates that the rostro-caudal spread of AAV9 needs to be confirmed in each animal, but in all cases the majority of RCs were targeted around the injection site: 83.3—100% of RCs in L4/5 or L1/L2 segments after caudal or rostral bias injections, respectively. EYFP labeling of RCs included the cell body, dendrites, axons and synapses on motoneurons (Fig. G), suggesting its utility for examining synaptic connectivity. Together, the results show that intraspinal AAV9 transduction in Calb1 -dgCre :: Pvalb -Flpo animals targets a large percentage of RCs in a region comprising approximately 2 segments above and below the injection site.
We have described two strategies for genetically targeting RCs, including the optimal timing and dosage of TMP for inducing Cre activity from Calb1 -dgCre alleles. Calb1 -dgCre combined with Pvalb -Flpo targeted 90% of RCs and 70% of RCs when combined with En1 -Flpo. Each model has unique properties. Genetic targeting of RCs continues to increase postnatally after TMP injection in the Calb1/Pvalb model due to progressive upregulation of Pvalb during late maturation. In contrast, RC targeting in the Calb1/En1 model is expected to occur within 36 h after TMP and not to change at later times after TMP injection. Therefore, the Calb1/En1 model is better suited for experiments requiring accurate timing, while the Calb1/Pvalb model increases coverage to almost the full RC population through and extended period of time. Both result in additional targeting of non-RC spinal interneurons, as well as neurons in the brain, quite prominently Purkinje cells. This limits their utility for whole-animal in vivo experiments using transgenic mice to introduce dual-conditional activity modifiers (optogenetics, DREADDs) or cellular ablation (diphteria toxin), as supraspinal systems will also be targeted. One alternative approach is to introduce dual Cre and Flp dependent transgenes locally in the spinal cord via viral delivery as demonstrated in our study. For this purpose, the Calb1/Pvalb model needs to be used because it retains Cre and Flp expression in postnatal RCs. Neither model, however, results in complete specificity within the spinal cord. Confounds introduced by a few dorsal horn interneurons in the Calb1/Pvalb model or a few other V1 interneurons in the Calb1/En1 model will need to be tested in each experiment. The non-RC spinal neurons labeled in each model are different. Therefore, increased specificity can be predicted in a triple intersection using novel commercially available En1 -Dreo or Pvalb -Dreo mice combined respectively with the Calb1 -dgCre/ Pvalb -Flpo or Calb1 -dgCre/ En1 -Flpo models. In particular, because the En1- Dreo relies on a 2A-Dre transgene that does not disrupt endogenous expression of En1 , this model has the further advantage of maintaining En1 expression. This could be relevant in some studies since En1 gene heterozygosis has been shown to accelerate aging deficits in some neurons , . Activity modifier genes dependent on combinations of Cre or Flp with Dreo are not yet available, but this will likely change in the future. We were surprised to find that En1 -Flpo is less effective than En1 -Cre targeting RCs, although both are constructed with similar insertions in the ATG of exon1 (see ). Originally, Cre was suggested to be more effective than Flp in mammalian cells because of differences in thermostability , leading to development of “enhanced” Flpe versions . We used “optimized” Flpo which was shown to have even higher recombination efficacy than Flpe . In RCs, however, En1-Flpo was less effective than En1-Cre, perhaps because RCs are the first En1 cells to be generated from the spinal cord p1 domain and downregulate En1 in early embryo. Thus, one possibility is that Cre or Flpo expression dependent on the En1 promoter might be short lived in the last-generated RCs, limiting recombinase activity and uncovering differences between Cre and Flpo not evident with longer or higher recombinase expression. RCs not targeted in En1 -Flpo mice did not display differences based on location or synaptic features. Future studies should more systemically study whether non-targeted RCs represent a unique subpopulation. Across all models using Calb1 -dgCre to target RCs, two features of this allele need careful consideration. First, there was considerable leakiness in the system, though this is not apparent in RCs in the first three postnatal weeks. The fact that increased spontaneous recombination in RCs is only observed after the first month and it is marginal until 6 months of age, suggests that it should not interfere with studies that aim to target RCs in young adults for anatomical or neurophysiological studies. Fortunately, genetic intersection with Pvalb or En1 dramatically diminishes the number of cells targeted through TMP-independent dgCre activity. Second, RC targeting efficiency changes with age of TMP injection. When considering the reduced RC targeting after TMP administration at older ages, it is possible that the Calb1 promoter becomes less active in older RCs despite CB-IR remaining detectable. Alternatively, proteosome degradation of dgCre might become more efficient in mature RCs. Whether any of these mechanisms can be overcome by more/higher doses of TMP will need to be tested. Another possibility is to set Calb1 -dgCre in homozygosis (the allele retains endogenous Calb1 expression), but it is predictable that higher dgCre expression would increase spontaneous recombination rates and reduce timing accuracy. It is also unclear from a mechanistic point of view why when using Calb1/Pvalb intersectional approaches, TMP injected in the first postnatal week induces recombination in fewer RCs compared to Calb1 -dgCre alone and dual injections of TMP were necessary at any age to maximize RC targeting. In contrast, a double TMP dose did not increase targeting efficiency in the Calb1/En1 intersection. Despite the lack of clear explanations for these observations, double TMP doses were empirically found to increase Cre recombination when this was weak and have no deleterious actions when this was maximized. Thus, in the future we will follow dual injection protocols when using Calb1 -dgCre alleles in intersectional paradigms. In summary for the two models proposed second postnatal week TMP injections induces efficient Cre recombination in RCs with relatively high specificity and avoids compensatory changes due to genetic targeting during earlier development. Future studies using these models should focus in relatively young adults to prevent accumulation of off-target cells due to TMP-independent dgCre recombination events. The models we describe here overcome the limitations reported for BAC Chrna2 -Cre mice , . BAC Chrna2 -Cre mice are similarly efficient at targeting 86–94% of RCs in the RCA; however, Cre recombination occurs through embryonic development, which can confound functional studies. Moreover, we systematically analyzed targeting of non-RCs across the brain and spinal cord. This level of characterization has not yet been reported for this BAC Chrna2 -Cre line with high levels of RC targeting, despite the fact that some dorsal horn neurons and many neurons in the brain express Chrna2 a. The exact spinal and brain cells targeted need to be fully mapped for each mouse BAC line because expression in BAC transgenics frequently differs from endogenous gene expression depending on the specifics of gene regulatory sequences and cell gene controls incorporated into the BAC . The published BAC Chrna2 -Cre line with high levels of RC targeting is not available.
We characterized two models for genetic targeting of RCs using currently available mouse lines that are widely available to the research community. Each has specific advantages or limitations that suit them to different types of experiments, enabling the targeting of almost all RCs, or alternatively, providing a way to sparsely label RCs or target RCs in defined spinal segments. Because labeling includes cell bodies, dendrites, axons and synapses, these approaches will be particularly useful in anatomical investigations of RC connectivity, facilitating analyses of RC output at the population or single-cell level. This information is critical to further refine computational models of RC function. Moreover, the viral strategies employed here can be used with optogenetic, chemogenetic or cellular ablation methodologies for temporal and spatial control of RC activity in selected spinal cord segments. Taken together, these models provide improved genetic access to RCs to facilitate future studies aimed at testing the functional role of RC circuitry in the spinal cord motor network.
Animals All experiments and procedures were performed according to NIH guidelines and approved by the Institutional Animal Care and Use Committee of Emory University and. Animals were maintained on a C57BL/6 background. Tail and toe tips from neonatal mice were routinely collected for PCR genotyping. All transgenic lines used in this study can be found in Table . En1 -Cre, En1 -Flpo, and Mafb-GFP mice were maintained in heterozygosis. All other lines were maintained in homozygosis. Various breeding schemes were used to generate single-conditional (Cre) and dual-conditional (Cre and Flp) experimental animals of various types. Animals were genotyped by in-house PCR or by Transnetyx with real-time PCR using primers listed in Supplemental Table . En1::Flpo mice were generated as described . Briefly, Flpo, a codon-optimized version of Flp recombinase was inserted into the ATG in the 1st exon of the En1 genomic locus, generating a null allele that simultaneously enables lineage tracing. Positive ES cell clones were screened by Southern blot analysis and microinjected into blastocysts, and the resulting chimeric mice were crossed to C57BL/6J females. The neomycin selectable cassette was removed using Protamine::Cre mice (JAX #003328). Mouse strains were maintained on a C57BL/6J background, and were backcrossed for > 6 generations. Trimethoprim (TMP) administration Trimethoprim (TMP; Sigma T7883) was reconstituted in DMSO to 100 mg/mL and diluted with saline to produce 5 or 10 mg/mL (25% DMSO) or 12.5 mg/mL solutions (35% DMSO) (prepared same day). Animals were injected intraperitoneally (50, 100, or 125 mg/kg) with a single dose or with two doses separated by 24–48 h. Virus and surgeries The Emory Viral Vector Core prepared an AAV9 carrying the pAAV-hSyn Con/Fon EYFP plasmid (Addgene #55650; Depositor: Karl Deisseroth) . Five animals between P5-P8 were anesthetized with isoflurane until a surgical plane of anesthesia was achieved (induction: 4%; maintenance: 2%, both in 100% O 2 ) and given a subcutaneous injection of 0.05 mg/kg buprenorphine to reduce postsurgical pain. A small skin incision was made in the dorsal surface below the last thoracic vertebrae. Using a glass micropipette, we slowly injected 0.5 μL of virus (2.6 × 10 13 genomic copies/mL) in the gap in vertebrae between Th13 and L1 (n = 2, rostral bias) or L3 and L4 (n = 3, caudal bias). The skin was then aligned and sutured back together. Animals were monitored daily for the first week after surgery; none exhibited signs of pain or distress. These animals received two injections of TMP at P10 or P15. Tissue preparation Animals were deeply anesthetized with Euthasol and perfused transcardially with 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4). Spinal cords and brains were collected and post-fixed overnight in the same fixative. The tissues were stored in 30% sucrose in 0.1 M PB at 4 °C until use. Histological processing and immunohistochemistry Lumbar segments 4 and 5 (L4/L5) were blocked and 50 µm thick freezing sliding microtome sections prepared. Sections from virus-injected animals were mounted serially and processed on slides; all other spinal cord sections were processed “free‐floating”. Full brains were sectioned longitudinally on a cryostat and processed on slides. The midbrain and hindbrain were blocked and sectioned coronally using a freezing sliding microtome and processed free-floating (50 µm thick). All sections were blocked with normal donkey serum diluted 1:10 in 0.01 M phosphate buffer saline (PBS) with 0.1% or 0.3% Triton-X-100 (PBST). Sections were then incubated at room temperature in different combinations of primary antibodies (Supplemental Table ) diluted in PBST for one day. For calretinin and parvalbumin staining, sections were incubated for two days to improve antibody penetration in high salt PBST (2.5% NaCl) to reduce background. Immunoreactive sites were revealed with mixtures of species-specific anti-IgG secondary antibodies made in donkey (Jackson Immunoresearch, Supplemental Table ) diluted 1:100 in PBST. Sections were thoroughly washed in PBS, mounted on glass slides if free-floating, and coverslipped with Vectashield anti-fading medium (Vector). Confocal imaging and image analysis All images were acquired using an Olympus FV1000 confocal microscope. Spinal cords were imaged at low magnification (10× , N.A. 0.4, 1 μm z steps). Brains were imaged using the same objective (4 μm z steps). Motor neurons in virus-injected animals were imaged using a 60× objective with × 2 digital zoom (N.A. 1.35, oil-immersion, 0.5 μm z steps). Images were imported into the neuron tracing software Neurolucida for analysis (version 12.0, MicroBrightField). RC area definition A topographical method for identifying Renshaw cells based on calbindin expression was developed in order to consistently assess the proportion of Renshaw cells that were successfully targeted. The vertical distance between the central canal and the ventral-most border of the gray-white matter in lamina IX was measured, and the Renshaw cell area (RCA) was defined as the ventral 45% of this region based on calbindin-immunoreactivity (IR) and lineage labeling or expression of the transcription factors En1 and MafB , respectively (Fig. C). The 45% limit was empirically obtained by confirming in spinal cord sections of different age inclusion of > 95% of Renshaw cells (RCs). The use of a percentage distance, rather than absolute distance, allowed us to apply the same criteria to spinal cords of different ages and with significantly different sizes. It needs to be emphasize that this criteria is valid for Lumbar 4 and 5 segments. In other segments the shape of the spinal cord varies and spatial criteria for the RCA will need to be validated in the future. Cell counting For each animal, approximately 12 ventral horns were analyzed (between 7 and 22; see ). Different antibodies and analyses were applied depending on the experiment and animal genotype and are described in Supplemental Table . Briefly, the RCA was traced in Neurolucida as described above, and counts were acquired for cells expressing genetic reporters and/or calcium binding proteins (calbindin, calretinin, parvalbumin). Only cells within the RCA were counted in single-conditional Calb1- dgCre/ + animals, as we wished to quantify efficiency of RC targeting in these animals. All genetically targeted cells (both inside and outside the RCA) were counted in dual-conditional animals ( Calb1- dgCre/ + :: Pvalb- Flpo/ + :: R26 RCE:dual-EGFP/ + or Calb1- dgCre/ + :: En1- Flpo/ + :: R26 FLTG). The percent of various populations of cells that were co-localized with a genetic reporter or another marker were then calculated, as described in each figure legend and corresponding . For virus-injected animals, these same analyses were applied to serial sections, which were aligned across animals using NeuN-IR to confirm segment transitions according to cytoarchitectonic landmarks. Each point in the resulting plots (Fig. F) is the average of two adjacent sections. Synaptic markers To confirm lack of universal targeting of RCs in En1 -Flpo animals, we combined CB-IR with either gephyrin or vesicular acetylcholine transporter (VAChT) immunolabeling to identify RCs based on location, calbindin expression, and synaptic characteristics. We used one En1 -Flpo :: R26 Ai9 lsl-tdTomato animal and analyzed all identified RCs in L4/5 segments through 6 hemicords (68 RCs) in the calbindin/gephyrin combination and 15 hemicords (73 RCs) in the calbindin/VAChT combinations. The percentage of identified RCs expressing tdTomato was then calculated. RCs were defined as CB-IR cells with large gephyrin clusters in the cell body and proximal dendrites, or CB-IR cells with a high density of large VAChT-IR contacts on their dendrites. More hemicords were used to analyze similar numbers of RCs based on VAChT immunolabeling because the higher difficulty of sampling RCs with long dendrites in 50 µm thick sections. Figure composition Figures were composed using CorelDraw. Pseudocolors were chosen from lookup tables in Fluoview or ImageJ. Image brightness and contrast were optimized with Image Pro Plus or ImageJ. Some images were sharpened using either a “sharpen” or “high-gauss” filter. All manipulations were done on the entire image. Digital manipulations were minimal and did not alter information content in the images. Statistics For each condition, we averaged data from approximately 3 to 5 animals (between 2 and 12 animals across all experiments). “n” usually refers to number of animals (except when indicated in “ ), and inter-animal variability was kept low by performing repetitive measurements in each animal before obtaining one average per animal. The exact details can be found in corresponding and in the Results and preceding sections detailing each of the analyses. We used one-way ANOVAs to reveal significant differences according to different experimental conditions. If we observed significant differences, we used Bonferroni post hoc t-tests for pairwise comparisons. All α values were set at 0.05. Sample sizes were set to power = 0.80 and varied according to sample variance and the size of the effect. If effect sizes were too small (10% difference), we did not seek incrementing sample sizes to increase power but interpreted any change too small to be of relevance. Ethics verification This study was conducted and reported in accordance with ARRIVE guidelines. The institutional and licensing committees approving the experiments are identified at the beginning of the Materials and Methods. All animal experiments were conducted in accordance with relevant guidelines and regulations. All experimentation and data analyses were performed in conformity with ARRIVE guidelines.
All experiments and procedures were performed according to NIH guidelines and approved by the Institutional Animal Care and Use Committee of Emory University and. Animals were maintained on a C57BL/6 background. Tail and toe tips from neonatal mice were routinely collected for PCR genotyping. All transgenic lines used in this study can be found in Table . En1 -Cre, En1 -Flpo, and Mafb-GFP mice were maintained in heterozygosis. All other lines were maintained in homozygosis. Various breeding schemes were used to generate single-conditional (Cre) and dual-conditional (Cre and Flp) experimental animals of various types. Animals were genotyped by in-house PCR or by Transnetyx with real-time PCR using primers listed in Supplemental Table . En1::Flpo mice were generated as described . Briefly, Flpo, a codon-optimized version of Flp recombinase was inserted into the ATG in the 1st exon of the En1 genomic locus, generating a null allele that simultaneously enables lineage tracing. Positive ES cell clones were screened by Southern blot analysis and microinjected into blastocysts, and the resulting chimeric mice were crossed to C57BL/6J females. The neomycin selectable cassette was removed using Protamine::Cre mice (JAX #003328). Mouse strains were maintained on a C57BL/6J background, and were backcrossed for > 6 generations.
Trimethoprim (TMP; Sigma T7883) was reconstituted in DMSO to 100 mg/mL and diluted with saline to produce 5 or 10 mg/mL (25% DMSO) or 12.5 mg/mL solutions (35% DMSO) (prepared same day). Animals were injected intraperitoneally (50, 100, or 125 mg/kg) with a single dose or with two doses separated by 24–48 h.
The Emory Viral Vector Core prepared an AAV9 carrying the pAAV-hSyn Con/Fon EYFP plasmid (Addgene #55650; Depositor: Karl Deisseroth) . Five animals between P5-P8 were anesthetized with isoflurane until a surgical plane of anesthesia was achieved (induction: 4%; maintenance: 2%, both in 100% O 2 ) and given a subcutaneous injection of 0.05 mg/kg buprenorphine to reduce postsurgical pain. A small skin incision was made in the dorsal surface below the last thoracic vertebrae. Using a glass micropipette, we slowly injected 0.5 μL of virus (2.6 × 10 13 genomic copies/mL) in the gap in vertebrae between Th13 and L1 (n = 2, rostral bias) or L3 and L4 (n = 3, caudal bias). The skin was then aligned and sutured back together. Animals were monitored daily for the first week after surgery; none exhibited signs of pain or distress. These animals received two injections of TMP at P10 or P15.
Animals were deeply anesthetized with Euthasol and perfused transcardially with 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4). Spinal cords and brains were collected and post-fixed overnight in the same fixative. The tissues were stored in 30% sucrose in 0.1 M PB at 4 °C until use.
Lumbar segments 4 and 5 (L4/L5) were blocked and 50 µm thick freezing sliding microtome sections prepared. Sections from virus-injected animals were mounted serially and processed on slides; all other spinal cord sections were processed “free‐floating”. Full brains were sectioned longitudinally on a cryostat and processed on slides. The midbrain and hindbrain were blocked and sectioned coronally using a freezing sliding microtome and processed free-floating (50 µm thick). All sections were blocked with normal donkey serum diluted 1:10 in 0.01 M phosphate buffer saline (PBS) with 0.1% or 0.3% Triton-X-100 (PBST). Sections were then incubated at room temperature in different combinations of primary antibodies (Supplemental Table ) diluted in PBST for one day. For calretinin and parvalbumin staining, sections were incubated for two days to improve antibody penetration in high salt PBST (2.5% NaCl) to reduce background. Immunoreactive sites were revealed with mixtures of species-specific anti-IgG secondary antibodies made in donkey (Jackson Immunoresearch, Supplemental Table ) diluted 1:100 in PBST. Sections were thoroughly washed in PBS, mounted on glass slides if free-floating, and coverslipped with Vectashield anti-fading medium (Vector).
All images were acquired using an Olympus FV1000 confocal microscope. Spinal cords were imaged at low magnification (10× , N.A. 0.4, 1 μm z steps). Brains were imaged using the same objective (4 μm z steps). Motor neurons in virus-injected animals were imaged using a 60× objective with × 2 digital zoom (N.A. 1.35, oil-immersion, 0.5 μm z steps). Images were imported into the neuron tracing software Neurolucida for analysis (version 12.0, MicroBrightField). RC area definition A topographical method for identifying Renshaw cells based on calbindin expression was developed in order to consistently assess the proportion of Renshaw cells that were successfully targeted. The vertical distance between the central canal and the ventral-most border of the gray-white matter in lamina IX was measured, and the Renshaw cell area (RCA) was defined as the ventral 45% of this region based on calbindin-immunoreactivity (IR) and lineage labeling or expression of the transcription factors En1 and MafB , respectively (Fig. C). The 45% limit was empirically obtained by confirming in spinal cord sections of different age inclusion of > 95% of Renshaw cells (RCs). The use of a percentage distance, rather than absolute distance, allowed us to apply the same criteria to spinal cords of different ages and with significantly different sizes. It needs to be emphasize that this criteria is valid for Lumbar 4 and 5 segments. In other segments the shape of the spinal cord varies and spatial criteria for the RCA will need to be validated in the future. Cell counting For each animal, approximately 12 ventral horns were analyzed (between 7 and 22; see ). Different antibodies and analyses were applied depending on the experiment and animal genotype and are described in Supplemental Table . Briefly, the RCA was traced in Neurolucida as described above, and counts were acquired for cells expressing genetic reporters and/or calcium binding proteins (calbindin, calretinin, parvalbumin). Only cells within the RCA were counted in single-conditional Calb1- dgCre/ + animals, as we wished to quantify efficiency of RC targeting in these animals. All genetically targeted cells (both inside and outside the RCA) were counted in dual-conditional animals ( Calb1- dgCre/ + :: Pvalb- Flpo/ + :: R26 RCE:dual-EGFP/ + or Calb1- dgCre/ + :: En1- Flpo/ + :: R26 FLTG). The percent of various populations of cells that were co-localized with a genetic reporter or another marker were then calculated, as described in each figure legend and corresponding . For virus-injected animals, these same analyses were applied to serial sections, which were aligned across animals using NeuN-IR to confirm segment transitions according to cytoarchitectonic landmarks. Each point in the resulting plots (Fig. F) is the average of two adjacent sections. Synaptic markers To confirm lack of universal targeting of RCs in En1 -Flpo animals, we combined CB-IR with either gephyrin or vesicular acetylcholine transporter (VAChT) immunolabeling to identify RCs based on location, calbindin expression, and synaptic characteristics. We used one En1 -Flpo :: R26 Ai9 lsl-tdTomato animal and analyzed all identified RCs in L4/5 segments through 6 hemicords (68 RCs) in the calbindin/gephyrin combination and 15 hemicords (73 RCs) in the calbindin/VAChT combinations. The percentage of identified RCs expressing tdTomato was then calculated. RCs were defined as CB-IR cells with large gephyrin clusters in the cell body and proximal dendrites, or CB-IR cells with a high density of large VAChT-IR contacts on their dendrites. More hemicords were used to analyze similar numbers of RCs based on VAChT immunolabeling because the higher difficulty of sampling RCs with long dendrites in 50 µm thick sections. Figure composition Figures were composed using CorelDraw. Pseudocolors were chosen from lookup tables in Fluoview or ImageJ. Image brightness and contrast were optimized with Image Pro Plus or ImageJ. Some images were sharpened using either a “sharpen” or “high-gauss” filter. All manipulations were done on the entire image. Digital manipulations were minimal and did not alter information content in the images.
A topographical method for identifying Renshaw cells based on calbindin expression was developed in order to consistently assess the proportion of Renshaw cells that were successfully targeted. The vertical distance between the central canal and the ventral-most border of the gray-white matter in lamina IX was measured, and the Renshaw cell area (RCA) was defined as the ventral 45% of this region based on calbindin-immunoreactivity (IR) and lineage labeling or expression of the transcription factors En1 and MafB , respectively (Fig. C). The 45% limit was empirically obtained by confirming in spinal cord sections of different age inclusion of > 95% of Renshaw cells (RCs). The use of a percentage distance, rather than absolute distance, allowed us to apply the same criteria to spinal cords of different ages and with significantly different sizes. It needs to be emphasize that this criteria is valid for Lumbar 4 and 5 segments. In other segments the shape of the spinal cord varies and spatial criteria for the RCA will need to be validated in the future.
For each animal, approximately 12 ventral horns were analyzed (between 7 and 22; see ). Different antibodies and analyses were applied depending on the experiment and animal genotype and are described in Supplemental Table . Briefly, the RCA was traced in Neurolucida as described above, and counts were acquired for cells expressing genetic reporters and/or calcium binding proteins (calbindin, calretinin, parvalbumin). Only cells within the RCA were counted in single-conditional Calb1- dgCre/ + animals, as we wished to quantify efficiency of RC targeting in these animals. All genetically targeted cells (both inside and outside the RCA) were counted in dual-conditional animals ( Calb1- dgCre/ + :: Pvalb- Flpo/ + :: R26 RCE:dual-EGFP/ + or Calb1- dgCre/ + :: En1- Flpo/ + :: R26 FLTG). The percent of various populations of cells that were co-localized with a genetic reporter or another marker were then calculated, as described in each figure legend and corresponding . For virus-injected animals, these same analyses were applied to serial sections, which were aligned across animals using NeuN-IR to confirm segment transitions according to cytoarchitectonic landmarks. Each point in the resulting plots (Fig. F) is the average of two adjacent sections.
To confirm lack of universal targeting of RCs in En1 -Flpo animals, we combined CB-IR with either gephyrin or vesicular acetylcholine transporter (VAChT) immunolabeling to identify RCs based on location, calbindin expression, and synaptic characteristics. We used one En1 -Flpo :: R26 Ai9 lsl-tdTomato animal and analyzed all identified RCs in L4/5 segments through 6 hemicords (68 RCs) in the calbindin/gephyrin combination and 15 hemicords (73 RCs) in the calbindin/VAChT combinations. The percentage of identified RCs expressing tdTomato was then calculated. RCs were defined as CB-IR cells with large gephyrin clusters in the cell body and proximal dendrites, or CB-IR cells with a high density of large VAChT-IR contacts on their dendrites. More hemicords were used to analyze similar numbers of RCs based on VAChT immunolabeling because the higher difficulty of sampling RCs with long dendrites in 50 µm thick sections.
Figures were composed using CorelDraw. Pseudocolors were chosen from lookup tables in Fluoview or ImageJ. Image brightness and contrast were optimized with Image Pro Plus or ImageJ. Some images were sharpened using either a “sharpen” or “high-gauss” filter. All manipulations were done on the entire image. Digital manipulations were minimal and did not alter information content in the images.
For each condition, we averaged data from approximately 3 to 5 animals (between 2 and 12 animals across all experiments). “n” usually refers to number of animals (except when indicated in “ ), and inter-animal variability was kept low by performing repetitive measurements in each animal before obtaining one average per animal. The exact details can be found in corresponding and in the Results and preceding sections detailing each of the analyses. We used one-way ANOVAs to reveal significant differences according to different experimental conditions. If we observed significant differences, we used Bonferroni post hoc t-tests for pairwise comparisons. All α values were set at 0.05. Sample sizes were set to power = 0.80 and varied according to sample variance and the size of the effect. If effect sizes were too small (10% difference), we did not seek incrementing sample sizes to increase power but interpreted any change too small to be of relevance.
This study was conducted and reported in accordance with ARRIVE guidelines. The institutional and licensing committees approving the experiments are identified at the beginning of the Materials and Methods. All animal experiments were conducted in accordance with relevant guidelines and regulations. All experimentation and data analyses were performed in conformity with ARRIVE guidelines.
Supplementary Information 1. Supplementary Information 2.
|
Smartphone adapters for flexible Nasolaryngoscopy: a systematic review | 05013e7c-d9b0-475f-bb4f-2f28f7f87a61 | 5941791 | Otolaryngology[mh] | Flexible nasolaryngoscopy (FNL) provides invaluable insight to the practicing Otolaryngologist and can be used to diagnose a wide variety of pathology. It is an essential skill that must be mastered by all trainees in Otolaryngology - Head and Neck Surgery (OTOHNS). While FNL is not technically challenging to the experienced otolaryngologist, there are many subtleties to its use and interpretation that may not be immediately appreciated by junior learners. As such, senior residents and attending physicians are often required to be present during the endoscopic exam to verify the junior trainee’s findings. As endoscopy towers are expensive and stationary, they are not conducive to remote locations such as emergency departments or patient wards, where patients commonly undergo FNL. Historically, FNL performed in these locations were not recorded and patients were then subjected to repeat examination, leading to redundancy and additional discomfort. Smartphone endoscope adapters are a relatively new technology which provide a mechanism to record flexible nasolaryngoscopy examinations performed in remote locations using portable scopes. In doing so, they allow attending physicians to remotely provide opinion and advice following a single assessment performed with a portable scope. Recorded videos obtained in this manner may additionally be retained for educational or research purposes. Since their introduction, there have been few articles examining the use of smartphone endoscope adapters. The purpose of this study was to perform a systematic review of the literature on the use of these devices. Specifically, we sought to assess the effect of smartphone endoscope adapters on video recording quality, patient satisfaction and care, and trainee educational experience. We also present our own institution’s experience with the use of smartphone endoscope adapters.
A systematic review of the literature based on the Cochrane handbook and the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) guidelines was performed . The study protocol was registered with the PROSPERO database (Trial Registration: CRD42018086714). Eligibility criteria Studies were selected using Population, Intervention, Comparator, Outcome, Study Design (PICOS) guidelines. We included all studies from 1946 to September 2017 published in English language in peer-reviewed journals. Information sources and search strategy With the help of an experienced librarian, we conducted a literature search using the following databases: Ovid MEDLINE, PubMed, and EMBASE. A MeSH terms and keywords search was conducted using truncation and adjacency operator and Boolean operators. MeSH terms were: endoscop*, otorhinolaryngologic diseases; otolaryngology; otorhinolaryngologic surgical procedures; nose; nasal; larynx*; laryngoscopy; laryngoscopes; nasolaryngoscop*; smart phone*; iphone*, andrid*, adaptor*, or adapter. We also performed a hand search of citations from relevant articles. Study selection Study inclusion and exclusion criteria were clearly defined, and are illustrated in Table . Data collection and extraction Data was extracted from the studies using a pre-written data entry form. Titles and abstracts were independently screened by two reviewers (A.Q., L.C) to assess for initial relevance. Titles or abstracts that were deemed relevant by either reviewer were obtained in full document or PDF form. Papers were then screened to determine if they met eligibility criteria, and if so, data was extracted accordingly. Data extraction was completed by two reviewers (A.Q., L.C.) and included important clinical baseline variables as well as primary outcomes measures (Tables , ). All disagreements between reviewers were discussed and resolved by a consensus meeting including all four authors. Data items The baseline variables that were extracted from each article included: type of exams performed (scope adapter +/− endoscopy video tower recording), scope operator trainee level, model of scope adapter and (when applicable) endoscopy tower used, model of smartphone used, and safety/ privacy measures. Primary outcomes of interest were: 1) patient care impacts of scope adapter video recordings, 2) resident educational impacts of scope adapter video recordings, 3) diagnostic accuracy of flexible nasolaryngoscopy videos recorded with smartphone adapters, and 4) costs of smartphone adapters for flexible nasolaryngoscopy. Secondary outcomes of interest were: 1) Quality of videos recorded using smartphone adapters compared to endoscopy tower video recordings, and 2) patient satisfaction with the use of smartphone adapters for flexible nasolaryngoscopy. Risk of Bias in individual studies Internal validity of study design and conduct was assessed independently by two reviewers (A.Q., L.C.). For non-randomized studies, the Newcastle-Ottawa Quality Assessment Tool was used . Discrepancies were resolved by a consensus meeting including all four authors.
Studies were selected using Population, Intervention, Comparator, Outcome, Study Design (PICOS) guidelines. We included all studies from 1946 to September 2017 published in English language in peer-reviewed journals.
With the help of an experienced librarian, we conducted a literature search using the following databases: Ovid MEDLINE, PubMed, and EMBASE. A MeSH terms and keywords search was conducted using truncation and adjacency operator and Boolean operators. MeSH terms were: endoscop*, otorhinolaryngologic diseases; otolaryngology; otorhinolaryngologic surgical procedures; nose; nasal; larynx*; laryngoscopy; laryngoscopes; nasolaryngoscop*; smart phone*; iphone*, andrid*, adaptor*, or adapter. We also performed a hand search of citations from relevant articles.
Study inclusion and exclusion criteria were clearly defined, and are illustrated in Table .
Data was extracted from the studies using a pre-written data entry form. Titles and abstracts were independently screened by two reviewers (A.Q., L.C) to assess for initial relevance. Titles or abstracts that were deemed relevant by either reviewer were obtained in full document or PDF form. Papers were then screened to determine if they met eligibility criteria, and if so, data was extracted accordingly. Data extraction was completed by two reviewers (A.Q., L.C.) and included important clinical baseline variables as well as primary outcomes measures (Tables , ). All disagreements between reviewers were discussed and resolved by a consensus meeting including all four authors.
The baseline variables that were extracted from each article included: type of exams performed (scope adapter +/− endoscopy video tower recording), scope operator trainee level, model of scope adapter and (when applicable) endoscopy tower used, model of smartphone used, and safety/ privacy measures. Primary outcomes of interest were: 1) patient care impacts of scope adapter video recordings, 2) resident educational impacts of scope adapter video recordings, 3) diagnostic accuracy of flexible nasolaryngoscopy videos recorded with smartphone adapters, and 4) costs of smartphone adapters for flexible nasolaryngoscopy. Secondary outcomes of interest were: 1) Quality of videos recorded using smartphone adapters compared to endoscopy tower video recordings, and 2) patient satisfaction with the use of smartphone adapters for flexible nasolaryngoscopy.
Internal validity of study design and conduct was assessed independently by two reviewers (A.Q., L.C.). For non-randomized studies, the Newcastle-Ottawa Quality Assessment Tool was used . Discrepancies were resolved by a consensus meeting including all four authors.
A total of 91 studies were screened (Fig. ). Studies were excluded for: duplicates, different topic/ intervention, non-English language, and insufficient data (abstract only, single case reports, no outcomes data). Three cohort studies were deemed eligible for inclusion [ – ]. Study characteristics The total of 152 examinations of patients using smartphone endoscope adapters were reported in the literature. Thirty of these patients also had endoscopy video tower recording of their flexible nasolaryngosocpy exams for direct comparison. The pertinent characteristics of the studies included for review are illustrated in Table . Study outcomes Two of the three studies assessed the diagnostic accuracy and quality of videos recorded using smartphone endoscope adapters (Table ) . In one of these studies (Liu H et al), videos recorded using smartphone endoscope adapters were compared to those recorded using endoscopy video towers, and mean differences in percent correct diagnoses made by blinded observers (senior residents and staff attending physicians) was calculated. The authors found that there was no significant difference in correct diagnoses made between endoscopy video tower recordings and smartphone endoscope adapter recordings (mean difference = 1.54%, p = 0.69). This study assessed video quality subjectively using a 5-point Likert scale, with a linear mixed effects model to determine differences in mobile and tower video quality. No significant difference in video quality ratings was found across 7 quality categories (illumination and brightness; ability to identify camera orientation; ability to identify important landmarks/ structures; picture clarify and texture; artefact and background noise; contrast, border, and sharpness; overall satisfaction with video quality) . In the other study (Lozara et al.), videos recorded by postgraduate year-1 residents using smartphone adapters were divided into diagnostic categories (airway evaluation, voice evaluation, dysphagia, aerodigestive tract mass) and were interpreted both by these same residents and staff attending physicians. Chi-squared statistics were used to compare the frequency of discordant exams. The authors found that there was an 11% frequency of discordant exams, with no statistical difference between diagnostic categories. They found that only 1 of 79 (1.3%) of exams had to be repeated due to poor quality . One study (Liu YF et al.) assessed the ability of flexible nasolaryngoscopy videos recorded with smartphone adapters to enhance resident learning. Post-graduate year-1 and -2 residents recorded flexible nasolaryngoscopy exams using smartphone endoscope adapters and then reviewed recorded videos with staff attending physicians, and subsequently were surveyed using a 5-point Likert scale on whether they believed the discussions afforded by the use of smartphone adapters enhanced their learning. The authors found that residents reported that reviewing videos they had recording using adapters enhanced their learning in 79% of cases, and that the ability to discuss video findings with attending physicians enhanced their learning in 88% of cases as reported by attendings, and 81% of cases as reported by residents . Two studies discussed methods employed to protect patient confidentiality. Liu H et al... used a Health Insurance Portability and Accountability Act (HIPAA)- compliant mobile application to record and store images and videos. Lozada et al. used a dedicated team iPhone, with encrypted email and password-secured files and computers. The third study (Liu YF et al) did not comment on the method used to ensure patient privacy and confidentiality. Risk of Bias The Newcastle-Ottawa Quality Assessment tool was used to appraise the selected studies (Additional file : Table S1) . The strength of evidence was overall low to moderate quality, with a Newcastle-Ottawa score from 5 to 9 (range 0–9; a lower score indicates methodological weakness). The analysis of the methodologic quality indicated that the principal risks of bias were a lack of objective data comparing outcomes of smartphone endoscope adapter recordings to recordings made with endoscopy video towers, and lack of objective outcome assessment.
The total of 152 examinations of patients using smartphone endoscope adapters were reported in the literature. Thirty of these patients also had endoscopy video tower recording of their flexible nasolaryngosocpy exams for direct comparison. The pertinent characteristics of the studies included for review are illustrated in Table .
Two of the three studies assessed the diagnostic accuracy and quality of videos recorded using smartphone endoscope adapters (Table ) . In one of these studies (Liu H et al), videos recorded using smartphone endoscope adapters were compared to those recorded using endoscopy video towers, and mean differences in percent correct diagnoses made by blinded observers (senior residents and staff attending physicians) was calculated. The authors found that there was no significant difference in correct diagnoses made between endoscopy video tower recordings and smartphone endoscope adapter recordings (mean difference = 1.54%, p = 0.69). This study assessed video quality subjectively using a 5-point Likert scale, with a linear mixed effects model to determine differences in mobile and tower video quality. No significant difference in video quality ratings was found across 7 quality categories (illumination and brightness; ability to identify camera orientation; ability to identify important landmarks/ structures; picture clarify and texture; artefact and background noise; contrast, border, and sharpness; overall satisfaction with video quality) . In the other study (Lozara et al.), videos recorded by postgraduate year-1 residents using smartphone adapters were divided into diagnostic categories (airway evaluation, voice evaluation, dysphagia, aerodigestive tract mass) and were interpreted both by these same residents and staff attending physicians. Chi-squared statistics were used to compare the frequency of discordant exams. The authors found that there was an 11% frequency of discordant exams, with no statistical difference between diagnostic categories. They found that only 1 of 79 (1.3%) of exams had to be repeated due to poor quality . One study (Liu YF et al.) assessed the ability of flexible nasolaryngoscopy videos recorded with smartphone adapters to enhance resident learning. Post-graduate year-1 and -2 residents recorded flexible nasolaryngoscopy exams using smartphone endoscope adapters and then reviewed recorded videos with staff attending physicians, and subsequently were surveyed using a 5-point Likert scale on whether they believed the discussions afforded by the use of smartphone adapters enhanced their learning. The authors found that residents reported that reviewing videos they had recording using adapters enhanced their learning in 79% of cases, and that the ability to discuss video findings with attending physicians enhanced their learning in 88% of cases as reported by attendings, and 81% of cases as reported by residents . Two studies discussed methods employed to protect patient confidentiality. Liu H et al... used a Health Insurance Portability and Accountability Act (HIPAA)- compliant mobile application to record and store images and videos. Lozada et al. used a dedicated team iPhone, with encrypted email and password-secured files and computers. The third study (Liu YF et al) did not comment on the method used to ensure patient privacy and confidentiality.
The Newcastle-Ottawa Quality Assessment tool was used to appraise the selected studies (Additional file : Table S1) . The strength of evidence was overall low to moderate quality, with a Newcastle-Ottawa score from 5 to 9 (range 0–9; a lower score indicates methodological weakness). The analysis of the methodologic quality indicated that the principal risks of bias were a lack of objective data comparing outcomes of smartphone endoscope adapter recordings to recordings made with endoscopy video towers, and lack of objective outcome assessment.
Flexible nasolaryngoscopy is an essential tool to the practicing Otolaryngologist. Smartphone endoscope adapters which allow video recording of flexible nasolaryngoscopy examinations are relatively new devices with a number of potential benefits, including enhanced patient care and satisfaction by means of fewer repeat examinations; enhanced resident education by virtue of the ability to store, analyze, and discuss findings of videos recorded in remote locations such as emergency departments and inpatient wards; and decreased costs compared to fixed endoscopy towers. There have been very few studies objectively evaluating the effects of these devices in Otolaryngology practice. In the present study, we have systematically reviewed the literature and found three studies which assessed the diagnostic accuracy, video quality, and educational benefits of smartphone endoscope adapters. These studies reported heterogeneous outcome data, but overall suggested a benefit of smartphone adapters on resident education, and demonstrated high diagnostic accuracy and video quality with the use of these devices. Lieu et al. objectively compared diagnostic accuracy and quality between videos recorded with endoscopy towers and smartphone adapters and found no difference in either metric. A study of diagnostic accuracy and video quality of smartphone adapters, by way of demonstrating staff physician ability to come to diagnostic and management decisions based on videos recorded with smartphone adapters, identified a low rate of repeat examinations as a result of poor quality (Liu et al., 2016) . Lozada et al. (2017) used self-reported surveys to show a resident educational benefit of smartphone adapters . Outcome data was unable to be combined due to its heterogeneous nature. No study in the literature objectively examined resident educational benefits of smartphone adapters, patient care outcomes with the use of smartphone adapters, patient satisfaction with the use of adapters, or cost-effectiveness smartphone adapters. At our own centre (The Ottawa Hospital, Department of Otolaryngology- Head & Neck Surgery), a tertiary care academic centre serving a catchment area of 1.2 million people, residents have been provided with and utilized smartphone endoscope adapters over a five-year period (ClearScope; Clearwater Clinical Limited, Ottawa, Canada) (Fig. ). Since their introduction, smartphone endoscope adapters have improved cross-departmental communications, being used in grand rounds, interdisciplinary meetings, and teaching rounds. The recordings made using these devices are securely shared with healthcare professionals including members of the OTOHNS team, anesthesiologists, and respiratory therapists, and have improved shared-decision making amongst airway consultants. Furthermore, a database of interesting cases has been curated, proving useful for medical education and research purposes. Endoscopic recordings are included in electronic medical records (EMRs) to ensure improved continuity of care in team handovers. Resident and staff physicians have reported that the frequency of repeat endoscopy by attending physicians to confirm resident diagnoses has decreased, as has the cleaning and maintenance costs associated with using a greater number of flexible scopes. A variety of models of smartphone endoscope adapter are available on the market. We critically appraised the literature for commercially available smartphone endoscope adapters. There were no head-to-head comparisons of these products available in the published literature. Additional file : Table S2 summarizes commercially available devices. Patient privacy and confidentiality is one concern which has increased in the era of omnipresent smartphone cameras and video recordings [ – ]. Smartphones can be misplaced or hacked, resulting in the breach of private medical information. Furthermore, images captured on smartphones are often stored in insecure mobile applications, many of which automatically sync the image to non-HIPAA-compliant cloud servers such as iCloud, Google+, and Dropbox. Conversely, encrypted mobile applications allow physicians to securely capture and save images and videos; some also provide HIPAA-compliant cloud sync services, allowing physicians to securely backup and share their photos and videos with the rest of the patient’s healthcare team. Additional file : Table S3 summarizes available HIPAA-compliant mobile applications for storage of captured images and videos. In our own department, MODICA (Clearwater Clinical Limited; Ottawa, Canada) was formerly used. There are several important limitations of the present study. There were only a small number of studies published in the literature examining the effects of smartphone endoscope adapters for FNL. Among existing studies, there was a lack of objective data examining our outcomes of interest, including lack of validated surveys; cost-effectiveness analyses; small patient populations; standardization between device operator level of training; and non-uniform use of a variety of different available adapters, endoscope video towers, and smartphones. Two of our three included studies were of poor quality based on Newcastle-Ottawa Scale ratings, due to lack of comparability data within the studies, and lack of objective outcome assessment (Additional file : Table S1). As well, among the three included studies, only two types of smartphone adapters were used (ClearScope, Clearwater Clinical Limited, Ottawa, Canada; and Mobile Optyx, MobileOptyx, Philadelphia, PA), and our own departmental experience is also with the ClearScope. The generalizability of our findings – especially scope video quality and diagnostic accuracy – is therefore limited by a lack of data derived from the use of other commercially available scope adapter products (Additional file : Table S2). Despite these limitations, we are able to conclude that the present study provides a sufficient overview of the current literature examining the use of smartphone adapters for flexible nasolaryngoscopy. In implementing our search strategy and study design as per the Cochrane handbook and PRISMA guidelines, we were able to effectively appraise the studies meeting our inclusion criteria.
The market for smartphone endoscope adapters has slowly evolved over the last decade such that new and innovative technology is now available for healthcare professionals to utilize. Accompanying these are a variety of HIPAA-compliant mobile applications to ensure the secure storage and sharing of captured images and videos. Few studies exist examining the utility of smartphone endoscope adapters in OTOHNS practice. This study has systematically reviewed the literature on the use of smartphone endoscope adapters. It has served to identify a significant lack of objective evidence exploring the use, benefits, and cost-effectiveness of these devices. However, we have shown that existing data supports the diagnostic accuracy, video quality, and educational benefits of smartphone endoscope adapters for flexible nasolaryngoscopy. Our study highlights the need for further research into the effects of incorporating these devices into practice.
Additional file 1: Table S1. Summary of critical appraisal of included studies using the Newcastle-Ottawa Quality Assessment tool for cohort studies. (DOCX 14 kb) Additional file 2: Table S2. Commercially available smartphone endoscope adapters. (DOCX 124 kb) Additional file 3: Table S3. Commercially available Health Insurance Portability and Accountability Act (HIPAA)-compliant secure mobile applications. (DOCX 46 kb)
|
Not too sick, not too well: reducing the diagnostic void in pediatric emergency medicine | 77af2e30-fd88-4c50-9dbd-3ff2dae1602a | 11624129 | Pediatrics[mh] | There is a considerable emphasis in pediatric emergency medicine on the task of recognising the unwell child. This focus tends to be based on decision making around individual patients and fails to acknowledge that emergency physicians are usually responsible for dealing with multiple patients at any one time. As such, the rapid recognition of the child with no emergent medical condition – the well child – is important in terms of mitigating iatrogenia, utilization of resources, and surge capacity for acutely ill and injured children. An additional benefit to the rapid discharge of children with non-emergent conditions is to the treating clinical team, in terms of offloading cognitive burden and workflow bottlenecks. We explore models for recognising wellness in order to improve the ability of clinicians to focus on the significantly unwell children in their care. Over 30 million emergency department visits are made by children less than 18 years old in the United States yearly. Over 96% of children are treated and sent home. In pre-pandemic 2018, there were 100 visits per 100 persons aged 1 year and younger and 58 visits per 100 persons aged 1–4 years. During the same year, an estimated 34% of primary care visits made by children under 1 were for a “new problem”. Due to an array of systemic, demographic, and financial reasons the emergency department is increasingly becoming a destination of first choice for care, independent of acuity. , Traditionally the emphasis in emergency medicine has been the recognition of the seriously unwell child with a secondary emphasis on recognising frank wellness. Attempts to give structure to the assessment of unwell children typically result in poor sensitivity and specificity. For example one UK primary care based study found that only 6% of children assessed using the traffic light system of signs and symptoms were categorised as “green”, that is, not requiring urgent intervention. This poor specificity likely results from the normal physiology of unwell younger children who have a tendency to extreme signs and symptoms during uncomplicated lower risk infections. The speedy recognition of children who are not requiring admission has clear benefits including improving the ability of emergency departments to reduce crowding and therefore focus more clinical time to those remaining. This in turn may improve the ability of teams to recognise and treat the seriously unwell children in their care. Large patient volumes may saturate or overwhelm the capacity of clinicians, auxiliary staff, and space. The stressed environment may impact the triage and initial management of children presenting to the emergency department. A particular challenge is the distinction of an emergency medical condition (i.e. requiring immediate or urgent attention) from a medical complaint or concern that may be addressed outside of the emergency department. A good example of this is in self-resolving minor illness or injury. Most previously healthy children with viral syndrome do not require medical evaluation or professional treatment. When a family brings a child with a simple viral syndrome to the emergency department, there is an inherent discordance involving parental fear, parental convenience, disease severity, expectations of the visit, and the clinician’s assessment. Ever-increasing emergency department utilization amplifies the effect of every decision made by emergency clinicians, affecting patients, clinicians, and the system. It is imperative we reduce the decision-making load on acute and emergency clinicians. The standard understanding is that the layperson determines the emergency. That may be true in terms of the decision to go to the emergency department but often has little to do with the presence of an emergency medical condition. Data both before and after COVID highlight parental perception of illness is often far higher than the child’s actual illness. However passing judgement on parental decision making risks paternalism, if it can be challenging for clinicians to recognise serious illness why not so parents? And this creates a dichotomy, on the one hand, asking the family to go the ED purely for emergencies may risk the ill child staying at home and worsening. On the other hand, the current use of the ED as an entry point to the medical system is not sustainable and may put emergent patients at risk. Take for example young Anya (case study: Part A) Anya is 18 months old. She has had a cold for a couple of days. This morning her mother finds her to be pale, clingy, and not her usual self. She vomited once, and now refuses food. Anya is brought to the emergency department . She is seen on arrival and noted to be flushed and quiet. She is aware of her surroundings and reaches for objects. She has a raised respiratory rate (42 breaths per minute) and a high heart rate (165 beats per minute). There is no rash but her hands and feet feel cold. Her temperature is 40.1 degrees centigrade. She had spat out the acetaminophen (paracetamol) her mother had tried to give her in the morning . What now? Does Anya need to be prioritized, or is she safe to wait? Is Anya going to require investigations or admission? Given that febrile illness is the second most common presentation to Emergency Departments (after breathing difficulty) it is easy to see why this type of case poses a challenge. Fever in the young child can markedly affect their appearance, a cardinal feature in the initial assessment of children. Compounding the initial assessment are the parent or caregiver’s preconceived notions of the significance of fever. , On the one hand, clinicians aim to ally with parents to take their concerns seriously. On the other hand, the dissonance between perception of illness and safety risk of fever in children between parents and clinicians is often marked. , In the presented scenario, historically, protocols would flag this child is at risk of sepsis. The clinician may elect to observe or even undertake initial investigations. If parental concern was strong and the treating clinician felt that Anya’s overall appearance was not in keeping with a virus they may also elect to treat with antibiotics awaiting the results of various cultures. Regardless of the path chosen, the clinician wrestles with a feared retrospective criticism if the child were to return more unwell with a confirmed bacterial illness. Sepsis is a dynamic disease process. Clinicians see the individual patient at various stages of illness. A healthy child’s physiologic response to both benign viral syndrome and early bacteremia may be identical, especially early in the course. Viral infections often cause prime conditions for a subsequent bacterial superinfection, also with varying degrees of acuity. Progression to sepsis is rare and unpredictable; screening every child with invasive testing is not warranted. , Uncertainty, patient volumes, medicolegal risk, established practice patterns, and increasing reliance of the healthcare system on emergency medical services run in contradistinction to the above ideal practice pattern. The highly sensitive and non-specific systemic inflammatory response syndrome (SIRS) schema has been used with mixed results. SIRS became protocolized in many institutions, resulting in “SIRS alerts” and mandatory, albeit low-yield testing. Even in the high-risk population of children under three months of age, the presence of SIRS was not predictive of invasive bacterial illness. The Phoenix pediatric sepsis criteria were developed as a response to over-testing and over-utilization of inpatient resources. The paradigm has shifted from physiological response to assessment of end-organ risk (Table ). These definitions are far more specific than the previous screening criteria that were associated with a systemic inflammatory response syndrome (SIRS); the previous method of determining which patients needed intervention. The vast majority of children who present to Emergency Departments will not have sepsis. , Even at current vaccination rates, children over 3 months of age (presenting to a Children’s Emergency Department) display a rate of serious bacterial infection of less than 7%. The majority of bacterial infections follow an uncomplicated course with sepsis being a rare outcome. Finding the ‘sepsis’ needle in the ’emergency department’ haystack is a daily task. This is particularly true for the children aged between 6 months and 6 years old due to their tendency to have more exaggerated changes in physiological parameters when unwell with an uncomplicated infection. A previously healthy 2-year-old who initially appears miserable with volume depletion and tachycardia has reasonable potential to improve rapidly with hydration. Serial examinations during an observation period are important to track his recovery or to make the decision to intervene further. The need for sequential review in an overcrowded department creates a high-risk, decision-rich environment which is a significant cognitive and emotional load. There is no standardized definition of wellness available to health care professionals. To recognise the well child requires training, time, and thought. Parental descriptions and use of terminology concerning the child’s condition may not match that of the clinician. Age, duration of symptoms, and access to care outside of the emergency department all influence how quick an emergency physician may be to deem a child “well” (read: safe for discharge home). From a systems lens, emergency departments could not function if every child with normal vital signs and common, benign, self-limiting condition received an in-depth invasive investigation. From an interpersonal lens, there may be a dissonance between the wellness that the emergency physician sees and the miserable symptomatology the parent experiences. Common scenarios include presentations for fever, cough, rhinorrhea, vomiting, or refusal to eat. The effect of fever in particular on decision making is complex. Fever is a common feature of uncomplicated low risk illness and is non-specific. Due to a weak association between higher fever and serious bacterial infection many guidelines include a fever threshold as a discriminating feature in decision making. Unfortunately fever is too non-specific to have any clinical usefulness, regardless of its degree. In practice fever does influence the decision-making process due to the features commonly associated with a raised temperature: typically tachycardia and altered peripheral perfusion, alongside a reduced activity level and changes in behaviour. While fever itself is non-specific the overall appearance and behaviour of a child that is febrile at the time of assessment is often one that impairs the decision making of the clinician. For this reason well timed analgesia can be an extremely effective strategy in the emergency department, allowing a clinician to see a child who had a high fever an hour ago with all the associated features of sepsis yet now looks and behaves like a well child. The Phoenix score, with its focus on evidence of organ dysfunction is an important progression in our approach to recognising true sepsis. However to achieve this the Phoenix score requires extensive blood work. In pediatrics the ideal is to avoid the trauma of venepunture wherever possible. We therefore need a way of deciding when organ dysfunction is very unlikely. One model that works well for recognition of the well child is to focus on activity and behaviour. In paediatric practice these features give valuable clinical information about the current metabolic status and end organ function of the child being assessed. While non-specific features such as cough and fever support a process of differential diagnosis (upper or lower respiratory tract infection) the behaviour and actions of the child give important information about the clinical effects of the infection regardless of what the diagnosis is. A child with fever and a cough who is seen to be running around, eating, drinking, playing and chatting is demonstrating metabolic and cardiorespiratory sufficiency. Arguably this supersedes the distinction between upper and lower respiratory infection as a lower respiratory tract infection with minimal clinical effect can be managed conservatively in a similar way to upper respiratory tract infection. This model of predominantly using activity and behaviour for recognition of wellness essentially relies on the sensitive nature of higher cerebral function. The brain has a high metabolic requirement and function is much more quickly affected by any lack in perfusion, hypoxia or hypoglycemia than other vital organs. This is particularly true for higher cerebral function which is affected first while hindbrain function is preserved for longer when the brain is under stress. This physiological model explains why it is valid in clinical practice to allow an interactive smile to be an extremely valid examination finding. There are two relevant factors to an approach that focuses on spotting wellness. The first is that some ‘well appearing’ children need to be in an emergency department (think safeguarding or oncology) and the second is that rapid recognition of the well child may be challenging in children who are neurologically atypical at baseline. , It has been increasingly recognised that children with neurodivergence or neurodisability are at significant risk for poor outcomes when they become ill. Clinicians may misjudge wellness for an abnormality, and vice versa. For health care professionals assessing children who are not neurotypical, it is important to be aware of the specific difficulty and risk in the emergency department setting. A parent or carer who knows the child well and is able to verbally compare current state to the child’s baseline activity and behaviour can be an important substitute for a direct comparison of observation to a presumed normal. This requires trust by the health care professional of the caregiver’s judgement and experience. Decision making can be straightforward at the extremes of the well to unwell spectrum. Non-specific signs or symptoms may sway the clinician to investigate further. If after treatment and reassessment the child “proves” his wellness (i.e., normalized vital signs, improved appearance and activity), then intervention can be de-escalated. Conversely, concerning signs such as severe tachycardia, cold peripheries, poor responsiveness, and inactivity flag the child as unwell; he is at high-risk for decompensation even after initial partial improvement with resuscitative measures. What commonly creates a dilemma in paediatric emergency medicine is the child with a mixed picture, the “in-betweener”. Typically this is a child who is alert and active but tachycardic and symptomatic. As there is no single and universally accepted strategy for decision making in such children a variety of approaches exist. These include: Treat and re-evaluate Risk stratify with biomarkers Treat and admit This strategy is commonly used in emergency departments. The validity of this approach stems from the dilemma that, especially in young children, physiological response to uncomplicated illness is often extreme and indistinguishable from physiological compensation or decompensation due to sepsis. Given no high-risk medical comorbidities, the previously well child with a self-limiting viral illness may respond well to this strategy. In this approach, administration of an antipyretic or analgesic is being used more as an investigation based on the presumption that the physiological changes due to the illness will return to baseline while dysregulated parameters and signs of organ dysfunction will remain deranged. This approach works well when any abnormal parameters make a significant positive change and this correlates with a healthy activity level. The pitfalls of this approach are many including a lack of clear parameters for success. Does the heart rate need to return to normal or is a degree of normalisation sufficient? This is particularly problematic when the reassessment is handed over to a different person (for example following a change of shift) as this model of analgesia and review works best when the same person sees the effect of analgesia directly. The use of biomarkers (e.g. CRP or procalcitonin) in decision-making is often used in patients with some risk of decompensation or invasive illness, such as young infants. Their use in older, previously well children is debated. Given the range of presentations and medical complexity of patients who present to the ED, it is tempting to shed cognitive load and depend on an objective test. Applicability of the test depends on its performance, or accuracy. Accuracy is a function of prevalence of the disease as well as the sensitivity and specificity of the test itself. While biomarkers do have some correlation with the significance of infection there are no clear thresholds which allow for these tests to reliably rule out or rule in serious bacterial infection. Biomarkers also give no information about the clinical effect of an illness. If there were any test or formula that had good sensitivity and specificity it would be the gold standard approach. Since no such test exists biomarkers tend to be used in those cases who are neither well enough to immediately decide to discharge nor unwell enough for an immediate decision to treat. The very decision to use a biomarker is a critical intervention in itself and dependant on the experience of the clinician, the clinical context of the decision and the prevalence of disease. We acknowledge the role of biomarkers in validated algorithms, where the biomarkers allow determination of an action as a critical decision making node when a disease process has been identified. However, using biomarkers in a defensive fashion for a well, vaccinated child with likely viral source only causes harm to the patient, increases costs, and delays the care of other, sicker children. Therefore while biomarkers appear to have inherent face validity they are not always necessary. While little work has been done that could claim to compare the effectiveness of the different approaches there is interesting proxy evidence that suggests clinical decision making alone is not just valid but as safe and more efficient than adding biomarkers to the decision making process. The Petechiae in Children (PIC) study used local guidelines for children with fever and petechial rash and applied these guidelines to a large dataset of children with fever and non-blanching rash. The original paper demonstrated that all of the local guidelines used had better specificity than the current national guideline (which essentially used a blanket treat and decide approach) without losing sensitivity. All of the guidelines using biomarkers in decision making achieved a specificity of up to 36%. Examination of a different guideline which relied wholly on clinical decision making without the use of biomarkers improved specificity to 69% without the loss of any sensitivity. This highlights a need to do similar research comparing different approaches to decision making in all febrile children and not just those with non-blanching rash. Ultimately given the PIC study demonstrated petechiae are no longer a risk factor for sepsis in a population vaccinated against meningococcus, it is arguable that this publication has shown that clinical decision making without the use of biomarkers is both safe and perhaps more effective in some populations. This approach involves an early decision to admit the child with treatment ongoing. The benefit to the child would be to allow further treatment and testing and to assure a safe disposition from the emergency department. In an overburdened system, routing obviously ill children to other available clinicians may be a shrewd strategy. Early anchoring of the not-so-sick child (or one who could benefit from the above treat and reassess or risk-stratify strategies) may conversely lead to overtreatment and could overwhelm inpatient capacity. A default treat-and-admit approach is most valid in high-risk groups where the specificity will be greatest. For example, a well appearing, febrile, and tachycardic 3 day old is very high-risk for serious bacterial infection whereas a well appearing, febrile, and tachycardic 3-year-old is very low-risk. This is because there is an incidence of invasive bacterial illness in a well appearing 3 day old just on the basis of having a fever, whereas the well appearing 3 year old with a fever has a negligible (but not non-existent) risk of invasive bacterial illness. Both cases demonstrate an innate vigorous physiological reaction to fever. The 3-day-old, however, is at high-risk for decompensation and serious sequelae; the three-year-old has proven his robustness to recover. If high fever and vital sign abnormality were the deciding (read: anchoring) factor to admit regardless of emergency department course, harm may be done to those who would not benefit from admission. Traditionally the approach has been to sift out the sickest children through senior staff, triage and/or Early Warning Systems and then work through the remaining equivocal cases. Early Warning Systems have become synonymous with Early Warning Scores i.e. individual numerical scores based on physiology which correspond to various levels of escalation however Early Warning Systems are much broader strategies to recognise unwell children. They incorporate not only physiological measurements through the use of scores but require the use of subjective judgements of staff, and increasingly incorporate the views of the caregivers. Furthermore they are predicated on healthcare cultures which are not beholden to hierarchy and utilise communication processes aligned with human factors theory. A combination of effective decision-making approaches which increase the number of well children discharged early in the patient journey, allow prompt treatment of the most unwell and maximise focus on the group in the middle (the void) is needed. The delivery of this is complex given it requires multiple staff members and a patient group who may have evolving disease process. Figure brings together an early warning system decision tree approach to decision making in the Emergency Department. At its heart is the use of clinical expertise to determine the disposition of patients, but in a tiered approach, so that not all decisions need to be made by the most senior personal. Early Warning Scores, biomarkers and caregiver concern all have a role to play but are adjuncts within a decision tree which aims to remove the most well and unwell from the patient load and thereby reducing the cognitive strain by working in the void area. The application of the decision tree may result in the following conclusion to the case study Case Study Part B (Evaluating Risk) Anya has no high risk criteria (Box A). The initial clinical evaluation, performed by a junior doctor finds that Anya has an upper respiratory tract infection. Due to the abnormal physiological parameters (Box B) the junior consults a senior doctor who reviews Anya. The senior’s opinion is that Anya’s peripheral coldness, raised heart rate and respiratory rate are explained by a combination of fever and discomfort. They recommend a period of observation to re-evaluate after the analgesia has had an effect . A fundamental aim of pediatric care is to seek out sick children and prioritize them. This culture ensures the delivery of education, and of support, departments must audit and evaluate near-miss cases. This leads to a predisposition that children are presumed ill and that perhaps an overemphasis on that fact that every discharge home carries some degree of risk. Because acute care presentations often occur at the beginning of a disease process, clinicians vary in their comfort with uncertainty or risk tolerance. This experience is often difficult to teach and so communities of practice and other ongoing professional development strategies may best help clinicians understand the practice patterns of their peers. Opportunities to discuss, adjust or update practices are multiple, regardless of locale. The careful clinician can only make the best decision with the information and resources at the time of presentation. The risk of decompensation of a discharged patient may only removed by admitting all children, negating the value of a primary assessment service. For the risk to be acceptable we must avoid criticism of the decision to discharge where a valid clinical assessment and decision process has taken place. Instead it is key to accept that rarely a child who appears well will later become unwell. “Sick Children look Sick” wrote Green et al. nearly a decade ago. While the medico-legal implications of discharging a child for them to return more unwell are significant this occurrence does not mean the initial discharge decision was incorrect. In order to protect patients and staff the discharging clinician must provide excellent verbal and written safety netting advice. , By doing so we employ the parent or carer in an ongoing decision-making process that is as dynamic as the illnesses with which children present. The well-child visit to the emergency department may also be a teachable moment to engage parents in what clinical setting best matches common complaints (e.g., clinic, urgent care, or emergency department). Case Study part C (Resolution) Anya received acetaminophen per rectum and the emergency physician confirmed with her parents that she had no high-risk features. Shared decision-making included a short observation time and reassessment. Forty-five minutes later, Anya had defervesced; she was taking fluids and hungrily eating a cookie. Her repeat vital signs showed a heart rate of 135 beats per minute and a respiratory rate of 32 breaths per minute. The emergency physician was transparent in his logic to the parents and cautioned that although there is still a risk of bacterial infection now or in the near future, home monitoring was a safe first step. Careful precautionary advice was given. The family was discharged home, and the emergency physician was immediately called to the bedside of an apneic infant . The emergency care system is overloaded and above capacity. The mission of emergency departments to care for acutely ill and injured children is in conflict with the present-day clearinghouse phenomenon in which the spectrum of presentations is widely expanded to include many well children. In terms of preservation of mission, resources, and reducing harm, spotting the well child will become increasingly valuable. Clinician awareness of societal needs, patients’ access to healthcare, and a culture of satisfaction are substantial barriers to re-routing patients to the best venue for medical attention. A cultural shift is needed to enable prompt decision making regarding children at lowest risk of serious illness. In addition to institutional support, this will depend on adequately experienced clinicians to make decisions based on clinical judgement rather than biomarker output. |
Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory | eaad99cc-8798-4544-85d5-4cedeac81aa2 | 8489684 | Physiology[mh] | Working memory, our ability to hold information in mind for short time periods, is a hallmark of cognition but is severely limited on several fronts . Some of its limitations, such as its capacity, precision, or specific quantitative biases have been successfully accounted for by a family of biophysically-constrained models, mostly on the basis of a ring attractor network that maintains memoranda through sustained reverberatory neural activity (activity bumps) . A feature of working memory that constrains the simultaneous storage of several items is the presence of swap errors . These errors occur when an inaccurate response to the target item is in fact accurate relative to a non-target item, reflecting the failure to maintain the appropriate association or “binding” between the separate features that define each item (e.g., color and location). The neural mechanisms supporting feature binding remain unclear, with different computational models implementing two alternative hypotheses . The first type of models are based on selective synchronization . In these models, different neuronal populations selective to each feature that define an object are bound together through synchronized oscillatory activity. This would answer the longstanding question of how independently encoded features could be flexibly encoded as a single concept . Thanks to this flexibility, at least conceptually, these models do not suffer from combinatorial explosion as an increasing number of feature combinations are considered. There are, however, important questions about the biological plausibility of this hypothesis. Crucially, such a framework would need a temporal encoder that tags bound features by a “temporal code” and a temporal decoder that is able to distinguish which features are associated by detecting ensembles oscillating in precise synchrony. Both the encoder and decoder would thus depend on undefined biological mechanisms for spike coincidence detection , which would struggle with the known high variability of neural spiking in sustained activity . However, there is ample evidence for oscillatory dynamics during working memory. For instance, oscillatory activity in the gamma band (roughly defined between 30 and 100 Hz) increases during the mnemonic periods, both locally and across sites , and further increases with memory load . Importantly, gamma-band activity seems to play a functional role, as working memory binding performance is increased when transcranial stimulation at gamma frequency (40 Hz) is applied at two different sites (left temporal and parietal), but only when in anti-phase in line with monkey electrophysiology showing that different items are stored in different oscillatory phases and the more general framework of phase-coding in working memory . Another class of models achieve feature binding through “conjunction neurons” – neurons that are selective to all features being bound. Since neurons with mixed selectivity are ubiquitous in the brain , these models seem more biologically plausible than those relying on unrealistically precise spike synchronization. Nevertheless, they suffer from some important limitations. First, the number of possible combinations explode quickly with an increasing number of features ( ; ; , ; but see ). Second, these models do not have independent storage systems for each feature that define an object, to which there is converging evidence . See and for recent reviews on the experimental evidence that should constrain multi-item working memory models, in particular those aiming to explain feature binding. Here, we propose a hybrid model that overcomes several limitations from both types of models. We connected two ring attractor networks – one ring representing and memorizing colors and another ring storing locations – via weak excitation. This is an explicit implementation of the independent storage of individual features, where each feature might be represented in different cortical areas (e.g., color in inferior temporal cortex and location in posterior parietal cortex). Within each area, oscillatory mnemonic activity occured naturally through the interplay between fast recurrent excitation and slower inhibitory feedback. Feature binding was accomplished through the selective synchronization of pairs of bumps across the two networks. Furthermore, encoding and decoding of specific color-location associations was accomplished through rate coding. Our hybrid model of rate/temporal coding shares the rich explanatory power of classical ring-attractor models of working memory and derives new predictions that can be tested on multiple levels.
Neural Network Model We extended a previously proposed computational model . In particular, we connected two one-dimensional ring networks via weak, cortico-cortical excitatory synapses governed by AMPAR-dynamics. Each network consists of 2,048 excitatory and 512 inhibitory leaky integrate-and-fire neurons fully connected through AMPAR-, NMDAR-, and GABA A R-mediated synaptic transmission as in . Moreover, excitatory and inhibitory neurons were spatially distributed on a ring so that nearby neurons encoded nearby spatial locations. All connections were all-to-all and spatially tuned, so that nearby neurons with similar preferred directions had stronger than average connections, while distant neurons had weaker connections. Inhibitory-to-inhibitory and across-network connectivity was untuned. Intrinsic parameters for both cell types and all the connectivity parameters were taken from , except the following for networks holding up to two stimuli or capacity-2 networks (notation consistent with ): G E E , A M P A = 0.09 nS , G E I , A M P A = 0.256 nS , G E E , N M D A = 0.24 nS , G E I , N M D A = 0.11 nS , G I I , G A B A = 2 nS , G I E , G A B A = 3 nS , g e x t , I = 2.74 nS , g e x t , E = 3.5 nS , J E E + = 10 , σ E E = 9 , J E I + = J I E + = 2.4 , σ E I = σ I E = 18 . For networks holding up to three stimuli (capacity-3 networks), G E E , A M P A = 0.126 nS , G E I , A M P A = 0.256 nS , G E E , N M D A = 0.2 nS , G E I , N M D A = 0.11 nS , G I I , G A B A = 2 nS , G I E , G A B A = 3 nS , g e x t , I = 2.8 nS , g e x t , E = 3.58 nS , J E E + = 11 , σ E E = 9 , J E I + = J I E + = 2.6 , σ E I = σ I E = 30 . Connectivity across networks was determined by the following conductances (for unconnected simulations, these conductances were set to zero): G E E , A M P A , a c r o s s = 0.45 nS , G E I , A M P A , a c r o s s = 0.18 nS , G E E , N M D A , a c r o s s = G E I , N M D A , a c r o s s = 0 nS . These parameters were adjusted to have within-network oscillations, which was accomplished by increasing the ratio between fast and slow excitation, supported, respectively, by AMPAR and NMDAR channels, as previously shown . The main dynamics described in this study were robust to a broad range of parameter values . Cross-Correlations For the cross-correlation analyses, we computed spike counts in bins of 5 ms, collapsing all neurons around the stimulus presentation location (here called a bump , ±340 neurons). Moreover, we computed within- and across-network correlations by, respectively, considering neurons in bumps from the same or different circuits. For the cross-frequency correlation plots (e.g., ), we further computed the power spectrum of the resulting cross-correlation functions, averaged across all possible (only within- or only across-) pairs of bumps. Conversion of Spikes Into Local Field Potentials For the conversion of simulated spike trains into local field potentials, we convolved the aggregated spike times ( t s )of all the neurons engaged in a bump (or in the network, depending on the analysis) with an alpha-function synaptic kernel: L F P ( t ) = ∑ t s Θ ( t - t s ) t - t s τ exp ( - t - t s τ ) with Θ( t ) being the Heaviside theta function, and τ = 5ms. Phase-Preservation Index To measure how an oscillating activity bump kept its oscillatory phase over multiple trials ( k = 1,…, N ) of our simulation, we first converted spike times into local-field potentials (see above). Through wavelet analysis, we determined the phase ϕ k ( f 0 , t ) of the LFP at f 0 = 30Hz (the approximate frequency of oscillations in the network) at all time points t of the simulation, and then we used the phase-preservation index (PPI), a method originally developed by for EEG data. The PPI is defined by taking a reference time point (in our case t r e f = stimulus offset ), and then computing the average consistency of the phases at the specific frequency of interest f 0 with the rest of the time points: P P I ( f 0 , t ) = 1 N | ∑ k = 1 N e i ϕ k ( f 0 , t r e f ) - i ϕ k ( f 0 , t ) | Phase-preservation index values thus vary between 0 and 1, with 1 indicating perfect phase consistency. Extracting Behavioral Output With a Mixture of Gaussians The final behavioral output, for simplicity, was extracted by fitting a mixture of two gaussians to the late-delay average activity of the color network. We then selected the central value (color) of the gaussian component with larger amplitude, or stronger mixture component. We fit the mixture of gaussians using the Python function sklearn.mixture.GMM . This algorithmic read-out could be replaced by a biologically plausible downstream network connected to the color circuit, and tuned to be in a winner-take-all regime – i.e., only able to maintain one bump at a time.
We extended a previously proposed computational model . In particular, we connected two one-dimensional ring networks via weak, cortico-cortical excitatory synapses governed by AMPAR-dynamics. Each network consists of 2,048 excitatory and 512 inhibitory leaky integrate-and-fire neurons fully connected through AMPAR-, NMDAR-, and GABA A R-mediated synaptic transmission as in . Moreover, excitatory and inhibitory neurons were spatially distributed on a ring so that nearby neurons encoded nearby spatial locations. All connections were all-to-all and spatially tuned, so that nearby neurons with similar preferred directions had stronger than average connections, while distant neurons had weaker connections. Inhibitory-to-inhibitory and across-network connectivity was untuned. Intrinsic parameters for both cell types and all the connectivity parameters were taken from , except the following for networks holding up to two stimuli or capacity-2 networks (notation consistent with ): G E E , A M P A = 0.09 nS , G E I , A M P A = 0.256 nS , G E E , N M D A = 0.24 nS , G E I , N M D A = 0.11 nS , G I I , G A B A = 2 nS , G I E , G A B A = 3 nS , g e x t , I = 2.74 nS , g e x t , E = 3.5 nS , J E E + = 10 , σ E E = 9 , J E I + = J I E + = 2.4 , σ E I = σ I E = 18 . For networks holding up to three stimuli (capacity-3 networks), G E E , A M P A = 0.126 nS , G E I , A M P A = 0.256 nS , G E E , N M D A = 0.2 nS , G E I , N M D A = 0.11 nS , G I I , G A B A = 2 nS , G I E , G A B A = 3 nS , g e x t , I = 2.8 nS , g e x t , E = 3.58 nS , J E E + = 11 , σ E E = 9 , J E I + = J I E + = 2.6 , σ E I = σ I E = 30 . Connectivity across networks was determined by the following conductances (for unconnected simulations, these conductances were set to zero): G E E , A M P A , a c r o s s = 0.45 nS , G E I , A M P A , a c r o s s = 0.18 nS , G E E , N M D A , a c r o s s = G E I , N M D A , a c r o s s = 0 nS . These parameters were adjusted to have within-network oscillations, which was accomplished by increasing the ratio between fast and slow excitation, supported, respectively, by AMPAR and NMDAR channels, as previously shown . The main dynamics described in this study were robust to a broad range of parameter values .
For the cross-correlation analyses, we computed spike counts in bins of 5 ms, collapsing all neurons around the stimulus presentation location (here called a bump , ±340 neurons). Moreover, we computed within- and across-network correlations by, respectively, considering neurons in bumps from the same or different circuits. For the cross-frequency correlation plots (e.g., ), we further computed the power spectrum of the resulting cross-correlation functions, averaged across all possible (only within- or only across-) pairs of bumps.
For the conversion of simulated spike trains into local field potentials, we convolved the aggregated spike times ( t s )of all the neurons engaged in a bump (or in the network, depending on the analysis) with an alpha-function synaptic kernel: L F P ( t ) = ∑ t s Θ ( t - t s ) t - t s τ exp ( - t - t s τ ) with Θ( t ) being the Heaviside theta function, and τ = 5ms.
To measure how an oscillating activity bump kept its oscillatory phase over multiple trials ( k = 1,…, N ) of our simulation, we first converted spike times into local-field potentials (see above). Through wavelet analysis, we determined the phase ϕ k ( f 0 , t ) of the LFP at f 0 = 30Hz (the approximate frequency of oscillations in the network) at all time points t of the simulation, and then we used the phase-preservation index (PPI), a method originally developed by for EEG data. The PPI is defined by taking a reference time point (in our case t r e f = stimulus offset ), and then computing the average consistency of the phases at the specific frequency of interest f 0 with the rest of the time points: P P I ( f 0 , t ) = 1 N | ∑ k = 1 N e i ϕ k ( f 0 , t r e f ) - i ϕ k ( f 0 , t ) | Phase-preservation index values thus vary between 0 and 1, with 1 indicating perfect phase consistency.
The final behavioral output, for simplicity, was extracted by fitting a mixture of two gaussians to the late-delay average activity of the color network. We then selected the central value (color) of the gaussian component with larger amplitude, or stronger mixture component. We fit the mixture of gaussians using the Python function sklearn.mixture.GMM . This algorithmic read-out could be replaced by a biologically plausible downstream network connected to the color circuit, and tuned to be in a winner-take-all regime – i.e., only able to maintain one bump at a time.
Working Memory Load Modulates Oscillation Power and Frequency We built a computational network model of a local neocortical circuit, with excitatory and inhibitory spiking neurons ( leaky integrate-and-fire neuron model) connected reciprocally via excitatory AMPAR-mediated and NMDAR-mediated synapses and inhibitory GABA A R-mediated synapses (see “Materials and Methods”). The ring-attractor network model was adjusted to support bump attractor dynamics with up to three simultaneous bumps , and further adjustment of the relative weights of AMPAR- and NMDAR-mediated currents was performed to set active reverberant neurons in the oscillatory regime . Using this computational model we started by investigating the dynamics that originated within each network. In our model, multiple bumps showed anti-correlated oscillatory activity . As we stored more bumps in the network, lateral inhibition originating from simultaneous memories established anti-phase oscillatory dynamics during the memory period. These oscillatory dynamics were irregular, as illustrated in quickly dampened correlation functions ( , bottom). Moreover, we found that the anti-phase behavior was robust in a wide range of values for AMPAR conductances , consistently in the gamma range of frequencies . Having seen these anti-phase dynamics between simultaneous bumps, we sought to contrast two opposite scenarios as we increased the number of stored memories ( memory load ). Under one alternative, bumps may oscillate at a fixed frequency irrespectively of load, so that the global network oscillation (adding up the activity of fixed-frequency out-of-phase bumps) would have a frequency that should increase linearly with memory load (scenario 1, dashed line ). Alternatively, the network global oscillation could have a fixed frequency for different loads, and simultaneous bumps would take turns to fire in the available active periods. This would lead to halving each bump’s oscillation frequency as we double the memory load (scenario 2, dashed line in ). We tested our model simulations to identify if our biophysical model adhered to one of these scenarios. To this end, we ran multiple simulations with three different loads (presenting 1–3 separate bumps during the encoding cue period) and we computed power spectra from either the aggregate activity of the whole network (network power) or from separate populations centered around each presented target (bump power). We then extracted the frequency of the peak network and bump power to study their dependency with load. We found signatures of both scenarios . As we increased the memory load, the overall network activity oscillated at slightly increasing frequencies . In contrast, each bump, corresponding to different memories, oscillated at markedly slower frequencies as load increased . We quantified which were the dominant dynamics by plotting both the network’s and each bump’s oscillating frequency against memory load. For better comparison, we normalized the frequency associated with different loads to the one of load 1. Moreover, we compared the effect of memory load against scenario 1 and 2 (dashed lines in ). Qualitatively, we found that our network dynamics was more consistent with the latter. We therefore conclude that our biophysical network maintains a relatively constant global oscillation as more items are loaded into memory, and individual memory oscillations instead start skipping cycles to sustain out-of-phase dynamics with other memories. Thus, the interplay between recurrent (fast) excitation and (slower) feedback inhibition acting locally is the basis of the oscillatory bump behavior. Moreover, we now show that anti-phase dynamics of simultaneous bumps occurs due to bump competition, accomplished by lateral inhibition. This competition increases with memory load, leading to longer periods of silence during the delay-activity of each bump. These dynamics generalize previous findings in simplified rate models , and extend them to biologically realistic ring attractor networks. Uniform Coupling Achieves Feature Binding The binding between color and location is accomplished through the spontaneous synchronization of pairs of bumps across two networks connected via weak cortico-cortical excitation . In particular, we connected two ring-attractors in the regime described above with all-to-all, untuned excitatory connectivity. This connectivity was weak and it was mediated exclusively by AMPARs , acting on all excitatory and inhibitory neurons. Interestingly, anti-phase dynamics within each network (as described above) was maintained robustly for a wide range of connectivity strength values . Across networks, each bump’s activity was in phase with one bump in the other network ( , black) but out of phase with the other ( , red). On the majority of the simulations, this selective synchronization was maintained through the whole delay period (see for an example simulation). This set of dynamics is an interesting possible mechanism that binds and maintains the information of each presented stimulus. To this end, however, there are several aspects to resolve in relation to the encoding and decoding of this bound information. On the one hand, synchronization selection was noise-induced in our simulations, resulting in across-networks associations between random pairs of bumps for different simulations. To control this association at the time of stimulus encoding, we stimulated strongly (7.5 times the intensity of sensory stimuli) and simultaneously one bump in each network for a brief period of 50 ms ( , , green period), forcing these two bumps (one in each network) to engage in correlated activity during the delay period. Nevertheless, this phase-locked dynamics could be broken by noisy fluctuations, leading to possible misbinding of memorized features and swap trials . On the other hand, our model raised the question of how this binding of information could reasonably be decoded without resorting to complex mechanisms for spike coincidence detection. In our task, the “behavioral” output consisted in answering which “color” was initially associated with a particular “location,” and this was accomplished by evaluating which bump of the color network maintained in-phase synchronization with the bump of the probed location at the end of the delay. We found that this did not require complex coincidence detection, but could instead be simulated in a rate formalism as follows. For each trial, we probed one location by stimulating weakly ( 1 4 of stimulus intensity) corresponding neurons in the location network at the end of the delay. This simulated the visual presentation of a location probe at the end of the delay. This increased the firing rate of the corresponding location bump, and we found that it also resulted in an increase of activity of the associated, in-phase color bump . Finally, we extracted the behavioral output by fitting a mixture of gaussians (“Materials and Methods”) applied to the mean firing rate activity across the color network during the location-probing period (0.5 s). shows color readouts from 1,000 of such simulated trials. Applying our encoding/decoding method to our simulations, resulted in 30% of trials wrongly associated with the non-target color (swap trials, ). We then separated swap trials from on-target trials and computed the spike-count correlation in windows of 5 ms through the whole trial period , and confirmed that on-target trials were in fact characterized by stable phase-locked activity, while the correlation between bumps in swap trials progressively approached the opposite dynamics (in-phase/anti-phase for the bound/unbound items, ). Importantly, networks maintained synchronized in-phase dynamics for bound features robustly over a broad range of inter-network connectivity parameter values . Additionally, we identified three sources of swap errors in our simulations, classified as memory swaps if the correct association based on in-phase bump synchronization changed abruptly during the delay (51% of the swap trials), attentional swaps if the wrong association was encoded during the encoding period (22%) or decoding swaps if the correct association was encoded and maintained during the memory period, but the decoding failed (27%). See for example simulations. Together, our biologically-constrained simulations demonstrate that feature-binding can be robustly accomplished through selective synchronization. Crucially, encoding/decoding location-color associations was done without a temporally precise code , a long-standing limitation in the binding by synchrony framework . Moreover, we identified three sources of swap errors. Based on these computational findings, we investigated model predictions that could be compared with existing data or could generate hypotheses for new experimental studies. Swap Errors Increase With Delay and Item Competition In our model, swap errors are induced by noisy fluctuations. This results in two behavioral predictions, congruent with previous findings . First, longer memory delays should increase the probability of a noisy fluctuation that is sufficiently large to induce a swap . Second, shows how swap errors decrease with target to non-target distances. For very close locations, feedback inhibition is strongest, leading to strong competition between nearby bumps, explaining an increase of swap errors for such distances. This is similar to previous studies , in which simultaneous bumps interfere (repulsively and through their phase relationship, which is in this case less stable through the delay). Experimentally, these two regimes correspond to different scenarios. In the first case, one color is forgotten, while in the second scenario, there is an actual swap error. This prediction could be tested experimentally by probing the subject’s memory on all items, instead of just one . In sum, our model is able to describe a previously found dependence of swap errors with delay duration and with target to non-target distance, and it offers mechanistic explanations for such dependencies. Neural Prediction: Swap Trials Show Less Phase Preservation Through the Delay Finally, abrupt changes in the phase relationship between oscillating bumps is the central mechanism of swap errors in our model . Therefore, it is worth deriving a testable neurophysiological prediction from this mechanism. Additionally, because these changes in phase relationships are abrupt, they require experiments using techniques with high temporal resolution such as MEG or EEG. Intuitively, swap errors in our model simulations are characterized by inconsistent phase relationships between brain signals when comparing the beginning and the end of the delay period. We therefore considered applying an analysis that has been proposed to test phase consistency in EEG/MEG: the phase-preservation index (PPI, ). We first derived LFP signals from our network’s spiking activity (“Materials and Methods”). We then calculated the phase-preservation index (PPI, see and “Materials and Methods”) at the end of the delay, relative to the beginning of the delay, and separately for on-target and swap trials defined “behaviorally” . As we expected based on our model simulations , this analysis applied to our simulated data showed that trials containing swap errors had a lower PPI, compared to on-target trials . This prediction can be tested with MEG/EEG data recorded from humans performing this task, based on an analysis of behavioral responses able to discriminate swap and correct error trials .
We built a computational network model of a local neocortical circuit, with excitatory and inhibitory spiking neurons ( leaky integrate-and-fire neuron model) connected reciprocally via excitatory AMPAR-mediated and NMDAR-mediated synapses and inhibitory GABA A R-mediated synapses (see “Materials and Methods”). The ring-attractor network model was adjusted to support bump attractor dynamics with up to three simultaneous bumps , and further adjustment of the relative weights of AMPAR- and NMDAR-mediated currents was performed to set active reverberant neurons in the oscillatory regime . Using this computational model we started by investigating the dynamics that originated within each network. In our model, multiple bumps showed anti-correlated oscillatory activity . As we stored more bumps in the network, lateral inhibition originating from simultaneous memories established anti-phase oscillatory dynamics during the memory period. These oscillatory dynamics were irregular, as illustrated in quickly dampened correlation functions ( , bottom). Moreover, we found that the anti-phase behavior was robust in a wide range of values for AMPAR conductances , consistently in the gamma range of frequencies . Having seen these anti-phase dynamics between simultaneous bumps, we sought to contrast two opposite scenarios as we increased the number of stored memories ( memory load ). Under one alternative, bumps may oscillate at a fixed frequency irrespectively of load, so that the global network oscillation (adding up the activity of fixed-frequency out-of-phase bumps) would have a frequency that should increase linearly with memory load (scenario 1, dashed line ). Alternatively, the network global oscillation could have a fixed frequency for different loads, and simultaneous bumps would take turns to fire in the available active periods. This would lead to halving each bump’s oscillation frequency as we double the memory load (scenario 2, dashed line in ). We tested our model simulations to identify if our biophysical model adhered to one of these scenarios. To this end, we ran multiple simulations with three different loads (presenting 1–3 separate bumps during the encoding cue period) and we computed power spectra from either the aggregate activity of the whole network (network power) or from separate populations centered around each presented target (bump power). We then extracted the frequency of the peak network and bump power to study their dependency with load. We found signatures of both scenarios . As we increased the memory load, the overall network activity oscillated at slightly increasing frequencies . In contrast, each bump, corresponding to different memories, oscillated at markedly slower frequencies as load increased . We quantified which were the dominant dynamics by plotting both the network’s and each bump’s oscillating frequency against memory load. For better comparison, we normalized the frequency associated with different loads to the one of load 1. Moreover, we compared the effect of memory load against scenario 1 and 2 (dashed lines in ). Qualitatively, we found that our network dynamics was more consistent with the latter. We therefore conclude that our biophysical network maintains a relatively constant global oscillation as more items are loaded into memory, and individual memory oscillations instead start skipping cycles to sustain out-of-phase dynamics with other memories. Thus, the interplay between recurrent (fast) excitation and (slower) feedback inhibition acting locally is the basis of the oscillatory bump behavior. Moreover, we now show that anti-phase dynamics of simultaneous bumps occurs due to bump competition, accomplished by lateral inhibition. This competition increases with memory load, leading to longer periods of silence during the delay-activity of each bump. These dynamics generalize previous findings in simplified rate models , and extend them to biologically realistic ring attractor networks.
The binding between color and location is accomplished through the spontaneous synchronization of pairs of bumps across two networks connected via weak cortico-cortical excitation . In particular, we connected two ring-attractors in the regime described above with all-to-all, untuned excitatory connectivity. This connectivity was weak and it was mediated exclusively by AMPARs , acting on all excitatory and inhibitory neurons. Interestingly, anti-phase dynamics within each network (as described above) was maintained robustly for a wide range of connectivity strength values . Across networks, each bump’s activity was in phase with one bump in the other network ( , black) but out of phase with the other ( , red). On the majority of the simulations, this selective synchronization was maintained through the whole delay period (see for an example simulation). This set of dynamics is an interesting possible mechanism that binds and maintains the information of each presented stimulus. To this end, however, there are several aspects to resolve in relation to the encoding and decoding of this bound information. On the one hand, synchronization selection was noise-induced in our simulations, resulting in across-networks associations between random pairs of bumps for different simulations. To control this association at the time of stimulus encoding, we stimulated strongly (7.5 times the intensity of sensory stimuli) and simultaneously one bump in each network for a brief period of 50 ms ( , , green period), forcing these two bumps (one in each network) to engage in correlated activity during the delay period. Nevertheless, this phase-locked dynamics could be broken by noisy fluctuations, leading to possible misbinding of memorized features and swap trials . On the other hand, our model raised the question of how this binding of information could reasonably be decoded without resorting to complex mechanisms for spike coincidence detection. In our task, the “behavioral” output consisted in answering which “color” was initially associated with a particular “location,” and this was accomplished by evaluating which bump of the color network maintained in-phase synchronization with the bump of the probed location at the end of the delay. We found that this did not require complex coincidence detection, but could instead be simulated in a rate formalism as follows. For each trial, we probed one location by stimulating weakly ( 1 4 of stimulus intensity) corresponding neurons in the location network at the end of the delay. This simulated the visual presentation of a location probe at the end of the delay. This increased the firing rate of the corresponding location bump, and we found that it also resulted in an increase of activity of the associated, in-phase color bump . Finally, we extracted the behavioral output by fitting a mixture of gaussians (“Materials and Methods”) applied to the mean firing rate activity across the color network during the location-probing period (0.5 s). shows color readouts from 1,000 of such simulated trials. Applying our encoding/decoding method to our simulations, resulted in 30% of trials wrongly associated with the non-target color (swap trials, ). We then separated swap trials from on-target trials and computed the spike-count correlation in windows of 5 ms through the whole trial period , and confirmed that on-target trials were in fact characterized by stable phase-locked activity, while the correlation between bumps in swap trials progressively approached the opposite dynamics (in-phase/anti-phase for the bound/unbound items, ). Importantly, networks maintained synchronized in-phase dynamics for bound features robustly over a broad range of inter-network connectivity parameter values . Additionally, we identified three sources of swap errors in our simulations, classified as memory swaps if the correct association based on in-phase bump synchronization changed abruptly during the delay (51% of the swap trials), attentional swaps if the wrong association was encoded during the encoding period (22%) or decoding swaps if the correct association was encoded and maintained during the memory period, but the decoding failed (27%). See for example simulations. Together, our biologically-constrained simulations demonstrate that feature-binding can be robustly accomplished through selective synchronization. Crucially, encoding/decoding location-color associations was done without a temporally precise code , a long-standing limitation in the binding by synchrony framework . Moreover, we identified three sources of swap errors. Based on these computational findings, we investigated model predictions that could be compared with existing data or could generate hypotheses for new experimental studies.
In our model, swap errors are induced by noisy fluctuations. This results in two behavioral predictions, congruent with previous findings . First, longer memory delays should increase the probability of a noisy fluctuation that is sufficiently large to induce a swap . Second, shows how swap errors decrease with target to non-target distances. For very close locations, feedback inhibition is strongest, leading to strong competition between nearby bumps, explaining an increase of swap errors for such distances. This is similar to previous studies , in which simultaneous bumps interfere (repulsively and through their phase relationship, which is in this case less stable through the delay). Experimentally, these two regimes correspond to different scenarios. In the first case, one color is forgotten, while in the second scenario, there is an actual swap error. This prediction could be tested experimentally by probing the subject’s memory on all items, instead of just one . In sum, our model is able to describe a previously found dependence of swap errors with delay duration and with target to non-target distance, and it offers mechanistic explanations for such dependencies.
Finally, abrupt changes in the phase relationship between oscillating bumps is the central mechanism of swap errors in our model . Therefore, it is worth deriving a testable neurophysiological prediction from this mechanism. Additionally, because these changes in phase relationships are abrupt, they require experiments using techniques with high temporal resolution such as MEG or EEG. Intuitively, swap errors in our model simulations are characterized by inconsistent phase relationships between brain signals when comparing the beginning and the end of the delay period. We therefore considered applying an analysis that has been proposed to test phase consistency in EEG/MEG: the phase-preservation index (PPI, ). We first derived LFP signals from our network’s spiking activity (“Materials and Methods”). We then calculated the phase-preservation index (PPI, see and “Materials and Methods”) at the end of the delay, relative to the beginning of the delay, and separately for on-target and swap trials defined “behaviorally” . As we expected based on our model simulations , this analysis applied to our simulated data showed that trials containing swap errors had a lower PPI, compared to on-target trials . This prediction can be tested with MEG/EEG data recorded from humans performing this task, based on an analysis of behavioral responses able to discriminate swap and correct error trials .
Aiming to account for swap-errors, a prominent source of multi-item working memory interference , we extended the ring-attractor model . Our biologically-constrained model offers a plausible mechanism for feature-binding. Briefly, the encoding and decoding of associations is accomplished through rate-coding, while their maintenance is accomplished through selective synchronization of oscillatory mnemonic activity. Oscillatory dynamics emerges naturally from bump competition, which increases with memory load and is in line with previous EEG experiments in humans and LFP recordings from monkey PFC . Finally, our model reveals different origins of swap errors , how they depend on delay duration and inter-item distances , and predicts that phase-locked oscillatory activity during the memory periods should reflect swap errors. Other Multi-Area Models for Working Memory Our multi-area model adds to a large body of computational work attempting to account for the distributed nature of working memory . While several of these models have implemented across-area interactions through oscillatory dynamics , they did not attribute a clear mechanistic role to inter-area synchronization dynamics. This is in contrast to our model, where feature-binding in working memory is accomplished through selective synchronization of oscillatory activity in different brain areas. Comparison With Previous Binding Models Previously proposed models by and as well as our model are explicit implementations of the synchronization mechanism for feature binding in working memory. While similar in the approach, there are important differences. As argued by , a major difficulty with previous synchronization models was that they were unable to show their capacity of reproducing the rich phenomenology of working memory behavior that other models can explain. Our model, on the basis of its architecture with ring attractor models of spiking neural networks, overcomes the limitation of earlier discrete population models and keeps all the demonstrated explanatory power that is characteristic of these attractor models, such as explaining several behavioral working memory biases in humans and monkeys ; as well as explaining key neurophysiological dynamics during working memory maintenance periods (see for a short review) in humans and monkeys . Our model also goes beyond previous synchronization models in that (1) by virtue of its 2-ring architecture, it explicitly implements the storage of different features in independent systems or brain areas, as shown experimentally , and that (2) it provides a plausible rate-based readout mechanism of working memory associations without resorting to complex synchrony detection processes, a major difficulty for this sort of models . Indeed, we show that our proposed mechanisms is robust to the noise inherent in spiking networks, which together with the need of precise spike coincidence detectors were major concerns of the binding through synchronized activity hypothesis in general and previous implementations in particular . Thus, our model now brings back synchronization-based feature binding in working memory as a plausible alternative to recent conjunction binding proposals, such as the binding pool and the conjunctive coding model . These models implement binding mechanisms that are fundamentally different from ours. In these models, binding of separated features is accomplished through conjunction neurons, which are neurons selective to mixtures of those features. While there is evidence for such neurons in the cortex , their role in feature-binding is not clear, given the consistent evidence for separate feature storage underlying working memory binding . Importantly, such a mechanism scales exponentially with the number of feature combinations, thus seemingly inconsistent with our ability to flexibly bind never seen combinations . However, it is to be noted that some conjunction models have mitigated this scaling problem through the construction of random conjunctions in an interposed network . Encoding With Rate Code In our hybrid model, only the maintenance of associations is accomplished through correlated oscillatory activity or, in other words, relies on a temporal code . Instead, encoding and decoding of associations is achieved through a rate code . Encoding and decoding is accomplished by delivering flat pulses (i.e., without the need to be temporally precise) to both the to-be-bound features exclusively ( encoding ) or just to one of them ( decoding ). Encoding the association between two different features through a pulse delivered simultaneously to each corresponding bump resembles the sequential encoding hypothesis in working memory . Moreover, there is evidence that a mechanism combining sequential and parallel encoding is implemented in the brain when solving multi-item working memory tasks . Our model implements such a combination. First, information about independent features arrives simultaneously to memory-encoding areas from upstream sensory areas. Then, the correct associations are sequentially encoded by brief excitatory pulses, possibly as a result of overt selective attention to each stimulus sequentially . Speaking to this, humans take longer to encode combined features than they take to encode the same amount of independent features . Decoding With Rate Code Works modelling multi-item working memory though the storage of several bumps in a network including our own often used approaches that are biologically implausible to extract the location of one bump, while ignoring other simultaneously maintained bumps. Our approach, however, matches closely the “cueing” period of a multi-item working memory task, which consists of stimulating the “cued” locations while reading out from the whole color network population. Moreover, our encoding/decoding mechanism proposes that swap errors can be of different origins (attention, memory, or decoding; ). Indeed, experimental designs that require subjects to rate their confidence on a trial-by-trial basis show that swap errors occur both in high- and low-confidence trials, suggesting different origins . Future Work: Toward Biological Plausibility of Binding Through Dynamics We found anti-phase dynamics within each network and phase-locking across networks, the central mechanisms for feature-binding in our model, to occur naturally in a broad range of parameters, indicating that the mechanisms proposed here do not require fine-tuning. Because our model is to some degree biologically constrained, it is a proof of concept that working memory binding through synchronized activity is at least possible to occur in the brain. In fact, we simulated noisy integrate-and-fire neurons, supporting that the central mechanism implemented in our model has some degree of robustness to noise. Our model is, however, limited in several ways that could be addressed in future studies. First, we did not simulate trials demanding binding of load 3 or higher. We expect that the main challenges associated with that improvement will be the encoding of more associations. We also did not explore conditions with asymmetric number of bumps (e.g., two colors/locations at/with one location/color), as this would lead to different experimental paradigms. Second, we did not investigate how feature-binding is impacted by incoming distractors. Previous work has shown that oscillatory activity on different bands can play a role in filtering distractors . Future work combining these models is necessary. Third, as a proof of concept, we only simulated two connected networks, while humans can encode and decode the association of many more features . Relatedly, our two-dimensional network architecture should be taken as a proof of concept, rather than being a literal anatomical representation of a specific brain structure. Finally, the oscillatory regime in which our model is operating, in which neurons are strongly synchronized with the population rhythm , however, derived from biologically constrained neuronal models, is arguably not biological itself. While there is abundant evidence that neuronal populations show strong oscillatory dynamics in working memory (e.g., ), single neuron dynamics approaches a Poisson process therefore not oscillatory at this scale (but see ). Early theoretical work has demonstrated that such oscillatory dynamics at the population level can coexist with noisy, unsynchronized neurons when randomly connected. Future work that connects randomly connected networks that store multiple stable bump-attractors , but operating in anti-correlated oscillatory activity such as in our simulations could be an appropriate avenue for the future work attempting to overcome these limitations.
Our multi-area model adds to a large body of computational work attempting to account for the distributed nature of working memory . While several of these models have implemented across-area interactions through oscillatory dynamics , they did not attribute a clear mechanistic role to inter-area synchronization dynamics. This is in contrast to our model, where feature-binding in working memory is accomplished through selective synchronization of oscillatory activity in different brain areas.
Previously proposed models by and as well as our model are explicit implementations of the synchronization mechanism for feature binding in working memory. While similar in the approach, there are important differences. As argued by , a major difficulty with previous synchronization models was that they were unable to show their capacity of reproducing the rich phenomenology of working memory behavior that other models can explain. Our model, on the basis of its architecture with ring attractor models of spiking neural networks, overcomes the limitation of earlier discrete population models and keeps all the demonstrated explanatory power that is characteristic of these attractor models, such as explaining several behavioral working memory biases in humans and monkeys ; as well as explaining key neurophysiological dynamics during working memory maintenance periods (see for a short review) in humans and monkeys . Our model also goes beyond previous synchronization models in that (1) by virtue of its 2-ring architecture, it explicitly implements the storage of different features in independent systems or brain areas, as shown experimentally , and that (2) it provides a plausible rate-based readout mechanism of working memory associations without resorting to complex synchrony detection processes, a major difficulty for this sort of models . Indeed, we show that our proposed mechanisms is robust to the noise inherent in spiking networks, which together with the need of precise spike coincidence detectors were major concerns of the binding through synchronized activity hypothesis in general and previous implementations in particular . Thus, our model now brings back synchronization-based feature binding in working memory as a plausible alternative to recent conjunction binding proposals, such as the binding pool and the conjunctive coding model . These models implement binding mechanisms that are fundamentally different from ours. In these models, binding of separated features is accomplished through conjunction neurons, which are neurons selective to mixtures of those features. While there is evidence for such neurons in the cortex , their role in feature-binding is not clear, given the consistent evidence for separate feature storage underlying working memory binding . Importantly, such a mechanism scales exponentially with the number of feature combinations, thus seemingly inconsistent with our ability to flexibly bind never seen combinations . However, it is to be noted that some conjunction models have mitigated this scaling problem through the construction of random conjunctions in an interposed network .
In our hybrid model, only the maintenance of associations is accomplished through correlated oscillatory activity or, in other words, relies on a temporal code . Instead, encoding and decoding of associations is achieved through a rate code . Encoding and decoding is accomplished by delivering flat pulses (i.e., without the need to be temporally precise) to both the to-be-bound features exclusively ( encoding ) or just to one of them ( decoding ). Encoding the association between two different features through a pulse delivered simultaneously to each corresponding bump resembles the sequential encoding hypothesis in working memory . Moreover, there is evidence that a mechanism combining sequential and parallel encoding is implemented in the brain when solving multi-item working memory tasks . Our model implements such a combination. First, information about independent features arrives simultaneously to memory-encoding areas from upstream sensory areas. Then, the correct associations are sequentially encoded by brief excitatory pulses, possibly as a result of overt selective attention to each stimulus sequentially . Speaking to this, humans take longer to encode combined features than they take to encode the same amount of independent features .
Works modelling multi-item working memory though the storage of several bumps in a network including our own often used approaches that are biologically implausible to extract the location of one bump, while ignoring other simultaneously maintained bumps. Our approach, however, matches closely the “cueing” period of a multi-item working memory task, which consists of stimulating the “cued” locations while reading out from the whole color network population. Moreover, our encoding/decoding mechanism proposes that swap errors can be of different origins (attention, memory, or decoding; ). Indeed, experimental designs that require subjects to rate their confidence on a trial-by-trial basis show that swap errors occur both in high- and low-confidence trials, suggesting different origins .
We found anti-phase dynamics within each network and phase-locking across networks, the central mechanisms for feature-binding in our model, to occur naturally in a broad range of parameters, indicating that the mechanisms proposed here do not require fine-tuning. Because our model is to some degree biologically constrained, it is a proof of concept that working memory binding through synchronized activity is at least possible to occur in the brain. In fact, we simulated noisy integrate-and-fire neurons, supporting that the central mechanism implemented in our model has some degree of robustness to noise. Our model is, however, limited in several ways that could be addressed in future studies. First, we did not simulate trials demanding binding of load 3 or higher. We expect that the main challenges associated with that improvement will be the encoding of more associations. We also did not explore conditions with asymmetric number of bumps (e.g., two colors/locations at/with one location/color), as this would lead to different experimental paradigms. Second, we did not investigate how feature-binding is impacted by incoming distractors. Previous work has shown that oscillatory activity on different bands can play a role in filtering distractors . Future work combining these models is necessary. Third, as a proof of concept, we only simulated two connected networks, while humans can encode and decode the association of many more features . Relatedly, our two-dimensional network architecture should be taken as a proof of concept, rather than being a literal anatomical representation of a specific brain structure. Finally, the oscillatory regime in which our model is operating, in which neurons are strongly synchronized with the population rhythm , however, derived from biologically constrained neuronal models, is arguably not biological itself. While there is abundant evidence that neuronal populations show strong oscillatory dynamics in working memory (e.g., ), single neuron dynamics approaches a Poisson process therefore not oscillatory at this scale (but see ). Early theoretical work has demonstrated that such oscillatory dynamics at the population level can coexist with noisy, unsynchronized neurons when randomly connected. Future work that connects randomly connected networks that store multiple stable bump-attractors , but operating in anti-correlated oscillatory activity such as in our simulations could be an appropriate avenue for the future work attempting to overcome these limitations.
The multi-area model as well as the code for measuring the phase-preservation index is available at https://github.com/comptelab/binding .
JB carried out the research. JB and AC conceived the research and wrote the manuscript. KKS, VB, and AT conceived the electrophysiological prediction. All authors edited and approved the final version of the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
Marginal bone loss and soft tissue health around two-implant mandibular overdenture retained with milled versus selective laser melted cobalt chromium bar: a randomized clinical trial | b1f1a2ec-aa6c-4353-a5fc-715a9803a8ab | 11452941 | Dentistry[mh] | Through the last decade, implant-retained overdentures have gained popularity as a mandibular ridge treatment modality due to their enhanced retention features, speech, patient satisfaction, chewing ability, biting force, and preservation of remaining bone . According to the literature, McGill and York declared that two implants can be considered the minimum standard number to retain overdentures with adequate retention and stability, improved ORHQL, cost efficacy, and higher patient satisfaction . Attachments are classified into splinted forms, such as bars and clips, or unsplinted attachments such as stud attachments, telescopes, and magnets . Bar attachment is known for its improved load distribution through splinting, decreased horizontal forces, compensation of implant malalignment, and low maintenance needed . Tongue cramping in v-shaped ridges, gingival hyperplasia, and plaque accumulation due to difficulties in cleaning procedures may be considered disadvantages of bars . Co-Cr alloy is one of the pioneer metal alloys used in implant-supported prostheses due to its biocompatibility, cost effectiveness, proper mechanical properties, and high modulus of elasticity, allowing the material to be used in thin Sect. . The lost wax technique is commonly used in metal framework fabrication . Nevertheless, it may result in a metal shrinkage problem with possible dimensional changes, porosity, defects in the casted framework, time consumption, and required laboratory skills . Computer aided design-computer aided manufacturer CAD-CAM fabrication techniques involve two systems: subtractive and additive techniques. The subtractive method depends on cutting the framework from a prefabricated block utilizing burs, drills, or diamond disks. While the additive method relies on forming a three- dimensional object through successive layering following a CAM design . Even though studies have reported higher biocompatibility, less metal shrinkage, and superior fit accuracy of milling technique over the casting technique , the former technique is expensive, may result in increased waste material or tool breakage with extra machine maintenance; besides it may produce inaccurate tiny details of complex geometrics . The additive manufacturing (AM) technique had overcome the problem of fine detail production and minimized waste material . Selective laser melting technique is one of the most popular AM techniques in metal powder production particularly, titanium and Co-Cr alloys. It involves layering the metal powder with an intense laser beam to melt and fuse it . Complex work pieces’ production, improved precision, passive production without force, and relatively decreased required laboratory work are considered advantages of the SLM technique . Marginal bone loss with a one year follow up is considered a recognition tool for implant osseointegration and success . Biological problems arising around implants may be described as peri-implantitis, which is inflammation of the soft tissue with continuous bone loss, or peri-mucositis, which is inflammation of the surrounding soft tissue without further bone loss. These conditions are best assessed through soft tissue parameters such as modified plaque index, modified gingival index, and probing depth . Various studies have reported the radiographic and soft tissue outcomes of bar retained implant mandibular overdentures constructed with the milling technique. On the contrast, scares clinical trials have reported the outcomes of bars fabricated with the SLM technique . Hence, the aim of this clinical trial was to assess marginal bone loss and soft tissue health around two-implant mandibular overdenture retained with milled versus selective laser-melted (SLM) Co-Cr bars after a one-year follow-up period. The null hypothesis was that there was no significant difference between the milled and SLM bars retaining implant-supported overdentures regarding marginal bone loss and soft tissue outcomes.
Sample size calculation The sample size was based on a previous published study , the least possible number was calculated to be 8 patients per group with 2 patients counted for dropout with total of 20 patients, as two equal groups were involved, with a power of 80% (b = 20) to detect a standardized effect size of 1.340 and a significance level of 5% (an error rate of 0.05). This work was done by an impartial statistician who wasn’t involved in the study. Study design The study was designed to be a parallel, triple-blinded, prospective, randomised controlled trial following the CONSORT guideline for clinical trials. http://www.consort-statement.org . All patients received two conical double-threaded internal hex implants of 13 mm length and 3.7 mm diameter in the mandibular canine regions, following the early loading protocol: implants were installed and splinted within two weeks. Patients were randomly allocated into two equal groups: the milled group (participants who received milled Co-Cr bars) and the SLM group (participants who received SLM Co-Cr bars). Figure . Patient selection The research protocol was accepted by the Faculty Ethical Committee of Scientific Research (no. 636, 4/10/2022). Patients were informed about the working strategy, prosthesis nature, and possible complications precisely in details. The participants who agreed signed written informed consents. Twenty completely edentulous patients (14 males and 6 females) were carefully chosen from the outpatient clinic of the Prosthodontic Department, Faculty of Dentistry, Minia University-Egypt. The eligible patients fulfilled the inclusion criteria, which were listed as follows: a completely edentulous patient, an age range of 50–60 years old, adequate bone volume in the canine area bilaterally to accommodate an implant of 3.7 mm diameter and 13 mm length, adequate inter-arch space (13–14 mm) to accommodate bar construction , normal maxillo-mandibular relationship (Angel’s class I), and proper oral hygiene. Exclusion criteria: absolute contra-indications: (patients with radiation therapy or bisphosphonate intake), relative contraindications: (metabolic or systemic disease that might affect osseointegration), local contraindications as (heavy smoking, and bruxism). Randomization and blinding Twenty participants were randomly assigned to either group A (the milled group) or group B (the SLM group) using simple randomization through closed envelop system, in which 10 cards having the symbol A and the other 10 cards having the symbol B were placed in a closed envelop. Eligible patients were asked to pick up a card randomly, (give it to a secretary who wasn’t engaged in the parameters assessment) who assigned the patients in their specific group according to the symbol on the chosen card. To maintain allocation concealment, access to the group treatment type was kept unknown for each of the following; the patient, the investigators (who evaluated the study parameters) and the statistician. Complete denture construction All patients received new sets of complete dentures and were instructed to wear them for a month to insure neuromuscular adaptation . Steps of complete denture were carried out in the conventional manner, the primary impression was made by a stock tray loaded with irreversible hydrocolloid material (Tropicalgin, Zhermack, Italy), then the impression was poured by dental stone (Cavex Istant stone, Holland) to obtain a primary cast on which a chemically cured acrylic resin (Acrostone, Co-Heliopolis, Egypt) custom tray was constructed. Secondary impressions were made by loading the custom trays with rubber base impression material (Cavex Outline, Holland). Maxillary and mandibular trial denture bases with wax occlusion rims were constructed, and a face bow (Whip Mix Corporation, USA) record to mount the maxillary cast to a semi-adjustable articulator (Whip Mix Corporation, USA) was made. The wax wafer method was utilised for recording centric relation and to mount lower cast. The articulator’s condylar guidance was accustomed using protrusive and lateral records. High-resistant cross-linked acrylic teeth (Acry Rock, Italy) were set up in accordance with the bilateral balanced occlusion concept. The waxed-up denture try in was made intraorally and checked for aesthetics, phonetics, and occlusion, then processed into heat-cured acrylic resin (Acrostone, Egypt) following conventional technique. The denture was finished, polished, and delivered to the patient with detailed instructions. The patient wore the denture for one month to acquire neuro muscular adaptation, and the lower denture was duplicated and processed into a transparent, self-cured acrylic resin radiographic stent. Ten holes were drilled in the stent’s fitting surface and packed with gutta percha markers. Cone beam computed tomography (SOREDEX 3DX, Finland) images were taken for all patients while wearing the radiographic stents. Surgical procedures Antibiotic (Augmentin, Galaxosmithkline) and mouth wash (Hexitol, ADCO, Egypt) were prescribed for all patients one hour before surgery. Infiltration anaesthesia was administered, a crestal incision was cut, and a mucoperiosteal flap was reflected. The radiographic stent was converted to a surgical stent by drilling holes at canine areas to determine the intended surgical sites Slowly successive drilling with copious irrigation was maintained until finalising the osteotomy site with a 3.2-mm-diameter drill. Parallism between the two implants was checked using paralling tools, and then two 3.7-mm-diameter, 13-mm-length implants (Conical SPI, Vitronix, Italy) were inserted manually with an implant ratchet and torqued by an implant wrench. Primary stability of 35–40 N was ensured by the torque wrench permitting early loading protocol . Two multi-unit straight abutments were tightened to the implants and torqued according to manufacturer instructions, Interrupted suturing was carried out to close the flap. Figure Post-operative medications such as antibiotic, (Brufen, Viatris, Egypt) analgesics and mouthwash were prescribed. Patients were instructed to have and soft diet and cold packs post-operatively. Prosthetic steps Within two weeks after surgery, an open tray impression technique was made through tightening the impression copings to the multi-unit straight abutments by a screw driver. A plastic tray was modified by making hole corresponding to the copings, and was checked intra-orally for proper fitting . A self-cure verification jig was fabricated to ensure immobility of the transfer copings during impression making . Additional silicon impression material was used for the final impression (Imicryl Dental, LLC, USA). A dental stone was used to pour the impression, creating a cast that was scanned by a laboratory scanner (DOF, SHINING 3D, South Korea). After words then the bar was designed via a software system (Dental DB Exocad 3 − 1 Rijeka, Vietnam). The selected bar design was the resilient OT castable bar (RHIEN83, Italy) from the library software. The bar had a rounded top cross section and a flat bottom surface with dimensions of 2 mm width and 4 mm height. The bar was designed to have one and a half mm of space beneath it for hygienic purposes . The Hader bar design was chosen for the study because it is thought to be a stress-breaking attachment, which results in improved stress distribution. Additionally, because it allows hinge movement along a single rotation axis, thus preventing individual implant mobility besides its positive impact on the health of the tissue around implants . The standard tessellation language (STL) file of the bar design was transferred to the CAM (MAMMOTH 3D printer 6.6, V-Ceram Shop) section, and a 3D-printed resin trial bar (Pro shape, Temp Resin, Turkey) was fabricated. Figure The passive fit of the resin bar was checked intra-orally using one screw test, in which one screw of the two abutments was tightened at one side and the other side was checked radiographically for any misfit discrepancy . Three resin bars were not accepted due to the in-complete screwing of the bar coping for the full length with tiny gaps showed between the multi-unit and the bar interface radiographically. Henceforth, the impression was repeated and rescanned. Once the resin bar’s passive fit was clinically and radiographically accepted, the bar STL file was transferred to the CAM machine and processed into either a milled or SLM Co-Cr bar according to patient allocation into groups. Regarding the milled group, the STL file was transferred to a milling machine (EMAR 5-axis dental milling, Egypt), where Co-Cr disc (Scheftner dental alloy, Mogucera c disc, Germany) was cut, finished, and polished with special discs (Vision abrasive disc 38 × 0.6, Italy) and stones (Sun Burst abrasive stones, USA). In the SLM group, the STL file was transferred to a 3D printing machine (Vulcan Tech laser, Istanbul, Turkey), where Co-Cr powder was mixed with resin liquid and sintered by a laser beam, and the final bar was finished and polished. In both groups, the bar passive fit was checked intraorally using a single screw test. Once accepted, the definitive bar was screwed by a screwdriver and torqued using a ratchet to the multi-unit straight abutments with a torque of 10–15 N according to manufacturer instructions. Overdenture construction: dental stone was poured into the fitting surface of the mandibular denture to construct a study cast, and modelling wax (Cavex Set Up Regular, Holland) was used to create a space. Furthermore, an auto-polymerizing, acrylic resin-perforated special tray was constructed on the cast. The space beneath the bar was blocked using condensation silicon rubber base impression material. Afterwards words, the secondary impression was made using condensation silicone impression material (Silaxil, LASCOD, Italy). The mandibular cast was mounted on a semi-adjustable articulator using a centric record, and the maxillary denture with its cast was mounted using a face bow record. The waxed-up trial denture base was tried intraorally, processed into heat-cured acrylic resin, finished, and polished following conventional techniques. After the overdenture was checked intraorally for proper seating, a space was created in its fitting surface corresponding to the bar area, the space beneath the bar was blocked by condensation silicon impression material. Figure . Two escape holes were made at the denture lingual flange. A regular retention blue plastic clip (OT bar clip RHIEN83, Italy) was placed on the bar and then picked up into the fitting surface of the overdenture using chemically cured acrylic resin. Once the resin was set, the excess material was removed and the overdenture was delivered. Figure (a, b), Fig. (a, b), Fig. .
The sample size was based on a previous published study , the least possible number was calculated to be 8 patients per group with 2 patients counted for dropout with total of 20 patients, as two equal groups were involved, with a power of 80% (b = 20) to detect a standardized effect size of 1.340 and a significance level of 5% (an error rate of 0.05). This work was done by an impartial statistician who wasn’t involved in the study.
The study was designed to be a parallel, triple-blinded, prospective, randomised controlled trial following the CONSORT guideline for clinical trials. http://www.consort-statement.org . All patients received two conical double-threaded internal hex implants of 13 mm length and 3.7 mm diameter in the mandibular canine regions, following the early loading protocol: implants were installed and splinted within two weeks. Patients were randomly allocated into two equal groups: the milled group (participants who received milled Co-Cr bars) and the SLM group (participants who received SLM Co-Cr bars). Figure .
The research protocol was accepted by the Faculty Ethical Committee of Scientific Research (no. 636, 4/10/2022). Patients were informed about the working strategy, prosthesis nature, and possible complications precisely in details. The participants who agreed signed written informed consents. Twenty completely edentulous patients (14 males and 6 females) were carefully chosen from the outpatient clinic of the Prosthodontic Department, Faculty of Dentistry, Minia University-Egypt. The eligible patients fulfilled the inclusion criteria, which were listed as follows: a completely edentulous patient, an age range of 50–60 years old, adequate bone volume in the canine area bilaterally to accommodate an implant of 3.7 mm diameter and 13 mm length, adequate inter-arch space (13–14 mm) to accommodate bar construction , normal maxillo-mandibular relationship (Angel’s class I), and proper oral hygiene. Exclusion criteria: absolute contra-indications: (patients with radiation therapy or bisphosphonate intake), relative contraindications: (metabolic or systemic disease that might affect osseointegration), local contraindications as (heavy smoking, and bruxism).
Twenty participants were randomly assigned to either group A (the milled group) or group B (the SLM group) using simple randomization through closed envelop system, in which 10 cards having the symbol A and the other 10 cards having the symbol B were placed in a closed envelop. Eligible patients were asked to pick up a card randomly, (give it to a secretary who wasn’t engaged in the parameters assessment) who assigned the patients in their specific group according to the symbol on the chosen card. To maintain allocation concealment, access to the group treatment type was kept unknown for each of the following; the patient, the investigators (who evaluated the study parameters) and the statistician.
All patients received new sets of complete dentures and were instructed to wear them for a month to insure neuromuscular adaptation . Steps of complete denture were carried out in the conventional manner, the primary impression was made by a stock tray loaded with irreversible hydrocolloid material (Tropicalgin, Zhermack, Italy), then the impression was poured by dental stone (Cavex Istant stone, Holland) to obtain a primary cast on which a chemically cured acrylic resin (Acrostone, Co-Heliopolis, Egypt) custom tray was constructed. Secondary impressions were made by loading the custom trays with rubber base impression material (Cavex Outline, Holland). Maxillary and mandibular trial denture bases with wax occlusion rims were constructed, and a face bow (Whip Mix Corporation, USA) record to mount the maxillary cast to a semi-adjustable articulator (Whip Mix Corporation, USA) was made. The wax wafer method was utilised for recording centric relation and to mount lower cast. The articulator’s condylar guidance was accustomed using protrusive and lateral records. High-resistant cross-linked acrylic teeth (Acry Rock, Italy) were set up in accordance with the bilateral balanced occlusion concept. The waxed-up denture try in was made intraorally and checked for aesthetics, phonetics, and occlusion, then processed into heat-cured acrylic resin (Acrostone, Egypt) following conventional technique. The denture was finished, polished, and delivered to the patient with detailed instructions. The patient wore the denture for one month to acquire neuro muscular adaptation, and the lower denture was duplicated and processed into a transparent, self-cured acrylic resin radiographic stent. Ten holes were drilled in the stent’s fitting surface and packed with gutta percha markers. Cone beam computed tomography (SOREDEX 3DX, Finland) images were taken for all patients while wearing the radiographic stents.
Antibiotic (Augmentin, Galaxosmithkline) and mouth wash (Hexitol, ADCO, Egypt) were prescribed for all patients one hour before surgery. Infiltration anaesthesia was administered, a crestal incision was cut, and a mucoperiosteal flap was reflected. The radiographic stent was converted to a surgical stent by drilling holes at canine areas to determine the intended surgical sites Slowly successive drilling with copious irrigation was maintained until finalising the osteotomy site with a 3.2-mm-diameter drill. Parallism between the two implants was checked using paralling tools, and then two 3.7-mm-diameter, 13-mm-length implants (Conical SPI, Vitronix, Italy) were inserted manually with an implant ratchet and torqued by an implant wrench. Primary stability of 35–40 N was ensured by the torque wrench permitting early loading protocol . Two multi-unit straight abutments were tightened to the implants and torqued according to manufacturer instructions, Interrupted suturing was carried out to close the flap. Figure Post-operative medications such as antibiotic, (Brufen, Viatris, Egypt) analgesics and mouthwash were prescribed. Patients were instructed to have and soft diet and cold packs post-operatively.
Within two weeks after surgery, an open tray impression technique was made through tightening the impression copings to the multi-unit straight abutments by a screw driver. A plastic tray was modified by making hole corresponding to the copings, and was checked intra-orally for proper fitting . A self-cure verification jig was fabricated to ensure immobility of the transfer copings during impression making . Additional silicon impression material was used for the final impression (Imicryl Dental, LLC, USA). A dental stone was used to pour the impression, creating a cast that was scanned by a laboratory scanner (DOF, SHINING 3D, South Korea). After words then the bar was designed via a software system (Dental DB Exocad 3 − 1 Rijeka, Vietnam). The selected bar design was the resilient OT castable bar (RHIEN83, Italy) from the library software. The bar had a rounded top cross section and a flat bottom surface with dimensions of 2 mm width and 4 mm height. The bar was designed to have one and a half mm of space beneath it for hygienic purposes . The Hader bar design was chosen for the study because it is thought to be a stress-breaking attachment, which results in improved stress distribution. Additionally, because it allows hinge movement along a single rotation axis, thus preventing individual implant mobility besides its positive impact on the health of the tissue around implants . The standard tessellation language (STL) file of the bar design was transferred to the CAM (MAMMOTH 3D printer 6.6, V-Ceram Shop) section, and a 3D-printed resin trial bar (Pro shape, Temp Resin, Turkey) was fabricated. Figure The passive fit of the resin bar was checked intra-orally using one screw test, in which one screw of the two abutments was tightened at one side and the other side was checked radiographically for any misfit discrepancy . Three resin bars were not accepted due to the in-complete screwing of the bar coping for the full length with tiny gaps showed between the multi-unit and the bar interface radiographically. Henceforth, the impression was repeated and rescanned. Once the resin bar’s passive fit was clinically and radiographically accepted, the bar STL file was transferred to the CAM machine and processed into either a milled or SLM Co-Cr bar according to patient allocation into groups. Regarding the milled group, the STL file was transferred to a milling machine (EMAR 5-axis dental milling, Egypt), where Co-Cr disc (Scheftner dental alloy, Mogucera c disc, Germany) was cut, finished, and polished with special discs (Vision abrasive disc 38 × 0.6, Italy) and stones (Sun Burst abrasive stones, USA). In the SLM group, the STL file was transferred to a 3D printing machine (Vulcan Tech laser, Istanbul, Turkey), where Co-Cr powder was mixed with resin liquid and sintered by a laser beam, and the final bar was finished and polished. In both groups, the bar passive fit was checked intraorally using a single screw test. Once accepted, the definitive bar was screwed by a screwdriver and torqued using a ratchet to the multi-unit straight abutments with a torque of 10–15 N according to manufacturer instructions. Overdenture construction: dental stone was poured into the fitting surface of the mandibular denture to construct a study cast, and modelling wax (Cavex Set Up Regular, Holland) was used to create a space. Furthermore, an auto-polymerizing, acrylic resin-perforated special tray was constructed on the cast. The space beneath the bar was blocked using condensation silicon rubber base impression material. Afterwards words, the secondary impression was made using condensation silicone impression material (Silaxil, LASCOD, Italy). The mandibular cast was mounted on a semi-adjustable articulator using a centric record, and the maxillary denture with its cast was mounted using a face bow record. The waxed-up trial denture base was tried intraorally, processed into heat-cured acrylic resin, finished, and polished following conventional techniques. After the overdenture was checked intraorally for proper seating, a space was created in its fitting surface corresponding to the bar area, the space beneath the bar was blocked by condensation silicon impression material. Figure . Two escape holes were made at the denture lingual flange. A regular retention blue plastic clip (OT bar clip RHIEN83, Italy) was placed on the bar and then picked up into the fitting surface of the overdenture using chemically cured acrylic resin. Once the resin was set, the excess material was removed and the overdenture was delivered. Figure (a, b), Fig. (a, b), Fig. .
Radiographic evaluation An independent radiologist who was blinded by the study design and grouping evaluated the radiographs at 0-month (base line), 6-month, and 12-month post-loading. A film holder (TPC film positioner, LK1900, China) was attached to an x-ray machine (Fona XDC, Italy). For image standardisation and reproducibility, a parallel long cone technique was used, and a duplicate of the patient’s lower denture was modified to be attached to the holder bite block with self-cured acrylic resin at which the patient bites each time of imaging. Digital periapical films (Fire CR dental, 3D imaging film, Korea) were inserted at the bite block with fixed imaging parameters for all patients (8 milliampere, 70 KV, 0.6 s). A software system (EZDent-i software, VATECH Co., Korea) was used for measuring the length of mesial and distal vertical bone loss for each implant, from the implant shoulder to the first implant bone contact . Measurements were calculated by subtraction of bone level values at 6-month and 12-month from their values at the baseline. Figure . Soft tissue outcomes modified plaque index (mPI), modified gingival index (mGI) and probing depth (PD) were used to assess soft tissue health . Assessment was done at the midpoint of four surfaces (buccal, lingual, mesial, and distal) at 0 (base line), 6, and 12- month follow up visits. The plaque accumulation assessment was assessed according to Mombelli index with the following scores: 0: no visible plaque is seen around implant copings. 1: local plaque film accumulation at the free gingival margin around implants (less than 25%). 2: general plaque accumulation around implant abutments or at the free gingival margin, which could be seen by naked eyes (more than 25%). 3: abundance of plaque around the implant abutment surface . The gingival health was evaluated according to Apsi index with the following criteria: 0: normal mucosa. 1: mild inflammation, slight change in color. 2: moderate inflammation, redness, and glazing. 3: severe inflammation, marked redness, and spontaneous bleeding . Probing depth was assessed in millimetres using a pressure-sensitive automated plastic probe (KerrHawe Click-probe, Switzerland) from a defined point at the abutment neck until the probe clicked . Figure . Statistical analysis An independent statistician blinded to the grouping and research design analyse the data using (IBM SPSS Statistics, Version 23.0. Armonk, NY: IBM Corp). The normality of numerical data was investigated through distribution analysis and the application of normality tests (Kolmogorov-Smirnov and Shapiro-Wilk tests). The distribution of the pocket depth data was normal (parametric), whereas the MBL, mPI and mGI scores had non-normal (non-parametric) distributions. The statistical data was displayed with the mean, standard deviation (SD), median, and range values, with a significance level of P ≤ 0.05. The repeated measures ANOVA test was utilised for parametric data in order to compare the PD of the two groups and examine the changes over time within each group. When ANOVA test was significant, pairwise comparisons were performed using Bonferroni’s post-hoc test. The Mann-Whitney U test was employed to compare the two groups’ non-parametric data. To examine the changes over time within each group, Friedman’s test was employed. For pairwise comparisons, Dunn’s test was employed when Friedman’s or Kruskal-Wallis tests were significant.
An independent radiologist who was blinded by the study design and grouping evaluated the radiographs at 0-month (base line), 6-month, and 12-month post-loading. A film holder (TPC film positioner, LK1900, China) was attached to an x-ray machine (Fona XDC, Italy). For image standardisation and reproducibility, a parallel long cone technique was used, and a duplicate of the patient’s lower denture was modified to be attached to the holder bite block with self-cured acrylic resin at which the patient bites each time of imaging. Digital periapical films (Fire CR dental, 3D imaging film, Korea) were inserted at the bite block with fixed imaging parameters for all patients (8 milliampere, 70 KV, 0.6 s). A software system (EZDent-i software, VATECH Co., Korea) was used for measuring the length of mesial and distal vertical bone loss for each implant, from the implant shoulder to the first implant bone contact . Measurements were calculated by subtraction of bone level values at 6-month and 12-month from their values at the baseline. Figure .
modified plaque index (mPI), modified gingival index (mGI) and probing depth (PD) were used to assess soft tissue health . Assessment was done at the midpoint of four surfaces (buccal, lingual, mesial, and distal) at 0 (base line), 6, and 12- month follow up visits. The plaque accumulation assessment was assessed according to Mombelli index with the following scores: 0: no visible plaque is seen around implant copings. 1: local plaque film accumulation at the free gingival margin around implants (less than 25%). 2: general plaque accumulation around implant abutments or at the free gingival margin, which could be seen by naked eyes (more than 25%). 3: abundance of plaque around the implant abutment surface . The gingival health was evaluated according to Apsi index with the following criteria: 0: normal mucosa. 1: mild inflammation, slight change in color. 2: moderate inflammation, redness, and glazing. 3: severe inflammation, marked redness, and spontaneous bleeding . Probing depth was assessed in millimetres using a pressure-sensitive automated plastic probe (KerrHawe Click-probe, Switzerland) from a defined point at the abutment neck until the probe clicked . Figure .
An independent statistician blinded to the grouping and research design analyse the data using (IBM SPSS Statistics, Version 23.0. Armonk, NY: IBM Corp). The normality of numerical data was investigated through distribution analysis and the application of normality tests (Kolmogorov-Smirnov and Shapiro-Wilk tests). The distribution of the pocket depth data was normal (parametric), whereas the MBL, mPI and mGI scores had non-normal (non-parametric) distributions. The statistical data was displayed with the mean, standard deviation (SD), median, and range values, with a significance level of P ≤ 0.05. The repeated measures ANOVA test was utilised for parametric data in order to compare the PD of the two groups and examine the changes over time within each group. When ANOVA test was significant, pairwise comparisons were performed using Bonferroni’s post-hoc test. The Mann-Whitney U test was employed to compare the two groups’ non-parametric data. To examine the changes over time within each group, Friedman’s test was employed. For pairwise comparisons, Dunn’s test was employed when Friedman’s or Kruskal-Wallis tests were significant.
Twenty completely edentulous patients with mean age 55 years have received two-implant retained overdentures with milled or SLM Co-Cr bars. All patients completed the study follow up periods without a dropout, and implant survival was 100% in both groups. All patients were satisfied with their prostheses throughout the whole study follow up periods. I -Marginal bone loss Comparison between groups The MBL values were 1.03 mm and 1.19 mm in the milled and SLM bar groups, respectively, there was no statistically significant difference between the two groups’ marginal bone loss at 0-month (base line), 6-month, and 12-month (P-value = 0.322, Effect size = 0.45, P-value = 0.940, Effect size = 0.034, and P-value = 0.290, Effect size = 0.487), respectively. Figure . Changes within each group The milled group exhibited a statistically significant shift in marginal bone loss over time (P-value = 0.013, Effect size = 0.433). Pair-wise comparisons of time periods demonstrated a statistically significant increase in marginal bone loss after 6 months, followed by a non-statistically significant shift in marginal bone loss between 6- and 12-months. On the other hand, in the SLM group, there was no statistically significant change in marginal bone loss over time (P-value = 0.122, Effect size = 0.210). Table . Soft tissue outcomes The modified Plaque index (mPI) Comparison between groups There was no statistically significant difference in mPI scores between the two groups at the 0-month (base line), 6-month, and 12-month follow ups (P-value = 1, Effect size = 0), (P-value = 0.177, Effect size = 0.469), and (P-value = 0.897, Effect size = 0.051), respectively. mPI scores were 0.55 and 0.58 for milled and SLM groups respectively. Figure . Changes within each group Both milling and SLM groups showed a substantial change in mPI scores over time (P-value < 0.001, Effect size = 0.86). Pair-wise comparisons between time periods demonstrated a statistically significant increase in mPI scores after 6 months, followed by no significant change from 6 to 12 months. (Table ) The modified Gingival index (mGI) Comparison between groups The mGI scores of the two groups did not differ statistically significantly at (base line) 0-month, 6-month, or 12-month follow ups (P-value = 0.170, Effect size = 0.581, P-value = 0.544, Effect size = 0.221, and P-value = 0.365, Effect size = 0.378), respectively. The mGI scores were 0.3 and 0.35 for milled and SLM groups, respectively. Figure . Changes within each group There was a statistically significant change in mGI scores by time in both the milling and SLM groups (P-value = 0.048, Effect size = 0.303 and P-value = 0.032, Effect size = 0.344), respectively. Pair-wise comparisons between time periods showed that the mGI values did not change statistically after six months, while the mGI scores decreased statistically between six and twelve months. (Table ) Pocket depth (PD) Comparison between groups At baseline (0-month), there was no statistically significant change in PD measures between the two groups (P-value = 0.100, effect size = 0.143). After 6 and 12 months, the milled bar group had statistically substantially lower PD measures than SLM bar group (P-value = 0.036, Effect size = 0.222) and (P-value = 0.045, Effect size = 0.205), respectively. The PD scores were 1.55 mm and 1.85 mm for milled and SLM group respectively. Figure . Changes within each group There was no statistically significant difference in the PD by time between the two groups (P-value = 0.744, Effect size = 0.022 and P-value = 0.714, Effect size = 0.026). (Table )
Comparison between groups The MBL values were 1.03 mm and 1.19 mm in the milled and SLM bar groups, respectively, there was no statistically significant difference between the two groups’ marginal bone loss at 0-month (base line), 6-month, and 12-month (P-value = 0.322, Effect size = 0.45, P-value = 0.940, Effect size = 0.034, and P-value = 0.290, Effect size = 0.487), respectively. Figure . Changes within each group The milled group exhibited a statistically significant shift in marginal bone loss over time (P-value = 0.013, Effect size = 0.433). Pair-wise comparisons of time periods demonstrated a statistically significant increase in marginal bone loss after 6 months, followed by a non-statistically significant shift in marginal bone loss between 6- and 12-months. On the other hand, in the SLM group, there was no statistically significant change in marginal bone loss over time (P-value = 0.122, Effect size = 0.210). Table .
The MBL values were 1.03 mm and 1.19 mm in the milled and SLM bar groups, respectively, there was no statistically significant difference between the two groups’ marginal bone loss at 0-month (base line), 6-month, and 12-month (P-value = 0.322, Effect size = 0.45, P-value = 0.940, Effect size = 0.034, and P-value = 0.290, Effect size = 0.487), respectively. Figure .
The milled group exhibited a statistically significant shift in marginal bone loss over time (P-value = 0.013, Effect size = 0.433). Pair-wise comparisons of time periods demonstrated a statistically significant increase in marginal bone loss after 6 months, followed by a non-statistically significant shift in marginal bone loss between 6- and 12-months. On the other hand, in the SLM group, there was no statistically significant change in marginal bone loss over time (P-value = 0.122, Effect size = 0.210). Table .
The modified Plaque index (mPI) Comparison between groups There was no statistically significant difference in mPI scores between the two groups at the 0-month (base line), 6-month, and 12-month follow ups (P-value = 1, Effect size = 0), (P-value = 0.177, Effect size = 0.469), and (P-value = 0.897, Effect size = 0.051), respectively. mPI scores were 0.55 and 0.58 for milled and SLM groups respectively. Figure . Changes within each group Both milling and SLM groups showed a substantial change in mPI scores over time (P-value < 0.001, Effect size = 0.86). Pair-wise comparisons between time periods demonstrated a statistically significant increase in mPI scores after 6 months, followed by no significant change from 6 to 12 months. (Table ) The modified Gingival index (mGI) Comparison between groups The mGI scores of the two groups did not differ statistically significantly at (base line) 0-month, 6-month, or 12-month follow ups (P-value = 0.170, Effect size = 0.581, P-value = 0.544, Effect size = 0.221, and P-value = 0.365, Effect size = 0.378), respectively. The mGI scores were 0.3 and 0.35 for milled and SLM groups, respectively. Figure . Changes within each group There was a statistically significant change in mGI scores by time in both the milling and SLM groups (P-value = 0.048, Effect size = 0.303 and P-value = 0.032, Effect size = 0.344), respectively. Pair-wise comparisons between time periods showed that the mGI values did not change statistically after six months, while the mGI scores decreased statistically between six and twelve months. (Table ) Pocket depth (PD) Comparison between groups At baseline (0-month), there was no statistically significant change in PD measures between the two groups (P-value = 0.100, effect size = 0.143). After 6 and 12 months, the milled bar group had statistically substantially lower PD measures than SLM bar group (P-value = 0.036, Effect size = 0.222) and (P-value = 0.045, Effect size = 0.205), respectively. The PD scores were 1.55 mm and 1.85 mm for milled and SLM group respectively. Figure . Changes within each group There was no statistically significant difference in the PD by time between the two groups (P-value = 0.744, Effect size = 0.022 and P-value = 0.714, Effect size = 0.026). (Table )
Comparison between groups There was no statistically significant difference in mPI scores between the two groups at the 0-month (base line), 6-month, and 12-month follow ups (P-value = 1, Effect size = 0), (P-value = 0.177, Effect size = 0.469), and (P-value = 0.897, Effect size = 0.051), respectively. mPI scores were 0.55 and 0.58 for milled and SLM groups respectively. Figure . Changes within each group Both milling and SLM groups showed a substantial change in mPI scores over time (P-value < 0.001, Effect size = 0.86). Pair-wise comparisons between time periods demonstrated a statistically significant increase in mPI scores after 6 months, followed by no significant change from 6 to 12 months. (Table )
There was no statistically significant difference in mPI scores between the two groups at the 0-month (base line), 6-month, and 12-month follow ups (P-value = 1, Effect size = 0), (P-value = 0.177, Effect size = 0.469), and (P-value = 0.897, Effect size = 0.051), respectively. mPI scores were 0.55 and 0.58 for milled and SLM groups respectively. Figure .
Both milling and SLM groups showed a substantial change in mPI scores over time (P-value < 0.001, Effect size = 0.86). Pair-wise comparisons between time periods demonstrated a statistically significant increase in mPI scores after 6 months, followed by no significant change from 6 to 12 months. (Table )
Comparison between groups The mGI scores of the two groups did not differ statistically significantly at (base line) 0-month, 6-month, or 12-month follow ups (P-value = 0.170, Effect size = 0.581, P-value = 0.544, Effect size = 0.221, and P-value = 0.365, Effect size = 0.378), respectively. The mGI scores were 0.3 and 0.35 for milled and SLM groups, respectively. Figure . Changes within each group There was a statistically significant change in mGI scores by time in both the milling and SLM groups (P-value = 0.048, Effect size = 0.303 and P-value = 0.032, Effect size = 0.344), respectively. Pair-wise comparisons between time periods showed that the mGI values did not change statistically after six months, while the mGI scores decreased statistically between six and twelve months. (Table )
The mGI scores of the two groups did not differ statistically significantly at (base line) 0-month, 6-month, or 12-month follow ups (P-value = 0.170, Effect size = 0.581, P-value = 0.544, Effect size = 0.221, and P-value = 0.365, Effect size = 0.378), respectively. The mGI scores were 0.3 and 0.35 for milled and SLM groups, respectively. Figure .
There was a statistically significant change in mGI scores by time in both the milling and SLM groups (P-value = 0.048, Effect size = 0.303 and P-value = 0.032, Effect size = 0.344), respectively. Pair-wise comparisons between time periods showed that the mGI values did not change statistically after six months, while the mGI scores decreased statistically between six and twelve months. (Table )
Comparison between groups At baseline (0-month), there was no statistically significant change in PD measures between the two groups (P-value = 0.100, effect size = 0.143). After 6 and 12 months, the milled bar group had statistically substantially lower PD measures than SLM bar group (P-value = 0.036, Effect size = 0.222) and (P-value = 0.045, Effect size = 0.205), respectively. The PD scores were 1.55 mm and 1.85 mm for milled and SLM group respectively. Figure . Changes within each group There was no statistically significant difference in the PD by time between the two groups (P-value = 0.744, Effect size = 0.022 and P-value = 0.714, Effect size = 0.026). (Table )
At baseline (0-month), there was no statistically significant change in PD measures between the two groups (P-value = 0.100, effect size = 0.143). After 6 and 12 months, the milled bar group had statistically substantially lower PD measures than SLM bar group (P-value = 0.036, Effect size = 0.222) and (P-value = 0.045, Effect size = 0.205), respectively. The PD scores were 1.55 mm and 1.85 mm for milled and SLM group respectively. Figure .
There was no statistically significant difference in the PD by time between the two groups (P-value = 0.744, Effect size = 0.022 and P-value = 0.714, Effect size = 0.026). (Table )
The aim of this study was to compare cobalt chromium bar retained implant mandibular overdentures fabricated with milled technique and SLM additive technique in terms of marginal bone loss and peri-implant soft tissue health. Many studies have discussed the clinical performance of CAD-CAM milled bar retained mandibular overdentures on two or more implants with different construction materials regarding survival rate, prosthetic complications, soft tissue health, and marginal bone loss . However, limited evidence is found discussing Co-Cr bars fabricated using the novel SLM additive technique, and that is why this study was carried out. In the current study, the periapical parallel long cone technique was employed in evaluating MBL, as it maintains a low radiation level, is reproducible when used with a radiographic stent, is cost-effective, and is suitable for monitoring and assessing bone resorption surrounding implants during asymptomatic implant follow-ups . Nevertheless, periapical imaging is a two-dimensional imaging modality with certain limitations that necessitate CBCT utilization, as when evaluating bone quality or quantity, placing critical borderline implants that require estimating the bucco-lingual width, superimposition which may obscure vital structures, magnification distortion, and when evaluating implants that exhibit symptoms (pain- mobility) which cannot be diagnosed by periapical radiography . In the current study, after 12-month post-loading, the mean MBL was 1.03 mm and 1.19 mm in the milled and SLM bar groups, respectively. While MBL at the peri-implant surfaces was traditionally thought to be within an acceptable range of 1–2 mm in the first year of function. Other MBL ranges of 1.5, 1.8, and 2.2 mm were mentioned by different authors. Recently, a MBL range following a one-year function has been found to be between 0.13 ± 0.35 mm and 1.03 ± 0.65 mm. It also has been suggested that a pathological bone loss is indicated by an increase in bone loss of more than 0.5 mm after six months of loading . Comparable MBL results for the Co-Cr milled bar (0.87 ± 0.26 mm) were reported in a study comparing it with the PEEK bar retained on two-implants mandibular overdentures . Moreover, Krennmair et al. reported similar bone loss of (1.4 ± 0.5 mm) in milled bar retained overdenture compared with telescopic attachment overdenture supported by four implants . Meanwhile, in a clinical randomized trial which compared MBL with titanium milled bar against retentive anchors (RA) ball attachment, the mean MBL was (-0.14) mm, indicating bone apposition around implants. This finding may be attributed to the different design with distal extension in the milled bar and the different clip material (gold clip) . On the other hand, lower mean MBL values were reported (0.29 ± 0.16 and 0.22 ± 0.09) mm, as reported by Pozzi et al. and Montanari et al., respectively, for milled titanium bar implant retained overdenture. Regarding Pozzi, lower MBL values were explained by the increased number of implants used and different bar materials. In the Montanari clinical trial, lower MBL values were attributed to increased implant number and the incorporation of a low-profile equator attachment on top of the bar . Comparing MBL results between the two groups, no statistically significant difference was found. This finding agrees with the results of a clinical trial that compared full-arch cobalt chrome metal frame work on four implants fabricated by selective laser melting versus soft milling techniques. In the latter study, the mean MBL of both anterior and posterior implants showed an insignificant difference between both groups . In the current study, mPI showed a significant increase reported from 0- to 6-months in both groups; this finding was in line with other studies , which reported a significantly increased mPI. This finding may be attributed to difficulty in bar cleansing procedures, elderly mal-ability, and the greater area crossed by bar design, which may induce more bacterial retention around bars. Meanwhile, mGI reported decreased values from six to twelve months for both groups. This could be attributed to the early loading protocol’ effect on incomplete gingival tissue healing at baseline follow-up, which was followed by stability of the gingival conditions at later follow-up visits. Within each group, PD didn’t show statistically significant difference at the follow up visits. However, the milled group showed a significantly lower PD compared to the SLM group at the 6- and 12-month follow up visits. This finding might be attributed to the relatively rough surface of SLM compared to the milled bar which might have induced slight gingival hyperplasia thus increasing the PD. These results were in line with other previously mentioned studies that reported the same findings . Seo et al. also evaluated the soft tissue outcomes around bar and locator attachments in implant-retained overdentures; although the locator seemed to be more hygienic, the bar showed stable mucosal parameters . Moreover, in a clinical study that compared the soft tissue outcomes of splinted milled bars on four versus six implants through a ten-year follow-up, no significant difference with very low mGI, mPI, and PD values was observed between the two groups . Although mPI reported increased values, they are still within accepted plaque scores of ≤ 1 . These results may be credited to the restricted oral hygiene measures followed by patients, which might explain the low stable values of mGI and PD reported in the current study. Additionally, a review of the literature revealed that the SLM and milling frameworks showed an acceptable range of surface roughness . This could lead to similar levels of bacterial adherence around the frameworks of both groups, which would account for the negligible differences in soft tissue outcomes between the two groups. The null hypothesis in the current study was accepted, as there was insignificant difference between the milled and the SLM groups regarding the hard and soft tissue outcomes. The current study had certain limitations: the small sample size and the short follow-up period (one year). Therefore, these data may be considered preliminary. Accordingly, further clinical investigations, along with longer follow-up periods and larger sample sizes, are required to judge the clinical performance of SLM frameworks.
Two-implant mandibular overdenture retained with milled or SLM Co-Cr bar can provide an acceptable treatment option for completely edentulous patients regarding marginal bone loss and soft tissue outcomes.
|
Antibacterial and antioxidant potentials, detection of host origin compounds, and metabolic profiling of endophytic Bacillus spp. isolated from | 8bc96b3e-e27a-4154-911e-d8f2704b01a9 | 11736096 | Biochemistry[mh] | Rauvolfia serpentina (L.) Benth. ex Kurz. is an evergreen, woody, glabrous and perennial shrub with a maximum height of 60 cm. The plant belongs to the family Apocynaceae and occurs in habitats of tropical and subtropical regions. The family is distributed worldwide in the region of the Himalayas, Indian peninsula, Myanmar, Indonesia and Sri Lanka and is native to India, Bangladesh and other regions of Asia (Fig. ). In Ayurvedic literature, the powdered root of this plant has been used for the treatment of snake bites, feverish illnesses and mental illness from ancient time. The available reports suggest for its antimicrobial, anti-inflammatory, antioxidant, anti-proliferative, anticancerous, antidiuretic, antifibrillar, antiarrhythmic, anticholinergic, anti-dysentery, antidiarrhoeal, anti-hypotensive, anti-contractile, sympathomimetic and tranquilizing activity , . The plant contains more than 70 distinct alkaloids belonging to the monoterpene indole alkaloid (MIA) family. These alkaloids are extensively located in the roots. The major indole alkaloids with biological and therapeutic potential reported from various Rauvolfia species are reserpine, serpentine, ajmaline, deserpidine, indobine, reserpiline, rescinnamine, and yohimbine . Alkaloids have therapeutic significance in treatment of cardiovascular diseases, hypertension, breast cancer, and human promyelocytic leukemia , psychotic disorders like schizophrenia, anxiety, epilepsy, insomnia, insanity, and furthermore, utilized as a sedative, a hypnotic drug . Rauvolfia has also been studied widely in research as a treatment for autistic children between the ages of 3.5 and 9 years . Endophytic microbes have been studied in majority of plant species, existing in either obligate or facultative relationships without causing harm to their host. Their interaction represents a highly researched domain aimed at deciphering their roles in various aspects . Host plants offers nutrients and a conducive environment for endophytes, which in turn aid in plant growth, disease management, phytoremediation, sustainable crop production, and the secondary metabolites production that aid the plants in resisting biotic and abiotic stresses. They are well-known for their ability to produce a wide range of pharmacologically important compounds (Taxol, Swainsonine, Huperzine, Rohutikine, Kaempferol, Maytansine, etc.,) with enormous therapeutic potentials that are similar or even identical to those of their hosts, especially medicinal plants . These bioactive compounds have been identified as antiviral, antifungal, antibacterial, antitumor and anticancer agents . Many antimicrobial compounds produced by endophytes belong to diverse structural classes such as peptides, alkaloids, steroids, quinones, terpenoids, phenols, and flavonoids . Our goal was to isolate and characterize endophytic bacteria from R. serpentina leaves, evaluate their antimicrobial potential,and detect host-derived compounds using RP-HPLC, as well as perform untargeted metabolic profiling via UHPLC-HRMS. Thus the study aims to explore the untapped potential of bacterial endophytes in producing both host-origin and novel bioactive metabolites of microbial origin. The overexploitation of R. serpentina for medicinal value especially for reserpine has threatened plant in an endangered stage. The metabolic profiling of Rauvolfia endophytes will offer a significant potential for discovering host-origin compounds and a hope to control the exploitation of medicinal plant. The bacterial endophytes can be easily optimized under a specific culture condition for fermentation and elucidating secondary metabolite pathways.
Collection of plant samples Healthy plant samples of R. serpentina were randomly collected during the month of April-May 2018 from five sites around the Botanical garden of Department of Botany, Banaras Hindu University, Varanasi, India. Isolation and biochemical characterization of endophytic bacteria The endophytic bacteria were isolated from leaves of R. serpentina plants . The plant parts were thoroughly washed with running tap water to remove soil particles and attached microbes. Leaves were individually surface sterilized. by serial washing in 70% ethanol (1 min), 2% sodium hypochlorite solution (2% available Cl − ) for 3–4 min and 70% ethanol (60 s) followed by rinsing three times with sterile distilled water. Isolation was performed using the direct plating method on nutrient agar (NA) medium supplemented with 2% sodium chloride (NaCl). The sterilized leaves were cut into 0.5 cm x 0.5 cm sections. A minimum of five sections were placed on NA plates and incubated at 35 ± 2 °C for one week. The plates were checked every 24 h for bacterial colony emergence. The emerging bacterial colonies were selected, sub-cultured, purified, and used for further studies. The bacterial isolates were characterized after 48 h for the following traits: color, form, elevation, margin, surface texture, and opacity. Biochemical tests such as indole production, methyl red and Vogues-Proskauer tests, citrate utilization, catalase and oxidase production tests, nitrate reduction, and oxidative fermentation tests were performed for the endophytic isolates – . Amylase, protease, lipase and cellulase tests were also performed for the endophytic isolates following standard protocols , . Extraction of secondary metabolites of endophytic bacterial isolates Previously isolated leaf endophytic bacterial strains were subjected to secondary metabolite extraction. A suspension of 10 7 CFU.mL −1 of endophytic bacteria was inoculated into 1 L of nutrient broth (NB) and incubated at 35 ± 2 °C for 4–5 days in a BOD shaker incubator at 150 rpm (Supertech, New Delhi). After incubation, the broth was centrifuged at 8000 × g for 20 min at room temperature. Secondary metabolites were extracted from the supernatant using a solvent extraction method. Equal volumes of ethyl acetate and culture filtrate were combined in a separatory funnel and shaken vigorously for 10 min. The ethyl acetate layer was then separated and concentrated using a Rotary Evaporator (Ika, Germany). Antibacterial activity of metabolites against human pathogenic bacteria The disc diffusion assay was conducted on Mueller-Hinton agar (MHA) plates against eight human pathogenic bacteria to identify the most potent crude secondary metabolites produced by the endophytic bacteria . Secondary metabolites showing significant zones of inhibition against the majority of pathogens were selected for determination of minimum inhibitory concentration (MIC) values. MIC values for two standard antibiotics and secondary metabolites were determined by broth dilution well plate assay in 96-well microtitre plates following established protocols . In broth dilution method crude extract were prepared and subjected to serial dilution (0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 35.6 mg.mL −1 ) using Mueller–Hinton broth and dimethyl sulfoxide (DMSO) in a 1:1 ratio. Tetracycline, nalidixic acid, and streptomycin served as standard positive controls (0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 35.6 µg.mL −1 , while a 1:1 ratio of DMSO and Mueller–Hinton broth was utilized as the negative control. Each well of the microplate was inoculated with 75 µl of a 24-hour cultured broth of human pathogenic bacteria containing 10 7 CFU.mL −1 , followed by addition of blank Mueller–Hinton broth. Antibiotic solutions and test metabolites (75 µL) were added to separate wells, resulting in a final assay volume of 150 µL. Absorbance readings were measured at 630 nm using a microplate reader (Agilent BioTek Synergy HTX Multimode Reader) immediately after inoculation to establish the baseline ( T 0). Plates were then incubated at 35 ± 2 °C without shaking until the stationary phase was reached. Absorbance was measured for TF (Final Time i.e. 48 h). The inhibition percentage was calculated as: [12pt]{minimal}
$$\:\:=1-(-0}{})\:\:100$$ The T 0 sample and TF sample represent the absorbance at 630 nm of bacterial growth in the presence of test metabolites before and after incubation, respectively. Inhibition values below 10% were deemed insignificant. Bacterial pathogens from wells were reinoculated onto MHA plates to observe colony appearance and determine the MIC of antibiotics and test metabolites. Molecular identification of endophytic bacterial isolates and their phylogenetic characterization Genomic DNA of isolates showing antibacterial activity was extracted following Wilson . The isolated genomic DNA strains were analysed on 0.8% agarose gel (Himedia) and amplification of the 16S rRNA (~ 1500 bp) gene was carried in PCR using universal primers [Forward primer (27 F) AGAGTTTGATCCTGGCTCAG; Reverse primer (1492R), CGGTTACCTTGTTACGACTT (Eurofin Genomics, India)]. PCR was performed with 25 µl reaction mixture composed of PCR buffer 2.5 µl; MgCl 2 1.5 µl; 0.2 mm dNTPs 2 µl; Primers each 1.25 µl; Taq DNA polymerase 0.25 µl (Thermo Fischer Scientific ); template DNA 2 µl; PCR water 14.75 µl. The PCR conditions were as follows: initial denaturation 94 °C for 4 min, followed by 30 cycles of 30 s denaturation at 94 °C, 1 min annealing at 55 °C, 1 min extension at 72 °C and a 7-min final extension at 72 °C. The PCR products were purified before they were sent for sequencing. The PCR products of the endophytes were sequenced by BIOKART INDIA Pvt. Ltd (Bengaluru, India) by Sanger’s dideoxy nucleotide sequencing method. All obtained sequences were compared with those in the GenBank database by using the BLASTN search program. Similar sequences were further aligned by CLUSTALW. Phylogenetic analysis was conducted by the neighbour-joining method. Bootstrap analysis was performed with 1000 replications to determine the support for each clade with grouping by the neighbour-joining method. A phylogenetic tree was constructed based on evolutionary distance data by using MEGA 11. The 16S rRNA nucleotide sequences acquired have been submitted to the National Center for Biotechnology Information (NCBI) GenBank to secure accession numbers. The unique accession numbers assigned to the bacterial isolates were MW7415340 and MW7415343. Biochemical characterization of metabolites Determination of total phenolic and total flavonoid content The total phenolic content in both the leaf extract and bacterial secondary metabolites was assessed using the Folin-Ciocalteu method , with Gallic acid as the standard. Phenolic compound levels were expressed as milligrams of Gallic acid equivalents (mg GAE.mg −1 dw). Total flavonoid content was quantified using the AlCl 3 method , expressed as mg Quercetin equivalents (mg QE.mg −1 dw) of extract. Determination of total antioxidant capacity (TAC), FRAP assay, and DPPH radical scavenging activity The total antioxidant capacity of the test metabolites was evaluated according to the method described by Prieto et al. 1999 . The absorbance of the aqueous solution of each sample was measured at 695 nm against blank. The antioxidant capacity was estimated using the following formula: [12pt]{minimal}
$$\:\:\:\:\:=(-}{})\:\:100$$ Where, Ac is absorbance of negative control; As is absorbance of sample at time (t) = 20 min. A concentration range (0.25, 0.5, 1, 2, 4, 8, 16, 32 mg.mL −1 ) of the leaf extracts and crude secondary metabolites of RSLB3 and RSLB18 was prepared for determining the EC 50 (Effective concentration). Total free radical scavenging capacity of the R. serpentina leaf extract and secondary metabolite of endophytic bacteria were estimated following Blois et al. (1958) with slight modification using the stable DPPH radical, which has an absorption maximum at 515 nm. A concentration range (0.25, 0.5, 1, 2, 4, 8, 16, 32 mg.mL −1 ) of the leaf extracts and crude secondary metabolites of RSLB3 and RSLB18 was prepared for determining the IC 50 . A calibration curve was plotted with % DPPH scavenged versus concentration of standard antioxidant (Ascorbic acid and Gallic acid). DPPH scavenging activity was calculated by using the following equation: [12pt]{minimal}
$$\:\:\:\:\:\:=1-(-}{})\:\:100$$ Where, Ac is absorbance of control; As is absorbance of sample at time (t) = 20 min. The ferric reducing antioxidant power (FRAP) assay for the leaf extract and bacterial secondary metabolites was conducted following Benzie and Strain, 1999 as a measure of antioxidant power. FRAP reagent was prepared by mixing acetate buffer (300 mM, pH 3.6), a solution of 10 mM TPTZ in 40 mM HCl, and 20 mM FeCl 3 at 10:1:1 (v/v/v). The reagent (150 µL) and sample solutions (50 µL) were added to each well and mixed thoroughly. The absorbance was taken at 593 nm at 4 min. A potential antioxidant will reduce the ferric ion (Fe 3+ ) to the ferrous ion (Fe 2+ ); the latter forms a blue complex (Fe 2+ /TPTZ), which increases the absorption at 593 nm. Standard curve was prepared using different concentrations of FeSO 4 . The results were expressed as mg Fe 2+ mg −1 dw. All analyses were performed in triplicate on each extract and absorbance readings were measured using a microplate reader (Agilent BioTek Synergy HTX Multimode Reader). HPLC detection of host origin compound reserpine in test metabolites of endophytic bacteria RP-HPLC analysis was conducted to detect the presence of the host-derived compound reserpine in the secondary metabolites produced by endophytic bacteria, compared to a standard curve of reserpine (Himedia). The HPLC instrument used was a Shimadzu Prominence model, equipped with an LC-20 AD pump and an SPD 20 A detector. The mobile phase comprised 0.1 M phosphate buffer (A) with 0.1% formic acid and methanol (B), both of which were filtered through a Millipore PTFE 0.45 μm membrane. Separations were carried out using a linear gradient, starting at 85% B for 0.01 min, maintaining at 85% B for 9 min, transitioning to 75% B at 9.01 min, maintaining at 75% B until 10 min, further transitioning to 70% B at 10.01 min, maintaining at 70% B until 12.0 min, transitioning to 65% B at 12.01 min, and maintaining at 65% B until 30 min. The flow rate of the mobile phase was set at 1.0 mL.min −1 , with an injection volume of 20 µL. Chromatographic runs were conducted at 40ºC, and UV detection was performed at 268 nm. Untargeted metabolite profiling of R. serpentina leaves and test metabolites of endophytic bacteria through UHPLC-HRMS R. serpentina fresh leaves were treated with liquid nitrogen and crushed in 80% methanol and suspended in 10 mL solvent for 10 min in a 55 kHz ultrasonic bath. Samples were filtered through a 0.25 μm PTFE membrane and stored at −20 °C until analysis. The resulting samples were filtered through a 0.25 μm PTFE membrane and stored at −20 °C until analysis. The hydro-alcoholic leaf extract and ethyl acetate extract containing secondary metabolites from endophytic bacteria were analyzed using Ultra-high performance liquid chromatography-high resolution electrospray ionization mass spectrometry (UHPLC-HRMS/MS) (Model: Q Exactive Plus; Make: Thermo Fischer Scientific; for small molecules UHPLC: Dionex Ultimate 3000 RS Series). The full scan MS was set at: resolution 70,000 (at m/z 100), AGC target 1e6, max IT 100 ms, scan range 100 to 1000 m/z. The MS2 conditions were: resolution 17,500 (at m/z 200), AGC target 1e5, max IT 50 ms, mass range m/z 200 to 2000, isolation window 4.0 m/z and (N)CE 30, 45, and 60. UHPLC separations were achieved using a Hypersil GOLD™ C18 Selectivity HPLC Column (Particle size 1.9 μm, Diameter 2.1 mm, Length 100 mm) with a column temperature set at 40 °C and a flow rate of 300 µL.min-1. The mobile phase consisted of a tertiary gradient of water (A), acetonitrile (B), and methanol (C), all containing 0.1% formic acid. The solvent composition gradient was programmed as follows: 0 min, 0% C; 0–7 min, 0–5% C; 7–15 min, 5–30% C; 15–20 min, 30–60% C; 20–25 min, 30–90% C; 25–28 min, 90 − 5% C; 28–30 min, 5 − 0% C; with a post-run of 8 min at 0.6 mL.min-1. Mass spectrometry, equipped with an ESI source, was operated in negative (NI) and positive (PI) ionization modes. Statistical analyses All samples underwent triplicate analysis, and the data were reported as the mean value accompanied by the standard deviation for each data point. Data processing and visualization software options include GraphPad Prism (version 10.1.0), Molecular Evolutionary Genetics Analysis (MEGA version 11.0.13). Data analysis from the UHPLC-HRMS for compound annotation were performed via Thermo Compound Discoverer 3.3.2.31 by using default settings and online databases.
Healthy plant samples of R. serpentina were randomly collected during the month of April-May 2018 from five sites around the Botanical garden of Department of Botany, Banaras Hindu University, Varanasi, India.
The endophytic bacteria were isolated from leaves of R. serpentina plants . The plant parts were thoroughly washed with running tap water to remove soil particles and attached microbes. Leaves were individually surface sterilized. by serial washing in 70% ethanol (1 min), 2% sodium hypochlorite solution (2% available Cl − ) for 3–4 min and 70% ethanol (60 s) followed by rinsing three times with sterile distilled water. Isolation was performed using the direct plating method on nutrient agar (NA) medium supplemented with 2% sodium chloride (NaCl). The sterilized leaves were cut into 0.5 cm x 0.5 cm sections. A minimum of five sections were placed on NA plates and incubated at 35 ± 2 °C for one week. The plates were checked every 24 h for bacterial colony emergence. The emerging bacterial colonies were selected, sub-cultured, purified, and used for further studies. The bacterial isolates were characterized after 48 h for the following traits: color, form, elevation, margin, surface texture, and opacity. Biochemical tests such as indole production, methyl red and Vogues-Proskauer tests, citrate utilization, catalase and oxidase production tests, nitrate reduction, and oxidative fermentation tests were performed for the endophytic isolates – . Amylase, protease, lipase and cellulase tests were also performed for the endophytic isolates following standard protocols , .
Previously isolated leaf endophytic bacterial strains were subjected to secondary metabolite extraction. A suspension of 10 7 CFU.mL −1 of endophytic bacteria was inoculated into 1 L of nutrient broth (NB) and incubated at 35 ± 2 °C for 4–5 days in a BOD shaker incubator at 150 rpm (Supertech, New Delhi). After incubation, the broth was centrifuged at 8000 × g for 20 min at room temperature. Secondary metabolites were extracted from the supernatant using a solvent extraction method. Equal volumes of ethyl acetate and culture filtrate were combined in a separatory funnel and shaken vigorously for 10 min. The ethyl acetate layer was then separated and concentrated using a Rotary Evaporator (Ika, Germany).
The disc diffusion assay was conducted on Mueller-Hinton agar (MHA) plates against eight human pathogenic bacteria to identify the most potent crude secondary metabolites produced by the endophytic bacteria . Secondary metabolites showing significant zones of inhibition against the majority of pathogens were selected for determination of minimum inhibitory concentration (MIC) values. MIC values for two standard antibiotics and secondary metabolites were determined by broth dilution well plate assay in 96-well microtitre plates following established protocols . In broth dilution method crude extract were prepared and subjected to serial dilution (0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 35.6 mg.mL −1 ) using Mueller–Hinton broth and dimethyl sulfoxide (DMSO) in a 1:1 ratio. Tetracycline, nalidixic acid, and streptomycin served as standard positive controls (0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 35.6 µg.mL −1 , while a 1:1 ratio of DMSO and Mueller–Hinton broth was utilized as the negative control. Each well of the microplate was inoculated with 75 µl of a 24-hour cultured broth of human pathogenic bacteria containing 10 7 CFU.mL −1 , followed by addition of blank Mueller–Hinton broth. Antibiotic solutions and test metabolites (75 µL) were added to separate wells, resulting in a final assay volume of 150 µL. Absorbance readings were measured at 630 nm using a microplate reader (Agilent BioTek Synergy HTX Multimode Reader) immediately after inoculation to establish the baseline ( T 0). Plates were then incubated at 35 ± 2 °C without shaking until the stationary phase was reached. Absorbance was measured for TF (Final Time i.e. 48 h). The inhibition percentage was calculated as: [12pt]{minimal}
$$\:\:=1-(-0}{})\:\:100$$ The T 0 sample and TF sample represent the absorbance at 630 nm of bacterial growth in the presence of test metabolites before and after incubation, respectively. Inhibition values below 10% were deemed insignificant. Bacterial pathogens from wells were reinoculated onto MHA plates to observe colony appearance and determine the MIC of antibiotics and test metabolites.
Genomic DNA of isolates showing antibacterial activity was extracted following Wilson . The isolated genomic DNA strains were analysed on 0.8% agarose gel (Himedia) and amplification of the 16S rRNA (~ 1500 bp) gene was carried in PCR using universal primers [Forward primer (27 F) AGAGTTTGATCCTGGCTCAG; Reverse primer (1492R), CGGTTACCTTGTTACGACTT (Eurofin Genomics, India)]. PCR was performed with 25 µl reaction mixture composed of PCR buffer 2.5 µl; MgCl 2 1.5 µl; 0.2 mm dNTPs 2 µl; Primers each 1.25 µl; Taq DNA polymerase 0.25 µl (Thermo Fischer Scientific ); template DNA 2 µl; PCR water 14.75 µl. The PCR conditions were as follows: initial denaturation 94 °C for 4 min, followed by 30 cycles of 30 s denaturation at 94 °C, 1 min annealing at 55 °C, 1 min extension at 72 °C and a 7-min final extension at 72 °C. The PCR products were purified before they were sent for sequencing. The PCR products of the endophytes were sequenced by BIOKART INDIA Pvt. Ltd (Bengaluru, India) by Sanger’s dideoxy nucleotide sequencing method. All obtained sequences were compared with those in the GenBank database by using the BLASTN search program. Similar sequences were further aligned by CLUSTALW. Phylogenetic analysis was conducted by the neighbour-joining method. Bootstrap analysis was performed with 1000 replications to determine the support for each clade with grouping by the neighbour-joining method. A phylogenetic tree was constructed based on evolutionary distance data by using MEGA 11. The 16S rRNA nucleotide sequences acquired have been submitted to the National Center for Biotechnology Information (NCBI) GenBank to secure accession numbers. The unique accession numbers assigned to the bacterial isolates were MW7415340 and MW7415343.
Determination of total phenolic and total flavonoid content The total phenolic content in both the leaf extract and bacterial secondary metabolites was assessed using the Folin-Ciocalteu method , with Gallic acid as the standard. Phenolic compound levels were expressed as milligrams of Gallic acid equivalents (mg GAE.mg −1 dw). Total flavonoid content was quantified using the AlCl 3 method , expressed as mg Quercetin equivalents (mg QE.mg −1 dw) of extract. Determination of total antioxidant capacity (TAC), FRAP assay, and DPPH radical scavenging activity The total antioxidant capacity of the test metabolites was evaluated according to the method described by Prieto et al. 1999 . The absorbance of the aqueous solution of each sample was measured at 695 nm against blank. The antioxidant capacity was estimated using the following formula: [12pt]{minimal}
$$\:\:\:\:\:=(-}{})\:\:100$$ Where, Ac is absorbance of negative control; As is absorbance of sample at time (t) = 20 min. A concentration range (0.25, 0.5, 1, 2, 4, 8, 16, 32 mg.mL −1 ) of the leaf extracts and crude secondary metabolites of RSLB3 and RSLB18 was prepared for determining the EC 50 (Effective concentration). Total free radical scavenging capacity of the R. serpentina leaf extract and secondary metabolite of endophytic bacteria were estimated following Blois et al. (1958) with slight modification using the stable DPPH radical, which has an absorption maximum at 515 nm. A concentration range (0.25, 0.5, 1, 2, 4, 8, 16, 32 mg.mL −1 ) of the leaf extracts and crude secondary metabolites of RSLB3 and RSLB18 was prepared for determining the IC 50 . A calibration curve was plotted with % DPPH scavenged versus concentration of standard antioxidant (Ascorbic acid and Gallic acid). DPPH scavenging activity was calculated by using the following equation: [12pt]{minimal}
$$\:\:\:\:\:\:=1-(-}{})\:\:100$$ Where, Ac is absorbance of control; As is absorbance of sample at time (t) = 20 min. The ferric reducing antioxidant power (FRAP) assay for the leaf extract and bacterial secondary metabolites was conducted following Benzie and Strain, 1999 as a measure of antioxidant power. FRAP reagent was prepared by mixing acetate buffer (300 mM, pH 3.6), a solution of 10 mM TPTZ in 40 mM HCl, and 20 mM FeCl 3 at 10:1:1 (v/v/v). The reagent (150 µL) and sample solutions (50 µL) were added to each well and mixed thoroughly. The absorbance was taken at 593 nm at 4 min. A potential antioxidant will reduce the ferric ion (Fe 3+ ) to the ferrous ion (Fe 2+ ); the latter forms a blue complex (Fe 2+ /TPTZ), which increases the absorption at 593 nm. Standard curve was prepared using different concentrations of FeSO 4 . The results were expressed as mg Fe 2+ mg −1 dw. All analyses were performed in triplicate on each extract and absorbance readings were measured using a microplate reader (Agilent BioTek Synergy HTX Multimode Reader). HPLC detection of host origin compound reserpine in test metabolites of endophytic bacteria RP-HPLC analysis was conducted to detect the presence of the host-derived compound reserpine in the secondary metabolites produced by endophytic bacteria, compared to a standard curve of reserpine (Himedia). The HPLC instrument used was a Shimadzu Prominence model, equipped with an LC-20 AD pump and an SPD 20 A detector. The mobile phase comprised 0.1 M phosphate buffer (A) with 0.1% formic acid and methanol (B), both of which were filtered through a Millipore PTFE 0.45 μm membrane. Separations were carried out using a linear gradient, starting at 85% B for 0.01 min, maintaining at 85% B for 9 min, transitioning to 75% B at 9.01 min, maintaining at 75% B until 10 min, further transitioning to 70% B at 10.01 min, maintaining at 70% B until 12.0 min, transitioning to 65% B at 12.01 min, and maintaining at 65% B until 30 min. The flow rate of the mobile phase was set at 1.0 mL.min −1 , with an injection volume of 20 µL. Chromatographic runs were conducted at 40ºC, and UV detection was performed at 268 nm. Untargeted metabolite profiling of R. serpentina leaves and test metabolites of endophytic bacteria through UHPLC-HRMS R. serpentina fresh leaves were treated with liquid nitrogen and crushed in 80% methanol and suspended in 10 mL solvent for 10 min in a 55 kHz ultrasonic bath. Samples were filtered through a 0.25 μm PTFE membrane and stored at −20 °C until analysis. The resulting samples were filtered through a 0.25 μm PTFE membrane and stored at −20 °C until analysis. The hydro-alcoholic leaf extract and ethyl acetate extract containing secondary metabolites from endophytic bacteria were analyzed using Ultra-high performance liquid chromatography-high resolution electrospray ionization mass spectrometry (UHPLC-HRMS/MS) (Model: Q Exactive Plus; Make: Thermo Fischer Scientific; for small molecules UHPLC: Dionex Ultimate 3000 RS Series). The full scan MS was set at: resolution 70,000 (at m/z 100), AGC target 1e6, max IT 100 ms, scan range 100 to 1000 m/z. The MS2 conditions were: resolution 17,500 (at m/z 200), AGC target 1e5, max IT 50 ms, mass range m/z 200 to 2000, isolation window 4.0 m/z and (N)CE 30, 45, and 60. UHPLC separations were achieved using a Hypersil GOLD™ C18 Selectivity HPLC Column (Particle size 1.9 μm, Diameter 2.1 mm, Length 100 mm) with a column temperature set at 40 °C and a flow rate of 300 µL.min-1. The mobile phase consisted of a tertiary gradient of water (A), acetonitrile (B), and methanol (C), all containing 0.1% formic acid. The solvent composition gradient was programmed as follows: 0 min, 0% C; 0–7 min, 0–5% C; 7–15 min, 5–30% C; 15–20 min, 30–60% C; 20–25 min, 30–90% C; 25–28 min, 90 − 5% C; 28–30 min, 5 − 0% C; with a post-run of 8 min at 0.6 mL.min-1. Mass spectrometry, equipped with an ESI source, was operated in negative (NI) and positive (PI) ionization modes. Statistical analyses All samples underwent triplicate analysis, and the data were reported as the mean value accompanied by the standard deviation for each data point. Data processing and visualization software options include GraphPad Prism (version 10.1.0), Molecular Evolutionary Genetics Analysis (MEGA version 11.0.13). Data analysis from the UHPLC-HRMS for compound annotation were performed via Thermo Compound Discoverer 3.3.2.31 by using default settings and online databases.
The total phenolic content in both the leaf extract and bacterial secondary metabolites was assessed using the Folin-Ciocalteu method , with Gallic acid as the standard. Phenolic compound levels were expressed as milligrams of Gallic acid equivalents (mg GAE.mg −1 dw). Total flavonoid content was quantified using the AlCl 3 method , expressed as mg Quercetin equivalents (mg QE.mg −1 dw) of extract.
The total antioxidant capacity of the test metabolites was evaluated according to the method described by Prieto et al. 1999 . The absorbance of the aqueous solution of each sample was measured at 695 nm against blank. The antioxidant capacity was estimated using the following formula: [12pt]{minimal}
$$\:\:\:\:\:=(-}{})\:\:100$$ Where, Ac is absorbance of negative control; As is absorbance of sample at time (t) = 20 min. A concentration range (0.25, 0.5, 1, 2, 4, 8, 16, 32 mg.mL −1 ) of the leaf extracts and crude secondary metabolites of RSLB3 and RSLB18 was prepared for determining the EC 50 (Effective concentration). Total free radical scavenging capacity of the R. serpentina leaf extract and secondary metabolite of endophytic bacteria were estimated following Blois et al. (1958) with slight modification using the stable DPPH radical, which has an absorption maximum at 515 nm. A concentration range (0.25, 0.5, 1, 2, 4, 8, 16, 32 mg.mL −1 ) of the leaf extracts and crude secondary metabolites of RSLB3 and RSLB18 was prepared for determining the IC 50 . A calibration curve was plotted with % DPPH scavenged versus concentration of standard antioxidant (Ascorbic acid and Gallic acid). DPPH scavenging activity was calculated by using the following equation: [12pt]{minimal}
$$\:\:\:\:\:\:=1-(-}{})\:\:100$$ Where, Ac is absorbance of control; As is absorbance of sample at time (t) = 20 min. The ferric reducing antioxidant power (FRAP) assay for the leaf extract and bacterial secondary metabolites was conducted following Benzie and Strain, 1999 as a measure of antioxidant power. FRAP reagent was prepared by mixing acetate buffer (300 mM, pH 3.6), a solution of 10 mM TPTZ in 40 mM HCl, and 20 mM FeCl 3 at 10:1:1 (v/v/v). The reagent (150 µL) and sample solutions (50 µL) were added to each well and mixed thoroughly. The absorbance was taken at 593 nm at 4 min. A potential antioxidant will reduce the ferric ion (Fe 3+ ) to the ferrous ion (Fe 2+ ); the latter forms a blue complex (Fe 2+ /TPTZ), which increases the absorption at 593 nm. Standard curve was prepared using different concentrations of FeSO 4 . The results were expressed as mg Fe 2+ mg −1 dw. All analyses were performed in triplicate on each extract and absorbance readings were measured using a microplate reader (Agilent BioTek Synergy HTX Multimode Reader).
RP-HPLC analysis was conducted to detect the presence of the host-derived compound reserpine in the secondary metabolites produced by endophytic bacteria, compared to a standard curve of reserpine (Himedia). The HPLC instrument used was a Shimadzu Prominence model, equipped with an LC-20 AD pump and an SPD 20 A detector. The mobile phase comprised 0.1 M phosphate buffer (A) with 0.1% formic acid and methanol (B), both of which were filtered through a Millipore PTFE 0.45 μm membrane. Separations were carried out using a linear gradient, starting at 85% B for 0.01 min, maintaining at 85% B for 9 min, transitioning to 75% B at 9.01 min, maintaining at 75% B until 10 min, further transitioning to 70% B at 10.01 min, maintaining at 70% B until 12.0 min, transitioning to 65% B at 12.01 min, and maintaining at 65% B until 30 min. The flow rate of the mobile phase was set at 1.0 mL.min −1 , with an injection volume of 20 µL. Chromatographic runs were conducted at 40ºC, and UV detection was performed at 268 nm.
R. serpentina leaves and test metabolites of endophytic bacteria through UHPLC-HRMS R. serpentina fresh leaves were treated with liquid nitrogen and crushed in 80% methanol and suspended in 10 mL solvent for 10 min in a 55 kHz ultrasonic bath. Samples were filtered through a 0.25 μm PTFE membrane and stored at −20 °C until analysis. The resulting samples were filtered through a 0.25 μm PTFE membrane and stored at −20 °C until analysis. The hydro-alcoholic leaf extract and ethyl acetate extract containing secondary metabolites from endophytic bacteria were analyzed using Ultra-high performance liquid chromatography-high resolution electrospray ionization mass spectrometry (UHPLC-HRMS/MS) (Model: Q Exactive Plus; Make: Thermo Fischer Scientific; for small molecules UHPLC: Dionex Ultimate 3000 RS Series). The full scan MS was set at: resolution 70,000 (at m/z 100), AGC target 1e6, max IT 100 ms, scan range 100 to 1000 m/z. The MS2 conditions were: resolution 17,500 (at m/z 200), AGC target 1e5, max IT 50 ms, mass range m/z 200 to 2000, isolation window 4.0 m/z and (N)CE 30, 45, and 60. UHPLC separations were achieved using a Hypersil GOLD™ C18 Selectivity HPLC Column (Particle size 1.9 μm, Diameter 2.1 mm, Length 100 mm) with a column temperature set at 40 °C and a flow rate of 300 µL.min-1. The mobile phase consisted of a tertiary gradient of water (A), acetonitrile (B), and methanol (C), all containing 0.1% formic acid. The solvent composition gradient was programmed as follows: 0 min, 0% C; 0–7 min, 0–5% C; 7–15 min, 5–30% C; 15–20 min, 30–60% C; 20–25 min, 30–90% C; 25–28 min, 90 − 5% C; 28–30 min, 5 − 0% C; with a post-run of 8 min at 0.6 mL.min-1. Mass spectrometry, equipped with an ESI source, was operated in negative (NI) and positive (PI) ionization modes.
All samples underwent triplicate analysis, and the data were reported as the mean value accompanied by the standard deviation for each data point. Data processing and visualization software options include GraphPad Prism (version 10.1.0), Molecular Evolutionary Genetics Analysis (MEGA version 11.0.13). Data analysis from the UHPLC-HRMS for compound annotation were performed via Thermo Compound Discoverer 3.3.2.31 by using default settings and online databases.
Isolation of bacterial endophytes and biochemical characterization A total of 9 endophytic strains (Table ) were isolated from the leaves of R. serpentina . The strains were designated as RSLB 1, 3, 4, 5, 6, 9, 12, 14, and 18. All the isolates were indole and oxidase negative with five being catalase positive. Most of the isolates exhibited positive result for Voges-Proskauer test, and oxidative fermentation, with less than half isolates showing positive result for nitrate reduction test citrate utilization. Protease and amylase activity was exhibited by all the isolates. All isolates except RSLB14 and RSLB18 had shown positive result for presence of cellulase activity. Four out of 9 isolates were positive for lipase activity (Table ). Antibacterial activity Antibacterial activity against human pathogenic bacteria was exhibited by majority of crude secondary metabolite extracts of endophytic bacteria when compared with standard antibiotics such as a tetracycline, streptomycin and nalidixic acid that showed inhibitory action against all pathogens. 22.22% of the isolates showed antibacterial activity in the form of zone of inhibition (Fig. a) against majority of pathogen. The Table highlights the endophytic bacterial isolates RSLB3 and RSLB18 as the most effective isolates in inhibiting the growth of the majority of the tested pathogens, with the largest zones of inhibitions. Zone of inhibition of secondary metabolite of RSLB3 was maximum, ranging between 20 mm and 25 mm, subsequently zone of inhibition of RSLB18 ranged between 7 mm and 26 mm (Fig. b). RSLB3 appears to be more effective against Shigella boydii and MRSA, while RSLB18 is more potent against Enterococcus faecalis . R. serpentina leaf extract also showed significant inhibitory activity (against 8 pathogens), with zone of inhibitions ranging from 7 mm to 12 mm. Endophtyic bacterial isolates RSLB9, RSLB14, RSLB4 and RSLB12 were active against 8, 4 and 3 pathogens respectively while isolates RSLB1 and RSLB6 inhibited growth of 1 pathogen each. The endophytic bacterial isolates, RSLB5 exhibited limited or no antibacterial activity, as indicated by the absence of zone of inhibition. MIC values of secondary metabolites of two endophytic bacteria (RSLB3 and RSLB18) against clinical isolates of human pathogenic bacteria was determined by broth dilution method. The MIC values obtained from broth dilution plate assay ranged from < 0.1 mg.mL −1 to 0.2 mg.mL −1 for RSLB3; maximum MIC value (0.2 mg.mL −1 ) being for M. morganii and minimum (< 0.05 mg.L.mL −1 ) for MRSA). RSLB18 had a MIC range of < 0.1 mg.mL −1 to 3.2 mg.mL −1 (< 0.1 mg.mL −1 for E. faecalis and 3.2 mg.mL −1 for MRSA). For standard antibiotics tetracycline and streptomycin MIC values ranged from 1 µg.mL −1 to 128 µg.mL −1 and 16 µg.mL −1 to 128 µg.mL −1 , respectively (Table ). This suggests that the crude secondary metabolites from RSLB3 and RSLB18 could be a promising source of novel antibacterial agents. Identification of bacterial endophytes The isolates exhibited 96–100% similarity to sequences in the National Center for Biotechnology Information (NCBI) database. The phylogenetic analysis grouped the two identified endophytic bacteria into the Bacillus group. The analysis is represented by order Bacillales. The 16S rRNA sequences were deposited in the GenBank with accession numbers MW680956–MW680964 (Table ). The resulting phylogenetic tree of bacterial isolates revealed that isolates and reference sequences were clustered according to established taxonomic orders, with high bootstrap support (Fig. ). Biochemical characterization of metabolites The results presented in Table provides insight into the total phenolic content, total flavonoid content, total antioxidant capacity, DPPH radical scavenging activity, and ferric reducing antioxidant power (FRAP) of the R. serpentina leaf extract and the secondary metabolites of the endophytic bacterial isolates RSLB3 and RSLB18. Total polyphenol and flavonoid content were measured and expressed as mg gallic acid equivalents (GAE) and mg Quercetin equivalents (QE) respectively. The total phenolic content of the R. serpentina leaf extract was determined to be 0.103 ± 0.003 mg GAE.mg −1 dw indicating the presence of a significant number of phenolic compounds in the plant material. In comparison, the secondary metabolites of the endophytic isolates RSLB3 and RSLB18 exhibited total phenolic contents of 0.027 ± 0.001 and 0.027 ± 0.001 mg GAE.mg −1 dw, respectively. The total flavonoid content followed a similar trend, with the leaf extract showing 0.016 ± 0.0008 mg QE.mg −1 dw, while the secondary metabolites of RSLB3 and RSLB18 contained 0.042 ± 0.0004 and 0.022 ± 0.001 mg QE.mg −1 dw, respectively (Fig. ). The total antioxidant capacity of extracts measured by phosphomolybdenum blue method was calculated as EC 50 was 1.2 ± 0.05 mg.mL −1 for leaf extract, 3.21 ± 0.05 mg.mL −1 for RSLB3 and 0.66 ± 0.1 mg.mL −1 for RSLB18 compared to the EC 50 of 0.197 ± 0.05 mg.mL −1 for ascorbic acid. The DPPH assay further corroborated the antioxidant properties of the samples. The leaf extract had an IC 50 value of 0.82 ± 0.003 mg.mL −1 , while the secondary metabolites of RSLB3 and RSLB18 IC 50 values of 0.53 ± 0.05 mg.mL −1 and 0.33 ± 0.04 mg.mL −1 , respectively when compared to IC 50 of ascorbic acid (0.0129 ± 0.03 mg.mL −1 ) and gallic acid (0.005 ± 0.0001 mg.mL −1 ). The ferric reducing antioxidant power (FRAP) assay showed that the R. serpentina leaf extract had an antioxidant capacity of 0.146 ± 0.0004 mg Fe 2+ mg −1 dw, while the RSLB3 and RSLB18 secondary metabolites demonstrated FRAP values of 0.043 ± 0.001 and 0.041 ± 0.001 mg Fe 2+ mg −1 dw, respectively (Fig. ). HPLC Analysis HPLC analysis was conducted to detect reserpine in the crude extracts of secondary metabolites from endophytic bacterial isolates RSLB3 and RSLB18, as well as in leaf extract. The distinct peak of the reserpine standard served as a reference for comparison. The concentrations found were 61.91 ppm in leaf extract, 53.51 ppm in RSLB3, and 63.02 ppm in RSLB18 (Fig. ). Metabolite profiling of R. Serpentina Leaf extracts and secondary metabolites of endophytic isolates RSLB3 and RSLB18 Untargeted analysis by Ultra high-performance liquid chromatography-high resolution electrospray ionization mass spectrometry (UHPLC-HRMS/MS) was performed to determine the profile of the main phytochemicals and bioactive metabolites present in the leaves of R. serpentina and endophytic bacterial isolates RSLB3 and RSLB18 respectively (Supplementary material S1). Based on the observed mass spectra (Figs. , and ), the compounds were identified by matching to different libraries, and bioactivities were reported by comparing them to literature. The list of some of the identified metabolites for leaf extract, RSLB3 and RSLB18 are reported in Tables , and respectively, and detailed HRMS, MS/MS data are reported in the Supplementary materials (S1). In the hydro-alcoholic leaf extract of R. serpentina , we observed the presence of a diverse array of plant metabolites, including alkaloids (e.g. gelsemine, ajmaline, yohimbine), indole alkaloids (e.g. catharanthine, vindoline, vincristine), iridoid glycosides (loganin, sarpagine), coumarins (fraxetin, rescinnamine), and flavonoids (kaempferol, kaempferitrin) (Table , Fig. :Supplementary file S2). The ethyl acetate extract of the leaf endophytic bacteria B. mojavensis (RSLB3) exhibited a diverse array of compounds, both plant and microbial origin (Table ; Fig. : Supplementary file S2). Among the identified compounds, the naphthoquinone plumbagin has been reported to exhibited anticancer, antimicrobial, and anti-inflammatory properties . The extract also contained compounds of microbial origin, including nucleoside antibiotics like sangivamycin produced by various microorganisms. The bacterium B. wiedmannii (RSLB18) produces a diverse array of secondary metabolites with various biological activities (Table , Supplementary file S2). Notably, this strain synthesizes muscimol, a mycotoxin acting as a GABA receptor agonist with neuroactive properties and also generates mycocyclosin, an antibiotic with significant antibacterial activity against Mycobacterium tuberculosis . Additionally, maculosin, a phytotoxin serving as a virulence factor, is another product of RSLB18 as well as Diacetylphloroglucinol, a compound known for its antimicrobial properties against plant pathogens . Scopoletin acetate, a coumarin, demonstrates antimicrobial, anti-inflammatory, and antioxidant properties. The strain also produced harmine, a β-carboline alkaloid with antimicrobial, antiparasitic, and psychoactive effects. Harmaline, another β-carboline alkaloid, shares similar properties with harmine. Several important intermediates of the alkaloid biosynthetic pathways in R. serpentina were also detected in the crude secondary metabolites of bacterial endophytes (Supplementary file S2). The identified key metabolites were utilized to construct a simplified metabolic pathway (Supplementary file S2) illustrates the synthesis of reserpine and indole alkaloids through the MIA pathway.
A total of 9 endophytic strains (Table ) were isolated from the leaves of R. serpentina . The strains were designated as RSLB 1, 3, 4, 5, 6, 9, 12, 14, and 18. All the isolates were indole and oxidase negative with five being catalase positive. Most of the isolates exhibited positive result for Voges-Proskauer test, and oxidative fermentation, with less than half isolates showing positive result for nitrate reduction test citrate utilization. Protease and amylase activity was exhibited by all the isolates. All isolates except RSLB14 and RSLB18 had shown positive result for presence of cellulase activity. Four out of 9 isolates were positive for lipase activity (Table ).
Antibacterial activity against human pathogenic bacteria was exhibited by majority of crude secondary metabolite extracts of endophytic bacteria when compared with standard antibiotics such as a tetracycline, streptomycin and nalidixic acid that showed inhibitory action against all pathogens. 22.22% of the isolates showed antibacterial activity in the form of zone of inhibition (Fig. a) against majority of pathogen. The Table highlights the endophytic bacterial isolates RSLB3 and RSLB18 as the most effective isolates in inhibiting the growth of the majority of the tested pathogens, with the largest zones of inhibitions. Zone of inhibition of secondary metabolite of RSLB3 was maximum, ranging between 20 mm and 25 mm, subsequently zone of inhibition of RSLB18 ranged between 7 mm and 26 mm (Fig. b). RSLB3 appears to be more effective against Shigella boydii and MRSA, while RSLB18 is more potent against Enterococcus faecalis . R. serpentina leaf extract also showed significant inhibitory activity (against 8 pathogens), with zone of inhibitions ranging from 7 mm to 12 mm. Endophtyic bacterial isolates RSLB9, RSLB14, RSLB4 and RSLB12 were active against 8, 4 and 3 pathogens respectively while isolates RSLB1 and RSLB6 inhibited growth of 1 pathogen each. The endophytic bacterial isolates, RSLB5 exhibited limited or no antibacterial activity, as indicated by the absence of zone of inhibition. MIC values of secondary metabolites of two endophytic bacteria (RSLB3 and RSLB18) against clinical isolates of human pathogenic bacteria was determined by broth dilution method. The MIC values obtained from broth dilution plate assay ranged from < 0.1 mg.mL −1 to 0.2 mg.mL −1 for RSLB3; maximum MIC value (0.2 mg.mL −1 ) being for M. morganii and minimum (< 0.05 mg.L.mL −1 ) for MRSA). RSLB18 had a MIC range of < 0.1 mg.mL −1 to 3.2 mg.mL −1 (< 0.1 mg.mL −1 for E. faecalis and 3.2 mg.mL −1 for MRSA). For standard antibiotics tetracycline and streptomycin MIC values ranged from 1 µg.mL −1 to 128 µg.mL −1 and 16 µg.mL −1 to 128 µg.mL −1 , respectively (Table ). This suggests that the crude secondary metabolites from RSLB3 and RSLB18 could be a promising source of novel antibacterial agents.
The isolates exhibited 96–100% similarity to sequences in the National Center for Biotechnology Information (NCBI) database. The phylogenetic analysis grouped the two identified endophytic bacteria into the Bacillus group. The analysis is represented by order Bacillales. The 16S rRNA sequences were deposited in the GenBank with accession numbers MW680956–MW680964 (Table ). The resulting phylogenetic tree of bacterial isolates revealed that isolates and reference sequences were clustered according to established taxonomic orders, with high bootstrap support (Fig. ).
The results presented in Table provides insight into the total phenolic content, total flavonoid content, total antioxidant capacity, DPPH radical scavenging activity, and ferric reducing antioxidant power (FRAP) of the R. serpentina leaf extract and the secondary metabolites of the endophytic bacterial isolates RSLB3 and RSLB18. Total polyphenol and flavonoid content were measured and expressed as mg gallic acid equivalents (GAE) and mg Quercetin equivalents (QE) respectively. The total phenolic content of the R. serpentina leaf extract was determined to be 0.103 ± 0.003 mg GAE.mg −1 dw indicating the presence of a significant number of phenolic compounds in the plant material. In comparison, the secondary metabolites of the endophytic isolates RSLB3 and RSLB18 exhibited total phenolic contents of 0.027 ± 0.001 and 0.027 ± 0.001 mg GAE.mg −1 dw, respectively. The total flavonoid content followed a similar trend, with the leaf extract showing 0.016 ± 0.0008 mg QE.mg −1 dw, while the secondary metabolites of RSLB3 and RSLB18 contained 0.042 ± 0.0004 and 0.022 ± 0.001 mg QE.mg −1 dw, respectively (Fig. ). The total antioxidant capacity of extracts measured by phosphomolybdenum blue method was calculated as EC 50 was 1.2 ± 0.05 mg.mL −1 for leaf extract, 3.21 ± 0.05 mg.mL −1 for RSLB3 and 0.66 ± 0.1 mg.mL −1 for RSLB18 compared to the EC 50 of 0.197 ± 0.05 mg.mL −1 for ascorbic acid. The DPPH assay further corroborated the antioxidant properties of the samples. The leaf extract had an IC 50 value of 0.82 ± 0.003 mg.mL −1 , while the secondary metabolites of RSLB3 and RSLB18 IC 50 values of 0.53 ± 0.05 mg.mL −1 and 0.33 ± 0.04 mg.mL −1 , respectively when compared to IC 50 of ascorbic acid (0.0129 ± 0.03 mg.mL −1 ) and gallic acid (0.005 ± 0.0001 mg.mL −1 ). The ferric reducing antioxidant power (FRAP) assay showed that the R. serpentina leaf extract had an antioxidant capacity of 0.146 ± 0.0004 mg Fe 2+ mg −1 dw, while the RSLB3 and RSLB18 secondary metabolites demonstrated FRAP values of 0.043 ± 0.001 and 0.041 ± 0.001 mg Fe 2+ mg −1 dw, respectively (Fig. ).
HPLC analysis was conducted to detect reserpine in the crude extracts of secondary metabolites from endophytic bacterial isolates RSLB3 and RSLB18, as well as in leaf extract. The distinct peak of the reserpine standard served as a reference for comparison. The concentrations found were 61.91 ppm in leaf extract, 53.51 ppm in RSLB3, and 63.02 ppm in RSLB18 (Fig. ).
R. Serpentina Leaf extracts and secondary metabolites of endophytic isolates RSLB3 and RSLB18 Untargeted analysis by Ultra high-performance liquid chromatography-high resolution electrospray ionization mass spectrometry (UHPLC-HRMS/MS) was performed to determine the profile of the main phytochemicals and bioactive metabolites present in the leaves of R. serpentina and endophytic bacterial isolates RSLB3 and RSLB18 respectively (Supplementary material S1). Based on the observed mass spectra (Figs. , and ), the compounds were identified by matching to different libraries, and bioactivities were reported by comparing them to literature. The list of some of the identified metabolites for leaf extract, RSLB3 and RSLB18 are reported in Tables , and respectively, and detailed HRMS, MS/MS data are reported in the Supplementary materials (S1). In the hydro-alcoholic leaf extract of R. serpentina , we observed the presence of a diverse array of plant metabolites, including alkaloids (e.g. gelsemine, ajmaline, yohimbine), indole alkaloids (e.g. catharanthine, vindoline, vincristine), iridoid glycosides (loganin, sarpagine), coumarins (fraxetin, rescinnamine), and flavonoids (kaempferol, kaempferitrin) (Table , Fig. :Supplementary file S2). The ethyl acetate extract of the leaf endophytic bacteria B. mojavensis (RSLB3) exhibited a diverse array of compounds, both plant and microbial origin (Table ; Fig. : Supplementary file S2). Among the identified compounds, the naphthoquinone plumbagin has been reported to exhibited anticancer, antimicrobial, and anti-inflammatory properties . The extract also contained compounds of microbial origin, including nucleoside antibiotics like sangivamycin produced by various microorganisms. The bacterium B. wiedmannii (RSLB18) produces a diverse array of secondary metabolites with various biological activities (Table , Supplementary file S2). Notably, this strain synthesizes muscimol, a mycotoxin acting as a GABA receptor agonist with neuroactive properties and also generates mycocyclosin, an antibiotic with significant antibacterial activity against Mycobacterium tuberculosis . Additionally, maculosin, a phytotoxin serving as a virulence factor, is another product of RSLB18 as well as Diacetylphloroglucinol, a compound known for its antimicrobial properties against plant pathogens . Scopoletin acetate, a coumarin, demonstrates antimicrobial, anti-inflammatory, and antioxidant properties. The strain also produced harmine, a β-carboline alkaloid with antimicrobial, antiparasitic, and psychoactive effects. Harmaline, another β-carboline alkaloid, shares similar properties with harmine. Several important intermediates of the alkaloid biosynthetic pathways in R. serpentina were also detected in the crude secondary metabolites of bacterial endophytes (Supplementary file S2). The identified key metabolites were utilized to construct a simplified metabolic pathway (Supplementary file S2) illustrates the synthesis of reserpine and indole alkaloids through the MIA pathway.
The isolation and biochemical characterization of the nine endophytic bacterial isolates (RSLB1, RSLB3, RSLB4, RSLB5, RSLB6, RSLB9, RSLB12, RSLB14, and RSLB18) from the leaves of R. serpentina revealed metabolically active strains. The positive results obtained in various biochemical tests, including indole production, methyl red, Voges-Proskauer, citrate utilization, catalase and oxidase production, nitrate reduction, oxidative fermentation, and citrate reduction, indicate the metabolic versatility of these endophytic bacteria and their potential to produce a wide range of secondary metabolites. The assessment of antibacterial activity of the crude secondary metabolites from the endophytic bacteria demonstrated significant inhibitory effects against a panel of clinically relevant human pathogenic bacteria. Isolate RSLB3 identified as B. mojavensis has been known for its ability to biosurfactant lipopeptides surfactins A, B, and C, pumilacidin, esperin, lichenysin, fengycin, iturin, Bacillomycin, etc . RSLB18, identified as B. wiedmannii , has been reported for production of hemolysin BL and, bacteriocins (Bawcin) having cytotoxic activity . The MIC values of two isolates (RSLB3 and RSLB18) were found to be in the range of 0.05 mg.mL −1 to 1.6 mg.mL −1 . This suggests that the crude secondary metabolites from RSLB3 and RSLB18 could be a promising source of novel antibacterial agents. The endophytic bacteria were capable of producing phenolic compounds, potentially through the biotransformation of host plant-derived precursors or de novo synthesis. The higher flavonoid content in the endophytic bacterial extracts indicates their potential to produce a diverse array of flavonoid-based compounds, which are known for their antioxidant, antimicrobial, and anti-inflammatory properties. The lower IC 50 value of RSLB3 and RSLB18 indicate a higher free radical scavenging ability of the endophytic bacterial extracts compared to the leaf extract. The data presented in Table collectively suggest that the secondary metabolites produced by the endophytic bacterial isolates RSLB3 and RSLB18 exhibit significant antioxidant and free radical scavenging abilities compared to the R. serpentina leaf extract. These findings highlight the potential of these endophytic bacteria as a valuable source of natural antioxidants and bioactive compounds with potential applications in the development of antimicrobial and therapeutic agents . The detection of the host-derived compound reserpine in the secondary metabolites of the endophytic bacteria, as confirmed by HPLC analysis, is a significant finding. The result suggests that the endophytic bacterial isolates acquired or evolved the necessary metabolic pathways to synthesize or accumulate this secondary metabolite. The ability of the endophytic bacteria to produce this bioactive compound, which is structurally similar to their host plant, highlights their potential as a renewable source of valuable natural products. The untargeted metabolomic profiling of the R. serpentina leaf extract and the secondary metabolites of the endophytic bacteria using UHPLC-HRMS/MS revealed a diverse range of compounds with known as well as unknown bioactive properties. These include alkaloids, terpenoids, flavonoids, quinones, and other secondary metabolites with antimicrobial, antioxidant, anticancer, and anti-inflammatory activities. It also allowed identification of various host origin as well as microbial origin secondary metabolites present in bacterial crude metabolite extracts. Some of the major compounds identified in R. serpentina leaf and their reported biological activities are noteworthy. Chlorogenic acid, a phenolic acid, exhibits potent antioxidant, anti-inflammatory, neuroprotective, and antidiabetic properties. The flavonoid glycoside kaempferol has been investigated for its antioxidant, anti-inflammatory, and anticancer activities. Fraxetin, a coumarin derivative, has demonstrated antioxidant, anti-inflammatory, and anticancer effects in preclinical studies . Kojic acid, a pyrone compound, is known for its antioxidant, tyrosinase inhibition, and antimicrobial capabilities . The iridoid glycoside loganin has been reported to exhibit neuroprotective, antidepressant, and anticonvulsant effects. Salicin, a phenolic glycoside found in willow bark, has analgesic, anti-inflammatory, and antioxidant applications. Harmine and the harmala alkaloids detected in the RSLB3 secondary metabolite, have been studied for their antidepressant, antitumor, and antimalarial effects . Phenolic compounds like guaiacol, vanillin, and eugenol have shown antimicrobial and antioxidant activities, while the phenylpropanoid cinnamaldehyde possesses antimicrobial and anti-inflammatory activities. Other notable plant-derived compounds include the analgesic and anti-inflammatory alkaloid capsaicin , the stimulant and neuroprotective caffeine, and cardioprotective stilbenoid resveratrol. The extract also contained compounds of microbial origin, including nucleoside antibiotics like sangivamycin produced by various microorganisms. Other microbially-derived compounds include the quaternary ammonium osmolyte betaine and the β-lactam antibacterial clavulanic acid . RSLB18 produced nigakinone, an alkaloid with antimicrobial properties, and cyclo (dehydrophenylalanyl-L-leucyl), a cyclic dipeptide with antimicrobial activity. Tetrahydropapaveroline, an isoquinoline alkaloid, exhibits both antimicrobial and neuroactive effects , while phoslactomycin B, a macrolide antibiotic, displays antitumor and antimicrobial activities . This extensive repertoire of secondary metabolites highlights the metabolic versatility of RSLB18 and its potential applications in pharmaceuticals, agriculture, and other biotechnological fields. The analysis of plant and endophyte metabolomes provide a direct insight into the contribution of microbiome toward host plant phenotype, as well as the impact of environment on plant-microbe interactions, thereby serving as sensitive and accurate markers. The application of microbial metabolomics enables the complete analysis of crude extracts as well as the identification of marker metabolites (associated with specific bioactivities), before undergoing tedious and laborious downstream processes . Research on endophytic fungi has had a longer historical context, with many known bioactive compounds derived from fungal sources, such as antibiotics and anticancer agents. For instance, the fungal endophyte Taxomyces andreanae was discovered to synthesize paclitaxel (Taxol), the first anticancer bioactive metabolite previously found only in Taxus plants. Many genera of endophytic fungi, including Alternaria , Aspergillus , Botryodiplodia , Botrytis , Cladosporium , Fusarium , etc. have been screened for their ability to produce paclitaxel and its analogues . Bacterial endophytes, particularly those within the Bacillus genus, are known for their ability to produce a wide range of secondary metabolites that exhibit various biological activities, including antimicrobial, antioxidant, and anti-inflammatory properties. This metabolic diversity is crucial for bioprospecting novel compounds that may not be found in fungal endophytes . While fungal endophytes are recognized for their complex metabolic capabilities and historical significance in bioactive compound research , bacterial endophytes present unique advantages; they are generally easier to culture than fungal endophytes, which often require more complex growth conditions. Bacteria can be rapidly grown in liquid media, allowing for quick scaling and experimentation. Their rapid growth rates and well-established protocols for genetic manipulation facilitate efficient experimentation and analysis, enabling targeted production of desired metabolites .
While the roots of R. serpentina are well-studied and known to contain over 70 distinct alkaloids with significant biological and therapeutic potential, the leaves may represent an underexplored source of bioactive compounds. Harvesting leaves is generally more sustainable and less destructive to the plant compared to harvesting roots. By focusing on the leaves and related bacterial endophytes, our research promotes conservation and sustainable use of R. serpentina , which is crucial for maintaining biodiversity and ecological balance. Investigating the leaves can provide complementary insights into the overall metabolite profile of R. serpentina , offering a more comprehensive view of the plant’s medicinal potential. Given that the roots are already well-studied, concentrating on the leaves increases the likelihood of discovering new compounds and bioactivities, further enriching our understanding of this valuable plant. In the symbiotic relationship between endophytic microbes and their host, the host provides nutrients and a suitable habitat for the endophytes, while the endophytes produce bioactive compounds that help the host resist biotic and abiotic stresses and promote its growth. Endophytes influence the chemical makeup of their host plants and promote the production of host-specific bioactive compounds, with genomic pathways involved in this process often distributed among all plant partners. Leveraging the biosynthetic capabilities of these endophytic bacteria could offer advantages in terms of scalability, sustainability, and potentially lower production costs compared to traditional plant-based extraction methods. Our future study could apply advanced fermentation techniques for improving the yield and purity of desired compounds.
Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2
|
Spatial relationships and interactions of immune cell niches are linked to the pathologic response of muscle-invasive bladder cancer to neoadjuvant therapy | b0e75982-7508-4235-902a-d62a50c35215 | 11948894 | Pathologic Processes[mh] | Bladder cancer is the ninth most frequently diagnosed cancer in the world . Neoadjuvant cisplatin-based chemotherapy, administered prior to radical cystectomy, has been shown to improve survival in patients with muscle-invasive bladder cancer (MIBC). However, only 30% of patients achieve a pathological complete response (pCR) following radical surgery . In recent years, neoadjuvant therapy with single-agent immune checkpoint inhibitors (ICIs) has demonstrated pCR rates of 30–40% in MIBC patients. Given that cisplatin may induce favorable immunomodulatory effects , several phase II clinical trials have explored ICI-chemotherapy combination regimens, resulting in pCR rates ranging from 40–50% . Although combination therapies have enhanced the sensitivity of immunotherapy, the underlying interaction mechanisms within the tumor microenvironment (TME) is still unknown. Advances in single-cell RNA sequencing (scRNA-seq) technology have significantly enhanced our understanding of the TME at the individual cell level . However, a major limitation to scRNA-seq is the loss of spatial and morphological context when tissues are dissociated into single cells, which complicates the study of tumor architecture. While in situ hybridization (ISH)-based techniques, such as multiple error-robust fluorescence in situ hybridization (MERFISH) and sequential fluorescence in situ hybridization (seqFISH) , are capable of providing spatial information, they are limited by the number of target genes that can be analyzed simultaneously. The recently developed and widely adopted spatial transcriptomics (ST) technology offers a promising solution to these limitations, enabling the study of spatial gene expression at a tissue-wide scale . In this study, we investigated the spatial transcriptomic architecture of MIBC to identify TME characteristics that contribute to the response to neoadjuvant therapy. Our findings indicate that patients who achieved a pCR exhibited a high degree of lymphocytic infiltration, immune activation, and TLS formation at the naïve stage of treatment. Additionally, we identified intrinsic heterogeneity within individual tumors, which was found to correlate with tumor's response to neoadjuvant therapy. These results provide new insights into the complex ecosystem of MIBC and offer potential strategies for enhancing the efficacy of precision medicine in treating MIBC.
Human MIBC samples This study enrolled eight patients diagnosed with MIBC at the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. Pretreatment samples were obtained from patients who consented to participate in a clinical trial, which is registered at https://www.chictr.org.cn (ChiCTR2000032359) . Prior to surgery, all patients were informed about the potential use of their pathological specimens for medical research and provided written informed consent. After diagnostic transurethral resection of bladder tumors (TURBT), patients received three cycles of camrelizumab in combination with gemcitabine and cisplatin as neoadjuvant therapy. Each treatment cycle consisted of 200 mg camrelizumab administered intravenously (IV) once on day 1, 1000 mg/m 2 gemcitabine IV once on days 1 and 8, and 70 mg/m 2 cisplatin IV once on day 2, administered every 3 weeks. Radical cystectomy (RC) was performed 3–4 weeks after the completion of neoadjuvant therapy. Among the 8 patients enrolled, the patients were classified into two groups based on their tumor response to neoadjuvant therapy: 5 patients with complete response (CR) and 3 patients with non-CR. Additionally, paired treatment-naïve and treatment-experienced formalin-fixed paraffin-embedded (FFPE) sections from 3 patients were included for further analysis, with the following classifications: progressive disease (PD) (n = 1), stable disease (SD) (n = 1), and CR (n = 1). ST sequencing The RNA quality of FFPE tissue blocks was evaluated by calculating the DV200 of RNA extracted from FFPE tissue sections, following the protocol provided by the Qiagen RNeasy FFPE Kit. Five-micron-thick tissue sections were mounted on a Sigma-Aldrich Poly Prep Slide in accordance with the Visium CytAssist Spatial Gene Expression Protocols for FFPE-Tissue Preparation Guide (10 × Genomics, CG000518 Rev C). After drying overnight, the slides were incubated at 60 °C for 2 h. Deparaffinization was then performed following the Visium CytAssist Spatial Gene Expression for FFPE Samples-Deparaffinization, Decrosslinking, Immunofluorescence Staining & Imaging Protocol (10 × Genomics, CG000519 Rev B). The tissue sections were stained with haematoxylin and eosin (H&E) and imaged at 20 × magnification via brightfield imaging using a Leica Aperio Versa8 whole-slide scanner. Decrosslinking was then performed immediately on H&E-stained sections. Next, human whole-transcriptome probe panels were added to the tissue. After these probe pairs hybridized to their target genes and were ligated to one another, the slides were placed on a Visium CytAssist instrument for RNase treatment and permeabilization. The ligated probes were then hybridized to the spatially barcoded oligonucleotides in the capture area. Spatial transcriptomics libraries were generated from the probes and sequenced on the Illumina NovaSeq 6000 system (performed by Beijing Novogene Technology Co., Ltd.). After cDNA library construction and sequencing, we used an in-house script to perform a basic statistical analysis of the raw data and to evaluate the data quality and GC content throughout the sequencing cycles. Raw FASTQ files and histology images were processed via the short-read probe alignment algorithm for the FFPE ‘count’ method in Space Ranger (v1.3.0) from 10 × Genomics to align probe reads to the human reference genome (GRCh38/mm10). The filtered count matrix and the fiducial-aligned low-resolution image were used for downstream data analyses using the R package Seurat (v.4.0.4). H&E and immunohistochemistry FFPE sections of MIBC were obtained from the clinical trial ChiCTR2000032359. A representative slide from each patient was stained with H&E. For immunohistochemistry (IHC), the MIBC tissue sections were stained with the following primary antibodies: anti-CD3 (Abcam, ab16669), anti-CD20 (Abcam, ab78237), anti-PNAd (BD Biosciences, 553863), anti-GATA3 (Abcam, ab199428), anti-ACTA2 (Thermo Fisher, 14–9760-82), anti-PEG10 (Novus, NBP2-13749), anti-FGFR3 (Thermo Fisher, 66954-1-IG) and anti-CK5/6 (NSJ Bioreagents, V8493-20UG). For the murine samples, sections were stained with anti-CD3 (Abcam, ab16669) and anti-CD19 (CST, 90176 T) antibodies. TLS identification and quantification We used the following criteria for the identification of TLSs: (1). Aggregations of large numbers of T cells and B cells; (2) Exhibits distinct T cell zone and B cell zones; (3) Presence of high endothelial venules (HEVs) in the surrounding of T cell zone and B cell zone. We performed quantitative analysis based on the TLS area and the TLS number per 1 mm 2 of tumor tissue. The “ratio of TLS/mm 2 ” was calculated as the total area of all TLS in each tumor section divided by the area of the tumor tissue. The “number of TLS/mm 2 ” was calculated as the total number of TLS in each tumor section divided by the area of the tumor tissue. ST data preprocessing The ST data were processed using the R programming language. Quality control for each of the six samples was performed individually using the default Seurat pipeline. We identified 3000 high variable features (HVFs) according to their expression means and variances and finally obtained common HVFs among the six samples from 3 patients (one PD, one SD and one CR patients respectively) for further analysis. The data from all six samples were then merged, followed by normalization, scaling, and principal component analysis (PCA). Integration and batch correction were performed using the harmony method with default parameters. Unsupervised clustering based on shared nearest neighbour (SNN) was applied to the spots, and the clustering results were visualized using (t-distributed stochastic neighbor embedding) t-SNE. Finally, these clusters were annotated based on canonical cell type markers. Statistical analysis The TLS quantification and mouse experiments data were presented as the mean ± SEM and analyzed using GraphPad Prism 10 software. Differences between groups were determined by Student’s t-tests. For downstream analysis of ST data, the differential expression analysis among the PD, SD, and CR groups was performed using the FindAllMarkers function of the Seurat package. The results were visualized as volcano plots with the ggplot2 package. Differentially expressed genes were determined by a p-value ≤ 0.05 and a |log 2 FC|≥ 1. Gene Ontology (GO) and gene set variation analysis (GSVA) were conducted with the clusterProfiler and GSVA packages under default parameters. To explore the temporal progression of TLS formation, a pseudotime analysis was conducted using the Monocle2 tools. Dominant ligand-receptor pairs in the TLS regions were identified using the SpaGene method with the default parameters. The downstream analysis of ST data was conducted by R software ( https://www.R-project.org/ ) v4.1.0. Cell culture, mice and preclinical experiments The mouse urothelial carcinoma cell line MB49 was procured from Shanghai Fuheng Biotechnology Company ( www.fudancell.com ) and cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% (v/v) fetal bovine serum (FBS) (Shanghai Fuheng Biotechnology Company, FH1136). 6- to 8 week-old wild-type C57BL/6 J mice were obtained from SiPeiFu, Inc. ( https://www.spfbiotech.com ) and maintained under specific pathogen-free conditions. To establish a xenograft tumor model, the mice were intraperitoneally injected with 4 × 10 5 MB49 cells. A mixture of 1.5 μg of Ccl19 (PeproTech, cat: 250-27B-20UG) and 1.5 μg of Ccl21 (PeproTech, cat: 250-13-20UG) was administered intraperitoneally on days 3, 5, and 7 after tumors were visible on the mice, and 250 μg of anti-mouse PD-1 (BioXcell, cat: BE0146) or 250 μg of anti-mouse IgG2a isotype (BioXcell, cat: BE0089) was administered intraperitoneally on days 10, 12, and 14 after tumors were visible. On day 17, after tumors were visible, the mice were sacrificed, and the final tumor weight was measured. Ethics This study was conducted in accordance with the principles of the Declaration of Helsinki. The use of patient samples was approved by the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (approval #20/084–2280). Informed consent was obtained from all patients prior to inclusion in the study. All animal experiments were also approved by the Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University (approval #2023KJT33) and were performed in compliance with institutional and national guidelines for the care and use of laboratory animals. Role of funders The study funders had no role in the study design, data collection, data analysis, data interpretation, manuscript writing, or in the decision to submit the manuscript for publication.
This study enrolled eight patients diagnosed with MIBC at the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. Pretreatment samples were obtained from patients who consented to participate in a clinical trial, which is registered at https://www.chictr.org.cn (ChiCTR2000032359) . Prior to surgery, all patients were informed about the potential use of their pathological specimens for medical research and provided written informed consent. After diagnostic transurethral resection of bladder tumors (TURBT), patients received three cycles of camrelizumab in combination with gemcitabine and cisplatin as neoadjuvant therapy. Each treatment cycle consisted of 200 mg camrelizumab administered intravenously (IV) once on day 1, 1000 mg/m 2 gemcitabine IV once on days 1 and 8, and 70 mg/m 2 cisplatin IV once on day 2, administered every 3 weeks. Radical cystectomy (RC) was performed 3–4 weeks after the completion of neoadjuvant therapy. Among the 8 patients enrolled, the patients were classified into two groups based on their tumor response to neoadjuvant therapy: 5 patients with complete response (CR) and 3 patients with non-CR. Additionally, paired treatment-naïve and treatment-experienced formalin-fixed paraffin-embedded (FFPE) sections from 3 patients were included for further analysis, with the following classifications: progressive disease (PD) (n = 1), stable disease (SD) (n = 1), and CR (n = 1).
The RNA quality of FFPE tissue blocks was evaluated by calculating the DV200 of RNA extracted from FFPE tissue sections, following the protocol provided by the Qiagen RNeasy FFPE Kit. Five-micron-thick tissue sections were mounted on a Sigma-Aldrich Poly Prep Slide in accordance with the Visium CytAssist Spatial Gene Expression Protocols for FFPE-Tissue Preparation Guide (10 × Genomics, CG000518 Rev C). After drying overnight, the slides were incubated at 60 °C for 2 h. Deparaffinization was then performed following the Visium CytAssist Spatial Gene Expression for FFPE Samples-Deparaffinization, Decrosslinking, Immunofluorescence Staining & Imaging Protocol (10 × Genomics, CG000519 Rev B). The tissue sections were stained with haematoxylin and eosin (H&E) and imaged at 20 × magnification via brightfield imaging using a Leica Aperio Versa8 whole-slide scanner. Decrosslinking was then performed immediately on H&E-stained sections. Next, human whole-transcriptome probe panels were added to the tissue. After these probe pairs hybridized to their target genes and were ligated to one another, the slides were placed on a Visium CytAssist instrument for RNase treatment and permeabilization. The ligated probes were then hybridized to the spatially barcoded oligonucleotides in the capture area. Spatial transcriptomics libraries were generated from the probes and sequenced on the Illumina NovaSeq 6000 system (performed by Beijing Novogene Technology Co., Ltd.). After cDNA library construction and sequencing, we used an in-house script to perform a basic statistical analysis of the raw data and to evaluate the data quality and GC content throughout the sequencing cycles. Raw FASTQ files and histology images were processed via the short-read probe alignment algorithm for the FFPE ‘count’ method in Space Ranger (v1.3.0) from 10 × Genomics to align probe reads to the human reference genome (GRCh38/mm10). The filtered count matrix and the fiducial-aligned low-resolution image were used for downstream data analyses using the R package Seurat (v.4.0.4).
FFPE sections of MIBC were obtained from the clinical trial ChiCTR2000032359. A representative slide from each patient was stained with H&E. For immunohistochemistry (IHC), the MIBC tissue sections were stained with the following primary antibodies: anti-CD3 (Abcam, ab16669), anti-CD20 (Abcam, ab78237), anti-PNAd (BD Biosciences, 553863), anti-GATA3 (Abcam, ab199428), anti-ACTA2 (Thermo Fisher, 14–9760-82), anti-PEG10 (Novus, NBP2-13749), anti-FGFR3 (Thermo Fisher, 66954-1-IG) and anti-CK5/6 (NSJ Bioreagents, V8493-20UG). For the murine samples, sections were stained with anti-CD3 (Abcam, ab16669) and anti-CD19 (CST, 90176 T) antibodies.
We used the following criteria for the identification of TLSs: (1). Aggregations of large numbers of T cells and B cells; (2) Exhibits distinct T cell zone and B cell zones; (3) Presence of high endothelial venules (HEVs) in the surrounding of T cell zone and B cell zone. We performed quantitative analysis based on the TLS area and the TLS number per 1 mm 2 of tumor tissue. The “ratio of TLS/mm 2 ” was calculated as the total area of all TLS in each tumor section divided by the area of the tumor tissue. The “number of TLS/mm 2 ” was calculated as the total number of TLS in each tumor section divided by the area of the tumor tissue.
The ST data were processed using the R programming language. Quality control for each of the six samples was performed individually using the default Seurat pipeline. We identified 3000 high variable features (HVFs) according to their expression means and variances and finally obtained common HVFs among the six samples from 3 patients (one PD, one SD and one CR patients respectively) for further analysis. The data from all six samples were then merged, followed by normalization, scaling, and principal component analysis (PCA). Integration and batch correction were performed using the harmony method with default parameters. Unsupervised clustering based on shared nearest neighbour (SNN) was applied to the spots, and the clustering results were visualized using (t-distributed stochastic neighbor embedding) t-SNE. Finally, these clusters were annotated based on canonical cell type markers.
The TLS quantification and mouse experiments data were presented as the mean ± SEM and analyzed using GraphPad Prism 10 software. Differences between groups were determined by Student’s t-tests. For downstream analysis of ST data, the differential expression analysis among the PD, SD, and CR groups was performed using the FindAllMarkers function of the Seurat package. The results were visualized as volcano plots with the ggplot2 package. Differentially expressed genes were determined by a p-value ≤ 0.05 and a |log 2 FC|≥ 1. Gene Ontology (GO) and gene set variation analysis (GSVA) were conducted with the clusterProfiler and GSVA packages under default parameters. To explore the temporal progression of TLS formation, a pseudotime analysis was conducted using the Monocle2 tools. Dominant ligand-receptor pairs in the TLS regions were identified using the SpaGene method with the default parameters. The downstream analysis of ST data was conducted by R software ( https://www.R-project.org/ ) v4.1.0.
The mouse urothelial carcinoma cell line MB49 was procured from Shanghai Fuheng Biotechnology Company ( www.fudancell.com ) and cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% (v/v) fetal bovine serum (FBS) (Shanghai Fuheng Biotechnology Company, FH1136). 6- to 8 week-old wild-type C57BL/6 J mice were obtained from SiPeiFu, Inc. ( https://www.spfbiotech.com ) and maintained under specific pathogen-free conditions. To establish a xenograft tumor model, the mice were intraperitoneally injected with 4 × 10 5 MB49 cells. A mixture of 1.5 μg of Ccl19 (PeproTech, cat: 250-27B-20UG) and 1.5 μg of Ccl21 (PeproTech, cat: 250-13-20UG) was administered intraperitoneally on days 3, 5, and 7 after tumors were visible on the mice, and 250 μg of anti-mouse PD-1 (BioXcell, cat: BE0146) or 250 μg of anti-mouse IgG2a isotype (BioXcell, cat: BE0089) was administered intraperitoneally on days 10, 12, and 14 after tumors were visible. On day 17, after tumors were visible, the mice were sacrificed, and the final tumor weight was measured.
This study was conducted in accordance with the principles of the Declaration of Helsinki. The use of patient samples was approved by the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (approval #20/084–2280). Informed consent was obtained from all patients prior to inclusion in the study. All animal experiments were also approved by the Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University (approval #2023KJT33) and were performed in compliance with institutional and national guidelines for the care and use of laboratory animals.
The study funders had no role in the study design, data collection, data analysis, data interpretation, manuscript writing, or in the decision to submit the manuscript for publication.
TLS are enriched in patients who achieve CR To examine the lymphocytic context in MIBC prior to neoadjuvant therapy, we assessed T-cell and B-cell infiltration using IHC on tissue sections obtained via electrotomy, including treatment-naïve samples from patients enrolled in a clinical trial (ChiCTR2000032359) patients with non-CR (n = 3) and CR (n = 5) responses (Fig. A). Our analysis revealed that the CR group exhibited a higher density of T/B-cell clustered structures, alongside numerous co-distributed HEVs, indicative of the presence of TLS structures (Fig. B, C). Quantitative analysis of the IHC maps demonstrated that the density of TLSs in tissues from patients who achieved CR was significantly higher compared to those from non-CR patients (Fig. D). Spatial heterogeneities of the cell-type niche in the MIBC microenvironment To further explore the spatial heterogeneity of the TME in CR and non-CR patients and to analyze the transcriptional characteristics of TLSs, we employed Visium ST technology from 10X Genomics. ST was performed on paired treatment-naïve and treatment-experienced FFPE sections from three patients in our cohort, including those with PD (n = 1), SD (n = 1), and CR (n = 1), generating six spatial expression maps (Fig. A). Following quality control, a total of 20,020 spots from these maps were retained for subsequent analysis. To characterize the spatial diversity of cell-type niche (CTN) within the MIBC TME, we combined the spots from different tissues and performed clustering analysis (Fig. B). Eight distinct CTNs were identified based on canonical cell-type markers and signatures. These included T/B cells (Immune/TB), myeloid cells (Immune/myeloid), immune cell mixtures (Immune/mixed), vascular vessels (Endovascular), lymphatic vessels (Endolymphatic), and fibroblast, epithelial and muscle cell-enriched niches (Fig. C, Fig. S1). All types of CTNs can be observed in the individual maps (Fig. D, Fig. S2), and the identification of specific markers of CNTs further validated the accuracy of the clustering analysis (Fig. E). Notably, the tumor purity signal was highly enriched in the epithelial and muscle cell niches, which is consistent with the pathological phenotype of MIBC (Fig. F). Patients in the CR group present increased lymphocyte infiltration and activation To further explore the neoadjuvant therapy-associated immune niches, we compared the abundance of CTNs between treatment-naïve tissues from patients who achieved a CR and those who did not, using the odds ratio test (Fig. A). We found that immune-related CTNs, such as Immune/TB, Immune/mixed, and endothelial- associated niches, were significantly enriched in the tissues from CR patients (Fig. A, B). Enhanced lymphocytic infiltration is known to contribute to better responses to immunotherapy, while an increased density of blood vessels aids in the efficient delivery of chemotherapy agents. We then compared the Immune/TB and Immune/mixed niches between CR and non-CR tissues through differential gene expression analysis (Fig. C). The CR group exhibited higher expression of several B-cell-related genes, including MS4A1, CD79A, BANK1, BLK, and FCRL1 (Fig. C). GO analysis revealed that the differentially expressed genes (DEGs) from both the Immune/TB and Immune/mixed niches were primarily involved in adaptive immune-related biological processes, particularly those associated with B-cell function (Fig. D, E). Moreover, we observed an increased expression of genes related to B-cell activation in CR tissues, suggesting that B-cell-mediated immunity may play a critical role in the response to neoadjuvant therapy (Fig. F). T cells undergo functional polarization, which is largely influenced by the TME . We hypothesized that infiltrating T cells in response to different treatment regimens would exhibit distinct phenotypes and functional statuses. To test this hypothesis, we extracted T-cell spots (defined as spots expressing both CD3 and either CD4 or CD8) and assessed T-cell populations in the different treatment-response groups based on the expression levels of T-cell subtype and activation-related marker genes. As anticipated, regulatory T cells (Tregs) predominantly infiltrated tissues from patients with PD, while cytotoxic CD8 + T cells were primarily found in tissues from patients who achieved SD and CR (Fig. G, H). Stem-like CD8 + T cells have garnered attention for their capacity to maintain long-term T-cell responses due to their stem-like properties . We found that, compared with tissues from non-CR patients, tissues from CR patients had a greater infiltration of stem-like CD8 + T cells (Fig. H). Furthermore, CD8 + T cells infiltrating CR tissues exhibited increased expression of exhaustion markers, suggesting that these patients may benefit from ICI therapy (Fig. H). We also observed that tissues from patients who achieved CR contained abundant vascular vessels (Fig. A, B). The vasculature in the tumor not only contributes to immunotherapy responses by facilitating lymphocyte trafficking but also supports chemotherapy efficacy by ensuring adequate drug delivery to the tumor bed . To further characterize the molecular and functional properties of the endothelial vessels in PD, SD, and CR tissues, we performed GSVA of DEGs within the Endovascular CTN. The GSVA results revealed that the vascular vessels of the CR patients exhibited a more mature phenotype and a greater capacity for T/B-cell recruitment (Fig. I). In contrast, vascular vessels in tissues from PD patients showed lower levels of leukocyte-endothelial cell adhesion, a key process for lymphocyte extravasation. Additionally, we found that the Endovascular CTN of CR tissues displayed increased expression of lymphocyte- and adhesion molecule-related genes (Fig. J). Taken together, our findings suggest that high levels of lymphocytes infiltrating blood vessels, B-cell activation, and enrichment of pro-immune-type T cells can serve as important indicators for predicting the response to neoadjuvant therapy. TLSs are observed in patients who achieve CR High numbers of tumor-infiltrating T cells and B cells are closely associated with the formation of TLSs, and the activation of B cells within the TME is dependent on the presence of TLSs . In our study, we found that the number of tumor-infiltrating T cells was comparable acros the different patient groups, while B cells were significantly enriched in the tissues from patients who achieved CR (Fig. A). Moreover, tissues from CR patients exhibited elevated expression levels of germinal center (GC) B cells, TLS-associated chemokines, adhesion molecules, the dendritic cell (DC) marker LAMP3, lymphotoxin signaling molecules, and HEV markers (Fig. A). As anticipated, signature-based analysis revealed that tissues from MIBC patients with CR showed high levels of lymph node development signals and TLS-associated signals, which were strongly colocalized with B-cell activation signals (Fig. B). By integrating H&E staining maps with gene expression data, we identified two distinct TLS regions in tissues from CR patients (Fig. C). Focusing on tissues from CR patients, which contain these TLSs, we evaluated the spatial distribution of T and B-cell according to the expression of specific cell markers (CD3 for T cells and MS4A1 for B cell). We observed that the core regions of the TLSs colocalized with both T and B cells, rather than with either T or B cells alone (Fig. C). The differentiation of B cells requires interactions with T cells. At the interface between the follicular and T-cell zones in secondary lymphoid organs, B cells interact with CD4 + helper T cells . These ongoing interactions between T cells and B cells are critical for the maintenance of GCs. Within the GCs, the interaction between B cells and T follicular helper (Tfh) cells promotes B-cell proliferation, class switch recombination, and differentiation into antibody-producing plasma cells or GC B cells . Recent studies have shown that B cells express major histocompatibility complex (MHC) class II molecules and possess the necessary machinery for antigen uptake, processing, and presentation, classifying them as professional antigen-presenting cells (APCs), akin to DCs and macrophages . In addition, B cells also express costimulatory molecules, including CD80 and CD86, which enhance T-cell activation by interacting with CD28 on T cells . To further explore the molecular and functional characteristics of T and B cells within TLSs, we compared T and B cell spots within and outside the TLS regions. Differential expression analysis revealed that chemokines involved in lymphocyte recruitment, as well as genes associated with B-cell activation and maturation, were more highly expressed within the TLS regions (Fig. S3A). GSEA further confirmed the increased activity of B-cell activation in T and B cell spots within the TLS regions (Fig. S3B). Furthermore, signature-based analysis showed that T cells within the TLS regions exhibited a greater cytotoxic potential, while B cells within these regions displayed characteristics consistent with antibody-secreting plasma cells. These findings suggest that the colocalization, or interaction, of T and B cells within the TLSs results in a stronger synergistic antitumor immune response (Fig. D). Since lymphocyte aggregation is primarily governed by chemokine gradients and cell–cell interactions, we investigated the ligand-receptor pairs (LRPs) contributing to lymphocyte aggregation using the R package SpaGene (version 0.1.0). This analysis identified nine chemokine-mediated cell–cell communication patterns within the tissue from patients who achieved CR (Fig. S3C). Among these, we identified TLS-specific LRPs (pattern 2 in Fig. S3C). Notably, the CCL19/CCL21-CCR7, CCL5/CCL14-ACKR1, and CXCL9-CXCR3 LRP axes were among the top five TLS-associated patterns (Fig. S3D). CCL19-CCR7 and CCL21-CCR7 are well-established canonical chemokine axes that promote TLS formation . We observed that CCL19/CCL21-CCR7 signaling was predominantly localized within the TLS regions (Fig. E). Within TLSs, CCL19 + and/or CCL21 + fibroblastic reticular cells (FRCs) guide the distribution of lymphocytes expressing the corresponding CCR7 receptors, thereby facilitating the formation of the T-cell zone formation . Additionally, CCL19/CCL21 recruit CCR7-expressing mature DCs, particularly LAMP3 + DCs, into the TLS regions to prime naïve T cells . In the TME, CXCL9, expressed by intratumoral conventional DCs, plays a key role in recruiting stem-like CD8 + T cells, promoting the replenishment of the effector T-cell pool . Furthermore, ACKR1, expressed on the blood endothelial cells (BECs) of postcapillary venules, facilitates the abluminal-to-luminal transcytosis of chemokines, thereby promoting the extravasation of immune cells from the vasculature into the tumor bed . We hypothesize that CCL5, retained at BEC junctions by ACKR1, aids immune cell extravasation, facilitating their infiltration into the TME. Subsequently, we explored the potential of the CCL19/CCL21-CCR7 axis as a therapeutic target for MIBC. The MB49 mouse bladder carcinoma cell line was intraperitoneally (i.p.) injected into C57BL /B6J mice. Starting on day 3, recombinant mouse Ccl19 and Ccl21 were administered i.p. six times every 2–3 days, and tumors were harvested on day 17. IHC analysis revealed that Ccl19/Ccl21 administration facilitated the formation of intratumoral TLSs (Fig. F, Fig. S4). More importantly, we observed that the delivery of Ccl19/Ccl21 enhanced the therapeutic efficacy of PD-1 inhibition, suggesting that these chemokines may serve as effective neoadjuvant therapeutic agents for MIBC (Fig. G). In summary, we identified the presence of TLSs in the tumors of patients who achieved CR, which was strongly associated with high levels of CCL19 and CCL21 enrichment in the TME. Furthermore, both CCL19 and CCL21 were shown to enhance the therapeutic efficacy of immunotherapy. TLS formation process To gain deeper insight into TLS formation in MIBC, we conducted a detailed analysis of the Immunity/TB CTN in tissues from patients who achieved CR. The formation of TLSs is a multistep process, which involves: (1) the aggregation of lymphocytes, (2) the establishment of distinct T-cell and B-cell zones, and (3) the formation of GC-like structures within the B-cell zone . Interestingly, we identified two primary distribution patterns within the Immune/TB and Immune/mixed CTNs. In the TLS region, the Immune/TB CTN was surrounded by Immune/mixed CTN and formed a follicular structure (pattern 1), while in the non-TLS region, the Immune/TB CTN was distributed around the Immune/mixed CTN (pattern 2) (Fig. A). We hypothesized whether this phenomenon represents a progression in TLS formation, i.e., if the non-TLS pattern (pattern 2) eventually transitions into the TLS pattern (pattern 1), or if these patterns represent distinct evolutionary paths for lymphocyte clusters. To test this hypothesis, we compared the cellular composition and molecular characteristics of the Immune/TB CTN between the TLS-pattern and non-TLS-pattern. Deconvolution analysis using the monocytic cell proportion (MCP)-counter method further identified lymphocyte subtypes, as well as other cell types that colocalized with lymphocytes (Fig. B). In addition to the organized T-zone and B-zone, TLSs encompass a variety of cellular components, with Tfh cells being the most prominent. As anticipated, we observed a significant enrichment of Tfh cells within the TLS_TB regions. The interaction between B cells and Tfh cells drives immunoglobulin class-switching and affinity maturation in GCs, resulting in the generation of high-affinity, long-lived plasma cells (Fig. B). We also noted that IgM-positive naïve B cells were predominantly found in the non-TLS region, while IgG-positive B cells and plasma cells were significantly enriched in the TLS regions (Fig. B). Furthermore, we identified that FRCs and follicular dendritic cells (FDCs), collectively termed lymphoid tissue organizer (LTo) cells, were significantly enriched in the pattern 1_TB (Fig. B). These findings align with previous reports by Jeremy Goc et al., who demonstrated that LAMP3 + DCs (also referred to as DC-LAMP) are predominantly localized within the T-cell zone of TLSs . Consistently, we found that LAMP3 + DCs were significantly enriched in the pattern 1_TB region compared with the pattern 2_TB region. Interestingly, we also observed that terminally exhausted CD8 + T cells were enriched in the pattern 1_TB region, while macrophages and fibroblasts were more abundant in the pattern 2_TB region. To further investigate the relationship between the TLS and non-TLS patterns, we performed pseudotime analysis using Monocle2 . This analysis revealed a trajectory that originated at immune spots within the Immune/mixed CTN and subsequently bifurcated into two distinct differentiation paths: one leading to the Immune/TB spots in the pattern 1 region and the other leading to Immune/TB spots in the pattern 2 region (Fig. C). As expected, key TLS-associated molecules were predominantly enriched along the path leading to the pattern 1 region spots (Fig. C). To identify the characteristic factors involved in TLS formation, we analyzed the genes whose expression changed significantly over the course of pseudotime progression. Notably, genes that were highly expressed along the TLS trajectory were involved in processes related to cell–cell communication, including chemokine-mediated signaling, cell adhesion, and activation, highlighting the dynamic cell–cell interactions occuring during TLS formation (Fig. D). In contrast, the pattern 2 region branch was characterized by the enrichment of extracellular matrix (ECM) remodeling-related signaling pathways, which may inhibit lymphocyte interactions and, consequently, prevent TLS formation (Fig. D). Additionally, angiogenesis-related terms were significantly enriched in the pattern 2_TB region. The angiogenesis-driven immunosuppressive TME is known to lead to T-cell dysfunction, which disrupts T-B-cell interactions and inhibits TLS formation . Recent studies have highlighted the role of tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) in ECM remodeling and angiogenesis , and we hypothesize that these immunosuppressive processes mediated by TAMs and CAFs may inhibit TLS formation (Fig. E). Moreover, we identified several previously unreported TLS-related genes that were significantly upregulated in TLSs (Fig. F). The signature score, calculated using these newly identified TLS-associated genes in the TCGA-BLCA cohort, was positively correlated with the lymphocyte infiltration levels (Fig. G). In summary, our results indicate that TLS formation requires not only the chemokine-mediated recruitment and cell–cell interactions of specific cell populations (such as LAMP3 + DCs, Tfh cells, FRCs and FDCs) but also an immunosupportive TME (Fig. E). Additionally, we identified novel TLS-associated genes that are significantly correlated with immune cell infiltration in MIBC. MIBC patients exhibit intrinsic heterogeneity within the tumor The neoadjuvant therapies administered in this cohort included camrelizumab and gemcitabine and cisplatin. The sensitivity to immunotherapy is influenced by the immunophenotype of the tumor, whereas the sensitivity to chemotherapy is more dependent on the intrinsic characteristics of the tumor. Therefore, the selection of an appropriate therapeutic strategy necessitates a comprehensive assessment of both the immune microenvironment and the tumor characteristics. To further investigate the tumor characteristics in responders, we conducted an analysis of the epithelial niches before and after therapy (Fig. A). Tumor purity scores were calculated at epithelial sites using the ESTIMATE package in R . As anticipated, tumor purity in tissues from patients who achieved a CR significantly decreased following neoadjuvant therapy (Fig. B). To evaluate the heterogeneity of the tumor regions across different response groups, we focused on samples from Clusters 3 and 4, which exhibited high tumor purity, for further analysis. Recently, MIBC has been classified into six consensus molecular subtypes: the luminal papillary (LumP), luminal nonspecified (LumNS), luminal unstable (LumU), stroma-rich, basal/squamous (Ba/Sq), and neuroendocrine-like (NE-like), each of which displays distinct responses to immunotherapy and chemotherapy . We applied a signature-based approach to calculate consensus class-related pathway scores across various tumor sites. This analysis revealed that malignant spots in patients who achieved PD and CR exhibited elevated urothelial differentiation signals (Fig. C). Furthermore, malignant spots from PD patients demonstrated increased cell cycle activity, while those from CR patients showed high expression of a noninvasive Ta pathway signature and an FGFR3 coexpressed gene signature, suggesting that the PD group primarily corresponds to the LumU phenotype, while the CR group aligns with the LumP phenotypes (Fig. C). Interestingly, we also observed that malignant spots from CR patients displayed high levels of basal differentiation and elevated cytotoxic lymphocyte infiltration, characteristics associated with the Ba/Sq phenotype. This suggests that CR patients exhibit a combination of LumP and Ba/Sq phenotypes (Fig. C). In contrast, malignant spots from patients with SD expressed signals indicative of neuroendocrine differentiation, cell cycle activity, and myofibroblasts, indicating a mixture of NE-like and stromal-rich phenotypes in these patients (Fig. C). These findings were further validated through IHC analysis (Fig. D, Fig. S5). In summary, our study revealed distinct molecular subtype compositions within individual MIBC patients. Specifically, patients with CR in our cohort exhibited a combination of LumP and Ba/Sq tumor phenotypes, while patients with SD displayed a mixture of NE-like and stromal-rich phenotypes. These observations underscore the importance of considering tumor heterogeneity when designing precision therapies.
To examine the lymphocytic context in MIBC prior to neoadjuvant therapy, we assessed T-cell and B-cell infiltration using IHC on tissue sections obtained via electrotomy, including treatment-naïve samples from patients enrolled in a clinical trial (ChiCTR2000032359) patients with non-CR (n = 3) and CR (n = 5) responses (Fig. A). Our analysis revealed that the CR group exhibited a higher density of T/B-cell clustered structures, alongside numerous co-distributed HEVs, indicative of the presence of TLS structures (Fig. B, C). Quantitative analysis of the IHC maps demonstrated that the density of TLSs in tissues from patients who achieved CR was significantly higher compared to those from non-CR patients (Fig. D).
To further explore the spatial heterogeneity of the TME in CR and non-CR patients and to analyze the transcriptional characteristics of TLSs, we employed Visium ST technology from 10X Genomics. ST was performed on paired treatment-naïve and treatment-experienced FFPE sections from three patients in our cohort, including those with PD (n = 1), SD (n = 1), and CR (n = 1), generating six spatial expression maps (Fig. A). Following quality control, a total of 20,020 spots from these maps were retained for subsequent analysis. To characterize the spatial diversity of cell-type niche (CTN) within the MIBC TME, we combined the spots from different tissues and performed clustering analysis (Fig. B). Eight distinct CTNs were identified based on canonical cell-type markers and signatures. These included T/B cells (Immune/TB), myeloid cells (Immune/myeloid), immune cell mixtures (Immune/mixed), vascular vessels (Endovascular), lymphatic vessels (Endolymphatic), and fibroblast, epithelial and muscle cell-enriched niches (Fig. C, Fig. S1). All types of CTNs can be observed in the individual maps (Fig. D, Fig. S2), and the identification of specific markers of CNTs further validated the accuracy of the clustering analysis (Fig. E). Notably, the tumor purity signal was highly enriched in the epithelial and muscle cell niches, which is consistent with the pathological phenotype of MIBC (Fig. F).
To further explore the neoadjuvant therapy-associated immune niches, we compared the abundance of CTNs between treatment-naïve tissues from patients who achieved a CR and those who did not, using the odds ratio test (Fig. A). We found that immune-related CTNs, such as Immune/TB, Immune/mixed, and endothelial- associated niches, were significantly enriched in the tissues from CR patients (Fig. A, B). Enhanced lymphocytic infiltration is known to contribute to better responses to immunotherapy, while an increased density of blood vessels aids in the efficient delivery of chemotherapy agents. We then compared the Immune/TB and Immune/mixed niches between CR and non-CR tissues through differential gene expression analysis (Fig. C). The CR group exhibited higher expression of several B-cell-related genes, including MS4A1, CD79A, BANK1, BLK, and FCRL1 (Fig. C). GO analysis revealed that the differentially expressed genes (DEGs) from both the Immune/TB and Immune/mixed niches were primarily involved in adaptive immune-related biological processes, particularly those associated with B-cell function (Fig. D, E). Moreover, we observed an increased expression of genes related to B-cell activation in CR tissues, suggesting that B-cell-mediated immunity may play a critical role in the response to neoadjuvant therapy (Fig. F). T cells undergo functional polarization, which is largely influenced by the TME . We hypothesized that infiltrating T cells in response to different treatment regimens would exhibit distinct phenotypes and functional statuses. To test this hypothesis, we extracted T-cell spots (defined as spots expressing both CD3 and either CD4 or CD8) and assessed T-cell populations in the different treatment-response groups based on the expression levels of T-cell subtype and activation-related marker genes. As anticipated, regulatory T cells (Tregs) predominantly infiltrated tissues from patients with PD, while cytotoxic CD8 + T cells were primarily found in tissues from patients who achieved SD and CR (Fig. G, H). Stem-like CD8 + T cells have garnered attention for their capacity to maintain long-term T-cell responses due to their stem-like properties . We found that, compared with tissues from non-CR patients, tissues from CR patients had a greater infiltration of stem-like CD8 + T cells (Fig. H). Furthermore, CD8 + T cells infiltrating CR tissues exhibited increased expression of exhaustion markers, suggesting that these patients may benefit from ICI therapy (Fig. H). We also observed that tissues from patients who achieved CR contained abundant vascular vessels (Fig. A, B). The vasculature in the tumor not only contributes to immunotherapy responses by facilitating lymphocyte trafficking but also supports chemotherapy efficacy by ensuring adequate drug delivery to the tumor bed . To further characterize the molecular and functional properties of the endothelial vessels in PD, SD, and CR tissues, we performed GSVA of DEGs within the Endovascular CTN. The GSVA results revealed that the vascular vessels of the CR patients exhibited a more mature phenotype and a greater capacity for T/B-cell recruitment (Fig. I). In contrast, vascular vessels in tissues from PD patients showed lower levels of leukocyte-endothelial cell adhesion, a key process for lymphocyte extravasation. Additionally, we found that the Endovascular CTN of CR tissues displayed increased expression of lymphocyte- and adhesion molecule-related genes (Fig. J). Taken together, our findings suggest that high levels of lymphocytes infiltrating blood vessels, B-cell activation, and enrichment of pro-immune-type T cells can serve as important indicators for predicting the response to neoadjuvant therapy.
High numbers of tumor-infiltrating T cells and B cells are closely associated with the formation of TLSs, and the activation of B cells within the TME is dependent on the presence of TLSs . In our study, we found that the number of tumor-infiltrating T cells was comparable acros the different patient groups, while B cells were significantly enriched in the tissues from patients who achieved CR (Fig. A). Moreover, tissues from CR patients exhibited elevated expression levels of germinal center (GC) B cells, TLS-associated chemokines, adhesion molecules, the dendritic cell (DC) marker LAMP3, lymphotoxin signaling molecules, and HEV markers (Fig. A). As anticipated, signature-based analysis revealed that tissues from MIBC patients with CR showed high levels of lymph node development signals and TLS-associated signals, which were strongly colocalized with B-cell activation signals (Fig. B). By integrating H&E staining maps with gene expression data, we identified two distinct TLS regions in tissues from CR patients (Fig. C). Focusing on tissues from CR patients, which contain these TLSs, we evaluated the spatial distribution of T and B-cell according to the expression of specific cell markers (CD3 for T cells and MS4A1 for B cell). We observed that the core regions of the TLSs colocalized with both T and B cells, rather than with either T or B cells alone (Fig. C). The differentiation of B cells requires interactions with T cells. At the interface between the follicular and T-cell zones in secondary lymphoid organs, B cells interact with CD4 + helper T cells . These ongoing interactions between T cells and B cells are critical for the maintenance of GCs. Within the GCs, the interaction between B cells and T follicular helper (Tfh) cells promotes B-cell proliferation, class switch recombination, and differentiation into antibody-producing plasma cells or GC B cells . Recent studies have shown that B cells express major histocompatibility complex (MHC) class II molecules and possess the necessary machinery for antigen uptake, processing, and presentation, classifying them as professional antigen-presenting cells (APCs), akin to DCs and macrophages . In addition, B cells also express costimulatory molecules, including CD80 and CD86, which enhance T-cell activation by interacting with CD28 on T cells . To further explore the molecular and functional characteristics of T and B cells within TLSs, we compared T and B cell spots within and outside the TLS regions. Differential expression analysis revealed that chemokines involved in lymphocyte recruitment, as well as genes associated with B-cell activation and maturation, were more highly expressed within the TLS regions (Fig. S3A). GSEA further confirmed the increased activity of B-cell activation in T and B cell spots within the TLS regions (Fig. S3B). Furthermore, signature-based analysis showed that T cells within the TLS regions exhibited a greater cytotoxic potential, while B cells within these regions displayed characteristics consistent with antibody-secreting plasma cells. These findings suggest that the colocalization, or interaction, of T and B cells within the TLSs results in a stronger synergistic antitumor immune response (Fig. D). Since lymphocyte aggregation is primarily governed by chemokine gradients and cell–cell interactions, we investigated the ligand-receptor pairs (LRPs) contributing to lymphocyte aggregation using the R package SpaGene (version 0.1.0). This analysis identified nine chemokine-mediated cell–cell communication patterns within the tissue from patients who achieved CR (Fig. S3C). Among these, we identified TLS-specific LRPs (pattern 2 in Fig. S3C). Notably, the CCL19/CCL21-CCR7, CCL5/CCL14-ACKR1, and CXCL9-CXCR3 LRP axes were among the top five TLS-associated patterns (Fig. S3D). CCL19-CCR7 and CCL21-CCR7 are well-established canonical chemokine axes that promote TLS formation . We observed that CCL19/CCL21-CCR7 signaling was predominantly localized within the TLS regions (Fig. E). Within TLSs, CCL19 + and/or CCL21 + fibroblastic reticular cells (FRCs) guide the distribution of lymphocytes expressing the corresponding CCR7 receptors, thereby facilitating the formation of the T-cell zone formation . Additionally, CCL19/CCL21 recruit CCR7-expressing mature DCs, particularly LAMP3 + DCs, into the TLS regions to prime naïve T cells . In the TME, CXCL9, expressed by intratumoral conventional DCs, plays a key role in recruiting stem-like CD8 + T cells, promoting the replenishment of the effector T-cell pool . Furthermore, ACKR1, expressed on the blood endothelial cells (BECs) of postcapillary venules, facilitates the abluminal-to-luminal transcytosis of chemokines, thereby promoting the extravasation of immune cells from the vasculature into the tumor bed . We hypothesize that CCL5, retained at BEC junctions by ACKR1, aids immune cell extravasation, facilitating their infiltration into the TME. Subsequently, we explored the potential of the CCL19/CCL21-CCR7 axis as a therapeutic target for MIBC. The MB49 mouse bladder carcinoma cell line was intraperitoneally (i.p.) injected into C57BL /B6J mice. Starting on day 3, recombinant mouse Ccl19 and Ccl21 were administered i.p. six times every 2–3 days, and tumors were harvested on day 17. IHC analysis revealed that Ccl19/Ccl21 administration facilitated the formation of intratumoral TLSs (Fig. F, Fig. S4). More importantly, we observed that the delivery of Ccl19/Ccl21 enhanced the therapeutic efficacy of PD-1 inhibition, suggesting that these chemokines may serve as effective neoadjuvant therapeutic agents for MIBC (Fig. G). In summary, we identified the presence of TLSs in the tumors of patients who achieved CR, which was strongly associated with high levels of CCL19 and CCL21 enrichment in the TME. Furthermore, both CCL19 and CCL21 were shown to enhance the therapeutic efficacy of immunotherapy.
To gain deeper insight into TLS formation in MIBC, we conducted a detailed analysis of the Immunity/TB CTN in tissues from patients who achieved CR. The formation of TLSs is a multistep process, which involves: (1) the aggregation of lymphocytes, (2) the establishment of distinct T-cell and B-cell zones, and (3) the formation of GC-like structures within the B-cell zone . Interestingly, we identified two primary distribution patterns within the Immune/TB and Immune/mixed CTNs. In the TLS region, the Immune/TB CTN was surrounded by Immune/mixed CTN and formed a follicular structure (pattern 1), while in the non-TLS region, the Immune/TB CTN was distributed around the Immune/mixed CTN (pattern 2) (Fig. A). We hypothesized whether this phenomenon represents a progression in TLS formation, i.e., if the non-TLS pattern (pattern 2) eventually transitions into the TLS pattern (pattern 1), or if these patterns represent distinct evolutionary paths for lymphocyte clusters. To test this hypothesis, we compared the cellular composition and molecular characteristics of the Immune/TB CTN between the TLS-pattern and non-TLS-pattern. Deconvolution analysis using the monocytic cell proportion (MCP)-counter method further identified lymphocyte subtypes, as well as other cell types that colocalized with lymphocytes (Fig. B). In addition to the organized T-zone and B-zone, TLSs encompass a variety of cellular components, with Tfh cells being the most prominent. As anticipated, we observed a significant enrichment of Tfh cells within the TLS_TB regions. The interaction between B cells and Tfh cells drives immunoglobulin class-switching and affinity maturation in GCs, resulting in the generation of high-affinity, long-lived plasma cells (Fig. B). We also noted that IgM-positive naïve B cells were predominantly found in the non-TLS region, while IgG-positive B cells and plasma cells were significantly enriched in the TLS regions (Fig. B). Furthermore, we identified that FRCs and follicular dendritic cells (FDCs), collectively termed lymphoid tissue organizer (LTo) cells, were significantly enriched in the pattern 1_TB (Fig. B). These findings align with previous reports by Jeremy Goc et al., who demonstrated that LAMP3 + DCs (also referred to as DC-LAMP) are predominantly localized within the T-cell zone of TLSs . Consistently, we found that LAMP3 + DCs were significantly enriched in the pattern 1_TB region compared with the pattern 2_TB region. Interestingly, we also observed that terminally exhausted CD8 + T cells were enriched in the pattern 1_TB region, while macrophages and fibroblasts were more abundant in the pattern 2_TB region. To further investigate the relationship between the TLS and non-TLS patterns, we performed pseudotime analysis using Monocle2 . This analysis revealed a trajectory that originated at immune spots within the Immune/mixed CTN and subsequently bifurcated into two distinct differentiation paths: one leading to the Immune/TB spots in the pattern 1 region and the other leading to Immune/TB spots in the pattern 2 region (Fig. C). As expected, key TLS-associated molecules were predominantly enriched along the path leading to the pattern 1 region spots (Fig. C). To identify the characteristic factors involved in TLS formation, we analyzed the genes whose expression changed significantly over the course of pseudotime progression. Notably, genes that were highly expressed along the TLS trajectory were involved in processes related to cell–cell communication, including chemokine-mediated signaling, cell adhesion, and activation, highlighting the dynamic cell–cell interactions occuring during TLS formation (Fig. D). In contrast, the pattern 2 region branch was characterized by the enrichment of extracellular matrix (ECM) remodeling-related signaling pathways, which may inhibit lymphocyte interactions and, consequently, prevent TLS formation (Fig. D). Additionally, angiogenesis-related terms were significantly enriched in the pattern 2_TB region. The angiogenesis-driven immunosuppressive TME is known to lead to T-cell dysfunction, which disrupts T-B-cell interactions and inhibits TLS formation . Recent studies have highlighted the role of tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) in ECM remodeling and angiogenesis , and we hypothesize that these immunosuppressive processes mediated by TAMs and CAFs may inhibit TLS formation (Fig. E). Moreover, we identified several previously unreported TLS-related genes that were significantly upregulated in TLSs (Fig. F). The signature score, calculated using these newly identified TLS-associated genes in the TCGA-BLCA cohort, was positively correlated with the lymphocyte infiltration levels (Fig. G). In summary, our results indicate that TLS formation requires not only the chemokine-mediated recruitment and cell–cell interactions of specific cell populations (such as LAMP3 + DCs, Tfh cells, FRCs and FDCs) but also an immunosupportive TME (Fig. E). Additionally, we identified novel TLS-associated genes that are significantly correlated with immune cell infiltration in MIBC.
The neoadjuvant therapies administered in this cohort included camrelizumab and gemcitabine and cisplatin. The sensitivity to immunotherapy is influenced by the immunophenotype of the tumor, whereas the sensitivity to chemotherapy is more dependent on the intrinsic characteristics of the tumor. Therefore, the selection of an appropriate therapeutic strategy necessitates a comprehensive assessment of both the immune microenvironment and the tumor characteristics. To further investigate the tumor characteristics in responders, we conducted an analysis of the epithelial niches before and after therapy (Fig. A). Tumor purity scores were calculated at epithelial sites using the ESTIMATE package in R . As anticipated, tumor purity in tissues from patients who achieved a CR significantly decreased following neoadjuvant therapy (Fig. B). To evaluate the heterogeneity of the tumor regions across different response groups, we focused on samples from Clusters 3 and 4, which exhibited high tumor purity, for further analysis. Recently, MIBC has been classified into six consensus molecular subtypes: the luminal papillary (LumP), luminal nonspecified (LumNS), luminal unstable (LumU), stroma-rich, basal/squamous (Ba/Sq), and neuroendocrine-like (NE-like), each of which displays distinct responses to immunotherapy and chemotherapy . We applied a signature-based approach to calculate consensus class-related pathway scores across various tumor sites. This analysis revealed that malignant spots in patients who achieved PD and CR exhibited elevated urothelial differentiation signals (Fig. C). Furthermore, malignant spots from PD patients demonstrated increased cell cycle activity, while those from CR patients showed high expression of a noninvasive Ta pathway signature and an FGFR3 coexpressed gene signature, suggesting that the PD group primarily corresponds to the LumU phenotype, while the CR group aligns with the LumP phenotypes (Fig. C). Interestingly, we also observed that malignant spots from CR patients displayed high levels of basal differentiation and elevated cytotoxic lymphocyte infiltration, characteristics associated with the Ba/Sq phenotype. This suggests that CR patients exhibit a combination of LumP and Ba/Sq phenotypes (Fig. C). In contrast, malignant spots from patients with SD expressed signals indicative of neuroendocrine differentiation, cell cycle activity, and myofibroblasts, indicating a mixture of NE-like and stromal-rich phenotypes in these patients (Fig. C). These findings were further validated through IHC analysis (Fig. D, Fig. S5). In summary, our study revealed distinct molecular subtype compositions within individual MIBC patients. Specifically, patients with CR in our cohort exhibited a combination of LumP and Ba/Sq tumor phenotypes, while patients with SD displayed a mixture of NE-like and stromal-rich phenotypes. These observations underscore the importance of considering tumor heterogeneity when designing precision therapies.
Cisplatin-based neoadjuvant chemotherapy (NAC) prior to radical cystectomy remains the standard treatment for MIBC. However, a study has shown that only 30–40% of patients undergoing NAC achieve ypT0N0 disease . Given the promising efficacy of immunotherapy in advanced bladder cancer, combining NAC with immunotherapy holds potential for improving survival outcomes in MIBC patients . Recent studies, however, have highlighted that bladder cancer encompasses a variety of histological and molecular subtypes, each of which exhibits differential sensitivities to current therapeutic modalities . Approximately 40% of urothelial carcinoma cases demonstrate differentiation, which can significantly affect treatment responses . A post hoc analysis of the VESPER trial revealed that patients with ≥ 50% squamous cell differentiation or ≥ 50% micropapillary subtype showed reduced progression-free survival (PFS) following NAC. In recent years, several molecular classifications have been developed to better stratify patients based on prognosis and therapeutic response [ , – ]. For instance, Robertson et al. identified five distinct subtypes in the PURE01 trial, characterized by unique genomic profiles, transcriptomic signatures, and distinct TME, which influence differential responses to neoadjuvant immunotherapy. Therefore, comparing tumor samples with varying responses before and after NAC combined with immunotherapy presents an opportunity to identify key biological features that could inform more effective and personalized treatment strategies for bladder cancer patients. In this study, we analyzed the genome-wide transcriptomic heterogeneity of patients with three distinct responses to treatment (PD, SD and CR) using ST methods. Our analysis revealed that patients with CR exhibited significantly higher levels of lymphocyte infiltration and activation. Notably, we observed that the high density of lymphocytes in CR patients led to the formation of specialized structures known as TLS. Recently, various studies have shown that the density and maturity of TLS are correlated with better responses to immunotherapy, especially ICI . The presence of TLSs in tumors is often associated with the increasing of immune Infiltration . Due to their lymph node-like structure and the presence of mature DCs, TLSs serve as an ideal site for T cell local priming . This also explains why patients with TLSs have a better response rate to ICI therapy. In addition, the GC structures within TLS can also promote the differentiation of B cells into plasma cells. The antibodies secreted by these plasma cells can mediate tumor killing through antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) . Therefore, inducing the intratumoral TLS is highly effective in enhancing the sensitivity of bladder cancer to immunotherapy. By comparing T-cell- and B-cell-dominant spots both within and outside the TLS regions, we found that spots within the TLS regions exhibited higher levels of antitumor signaling compared to those outside the TLS regions. This may be attributed to the closer proximity of T and B cells within the TLS, which enhances their interaction capacity and thereby promotes more effective immune functions. To further investigate the mechanisms underlying TLS formation, we conducted pseudotime analysis, mapping the pathways associated with changes in cellular components during TLS development. Our findings highlighted that the CCL19/CCL21-CCR7 axis plays a dominant role in this process, positioning CCL19 and CCL21 as promising therapeutic targets for MIBC. Additionally, we validated the therapeutic potential of CCL19/CCL21 in mouse models. Chemokine-mediated cancer therapy is increasingly gaining attention. Generally, there are two therapeutic approaches for harnessing chemokines in cancer therapy: one is to inhibit immune-suppressive chemokines, and the other is to enhance the enrichment of inflammatory chemokines. As important inflammatory chemokines, the therapeutic strategy of CCL19/CCL21 is to increase the concentration of CCL19 and CCL21 in the tumor area through rational drug delivery methods, forming a chemokine gradient to promote greater lymphocyte infiltration. This can be achieved by delivering recombinant proteins, as employed in this study, or by using adeno-associated virus (AAV). The anti-tumor effects of CCL19 and CCL21 proteins have been validated in several preclinical mouse models . Therefore, we believe that the administration of CCL19/CCL21 recombinant protein drugs via intravenous, intravesical, and intratumoral injection could be effective in the treatment of bladder cancer. Moreover, we explored the intrinsic heterogeneity of spots of malignant cell spots and observed that a single patient could harbor multiple tumor subtypes, providing an explanation for the variable efficacy of MIBC treatments. Our study has several limitations. The primary constraint is the limited sample size, which may introduce bias and affect the generalizability of our findings. Additionally, the inherent limitations of ST technology complicate the dissection of the TME at the single-cell resolution level, which in turn poses challenges for the analysis and interpretation of the results. To mitigate these limitations, we took measures to enhance the robustness of our study through deeper analyses and experimental validations. Despite these challenges, our research provides valuable insights and contributes to a broader clinical perspective, advancing our understanding of MIBC.
Our study demonstrates that patients who achieve a pCR to neoadjuvant therapy exhibit high levels of lymphocyte infiltration, which leads to the formation of TLS. A comprehensive analysis of ST data revealed that the CCL19/CCL21-CCR7 ligand-receptor pair plays a critical role in TLS formation. Findings from mouse experiments further support the potential of CCL19 and CCL21 as therapeutic targets to enhance the ICI response rate in patients with MIBC. Moreover, we identified intrinsic heterogeneity within tumors from individual patients, which may influence the response to ICI therapy. Following these findings, we have also come up with new questions: What are the clinical methods to deliver these chemokines to the tumor? Among intravenous injection, intravesical instillation, and intratumoral injection, which method is the most effective and the most acceptable to patients? In addition to CCL19/CCL21, what other molecules can promote the formation of tertiary lymphoid structures in bladder cancer? We will conduct more research in the future to try to address these questions.
Supplementary Material 1: Figure S1. TSNE map shows cell type markers. Figure S2. Spatial feature plots show cell type niche score. Figure S3. TLS associated genes and pathway.Volcano plots shows differential expressed genes of T&B cell spots inside and outside the TLS regions.GSEA analysis identified the biological processes involved by differential expressed genes of T&B cell spots inside and outside the TLS regions.SpaGene analysis identified ligand-receptor pairs in treatment naïve tissue of CR patient. Figure S4. Whole pictures of IHC staining mouse slides. Figure S5. Whole pictures of IHC staining MIBC slides Supplementary Material 2
|
Framing Effects on Decision-Making for Diagnostic Genetic Testing: Results from a Randomized Trial | 505effc7-1fc4-4ae6-adf9-e1f653bd1d36 | 8234313 | Patient Education as Topic[mh] | Technologic advances and falling costs have led to increasing use of next-generation sequencing (e.g., whole exome sequencing) in routine clinical care . Genetic testing is no longer solely the domain of specialty clinics and test results are used to guide health behavior, disease management, and reproductive choices. Genetic testing decisions are challenging for patients for numerous reasons . First, genetic information can be complex and difficult to comprehend without adequate genetic literacy . Second, the benefit of testing may not be evident. For example, results are not always definitive (i.e., variants of unknown significance), contributing to prognostic uncertainty. Deciding to have genetic testing spurs a sequence of additional decisions arising from test results that may include preventative efforts such as significant lifestyle changes, risk-reducing surgery (e.g., hereditary breast and ovarian cancer syndrome), or reproductive decisions (e.g., pregnancy termination). Last, genetic tests are unlike other medical tests because results also implicate family members . Thus, family dynamics add to the complexity of testing decisions and may contribute to decisional conflict and regret . Genetic counselors have long played an important role in supporting patients and families to make informed testing decisions . Broadly, the goal of genetic counseling is to support autonomy, self-determination, and high-quality decisions that are informed and aligned with individual values and preferences. The pre-test counseling process is characterized by a non-directive approach that involves providing information and focusing statements to elicit values and preferences that shape behavioral intention (decisions), while post-test counseling aims to support individuals in interpreting genetic test results. The rise of direct-to-consumer (DTC) genetic testing has altered the landscape of genetic testing as individuals make autonomous decisions outside the context of healthcare delivery . Further, DTC services raise important ethical concerns because testing decisions and return of results occur without genetic counseling . To date, it is unclear if genetic testing decision-making can be affected by the manner that information about testing is presented. Choice architecture refers to the variety of ways that choices can be presented to consumers and the impact that a particular presentation has on decision-making . For example, the context of a decision can be framed positively (as a gain), or negatively (as a loss) to affect decision-making in a predictable way. Choice architecture has been widely examined in consumer research and policy. Today, insights from behavior economics and choice architecture are quite well established in consumer research and policy to “nudge” people towards better financial decisions, healthier food choices, and more ecologically conscious consumption . However, principles of behavioral economics and framing effects are virtually unexplored in relation to genetics. Such information would hold significant relevance for ensuring autonomy and self-determination for genetic testing decisions. Studies of framing effects on health information messages has shown mixed results . A Cochrane review found attribute framing (positive vs. negative words, e.g., “60% chance of survival” vs. “40% chance of dying”) does not influence persuasiveness yet negative goal framing (gain vs. loss, e.g., “screening will prolong your survival” vs. “not having screening will shorten your survival”) elicits more positive views of treatment effectiveness. A small proof-of-concept study compared opt-in, opt-out, and choice frames for a hypothetical oncology trial and found that the choice frame was less likely to bias preferences for participating in a hypothetical clinical trial . Few studies have examined framing effects on decision-making for genetic screening. Framing had no effect on pre-conception expanded carrier screening . A study examining optional bloodspot screening tests for newborns identified that the type of information provided influenced parents choosing optional testing . Notably, there is a paucity of data on framing effects in the setting of diagnostic genetic testing. We sought to determine the role of framing effects on genetic testing decision-making to inform clinical practices for pre-test genetic counseling. The Theory of Planned Behavior (TPB) has been employed in the field of genetic counseling to better understand and predict behaviors around prenatal genetic testing , testing for genetic susceptibility , and expanded carrier screening . Guided by the TPB, we aimed to examine how presenting information in different ways (i.e., choice architecture, ‘framing’) affects cognitions/decisions of individuals facing a hypothetical genetic testing decision. The purpose of this study was threefold. First, we aimed to examine the effect of framing on genetic testing decision-making compared with a non-directive choice presentation. Second, we sought to examine the predictors of satisfaction with decision and decision regret. Last, we aimed to compare genetic testing decision-making for a common, life-threatening condition (hereditary breast and ovarian cancer, HBOC) with a rare, life-altering condition (congenital hypogonadotropic hypogonadism, CHH). Thus, we tested two null hypotheses: (i) there is no significant difference in opting for genetic testing between choice and the other frames, and (ii) there is no significant difference in opting for genetic testing between disease scenarios (HBOC vs. CHH).
This study was reviewed and approved by the Boston College Institutional Review Board (protocol 20.205.01) and the randomized trial was registered with clinicaltrials.gov (NCT04372888). This study was conducted in accordance with the Declaration of Helsinki and all participants provided opt-in electronic consent prior to study participation. Results are reported using the CONSORT-SPI 2018 extension for randomized social & psychological interventions . 2.1. Trial Design The study employed a randomized factorial design with two factors. The first factor was disease type (hereditary breast and ovarian cancer, HBOC, and congenital hypogonadotropic hypogonadism, CHH). The second factor was decision frame (six levels). Participants were allocated in a 1:1 ratio. After being randomized to a hypothetical genetic testing scenario (i.e., HBOC or CHH), participants were randomized to receive one of six frames for decision-making (choice, opt-in, opt-out, enhanced choice [context], enhanced choice [norms], enhanced choice [affect])—yielding 12 groups in total ( ). No changes to the methods were made during the study. 2.2. Participants A national sample of diverse, English-speaking adults (18+ years) living in North America were recruited (24–31 March 2020). Participants were users of Amazon’s Mechanical Turk (AMT) platform . Briefly, AMT is a large, secure, web-based crowdsourcing tool for recruiting diverse participants (100,000+ members) used widely for online social and behavioral sciences research . Studies have demonstrated AMT data and results are comparable to traditional data collection methods and validity is supported by studies replicating the classic behavioral economics framing studies . All participants provided opt-in electronic consent prior to participation in the online survey. 2.3. Interventions Following opt-in informed consent, participants provided sociodemographic information, including personal experience with breast cancer or a rare disease, and were randomized to view either the HBOC or CHH clinical scenario. Each scenario includes: (i) contextual information (i.e., hypothetical clinical information leading the individual to seek medical attention); (ii) clinical information (i.e., a summary of how the diagnosis is made, whether life threatening or life altering, treatment options, hereditary nature of the condition (that it can be passed on to offspring)), (iii) diagnostic information including approach to diagnosis (i.e., blood tests, imaging studies, with/without genetic testing, and costs) as well as possible results (i.e., making/confirming a diagnosis, effect on treatment choice, identifying at-risk blood relatives, and risk of passing on to offspring) ( ). Participants were then randomized to one of six frames and asked to make a decision about genetic testing. The wording/phrasing for each frame is provided in . The comparator frame was active choice reflecting current genetic counseling practices. (i.e., you have two options—standard testing only or standard testing and a DNA test). Two frames were passive/default frames (opt-in, opt-out) addressing the so-called status quo bias. The other three frames (enhanced choice) were derived from the Theory of Planned Behavior (TPB) . The TPB is a well-validated framework that has been applied widely to understand and predict social and health behavior that has also been applied to decision-making in genetic counseling [ , , , ]. The TPB posits that intention is the immediate precursor of behavior. Intention is mediated by attitudes, subjective norms, and perceived behavioral control—all of which are influenced by beliefs. Genetic tests are unlike any other test in healthcare as results implicate at-risk blood relatives. Accordingly, we hypothesized that affect/commitments, consequences, and testing norms would be important factors in decision-making. The enhanced choice frames were labelled as such because they ‘enhanced’ certain aspects of the option by making it more salient over other aspects. The three enhanced choice frames included nudges relating to affect/commitments (i.e., ability to inform at-risk blood relatives or not), testing consequences (early vs. late detection), and testing norms (what most people do) ( ). Prior to launch, the survey was reviewed for health literacy and pilot tested ( n = 6) using a qualitative “think aloud” method . Briefly, individuals verbalized cognitive processes during problem-solving tasks, feedback that informed refining content presentation, and design and user engagement. 2.4. Outcomes Primary outcome measures included choosing to have genetic testing (yes/no), decision satisfaction, and regret. The Satisfaction with Decision Scale (SWD) is an easy to read, validated measure of patient satisfaction with a healthcare decision across a range of conditions and patient populations . It has good discriminant validity, correlates with decision confidence (r = 0.64), and has sufficient internal consistency (α 0.86). The decision regret scale (DRS) is a brief tool (five items) that uses five-point Likert type questions to assess distress and remorse related to a healthcare decision. The DRS has good internal consistency (α 0.92). We also employed Likert type questions (7-point scale: 1 = strongly disagree and 7 = strongly agree) to assess decision cognitions (i.e., TPB motivational drivers). Questions addressed attitudes toward genetic testing ( n = 3), subjective norms ( n = 2) assessing norms of dyadic relationships for genetic testing (family and physician respectively), and the perceived voluntariness and ability to make a testing decision (perceived behavioral control, n = 3). Additionally, perceived risk (and consequences) of the condition (common vs. rare) were measured. Questions derived from the TPB had an internal consistency of (α 0.71)—generally internal consistency >0.70 is considered ‘good’. We considered that health/genetic literacy could be an important variable. As such, participants completed subjective and objective measures of health literacy. The subjective measure of health literacy has been shown to detect limited health literacy as assessed by the Rapid Estimate of Adult Literacy in Medicine (REALM), a lengthier validated instrument (AUROC: 0.82) . The objective measure of health literacy, Newest Vital Sign (NVS), is a brief 6-item instrument that requires individuals to identify and interpret information from a nutrition label . The NVS has good internal consistency (α > 0.76) and correlates with the lengthier Test of Functional Health Literacy in Adults (TOFHLA) (AUROC: 0.88). Outcomes were measured following participant decision regarding genetic testing. No changes were made to trial outcomes after launching the study. 2.5. Sample Size A power calculation was based on (common, life-threatening and rare, and life-altering) multivariate analyses testing for pairwise differences using post hoc t-tests adjusted for multiple comparisons using Tukey’s HSD. We assumed a significance level of 0.05. For a Cohen effect size = 0.25 (error standard deviation assumed to be 1.0). We estimated 85 subjects would be needed per treatment level combination (680 total subjects) to achieve a power level of 0.80. We set target recruitment at 1000 participants (i.e., 500 in each arm). Interim analysis was not performed and there were no stopping guidelines. 2.6. Randomization/Sequence Generation Mechanical Turk users interested in participating were linked to a Qualtrics™ survey to review the informed consent. After providing consent, the Qualtrics™ program randomized participants in blocks of 12. Participants were blinded to randomization and data were not reviewed by investigators until data collection was completed. 2.7. Statistical Methods We used ANOVA to assess the relationship between frames and satisfaction (SWD) and regret (DRS), respectively. One-way ANOVA was applied to detect relationships between TPB responses and frames. Scheffe and Games–Howell post hoc tests were used as appropriate for between-group comparisons. Student’s t -tests were employed to assess relationships between subjective and objective health literacy (NVS), respectively, and testing decision. Linear regression was used to assess relationships between health literacy and education (collapsed into less than college education vs. college education or more). Logistic regression was used to examine if personal family experience with breast cancer or a rare disease affected genetic testing decision. Similarly, logistic regression was used to compare genetic test decision between and across groups. Multiple linear regression was utilized to explore the relationship between TPB responses and satisfaction (SWD) and regret (DRS), respectively. Significant standardized coefficients were compared to identify the largest effect satisfaction and regret, respectively. A p value of < 0.05 was considered statistically significant.
The study employed a randomized factorial design with two factors. The first factor was disease type (hereditary breast and ovarian cancer, HBOC, and congenital hypogonadotropic hypogonadism, CHH). The second factor was decision frame (six levels). Participants were allocated in a 1:1 ratio. After being randomized to a hypothetical genetic testing scenario (i.e., HBOC or CHH), participants were randomized to receive one of six frames for decision-making (choice, opt-in, opt-out, enhanced choice [context], enhanced choice [norms], enhanced choice [affect])—yielding 12 groups in total ( ). No changes to the methods were made during the study.
A national sample of diverse, English-speaking adults (18+ years) living in North America were recruited (24–31 March 2020). Participants were users of Amazon’s Mechanical Turk (AMT) platform . Briefly, AMT is a large, secure, web-based crowdsourcing tool for recruiting diverse participants (100,000+ members) used widely for online social and behavioral sciences research . Studies have demonstrated AMT data and results are comparable to traditional data collection methods and validity is supported by studies replicating the classic behavioral economics framing studies . All participants provided opt-in electronic consent prior to participation in the online survey.
Following opt-in informed consent, participants provided sociodemographic information, including personal experience with breast cancer or a rare disease, and were randomized to view either the HBOC or CHH clinical scenario. Each scenario includes: (i) contextual information (i.e., hypothetical clinical information leading the individual to seek medical attention); (ii) clinical information (i.e., a summary of how the diagnosis is made, whether life threatening or life altering, treatment options, hereditary nature of the condition (that it can be passed on to offspring)), (iii) diagnostic information including approach to diagnosis (i.e., blood tests, imaging studies, with/without genetic testing, and costs) as well as possible results (i.e., making/confirming a diagnosis, effect on treatment choice, identifying at-risk blood relatives, and risk of passing on to offspring) ( ). Participants were then randomized to one of six frames and asked to make a decision about genetic testing. The wording/phrasing for each frame is provided in . The comparator frame was active choice reflecting current genetic counseling practices. (i.e., you have two options—standard testing only or standard testing and a DNA test). Two frames were passive/default frames (opt-in, opt-out) addressing the so-called status quo bias. The other three frames (enhanced choice) were derived from the Theory of Planned Behavior (TPB) . The TPB is a well-validated framework that has been applied widely to understand and predict social and health behavior that has also been applied to decision-making in genetic counseling [ , , , ]. The TPB posits that intention is the immediate precursor of behavior. Intention is mediated by attitudes, subjective norms, and perceived behavioral control—all of which are influenced by beliefs. Genetic tests are unlike any other test in healthcare as results implicate at-risk blood relatives. Accordingly, we hypothesized that affect/commitments, consequences, and testing norms would be important factors in decision-making. The enhanced choice frames were labelled as such because they ‘enhanced’ certain aspects of the option by making it more salient over other aspects. The three enhanced choice frames included nudges relating to affect/commitments (i.e., ability to inform at-risk blood relatives or not), testing consequences (early vs. late detection), and testing norms (what most people do) ( ). Prior to launch, the survey was reviewed for health literacy and pilot tested ( n = 6) using a qualitative “think aloud” method . Briefly, individuals verbalized cognitive processes during problem-solving tasks, feedback that informed refining content presentation, and design and user engagement.
Primary outcome measures included choosing to have genetic testing (yes/no), decision satisfaction, and regret. The Satisfaction with Decision Scale (SWD) is an easy to read, validated measure of patient satisfaction with a healthcare decision across a range of conditions and patient populations . It has good discriminant validity, correlates with decision confidence (r = 0.64), and has sufficient internal consistency (α 0.86). The decision regret scale (DRS) is a brief tool (five items) that uses five-point Likert type questions to assess distress and remorse related to a healthcare decision. The DRS has good internal consistency (α 0.92). We also employed Likert type questions (7-point scale: 1 = strongly disagree and 7 = strongly agree) to assess decision cognitions (i.e., TPB motivational drivers). Questions addressed attitudes toward genetic testing ( n = 3), subjective norms ( n = 2) assessing norms of dyadic relationships for genetic testing (family and physician respectively), and the perceived voluntariness and ability to make a testing decision (perceived behavioral control, n = 3). Additionally, perceived risk (and consequences) of the condition (common vs. rare) were measured. Questions derived from the TPB had an internal consistency of (α 0.71)—generally internal consistency >0.70 is considered ‘good’. We considered that health/genetic literacy could be an important variable. As such, participants completed subjective and objective measures of health literacy. The subjective measure of health literacy has been shown to detect limited health literacy as assessed by the Rapid Estimate of Adult Literacy in Medicine (REALM), a lengthier validated instrument (AUROC: 0.82) . The objective measure of health literacy, Newest Vital Sign (NVS), is a brief 6-item instrument that requires individuals to identify and interpret information from a nutrition label . The NVS has good internal consistency (α > 0.76) and correlates with the lengthier Test of Functional Health Literacy in Adults (TOFHLA) (AUROC: 0.88). Outcomes were measured following participant decision regarding genetic testing. No changes were made to trial outcomes after launching the study.
A power calculation was based on (common, life-threatening and rare, and life-altering) multivariate analyses testing for pairwise differences using post hoc t-tests adjusted for multiple comparisons using Tukey’s HSD. We assumed a significance level of 0.05. For a Cohen effect size = 0.25 (error standard deviation assumed to be 1.0). We estimated 85 subjects would be needed per treatment level combination (680 total subjects) to achieve a power level of 0.80. We set target recruitment at 1000 participants (i.e., 500 in each arm). Interim analysis was not performed and there were no stopping guidelines.
Mechanical Turk users interested in participating were linked to a Qualtrics™ survey to review the informed consent. After providing consent, the Qualtrics™ program randomized participants in blocks of 12. Participants were blinded to randomization and data were not reviewed by investigators until data collection was completed.
We used ANOVA to assess the relationship between frames and satisfaction (SWD) and regret (DRS), respectively. One-way ANOVA was applied to detect relationships between TPB responses and frames. Scheffe and Games–Howell post hoc tests were used as appropriate for between-group comparisons. Student’s t -tests were employed to assess relationships between subjective and objective health literacy (NVS), respectively, and testing decision. Linear regression was used to assess relationships between health literacy and education (collapsed into less than college education vs. college education or more). Logistic regression was used to examine if personal family experience with breast cancer or a rare disease affected genetic testing decision. Similarly, logistic regression was used to compare genetic test decision between and across groups. Multiple linear regression was utilized to explore the relationship between TPB responses and satisfaction (SWD) and regret (DRS), respectively. Significant standardized coefficients were compared to identify the largest effect satisfaction and regret, respectively. A p value of < 0.05 was considered statistically significant.
3.1. Participant Characteristics In total, 1012 participants were recruited using Amazon’s Mechanical Turk platform (see Methods) and completed the study. Briefly, participants were randomized to one of two scenarios: either a common, life-threatening genetic condition (HBOC, n = 507) or a rare, life altering genetic condition (CHH, n = 505). After reviewing structured information (see Methods), participants were randomized to one of six frames for decision-making ( ) and then declared their decision regarding genetic testing. Participants reported no harms. Participants were evenly distributed between the groups and did not differ in terms of sociodemographics ( ). Overall, the mean age was 36 ± 11 years (95% CI: 34.4–42.1 years), the majority of participants were male (604/1012, 60%), and 424/1012 (42%) self-identified as white. Participants were generally well educated with 690/1012 (42%) having attained a bachelor’s degree or higher and 822/1012 (81%) had adequate health literacy, subjectively. In terms of objective health literacy, the participant mean score (2.97 ± 0.06, 95% CI: 2.85–3.08) is at the high end of the intermediate range (NVS score 0–1: high likelihood of limited health literacy, 2–3: possibility of limited health literacy, 4–6: almost always indicates adequate health literacy) (32). More than half were married (536/1012, 53%) and 502/1012 (81%) reported having children. 3.2. Effect of Choice Architecture (Framing) on Genetic Testing Decisions Examining choice architecture in the overall group, participants randomized to the ‘choice’ frame ( n = 171) were least likely to opt for testing compared with all other frames (79% vs. 83–91%, p < 0.05) ( ). Using choice as the base, we used logistic regression to determine the magnitude of the framing effect. One passive/default frame and two enhanced choice frames exhibited significant effects. Using the ‘opt out’ frame increased the odds for choosing genetic testing (OR 1.79, p = 0.048). The enhanced choice frames, derived from the TPB, increased the likelihood of choosing to have genetic testing (‘norms’: OR 2.73, p = 0.002, ‘affect/commitment’: OR 2.36, p = 0.007). We hypothesized that objective health literacy (NVS) could play a role in genetic testing decisions. However, we did not observe any association between NVS and overall decision to opt for genetic testing (OR = 1.048, t = −0.462, p = 0.64) regardless of frame. Among frames, there was no association observed between objective health literacy and decision to opt for genetic testing. In terms of disease scenario, framing neither had an effect on satisfaction (HBOC: F = 1.819, p = 0.10; CHH: F = 0.699, p = 0.62) nor regret (HBOC: F = 1.735, p = 0.12; CHH: F = 1.118, p = 0.35). We considered that framing could affect decision cognitions—yet no significant differences were observed across frames. Specifically, decision cognitions relating to “norms” ( n = 3) did not differ between active choice, opt-out, and enhanced choice ( p = 0.86, p = 0.12, p = 0.29 respectively). Similarly, decision cognitions relating to “consequences” ( n = 3) did not differ between the enhanced choice (consequences) frame and active choice ( p = 0.66, p = 0.42, p = 0.87, respectively). Neither satisfaction nor regret differed across the six frames (F = 1.353, p = 0.24; F = 0.875, p = 0.49, respectively) ( ). As satisfaction and regret are important outcomes for genetic testing decision-making, we used multiple linear regression to identify if elements of the TPB predict satisfaction and regret, respectively ( ). First, we examined for collinearity to determine if multiple significant effects could be masked in the multiple linear regression. No collinearity effects were observed, therefore, we did not consider this problematic for the analysis. The TPB concept of behavioral control (i.e., “Having genetic testing is entirely up to me”) was a predictor of satisfaction (B = 0.085, p < 0.001). Conversely, feeling one lacked behavioral control (i.e., “I feel I have no control over my decision to have genetic testing”, B = 0.346, p < 0.001) predicted decisional regret. Considering genetic testing as being beneficial for family members predicted satisfaction with decision yet TPB elements relating to consequences (physical, psychological, and social) had little effect on satisfaction. Several TPB factors predicted decision regret including perceiving that the condition would affect one personally, have physical consequences, or social (e.g., discrimination) consequences. Interestingly, perceiving the decision as being “easy” was associated with both satisfaction and regret. 3.3. Common, Life-Altering Scenario vs. Rare, Life-Altering Scenario Participants were randomized to make a genetic testing decision for genetic conditions with disparate consequences—either a common/life-threatening condition (hereditary breast and ovarian cancer, HBOC) or a rare, life-altering condition (congenital hypogonadotropic hypogonadism, CHH). A secondary aim of the study was to examine if differences were noted in genetic testing decisions between conditions with divergent frequencies that are at opposing ends of the lethality spectrum. In this hypothetical setting, the decision to opt for genetic testing did not differ between HBOC and CHH (443/507 [87.4%] vs. 428/505 [84.8%], respectively, p = 0.23). Framing neither had an effect on satisfaction (HBOC: F = 1.819, p = 0.10; CHH: F = 0.699, p = 0.62) nor regret (HBOC: F = 1.735, p = 0.12; CHH: F = 1.118, p = 0.35). We hypothesized that having experience (personal or family) with either breast cancer or a rare disease could affect testing decisions. Groups did not differ in terms of the rates of having prior experience (HBOC: 93/507 (18%), CHH: 87/505 (17%), ) and rates of individuals with prior experience did not differ across frames. Logistic regression revealed that having prior experience did not have any effect on opting to have genetic testing (HBOC: OR = 0.86, 95%CI: 0.60–1.24, p = 0.42; CHH: OR = 0.60, 95%CI: 0.39–0.92, p = 0.21). Similarly, no significant differences were observed according to individual frame. Thus, individuals with prior experience of the conditions (and presumably strong views about genetic testing) do not appear to be more influenced by the frames. We compared decision cognitions (based on the TPB) between the common and rare scenarios. Within-group scores did not differ across frames and scores were similar in 11/13 decision cognition questions ( ). The HBOC group assigned higher ratings than the CHH group for perceived risk (i.e., “The health scenario would affect me personally”, 5.97 ± 1.18 (95%CI: 5.86–6.07) vs. 5.77 ± 1.31 (95%CI: 5.65–5.88), p = 0.012) and norms (i.e., “Having genetic testing would be important for people I care about”, 5.92 ± 1.24 (95%CI: 5.81–6.03) vs. 5.72 ± 1.32 (95%CI: 5.61–5.84), p = 0.012). While the differences reached statistical significance, it is not clear that the magnitude of difference on a seven-point Likert-type scale would be clinically meaningful.
In total, 1012 participants were recruited using Amazon’s Mechanical Turk platform (see Methods) and completed the study. Briefly, participants were randomized to one of two scenarios: either a common, life-threatening genetic condition (HBOC, n = 507) or a rare, life altering genetic condition (CHH, n = 505). After reviewing structured information (see Methods), participants were randomized to one of six frames for decision-making ( ) and then declared their decision regarding genetic testing. Participants reported no harms. Participants were evenly distributed between the groups and did not differ in terms of sociodemographics ( ). Overall, the mean age was 36 ± 11 years (95% CI: 34.4–42.1 years), the majority of participants were male (604/1012, 60%), and 424/1012 (42%) self-identified as white. Participants were generally well educated with 690/1012 (42%) having attained a bachelor’s degree or higher and 822/1012 (81%) had adequate health literacy, subjectively. In terms of objective health literacy, the participant mean score (2.97 ± 0.06, 95% CI: 2.85–3.08) is at the high end of the intermediate range (NVS score 0–1: high likelihood of limited health literacy, 2–3: possibility of limited health literacy, 4–6: almost always indicates adequate health literacy) (32). More than half were married (536/1012, 53%) and 502/1012 (81%) reported having children.
Examining choice architecture in the overall group, participants randomized to the ‘choice’ frame ( n = 171) were least likely to opt for testing compared with all other frames (79% vs. 83–91%, p < 0.05) ( ). Using choice as the base, we used logistic regression to determine the magnitude of the framing effect. One passive/default frame and two enhanced choice frames exhibited significant effects. Using the ‘opt out’ frame increased the odds for choosing genetic testing (OR 1.79, p = 0.048). The enhanced choice frames, derived from the TPB, increased the likelihood of choosing to have genetic testing (‘norms’: OR 2.73, p = 0.002, ‘affect/commitment’: OR 2.36, p = 0.007). We hypothesized that objective health literacy (NVS) could play a role in genetic testing decisions. However, we did not observe any association between NVS and overall decision to opt for genetic testing (OR = 1.048, t = −0.462, p = 0.64) regardless of frame. Among frames, there was no association observed between objective health literacy and decision to opt for genetic testing. In terms of disease scenario, framing neither had an effect on satisfaction (HBOC: F = 1.819, p = 0.10; CHH: F = 0.699, p = 0.62) nor regret (HBOC: F = 1.735, p = 0.12; CHH: F = 1.118, p = 0.35). We considered that framing could affect decision cognitions—yet no significant differences were observed across frames. Specifically, decision cognitions relating to “norms” ( n = 3) did not differ between active choice, opt-out, and enhanced choice ( p = 0.86, p = 0.12, p = 0.29 respectively). Similarly, decision cognitions relating to “consequences” ( n = 3) did not differ between the enhanced choice (consequences) frame and active choice ( p = 0.66, p = 0.42, p = 0.87, respectively). Neither satisfaction nor regret differed across the six frames (F = 1.353, p = 0.24; F = 0.875, p = 0.49, respectively) ( ). As satisfaction and regret are important outcomes for genetic testing decision-making, we used multiple linear regression to identify if elements of the TPB predict satisfaction and regret, respectively ( ). First, we examined for collinearity to determine if multiple significant effects could be masked in the multiple linear regression. No collinearity effects were observed, therefore, we did not consider this problematic for the analysis. The TPB concept of behavioral control (i.e., “Having genetic testing is entirely up to me”) was a predictor of satisfaction (B = 0.085, p < 0.001). Conversely, feeling one lacked behavioral control (i.e., “I feel I have no control over my decision to have genetic testing”, B = 0.346, p < 0.001) predicted decisional regret. Considering genetic testing as being beneficial for family members predicted satisfaction with decision yet TPB elements relating to consequences (physical, psychological, and social) had little effect on satisfaction. Several TPB factors predicted decision regret including perceiving that the condition would affect one personally, have physical consequences, or social (e.g., discrimination) consequences. Interestingly, perceiving the decision as being “easy” was associated with both satisfaction and regret.
Participants were randomized to make a genetic testing decision for genetic conditions with disparate consequences—either a common/life-threatening condition (hereditary breast and ovarian cancer, HBOC) or a rare, life-altering condition (congenital hypogonadotropic hypogonadism, CHH). A secondary aim of the study was to examine if differences were noted in genetic testing decisions between conditions with divergent frequencies that are at opposing ends of the lethality spectrum. In this hypothetical setting, the decision to opt for genetic testing did not differ between HBOC and CHH (443/507 [87.4%] vs. 428/505 [84.8%], respectively, p = 0.23). Framing neither had an effect on satisfaction (HBOC: F = 1.819, p = 0.10; CHH: F = 0.699, p = 0.62) nor regret (HBOC: F = 1.735, p = 0.12; CHH: F = 1.118, p = 0.35). We hypothesized that having experience (personal or family) with either breast cancer or a rare disease could affect testing decisions. Groups did not differ in terms of the rates of having prior experience (HBOC: 93/507 (18%), CHH: 87/505 (17%), ) and rates of individuals with prior experience did not differ across frames. Logistic regression revealed that having prior experience did not have any effect on opting to have genetic testing (HBOC: OR = 0.86, 95%CI: 0.60–1.24, p = 0.42; CHH: OR = 0.60, 95%CI: 0.39–0.92, p = 0.21). Similarly, no significant differences were observed according to individual frame. Thus, individuals with prior experience of the conditions (and presumably strong views about genetic testing) do not appear to be more influenced by the frames. We compared decision cognitions (based on the TPB) between the common and rare scenarios. Within-group scores did not differ across frames and scores were similar in 11/13 decision cognition questions ( ). The HBOC group assigned higher ratings than the CHH group for perceived risk (i.e., “The health scenario would affect me personally”, 5.97 ± 1.18 (95%CI: 5.86–6.07) vs. 5.77 ± 1.31 (95%CI: 5.65–5.88), p = 0.012) and norms (i.e., “Having genetic testing would be important for people I care about”, 5.92 ± 1.24 (95%CI: 5.81–6.03) vs. 5.72 ± 1.32 (95%CI: 5.61–5.84), p = 0.012). While the differences reached statistical significance, it is not clear that the magnitude of difference on a seven-point Likert-type scale would be clinically meaningful.
Herein we present findings of an experiment examining genetic testing decision-making in two hypothetical scenarios (common/life-threatening and rare/life-altering). Traditionally, genetic counseling employs a non-directive approach (i.e., choice) to support patients and families in making testing decisions that are informed and aligned with values and preferences . We observed that default frames (i.e., opt-in, opt-out) as well as enhanced choice frames (based on the TPB) all increased the likelihood of individuals opting for genetic testing compared with the ‘choice’ frame. Findings from these hypothetical testing scenarios suggest that the manner in which a decision is framed influences individuals to opt for genetic testing (compared with standard choice). Notably, neither satisfaction with decision nor decision regret differed across the decision frames. Perceived autonomy was an important predictor satisfaction while lack of autonomy predicted decision regret. Genomic medicine is relevant throughout the lifespan from pre-conception (i.e., expanded carrier screening) to the newborn period (i.e., newborn bloodspot screening), to childhood/young adulthood (i.e., diagnosing Mendelian disorders), and into adult life (i.e., polygenic risk scores and cancer risk) . Few studies have examined the effect of framing and genetic testing decisions. Voorwinden and colleagues examined the effect of framing and narrative information on intended participation in expanded carrier screening for autosomal recessive conditions (i.e., pre-conception carrier screening). Investigators found no significant effect on intended participation in pre-conception carrier screening . Considering genetic testing in the newborn period, Lillie et al. found evidence of framing effects in the context of mandatory newborn bloodspot screening. Participants were more likely to select optional testing for a recessive condition (Duchenne muscular dystrophy) when receiving information about mandatory/standard newborn blood screening—compared with being offered testing for DMD in isolation . In a proof-of-concept study using a hypothetical cancer clinical trial scenario, Abhyankar and colleagues presented participants with three frames (choice, opt-in, and opt-out) then asked participants to make a decision (i.e., enroll in the trial, pursue standard treatment, or undecided) . Subsequently, participants received detailed information about the clinical trial and standard treatment and were then given the opportunity to change their initial decision. When the initial decision was presented using a default frame (opt-in or opt-out), participants were more likely to opt for the trial (or be undecided) rather than choosing standard treatment. In total, 16% of participants changed their decision after seeing detailed information. Notably, satisfaction with decision did not differ across frames—similar to the findings in the present study. Investigators concluded that presenting balanced and comprehensive information in parallel (i.e., side-by-side) prior to decision-making can help de-bias the decision frame. In contrast to Abhyankar and colleagues, participants in the present study were presented with side-by-side information prior to making a decision—yet we still observed significant framing effects. Thus, it is not clear that presenting detailed information in a side-by-side format is sufficient to de-bias decision framing. Notably, the presentation of information differed between studies. Abhyankar et al. depicted information on clinical trial vs. standard treatment more like a decision aid. Accordingly, one must be cautious not to over-interpret disparate findings between the studies. Findings from the present study raise important questions about self-determination in genetic testing decisions. Importantly, autonomy in genetic testing may relate to the individual (i.e., agency and the right to determine what happens to an individual) as well as blood relatives . For example, if an individual opts for genetic testing and gets results, the information from the test may rob blood relatives of autonomy as they may not have desired to know their potential risk. Thus, unlike other medical testing situations, genetic testing does not exist in a social vacuum—as findings also implicate blood relatives. Such ethical dilemmas are heightened by direct-to-consumer genetic testing that typically occurs without genetic counseling or clinician input . Data indicate that the lay public often has high expectations regarding what genetic test results can deliver (i.e., that results are actionable) . In contrast, findings of variants of unknown significance and uncertainty regarding penetrance and expressivity of variants makes interpretation challenging. Thus, genetic test results are not as definitive as the lay public perceives them to be . The gap between the state of the science in interpreting genetic test results and public perception raises questions about just how informed genetic testing decisions are. The American College of Medical Genetics has designated 73 genes as medically actionable . Actionable means that finding a deleterious mutation would result in specific evidence-based medical recommendations that could reduce mortality and disease risk. Similarly, the Centers for Disease Control (CDC) advocates cascade carrier screening for “Tier 1” conditions (e.g., hereditary breast and ovarian cancer syndrome, Lynch syndrome, and familial hypercholesterolemia). Cascade carrier screening is a process for identifying, informing, and managing at-risk blood relatives of individuals at risk for heritable conditions (e.g., CDC Tier 1 conditions) . By identifying potentially at-risk relatives, genetic testing can cascade through the family to inform individuals of their hereditary disease risk and guide interventions to improve outcomes. Our current findings demonstrate nudges can promote decisions for genetic testing. An ethical debate may examine the utility and appropriateness of framing decisions for Tier 1 genetic testing decisions. One may argue that framing could be applied to genetic-testing decisions because neither satisfaction nor regret are affected by choice architecture. However, as shown in , initial expectations (i.e., TPB attitudes, norms, as well as perceived consequences and behavioral control) play important roles in satisfaction and regret. This study did not assess if participants felt fully informed—another key element of high quality decisions. Thus, more clarity is needed to determine if framing encourages individuals to make less informed decisions. The enhanced choice frame relating to norms (i.e., TPB normative beliefs) was observed to be effective for nudging individuals to opt for genetic testing. Subjective norms refer to an individual’s perception of social pressures to adopt a specific behavior . Interestingly, a recent systematic review and meta-analysis examined whether subjective norms predict screening of cancer patients’ first-degree relatives . Investigators found that recommendation from a physician, healthcare provider, or family/friend significantly increased the likelihood of referring for screening and/or preventive measures. Thus, it appears that normative beliefs can play an important role in decision-making as well as actions that facilitate expanding genetic screening to potentially at-risk first-degree relatives (cascade carrier screening). It is worthwhile to note that not all genetic conditions have the same impact on health and quality of life. We compared genetic testing decision-making between two conditions with disparate prevalence (common vs. rare) and divergent disease trajectories (life-threatening vs. life-altering). Participants opting for genetic testing did not differ between the Tier 1 condition HBOC and the rare-like altering condition of CHH . Having prior family experience with breast cancer or a rare disease did not affect the decision for testing. Moreover, decision cognitions were strikingly similar between the hypothetical scenarios suggesting that decision-making was similar across a lethality index. Such findings hold relevance for providing decisional support for a wide range of genetic conditions regardless of prevalence or lethality. Presently, there is a shortfall of trained health professionals (i.e., genetic counselors) to meet growing genetic healthcare needs—contributing to growing health disparities . To reap the full potential of genomics for improving clinical outcomes and quality of life, novel approaches are needed to extend the reach of genetic testing decisional support (i.e., telegenetics). Findings from this hypothetical experiment point to the possible role for a modular approach to decisional support in supporting high quality decisions that are informed and aligned with patient values and preferences. For example, one might imagine web-based decisional support to increase access for patients wherein set modules addressing decision cognitions could be static (i.e., eliciting and inviting reflection on “values and preferences”—how one would use the test result to inform personal/family health decisions) while disease-specific modules could be introduced to help individuals be “informed” about the disease specific to the testing situation (i.e., HBOC, CHH). Such a modular approach could be a potential scalable solution to meeting the current shortfall of genetic healthcare professionals. However, further qualitative inquiry is needed in real-life situations to determine if our findings on common/rare decision cognitions hold up beyond hypothetical testing decisions. Findings from the present study are relevant to healthcare professionals (i.e., genetic counselors) as well as for direct-to-consumer genetic testing. First, a key tenet of genetic counseling is a non-directive approach (i.e., “choice”) that involves providing information and eliciting values and preferences to support high-quality decisions. Our observations of framing effects indicate that in hypothetical testing scenarios, testing decisions can be influenced by the way information is presented. Thus, a non-directive approach remains central for supporting patient autonomy. Moreover, our findings should caution clinicians that the way they present genetic testing information can nudge and bias patients towards testing. Similarly, study findings may be useful for informing guidelines for direct-to-consumer testing. For example, it is conceivable that marketing strategies could employ behavioral economics. Such framing of genetic testing decisions could undermine individuals’ agency and possibly be considered coercive. A relative strength of this study is the large, relatively diverse sample that mirrors the age of individuals who are typically presented with genetic testing decisions. It merits mention that while the mean sample age reflects typical timing of HBOC testing, it is less representative of CHH (as testing often occurs between 18–20 years of age). We also utilized recommendations from the International Patient Decision Aid Standards, i.e., presenting balanced information in a side-by-side format ( ), to help mitigate potential bias in presenting detailed information . Study findings should be interpreted with the understanding that genetic tests are typically offered to supplement an individual’s clinical, biochemical, and/or imaging data. Thus, the decision to have genetic testing may relate to other consequences (i.e., communicating genetic risk to blood relatives, guide treatment, and informing reproductive choices) rather than whether or not an individual wishes to make/confirm a diagnosis. Limitations of this investigation include the hypothetical nature of the experiment. However, it would be unethical to manipulate the frames in a real-world setting with patients. The majority of participants were white and well educated. As such, one should be cautious in extrapolating findings to communities of color and/or populations with less than a college education. Another caveat is that the sample had a relatively high level of health literacy and numeracy. Thus, findings may not be generalizable to individuals with limited health literacy and numeracy skills.
In conclusion, we found framing genetic testing decisions increases the likelihood of individuals opting for genetic testing. We believe these findings have implications for non-directive genetic counseling as framing that differs from ‘choice’ may nudge individuals to have genetic testing. Findings also raise important questions about patient autonomy and self-determination in making genetic testing decisions. Examining decision cognitions revealed that perceived behavioral control is important for increasing satisfaction and minimizing regret. We neither identified differences based on disease prevalence (common/rare) nor lethality (life-altering/life-threatening), raising the possibility of a modular approach to decisional support for genetic testing.
|
Genetic Variations Associated with Long-Term Treatment Response in Bipolar Depression | f6457519-b270-41bc-9485-427b6114e1b8 | 8391230 | Pharmacology[mh] | Bipolar disorder (BD) is a complex and severe psychiatric condition characterized by biphasic mood episodes of mania or hypomania and depression, which are expressed as recurrent episodes of changes in energy levels and behavior that last from days to weeks, each with subsyndromal symptoms commonly present between major episodes . The total lifetime prevalence of BD is approximately 2.4% . Despite an apparent lower prevalence than unipolar depression , BD is associated with higher levels of functional impairment and compromised quality of life, posing a greater socioeconomic burden . With a typical age at onset between late adolescence and early adulthood, BD represents one of the leading causes of disability among young people . Regarding gender distribution, while BD type I is considered to affect men and women equally, BD type II has been more frequently reported among women . Due to the progressive and debilitating course of this disorder, an early and effective therapeutic approach is key for patient prognosis . Pharmacological intervention, combined with psychotherapy and psychoeducation, are the therapeutic tools for managing BD. The choice of treatment options is guided by the phase of illness (mania/hypomania/depression/mixed) and past treatment history. Therefore, treatment guidelines include the use of mood stabilizers (lithium and some anticonvulsants such as carbamazepine and valproate) but also a variety of other psychoactive drugs, including antipsychotics, antidepressants, anxiolytics, and combinations thereof . However, treatment response is often inadequate, and the rate of remission, particularly in patients in a depressive episode, is low. This leads to multiple unsuccessful drug trials for many patients, with the probability of achieving remission decreasing with every additional treatment line. In addition, common side effects and poor tolerability to treatment are usually observed, particularly when multimodal drug therapy is used. Patient’s characteristics impacting drug response include endogenous and environmental factors, such as age, sex, body mass index, organ function, disease, concomitant medications, or lifestyle. Variability in treatment efficacy and tolerability has also been shown to be influenced by inherited genetic variation. Common genetic variation has been estimated to explain up to 42% of the variance in antidepressant response , and several genes with polymorphisms of pharmacogenetic (PGx) relevance in the treatment of major depression have been identified , although genome-wide association studies (GWAS) suggest no single polymorphisms of large effect . The effect of genetic variants in BD has been understudied in comparison with major depression, with lithium being the medication most extensively investigated . The largest GWAS to date in BD suggests that 15 genes robustly linked to BD encode druggable targets such as HTR6—a serotonin receptor targeted by antidepressants and antipsychotics . Another important GWAS on lithium response, conducted by the International Consortium on Lithium Genetics (ConLiGen) in 2563 patients with BD, found a significant association with a single locus of four linked single nucleotide polymorphisms (SNPs) on chromosome 21, which contained two genes coding for long non-coding RNAs of unknown function . Remarkably, an independent prospective study with 73 participants treated with lithium monotherapy showed an association of this region with the rate of relapse in the two-year follow-up . Important PGx progress has also been made in the context of severe life-threatening cutaneous drug reactions observed with antiepileptic mood-stabilizers. Carriers of the human leukocyte antigen HLA-B*15:02 allele who are of Asian descent are at an increased risk of developing Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) . Likewise, the presence of the HLA-A*31:01 allele has been associated with carbamazepine-induced hypersensitivity reactions in individuals of European, Korean, and Japanese ancestry . Therefore, recommendations for HLA-B*15:02 and/or HLA-A*31:01 are included in the summary of products characteristics (SmPC) of carbamazepine, as well as in guidelines by the Clinical Pharmacogenetics Implementation Consortium (CPIC) or the Canadian Pharmacogenomics Network for Drug Safety (CPNDS) . Another interesting example is the cytochrome (CYP) P450 superfamily, which includes the major drug-metabolizing enzymes involved in phase I reactions. Even though specific studies in BD are generally lacking, most drugs used to treat BD are metabolized by CYP450 enzymes. Therefore, genotyping the CYP450 genes to characterize the metabolizer profile of a patient becomes a potentially useful tool for medication selection and dose adjustment . Neuropharmagen ® (AB-Biotics S.A., Barcelona, Spain) is a commercial PGx-based tool that generates psychopharmacological recommendations based on pharmacogenetic guidelines issued by the CPIC and other reference organizations, PGx information included in the SmPC approved by regulatory agencies, and additional relevant information from selected clinical studies. The psychopharmacological recommendations are displayed using a safety-first approach that grades genetic polymorphisms associated with risk of adverse effects above the genetically determined metabolizer status and genetic variants of efficacy. The clinical utility of Neuropharmagen ® in major depression was initially evaluated in a twelve-week randomized controlled trial (RCT) in Spain, including 316 participants with major depressive disorder (MDD). In this trial, the PGx-guided arm showed a higher responder rate when compared to treatment as usual (TAU), with the PGx-guided treatment effects being more consistent in patients with 1–3 failed drug trials, as well as higher odds of achieving better tolerability . A reanalysis of this study showed that PGx-guided treatment was mostly beneficial in non-elderly subjects, and in those with moderate-to-severe depression, but not in patients with mild depression . These results were further replicated in an eight-week, industry-independent RCT in South Korea, including 100 adult patients with MDD, in which the PGx-guided arm showed higher response rates, less depressive symptoms, and better tolerability of treatments compared to TAU . The output of multigene PGx-based tools is based on the integrated analysis of several genes which could differ in their individual clinical utility in a disorder-related manner. Two small pilot trials have suggested a potential clinical utility of Neuropharmagen ® in patients with BD. A three-month prospective, observational trial including 30 patients with BD suggested that at baseline less than 5% of patients had an optimal treatment according to the PGx-guided information provided by Neuropharmagen ® . The evolution of severity (as assessed by the Clinical Global Impression of Severity, CGI-S) displayed a statistically significant treatment x time interaction, favoring those patients whose therapy had been changed following the test recommendations. Additionally, after the three-month follow-up period, the percentage of adverse effects was reduced when prescribing treatments concordant with the test . The second pilot study with Neuropharmagen ® in a BD cohort consisted of a 2-year mirror analysis of the expenditure of 30 patients with BD. Comparison of the expenditure before and after the change of therapy in agreement with the Neuropharmagen ® recommendations resulted in cost savings in terms of the number of consultations to the emergency room, and the number and mean duration of hospitalizations . The aim of the present pilot study was to objectively evaluate the long-term impact (versus the typical 8–12-week follow-up periods) of the genetic variation in individual genes—already demonstrated to be relevant in the pharmacological treatment of other psychiatric conditions such as MDD or schizophrenia—on the therapeutic response and side effects profile in a cohort of well-characterized patients with bipolar depression, using Neuropharmagen ® .
2.1. Study Design This observational, retrospective, epidemiological study enrolled a total of 76 patients from 1 March 2016 to 31 March 2016, among those attending the Bipolar and Depressive Disorders Unit Program of the Psychiatry Service of the Hospital Clinic de Barcelona (Barcelona, Spain). The follow-up was at least six months from the beginning of the index episode (IE), and the frequency of visits was determined by the usual practice at the Bipolar and Depressive Disorders Unit Program. depicts a schematic view of the study procedures. The study was conducted in compliance with Good Clinical Practices (as described in the Guidelines for Good Pharmacoepidemiology Practices (GPP) of the International Society for Pharmacoepidemiology, 2008 review) and the guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board (IRB) of Hospital Clínic de Barcelona, Spain (protocol code: PGx-BP; approval number and date: HCB/2015/0990, 4 January 2016), and the protocol was retrospectively registered at ClinicalTrials.gov as NCT04923204. All patients provided written informed consent to participate. In the case of disabled patients, informed consent was provided by the legal representative or responsible family. 2.2. Subjects The study included patients 18 years and older, with a diagnosis of BD with an IE of major depression with or without associated psychotic symptoms—according to the Diagnostic Manual of Mental Disorder 4th Edition Text Revision (DSM-IV-TR). Patients undergoing electroconvulsive therapy (ECT) or with any serious or terminal medical organic disease or an intelligence quotient <85 were excluded. 2.3. Data Collection The data were retrospectively collected from the patient clinical records of the hospital and added into an anonymized electronic database. Sociodemographic and clinical data were extracted from all enrolled subjects, including the pharmacological treatment in the IE and mood switch during the IE—the latter for those patients applicable—and the presence and type of side effect associated with the pharmacological treatment in the IE. The adverse events present in the reviewed patients’ charts were as follows: weight gain, dyslipidemia (cholesterol, LDL, HDL, triglycerides), glucose disturbance, sedation, extrapyramidal symptoms, seizures, neuroleptic malignant syndrome, QTc prolongation, sexual dysfunction, and hyperprolactinemia. Other data recorded were gender, age, ethnicity, marital status, academic level, weight and height, psychiatric and/or non-psychiatric comorbidities, smoking, alcohol intake, substance use disorder, psychiatric family history, type of bipolar disorder (I, II, or not otherwise specified—NOS), time since diagnosis, date and duration of IE, treatments prior to IE, bipolar episodes (type and number), need of hospitalization in IE, psychosis, suicidal ideation and/or suicidal attempt, use of anxiolytics, presence of mood switch during the IE, and time to mood switch. The patient’s clinical status was assessed by the treating psychiatrist according to the modified version of the Clinical Global Impression for Bipolar Disorder (CGI-BP-M) at the beginning of the IE of major depression, at the end of the IE, and at enrolment. The 17-item Hamilton Depression Rating Scale (HDRS-17) and Functioning Assessment Short Test (FAST) were also administered at enrolment. 2.4. Genotyping and Reporting of Results At enrolment, saliva samples from patients who gave their informed consent were collected with the commercial kit OG-510 (DNA Genotek Inc., Ottawa, ON, Canada) and sent to the laboratory of AB-Biotics S.A. (Barcelona, Spain) for DNA isolation and subsequent genotyping of selected variants. Details on the pharmacogenetic analysis have been previously described . Briefly, Neuropharmagen ® provided an interpretive report assigning one or more colors to each drug depending on the subject’s genotype: green for the presence of pharmacogenetic variants associated with increased positive response or lower likelihood of side effects, amber for variants associated with altered metabolism or reduced efficacy, red for variants associated with side effects not related to reduced plasma clearance, and white (or “standard”) when none of the genetic variants analyzed for the said drug were detected . The Neuropharmagen ® results were used for the purpose of the current retrospective association study. 2.5. Statistics Continuous variables were summarized as the means and standard deviations or median and range, while categorical variables were summarized as percentages. Because several genes, as well as clinical variables, may contribute to a given clinical outcome, the effect of the individual gene-drug pairs together with several clinical variables was analyzed by means of multivariate linear regression for continuous outcomes and multivariate binary logistic regression for dichotomous outcomes. A forward-selection approach with a p -value cutoff of 0.05 was used to identify the pharmacogenetic and clinical predictor variables with a significant contribution to each clinical outcome. 2.5.1. Predictor Variables Two types of predictor variables were considered: clinical and pharmacogenetic. The clinical variables included in the analyses were: Gender (male/female); Age (years); Presence of other psychiatric comorbidities (yes/no); Presence of non-psychiatric comorbidities (yes/no); Smoking (number of cigarettes per day at enrolment); Alcohol consumption (number of standard units per day at enrollment); Previous bipolar episodes (number); Type of bipolar disorder (I or II); Overall CGI-BP score at the onset of the IE; Presence of suicidal ideation (yes/no); Use of anxiolytics (yes/no); and Presence of mood switch (yes/no; not included when switch was the predicted variable). The pharmacogenetic variables included in the analysis were related to the following medications: Response to lithium ( CACNG2 ); Reduced metabolism of quetiapine ( CYP3A4 ); Variable metabolism of second-generation antipsychotics other than quetiapine ( CYP1A2 , CYP2D6 ); Lower side effects of risperidone or paliperidone ( AKT1-FCHSDQ-RPTOR-DDIT4 ); Response to selective serotonin reuptake inhibitors (SSRIs) ( SLC6A4 , BDNF , ABCB1 ); Increased side effects of SSRIs ( SLC6A4 , HTR2A ); Response to serotonin norepinephrine reuptake inhibitors (SNRIs) ( ABCB1 ); Variable metabolism of SNRIs ( CYP2D6 ); Response to anticonvulsants ( ABCB1 ); and Increased side effects of anticonvulsants ( HLA-A ). Pharmacogenetic variables were coded as +1 when the relevant genetic variant was detected in the genome of the patient and the patient was taking the particular affected drug, −1 when the genetic variant was not detected (i.e., the patient was wild-type for the polymorphism) and the patient was taking said affected drug, and 0 when the patient was not taking the affected drug, regardless of the patient’s genotype. Therefore, this codification system allowed to incorporate the aforementioned specific gene-drug interactions in the regression models, using the whole sample population ( n = 76) per each gene-drug interaction evaluated. 2.5.2. Clinical Outcomes For the purposes of this study, long-term treatment response in bipolar depression was analyzed using the CGI-BP-M scores at enrollment (primary outcome measure) as the dependent variable. The score-change in this scale as a measure of treatment efficacy (i.e., patient improvement or worsening) was not assessed due to the lack of baseline scores for the HDRS-17 and FAST scales (secondary outcomes). To account for the possible effect of the severity of patients at the beginning of the disorder, the baseline CGI-BP-M score was considered as an independent variable in the statistical models, but it was not significant in any model. Treatment tolerability was also assessed as a secondary outcome using the total number of adverse effects as a dependent variable in the regression models. Additionally, as autolytic ideation and mood switch could be characteristics of the disorder itself but also be driven by the pharmacological treatment, we chose to repeat the analysis adding these two factors to the definition of the dependent variable “adverse effects”. All statistical analyses were performed with the application R studio version 1.2.1335 of R statistical software version 3.6.1 .
This observational, retrospective, epidemiological study enrolled a total of 76 patients from 1 March 2016 to 31 March 2016, among those attending the Bipolar and Depressive Disorders Unit Program of the Psychiatry Service of the Hospital Clinic de Barcelona (Barcelona, Spain). The follow-up was at least six months from the beginning of the index episode (IE), and the frequency of visits was determined by the usual practice at the Bipolar and Depressive Disorders Unit Program. depicts a schematic view of the study procedures. The study was conducted in compliance with Good Clinical Practices (as described in the Guidelines for Good Pharmacoepidemiology Practices (GPP) of the International Society for Pharmacoepidemiology, 2008 review) and the guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board (IRB) of Hospital Clínic de Barcelona, Spain (protocol code: PGx-BP; approval number and date: HCB/2015/0990, 4 January 2016), and the protocol was retrospectively registered at ClinicalTrials.gov as NCT04923204. All patients provided written informed consent to participate. In the case of disabled patients, informed consent was provided by the legal representative or responsible family.
The study included patients 18 years and older, with a diagnosis of BD with an IE of major depression with or without associated psychotic symptoms—according to the Diagnostic Manual of Mental Disorder 4th Edition Text Revision (DSM-IV-TR). Patients undergoing electroconvulsive therapy (ECT) or with any serious or terminal medical organic disease or an intelligence quotient <85 were excluded.
The data were retrospectively collected from the patient clinical records of the hospital and added into an anonymized electronic database. Sociodemographic and clinical data were extracted from all enrolled subjects, including the pharmacological treatment in the IE and mood switch during the IE—the latter for those patients applicable—and the presence and type of side effect associated with the pharmacological treatment in the IE. The adverse events present in the reviewed patients’ charts were as follows: weight gain, dyslipidemia (cholesterol, LDL, HDL, triglycerides), glucose disturbance, sedation, extrapyramidal symptoms, seizures, neuroleptic malignant syndrome, QTc prolongation, sexual dysfunction, and hyperprolactinemia. Other data recorded were gender, age, ethnicity, marital status, academic level, weight and height, psychiatric and/or non-psychiatric comorbidities, smoking, alcohol intake, substance use disorder, psychiatric family history, type of bipolar disorder (I, II, or not otherwise specified—NOS), time since diagnosis, date and duration of IE, treatments prior to IE, bipolar episodes (type and number), need of hospitalization in IE, psychosis, suicidal ideation and/or suicidal attempt, use of anxiolytics, presence of mood switch during the IE, and time to mood switch. The patient’s clinical status was assessed by the treating psychiatrist according to the modified version of the Clinical Global Impression for Bipolar Disorder (CGI-BP-M) at the beginning of the IE of major depression, at the end of the IE, and at enrolment. The 17-item Hamilton Depression Rating Scale (HDRS-17) and Functioning Assessment Short Test (FAST) were also administered at enrolment.
At enrolment, saliva samples from patients who gave their informed consent were collected with the commercial kit OG-510 (DNA Genotek Inc., Ottawa, ON, Canada) and sent to the laboratory of AB-Biotics S.A. (Barcelona, Spain) for DNA isolation and subsequent genotyping of selected variants. Details on the pharmacogenetic analysis have been previously described . Briefly, Neuropharmagen ® provided an interpretive report assigning one or more colors to each drug depending on the subject’s genotype: green for the presence of pharmacogenetic variants associated with increased positive response or lower likelihood of side effects, amber for variants associated with altered metabolism or reduced efficacy, red for variants associated with side effects not related to reduced plasma clearance, and white (or “standard”) when none of the genetic variants analyzed for the said drug were detected . The Neuropharmagen ® results were used for the purpose of the current retrospective association study.
Continuous variables were summarized as the means and standard deviations or median and range, while categorical variables were summarized as percentages. Because several genes, as well as clinical variables, may contribute to a given clinical outcome, the effect of the individual gene-drug pairs together with several clinical variables was analyzed by means of multivariate linear regression for continuous outcomes and multivariate binary logistic regression for dichotomous outcomes. A forward-selection approach with a p -value cutoff of 0.05 was used to identify the pharmacogenetic and clinical predictor variables with a significant contribution to each clinical outcome. 2.5.1. Predictor Variables Two types of predictor variables were considered: clinical and pharmacogenetic. The clinical variables included in the analyses were: Gender (male/female); Age (years); Presence of other psychiatric comorbidities (yes/no); Presence of non-psychiatric comorbidities (yes/no); Smoking (number of cigarettes per day at enrolment); Alcohol consumption (number of standard units per day at enrollment); Previous bipolar episodes (number); Type of bipolar disorder (I or II); Overall CGI-BP score at the onset of the IE; Presence of suicidal ideation (yes/no); Use of anxiolytics (yes/no); and Presence of mood switch (yes/no; not included when switch was the predicted variable). The pharmacogenetic variables included in the analysis were related to the following medications: Response to lithium ( CACNG2 ); Reduced metabolism of quetiapine ( CYP3A4 ); Variable metabolism of second-generation antipsychotics other than quetiapine ( CYP1A2 , CYP2D6 ); Lower side effects of risperidone or paliperidone ( AKT1-FCHSDQ-RPTOR-DDIT4 ); Response to selective serotonin reuptake inhibitors (SSRIs) ( SLC6A4 , BDNF , ABCB1 ); Increased side effects of SSRIs ( SLC6A4 , HTR2A ); Response to serotonin norepinephrine reuptake inhibitors (SNRIs) ( ABCB1 ); Variable metabolism of SNRIs ( CYP2D6 ); Response to anticonvulsants ( ABCB1 ); and Increased side effects of anticonvulsants ( HLA-A ). Pharmacogenetic variables were coded as +1 when the relevant genetic variant was detected in the genome of the patient and the patient was taking the particular affected drug, −1 when the genetic variant was not detected (i.e., the patient was wild-type for the polymorphism) and the patient was taking said affected drug, and 0 when the patient was not taking the affected drug, regardless of the patient’s genotype. Therefore, this codification system allowed to incorporate the aforementioned specific gene-drug interactions in the regression models, using the whole sample population ( n = 76) per each gene-drug interaction evaluated. 2.5.2. Clinical Outcomes For the purposes of this study, long-term treatment response in bipolar depression was analyzed using the CGI-BP-M scores at enrollment (primary outcome measure) as the dependent variable. The score-change in this scale as a measure of treatment efficacy (i.e., patient improvement or worsening) was not assessed due to the lack of baseline scores for the HDRS-17 and FAST scales (secondary outcomes). To account for the possible effect of the severity of patients at the beginning of the disorder, the baseline CGI-BP-M score was considered as an independent variable in the statistical models, but it was not significant in any model. Treatment tolerability was also assessed as a secondary outcome using the total number of adverse effects as a dependent variable in the regression models. Additionally, as autolytic ideation and mood switch could be characteristics of the disorder itself but also be driven by the pharmacological treatment, we chose to repeat the analysis adding these two factors to the definition of the dependent variable “adverse effects”. All statistical analyses were performed with the application R studio version 1.2.1335 of R statistical software version 3.6.1 .
Two types of predictor variables were considered: clinical and pharmacogenetic. The clinical variables included in the analyses were: Gender (male/female); Age (years); Presence of other psychiatric comorbidities (yes/no); Presence of non-psychiatric comorbidities (yes/no); Smoking (number of cigarettes per day at enrolment); Alcohol consumption (number of standard units per day at enrollment); Previous bipolar episodes (number); Type of bipolar disorder (I or II); Overall CGI-BP score at the onset of the IE; Presence of suicidal ideation (yes/no); Use of anxiolytics (yes/no); and Presence of mood switch (yes/no; not included when switch was the predicted variable). The pharmacogenetic variables included in the analysis were related to the following medications: Response to lithium ( CACNG2 ); Reduced metabolism of quetiapine ( CYP3A4 ); Variable metabolism of second-generation antipsychotics other than quetiapine ( CYP1A2 , CYP2D6 ); Lower side effects of risperidone or paliperidone ( AKT1-FCHSDQ-RPTOR-DDIT4 ); Response to selective serotonin reuptake inhibitors (SSRIs) ( SLC6A4 , BDNF , ABCB1 ); Increased side effects of SSRIs ( SLC6A4 , HTR2A ); Response to serotonin norepinephrine reuptake inhibitors (SNRIs) ( ABCB1 ); Variable metabolism of SNRIs ( CYP2D6 ); Response to anticonvulsants ( ABCB1 ); and Increased side effects of anticonvulsants ( HLA-A ). Pharmacogenetic variables were coded as +1 when the relevant genetic variant was detected in the genome of the patient and the patient was taking the particular affected drug, −1 when the genetic variant was not detected (i.e., the patient was wild-type for the polymorphism) and the patient was taking said affected drug, and 0 when the patient was not taking the affected drug, regardless of the patient’s genotype. Therefore, this codification system allowed to incorporate the aforementioned specific gene-drug interactions in the regression models, using the whole sample population ( n = 76) per each gene-drug interaction evaluated.
For the purposes of this study, long-term treatment response in bipolar depression was analyzed using the CGI-BP-M scores at enrollment (primary outcome measure) as the dependent variable. The score-change in this scale as a measure of treatment efficacy (i.e., patient improvement or worsening) was not assessed due to the lack of baseline scores for the HDRS-17 and FAST scales (secondary outcomes). To account for the possible effect of the severity of patients at the beginning of the disorder, the baseline CGI-BP-M score was considered as an independent variable in the statistical models, but it was not significant in any model. Treatment tolerability was also assessed as a secondary outcome using the total number of adverse effects as a dependent variable in the regression models. Additionally, as autolytic ideation and mood switch could be characteristics of the disorder itself but also be driven by the pharmacological treatment, we chose to repeat the analysis adding these two factors to the definition of the dependent variable “adverse effects”. All statistical analyses were performed with the application R studio version 1.2.1335 of R statistical software version 3.6.1 .
3.1. Patient Characteristics Sociodemographic and clinical characteristics of the study participants are summarized in . The evaluated subjects had a mean age of 43.5 ± 9.1 years, 65.8% were females, and 92.1% were of European descent. All patients had a diagnosis of BD with an index episode of major depression. A total of 53.9% of patients had BD type I and 44.7% BD type II. The mean age of onset was 31.2 ± 10.7 years, and the average duration of illness was 11.9 ± 8.6 years. Mood switch was experienced by 26.3% of the patients. Patients were prescribed an average of 4.5 ± 1.4 medications for their index episode. The most prescribed medications for treating the IE were lithium, lamotrigine, and valproate among mood stabilizers; selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs) among antidepressants; and quetiapine and aripiprazole among antipsychotics . Baseline severity at the onset of the IE was evaluated using a modified version of the Clinical Global Impression for Bipolar Disorder (CGI-BP-M), yielding an average score of 3.9 ± 0.7 (overall), 4.0 ± 0.7 (depression subscale), and 1.4 ± 0.5 (mania subscale) . At the end of the episode, a statistically significant improvement was observed in the CGI-BP-M depression and overall subscales ( p -value < 0.001). This improvement was maintained at the enrollment visit . 3.2. Primary Results Based on the premise that response to a pharmacological treatment, in terms of efficacy and/or tolerability, may be different when a medication is taken by a patient who carries a genetic polymorphism associated with efficacy, metabolism, and/or side effects, compared with a person with a wild-type genotype, multiple linear regression analysis was used to detect statistically significant associations between the clinical outcome of patients and the putatively predictive variables. Given the high number of drugs per case and the impossibility to assign a unique type of gene-drug interaction to each patient based on the PGx report, the analysis was focused on the effect of individual gene-drug pairs. Specifically, the analysis was conducted on those drugs for which, among patients taking them, there was a minimum of two cases with and without a particular pharmacogenetic variant. The primary clinical outcome (dependent variable) analyzed was the CGI-BP-M score at enrollment. The CGI-BP-M score at the IE of major depression was considered as an independent variable in the statistical models to account for the possible effect of the severity of patients at beginning of the IE. The HDRS-17 score at enrollment and the number of adverse effects were included as secondary dependent variables in this study. Basic descriptive statistics and regression coefficients are shown in . Pharmacogenetic variables that significantly correlated with the scores in the CGI-BP-M scale at enrollment were a response to SNRIs and reduced metabolism of quetiapine . The genetically determined response to SNRIs—based on the analysis of the ABCB1 gene—showed a significant negative correlation with the CGI-BP-M overall score ( p -value < 0.030) and the depression subscore ( p -value < 0.026) at enrollment, indicating an association with lower scores in both domains of this scale . The response to SNRIs was also correlated with lower scores in the HDRS-17 scale at the time of analysis, and with a higher number of adverse effects ( p -value < 0.024) . The variable “reduced metabolism of quetiapine”—determined by the presence of CYP3A4*22 allele—was identified as a significant predictor of lower scores in the overall CGI-BP-M ( p -value < 0.038) and the depression subdomain ( p -value < 0.025) at enrollment. The pharmacogenetic variable lower risk of side effects of risperidone or paliperidone—based on the analysis of an mTOR-related multigenic predictor—was negatively correlated with the number of adverse effects, when including suicidal ideation and mood switch ( p = 0.013) Non-pharmacogenetic variables with a significant effect on the CGI-BP-M scores were the amount of tobacco and the treatment with anxiolytics . The presence of comorbidities significantly predicted higher HDRS-17 scores. The number of previous episodes and the presence of psychiatric comorbidities were also positively correlated with the number of adverse effects.
Sociodemographic and clinical characteristics of the study participants are summarized in . The evaluated subjects had a mean age of 43.5 ± 9.1 years, 65.8% were females, and 92.1% were of European descent. All patients had a diagnosis of BD with an index episode of major depression. A total of 53.9% of patients had BD type I and 44.7% BD type II. The mean age of onset was 31.2 ± 10.7 years, and the average duration of illness was 11.9 ± 8.6 years. Mood switch was experienced by 26.3% of the patients. Patients were prescribed an average of 4.5 ± 1.4 medications for their index episode. The most prescribed medications for treating the IE were lithium, lamotrigine, and valproate among mood stabilizers; selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs) among antidepressants; and quetiapine and aripiprazole among antipsychotics . Baseline severity at the onset of the IE was evaluated using a modified version of the Clinical Global Impression for Bipolar Disorder (CGI-BP-M), yielding an average score of 3.9 ± 0.7 (overall), 4.0 ± 0.7 (depression subscale), and 1.4 ± 0.5 (mania subscale) . At the end of the episode, a statistically significant improvement was observed in the CGI-BP-M depression and overall subscales ( p -value < 0.001). This improvement was maintained at the enrollment visit .
Based on the premise that response to a pharmacological treatment, in terms of efficacy and/or tolerability, may be different when a medication is taken by a patient who carries a genetic polymorphism associated with efficacy, metabolism, and/or side effects, compared with a person with a wild-type genotype, multiple linear regression analysis was used to detect statistically significant associations between the clinical outcome of patients and the putatively predictive variables. Given the high number of drugs per case and the impossibility to assign a unique type of gene-drug interaction to each patient based on the PGx report, the analysis was focused on the effect of individual gene-drug pairs. Specifically, the analysis was conducted on those drugs for which, among patients taking them, there was a minimum of two cases with and without a particular pharmacogenetic variant. The primary clinical outcome (dependent variable) analyzed was the CGI-BP-M score at enrollment. The CGI-BP-M score at the IE of major depression was considered as an independent variable in the statistical models to account for the possible effect of the severity of patients at beginning of the IE. The HDRS-17 score at enrollment and the number of adverse effects were included as secondary dependent variables in this study. Basic descriptive statistics and regression coefficients are shown in . Pharmacogenetic variables that significantly correlated with the scores in the CGI-BP-M scale at enrollment were a response to SNRIs and reduced metabolism of quetiapine . The genetically determined response to SNRIs—based on the analysis of the ABCB1 gene—showed a significant negative correlation with the CGI-BP-M overall score ( p -value < 0.030) and the depression subscore ( p -value < 0.026) at enrollment, indicating an association with lower scores in both domains of this scale . The response to SNRIs was also correlated with lower scores in the HDRS-17 scale at the time of analysis, and with a higher number of adverse effects ( p -value < 0.024) . The variable “reduced metabolism of quetiapine”—determined by the presence of CYP3A4*22 allele—was identified as a significant predictor of lower scores in the overall CGI-BP-M ( p -value < 0.038) and the depression subdomain ( p -value < 0.025) at enrollment. The pharmacogenetic variable lower risk of side effects of risperidone or paliperidone—based on the analysis of an mTOR-related multigenic predictor—was negatively correlated with the number of adverse effects, when including suicidal ideation and mood switch ( p = 0.013) Non-pharmacogenetic variables with a significant effect on the CGI-BP-M scores were the amount of tobacco and the treatment with anxiolytics . The presence of comorbidities significantly predicted higher HDRS-17 scores. The number of previous episodes and the presence of psychiatric comorbidities were also positively correlated with the number of adverse effects.
In recent years, substantial progress has been made in the field of pharmacogenetics in psychiatry. Particularly, several studies have demonstrated that pharmacogenetic-guided treatment significantly improves the efficacy and tolerability of medication in patients with a main diagnosis of major depressive disorder . However, the clinical utility of pharmacogenetic testing for the treatment of BD has been less studied . This observational retrospective study objectively evaluated the impact of the gene–drug pairs analyzed by a commercial pharmacogenetic-based tool in the response to pharmacological treatment in a well-characterized cohort of patients with BD . As expected, the pharmacogenetic variable “response to SNRIs”, determined from the analysis of the genetic variation in the transporter gene ABCB1 , was associated with lower scores in the CGI-BP-M overall and depression subscales (as well as the HDRS-17 scale), suggesting a role for this gene in predicting a good response to SNRI antidepressants in the context of BD treatment. These results are in accordance with previous studies linking genetic variation in ABCB1 to the efficacy of antidepressant medications that are substrates of this protein . Interestingly, in line with other studies, our results also suggested the association of ABCB1 polymorphisms with an increased number of adverse effects . The ABCB1 gene—formerly known as the multi-drug resistance gene MDR1 —codes for the drug efflux transporter permeability-glycoprotein (P-gp). P-gp actively drives the efflux of its substrates across cellular membranes in the intestine, liver, kidney, and the blood–brain barrier . The presence of polymorphisms lowering the activity of this protein is thought to be linked with higher concentrations of its substrates in the brain, which may explain the increased likelihood of response, but also the poorer tolerability observed in our study. The pharmacogenetic variable “reduced metabolism of quetiapine”, based on the analysis of the CYP3A4*22 allele, was associated with lower scores in the CGI-BP-M overall and depression subscales, thus positively impacting the likelihood of response. The CYP3A subfamily of enzymes is responsible for the metabolism of more than 50% of medications that undergo first-pass hepatic metabolism. The activity of CYP3A4 is highly variable due to changes in its expression levels that mainly occur in response to physiological states and environmental factors. Genetic variation has also been described to impact CYP3A4 activity. Particularly, the CYP3A4*22 polymorphism has been associated with reduced CYP3A4 expression levels and decreased enzyme activity in human livers . Individuals carrying this variant have plasma levels of quetiapine 2.5 times higher than non-carriers at comparable doses . Higher than expected serum concentrations of quetiapine in carriers of the CYP3A4*22 allele could explain a good response to this antipsychotic in our population. It is remarkable that the presence of this polymorphism did not correlate with an increased number of adverse effects in the studied cohort. The genetic variable “lower side effects of risperidone or paliperidone” was correlated with a lower number of adverse effects when this outcome variable included switch and autolytical ideation. This genetic variable is based on the research carried out by Mas and collaborators , who developed a model for the prediction of antipsychotic-induced extrapyramidal symptoms through the analysis of the epistatic interactions of four genes of the DRD1-activated mTOR pathway ( AKT1 , FCHSDQ , RPTOR , and DDIT4 ). Surprisingly, this pharmacogenetic marker was associated with a much broader definition of adverse effects in our population, which warrants further studies. Due to the observational uncontrolled design of this study, other available clinic-physiological characteristics of the patient sample were included in the statistical models to avoid the detection of false correlations with pharmacogenetic variables. Among non-pharmacogenetic variables associated with long-term patients’ status, the amount of tobacco and the treatment with anxiolytics were significantly positively correlated with the CGI-BP-M scores. The polycyclic aromatic hydrocarbons in cigarette smoke have been shown to induce CYP1A2, and different studies have reported decreased plasmatic concentrations of drugs primarily metabolized by CYP1A2 in smokers, including the antipsychotics clozapine and olanzapine , with the consequent poorer treatment response. We could not find a correlation between smokers with higher CGI-BP-M scores and treatment with clozapine or olanzapine, probably because of the small sample used in this study and the low number of patients treated with those drugs ( n ≤ 5). Additionally, other patients’ characteristics, such as the severity of the disease itself may account for increased tobacco consumption. Likewise, treatment with anxiolytics, as a marker of anxiety in the study sample, was also associated with the long-term severity in our cohort. Finally, the number of adverse effects was found to be associated with the number of previous episodes and the presence of psychiatric comorbidities, which might be indirectly indicative of a higher number of medication trials or polymedication in these patients. The predictors described in the present study could be of value for targeting patients with BD that most benefit from a particular medication in the long term. However, this study has some limitations. First, the reported R 2 for the models obtained was between 0.14 and 0.33, meaning that 67% to 86% of the variation in the clinical outcomes assessed would not be explained by the models. Nevertheless, since the statistic is strongly influenced by variation in the independent variables, a small R 2 may not necessarily be indicative of a weak relationship . Additionally, a low R 2 can be a good model in certain cases, such as in fields of study that have an inherently greater amount of variation, making it inevitable to obtain lower R 2 values. This is the scenario found in the genetic study of many phenotypes. While in monogenic disorders such as cystic fibrosis or Duchenne muscular dystrophy a single gene/mutation is the cause of the disorder, complex disorders such as diabetes, cancer susceptibility, or psychiatric disorders themselves are known to be polygenic, being influenced by several genes/variants, each of them having a small contribution, and often in conjunction with environmental factors. Drug response is indeed a complex trait, affected by several pharmacokinetic and pharmacodynamic genes, but also by the biology of the treated disease itself and a number of environmental factors. It is in this context that the predictive value of the significant variables in this study is valuable, adding to the body of evidence of the clinical validity of these gene-drug interactions in bipolar disorder. Second, the high number of medications per patient in the study population made it impossible to assign each patient to only one response-related category (i.e., efficacy, metabolism, side effects, or standard/wild-type genotype) according to the Neuropharmagen ® report. Most patients were treated with different medications associated with different response categories and, in some cases, one medication was associated with more than one category. The frequency of the medications prescribed in the study population was markedly unequal: lithium, SSRIs, or quetiapine were used in many cases, while other psychotropic medications were much less used. In addition, the pharmacogenetic tool did not provide information for some of the medications used in some patients (black bars in ). All these factors, together with the small sample size, may have restricted the number of significant treatment predictors found in the study. Finally, although the cohort of patients studied is representative of what clinicians encounter in their usual practice, the retrospective observational design is an additional limitation of this exploratory analysis. The results of this pilot study add to the evidence of the clinical validity of several gene–drug interactions in bipolar disorder (previously described to be relevant in other psychiatry conditions). However, further studies with larger samples of patients and a prospective, double-blind, randomized design will help determine if pharmacogenetic-guided treatment should become usual practice in the management of patients with BD.
Although studies proving the clinical utility of pharmacogenomics in BD are still in their early days, they offer a promise in improving clinical care. As more evidence becomes available, predictor models incorporating pharmacogenomics and clinical characteristics may one day help to decode the complexities of treatment responses in BD and successfully predict patient responses to medication.
|
Gene expression profiling of meningiomas: current status after a decade of microarray-based transcriptomic studies | 7a1a7739-6a48-48dc-91bc-215f7a76d5a4 | 3040823 | Pathology[mh] | Medicine has been transformed by the genomic revolution, and classical population risk assessment and empirical treatment challenged by molecular classification and the concept of personalized therapy . With high-throughput molecular genetic analyses, genes and pathways associated with, e.g., clinical progression, response to irradiation or drugs, and environmental exposure, can be discovered . Thus, new biomarkers and therapeutic targets may be developed. During the last decade, microarray technology has been implemented in molecular biological laboratories world-wide, and numerous studies have been performed in a wide range of human tissues and conditions. Microarray chips of the size of histology slides including all known human genes are currently manufactured with high precision. The main methods for the production of such chips are based on robotic spotting of cDNA probes or oligonucleotides to a surface (Fig. ). By labeling the RNA in the samples at study and hybridization to the corresponding gene probes on the microarray chip it is subsequently possible to measure the abundance of every gene expressed at time of tissue sampling. The measured signals are then converted to numerical values and interpreted with bioinformatic tools to produce the gene expression profile . Meningiomas (Fig. ), being the second most common intracranial neoplasm (20–30% of all cases) , have been extensively studied in the past. An important contribution to the understanding of the pathogenesis was the identification of the NF2 gene located on chromosome 22q12.2 [ – ]. Loss of one allele of this gene is causing the autosomal dominant syndrome neurofibromatosis type 2, in which bilateral vestibular schwannomas are pathognomonic, and multiple meningiomas often develop. From research on sporadic (non-NF2-related) meningiomas it has been found that loss of heterozygosity is found in 40–70% of cases and mutations in the remaining allele present in 60% [ – ]. Thus, a main mechanism of meningioma initiation follows the pattern of Knudsons two-hit hypothesis: first, a risk allele is deleted, and then a mutation in the remaining allele containing a tumor suppressor gene initiates neoplastic growth . However, it follows that this mechanism is not uniformly causing meningiomas, and that other genes or pathways are contributing to the tumorigenesis. Hence, gene expression profiling studies have the potential to discover novel genes and signaling pathways with a role in meningioma biology. The aim behind this article is to provide a review of the mRNA microarray studies that have been performed on meningiomas, and to reflect the advances in the biological understanding of the tumors gained by this technology. We also address some future prospects and challenges ahead in the field of gene expression profiling.
We performed a systematic search in the PubMed and EMBASE databases in May 2010 with the following medical subject headings (MeSH) terms: “meningioma”, “microarray analysis”, “oligonucleotide array sequence analysis”, “gene expression profiling” (Table ). Upon literature review only original research articles in English that had used RNA hybridized to high-resolution microarray chips to generate gene expression profiles were included.
Based on our search criteria we detected a total of 70 studies. There were no additional studies found in EMBASE to those located in PubMed. All microarray studies found by search term 1 were also found with search term 3. With search term 2, one additional study was identified; however, it was excluded since it was a review article. With search term 3, we found one additional RNA-based microarray study that was included. However, many of the studies were based on quantitative reverse transcriptase real time polymerase chain reaction (qRT-PCR) for selected genes with an a priori hypothesis. Other excluded studies were proteomic profiling projects, tissue arrays, review articles, and non-human material. We identified 13 original research articles that analyzed meningioma RNA with microarray chip technology (Table ). All studies have been performed during the last decade. Microarray platforms Six different microarray platforms had been used in the studies, with technology from Affymetrix being most frequently used (eight of 13 studies). Three of the studies used chips that were spotted with cDNA clones; the remaining used oligonucleotide microarrays. A high number of transcripts were represented on the chips; however, some of the early works included relatively few gene probes due to technical limitations at the time of study. Meningioma subtypes Altogether, 227 meningiomas were included in the studies; according to the WHO classification these were 151 grade I, 42 grade II, and 34 grade III. Some papers did not describe the histological subtypes present in their material; however, from the studies with histology data available we found the following subtypes present among the grade I tumors: fibrous ( n = 31), meningothelial (48), transitional ( n = 27), secretory ( n = 1), and psammomatous ( n = 1). Apart from two cases of clear cell meningiomas (WHO grade II), there was no further information on histological subtype for the grades II and III cases. Source of control tissue The aims of the various studies are different, and thus the source of control tissue is variable. For those studies aiming at detecting gene expression profiles separating meningiomas from tissue of origin the authors have extracted RNA from meninges, dura, arachnoid cyst membranes, normal brain, pooled normal tissue from various sources, and other brain tumors. Other studies have aimed at detecting gene expression changes due to irradiation, anatomical location (spinal vs. intracranial), and WHO grade; hence, samples from each of the categories have been compared with no external controls applied. Statistics Of the 13 microarray studies, ten provided a clear description of statistical procedure for the detection of differentially expressed genes between cases. Most frequently the Student’s t test was applied (seven studies), two studies used the Mann–Whitney test, one study used significance analysis of microarrays (SAM), and finally one study used receiver operating characteristic curve. Summary of main results (Table ) In the pioneering oligonucleotide microarray study on meningiomas by Watson and colleagues , 15 meningiomas (WHO grade I, n = 6; WHO grade II, n = 6; WHO grade III, n = 3) and three post mortem leptomeninges were studied. The main result was the detection of gene expression profiles associated with WHO grade subtypes (growth hormone receptors, endothelin receptor A, IGF2, IGFBP-7). Selected genes were confirmed using qRT-PCR. Sasaki and colleagues aimed at comparing the transcriptomic profiles of original meningiomas ( n = 3; one of each WHO grade) and primary cultures of the same samples. They found that 51 genes were up-regulated > fivefold, and 19 genes were down-regulated by twofold or more in the primary cultures. The results were validated with qRT-PCR. Fathallah-Shaykh and colleagues studied ten meningiomas and compared them with pooled post mortem brain RNA from the occipital lobes of four individuals. With their approach 364 genes were differentially expressed, and they found evidence of activation of different signaling pathways like Wnt, MAP kinase, PI3K, and notch. No validation of the findings with alternative approaches was performed. In a study of 30 meningiomas (WHO grade I, n = 13; WHO grade II, n = 12; WHO grade III, n = 5) Wrobel and colleagues investigated 2,600 genes using cDNA microarrays. The gene expression profiles of each category were compared with each other. The main finding was that 37 genes were decreased and 27 increases in grades II and II meningiomas compared with grade I. Compared with a pool of RNA from various human tissues (heart, spleen, placenta, kidney, skeletal muscle, liver, brain, and lung) a gene signature of the meningiomas was identified: PTGDS, CLU, BAD, MGP, LIG1, ANXA2, MMP12, VIM, TIMP1, and CCND1 were highly expressed in the meningiomas. Selected genes were validated with qRT-PCR, and for several candidates the results corroborated with those found by microarray. The authors concluded that the study showed that genes related to cell cycle regulation, cellular proliferation, as well as the IGF and WNT signaling pathways were up-regulated in grades II and III meningiomas. However, a main limitation of the study was the low number of cDNA probes, covering <10% of all human genes. The aim of the study of Lusis et al. was to identify genetic events responsible for malignant progression of meningiomas. Using the Affymetrix U133A/B GeneChip Microarrays the authors searched for transcripts that were lost in grade III meningiomas compared with grade I. They found that approximately 40% of down-regulated genes in grade III meningiomas were located at chromosomes 1p and 14q. One of the candidates, the NDRG2 genes, was consistently down-regulated in all grade III meningiomas both at the mRNA and protein level, and that this was correlated with hypermethylation of the corresponding promoter. As a part of a larger study of the genomics of spinal meningiomas Sayagues et al. performed gene expression profiling to compare spinal and intracranial meningiomas. They included seven spinal and 11 intracranial meningiomas in the study. The main result was differential expression of 1,555 genes, of which 35 genes showed the highest correlation ( r 2 > 0.7 or r 2 < −0.7). Thirty of these had lower expression in the intracranial tumors, whereas the remaining five genes were up-regulated. Three genes were selected for qRT-PCR validation, and a significant correlation ( p < 0.001) with microarray expression was found for all genes (NR4A3, DUSP5, and HOXA5). In the study of Carvalho et al. the purpose was to identify molecular signatures that characterize the different grades of meningiomas and molecular mechanisms driving meningioma tumorigenesis. They included 23 meningiomas (WHO grade I, n = 8; WHO grade II, n = 7; WHO grade III, n = 8). Using SAM, the authors found 28 genes differentially expressed between grades I and II meningiomas, and no differential expression between grades II and III. A total of 1,212 genes were differentially expressed between grades I and III meningiomas. In an unbiased unsupervised cluster analysis the 23 meningiomas grouped in two branches. All grades I and grade III meningiomas were located in separate branches, and the authors thus designated each branch as “low proliferative” and “high proliferative”, respectively. The grade II meningiomas were located in both branches, three of seven in the low-proliferative group, and the remaining four in the high-proliferative group. A selection of genes were validated using qRT-PCR. In 2008, our group published a microarray study on meningiomas of grades I ( n = 22) and II ( n = 5), where the aim was to study the gene expression profiles of meningiomas in comparison to progenitor meningioma tissue (arachnoid cells). Unsupervised cluster analysis of a filtered data set of 16,430 genes showed that five of seven fibrous meningiomas clustered together, while the remaining samples (meningothelial, transitional, and atypical) made no clear branching. As control tissue we used samples from the membranes of four arachnoid cysts (AC), and all these samples formed a separate cluster indicating a very homogeneous transcription profile. Using the t test, we detected 20 genes that differentiated between meningiomas and ACs ( p < 4.3 × 10 −7 ), in which the tumor suppressor gene WWOX was down-regulated and the oncogene TYMS was up-regulated. We also found 20 genes separating fibrous from meningothelial meningiomas ( p < 1.1 × 10 −5 ), where DMD and BMPR1B were up-regulated in the fibrous, and RAMP1 was down-regulated in the meningothelial meningiomas. qRT-PCR was performed on a selection of genes and showed similar expression profiles as those generated using microarray analysis. Since a recognized mechanism of meningioma initiation is irradiation, Lillehei et al. performed a microarray study of five radiation-induced meningiomas (RIM) and six spontaneous meningiomas to find unique genes behind this phenomenon. Interestingly, based on a microarray of 54,675 genes unsupervised hierarchical cluster analysis did not show separate clustering of RIMs and spontaneous meningiomas. Using a t test to compare the gene expression profiles of RIMs and spontaneous meningiomas the authors found a small subset of 20 genes separating the two groups ( p < 0.001). Hankins et al. studied the expression profiles of 12,000 genes in six meningiomas (WHO grade I) and four dural samples. By this approach, the authors found five up-regulated and 35 down-regulated genes in the meningiomas. The down-regulation of the DLC1 gene was confirmed with qRT-PCR and immunohistochemical staining. No evidence of CpG methylation of the corresponding promoter was found. The authors concluded that DLC1 may function as a tumor suppressor gene in meningiomas. Claus and colleagues studied 31 samples from sporadic meningiomas (WHO grade I, n = 25; WHO grade II, n = 6) with the aim of examining the gene expression profiles in relation to hormone receptor status. Estrogen receptor positivity was present in 33% and progesterone receptor positivity in 84%. In a comparison of PR+ and PR− meningiomas, the study showed up-regulation of ten genes, and down-regulation of 14 genes. No genes separated ER+ from ER− meningiomas. As the number of candidate genes was small, no single pathways or groups of genes were clearly identified. In 2009, Fèvre-Montange and colleagues published a transcriptomic study of 17 meningiomas (WHO grade I, n = 10; WHO grade II, n = 5; WHO grade III, n = 2). As control tissue RNA from a human whole brain (72 years of age) was used. The aim was to distinguish between the different WHO grades and histopathological subtypes, and to identify factors predicating recurrence. Unsupervised cluster analysis showed three groups of samples: group A consisted of seven of ten grade I cases, group B of the remaining three grade I samples and all five grade II meningiomas, and finally group C consisted of the two grade III tumors. Statistical analysis revealed that 346 and 2,995 genes showed more than twofold over-expression in groups B and C, respectively. Similarly, 184 and 1,380 genes were down-regulated, respectively. Furthermore, the study showed differential gene expression between fibrous and meningothelial meningiomas, with 12 up-regulated and 20 down-regulated genes in the fibrous subset. Selected genes were validated with qRT-PCR. The last published microarray study on meningiomas so far was published in 2009 and performed by Castells et al. . The aim was to assess whether automated categorization of brain tumors can be made by the use of microarray. Biopsies from 35 patients (17 glioblastomas and 18 meningothelial meningiomas) were subjected to cDNA-based microarray analysis. The study showed up to 100% prediction accuracy by using microarrays, thus providing evidence of possible clinical diagnostic use of this technology.
Six different microarray platforms had been used in the studies, with technology from Affymetrix being most frequently used (eight of 13 studies). Three of the studies used chips that were spotted with cDNA clones; the remaining used oligonucleotide microarrays. A high number of transcripts were represented on the chips; however, some of the early works included relatively few gene probes due to technical limitations at the time of study.
Altogether, 227 meningiomas were included in the studies; according to the WHO classification these were 151 grade I, 42 grade II, and 34 grade III. Some papers did not describe the histological subtypes present in their material; however, from the studies with histology data available we found the following subtypes present among the grade I tumors: fibrous ( n = 31), meningothelial (48), transitional ( n = 27), secretory ( n = 1), and psammomatous ( n = 1). Apart from two cases of clear cell meningiomas (WHO grade II), there was no further information on histological subtype for the grades II and III cases.
The aims of the various studies are different, and thus the source of control tissue is variable. For those studies aiming at detecting gene expression profiles separating meningiomas from tissue of origin the authors have extracted RNA from meninges, dura, arachnoid cyst membranes, normal brain, pooled normal tissue from various sources, and other brain tumors. Other studies have aimed at detecting gene expression changes due to irradiation, anatomical location (spinal vs. intracranial), and WHO grade; hence, samples from each of the categories have been compared with no external controls applied.
Of the 13 microarray studies, ten provided a clear description of statistical procedure for the detection of differentially expressed genes between cases. Most frequently the Student’s t test was applied (seven studies), two studies used the Mann–Whitney test, one study used significance analysis of microarrays (SAM), and finally one study used receiver operating characteristic curve.
) In the pioneering oligonucleotide microarray study on meningiomas by Watson and colleagues , 15 meningiomas (WHO grade I, n = 6; WHO grade II, n = 6; WHO grade III, n = 3) and three post mortem leptomeninges were studied. The main result was the detection of gene expression profiles associated with WHO grade subtypes (growth hormone receptors, endothelin receptor A, IGF2, IGFBP-7). Selected genes were confirmed using qRT-PCR. Sasaki and colleagues aimed at comparing the transcriptomic profiles of original meningiomas ( n = 3; one of each WHO grade) and primary cultures of the same samples. They found that 51 genes were up-regulated > fivefold, and 19 genes were down-regulated by twofold or more in the primary cultures. The results were validated with qRT-PCR. Fathallah-Shaykh and colleagues studied ten meningiomas and compared them with pooled post mortem brain RNA from the occipital lobes of four individuals. With their approach 364 genes were differentially expressed, and they found evidence of activation of different signaling pathways like Wnt, MAP kinase, PI3K, and notch. No validation of the findings with alternative approaches was performed. In a study of 30 meningiomas (WHO grade I, n = 13; WHO grade II, n = 12; WHO grade III, n = 5) Wrobel and colleagues investigated 2,600 genes using cDNA microarrays. The gene expression profiles of each category were compared with each other. The main finding was that 37 genes were decreased and 27 increases in grades II and II meningiomas compared with grade I. Compared with a pool of RNA from various human tissues (heart, spleen, placenta, kidney, skeletal muscle, liver, brain, and lung) a gene signature of the meningiomas was identified: PTGDS, CLU, BAD, MGP, LIG1, ANXA2, MMP12, VIM, TIMP1, and CCND1 were highly expressed in the meningiomas. Selected genes were validated with qRT-PCR, and for several candidates the results corroborated with those found by microarray. The authors concluded that the study showed that genes related to cell cycle regulation, cellular proliferation, as well as the IGF and WNT signaling pathways were up-regulated in grades II and III meningiomas. However, a main limitation of the study was the low number of cDNA probes, covering <10% of all human genes. The aim of the study of Lusis et al. was to identify genetic events responsible for malignant progression of meningiomas. Using the Affymetrix U133A/B GeneChip Microarrays the authors searched for transcripts that were lost in grade III meningiomas compared with grade I. They found that approximately 40% of down-regulated genes in grade III meningiomas were located at chromosomes 1p and 14q. One of the candidates, the NDRG2 genes, was consistently down-regulated in all grade III meningiomas both at the mRNA and protein level, and that this was correlated with hypermethylation of the corresponding promoter. As a part of a larger study of the genomics of spinal meningiomas Sayagues et al. performed gene expression profiling to compare spinal and intracranial meningiomas. They included seven spinal and 11 intracranial meningiomas in the study. The main result was differential expression of 1,555 genes, of which 35 genes showed the highest correlation ( r 2 > 0.7 or r 2 < −0.7). Thirty of these had lower expression in the intracranial tumors, whereas the remaining five genes were up-regulated. Three genes were selected for qRT-PCR validation, and a significant correlation ( p < 0.001) with microarray expression was found for all genes (NR4A3, DUSP5, and HOXA5). In the study of Carvalho et al. the purpose was to identify molecular signatures that characterize the different grades of meningiomas and molecular mechanisms driving meningioma tumorigenesis. They included 23 meningiomas (WHO grade I, n = 8; WHO grade II, n = 7; WHO grade III, n = 8). Using SAM, the authors found 28 genes differentially expressed between grades I and II meningiomas, and no differential expression between grades II and III. A total of 1,212 genes were differentially expressed between grades I and III meningiomas. In an unbiased unsupervised cluster analysis the 23 meningiomas grouped in two branches. All grades I and grade III meningiomas were located in separate branches, and the authors thus designated each branch as “low proliferative” and “high proliferative”, respectively. The grade II meningiomas were located in both branches, three of seven in the low-proliferative group, and the remaining four in the high-proliferative group. A selection of genes were validated using qRT-PCR. In 2008, our group published a microarray study on meningiomas of grades I ( n = 22) and II ( n = 5), where the aim was to study the gene expression profiles of meningiomas in comparison to progenitor meningioma tissue (arachnoid cells). Unsupervised cluster analysis of a filtered data set of 16,430 genes showed that five of seven fibrous meningiomas clustered together, while the remaining samples (meningothelial, transitional, and atypical) made no clear branching. As control tissue we used samples from the membranes of four arachnoid cysts (AC), and all these samples formed a separate cluster indicating a very homogeneous transcription profile. Using the t test, we detected 20 genes that differentiated between meningiomas and ACs ( p < 4.3 × 10 −7 ), in which the tumor suppressor gene WWOX was down-regulated and the oncogene TYMS was up-regulated. We also found 20 genes separating fibrous from meningothelial meningiomas ( p < 1.1 × 10 −5 ), where DMD and BMPR1B were up-regulated in the fibrous, and RAMP1 was down-regulated in the meningothelial meningiomas. qRT-PCR was performed on a selection of genes and showed similar expression profiles as those generated using microarray analysis. Since a recognized mechanism of meningioma initiation is irradiation, Lillehei et al. performed a microarray study of five radiation-induced meningiomas (RIM) and six spontaneous meningiomas to find unique genes behind this phenomenon. Interestingly, based on a microarray of 54,675 genes unsupervised hierarchical cluster analysis did not show separate clustering of RIMs and spontaneous meningiomas. Using a t test to compare the gene expression profiles of RIMs and spontaneous meningiomas the authors found a small subset of 20 genes separating the two groups ( p < 0.001). Hankins et al. studied the expression profiles of 12,000 genes in six meningiomas (WHO grade I) and four dural samples. By this approach, the authors found five up-regulated and 35 down-regulated genes in the meningiomas. The down-regulation of the DLC1 gene was confirmed with qRT-PCR and immunohistochemical staining. No evidence of CpG methylation of the corresponding promoter was found. The authors concluded that DLC1 may function as a tumor suppressor gene in meningiomas. Claus and colleagues studied 31 samples from sporadic meningiomas (WHO grade I, n = 25; WHO grade II, n = 6) with the aim of examining the gene expression profiles in relation to hormone receptor status. Estrogen receptor positivity was present in 33% and progesterone receptor positivity in 84%. In a comparison of PR+ and PR− meningiomas, the study showed up-regulation of ten genes, and down-regulation of 14 genes. No genes separated ER+ from ER− meningiomas. As the number of candidate genes was small, no single pathways or groups of genes were clearly identified. In 2009, Fèvre-Montange and colleagues published a transcriptomic study of 17 meningiomas (WHO grade I, n = 10; WHO grade II, n = 5; WHO grade III, n = 2). As control tissue RNA from a human whole brain (72 years of age) was used. The aim was to distinguish between the different WHO grades and histopathological subtypes, and to identify factors predicating recurrence. Unsupervised cluster analysis showed three groups of samples: group A consisted of seven of ten grade I cases, group B of the remaining three grade I samples and all five grade II meningiomas, and finally group C consisted of the two grade III tumors. Statistical analysis revealed that 346 and 2,995 genes showed more than twofold over-expression in groups B and C, respectively. Similarly, 184 and 1,380 genes were down-regulated, respectively. Furthermore, the study showed differential gene expression between fibrous and meningothelial meningiomas, with 12 up-regulated and 20 down-regulated genes in the fibrous subset. Selected genes were validated with qRT-PCR. The last published microarray study on meningiomas so far was published in 2009 and performed by Castells et al. . The aim was to assess whether automated categorization of brain tumors can be made by the use of microarray. Biopsies from 35 patients (17 glioblastomas and 18 meningothelial meningiomas) were subjected to cDNA-based microarray analysis. The study showed up to 100% prediction accuracy by using microarrays, thus providing evidence of possible clinical diagnostic use of this technology.
During the last decade, a total of 13 microarray-based studies have been performed on RNA from altogether 227 meningiomas. The aims, sources of control tissue, microarray platforms, and statistical approaches vary between the studies. The main results of the studies can be grouped in three categories: (1) several groups have identified meningioma-specific genes, and genes associated with the three WHO grades and the main histological subtypes of grade I meningiomas. (2) One publication has shown that the general transcription profile of samples of all WHO grades differs in vivo and in vitro. (3) One report provides evidence that microarray technology can be used in an automated fashion to classify tumors. We found no clear overlap between the studies regarding individual candidate genes. Other than the gross signature differences identified in the studies, we were not able to detect any shared deregulated genes. This illustrates a big challenge in gene expression profiling studies: the vast number of transcripts present on the chips makes it very difficult to compare the studies and isolate key candidate genes with a possible role in the biology. Critiques may regard this as a weakness of the technology; however, the lack of external validity may in fact be explained by the variation in microarray platforms, study populations, chip manufacturer, statistical approach, and quality of RNA in the tissue at study. In fact, the Microarray Quality Control (MAQC) project ( http://www.fda.gov/ScienceResearch/BioinformaticsTools/MicroarrayQualityControlProject/default.htm ) has shown that the microarray quality at present may be higher than that of, e.g., immunohistochemical analysis . So the question is how should we perform microarray studies? Would it be appropriate to standardize some factors, like the source of control tissue and statistical procedure? And should it be mandatory to post microarray data in publicly available databases so that independent researchers could perform meta-analyses with the aim of identifying biological markers that could be used clinically? If so, would it be reasonable to demand posting of certain data, e.g., the overall best candidate genes for comparison purposes? Some journals have implemented this policy, however it is our opinion that it should be adopted globally. To draw firmer conclusions of the studies, consensus on source of control tissue and statistical approach, as well as replication of the most biological relevant findings so far is required. The time is therefore now due for a large replication study that includes the most significant and biologically relevant candidate genes generated from the various transcription studies that have been performed. This means that a custom-made microarray chip that includes these consensus genes should be designed, and quality controlled samples from several laboratories included. The data should also be compared with clinical outcome in order to identify clinically relevant genes. Recently, The Cancer Genome Atlas Research Network successfully applied this method to a large panel of glioblastomas . Here, 206 glioblastomas were subjected to gene expression profiling, DNA copy number variation analysis, and CpG methylation status assessment of 601 selected genes. It follows that such a project demands considerable dedication, coordination, and financial support. An interesting result was the difference in transcriptional profiles between original frozen specimens and primary culture of all three WHO grades. The study provides evidence that as tumors are removed from their native environment, the gene expression changes accordingly. Thus, results gained from cell culture experiments have to be interpreted with caution. Validation in vivo seems to be mandatory before firm conclusion can be made. It is however no surprise that the gene expression profile is altered when a tumor is removed from its site of origin. The dynamic transcriptome will rapidly adjust to changes in local environment to provide the substrates necessary to maintain the tumor. Medicine has been transformed by the genomic revolution, and classical population risk assessment and empirical treatment has been challenged by molecular classification and prospects of individualized therapy . With the help of microarray technology novel candidate genes, pathways, and networks may be linked with clinical scenarios, such as treatment response or environmental exposure. Thus, microarrays may be helpful in identifying biomarkers, developing new treatment strategies, and in tumor classification (Fig. ). Hence, a reasonable application of global gene expression profiling in oncology is to subclassify brain tumors of WHO grade II. Such tumors are generally infiltrative, have low-proliferative activity, tend to recur, and can undergo malignant transformation. Often there is marked clinical and morphological heterogeneity within tumors of this grade. Thus, the categorization of grade II tumors may be more challenging than that of purely benign or malignant entities. With microarray analysis, high- or low-proliferative gene expression signatures, or hyper- or hypo-mutator phenotypes may be revealed. Consequently, signatures indicating high or low risk of recurrence or transformation may be found. Such information may be useful when the addition of adjuvant therapy or follow-up schedule is discussed. However, some technical issues make the use of this powerful technology difficult: (1) Since only minute amounts of RNA are required and probes corresponding to all known genes are analyzed, one must ensure that only representative tissue is subjected to hybridization to the microarray chips. Contamination with leucocytes, neighboring blood vessels or other normal tissue will contribute to the overall transcriptomic profile. (2) If the goal of the analysis is to measure therapeutic response to e.g. irradiation or cytotoxic drugs one must sample tissue at first-time surgery as well as after completed adjuvant therapy. Thus, the patient must undergo repeated surgery with the inherent risk factors. (3) RNA is very susceptible to time dependent degradation due to abundant RNase in the environment. Low quality RNA cannot be subjected to microarray analysis. Hence, the personnel involved in the sampling and handling of tissue must be dedicated and have a proper logistic procedure in order to preserve RNA. This means that tissue must be snap-frozen in liquid nitrogen in the operating theater and then transferred to permanent storage at ultra cold temperatures or in media containing RNase inhibitors. 4) Since transcriptome analysis can reveal genes that differ between tissue types, appropriate controls and groups of e.g. tumors have to be selected long before time of study. As meningiomas develop from arachnoid cells , it follows that such cells should be selected if the purpose was to find meningioma-specific genes. If dura or whole brain were used as controls the transcriptional profile would presumably be different due to the comparison of different tissue types. It would thus be impossible to call these genes different because of tumorigenesis per se. Only one of the 13 studies states that arachnoid tissue has been used as controls . However, if the purpose is to identify genes associated with the different WHO grades it follows that no additional controls would be needed. The main limitations of the studies were small sample size and unequal group sizes when performing statistical comparison. This illustrates how difficult it is to recruit patients and controls to such studies. It may also be due to the relatively high cost of performing microarray analysis. Furthermore, the results of the studies have to be functionally tested in order to safely identify biological markers and key players in meningioma pathogenesis. As in most microarray studies of the transcriptome, new genes and pathways have been revealed in all the meningioma studies. The challenge is to make use of the results clinically. At present, microarray chip technology is mature and with low technical error rate. Thus, the main issues that have to be handled in transcriptomic projects are study design, representative tissue source, logistics, and data interpretation. Since transcriptome analysis only provides snapshots of the gene expression state in cells constituting the tissue at study, clear improvements in diagnostic accuracy or clinical endpoints (e.g., improved recurrence free survival) must be objectively shown before the results of such studies can be safely implemented in the clinical armamentarium. This notion is by no means reserved for microarray studies, as all new methods have to be quality checked accordingly before clinical approval. Microarray studies have been taken into account for a trend towards personalized medicine, e.g. the genetic profile of a patient’s tumor could tell which pathways that could be pharmacologically inhibited. However, as long as there are no individually designed drugs, such treatment is reserved for the future. How eager the pharmacological industry is to make such designed drugs, which presumably are more expensive than mass production, remains to be seen. Nevertheless, the molecular technology is continuously developing new and faster methods for the analysis of the nucleic acids. Next-generation sequencing is already “this generation”; the first people and cancers have been deciphered at base-pair resolution by the so-called deep sequencing technology. The complete sequencing of the human genome that took 10 years in the last decade of the twentieth century can now be performed in a few days. Therefore, awaiting individualized therapy, the era of the personalized genome is inevitably about to begin.
|
International external quality assurance of | b652b867-8f92-40e3-92ed-5732415e5d15 | 6469832 | Pathology[mh] | The discovery of the c.1849G>T mutation leading to the p.Val617Phe (V617F) substitution in JAK2 has been a landmark in molecular diagnosis of the myeloproliferative neoplasms (MPN) polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). Quantification of the mutation has shown that mutation burden also could reflect different subtypes of MPN. The majority of patients with PV or fibrotic PMF have been reported to have more than 50% JAK2 V617F while the opposite has been seen in ET patients . In addition, quantification of the allelic burden in JAK2 V617F-positive patients is increasingly used to monitor treatment response of new targeted therapies as well as in transplanted patients . For molecular diagnosis, it has been recommended that the assay should be sensitive enough to detect a mutant burden around 1% . The combination of a sensitive detection and reproducible quantification of JAK2 V617F challenges the methodology used in a routine setting. Conventional Sanger sequencing does not show the required sensitivity in cases with low mutation burden, and methodologies involving next generation sequencing are unnecessarily labor intensive and expensive for mutation detection of a single nucleotide substitution. Instead, the use of quantitative polymerase chain reaction (qPCR) has been shown to be a both sensitive and cost-effective method and superior in sensitivity compared to qualitative methods . As a step towards standardization of reliable molecular diagnostics, the European Leukemia Net (ELN) and MPN&MPNr-EuroNet have evaluated performance of different allele-specific (AS)-qPCR assays . This work, involving 12 laboratories from seven countries recommended a JAK2 V617F qPCR assay which showed consistent performance across different qPCR platforms . Even so, variation between laboratories and different instrumental setups can be substantial despite the use of the same experimental protocol. To ensure high quality and standardized quantitative results, external quality assurance (EQA) programs are vital. A program dedicated to JAK2 V617F detection by qPCR is advantageous since no additional bias on quantification would be introduced by comparison to a different methodology. MPN&MPNr-EuroNet has performed two rounds of EQA based on qPCR assays. In addition to providing an EQA to participating laboratories in the network, the aim was to identify parameters critical for the quantification of JAK2 V617F. Such factors would have a substantial impact also on an EQA result, and thus need to be identified in order to design a beneficial EQA program which would be useful in clinical routine.
Participants For the first quality assurance round (QA1), 19 laboratories from 11 countries across Europe analyzing JAK2 V617F by qPCR as part of their routine diagnostics returned results obtained with in-house assays. In the second QA (QA2), 25 laboratories from 17 countries participated. Samples and references Blood samples from JAK2 V617F-positive patients were collected after informed consent according to the guidelines of the Danish Regional Science Ethics Committee. In QA1, ten blood samples were collected, aliquoted, and distributed to participating laboratories by an overnight courier. DNA was extracted locally from whole blood according to each participant’s standard procedure. Six participants received extra blood and extracted DNA also from hemolyzed blood ( n = 3) or granulocytes ( n = 3) in addition to whole blood. In QA2, six unknown samples prepared by spiking JAK2 V617F-positive HEL cell line DNA into normal wild-type donor DNA was sent out. In both QA1 and QA2, a common reference for calibration corresponding to 75%, 23%, 3%, and 0.3% JAK2 V617F was created by spiking a 648 bp PCR fragment containing the c.1849G>T mutation into normal wild-type donor DNA and distributed with the samples. Droplet digital PCR (ddPCR, Bio-Rad, Hercules, CA, USA) was used to obtain a reference value for each sample in the trials by taking the mean of four replicates repeated three times. In QA2, values obtained by ddPCR in a separate laboratory were added to the mean as well. Quantification of JAK2 V617F by qPCR Copy numbers for JAK2 V617F and JAK2 WT and the allelic ratios of JAK2 V617F expressed as % [ JAK2 V617F copy number/( JAK2 WT copy number + JAK2 V617F copy number)] were determined by the participating laboratories according to the assay used in the clinical routine. All results were sent to one laboratory for further analysis. To determine general variation of qPCR within an assay, data was collected from control samples and repeatedly analyzed according to the Larsen protocol during 12 months in one laboratory. The analysis was performed by different persons on two PCR instruments, and batches for reagents were changed during the 12-month period. Percentage JAK2 V617F was calculated for each sample and the coefficient of variation (CV) for the assay was determined.
For the first quality assurance round (QA1), 19 laboratories from 11 countries across Europe analyzing JAK2 V617F by qPCR as part of their routine diagnostics returned results obtained with in-house assays. In the second QA (QA2), 25 laboratories from 17 countries participated.
Blood samples from JAK2 V617F-positive patients were collected after informed consent according to the guidelines of the Danish Regional Science Ethics Committee. In QA1, ten blood samples were collected, aliquoted, and distributed to participating laboratories by an overnight courier. DNA was extracted locally from whole blood according to each participant’s standard procedure. Six participants received extra blood and extracted DNA also from hemolyzed blood ( n = 3) or granulocytes ( n = 3) in addition to whole blood. In QA2, six unknown samples prepared by spiking JAK2 V617F-positive HEL cell line DNA into normal wild-type donor DNA was sent out. In both QA1 and QA2, a common reference for calibration corresponding to 75%, 23%, 3%, and 0.3% JAK2 V617F was created by spiking a 648 bp PCR fragment containing the c.1849G>T mutation into normal wild-type donor DNA and distributed with the samples. Droplet digital PCR (ddPCR, Bio-Rad, Hercules, CA, USA) was used to obtain a reference value for each sample in the trials by taking the mean of four replicates repeated three times. In QA2, values obtained by ddPCR in a separate laboratory were added to the mean as well.
Copy numbers for JAK2 V617F and JAK2 WT and the allelic ratios of JAK2 V617F expressed as % [ JAK2 V617F copy number/( JAK2 WT copy number + JAK2 V617F copy number)] were determined by the participating laboratories according to the assay used in the clinical routine. All results were sent to one laboratory for further analysis. To determine general variation of qPCR within an assay, data was collected from control samples and repeatedly analyzed according to the Larsen protocol during 12 months in one laboratory. The analysis was performed by different persons on two PCR instruments, and batches for reagents were changed during the 12-month period. Percentage JAK2 V617F was calculated for each sample and the coefficient of variation (CV) for the assay was determined.
Similar EQA results with different starting materials, qPCR assays, and qPCR instruments To identify the parameters of specific importance for causing outliers in a JAK2 V617F EQA where a quantitative value of mutation burden is determined by qPCR, different starting materials, different qPCR assays, and different technical platforms were included. In total, 16 samples with unknown mutation burden were issued to participating laboratories. In QA1, samples were divided into four groups based on the reference levels of JAK2 V617F as determined by ddPCR: < 2% ( n = 4), 2–10% ( n = 3), 10–20% ( n = 2), and > 30% ( n = 2). Results were analyzed in detail for one sample in each group. To test starting material for the analysis, six different laboratories extracted DNA from purified granulocytes or hemolyzed blood in addition to whole blood. JAK2 V617F was analyzed from both types of starting materials in parallel using routine protocol(s). Although differences could be noted between starting materials when comparisons were made within the same laboratory, the difference was in the same range as between the laboratories and different assays (Fig. ). To study the influence of assay protocols on EQA results, 19 laboratories from 11 countries analyzing JAK2 V617F by qPCR as part of their routine diagnostics returned results from their assay protocol used in clinical diagnostics in QA1. One of the laboratories returned results from two different assays yielding 20 sets of data in total. Various qPCR assay protocols were used: Larsen , n = 6; Lippert , n = 5; Ipsogen Mutaquant kit (Qiagen, Marseille, France), n = 4; and other protocols (in-house assays), n = 5. Although reported copy numbers in samples varied between laboratories (data not shown), the % JAK2 V617F was rather consistent across different assays (Table ). In QA2, 25 laboratories from 17 countries returned results. Two of the laboratories returned results from two different assays yielding 27 sets of data. In QA2, the majority of participating laboratories used the Larsen assay ( n = 18) or a modification of this assay ( n = 4). Five laboratories reported results obtained by another assay. The six samples issued in QA2 were divided into the same groups as for QA1 (< 2% ( n = 2), 2–10% ( n = 1), 10–20% ( n = 2), and > 30% ( n = 1)) and one sample from each group was analyzed in detail. Overall, variations were similar in QA1 and in QA2 (Table ). Although there was a relative consistency in quantification of JAK2 V617F allelic burdens above 2%, a higher variation was noted in samples with low mutation burden (< 2%). Next, we studied whether different qPCR platforms could introduce substantial variation. The majority of QA1 participants used instruments from Applied Biosystems (Foster City, CA, USA). Eleven sets of data were analyzed on these instruments (ABI7300/7500/7500FAST/7900HT). The remaining laboratories used Lightcycler LC480 (Roche Applied Science, Penzberg, Germany, n = 4), Rotorgene (3000A/Q; Corbett Life Science, Sydney, Australia; Qiagen, n = 3), or Stratagene (MX3000/MX3500; Agilent Technologies, Santa Clara, CA, USA, n = 2) for analysis. For all but Applied Biosystems instruments, groups were very small, which resulted in single outliers having a substantial impact on the results. In addition, different versions of instruments from the same manufacturer were used in all groups. Nonetheless, no major difference depending on qPCR instrument could be seen (Fig. ). For comparison, CV for the Larsen assay over a stretch of one year was determined in one participating laboratory. During that period of time, a control sample of 4.5% JAK2 V617F was analyzed 97 times and a sample of 13% was analyzed 64 times on two instruments (Rotorgene Q, Fig. ). CV for calculated % JAK2 V617F was 26% in both cases. To evaluate whether the differences between assays and qPCR instruments were substantial enough to affect the result of an EQA, z-scores were determined for selected samples in QA1. A z-score between 2 and − 2 was considered as satisfactory performance, a z-score between 2 and 3 or between − 2 and − 3 was considered as a warning, and a z-score above 3 or below − 3 was considered as critical. Results showed that three participants obtained a warning, while the remaining participants got a satisfactory performance. None was scored as “critical” (Table ).
To identify the parameters of specific importance for causing outliers in a JAK2 V617F EQA where a quantitative value of mutation burden is determined by qPCR, different starting materials, different qPCR assays, and different technical platforms were included. In total, 16 samples with unknown mutation burden were issued to participating laboratories. In QA1, samples were divided into four groups based on the reference levels of JAK2 V617F as determined by ddPCR: < 2% ( n = 4), 2–10% ( n = 3), 10–20% ( n = 2), and > 30% ( n = 2). Results were analyzed in detail for one sample in each group. To test starting material for the analysis, six different laboratories extracted DNA from purified granulocytes or hemolyzed blood in addition to whole blood. JAK2 V617F was analyzed from both types of starting materials in parallel using routine protocol(s). Although differences could be noted between starting materials when comparisons were made within the same laboratory, the difference was in the same range as between the laboratories and different assays (Fig. ). To study the influence of assay protocols on EQA results, 19 laboratories from 11 countries analyzing JAK2 V617F by qPCR as part of their routine diagnostics returned results from their assay protocol used in clinical diagnostics in QA1. One of the laboratories returned results from two different assays yielding 20 sets of data in total. Various qPCR assay protocols were used: Larsen , n = 6; Lippert , n = 5; Ipsogen Mutaquant kit (Qiagen, Marseille, France), n = 4; and other protocols (in-house assays), n = 5. Although reported copy numbers in samples varied between laboratories (data not shown), the % JAK2 V617F was rather consistent across different assays (Table ). In QA2, 25 laboratories from 17 countries returned results. Two of the laboratories returned results from two different assays yielding 27 sets of data. In QA2, the majority of participating laboratories used the Larsen assay ( n = 18) or a modification of this assay ( n = 4). Five laboratories reported results obtained by another assay. The six samples issued in QA2 were divided into the same groups as for QA1 (< 2% ( n = 2), 2–10% ( n = 1), 10–20% ( n = 2), and > 30% ( n = 1)) and one sample from each group was analyzed in detail. Overall, variations were similar in QA1 and in QA2 (Table ). Although there was a relative consistency in quantification of JAK2 V617F allelic burdens above 2%, a higher variation was noted in samples with low mutation burden (< 2%). Next, we studied whether different qPCR platforms could introduce substantial variation. The majority of QA1 participants used instruments from Applied Biosystems (Foster City, CA, USA). Eleven sets of data were analyzed on these instruments (ABI7300/7500/7500FAST/7900HT). The remaining laboratories used Lightcycler LC480 (Roche Applied Science, Penzberg, Germany, n = 4), Rotorgene (3000A/Q; Corbett Life Science, Sydney, Australia; Qiagen, n = 3), or Stratagene (MX3000/MX3500; Agilent Technologies, Santa Clara, CA, USA, n = 2) for analysis. For all but Applied Biosystems instruments, groups were very small, which resulted in single outliers having a substantial impact on the results. In addition, different versions of instruments from the same manufacturer were used in all groups. Nonetheless, no major difference depending on qPCR instrument could be seen (Fig. ). For comparison, CV for the Larsen assay over a stretch of one year was determined in one participating laboratory. During that period of time, a control sample of 4.5% JAK2 V617F was analyzed 97 times and a sample of 13% was analyzed 64 times on two instruments (Rotorgene Q, Fig. ). CV for calculated % JAK2 V617F was 26% in both cases. To evaluate whether the differences between assays and qPCR instruments were substantial enough to affect the result of an EQA, z-scores were determined for selected samples in QA1. A z-score between 2 and − 2 was considered as satisfactory performance, a z-score between 2 and 3 or between − 2 and − 3 was considered as a warning, and a z-score above 3 or below − 3 was considered as critical. Results showed that three participants obtained a warning, while the remaining participants got a satisfactory performance. None was scored as “critical” (Table ).
Bias altering qPCR results may occur at several steps of JAK2 V617F assays, even when laboratories use the same methodology. Starting material for the analysis as well as technical platform, assay design and batch variations can influence the results. Even among laboratories using the same qPCR protocol for quantitative assays, considerable variation has been reported . Standardized results are vital not only to aid in diagnosis of patients but also in clinical, multicenter studies. One way to test how well individual laboratories align to predicted results is through participating in EQA. Moreover, EQA are central tools for the accreditation and assessment of laboratory performance. To design a useful EQA for quantitative analysis, it is important to take into account the variation of the methodology in focus. If expectations of consistency in results are set too high, beyond the limits of the technology used, there is a risk that a well-performing laboratory will get poor or inadequate results just because of natural variation in the method, or because of the influence of a particular parameter which has not been identified as important for outcome. Therefore, it is essential to recognize factors which would cause substantial outliers in the tests, as well as which variation could be expected from different qPCR technical platforms. A previous study has shown that the results obtained for the detection of the JAK2 mutation were comparable in whole blood and in purified granulocytes, and that no false negative was reported in whole blood if the qPCR assay used was able to detect < 1% JAK2 V617F . However, in this study, the allelic ratio was reported to be on average 15% lower in whole blood than in purified granulocytes; the low-average JAK2 V617F values was due to a minority of the whole blood samples. The choice of the starting material could thus be of importance in individual cases depending on the question asked. In the present study, the starting material used for the analysis did not affect the performance in EQA for the majority of laboratories. In both QA1 and QA2, samples with low mutation burden (< 2% JAK2 V617F) were included, and a greater variation was seen for these samples. This reflects the sensitivity of the assay and the qPCR setup in each laboratory. In addition, when dealing with low JAK2 V617F copy numbers stochastic variation will add to the overall variation. However, for low mutation burden, specificity of the assay is an equally important issue. The background level where cross-reaction with the wild-type allele could occur must be clearly defined by each laboratory to avoid false positive results . To be able to compare results, over time as well as between laboratories, there is a need to standardize the results with respect to the quantitative level of mutation burden. In chronic myeloid leukemia, where the level of expression of the fusion gene BCR-ABL1 is correlated to prognosis, a conversion factor has been established to correct for differences across laboratories. This factor is used to align results to an international scale which is anchored to clinical results . However, the original conversion factor was based on the sample exchange with a reference laboratory and this procedure is both time-consuming and expensive and a risk for inborn errors due to bias cannot be ruled out. To overcome this, primary references intended for the calibration of a secondary reference material have been established . In addition, a certified reference plasmid for the calibration of BCR-ABL1 quantification has been manufactured . As reported in a previous international study , a common reference material remains a useful tool for laboratories also for JAK2 V617F, as it allows decreasing or suppressing differences in copy numbers in certain laboratories. In addition, it also allows adjustment for batch variations, e.g., due to differences in quality of primer oligonucleotides. A first WHO reference panel for JAK2 V617F has recently been established and is now available . This holds promise to further improve assay standardization. With increasing clinical demands for molecular monitoring, both EQA programs and standardized JAK2 V617F reference material are needed to identify and maintain validated laboratories. In conclusion, variation in method due to the starting material, assay set-up, or qPCR equipment did not result in significant outliers in the EQA programs included in this study. However, EQA based on a single technology remains a valuable tool to achieve standardization of JAK2 V617F quantification.
|
Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review | 5397092c-fe1e-4efe-a9c6-d428eb1b58b3 | 11843972 | Internal Medicine[mh] | Finding a cure for cancer, in its many forms, is still a tremendously complex problem. Despite continuous advances in understanding its biological foundations and the emergence of new treatment possibilities, this disease is still the world's second-leading cause of death , causing an enormous socioeconomic burden and an immense workload for physicians . As part of the procedures to diagnose and treat patients with cancer, practitioners collect massive amounts of data, including clinical notes, previous conditions, diagnoses, treatments, prescriptions, laboratory test results, radiological images, and phenotypic and genotypic features. Along with any prior patient-specific knowledge in the same or other healthcare contexts, this information is increasingly stored in virtual collections – electronic health records (EHRs) . Notwithstanding the potential of this digitization, the resulting exponential, ever-increasing data expansion – both in volume and complexity – has inevitably shortened the time for clinicians to learn, follow emerging clinical guidelines, and gather all relevant information for proper care . Indeed, with a single patient estimated to generate up to 8 Gb of raw input ranging from unstructured clinical narratives to scanned documents , automated techniques have undeniably become required to distill insight from EHRs and assist in decision-making. In that vein, machine learning (ML) – a branch of Artificial Intelligence (AI) with the ability to learn from and identify patterns in the available data – is increasingly used in healthcare to model patient-specific predictive, prognostic, or prescriptive assessments at the point of decision-making . In this context, ML models can be deployed as standalone applications or fall into several technologies, such as clinical decision support (CDS) and computer-aided detection (CADe) or diagnosis (CADx) systems . The main difference between these tools concerns the type of data used for model development: while CADx and CADe approaches rely on imaging, CDS systems usually involve text-based information, such as test results, comorbidities, patient history, and other relevant clinical variables . Machine learning can be divided into two subtypes, supervised and unsupervised learning, separated by the use of labeled or unlabeled datasets . On the one hand, supervised learning models – e.g., support vector machines (SVMs), gradient boosting (GB), random forests (RF), and logistic regression – correlate previously organized features (such as unique patient characteristics) with known outcomes . This approach deals with two types of problems: (i) classification, to produce discrete outputs (or classes), for example, to predict tumor malignancy ; and (ii) regression, to estimate continuous values . In healthcare, regression algorithms can be used, for instance, to determine the risk of developing lesions or sequelae over time ; or to establish an adequate dose of medication to administer to a specific patient . On the other hand, unsupervised learning methods focus on finding natural patterns in unlabeled data . These models – including principal component analysis (PCA), k-means, gaussian mixture models, density-based spatial clustering of applications with noise (DBSCAN), and balanced iterative reducing and clustering (BIRCH) – are used to find relationships between variables, assign them to different groups according to their similarities (clustering), and to prioritize and reduce the number of features in the dataset (dimensionality reduction) . A specific set of methods, artificial neural networks (ANNs), has even been the basis for a subcategory of machine learning termed deep learning (DL) . Designed to (partially) emulate human neuronal processing, ANNs are composed of artificial neurons (or nodes), interconnected and stacked into three types of layers : (i) input layer , containing the original dataset variables; (ii) hidden layer(s) , where the data is processed at a certain level of representation ; and (iii) output layer , with the attained results. In contrast to standard ANNs, usually limited to one hidden layer and still requiring labeled features , deep neural networks (DNNs) can derive knowledge from two or more increasing levels of abstraction, with their depth growing along with the number of hidden layers . DNNs (e.g., convolutional and recurrent neural networks) can accurately detect and classify patterns in complex labeled or unlabeled datasets , having produced ground-breaking results in numerous areas, including image, pattern, and language recognition . Over the years, several ML- and DL-based tools have been developed to support clinical decision-making in oncology, with many reported benefits. First, these methods can accurately predict cancer susceptibility, recurrence, survival, and risk of complications according to multiple constraints and therapeutic paths . Second, these can be employed in gene expression analysis to predict mutations, proving useful in targeted gene therapy . Third, artificially intelligent approaches in imaging analysis are usually used for tumor monitoring, detection (CADe), segmentation, diagnosis (CADx), and staging . In particular, ML can be paired with radiomics, a quantitative imaging approach that deconstructs medical images into mineable features. ML-based radiomic pipelines, most commonly applied in oncology , are usually composed of four sequential stages : (i) image retrieval and segmentation, to delineate regions or volumes of interest (for two- or three-dimensional images, respectively); (ii) high-dimensional quantitative feature extraction, to unravel tumor pathophysiology into measurable biomarkers, such as size, volume, texture, shape, and intensity; (iii) feature reduction, to explore relationships between variables to remove redundant or correlated features; and (iv) prognostic/predictive, to link specific features with possible outcomes. By mapping the whole tumor and its adjacent tissues , this technique allows performing dynamic virtual biopsies, which can be used to capture spatial and temporal intra-tumoral heterogeneity , a key factor linked with tumor aggressiveness and poor treatment responses and survival . These results can be integrated with other available sources of clinical, pathological, and genomic data and leveraged for individualized decision-making to, for example, determine chemo- or radiotherapy doses, treatment-resistant regions, or the best sites to perform an actual biopsy . Finally, efforts have recently been shown to develop digital twins (DTs) for cancer patients . In a medical context, DTs can be described as dynamic virtual replicas modeled intelligently after each physical patient's medical, behavioral, and environmental variables, used for real-time simulations . Here, DTs provide clinical support by non-invasively anticipating treatment responses, predicting drug effectiveness, monitoring health indicators, and detecting abnormalities, thus easing decision-making and avoiding unnecessary costs and ineffective procedures . Because of these unique capabilities, ML-and DL-based approaches have unleashed the potential to revolutionize standard healthcare, especially when made available to practitioners at the point of care. Nonetheless, the overwhelming majority of algorithms developed for cancer-related decisions have yet to reach oncology practice , mainly due to subpar methodological reporting and validation standards . Showing performance on the patients used for development (internal validation) is insufficient, particularly for small sample sizes ; as predictions are modeled after that specific cohort, results can be misleading (e.g., biased or overfitted) and non-generalizable to new case mixes . Thus, before a new or updated artificially intelligent method can be adopted in clinical practice, it must undergo a thorough evaluation process, which usually consists of external (ideally, clinical) validation and the assessment of clinical utility. First, to ensure model reproducibility (or external validity) and increase confidence in its predictions or estimates, its performance should be evaluated in separate, independent, and comprehensive patient datasets representing the intended target setting(s) . Specifically, the following metrics should be reported : (i) calibration, i.e., the ratio between predicted and observed outcomes, ideally revealed graphically in a calibration plot (to depict the whole range of predictions); and (ii) discrimination, that is, the ability to separate individuals with or without the event of interest. For regression models (continuous outcomes), discrimination is usually shown via concordance (C) index or mean absolute or squared error . For classification tasks (discrete/binary outcomes), discrimination metrics can include the area under the ROC curve (AUC), accuracy, sensitivity, specificity, precision (or positive predictive value), or F-score (i.e., Dice similarity) . Second, this information should always be complemented by evaluating clinical utility, i.e., quantifying the impact of the developed tool on decision-making – and, subsequently, on patient outcomes – through comparative analyses . These comparisons include, for example, clinicians performing the same task with or without assistance, patient outcomes before and after implementation, or, although less informative, direct comparison between well-established models developed for the same end . If possible, this evaluation should preferably be carried out in randomized clinical trials (to minimize confounding variables) or, at least, prospective observational studies so that impact may be assessed over time . Lastly, to ensure clinical validity, this process should involve real-world data (RWD), that is, information routinely collected from actual patients (for example, from EHRs and wearable or mobile devices) . In the context of this scoping review, we conducted a preliminary search of the PubMed database regarding externally validated ML models developed for patient-related decision-making in oncology. Firstly, although several decision support-focused publications do continue to emerge, they are either: (i) focused on context-specific applications, such as evaluation for a specific type of tumor or field ; (ii) not approached from a machine learning perspective, that is, not stating which algorithms were used, their performance or their clinical validity ; or (iii) outdated . Secondly, only two reports concerning external validation in oncology were found, focused on shortcomings, lack of reporting standards, and risk of bias . However, none of these reviews report: (i) which were the externally validated algorithms; (ii) how the validation studies were designed and their target populations; (ii) if performance was compared against expert clinicians or gold standards and using real-world data; and (iii) if any links can be made between specific algorithms and cancer variants. Since processing mechanisms for different cancer types can be contrasting, addressing the last issue is particularly relevant. Stellar examples are melanomas and other tumors requiring imaging analysis, which have proven to be accurately identified with neural networks (see , for example). Accordingly, further connections could and should be made between different types of cancer and frameworks with specific ML techniques. For the reasons abovementioned, we conducted a scoping review to systematically map externally validated and clinically useful ML-based models developed for patient-related decision-making in the broad scope of oncology practice. Namely, we aimed to report on their validation and impact on decision-making (clinical utility), attempt to associate specific models with particular types of cancer and decisions to make by quantifying their performance, and unveil research gaps in this field. We hope that our findings can be translated into efficient implementations. The ultimate goal is to simplify the decision process and reduce misprescriptions, thus lowering clinicians' workload, increasing confidence, and avoiding misuse and malapplied AI, potentially leading to better healthcare. The remainder of this paper is organized as follows. Section " " details the methodological approach for the scoping review, including the databases and search terms used and the inclusion and exclusion criteria for data analyses. Section " " provides a critical qualitative and quantitative synthesis of external validation and clinical utility assessment. Finally, Section " " concerns the discussion, where feasible associations between particular types of cancer and ML algorithms are established, the limitations currently faced by ML are outlined, research gaps are presented, conclusions are drawn, and guidance is provided for further work. This scoping review was conducted according to the updated Joanna Briggs Institute (JBI) methodology for scoping reviews , which we also used to develop our protocol (see Additional file ). In addition, it followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist, adapted to encompass the PRISMA 2020 statement (the filled PRISMA2020 Checklist is provided in Additional file ). Furthermore, as recommended by JBI , the Population/Concept/Context (PCC) mnemonic guided the identification of the main concepts, research questions, and search strategy in this review. Here, the population consists of cancer patients (with no restrictions). The concept is externally validated and clinically useful machine or deep learning algorithms to assist decision-making regarding clinical outcomes for cancer patients. The context is oncology care in any setting. Our methodology (and PCC elements) is described in detail in our protocol and summarily presented below. Research questions The research questions and sub-questions were outlined as follows: What externally validated machine learning algorithms have been developed to assist patient-related decision-making in oncology practice? ◦ For what types of cancer variants and clinical outcomes were these models developed? ◦ How were the validation studies designed? ◦ Which populations and types of inputs were used? ◦ Have these methods been tested on real-world data? ◦ Have the models been implemented in clinical practice? ◦ How was performance assessed during external validation? How was clinical utility established for these methods? ◦ Which comparators and metrics have been used? Which machine learning algorithms show the best performance depending on the type of cancer, clinical modalities, and the decision(s) to be made? ◦ What are the reported effects of these ML-based models on decision-making and outcomes? What are the research gaps in this field? Types of sources and search strategy This scoping review considered quantitative experimental, quasi-experimental, and observational study designs, including randomized and non-randomized controlled trials, before and after studies, prospective and retrospective cohort studies, and any additional relevant quantitative and comparative research frameworks. Conference abstracts, qualitative studies, and secondary research designs (such as reviews, editorials, letters, and book chapters) were not considered due to not typically reporting individual (if any) performance metrics, thus impeding quantitative analyses. Grey literature was also not included. To limit the scope of this review and increase reproducibility, it only encompassed peer-reviewed journal articles with institutional or open full-text access. Furthermore, to ensure quality and reliable reporting, papers were only assessed for eligibility if published in journals whose Scimago Journal and Country Rank (SJR, 2021), an indicator of scientific journal prestige, is higher than one and whose best quartile is Q1 . The search strategy aimed to locate primary research papers published in peer-reviewed journals. As suggested by JBI, a 3-step search strategy was executed. First, the first author undertook a limited search of PubMed to identify articles on the topic. As a result of this search, keywords were divided into three categories: machine-learning-based decision-making ( " machine learning " OR " deep learning " OR " classification " OR " regression " OR " clinical decision support " OR " computer-aided diagnosis " OR " computer-aided detection " OR " digital twin(s) " OR " decision-making " ), cancer ( " cancer " OR " oncology " OR " tumor(s) " OR " neoplasm(s) " OR " malignancy " ), and evaluation ( " comparison " OR " performance " OR " valid* " ). This search strategy and the inclusion criteria were deliberately designed without imposing limitations on ML, patient profiles, or specific cancer-related settings, ensuring the inclusion of a wide range of relevant papers and maximizing the comprehensiveness of the review. Second, these keywords were used to develop a complete search strategy for the EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science databases. The search terms were adapted to each database (see Additional file ). This study selected IEEE Xplore to address computing articles, PubMed and EMBASE to include biomedical literature, and Scopus and Web of Science to cover multidisciplinary reports. Only publications written in English were considered for inclusion. Studies published from January 1, 2014 were searched, as this year aligns with when deep learning became mainstream . Third, the reference list of all included sources of evidence was screened for additional studies. Eligibility criteria This review included new or updated externally validated machine or deep learning algorithms to assist decision-making regarding clinical outcomes for cancer patients, with no restrictions regarding cancer types or specific demographics. Samples could consist of human patients or lesions (for image analysis), provided that the focus was on cancer patient outcomes and data routinely available in clinical settings were used. All commonly known machine learning algorithms and digital twin approaches were considered, as these align with clinical prediction models. Although not universally qualified as an external assessment , papers reporting model performance on temporally different datasets (temporal validation) were also included. The assessment of clinical utility was mandatory, but all clinical comparators were included (e.g., comparison against standard care, before-after studies, and clinician performance with and without the tool, among many others). Studies were discarded if they: Were not primary research articles published in peer-reviewed journals whose SJR was equal to or higher than one. This criterion was established to ensure the inclusion of research from sources recognized for their quality and impact, thereby enhancing the reliability and relevance of the synthesized evidence. Used synthetic patients or animals. This restriction was imposed to prioritize real-world applicability in clinical settings, where outcomes and decisions are based on authentic human patient data. Although an instrumental resource, synthetic data may not fully encapsulate the complexity and variability inherent in clinical practice . Concerned sequencing, omics, and molecular biomarker discovery. These studies were excluded due to the specialized and currently less accessible nature of omic information in routine clinical settings, a challenge particularly pronounced for proteomics and metabolomics . This review centers on algorithms ready for immediate use in clinical decision-making, aligning with the immediate needs of healthcare practices. Used non-machine learning approaches (for traditional statistical algorithms such as logistic regression and naïve Bayes, these were excluded unless explicitly described as machine learning models); Developed algorithms for anything other than patient care (such as medical education, structured data collection, text classification, cohort-specific assessments, or EHR dashboards); Were not primarily focused on oncology; Did not present performance metrics for external validation (either in the current or previous papers). These metrics are required to verify the algorithms' reliability and generalizability beyond the development environment, a key indicator of their readiness for clinical application. Had not assessed clinical utility. This assessment is critical for demonstrating an algorithm's palpable benefit in improving patient care, an essential aspect of its value to the medical community. Were not written in English. This requirement ensures wide accessibility and comprehension of the review's findings within the global scientific community. Did not have full-text access (inaccessible or inexistent), as this limitation prevents an in-depth analysis of the studies’ methodologies and outcomes. Study selection Following the search, all identified citations were collated in RIS format, uploaded into EndNote 20.4.1 /2022 (Clarivate Analytics, PA, USA), and deduplicated (first electronically, followed by a manual sweep). A Python script was then used to filter publications by SJR ranking (available at Additional file ). The remaining citations were imported into a spreadsheet, and titles and abstracts were screened for assessment against the inclusion criteria for the review. Next, a full-text inspection of the potentially relevant sources was carried out. Disagreements at each stage of the selection process were resolved through discussion among the authors. The search and study inclusion process results are presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review (PRISMA-ScR) flow diagram updated per the PRISMA 2020 statement (see Fig. in Results ). Data charting Data were extracted using a data extraction form (available in our protocol – see Additional file ). These data were stored in Excel spreadsheets and included general information and specific details about the participants, concept, context, study methods, and critical findings relevant to the review questions. No modifications were made to the original form. General study characteristics included the first author, title, year of publication, journal, SJR ranking, and whether limitations were reported and any reporting guidelines were followed. The following information was charted from each source: development design (development and validation or validation only), study design (retrospective versus prospective), care type (primary, secondary, tertiary, or quaternary), general and specific cancer type, the study's focus (e.g., survival or diagnosis), best-performing machine learning method(s), task (classification, regression, or both), type of implementation, interface, system classification (e.g., CADx, CDSS), processing time, software, number of institutions in validation, data availability, validation type, data source (i.e., the country from which the data were obtained), population details (age group, number of patients, number of female and male patients, sample type, and sample size), whether independent validation was performed and real-world data were used, which discrimination and calibration metrics were used to evaluate validation performance, and which comparators and metrics were used to assess the models’ clinical utility. The data is presented in tabular and graphical form, accompanied by a narrative summary. All statistical analyses and graphic illustrations were performed using Pandas 1.3.4 and Matplotlib 3.4.3 (Python 3.9.7). Critical appraisal and risk of bias Besides discarding publications whose SJR was lower than one, no other evaluations concerning data quality were carried out, which aligns with the JBI's protocol for scoping reviews . The research questions and sub-questions were outlined as follows: What externally validated machine learning algorithms have been developed to assist patient-related decision-making in oncology practice? ◦ For what types of cancer variants and clinical outcomes were these models developed? ◦ How were the validation studies designed? ◦ Which populations and types of inputs were used? ◦ Have these methods been tested on real-world data? ◦ Have the models been implemented in clinical practice? ◦ How was performance assessed during external validation? How was clinical utility established for these methods? ◦ Which comparators and metrics have been used? Which machine learning algorithms show the best performance depending on the type of cancer, clinical modalities, and the decision(s) to be made? ◦ What are the reported effects of these ML-based models on decision-making and outcomes? What are the research gaps in this field? This scoping review considered quantitative experimental, quasi-experimental, and observational study designs, including randomized and non-randomized controlled trials, before and after studies, prospective and retrospective cohort studies, and any additional relevant quantitative and comparative research frameworks. Conference abstracts, qualitative studies, and secondary research designs (such as reviews, editorials, letters, and book chapters) were not considered due to not typically reporting individual (if any) performance metrics, thus impeding quantitative analyses. Grey literature was also not included. To limit the scope of this review and increase reproducibility, it only encompassed peer-reviewed journal articles with institutional or open full-text access. Furthermore, to ensure quality and reliable reporting, papers were only assessed for eligibility if published in journals whose Scimago Journal and Country Rank (SJR, 2021), an indicator of scientific journal prestige, is higher than one and whose best quartile is Q1 . The search strategy aimed to locate primary research papers published in peer-reviewed journals. As suggested by JBI, a 3-step search strategy was executed. First, the first author undertook a limited search of PubMed to identify articles on the topic. As a result of this search, keywords were divided into three categories: machine-learning-based decision-making ( " machine learning " OR " deep learning " OR " classification " OR " regression " OR " clinical decision support " OR " computer-aided diagnosis " OR " computer-aided detection " OR " digital twin(s) " OR " decision-making " ), cancer ( " cancer " OR " oncology " OR " tumor(s) " OR " neoplasm(s) " OR " malignancy " ), and evaluation ( " comparison " OR " performance " OR " valid* " ). This search strategy and the inclusion criteria were deliberately designed without imposing limitations on ML, patient profiles, or specific cancer-related settings, ensuring the inclusion of a wide range of relevant papers and maximizing the comprehensiveness of the review. Second, these keywords were used to develop a complete search strategy for the EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science databases. The search terms were adapted to each database (see Additional file ). This study selected IEEE Xplore to address computing articles, PubMed and EMBASE to include biomedical literature, and Scopus and Web of Science to cover multidisciplinary reports. Only publications written in English were considered for inclusion. Studies published from January 1, 2014 were searched, as this year aligns with when deep learning became mainstream . Third, the reference list of all included sources of evidence was screened for additional studies. This review included new or updated externally validated machine or deep learning algorithms to assist decision-making regarding clinical outcomes for cancer patients, with no restrictions regarding cancer types or specific demographics. Samples could consist of human patients or lesions (for image analysis), provided that the focus was on cancer patient outcomes and data routinely available in clinical settings were used. All commonly known machine learning algorithms and digital twin approaches were considered, as these align with clinical prediction models. Although not universally qualified as an external assessment , papers reporting model performance on temporally different datasets (temporal validation) were also included. The assessment of clinical utility was mandatory, but all clinical comparators were included (e.g., comparison against standard care, before-after studies, and clinician performance with and without the tool, among many others). Studies were discarded if they: Were not primary research articles published in peer-reviewed journals whose SJR was equal to or higher than one. This criterion was established to ensure the inclusion of research from sources recognized for their quality and impact, thereby enhancing the reliability and relevance of the synthesized evidence. Used synthetic patients or animals. This restriction was imposed to prioritize real-world applicability in clinical settings, where outcomes and decisions are based on authentic human patient data. Although an instrumental resource, synthetic data may not fully encapsulate the complexity and variability inherent in clinical practice . Concerned sequencing, omics, and molecular biomarker discovery. These studies were excluded due to the specialized and currently less accessible nature of omic information in routine clinical settings, a challenge particularly pronounced for proteomics and metabolomics . This review centers on algorithms ready for immediate use in clinical decision-making, aligning with the immediate needs of healthcare practices. Used non-machine learning approaches (for traditional statistical algorithms such as logistic regression and naïve Bayes, these were excluded unless explicitly described as machine learning models); Developed algorithms for anything other than patient care (such as medical education, structured data collection, text classification, cohort-specific assessments, or EHR dashboards); Were not primarily focused on oncology; Did not present performance metrics for external validation (either in the current or previous papers). These metrics are required to verify the algorithms' reliability and generalizability beyond the development environment, a key indicator of their readiness for clinical application. Had not assessed clinical utility. This assessment is critical for demonstrating an algorithm's palpable benefit in improving patient care, an essential aspect of its value to the medical community. Were not written in English. This requirement ensures wide accessibility and comprehension of the review's findings within the global scientific community. Did not have full-text access (inaccessible or inexistent), as this limitation prevents an in-depth analysis of the studies’ methodologies and outcomes. Following the search, all identified citations were collated in RIS format, uploaded into EndNote 20.4.1 /2022 (Clarivate Analytics, PA, USA), and deduplicated (first electronically, followed by a manual sweep). A Python script was then used to filter publications by SJR ranking (available at Additional file ). The remaining citations were imported into a spreadsheet, and titles and abstracts were screened for assessment against the inclusion criteria for the review. Next, a full-text inspection of the potentially relevant sources was carried out. Disagreements at each stage of the selection process were resolved through discussion among the authors. The search and study inclusion process results are presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review (PRISMA-ScR) flow diagram updated per the PRISMA 2020 statement (see Fig. in Results ). Data were extracted using a data extraction form (available in our protocol – see Additional file ). These data were stored in Excel spreadsheets and included general information and specific details about the participants, concept, context, study methods, and critical findings relevant to the review questions. No modifications were made to the original form. General study characteristics included the first author, title, year of publication, journal, SJR ranking, and whether limitations were reported and any reporting guidelines were followed. The following information was charted from each source: development design (development and validation or validation only), study design (retrospective versus prospective), care type (primary, secondary, tertiary, or quaternary), general and specific cancer type, the study's focus (e.g., survival or diagnosis), best-performing machine learning method(s), task (classification, regression, or both), type of implementation, interface, system classification (e.g., CADx, CDSS), processing time, software, number of institutions in validation, data availability, validation type, data source (i.e., the country from which the data were obtained), population details (age group, number of patients, number of female and male patients, sample type, and sample size), whether independent validation was performed and real-world data were used, which discrimination and calibration metrics were used to evaluate validation performance, and which comparators and metrics were used to assess the models’ clinical utility. The data is presented in tabular and graphical form, accompanied by a narrative summary. All statistical analyses and graphic illustrations were performed using Pandas 1.3.4 and Matplotlib 3.4.3 (Python 3.9.7). Besides discarding publications whose SJR was lower than one, no other evaluations concerning data quality were carried out, which aligns with the JBI's protocol for scoping reviews . Study selection A total of 13 708 records were identified in our search, which was last updated on September 30, 2022. As shown in Fig. , after duplicate removal and filtering by SJR ranking, the titles and abstracts of 4023 citations from Embase, IEEE Xplore, PubMed, Scopus, and Web of Science were assessed. In this stage, 3325 papers were excluded for not being machine learning-based ( n = 1204, 29.9%), using genetic variables or omics ( n = 705, 17.5%), not being externally validated (clearly mentioning performance evaluation by cross-validation or hold-out sampling, n = 587, 14.6%), not being focused on oncology ( n = 534, 13.3%), not regarding patient care or clinical decision-making (e.g., creation of data infrastructures or organizing EHRs, n = 166, 4.1%), not being primary research articles ( n = 101, 2.5%), and not including human patients ( n = 28, 0.7%). This left 698 papers eligible for full-text inspection, of which 62 were excluded for unavailability. From the remaining 636 reports, 274 (43.1%) were discarded for not assessing or quantifying clinical utility, 252 (39.6%) for not being externally validated, 17 (2.7%) for not directly concerning patient care, 13 (2%) for not reporting performance metrics, 13 (2%) for focusing on gene expression or omics, 4 (0.6%) for not containing machine learning models, 2 (0.3%) for not focusing on oncology and 1 (0.2%) secondary research paper. For example, although seemingly relevant, that is, describing external validation and comparison of diagnostic competence against pathologists, other than reporting intraclass correlation coefficients, Yang et al.'s study did not quantify clinicians' performance, which led to its exclusion. No additional relevant documents were found by screening the included studies. Finally, 56 articles were included in this scoping review. The completed form for the included studies can be found in Additional file . Study overview Table summarizes key findings from the 56 studies on patient-centered ML applications in oncology, providing an overview of algorithms, clinical applications, data types, and evaluation methods for clinical utility. The following subsections offer insights into different aspects of the data. Journals, years of publication and reporting guidelines As depicted in Fig. A, the included articles were retrieved from 31 journals with an average SJR (2021) of 2.496, from a minimum of 1.005 ( Scientific Reports ) and a maximum of 7.689 ( Gastroenterology ). Frontiers in Oncology was the most common source ( n = 9, 16.07%, SJR = 1.291), followed by eBioMedicine ( n = 6, 10.71%, SJR = 2.9) and European Radiology ( n = 5, 8.93%, SJR = 1.73) . Eight (25.8%) of these journals were primarily dedicated to methodological issues and computational methods within artificial intelligence (dashed bars in Fig. A), while the remaining twenty-three (74.2%) focused on medical applications and patient-related topics. Concerning the year of publication, although citations since 2014 were screened, only papers from 2018 and onwards met the inclusion criteria. The number of reports increased substantially after 2020, with 23% ( n = 13), 27% ( n = 15), and 43% ( n = 24) of the sources being from 2020, 2021, and 2022, respectively, versus 2% ( n = 1) in 2018 and 5% ( n = 3) in 2019 (Fig. B). While the majority did not adhere to any reporting guidelines ( n = 48, 85.714%), 3 (5.357% ) used TRIPOD , 3 (5.357% ) followed STARD 2015 (commonly used for diagnostic and prognostic studies) , and 2 used CONSORT-AI and STROBE (1 each, 1.786%, and , respectively). Lastly, caveats were not reported for a small percentage of studies (7.14%, n = 4) . Algorithms, cancer types and clinical outcomes The features of the machine learning algorithms found in the included articles are detailed in Table . Sixty-two models were described in the 56 documents, with 55.4% (31/56) of the authors explicitly mentioning which algorithms were used in the paper's abstract. Most developers opted for an ensemble approach ( n = 27, 48.2%), 26 (46.4%) for single models, and three (5.4%) for both . Of the selected studies, 50 (89.3%) were exclusively devoted to classification, 4 to regression (7.1%) , and 2 developed both types of models (3.6%) . All models were supervised except in one study (semi-supervised) , and 50% of the researchers ( n = 28) compared their systems against other ML algorithms. Apart from work developed in , where the model was silently integrated into the patients' EHRs, all models were deployed as standalone systems. Overall, 30 (53.6%) can be classified as CADx, 19 (33.9%) as CDSS, 2 (3.6%) as CADe , and 5 as both CADe and CADx (8.9%) . Regarding interfaces, most tools were desktop-based ( n = 46, 82.1%), and 10 (17.9%) were deployed as web-based applications . All websites were reported, 43 articles (76.79%) disclosed which software was used, and codes were provided for 11 models (19.6%) . Most studies were deep-learning based ( n = 36, 64.3%). From these, the most frequently reported models were Convolutional Neural Networks (CNNs), either alone (29/36, 80.55%), coupled with a Recurrent Neural Network (RNN, 3/36, 8.34%) , or with Logistic Regression (LR), a shallow ANN, Gradient Boosting (GB), a Support Vector Machine (SVM), and Random Forest (RF, 1/36, 2.78%) . Specific CNN architectures were reported for approximately 76% of the articles (25/33), which, as shown in Fig. , primarily consisted of ResNet- ( n = 9, 36%) and DenseNet-based frameworks ( n = 8, 32%), used individually or in conjunction. To overcome data scarcity, transfer learning was used in 16 of the 33 CNN-based articles 48.5%), which involves pre-training the network on a specific problem and transferring that base knowledge to a new, related task (see Table : pre-trained in column General Focus and Models ). Besides CNNs, other DL algorithms were described in four articles . Multilayer Perceptrons (MLPs) were used in three (5.56%) , two of which applied a DeepSurv architecture, a deep Cox proportional hazards feed-forward neural network . The last (2.78%) involved a neural multitask logistic regression model (N-MTLR) . The remaining documents ( n = 20, 35.7%) described a non-deep-learning-based workflow encompassing fifteen unique algorithms applied in twenty-eight configurations. From these, boosting-based techniques were the most widely reported, consisting of eXtreme Gradient Boosting (XGBoost, 6/28, 21.43%) , a Light Gradient Boosting Machine (LightGBM, 1/28, 3.57%) , LogitBoost (1/28, 3.57%) , Adaptive Boosting (AdaBoost, 1/28, 3.57%) , and Gradient-Boosted Decision Trees (GBDT, 2/28, 7.14%) . Other decision tree designs were also used, including RF (6/28, 21.43%) and extremely randomized trees (ExtraTrees, 1/28, 3.57%) . The third most reported group of algorithms were SVMs , a Support Vector Classifier (SVC) , and a Quadratic SVM (4/28, 14.28%), followed by shallow ANNs (2/28, 7.14%) and LR (1/28, 3.57%) . Lastly, Mixture Discriminant Analysis (MDA), k-nearest Neighbors (kNNs), and naïve Bayes (NB) were also found, all used in the same article (total of 3/28, 10.71%) . Regarding general cancer types, the selected papers can be broadly divided into two categories: those concentrating on primary tumors and those mainly examining metastasized (secondary) cancers. Most articles focused on primary tumors (51/56, 91.1%), although four also included metastases . These cancers can be further branched into the specific system where the malignancy was formed: (i) central nervous system (CNS), including the brain (3/51, 5.88%) ; (ii) digestive system, encompassing colorectal (7/51, 13.73%) , esophageal (3/51, 5.88%), gastric (5/51, 9.8%) , and liver cancers (2/51, 3.92%) ; (iii) endocrine system, involving cancers of the pancreas (2/51, 3.92%) and thymus (1/51, 1.96%) ; (iv) genitourinary system, consisting of bladder (1/51, 1.96%) , cervical (1/51, 1.96%) , prostate (2/51, 3.92%) , and endometrial (2/51, 3.92%) cancers; (v) integumentary system, with tumors of the breast (4/51, 7.84%) and skin (2/51, 3.92%); (vi) respiratory system, studying neoplasms of the larynx (1/51, 1.96%) , lung (10/51, 19.61%) , mesothelium (1/51, 1.96%) , and nasopharynx (1/51, 1.96%) ; and (vii) the skeletal system, comprising the bones (4/51, 7.84%) . In addition, five papers analyzed metastatic cancers (5/56, 8.9%), which can also be bifurcated into malignancies spread to nodes or organs. The former includes solid metastatic breast, lung, and gastrointestinal and genitourinary tract tumors , bone metastases in kidney cancer patients , and liver metastases from colorectal cancers . The latter encompasses thyroid cancer spread to lymph nodes and sentinel lymph node metastasis from primary breast lesions . Seventy-six cancer-related goals were addressed in the 56 documents, with an average of one task performed per paper and a maximum of three . These included the development or improvement of systems for: (i) diagnosis alone ( n = 28, 50%) or combined with detection ( n = 5, 8.93%) or prognosis ( n = 1, 1.79%) ; (ii) detection by itself ( n = 2, 3.58%) or coupled with outcome prediction ( n = 1, 1.79%) ; and (iii) outcome prediction, including prognosis ( n = 16, 28.58%) ; and risk stratification ( n = 3, 5.36%) . Finally, fifteen studies resorted to explainable AI (XAI) to increase the transparency behind the models' decisions. Unlike black-box methods, whose reasoning is indecipherable, XAI allows the creation of interpretable models to determine how each prediction was reached and which clinical predictors bore the most weight. Three packages were used for this purpose: (i) SHapley Additive exPlanations (SHAP), which can be employed in any ML algorithm ( n = 6, 40%) ; and (ii) Class Activation Mapping (CAM, n = 1, 6.67%) and Gradient-weighted CAM (Grad-CAM, n = 8, 53.33%) , explicitly developed for CNNs. Clinical inputs and populations According to the clinical variables used as input, the models validated in the 56 studies can be divided into three types: image-based (including video, n = 37, 66.1%), text-based ( n = 10, 17.9%), and mixed, using both clinical modalities ( n = 9, 16.1%). Image-based Studies A total of 335 085 high-resolution images from 112 538 patients (102 117 female, 8 215 male ) were used for classification in 36 of the 37 image-based studies and for classification (recurrence) and regression (recurrence-free survival) in the last study . Except for one paper including both pediatric and adult patients (unknown age proportion, 175 female, 116 male) and two other articles not listing the patients’ age group (698 in , unknown in , unidentified male–female ratio in both), all studies consisted of adults (111 469 patients, 101 942 women, 8 099 men). Eight studies (21.6%) extracted radiomic features from the retrieved images . The studies encompassed X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography – Computed Tomography (PET-CT) scans, endoscopic images and videos, photographs, ultrasounds, histological slides, and whole-slide images (WSI). Besides digital pictures, which are limited to the surface, these imaging techniques capture the body's internal structures. However, they differ in the way they create images and the type of information they provide. X-rays use and expose the patient to ionizing radiation to create scans . Although time- and cost-effective, these do not provide as much detail as CT or MRI scans. In this review, two studies used radiographic images (2/37, 5.4%) to: (i) classify pathologically-confirmed primary bone tumors in children and adults (639 radiographs, 175 female, 116 male) ; and (ii) for breast cancer screening in adult women ( n = 1, 213 694 X-rays, 92 585 women) . CT scans combine X-rays from different angles to create high-quality, three-dimensional images. Nevertheless, since they are generated from controlled motions of X-rays, CTs are still unfit for extracting molecular information . Furthermore, these scans subject the patient to higher radiation levels than X-rays and may require contrast agents depending on the adopted modality – contrast-enhanced CTs (CECTs) versus non-contrast CTs (NECTs). CT scans were commonly collected variables in the selected articles (8/37, 21.6%), amounting to 7 540 images from: (i) the lungs ( n = 4, 2 323 nodules, 2 113 patients) ; (ii) gastric cancers ( n = 2, 1 129 images, 352 women, 777 men) ; (iii) cervical lymph nodes ( n = 1, 3 838 images, 698 patients of unknown gender) ; and (iv) hepatic metastasis from colorectal cancer ( n = 1, 250 lesions, 31 women, 54 men) . MRI scans do not depend on radiation and use a strong magnetic field and radio waves to create detailed images. This type of imaging can be separated into two subtypes: conventional and advanced . Conventional MRI (cMRI) sequences include standard MRI protocols commonly used in clinical practice, such as (i) T1-weighted: used to identify structural abnormalities; (ii) Axial fluid-attenuated inversion recovery MRI (FLAIR), applied to identify abnormalities that affect the tissues' water content; and (iii) T2-weighted: also appropriate to assess irregularities in water content. Advanced MRI (advMRI) techniques generate deeper information regarding the tissue's function, structure, and metabolic processes, including: (i) multiparametric MRI (mpMRI), which combine several other MRI sequences to enrich its output; (ii) axial diffusion-weighted (DWI) MRI, which measure the movement of water molecules in tissues; (iii) Vascular architecture mapping (VAM) MRI, providing information about the tissue's blood vessels; (iv) Gradient echo dynamic susceptibility contrast (DSC) MRI, used to measure blood movement; (v) Quantitative blood-oxygenation-level-dependent (qBOLD) MRI, able to measure the oxygen content in the blood; (vi) General Electric-Dynamic Susceptibility Contrast (GE-DSC) MRI, which resorts to a contrast agent to measure blood flow; and (vii) Magnetic resonance spectroscopy (MRS), which calculate the levels of certain chemicals and metabolites in the tissues. Although some types of MRIs – such as MR spectroscopy and diffusion-weighted imaging – allow assessing molecular details without contrasts, most are better equipped to analyze gross internal structures and are more expensive than CTs and X-rays . MRI scans were also frequently used as input for the models, with 64 941 combined images from 8 studies (21.6%), including (i) the brain ( n = 3, 64 459 lesions, 623 women, 461 men) ; (ii) the prostate ( n = 2, 262 nodules, 300 men) ; (iii) colorectal malignancies ( n = 2, 154 images, 54 women, 64 men) ; and (iv) bones and cartilages ( n = 1, 65 scans, 34 women, 31 men) . PET scans, which are also radiation-free, allow for examining the internal body structure and underlying molecular tissues. However, these are extremely expensive, usually unavailable in routine practice, and due to their low spatial resolution, require pairing with a second modality, such as CTs and MRIs . In this review, one study (2.7%) used PET-CT scans to examine atypical cartilaginous tumors and appendicular chondrosarcomas (36 scans, 23 women, 13 men) . Similarly to X-rays, ultrasounds – which use high-frequency sound waves to create images – provide an inexpensive method to inspect organ structures without detailing underlying molecular information, with the upside of not involving radiation . Ultrasonographic imaging was mentioned in 2 articles ( n = 2, 5.4%, 328), which studied breast cancers (116 ultrasounds, 107 women) . Eight reports describe images captured with standard endoscopes ( n = 8, 24.3%, 3681 items), which cannot capture molecular features. Four studies used colonoscopic lesions from the colon and rectum (995 images, 105 women, 224 men) . Four studies analyzed endoscopic pictures of the esophagus ( n = 2, 260 images, 260 patients of unknown gender) , the larynx ( n = 1, 1 176 images, unknown number of patients) , and the nasopharynx ( n = 1, 1 430 images, 124 women, 231 men) . Lastly, one study examined endoscopic videos from intramucosal gastric cancer patients (54 videos, 38 women, 16 men) . Two studies used advanced endoscopes. One involved endoscopic ultrasonography (EUS), a technique that combines endoscopy and ultrasonography to gather gastrointestinal images ( n = 1, 2.7%, 212 ultrasounds, 38 women, 31 men) . The other resorted to endocytoscopy, a relatively new high-magnification imaging approach that allows tissue analysis at a cellular level, to collect 100 colorectal images from 89 patients ( n = 1, 2.7%, 26 women, 63 men) . A histological image is a high-resolution, microscopic image of a tissue slide after it's been processed with one or more stains to reveal its composition . This method allows distinguishing between different histological cancer subtypes but involves a long preparation time and offers a limited depth of view. One paper used hematoxylin-and-eosin (H&E)-stained histological images to study endometrium hyperplasia and intraepithelial neoplasia ( n = 1, 2.7%, 1 631 slides, 102 women) . Whole-slide images (WSIs) are virtual representations of a tissue section scanned at high resolution and magnification. WSIs are created by scanning stained histological slides and usually combine and magnify multiple slides using specialized software . This technique allows thorough tissue examination at cellular and sub-cellular levels, but it is still cost-, storage- and technically heavy. WSIs were used to feed the models in three studies (8.1%, 3 315 images), using 30 × or 40 × magnification. Two included H&E stained slides of the liver ( n = 1, 80 slides, 24 women, 56 men) and the mesothelium ( n = 1, 39 images, 39 patients of unreported gender) . One was composed of stained slides (unknown stain) for the cervical screening of women without any known conditions and with the Human papillomavirus (HPV) ( n = 1, 1565 images and women) . Finally, 46 962 digital photographs (captured with a camera) were analyzed across two documents (5.4%). Both inspected skin malignancies ( n = 2, 10 602 patients). Detailed information regarding the samples, type of CTs, MRIs, and endoscopes used in the image-based studies, as well as population details and counts (age group, total patients, female, and male), is itemized in Table . Text-based Studies The populations and specific clinical variables used in each text-based study are compiled in Table . Clinical data from 6 803 patients (2 772 women, 4 031 men, 7 861 encounters) was collected for validation across ten papers . Apart from one work including senior citizens , all studies consisted of adult patients (6 644 subjects, 2 701 women, 3 943 men). An average of 17 clinical variables was used per study (range = 6 – 31 ), encompassing information on demographics, tumoral values, and laboratory test results. The machine learning models used in 6 of the articles (60%) were exclusively developed for classification (1 960 women, 3 097 men) , while 4 (40%) solely concerned regression (812 women, 934 men) . In the four regression-based articles, the developed prognostic models assessed (i) patients with a single lesion of primary stage I to IV esophageal adenocarcinoma or squamous cell carcinoma ( n = 1, 150 women, 350 men) ; (ii) patients with pathologically confirmed and resected intrahepatic cholangiocarcinoma (12 women, 30 men) ; (iii) patients with stage I to III non-small cell lung cancer (642 women, 540 men) ; and (iv) patients in palliative care with unresectable advanced pancreatic ductal adenocarcinoma with liver metastases (8 women, 14 men) . The six classification papers included: (i) seniors with stage I to III non-small cell lung cancer treated with curative-intent radiotherapy (159 individuals, 71 women, 88 men) ; (ii) bone metastasis in kidney cancer patients with complete survival data (323 women, 640 men) ; (iii) women with primary breast cancer diagnosed by pathological examination (150 women) ; (iv) patients with primary colorectal cancer with survival-related data who underwent surgery (1 572 patients, 607 female, 965 male) ; (v) patients with confirmed stage III non-small cell lung cancer (39 women, 133 men) ; and (vi) patients with solid metastatic tumors for several types of cancer with and without alterations in treatment in an outpatient setting (3 099 encounters, 2 041 individuals, 770 women, 1 271 men) . Mixed Studies An average of 9 clinical variables (range = 3 – 17 ), 784 images, and 720 patients (range = 44 – 5 493 for both) were used in the nine mixed studies, whose information is highlighted in Table . These papers combined patients’ demographics, cancer-specific data, laboratory results, and imaging features extracted from different modalities for cancer-specific populations (7 053 images, 6 482 patients, 3 009 women, 3 478 men). Radiomics approaches were used in three studies . Six reports included CT images to study: (i) patients who underwent curative-intent resection for pancreatic ductal adenocarcinoma ( n = 1, 53 images, 27 women, 26 men) ; (ii) patients with benign and malignant pulmonary ground-glass nodules with less than 30 mm ( n = 1, 63 images, 39 women, 22 men) ; (iii) individuals with multiple lungs nodes in a post-operative setting ( n = 1, 200 images, 51 women, 27 men) ; (iv) lung cancer patients with an available baseline radiograph ( n = 1, 5 493 patients and images, 2456 women, 3037 men) ; (v) patients with muscle-invasive bladder cancer who underwent surgery ( n = 1, 75 images, 13 women, 62 men) ; and (vi) adults with pathologically confirmed thymomas and thymic carcinomas ( n = 1, 76 preoperative scans, 33 women, 48 men) . Additionally, three studies used other types of scans. One work paired breast-specific data with features derived from three types of MRI scans for women with endometrial lesions and complete clinical data (44 images, 44 women) . One paper combined patients’ age, sex, tumor type, location, and radiomic features extracted from X-rays to analyze primary bone tumors (40 women, 56 men) . Finally, one study evaluated survival- and gross-tumor-related data in conjunction with H&E slides magnified 30 times (whole-slide images) to estimate outcomes for patients diagnosed with gastric cancer (175 images, 91 patients, 60 female, 31 male) . Except for the models developed in this study, where the first used only WSIs for classification and the second used these images and clinical data for prognostication (regression), all algorithms were classifiers. Validation design, clinical settings and performance metrics Information concerning institutional, study, and validation designs, care types, datasets, clinical settings, and the number of institutions involved in validation in the selected documents is illustrated in Table . Model development and validation were performed simultaneously in most studies ( n = 50, 87.5%), while 4 (7.14%) evaluated external validity separately, and 3 (5.36%) entailed model updating and validation. Of the 56 documents included in this review, 44 (78.57%) directly reference external validation in the abstract, 10 (17.86%) indirectly mention it, and 2 (3.57%) omit this information. Overall, 74 medical datasets were used for external validation across the 56 studies, averaging 1.3 per paper (range = 1—8). All studies used real-world data acquired prospectively or collected from the patients' EHRs and imaging archiving platforms. Except for three articles using both standard and uncommon types of MRI scans and one using endocytoscopy (whose use is still growing) , all studies used text- and image-based data routinely collected in clinical practice. However, only nine reports describe external validation in clinically realistic scenarios , and solely two systems are currently implemented in practice . The papers involved several cancer-related settings, including secondary ( n = 1, 2%), tertiary ( n = 34, 61%), and quaternary (12, 21%) oncology care. However, 6 (11%) studies did not report from which centers data were retrieved, and 3 (5%) used databases without this information. Among the collected studies, 49 (87.5%) were conducted retrospectively, 3 (5.36%) were prospective, 4 (7.15%) were mixed: one performed internal validation prospectively and external validation retrospectively , one proceeded inversely , and two used both retrospective and prospective cohorts . Only one report used randomized data . Regarding validation design, 31 (55.357%) studies followed a multi-institutional approach, 14 (25%) collected information from a single center, 1 (1.786%) only used public databases, 2 (3,572%) used public multi-institutional databases, and 8 (14,286%) used both types of sources. For the multi-institutional studies (including databases), the average number of facilities used for validation was 3, with a maximum of 33 . One study did not report the number of institutions involved . The following freely available data sources were used: (i) the Surveillance, Epidemiology, and End Results (SEER) database, which covers population-based cancer registries of approximately 47.8% of the United States Population ; (ii) The Cancer Genome Atlas (TCGA, from the USA), which molecularly characterizes over 20,000 primary cancers, and contains whole-slide images ; (iii) The Cancer Imaging Archive, which hosts a large number of medical images for various types of cancer ; (iv) the Edinburgh dataset, containing data from the University of Edinburgh (Scottland, United Kingdom) ; (v) the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial sponsored by the by the National Cancer Institute (NCI), designed to evaluate the impact of cancer screening on mortality rates, as well as to assess the potential risks and benefits associated with screening ; (vi) the National Lung Screening Trial (NLST), a randomized controlled trial also supported by the NCI that aimed to evaluate the impact of using low-dose helical CT scans on patient mortality ; (vii) the PROSTATEx dataset, which contains a retrospective set of prostate MRI studies ; (viii) the PICTURE dataset, containing data from a single-center trial, and intended to evaluate the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in men with prostate lesions ; and (ix) the National Human Genetic Resources Sharing Service Platform (NHGRP), for which we could not find any details . In two studies, models were trained using data from multiple countries. One developed their model using patients from three Chinese institutions and one center from the United States of America (USA) and validated it on a Chinese dataset ( n = 1, 1.8%) . The other gathered data from a Chinese institution and TCGA and validated their model on images from NHGRP . Additionally, one document did not report which countries were involved in their model’s development or validation . All other authors developed their model on data from a single country. These included China ( n = 19, 33.7%), the USA ( n = 12, 21.4%), South Korea ( n = 9, 16.1%), Italy and Germany (3 each, 5.4%), Japan and the Netherlands (2 each, 3.6%), and the United Kingdom (UK), Canada, and Austria (1 each, 1.8%). Besides the two abovementioned papers , twelve other studies performed international validation. Of these, six included ethnically different sources. Two authors trained their model with data from South Korea: one validated it on South Korean and American datasets , and the other validated it on a South Korean dataset and the Edinburgh dataset (UK) . Additionally, five reports mention training their model on the SEER database (USA), with four validating it with Chinese patients and one with South Korean patients . For the five remaining studies, patients with the same ethnicity were included: (i) one was developed with the NLST trial dataset (USA) and validated on data from the UK ; (ii) one was trained with data from TCGA (USA) and validated on an institution from the UK ; (iii) one used data from Italy for training and patients from The Netherlands for validation ; (iii) one trained their model on the PROSTATEx dataset (from The Netherlands) and validated it on the PICTURE dataset (from the UK) ; and (iv) one used a Chinese dataset for training and Chinese and South Korean patients for validation . Regarding validation types, 12 studies (21.48%) were limited to temporal validation from a single institution, which cannot be interpreted as a fully independent validation . Five other studies also only temporally validated their model. However, two used a multi-institutional approach (3.58%) , two (3.58%) used different data acquisition designs (retrospective internal validation and prospective external validation) , and one evaluated performance for patients at different treatment stages (1.78%) . Nine studies (16,08%) only validated their model geographically, seven within the same country , one internationally , and one with internationally and ethnically different patients . Twenty-nine reports (51.8%) included both temporal and geographical validation. Sixteen (28.57%) used local data, one evaluated temporally and geographically different patients from the same country with images captured using various scanners , and one (1.79%) used national data and mixed data acquisition (prospective internal validation and retrospective external validation) . Lastly, one study that did not report data sources validated their model on different types of computed tomography (CT) scanners . The external datasets were used to evaluate the models’ generalizability to populations differing – geographically, temporally, or both – from the development cohort. The performance metrics reported in the articles can be branched into three categories: discrimination, calibration, and processing time. For classification models, an average of 5 metrics were used to assess discrimination, up to a maximum of seven (range = 1 – 7). These consisted of (i) sensitivity, reported in 48 papers; (ii) area under the receiver operating characteristic (ROC) curve (AUC), calculated in 43 studies; (iii) specificity, used in 42 articles; (iv) accuracy, presented in 35 documents; (v and vi) positive and negative predictive values (PPV and NPV), computed in 29 and 19 reports, respectively; (vii) F1-score, considered in 13 papers; (viii) C-index, used in 2 articles ; (ix) false positive rate, reported in two papers ; (x) area under the alternative free-response ROC curve (AUAFROC) , calculated for one model; (xi) jackknife alternative free-response ROC (JAFROC), also computed for one algorithm ; and (xii) Softspot (Sos) and Sweetspot (Sws) flags, both used in the same two papers . However, decision thresholds were only disclosed for half of the articles (26/52, 50%), and only three papers presented results for different cut-off values/settings . Likewise, 39 classification studies did not assess calibration. When evaluated (13/52, 25%), calibration was illustrated graphically in five studies (9.62%) , via Brier Score in three documents (5.77%) , using both approaches in four papers (7.69%) , and with mean absolute error (MAE) in one report . Lastly, the models’ processing time was also seldomly revealed, with only seven studies reporting it . For the regression-based algorithms, discriminative performance was assessed via C-index . Regarding calibration, the model’s Brier Score was presented in one study , calibration plots in two , both metrics in one , and none in two . The models’ processing time and decision thresholds were not reported in any of these studies. Clinical utility From the selected studies, the majority ( n = 50, 89.29%) explicitly mentions the assessment of the models' clinical utility, that is, its relevance to clinicians and patient outcomes, in the paper's abstract. However, one only refers to it indirectly (1.79%) , and the remaining five (8.93%) do not state this aspect in their summaries . Two approaches were used to assess the models’ utility: comparison against clinician performance, adopted in most studies (40/56, 71.4%), and benchmarking against established clinical tools (15/56, 26.8%). Additionally, one study used both: retrospective comparisons were performed against routine clinical scores, while prospective assessments involved clinicians (1/56, 1.8%) . Comparison Against Clinicians Four hundred-ninety-nine medical professionals of varying expertise were involved in these studies, with an average of 12 clinicians compared against each model (range = 1 – 109 ). These included endoscopists ( n = 204), oncologists ( n = 77), radiologists ( n = 76), general physicians ( n = 71), dermatologists ( n = 44), pathologists ( n = 21), ophthalmologists ( n = 3), and thoracic surgeons ( n = 3). A subset of 113 115 patients (102 178 female, 9 619 male) was used for these assessments, and identical performance metrics as those documented for external validation were observed, plus time until diagnosis. Specific clinicians’ years of experience were reported in 20 papers (48.8%), ranks (without years) in 11 (26.8%), and no information concerning expertise in 10 (24.4%). The 41 classification studies encompassing model comparison against clinicians can be divided into two designs: with and without the model and independent evaluation of the models and the clinicians. The most commonly adopted technique was separately assessing model and clinician performance and comparing it posteriorly ( n = 30, 73.2%). Four hundred-one clinicians (μ = 15 per report, range = 1 – 109) and 109 720 patients (μ = 3 657 per paper, 100 965 female, 8 203 male ) were involved in these papers, and model-clinician performance was compared for detection and diagnostic capabilities. An average of 4 performance metrics (range = 1 – 7 ) were computed per paper, with sensitivity being the most calculated ( n = 23), followed by specificity ( n = 18) and accuracy ( n = 15), AUC ( n = 11), PPV ( n = 11), NPV ( n = 7), F1-score ( n = 3) , false positive rate ( n = 2) , Sweetspot and Softsoft flags ( n = 2) , diagnostic time ( n = 1) , and AUAFROC ( n = 1) , and JAFROC ( n = 1) . The second approach involved comparing clinician performance with and without the assistance of the artificially intelligent systems developed by the authors ( n = 11, 26.8%). The eleven studies employing this method comprised 92 clinicians (μ = 8, minimum = 1, maximum = 20 ) and 3 337 patients (μ = 370, 1 223 female, 1 416 male ). Similarly to the previous technique, an average of 4 performance metrics were used per paper (range = 1 – 6 ), including sensitivity ( n = 9), specificity ( n = 8), accuracy ( n = 8), PPV ( n = 6), NPV ( n = 5), AUC ( n = 2) , mean diagnostic time ( n = 2) , and error rate ( n = 1) . Comparison Against Standard/Established Clinical Tools In sixteen studies, assessing the usefulness of the models involved comparing their performance against well-established and routinely used clinical tools. In total, 11 659 patients (μ = 777 per paper, 4 521 female, 5 694 male ) were encompassed in these assessments, and twelve standard tools were used for comparisons. These included: (i) the 7th and 8th editions of the Tumor, Node, and Metastasis (TNM) staging system; (ii) the Brock University Model; (iii) the Fracture Risk Assessment Tool (FRAX); (iv) the Liver Cancer Study Group of Japan (LCSGJ); (v) the Mayo clinic model; (vi) the modified Glasgow Prognostic Score (mGPS); (vii) the Osteoporosis Self-Assessment Tool for Asians (OSTA); (viii) the second version of the Prostate Imaging Reporting and Data System (PI-RADS v2); (ix) the Peking University (PKU) model; (x) the PLCOm2012 model; (iv) the Response Evaluation Criteria in Solid Tumors (RECIST); (xi) the Veterans Affairs (VA) model; and (xii) the World Health Organization (WHO) performance status. Except for one study , all papers explicitly mention comparisons against these tools in the abstract. The TNM system, created by the American Joint Committee on Cancer (AJCC), is globally used in routine clinical procedures. It categorizes cancer progression and guides subsequent treatment decisions depending on (i) the size and extent of the primary tumor (T), (ii) if it has spread to nearby lymph nodes (N), and (iii) if it has metastasized to distant organs (M) . In this review, two text-based classification studies compared their models against the 7th edition of this staging system (TNM-7): one juxtaposed diagnostic and prognostic (3-year overall survival) predictions for bone metastasis in kidney cancer patients (323 women, 640 men) , while the other compared 1–10-year postoperative survival predictions for patients with colorectal cancer (607 women, 965 men) . Similarly, seven papers resorted to the 8th edition of AJCC TMN (TNM-8), its revised and updated version. On the one hand, in four articles, the models were only compared against this system. Two analyzed their text- and regression-based models to predict cancer-specific survival for esophageal (500 patients, 150 women, 350 men) and lung tumors (1 182 individuals, 642 female, 540 male) . The other two concerned the evaluation of classification models. Using preoperative images and descriptive data, one compared 2-year overall survival and 1-year recurrence-free survival predictions for patients with pancreatic cancer (27 female, 26 male) . The other compared risk stratification performance for overall survival for lung cancer patients (39 women, 133 men) between their model and the TMN-8 system using only text-based data . On the other hand, in three text-based studies, models were compared against TNM-8 and other tools. One paper also contrasted model performance for recurrence, recurrence-free survival, and overall survival for lung cancer patients (71 women, 88 men) with the WHO performance status, often used in oncology to determine patients' overall health status, prognosis, and the ability to tolerate treatment . This scaling system ranges from 0 to 4, where 0 represents no symptoms and pre-disease performance, and 4 translates to total disability. In the second article, predictions of overall postoperative survival were benchmarked against TNM-8 and LCSGJ (42 liver cancer patients, 12 women, 30 men) . LCSGJ is a group of Japanese medical professionals specializing in diagnosing and treating liver cancer, recognized as a leading authority in this cancer research field. Lastly, the third study describes the development of three risk models for breast cancer patients (150 women) : (i) fracture, whose predictions were contrasted with those generated by FRAX; (ii) osteoporosis, compared against and FRAX and OSTA; (iii) and survival, benchmarked against TNM-8. FRAX is a web-based tool designed to stratify 10-year bone fracture risk, and OSTA assesses the risk of osteoporosis in Asian populations . The Brock University (also known as PanCan) model is a logistic regression model devised to assist in risk stratification for lung cancer. It is recommended in the British Thoracic Society guideline as a tool to decide if nodules measuring 8 mm or more in maximum diameter should be assessed further with PET-CT . Here, it was applied in one of the selected papers to compare predictions of malignancy risk for lung cancer from CECT and NECT scans (1 397 images, 1187 patients, unknown gender proportion) . In addition to the Brock Model, comparisons in a second paper (978 CTs, 493 patients, 297 women, 196 men) were also performed against three other tools: (i) the Mayo model, which the Mayo Clinic developed to assess cancer prognosis and predict patient outcomes; (ii) the PKU model, created by the Peking University; and (iii) the VA model, which includes a comprehensive cancer care system that aims to provide high-quality, evidence-based care to veterans with cancer . The mGPS scale is a validated scoring system formulated to assess the prognosis of patients with advanced or metastatic cancer based on nutritional and inflammatory markers . In this review, it was used to establish clinical utility for a text-based classification model developed to predict overall survival for patients with unresectable pancreatic tumors (22 patients, 8 women, 14 men) . PI-RADS is a standardized system for interpreting and reporting findings from prostate MRI scans, created to guide clinical decision-making in diagnosing and treating prostate cancer. In this context, it was contrasted against a model developed to stratify low- and high-risk patients (39 and 14 men, respectively) . PLCOm2012 is a validated risk score that uses logistic regression to predict the probability of lung cancer occurrence within six years based on demographic and clinical information . It was the chosen comparator in a study predicting 12-year lung cancer incidence using low-dose CT images and patients’ age, sex, and smoking status (5493 images and patients, 2456 women, 3037 men) . Finally, RECIST is a set of guidelines used to evaluate the response of solid tumors to treatment in clinical trials and clinical practice. It was compared against two classification models: one aimed at detecting pathological downstaging in advanced gastric cancer patients from CECT images (86 patients and images, 23 women, 27 men) ; the other was designed to predict pathological tumor regression grade response to neoadjuvant chemotherapy in patients with colorectal liver metastases from MRI scans (61 images, 25 patients, 13 female, 12 male) . A few performance metrics were reported for the comparisons between the models developed in the selected papers and routinely used clinical tools, with an average of 3 metrics reported per document (range = 1 – 6). Here, the most frequently calculated metrics were AUC ( n = 11) and sensitivity ( n = 8), but PPV ( n = 5), C-index ( n = 4), specificity ( n = 4), accuracy ( n = 3), NPV ( n = 3), Brier Score ( n = 2) and F1-score ( n = 1) were also used in the evaluations. Primary tumors Fifty-one papers (91.1%) describe models developed for primary tumor-related assessments. These include cancers of the CNS (brain ), digestive (colorectal , esophageal , gastric , and hepatic malignancies), endocrine (pancreas and thymus ), genitourinary (bladder , cervix , prostate , and uterus ), and integumentary (breast and skin ) systems, respiratory system and associated tissues (larynx , lung , mesothelium , and nasopharynx ), and the skeleton (cartilages and bones ). Central nervous system Three retrospective studies were developed to diagnose brain cancers using MRI scans, amounting to 1 084 patients and 64 459 images, resulting in an average sensitivity of 81.97% and specificity of 91.63 (Table ) . The first involved the following conditions: acoustic neuroma, pituitary tumor, epidermoid cyst, meningioma, paraganglioma, craniopharyngioma, glioma, hemangioblastoma, metastatic tumor, germ cell tumor, medulloblastoma, chordoma, lymphomas, choroid plexus, papilloma, gangliocytoma, dysembryoplastic neuroepithelial tumor, and hemangiopericytoma . The CNN-based model was trained on images from 37 871 patients and externally validated using 64 414 T1-weighted, T2-weighted, and T1c MRI scans from 1039 subjects (600 female, 349 male) from three institutions. Its diagnostic performance was compared against nine neuroradiologists (5 to 20 years of experience) to assess clinical utility. This CNN classified brain tumors with high accuracy, sensitivity, and specificity, performing particularly well in identifying gliomas, which are difficult to diagnose using traditional imaging methods. When aided by the model, the neuroradiologists' accuracy increased by 18.9%, which was still lower than the model alone. AI assistance also boosted the neuroradiologists' sensitivity, specificity, and PPV. However, only three types of scans were considered, training data was obtained from a single center, and few rare tumors were included. In the second paper, the authors explored the combination of 9 different ML models – NB, logistic regression, SVM with a polynomial kernel, kNN (k = 3), DT, MLP, RF, AdaBoost, and bootstrap aggregating – to distinguish between different types of brain tumors (glioblastoma, anaplastic glioma, meningioma, primary central nervous system lymphoma, and brain metastasis) . MRI techniques were analyzed in a combination of 135 classifiers and radiomics: cMRI, advMRI, phyMRI, cMRI + phyMRI, and advMRI + phyMRI. A dataset of 167 patients was used for training, and temporal validation was performed on 20 subjects. Physiological MRI scans (phyMRI), named radiophysiomics, achieved the best results using AdaBoost with cMRI and phyMRI and RF with phyMRI. Both models surpassed the radiologists in AUC and F1-score but were outperformed in sensitivity and specificity. The AdaBoost model also had a higher PPV than the clinicians. However, this was a single-center, retrospective study, and the application and tuning of the models were performed manually. The third study evaluated the usefulness of preoperative contrast-enhanced T1- and T2-weighted MRI in differentiating low-grade gliomas (LGG) from glioblastomas (GBM) . The authors trained a radiomics-based RF classifier on 142 patients from 8 American centers and externally validated it on 25 patients from another institution (all from The Cancer Imaging Archive). The results showed that the machine learning algorithm was highly accurate in differentiating between GBM and LGG based on preoperative contrast-enhanced MRI scans, surpassing two neuroradiologists (15 and 1 year of experience) and a radiologist (3 years of experience). However, few patients from a public database were collected, possibly resulting in selection bias (non-random selection). Digestive system Malignancies of the digestive system – highlighted in Table – were the most comprehensively studied (17/56, 30.4%), encompassing colorectal ( n = 7, 41.2%), esophageal ( n = 3, 17.6%), gastric ( n = 5, 29.4%), and liver ( n = 2, 11.8%) cancers. Colorectal Cancer Three sets of articles addressed colorectal cancers (7 papers). The goal of the first set, consisting of four multi-institutional retrospective studies, was its diagnosis, averaging a sensitivity of 77.3% and a specificity of 93.2% for tests on 995 images from different sources . The authors in developed an ensemble of three CNNs (Inception-v3, ResNet-50, and DenseNet-161) to predict the histology of colorectal neoplasms based on white light colonoscopic images. The ensemble model transferred knowledge from digital photography and learned with colonoscopic images to classify the images into one of 4 different pathologic categories: normal (healthy), adenoma with low-grade dysplasia (A-LGD), adenoma with high-grade dysplasia (A-HGD), and adenocarcinoma. The system's diagnostic performance was compared against four experts (more than five years of experience) and six trainees (less than two years). In the external validation dataset (400 images, 100 of each type), the CNN-CAD model achieved high accuracy in predicting the histology of the lesions. Compared to endoscopists, the model's performance was slightly better than the experts' and significantly outperformed the trainees. In addition, the authors used Grad-CAM to create a heatmap highlighting the regions of the input image that were most relevant to the network's decision. However, only one image per polyp was used; consequently, tumors that cannot be contained within a single image were neglected. The second work concerns the external validation and clinical utility assessment of EndoBRAIN, an AI-assisted system to classify colorectal polyps into malignant or non-malignant. EndoBRAIN was trained with 69 142 endocytoscopic images from patients with colorectal polyps from five academic centers in Japan. Its clinical validity had previously been confirmed in a single-center prospective study. However, since its implementation depends on governmental regulatory approval, the current study compared EndoBRAIN's diagnostic performance against 30 endoscopists (20 trainees, 10 experts) using stained and narrow-band endocytoscopic images in a web-based trial. The authors found their CADx tool accurately differentiated neoplastic from non-neoplastic lesions, outperforming all endoscopists for stained images, achieving similar performance in narrow-band images, and being accepted for clinical use. The third diagnostic model concerns the development of a deep learning model to predict the revised Vienna Classification in colonoscopy, which categorizes colorectal neoplasms into different levels of malignancy using standard endoscopic colonoscopy images . Several CNN architectures were compared, namely AlexNet, ResNet152, and EfficientNet-B8, with ResNet152 being chosen as the prediction model due to its higher accuracy and fastest inference time. The model was trained using 56,872 colonoscopy images (6775 lesions) and validated on 255 images (128 lesions) from 7 external institutions in Japan. The authors also compared diagnostic performance against endoscopists (five novices, three fellows, and four experts). The AI system’s sensitivity and specificity exceeded that of all endoscopists. Nevertheless, the model cannot discriminate between high-grade dysplasia and invasive cancer (categories 4 and 5 of the revised Vienna Classification), and only binary classification is supported. In the fourth document, the authors tested two pre-trained radiomics-based CNN architectures (Inception-ResNet-v2 and ResNet-152) to classify colorectal neoplasms into three types of sets automatically: 7-class (T1-4 colorectal cancer, high-grade dysplasia, tubular adenoma, vs. non-neoplasms), 4-class (neoplastic vs. non-neoplastic – advanced vs. early CRC vs. adenoma vs. healthy), and 2-class (neoplastic versus non-neoplastic and advanced versus non-advanced lesions) . The CNNs were trained on a South Korean dataset (3453 colonoscopy images, 1446 patients) and temporally and geographically validated on 240 images (and as many patients) from another institution. CAM was used to highlight its decisions. The best-performing architecture was ResNet-152 for 7-way and 4-way diagnoses, but Inception-ResNet-v2 achieved better results on binary classifications. In addition, the model's performance was compared with one novice and two experienced endoscopists with six months and more than five years of colonoscopy experience, respectively. Although resulting in high accuracy, neither CNN architecture could outperform the endoscopists. Furthermore, this retrospective study only considered three types of diseases and white-light colonoscopy images. The second set of articles was devoted to predicting outcomes from MRI scans in patients with colorectal cancer undergoing neoadjuvant chemotherapy (NCRT), accruing 143 MRIs from 118 patients and a mean AUC and accuracy of 0.77 and 81.9%, respectively . The first was a prospective study using a multipath CNN on MRI scans (diffusion kurtosis and T2-weighted) . The authors used a dataset of 412 patients (290 for development and 93 for temporal validation) with locally advanced rectal adenocarcinoma scheduled for NCRT. The researchers developed three multipath CNN-based models: one to preoperatively predict pathologic complete response (pCR) to neoadjuvant chemoradiotherapy, one to assess tumor regression grade (TRG) (TRG0 and TRG1 vs. TRG2 and TRG3), and one to predict T downstaging. In addition, the authors evaluated the models' utility by comparing two radiologists' – with 10 and 15 years of experience – performance with and without their assistance. The results showed excellent performance in predicting pCR, superior to the assessment by the two radiologists, whose error rate was also reduced when assisted by the DL model. Although with lower performance, the TRG and T downstaging models also achieved promising results with an AUC of 0.70 and 0.79, respectively (although not outperforming the clinicians). Nevertheless, this monoinstitutional research required manual delineation, and interobserver variability was not analyzed. Moreover, further validation studies are necessary to assess performance with different MRI scanners. The second group of researchers developed an MRI-based CNN (DC3CNN) to predict tumor regression grade (assessment of tumor size) in response to NCRT in patients with colorectal liver metastases . The authors used prospective internal (328 lesions from 155 patients) and retrospective external cohorts (61 images, 25 patients) to collect pre and post-treatment T2-weighted- and DW-MRI scans. The model surpassed the diagnostic accuracy of RECIST, the most commonly used criteria for clinical evaluation of solid tumor response to chemotherapy. However, the study was retrospective, and further studies are needed to validate its performance in larger ethnically diverse patient populations. Lastly, only one model assessed postoperative survival of colorectal cancer using text-based data . The model was trained on the SEER database (364 316 patients) and externally validated (temporally and ethnically) on a Korean dataset (1 572 subjects, 607 women, 965 men). The authors compared 4 ML algorithms, namely logistic regression, DTs, RFs, and LightGBM, to obtain an optimal prognostic model. The best-performing model – LightGBM – outperformed TNM-7 in predicting survival for all tested periods (1, 2, 3, 4, 5, 6, 8, and 10 years). Still, data were collected retrospectively from a public database and a single institution using only text-based data, so prospective studies are necessary, and clinicopathological, molecular, and radiologic variables should also be incorporated. Esophageal Cancer Three studies involved esophageal cancers. Two papers studied neoplasia detection in patients with Barrett’s esophagus, a medical condition resulting from long-term acid-reflux damage, causing esophageal tissue lining to thicken and become irritated, increasing cancer risk . The same group of researchers conducted both studies: the first paper describes model development for detection , while the second encompasses its tuning and update to include location . The authors proposed a multi-stage pretraining approach that involved training a CNN learning model on 494,355 gastrointestinal images before fine-tuning it on a smaller dataset of medical images specific to Barrett's neoplasia. The model was trained with images from different endoscopes. In the first paper , using data from separate institutions, the authors used a retrospective dataset of early Barrett’s neoplasia for primary validation (80 patients, unknown proportion) and a second prospectively acquired dataset (80 patients and images) to compare their model’s performance against fifty-three endoscopists (17 seniors, 8 juniors, 18 fellows, and 10 novices). In the second paper, the researchers validated their model on three prospective datasets: one with clinically representative images (80 individuals), one with subtle lesions (80 subjects), and one in a live setting with dysplastic and nondysplastic patients (ten each) . It showed excellent performance on the three external validation datasets, and its detection and location performances were also compared against the 53 experienced endoscopists on the subtle lesions. The CAD system outperformed all 53 endoscopists for all tested metrics in both papers, obtaining an average accuracy, sensitivity, and specificity of 87.9%, 91.7%, and 84.16%, respectively. The models developed in both articles performed similarly and were tested in clinically realistic scenarios, with an average accuracy, sensitivity, and specificity of 88.45%, 91.25%, and 85.63%, respectively, enhancing CNNs’ predictive power. Additionally, a retrospective study evaluated cancer-specific survival for esophageal adenocarcinoma and squamous cell carcinoma according to individual treatment recommendations . The authors trained a deep-, regression-, and text-based survival neural network (DeepSurv, multi-layer perceptron) using the SEER database (6855 patients) and validated it on 150 women and 350 men from their institution (China). Additionally, prognostic performance was compared against TNM-8, having exceeded it. However, only one medical center was used, and research was not performed in an accurately representative clinical setting. Gastric Cancer In five articles, models were developed for gastric-related tasks. The first three studies had a diagnostic component. In the first research, the authors developed two models – GastroMIL and MIL-GC –, training them on WSIs from H&E slides magnified 30 times collected from TCGA and a Chinese institution. They also temporally and geographically validated them with 175 WSIs from 91 patients from NHGRP . GastroMIL used an ensemble of a CNN and an RNN to distinguish gastric cancer from normal gastric tissue images. Its performance was compared against one junior and three expert pathologists. MIL-GC, a regression-based model, was created to predict patients’ overall survival. Besides WSIs, MIL-GC uses clinical data, namely survival state, overall survival time, age, sex, tumor size, neoplasm histologic grade, and pathologic T, N, M, and TNM-8 stages. The deep learning models achieved high performance in both tasks, with an overall accuracy of 92% for diagnosis and a C-index of 0.657 for prognosis prediction in the external dataset. Compared to human performance, GastroMIL outperformed the junior pathologist in accuracy and sensitivity but was surpassed by the experienced pathologists (in accuracy, sensitivity, and specificity). However, the tested cohorts were retrospective and had unbalanced survival times, and clinical utility was not evaluated for the prognostic model. The second study used a CNN (ResNet-50) for real-time gastric cancer diagnosis . The model was developed with 3 407 endoscopic images of 666 patients with gastric lesions from two institutions. The DCNN model was tested on a temporally different dataset of endoscopic videos from a separate institution (54 videos from 54 patients), and performance was compared against 20 endoscopists (6 experts, 14 novices). The model achieved better performance than any of the endoscopists, and diagnostic accuracy, sensitivity, and specificity increased for all clinicians while assisted by the model. Nevertheless, despite decreasing the aggregate diagnostic time from 4.35 s to 3.01 s, it increased experts’ by 0.10 s. In addition, the diagnostic model was only tested on high-quality images, and the validation dataset was small and domestic. Although slightly less sensitive than Gastro-MIL (93.2% vs. 93.4%), the model developed in achieved the best accuracy and sensitivity, evidencing that endoscopic images and videos might be more appropriate to diagnose gastric cancer. The third model was created using endoscopic ultrasonography images (EUS) for the differential diagnosis of gastric mesenchymal tumors, including GISTs, leiomyomas, and schwannomas . This model was trained with EUS from three Korean institutions and tested on a temporally separate set of 212 images from the same centers (69 patients, 38 female, 31 male). A sequential analysis approach was adopted using two CNNs: the first classifies the tumor as GIST or non-GIST; for non-GISTs, the second CNN classifies it as either a leiomyoma or schwannoma. The results were compared against junior ( n = 3, less than 200 examinations) and expert endoscopists ( n = 3, more than 500 examinations) who evaluated the same images, having surpassed them in both types of classification. However, this study was retrospective and involved a small number of patients, and the types of equipment used to perform ultrasounds varied considerably across the facilities. The last two papers concerned outcome predictions. The first presents a multi-institutional study that uses multitask deep learning to predict peritoneal recurrence and disease-free survival in gastric cancer patients after curative-intent surgery based on CT images . Supervised contrastive learning and a dynamic convolutional neural network were combined to achieve this purpose, and Grad-CAM was used to explain the model’s decisions. The model included CT scans from three patient cohorts, and external validation included 1 043 patients (329 women, 714 men) and as many images from another Chinese institution. In addition, the authors investigated clinician performance for peritoneal recurrence prediction with and without the assistance of the AI model, having found that performance was significantly enhanced after integrating it and that the model alone surpassed all physicians. Nonetheless, only East Asian patients were included in this retrospective study, which was not performed in a real clinical setting, and sensitivity was only reported for one of the clinicians. The last study discusses the use of CT radiomics to predict the response of advanced gastric cancer to neoadjuvant chemotherapy and to detect pathological downstaging at an early stage . The authors trained two SVCs on 206 patients who had undergone three or four cycles of chemotherapy and externally validated them on two testing cohorts, which were also used for benchmarking detection against RECIST. The first testing cohort consists of temporal validation (40 patients and CTs, 13 women, 27 men), while the second differs in the number of chemotherapy cycles (46 individuals and CTs, 10 women, 36 men). Performance for the detection model surpassed RECIST in both cohorts, and, except for sensitivity, the response prediction model also produced positive results. However, retrospective data and a small, unbalanced sample size constrain this study, which was not evaluated in a clinically representative setting. Liver Cancer Two models were developed for liver cancer-related predictions. The first aimed at classifying hepatocellular carcinomas and cholangiocarcinomas (differential diagnosis) . The authors developed a web-based (cloud-deployed AI model and browser-based interface) CNN (DenseNet architecture) using WSIs from H&E slides magnified 40 times and used Grad-CAM to increase the model’s explainability. The training dataset was obtained from TCGA (70 slides from 70 unique patients). The external validation dataset was collected from the Department of Pathology at Stanford University Medical Center (80 slides from 24 women and 56 men). The model achieved a diagnostic accuracy of 84.2% in the validation cohort. Diagnostic performance was also compared to that of 11 pathologists. Except for the two unspecified pathologists, performance (AUC) increased for all clinicians when assisted by this tool. However, the pathologists only had access to the WSIs (as opposed to being complemented with clinical data), the model required manual intervention for patch selection, and the study was retrospective with a small sample size (development and external validation with a total of 150 WSIs and patients). The second model was designed to predict three-year overall survival for intrahepatic cholangiocarcinoma patients after undergoing hepatectomy using an ensemble of Random Forests, XGBoost, and GBDT . Using a single quaternary Chinese institution, the authors collected 1390 patients for training and 42 patients (12 women, 30 men) for external temporal validation. Results were compared against the TNM-8 and LCSGJ staging systems, with model performance exceeding that of the routinely used tools. Nonetheless, this was a monoinstitutional endeavor limited to a small number of Asian patients. Furthermore, only six prognostic factors were used: carcinoembryonic antigen, carbohydrate antigen 19–9, alpha-fetoprotein, pre-albumin, and T and N stages. Endocrine system Three papers described prognostic models for cancers in organs affecting the endocrine system (pancreas and thymus), whose results are depicted in Table . Pancreatic Cancer The first two studies assessed survival for pancreatic ductal adenocarcinoma (PDAC) patients but adopted disparate research designs and clinical inputs . The first group of researchers used a regression-based random survival forest model to prognosticate patients with advanced pancreatic cancer . Aimed at predicting overall survival for patients with unresectable PDAC, the model was developed with clinical data and CT scans from a German institution (203 patients). It was temporally and geographically validated using only text-based clinical data from patients with liver metastases from the same country (8 women, 14 men) and compared against mGPS, having outperformed it. Additionally, the authors used SHAP to explain their model, finding that inflammatory markers C-reactive protein and neutrophil-to-lymphocyte ratio had the most significant influence on its decision-making. Nonetheless, only twenty national patients were used to validate the model externally, and different types of inputs were used for training and testing. The second set of authors used an ensemble of ML methods – ANN, logistic regression, RF, GB, SVM, and CNNs (3D ResNet-18, R(2 + 1)D-18, 3D ResNeXt-50, and 3D DenseNet-121) – to predict 2-year overall and 1-year recurrence-free survival for PDAC patients after surgical resection . The classifier was trained and tuned using 229 patients and temporally validated with CECT images and seventeen clinical variables from the same South Korean institution (53 CECTs from 27 women and 26 men). Grad-CAM was used to explain the model’s decisions, and comparisons were made against TMN-8 to evaluate clinical utility. Although more accurate, specific, and with a higher PPV than TNM-8, it was less sensitive for both predictions and had a lower NPV for overall survival prediction. Furthermore, tumor margins were manually segmented, and the model did not consider histopathologic data. Thymic Cancer One study was designed for the simplified risk categorization of thymic epithelial tumors (TETs), rare cancer forms . Here, three types of tumors were evaluated: low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC). Three triple classification models were developed using radiomic features extracted from preoperative NECT images and clinical data from 433 patients: (i) LRT vs. HRT + TC; (ii) HRT vs. LRT + TC; (iii) TC vs. LRT + HRT. The authors compared several CT-based classifiers: logistic regression, linear SVC, Bernoulli and Gaussian Naïve Bayes, LDA, Stochastic Gradient Descent, SVM, DT, kNN, MLP, RF, AdaBoost, gradient boosting, and XGBoost. Combined with clinical data, the SVM model demonstrated the best performance for predicting the simplified TETs risk categorization. In addition, the SVM model was validated in a temporally different cohort using images from 5 types of scanners (76 scans and patients, 33 women, 48 men). Finally, its diagnostic performance was compared against three radiologists (3, 6, and 12 years of experience), having exceeded them regarding AUC (0.844 versus 0.645, 0.813, and 0.724) but not for other metrics (accuracy, sensitivity, and specificity). Caveats include the reduced amount of patients, low number of thymic carcinomas, and incomplete automation of the models. Genitourinary system Table illustrates the models developed for genitourinary cancers, including the bladder, cervix, prostate, and uterus. Bladder Cancer From the retrieved models, only one assesses outcomes for primary bladder cancers . This article presents a CNN-based strategy to predict the muscular invasiveness of bladder cancer based on CT images and clinical data. The model was developed with 183 patients. Its performance was tested on an independent institution's temporally and geographically different validation cohort of patients with urothelial carcinoma (13 women, 62 men, and as many images). The model’s predictions were juxtaposed with diagnoses from two radiologists with nine and two years of experience, having achieved better accuracy and specificity than the two clinicians but a lower sensitivity. Overall, the authors found that the deep learning algorithm achieved a high accuracy rate in predicting muscular invasiveness, an essential factor in determining the prognosis and treatment of bladder cancer. However, the study is limited by its retrospective nature, exclusion of tumors not visible in CT images, and small sample size. Cervical Cancer Similarly, primary tumors of the cervix were only screened in one paper . Here, the authors trained an ensemble of convolutional and recurrent neural networks on whole-slide images from patients' cervical biopsies and 79 911 annotations from five hospitals and five kinds of scanners. The system comprises (i) two CNNs – the first scans WSIs at low resolution and the second at high resolution – to identify and locate the ten most suspicious areas in each slide; (ii) and an RNN to predict corresponding probabilities. The system classifies squamous and glandular epithelial cell abnormalities as positive (neoplastic) and normal findings as negative for intraepithelial lesions or malignancies (non-neoplastic). The method was externally validated on multi-center independent test sets of 1 565 women (1 170 without additional conditions and 395 with HPV), and classification performance was compared against three cytopathologists. Although obtaining promising results and surpassing clinician performance for both types of women, the authors highlight that the model was designed for the general women population, implying that further refinements are required for specific comorbidities. Prostate Cancer Two models were developed for prostate-cancer-related classifications using multiparametric MRI scans . In the first paper, the authors describe the development of Autoprostate, a system employing deep learning to generate a report summarizing the probability of suspicious lesions qualifying as clinically significant prostate cancer (CSPCa) . The authors trained their approach on the PROSTATEx dataset (249 men), externally validated it on the PICTURE dataset (247 patients), and compared its reports (with post-thresholding and false positive reduction) to those generated by a radiologist with ten years of experience. The system achieved a high level of agreement with the human reports (surpassing the radiologist in AUC and specificity) and could accurately identify CSPCa. However, this study was retrospective, a single (public) dataset was used for external validation, and only two types of prostate lesions were considered. The second article presented an ML-based approach for prostate cancer risk stratification using radiomics applied to multiparametric MRI scans . In this retrospective, monoinstitutional study, the authors compared seven classification algorithms: logistic regression, linear, quadratic (Q), cubic, and Gaussian kernel-based SVM, linear discriminant analysis, and RF. After training with 68 patients, the best-performing method – QSVM – was validated on a temporally independent dataset (14 high- and 39 low-risk patients). Its performance was compared against PI-RADS v2, having found that the model could accurately predict the risk of clinically significant prostate cancer. Although the classifier performed equivalently to PI-RADS v2 regarding AUC, it performed substantially better in class-specific measures (F1-score, sensitivity, and PPV), especially for the high-risk class. However, the study is limited by its retrospective nature and small sample size from a single source. Uterine Cancer Two studies for primary cancers focused on classifying lesions of the endometrium, the layer of tissue lining the uterus . In the first article, using 245 women as the training cohort, the authors compared nine models – logistic regression (LR), SVM, stochastic gradient descent, kNN, DT, RF, ExtraTrees, XGBoost, and LightGBM – to obtain an optimal algorithm for differential diagnosis (malignant versus benign tumors) . A radiomics score (radscore) was computed for the best-performing algorithm (logistic regression), and four models were selected using different combinations of T1-weighted, T2-weighted, and DWI MRI features: (i) the radiomics model; (ii) a nomogram, combining the radscore and clinical predictive parameters; (iii) a two-tiered stacking model, where the first tier was the clinical model and the optimal radiomics model (LR), and the second tier used the output of the first tier as the input of the multivariate LR; and (iv) an ensemble model, where the predictions obtained from the preceding clinical model and radiomics model were calculated by an accuracy-weighted average. The results showed that all four models accurately differentiated stage IA endometrial cancer and benign endometrial lesions. Furthermore, during external validation (44 MRIs from 44 women), the authors found that the nomogram had a higher AUC than the radiomics model, revealing more stable discrimination efficiency and better generalizability than the stacking and ensemble models and a radiologist with 30 years of experience (except in sensitivity). Nevertheless, data was collected from two same-country centers (Chinese institutions), only standard radiomics features were extracted, and lesions were manually segmented, which is highly time-consuming. The second paper encompassed a global-to-local multi-scale CNN to diagnose endometrial hyperplasia and screen endometrial intraepithelial neoplasia (EIN) in histopathological images . The researchers trained the CNN using a large annotated dataset (6 248 images) and tested it on a temporally different set of patients (1631 images, 135 specimens, 102 women). They found that it performed well in diagnosing endometrial hyperplasia and detecting EIN, outperforming a junior pathologist (2 years of experience) and obtaining comparable performance to a mid-level and a senior pathologist (6 and 25 years of experience, respectively). The authors used Grad-CAM to emphasize the regions the model deemed relevant for diagnosis. However, this retrospective study only used histopathological images (as opposed to WSIs). Besides, it focused solely on classifying healthy slides, hyperplasia without atypia, and endometrial intraepithelial neoplasia, thus neglecting the differentiation between benign lesions and endometrial cancer. Integumentary system As illustrated in Table , five papers studied cancers of the integumentary system, focusing on the breasts and skin. Breast Cancer Three studies developed models for cancers originating in the breasts, each with a specific purpose and using different clinical modalities. In , several text-based machine learning classifiers, namely, DTs, RFs, MLPs, logistic regression, naïve Bayes, and XGBoost, were compared to establish optimal classifiers for osteoporosis, relative fracture, and 8-year overall survival predictions. The algorithm was trained on 420 patients from a Chinese institution and geographically validated on 150 women from a separate local institution. The osteoporosis model was compared against OSTA and FRAX, the fracture model against FRAX, and the prognostic model against TNM-8. The results showed that the XGBoost classifier performed the best for the three tasks and outperformed the other clinical models. Additionally, for explainability, the authors also used SHAP for feature importance analysis for each model: (i) age, use of anti-estrogens, and molecular type are the most predictive of osteoporosis; (ii) osteoporosis, age, and bone-specific alkaline phosphatase are the best predictors for fracture; and (iii) N-stage, molecular type, and age have the highest prognostic value for overall survival. Despite its positive results, prospective studies are needed to validate the model in more diverse patient populations. In , authors explored how combining AI and radiologists can improve breast cancer screening. Using 213 694 retrospectively collected mammograms (X-ray images) from 92 585 women, it was found that the combination of radiologists and AI (CNN-based classifier) achieved the highest accuracy in detecting breast cancer. The sensitivity and specificity of the standalone AI system were significantly lower than an unaided radiologist. However, the decision-referral approach outperformed the unaided radiologist on both sensitivity and specificity for several tested thresholds. Nonetheless, the study only included mammogram images and did not consider other factors, such as patient history or clinical data, which may impact the accuracy of breast cancer screening. Furthermore, the AI algorithm used in the study was not optimized for clinical use and may require further development and testing before it can be implemented in a clinical setting. Lastly, the work developed in entailed diagnosing non-cystic benign and malignant breast lesions from ultrasonographic images. Radiomic features were extracted from the ultrasound images, and a random forest model was trained with 135 lesions and externally validated to predict malignancy for each lesion. Moreover, the performance of an experienced radiologist (8 years) was compared with and without the model’s assistance. Although not with statistical significance, the radiologist's assessments improved when using the AI system. However, the final validation population was small (66 ultrasounds from 57 women) and showed different proportions of malignant lesions. Skin Cancer Two models were developed to diagnose skin tumors using photographs, producing an average AUC, sensitivity, and specificity of 0.89, 77.1%, and 81.74% . The first was a retrospective validation study assessing the performance of deep neural networks in detecting and diagnosing benign and malignant skin neoplasms of the head and neck, trunk, arms, and legs . In a previous study, the authors trained an ensemble of CNNs (SENet + SE-ResNeXt-50 + faster RCNN) with 1 106 886 image crops from South Korean patients to detect potential lesions and classify skin malignancies. Here, performance was tested on three new temporal and geographical validation datasets of skin lesions (two national, one international, 46 696 photographs from 10 876 patients): (i) one dataset was used to compare the model’s classification performance against 65 attending physicians in real-world practice; (ii) one’s goal was to evaluate classification performance against with 44 dermatologists in an experimental setting; and (iv) the last two were meant to predict exact diagnosis (1 of 43 primary skin neoplasms) in a local (South Korean) and an international (UK, 1 300 images) dataset, with the first also being compared against physicians. In (i) and (ii), performance was calculated for high specificity and high sensitivity thresholds. The algorithm was more sensitive and specific than the dermatologists in the experimental setting. However, attending physicians outperformed it in real-world practice in all tested metrics (sensitivity, specificity, PPV, and NPV). In addition, the model only dealt with high-quality clinical photographs, and there was a lack of ethnic diversity in the study population. The second paper presented a set of CNNs – DenseNet-121 (Faster R-CNN and deep classification network) – developed to detect malignant eyelid tumors from photographic images . The researchers used a 1 417 clinical images dataset with 1 533 eyelid tumors from 851 patients across three Chinese institutions (one for development and two for external validation). Besides using Grad-CAM for interpretation, the AI’s performance on the external dataset (266 pictures from 176 patients) was compared to three ophthalmologists: one junior, one senior, and one expert (3, 7, and 15 years of experience, respectively). It surpassed the junior and senior ophthalmologists’ performance and achieved similar results to the expert. Notwithstanding its potential, the system still needs evaluation on non-Asian populations and prospectively acquired datasets, and it was only developed for detection (it cannot provide a specific diagnosis). Respiratory system and associated tissues Thirteen papers addressed respiratory system cancers, which predominantly concerned the lungs, but also included the larynx, nasopharynx, and mesothelium (Table ). Lung Cancer Ten approaches were developed for lung cancer assessments. The first document describes a validation study of a CNN-based tool (DenseNet) designed to predict the malignancy of pulmonary nodules . The model was previously trained with the NLST dataset and was now externally validated in 3 UK centers with different CT scanners (1 397 CECTs and NECTs, 1 187 patients of unknown gender ratio). The authors also evaluated its clinical utility by comparing it to the Brock Model. Although slightly less specific than the Brock model, the detection algorithm developed by the authors had a higher AUC and sensitivity. Despite having undergone international validation, prospective studies in ethnically diverse populations are still amiss. The second paper involved developing and validating a model to predict the malignancy of multiple pulmonary nodules from CT scans and eleven clinical variables . The study analyzed data from various medical centers. Ten ML methods were compared to identify the best malignancy predictor: AdaBoost, DT, Logistic Regression, Linear SVM, Radial Basis Function Kernel SVM, NB, kNN, Neural Net, Quadratic Discriminant Analysis, RF, and XGBoost. The best-performing model – XGBoost – was tested on three datasets. The first was retrospective, compiled from 6 institutions (five from China and one from South Korea), used for primary external validation (220 patients, 583 CT scans), and compared against four well-established models: Brock, Mayo, PKU, and VA. The second retrospective dataset was used for generalizability, containing patients from a Chinese institution with solitary pulmonary nodules (195 patients and images, 110 women, 85 men), whose results were also compared against the four just-mentioned models. The third and last dataset included data from 4 Chinese centers and was collected prospectively for secondary validation and comparisons against clinicians (200 CTs, 78 patients, 51 women, 27 men). This comparison involved three thoracic surgeons and one radiologist, who achieved an average sensitivity of 0.651 and specificity of 0.679. The model significantly outperformed this average and each clinician’s AUC, as well as in all comparisons against the routinely used models. In addition, SHAP was used to identify the most predictive nodule characteristics, finding that the model's most predictive features were nodule size, type, count, border, patient age, spiculation, lobulation, emphysema, nodule location, and distribution. Nonetheless, besides not reporting individual clinician sensitivity and specificity in the prospective cohort, the drawbacks of this study include only assessing typical high-risk patients and the lack of validation with different ethnicities. The work in involved a CNN-based model for predicting the presence of visceral pleural invasion in patients with early-stage lung cancer. The deep learning model was trained using a dataset of CT scans from 676 patients and externally validated on a temporally different cohort from the same South Korean institution (141 CTs from 84 women and 57 men). Besides using Grad-CAM to evidence its decisions, this CNN can adapt its sensitivity and specificity to meet the clinical needs of individual patients and clinicians. The model achieved a performance level comparable to three expert radiologists but did not surpass it except in PPV. Besides, these are results from a monoinstitutional retrospective study where geographical validation was not performed. In addition to using a small number of patients, data was also imbalanced, and the model was not fully automated (required manual tumor annotations). The fourth article concerns developing an EfficientNetV2-based CNN system to predict the survival benefit of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in patients with stage IV non-small cell lung cancer . The model was developed with accessible pre-therapy CT images from five centers and externally validated on a monoinstitutional dataset from a national dataset (China, 92 CTs from 92 patients). The authors also compared radiologists' and oncologists' (three each, 2, 5, and 10 years of experience) performance with and without ESBP. The results showed that, while assisted by the system, all radiologists improved their diagnostic accuracy, sensibility, specificity, PPV, and NPV (except for the trainee oncologist, who achieved better sensitivity without the model). However, prospective studies in ethnically rich cohorts are still necessary to implement this tool in clinical practice. The fifth study aimed at finding optimal predictors of two-year recurrence, recurrence-free survival, and overall survival after curative-intent radiotherapy for non-small cell lung cancer . Ten text-based ML models were trained on 498 patients and compared: ANN, Linear and Non-linear SVM, Generalized Linear Model, kNN, RF, MDA, Partial Least Squares, NB, and XGBoost. The best-performing models were as follows: (i) an ensemble of kNN, NB, and RF for recurrence classification; (ii) kNN for recurrence-free survival prediction; and (iii) a combination of XGBoost, ANN, and MDA for overall survival. The three optimal predictors were externally validated using routinely collected data from 5 UK institutions (159 seniors, 71 women, 88 men) and compared against TNM-8 and WHO performance status. The recurrence and overall survival models outperformed both routinely used systems, but these tools surpassed the recurrence-free survival predictor’s performance. Moreover, this study was retrospective and had a small sample size with missing data. The sixth study was designed to identify high-risk smokers to predict long-term lung cancer incidence (12 years) . In this paper, the authors developed a convolutional neural inception V4 network based on low-dose chest CT images, age, sex, and current versus former smoking statuses. The CNN was trained using patients from the PLCO trial and externally validated on data from the NLST randomized controlled trial (2456 women and 3037 men from 33 USA institutions). The model was also compared against PLCOm2012 to evaluate clinical utility, having exceeded its performance for all assessed metrics (AUC, sensitivity, specificity, PPV, and NPV). However, this study was retrospective, lacked ethnic diversity, and was not evaluated in a clinically realistic scenario. Additionally, information from symptomatic patients was unavailable due to using data from a screening trial. In the seventh article, a CNN-based model was developed for the automated detection and diagnosis of malignant pulmonary nodules on CECT scans . The algorithm was externally validated on four separate datasets with ethnic differences (three from South Korea and one from the USA, amounting to 693 patients and CTs). Furthermore, the diagnostic performance of 18 physicians (from non-radiologists to radiologists with 26 years of experience) was compared while assisted and not assisted by the algorithm for one dataset. The model achieved an excellent performance in the four tested datasets, outperforming all clinicians, and the professionals’ accuracy increased while aided by the model for all tested groups. Nonetheless, the model was undertrained for small nodules (< 1 cm) and trained only for malignant nodule detection for one type of CT (posterior-anterior projections), and the study was retrospective and not representative of a real-world clinical setting. The eighth algorithm consisted of a multilayer perceptron (Feed-Forward Neural Network) paired with a Cox proportional hazards model to predict cancer-specific survival for non-small cell lung cancer . The text-based model was trained using the SEER database and externally validated on patients from a Chinese tertiary pulmonary hospital (642 women, 540 men). It was compared against TNM-8, having outperformed it with statistical significance. Although tested with real-world clinical data, prospective multi-institutional studies are needed before the deep learning model can be used in clinical practice. The ninth article described developing, validating, and comparing three CNN models to differentiate between benign and malignant pulmonary ground-glass nodules (GGNs) . The first CNN only used CT images. The second CNN used clinical data: age, sex, and smoking history. The third was a fusion model combining CTs and clinical features, achieving the best performance. This model was temporally and geographically validated with 63 CT scans from 61 patients (39 women, 22 men). Its classification performance was compared against two radiologists (5 and 10 years of experience) for clinical utility assessment. Despite performing satisfactorily in external validation, the model was surpassed by both clinicians in accuracy, sensitivity, and NPV, only producing higher results for specificity and NPV. Furthermore, this study was retrospective, and validation was neither international nor evaluated in a correct clinical setting. In the tenth and final paper, a Neural Multitask Logistic Regression (N-MTLR) network was developed for survival risk stratification for stage III non-small cell lung cancer . The text-based deep learning system was trained on 16 613 patients from the SEER database and externally validated on subjects from a Chinese institution (172 patients, 39 women, 133 men). The results in the external dataset showed that the DSNN could predict survival outcomes more accurately than TNM-8 (AUC of 0.7439 vs. 0.561). The study results suggest that the deep learning system could be used for personalized treatment planning and stratification for patients with stage III non-small cell lung cancer. However, prospective studies in multi-institutional datasets are still required. Laryngeal, Mesothelial and Nasopharyngeal Cancers Three models were developed to assess tumors of other elements of the respiratory system. In , the authors trained a CNN (GoogLeNet Inception v3 network) with 13 721 raw endoscopic laryngeal images – including laryngeal cancer (LCA), precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT), and healthy tissue – from three Chinese institutions (1 816 patients). External validation was performed on 1 176 white-light endoscopic images from two additional institutions in the same country (392 patients), testing the model for binary classification – urgent (LCA and PRELCA) or non-urgent (BLT and healthy) – and between the four conditions. Predictions for both classification types were compared against three endoscopists (3, 3 to 10, and 10 to 20 years of experience). In two-way classification, the algorithm was less accurate than one endoscopist and less sensitive than two but outperformed all clinicians in four-way diagnostic accuracy. Still, this accuracy was relatively low (less than 80%), the study was retrospective, and all tested laryngoscopic images were obtained by the same type of standard endoscopes. Cancers of the mesothelium were approached in a single retrospective multi-center study . The paper uses DL to distinguish between two types of mesothelial cell proliferations: sarcomatoid malignant mesotheliomas (SMM) and benign spindle cell mesothelial proliferations (BSCMP). SMMs and BSCMPs are difficult to distinguish using traditional histopathological methods, resulting in misdiagnoses. The authors propose a new strategy—SpindleMesoNET—that uses an ensemble of a CNN and an RNN to analyze WSIs of H&E-stained mesothelial slides magnified 40 times. The model was trained on a Canadian dataset, externally validated on 39 images from 39 patients from a Chinese center, and compared against the diagnostic performance of three pathologists on a referral test set (40 WSIs from 40 patients). The accuracy and specificity of SpindleMesoNET on the referral set cases (92.5% and 100%, respectively) exceeded that of the three pathologists on the same slide set (91.7% and 96.5%). However, the pathologists were more sensitive than the diagnostic model (87.3% vs. 85.3%). In addition, the study had a minimal sample size, and only AUC was reported for the external validation dataset (0.989), which, although considerably high, is insufficient to assess the model’s effectiveness. The last study entailed developing and validating a CNN-based model to differentiate malignant carcinoma from benign nasopharyngeal lesions using white-light endoscopic images . Malignant conditions included lymphoma, rhabdomyosarcoma, olfactory neuroblastoma, malignant melanoma, and plasmacytoma. Benign subtypes encompassed precancerous or atypical hyperplasia, fibroangioma, leiomyoma, meningioma, minor salivary gland tumor, fungal infection, tuberculosis, chronic inflammation, adenoids or lymphoid hyperplasia, nasopharyngeal cyst, and foreign body. The model was trained on 27 536 images collected retrospectively (7 951 subjects) and temporally (prospectively) externally validated with 1 430 images (from 355 patients) from the same Chinese institution. Diagnostic performance was compared against 14 endoscopists: (i) three experts with more than five years of experience; (ii) eight residents with one year of experience; and (iii) interns with less than three months of experience. Except for the interns’ sensitivity, the model’s diagnostic performance surpassed the endoscopists in all tested metrics. However, data were collected from a single tertiary institution, and more malignancies should be included. Although not developed for the same cancer type, the two cancer detection studies for the larynx and nasopharynx are comparable due to using white-light endoscopic images. Both used CNNs and involved more than 300 patients and 1000 images, but the optimal diagnostic performance – although less sensitive (72% vs. 90.2% in ) – was achieved for the GoogLeNet Inception v3 network CNN with an AUC of 0.953, an accuracy of 89.7%, and a specificity of 94.8%, enhancing the value of pre-training CNNs. Skeletal system Four studies using different imaging techniques were designed to diagnose bone cancers, producing an average AUC of 0.88 (Table ). The first two radiomics-based models were developed for the binary classification of atypical cartilaginous tumors (ACT) and appendicular chondrosarcomas (CS) . In , a LogitBoost algorithm was temporally and geographically validated on 36 PET-CT scans from 23 women and 13 men. Besides externally validating their method, the authors evaluated clinical utility by comparing its diagnostic performance against a radiologist. The model performed satisfactorily in all calculated metrics (AUC, accuracy, sensitivity, PPV, and F1-score), but its accuracy was lower than the radiologist. In addition, only non-contrast PET-CT scans were included in the analyses. In the following year, research performed by the same first author evaluated bone tumor diagnosis from MRI scans . Radiomic features were extracted from T1-weighted MRI scans, and an ExtraTrees algorithm was trained to classify the tumors. On an external validation dataset of 65 images (34 women, 31 men), the model achieved a PPV, sensitivity, and F1-score of 92%, 98%, and 0.95 in classifying ACTs, while 94%, 80%, and 86% for the classification of grade II CS of long bones, respectively (weighted average is presented in Table ). The model's classification performance was compared against an experienced radiologist (with 35 years of experience) to assess clinical utility, finding that it could not match the radiologist's performance. Using SHAP, it was also found that certain radiomic features, such as the mean and standard deviation of gradient magnitude and entropy, significantly differed between the two tumor types. Drawbacks include the study’s retrospective nature, using only one type of MRI, and over-representing appendicular chondrosarcomas compared to cartilaginous tumors in the study population. The second set of papers used neural networks to differentiate benign from malignant bone tumors from X-ray images . On the one hand, in , a CNN (EfficientNet-B0) was developed on a dataset of 2899 radiographic images from 1356 patients with primary bone tumors from 5 institutions (3 for training, 2 for validation), including benign (1523 images, 679 patients), intermediate (635 images, 317 patients), and malignant (741 images, 360 patients) growths. The CNN model was developed for binary (benign versus not benign and malignant versus not malignant) and three-way (benign versus intermediate versus malignant) tumor classification. The authors also compared the model’s triple-way classification performance against two musculoskeletal subspecialists with 25 and 23 years of experience and three junior radiologists with 6, 1, and 7 years of experience. The deep learning algorithm had similar accuracy to the subspecialists and better performance than junior radiologists. However, only a modest number of patients was used for validation (639 X-rays from 291 patients), tumor classes were unbalanced (smaller number of benign bone tumors compared to intermediate and malignant), and the pipeline was not fully automated. In contrast, other authors resorted to a non-deep ANN that uses radiomic features extracted from X-ray images and demographic data to classify and differentiate malignant and benign bone tumors . The ANN was developed on 880 patients with the following conditions: (i) malignant tumors: chondrosarcoma, osteosarcoma, Ewing’s sarcoma, plasma cell myeloma, non-Hodgkin lymphoma B cell, and chordoma; (ii) benign subtypes: osteochondroma, enchondroma, chondroblastoma, osteoid osteoma, giant cell tumor, non-ossifying fibroma, haemangioma, aneurysmal bone cyst, simple bone cyst, fibrous dysplasia. The method was externally validated on 96 patients from a different institution, and performance was compared against four radiologists (two residents and two specialized). The model was more sensitive than both radiologist groups but was outperformed by the specialized radiologists in accuracy and specificity. In addition, the model requires manual segmentations and can only distinguish between benign and malignant tumors and not specific subtypes. Metastases (Secondary Tumors) As shown in Table , five studies entailed the assessment of metastatic cancer, that is, secondary tumors spread from different tissues. From these, three focused on cancer spread to organs , while two evaluated metastasized nodes. Organ metastases In , models were created to predict the risk of bone metastasis and prognosis (three-year overall survival) for kidney cancer patients. To achieve optimal performance, the researchers developed and compared eight ML models: DTs, RFs, MLPs, Logistic Regression, Naïve Bayes BS classifier, XGBoost, SVMs, and kNN. The text-based models were trained with 71 414 patients from the SEER database (USA) and externally validated with 963 patients from a Chinese institution (323 women, 640 men). The results showed that their XGBoost-based models had the best accuracy in predicting bone metastasis risk and prognosis. The risk prediction model (diagnosis) outperformed TNM-7 only regarding AUC (0.98 vs. 0.93), while the prognostic model exceeded TNM-7’s predictions for all tested metrics (AUC, accuracy, sensitivity, PPV, and F1-score). Using SHAP analysis, the authors also unveiled that the key factors influencing these outcomes were age, sex, and tumor characteristics. Although trained on ethnically different patients, these models were only validated on Asian subjects and not compared against clinicians, so further studies are required to establish clinical validity and utility. The second paper explores the effectiveness of a deep learning-based algorithm (CNN) in detecting and classifying liver metastases from colorectal cancer using CT scans . In this South Korean monoinstitutional study, 502 patients were used for training, and temporally different patients (40 with 99 metastatic lesions, 45 without metastases) were used for validation. The algorithm's detection and classification performance was compared to three radiologists (with 2, 3, and 20 years of experience in liver imaging) and three second-year radiology residents. Although showing a higher diagnostic sensitivity than both types of clinicians, the six radiologists outperformed the model in AUAFROC (detection) and false positives per patient (FPP, classification). In addition, the CT scans had been captured eight years before the analyses. The third study was conducted in a clinically realistic scenario, and the model has been implemented in practice . The model was designed to predict 3-month mortality in patients with solid metastatic tumors for several types of cancer (breast, gastrointestinal, genitourinary, lung, rare) and treatment alterations in an outpatient setting. The authors trained a Gradient-Boosted Trees Binary Classifier with observations from 28 484 deceased and alive patients and 493 features from demographic characteristics, laboratory test results, flowsheets, and diagnoses. The model was silently deployed in the patients’ EHRs for 20 months to compare its predictions against 74 oncologists. This prospective temporal validation study involved 3099 encounters from 2041 ethnically diverse patients. The model outperformed oncologists in all metrics for aggregate (general, with and without treatment alterations), gastrointestinal, genitourinary, and lung cancers but was less sensitive than the professionals for rare and breast metastatic tumors. Although currently available in medical practice, the authors note that further research is needed to validate whether using the model improves prognostic confidence and patient engagement. Node metastases Two models were developed to diagnose node metastases. In , the authors aimed to classify cervical lymph node metastasis from thyroid cancer using CT scans . The researchers had previously developed a CNN (Xception architecture) trained on a 787 axial preoperative CT scans dataset. This study validated the systems' performance on 3 838 images from 698 patients (unknown female-male ratio) and used Grad-CAM to explain the model’s reasoning. The researchers also evaluated the clinical utility of the model by comparing seven radiologists’ performance (one expert, six trainees) with and without its assistance. While aided by the system, the expert’s accuracy, sensitivity, specificity, PPV, and NPV were all found to increase, while only accuracy, specificity, and NPV improved for the trainees. This study was retrospective and conducted in a single institution, and the results obtained were not satisfying enough to justify clinical implementation. The second and last document describes developing an ultrasound-based ML model to assess the risk of sentinel lymph node metastasis (SLNM) in breast cancer patients . First, the authors compared ten algorithms to achieve an optimal model: SVM, RF, LDA, Logistic Regression, NB, kNN, MLP, Long Short-Term Memory, and CNN. The best algorithm (XGBoost) was then integrated into a clinical model, and SHAP was used to analyze its diagnostic performance. XGBoost was trained with 902 patients, and external validation consisted of 50 temporally separate women. The authors also compared their tool with a radiologist’s diagnostic evaluations (unknown years of experience). The results showed that the ML model could predict the risk of SLNM in breast cancer patients based on ultrasound image features with high accuracy (84.6%), having outperformed the radiologist. In addition, SHAP analysis deemed suspicious lymph nodes, microcalcifications, spiculation at the edge of the lesion, and distorted tissue structure around the lesion as the model’s most significant features. Nonetheless, this research was retrospective and used a minimal number of patients from a single institution with limited pathological types of breast cancer. A total of 13 708 records were identified in our search, which was last updated on September 30, 2022. As shown in Fig. , after duplicate removal and filtering by SJR ranking, the titles and abstracts of 4023 citations from Embase, IEEE Xplore, PubMed, Scopus, and Web of Science were assessed. In this stage, 3325 papers were excluded for not being machine learning-based ( n = 1204, 29.9%), using genetic variables or omics ( n = 705, 17.5%), not being externally validated (clearly mentioning performance evaluation by cross-validation or hold-out sampling, n = 587, 14.6%), not being focused on oncology ( n = 534, 13.3%), not regarding patient care or clinical decision-making (e.g., creation of data infrastructures or organizing EHRs, n = 166, 4.1%), not being primary research articles ( n = 101, 2.5%), and not including human patients ( n = 28, 0.7%). This left 698 papers eligible for full-text inspection, of which 62 were excluded for unavailability. From the remaining 636 reports, 274 (43.1%) were discarded for not assessing or quantifying clinical utility, 252 (39.6%) for not being externally validated, 17 (2.7%) for not directly concerning patient care, 13 (2%) for not reporting performance metrics, 13 (2%) for focusing on gene expression or omics, 4 (0.6%) for not containing machine learning models, 2 (0.3%) for not focusing on oncology and 1 (0.2%) secondary research paper. For example, although seemingly relevant, that is, describing external validation and comparison of diagnostic competence against pathologists, other than reporting intraclass correlation coefficients, Yang et al.'s study did not quantify clinicians' performance, which led to its exclusion. No additional relevant documents were found by screening the included studies. Finally, 56 articles were included in this scoping review. The completed form for the included studies can be found in Additional file . Table summarizes key findings from the 56 studies on patient-centered ML applications in oncology, providing an overview of algorithms, clinical applications, data types, and evaluation methods for clinical utility. The following subsections offer insights into different aspects of the data. Journals, years of publication and reporting guidelines As depicted in Fig. A, the included articles were retrieved from 31 journals with an average SJR (2021) of 2.496, from a minimum of 1.005 ( Scientific Reports ) and a maximum of 7.689 ( Gastroenterology ). Frontiers in Oncology was the most common source ( n = 9, 16.07%, SJR = 1.291), followed by eBioMedicine ( n = 6, 10.71%, SJR = 2.9) and European Radiology ( n = 5, 8.93%, SJR = 1.73) . Eight (25.8%) of these journals were primarily dedicated to methodological issues and computational methods within artificial intelligence (dashed bars in Fig. A), while the remaining twenty-three (74.2%) focused on medical applications and patient-related topics. Concerning the year of publication, although citations since 2014 were screened, only papers from 2018 and onwards met the inclusion criteria. The number of reports increased substantially after 2020, with 23% ( n = 13), 27% ( n = 15), and 43% ( n = 24) of the sources being from 2020, 2021, and 2022, respectively, versus 2% ( n = 1) in 2018 and 5% ( n = 3) in 2019 (Fig. B). While the majority did not adhere to any reporting guidelines ( n = 48, 85.714%), 3 (5.357% ) used TRIPOD , 3 (5.357% ) followed STARD 2015 (commonly used for diagnostic and prognostic studies) , and 2 used CONSORT-AI and STROBE (1 each, 1.786%, and , respectively). Lastly, caveats were not reported for a small percentage of studies (7.14%, n = 4) . Algorithms, cancer types and clinical outcomes The features of the machine learning algorithms found in the included articles are detailed in Table . Sixty-two models were described in the 56 documents, with 55.4% (31/56) of the authors explicitly mentioning which algorithms were used in the paper's abstract. Most developers opted for an ensemble approach ( n = 27, 48.2%), 26 (46.4%) for single models, and three (5.4%) for both . Of the selected studies, 50 (89.3%) were exclusively devoted to classification, 4 to regression (7.1%) , and 2 developed both types of models (3.6%) . All models were supervised except in one study (semi-supervised) , and 50% of the researchers ( n = 28) compared their systems against other ML algorithms. Apart from work developed in , where the model was silently integrated into the patients' EHRs, all models were deployed as standalone systems. Overall, 30 (53.6%) can be classified as CADx, 19 (33.9%) as CDSS, 2 (3.6%) as CADe , and 5 as both CADe and CADx (8.9%) . Regarding interfaces, most tools were desktop-based ( n = 46, 82.1%), and 10 (17.9%) were deployed as web-based applications . All websites were reported, 43 articles (76.79%) disclosed which software was used, and codes were provided for 11 models (19.6%) . Most studies were deep-learning based ( n = 36, 64.3%). From these, the most frequently reported models were Convolutional Neural Networks (CNNs), either alone (29/36, 80.55%), coupled with a Recurrent Neural Network (RNN, 3/36, 8.34%) , or with Logistic Regression (LR), a shallow ANN, Gradient Boosting (GB), a Support Vector Machine (SVM), and Random Forest (RF, 1/36, 2.78%) . Specific CNN architectures were reported for approximately 76% of the articles (25/33), which, as shown in Fig. , primarily consisted of ResNet- ( n = 9, 36%) and DenseNet-based frameworks ( n = 8, 32%), used individually or in conjunction. To overcome data scarcity, transfer learning was used in 16 of the 33 CNN-based articles 48.5%), which involves pre-training the network on a specific problem and transferring that base knowledge to a new, related task (see Table : pre-trained in column General Focus and Models ). Besides CNNs, other DL algorithms were described in four articles . Multilayer Perceptrons (MLPs) were used in three (5.56%) , two of which applied a DeepSurv architecture, a deep Cox proportional hazards feed-forward neural network . The last (2.78%) involved a neural multitask logistic regression model (N-MTLR) . The remaining documents ( n = 20, 35.7%) described a non-deep-learning-based workflow encompassing fifteen unique algorithms applied in twenty-eight configurations. From these, boosting-based techniques were the most widely reported, consisting of eXtreme Gradient Boosting (XGBoost, 6/28, 21.43%) , a Light Gradient Boosting Machine (LightGBM, 1/28, 3.57%) , LogitBoost (1/28, 3.57%) , Adaptive Boosting (AdaBoost, 1/28, 3.57%) , and Gradient-Boosted Decision Trees (GBDT, 2/28, 7.14%) . Other decision tree designs were also used, including RF (6/28, 21.43%) and extremely randomized trees (ExtraTrees, 1/28, 3.57%) . The third most reported group of algorithms were SVMs , a Support Vector Classifier (SVC) , and a Quadratic SVM (4/28, 14.28%), followed by shallow ANNs (2/28, 7.14%) and LR (1/28, 3.57%) . Lastly, Mixture Discriminant Analysis (MDA), k-nearest Neighbors (kNNs), and naïve Bayes (NB) were also found, all used in the same article (total of 3/28, 10.71%) . Regarding general cancer types, the selected papers can be broadly divided into two categories: those concentrating on primary tumors and those mainly examining metastasized (secondary) cancers. Most articles focused on primary tumors (51/56, 91.1%), although four also included metastases . These cancers can be further branched into the specific system where the malignancy was formed: (i) central nervous system (CNS), including the brain (3/51, 5.88%) ; (ii) digestive system, encompassing colorectal (7/51, 13.73%) , esophageal (3/51, 5.88%), gastric (5/51, 9.8%) , and liver cancers (2/51, 3.92%) ; (iii) endocrine system, involving cancers of the pancreas (2/51, 3.92%) and thymus (1/51, 1.96%) ; (iv) genitourinary system, consisting of bladder (1/51, 1.96%) , cervical (1/51, 1.96%) , prostate (2/51, 3.92%) , and endometrial (2/51, 3.92%) cancers; (v) integumentary system, with tumors of the breast (4/51, 7.84%) and skin (2/51, 3.92%); (vi) respiratory system, studying neoplasms of the larynx (1/51, 1.96%) , lung (10/51, 19.61%) , mesothelium (1/51, 1.96%) , and nasopharynx (1/51, 1.96%) ; and (vii) the skeletal system, comprising the bones (4/51, 7.84%) . In addition, five papers analyzed metastatic cancers (5/56, 8.9%), which can also be bifurcated into malignancies spread to nodes or organs. The former includes solid metastatic breast, lung, and gastrointestinal and genitourinary tract tumors , bone metastases in kidney cancer patients , and liver metastases from colorectal cancers . The latter encompasses thyroid cancer spread to lymph nodes and sentinel lymph node metastasis from primary breast lesions . Seventy-six cancer-related goals were addressed in the 56 documents, with an average of one task performed per paper and a maximum of three . These included the development or improvement of systems for: (i) diagnosis alone ( n = 28, 50%) or combined with detection ( n = 5, 8.93%) or prognosis ( n = 1, 1.79%) ; (ii) detection by itself ( n = 2, 3.58%) or coupled with outcome prediction ( n = 1, 1.79%) ; and (iii) outcome prediction, including prognosis ( n = 16, 28.58%) ; and risk stratification ( n = 3, 5.36%) . Finally, fifteen studies resorted to explainable AI (XAI) to increase the transparency behind the models' decisions. Unlike black-box methods, whose reasoning is indecipherable, XAI allows the creation of interpretable models to determine how each prediction was reached and which clinical predictors bore the most weight. Three packages were used for this purpose: (i) SHapley Additive exPlanations (SHAP), which can be employed in any ML algorithm ( n = 6, 40%) ; and (ii) Class Activation Mapping (CAM, n = 1, 6.67%) and Gradient-weighted CAM (Grad-CAM, n = 8, 53.33%) , explicitly developed for CNNs. Clinical inputs and populations According to the clinical variables used as input, the models validated in the 56 studies can be divided into three types: image-based (including video, n = 37, 66.1%), text-based ( n = 10, 17.9%), and mixed, using both clinical modalities ( n = 9, 16.1%). Image-based Studies A total of 335 085 high-resolution images from 112 538 patients (102 117 female, 8 215 male ) were used for classification in 36 of the 37 image-based studies and for classification (recurrence) and regression (recurrence-free survival) in the last study . Except for one paper including both pediatric and adult patients (unknown age proportion, 175 female, 116 male) and two other articles not listing the patients’ age group (698 in , unknown in , unidentified male–female ratio in both), all studies consisted of adults (111 469 patients, 101 942 women, 8 099 men). Eight studies (21.6%) extracted radiomic features from the retrieved images . The studies encompassed X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography – Computed Tomography (PET-CT) scans, endoscopic images and videos, photographs, ultrasounds, histological slides, and whole-slide images (WSI). Besides digital pictures, which are limited to the surface, these imaging techniques capture the body's internal structures. However, they differ in the way they create images and the type of information they provide. X-rays use and expose the patient to ionizing radiation to create scans . Although time- and cost-effective, these do not provide as much detail as CT or MRI scans. In this review, two studies used radiographic images (2/37, 5.4%) to: (i) classify pathologically-confirmed primary bone tumors in children and adults (639 radiographs, 175 female, 116 male) ; and (ii) for breast cancer screening in adult women ( n = 1, 213 694 X-rays, 92 585 women) . CT scans combine X-rays from different angles to create high-quality, three-dimensional images. Nevertheless, since they are generated from controlled motions of X-rays, CTs are still unfit for extracting molecular information . Furthermore, these scans subject the patient to higher radiation levels than X-rays and may require contrast agents depending on the adopted modality – contrast-enhanced CTs (CECTs) versus non-contrast CTs (NECTs). CT scans were commonly collected variables in the selected articles (8/37, 21.6%), amounting to 7 540 images from: (i) the lungs ( n = 4, 2 323 nodules, 2 113 patients) ; (ii) gastric cancers ( n = 2, 1 129 images, 352 women, 777 men) ; (iii) cervical lymph nodes ( n = 1, 3 838 images, 698 patients of unknown gender) ; and (iv) hepatic metastasis from colorectal cancer ( n = 1, 250 lesions, 31 women, 54 men) . MRI scans do not depend on radiation and use a strong magnetic field and radio waves to create detailed images. This type of imaging can be separated into two subtypes: conventional and advanced . Conventional MRI (cMRI) sequences include standard MRI protocols commonly used in clinical practice, such as (i) T1-weighted: used to identify structural abnormalities; (ii) Axial fluid-attenuated inversion recovery MRI (FLAIR), applied to identify abnormalities that affect the tissues' water content; and (iii) T2-weighted: also appropriate to assess irregularities in water content. Advanced MRI (advMRI) techniques generate deeper information regarding the tissue's function, structure, and metabolic processes, including: (i) multiparametric MRI (mpMRI), which combine several other MRI sequences to enrich its output; (ii) axial diffusion-weighted (DWI) MRI, which measure the movement of water molecules in tissues; (iii) Vascular architecture mapping (VAM) MRI, providing information about the tissue's blood vessels; (iv) Gradient echo dynamic susceptibility contrast (DSC) MRI, used to measure blood movement; (v) Quantitative blood-oxygenation-level-dependent (qBOLD) MRI, able to measure the oxygen content in the blood; (vi) General Electric-Dynamic Susceptibility Contrast (GE-DSC) MRI, which resorts to a contrast agent to measure blood flow; and (vii) Magnetic resonance spectroscopy (MRS), which calculate the levels of certain chemicals and metabolites in the tissues. Although some types of MRIs – such as MR spectroscopy and diffusion-weighted imaging – allow assessing molecular details without contrasts, most are better equipped to analyze gross internal structures and are more expensive than CTs and X-rays . MRI scans were also frequently used as input for the models, with 64 941 combined images from 8 studies (21.6%), including (i) the brain ( n = 3, 64 459 lesions, 623 women, 461 men) ; (ii) the prostate ( n = 2, 262 nodules, 300 men) ; (iii) colorectal malignancies ( n = 2, 154 images, 54 women, 64 men) ; and (iv) bones and cartilages ( n = 1, 65 scans, 34 women, 31 men) . PET scans, which are also radiation-free, allow for examining the internal body structure and underlying molecular tissues. However, these are extremely expensive, usually unavailable in routine practice, and due to their low spatial resolution, require pairing with a second modality, such as CTs and MRIs . In this review, one study (2.7%) used PET-CT scans to examine atypical cartilaginous tumors and appendicular chondrosarcomas (36 scans, 23 women, 13 men) . Similarly to X-rays, ultrasounds – which use high-frequency sound waves to create images – provide an inexpensive method to inspect organ structures without detailing underlying molecular information, with the upside of not involving radiation . Ultrasonographic imaging was mentioned in 2 articles ( n = 2, 5.4%, 328), which studied breast cancers (116 ultrasounds, 107 women) . Eight reports describe images captured with standard endoscopes ( n = 8, 24.3%, 3681 items), which cannot capture molecular features. Four studies used colonoscopic lesions from the colon and rectum (995 images, 105 women, 224 men) . Four studies analyzed endoscopic pictures of the esophagus ( n = 2, 260 images, 260 patients of unknown gender) , the larynx ( n = 1, 1 176 images, unknown number of patients) , and the nasopharynx ( n = 1, 1 430 images, 124 women, 231 men) . Lastly, one study examined endoscopic videos from intramucosal gastric cancer patients (54 videos, 38 women, 16 men) . Two studies used advanced endoscopes. One involved endoscopic ultrasonography (EUS), a technique that combines endoscopy and ultrasonography to gather gastrointestinal images ( n = 1, 2.7%, 212 ultrasounds, 38 women, 31 men) . The other resorted to endocytoscopy, a relatively new high-magnification imaging approach that allows tissue analysis at a cellular level, to collect 100 colorectal images from 89 patients ( n = 1, 2.7%, 26 women, 63 men) . A histological image is a high-resolution, microscopic image of a tissue slide after it's been processed with one or more stains to reveal its composition . This method allows distinguishing between different histological cancer subtypes but involves a long preparation time and offers a limited depth of view. One paper used hematoxylin-and-eosin (H&E)-stained histological images to study endometrium hyperplasia and intraepithelial neoplasia ( n = 1, 2.7%, 1 631 slides, 102 women) . Whole-slide images (WSIs) are virtual representations of a tissue section scanned at high resolution and magnification. WSIs are created by scanning stained histological slides and usually combine and magnify multiple slides using specialized software . This technique allows thorough tissue examination at cellular and sub-cellular levels, but it is still cost-, storage- and technically heavy. WSIs were used to feed the models in three studies (8.1%, 3 315 images), using 30 × or 40 × magnification. Two included H&E stained slides of the liver ( n = 1, 80 slides, 24 women, 56 men) and the mesothelium ( n = 1, 39 images, 39 patients of unreported gender) . One was composed of stained slides (unknown stain) for the cervical screening of women without any known conditions and with the Human papillomavirus (HPV) ( n = 1, 1565 images and women) . Finally, 46 962 digital photographs (captured with a camera) were analyzed across two documents (5.4%). Both inspected skin malignancies ( n = 2, 10 602 patients). Detailed information regarding the samples, type of CTs, MRIs, and endoscopes used in the image-based studies, as well as population details and counts (age group, total patients, female, and male), is itemized in Table . Text-based Studies The populations and specific clinical variables used in each text-based study are compiled in Table . Clinical data from 6 803 patients (2 772 women, 4 031 men, 7 861 encounters) was collected for validation across ten papers . Apart from one work including senior citizens , all studies consisted of adult patients (6 644 subjects, 2 701 women, 3 943 men). An average of 17 clinical variables was used per study (range = 6 – 31 ), encompassing information on demographics, tumoral values, and laboratory test results. The machine learning models used in 6 of the articles (60%) were exclusively developed for classification (1 960 women, 3 097 men) , while 4 (40%) solely concerned regression (812 women, 934 men) . In the four regression-based articles, the developed prognostic models assessed (i) patients with a single lesion of primary stage I to IV esophageal adenocarcinoma or squamous cell carcinoma ( n = 1, 150 women, 350 men) ; (ii) patients with pathologically confirmed and resected intrahepatic cholangiocarcinoma (12 women, 30 men) ; (iii) patients with stage I to III non-small cell lung cancer (642 women, 540 men) ; and (iv) patients in palliative care with unresectable advanced pancreatic ductal adenocarcinoma with liver metastases (8 women, 14 men) . The six classification papers included: (i) seniors with stage I to III non-small cell lung cancer treated with curative-intent radiotherapy (159 individuals, 71 women, 88 men) ; (ii) bone metastasis in kidney cancer patients with complete survival data (323 women, 640 men) ; (iii) women with primary breast cancer diagnosed by pathological examination (150 women) ; (iv) patients with primary colorectal cancer with survival-related data who underwent surgery (1 572 patients, 607 female, 965 male) ; (v) patients with confirmed stage III non-small cell lung cancer (39 women, 133 men) ; and (vi) patients with solid metastatic tumors for several types of cancer with and without alterations in treatment in an outpatient setting (3 099 encounters, 2 041 individuals, 770 women, 1 271 men) . Mixed Studies An average of 9 clinical variables (range = 3 – 17 ), 784 images, and 720 patients (range = 44 – 5 493 for both) were used in the nine mixed studies, whose information is highlighted in Table . These papers combined patients’ demographics, cancer-specific data, laboratory results, and imaging features extracted from different modalities for cancer-specific populations (7 053 images, 6 482 patients, 3 009 women, 3 478 men). Radiomics approaches were used in three studies . Six reports included CT images to study: (i) patients who underwent curative-intent resection for pancreatic ductal adenocarcinoma ( n = 1, 53 images, 27 women, 26 men) ; (ii) patients with benign and malignant pulmonary ground-glass nodules with less than 30 mm ( n = 1, 63 images, 39 women, 22 men) ; (iii) individuals with multiple lungs nodes in a post-operative setting ( n = 1, 200 images, 51 women, 27 men) ; (iv) lung cancer patients with an available baseline radiograph ( n = 1, 5 493 patients and images, 2456 women, 3037 men) ; (v) patients with muscle-invasive bladder cancer who underwent surgery ( n = 1, 75 images, 13 women, 62 men) ; and (vi) adults with pathologically confirmed thymomas and thymic carcinomas ( n = 1, 76 preoperative scans, 33 women, 48 men) . Additionally, three studies used other types of scans. One work paired breast-specific data with features derived from three types of MRI scans for women with endometrial lesions and complete clinical data (44 images, 44 women) . One paper combined patients’ age, sex, tumor type, location, and radiomic features extracted from X-rays to analyze primary bone tumors (40 women, 56 men) . Finally, one study evaluated survival- and gross-tumor-related data in conjunction with H&E slides magnified 30 times (whole-slide images) to estimate outcomes for patients diagnosed with gastric cancer (175 images, 91 patients, 60 female, 31 male) . Except for the models developed in this study, where the first used only WSIs for classification and the second used these images and clinical data for prognostication (regression), all algorithms were classifiers. Validation design, clinical settings and performance metrics Information concerning institutional, study, and validation designs, care types, datasets, clinical settings, and the number of institutions involved in validation in the selected documents is illustrated in Table . Model development and validation were performed simultaneously in most studies ( n = 50, 87.5%), while 4 (7.14%) evaluated external validity separately, and 3 (5.36%) entailed model updating and validation. Of the 56 documents included in this review, 44 (78.57%) directly reference external validation in the abstract, 10 (17.86%) indirectly mention it, and 2 (3.57%) omit this information. Overall, 74 medical datasets were used for external validation across the 56 studies, averaging 1.3 per paper (range = 1—8). All studies used real-world data acquired prospectively or collected from the patients' EHRs and imaging archiving platforms. Except for three articles using both standard and uncommon types of MRI scans and one using endocytoscopy (whose use is still growing) , all studies used text- and image-based data routinely collected in clinical practice. However, only nine reports describe external validation in clinically realistic scenarios , and solely two systems are currently implemented in practice . The papers involved several cancer-related settings, including secondary ( n = 1, 2%), tertiary ( n = 34, 61%), and quaternary (12, 21%) oncology care. However, 6 (11%) studies did not report from which centers data were retrieved, and 3 (5%) used databases without this information. Among the collected studies, 49 (87.5%) were conducted retrospectively, 3 (5.36%) were prospective, 4 (7.15%) were mixed: one performed internal validation prospectively and external validation retrospectively , one proceeded inversely , and two used both retrospective and prospective cohorts . Only one report used randomized data . Regarding validation design, 31 (55.357%) studies followed a multi-institutional approach, 14 (25%) collected information from a single center, 1 (1.786%) only used public databases, 2 (3,572%) used public multi-institutional databases, and 8 (14,286%) used both types of sources. For the multi-institutional studies (including databases), the average number of facilities used for validation was 3, with a maximum of 33 . One study did not report the number of institutions involved . The following freely available data sources were used: (i) the Surveillance, Epidemiology, and End Results (SEER) database, which covers population-based cancer registries of approximately 47.8% of the United States Population ; (ii) The Cancer Genome Atlas (TCGA, from the USA), which molecularly characterizes over 20,000 primary cancers, and contains whole-slide images ; (iii) The Cancer Imaging Archive, which hosts a large number of medical images for various types of cancer ; (iv) the Edinburgh dataset, containing data from the University of Edinburgh (Scottland, United Kingdom) ; (v) the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial sponsored by the by the National Cancer Institute (NCI), designed to evaluate the impact of cancer screening on mortality rates, as well as to assess the potential risks and benefits associated with screening ; (vi) the National Lung Screening Trial (NLST), a randomized controlled trial also supported by the NCI that aimed to evaluate the impact of using low-dose helical CT scans on patient mortality ; (vii) the PROSTATEx dataset, which contains a retrospective set of prostate MRI studies ; (viii) the PICTURE dataset, containing data from a single-center trial, and intended to evaluate the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in men with prostate lesions ; and (ix) the National Human Genetic Resources Sharing Service Platform (NHGRP), for which we could not find any details . In two studies, models were trained using data from multiple countries. One developed their model using patients from three Chinese institutions and one center from the United States of America (USA) and validated it on a Chinese dataset ( n = 1, 1.8%) . The other gathered data from a Chinese institution and TCGA and validated their model on images from NHGRP . Additionally, one document did not report which countries were involved in their model’s development or validation . All other authors developed their model on data from a single country. These included China ( n = 19, 33.7%), the USA ( n = 12, 21.4%), South Korea ( n = 9, 16.1%), Italy and Germany (3 each, 5.4%), Japan and the Netherlands (2 each, 3.6%), and the United Kingdom (UK), Canada, and Austria (1 each, 1.8%). Besides the two abovementioned papers , twelve other studies performed international validation. Of these, six included ethnically different sources. Two authors trained their model with data from South Korea: one validated it on South Korean and American datasets , and the other validated it on a South Korean dataset and the Edinburgh dataset (UK) . Additionally, five reports mention training their model on the SEER database (USA), with four validating it with Chinese patients and one with South Korean patients . For the five remaining studies, patients with the same ethnicity were included: (i) one was developed with the NLST trial dataset (USA) and validated on data from the UK ; (ii) one was trained with data from TCGA (USA) and validated on an institution from the UK ; (iii) one used data from Italy for training and patients from The Netherlands for validation ; (iii) one trained their model on the PROSTATEx dataset (from The Netherlands) and validated it on the PICTURE dataset (from the UK) ; and (iv) one used a Chinese dataset for training and Chinese and South Korean patients for validation . Regarding validation types, 12 studies (21.48%) were limited to temporal validation from a single institution, which cannot be interpreted as a fully independent validation . Five other studies also only temporally validated their model. However, two used a multi-institutional approach (3.58%) , two (3.58%) used different data acquisition designs (retrospective internal validation and prospective external validation) , and one evaluated performance for patients at different treatment stages (1.78%) . Nine studies (16,08%) only validated their model geographically, seven within the same country , one internationally , and one with internationally and ethnically different patients . Twenty-nine reports (51.8%) included both temporal and geographical validation. Sixteen (28.57%) used local data, one evaluated temporally and geographically different patients from the same country with images captured using various scanners , and one (1.79%) used national data and mixed data acquisition (prospective internal validation and retrospective external validation) . Lastly, one study that did not report data sources validated their model on different types of computed tomography (CT) scanners . The external datasets were used to evaluate the models’ generalizability to populations differing – geographically, temporally, or both – from the development cohort. The performance metrics reported in the articles can be branched into three categories: discrimination, calibration, and processing time. For classification models, an average of 5 metrics were used to assess discrimination, up to a maximum of seven (range = 1 – 7). These consisted of (i) sensitivity, reported in 48 papers; (ii) area under the receiver operating characteristic (ROC) curve (AUC), calculated in 43 studies; (iii) specificity, used in 42 articles; (iv) accuracy, presented in 35 documents; (v and vi) positive and negative predictive values (PPV and NPV), computed in 29 and 19 reports, respectively; (vii) F1-score, considered in 13 papers; (viii) C-index, used in 2 articles ; (ix) false positive rate, reported in two papers ; (x) area under the alternative free-response ROC curve (AUAFROC) , calculated for one model; (xi) jackknife alternative free-response ROC (JAFROC), also computed for one algorithm ; and (xii) Softspot (Sos) and Sweetspot (Sws) flags, both used in the same two papers . However, decision thresholds were only disclosed for half of the articles (26/52, 50%), and only three papers presented results for different cut-off values/settings . Likewise, 39 classification studies did not assess calibration. When evaluated (13/52, 25%), calibration was illustrated graphically in five studies (9.62%) , via Brier Score in three documents (5.77%) , using both approaches in four papers (7.69%) , and with mean absolute error (MAE) in one report . Lastly, the models’ processing time was also seldomly revealed, with only seven studies reporting it . For the regression-based algorithms, discriminative performance was assessed via C-index . Regarding calibration, the model’s Brier Score was presented in one study , calibration plots in two , both metrics in one , and none in two . The models’ processing time and decision thresholds were not reported in any of these studies. Clinical utility From the selected studies, the majority ( n = 50, 89.29%) explicitly mentions the assessment of the models' clinical utility, that is, its relevance to clinicians and patient outcomes, in the paper's abstract. However, one only refers to it indirectly (1.79%) , and the remaining five (8.93%) do not state this aspect in their summaries . Two approaches were used to assess the models’ utility: comparison against clinician performance, adopted in most studies (40/56, 71.4%), and benchmarking against established clinical tools (15/56, 26.8%). Additionally, one study used both: retrospective comparisons were performed against routine clinical scores, while prospective assessments involved clinicians (1/56, 1.8%) . Comparison Against Clinicians Four hundred-ninety-nine medical professionals of varying expertise were involved in these studies, with an average of 12 clinicians compared against each model (range = 1 – 109 ). These included endoscopists ( n = 204), oncologists ( n = 77), radiologists ( n = 76), general physicians ( n = 71), dermatologists ( n = 44), pathologists ( n = 21), ophthalmologists ( n = 3), and thoracic surgeons ( n = 3). A subset of 113 115 patients (102 178 female, 9 619 male) was used for these assessments, and identical performance metrics as those documented for external validation were observed, plus time until diagnosis. Specific clinicians’ years of experience were reported in 20 papers (48.8%), ranks (without years) in 11 (26.8%), and no information concerning expertise in 10 (24.4%). The 41 classification studies encompassing model comparison against clinicians can be divided into two designs: with and without the model and independent evaluation of the models and the clinicians. The most commonly adopted technique was separately assessing model and clinician performance and comparing it posteriorly ( n = 30, 73.2%). Four hundred-one clinicians (μ = 15 per report, range = 1 – 109) and 109 720 patients (μ = 3 657 per paper, 100 965 female, 8 203 male ) were involved in these papers, and model-clinician performance was compared for detection and diagnostic capabilities. An average of 4 performance metrics (range = 1 – 7 ) were computed per paper, with sensitivity being the most calculated ( n = 23), followed by specificity ( n = 18) and accuracy ( n = 15), AUC ( n = 11), PPV ( n = 11), NPV ( n = 7), F1-score ( n = 3) , false positive rate ( n = 2) , Sweetspot and Softsoft flags ( n = 2) , diagnostic time ( n = 1) , and AUAFROC ( n = 1) , and JAFROC ( n = 1) . The second approach involved comparing clinician performance with and without the assistance of the artificially intelligent systems developed by the authors ( n = 11, 26.8%). The eleven studies employing this method comprised 92 clinicians (μ = 8, minimum = 1, maximum = 20 ) and 3 337 patients (μ = 370, 1 223 female, 1 416 male ). Similarly to the previous technique, an average of 4 performance metrics were used per paper (range = 1 – 6 ), including sensitivity ( n = 9), specificity ( n = 8), accuracy ( n = 8), PPV ( n = 6), NPV ( n = 5), AUC ( n = 2) , mean diagnostic time ( n = 2) , and error rate ( n = 1) . Comparison Against Standard/Established Clinical Tools In sixteen studies, assessing the usefulness of the models involved comparing their performance against well-established and routinely used clinical tools. In total, 11 659 patients (μ = 777 per paper, 4 521 female, 5 694 male ) were encompassed in these assessments, and twelve standard tools were used for comparisons. These included: (i) the 7th and 8th editions of the Tumor, Node, and Metastasis (TNM) staging system; (ii) the Brock University Model; (iii) the Fracture Risk Assessment Tool (FRAX); (iv) the Liver Cancer Study Group of Japan (LCSGJ); (v) the Mayo clinic model; (vi) the modified Glasgow Prognostic Score (mGPS); (vii) the Osteoporosis Self-Assessment Tool for Asians (OSTA); (viii) the second version of the Prostate Imaging Reporting and Data System (PI-RADS v2); (ix) the Peking University (PKU) model; (x) the PLCOm2012 model; (iv) the Response Evaluation Criteria in Solid Tumors (RECIST); (xi) the Veterans Affairs (VA) model; and (xii) the World Health Organization (WHO) performance status. Except for one study , all papers explicitly mention comparisons against these tools in the abstract. The TNM system, created by the American Joint Committee on Cancer (AJCC), is globally used in routine clinical procedures. It categorizes cancer progression and guides subsequent treatment decisions depending on (i) the size and extent of the primary tumor (T), (ii) if it has spread to nearby lymph nodes (N), and (iii) if it has metastasized to distant organs (M) . In this review, two text-based classification studies compared their models against the 7th edition of this staging system (TNM-7): one juxtaposed diagnostic and prognostic (3-year overall survival) predictions for bone metastasis in kidney cancer patients (323 women, 640 men) , while the other compared 1–10-year postoperative survival predictions for patients with colorectal cancer (607 women, 965 men) . Similarly, seven papers resorted to the 8th edition of AJCC TMN (TNM-8), its revised and updated version. On the one hand, in four articles, the models were only compared against this system. Two analyzed their text- and regression-based models to predict cancer-specific survival for esophageal (500 patients, 150 women, 350 men) and lung tumors (1 182 individuals, 642 female, 540 male) . The other two concerned the evaluation of classification models. Using preoperative images and descriptive data, one compared 2-year overall survival and 1-year recurrence-free survival predictions for patients with pancreatic cancer (27 female, 26 male) . The other compared risk stratification performance for overall survival for lung cancer patients (39 women, 133 men) between their model and the TMN-8 system using only text-based data . On the other hand, in three text-based studies, models were compared against TNM-8 and other tools. One paper also contrasted model performance for recurrence, recurrence-free survival, and overall survival for lung cancer patients (71 women, 88 men) with the WHO performance status, often used in oncology to determine patients' overall health status, prognosis, and the ability to tolerate treatment . This scaling system ranges from 0 to 4, where 0 represents no symptoms and pre-disease performance, and 4 translates to total disability. In the second article, predictions of overall postoperative survival were benchmarked against TNM-8 and LCSGJ (42 liver cancer patients, 12 women, 30 men) . LCSGJ is a group of Japanese medical professionals specializing in diagnosing and treating liver cancer, recognized as a leading authority in this cancer research field. Lastly, the third study describes the development of three risk models for breast cancer patients (150 women) : (i) fracture, whose predictions were contrasted with those generated by FRAX; (ii) osteoporosis, compared against and FRAX and OSTA; (iii) and survival, benchmarked against TNM-8. FRAX is a web-based tool designed to stratify 10-year bone fracture risk, and OSTA assesses the risk of osteoporosis in Asian populations . The Brock University (also known as PanCan) model is a logistic regression model devised to assist in risk stratification for lung cancer. It is recommended in the British Thoracic Society guideline as a tool to decide if nodules measuring 8 mm or more in maximum diameter should be assessed further with PET-CT . Here, it was applied in one of the selected papers to compare predictions of malignancy risk for lung cancer from CECT and NECT scans (1 397 images, 1187 patients, unknown gender proportion) . In addition to the Brock Model, comparisons in a second paper (978 CTs, 493 patients, 297 women, 196 men) were also performed against three other tools: (i) the Mayo model, which the Mayo Clinic developed to assess cancer prognosis and predict patient outcomes; (ii) the PKU model, created by the Peking University; and (iii) the VA model, which includes a comprehensive cancer care system that aims to provide high-quality, evidence-based care to veterans with cancer . The mGPS scale is a validated scoring system formulated to assess the prognosis of patients with advanced or metastatic cancer based on nutritional and inflammatory markers . In this review, it was used to establish clinical utility for a text-based classification model developed to predict overall survival for patients with unresectable pancreatic tumors (22 patients, 8 women, 14 men) . PI-RADS is a standardized system for interpreting and reporting findings from prostate MRI scans, created to guide clinical decision-making in diagnosing and treating prostate cancer. In this context, it was contrasted against a model developed to stratify low- and high-risk patients (39 and 14 men, respectively) . PLCOm2012 is a validated risk score that uses logistic regression to predict the probability of lung cancer occurrence within six years based on demographic and clinical information . It was the chosen comparator in a study predicting 12-year lung cancer incidence using low-dose CT images and patients’ age, sex, and smoking status (5493 images and patients, 2456 women, 3037 men) . Finally, RECIST is a set of guidelines used to evaluate the response of solid tumors to treatment in clinical trials and clinical practice. It was compared against two classification models: one aimed at detecting pathological downstaging in advanced gastric cancer patients from CECT images (86 patients and images, 23 women, 27 men) ; the other was designed to predict pathological tumor regression grade response to neoadjuvant chemotherapy in patients with colorectal liver metastases from MRI scans (61 images, 25 patients, 13 female, 12 male) . A few performance metrics were reported for the comparisons between the models developed in the selected papers and routinely used clinical tools, with an average of 3 metrics reported per document (range = 1 – 6). Here, the most frequently calculated metrics were AUC ( n = 11) and sensitivity ( n = 8), but PPV ( n = 5), C-index ( n = 4), specificity ( n = 4), accuracy ( n = 3), NPV ( n = 3), Brier Score ( n = 2) and F1-score ( n = 1) were also used in the evaluations. As depicted in Fig. A, the included articles were retrieved from 31 journals with an average SJR (2021) of 2.496, from a minimum of 1.005 ( Scientific Reports ) and a maximum of 7.689 ( Gastroenterology ). Frontiers in Oncology was the most common source ( n = 9, 16.07%, SJR = 1.291), followed by eBioMedicine ( n = 6, 10.71%, SJR = 2.9) and European Radiology ( n = 5, 8.93%, SJR = 1.73) . Eight (25.8%) of these journals were primarily dedicated to methodological issues and computational methods within artificial intelligence (dashed bars in Fig. A), while the remaining twenty-three (74.2%) focused on medical applications and patient-related topics. Concerning the year of publication, although citations since 2014 were screened, only papers from 2018 and onwards met the inclusion criteria. The number of reports increased substantially after 2020, with 23% ( n = 13), 27% ( n = 15), and 43% ( n = 24) of the sources being from 2020, 2021, and 2022, respectively, versus 2% ( n = 1) in 2018 and 5% ( n = 3) in 2019 (Fig. B). While the majority did not adhere to any reporting guidelines ( n = 48, 85.714%), 3 (5.357% ) used TRIPOD , 3 (5.357% ) followed STARD 2015 (commonly used for diagnostic and prognostic studies) , and 2 used CONSORT-AI and STROBE (1 each, 1.786%, and , respectively). Lastly, caveats were not reported for a small percentage of studies (7.14%, n = 4) . The features of the machine learning algorithms found in the included articles are detailed in Table . Sixty-two models were described in the 56 documents, with 55.4% (31/56) of the authors explicitly mentioning which algorithms were used in the paper's abstract. Most developers opted for an ensemble approach ( n = 27, 48.2%), 26 (46.4%) for single models, and three (5.4%) for both . Of the selected studies, 50 (89.3%) were exclusively devoted to classification, 4 to regression (7.1%) , and 2 developed both types of models (3.6%) . All models were supervised except in one study (semi-supervised) , and 50% of the researchers ( n = 28) compared their systems against other ML algorithms. Apart from work developed in , where the model was silently integrated into the patients' EHRs, all models were deployed as standalone systems. Overall, 30 (53.6%) can be classified as CADx, 19 (33.9%) as CDSS, 2 (3.6%) as CADe , and 5 as both CADe and CADx (8.9%) . Regarding interfaces, most tools were desktop-based ( n = 46, 82.1%), and 10 (17.9%) were deployed as web-based applications . All websites were reported, 43 articles (76.79%) disclosed which software was used, and codes were provided for 11 models (19.6%) . Most studies were deep-learning based ( n = 36, 64.3%). From these, the most frequently reported models were Convolutional Neural Networks (CNNs), either alone (29/36, 80.55%), coupled with a Recurrent Neural Network (RNN, 3/36, 8.34%) , or with Logistic Regression (LR), a shallow ANN, Gradient Boosting (GB), a Support Vector Machine (SVM), and Random Forest (RF, 1/36, 2.78%) . Specific CNN architectures were reported for approximately 76% of the articles (25/33), which, as shown in Fig. , primarily consisted of ResNet- ( n = 9, 36%) and DenseNet-based frameworks ( n = 8, 32%), used individually or in conjunction. To overcome data scarcity, transfer learning was used in 16 of the 33 CNN-based articles 48.5%), which involves pre-training the network on a specific problem and transferring that base knowledge to a new, related task (see Table : pre-trained in column General Focus and Models ). Besides CNNs, other DL algorithms were described in four articles . Multilayer Perceptrons (MLPs) were used in three (5.56%) , two of which applied a DeepSurv architecture, a deep Cox proportional hazards feed-forward neural network . The last (2.78%) involved a neural multitask logistic regression model (N-MTLR) . The remaining documents ( n = 20, 35.7%) described a non-deep-learning-based workflow encompassing fifteen unique algorithms applied in twenty-eight configurations. From these, boosting-based techniques were the most widely reported, consisting of eXtreme Gradient Boosting (XGBoost, 6/28, 21.43%) , a Light Gradient Boosting Machine (LightGBM, 1/28, 3.57%) , LogitBoost (1/28, 3.57%) , Adaptive Boosting (AdaBoost, 1/28, 3.57%) , and Gradient-Boosted Decision Trees (GBDT, 2/28, 7.14%) . Other decision tree designs were also used, including RF (6/28, 21.43%) and extremely randomized trees (ExtraTrees, 1/28, 3.57%) . The third most reported group of algorithms were SVMs , a Support Vector Classifier (SVC) , and a Quadratic SVM (4/28, 14.28%), followed by shallow ANNs (2/28, 7.14%) and LR (1/28, 3.57%) . Lastly, Mixture Discriminant Analysis (MDA), k-nearest Neighbors (kNNs), and naïve Bayes (NB) were also found, all used in the same article (total of 3/28, 10.71%) . Regarding general cancer types, the selected papers can be broadly divided into two categories: those concentrating on primary tumors and those mainly examining metastasized (secondary) cancers. Most articles focused on primary tumors (51/56, 91.1%), although four also included metastases . These cancers can be further branched into the specific system where the malignancy was formed: (i) central nervous system (CNS), including the brain (3/51, 5.88%) ; (ii) digestive system, encompassing colorectal (7/51, 13.73%) , esophageal (3/51, 5.88%), gastric (5/51, 9.8%) , and liver cancers (2/51, 3.92%) ; (iii) endocrine system, involving cancers of the pancreas (2/51, 3.92%) and thymus (1/51, 1.96%) ; (iv) genitourinary system, consisting of bladder (1/51, 1.96%) , cervical (1/51, 1.96%) , prostate (2/51, 3.92%) , and endometrial (2/51, 3.92%) cancers; (v) integumentary system, with tumors of the breast (4/51, 7.84%) and skin (2/51, 3.92%); (vi) respiratory system, studying neoplasms of the larynx (1/51, 1.96%) , lung (10/51, 19.61%) , mesothelium (1/51, 1.96%) , and nasopharynx (1/51, 1.96%) ; and (vii) the skeletal system, comprising the bones (4/51, 7.84%) . In addition, five papers analyzed metastatic cancers (5/56, 8.9%), which can also be bifurcated into malignancies spread to nodes or organs. The former includes solid metastatic breast, lung, and gastrointestinal and genitourinary tract tumors , bone metastases in kidney cancer patients , and liver metastases from colorectal cancers . The latter encompasses thyroid cancer spread to lymph nodes and sentinel lymph node metastasis from primary breast lesions . Seventy-six cancer-related goals were addressed in the 56 documents, with an average of one task performed per paper and a maximum of three . These included the development or improvement of systems for: (i) diagnosis alone ( n = 28, 50%) or combined with detection ( n = 5, 8.93%) or prognosis ( n = 1, 1.79%) ; (ii) detection by itself ( n = 2, 3.58%) or coupled with outcome prediction ( n = 1, 1.79%) ; and (iii) outcome prediction, including prognosis ( n = 16, 28.58%) ; and risk stratification ( n = 3, 5.36%) . Finally, fifteen studies resorted to explainable AI (XAI) to increase the transparency behind the models' decisions. Unlike black-box methods, whose reasoning is indecipherable, XAI allows the creation of interpretable models to determine how each prediction was reached and which clinical predictors bore the most weight. Three packages were used for this purpose: (i) SHapley Additive exPlanations (SHAP), which can be employed in any ML algorithm ( n = 6, 40%) ; and (ii) Class Activation Mapping (CAM, n = 1, 6.67%) and Gradient-weighted CAM (Grad-CAM, n = 8, 53.33%) , explicitly developed for CNNs. According to the clinical variables used as input, the models validated in the 56 studies can be divided into three types: image-based (including video, n = 37, 66.1%), text-based ( n = 10, 17.9%), and mixed, using both clinical modalities ( n = 9, 16.1%). Image-based Studies A total of 335 085 high-resolution images from 112 538 patients (102 117 female, 8 215 male ) were used for classification in 36 of the 37 image-based studies and for classification (recurrence) and regression (recurrence-free survival) in the last study . Except for one paper including both pediatric and adult patients (unknown age proportion, 175 female, 116 male) and two other articles not listing the patients’ age group (698 in , unknown in , unidentified male–female ratio in both), all studies consisted of adults (111 469 patients, 101 942 women, 8 099 men). Eight studies (21.6%) extracted radiomic features from the retrieved images . The studies encompassed X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography – Computed Tomography (PET-CT) scans, endoscopic images and videos, photographs, ultrasounds, histological slides, and whole-slide images (WSI). Besides digital pictures, which are limited to the surface, these imaging techniques capture the body's internal structures. However, they differ in the way they create images and the type of information they provide. X-rays use and expose the patient to ionizing radiation to create scans . Although time- and cost-effective, these do not provide as much detail as CT or MRI scans. In this review, two studies used radiographic images (2/37, 5.4%) to: (i) classify pathologically-confirmed primary bone tumors in children and adults (639 radiographs, 175 female, 116 male) ; and (ii) for breast cancer screening in adult women ( n = 1, 213 694 X-rays, 92 585 women) . CT scans combine X-rays from different angles to create high-quality, three-dimensional images. Nevertheless, since they are generated from controlled motions of X-rays, CTs are still unfit for extracting molecular information . Furthermore, these scans subject the patient to higher radiation levels than X-rays and may require contrast agents depending on the adopted modality – contrast-enhanced CTs (CECTs) versus non-contrast CTs (NECTs). CT scans were commonly collected variables in the selected articles (8/37, 21.6%), amounting to 7 540 images from: (i) the lungs ( n = 4, 2 323 nodules, 2 113 patients) ; (ii) gastric cancers ( n = 2, 1 129 images, 352 women, 777 men) ; (iii) cervical lymph nodes ( n = 1, 3 838 images, 698 patients of unknown gender) ; and (iv) hepatic metastasis from colorectal cancer ( n = 1, 250 lesions, 31 women, 54 men) . MRI scans do not depend on radiation and use a strong magnetic field and radio waves to create detailed images. This type of imaging can be separated into two subtypes: conventional and advanced . Conventional MRI (cMRI) sequences include standard MRI protocols commonly used in clinical practice, such as (i) T1-weighted: used to identify structural abnormalities; (ii) Axial fluid-attenuated inversion recovery MRI (FLAIR), applied to identify abnormalities that affect the tissues' water content; and (iii) T2-weighted: also appropriate to assess irregularities in water content. Advanced MRI (advMRI) techniques generate deeper information regarding the tissue's function, structure, and metabolic processes, including: (i) multiparametric MRI (mpMRI), which combine several other MRI sequences to enrich its output; (ii) axial diffusion-weighted (DWI) MRI, which measure the movement of water molecules in tissues; (iii) Vascular architecture mapping (VAM) MRI, providing information about the tissue's blood vessels; (iv) Gradient echo dynamic susceptibility contrast (DSC) MRI, used to measure blood movement; (v) Quantitative blood-oxygenation-level-dependent (qBOLD) MRI, able to measure the oxygen content in the blood; (vi) General Electric-Dynamic Susceptibility Contrast (GE-DSC) MRI, which resorts to a contrast agent to measure blood flow; and (vii) Magnetic resonance spectroscopy (MRS), which calculate the levels of certain chemicals and metabolites in the tissues. Although some types of MRIs – such as MR spectroscopy and diffusion-weighted imaging – allow assessing molecular details without contrasts, most are better equipped to analyze gross internal structures and are more expensive than CTs and X-rays . MRI scans were also frequently used as input for the models, with 64 941 combined images from 8 studies (21.6%), including (i) the brain ( n = 3, 64 459 lesions, 623 women, 461 men) ; (ii) the prostate ( n = 2, 262 nodules, 300 men) ; (iii) colorectal malignancies ( n = 2, 154 images, 54 women, 64 men) ; and (iv) bones and cartilages ( n = 1, 65 scans, 34 women, 31 men) . PET scans, which are also radiation-free, allow for examining the internal body structure and underlying molecular tissues. However, these are extremely expensive, usually unavailable in routine practice, and due to their low spatial resolution, require pairing with a second modality, such as CTs and MRIs . In this review, one study (2.7%) used PET-CT scans to examine atypical cartilaginous tumors and appendicular chondrosarcomas (36 scans, 23 women, 13 men) . Similarly to X-rays, ultrasounds – which use high-frequency sound waves to create images – provide an inexpensive method to inspect organ structures without detailing underlying molecular information, with the upside of not involving radiation . Ultrasonographic imaging was mentioned in 2 articles ( n = 2, 5.4%, 328), which studied breast cancers (116 ultrasounds, 107 women) . Eight reports describe images captured with standard endoscopes ( n = 8, 24.3%, 3681 items), which cannot capture molecular features. Four studies used colonoscopic lesions from the colon and rectum (995 images, 105 women, 224 men) . Four studies analyzed endoscopic pictures of the esophagus ( n = 2, 260 images, 260 patients of unknown gender) , the larynx ( n = 1, 1 176 images, unknown number of patients) , and the nasopharynx ( n = 1, 1 430 images, 124 women, 231 men) . Lastly, one study examined endoscopic videos from intramucosal gastric cancer patients (54 videos, 38 women, 16 men) . Two studies used advanced endoscopes. One involved endoscopic ultrasonography (EUS), a technique that combines endoscopy and ultrasonography to gather gastrointestinal images ( n = 1, 2.7%, 212 ultrasounds, 38 women, 31 men) . The other resorted to endocytoscopy, a relatively new high-magnification imaging approach that allows tissue analysis at a cellular level, to collect 100 colorectal images from 89 patients ( n = 1, 2.7%, 26 women, 63 men) . A histological image is a high-resolution, microscopic image of a tissue slide after it's been processed with one or more stains to reveal its composition . This method allows distinguishing between different histological cancer subtypes but involves a long preparation time and offers a limited depth of view. One paper used hematoxylin-and-eosin (H&E)-stained histological images to study endometrium hyperplasia and intraepithelial neoplasia ( n = 1, 2.7%, 1 631 slides, 102 women) . Whole-slide images (WSIs) are virtual representations of a tissue section scanned at high resolution and magnification. WSIs are created by scanning stained histological slides and usually combine and magnify multiple slides using specialized software . This technique allows thorough tissue examination at cellular and sub-cellular levels, but it is still cost-, storage- and technically heavy. WSIs were used to feed the models in three studies (8.1%, 3 315 images), using 30 × or 40 × magnification. Two included H&E stained slides of the liver ( n = 1, 80 slides, 24 women, 56 men) and the mesothelium ( n = 1, 39 images, 39 patients of unreported gender) . One was composed of stained slides (unknown stain) for the cervical screening of women without any known conditions and with the Human papillomavirus (HPV) ( n = 1, 1565 images and women) . Finally, 46 962 digital photographs (captured with a camera) were analyzed across two documents (5.4%). Both inspected skin malignancies ( n = 2, 10 602 patients). Detailed information regarding the samples, type of CTs, MRIs, and endoscopes used in the image-based studies, as well as population details and counts (age group, total patients, female, and male), is itemized in Table . Text-based Studies The populations and specific clinical variables used in each text-based study are compiled in Table . Clinical data from 6 803 patients (2 772 women, 4 031 men, 7 861 encounters) was collected for validation across ten papers . Apart from one work including senior citizens , all studies consisted of adult patients (6 644 subjects, 2 701 women, 3 943 men). An average of 17 clinical variables was used per study (range = 6 – 31 ), encompassing information on demographics, tumoral values, and laboratory test results. The machine learning models used in 6 of the articles (60%) were exclusively developed for classification (1 960 women, 3 097 men) , while 4 (40%) solely concerned regression (812 women, 934 men) . In the four regression-based articles, the developed prognostic models assessed (i) patients with a single lesion of primary stage I to IV esophageal adenocarcinoma or squamous cell carcinoma ( n = 1, 150 women, 350 men) ; (ii) patients with pathologically confirmed and resected intrahepatic cholangiocarcinoma (12 women, 30 men) ; (iii) patients with stage I to III non-small cell lung cancer (642 women, 540 men) ; and (iv) patients in palliative care with unresectable advanced pancreatic ductal adenocarcinoma with liver metastases (8 women, 14 men) . The six classification papers included: (i) seniors with stage I to III non-small cell lung cancer treated with curative-intent radiotherapy (159 individuals, 71 women, 88 men) ; (ii) bone metastasis in kidney cancer patients with complete survival data (323 women, 640 men) ; (iii) women with primary breast cancer diagnosed by pathological examination (150 women) ; (iv) patients with primary colorectal cancer with survival-related data who underwent surgery (1 572 patients, 607 female, 965 male) ; (v) patients with confirmed stage III non-small cell lung cancer (39 women, 133 men) ; and (vi) patients with solid metastatic tumors for several types of cancer with and without alterations in treatment in an outpatient setting (3 099 encounters, 2 041 individuals, 770 women, 1 271 men) . Mixed Studies An average of 9 clinical variables (range = 3 – 17 ), 784 images, and 720 patients (range = 44 – 5 493 for both) were used in the nine mixed studies, whose information is highlighted in Table . These papers combined patients’ demographics, cancer-specific data, laboratory results, and imaging features extracted from different modalities for cancer-specific populations (7 053 images, 6 482 patients, 3 009 women, 3 478 men). Radiomics approaches were used in three studies . Six reports included CT images to study: (i) patients who underwent curative-intent resection for pancreatic ductal adenocarcinoma ( n = 1, 53 images, 27 women, 26 men) ; (ii) patients with benign and malignant pulmonary ground-glass nodules with less than 30 mm ( n = 1, 63 images, 39 women, 22 men) ; (iii) individuals with multiple lungs nodes in a post-operative setting ( n = 1, 200 images, 51 women, 27 men) ; (iv) lung cancer patients with an available baseline radiograph ( n = 1, 5 493 patients and images, 2456 women, 3037 men) ; (v) patients with muscle-invasive bladder cancer who underwent surgery ( n = 1, 75 images, 13 women, 62 men) ; and (vi) adults with pathologically confirmed thymomas and thymic carcinomas ( n = 1, 76 preoperative scans, 33 women, 48 men) . Additionally, three studies used other types of scans. One work paired breast-specific data with features derived from three types of MRI scans for women with endometrial lesions and complete clinical data (44 images, 44 women) . One paper combined patients’ age, sex, tumor type, location, and radiomic features extracted from X-rays to analyze primary bone tumors (40 women, 56 men) . Finally, one study evaluated survival- and gross-tumor-related data in conjunction with H&E slides magnified 30 times (whole-slide images) to estimate outcomes for patients diagnosed with gastric cancer (175 images, 91 patients, 60 female, 31 male) . Except for the models developed in this study, where the first used only WSIs for classification and the second used these images and clinical data for prognostication (regression), all algorithms were classifiers. A total of 335 085 high-resolution images from 112 538 patients (102 117 female, 8 215 male ) were used for classification in 36 of the 37 image-based studies and for classification (recurrence) and regression (recurrence-free survival) in the last study . Except for one paper including both pediatric and adult patients (unknown age proportion, 175 female, 116 male) and two other articles not listing the patients’ age group (698 in , unknown in , unidentified male–female ratio in both), all studies consisted of adults (111 469 patients, 101 942 women, 8 099 men). Eight studies (21.6%) extracted radiomic features from the retrieved images . The studies encompassed X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography – Computed Tomography (PET-CT) scans, endoscopic images and videos, photographs, ultrasounds, histological slides, and whole-slide images (WSI). Besides digital pictures, which are limited to the surface, these imaging techniques capture the body's internal structures. However, they differ in the way they create images and the type of information they provide. X-rays use and expose the patient to ionizing radiation to create scans . Although time- and cost-effective, these do not provide as much detail as CT or MRI scans. In this review, two studies used radiographic images (2/37, 5.4%) to: (i) classify pathologically-confirmed primary bone tumors in children and adults (639 radiographs, 175 female, 116 male) ; and (ii) for breast cancer screening in adult women ( n = 1, 213 694 X-rays, 92 585 women) . CT scans combine X-rays from different angles to create high-quality, three-dimensional images. Nevertheless, since they are generated from controlled motions of X-rays, CTs are still unfit for extracting molecular information . Furthermore, these scans subject the patient to higher radiation levels than X-rays and may require contrast agents depending on the adopted modality – contrast-enhanced CTs (CECTs) versus non-contrast CTs (NECTs). CT scans were commonly collected variables in the selected articles (8/37, 21.6%), amounting to 7 540 images from: (i) the lungs ( n = 4, 2 323 nodules, 2 113 patients) ; (ii) gastric cancers ( n = 2, 1 129 images, 352 women, 777 men) ; (iii) cervical lymph nodes ( n = 1, 3 838 images, 698 patients of unknown gender) ; and (iv) hepatic metastasis from colorectal cancer ( n = 1, 250 lesions, 31 women, 54 men) . MRI scans do not depend on radiation and use a strong magnetic field and radio waves to create detailed images. This type of imaging can be separated into two subtypes: conventional and advanced . Conventional MRI (cMRI) sequences include standard MRI protocols commonly used in clinical practice, such as (i) T1-weighted: used to identify structural abnormalities; (ii) Axial fluid-attenuated inversion recovery MRI (FLAIR), applied to identify abnormalities that affect the tissues' water content; and (iii) T2-weighted: also appropriate to assess irregularities in water content. Advanced MRI (advMRI) techniques generate deeper information regarding the tissue's function, structure, and metabolic processes, including: (i) multiparametric MRI (mpMRI), which combine several other MRI sequences to enrich its output; (ii) axial diffusion-weighted (DWI) MRI, which measure the movement of water molecules in tissues; (iii) Vascular architecture mapping (VAM) MRI, providing information about the tissue's blood vessels; (iv) Gradient echo dynamic susceptibility contrast (DSC) MRI, used to measure blood movement; (v) Quantitative blood-oxygenation-level-dependent (qBOLD) MRI, able to measure the oxygen content in the blood; (vi) General Electric-Dynamic Susceptibility Contrast (GE-DSC) MRI, which resorts to a contrast agent to measure blood flow; and (vii) Magnetic resonance spectroscopy (MRS), which calculate the levels of certain chemicals and metabolites in the tissues. Although some types of MRIs – such as MR spectroscopy and diffusion-weighted imaging – allow assessing molecular details without contrasts, most are better equipped to analyze gross internal structures and are more expensive than CTs and X-rays . MRI scans were also frequently used as input for the models, with 64 941 combined images from 8 studies (21.6%), including (i) the brain ( n = 3, 64 459 lesions, 623 women, 461 men) ; (ii) the prostate ( n = 2, 262 nodules, 300 men) ; (iii) colorectal malignancies ( n = 2, 154 images, 54 women, 64 men) ; and (iv) bones and cartilages ( n = 1, 65 scans, 34 women, 31 men) . PET scans, which are also radiation-free, allow for examining the internal body structure and underlying molecular tissues. However, these are extremely expensive, usually unavailable in routine practice, and due to their low spatial resolution, require pairing with a second modality, such as CTs and MRIs . In this review, one study (2.7%) used PET-CT scans to examine atypical cartilaginous tumors and appendicular chondrosarcomas (36 scans, 23 women, 13 men) . Similarly to X-rays, ultrasounds – which use high-frequency sound waves to create images – provide an inexpensive method to inspect organ structures without detailing underlying molecular information, with the upside of not involving radiation . Ultrasonographic imaging was mentioned in 2 articles ( n = 2, 5.4%, 328), which studied breast cancers (116 ultrasounds, 107 women) . Eight reports describe images captured with standard endoscopes ( n = 8, 24.3%, 3681 items), which cannot capture molecular features. Four studies used colonoscopic lesions from the colon and rectum (995 images, 105 women, 224 men) . Four studies analyzed endoscopic pictures of the esophagus ( n = 2, 260 images, 260 patients of unknown gender) , the larynx ( n = 1, 1 176 images, unknown number of patients) , and the nasopharynx ( n = 1, 1 430 images, 124 women, 231 men) . Lastly, one study examined endoscopic videos from intramucosal gastric cancer patients (54 videos, 38 women, 16 men) . Two studies used advanced endoscopes. One involved endoscopic ultrasonography (EUS), a technique that combines endoscopy and ultrasonography to gather gastrointestinal images ( n = 1, 2.7%, 212 ultrasounds, 38 women, 31 men) . The other resorted to endocytoscopy, a relatively new high-magnification imaging approach that allows tissue analysis at a cellular level, to collect 100 colorectal images from 89 patients ( n = 1, 2.7%, 26 women, 63 men) . A histological image is a high-resolution, microscopic image of a tissue slide after it's been processed with one or more stains to reveal its composition . This method allows distinguishing between different histological cancer subtypes but involves a long preparation time and offers a limited depth of view. One paper used hematoxylin-and-eosin (H&E)-stained histological images to study endometrium hyperplasia and intraepithelial neoplasia ( n = 1, 2.7%, 1 631 slides, 102 women) . Whole-slide images (WSIs) are virtual representations of a tissue section scanned at high resolution and magnification. WSIs are created by scanning stained histological slides and usually combine and magnify multiple slides using specialized software . This technique allows thorough tissue examination at cellular and sub-cellular levels, but it is still cost-, storage- and technically heavy. WSIs were used to feed the models in three studies (8.1%, 3 315 images), using 30 × or 40 × magnification. Two included H&E stained slides of the liver ( n = 1, 80 slides, 24 women, 56 men) and the mesothelium ( n = 1, 39 images, 39 patients of unreported gender) . One was composed of stained slides (unknown stain) for the cervical screening of women without any known conditions and with the Human papillomavirus (HPV) ( n = 1, 1565 images and women) . Finally, 46 962 digital photographs (captured with a camera) were analyzed across two documents (5.4%). Both inspected skin malignancies ( n = 2, 10 602 patients). Detailed information regarding the samples, type of CTs, MRIs, and endoscopes used in the image-based studies, as well as population details and counts (age group, total patients, female, and male), is itemized in Table . The populations and specific clinical variables used in each text-based study are compiled in Table . Clinical data from 6 803 patients (2 772 women, 4 031 men, 7 861 encounters) was collected for validation across ten papers . Apart from one work including senior citizens , all studies consisted of adult patients (6 644 subjects, 2 701 women, 3 943 men). An average of 17 clinical variables was used per study (range = 6 – 31 ), encompassing information on demographics, tumoral values, and laboratory test results. The machine learning models used in 6 of the articles (60%) were exclusively developed for classification (1 960 women, 3 097 men) , while 4 (40%) solely concerned regression (812 women, 934 men) . In the four regression-based articles, the developed prognostic models assessed (i) patients with a single lesion of primary stage I to IV esophageal adenocarcinoma or squamous cell carcinoma ( n = 1, 150 women, 350 men) ; (ii) patients with pathologically confirmed and resected intrahepatic cholangiocarcinoma (12 women, 30 men) ; (iii) patients with stage I to III non-small cell lung cancer (642 women, 540 men) ; and (iv) patients in palliative care with unresectable advanced pancreatic ductal adenocarcinoma with liver metastases (8 women, 14 men) . The six classification papers included: (i) seniors with stage I to III non-small cell lung cancer treated with curative-intent radiotherapy (159 individuals, 71 women, 88 men) ; (ii) bone metastasis in kidney cancer patients with complete survival data (323 women, 640 men) ; (iii) women with primary breast cancer diagnosed by pathological examination (150 women) ; (iv) patients with primary colorectal cancer with survival-related data who underwent surgery (1 572 patients, 607 female, 965 male) ; (v) patients with confirmed stage III non-small cell lung cancer (39 women, 133 men) ; and (vi) patients with solid metastatic tumors for several types of cancer with and without alterations in treatment in an outpatient setting (3 099 encounters, 2 041 individuals, 770 women, 1 271 men) . An average of 9 clinical variables (range = 3 – 17 ), 784 images, and 720 patients (range = 44 – 5 493 for both) were used in the nine mixed studies, whose information is highlighted in Table . These papers combined patients’ demographics, cancer-specific data, laboratory results, and imaging features extracted from different modalities for cancer-specific populations (7 053 images, 6 482 patients, 3 009 women, 3 478 men). Radiomics approaches were used in three studies . Six reports included CT images to study: (i) patients who underwent curative-intent resection for pancreatic ductal adenocarcinoma ( n = 1, 53 images, 27 women, 26 men) ; (ii) patients with benign and malignant pulmonary ground-glass nodules with less than 30 mm ( n = 1, 63 images, 39 women, 22 men) ; (iii) individuals with multiple lungs nodes in a post-operative setting ( n = 1, 200 images, 51 women, 27 men) ; (iv) lung cancer patients with an available baseline radiograph ( n = 1, 5 493 patients and images, 2456 women, 3037 men) ; (v) patients with muscle-invasive bladder cancer who underwent surgery ( n = 1, 75 images, 13 women, 62 men) ; and (vi) adults with pathologically confirmed thymomas and thymic carcinomas ( n = 1, 76 preoperative scans, 33 women, 48 men) . Additionally, three studies used other types of scans. One work paired breast-specific data with features derived from three types of MRI scans for women with endometrial lesions and complete clinical data (44 images, 44 women) . One paper combined patients’ age, sex, tumor type, location, and radiomic features extracted from X-rays to analyze primary bone tumors (40 women, 56 men) . Finally, one study evaluated survival- and gross-tumor-related data in conjunction with H&E slides magnified 30 times (whole-slide images) to estimate outcomes for patients diagnosed with gastric cancer (175 images, 91 patients, 60 female, 31 male) . Except for the models developed in this study, where the first used only WSIs for classification and the second used these images and clinical data for prognostication (regression), all algorithms were classifiers. Information concerning institutional, study, and validation designs, care types, datasets, clinical settings, and the number of institutions involved in validation in the selected documents is illustrated in Table . Model development and validation were performed simultaneously in most studies ( n = 50, 87.5%), while 4 (7.14%) evaluated external validity separately, and 3 (5.36%) entailed model updating and validation. Of the 56 documents included in this review, 44 (78.57%) directly reference external validation in the abstract, 10 (17.86%) indirectly mention it, and 2 (3.57%) omit this information. Overall, 74 medical datasets were used for external validation across the 56 studies, averaging 1.3 per paper (range = 1—8). All studies used real-world data acquired prospectively or collected from the patients' EHRs and imaging archiving platforms. Except for three articles using both standard and uncommon types of MRI scans and one using endocytoscopy (whose use is still growing) , all studies used text- and image-based data routinely collected in clinical practice. However, only nine reports describe external validation in clinically realistic scenarios , and solely two systems are currently implemented in practice . The papers involved several cancer-related settings, including secondary ( n = 1, 2%), tertiary ( n = 34, 61%), and quaternary (12, 21%) oncology care. However, 6 (11%) studies did not report from which centers data were retrieved, and 3 (5%) used databases without this information. Among the collected studies, 49 (87.5%) were conducted retrospectively, 3 (5.36%) were prospective, 4 (7.15%) were mixed: one performed internal validation prospectively and external validation retrospectively , one proceeded inversely , and two used both retrospective and prospective cohorts . Only one report used randomized data . Regarding validation design, 31 (55.357%) studies followed a multi-institutional approach, 14 (25%) collected information from a single center, 1 (1.786%) only used public databases, 2 (3,572%) used public multi-institutional databases, and 8 (14,286%) used both types of sources. For the multi-institutional studies (including databases), the average number of facilities used for validation was 3, with a maximum of 33 . One study did not report the number of institutions involved . The following freely available data sources were used: (i) the Surveillance, Epidemiology, and End Results (SEER) database, which covers population-based cancer registries of approximately 47.8% of the United States Population ; (ii) The Cancer Genome Atlas (TCGA, from the USA), which molecularly characterizes over 20,000 primary cancers, and contains whole-slide images ; (iii) The Cancer Imaging Archive, which hosts a large number of medical images for various types of cancer ; (iv) the Edinburgh dataset, containing data from the University of Edinburgh (Scottland, United Kingdom) ; (v) the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial sponsored by the by the National Cancer Institute (NCI), designed to evaluate the impact of cancer screening on mortality rates, as well as to assess the potential risks and benefits associated with screening ; (vi) the National Lung Screening Trial (NLST), a randomized controlled trial also supported by the NCI that aimed to evaluate the impact of using low-dose helical CT scans on patient mortality ; (vii) the PROSTATEx dataset, which contains a retrospective set of prostate MRI studies ; (viii) the PICTURE dataset, containing data from a single-center trial, and intended to evaluate the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in men with prostate lesions ; and (ix) the National Human Genetic Resources Sharing Service Platform (NHGRP), for which we could not find any details . In two studies, models were trained using data from multiple countries. One developed their model using patients from three Chinese institutions and one center from the United States of America (USA) and validated it on a Chinese dataset ( n = 1, 1.8%) . The other gathered data from a Chinese institution and TCGA and validated their model on images from NHGRP . Additionally, one document did not report which countries were involved in their model’s development or validation . All other authors developed their model on data from a single country. These included China ( n = 19, 33.7%), the USA ( n = 12, 21.4%), South Korea ( n = 9, 16.1%), Italy and Germany (3 each, 5.4%), Japan and the Netherlands (2 each, 3.6%), and the United Kingdom (UK), Canada, and Austria (1 each, 1.8%). Besides the two abovementioned papers , twelve other studies performed international validation. Of these, six included ethnically different sources. Two authors trained their model with data from South Korea: one validated it on South Korean and American datasets , and the other validated it on a South Korean dataset and the Edinburgh dataset (UK) . Additionally, five reports mention training their model on the SEER database (USA), with four validating it with Chinese patients and one with South Korean patients . For the five remaining studies, patients with the same ethnicity were included: (i) one was developed with the NLST trial dataset (USA) and validated on data from the UK ; (ii) one was trained with data from TCGA (USA) and validated on an institution from the UK ; (iii) one used data from Italy for training and patients from The Netherlands for validation ; (iii) one trained their model on the PROSTATEx dataset (from The Netherlands) and validated it on the PICTURE dataset (from the UK) ; and (iv) one used a Chinese dataset for training and Chinese and South Korean patients for validation . Regarding validation types, 12 studies (21.48%) were limited to temporal validation from a single institution, which cannot be interpreted as a fully independent validation . Five other studies also only temporally validated their model. However, two used a multi-institutional approach (3.58%) , two (3.58%) used different data acquisition designs (retrospective internal validation and prospective external validation) , and one evaluated performance for patients at different treatment stages (1.78%) . Nine studies (16,08%) only validated their model geographically, seven within the same country , one internationally , and one with internationally and ethnically different patients . Twenty-nine reports (51.8%) included both temporal and geographical validation. Sixteen (28.57%) used local data, one evaluated temporally and geographically different patients from the same country with images captured using various scanners , and one (1.79%) used national data and mixed data acquisition (prospective internal validation and retrospective external validation) . Lastly, one study that did not report data sources validated their model on different types of computed tomography (CT) scanners . The external datasets were used to evaluate the models’ generalizability to populations differing – geographically, temporally, or both – from the development cohort. The performance metrics reported in the articles can be branched into three categories: discrimination, calibration, and processing time. For classification models, an average of 5 metrics were used to assess discrimination, up to a maximum of seven (range = 1 – 7). These consisted of (i) sensitivity, reported in 48 papers; (ii) area under the receiver operating characteristic (ROC) curve (AUC), calculated in 43 studies; (iii) specificity, used in 42 articles; (iv) accuracy, presented in 35 documents; (v and vi) positive and negative predictive values (PPV and NPV), computed in 29 and 19 reports, respectively; (vii) F1-score, considered in 13 papers; (viii) C-index, used in 2 articles ; (ix) false positive rate, reported in two papers ; (x) area under the alternative free-response ROC curve (AUAFROC) , calculated for one model; (xi) jackknife alternative free-response ROC (JAFROC), also computed for one algorithm ; and (xii) Softspot (Sos) and Sweetspot (Sws) flags, both used in the same two papers . However, decision thresholds were only disclosed for half of the articles (26/52, 50%), and only three papers presented results for different cut-off values/settings . Likewise, 39 classification studies did not assess calibration. When evaluated (13/52, 25%), calibration was illustrated graphically in five studies (9.62%) , via Brier Score in three documents (5.77%) , using both approaches in four papers (7.69%) , and with mean absolute error (MAE) in one report . Lastly, the models’ processing time was also seldomly revealed, with only seven studies reporting it . For the regression-based algorithms, discriminative performance was assessed via C-index . Regarding calibration, the model’s Brier Score was presented in one study , calibration plots in two , both metrics in one , and none in two . The models’ processing time and decision thresholds were not reported in any of these studies. From the selected studies, the majority ( n = 50, 89.29%) explicitly mentions the assessment of the models' clinical utility, that is, its relevance to clinicians and patient outcomes, in the paper's abstract. However, one only refers to it indirectly (1.79%) , and the remaining five (8.93%) do not state this aspect in their summaries . Two approaches were used to assess the models’ utility: comparison against clinician performance, adopted in most studies (40/56, 71.4%), and benchmarking against established clinical tools (15/56, 26.8%). Additionally, one study used both: retrospective comparisons were performed against routine clinical scores, while prospective assessments involved clinicians (1/56, 1.8%) . Comparison Against Clinicians Four hundred-ninety-nine medical professionals of varying expertise were involved in these studies, with an average of 12 clinicians compared against each model (range = 1 – 109 ). These included endoscopists ( n = 204), oncologists ( n = 77), radiologists ( n = 76), general physicians ( n = 71), dermatologists ( n = 44), pathologists ( n = 21), ophthalmologists ( n = 3), and thoracic surgeons ( n = 3). A subset of 113 115 patients (102 178 female, 9 619 male) was used for these assessments, and identical performance metrics as those documented for external validation were observed, plus time until diagnosis. Specific clinicians’ years of experience were reported in 20 papers (48.8%), ranks (without years) in 11 (26.8%), and no information concerning expertise in 10 (24.4%). The 41 classification studies encompassing model comparison against clinicians can be divided into two designs: with and without the model and independent evaluation of the models and the clinicians. The most commonly adopted technique was separately assessing model and clinician performance and comparing it posteriorly ( n = 30, 73.2%). Four hundred-one clinicians (μ = 15 per report, range = 1 – 109) and 109 720 patients (μ = 3 657 per paper, 100 965 female, 8 203 male ) were involved in these papers, and model-clinician performance was compared for detection and diagnostic capabilities. An average of 4 performance metrics (range = 1 – 7 ) were computed per paper, with sensitivity being the most calculated ( n = 23), followed by specificity ( n = 18) and accuracy ( n = 15), AUC ( n = 11), PPV ( n = 11), NPV ( n = 7), F1-score ( n = 3) , false positive rate ( n = 2) , Sweetspot and Softsoft flags ( n = 2) , diagnostic time ( n = 1) , and AUAFROC ( n = 1) , and JAFROC ( n = 1) . The second approach involved comparing clinician performance with and without the assistance of the artificially intelligent systems developed by the authors ( n = 11, 26.8%). The eleven studies employing this method comprised 92 clinicians (μ = 8, minimum = 1, maximum = 20 ) and 3 337 patients (μ = 370, 1 223 female, 1 416 male ). Similarly to the previous technique, an average of 4 performance metrics were used per paper (range = 1 – 6 ), including sensitivity ( n = 9), specificity ( n = 8), accuracy ( n = 8), PPV ( n = 6), NPV ( n = 5), AUC ( n = 2) , mean diagnostic time ( n = 2) , and error rate ( n = 1) . Comparison Against Standard/Established Clinical Tools In sixteen studies, assessing the usefulness of the models involved comparing their performance against well-established and routinely used clinical tools. In total, 11 659 patients (μ = 777 per paper, 4 521 female, 5 694 male ) were encompassed in these assessments, and twelve standard tools were used for comparisons. These included: (i) the 7th and 8th editions of the Tumor, Node, and Metastasis (TNM) staging system; (ii) the Brock University Model; (iii) the Fracture Risk Assessment Tool (FRAX); (iv) the Liver Cancer Study Group of Japan (LCSGJ); (v) the Mayo clinic model; (vi) the modified Glasgow Prognostic Score (mGPS); (vii) the Osteoporosis Self-Assessment Tool for Asians (OSTA); (viii) the second version of the Prostate Imaging Reporting and Data System (PI-RADS v2); (ix) the Peking University (PKU) model; (x) the PLCOm2012 model; (iv) the Response Evaluation Criteria in Solid Tumors (RECIST); (xi) the Veterans Affairs (VA) model; and (xii) the World Health Organization (WHO) performance status. Except for one study , all papers explicitly mention comparisons against these tools in the abstract. The TNM system, created by the American Joint Committee on Cancer (AJCC), is globally used in routine clinical procedures. It categorizes cancer progression and guides subsequent treatment decisions depending on (i) the size and extent of the primary tumor (T), (ii) if it has spread to nearby lymph nodes (N), and (iii) if it has metastasized to distant organs (M) . In this review, two text-based classification studies compared their models against the 7th edition of this staging system (TNM-7): one juxtaposed diagnostic and prognostic (3-year overall survival) predictions for bone metastasis in kidney cancer patients (323 women, 640 men) , while the other compared 1–10-year postoperative survival predictions for patients with colorectal cancer (607 women, 965 men) . Similarly, seven papers resorted to the 8th edition of AJCC TMN (TNM-8), its revised and updated version. On the one hand, in four articles, the models were only compared against this system. Two analyzed their text- and regression-based models to predict cancer-specific survival for esophageal (500 patients, 150 women, 350 men) and lung tumors (1 182 individuals, 642 female, 540 male) . The other two concerned the evaluation of classification models. Using preoperative images and descriptive data, one compared 2-year overall survival and 1-year recurrence-free survival predictions for patients with pancreatic cancer (27 female, 26 male) . The other compared risk stratification performance for overall survival for lung cancer patients (39 women, 133 men) between their model and the TMN-8 system using only text-based data . On the other hand, in three text-based studies, models were compared against TNM-8 and other tools. One paper also contrasted model performance for recurrence, recurrence-free survival, and overall survival for lung cancer patients (71 women, 88 men) with the WHO performance status, often used in oncology to determine patients' overall health status, prognosis, and the ability to tolerate treatment . This scaling system ranges from 0 to 4, where 0 represents no symptoms and pre-disease performance, and 4 translates to total disability. In the second article, predictions of overall postoperative survival were benchmarked against TNM-8 and LCSGJ (42 liver cancer patients, 12 women, 30 men) . LCSGJ is a group of Japanese medical professionals specializing in diagnosing and treating liver cancer, recognized as a leading authority in this cancer research field. Lastly, the third study describes the development of three risk models for breast cancer patients (150 women) : (i) fracture, whose predictions were contrasted with those generated by FRAX; (ii) osteoporosis, compared against and FRAX and OSTA; (iii) and survival, benchmarked against TNM-8. FRAX is a web-based tool designed to stratify 10-year bone fracture risk, and OSTA assesses the risk of osteoporosis in Asian populations . The Brock University (also known as PanCan) model is a logistic regression model devised to assist in risk stratification for lung cancer. It is recommended in the British Thoracic Society guideline as a tool to decide if nodules measuring 8 mm or more in maximum diameter should be assessed further with PET-CT . Here, it was applied in one of the selected papers to compare predictions of malignancy risk for lung cancer from CECT and NECT scans (1 397 images, 1187 patients, unknown gender proportion) . In addition to the Brock Model, comparisons in a second paper (978 CTs, 493 patients, 297 women, 196 men) were also performed against three other tools: (i) the Mayo model, which the Mayo Clinic developed to assess cancer prognosis and predict patient outcomes; (ii) the PKU model, created by the Peking University; and (iii) the VA model, which includes a comprehensive cancer care system that aims to provide high-quality, evidence-based care to veterans with cancer . The mGPS scale is a validated scoring system formulated to assess the prognosis of patients with advanced or metastatic cancer based on nutritional and inflammatory markers . In this review, it was used to establish clinical utility for a text-based classification model developed to predict overall survival for patients with unresectable pancreatic tumors (22 patients, 8 women, 14 men) . PI-RADS is a standardized system for interpreting and reporting findings from prostate MRI scans, created to guide clinical decision-making in diagnosing and treating prostate cancer. In this context, it was contrasted against a model developed to stratify low- and high-risk patients (39 and 14 men, respectively) . PLCOm2012 is a validated risk score that uses logistic regression to predict the probability of lung cancer occurrence within six years based on demographic and clinical information . It was the chosen comparator in a study predicting 12-year lung cancer incidence using low-dose CT images and patients’ age, sex, and smoking status (5493 images and patients, 2456 women, 3037 men) . Finally, RECIST is a set of guidelines used to evaluate the response of solid tumors to treatment in clinical trials and clinical practice. It was compared against two classification models: one aimed at detecting pathological downstaging in advanced gastric cancer patients from CECT images (86 patients and images, 23 women, 27 men) ; the other was designed to predict pathological tumor regression grade response to neoadjuvant chemotherapy in patients with colorectal liver metastases from MRI scans (61 images, 25 patients, 13 female, 12 male) . A few performance metrics were reported for the comparisons between the models developed in the selected papers and routinely used clinical tools, with an average of 3 metrics reported per document (range = 1 – 6). Here, the most frequently calculated metrics were AUC ( n = 11) and sensitivity ( n = 8), but PPV ( n = 5), C-index ( n = 4), specificity ( n = 4), accuracy ( n = 3), NPV ( n = 3), Brier Score ( n = 2) and F1-score ( n = 1) were also used in the evaluations. Four hundred-ninety-nine medical professionals of varying expertise were involved in these studies, with an average of 12 clinicians compared against each model (range = 1 – 109 ). These included endoscopists ( n = 204), oncologists ( n = 77), radiologists ( n = 76), general physicians ( n = 71), dermatologists ( n = 44), pathologists ( n = 21), ophthalmologists ( n = 3), and thoracic surgeons ( n = 3). A subset of 113 115 patients (102 178 female, 9 619 male) was used for these assessments, and identical performance metrics as those documented for external validation were observed, plus time until diagnosis. Specific clinicians’ years of experience were reported in 20 papers (48.8%), ranks (without years) in 11 (26.8%), and no information concerning expertise in 10 (24.4%). The 41 classification studies encompassing model comparison against clinicians can be divided into two designs: with and without the model and independent evaluation of the models and the clinicians. The most commonly adopted technique was separately assessing model and clinician performance and comparing it posteriorly ( n = 30, 73.2%). Four hundred-one clinicians (μ = 15 per report, range = 1 – 109) and 109 720 patients (μ = 3 657 per paper, 100 965 female, 8 203 male ) were involved in these papers, and model-clinician performance was compared for detection and diagnostic capabilities. An average of 4 performance metrics (range = 1 – 7 ) were computed per paper, with sensitivity being the most calculated ( n = 23), followed by specificity ( n = 18) and accuracy ( n = 15), AUC ( n = 11), PPV ( n = 11), NPV ( n = 7), F1-score ( n = 3) , false positive rate ( n = 2) , Sweetspot and Softsoft flags ( n = 2) , diagnostic time ( n = 1) , and AUAFROC ( n = 1) , and JAFROC ( n = 1) . The second approach involved comparing clinician performance with and without the assistance of the artificially intelligent systems developed by the authors ( n = 11, 26.8%). The eleven studies employing this method comprised 92 clinicians (μ = 8, minimum = 1, maximum = 20 ) and 3 337 patients (μ = 370, 1 223 female, 1 416 male ). Similarly to the previous technique, an average of 4 performance metrics were used per paper (range = 1 – 6 ), including sensitivity ( n = 9), specificity ( n = 8), accuracy ( n = 8), PPV ( n = 6), NPV ( n = 5), AUC ( n = 2) , mean diagnostic time ( n = 2) , and error rate ( n = 1) . In sixteen studies, assessing the usefulness of the models involved comparing their performance against well-established and routinely used clinical tools. In total, 11 659 patients (μ = 777 per paper, 4 521 female, 5 694 male ) were encompassed in these assessments, and twelve standard tools were used for comparisons. These included: (i) the 7th and 8th editions of the Tumor, Node, and Metastasis (TNM) staging system; (ii) the Brock University Model; (iii) the Fracture Risk Assessment Tool (FRAX); (iv) the Liver Cancer Study Group of Japan (LCSGJ); (v) the Mayo clinic model; (vi) the modified Glasgow Prognostic Score (mGPS); (vii) the Osteoporosis Self-Assessment Tool for Asians (OSTA); (viii) the second version of the Prostate Imaging Reporting and Data System (PI-RADS v2); (ix) the Peking University (PKU) model; (x) the PLCOm2012 model; (iv) the Response Evaluation Criteria in Solid Tumors (RECIST); (xi) the Veterans Affairs (VA) model; and (xii) the World Health Organization (WHO) performance status. Except for one study , all papers explicitly mention comparisons against these tools in the abstract. The TNM system, created by the American Joint Committee on Cancer (AJCC), is globally used in routine clinical procedures. It categorizes cancer progression and guides subsequent treatment decisions depending on (i) the size and extent of the primary tumor (T), (ii) if it has spread to nearby lymph nodes (N), and (iii) if it has metastasized to distant organs (M) . In this review, two text-based classification studies compared their models against the 7th edition of this staging system (TNM-7): one juxtaposed diagnostic and prognostic (3-year overall survival) predictions for bone metastasis in kidney cancer patients (323 women, 640 men) , while the other compared 1–10-year postoperative survival predictions for patients with colorectal cancer (607 women, 965 men) . Similarly, seven papers resorted to the 8th edition of AJCC TMN (TNM-8), its revised and updated version. On the one hand, in four articles, the models were only compared against this system. Two analyzed their text- and regression-based models to predict cancer-specific survival for esophageal (500 patients, 150 women, 350 men) and lung tumors (1 182 individuals, 642 female, 540 male) . The other two concerned the evaluation of classification models. Using preoperative images and descriptive data, one compared 2-year overall survival and 1-year recurrence-free survival predictions for patients with pancreatic cancer (27 female, 26 male) . The other compared risk stratification performance for overall survival for lung cancer patients (39 women, 133 men) between their model and the TMN-8 system using only text-based data . On the other hand, in three text-based studies, models were compared against TNM-8 and other tools. One paper also contrasted model performance for recurrence, recurrence-free survival, and overall survival for lung cancer patients (71 women, 88 men) with the WHO performance status, often used in oncology to determine patients' overall health status, prognosis, and the ability to tolerate treatment . This scaling system ranges from 0 to 4, where 0 represents no symptoms and pre-disease performance, and 4 translates to total disability. In the second article, predictions of overall postoperative survival were benchmarked against TNM-8 and LCSGJ (42 liver cancer patients, 12 women, 30 men) . LCSGJ is a group of Japanese medical professionals specializing in diagnosing and treating liver cancer, recognized as a leading authority in this cancer research field. Lastly, the third study describes the development of three risk models for breast cancer patients (150 women) : (i) fracture, whose predictions were contrasted with those generated by FRAX; (ii) osteoporosis, compared against and FRAX and OSTA; (iii) and survival, benchmarked against TNM-8. FRAX is a web-based tool designed to stratify 10-year bone fracture risk, and OSTA assesses the risk of osteoporosis in Asian populations . The Brock University (also known as PanCan) model is a logistic regression model devised to assist in risk stratification for lung cancer. It is recommended in the British Thoracic Society guideline as a tool to decide if nodules measuring 8 mm or more in maximum diameter should be assessed further with PET-CT . Here, it was applied in one of the selected papers to compare predictions of malignancy risk for lung cancer from CECT and NECT scans (1 397 images, 1187 patients, unknown gender proportion) . In addition to the Brock Model, comparisons in a second paper (978 CTs, 493 patients, 297 women, 196 men) were also performed against three other tools: (i) the Mayo model, which the Mayo Clinic developed to assess cancer prognosis and predict patient outcomes; (ii) the PKU model, created by the Peking University; and (iii) the VA model, which includes a comprehensive cancer care system that aims to provide high-quality, evidence-based care to veterans with cancer . The mGPS scale is a validated scoring system formulated to assess the prognosis of patients with advanced or metastatic cancer based on nutritional and inflammatory markers . In this review, it was used to establish clinical utility for a text-based classification model developed to predict overall survival for patients with unresectable pancreatic tumors (22 patients, 8 women, 14 men) . PI-RADS is a standardized system for interpreting and reporting findings from prostate MRI scans, created to guide clinical decision-making in diagnosing and treating prostate cancer. In this context, it was contrasted against a model developed to stratify low- and high-risk patients (39 and 14 men, respectively) . PLCOm2012 is a validated risk score that uses logistic regression to predict the probability of lung cancer occurrence within six years based on demographic and clinical information . It was the chosen comparator in a study predicting 12-year lung cancer incidence using low-dose CT images and patients’ age, sex, and smoking status (5493 images and patients, 2456 women, 3037 men) . Finally, RECIST is a set of guidelines used to evaluate the response of solid tumors to treatment in clinical trials and clinical practice. It was compared against two classification models: one aimed at detecting pathological downstaging in advanced gastric cancer patients from CECT images (86 patients and images, 23 women, 27 men) ; the other was designed to predict pathological tumor regression grade response to neoadjuvant chemotherapy in patients with colorectal liver metastases from MRI scans (61 images, 25 patients, 13 female, 12 male) . A few performance metrics were reported for the comparisons between the models developed in the selected papers and routinely used clinical tools, with an average of 3 metrics reported per document (range = 1 – 6). Here, the most frequently calculated metrics were AUC ( n = 11) and sensitivity ( n = 8), but PPV ( n = 5), C-index ( n = 4), specificity ( n = 4), accuracy ( n = 3), NPV ( n = 3), Brier Score ( n = 2) and F1-score ( n = 1) were also used in the evaluations. Fifty-one papers (91.1%) describe models developed for primary tumor-related assessments. These include cancers of the CNS (brain ), digestive (colorectal , esophageal , gastric , and hepatic malignancies), endocrine (pancreas and thymus ), genitourinary (bladder , cervix , prostate , and uterus ), and integumentary (breast and skin ) systems, respiratory system and associated tissues (larynx , lung , mesothelium , and nasopharynx ), and the skeleton (cartilages and bones ). Central nervous system Three retrospective studies were developed to diagnose brain cancers using MRI scans, amounting to 1 084 patients and 64 459 images, resulting in an average sensitivity of 81.97% and specificity of 91.63 (Table ) . The first involved the following conditions: acoustic neuroma, pituitary tumor, epidermoid cyst, meningioma, paraganglioma, craniopharyngioma, glioma, hemangioblastoma, metastatic tumor, germ cell tumor, medulloblastoma, chordoma, lymphomas, choroid plexus, papilloma, gangliocytoma, dysembryoplastic neuroepithelial tumor, and hemangiopericytoma . The CNN-based model was trained on images from 37 871 patients and externally validated using 64 414 T1-weighted, T2-weighted, and T1c MRI scans from 1039 subjects (600 female, 349 male) from three institutions. Its diagnostic performance was compared against nine neuroradiologists (5 to 20 years of experience) to assess clinical utility. This CNN classified brain tumors with high accuracy, sensitivity, and specificity, performing particularly well in identifying gliomas, which are difficult to diagnose using traditional imaging methods. When aided by the model, the neuroradiologists' accuracy increased by 18.9%, which was still lower than the model alone. AI assistance also boosted the neuroradiologists' sensitivity, specificity, and PPV. However, only three types of scans were considered, training data was obtained from a single center, and few rare tumors were included. In the second paper, the authors explored the combination of 9 different ML models – NB, logistic regression, SVM with a polynomial kernel, kNN (k = 3), DT, MLP, RF, AdaBoost, and bootstrap aggregating – to distinguish between different types of brain tumors (glioblastoma, anaplastic glioma, meningioma, primary central nervous system lymphoma, and brain metastasis) . MRI techniques were analyzed in a combination of 135 classifiers and radiomics: cMRI, advMRI, phyMRI, cMRI + phyMRI, and advMRI + phyMRI. A dataset of 167 patients was used for training, and temporal validation was performed on 20 subjects. Physiological MRI scans (phyMRI), named radiophysiomics, achieved the best results using AdaBoost with cMRI and phyMRI and RF with phyMRI. Both models surpassed the radiologists in AUC and F1-score but were outperformed in sensitivity and specificity. The AdaBoost model also had a higher PPV than the clinicians. However, this was a single-center, retrospective study, and the application and tuning of the models were performed manually. The third study evaluated the usefulness of preoperative contrast-enhanced T1- and T2-weighted MRI in differentiating low-grade gliomas (LGG) from glioblastomas (GBM) . The authors trained a radiomics-based RF classifier on 142 patients from 8 American centers and externally validated it on 25 patients from another institution (all from The Cancer Imaging Archive). The results showed that the machine learning algorithm was highly accurate in differentiating between GBM and LGG based on preoperative contrast-enhanced MRI scans, surpassing two neuroradiologists (15 and 1 year of experience) and a radiologist (3 years of experience). However, few patients from a public database were collected, possibly resulting in selection bias (non-random selection). Digestive system Malignancies of the digestive system – highlighted in Table – were the most comprehensively studied (17/56, 30.4%), encompassing colorectal ( n = 7, 41.2%), esophageal ( n = 3, 17.6%), gastric ( n = 5, 29.4%), and liver ( n = 2, 11.8%) cancers. Colorectal Cancer Three sets of articles addressed colorectal cancers (7 papers). The goal of the first set, consisting of four multi-institutional retrospective studies, was its diagnosis, averaging a sensitivity of 77.3% and a specificity of 93.2% for tests on 995 images from different sources . The authors in developed an ensemble of three CNNs (Inception-v3, ResNet-50, and DenseNet-161) to predict the histology of colorectal neoplasms based on white light colonoscopic images. The ensemble model transferred knowledge from digital photography and learned with colonoscopic images to classify the images into one of 4 different pathologic categories: normal (healthy), adenoma with low-grade dysplasia (A-LGD), adenoma with high-grade dysplasia (A-HGD), and adenocarcinoma. The system's diagnostic performance was compared against four experts (more than five years of experience) and six trainees (less than two years). In the external validation dataset (400 images, 100 of each type), the CNN-CAD model achieved high accuracy in predicting the histology of the lesions. Compared to endoscopists, the model's performance was slightly better than the experts' and significantly outperformed the trainees. In addition, the authors used Grad-CAM to create a heatmap highlighting the regions of the input image that were most relevant to the network's decision. However, only one image per polyp was used; consequently, tumors that cannot be contained within a single image were neglected. The second work concerns the external validation and clinical utility assessment of EndoBRAIN, an AI-assisted system to classify colorectal polyps into malignant or non-malignant. EndoBRAIN was trained with 69 142 endocytoscopic images from patients with colorectal polyps from five academic centers in Japan. Its clinical validity had previously been confirmed in a single-center prospective study. However, since its implementation depends on governmental regulatory approval, the current study compared EndoBRAIN's diagnostic performance against 30 endoscopists (20 trainees, 10 experts) using stained and narrow-band endocytoscopic images in a web-based trial. The authors found their CADx tool accurately differentiated neoplastic from non-neoplastic lesions, outperforming all endoscopists for stained images, achieving similar performance in narrow-band images, and being accepted for clinical use. The third diagnostic model concerns the development of a deep learning model to predict the revised Vienna Classification in colonoscopy, which categorizes colorectal neoplasms into different levels of malignancy using standard endoscopic colonoscopy images . Several CNN architectures were compared, namely AlexNet, ResNet152, and EfficientNet-B8, with ResNet152 being chosen as the prediction model due to its higher accuracy and fastest inference time. The model was trained using 56,872 colonoscopy images (6775 lesions) and validated on 255 images (128 lesions) from 7 external institutions in Japan. The authors also compared diagnostic performance against endoscopists (five novices, three fellows, and four experts). The AI system’s sensitivity and specificity exceeded that of all endoscopists. Nevertheless, the model cannot discriminate between high-grade dysplasia and invasive cancer (categories 4 and 5 of the revised Vienna Classification), and only binary classification is supported. In the fourth document, the authors tested two pre-trained radiomics-based CNN architectures (Inception-ResNet-v2 and ResNet-152) to classify colorectal neoplasms into three types of sets automatically: 7-class (T1-4 colorectal cancer, high-grade dysplasia, tubular adenoma, vs. non-neoplasms), 4-class (neoplastic vs. non-neoplastic – advanced vs. early CRC vs. adenoma vs. healthy), and 2-class (neoplastic versus non-neoplastic and advanced versus non-advanced lesions) . The CNNs were trained on a South Korean dataset (3453 colonoscopy images, 1446 patients) and temporally and geographically validated on 240 images (and as many patients) from another institution. CAM was used to highlight its decisions. The best-performing architecture was ResNet-152 for 7-way and 4-way diagnoses, but Inception-ResNet-v2 achieved better results on binary classifications. In addition, the model's performance was compared with one novice and two experienced endoscopists with six months and more than five years of colonoscopy experience, respectively. Although resulting in high accuracy, neither CNN architecture could outperform the endoscopists. Furthermore, this retrospective study only considered three types of diseases and white-light colonoscopy images. The second set of articles was devoted to predicting outcomes from MRI scans in patients with colorectal cancer undergoing neoadjuvant chemotherapy (NCRT), accruing 143 MRIs from 118 patients and a mean AUC and accuracy of 0.77 and 81.9%, respectively . The first was a prospective study using a multipath CNN on MRI scans (diffusion kurtosis and T2-weighted) . The authors used a dataset of 412 patients (290 for development and 93 for temporal validation) with locally advanced rectal adenocarcinoma scheduled for NCRT. The researchers developed three multipath CNN-based models: one to preoperatively predict pathologic complete response (pCR) to neoadjuvant chemoradiotherapy, one to assess tumor regression grade (TRG) (TRG0 and TRG1 vs. TRG2 and TRG3), and one to predict T downstaging. In addition, the authors evaluated the models' utility by comparing two radiologists' – with 10 and 15 years of experience – performance with and without their assistance. The results showed excellent performance in predicting pCR, superior to the assessment by the two radiologists, whose error rate was also reduced when assisted by the DL model. Although with lower performance, the TRG and T downstaging models also achieved promising results with an AUC of 0.70 and 0.79, respectively (although not outperforming the clinicians). Nevertheless, this monoinstitutional research required manual delineation, and interobserver variability was not analyzed. Moreover, further validation studies are necessary to assess performance with different MRI scanners. The second group of researchers developed an MRI-based CNN (DC3CNN) to predict tumor regression grade (assessment of tumor size) in response to NCRT in patients with colorectal liver metastases . The authors used prospective internal (328 lesions from 155 patients) and retrospective external cohorts (61 images, 25 patients) to collect pre and post-treatment T2-weighted- and DW-MRI scans. The model surpassed the diagnostic accuracy of RECIST, the most commonly used criteria for clinical evaluation of solid tumor response to chemotherapy. However, the study was retrospective, and further studies are needed to validate its performance in larger ethnically diverse patient populations. Lastly, only one model assessed postoperative survival of colorectal cancer using text-based data . The model was trained on the SEER database (364 316 patients) and externally validated (temporally and ethnically) on a Korean dataset (1 572 subjects, 607 women, 965 men). The authors compared 4 ML algorithms, namely logistic regression, DTs, RFs, and LightGBM, to obtain an optimal prognostic model. The best-performing model – LightGBM – outperformed TNM-7 in predicting survival for all tested periods (1, 2, 3, 4, 5, 6, 8, and 10 years). Still, data were collected retrospectively from a public database and a single institution using only text-based data, so prospective studies are necessary, and clinicopathological, molecular, and radiologic variables should also be incorporated. Esophageal Cancer Three studies involved esophageal cancers. Two papers studied neoplasia detection in patients with Barrett’s esophagus, a medical condition resulting from long-term acid-reflux damage, causing esophageal tissue lining to thicken and become irritated, increasing cancer risk . The same group of researchers conducted both studies: the first paper describes model development for detection , while the second encompasses its tuning and update to include location . The authors proposed a multi-stage pretraining approach that involved training a CNN learning model on 494,355 gastrointestinal images before fine-tuning it on a smaller dataset of medical images specific to Barrett's neoplasia. The model was trained with images from different endoscopes. In the first paper , using data from separate institutions, the authors used a retrospective dataset of early Barrett’s neoplasia for primary validation (80 patients, unknown proportion) and a second prospectively acquired dataset (80 patients and images) to compare their model’s performance against fifty-three endoscopists (17 seniors, 8 juniors, 18 fellows, and 10 novices). In the second paper, the researchers validated their model on three prospective datasets: one with clinically representative images (80 individuals), one with subtle lesions (80 subjects), and one in a live setting with dysplastic and nondysplastic patients (ten each) . It showed excellent performance on the three external validation datasets, and its detection and location performances were also compared against the 53 experienced endoscopists on the subtle lesions. The CAD system outperformed all 53 endoscopists for all tested metrics in both papers, obtaining an average accuracy, sensitivity, and specificity of 87.9%, 91.7%, and 84.16%, respectively. The models developed in both articles performed similarly and were tested in clinically realistic scenarios, with an average accuracy, sensitivity, and specificity of 88.45%, 91.25%, and 85.63%, respectively, enhancing CNNs’ predictive power. Additionally, a retrospective study evaluated cancer-specific survival for esophageal adenocarcinoma and squamous cell carcinoma according to individual treatment recommendations . The authors trained a deep-, regression-, and text-based survival neural network (DeepSurv, multi-layer perceptron) using the SEER database (6855 patients) and validated it on 150 women and 350 men from their institution (China). Additionally, prognostic performance was compared against TNM-8, having exceeded it. However, only one medical center was used, and research was not performed in an accurately representative clinical setting. Gastric Cancer In five articles, models were developed for gastric-related tasks. The first three studies had a diagnostic component. In the first research, the authors developed two models – GastroMIL and MIL-GC –, training them on WSIs from H&E slides magnified 30 times collected from TCGA and a Chinese institution. They also temporally and geographically validated them with 175 WSIs from 91 patients from NHGRP . GastroMIL used an ensemble of a CNN and an RNN to distinguish gastric cancer from normal gastric tissue images. Its performance was compared against one junior and three expert pathologists. MIL-GC, a regression-based model, was created to predict patients’ overall survival. Besides WSIs, MIL-GC uses clinical data, namely survival state, overall survival time, age, sex, tumor size, neoplasm histologic grade, and pathologic T, N, M, and TNM-8 stages. The deep learning models achieved high performance in both tasks, with an overall accuracy of 92% for diagnosis and a C-index of 0.657 for prognosis prediction in the external dataset. Compared to human performance, GastroMIL outperformed the junior pathologist in accuracy and sensitivity but was surpassed by the experienced pathologists (in accuracy, sensitivity, and specificity). However, the tested cohorts were retrospective and had unbalanced survival times, and clinical utility was not evaluated for the prognostic model. The second study used a CNN (ResNet-50) for real-time gastric cancer diagnosis . The model was developed with 3 407 endoscopic images of 666 patients with gastric lesions from two institutions. The DCNN model was tested on a temporally different dataset of endoscopic videos from a separate institution (54 videos from 54 patients), and performance was compared against 20 endoscopists (6 experts, 14 novices). The model achieved better performance than any of the endoscopists, and diagnostic accuracy, sensitivity, and specificity increased for all clinicians while assisted by the model. Nevertheless, despite decreasing the aggregate diagnostic time from 4.35 s to 3.01 s, it increased experts’ by 0.10 s. In addition, the diagnostic model was only tested on high-quality images, and the validation dataset was small and domestic. Although slightly less sensitive than Gastro-MIL (93.2% vs. 93.4%), the model developed in achieved the best accuracy and sensitivity, evidencing that endoscopic images and videos might be more appropriate to diagnose gastric cancer. The third model was created using endoscopic ultrasonography images (EUS) for the differential diagnosis of gastric mesenchymal tumors, including GISTs, leiomyomas, and schwannomas . This model was trained with EUS from three Korean institutions and tested on a temporally separate set of 212 images from the same centers (69 patients, 38 female, 31 male). A sequential analysis approach was adopted using two CNNs: the first classifies the tumor as GIST or non-GIST; for non-GISTs, the second CNN classifies it as either a leiomyoma or schwannoma. The results were compared against junior ( n = 3, less than 200 examinations) and expert endoscopists ( n = 3, more than 500 examinations) who evaluated the same images, having surpassed them in both types of classification. However, this study was retrospective and involved a small number of patients, and the types of equipment used to perform ultrasounds varied considerably across the facilities. The last two papers concerned outcome predictions. The first presents a multi-institutional study that uses multitask deep learning to predict peritoneal recurrence and disease-free survival in gastric cancer patients after curative-intent surgery based on CT images . Supervised contrastive learning and a dynamic convolutional neural network were combined to achieve this purpose, and Grad-CAM was used to explain the model’s decisions. The model included CT scans from three patient cohorts, and external validation included 1 043 patients (329 women, 714 men) and as many images from another Chinese institution. In addition, the authors investigated clinician performance for peritoneal recurrence prediction with and without the assistance of the AI model, having found that performance was significantly enhanced after integrating it and that the model alone surpassed all physicians. Nonetheless, only East Asian patients were included in this retrospective study, which was not performed in a real clinical setting, and sensitivity was only reported for one of the clinicians. The last study discusses the use of CT radiomics to predict the response of advanced gastric cancer to neoadjuvant chemotherapy and to detect pathological downstaging at an early stage . The authors trained two SVCs on 206 patients who had undergone three or four cycles of chemotherapy and externally validated them on two testing cohorts, which were also used for benchmarking detection against RECIST. The first testing cohort consists of temporal validation (40 patients and CTs, 13 women, 27 men), while the second differs in the number of chemotherapy cycles (46 individuals and CTs, 10 women, 36 men). Performance for the detection model surpassed RECIST in both cohorts, and, except for sensitivity, the response prediction model also produced positive results. However, retrospective data and a small, unbalanced sample size constrain this study, which was not evaluated in a clinically representative setting. Liver Cancer Two models were developed for liver cancer-related predictions. The first aimed at classifying hepatocellular carcinomas and cholangiocarcinomas (differential diagnosis) . The authors developed a web-based (cloud-deployed AI model and browser-based interface) CNN (DenseNet architecture) using WSIs from H&E slides magnified 40 times and used Grad-CAM to increase the model’s explainability. The training dataset was obtained from TCGA (70 slides from 70 unique patients). The external validation dataset was collected from the Department of Pathology at Stanford University Medical Center (80 slides from 24 women and 56 men). The model achieved a diagnostic accuracy of 84.2% in the validation cohort. Diagnostic performance was also compared to that of 11 pathologists. Except for the two unspecified pathologists, performance (AUC) increased for all clinicians when assisted by this tool. However, the pathologists only had access to the WSIs (as opposed to being complemented with clinical data), the model required manual intervention for patch selection, and the study was retrospective with a small sample size (development and external validation with a total of 150 WSIs and patients). The second model was designed to predict three-year overall survival for intrahepatic cholangiocarcinoma patients after undergoing hepatectomy using an ensemble of Random Forests, XGBoost, and GBDT . Using a single quaternary Chinese institution, the authors collected 1390 patients for training and 42 patients (12 women, 30 men) for external temporal validation. Results were compared against the TNM-8 and LCSGJ staging systems, with model performance exceeding that of the routinely used tools. Nonetheless, this was a monoinstitutional endeavor limited to a small number of Asian patients. Furthermore, only six prognostic factors were used: carcinoembryonic antigen, carbohydrate antigen 19–9, alpha-fetoprotein, pre-albumin, and T and N stages. Endocrine system Three papers described prognostic models for cancers in organs affecting the endocrine system (pancreas and thymus), whose results are depicted in Table . Pancreatic Cancer The first two studies assessed survival for pancreatic ductal adenocarcinoma (PDAC) patients but adopted disparate research designs and clinical inputs . The first group of researchers used a regression-based random survival forest model to prognosticate patients with advanced pancreatic cancer . Aimed at predicting overall survival for patients with unresectable PDAC, the model was developed with clinical data and CT scans from a German institution (203 patients). It was temporally and geographically validated using only text-based clinical data from patients with liver metastases from the same country (8 women, 14 men) and compared against mGPS, having outperformed it. Additionally, the authors used SHAP to explain their model, finding that inflammatory markers C-reactive protein and neutrophil-to-lymphocyte ratio had the most significant influence on its decision-making. Nonetheless, only twenty national patients were used to validate the model externally, and different types of inputs were used for training and testing. The second set of authors used an ensemble of ML methods – ANN, logistic regression, RF, GB, SVM, and CNNs (3D ResNet-18, R(2 + 1)D-18, 3D ResNeXt-50, and 3D DenseNet-121) – to predict 2-year overall and 1-year recurrence-free survival for PDAC patients after surgical resection . The classifier was trained and tuned using 229 patients and temporally validated with CECT images and seventeen clinical variables from the same South Korean institution (53 CECTs from 27 women and 26 men). Grad-CAM was used to explain the model’s decisions, and comparisons were made against TMN-8 to evaluate clinical utility. Although more accurate, specific, and with a higher PPV than TNM-8, it was less sensitive for both predictions and had a lower NPV for overall survival prediction. Furthermore, tumor margins were manually segmented, and the model did not consider histopathologic data. Thymic Cancer One study was designed for the simplified risk categorization of thymic epithelial tumors (TETs), rare cancer forms . Here, three types of tumors were evaluated: low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC). Three triple classification models were developed using radiomic features extracted from preoperative NECT images and clinical data from 433 patients: (i) LRT vs. HRT + TC; (ii) HRT vs. LRT + TC; (iii) TC vs. LRT + HRT. The authors compared several CT-based classifiers: logistic regression, linear SVC, Bernoulli and Gaussian Naïve Bayes, LDA, Stochastic Gradient Descent, SVM, DT, kNN, MLP, RF, AdaBoost, gradient boosting, and XGBoost. Combined with clinical data, the SVM model demonstrated the best performance for predicting the simplified TETs risk categorization. In addition, the SVM model was validated in a temporally different cohort using images from 5 types of scanners (76 scans and patients, 33 women, 48 men). Finally, its diagnostic performance was compared against three radiologists (3, 6, and 12 years of experience), having exceeded them regarding AUC (0.844 versus 0.645, 0.813, and 0.724) but not for other metrics (accuracy, sensitivity, and specificity). Caveats include the reduced amount of patients, low number of thymic carcinomas, and incomplete automation of the models. Genitourinary system Table illustrates the models developed for genitourinary cancers, including the bladder, cervix, prostate, and uterus. Bladder Cancer From the retrieved models, only one assesses outcomes for primary bladder cancers . This article presents a CNN-based strategy to predict the muscular invasiveness of bladder cancer based on CT images and clinical data. The model was developed with 183 patients. Its performance was tested on an independent institution's temporally and geographically different validation cohort of patients with urothelial carcinoma (13 women, 62 men, and as many images). The model’s predictions were juxtaposed with diagnoses from two radiologists with nine and two years of experience, having achieved better accuracy and specificity than the two clinicians but a lower sensitivity. Overall, the authors found that the deep learning algorithm achieved a high accuracy rate in predicting muscular invasiveness, an essential factor in determining the prognosis and treatment of bladder cancer. However, the study is limited by its retrospective nature, exclusion of tumors not visible in CT images, and small sample size. Cervical Cancer Similarly, primary tumors of the cervix were only screened in one paper . Here, the authors trained an ensemble of convolutional and recurrent neural networks on whole-slide images from patients' cervical biopsies and 79 911 annotations from five hospitals and five kinds of scanners. The system comprises (i) two CNNs – the first scans WSIs at low resolution and the second at high resolution – to identify and locate the ten most suspicious areas in each slide; (ii) and an RNN to predict corresponding probabilities. The system classifies squamous and glandular epithelial cell abnormalities as positive (neoplastic) and normal findings as negative for intraepithelial lesions or malignancies (non-neoplastic). The method was externally validated on multi-center independent test sets of 1 565 women (1 170 without additional conditions and 395 with HPV), and classification performance was compared against three cytopathologists. Although obtaining promising results and surpassing clinician performance for both types of women, the authors highlight that the model was designed for the general women population, implying that further refinements are required for specific comorbidities. Prostate Cancer Two models were developed for prostate-cancer-related classifications using multiparametric MRI scans . In the first paper, the authors describe the development of Autoprostate, a system employing deep learning to generate a report summarizing the probability of suspicious lesions qualifying as clinically significant prostate cancer (CSPCa) . The authors trained their approach on the PROSTATEx dataset (249 men), externally validated it on the PICTURE dataset (247 patients), and compared its reports (with post-thresholding and false positive reduction) to those generated by a radiologist with ten years of experience. The system achieved a high level of agreement with the human reports (surpassing the radiologist in AUC and specificity) and could accurately identify CSPCa. However, this study was retrospective, a single (public) dataset was used for external validation, and only two types of prostate lesions were considered. The second article presented an ML-based approach for prostate cancer risk stratification using radiomics applied to multiparametric MRI scans . In this retrospective, monoinstitutional study, the authors compared seven classification algorithms: logistic regression, linear, quadratic (Q), cubic, and Gaussian kernel-based SVM, linear discriminant analysis, and RF. After training with 68 patients, the best-performing method – QSVM – was validated on a temporally independent dataset (14 high- and 39 low-risk patients). Its performance was compared against PI-RADS v2, having found that the model could accurately predict the risk of clinically significant prostate cancer. Although the classifier performed equivalently to PI-RADS v2 regarding AUC, it performed substantially better in class-specific measures (F1-score, sensitivity, and PPV), especially for the high-risk class. However, the study is limited by its retrospective nature and small sample size from a single source. Uterine Cancer Two studies for primary cancers focused on classifying lesions of the endometrium, the layer of tissue lining the uterus . In the first article, using 245 women as the training cohort, the authors compared nine models – logistic regression (LR), SVM, stochastic gradient descent, kNN, DT, RF, ExtraTrees, XGBoost, and LightGBM – to obtain an optimal algorithm for differential diagnosis (malignant versus benign tumors) . A radiomics score (radscore) was computed for the best-performing algorithm (logistic regression), and four models were selected using different combinations of T1-weighted, T2-weighted, and DWI MRI features: (i) the radiomics model; (ii) a nomogram, combining the radscore and clinical predictive parameters; (iii) a two-tiered stacking model, where the first tier was the clinical model and the optimal radiomics model (LR), and the second tier used the output of the first tier as the input of the multivariate LR; and (iv) an ensemble model, where the predictions obtained from the preceding clinical model and radiomics model were calculated by an accuracy-weighted average. The results showed that all four models accurately differentiated stage IA endometrial cancer and benign endometrial lesions. Furthermore, during external validation (44 MRIs from 44 women), the authors found that the nomogram had a higher AUC than the radiomics model, revealing more stable discrimination efficiency and better generalizability than the stacking and ensemble models and a radiologist with 30 years of experience (except in sensitivity). Nevertheless, data was collected from two same-country centers (Chinese institutions), only standard radiomics features were extracted, and lesions were manually segmented, which is highly time-consuming. The second paper encompassed a global-to-local multi-scale CNN to diagnose endometrial hyperplasia and screen endometrial intraepithelial neoplasia (EIN) in histopathological images . The researchers trained the CNN using a large annotated dataset (6 248 images) and tested it on a temporally different set of patients (1631 images, 135 specimens, 102 women). They found that it performed well in diagnosing endometrial hyperplasia and detecting EIN, outperforming a junior pathologist (2 years of experience) and obtaining comparable performance to a mid-level and a senior pathologist (6 and 25 years of experience, respectively). The authors used Grad-CAM to emphasize the regions the model deemed relevant for diagnosis. However, this retrospective study only used histopathological images (as opposed to WSIs). Besides, it focused solely on classifying healthy slides, hyperplasia without atypia, and endometrial intraepithelial neoplasia, thus neglecting the differentiation between benign lesions and endometrial cancer. Integumentary system As illustrated in Table , five papers studied cancers of the integumentary system, focusing on the breasts and skin. Breast Cancer Three studies developed models for cancers originating in the breasts, each with a specific purpose and using different clinical modalities. In , several text-based machine learning classifiers, namely, DTs, RFs, MLPs, logistic regression, naïve Bayes, and XGBoost, were compared to establish optimal classifiers for osteoporosis, relative fracture, and 8-year overall survival predictions. The algorithm was trained on 420 patients from a Chinese institution and geographically validated on 150 women from a separate local institution. The osteoporosis model was compared against OSTA and FRAX, the fracture model against FRAX, and the prognostic model against TNM-8. The results showed that the XGBoost classifier performed the best for the three tasks and outperformed the other clinical models. Additionally, for explainability, the authors also used SHAP for feature importance analysis for each model: (i) age, use of anti-estrogens, and molecular type are the most predictive of osteoporosis; (ii) osteoporosis, age, and bone-specific alkaline phosphatase are the best predictors for fracture; and (iii) N-stage, molecular type, and age have the highest prognostic value for overall survival. Despite its positive results, prospective studies are needed to validate the model in more diverse patient populations. In , authors explored how combining AI and radiologists can improve breast cancer screening. Using 213 694 retrospectively collected mammograms (X-ray images) from 92 585 women, it was found that the combination of radiologists and AI (CNN-based classifier) achieved the highest accuracy in detecting breast cancer. The sensitivity and specificity of the standalone AI system were significantly lower than an unaided radiologist. However, the decision-referral approach outperformed the unaided radiologist on both sensitivity and specificity for several tested thresholds. Nonetheless, the study only included mammogram images and did not consider other factors, such as patient history or clinical data, which may impact the accuracy of breast cancer screening. Furthermore, the AI algorithm used in the study was not optimized for clinical use and may require further development and testing before it can be implemented in a clinical setting. Lastly, the work developed in entailed diagnosing non-cystic benign and malignant breast lesions from ultrasonographic images. Radiomic features were extracted from the ultrasound images, and a random forest model was trained with 135 lesions and externally validated to predict malignancy for each lesion. Moreover, the performance of an experienced radiologist (8 years) was compared with and without the model’s assistance. Although not with statistical significance, the radiologist's assessments improved when using the AI system. However, the final validation population was small (66 ultrasounds from 57 women) and showed different proportions of malignant lesions. Skin Cancer Two models were developed to diagnose skin tumors using photographs, producing an average AUC, sensitivity, and specificity of 0.89, 77.1%, and 81.74% . The first was a retrospective validation study assessing the performance of deep neural networks in detecting and diagnosing benign and malignant skin neoplasms of the head and neck, trunk, arms, and legs . In a previous study, the authors trained an ensemble of CNNs (SENet + SE-ResNeXt-50 + faster RCNN) with 1 106 886 image crops from South Korean patients to detect potential lesions and classify skin malignancies. Here, performance was tested on three new temporal and geographical validation datasets of skin lesions (two national, one international, 46 696 photographs from 10 876 patients): (i) one dataset was used to compare the model’s classification performance against 65 attending physicians in real-world practice; (ii) one’s goal was to evaluate classification performance against with 44 dermatologists in an experimental setting; and (iv) the last two were meant to predict exact diagnosis (1 of 43 primary skin neoplasms) in a local (South Korean) and an international (UK, 1 300 images) dataset, with the first also being compared against physicians. In (i) and (ii), performance was calculated for high specificity and high sensitivity thresholds. The algorithm was more sensitive and specific than the dermatologists in the experimental setting. However, attending physicians outperformed it in real-world practice in all tested metrics (sensitivity, specificity, PPV, and NPV). In addition, the model only dealt with high-quality clinical photographs, and there was a lack of ethnic diversity in the study population. The second paper presented a set of CNNs – DenseNet-121 (Faster R-CNN and deep classification network) – developed to detect malignant eyelid tumors from photographic images . The researchers used a 1 417 clinical images dataset with 1 533 eyelid tumors from 851 patients across three Chinese institutions (one for development and two for external validation). Besides using Grad-CAM for interpretation, the AI’s performance on the external dataset (266 pictures from 176 patients) was compared to three ophthalmologists: one junior, one senior, and one expert (3, 7, and 15 years of experience, respectively). It surpassed the junior and senior ophthalmologists’ performance and achieved similar results to the expert. Notwithstanding its potential, the system still needs evaluation on non-Asian populations and prospectively acquired datasets, and it was only developed for detection (it cannot provide a specific diagnosis). Respiratory system and associated tissues Thirteen papers addressed respiratory system cancers, which predominantly concerned the lungs, but also included the larynx, nasopharynx, and mesothelium (Table ). Lung Cancer Ten approaches were developed for lung cancer assessments. The first document describes a validation study of a CNN-based tool (DenseNet) designed to predict the malignancy of pulmonary nodules . The model was previously trained with the NLST dataset and was now externally validated in 3 UK centers with different CT scanners (1 397 CECTs and NECTs, 1 187 patients of unknown gender ratio). The authors also evaluated its clinical utility by comparing it to the Brock Model. Although slightly less specific than the Brock model, the detection algorithm developed by the authors had a higher AUC and sensitivity. Despite having undergone international validation, prospective studies in ethnically diverse populations are still amiss. The second paper involved developing and validating a model to predict the malignancy of multiple pulmonary nodules from CT scans and eleven clinical variables . The study analyzed data from various medical centers. Ten ML methods were compared to identify the best malignancy predictor: AdaBoost, DT, Logistic Regression, Linear SVM, Radial Basis Function Kernel SVM, NB, kNN, Neural Net, Quadratic Discriminant Analysis, RF, and XGBoost. The best-performing model – XGBoost – was tested on three datasets. The first was retrospective, compiled from 6 institutions (five from China and one from South Korea), used for primary external validation (220 patients, 583 CT scans), and compared against four well-established models: Brock, Mayo, PKU, and VA. The second retrospective dataset was used for generalizability, containing patients from a Chinese institution with solitary pulmonary nodules (195 patients and images, 110 women, 85 men), whose results were also compared against the four just-mentioned models. The third and last dataset included data from 4 Chinese centers and was collected prospectively for secondary validation and comparisons against clinicians (200 CTs, 78 patients, 51 women, 27 men). This comparison involved three thoracic surgeons and one radiologist, who achieved an average sensitivity of 0.651 and specificity of 0.679. The model significantly outperformed this average and each clinician’s AUC, as well as in all comparisons against the routinely used models. In addition, SHAP was used to identify the most predictive nodule characteristics, finding that the model's most predictive features were nodule size, type, count, border, patient age, spiculation, lobulation, emphysema, nodule location, and distribution. Nonetheless, besides not reporting individual clinician sensitivity and specificity in the prospective cohort, the drawbacks of this study include only assessing typical high-risk patients and the lack of validation with different ethnicities. The work in involved a CNN-based model for predicting the presence of visceral pleural invasion in patients with early-stage lung cancer. The deep learning model was trained using a dataset of CT scans from 676 patients and externally validated on a temporally different cohort from the same South Korean institution (141 CTs from 84 women and 57 men). Besides using Grad-CAM to evidence its decisions, this CNN can adapt its sensitivity and specificity to meet the clinical needs of individual patients and clinicians. The model achieved a performance level comparable to three expert radiologists but did not surpass it except in PPV. Besides, these are results from a monoinstitutional retrospective study where geographical validation was not performed. In addition to using a small number of patients, data was also imbalanced, and the model was not fully automated (required manual tumor annotations). The fourth article concerns developing an EfficientNetV2-based CNN system to predict the survival benefit of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in patients with stage IV non-small cell lung cancer . The model was developed with accessible pre-therapy CT images from five centers and externally validated on a monoinstitutional dataset from a national dataset (China, 92 CTs from 92 patients). The authors also compared radiologists' and oncologists' (three each, 2, 5, and 10 years of experience) performance with and without ESBP. The results showed that, while assisted by the system, all radiologists improved their diagnostic accuracy, sensibility, specificity, PPV, and NPV (except for the trainee oncologist, who achieved better sensitivity without the model). However, prospective studies in ethnically rich cohorts are still necessary to implement this tool in clinical practice. The fifth study aimed at finding optimal predictors of two-year recurrence, recurrence-free survival, and overall survival after curative-intent radiotherapy for non-small cell lung cancer . Ten text-based ML models were trained on 498 patients and compared: ANN, Linear and Non-linear SVM, Generalized Linear Model, kNN, RF, MDA, Partial Least Squares, NB, and XGBoost. The best-performing models were as follows: (i) an ensemble of kNN, NB, and RF for recurrence classification; (ii) kNN for recurrence-free survival prediction; and (iii) a combination of XGBoost, ANN, and MDA for overall survival. The three optimal predictors were externally validated using routinely collected data from 5 UK institutions (159 seniors, 71 women, 88 men) and compared against TNM-8 and WHO performance status. The recurrence and overall survival models outperformed both routinely used systems, but these tools surpassed the recurrence-free survival predictor’s performance. Moreover, this study was retrospective and had a small sample size with missing data. The sixth study was designed to identify high-risk smokers to predict long-term lung cancer incidence (12 years) . In this paper, the authors developed a convolutional neural inception V4 network based on low-dose chest CT images, age, sex, and current versus former smoking statuses. The CNN was trained using patients from the PLCO trial and externally validated on data from the NLST randomized controlled trial (2456 women and 3037 men from 33 USA institutions). The model was also compared against PLCOm2012 to evaluate clinical utility, having exceeded its performance for all assessed metrics (AUC, sensitivity, specificity, PPV, and NPV). However, this study was retrospective, lacked ethnic diversity, and was not evaluated in a clinically realistic scenario. Additionally, information from symptomatic patients was unavailable due to using data from a screening trial. In the seventh article, a CNN-based model was developed for the automated detection and diagnosis of malignant pulmonary nodules on CECT scans . The algorithm was externally validated on four separate datasets with ethnic differences (three from South Korea and one from the USA, amounting to 693 patients and CTs). Furthermore, the diagnostic performance of 18 physicians (from non-radiologists to radiologists with 26 years of experience) was compared while assisted and not assisted by the algorithm for one dataset. The model achieved an excellent performance in the four tested datasets, outperforming all clinicians, and the professionals’ accuracy increased while aided by the model for all tested groups. Nonetheless, the model was undertrained for small nodules (< 1 cm) and trained only for malignant nodule detection for one type of CT (posterior-anterior projections), and the study was retrospective and not representative of a real-world clinical setting. The eighth algorithm consisted of a multilayer perceptron (Feed-Forward Neural Network) paired with a Cox proportional hazards model to predict cancer-specific survival for non-small cell lung cancer . The text-based model was trained using the SEER database and externally validated on patients from a Chinese tertiary pulmonary hospital (642 women, 540 men). It was compared against TNM-8, having outperformed it with statistical significance. Although tested with real-world clinical data, prospective multi-institutional studies are needed before the deep learning model can be used in clinical practice. The ninth article described developing, validating, and comparing three CNN models to differentiate between benign and malignant pulmonary ground-glass nodules (GGNs) . The first CNN only used CT images. The second CNN used clinical data: age, sex, and smoking history. The third was a fusion model combining CTs and clinical features, achieving the best performance. This model was temporally and geographically validated with 63 CT scans from 61 patients (39 women, 22 men). Its classification performance was compared against two radiologists (5 and 10 years of experience) for clinical utility assessment. Despite performing satisfactorily in external validation, the model was surpassed by both clinicians in accuracy, sensitivity, and NPV, only producing higher results for specificity and NPV. Furthermore, this study was retrospective, and validation was neither international nor evaluated in a correct clinical setting. In the tenth and final paper, a Neural Multitask Logistic Regression (N-MTLR) network was developed for survival risk stratification for stage III non-small cell lung cancer . The text-based deep learning system was trained on 16 613 patients from the SEER database and externally validated on subjects from a Chinese institution (172 patients, 39 women, 133 men). The results in the external dataset showed that the DSNN could predict survival outcomes more accurately than TNM-8 (AUC of 0.7439 vs. 0.561). The study results suggest that the deep learning system could be used for personalized treatment planning and stratification for patients with stage III non-small cell lung cancer. However, prospective studies in multi-institutional datasets are still required. Laryngeal, Mesothelial and Nasopharyngeal Cancers Three models were developed to assess tumors of other elements of the respiratory system. In , the authors trained a CNN (GoogLeNet Inception v3 network) with 13 721 raw endoscopic laryngeal images – including laryngeal cancer (LCA), precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT), and healthy tissue – from three Chinese institutions (1 816 patients). External validation was performed on 1 176 white-light endoscopic images from two additional institutions in the same country (392 patients), testing the model for binary classification – urgent (LCA and PRELCA) or non-urgent (BLT and healthy) – and between the four conditions. Predictions for both classification types were compared against three endoscopists (3, 3 to 10, and 10 to 20 years of experience). In two-way classification, the algorithm was less accurate than one endoscopist and less sensitive than two but outperformed all clinicians in four-way diagnostic accuracy. Still, this accuracy was relatively low (less than 80%), the study was retrospective, and all tested laryngoscopic images were obtained by the same type of standard endoscopes. Cancers of the mesothelium were approached in a single retrospective multi-center study . The paper uses DL to distinguish between two types of mesothelial cell proliferations: sarcomatoid malignant mesotheliomas (SMM) and benign spindle cell mesothelial proliferations (BSCMP). SMMs and BSCMPs are difficult to distinguish using traditional histopathological methods, resulting in misdiagnoses. The authors propose a new strategy—SpindleMesoNET—that uses an ensemble of a CNN and an RNN to analyze WSIs of H&E-stained mesothelial slides magnified 40 times. The model was trained on a Canadian dataset, externally validated on 39 images from 39 patients from a Chinese center, and compared against the diagnostic performance of three pathologists on a referral test set (40 WSIs from 40 patients). The accuracy and specificity of SpindleMesoNET on the referral set cases (92.5% and 100%, respectively) exceeded that of the three pathologists on the same slide set (91.7% and 96.5%). However, the pathologists were more sensitive than the diagnostic model (87.3% vs. 85.3%). In addition, the study had a minimal sample size, and only AUC was reported for the external validation dataset (0.989), which, although considerably high, is insufficient to assess the model’s effectiveness. The last study entailed developing and validating a CNN-based model to differentiate malignant carcinoma from benign nasopharyngeal lesions using white-light endoscopic images . Malignant conditions included lymphoma, rhabdomyosarcoma, olfactory neuroblastoma, malignant melanoma, and plasmacytoma. Benign subtypes encompassed precancerous or atypical hyperplasia, fibroangioma, leiomyoma, meningioma, minor salivary gland tumor, fungal infection, tuberculosis, chronic inflammation, adenoids or lymphoid hyperplasia, nasopharyngeal cyst, and foreign body. The model was trained on 27 536 images collected retrospectively (7 951 subjects) and temporally (prospectively) externally validated with 1 430 images (from 355 patients) from the same Chinese institution. Diagnostic performance was compared against 14 endoscopists: (i) three experts with more than five years of experience; (ii) eight residents with one year of experience; and (iii) interns with less than three months of experience. Except for the interns’ sensitivity, the model’s diagnostic performance surpassed the endoscopists in all tested metrics. However, data were collected from a single tertiary institution, and more malignancies should be included. Although not developed for the same cancer type, the two cancer detection studies for the larynx and nasopharynx are comparable due to using white-light endoscopic images. Both used CNNs and involved more than 300 patients and 1000 images, but the optimal diagnostic performance – although less sensitive (72% vs. 90.2% in ) – was achieved for the GoogLeNet Inception v3 network CNN with an AUC of 0.953, an accuracy of 89.7%, and a specificity of 94.8%, enhancing the value of pre-training CNNs. Skeletal system Four studies using different imaging techniques were designed to diagnose bone cancers, producing an average AUC of 0.88 (Table ). The first two radiomics-based models were developed for the binary classification of atypical cartilaginous tumors (ACT) and appendicular chondrosarcomas (CS) . In , a LogitBoost algorithm was temporally and geographically validated on 36 PET-CT scans from 23 women and 13 men. Besides externally validating their method, the authors evaluated clinical utility by comparing its diagnostic performance against a radiologist. The model performed satisfactorily in all calculated metrics (AUC, accuracy, sensitivity, PPV, and F1-score), but its accuracy was lower than the radiologist. In addition, only non-contrast PET-CT scans were included in the analyses. In the following year, research performed by the same first author evaluated bone tumor diagnosis from MRI scans . Radiomic features were extracted from T1-weighted MRI scans, and an ExtraTrees algorithm was trained to classify the tumors. On an external validation dataset of 65 images (34 women, 31 men), the model achieved a PPV, sensitivity, and F1-score of 92%, 98%, and 0.95 in classifying ACTs, while 94%, 80%, and 86% for the classification of grade II CS of long bones, respectively (weighted average is presented in Table ). The model's classification performance was compared against an experienced radiologist (with 35 years of experience) to assess clinical utility, finding that it could not match the radiologist's performance. Using SHAP, it was also found that certain radiomic features, such as the mean and standard deviation of gradient magnitude and entropy, significantly differed between the two tumor types. Drawbacks include the study’s retrospective nature, using only one type of MRI, and over-representing appendicular chondrosarcomas compared to cartilaginous tumors in the study population. The second set of papers used neural networks to differentiate benign from malignant bone tumors from X-ray images . On the one hand, in , a CNN (EfficientNet-B0) was developed on a dataset of 2899 radiographic images from 1356 patients with primary bone tumors from 5 institutions (3 for training, 2 for validation), including benign (1523 images, 679 patients), intermediate (635 images, 317 patients), and malignant (741 images, 360 patients) growths. The CNN model was developed for binary (benign versus not benign and malignant versus not malignant) and three-way (benign versus intermediate versus malignant) tumor classification. The authors also compared the model’s triple-way classification performance against two musculoskeletal subspecialists with 25 and 23 years of experience and three junior radiologists with 6, 1, and 7 years of experience. The deep learning algorithm had similar accuracy to the subspecialists and better performance than junior radiologists. However, only a modest number of patients was used for validation (639 X-rays from 291 patients), tumor classes were unbalanced (smaller number of benign bone tumors compared to intermediate and malignant), and the pipeline was not fully automated. In contrast, other authors resorted to a non-deep ANN that uses radiomic features extracted from X-ray images and demographic data to classify and differentiate malignant and benign bone tumors . The ANN was developed on 880 patients with the following conditions: (i) malignant tumors: chondrosarcoma, osteosarcoma, Ewing’s sarcoma, plasma cell myeloma, non-Hodgkin lymphoma B cell, and chordoma; (ii) benign subtypes: osteochondroma, enchondroma, chondroblastoma, osteoid osteoma, giant cell tumor, non-ossifying fibroma, haemangioma, aneurysmal bone cyst, simple bone cyst, fibrous dysplasia. The method was externally validated on 96 patients from a different institution, and performance was compared against four radiologists (two residents and two specialized). The model was more sensitive than both radiologist groups but was outperformed by the specialized radiologists in accuracy and specificity. In addition, the model requires manual segmentations and can only distinguish between benign and malignant tumors and not specific subtypes. Three retrospective studies were developed to diagnose brain cancers using MRI scans, amounting to 1 084 patients and 64 459 images, resulting in an average sensitivity of 81.97% and specificity of 91.63 (Table ) . The first involved the following conditions: acoustic neuroma, pituitary tumor, epidermoid cyst, meningioma, paraganglioma, craniopharyngioma, glioma, hemangioblastoma, metastatic tumor, germ cell tumor, medulloblastoma, chordoma, lymphomas, choroid plexus, papilloma, gangliocytoma, dysembryoplastic neuroepithelial tumor, and hemangiopericytoma . The CNN-based model was trained on images from 37 871 patients and externally validated using 64 414 T1-weighted, T2-weighted, and T1c MRI scans from 1039 subjects (600 female, 349 male) from three institutions. Its diagnostic performance was compared against nine neuroradiologists (5 to 20 years of experience) to assess clinical utility. This CNN classified brain tumors with high accuracy, sensitivity, and specificity, performing particularly well in identifying gliomas, which are difficult to diagnose using traditional imaging methods. When aided by the model, the neuroradiologists' accuracy increased by 18.9%, which was still lower than the model alone. AI assistance also boosted the neuroradiologists' sensitivity, specificity, and PPV. However, only three types of scans were considered, training data was obtained from a single center, and few rare tumors were included. In the second paper, the authors explored the combination of 9 different ML models – NB, logistic regression, SVM with a polynomial kernel, kNN (k = 3), DT, MLP, RF, AdaBoost, and bootstrap aggregating – to distinguish between different types of brain tumors (glioblastoma, anaplastic glioma, meningioma, primary central nervous system lymphoma, and brain metastasis) . MRI techniques were analyzed in a combination of 135 classifiers and radiomics: cMRI, advMRI, phyMRI, cMRI + phyMRI, and advMRI + phyMRI. A dataset of 167 patients was used for training, and temporal validation was performed on 20 subjects. Physiological MRI scans (phyMRI), named radiophysiomics, achieved the best results using AdaBoost with cMRI and phyMRI and RF with phyMRI. Both models surpassed the radiologists in AUC and F1-score but were outperformed in sensitivity and specificity. The AdaBoost model also had a higher PPV than the clinicians. However, this was a single-center, retrospective study, and the application and tuning of the models were performed manually. The third study evaluated the usefulness of preoperative contrast-enhanced T1- and T2-weighted MRI in differentiating low-grade gliomas (LGG) from glioblastomas (GBM) . The authors trained a radiomics-based RF classifier on 142 patients from 8 American centers and externally validated it on 25 patients from another institution (all from The Cancer Imaging Archive). The results showed that the machine learning algorithm was highly accurate in differentiating between GBM and LGG based on preoperative contrast-enhanced MRI scans, surpassing two neuroradiologists (15 and 1 year of experience) and a radiologist (3 years of experience). However, few patients from a public database were collected, possibly resulting in selection bias (non-random selection). Malignancies of the digestive system – highlighted in Table – were the most comprehensively studied (17/56, 30.4%), encompassing colorectal ( n = 7, 41.2%), esophageal ( n = 3, 17.6%), gastric ( n = 5, 29.4%), and liver ( n = 2, 11.8%) cancers. Colorectal Cancer Three sets of articles addressed colorectal cancers (7 papers). The goal of the first set, consisting of four multi-institutional retrospective studies, was its diagnosis, averaging a sensitivity of 77.3% and a specificity of 93.2% for tests on 995 images from different sources . The authors in developed an ensemble of three CNNs (Inception-v3, ResNet-50, and DenseNet-161) to predict the histology of colorectal neoplasms based on white light colonoscopic images. The ensemble model transferred knowledge from digital photography and learned with colonoscopic images to classify the images into one of 4 different pathologic categories: normal (healthy), adenoma with low-grade dysplasia (A-LGD), adenoma with high-grade dysplasia (A-HGD), and adenocarcinoma. The system's diagnostic performance was compared against four experts (more than five years of experience) and six trainees (less than two years). In the external validation dataset (400 images, 100 of each type), the CNN-CAD model achieved high accuracy in predicting the histology of the lesions. Compared to endoscopists, the model's performance was slightly better than the experts' and significantly outperformed the trainees. In addition, the authors used Grad-CAM to create a heatmap highlighting the regions of the input image that were most relevant to the network's decision. However, only one image per polyp was used; consequently, tumors that cannot be contained within a single image were neglected. The second work concerns the external validation and clinical utility assessment of EndoBRAIN, an AI-assisted system to classify colorectal polyps into malignant or non-malignant. EndoBRAIN was trained with 69 142 endocytoscopic images from patients with colorectal polyps from five academic centers in Japan. Its clinical validity had previously been confirmed in a single-center prospective study. However, since its implementation depends on governmental regulatory approval, the current study compared EndoBRAIN's diagnostic performance against 30 endoscopists (20 trainees, 10 experts) using stained and narrow-band endocytoscopic images in a web-based trial. The authors found their CADx tool accurately differentiated neoplastic from non-neoplastic lesions, outperforming all endoscopists for stained images, achieving similar performance in narrow-band images, and being accepted for clinical use. The third diagnostic model concerns the development of a deep learning model to predict the revised Vienna Classification in colonoscopy, which categorizes colorectal neoplasms into different levels of malignancy using standard endoscopic colonoscopy images . Several CNN architectures were compared, namely AlexNet, ResNet152, and EfficientNet-B8, with ResNet152 being chosen as the prediction model due to its higher accuracy and fastest inference time. The model was trained using 56,872 colonoscopy images (6775 lesions) and validated on 255 images (128 lesions) from 7 external institutions in Japan. The authors also compared diagnostic performance against endoscopists (five novices, three fellows, and four experts). The AI system’s sensitivity and specificity exceeded that of all endoscopists. Nevertheless, the model cannot discriminate between high-grade dysplasia and invasive cancer (categories 4 and 5 of the revised Vienna Classification), and only binary classification is supported. In the fourth document, the authors tested two pre-trained radiomics-based CNN architectures (Inception-ResNet-v2 and ResNet-152) to classify colorectal neoplasms into three types of sets automatically: 7-class (T1-4 colorectal cancer, high-grade dysplasia, tubular adenoma, vs. non-neoplasms), 4-class (neoplastic vs. non-neoplastic – advanced vs. early CRC vs. adenoma vs. healthy), and 2-class (neoplastic versus non-neoplastic and advanced versus non-advanced lesions) . The CNNs were trained on a South Korean dataset (3453 colonoscopy images, 1446 patients) and temporally and geographically validated on 240 images (and as many patients) from another institution. CAM was used to highlight its decisions. The best-performing architecture was ResNet-152 for 7-way and 4-way diagnoses, but Inception-ResNet-v2 achieved better results on binary classifications. In addition, the model's performance was compared with one novice and two experienced endoscopists with six months and more than five years of colonoscopy experience, respectively. Although resulting in high accuracy, neither CNN architecture could outperform the endoscopists. Furthermore, this retrospective study only considered three types of diseases and white-light colonoscopy images. The second set of articles was devoted to predicting outcomes from MRI scans in patients with colorectal cancer undergoing neoadjuvant chemotherapy (NCRT), accruing 143 MRIs from 118 patients and a mean AUC and accuracy of 0.77 and 81.9%, respectively . The first was a prospective study using a multipath CNN on MRI scans (diffusion kurtosis and T2-weighted) . The authors used a dataset of 412 patients (290 for development and 93 for temporal validation) with locally advanced rectal adenocarcinoma scheduled for NCRT. The researchers developed three multipath CNN-based models: one to preoperatively predict pathologic complete response (pCR) to neoadjuvant chemoradiotherapy, one to assess tumor regression grade (TRG) (TRG0 and TRG1 vs. TRG2 and TRG3), and one to predict T downstaging. In addition, the authors evaluated the models' utility by comparing two radiologists' – with 10 and 15 years of experience – performance with and without their assistance. The results showed excellent performance in predicting pCR, superior to the assessment by the two radiologists, whose error rate was also reduced when assisted by the DL model. Although with lower performance, the TRG and T downstaging models also achieved promising results with an AUC of 0.70 and 0.79, respectively (although not outperforming the clinicians). Nevertheless, this monoinstitutional research required manual delineation, and interobserver variability was not analyzed. Moreover, further validation studies are necessary to assess performance with different MRI scanners. The second group of researchers developed an MRI-based CNN (DC3CNN) to predict tumor regression grade (assessment of tumor size) in response to NCRT in patients with colorectal liver metastases . The authors used prospective internal (328 lesions from 155 patients) and retrospective external cohorts (61 images, 25 patients) to collect pre and post-treatment T2-weighted- and DW-MRI scans. The model surpassed the diagnostic accuracy of RECIST, the most commonly used criteria for clinical evaluation of solid tumor response to chemotherapy. However, the study was retrospective, and further studies are needed to validate its performance in larger ethnically diverse patient populations. Lastly, only one model assessed postoperative survival of colorectal cancer using text-based data . The model was trained on the SEER database (364 316 patients) and externally validated (temporally and ethnically) on a Korean dataset (1 572 subjects, 607 women, 965 men). The authors compared 4 ML algorithms, namely logistic regression, DTs, RFs, and LightGBM, to obtain an optimal prognostic model. The best-performing model – LightGBM – outperformed TNM-7 in predicting survival for all tested periods (1, 2, 3, 4, 5, 6, 8, and 10 years). Still, data were collected retrospectively from a public database and a single institution using only text-based data, so prospective studies are necessary, and clinicopathological, molecular, and radiologic variables should also be incorporated. Esophageal Cancer Three studies involved esophageal cancers. Two papers studied neoplasia detection in patients with Barrett’s esophagus, a medical condition resulting from long-term acid-reflux damage, causing esophageal tissue lining to thicken and become irritated, increasing cancer risk . The same group of researchers conducted both studies: the first paper describes model development for detection , while the second encompasses its tuning and update to include location . The authors proposed a multi-stage pretraining approach that involved training a CNN learning model on 494,355 gastrointestinal images before fine-tuning it on a smaller dataset of medical images specific to Barrett's neoplasia. The model was trained with images from different endoscopes. In the first paper , using data from separate institutions, the authors used a retrospective dataset of early Barrett’s neoplasia for primary validation (80 patients, unknown proportion) and a second prospectively acquired dataset (80 patients and images) to compare their model’s performance against fifty-three endoscopists (17 seniors, 8 juniors, 18 fellows, and 10 novices). In the second paper, the researchers validated their model on three prospective datasets: one with clinically representative images (80 individuals), one with subtle lesions (80 subjects), and one in a live setting with dysplastic and nondysplastic patients (ten each) . It showed excellent performance on the three external validation datasets, and its detection and location performances were also compared against the 53 experienced endoscopists on the subtle lesions. The CAD system outperformed all 53 endoscopists for all tested metrics in both papers, obtaining an average accuracy, sensitivity, and specificity of 87.9%, 91.7%, and 84.16%, respectively. The models developed in both articles performed similarly and were tested in clinically realistic scenarios, with an average accuracy, sensitivity, and specificity of 88.45%, 91.25%, and 85.63%, respectively, enhancing CNNs’ predictive power. Additionally, a retrospective study evaluated cancer-specific survival for esophageal adenocarcinoma and squamous cell carcinoma according to individual treatment recommendations . The authors trained a deep-, regression-, and text-based survival neural network (DeepSurv, multi-layer perceptron) using the SEER database (6855 patients) and validated it on 150 women and 350 men from their institution (China). Additionally, prognostic performance was compared against TNM-8, having exceeded it. However, only one medical center was used, and research was not performed in an accurately representative clinical setting. Gastric Cancer In five articles, models were developed for gastric-related tasks. The first three studies had a diagnostic component. In the first research, the authors developed two models – GastroMIL and MIL-GC –, training them on WSIs from H&E slides magnified 30 times collected from TCGA and a Chinese institution. They also temporally and geographically validated them with 175 WSIs from 91 patients from NHGRP . GastroMIL used an ensemble of a CNN and an RNN to distinguish gastric cancer from normal gastric tissue images. Its performance was compared against one junior and three expert pathologists. MIL-GC, a regression-based model, was created to predict patients’ overall survival. Besides WSIs, MIL-GC uses clinical data, namely survival state, overall survival time, age, sex, tumor size, neoplasm histologic grade, and pathologic T, N, M, and TNM-8 stages. The deep learning models achieved high performance in both tasks, with an overall accuracy of 92% for diagnosis and a C-index of 0.657 for prognosis prediction in the external dataset. Compared to human performance, GastroMIL outperformed the junior pathologist in accuracy and sensitivity but was surpassed by the experienced pathologists (in accuracy, sensitivity, and specificity). However, the tested cohorts were retrospective and had unbalanced survival times, and clinical utility was not evaluated for the prognostic model. The second study used a CNN (ResNet-50) for real-time gastric cancer diagnosis . The model was developed with 3 407 endoscopic images of 666 patients with gastric lesions from two institutions. The DCNN model was tested on a temporally different dataset of endoscopic videos from a separate institution (54 videos from 54 patients), and performance was compared against 20 endoscopists (6 experts, 14 novices). The model achieved better performance than any of the endoscopists, and diagnostic accuracy, sensitivity, and specificity increased for all clinicians while assisted by the model. Nevertheless, despite decreasing the aggregate diagnostic time from 4.35 s to 3.01 s, it increased experts’ by 0.10 s. In addition, the diagnostic model was only tested on high-quality images, and the validation dataset was small and domestic. Although slightly less sensitive than Gastro-MIL (93.2% vs. 93.4%), the model developed in achieved the best accuracy and sensitivity, evidencing that endoscopic images and videos might be more appropriate to diagnose gastric cancer. The third model was created using endoscopic ultrasonography images (EUS) for the differential diagnosis of gastric mesenchymal tumors, including GISTs, leiomyomas, and schwannomas . This model was trained with EUS from three Korean institutions and tested on a temporally separate set of 212 images from the same centers (69 patients, 38 female, 31 male). A sequential analysis approach was adopted using two CNNs: the first classifies the tumor as GIST or non-GIST; for non-GISTs, the second CNN classifies it as either a leiomyoma or schwannoma. The results were compared against junior ( n = 3, less than 200 examinations) and expert endoscopists ( n = 3, more than 500 examinations) who evaluated the same images, having surpassed them in both types of classification. However, this study was retrospective and involved a small number of patients, and the types of equipment used to perform ultrasounds varied considerably across the facilities. The last two papers concerned outcome predictions. The first presents a multi-institutional study that uses multitask deep learning to predict peritoneal recurrence and disease-free survival in gastric cancer patients after curative-intent surgery based on CT images . Supervised contrastive learning and a dynamic convolutional neural network were combined to achieve this purpose, and Grad-CAM was used to explain the model’s decisions. The model included CT scans from three patient cohorts, and external validation included 1 043 patients (329 women, 714 men) and as many images from another Chinese institution. In addition, the authors investigated clinician performance for peritoneal recurrence prediction with and without the assistance of the AI model, having found that performance was significantly enhanced after integrating it and that the model alone surpassed all physicians. Nonetheless, only East Asian patients were included in this retrospective study, which was not performed in a real clinical setting, and sensitivity was only reported for one of the clinicians. The last study discusses the use of CT radiomics to predict the response of advanced gastric cancer to neoadjuvant chemotherapy and to detect pathological downstaging at an early stage . The authors trained two SVCs on 206 patients who had undergone three or four cycles of chemotherapy and externally validated them on two testing cohorts, which were also used for benchmarking detection against RECIST. The first testing cohort consists of temporal validation (40 patients and CTs, 13 women, 27 men), while the second differs in the number of chemotherapy cycles (46 individuals and CTs, 10 women, 36 men). Performance for the detection model surpassed RECIST in both cohorts, and, except for sensitivity, the response prediction model also produced positive results. However, retrospective data and a small, unbalanced sample size constrain this study, which was not evaluated in a clinically representative setting. Liver Cancer Two models were developed for liver cancer-related predictions. The first aimed at classifying hepatocellular carcinomas and cholangiocarcinomas (differential diagnosis) . The authors developed a web-based (cloud-deployed AI model and browser-based interface) CNN (DenseNet architecture) using WSIs from H&E slides magnified 40 times and used Grad-CAM to increase the model’s explainability. The training dataset was obtained from TCGA (70 slides from 70 unique patients). The external validation dataset was collected from the Department of Pathology at Stanford University Medical Center (80 slides from 24 women and 56 men). The model achieved a diagnostic accuracy of 84.2% in the validation cohort. Diagnostic performance was also compared to that of 11 pathologists. Except for the two unspecified pathologists, performance (AUC) increased for all clinicians when assisted by this tool. However, the pathologists only had access to the WSIs (as opposed to being complemented with clinical data), the model required manual intervention for patch selection, and the study was retrospective with a small sample size (development and external validation with a total of 150 WSIs and patients). The second model was designed to predict three-year overall survival for intrahepatic cholangiocarcinoma patients after undergoing hepatectomy using an ensemble of Random Forests, XGBoost, and GBDT . Using a single quaternary Chinese institution, the authors collected 1390 patients for training and 42 patients (12 women, 30 men) for external temporal validation. Results were compared against the TNM-8 and LCSGJ staging systems, with model performance exceeding that of the routinely used tools. Nonetheless, this was a monoinstitutional endeavor limited to a small number of Asian patients. Furthermore, only six prognostic factors were used: carcinoembryonic antigen, carbohydrate antigen 19–9, alpha-fetoprotein, pre-albumin, and T and N stages. Three sets of articles addressed colorectal cancers (7 papers). The goal of the first set, consisting of four multi-institutional retrospective studies, was its diagnosis, averaging a sensitivity of 77.3% and a specificity of 93.2% for tests on 995 images from different sources . The authors in developed an ensemble of three CNNs (Inception-v3, ResNet-50, and DenseNet-161) to predict the histology of colorectal neoplasms based on white light colonoscopic images. The ensemble model transferred knowledge from digital photography and learned with colonoscopic images to classify the images into one of 4 different pathologic categories: normal (healthy), adenoma with low-grade dysplasia (A-LGD), adenoma with high-grade dysplasia (A-HGD), and adenocarcinoma. The system's diagnostic performance was compared against four experts (more than five years of experience) and six trainees (less than two years). In the external validation dataset (400 images, 100 of each type), the CNN-CAD model achieved high accuracy in predicting the histology of the lesions. Compared to endoscopists, the model's performance was slightly better than the experts' and significantly outperformed the trainees. In addition, the authors used Grad-CAM to create a heatmap highlighting the regions of the input image that were most relevant to the network's decision. However, only one image per polyp was used; consequently, tumors that cannot be contained within a single image were neglected. The second work concerns the external validation and clinical utility assessment of EndoBRAIN, an AI-assisted system to classify colorectal polyps into malignant or non-malignant. EndoBRAIN was trained with 69 142 endocytoscopic images from patients with colorectal polyps from five academic centers in Japan. Its clinical validity had previously been confirmed in a single-center prospective study. However, since its implementation depends on governmental regulatory approval, the current study compared EndoBRAIN's diagnostic performance against 30 endoscopists (20 trainees, 10 experts) using stained and narrow-band endocytoscopic images in a web-based trial. The authors found their CADx tool accurately differentiated neoplastic from non-neoplastic lesions, outperforming all endoscopists for stained images, achieving similar performance in narrow-band images, and being accepted for clinical use. The third diagnostic model concerns the development of a deep learning model to predict the revised Vienna Classification in colonoscopy, which categorizes colorectal neoplasms into different levels of malignancy using standard endoscopic colonoscopy images . Several CNN architectures were compared, namely AlexNet, ResNet152, and EfficientNet-B8, with ResNet152 being chosen as the prediction model due to its higher accuracy and fastest inference time. The model was trained using 56,872 colonoscopy images (6775 lesions) and validated on 255 images (128 lesions) from 7 external institutions in Japan. The authors also compared diagnostic performance against endoscopists (five novices, three fellows, and four experts). The AI system’s sensitivity and specificity exceeded that of all endoscopists. Nevertheless, the model cannot discriminate between high-grade dysplasia and invasive cancer (categories 4 and 5 of the revised Vienna Classification), and only binary classification is supported. In the fourth document, the authors tested two pre-trained radiomics-based CNN architectures (Inception-ResNet-v2 and ResNet-152) to classify colorectal neoplasms into three types of sets automatically: 7-class (T1-4 colorectal cancer, high-grade dysplasia, tubular adenoma, vs. non-neoplasms), 4-class (neoplastic vs. non-neoplastic – advanced vs. early CRC vs. adenoma vs. healthy), and 2-class (neoplastic versus non-neoplastic and advanced versus non-advanced lesions) . The CNNs were trained on a South Korean dataset (3453 colonoscopy images, 1446 patients) and temporally and geographically validated on 240 images (and as many patients) from another institution. CAM was used to highlight its decisions. The best-performing architecture was ResNet-152 for 7-way and 4-way diagnoses, but Inception-ResNet-v2 achieved better results on binary classifications. In addition, the model's performance was compared with one novice and two experienced endoscopists with six months and more than five years of colonoscopy experience, respectively. Although resulting in high accuracy, neither CNN architecture could outperform the endoscopists. Furthermore, this retrospective study only considered three types of diseases and white-light colonoscopy images. The second set of articles was devoted to predicting outcomes from MRI scans in patients with colorectal cancer undergoing neoadjuvant chemotherapy (NCRT), accruing 143 MRIs from 118 patients and a mean AUC and accuracy of 0.77 and 81.9%, respectively . The first was a prospective study using a multipath CNN on MRI scans (diffusion kurtosis and T2-weighted) . The authors used a dataset of 412 patients (290 for development and 93 for temporal validation) with locally advanced rectal adenocarcinoma scheduled for NCRT. The researchers developed three multipath CNN-based models: one to preoperatively predict pathologic complete response (pCR) to neoadjuvant chemoradiotherapy, one to assess tumor regression grade (TRG) (TRG0 and TRG1 vs. TRG2 and TRG3), and one to predict T downstaging. In addition, the authors evaluated the models' utility by comparing two radiologists' – with 10 and 15 years of experience – performance with and without their assistance. The results showed excellent performance in predicting pCR, superior to the assessment by the two radiologists, whose error rate was also reduced when assisted by the DL model. Although with lower performance, the TRG and T downstaging models also achieved promising results with an AUC of 0.70 and 0.79, respectively (although not outperforming the clinicians). Nevertheless, this monoinstitutional research required manual delineation, and interobserver variability was not analyzed. Moreover, further validation studies are necessary to assess performance with different MRI scanners. The second group of researchers developed an MRI-based CNN (DC3CNN) to predict tumor regression grade (assessment of tumor size) in response to NCRT in patients with colorectal liver metastases . The authors used prospective internal (328 lesions from 155 patients) and retrospective external cohorts (61 images, 25 patients) to collect pre and post-treatment T2-weighted- and DW-MRI scans. The model surpassed the diagnostic accuracy of RECIST, the most commonly used criteria for clinical evaluation of solid tumor response to chemotherapy. However, the study was retrospective, and further studies are needed to validate its performance in larger ethnically diverse patient populations. Lastly, only one model assessed postoperative survival of colorectal cancer using text-based data . The model was trained on the SEER database (364 316 patients) and externally validated (temporally and ethnically) on a Korean dataset (1 572 subjects, 607 women, 965 men). The authors compared 4 ML algorithms, namely logistic regression, DTs, RFs, and LightGBM, to obtain an optimal prognostic model. The best-performing model – LightGBM – outperformed TNM-7 in predicting survival for all tested periods (1, 2, 3, 4, 5, 6, 8, and 10 years). Still, data were collected retrospectively from a public database and a single institution using only text-based data, so prospective studies are necessary, and clinicopathological, molecular, and radiologic variables should also be incorporated. Three studies involved esophageal cancers. Two papers studied neoplasia detection in patients with Barrett’s esophagus, a medical condition resulting from long-term acid-reflux damage, causing esophageal tissue lining to thicken and become irritated, increasing cancer risk . The same group of researchers conducted both studies: the first paper describes model development for detection , while the second encompasses its tuning and update to include location . The authors proposed a multi-stage pretraining approach that involved training a CNN learning model on 494,355 gastrointestinal images before fine-tuning it on a smaller dataset of medical images specific to Barrett's neoplasia. The model was trained with images from different endoscopes. In the first paper , using data from separate institutions, the authors used a retrospective dataset of early Barrett’s neoplasia for primary validation (80 patients, unknown proportion) and a second prospectively acquired dataset (80 patients and images) to compare their model’s performance against fifty-three endoscopists (17 seniors, 8 juniors, 18 fellows, and 10 novices). In the second paper, the researchers validated their model on three prospective datasets: one with clinically representative images (80 individuals), one with subtle lesions (80 subjects), and one in a live setting with dysplastic and nondysplastic patients (ten each) . It showed excellent performance on the three external validation datasets, and its detection and location performances were also compared against the 53 experienced endoscopists on the subtle lesions. The CAD system outperformed all 53 endoscopists for all tested metrics in both papers, obtaining an average accuracy, sensitivity, and specificity of 87.9%, 91.7%, and 84.16%, respectively. The models developed in both articles performed similarly and were tested in clinically realistic scenarios, with an average accuracy, sensitivity, and specificity of 88.45%, 91.25%, and 85.63%, respectively, enhancing CNNs’ predictive power. Additionally, a retrospective study evaluated cancer-specific survival for esophageal adenocarcinoma and squamous cell carcinoma according to individual treatment recommendations . The authors trained a deep-, regression-, and text-based survival neural network (DeepSurv, multi-layer perceptron) using the SEER database (6855 patients) and validated it on 150 women and 350 men from their institution (China). Additionally, prognostic performance was compared against TNM-8, having exceeded it. However, only one medical center was used, and research was not performed in an accurately representative clinical setting. In five articles, models were developed for gastric-related tasks. The first three studies had a diagnostic component. In the first research, the authors developed two models – GastroMIL and MIL-GC –, training them on WSIs from H&E slides magnified 30 times collected from TCGA and a Chinese institution. They also temporally and geographically validated them with 175 WSIs from 91 patients from NHGRP . GastroMIL used an ensemble of a CNN and an RNN to distinguish gastric cancer from normal gastric tissue images. Its performance was compared against one junior and three expert pathologists. MIL-GC, a regression-based model, was created to predict patients’ overall survival. Besides WSIs, MIL-GC uses clinical data, namely survival state, overall survival time, age, sex, tumor size, neoplasm histologic grade, and pathologic T, N, M, and TNM-8 stages. The deep learning models achieved high performance in both tasks, with an overall accuracy of 92% for diagnosis and a C-index of 0.657 for prognosis prediction in the external dataset. Compared to human performance, GastroMIL outperformed the junior pathologist in accuracy and sensitivity but was surpassed by the experienced pathologists (in accuracy, sensitivity, and specificity). However, the tested cohorts were retrospective and had unbalanced survival times, and clinical utility was not evaluated for the prognostic model. The second study used a CNN (ResNet-50) for real-time gastric cancer diagnosis . The model was developed with 3 407 endoscopic images of 666 patients with gastric lesions from two institutions. The DCNN model was tested on a temporally different dataset of endoscopic videos from a separate institution (54 videos from 54 patients), and performance was compared against 20 endoscopists (6 experts, 14 novices). The model achieved better performance than any of the endoscopists, and diagnostic accuracy, sensitivity, and specificity increased for all clinicians while assisted by the model. Nevertheless, despite decreasing the aggregate diagnostic time from 4.35 s to 3.01 s, it increased experts’ by 0.10 s. In addition, the diagnostic model was only tested on high-quality images, and the validation dataset was small and domestic. Although slightly less sensitive than Gastro-MIL (93.2% vs. 93.4%), the model developed in achieved the best accuracy and sensitivity, evidencing that endoscopic images and videos might be more appropriate to diagnose gastric cancer. The third model was created using endoscopic ultrasonography images (EUS) for the differential diagnosis of gastric mesenchymal tumors, including GISTs, leiomyomas, and schwannomas . This model was trained with EUS from three Korean institutions and tested on a temporally separate set of 212 images from the same centers (69 patients, 38 female, 31 male). A sequential analysis approach was adopted using two CNNs: the first classifies the tumor as GIST or non-GIST; for non-GISTs, the second CNN classifies it as either a leiomyoma or schwannoma. The results were compared against junior ( n = 3, less than 200 examinations) and expert endoscopists ( n = 3, more than 500 examinations) who evaluated the same images, having surpassed them in both types of classification. However, this study was retrospective and involved a small number of patients, and the types of equipment used to perform ultrasounds varied considerably across the facilities. The last two papers concerned outcome predictions. The first presents a multi-institutional study that uses multitask deep learning to predict peritoneal recurrence and disease-free survival in gastric cancer patients after curative-intent surgery based on CT images . Supervised contrastive learning and a dynamic convolutional neural network were combined to achieve this purpose, and Grad-CAM was used to explain the model’s decisions. The model included CT scans from three patient cohorts, and external validation included 1 043 patients (329 women, 714 men) and as many images from another Chinese institution. In addition, the authors investigated clinician performance for peritoneal recurrence prediction with and without the assistance of the AI model, having found that performance was significantly enhanced after integrating it and that the model alone surpassed all physicians. Nonetheless, only East Asian patients were included in this retrospective study, which was not performed in a real clinical setting, and sensitivity was only reported for one of the clinicians. The last study discusses the use of CT radiomics to predict the response of advanced gastric cancer to neoadjuvant chemotherapy and to detect pathological downstaging at an early stage . The authors trained two SVCs on 206 patients who had undergone three or four cycles of chemotherapy and externally validated them on two testing cohorts, which were also used for benchmarking detection against RECIST. The first testing cohort consists of temporal validation (40 patients and CTs, 13 women, 27 men), while the second differs in the number of chemotherapy cycles (46 individuals and CTs, 10 women, 36 men). Performance for the detection model surpassed RECIST in both cohorts, and, except for sensitivity, the response prediction model also produced positive results. However, retrospective data and a small, unbalanced sample size constrain this study, which was not evaluated in a clinically representative setting. Two models were developed for liver cancer-related predictions. The first aimed at classifying hepatocellular carcinomas and cholangiocarcinomas (differential diagnosis) . The authors developed a web-based (cloud-deployed AI model and browser-based interface) CNN (DenseNet architecture) using WSIs from H&E slides magnified 40 times and used Grad-CAM to increase the model’s explainability. The training dataset was obtained from TCGA (70 slides from 70 unique patients). The external validation dataset was collected from the Department of Pathology at Stanford University Medical Center (80 slides from 24 women and 56 men). The model achieved a diagnostic accuracy of 84.2% in the validation cohort. Diagnostic performance was also compared to that of 11 pathologists. Except for the two unspecified pathologists, performance (AUC) increased for all clinicians when assisted by this tool. However, the pathologists only had access to the WSIs (as opposed to being complemented with clinical data), the model required manual intervention for patch selection, and the study was retrospective with a small sample size (development and external validation with a total of 150 WSIs and patients). The second model was designed to predict three-year overall survival for intrahepatic cholangiocarcinoma patients after undergoing hepatectomy using an ensemble of Random Forests, XGBoost, and GBDT . Using a single quaternary Chinese institution, the authors collected 1390 patients for training and 42 patients (12 women, 30 men) for external temporal validation. Results were compared against the TNM-8 and LCSGJ staging systems, with model performance exceeding that of the routinely used tools. Nonetheless, this was a monoinstitutional endeavor limited to a small number of Asian patients. Furthermore, only six prognostic factors were used: carcinoembryonic antigen, carbohydrate antigen 19–9, alpha-fetoprotein, pre-albumin, and T and N stages. Three papers described prognostic models for cancers in organs affecting the endocrine system (pancreas and thymus), whose results are depicted in Table . Pancreatic Cancer The first two studies assessed survival for pancreatic ductal adenocarcinoma (PDAC) patients but adopted disparate research designs and clinical inputs . The first group of researchers used a regression-based random survival forest model to prognosticate patients with advanced pancreatic cancer . Aimed at predicting overall survival for patients with unresectable PDAC, the model was developed with clinical data and CT scans from a German institution (203 patients). It was temporally and geographically validated using only text-based clinical data from patients with liver metastases from the same country (8 women, 14 men) and compared against mGPS, having outperformed it. Additionally, the authors used SHAP to explain their model, finding that inflammatory markers C-reactive protein and neutrophil-to-lymphocyte ratio had the most significant influence on its decision-making. Nonetheless, only twenty national patients were used to validate the model externally, and different types of inputs were used for training and testing. The second set of authors used an ensemble of ML methods – ANN, logistic regression, RF, GB, SVM, and CNNs (3D ResNet-18, R(2 + 1)D-18, 3D ResNeXt-50, and 3D DenseNet-121) – to predict 2-year overall and 1-year recurrence-free survival for PDAC patients after surgical resection . The classifier was trained and tuned using 229 patients and temporally validated with CECT images and seventeen clinical variables from the same South Korean institution (53 CECTs from 27 women and 26 men). Grad-CAM was used to explain the model’s decisions, and comparisons were made against TMN-8 to evaluate clinical utility. Although more accurate, specific, and with a higher PPV than TNM-8, it was less sensitive for both predictions and had a lower NPV for overall survival prediction. Furthermore, tumor margins were manually segmented, and the model did not consider histopathologic data. Thymic Cancer One study was designed for the simplified risk categorization of thymic epithelial tumors (TETs), rare cancer forms . Here, three types of tumors were evaluated: low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC). Three triple classification models were developed using radiomic features extracted from preoperative NECT images and clinical data from 433 patients: (i) LRT vs. HRT + TC; (ii) HRT vs. LRT + TC; (iii) TC vs. LRT + HRT. The authors compared several CT-based classifiers: logistic regression, linear SVC, Bernoulli and Gaussian Naïve Bayes, LDA, Stochastic Gradient Descent, SVM, DT, kNN, MLP, RF, AdaBoost, gradient boosting, and XGBoost. Combined with clinical data, the SVM model demonstrated the best performance for predicting the simplified TETs risk categorization. In addition, the SVM model was validated in a temporally different cohort using images from 5 types of scanners (76 scans and patients, 33 women, 48 men). Finally, its diagnostic performance was compared against three radiologists (3, 6, and 12 years of experience), having exceeded them regarding AUC (0.844 versus 0.645, 0.813, and 0.724) but not for other metrics (accuracy, sensitivity, and specificity). Caveats include the reduced amount of patients, low number of thymic carcinomas, and incomplete automation of the models. The first two studies assessed survival for pancreatic ductal adenocarcinoma (PDAC) patients but adopted disparate research designs and clinical inputs . The first group of researchers used a regression-based random survival forest model to prognosticate patients with advanced pancreatic cancer . Aimed at predicting overall survival for patients with unresectable PDAC, the model was developed with clinical data and CT scans from a German institution (203 patients). It was temporally and geographically validated using only text-based clinical data from patients with liver metastases from the same country (8 women, 14 men) and compared against mGPS, having outperformed it. Additionally, the authors used SHAP to explain their model, finding that inflammatory markers C-reactive protein and neutrophil-to-lymphocyte ratio had the most significant influence on its decision-making. Nonetheless, only twenty national patients were used to validate the model externally, and different types of inputs were used for training and testing. The second set of authors used an ensemble of ML methods – ANN, logistic regression, RF, GB, SVM, and CNNs (3D ResNet-18, R(2 + 1)D-18, 3D ResNeXt-50, and 3D DenseNet-121) – to predict 2-year overall and 1-year recurrence-free survival for PDAC patients after surgical resection . The classifier was trained and tuned using 229 patients and temporally validated with CECT images and seventeen clinical variables from the same South Korean institution (53 CECTs from 27 women and 26 men). Grad-CAM was used to explain the model’s decisions, and comparisons were made against TMN-8 to evaluate clinical utility. Although more accurate, specific, and with a higher PPV than TNM-8, it was less sensitive for both predictions and had a lower NPV for overall survival prediction. Furthermore, tumor margins were manually segmented, and the model did not consider histopathologic data. One study was designed for the simplified risk categorization of thymic epithelial tumors (TETs), rare cancer forms . Here, three types of tumors were evaluated: low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC). Three triple classification models were developed using radiomic features extracted from preoperative NECT images and clinical data from 433 patients: (i) LRT vs. HRT + TC; (ii) HRT vs. LRT + TC; (iii) TC vs. LRT + HRT. The authors compared several CT-based classifiers: logistic regression, linear SVC, Bernoulli and Gaussian Naïve Bayes, LDA, Stochastic Gradient Descent, SVM, DT, kNN, MLP, RF, AdaBoost, gradient boosting, and XGBoost. Combined with clinical data, the SVM model demonstrated the best performance for predicting the simplified TETs risk categorization. In addition, the SVM model was validated in a temporally different cohort using images from 5 types of scanners (76 scans and patients, 33 women, 48 men). Finally, its diagnostic performance was compared against three radiologists (3, 6, and 12 years of experience), having exceeded them regarding AUC (0.844 versus 0.645, 0.813, and 0.724) but not for other metrics (accuracy, sensitivity, and specificity). Caveats include the reduced amount of patients, low number of thymic carcinomas, and incomplete automation of the models. Table illustrates the models developed for genitourinary cancers, including the bladder, cervix, prostate, and uterus. Bladder Cancer From the retrieved models, only one assesses outcomes for primary bladder cancers . This article presents a CNN-based strategy to predict the muscular invasiveness of bladder cancer based on CT images and clinical data. The model was developed with 183 patients. Its performance was tested on an independent institution's temporally and geographically different validation cohort of patients with urothelial carcinoma (13 women, 62 men, and as many images). The model’s predictions were juxtaposed with diagnoses from two radiologists with nine and two years of experience, having achieved better accuracy and specificity than the two clinicians but a lower sensitivity. Overall, the authors found that the deep learning algorithm achieved a high accuracy rate in predicting muscular invasiveness, an essential factor in determining the prognosis and treatment of bladder cancer. However, the study is limited by its retrospective nature, exclusion of tumors not visible in CT images, and small sample size. Cervical Cancer Similarly, primary tumors of the cervix were only screened in one paper . Here, the authors trained an ensemble of convolutional and recurrent neural networks on whole-slide images from patients' cervical biopsies and 79 911 annotations from five hospitals and five kinds of scanners. The system comprises (i) two CNNs – the first scans WSIs at low resolution and the second at high resolution – to identify and locate the ten most suspicious areas in each slide; (ii) and an RNN to predict corresponding probabilities. The system classifies squamous and glandular epithelial cell abnormalities as positive (neoplastic) and normal findings as negative for intraepithelial lesions or malignancies (non-neoplastic). The method was externally validated on multi-center independent test sets of 1 565 women (1 170 without additional conditions and 395 with HPV), and classification performance was compared against three cytopathologists. Although obtaining promising results and surpassing clinician performance for both types of women, the authors highlight that the model was designed for the general women population, implying that further refinements are required for specific comorbidities. Prostate Cancer Two models were developed for prostate-cancer-related classifications using multiparametric MRI scans . In the first paper, the authors describe the development of Autoprostate, a system employing deep learning to generate a report summarizing the probability of suspicious lesions qualifying as clinically significant prostate cancer (CSPCa) . The authors trained their approach on the PROSTATEx dataset (249 men), externally validated it on the PICTURE dataset (247 patients), and compared its reports (with post-thresholding and false positive reduction) to those generated by a radiologist with ten years of experience. The system achieved a high level of agreement with the human reports (surpassing the radiologist in AUC and specificity) and could accurately identify CSPCa. However, this study was retrospective, a single (public) dataset was used for external validation, and only two types of prostate lesions were considered. The second article presented an ML-based approach for prostate cancer risk stratification using radiomics applied to multiparametric MRI scans . In this retrospective, monoinstitutional study, the authors compared seven classification algorithms: logistic regression, linear, quadratic (Q), cubic, and Gaussian kernel-based SVM, linear discriminant analysis, and RF. After training with 68 patients, the best-performing method – QSVM – was validated on a temporally independent dataset (14 high- and 39 low-risk patients). Its performance was compared against PI-RADS v2, having found that the model could accurately predict the risk of clinically significant prostate cancer. Although the classifier performed equivalently to PI-RADS v2 regarding AUC, it performed substantially better in class-specific measures (F1-score, sensitivity, and PPV), especially for the high-risk class. However, the study is limited by its retrospective nature and small sample size from a single source. Uterine Cancer Two studies for primary cancers focused on classifying lesions of the endometrium, the layer of tissue lining the uterus . In the first article, using 245 women as the training cohort, the authors compared nine models – logistic regression (LR), SVM, stochastic gradient descent, kNN, DT, RF, ExtraTrees, XGBoost, and LightGBM – to obtain an optimal algorithm for differential diagnosis (malignant versus benign tumors) . A radiomics score (radscore) was computed for the best-performing algorithm (logistic regression), and four models were selected using different combinations of T1-weighted, T2-weighted, and DWI MRI features: (i) the radiomics model; (ii) a nomogram, combining the radscore and clinical predictive parameters; (iii) a two-tiered stacking model, where the first tier was the clinical model and the optimal radiomics model (LR), and the second tier used the output of the first tier as the input of the multivariate LR; and (iv) an ensemble model, where the predictions obtained from the preceding clinical model and radiomics model were calculated by an accuracy-weighted average. The results showed that all four models accurately differentiated stage IA endometrial cancer and benign endometrial lesions. Furthermore, during external validation (44 MRIs from 44 women), the authors found that the nomogram had a higher AUC than the radiomics model, revealing more stable discrimination efficiency and better generalizability than the stacking and ensemble models and a radiologist with 30 years of experience (except in sensitivity). Nevertheless, data was collected from two same-country centers (Chinese institutions), only standard radiomics features were extracted, and lesions were manually segmented, which is highly time-consuming. The second paper encompassed a global-to-local multi-scale CNN to diagnose endometrial hyperplasia and screen endometrial intraepithelial neoplasia (EIN) in histopathological images . The researchers trained the CNN using a large annotated dataset (6 248 images) and tested it on a temporally different set of patients (1631 images, 135 specimens, 102 women). They found that it performed well in diagnosing endometrial hyperplasia and detecting EIN, outperforming a junior pathologist (2 years of experience) and obtaining comparable performance to a mid-level and a senior pathologist (6 and 25 years of experience, respectively). The authors used Grad-CAM to emphasize the regions the model deemed relevant for diagnosis. However, this retrospective study only used histopathological images (as opposed to WSIs). Besides, it focused solely on classifying healthy slides, hyperplasia without atypia, and endometrial intraepithelial neoplasia, thus neglecting the differentiation between benign lesions and endometrial cancer. From the retrieved models, only one assesses outcomes for primary bladder cancers . This article presents a CNN-based strategy to predict the muscular invasiveness of bladder cancer based on CT images and clinical data. The model was developed with 183 patients. Its performance was tested on an independent institution's temporally and geographically different validation cohort of patients with urothelial carcinoma (13 women, 62 men, and as many images). The model’s predictions were juxtaposed with diagnoses from two radiologists with nine and two years of experience, having achieved better accuracy and specificity than the two clinicians but a lower sensitivity. Overall, the authors found that the deep learning algorithm achieved a high accuracy rate in predicting muscular invasiveness, an essential factor in determining the prognosis and treatment of bladder cancer. However, the study is limited by its retrospective nature, exclusion of tumors not visible in CT images, and small sample size. Similarly, primary tumors of the cervix were only screened in one paper . Here, the authors trained an ensemble of convolutional and recurrent neural networks on whole-slide images from patients' cervical biopsies and 79 911 annotations from five hospitals and five kinds of scanners. The system comprises (i) two CNNs – the first scans WSIs at low resolution and the second at high resolution – to identify and locate the ten most suspicious areas in each slide; (ii) and an RNN to predict corresponding probabilities. The system classifies squamous and glandular epithelial cell abnormalities as positive (neoplastic) and normal findings as negative for intraepithelial lesions or malignancies (non-neoplastic). The method was externally validated on multi-center independent test sets of 1 565 women (1 170 without additional conditions and 395 with HPV), and classification performance was compared against three cytopathologists. Although obtaining promising results and surpassing clinician performance for both types of women, the authors highlight that the model was designed for the general women population, implying that further refinements are required for specific comorbidities. Two models were developed for prostate-cancer-related classifications using multiparametric MRI scans . In the first paper, the authors describe the development of Autoprostate, a system employing deep learning to generate a report summarizing the probability of suspicious lesions qualifying as clinically significant prostate cancer (CSPCa) . The authors trained their approach on the PROSTATEx dataset (249 men), externally validated it on the PICTURE dataset (247 patients), and compared its reports (with post-thresholding and false positive reduction) to those generated by a radiologist with ten years of experience. The system achieved a high level of agreement with the human reports (surpassing the radiologist in AUC and specificity) and could accurately identify CSPCa. However, this study was retrospective, a single (public) dataset was used for external validation, and only two types of prostate lesions were considered. The second article presented an ML-based approach for prostate cancer risk stratification using radiomics applied to multiparametric MRI scans . In this retrospective, monoinstitutional study, the authors compared seven classification algorithms: logistic regression, linear, quadratic (Q), cubic, and Gaussian kernel-based SVM, linear discriminant analysis, and RF. After training with 68 patients, the best-performing method – QSVM – was validated on a temporally independent dataset (14 high- and 39 low-risk patients). Its performance was compared against PI-RADS v2, having found that the model could accurately predict the risk of clinically significant prostate cancer. Although the classifier performed equivalently to PI-RADS v2 regarding AUC, it performed substantially better in class-specific measures (F1-score, sensitivity, and PPV), especially for the high-risk class. However, the study is limited by its retrospective nature and small sample size from a single source. Two studies for primary cancers focused on classifying lesions of the endometrium, the layer of tissue lining the uterus . In the first article, using 245 women as the training cohort, the authors compared nine models – logistic regression (LR), SVM, stochastic gradient descent, kNN, DT, RF, ExtraTrees, XGBoost, and LightGBM – to obtain an optimal algorithm for differential diagnosis (malignant versus benign tumors) . A radiomics score (radscore) was computed for the best-performing algorithm (logistic regression), and four models were selected using different combinations of T1-weighted, T2-weighted, and DWI MRI features: (i) the radiomics model; (ii) a nomogram, combining the radscore and clinical predictive parameters; (iii) a two-tiered stacking model, where the first tier was the clinical model and the optimal radiomics model (LR), and the second tier used the output of the first tier as the input of the multivariate LR; and (iv) an ensemble model, where the predictions obtained from the preceding clinical model and radiomics model were calculated by an accuracy-weighted average. The results showed that all four models accurately differentiated stage IA endometrial cancer and benign endometrial lesions. Furthermore, during external validation (44 MRIs from 44 women), the authors found that the nomogram had a higher AUC than the radiomics model, revealing more stable discrimination efficiency and better generalizability than the stacking and ensemble models and a radiologist with 30 years of experience (except in sensitivity). Nevertheless, data was collected from two same-country centers (Chinese institutions), only standard radiomics features were extracted, and lesions were manually segmented, which is highly time-consuming. The second paper encompassed a global-to-local multi-scale CNN to diagnose endometrial hyperplasia and screen endometrial intraepithelial neoplasia (EIN) in histopathological images . The researchers trained the CNN using a large annotated dataset (6 248 images) and tested it on a temporally different set of patients (1631 images, 135 specimens, 102 women). They found that it performed well in diagnosing endometrial hyperplasia and detecting EIN, outperforming a junior pathologist (2 years of experience) and obtaining comparable performance to a mid-level and a senior pathologist (6 and 25 years of experience, respectively). The authors used Grad-CAM to emphasize the regions the model deemed relevant for diagnosis. However, this retrospective study only used histopathological images (as opposed to WSIs). Besides, it focused solely on classifying healthy slides, hyperplasia without atypia, and endometrial intraepithelial neoplasia, thus neglecting the differentiation between benign lesions and endometrial cancer. As illustrated in Table , five papers studied cancers of the integumentary system, focusing on the breasts and skin. Breast Cancer Three studies developed models for cancers originating in the breasts, each with a specific purpose and using different clinical modalities. In , several text-based machine learning classifiers, namely, DTs, RFs, MLPs, logistic regression, naïve Bayes, and XGBoost, were compared to establish optimal classifiers for osteoporosis, relative fracture, and 8-year overall survival predictions. The algorithm was trained on 420 patients from a Chinese institution and geographically validated on 150 women from a separate local institution. The osteoporosis model was compared against OSTA and FRAX, the fracture model against FRAX, and the prognostic model against TNM-8. The results showed that the XGBoost classifier performed the best for the three tasks and outperformed the other clinical models. Additionally, for explainability, the authors also used SHAP for feature importance analysis for each model: (i) age, use of anti-estrogens, and molecular type are the most predictive of osteoporosis; (ii) osteoporosis, age, and bone-specific alkaline phosphatase are the best predictors for fracture; and (iii) N-stage, molecular type, and age have the highest prognostic value for overall survival. Despite its positive results, prospective studies are needed to validate the model in more diverse patient populations. In , authors explored how combining AI and radiologists can improve breast cancer screening. Using 213 694 retrospectively collected mammograms (X-ray images) from 92 585 women, it was found that the combination of radiologists and AI (CNN-based classifier) achieved the highest accuracy in detecting breast cancer. The sensitivity and specificity of the standalone AI system were significantly lower than an unaided radiologist. However, the decision-referral approach outperformed the unaided radiologist on both sensitivity and specificity for several tested thresholds. Nonetheless, the study only included mammogram images and did not consider other factors, such as patient history or clinical data, which may impact the accuracy of breast cancer screening. Furthermore, the AI algorithm used in the study was not optimized for clinical use and may require further development and testing before it can be implemented in a clinical setting. Lastly, the work developed in entailed diagnosing non-cystic benign and malignant breast lesions from ultrasonographic images. Radiomic features were extracted from the ultrasound images, and a random forest model was trained with 135 lesions and externally validated to predict malignancy for each lesion. Moreover, the performance of an experienced radiologist (8 years) was compared with and without the model’s assistance. Although not with statistical significance, the radiologist's assessments improved when using the AI system. However, the final validation population was small (66 ultrasounds from 57 women) and showed different proportions of malignant lesions. Skin Cancer Two models were developed to diagnose skin tumors using photographs, producing an average AUC, sensitivity, and specificity of 0.89, 77.1%, and 81.74% . The first was a retrospective validation study assessing the performance of deep neural networks in detecting and diagnosing benign and malignant skin neoplasms of the head and neck, trunk, arms, and legs . In a previous study, the authors trained an ensemble of CNNs (SENet + SE-ResNeXt-50 + faster RCNN) with 1 106 886 image crops from South Korean patients to detect potential lesions and classify skin malignancies. Here, performance was tested on three new temporal and geographical validation datasets of skin lesions (two national, one international, 46 696 photographs from 10 876 patients): (i) one dataset was used to compare the model’s classification performance against 65 attending physicians in real-world practice; (ii) one’s goal was to evaluate classification performance against with 44 dermatologists in an experimental setting; and (iv) the last two were meant to predict exact diagnosis (1 of 43 primary skin neoplasms) in a local (South Korean) and an international (UK, 1 300 images) dataset, with the first also being compared against physicians. In (i) and (ii), performance was calculated for high specificity and high sensitivity thresholds. The algorithm was more sensitive and specific than the dermatologists in the experimental setting. However, attending physicians outperformed it in real-world practice in all tested metrics (sensitivity, specificity, PPV, and NPV). In addition, the model only dealt with high-quality clinical photographs, and there was a lack of ethnic diversity in the study population. The second paper presented a set of CNNs – DenseNet-121 (Faster R-CNN and deep classification network) – developed to detect malignant eyelid tumors from photographic images . The researchers used a 1 417 clinical images dataset with 1 533 eyelid tumors from 851 patients across three Chinese institutions (one for development and two for external validation). Besides using Grad-CAM for interpretation, the AI’s performance on the external dataset (266 pictures from 176 patients) was compared to three ophthalmologists: one junior, one senior, and one expert (3, 7, and 15 years of experience, respectively). It surpassed the junior and senior ophthalmologists’ performance and achieved similar results to the expert. Notwithstanding its potential, the system still needs evaluation on non-Asian populations and prospectively acquired datasets, and it was only developed for detection (it cannot provide a specific diagnosis). Three studies developed models for cancers originating in the breasts, each with a specific purpose and using different clinical modalities. In , several text-based machine learning classifiers, namely, DTs, RFs, MLPs, logistic regression, naïve Bayes, and XGBoost, were compared to establish optimal classifiers for osteoporosis, relative fracture, and 8-year overall survival predictions. The algorithm was trained on 420 patients from a Chinese institution and geographically validated on 150 women from a separate local institution. The osteoporosis model was compared against OSTA and FRAX, the fracture model against FRAX, and the prognostic model against TNM-8. The results showed that the XGBoost classifier performed the best for the three tasks and outperformed the other clinical models. Additionally, for explainability, the authors also used SHAP for feature importance analysis for each model: (i) age, use of anti-estrogens, and molecular type are the most predictive of osteoporosis; (ii) osteoporosis, age, and bone-specific alkaline phosphatase are the best predictors for fracture; and (iii) N-stage, molecular type, and age have the highest prognostic value for overall survival. Despite its positive results, prospective studies are needed to validate the model in more diverse patient populations. In , authors explored how combining AI and radiologists can improve breast cancer screening. Using 213 694 retrospectively collected mammograms (X-ray images) from 92 585 women, it was found that the combination of radiologists and AI (CNN-based classifier) achieved the highest accuracy in detecting breast cancer. The sensitivity and specificity of the standalone AI system were significantly lower than an unaided radiologist. However, the decision-referral approach outperformed the unaided radiologist on both sensitivity and specificity for several tested thresholds. Nonetheless, the study only included mammogram images and did not consider other factors, such as patient history or clinical data, which may impact the accuracy of breast cancer screening. Furthermore, the AI algorithm used in the study was not optimized for clinical use and may require further development and testing before it can be implemented in a clinical setting. Lastly, the work developed in entailed diagnosing non-cystic benign and malignant breast lesions from ultrasonographic images. Radiomic features were extracted from the ultrasound images, and a random forest model was trained with 135 lesions and externally validated to predict malignancy for each lesion. Moreover, the performance of an experienced radiologist (8 years) was compared with and without the model’s assistance. Although not with statistical significance, the radiologist's assessments improved when using the AI system. However, the final validation population was small (66 ultrasounds from 57 women) and showed different proportions of malignant lesions. Two models were developed to diagnose skin tumors using photographs, producing an average AUC, sensitivity, and specificity of 0.89, 77.1%, and 81.74% . The first was a retrospective validation study assessing the performance of deep neural networks in detecting and diagnosing benign and malignant skin neoplasms of the head and neck, trunk, arms, and legs . In a previous study, the authors trained an ensemble of CNNs (SENet + SE-ResNeXt-50 + faster RCNN) with 1 106 886 image crops from South Korean patients to detect potential lesions and classify skin malignancies. Here, performance was tested on three new temporal and geographical validation datasets of skin lesions (two national, one international, 46 696 photographs from 10 876 patients): (i) one dataset was used to compare the model’s classification performance against 65 attending physicians in real-world practice; (ii) one’s goal was to evaluate classification performance against with 44 dermatologists in an experimental setting; and (iv) the last two were meant to predict exact diagnosis (1 of 43 primary skin neoplasms) in a local (South Korean) and an international (UK, 1 300 images) dataset, with the first also being compared against physicians. In (i) and (ii), performance was calculated for high specificity and high sensitivity thresholds. The algorithm was more sensitive and specific than the dermatologists in the experimental setting. However, attending physicians outperformed it in real-world practice in all tested metrics (sensitivity, specificity, PPV, and NPV). In addition, the model only dealt with high-quality clinical photographs, and there was a lack of ethnic diversity in the study population. The second paper presented a set of CNNs – DenseNet-121 (Faster R-CNN and deep classification network) – developed to detect malignant eyelid tumors from photographic images . The researchers used a 1 417 clinical images dataset with 1 533 eyelid tumors from 851 patients across three Chinese institutions (one for development and two for external validation). Besides using Grad-CAM for interpretation, the AI’s performance on the external dataset (266 pictures from 176 patients) was compared to three ophthalmologists: one junior, one senior, and one expert (3, 7, and 15 years of experience, respectively). It surpassed the junior and senior ophthalmologists’ performance and achieved similar results to the expert. Notwithstanding its potential, the system still needs evaluation on non-Asian populations and prospectively acquired datasets, and it was only developed for detection (it cannot provide a specific diagnosis). Thirteen papers addressed respiratory system cancers, which predominantly concerned the lungs, but also included the larynx, nasopharynx, and mesothelium (Table ). Lung Cancer Ten approaches were developed for lung cancer assessments. The first document describes a validation study of a CNN-based tool (DenseNet) designed to predict the malignancy of pulmonary nodules . The model was previously trained with the NLST dataset and was now externally validated in 3 UK centers with different CT scanners (1 397 CECTs and NECTs, 1 187 patients of unknown gender ratio). The authors also evaluated its clinical utility by comparing it to the Brock Model. Although slightly less specific than the Brock model, the detection algorithm developed by the authors had a higher AUC and sensitivity. Despite having undergone international validation, prospective studies in ethnically diverse populations are still amiss. The second paper involved developing and validating a model to predict the malignancy of multiple pulmonary nodules from CT scans and eleven clinical variables . The study analyzed data from various medical centers. Ten ML methods were compared to identify the best malignancy predictor: AdaBoost, DT, Logistic Regression, Linear SVM, Radial Basis Function Kernel SVM, NB, kNN, Neural Net, Quadratic Discriminant Analysis, RF, and XGBoost. The best-performing model – XGBoost – was tested on three datasets. The first was retrospective, compiled from 6 institutions (five from China and one from South Korea), used for primary external validation (220 patients, 583 CT scans), and compared against four well-established models: Brock, Mayo, PKU, and VA. The second retrospective dataset was used for generalizability, containing patients from a Chinese institution with solitary pulmonary nodules (195 patients and images, 110 women, 85 men), whose results were also compared against the four just-mentioned models. The third and last dataset included data from 4 Chinese centers and was collected prospectively for secondary validation and comparisons against clinicians (200 CTs, 78 patients, 51 women, 27 men). This comparison involved three thoracic surgeons and one radiologist, who achieved an average sensitivity of 0.651 and specificity of 0.679. The model significantly outperformed this average and each clinician’s AUC, as well as in all comparisons against the routinely used models. In addition, SHAP was used to identify the most predictive nodule characteristics, finding that the model's most predictive features were nodule size, type, count, border, patient age, spiculation, lobulation, emphysema, nodule location, and distribution. Nonetheless, besides not reporting individual clinician sensitivity and specificity in the prospective cohort, the drawbacks of this study include only assessing typical high-risk patients and the lack of validation with different ethnicities. The work in involved a CNN-based model for predicting the presence of visceral pleural invasion in patients with early-stage lung cancer. The deep learning model was trained using a dataset of CT scans from 676 patients and externally validated on a temporally different cohort from the same South Korean institution (141 CTs from 84 women and 57 men). Besides using Grad-CAM to evidence its decisions, this CNN can adapt its sensitivity and specificity to meet the clinical needs of individual patients and clinicians. The model achieved a performance level comparable to three expert radiologists but did not surpass it except in PPV. Besides, these are results from a monoinstitutional retrospective study where geographical validation was not performed. In addition to using a small number of patients, data was also imbalanced, and the model was not fully automated (required manual tumor annotations). The fourth article concerns developing an EfficientNetV2-based CNN system to predict the survival benefit of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in patients with stage IV non-small cell lung cancer . The model was developed with accessible pre-therapy CT images from five centers and externally validated on a monoinstitutional dataset from a national dataset (China, 92 CTs from 92 patients). The authors also compared radiologists' and oncologists' (three each, 2, 5, and 10 years of experience) performance with and without ESBP. The results showed that, while assisted by the system, all radiologists improved their diagnostic accuracy, sensibility, specificity, PPV, and NPV (except for the trainee oncologist, who achieved better sensitivity without the model). However, prospective studies in ethnically rich cohorts are still necessary to implement this tool in clinical practice. The fifth study aimed at finding optimal predictors of two-year recurrence, recurrence-free survival, and overall survival after curative-intent radiotherapy for non-small cell lung cancer . Ten text-based ML models were trained on 498 patients and compared: ANN, Linear and Non-linear SVM, Generalized Linear Model, kNN, RF, MDA, Partial Least Squares, NB, and XGBoost. The best-performing models were as follows: (i) an ensemble of kNN, NB, and RF for recurrence classification; (ii) kNN for recurrence-free survival prediction; and (iii) a combination of XGBoost, ANN, and MDA for overall survival. The three optimal predictors were externally validated using routinely collected data from 5 UK institutions (159 seniors, 71 women, 88 men) and compared against TNM-8 and WHO performance status. The recurrence and overall survival models outperformed both routinely used systems, but these tools surpassed the recurrence-free survival predictor’s performance. Moreover, this study was retrospective and had a small sample size with missing data. The sixth study was designed to identify high-risk smokers to predict long-term lung cancer incidence (12 years) . In this paper, the authors developed a convolutional neural inception V4 network based on low-dose chest CT images, age, sex, and current versus former smoking statuses. The CNN was trained using patients from the PLCO trial and externally validated on data from the NLST randomized controlled trial (2456 women and 3037 men from 33 USA institutions). The model was also compared against PLCOm2012 to evaluate clinical utility, having exceeded its performance for all assessed metrics (AUC, sensitivity, specificity, PPV, and NPV). However, this study was retrospective, lacked ethnic diversity, and was not evaluated in a clinically realistic scenario. Additionally, information from symptomatic patients was unavailable due to using data from a screening trial. In the seventh article, a CNN-based model was developed for the automated detection and diagnosis of malignant pulmonary nodules on CECT scans . The algorithm was externally validated on four separate datasets with ethnic differences (three from South Korea and one from the USA, amounting to 693 patients and CTs). Furthermore, the diagnostic performance of 18 physicians (from non-radiologists to radiologists with 26 years of experience) was compared while assisted and not assisted by the algorithm for one dataset. The model achieved an excellent performance in the four tested datasets, outperforming all clinicians, and the professionals’ accuracy increased while aided by the model for all tested groups. Nonetheless, the model was undertrained for small nodules (< 1 cm) and trained only for malignant nodule detection for one type of CT (posterior-anterior projections), and the study was retrospective and not representative of a real-world clinical setting. The eighth algorithm consisted of a multilayer perceptron (Feed-Forward Neural Network) paired with a Cox proportional hazards model to predict cancer-specific survival for non-small cell lung cancer . The text-based model was trained using the SEER database and externally validated on patients from a Chinese tertiary pulmonary hospital (642 women, 540 men). It was compared against TNM-8, having outperformed it with statistical significance. Although tested with real-world clinical data, prospective multi-institutional studies are needed before the deep learning model can be used in clinical practice. The ninth article described developing, validating, and comparing three CNN models to differentiate between benign and malignant pulmonary ground-glass nodules (GGNs) . The first CNN only used CT images. The second CNN used clinical data: age, sex, and smoking history. The third was a fusion model combining CTs and clinical features, achieving the best performance. This model was temporally and geographically validated with 63 CT scans from 61 patients (39 women, 22 men). Its classification performance was compared against two radiologists (5 and 10 years of experience) for clinical utility assessment. Despite performing satisfactorily in external validation, the model was surpassed by both clinicians in accuracy, sensitivity, and NPV, only producing higher results for specificity and NPV. Furthermore, this study was retrospective, and validation was neither international nor evaluated in a correct clinical setting. In the tenth and final paper, a Neural Multitask Logistic Regression (N-MTLR) network was developed for survival risk stratification for stage III non-small cell lung cancer . The text-based deep learning system was trained on 16 613 patients from the SEER database and externally validated on subjects from a Chinese institution (172 patients, 39 women, 133 men). The results in the external dataset showed that the DSNN could predict survival outcomes more accurately than TNM-8 (AUC of 0.7439 vs. 0.561). The study results suggest that the deep learning system could be used for personalized treatment planning and stratification for patients with stage III non-small cell lung cancer. However, prospective studies in multi-institutional datasets are still required. Laryngeal, Mesothelial and Nasopharyngeal Cancers Three models were developed to assess tumors of other elements of the respiratory system. In , the authors trained a CNN (GoogLeNet Inception v3 network) with 13 721 raw endoscopic laryngeal images – including laryngeal cancer (LCA), precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT), and healthy tissue – from three Chinese institutions (1 816 patients). External validation was performed on 1 176 white-light endoscopic images from two additional institutions in the same country (392 patients), testing the model for binary classification – urgent (LCA and PRELCA) or non-urgent (BLT and healthy) – and between the four conditions. Predictions for both classification types were compared against three endoscopists (3, 3 to 10, and 10 to 20 years of experience). In two-way classification, the algorithm was less accurate than one endoscopist and less sensitive than two but outperformed all clinicians in four-way diagnostic accuracy. Still, this accuracy was relatively low (less than 80%), the study was retrospective, and all tested laryngoscopic images were obtained by the same type of standard endoscopes. Cancers of the mesothelium were approached in a single retrospective multi-center study . The paper uses DL to distinguish between two types of mesothelial cell proliferations: sarcomatoid malignant mesotheliomas (SMM) and benign spindle cell mesothelial proliferations (BSCMP). SMMs and BSCMPs are difficult to distinguish using traditional histopathological methods, resulting in misdiagnoses. The authors propose a new strategy—SpindleMesoNET—that uses an ensemble of a CNN and an RNN to analyze WSIs of H&E-stained mesothelial slides magnified 40 times. The model was trained on a Canadian dataset, externally validated on 39 images from 39 patients from a Chinese center, and compared against the diagnostic performance of three pathologists on a referral test set (40 WSIs from 40 patients). The accuracy and specificity of SpindleMesoNET on the referral set cases (92.5% and 100%, respectively) exceeded that of the three pathologists on the same slide set (91.7% and 96.5%). However, the pathologists were more sensitive than the diagnostic model (87.3% vs. 85.3%). In addition, the study had a minimal sample size, and only AUC was reported for the external validation dataset (0.989), which, although considerably high, is insufficient to assess the model’s effectiveness. The last study entailed developing and validating a CNN-based model to differentiate malignant carcinoma from benign nasopharyngeal lesions using white-light endoscopic images . Malignant conditions included lymphoma, rhabdomyosarcoma, olfactory neuroblastoma, malignant melanoma, and plasmacytoma. Benign subtypes encompassed precancerous or atypical hyperplasia, fibroangioma, leiomyoma, meningioma, minor salivary gland tumor, fungal infection, tuberculosis, chronic inflammation, adenoids or lymphoid hyperplasia, nasopharyngeal cyst, and foreign body. The model was trained on 27 536 images collected retrospectively (7 951 subjects) and temporally (prospectively) externally validated with 1 430 images (from 355 patients) from the same Chinese institution. Diagnostic performance was compared against 14 endoscopists: (i) three experts with more than five years of experience; (ii) eight residents with one year of experience; and (iii) interns with less than three months of experience. Except for the interns’ sensitivity, the model’s diagnostic performance surpassed the endoscopists in all tested metrics. However, data were collected from a single tertiary institution, and more malignancies should be included. Although not developed for the same cancer type, the two cancer detection studies for the larynx and nasopharynx are comparable due to using white-light endoscopic images. Both used CNNs and involved more than 300 patients and 1000 images, but the optimal diagnostic performance – although less sensitive (72% vs. 90.2% in ) – was achieved for the GoogLeNet Inception v3 network CNN with an AUC of 0.953, an accuracy of 89.7%, and a specificity of 94.8%, enhancing the value of pre-training CNNs. Ten approaches were developed for lung cancer assessments. The first document describes a validation study of a CNN-based tool (DenseNet) designed to predict the malignancy of pulmonary nodules . The model was previously trained with the NLST dataset and was now externally validated in 3 UK centers with different CT scanners (1 397 CECTs and NECTs, 1 187 patients of unknown gender ratio). The authors also evaluated its clinical utility by comparing it to the Brock Model. Although slightly less specific than the Brock model, the detection algorithm developed by the authors had a higher AUC and sensitivity. Despite having undergone international validation, prospective studies in ethnically diverse populations are still amiss. The second paper involved developing and validating a model to predict the malignancy of multiple pulmonary nodules from CT scans and eleven clinical variables . The study analyzed data from various medical centers. Ten ML methods were compared to identify the best malignancy predictor: AdaBoost, DT, Logistic Regression, Linear SVM, Radial Basis Function Kernel SVM, NB, kNN, Neural Net, Quadratic Discriminant Analysis, RF, and XGBoost. The best-performing model – XGBoost – was tested on three datasets. The first was retrospective, compiled from 6 institutions (five from China and one from South Korea), used for primary external validation (220 patients, 583 CT scans), and compared against four well-established models: Brock, Mayo, PKU, and VA. The second retrospective dataset was used for generalizability, containing patients from a Chinese institution with solitary pulmonary nodules (195 patients and images, 110 women, 85 men), whose results were also compared against the four just-mentioned models. The third and last dataset included data from 4 Chinese centers and was collected prospectively for secondary validation and comparisons against clinicians (200 CTs, 78 patients, 51 women, 27 men). This comparison involved three thoracic surgeons and one radiologist, who achieved an average sensitivity of 0.651 and specificity of 0.679. The model significantly outperformed this average and each clinician’s AUC, as well as in all comparisons against the routinely used models. In addition, SHAP was used to identify the most predictive nodule characteristics, finding that the model's most predictive features were nodule size, type, count, border, patient age, spiculation, lobulation, emphysema, nodule location, and distribution. Nonetheless, besides not reporting individual clinician sensitivity and specificity in the prospective cohort, the drawbacks of this study include only assessing typical high-risk patients and the lack of validation with different ethnicities. The work in involved a CNN-based model for predicting the presence of visceral pleural invasion in patients with early-stage lung cancer. The deep learning model was trained using a dataset of CT scans from 676 patients and externally validated on a temporally different cohort from the same South Korean institution (141 CTs from 84 women and 57 men). Besides using Grad-CAM to evidence its decisions, this CNN can adapt its sensitivity and specificity to meet the clinical needs of individual patients and clinicians. The model achieved a performance level comparable to three expert radiologists but did not surpass it except in PPV. Besides, these are results from a monoinstitutional retrospective study where geographical validation was not performed. In addition to using a small number of patients, data was also imbalanced, and the model was not fully automated (required manual tumor annotations). The fourth article concerns developing an EfficientNetV2-based CNN system to predict the survival benefit of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in patients with stage IV non-small cell lung cancer . The model was developed with accessible pre-therapy CT images from five centers and externally validated on a monoinstitutional dataset from a national dataset (China, 92 CTs from 92 patients). The authors also compared radiologists' and oncologists' (three each, 2, 5, and 10 years of experience) performance with and without ESBP. The results showed that, while assisted by the system, all radiologists improved their diagnostic accuracy, sensibility, specificity, PPV, and NPV (except for the trainee oncologist, who achieved better sensitivity without the model). However, prospective studies in ethnically rich cohorts are still necessary to implement this tool in clinical practice. The fifth study aimed at finding optimal predictors of two-year recurrence, recurrence-free survival, and overall survival after curative-intent radiotherapy for non-small cell lung cancer . Ten text-based ML models were trained on 498 patients and compared: ANN, Linear and Non-linear SVM, Generalized Linear Model, kNN, RF, MDA, Partial Least Squares, NB, and XGBoost. The best-performing models were as follows: (i) an ensemble of kNN, NB, and RF for recurrence classification; (ii) kNN for recurrence-free survival prediction; and (iii) a combination of XGBoost, ANN, and MDA for overall survival. The three optimal predictors were externally validated using routinely collected data from 5 UK institutions (159 seniors, 71 women, 88 men) and compared against TNM-8 and WHO performance status. The recurrence and overall survival models outperformed both routinely used systems, but these tools surpassed the recurrence-free survival predictor’s performance. Moreover, this study was retrospective and had a small sample size with missing data. The sixth study was designed to identify high-risk smokers to predict long-term lung cancer incidence (12 years) . In this paper, the authors developed a convolutional neural inception V4 network based on low-dose chest CT images, age, sex, and current versus former smoking statuses. The CNN was trained using patients from the PLCO trial and externally validated on data from the NLST randomized controlled trial (2456 women and 3037 men from 33 USA institutions). The model was also compared against PLCOm2012 to evaluate clinical utility, having exceeded its performance for all assessed metrics (AUC, sensitivity, specificity, PPV, and NPV). However, this study was retrospective, lacked ethnic diversity, and was not evaluated in a clinically realistic scenario. Additionally, information from symptomatic patients was unavailable due to using data from a screening trial. In the seventh article, a CNN-based model was developed for the automated detection and diagnosis of malignant pulmonary nodules on CECT scans . The algorithm was externally validated on four separate datasets with ethnic differences (three from South Korea and one from the USA, amounting to 693 patients and CTs). Furthermore, the diagnostic performance of 18 physicians (from non-radiologists to radiologists with 26 years of experience) was compared while assisted and not assisted by the algorithm for one dataset. The model achieved an excellent performance in the four tested datasets, outperforming all clinicians, and the professionals’ accuracy increased while aided by the model for all tested groups. Nonetheless, the model was undertrained for small nodules (< 1 cm) and trained only for malignant nodule detection for one type of CT (posterior-anterior projections), and the study was retrospective and not representative of a real-world clinical setting. The eighth algorithm consisted of a multilayer perceptron (Feed-Forward Neural Network) paired with a Cox proportional hazards model to predict cancer-specific survival for non-small cell lung cancer . The text-based model was trained using the SEER database and externally validated on patients from a Chinese tertiary pulmonary hospital (642 women, 540 men). It was compared against TNM-8, having outperformed it with statistical significance. Although tested with real-world clinical data, prospective multi-institutional studies are needed before the deep learning model can be used in clinical practice. The ninth article described developing, validating, and comparing three CNN models to differentiate between benign and malignant pulmonary ground-glass nodules (GGNs) . The first CNN only used CT images. The second CNN used clinical data: age, sex, and smoking history. The third was a fusion model combining CTs and clinical features, achieving the best performance. This model was temporally and geographically validated with 63 CT scans from 61 patients (39 women, 22 men). Its classification performance was compared against two radiologists (5 and 10 years of experience) for clinical utility assessment. Despite performing satisfactorily in external validation, the model was surpassed by both clinicians in accuracy, sensitivity, and NPV, only producing higher results for specificity and NPV. Furthermore, this study was retrospective, and validation was neither international nor evaluated in a correct clinical setting. In the tenth and final paper, a Neural Multitask Logistic Regression (N-MTLR) network was developed for survival risk stratification for stage III non-small cell lung cancer . The text-based deep learning system was trained on 16 613 patients from the SEER database and externally validated on subjects from a Chinese institution (172 patients, 39 women, 133 men). The results in the external dataset showed that the DSNN could predict survival outcomes more accurately than TNM-8 (AUC of 0.7439 vs. 0.561). The study results suggest that the deep learning system could be used for personalized treatment planning and stratification for patients with stage III non-small cell lung cancer. However, prospective studies in multi-institutional datasets are still required. Three models were developed to assess tumors of other elements of the respiratory system. In , the authors trained a CNN (GoogLeNet Inception v3 network) with 13 721 raw endoscopic laryngeal images – including laryngeal cancer (LCA), precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT), and healthy tissue – from three Chinese institutions (1 816 patients). External validation was performed on 1 176 white-light endoscopic images from two additional institutions in the same country (392 patients), testing the model for binary classification – urgent (LCA and PRELCA) or non-urgent (BLT and healthy) – and between the four conditions. Predictions for both classification types were compared against three endoscopists (3, 3 to 10, and 10 to 20 years of experience). In two-way classification, the algorithm was less accurate than one endoscopist and less sensitive than two but outperformed all clinicians in four-way diagnostic accuracy. Still, this accuracy was relatively low (less than 80%), the study was retrospective, and all tested laryngoscopic images were obtained by the same type of standard endoscopes. Cancers of the mesothelium were approached in a single retrospective multi-center study . The paper uses DL to distinguish between two types of mesothelial cell proliferations: sarcomatoid malignant mesotheliomas (SMM) and benign spindle cell mesothelial proliferations (BSCMP). SMMs and BSCMPs are difficult to distinguish using traditional histopathological methods, resulting in misdiagnoses. The authors propose a new strategy—SpindleMesoNET—that uses an ensemble of a CNN and an RNN to analyze WSIs of H&E-stained mesothelial slides magnified 40 times. The model was trained on a Canadian dataset, externally validated on 39 images from 39 patients from a Chinese center, and compared against the diagnostic performance of three pathologists on a referral test set (40 WSIs from 40 patients). The accuracy and specificity of SpindleMesoNET on the referral set cases (92.5% and 100%, respectively) exceeded that of the three pathologists on the same slide set (91.7% and 96.5%). However, the pathologists were more sensitive than the diagnostic model (87.3% vs. 85.3%). In addition, the study had a minimal sample size, and only AUC was reported for the external validation dataset (0.989), which, although considerably high, is insufficient to assess the model’s effectiveness. The last study entailed developing and validating a CNN-based model to differentiate malignant carcinoma from benign nasopharyngeal lesions using white-light endoscopic images . Malignant conditions included lymphoma, rhabdomyosarcoma, olfactory neuroblastoma, malignant melanoma, and plasmacytoma. Benign subtypes encompassed precancerous or atypical hyperplasia, fibroangioma, leiomyoma, meningioma, minor salivary gland tumor, fungal infection, tuberculosis, chronic inflammation, adenoids or lymphoid hyperplasia, nasopharyngeal cyst, and foreign body. The model was trained on 27 536 images collected retrospectively (7 951 subjects) and temporally (prospectively) externally validated with 1 430 images (from 355 patients) from the same Chinese institution. Diagnostic performance was compared against 14 endoscopists: (i) three experts with more than five years of experience; (ii) eight residents with one year of experience; and (iii) interns with less than three months of experience. Except for the interns’ sensitivity, the model’s diagnostic performance surpassed the endoscopists in all tested metrics. However, data were collected from a single tertiary institution, and more malignancies should be included. Although not developed for the same cancer type, the two cancer detection studies for the larynx and nasopharynx are comparable due to using white-light endoscopic images. Both used CNNs and involved more than 300 patients and 1000 images, but the optimal diagnostic performance – although less sensitive (72% vs. 90.2% in ) – was achieved for the GoogLeNet Inception v3 network CNN with an AUC of 0.953, an accuracy of 89.7%, and a specificity of 94.8%, enhancing the value of pre-training CNNs. Four studies using different imaging techniques were designed to diagnose bone cancers, producing an average AUC of 0.88 (Table ). The first two radiomics-based models were developed for the binary classification of atypical cartilaginous tumors (ACT) and appendicular chondrosarcomas (CS) . In , a LogitBoost algorithm was temporally and geographically validated on 36 PET-CT scans from 23 women and 13 men. Besides externally validating their method, the authors evaluated clinical utility by comparing its diagnostic performance against a radiologist. The model performed satisfactorily in all calculated metrics (AUC, accuracy, sensitivity, PPV, and F1-score), but its accuracy was lower than the radiologist. In addition, only non-contrast PET-CT scans were included in the analyses. In the following year, research performed by the same first author evaluated bone tumor diagnosis from MRI scans . Radiomic features were extracted from T1-weighted MRI scans, and an ExtraTrees algorithm was trained to classify the tumors. On an external validation dataset of 65 images (34 women, 31 men), the model achieved a PPV, sensitivity, and F1-score of 92%, 98%, and 0.95 in classifying ACTs, while 94%, 80%, and 86% for the classification of grade II CS of long bones, respectively (weighted average is presented in Table ). The model's classification performance was compared against an experienced radiologist (with 35 years of experience) to assess clinical utility, finding that it could not match the radiologist's performance. Using SHAP, it was also found that certain radiomic features, such as the mean and standard deviation of gradient magnitude and entropy, significantly differed between the two tumor types. Drawbacks include the study’s retrospective nature, using only one type of MRI, and over-representing appendicular chondrosarcomas compared to cartilaginous tumors in the study population. The second set of papers used neural networks to differentiate benign from malignant bone tumors from X-ray images . On the one hand, in , a CNN (EfficientNet-B0) was developed on a dataset of 2899 radiographic images from 1356 patients with primary bone tumors from 5 institutions (3 for training, 2 for validation), including benign (1523 images, 679 patients), intermediate (635 images, 317 patients), and malignant (741 images, 360 patients) growths. The CNN model was developed for binary (benign versus not benign and malignant versus not malignant) and three-way (benign versus intermediate versus malignant) tumor classification. The authors also compared the model’s triple-way classification performance against two musculoskeletal subspecialists with 25 and 23 years of experience and three junior radiologists with 6, 1, and 7 years of experience. The deep learning algorithm had similar accuracy to the subspecialists and better performance than junior radiologists. However, only a modest number of patients was used for validation (639 X-rays from 291 patients), tumor classes were unbalanced (smaller number of benign bone tumors compared to intermediate and malignant), and the pipeline was not fully automated. In contrast, other authors resorted to a non-deep ANN that uses radiomic features extracted from X-ray images and demographic data to classify and differentiate malignant and benign bone tumors . The ANN was developed on 880 patients with the following conditions: (i) malignant tumors: chondrosarcoma, osteosarcoma, Ewing’s sarcoma, plasma cell myeloma, non-Hodgkin lymphoma B cell, and chordoma; (ii) benign subtypes: osteochondroma, enchondroma, chondroblastoma, osteoid osteoma, giant cell tumor, non-ossifying fibroma, haemangioma, aneurysmal bone cyst, simple bone cyst, fibrous dysplasia. The method was externally validated on 96 patients from a different institution, and performance was compared against four radiologists (two residents and two specialized). The model was more sensitive than both radiologist groups but was outperformed by the specialized radiologists in accuracy and specificity. In addition, the model requires manual segmentations and can only distinguish between benign and malignant tumors and not specific subtypes. As shown in Table , five studies entailed the assessment of metastatic cancer, that is, secondary tumors spread from different tissues. From these, three focused on cancer spread to organs , while two evaluated metastasized nodes. Organ metastases In , models were created to predict the risk of bone metastasis and prognosis (three-year overall survival) for kidney cancer patients. To achieve optimal performance, the researchers developed and compared eight ML models: DTs, RFs, MLPs, Logistic Regression, Naïve Bayes BS classifier, XGBoost, SVMs, and kNN. The text-based models were trained with 71 414 patients from the SEER database (USA) and externally validated with 963 patients from a Chinese institution (323 women, 640 men). The results showed that their XGBoost-based models had the best accuracy in predicting bone metastasis risk and prognosis. The risk prediction model (diagnosis) outperformed TNM-7 only regarding AUC (0.98 vs. 0.93), while the prognostic model exceeded TNM-7’s predictions for all tested metrics (AUC, accuracy, sensitivity, PPV, and F1-score). Using SHAP analysis, the authors also unveiled that the key factors influencing these outcomes were age, sex, and tumor characteristics. Although trained on ethnically different patients, these models were only validated on Asian subjects and not compared against clinicians, so further studies are required to establish clinical validity and utility. The second paper explores the effectiveness of a deep learning-based algorithm (CNN) in detecting and classifying liver metastases from colorectal cancer using CT scans . In this South Korean monoinstitutional study, 502 patients were used for training, and temporally different patients (40 with 99 metastatic lesions, 45 without metastases) were used for validation. The algorithm's detection and classification performance was compared to three radiologists (with 2, 3, and 20 years of experience in liver imaging) and three second-year radiology residents. Although showing a higher diagnostic sensitivity than both types of clinicians, the six radiologists outperformed the model in AUAFROC (detection) and false positives per patient (FPP, classification). In addition, the CT scans had been captured eight years before the analyses. The third study was conducted in a clinically realistic scenario, and the model has been implemented in practice . The model was designed to predict 3-month mortality in patients with solid metastatic tumors for several types of cancer (breast, gastrointestinal, genitourinary, lung, rare) and treatment alterations in an outpatient setting. The authors trained a Gradient-Boosted Trees Binary Classifier with observations from 28 484 deceased and alive patients and 493 features from demographic characteristics, laboratory test results, flowsheets, and diagnoses. The model was silently deployed in the patients’ EHRs for 20 months to compare its predictions against 74 oncologists. This prospective temporal validation study involved 3099 encounters from 2041 ethnically diverse patients. The model outperformed oncologists in all metrics for aggregate (general, with and without treatment alterations), gastrointestinal, genitourinary, and lung cancers but was less sensitive than the professionals for rare and breast metastatic tumors. Although currently available in medical practice, the authors note that further research is needed to validate whether using the model improves prognostic confidence and patient engagement. Node metastases Two models were developed to diagnose node metastases. In , the authors aimed to classify cervical lymph node metastasis from thyroid cancer using CT scans . The researchers had previously developed a CNN (Xception architecture) trained on a 787 axial preoperative CT scans dataset. This study validated the systems' performance on 3 838 images from 698 patients (unknown female-male ratio) and used Grad-CAM to explain the model’s reasoning. The researchers also evaluated the clinical utility of the model by comparing seven radiologists’ performance (one expert, six trainees) with and without its assistance. While aided by the system, the expert’s accuracy, sensitivity, specificity, PPV, and NPV were all found to increase, while only accuracy, specificity, and NPV improved for the trainees. This study was retrospective and conducted in a single institution, and the results obtained were not satisfying enough to justify clinical implementation. The second and last document describes developing an ultrasound-based ML model to assess the risk of sentinel lymph node metastasis (SLNM) in breast cancer patients . First, the authors compared ten algorithms to achieve an optimal model: SVM, RF, LDA, Logistic Regression, NB, kNN, MLP, Long Short-Term Memory, and CNN. The best algorithm (XGBoost) was then integrated into a clinical model, and SHAP was used to analyze its diagnostic performance. XGBoost was trained with 902 patients, and external validation consisted of 50 temporally separate women. The authors also compared their tool with a radiologist’s diagnostic evaluations (unknown years of experience). The results showed that the ML model could predict the risk of SLNM in breast cancer patients based on ultrasound image features with high accuracy (84.6%), having outperformed the radiologist. In addition, SHAP analysis deemed suspicious lymph nodes, microcalcifications, spiculation at the edge of the lesion, and distorted tissue structure around the lesion as the model’s most significant features. Nonetheless, this research was retrospective and used a minimal number of patients from a single institution with limited pathological types of breast cancer. In , models were created to predict the risk of bone metastasis and prognosis (three-year overall survival) for kidney cancer patients. To achieve optimal performance, the researchers developed and compared eight ML models: DTs, RFs, MLPs, Logistic Regression, Naïve Bayes BS classifier, XGBoost, SVMs, and kNN. The text-based models were trained with 71 414 patients from the SEER database (USA) and externally validated with 963 patients from a Chinese institution (323 women, 640 men). The results showed that their XGBoost-based models had the best accuracy in predicting bone metastasis risk and prognosis. The risk prediction model (diagnosis) outperformed TNM-7 only regarding AUC (0.98 vs. 0.93), while the prognostic model exceeded TNM-7’s predictions for all tested metrics (AUC, accuracy, sensitivity, PPV, and F1-score). Using SHAP analysis, the authors also unveiled that the key factors influencing these outcomes were age, sex, and tumor characteristics. Although trained on ethnically different patients, these models were only validated on Asian subjects and not compared against clinicians, so further studies are required to establish clinical validity and utility. The second paper explores the effectiveness of a deep learning-based algorithm (CNN) in detecting and classifying liver metastases from colorectal cancer using CT scans . In this South Korean monoinstitutional study, 502 patients were used for training, and temporally different patients (40 with 99 metastatic lesions, 45 without metastases) were used for validation. The algorithm's detection and classification performance was compared to three radiologists (with 2, 3, and 20 years of experience in liver imaging) and three second-year radiology residents. Although showing a higher diagnostic sensitivity than both types of clinicians, the six radiologists outperformed the model in AUAFROC (detection) and false positives per patient (FPP, classification). In addition, the CT scans had been captured eight years before the analyses. The third study was conducted in a clinically realistic scenario, and the model has been implemented in practice . The model was designed to predict 3-month mortality in patients with solid metastatic tumors for several types of cancer (breast, gastrointestinal, genitourinary, lung, rare) and treatment alterations in an outpatient setting. The authors trained a Gradient-Boosted Trees Binary Classifier with observations from 28 484 deceased and alive patients and 493 features from demographic characteristics, laboratory test results, flowsheets, and diagnoses. The model was silently deployed in the patients’ EHRs for 20 months to compare its predictions against 74 oncologists. This prospective temporal validation study involved 3099 encounters from 2041 ethnically diverse patients. The model outperformed oncologists in all metrics for aggregate (general, with and without treatment alterations), gastrointestinal, genitourinary, and lung cancers but was less sensitive than the professionals for rare and breast metastatic tumors. Although currently available in medical practice, the authors note that further research is needed to validate whether using the model improves prognostic confidence and patient engagement. Two models were developed to diagnose node metastases. In , the authors aimed to classify cervical lymph node metastasis from thyroid cancer using CT scans . The researchers had previously developed a CNN (Xception architecture) trained on a 787 axial preoperative CT scans dataset. This study validated the systems' performance on 3 838 images from 698 patients (unknown female-male ratio) and used Grad-CAM to explain the model’s reasoning. The researchers also evaluated the clinical utility of the model by comparing seven radiologists’ performance (one expert, six trainees) with and without its assistance. While aided by the system, the expert’s accuracy, sensitivity, specificity, PPV, and NPV were all found to increase, while only accuracy, specificity, and NPV improved for the trainees. This study was retrospective and conducted in a single institution, and the results obtained were not satisfying enough to justify clinical implementation. The second and last document describes developing an ultrasound-based ML model to assess the risk of sentinel lymph node metastasis (SLNM) in breast cancer patients . First, the authors compared ten algorithms to achieve an optimal model: SVM, RF, LDA, Logistic Regression, NB, kNN, MLP, Long Short-Term Memory, and CNN. The best algorithm (XGBoost) was then integrated into a clinical model, and SHAP was used to analyze its diagnostic performance. XGBoost was trained with 902 patients, and external validation consisted of 50 temporally separate women. The authors also compared their tool with a radiologist’s diagnostic evaluations (unknown years of experience). The results showed that the ML model could predict the risk of SLNM in breast cancer patients based on ultrasound image features with high accuracy (84.6%), having outperformed the radiologist. In addition, SHAP analysis deemed suspicious lymph nodes, microcalcifications, spiculation at the edge of the lesion, and distorted tissue structure around the lesion as the model’s most significant features. Nonetheless, this research was retrospective and used a minimal number of patients from a single institution with limited pathological types of breast cancer. We conducted a scoping review to gather externally validated ML algorithms developed for patient care in oncology whose clinical utility has also been assessed. Given the rapidly evolving nature of the field and the potential for novel approaches and emerging research, and unlike previous reviews , a deliberate decision was made not to restrict the search strategy or outcomes stringently. The objective was to adopt a comprehensive and inclusive process to capture a diverse range of literature that could potentially contribute to our understanding of externally validated machine learning algorithms in the context of oncology practice. This approach allowed for exploring various cancer variants, clinical outcomes, validation methodologies, and clinical utility assessments without preconceived limitations that might have excluded relevant studies. Principal findings The findings from this scoping review reveal several critical insights into the landscape of ML and DL applications in cancer-patient-related decision-making. A notable prominent trend is their increasing recognition and interest. The dominance of papers focused on patients and medical issues (versus computational journals, Fig. A) highlights this growing enthusiasm and a strong emphasis on tackling clinical challenges and reflects a paradigmatic transition from theoretical and computational considerations toward practical, patient-oriented solutions. This is underscored by the significant rise in relevant sources after 2018, particularly in 2020, 2021, and 2022 (Fig. B). However, it's crucial to note that many papers were excluded due to insufficient external validation and clinical utility assessment (Fig. ), showing that the model development and testing methodology still lack standardization, which agrees with the literature . These observations collectively emphasize the evolution and maturation of the field, yet they also serve as a call to action for enhancing the methodological rigor of research endeavors. Concerning the first research question, we found that CNNs have risen to prominence and are now the backbone of most research initiatives (33/56 papers). Random Forests and XGBoost, while less common, still played significant roles, featuring in 7/56 and 6/56 of the studies, respectively, adding diversity to the oncology decision-making landscape. While lung cancer and digestive system assessments were the primary focus, these algorithms demonstrated versatile applicability across various cancer types. Moreover, the emphasis on image-based analyses reflects the potential of ML in augmenting the accuracy of diagnostic processes. However, the limited attention to risk stratification and pharmacotherapy research is a notable caveat. Likewise, the underutilization of radiomics in image studies indicates a missed opportunity. Incorporating radiomics can provide a wealth of information about tumor characteristics and heterogeneity, enriching our understanding and predictive capabilities in oncology. These are areas where ML can make significant contributions to the field, highlighting future directions for research and untapped potential for exploring alternative methodologies. Indeed, methodological considerations highlight several areas that demand attention. The simultaneous development and validation of models in most papers could potentially introduce partiality . Further, the limited sample sizes in many studies, with the majority involving fewer than 200 patients, raise concerns about the generalizability and robustness of these models . Equally, except for three prospective studies and four pieces of research encompassing both retrospective and prospective datasets, the selected papers were mainly retrospective (49/56), a less rigorous design potentially lowering data quality and compromising reliability . Nonetheless, in contrast to previous reviews , we witnessed a substantial increase in multi-institutional studies, marking a positive transformation in the landscape of oncological research. The shift towards collaborative efforts involving multiple centers brings diversity to the study populations, which is critical for generalizing findings to broader patient groups and instilling confidence in research outcomes. Collaborative research involving several institutions augments resources, expertise, and data access, offering a deeper understanding of research questions. However, the infrequent international validation and the paucity of data and code sharing in multi-institutional studies present substantial hurdles. These challenges obstruct the path to enhanced reproducibility and collaborative progress. Scientifically, they emphasize the importance of standardizing data-sharing practices and code accessibility to facilitate transparency, rigor, and cooperation in the field. Besides, the disconnect between data used in research and real-world clinical scenarios is an essential finding. In a clinical environment, both text and image-based information are often simultaneously available, making it crucial for ML models to adapt to such real-world complexities. The prevalence of models designed for binary classification, while suitable for emergency settings, reveals a limitation. Clinical decision-making is a complex process that often involves navigating numerous potential diseases, each with unique characteristics, presentations, and treatment considerations. The overreliance on binary classification fails to capture this richness and underscores the need for more nuanced approaches. Furthermore, the observation that only two models have been effectively implemented in clinical practice highlights the gap between research findings and practical implementation. This finding underscores the challenges in translating scientific progress into real-world healthcare contexts. It draws attention to the necessity of comprehensive validation, addressing regulatory considerations, and managing the integration of new technologies into existing clinical workflows . Additionally, building trust in AI systems is a crucial scientific contribution. The employment of XAI models in 15 reviewed papers demonstrates a proactive effort to enhance transparency and accountability. XAI provides insights into the underlying features, variables, or patterns that contribute to the model's decision-making process, enabling clinicians to comprehend and validate outputs and allay their wariness . This multi-dimensional approach acknowledges the technical and human factors critical for AI's successful implementation in healthcare. Regarding the second research question, two main comparators were used to evaluate clinical utility: clinicians and routine clinical scoring systems and tools, with only one study adopting both types of comparative analyses . An important consideration is the presence of a wide inter- and intra-variability in the number of included clinicians. While 499 medical professionals were identified across the reviewed studies, it is crucial to note that the distribution was heavily skewed. Specifically, only six studies involved a substantial number of clinicians (twenty or more). At the same time, eleven included a moderate number (between five and eighteen), and most had a considerably smaller sample size (four or fewer clinicians, 24 studies). Furthermore, the observed variability underscores the importance of reporting detailed clinician characteristics. Although the number of clinicians was reported in the studies, there was limited information regarding their specific backgrounds, years of experience, and areas of specialization. Of the 41 studies comparing models against clinicians, eleven did not report years of experience, and ten only reported rank. Clinician expertise and experience can significantly influence diagnostic accuracy and decision-making outcomes, so studies with limited physicians with unreported proficiency may be more susceptible to bias and not fully encompass the full spectrum of clinical decision-making . Besides, none of the comparisons were carried out in randomized trials, which is the most accurate way of testing utility . Clinical utility was assessed mainly by comparing model and clinician performance separately, intended to evaluate each entity’s capabilities independently and capture the variations in clinical decision-making among different individuals or groups. Although helpful in calculating inter- and intra-observer variability, this approach may overlook the interaction dynamics between AI and clinicians and not fully reflect the complexities and challenges of real-world clinical practice. Conversely, performance with and without AI assistance was evaluated in ten papers, which helps discern the unique contributions of AI in terms of augmenting clinician judgment, providing additional insights, or improving efficiency. In addition, sixteen studies benchmarked the clinical utility of machine learning models against twelve commonly used clinical tools. Although more prone to bias and less generalizable , this type of comparison provides a uniform reference point for evaluating performance, assessing the practical impact and potential improvements the new method offers over the current standard of care. There is also a clear need for more comprehensive and standardized research in clinical utility, fostering a more effective and seamless integration of AI into healthcare decision-making. Future studies should strive for a more inclusive representation of clinicians, prioritize randomized trials for robust validation, and aim for a thorough understanding of how AI can complement and enhance human expertise. Answering the third research question involves the reported performance during both external validation and clinical utility assessment. The impressive performance of CNNs across various cancer types presents a vital scientific contribution. Their consistently high performance underscores their reputation as a powerful tool in patient-focused cancer research. Additionally, the strong performance of Gradient and Decision Tree-based algorithms in diverse cancer-related tasks reveals an underrepresented facet of ML research. This finding highlights an opportunity to explore and evaluate different ML approaches in oncology applications. The variability in reporting discrimination metrics and calibration metrics, while illuminating the diversity of evaluation methods, raises a critical concern. The lack of standardization hampers the reliability and accuracy of risk assessments , emphasizing the need for consistency in reporting and metrics. In assessing clinical utility, the notable superiority of ML models over clinical tools marks a significant scientific advancement. These findings signify the potential for ML to enhance clinical decision-making processes significantly. However, they also reveal that ML models have not yet reached the same level of expertise as human clinicians in certain aspects, pointing to a collaborative approach where AI systems complement and support clinicians rather than replace them. This collaborative model could offer a path forward to augmenting healthcare capabilities. Finally, six main research gaps were found throughout the review. First, although common cancers were extensively studied in adults, metastases, rare tumors, and different age groups were only investigated in five , three , and two papers, respectively. For example, an evident instance of the limited research focus on rare tumors can be observed in the absence of studies examining breast cancers in men. This paucity might be attributable to insufficient publicly available data, the high cost of collecting new data in bulk , and scarce interaction between medical centers. Second, most models were developed for diagnosis, outcome predictions, or risk stratification, while studies on optimal treatment and drug administration options are still lacking. Third, most studies were retrospective with small sample sizes, thus requiring further prospective validations in diverse patient populations to ensure generalizability. Fourth, none of the image-based studies addressed low-quality images; this is essential for real-world clinical applications, as not all images obtained in practice may be optimal. Fifth, no study assessed utility on patient outcomes, which is not only the ultimate goal but also required by insurance coverage and crucial for determining actual clinical utility and effectiveness. Sixth and last, the absence of studies involving digital twins – even during abstract inspection – is worth mentioning. Further exploration of ML models with these virtual replica technologies could provide meaningful contributions to their application in clinical practice. Likewise, these gaps could be bridged by encouraging and implementing collaboration in healthcare, as merging – ideally, at an international level – information from several institutes would result in more comprehensive data, less bias from country-specific patients and treatment recommendations and larger sample sizes, and consequently, in a higher generalization capacity, and even faster and more accurate diagnoses and treatment decisions. This research stands out for its inclusivity, encompassing diverse patient populations, ML algorithms, and hospital settings, enhancing the applicability of its findings. Its contributions lie in systematically exploring external validation and clinical utility evaluation for ML algorithms, bridging the gap between AI researchers and medical professionals. Lastly, this work highlights the paramount significance of the synergy between AI researchers and medical practitioners. Interdisciplinary collaboration is foundational for promoting the adoption of AI technologies in healthcare and enhancing their scientific and clinical contributions. It ensures that research is translated into innovative, hands-on solutions that align with clinical needs and standards, disease management, and clinical decision-making and positively impact patient care. Study limitations Despite the valuable insights gained from this study, it is essential to acknowledge its limitations. First, relevant studies might have been missed despite efforts to design a comprehensive search strategy and the inclusion of databases from different research fields. For example, sequencing, omics, and molecular biomarker discovery studies were excluded from this review. Notwithstanding their critical role in advancing personalized medicine, genomic, transcriptomic, and proteomic approaches still face obstacles to widespread clinical adoption due to their complexity, the specialized analytical skills required, the need for substantial adjustments in clinical workflows, and significant regulatory challenges . Given these constraints, this review emphasized machine learning algorithms immediately employable in clinical operations, ensuring research is relevant and actionable within healthcare settings. However, this selection reflects a limitation. While narrowing the focus to technologies with broader immediate applicability, not incorporating genetics and omics studies may have inadvertently excluded a subset of literature that explicitly investigates the interplay between genetic factors, treatment regimens, and therapeutic responses, offering a potential explanation for the absence of papers exploring drug and treatment responses and digital twin approaches. Second, this review did not extensively cover the emerging challenges and opportunities of stringent data protection laws, notably the potential for synthetic data in research. While this exclusion aimed at evaluating model performance in genuine patient data, thereby accounting for the complexities and variabilities inherent in healthcare, synthetic data offers a promising avenue for navigating privacy concerns and enhancing dataset diversity . Hence, its absence marks a limitation, reflecting areas beyond the immediate scope of this review yet critical for the future of ML applications in oncology. Third, although the review revealed mostly positive results highlighting ML’s promise, the risk of publication bias cannot be discarded, as studies with positive or significant findings are more likely to be published than those with unfavorable or nonsignificant verdicts . Similarly, the emphasis on SJR as a quality measure, while aiming to ensure the inclusion of high-impact research, acknowledges the potential oversight of specialized, significant studies that might not yet have achieved wide recognition but contribute meaningfully to the field. Furthermore, the selection process did not extend to evaluating the methodological quality or risk of bias within the included studies, potentially limiting the ability to characterize the overall strength of the evidence. Fourth, a significant portion of the studies was retrospective, which increases susceptibility to selection bias and data quality concerns compared to prospective analyses, which may affect the conclusions' robustness . Small sample sizes and the lack of diversity within study populations further challenge the findings' generalizability, emphasizing the need for broader testing of machine learning models across diverse clinical contexts . Additionally, external validation and clinical utility evaluations, often conducted within restricted scopes, may fail to represent the complexities encountered in real-world healthcare settings fully. This limitation suggests that the current body of research may not adequately reflect the potential challenges and applicability of machine learning solutions across the healthcare spectrum, restricting extrapolations. Lastly, a notable methodological concern within the broader field, rather than this review alone, is the variability in performance metrics and a lack of standardized reporting practices across studies. This inconsistency hinders direct comparisons between research outcomes, urging standardized reporting guidelines to facilitate a more effective synthesis of research findings and accurately evaluate the progress of ML-based applications in oncology. Conclusions Although facing challenges primarily tied to data availability and quality, machine learning models, with CNNs in the forefront, have consistently demonstrated substantial potential to revolutionize modern medicine and ultimately improve overall healthcare quality. These models have been especially impactful in lung, colorectal, gastric, bone, and breast cancers, offering a promising pathway for clinicians to make more accurate and personalized clinical decisions and reducing the need for invasive procedures. For instance, in lung cancer, CNNs have enhanced lesion detection, while in colorectal cancers, they have improved early neoplasm detection. Gastric cancer research has also benefited from AI’s ability to diagnose and predict treatment responses, offering new avenues for patient care. Similarly, using ML in breast cancer resulted in streamlined screening processes, and in bone cancer, these algorithms assisted in distinguishing benign from malignant lesions, allowing for earlier detection and treatment. However, the path to fully leveraging ML in oncology highlights a pronounced need for model sensitivity and specificity refinement. Minimizing false positives and negatives is critical, particularly for cancers with intricate presentation patterns. Furthermore, our findings reveal a substantial gap in addressing less common and rare cancers, rising an imperative for the research community to extend its investigative efforts. By broadening the application of ML technologies to encompass these lesser-studied cancers, there is an opportunity to deepen their understanding and craft more inclusive and precise diagnostic and therapeutic approaches, thereby maximizing AI's impact across the full spectrum of oncological patient care. Moving forward, we propose a comprehensive roadmap to guide the implementation of AI in clinical settings. The initial step involves standardized data collection and curation, emphasizing the creation of diverse, well-annotated datasets that accurately represent the complexity of real-world clinical scenarios. These datasets ensure consistency and reliability in model performance across various studies and healthcare institutions. The subsequent stages are centered around the rigorous development, external validation, and utility testing of AI models, placing a premium on homogeneity in discrimination and calibration metrics, robustness, and generalizability. The developed models should be integrated into clinical workflows in close collaboration with healthcare professionals, and ongoing training programs should be implemented to enhance their understanding of AI concepts. Simultaneously, establishing frameworks that address ethical governance, privacy protection, and regulatory compliance is crucial for navigating the legal and ethical considerations associated with AI implementation and promoting data sharing. Finally, fostering a culture of continuous improvement is essential, where AI models are regularly updated and refined based on feedback from clinicians, new data, and advancements in the field. In conclusion, this review issues a resounding call for collective action from oncology stakeholders – clinicians, researchers, policymakers, and healthcare institutions. The findings reinforce the pressing need to fully embrace machine learning as an asset for patient-centered cancer research and decision-making. In this cooperative endeavor, it is imperative to ensure equitable access to high-quality data, engage in large-scale prospective studies, and foster international collaboration for the robust validation of AI models across diverse patient populations. Furthermore, prioritizing investments in transparency, explainability, and the ongoing refinement of AI algorithms is paramount to achieving clinical utility. The dawn of realizing the full potential of medical AI is upon us, and this journey mandates an unwavering commitment to ethics and an unceasing quest for progress. The future of cancer care beckons, and it's our collective responsibility to answer that call. The findings from this scoping review reveal several critical insights into the landscape of ML and DL applications in cancer-patient-related decision-making. A notable prominent trend is their increasing recognition and interest. The dominance of papers focused on patients and medical issues (versus computational journals, Fig. A) highlights this growing enthusiasm and a strong emphasis on tackling clinical challenges and reflects a paradigmatic transition from theoretical and computational considerations toward practical, patient-oriented solutions. This is underscored by the significant rise in relevant sources after 2018, particularly in 2020, 2021, and 2022 (Fig. B). However, it's crucial to note that many papers were excluded due to insufficient external validation and clinical utility assessment (Fig. ), showing that the model development and testing methodology still lack standardization, which agrees with the literature . These observations collectively emphasize the evolution and maturation of the field, yet they also serve as a call to action for enhancing the methodological rigor of research endeavors. Concerning the first research question, we found that CNNs have risen to prominence and are now the backbone of most research initiatives (33/56 papers). Random Forests and XGBoost, while less common, still played significant roles, featuring in 7/56 and 6/56 of the studies, respectively, adding diversity to the oncology decision-making landscape. While lung cancer and digestive system assessments were the primary focus, these algorithms demonstrated versatile applicability across various cancer types. Moreover, the emphasis on image-based analyses reflects the potential of ML in augmenting the accuracy of diagnostic processes. However, the limited attention to risk stratification and pharmacotherapy research is a notable caveat. Likewise, the underutilization of radiomics in image studies indicates a missed opportunity. Incorporating radiomics can provide a wealth of information about tumor characteristics and heterogeneity, enriching our understanding and predictive capabilities in oncology. These are areas where ML can make significant contributions to the field, highlighting future directions for research and untapped potential for exploring alternative methodologies. Indeed, methodological considerations highlight several areas that demand attention. The simultaneous development and validation of models in most papers could potentially introduce partiality . Further, the limited sample sizes in many studies, with the majority involving fewer than 200 patients, raise concerns about the generalizability and robustness of these models . Equally, except for three prospective studies and four pieces of research encompassing both retrospective and prospective datasets, the selected papers were mainly retrospective (49/56), a less rigorous design potentially lowering data quality and compromising reliability . Nonetheless, in contrast to previous reviews , we witnessed a substantial increase in multi-institutional studies, marking a positive transformation in the landscape of oncological research. The shift towards collaborative efforts involving multiple centers brings diversity to the study populations, which is critical for generalizing findings to broader patient groups and instilling confidence in research outcomes. Collaborative research involving several institutions augments resources, expertise, and data access, offering a deeper understanding of research questions. However, the infrequent international validation and the paucity of data and code sharing in multi-institutional studies present substantial hurdles. These challenges obstruct the path to enhanced reproducibility and collaborative progress. Scientifically, they emphasize the importance of standardizing data-sharing practices and code accessibility to facilitate transparency, rigor, and cooperation in the field. Besides, the disconnect between data used in research and real-world clinical scenarios is an essential finding. In a clinical environment, both text and image-based information are often simultaneously available, making it crucial for ML models to adapt to such real-world complexities. The prevalence of models designed for binary classification, while suitable for emergency settings, reveals a limitation. Clinical decision-making is a complex process that often involves navigating numerous potential diseases, each with unique characteristics, presentations, and treatment considerations. The overreliance on binary classification fails to capture this richness and underscores the need for more nuanced approaches. Furthermore, the observation that only two models have been effectively implemented in clinical practice highlights the gap between research findings and practical implementation. This finding underscores the challenges in translating scientific progress into real-world healthcare contexts. It draws attention to the necessity of comprehensive validation, addressing regulatory considerations, and managing the integration of new technologies into existing clinical workflows . Additionally, building trust in AI systems is a crucial scientific contribution. The employment of XAI models in 15 reviewed papers demonstrates a proactive effort to enhance transparency and accountability. XAI provides insights into the underlying features, variables, or patterns that contribute to the model's decision-making process, enabling clinicians to comprehend and validate outputs and allay their wariness . This multi-dimensional approach acknowledges the technical and human factors critical for AI's successful implementation in healthcare. Regarding the second research question, two main comparators were used to evaluate clinical utility: clinicians and routine clinical scoring systems and tools, with only one study adopting both types of comparative analyses . An important consideration is the presence of a wide inter- and intra-variability in the number of included clinicians. While 499 medical professionals were identified across the reviewed studies, it is crucial to note that the distribution was heavily skewed. Specifically, only six studies involved a substantial number of clinicians (twenty or more). At the same time, eleven included a moderate number (between five and eighteen), and most had a considerably smaller sample size (four or fewer clinicians, 24 studies). Furthermore, the observed variability underscores the importance of reporting detailed clinician characteristics. Although the number of clinicians was reported in the studies, there was limited information regarding their specific backgrounds, years of experience, and areas of specialization. Of the 41 studies comparing models against clinicians, eleven did not report years of experience, and ten only reported rank. Clinician expertise and experience can significantly influence diagnostic accuracy and decision-making outcomes, so studies with limited physicians with unreported proficiency may be more susceptible to bias and not fully encompass the full spectrum of clinical decision-making . Besides, none of the comparisons were carried out in randomized trials, which is the most accurate way of testing utility . Clinical utility was assessed mainly by comparing model and clinician performance separately, intended to evaluate each entity’s capabilities independently and capture the variations in clinical decision-making among different individuals or groups. Although helpful in calculating inter- and intra-observer variability, this approach may overlook the interaction dynamics between AI and clinicians and not fully reflect the complexities and challenges of real-world clinical practice. Conversely, performance with and without AI assistance was evaluated in ten papers, which helps discern the unique contributions of AI in terms of augmenting clinician judgment, providing additional insights, or improving efficiency. In addition, sixteen studies benchmarked the clinical utility of machine learning models against twelve commonly used clinical tools. Although more prone to bias and less generalizable , this type of comparison provides a uniform reference point for evaluating performance, assessing the practical impact and potential improvements the new method offers over the current standard of care. There is also a clear need for more comprehensive and standardized research in clinical utility, fostering a more effective and seamless integration of AI into healthcare decision-making. Future studies should strive for a more inclusive representation of clinicians, prioritize randomized trials for robust validation, and aim for a thorough understanding of how AI can complement and enhance human expertise. Answering the third research question involves the reported performance during both external validation and clinical utility assessment. The impressive performance of CNNs across various cancer types presents a vital scientific contribution. Their consistently high performance underscores their reputation as a powerful tool in patient-focused cancer research. Additionally, the strong performance of Gradient and Decision Tree-based algorithms in diverse cancer-related tasks reveals an underrepresented facet of ML research. This finding highlights an opportunity to explore and evaluate different ML approaches in oncology applications. The variability in reporting discrimination metrics and calibration metrics, while illuminating the diversity of evaluation methods, raises a critical concern. The lack of standardization hampers the reliability and accuracy of risk assessments , emphasizing the need for consistency in reporting and metrics. In assessing clinical utility, the notable superiority of ML models over clinical tools marks a significant scientific advancement. These findings signify the potential for ML to enhance clinical decision-making processes significantly. However, they also reveal that ML models have not yet reached the same level of expertise as human clinicians in certain aspects, pointing to a collaborative approach where AI systems complement and support clinicians rather than replace them. This collaborative model could offer a path forward to augmenting healthcare capabilities. Finally, six main research gaps were found throughout the review. First, although common cancers were extensively studied in adults, metastases, rare tumors, and different age groups were only investigated in five , three , and two papers, respectively. For example, an evident instance of the limited research focus on rare tumors can be observed in the absence of studies examining breast cancers in men. This paucity might be attributable to insufficient publicly available data, the high cost of collecting new data in bulk , and scarce interaction between medical centers. Second, most models were developed for diagnosis, outcome predictions, or risk stratification, while studies on optimal treatment and drug administration options are still lacking. Third, most studies were retrospective with small sample sizes, thus requiring further prospective validations in diverse patient populations to ensure generalizability. Fourth, none of the image-based studies addressed low-quality images; this is essential for real-world clinical applications, as not all images obtained in practice may be optimal. Fifth, no study assessed utility on patient outcomes, which is not only the ultimate goal but also required by insurance coverage and crucial for determining actual clinical utility and effectiveness. Sixth and last, the absence of studies involving digital twins – even during abstract inspection – is worth mentioning. Further exploration of ML models with these virtual replica technologies could provide meaningful contributions to their application in clinical practice. Likewise, these gaps could be bridged by encouraging and implementing collaboration in healthcare, as merging – ideally, at an international level – information from several institutes would result in more comprehensive data, less bias from country-specific patients and treatment recommendations and larger sample sizes, and consequently, in a higher generalization capacity, and even faster and more accurate diagnoses and treatment decisions. This research stands out for its inclusivity, encompassing diverse patient populations, ML algorithms, and hospital settings, enhancing the applicability of its findings. Its contributions lie in systematically exploring external validation and clinical utility evaluation for ML algorithms, bridging the gap between AI researchers and medical professionals. Lastly, this work highlights the paramount significance of the synergy between AI researchers and medical practitioners. Interdisciplinary collaboration is foundational for promoting the adoption of AI technologies in healthcare and enhancing their scientific and clinical contributions. It ensures that research is translated into innovative, hands-on solutions that align with clinical needs and standards, disease management, and clinical decision-making and positively impact patient care. Despite the valuable insights gained from this study, it is essential to acknowledge its limitations. First, relevant studies might have been missed despite efforts to design a comprehensive search strategy and the inclusion of databases from different research fields. For example, sequencing, omics, and molecular biomarker discovery studies were excluded from this review. Notwithstanding their critical role in advancing personalized medicine, genomic, transcriptomic, and proteomic approaches still face obstacles to widespread clinical adoption due to their complexity, the specialized analytical skills required, the need for substantial adjustments in clinical workflows, and significant regulatory challenges . Given these constraints, this review emphasized machine learning algorithms immediately employable in clinical operations, ensuring research is relevant and actionable within healthcare settings. However, this selection reflects a limitation. While narrowing the focus to technologies with broader immediate applicability, not incorporating genetics and omics studies may have inadvertently excluded a subset of literature that explicitly investigates the interplay between genetic factors, treatment regimens, and therapeutic responses, offering a potential explanation for the absence of papers exploring drug and treatment responses and digital twin approaches. Second, this review did not extensively cover the emerging challenges and opportunities of stringent data protection laws, notably the potential for synthetic data in research. While this exclusion aimed at evaluating model performance in genuine patient data, thereby accounting for the complexities and variabilities inherent in healthcare, synthetic data offers a promising avenue for navigating privacy concerns and enhancing dataset diversity . Hence, its absence marks a limitation, reflecting areas beyond the immediate scope of this review yet critical for the future of ML applications in oncology. Third, although the review revealed mostly positive results highlighting ML’s promise, the risk of publication bias cannot be discarded, as studies with positive or significant findings are more likely to be published than those with unfavorable or nonsignificant verdicts . Similarly, the emphasis on SJR as a quality measure, while aiming to ensure the inclusion of high-impact research, acknowledges the potential oversight of specialized, significant studies that might not yet have achieved wide recognition but contribute meaningfully to the field. Furthermore, the selection process did not extend to evaluating the methodological quality or risk of bias within the included studies, potentially limiting the ability to characterize the overall strength of the evidence. Fourth, a significant portion of the studies was retrospective, which increases susceptibility to selection bias and data quality concerns compared to prospective analyses, which may affect the conclusions' robustness . Small sample sizes and the lack of diversity within study populations further challenge the findings' generalizability, emphasizing the need for broader testing of machine learning models across diverse clinical contexts . Additionally, external validation and clinical utility evaluations, often conducted within restricted scopes, may fail to represent the complexities encountered in real-world healthcare settings fully. This limitation suggests that the current body of research may not adequately reflect the potential challenges and applicability of machine learning solutions across the healthcare spectrum, restricting extrapolations. Lastly, a notable methodological concern within the broader field, rather than this review alone, is the variability in performance metrics and a lack of standardized reporting practices across studies. This inconsistency hinders direct comparisons between research outcomes, urging standardized reporting guidelines to facilitate a more effective synthesis of research findings and accurately evaluate the progress of ML-based applications in oncology. Although facing challenges primarily tied to data availability and quality, machine learning models, with CNNs in the forefront, have consistently demonstrated substantial potential to revolutionize modern medicine and ultimately improve overall healthcare quality. These models have been especially impactful in lung, colorectal, gastric, bone, and breast cancers, offering a promising pathway for clinicians to make more accurate and personalized clinical decisions and reducing the need for invasive procedures. For instance, in lung cancer, CNNs have enhanced lesion detection, while in colorectal cancers, they have improved early neoplasm detection. Gastric cancer research has also benefited from AI’s ability to diagnose and predict treatment responses, offering new avenues for patient care. Similarly, using ML in breast cancer resulted in streamlined screening processes, and in bone cancer, these algorithms assisted in distinguishing benign from malignant lesions, allowing for earlier detection and treatment. However, the path to fully leveraging ML in oncology highlights a pronounced need for model sensitivity and specificity refinement. Minimizing false positives and negatives is critical, particularly for cancers with intricate presentation patterns. Furthermore, our findings reveal a substantial gap in addressing less common and rare cancers, rising an imperative for the research community to extend its investigative efforts. By broadening the application of ML technologies to encompass these lesser-studied cancers, there is an opportunity to deepen their understanding and craft more inclusive and precise diagnostic and therapeutic approaches, thereby maximizing AI's impact across the full spectrum of oncological patient care. Moving forward, we propose a comprehensive roadmap to guide the implementation of AI in clinical settings. The initial step involves standardized data collection and curation, emphasizing the creation of diverse, well-annotated datasets that accurately represent the complexity of real-world clinical scenarios. These datasets ensure consistency and reliability in model performance across various studies and healthcare institutions. The subsequent stages are centered around the rigorous development, external validation, and utility testing of AI models, placing a premium on homogeneity in discrimination and calibration metrics, robustness, and generalizability. The developed models should be integrated into clinical workflows in close collaboration with healthcare professionals, and ongoing training programs should be implemented to enhance their understanding of AI concepts. Simultaneously, establishing frameworks that address ethical governance, privacy protection, and regulatory compliance is crucial for navigating the legal and ethical considerations associated with AI implementation and promoting data sharing. Finally, fostering a culture of continuous improvement is essential, where AI models are regularly updated and refined based on feedback from clinicians, new data, and advancements in the field. In conclusion, this review issues a resounding call for collective action from oncology stakeholders – clinicians, researchers, policymakers, and healthcare institutions. The findings reinforce the pressing need to fully embrace machine learning as an asset for patient-centered cancer research and decision-making. In this cooperative endeavor, it is imperative to ensure equitable access to high-quality data, engage in large-scale prospective studies, and foster international collaboration for the robust validation of AI models across diverse patient populations. Furthermore, prioritizing investments in transparency, explainability, and the ongoing refinement of AI algorithms is paramount to achieving clinical utility. The dawn of realizing the full potential of medical AI is upon us, and this journey mandates an unwavering commitment to ethics and an unceasing quest for progress. The future of cancer care beckons, and it's our collective responsibility to answer that call. Additional file 1. Protocol. This document presents the protocol developed for the scoping review. Additional file 2. PRISMA 2020 Checklist. This file contains the completed PRISMA 2020 checklist documenting the reporting of the scoping review methodology and findings. Additional file 3. Search Strategy. This document details the complete search strategy and database-specific filters applied in the scoping review. Additional file 4. Ranking Filter. This document contains the Python-based ranking filter developed to filter journals based on SCImago Journal Rank metrics. Additional file 5. Data Charting. This spreadsheet presents the comprehensive data extraction and charting results from the articles selected for inclusion in the scoping review. |
Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach | 993b606b-cba1-4fd3-b3f7-fad92d53d671 | 11835785 | Surgical Procedures, Operative[mh] | Atrial fibrillation (AF), the most common sustained cardiac arrhythmia , poses significant challenges in the clinical management and prediction of disease progression. Currently, the ATLAS score provides a reliable risk estimate to predict the rate of AF recurrence after a pulmonary vein isolation (PVI) procedure. However, it suffers from typical limitations of clinical scores, such as the use of a fixed number of independent variables for the prediction of a single dependent variable, its static nature, and its inability to be adjusted as new knowledge becomes available. All these issues can be addressed by artificial intelligence (AI) models based on machine learning algorithms, which can learn from available data, be quickly updated with new data, and perform complex calculations in a short time. In recent years, such machine learning techniques have emerged as powerful tools in various medical domains, including cardiology . There have been some recent successful attempts to develop AI models to predict the recurrence of AF after ablation procedure. However, despite the good performance of those models, they either lack the explainability required to allow their acceptance by health care professionals , or share the same limitations of medical scores discussed above . In fact, although many physicians have recognized that AI models may be useful both for diagnosis and prognosis in medical practice, many authors raise legitimate questions about the lack of explainability of some AI models . Bayesian networks, despite being still poorly adopted in health care, have gained popularity as clinical decision support models in medicine due to their ability to handle complex problems with causal dependencies, integrate both data and domain knowledge, provide an interpretable graphical structure, and support both diagnostic and prognostic reasoning . In addition, these models can be updated with new medical knowledge, enabling the incorporation of novel risk factors and advancements in the field of arrhythmology. This adaptability and scalability make Bayesian networks a promising tool for decision-making in medicine and long-term monitoring of patients with AF. This study aims to address key research gaps in the prediction of AF relapse by developing a more reliable and adaptable predictive model based on Bayesian networks. Traditional medical scoring systems are limited by their reliance on a fixed set of independent variables, which reduces their generalizability across diverse patient populations. In addition, many existing AI models for AF prediction lack the necessary explainability required to foster trust and acceptance among health care professionals. To bridge these gaps, this study makes several significant contributions. First, it introduces a novel explainable AI model based on Bayesian networks, which allows for the calculation of conditional probabilities tailored to individual patient profiles, thus enhancing both the interpretability of the predictions and their clinical acceptance. Second, the study overcomes the limitations of traditional scoring systems by offering a dynamic and adaptable model that can incorporate new risk factors and learn from evolving patient data, thereby improving predictive accuracy over time. Third, the proposed model demonstrates flexibility and robustness, making it suitable for real-world clinical scenarios where incomplete data may be present. Finally, by integrating this model into clinical decision support systems, the study has the potential to enhance decision-making processes and improve patient outcomes in the management of AF. In this work, we investigate the use of Bayesian networks to predict AF relapse before a percutaneous PVI procedure and evaluate its potential as a valuable clinical tool, with the primary aim of improving clinical decision-making and patient care. Study Population All consecutive patients with symptomatic drug-refractory AF undergoing cardiac computed tomography (CT) before percutaneous PVI at Hospital Santa Cruz (Carnaxide, Portugal) between November 2015 and July 2019 were included in an observational registry used for this retrospective study. Patients with moderate or severe valvular heart disease, left atrial thrombus, abnormal thyroid function, or contraindication to anticoagulation were excluded. Baseline demographic and clinical characteristics, including age, sex, height, weight, and presence of hypertension, diabetes, smoking, and known coronary artery disease, were recorded for all patients. AF was categorized as paroxysmal if it self-terminated in less than 7 days, persistent if episodes lasted ≥7 days or required cardioversion, or long-standing persistent if AF was maintained for more than 12 months. PVI Protocol PVI was guided by electroanatomical mapping, using either NavX (St Jude Medical) or CARTO (Biosense Webster) systems. The right femoral vein was used as the preferred vascular access, through which three catheter electrodes were introduced: (1) a decapolar catheter, advanced through the coronary sinus; (2) a variable circular mapping catheter, placed in the pulmonary veins (PVs); and (3) an irrigated contact force-sensing ablation catheter. Left atrial access was established by a transseptal puncture. Radiofrequency ablation was performed more than 5 mm from the PV ostia, with continuous lesions enclosing the left and right pairs of PVs. The treatment was considered successful if complete electrophysiological PVI was achieved. When required, electrical cardioversion was performed at the end of the procedure. Oral anticoagulation was resumed 6 hours after the ablation, maintained for 6 months, and then withdrawn or continued according to CHA2DS2-VASc criteria. Generally, class I/III antiarrhythmic drugs were maintained in all patients for the first 3 months after the procedure and then withdrawn if there was no AF recurrence. A proton pump inhibitor was also prescribed for the first month after the ablation. Study End Point and Patient Follow-Up The study end point was AF recurrence, defined as symptomatic or documented AF or other atrial arrhythmias, after a 3-month blanking period. Symptomatic AF was defined as the presence of symptoms considered to be likely due to AF episodes. Documented AF was defined by the presence of at least one episode of AF lasting more than 30 seconds in an ECG, 24-hour Holter monitoring, or event-loop recording. The follow-up protocol comprised outpatient visits with 12-lead ECG and 24-hour Holter monitoring at the assistant physicians’ discretion (typically at 6 and 12 months, and yearly thereafter). Patients were encouraged to contact the department if they experienced symptoms of AF recurrence. Whenever clinical records were insufficient, a structured telephonic interview was conducted. Patients who were kept on antiarrhythmic drugs after the third month of follow-up were not considered as failed ablation. Population Characteristics The analyzed sample comprised demographic and clinical data from 480 patients who underwent follow-up after the PVI procedure described above. The cohort included 295 (61.5%) men and 185 (38.5%) women, with a mean age of 61.1 (SD 11.5) years. The median duration of the follow-up time of the patients was 392 (IQR 150‐674) days. For the purpose of this study, all numeric variables in the dataset (including age, BMI, left atrial volume, and epicardial fat) were discretized into classes. Data characterization is shown in . The variable preablation AF type represents the type of AF identified in each patient before the ablation procedure, being coded either as paroxysmal or persistent. The variable sex is categorized as binary (female or male). All other binary variables such as alcoholism, smoking, diabetes, high blood pressure, and obstructive sleep apnea, were coded as logical (true or false), indicating the presence or absence of that condition. The variable AF relapse represents the identification of postprocedural AF relapse in patients during follow-up examinations, also coded as logical (true or false). It was targeted as the outcome variable for this study. Bayesian Network Model Training Network Structure Considering that Bayesian networks are probabilistic graphical models made to represent knowledge, we started by building our network structure primarily based on medical knowledge in this field. In a first step, we opted to include (whitelist) some of the most noteworthy known clinical relationships between features, such as (1) known risk factors for diseases expressed in the dataset, namely diabetes, high blood pressure (HBP), and obstructive sleep apnea (OSA); and (2) known predictive features of AF relapse, such as the ATLAS score features (age, sex, smoking, persistent AF and left atrial volume), as well as epicardial fat and OSA , as suggested by recent medical literature. In the second step, we explored additional potential relationships between features that could improve model fit and better explain the observed data through data-driven inference. To achieve this, we applied a score-based structure learning method, using the Bayesian Information Criterion (BIC) as the scoring metric to be optimized. The optimization of the BIC score was performed using a hill-climbing algorithm . This approach allowed us to learn the remaining structure of the network, resulting in a model that aligns with current medical knowledge while effectively capturing the relationships between the variables. Model Fitting After the network structure was defined, a model could be set to learn the conditional probabilities among all related features. The parameters of the Bayesian network were thus fit given the previously learned structure and the available data, by means of a Bayesian posterior estimator with a uniform before. With the model fitted in this fashion, it was now possible to use the model to compute the estimated probability that a given patient has AF relapse given her clinical characteristics, for example, the model can be asked “based on the available data, what is the probability that a patient has AF relapse knowing that she is female,+65 years old and non-smoking . ” Further examples of computed conditional probabilities for AF relapse based on patients’ conditions are presented in the Results section. Model Validation Model validation was executed by out-of-sample testing to assess the predictive performance of the model on unseen data, as follows: from the full dataset, a random sample was taken to be used as training data for the model. This sample was used to train a conditional probabilities model, as previously described. Following that, the remaining observations that were not included in the training set were used as a test set, upon which the model predictions were tested. For this testing step, we used the model to compute the conditional probability of AF relapse for each patient in the test set, and stored the prediction results for each tested observation. This process was cyclically repeated multiple times until each observation had been used for testing at least 30 times. Finally, the calculated probability of AF relapse for each patient was assumed to be the average of all estimated probabilities for that patient. We then compared the average predicted probability with the true observation of AF relapse for each patient, and measured the performance through the area under the receiver operating characteristic curve (AUC-ROC). Regarding the sampling process at the beginning of each cycle, it is worth mentioning that the random samples for training the model were obtained through one of four different sampling processes: (1) bootstrapping, which on average uses 63.2% of the observations for training, or (2) hold-out, using fixed splitting ratios for the train and test of 80:20, (3) 90:10, and (4) 95:5, that is, with 80%, 90%, and 95% of the observations, respectively, being used for training the model, and the remaining proportion used for testing. With these processes, we aimed to assess the model’s ability to generalize for unknown data and achieve a good estimator for the generalization error. This analysis was carried out using R (version 4.2.2; R Foundation for Statistical Computing) , with packages bnlearn and pROC . Ethical Considerations This study adheres to the ethical guidelines of the Declaration of Helsinki, including its later amendments. It has been approved by the Health Ethics Commission of the Western Lisbon Hospital Center, with the approval number 2117. All patients provided written informed consent before this study for both the procedure and the publication of any relevant data. Patient confidentiality was maintained by removing any personally identifiable information from all data used in this study and its supplementary materials. All consecutive patients with symptomatic drug-refractory AF undergoing cardiac computed tomography (CT) before percutaneous PVI at Hospital Santa Cruz (Carnaxide, Portugal) between November 2015 and July 2019 were included in an observational registry used for this retrospective study. Patients with moderate or severe valvular heart disease, left atrial thrombus, abnormal thyroid function, or contraindication to anticoagulation were excluded. Baseline demographic and clinical characteristics, including age, sex, height, weight, and presence of hypertension, diabetes, smoking, and known coronary artery disease, were recorded for all patients. AF was categorized as paroxysmal if it self-terminated in less than 7 days, persistent if episodes lasted ≥7 days or required cardioversion, or long-standing persistent if AF was maintained for more than 12 months. PVI was guided by electroanatomical mapping, using either NavX (St Jude Medical) or CARTO (Biosense Webster) systems. The right femoral vein was used as the preferred vascular access, through which three catheter electrodes were introduced: (1) a decapolar catheter, advanced through the coronary sinus; (2) a variable circular mapping catheter, placed in the pulmonary veins (PVs); and (3) an irrigated contact force-sensing ablation catheter. Left atrial access was established by a transseptal puncture. Radiofrequency ablation was performed more than 5 mm from the PV ostia, with continuous lesions enclosing the left and right pairs of PVs. The treatment was considered successful if complete electrophysiological PVI was achieved. When required, electrical cardioversion was performed at the end of the procedure. Oral anticoagulation was resumed 6 hours after the ablation, maintained for 6 months, and then withdrawn or continued according to CHA2DS2-VASc criteria. Generally, class I/III antiarrhythmic drugs were maintained in all patients for the first 3 months after the procedure and then withdrawn if there was no AF recurrence. A proton pump inhibitor was also prescribed for the first month after the ablation. The study end point was AF recurrence, defined as symptomatic or documented AF or other atrial arrhythmias, after a 3-month blanking period. Symptomatic AF was defined as the presence of symptoms considered to be likely due to AF episodes. Documented AF was defined by the presence of at least one episode of AF lasting more than 30 seconds in an ECG, 24-hour Holter monitoring, or event-loop recording. The follow-up protocol comprised outpatient visits with 12-lead ECG and 24-hour Holter monitoring at the assistant physicians’ discretion (typically at 6 and 12 months, and yearly thereafter). Patients were encouraged to contact the department if they experienced symptoms of AF recurrence. Whenever clinical records were insufficient, a structured telephonic interview was conducted. Patients who were kept on antiarrhythmic drugs after the third month of follow-up were not considered as failed ablation. The analyzed sample comprised demographic and clinical data from 480 patients who underwent follow-up after the PVI procedure described above. The cohort included 295 (61.5%) men and 185 (38.5%) women, with a mean age of 61.1 (SD 11.5) years. The median duration of the follow-up time of the patients was 392 (IQR 150‐674) days. For the purpose of this study, all numeric variables in the dataset (including age, BMI, left atrial volume, and epicardial fat) were discretized into classes. Data characterization is shown in . The variable preablation AF type represents the type of AF identified in each patient before the ablation procedure, being coded either as paroxysmal or persistent. The variable sex is categorized as binary (female or male). All other binary variables such as alcoholism, smoking, diabetes, high blood pressure, and obstructive sleep apnea, were coded as logical (true or false), indicating the presence or absence of that condition. The variable AF relapse represents the identification of postprocedural AF relapse in patients during follow-up examinations, also coded as logical (true or false). It was targeted as the outcome variable for this study. Network Structure Considering that Bayesian networks are probabilistic graphical models made to represent knowledge, we started by building our network structure primarily based on medical knowledge in this field. In a first step, we opted to include (whitelist) some of the most noteworthy known clinical relationships between features, such as (1) known risk factors for diseases expressed in the dataset, namely diabetes, high blood pressure (HBP), and obstructive sleep apnea (OSA); and (2) known predictive features of AF relapse, such as the ATLAS score features (age, sex, smoking, persistent AF and left atrial volume), as well as epicardial fat and OSA , as suggested by recent medical literature. In the second step, we explored additional potential relationships between features that could improve model fit and better explain the observed data through data-driven inference. To achieve this, we applied a score-based structure learning method, using the Bayesian Information Criterion (BIC) as the scoring metric to be optimized. The optimization of the BIC score was performed using a hill-climbing algorithm . This approach allowed us to learn the remaining structure of the network, resulting in a model that aligns with current medical knowledge while effectively capturing the relationships between the variables. Model Fitting After the network structure was defined, a model could be set to learn the conditional probabilities among all related features. The parameters of the Bayesian network were thus fit given the previously learned structure and the available data, by means of a Bayesian posterior estimator with a uniform before. With the model fitted in this fashion, it was now possible to use the model to compute the estimated probability that a given patient has AF relapse given her clinical characteristics, for example, the model can be asked “based on the available data, what is the probability that a patient has AF relapse knowing that she is female,+65 years old and non-smoking . ” Further examples of computed conditional probabilities for AF relapse based on patients’ conditions are presented in the Results section. Considering that Bayesian networks are probabilistic graphical models made to represent knowledge, we started by building our network structure primarily based on medical knowledge in this field. In a first step, we opted to include (whitelist) some of the most noteworthy known clinical relationships between features, such as (1) known risk factors for diseases expressed in the dataset, namely diabetes, high blood pressure (HBP), and obstructive sleep apnea (OSA); and (2) known predictive features of AF relapse, such as the ATLAS score features (age, sex, smoking, persistent AF and left atrial volume), as well as epicardial fat and OSA , as suggested by recent medical literature. In the second step, we explored additional potential relationships between features that could improve model fit and better explain the observed data through data-driven inference. To achieve this, we applied a score-based structure learning method, using the Bayesian Information Criterion (BIC) as the scoring metric to be optimized. The optimization of the BIC score was performed using a hill-climbing algorithm . This approach allowed us to learn the remaining structure of the network, resulting in a model that aligns with current medical knowledge while effectively capturing the relationships between the variables. After the network structure was defined, a model could be set to learn the conditional probabilities among all related features. The parameters of the Bayesian network were thus fit given the previously learned structure and the available data, by means of a Bayesian posterior estimator with a uniform before. With the model fitted in this fashion, it was now possible to use the model to compute the estimated probability that a given patient has AF relapse given her clinical characteristics, for example, the model can be asked “based on the available data, what is the probability that a patient has AF relapse knowing that she is female,+65 years old and non-smoking . ” Further examples of computed conditional probabilities for AF relapse based on patients’ conditions are presented in the Results section. Model validation was executed by out-of-sample testing to assess the predictive performance of the model on unseen data, as follows: from the full dataset, a random sample was taken to be used as training data for the model. This sample was used to train a conditional probabilities model, as previously described. Following that, the remaining observations that were not included in the training set were used as a test set, upon which the model predictions were tested. For this testing step, we used the model to compute the conditional probability of AF relapse for each patient in the test set, and stored the prediction results for each tested observation. This process was cyclically repeated multiple times until each observation had been used for testing at least 30 times. Finally, the calculated probability of AF relapse for each patient was assumed to be the average of all estimated probabilities for that patient. We then compared the average predicted probability with the true observation of AF relapse for each patient, and measured the performance through the area under the receiver operating characteristic curve (AUC-ROC). Regarding the sampling process at the beginning of each cycle, it is worth mentioning that the random samples for training the model were obtained through one of four different sampling processes: (1) bootstrapping, which on average uses 63.2% of the observations for training, or (2) hold-out, using fixed splitting ratios for the train and test of 80:20, (3) 90:10, and (4) 95:5, that is, with 80%, 90%, and 95% of the observations, respectively, being used for training the model, and the remaining proportion used for testing. With these processes, we aimed to assess the model’s ability to generalize for unknown data and achieve a good estimator for the generalization error. This analysis was carried out using R (version 4.2.2; R Foundation for Statistical Computing) , with packages bnlearn and pROC . This study adheres to the ethical guidelines of the Declaration of Helsinki, including its later amendments. It has been approved by the Health Ethics Commission of the Western Lisbon Hospital Center, with the approval number 2117. All patients provided written informed consent before this study for both the procedure and the publication of any relevant data. Patient confidentiality was maintained by removing any personally identifiable information from all data used in this study and its supplementary materials. Bayesian Network Structure The Bayesian network structure defined by expert knowledge and inference from data is represented in . As noted in this representation, the model suggests relationships that were not initially declared, such as BMI→Epicardial fat, OSA→preablation AF type, and preablation AF type→Left atrial volume. Furthermore, sex appears to be related to active smoking, alcoholism, and BMI. All these relationships are not surprising and are even supported by the current medical literature, thus providing a reasonable representation of clinical knowledge in this field. Regarding the outcome variable AF relapse, the model did not find any other relevant relations apart from those previously whitelisted. An alternative representation of this network is exhibited in , showing relative frequencies per class at each node. Conditional Probability Calculation With each trained model, we calculated the conditional probability of AF relapse for each patient in the test set, considering their reported clinical conditions. These probabilities were compared with the true values of AF relapse for each patient and plotted in a receiver operating characteristic (ROC) curve, with cutoff values for classification determined as those that maximize the Youden J statistic. We tested in turns 7, 5, or 6 predictive features, as explained in the sections to follow. For illustration purposes, presents a few examples of different combinations of patients’ conditions and their calculated conditional probability of AF relapse. These calculations were conducted for hypothetical patients, while considering as predictors all 7 parent nodes of AF relapse as represented in the network structure. The 7 Predictors In the first stage, the calculation considered the clinical state of the patients for the 7 parent nodes of AF relapse represented in the network structure: age, sex, smoking, preablation AF type, left atrial volume, epicardial fat, and OSA. The performance of the model in classifying AF relapse with all parent nodes (7 predictors) was calculated to an average area under the curve (AUC) value of 0.752 (95% CI 0.701‐0.800) for all sampling methods. ROC curves for each validation test are shown in . The 5 Predictors Out of the 7 predictive features used in the previous test, 2 are usually difficult to obtain: left atrial volume and epicardial fat. These 2 features are typically calculated by diagnostic imaging, which is not always performed for all patients. In some cases, the physician does not have access to those measurements, which frustrates the calculation of medical scores that require any of those values, as is the case with the ATLAS score. The purpose of this test was to evaluate the performance of the model without these 2 features, thus simulating a frequent real-life scenario. As such, we calculated the conditional probability of AF relapse for each patient in the test set, considering only 5 of its parent nodes: age, sex, smoking, preablation AF type, and OSA. The remaining 2 parent nodes (left atrial volume and epicardial fat) were disregarded from evidence to calculate conditional probabilities. The performance of the model for classifying AF relapse with these 5 predictors was as expectably lower than with 7 predictors, with a calculated AUC average of 0.661 (95% CI 0.603‐0.718) for all sampling methods. ROC curves for each validation test are shown in . The 6 Predictors The predictive performance with only the previous 5 predictors appears to be slightly more than average. However, it can be observed from the defined Bayesian network structure that the epicardial fat node has BMI as its single parent, meaning that the latter directly influences the former. As such, the lack of information on epicardial fat for a given patient can be partially compensated by its information on the BMI value. This poses an interesting possibility, especially when observed that BMI is usually an available or easy to obtain feature for any patient. The rationale for this test was therefore to gauge the predictive power of a model when using the 5 predictors in the previous experience, plus the information on the BMI node. All these 6 features—age, sex, smoking, preablation AF type, OSA, and BMI—are usually easily available clinical variables for physicians’ evaluation, which do not require the use of additional complex or expensive diagnostic means. Therefore, this setting simulates the predictive power of the model in a likely real-life scenario. For this test, we calculated the conditional probability of AF relapse for each patient in the test set, considering evidence on age, sex, smoking, preablation AF type, OSA, and BMI. Any information on left atrial volume and epicardial fat was ignored for this purpose. The performance of the model for classifying AF relapse with these 6 predictors resulted in a computed AUC average of 0.703 (95% CI 0.652‐0.753) for all sampling methods. ROC curves for each validation test are shown in . presents a comparative analysis of the three models developed using 5, 6, and 7 predictors, respectively. As shown, the AUC-ROC progressively increases with the addition of predictors, indicating improved model performance. Furthermore, the 95% CI narrows as the number of predictors increases, suggesting greater precision in the model’s estimates. The Bayesian network structure defined by expert knowledge and inference from data is represented in . As noted in this representation, the model suggests relationships that were not initially declared, such as BMI→Epicardial fat, OSA→preablation AF type, and preablation AF type→Left atrial volume. Furthermore, sex appears to be related to active smoking, alcoholism, and BMI. All these relationships are not surprising and are even supported by the current medical literature, thus providing a reasonable representation of clinical knowledge in this field. Regarding the outcome variable AF relapse, the model did not find any other relevant relations apart from those previously whitelisted. An alternative representation of this network is exhibited in , showing relative frequencies per class at each node. With each trained model, we calculated the conditional probability of AF relapse for each patient in the test set, considering their reported clinical conditions. These probabilities were compared with the true values of AF relapse for each patient and plotted in a receiver operating characteristic (ROC) curve, with cutoff values for classification determined as those that maximize the Youden J statistic. We tested in turns 7, 5, or 6 predictive features, as explained in the sections to follow. For illustration purposes, presents a few examples of different combinations of patients’ conditions and their calculated conditional probability of AF relapse. These calculations were conducted for hypothetical patients, while considering as predictors all 7 parent nodes of AF relapse as represented in the network structure. In the first stage, the calculation considered the clinical state of the patients for the 7 parent nodes of AF relapse represented in the network structure: age, sex, smoking, preablation AF type, left atrial volume, epicardial fat, and OSA. The performance of the model in classifying AF relapse with all parent nodes (7 predictors) was calculated to an average area under the curve (AUC) value of 0.752 (95% CI 0.701‐0.800) for all sampling methods. ROC curves for each validation test are shown in . Out of the 7 predictive features used in the previous test, 2 are usually difficult to obtain: left atrial volume and epicardial fat. These 2 features are typically calculated by diagnostic imaging, which is not always performed for all patients. In some cases, the physician does not have access to those measurements, which frustrates the calculation of medical scores that require any of those values, as is the case with the ATLAS score. The purpose of this test was to evaluate the performance of the model without these 2 features, thus simulating a frequent real-life scenario. As such, we calculated the conditional probability of AF relapse for each patient in the test set, considering only 5 of its parent nodes: age, sex, smoking, preablation AF type, and OSA. The remaining 2 parent nodes (left atrial volume and epicardial fat) were disregarded from evidence to calculate conditional probabilities. The performance of the model for classifying AF relapse with these 5 predictors was as expectably lower than with 7 predictors, with a calculated AUC average of 0.661 (95% CI 0.603‐0.718) for all sampling methods. ROC curves for each validation test are shown in . The predictive performance with only the previous 5 predictors appears to be slightly more than average. However, it can be observed from the defined Bayesian network structure that the epicardial fat node has BMI as its single parent, meaning that the latter directly influences the former. As such, the lack of information on epicardial fat for a given patient can be partially compensated by its information on the BMI value. This poses an interesting possibility, especially when observed that BMI is usually an available or easy to obtain feature for any patient. The rationale for this test was therefore to gauge the predictive power of a model when using the 5 predictors in the previous experience, plus the information on the BMI node. All these 6 features—age, sex, smoking, preablation AF type, OSA, and BMI—are usually easily available clinical variables for physicians’ evaluation, which do not require the use of additional complex or expensive diagnostic means. Therefore, this setting simulates the predictive power of the model in a likely real-life scenario. For this test, we calculated the conditional probability of AF relapse for each patient in the test set, considering evidence on age, sex, smoking, preablation AF type, OSA, and BMI. Any information on left atrial volume and epicardial fat was ignored for this purpose. The performance of the model for classifying AF relapse with these 6 predictors resulted in a computed AUC average of 0.703 (95% CI 0.652‐0.753) for all sampling methods. ROC curves for each validation test are shown in . presents a comparative analysis of the three models developed using 5, 6, and 7 predictors, respectively. As shown, the AUC-ROC progressively increases with the addition of predictors, indicating improved model performance. Furthermore, the 95% CI narrows as the number of predictors increases, suggesting greater precision in the model’s estimates. Principal Findings The ability to accurately predict clinical outcomes is vital for improving the quality of medical care and increasing the efficiency of resource allocation in health care. For such predictions, cardiologists often use clinical scores that have various limitations, such as being dependent on a set number of medical variables or not being adaptable to new medical knowledge. Nonetheless, these professionals have also been witnessing the development of AI models for applications in cardiology in general and for the management of arrhythmias in particular . In this context, our aim was to develop an alternative model to clinical scores that was not susceptible to these limitations, to predict the relapse of AF after PVI procedure. For this purpose, we have resorted to Bayesian networks, a type of probabilistic graphical model that can represent knowledge as a set of variables and their conditional dependencies. Unlike traditional prognostic models based on linear or logistic regressions, Bayesian networks offer an interpretable graphical structure, which enhances the model’s clarity and facilitates its adoption among physicians. In addition, Bayesian networks manage missing data more efficiently than other machine learning methods like classification and regression trees or random forests, as they can compute the probability of an outcome even when predictive variables have missing values. This makes them particularly well suited for medical datasets, where missing data are often a challenge. We have therefore chosen to develop our models based on Bayesian networks due to their explainability, flexibility, and robustness. Their explainability derives from their ability to represent relationships between variables as a graphical model, thus rendering their results more comprehensible. This capability is of paramount importance for the acceptance of AI models by medical professionals, who can thus integrate them safely into clinical practice . Further, the models’ flexibility derives from the ability to accommodate and represent new medical knowledge by reshaping the network structure accordingly and recalculating the conditional dependencies among multiple variables. Therefore, new suspected or known risk factors or predictors for AF relapse can be incorporated into a Bayesian network model at any time, with minimal resetting of the model. Additionally, the models’ robustness derives from the fact that they can make predictions for the outcome variable even when there are missing data on some predictive variables, thus allowing them to be used in cases of incomplete information on any given patient. Thus, unlike clinical scores, Bayesian networks do not require the full set of clinical explanatory variables to deliver useful results. Despite none of these characteristics being unique to Bayesian networks on its own, this combination of characteristics makes these models highly interesting to be used as basis for clinical decision support tools.The first stage of the construction of our model was to create the network structure, that is, the network of relationships between the clinical variables. As stated in the Methods section, this was achieved in 2 steps: initially the known relationships were set manually based on expert knowledge; then, in a second step, the network structure was improved upon inference from data by the use of an AI algorithm. At this last step, the algorithm suggested a relationship between BMI and epicardial fat, which was considered acceptable, as there is significant evidence of a correlation between these two variables . This finding proved useful since it enabled the use of the path “BMI → epicardial fat → AF relapse” when there was no information on the middle variable. The algorithm also suggested a path “OSA → pre-ablation AF type → left atrial volume.” In this study, we opted to retain this suggestion in the network structure as a potential motivation for further exploration in future research. Although these relationships were considered to represent knowledge derived from the data, they were not particularly relevant for the model calculations, since each of these variables is also directly related to the outcome variable. The second stage of the construction of our model was to train and validate the model based on the previous network structure. When validating the use of evidence from the 7 parent nodes of our outcome variable, the model performed with a calculated AUC value of approximately 0.75, interpreted as acceptable diagnostic accuracy . These results implied using as predictive variables age, sex, smoking, preablation AF type, left atrial volume, epicardial fat, and OSA. However, some of these features are not always available in patients’ clinical records. Thus, we have validated the model in the absence of information on left atrial volume and epicardial fat as predictive features. In this case, the model exhibited an expectedly lower performance, with a calculated mean AUC value close to 0.66. Despite the observed difference was not statistically significant, as noted from the overlapping confidence intervals, it suggests that these 2 features have a high weight on the performance of the model. This finding is consistent with those reported in the ATLAS score that the left atrial volume has the highest weight on the predictive power of that score . Going further, our experiment also showed that the lack of information on epicardial fat can be partially compensated for by evidence of BMI, as this is its parent node. Taking into account daily clinical practice, this poses an interesting possibility, since BMI measurements are generally available for clinical evaluation for most patients. In these 6-variable cases, the model response exhibited a calculated mean AUC value of 0.70. Also here, despite the observed differences for the previous scenarios not being statistically significant, these outcomes fit within an acceptable range for a prediction tool. Such results implied using as predictive variables age, sex, smoking, preablation AF type, OSA, and BMI, all of which are typically easy to obtain in a clinical setting. To put these results in perspective, the AFA Recur tool developed by Saglietto et al achieves a performance of AUC 0.72 using a 19-variable AI model with little to no explainability. Future research in the context of predicting AF relapse using Bayesian networks should address several key challenges and directions. The first is ensuring the generalizability of the model across diverse populations and clinical settings to seek validation in varied patient cohorts. Second, it would be essential to conduct longitudinal studies to assess the model’s long-term performance and capture patient evolution over extended time horizons. In addition, future studies could explore the inclusion of expanded predictive factors, such as genetic influences, lifestyle changes, and comorbidities, to enhance the model’s accuracy and clinical use. Finally, incorporating patient-reported outcomes and preferences into the predictive framework may improve the model’s relevance and acceptance, fostering a more patient-centric approach to clinical decision-making. We consider that this data-based approach based on a Bayesian network model can be the backbone for a future clinical decision support system. Being an AI model, it opens the possibility of being continuously retrained as new patient information becomes available in clinical records, hence progressively providing more accurate results upon new accumulated data. Such a retraining process can be automatized on a schedule or upon a trigger, for example, recalculating conditional dependencies between clinical features on a monthly basis or at every new 100 patient observations. This retraining of the model based on the recalculation of conditional probabilities from new patient data is not expected to represent significant computational costs, even for exceptionally large amounts of patient observations. This model can also be considered as an enhancement of the ATLAS score, as it is based on its 5 predictive features, to which 2 additional features were added. Nonetheless, it may serve as a starting point for the representation of knowledge in this field, being open to incorporating new evidence as it becomes available. For such a reason, we believe that the findings of this research contribute to the growing body of knowledge on the application of AI methods in cardiology and pave the way for future advancements in predictive analytics for cardiovascular diseases. Strengths and Limitations The model was developed and evaluated on a dataset with a limited number of features. Although the current literature identifies other potential risk factors for relapse of AF, these were not considered in this work, as there was no information from patients on such features. Nevertheless, this type of model allows the incorporation of other risk factors at any time, provided that the network structure is rebuilt for that knowledge representation and the model is retrained accordingly. In addition, the size of the dataset used in this work was below optimal for this type of probabilistic model. This is particularly relevant if we consider the subsample sizes for a given combination of clinical conditions (eg, in this dataset, there was only one observation that simultaneously satisfies the multiple conditions sex = female + smoking = true + OSA = true). However, this type of model can be set to learn from new patient data as they becomes available. In this fashion, as it continuously builds on new evidence, the model becomes more accurate and reliable, even for less frequent clinical conditions. The ability to accurately predict clinical outcomes is vital for improving the quality of medical care and increasing the efficiency of resource allocation in health care. For such predictions, cardiologists often use clinical scores that have various limitations, such as being dependent on a set number of medical variables or not being adaptable to new medical knowledge. Nonetheless, these professionals have also been witnessing the development of AI models for applications in cardiology in general and for the management of arrhythmias in particular . In this context, our aim was to develop an alternative model to clinical scores that was not susceptible to these limitations, to predict the relapse of AF after PVI procedure. For this purpose, we have resorted to Bayesian networks, a type of probabilistic graphical model that can represent knowledge as a set of variables and their conditional dependencies. Unlike traditional prognostic models based on linear or logistic regressions, Bayesian networks offer an interpretable graphical structure, which enhances the model’s clarity and facilitates its adoption among physicians. In addition, Bayesian networks manage missing data more efficiently than other machine learning methods like classification and regression trees or random forests, as they can compute the probability of an outcome even when predictive variables have missing values. This makes them particularly well suited for medical datasets, where missing data are often a challenge. We have therefore chosen to develop our models based on Bayesian networks due to their explainability, flexibility, and robustness. Their explainability derives from their ability to represent relationships between variables as a graphical model, thus rendering their results more comprehensible. This capability is of paramount importance for the acceptance of AI models by medical professionals, who can thus integrate them safely into clinical practice . Further, the models’ flexibility derives from the ability to accommodate and represent new medical knowledge by reshaping the network structure accordingly and recalculating the conditional dependencies among multiple variables. Therefore, new suspected or known risk factors or predictors for AF relapse can be incorporated into a Bayesian network model at any time, with minimal resetting of the model. Additionally, the models’ robustness derives from the fact that they can make predictions for the outcome variable even when there are missing data on some predictive variables, thus allowing them to be used in cases of incomplete information on any given patient. Thus, unlike clinical scores, Bayesian networks do not require the full set of clinical explanatory variables to deliver useful results. Despite none of these characteristics being unique to Bayesian networks on its own, this combination of characteristics makes these models highly interesting to be used as basis for clinical decision support tools.The first stage of the construction of our model was to create the network structure, that is, the network of relationships between the clinical variables. As stated in the Methods section, this was achieved in 2 steps: initially the known relationships were set manually based on expert knowledge; then, in a second step, the network structure was improved upon inference from data by the use of an AI algorithm. At this last step, the algorithm suggested a relationship between BMI and epicardial fat, which was considered acceptable, as there is significant evidence of a correlation between these two variables . This finding proved useful since it enabled the use of the path “BMI → epicardial fat → AF relapse” when there was no information on the middle variable. The algorithm also suggested a path “OSA → pre-ablation AF type → left atrial volume.” In this study, we opted to retain this suggestion in the network structure as a potential motivation for further exploration in future research. Although these relationships were considered to represent knowledge derived from the data, they were not particularly relevant for the model calculations, since each of these variables is also directly related to the outcome variable. The second stage of the construction of our model was to train and validate the model based on the previous network structure. When validating the use of evidence from the 7 parent nodes of our outcome variable, the model performed with a calculated AUC value of approximately 0.75, interpreted as acceptable diagnostic accuracy . These results implied using as predictive variables age, sex, smoking, preablation AF type, left atrial volume, epicardial fat, and OSA. However, some of these features are not always available in patients’ clinical records. Thus, we have validated the model in the absence of information on left atrial volume and epicardial fat as predictive features. In this case, the model exhibited an expectedly lower performance, with a calculated mean AUC value close to 0.66. Despite the observed difference was not statistically significant, as noted from the overlapping confidence intervals, it suggests that these 2 features have a high weight on the performance of the model. This finding is consistent with those reported in the ATLAS score that the left atrial volume has the highest weight on the predictive power of that score . Going further, our experiment also showed that the lack of information on epicardial fat can be partially compensated for by evidence of BMI, as this is its parent node. Taking into account daily clinical practice, this poses an interesting possibility, since BMI measurements are generally available for clinical evaluation for most patients. In these 6-variable cases, the model response exhibited a calculated mean AUC value of 0.70. Also here, despite the observed differences for the previous scenarios not being statistically significant, these outcomes fit within an acceptable range for a prediction tool. Such results implied using as predictive variables age, sex, smoking, preablation AF type, OSA, and BMI, all of which are typically easy to obtain in a clinical setting. To put these results in perspective, the AFA Recur tool developed by Saglietto et al achieves a performance of AUC 0.72 using a 19-variable AI model with little to no explainability. Future research in the context of predicting AF relapse using Bayesian networks should address several key challenges and directions. The first is ensuring the generalizability of the model across diverse populations and clinical settings to seek validation in varied patient cohorts. Second, it would be essential to conduct longitudinal studies to assess the model’s long-term performance and capture patient evolution over extended time horizons. In addition, future studies could explore the inclusion of expanded predictive factors, such as genetic influences, lifestyle changes, and comorbidities, to enhance the model’s accuracy and clinical use. Finally, incorporating patient-reported outcomes and preferences into the predictive framework may improve the model’s relevance and acceptance, fostering a more patient-centric approach to clinical decision-making. We consider that this data-based approach based on a Bayesian network model can be the backbone for a future clinical decision support system. Being an AI model, it opens the possibility of being continuously retrained as new patient information becomes available in clinical records, hence progressively providing more accurate results upon new accumulated data. Such a retraining process can be automatized on a schedule or upon a trigger, for example, recalculating conditional dependencies between clinical features on a monthly basis or at every new 100 patient observations. This retraining of the model based on the recalculation of conditional probabilities from new patient data is not expected to represent significant computational costs, even for exceptionally large amounts of patient observations. This model can also be considered as an enhancement of the ATLAS score, as it is based on its 5 predictive features, to which 2 additional features were added. Nonetheless, it may serve as a starting point for the representation of knowledge in this field, being open to incorporating new evidence as it becomes available. For such a reason, we believe that the findings of this research contribute to the growing body of knowledge on the application of AI methods in cardiology and pave the way for future advancements in predictive analytics for cardiovascular diseases. The model was developed and evaluated on a dataset with a limited number of features. Although the current literature identifies other potential risk factors for relapse of AF, these were not considered in this work, as there was no information from patients on such features. Nevertheless, this type of model allows the incorporation of other risk factors at any time, provided that the network structure is rebuilt for that knowledge representation and the model is retrained accordingly. In addition, the size of the dataset used in this work was below optimal for this type of probabilistic model. This is particularly relevant if we consider the subsample sizes for a given combination of clinical conditions (eg, in this dataset, there was only one observation that simultaneously satisfies the multiple conditions sex = female + smoking = true + OSA = true). However, this type of model can be set to learn from new patient data as they becomes available. In this fashion, as it continuously builds on new evidence, the model becomes more accurate and reliable, even for less frequent clinical conditions. |
CyberKnife in Pediatric Oncology: A Narrative Review of Treatment Approaches and Outcomes | fa519742-b417-411a-b86f-2f1086b1294a | 11854067 | Surgical Procedures, Operative[mh] | Pediatric cancers, though rare, present unique challenges in terms of treatment due to their aggressive nature and different tolerance to therapy, compared to adults, because of dissimilar host characteristics, such as physiology and organ maturation . Among the available treatment modalities, radiotherapy (RT) has demonstrated efficacy in tumor control. However, despite recent technological advances , its application in pediatric patients is often limited due to concerns about potential long-term toxicity in developing tissues. In fact, delivering RT to pediatric patients requires extreme caution due to several factors: (i) increased sensitivity to radiation of children’s developing tissues and organs; (ii) longer life expectancy and therefore more time for radiation-induced late effects to manifest; (iii) risk of developing secondary cancers later in life; and (iv) risk of growth abnormalities and cognitive deficits . CyberKnife (CK), also known as robotic radiosurgery or frameless radiosurgery, is an advanced and precise system for delivering high-dose radiation to tumors. Unlike conventional RT, CK employs a robotic arm to maneuver the treatment delivery, enabling exceptional targeting accuracy and real-time tracking of tumor movement during treatment. This frameless system utilizes non-invasive image-guided localization and a lightweight high-energy radiation source to deliver stereotactic radiosurgery in single or multiple sessions, often referred to as “ultra-hypofractionated” treatments typically involving two to five fractions, allowing ablative radiosurgical doses to the lesion while enhancing protection of adjacent tissues ( A,B). CK is equipped with sophisticated image guidance technologies, enabling precise tumor localization within the body. During treatment setup, patients are immobilized using a custom-fitted mask, and in-room lasers define the center of the imaging system for initial alignment. The treatment location system employs orthogonal kV X-ray pairs, or live images, to compare the patient’s position against planning system-generated digitally reconstructed radiographs from the planning CT scan. This ensures alignment to within a millimeter of the planned treatment site. Additionally, the robotic couch performs fine adjustments in translation and rotation, including pitch, roll, and yaw, until residual offsets are within acceptable thresholds (<1 mm in translation and <0.5° in rotation). These offsets are continuously monitored and corrected during treatment. CK’s frameless design eliminates the need for rigid head fixation, making it particularly suitable for treating tumors in challenging locations, such as the brain and spine. Furthermore, the system can perform real-time positional adjustments to account for any intrafraction movement, ensuring sub-millimeter precision throughout treatment. This combination of advanced tracking, non-invasiveness, and high precision positions CK as an effective and versatile tool for treating various tumor types while maintaining an enhanced focus on patient safety and comfort . This high precision in treatment delivery, particularly in the capacity to minimize radiation exposure to healthy organs, especially those that are radiosensitive during their development, makes CK an appealing therapeutic choice in the pediatric population. Despite these advantages, CK is associated with significant limitations, including a higher potential for low-dose scatter (often referred to as the “dose-bath” effect) and extended treatment times in some protocols, particularly when compared to other modalities such as Gamma Knife or proton therapy. Additionally, the high cost of CK systems and the specialized expertise required for their operation further restrict their widespread adoption, especially in resource-limited settings. Moreover, while experiences with CK in adult patients have shown promising outcomes, including improved tumor control and reduced toxicity , the use of CK in the pediatric setting remains relatively unexplored, with only a few reports available in the literature predominantly consisting of small case series and retrospective reviews . Specifically, the literature is deficient in reports on prospective studies, as well as in reviews of the existing evidence. The present review seeks to address this gap by critically evaluating the existing literature on CK application in pediatric patients. In doing so, the review aims to offer a balanced perspective, acknowledging both the potential advantages and significant challenges of CK in this sensitive population. Another aim of this review is to explore and discuss possible comparisons between the results of CK and those of other established techniques (such as image-guided RT, proton therapy, intensity-modulated proton therapy, and Gamma Knife) or emerging techniques (such as 4π RT), specifically within the clinical settings of pediatric tumors for which CK is intended, namely small tumor lesions treated with few fractions. 2.1. Inclusion and Exclusion Criteria This narrative review focused exclusively on studies that investigated the use of CK in pediatric patients. The review was conducted by a multidisciplinary team, composed of radiation oncologists, pediatric oncologists, and medical physicists, based on the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines . Abstracts, letters, editorials, and papers not written in English were excluded from the review. 2.2. Literature Search A literature search was performed on PubMed on 13 July 13 2023, using the following search strategy: (“cyberknife” AND (“pediatric” OR “paediatric” OR “children”)) with filters applied: “Child: birth-18 years”. Additionally, the snowball technique was utilized to identify relevant articles by manually reviewing the reference lists of retrieved studies. 2.3. Study Selection and Data Extraction Two authors (CMD, FM) independently screened the titles and abstracts of the identified articles to determine their relevance to the topic. Any disagreements between the authors were resolved through discussion and consultation with the senior author (AGM). Only studies meeting the inclusion criteria were considered for further analysis. Two authors (MB, SCa) independently extracted relevant information from the selected studies, including study characteristics, patient demographics, treatment protocols, tumor response, and toxicity outcomes. Any discrepancies or conflicts in data extraction were resolved through discussion and consensus. 2.4. Narrative Review Checklist In order to ensure a thorough and comprehensive review of the topic, we adhered to a narrative review checklist. outlines the checklist items and their corresponding assessment criteria, which guided our approach. By following this methodology, our objective was to conduct a comprehensive exploration of the literature pertaining to the application of CK in pediatric oncology. This narrative review focused exclusively on studies that investigated the use of CK in pediatric patients. The review was conducted by a multidisciplinary team, composed of radiation oncologists, pediatric oncologists, and medical physicists, based on the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines . Abstracts, letters, editorials, and papers not written in English were excluded from the review. A literature search was performed on PubMed on 13 July 13 2023, using the following search strategy: (“cyberknife” AND (“pediatric” OR “paediatric” OR “children”)) with filters applied: “Child: birth-18 years”. Additionally, the snowball technique was utilized to identify relevant articles by manually reviewing the reference lists of retrieved studies. Two authors (CMD, FM) independently screened the titles and abstracts of the identified articles to determine their relevance to the topic. Any disagreements between the authors were resolved through discussion and consultation with the senior author (AGM). Only studies meeting the inclusion criteria were considered for further analysis. Two authors (MB, SCa) independently extracted relevant information from the selected studies, including study characteristics, patient demographics, treatment protocols, tumor response, and toxicity outcomes. Any discrepancies or conflicts in data extraction were resolved through discussion and consensus. In order to ensure a thorough and comprehensive review of the topic, we adhered to a narrative review checklist. outlines the checklist items and their corresponding assessment criteria, which guided our approach. By following this methodology, our objective was to conduct a comprehensive exploration of the literature pertaining to the application of CK in pediatric oncology. The initial search yielded a total of 59 items. After screening the titles and abstracts, 13 papers were identified as meeting the inclusion criteria, while 46 papers were excluded. All included papers were published since 2000, with 7 of them published since 2015, indicating a recent focus on the topic. The publications were sourced from various centers worldwide, including the USA (5 papers), Japan (4 papers), Costa Rica (1 paper), France (1 paper), UK (1 paper), and Turkey (1 paper). The selected papers covered a range of clinical settings, including brain tumors , oculomotor schwannomas , craniopharyngiomas , ameloblastic fibro-odontosarcoma , optic nerve glioma , clear cell meningioma , acoustic schwannoma , and juvenile nasopharyngeal angiofibroma . However, all the cases and series analyzed focused on the treatment of tumors located in the intracranial or head and neck region. A summary of the findings of the selected studies is presented in . Literature Review The application of CK in pediatric oncology, as reflected in the current literature, spans a spectrum of tumor types and treatment scenarios. Case reports and small series underscore CK’s capacity for precision in targeting a range of pediatric tumors, including but not limited to acoustic schwannoma, brain metastases, recurrent medulloblastoma, nasopharyngeal angiofibroma, optic gliomas, and clear cell meningioma. These studies collectively highlight CK’s potential advantages in minimizing radiation exposure to non-target tissues. However, the nature of these reports limits their ability to inform robust conclusions due to the absence of control groups, small sample sizes, and often short follow-up periods, which are insufficient for assessing long-term outcomes and late-onset toxicities. Additionally, these studies do not provide sufficient data on the comparative effectiveness of CK versus other high-precision techniques like Gamma Knife, raising questions about its specific niche in pediatric oncology. Giller et al.’s experience with 21 children with central nervous system tumors and Mohamad et al.’s study involving 52 pediatric patients with brain tumors provide a broader perspective on CK utility, demonstrating notable success in achieving local control with minimal immediate adverse effects. These larger cohort studies contribute valuable insights into CK’s efficacy, yet still leave questions regarding the long-term safety profile and risk of secondary malignancies largely unanswered. For instance, the lack of comprehensive follow-up data on secondary malignancies remains a critical gap, particularly given CK’s association with a higher “dose-bath” effect compared to other technologies. In the first paper , the authors discussed their experience using CK radiosurgery in the treatment of 21 pediatric patients with unresectable tumors. A total of 38 procedures were performed on children aged between 8 months and 16 years (average age 7 years). The tumors treated included pilocytic astrocytomas (3 cases), anaplastic astrocytomas (2 cases), ependymomas (3 cases, including 2 anaplastic), medulloblastomas (4 cases), atypical teratoid/rhabdoid tumors (3 cases), craniopharyngiomas (3 cases), and other pathologies (3 cases). The average target volume was 10.7 cm 3 , with a mean marginal dose of 18.8 Gy, and the follow-up period averaged 18 months. Of the procedures, 71% were single-session treatments, and 38% of patients did not require general anesthesia. Results indicated successful local control for patients with pilocytic and anaplastic astrocytomas, three patients with medulloblastomas, and all patients with craniopharyngiomas. However, local control was not achieved for those with ependymomas. Two patients with atypical teratoid/rhabdoid tumors survived for 16 and 35 months post-diagnosis. Notably, there were no deaths or complications related to the procedures. The authors concluded that CK radiosurgery proved effective in achieving local control for certain pediatric CNS tumors, without the need for rigid head fixation . While these results are promising, the study’s small sample size and short follow-up highlight the need for further investigation into long-term outcomes and broader applicability. The authors of the second paper treated 52 pediatric brain tumor patients using CK stereotactic RT with doses of 1.8 to 2 Gy per fraction between 2008 and 2017. They compared thirty cases with intensity-modulated RT plans and assessed normal tissue exposure, plan quality, and dose–volume parameters, as well as overall survival, progression-free survival, and local control. Results indicated that CK plans exposed significantly less normal tissue to high doses (defined as ≥80% of the prescription dose or ≥40 Gy) and intermediate doses (defined as 80% > dose ≥ 50% of the prescription dose or 40 Gy > dose ≥ 25 Gy) compared to IMRT plans. With a median follow-up of 3.7 years, the 3-year local control rate was 92%. There were eight treatment failures: one craniopharyngioma, two ependymomas, and five low-grade gliomas. The authors concluded that CK SRT reduces the volume of irradiated tissue without significantly compromising local control in pediatric brain tumors, suggesting the need for further validation in prospective studies . However, the study does not address the impact on broader clinical decision-making, particularly in the context of other available high-precision modalities. Furthermore, the authors of this study did not report data on the volume irradiated at low doses, which may be correlated with the incidence of secondary malignancies. The review of Fadel et al. offers a deeper dive into the technical merits and potential of CK, especially with respect to non-isocentric planning and the treatment of complex tumor geometries. These contributions emphasize the technological advancements that facilitate the tailored application of CK in pediatric cases, suggesting an improvement in radiation dose distribution and a theoretical reduction in harm to surrounding healthy tissue. Nevertheless, these technical advantages must be weighed against practical challenges such as longer treatment durations, higher costs, and the need for specialized expertise. Overall, despite their limitations, the selected studies suggest that CK has a favorable toxicity profile for both acute and late deterministic effects. However, this observation applies strictly to the specific clinical settings for which this technique is designed and potentially effective (namely, small tumor lesions treated with few fractions) and must be interpreted with caution given the absence of comparative studies. Among the 98 patients included in the selected studies, only 1 case was reported to have a serious side effect (suspected osteonecrosis in one patient re-irradiated with CK) . Yet, the lack of long-term data precludes a reliable assessment of potential stochastic effects. This underscores the critical need for standardized, prospective studies to better understand CK’s role in pediatric oncology. In addition, Paddick et al. measured extracranial doses from Gamma Knife Perfexion (GKP) intracranial stereotactic radiosurgery and modeled the malignancy risk from different treatment platforms. Doses were measured for 20 patients at distances of 18, 43, and 75 cm from the target, corresponding to the thyroid, breast, and gonads, respectively. Comparative data from other radiosurgery platforms were collected from the literature, and the National Cancer Institute RadRAT calculator was used to estimate excess lifetime cancer risk for different age groups. Results showed extracranial doses for GKP were 0.04%, 0.008%, and 0.002% of the prescription dose at 18, 43, and 75 cm, respectively. GKP had the lowest extracranial dose compared to linacs with micro-multileaf collimators (mMLC), linacs with circular collimators (cones), and CK. Estimated lifetime risks of radiation-induced malignancy were 0.03–0.88% for GKP, 0.36–11% for mMLC, 0.61–18% for cones, and 2.2–39% for CK . This finding highlights a critical area of concern: the potential for increased risk of second tumors. In fact, this analysis underscores the importance of cautious application and rigorous long-term follow-up in pediatric patients treated with CK, reflecting the broader need for a balanced consideration of risks and benefits in employing this technology. In addition, while this study highlights the dosimetric advantages of the Gamma Knife, it is important to note that this treatment modality is limited to selected intracranial indications. The application of CK in pediatric oncology, as reflected in the current literature, spans a spectrum of tumor types and treatment scenarios. Case reports and small series underscore CK’s capacity for precision in targeting a range of pediatric tumors, including but not limited to acoustic schwannoma, brain metastases, recurrent medulloblastoma, nasopharyngeal angiofibroma, optic gliomas, and clear cell meningioma. These studies collectively highlight CK’s potential advantages in minimizing radiation exposure to non-target tissues. However, the nature of these reports limits their ability to inform robust conclusions due to the absence of control groups, small sample sizes, and often short follow-up periods, which are insufficient for assessing long-term outcomes and late-onset toxicities. Additionally, these studies do not provide sufficient data on the comparative effectiveness of CK versus other high-precision techniques like Gamma Knife, raising questions about its specific niche in pediatric oncology. Giller et al.’s experience with 21 children with central nervous system tumors and Mohamad et al.’s study involving 52 pediatric patients with brain tumors provide a broader perspective on CK utility, demonstrating notable success in achieving local control with minimal immediate adverse effects. These larger cohort studies contribute valuable insights into CK’s efficacy, yet still leave questions regarding the long-term safety profile and risk of secondary malignancies largely unanswered. For instance, the lack of comprehensive follow-up data on secondary malignancies remains a critical gap, particularly given CK’s association with a higher “dose-bath” effect compared to other technologies. In the first paper , the authors discussed their experience using CK radiosurgery in the treatment of 21 pediatric patients with unresectable tumors. A total of 38 procedures were performed on children aged between 8 months and 16 years (average age 7 years). The tumors treated included pilocytic astrocytomas (3 cases), anaplastic astrocytomas (2 cases), ependymomas (3 cases, including 2 anaplastic), medulloblastomas (4 cases), atypical teratoid/rhabdoid tumors (3 cases), craniopharyngiomas (3 cases), and other pathologies (3 cases). The average target volume was 10.7 cm 3 , with a mean marginal dose of 18.8 Gy, and the follow-up period averaged 18 months. Of the procedures, 71% were single-session treatments, and 38% of patients did not require general anesthesia. Results indicated successful local control for patients with pilocytic and anaplastic astrocytomas, three patients with medulloblastomas, and all patients with craniopharyngiomas. However, local control was not achieved for those with ependymomas. Two patients with atypical teratoid/rhabdoid tumors survived for 16 and 35 months post-diagnosis. Notably, there were no deaths or complications related to the procedures. The authors concluded that CK radiosurgery proved effective in achieving local control for certain pediatric CNS tumors, without the need for rigid head fixation . While these results are promising, the study’s small sample size and short follow-up highlight the need for further investigation into long-term outcomes and broader applicability. The authors of the second paper treated 52 pediatric brain tumor patients using CK stereotactic RT with doses of 1.8 to 2 Gy per fraction between 2008 and 2017. They compared thirty cases with intensity-modulated RT plans and assessed normal tissue exposure, plan quality, and dose–volume parameters, as well as overall survival, progression-free survival, and local control. Results indicated that CK plans exposed significantly less normal tissue to high doses (defined as ≥80% of the prescription dose or ≥40 Gy) and intermediate doses (defined as 80% > dose ≥ 50% of the prescription dose or 40 Gy > dose ≥ 25 Gy) compared to IMRT plans. With a median follow-up of 3.7 years, the 3-year local control rate was 92%. There were eight treatment failures: one craniopharyngioma, two ependymomas, and five low-grade gliomas. The authors concluded that CK SRT reduces the volume of irradiated tissue without significantly compromising local control in pediatric brain tumors, suggesting the need for further validation in prospective studies . However, the study does not address the impact on broader clinical decision-making, particularly in the context of other available high-precision modalities. Furthermore, the authors of this study did not report data on the volume irradiated at low doses, which may be correlated with the incidence of secondary malignancies. The review of Fadel et al. offers a deeper dive into the technical merits and potential of CK, especially with respect to non-isocentric planning and the treatment of complex tumor geometries. These contributions emphasize the technological advancements that facilitate the tailored application of CK in pediatric cases, suggesting an improvement in radiation dose distribution and a theoretical reduction in harm to surrounding healthy tissue. Nevertheless, these technical advantages must be weighed against practical challenges such as longer treatment durations, higher costs, and the need for specialized expertise. Overall, despite their limitations, the selected studies suggest that CK has a favorable toxicity profile for both acute and late deterministic effects. However, this observation applies strictly to the specific clinical settings for which this technique is designed and potentially effective (namely, small tumor lesions treated with few fractions) and must be interpreted with caution given the absence of comparative studies. Among the 98 patients included in the selected studies, only 1 case was reported to have a serious side effect (suspected osteonecrosis in one patient re-irradiated with CK) . Yet, the lack of long-term data precludes a reliable assessment of potential stochastic effects. This underscores the critical need for standardized, prospective studies to better understand CK’s role in pediatric oncology. In addition, Paddick et al. measured extracranial doses from Gamma Knife Perfexion (GKP) intracranial stereotactic radiosurgery and modeled the malignancy risk from different treatment platforms. Doses were measured for 20 patients at distances of 18, 43, and 75 cm from the target, corresponding to the thyroid, breast, and gonads, respectively. Comparative data from other radiosurgery platforms were collected from the literature, and the National Cancer Institute RadRAT calculator was used to estimate excess lifetime cancer risk for different age groups. Results showed extracranial doses for GKP were 0.04%, 0.008%, and 0.002% of the prescription dose at 18, 43, and 75 cm, respectively. GKP had the lowest extracranial dose compared to linacs with micro-multileaf collimators (mMLC), linacs with circular collimators (cones), and CK. Estimated lifetime risks of radiation-induced malignancy were 0.03–0.88% for GKP, 0.36–11% for mMLC, 0.61–18% for cones, and 2.2–39% for CK . This finding highlights a critical area of concern: the potential for increased risk of second tumors. In fact, this analysis underscores the importance of cautious application and rigorous long-term follow-up in pediatric patients treated with CK, reflecting the broader need for a balanced consideration of risks and benefits in employing this technology. In addition, while this study highlights the dosimetric advantages of the Gamma Knife, it is important to note that this treatment modality is limited to selected intracranial indications. Narrative Our narrative review delves into the limited yet emerging evidence regarding the application of CK in pediatric oncology. It is noteworthy that all the studies analyzed pertained to patients with intracranial or head and neck tumors, where the minimal or absent organ motion obviates the need to leverage CK’s advantages in real-time target tracking. Not surprisingly, of all the studies analyzed, only one reported on the treatment of gliomas. In fact, CK stereotactic RT is generally best suited for well-delineated tumor lesions due to its reliance on precise imaging and highly conformal dose delivery. In the case of infiltrative tumors, such as gliomas, the diffuse nature of these lesions poses challenges for achieving optimal target definition and dose conformity. While CK has been used for specific cases of gliomas with limited infiltration or well-delineated regions requiring focal treatment, its application in these scenarios remains limited. The findings across the reviewed publications indicate a potential for CK’s safety and efficacy in treating pediatric patients, with a rare report of severe complications like bone necrosis in the context of re-irradiation . Notably, the reported 92% 3-year local control rate in one study on the treatment of brain tumors using a margin-free technique points towards the potential benefits of CK in achieving effective tumor control with precise radiation delivery. However, the current landscape of evidence, predominantly comprised of single-case reports and small case series, underscores the nascent state of knowledge regarding CK’s application in pediatric patients. The absence of large-scale, prospective clinical trials and the variability in reported outcomes highlight significant gaps in our understanding of CK’s long-term safety and efficacy. These limitations necessitate a cautious interpretation of the findings and a careful consideration of CK’s role in pediatric oncology. CK offers several advantages that are particularly appealing in the treatment of pediatric tumors, including its precise imaging and tracking capabilities, non-invasive nature, and potential for reducing the number of treatment sessions . These features suggest that CK could minimize exposure to healthy tissues and improve patient comfort, especially important in pediatric care. However, these advantages are tempered by notable concerns and limitations. In fact, CyberKnife has well-recognized technical limitations. It is particularly effective only for tumors up to 3 cm in diameter, although in some cases, tumors up to a maximum size of 6 cm can be treated, depending on their location and shape. Furthermore, the use of CK requires tumors to be well-delineated on imaging studies, such as MRI or PET-CT, to ensure precise targeting during treatment. Additionally, the use of CK delivered with conventional fractionation poses significant challenges, including its impact on departmental activity due to prolonged machine occupancy times and the intrinsic inhomogeneity of dose distribution produced by CK. Moreover, the high initial costs, the complexity of treatment planning, and the potential for longer treatment sessions with CK present practical challenges to its widespread adoption . Finally, the precision of CK, while a strength, also introduces the risk of a “dose-bath” effect, wherein low-dose radiation is distributed to a larger volume of healthy tissue than with traditional radiation therapy approaches . This aspect is particularly concerning in pediatric patients, whose growing tissues are more susceptible to radiation-induced damage and who have a longer lifespan during which radiation-induced secondary cancers could develop . In fact, the study by Paddick et al. highlights the risk of radiation-induced malignancy with CK, confirming the need for a better understanding of the risk-benefit ratio in using CK for pediatric patients. Therefore, our review suggests that considering the risks of carcinogenesis, CK is a reasonable option in two specific cases: i) treatments or retreatments in patients with an unfavorable prognosis, where CK offers a lower risk of acute or subacute side effects that could negatively impact the patient’s quality of life; and ii) treatments or retreatments in patients with a favorable prognosis, where conventional irradiation at curative doses is associated with an unacceptably high risk of acute or late side effects. Moreover, our review indicates that future studies should focus on better quantifying the advantages of CK in reducing non-stochastic toxic effects and exploring its potential for dose escalation to enhance local tumor control. Additionally, it is crucial to quantify the risks of stochastic radio-induced effects, including carcinogenesis and transmissible mutations. In fact, the study by Paddick et al. points out a broad range of risks for second cancers and does not address the risk of transmissible genetic effects, highlighting an area in need of further research. Given these considerations, the potential of CK in pediatric oncology should be explored cautiously. Future studies with long-term follow-up and comparative data with conventional treatments are essential to clearly delineate CK's safety profile and therapeutic value. Until such evidence is available, clinical decisions regarding CK use in pediatric patients should be made within a multidisciplinary context, weighing potential benefits against risks and considering each patient’s unique clinical scenario. Specifically, CK for treating pediatric patients, particularly those with tumors having a potentially favorable prognosis, seems justifiable only when conventional techniques pose an unacceptable risk of adverse effects. In conclusion, the therapies currently available for pediatric RT, including linac-based IMRT/VMAT , proton therapy , 4π RT (an advanced technique that delivers radiation from nearly unlimited angles around the patient, maximizing dose conformity to the tumor while minimizing exposure to surrounding healthy tissues), and CK, have been studied with varying levels of depth. While IMRT/VMAT and proton therapy are supported by a relatively robust body of clinical evidence, research on 4π RT and CK is less extensive, particularly in the pediatric population. This disparity highlights the need for a more systematic evaluation of emerging techniques to determine their optimal role in treatment planning. At a minimum, comparative planning studies are crucial to identify which modality offers the best therapeutic advantage for specific cases, balancing precision, safety, and accessibility. CK and 4π RT, with their advanced precision and sparing of healthy tissue, hold significant promise for treating complex or inoperable pediatric tumors. CK real-time tracking and frameless delivery reduce treatment times, while 4π RT non-coplanar beam arrangements allow for unparalleled dose conformity. However, both techniques present unique challenges, including the potential for a low-dose bath with CK and concerns about the integral dose in 4π therapy, which could increase the risk of secondary malignancies. Proton therapy remains a benchmark for minimizing long-term toxicities due to its sharp dose fall-off, but its limited availability and cost are notable constraints. shows a brief comparison of the main differences between the techniques currently available or being introduced into clinical practice in the pediatric tumor setting. Future studies should not only aim to quantify and compare these techniques’ clinical outcomes but also evaluate their dosimetric advantages through rigorous planning studies. Such research is essential to establish evidence-based guidelines for selecting the most appropriate technique tailored to individual pediatric patients, ensuring both efficacy and safety in long-term outcomes. Our narrative review delves into the limited yet emerging evidence regarding the application of CK in pediatric oncology. It is noteworthy that all the studies analyzed pertained to patients with intracranial or head and neck tumors, where the minimal or absent organ motion obviates the need to leverage CK’s advantages in real-time target tracking. Not surprisingly, of all the studies analyzed, only one reported on the treatment of gliomas. In fact, CK stereotactic RT is generally best suited for well-delineated tumor lesions due to its reliance on precise imaging and highly conformal dose delivery. In the case of infiltrative tumors, such as gliomas, the diffuse nature of these lesions poses challenges for achieving optimal target definition and dose conformity. While CK has been used for specific cases of gliomas with limited infiltration or well-delineated regions requiring focal treatment, its application in these scenarios remains limited. The findings across the reviewed publications indicate a potential for CK’s safety and efficacy in treating pediatric patients, with a rare report of severe complications like bone necrosis in the context of re-irradiation . Notably, the reported 92% 3-year local control rate in one study on the treatment of brain tumors using a margin-free technique points towards the potential benefits of CK in achieving effective tumor control with precise radiation delivery. However, the current landscape of evidence, predominantly comprised of single-case reports and small case series, underscores the nascent state of knowledge regarding CK’s application in pediatric patients. The absence of large-scale, prospective clinical trials and the variability in reported outcomes highlight significant gaps in our understanding of CK’s long-term safety and efficacy. These limitations necessitate a cautious interpretation of the findings and a careful consideration of CK’s role in pediatric oncology. CK offers several advantages that are particularly appealing in the treatment of pediatric tumors, including its precise imaging and tracking capabilities, non-invasive nature, and potential for reducing the number of treatment sessions . These features suggest that CK could minimize exposure to healthy tissues and improve patient comfort, especially important in pediatric care. However, these advantages are tempered by notable concerns and limitations. In fact, CyberKnife has well-recognized technical limitations. It is particularly effective only for tumors up to 3 cm in diameter, although in some cases, tumors up to a maximum size of 6 cm can be treated, depending on their location and shape. Furthermore, the use of CK requires tumors to be well-delineated on imaging studies, such as MRI or PET-CT, to ensure precise targeting during treatment. Additionally, the use of CK delivered with conventional fractionation poses significant challenges, including its impact on departmental activity due to prolonged machine occupancy times and the intrinsic inhomogeneity of dose distribution produced by CK. Moreover, the high initial costs, the complexity of treatment planning, and the potential for longer treatment sessions with CK present practical challenges to its widespread adoption . Finally, the precision of CK, while a strength, also introduces the risk of a “dose-bath” effect, wherein low-dose radiation is distributed to a larger volume of healthy tissue than with traditional radiation therapy approaches . This aspect is particularly concerning in pediatric patients, whose growing tissues are more susceptible to radiation-induced damage and who have a longer lifespan during which radiation-induced secondary cancers could develop . In fact, the study by Paddick et al. highlights the risk of radiation-induced malignancy with CK, confirming the need for a better understanding of the risk-benefit ratio in using CK for pediatric patients. Therefore, our review suggests that considering the risks of carcinogenesis, CK is a reasonable option in two specific cases: i) treatments or retreatments in patients with an unfavorable prognosis, where CK offers a lower risk of acute or subacute side effects that could negatively impact the patient’s quality of life; and ii) treatments or retreatments in patients with a favorable prognosis, where conventional irradiation at curative doses is associated with an unacceptably high risk of acute or late side effects. Moreover, our review indicates that future studies should focus on better quantifying the advantages of CK in reducing non-stochastic toxic effects and exploring its potential for dose escalation to enhance local tumor control. Additionally, it is crucial to quantify the risks of stochastic radio-induced effects, including carcinogenesis and transmissible mutations. In fact, the study by Paddick et al. points out a broad range of risks for second cancers and does not address the risk of transmissible genetic effects, highlighting an area in need of further research. Given these considerations, the potential of CK in pediatric oncology should be explored cautiously. Future studies with long-term follow-up and comparative data with conventional treatments are essential to clearly delineate CK's safety profile and therapeutic value. Until such evidence is available, clinical decisions regarding CK use in pediatric patients should be made within a multidisciplinary context, weighing potential benefits against risks and considering each patient’s unique clinical scenario. Specifically, CK for treating pediatric patients, particularly those with tumors having a potentially favorable prognosis, seems justifiable only when conventional techniques pose an unacceptable risk of adverse effects. In conclusion, the therapies currently available for pediatric RT, including linac-based IMRT/VMAT , proton therapy , 4π RT (an advanced technique that delivers radiation from nearly unlimited angles around the patient, maximizing dose conformity to the tumor while minimizing exposure to surrounding healthy tissues), and CK, have been studied with varying levels of depth. While IMRT/VMAT and proton therapy are supported by a relatively robust body of clinical evidence, research on 4π RT and CK is less extensive, particularly in the pediatric population. This disparity highlights the need for a more systematic evaluation of emerging techniques to determine their optimal role in treatment planning. At a minimum, comparative planning studies are crucial to identify which modality offers the best therapeutic advantage for specific cases, balancing precision, safety, and accessibility. CK and 4π RT, with their advanced precision and sparing of healthy tissue, hold significant promise for treating complex or inoperable pediatric tumors. CK real-time tracking and frameless delivery reduce treatment times, while 4π RT non-coplanar beam arrangements allow for unparalleled dose conformity. However, both techniques present unique challenges, including the potential for a low-dose bath with CK and concerns about the integral dose in 4π therapy, which could increase the risk of secondary malignancies. Proton therapy remains a benchmark for minimizing long-term toxicities due to its sharp dose fall-off, but its limited availability and cost are notable constraints. shows a brief comparison of the main differences between the techniques currently available or being introduced into clinical practice in the pediatric tumor setting. Future studies should not only aim to quantify and compare these techniques’ clinical outcomes but also evaluate their dosimetric advantages through rigorous planning studies. Such research is essential to establish evidence-based guidelines for selecting the most appropriate technique tailored to individual pediatric patients, ensuring both efficacy and safety in long-term outcomes. CK offers potential advantages in precision and non-invasiveness for pediatric oncology, suggesting a promising role in treating various cancers. However, the evidence for its use in pediatric patients is limited, necessitating careful consideration. Key challenges include high initial costs, longer treatment times, and the need for specialized expertise. In pediatric settings, cautious application of CK is necessary to avoid risks associated with low-dose radiation over large body volumes. Therefore, decisions on CK application must be carefully weighed, focusing on minimizing long-term risks to young patients. Future research is needed to expand our understanding of CK safety and efficacy in pediatric oncology and guide its informed and judicious use. A recent German initiative has the potential to significantly advance our understanding of CK's role in pediatric CNS lesions. By systematically collecting and analyzing long-term data, it could pave the way for optimizing treatment strategies, improving patient outcomes, and fostering evidence-based integration of CK into pediatric oncology practice . |
CXCL5 Downregulation in Villous Tissue Is Correlated With Recurrent Spontaneous Abortion | de26d43e-103d-4c5c-8524-2d7361a3389a | 8486294 | Anatomy[mh] | Recurrent spontaneous abortion (RSA) is defined as two or more times of consecutive miscarriages before 20 weeks of gestation and impacts approximately 5% of childbearing-age women throughout the world ( – ). There is no unified theory in the pathogenesis of RSA. Several known factors for RSA include endocrine diseases, genetic abnormalities, immune diseases, and anatomical abnormalities, and approximately 50% of RSA cases are unexplained ( ). The underlying molecular and cytological mechanisms for RSA also remain largely enigmatic. As the most common pregnancy-associated complication, RSA seriously disturbs the physical and mental health of the female population and is also frustrating for the physician. Trophoblast invasion is a key event during pregnancy and plays a vital role in the process of embryo implantation and placentation ( ). In the early stage of pregnancy, trophoblast cells from the trophectoderm differentiate into two main lineages, villous cytotrophoblasts (VCTs) and extravillous trophoblasts (EVTs). Subsequently, EVTs undergo migration and invasion into the maternal decidua and myometrium, during which process embryos are anchored to the uterine wall and the uterine vessels are remodeled to form the low-resistance spiral arteries ( , ). Proper trophoblast invasion is necessary for a successful pregnancy. Studies show that poor trophoblast invasion is related to a series of pregnancy complications, including RSA, preeclampsia, and fetal growth restriction ( , ). Inadequate invasion of the trophoblast is even reported to be one of the main reasons for RSA ( – ). Therefore, exploring the factors that affect trophoblast invasion is of great significance for improving our understanding of the pathogenesis of RSA. Many molecules including hormones, chemokines, and growth factors, are associated with trophoblast invasion within the maternal–fetal microenvironment. Of these, chemokines are a large family of small-molecular-weight peptides that are initially involved in the pro-inflammatory process ( ). Recently, studies have demonstrated that a wide range of chemokines are expressed at the maternal–fetal interface, and these chemokines can directly participate in the regulation of trophoblast invasion and the establishment of maternal–fetal tolerance ( , ). C-X-C motif chemokine ligand 5 (CXCL5), also known as epithelial neutrophil-activating peptide 78 (ENA-78), is a member of the CXC chemokine subfamily and is originally identified in neutrophils ( ). It has been demonstrated that CXCL5 is a potent mediator of neoangiogenesis and is mainly expressed in epithelial cancer cells and immune cells ( ). Chorionic trophoblast and amniotic epithelium membranes have also been confirmed to express CXCL5 ( ). Abnormal expression of CXCL5 is found to be correlated with a large number of diseases, such as autoinflammation, cancer, obesity, and diabetes ( – ). CXCL5 exerts its action by binding to its G-protein-coupled receptors chemokine receptor 2 (CXCR2), and the CXCL5/CXCR2 axis has been widely investigated in various types of cancers. Numerous studies have confirmed that CXCL5 can induce the epithelial-to-mesenchymal transition (EMT) process in cancer cells and thus promotes cancer cells invasion and metastasis ( , ). Trophoblast cells and cancer cells share striking behavioral similarities in invasion, migration, and proliferation capacities ( ). Whether CXCL5 can affect trophoblast invasion has not been reported. A previous study identified that human villi expressed CXCR2 ( ). It was also found that as one of the ligands of CXCR2, IL-8, was expressed in human decidua and trophoblast and promoted trophoblast migration and invasion in an autocrine or paracrine manner ( ). Similar results were found in CXCL3 ( ). These findings draw our attention to the CXCL5. Additionally, one study evaluated the relationship between the levels of chemokine and the risk of miscarriage. The study found that elevated CXCL5 levels from serum samples were associated with an increased risk of miscarriage as the collection-outcome interval increased, although the authors did not observe statistical significance ( ). Based on these results, we hypothesized that CXCL5 may exert an effect on trophoblast migration and invasion and thus could participate in the occurrence of RSA. In the present study, we compared CXCL5 levels in the placental villous tissue of RSA patients and control patients. We also investigated its effects on the trophoblast and explored the underlying mechanism. Our results showed that the expression of CXCL5 was significantly lower in RSA patients. We also further demonstrated that CXCL5 can induce the EMT process through PI3K/AKT/ERK1/2 signaling pathway and thus promoted trophoblast invasion and migration. Collectively, these data provide the first evidence that CXCL5 downregulation in villous tissue is correlated with RSA. In addition, we found that local factors, including estrogen (E), progesterone (P), human chorionic gonadotropin (HCG), and decidual stromal cells (DSCs) regulated CXCL5 and CXCR2 expression of trophoblast cells.
Patients and Clinical Samples This study was approved by the ethics committee of Renmin Hospital of Wuhan University, and consent was obtained from each patient before sample collection. Fifteen patients with RSA and 13 control patients from the reproductive medical center in Renmin Hospital of Wuhan University were included between December 2017 and October 2019. Clinical data and placental villous tissue samples were obtained from the two groups. RSA was defined as the loss of two or more sequential pregnancies with the same partner before a gestational of 20 weeks. The exclusion criteria were as follows: a) symptoms of endocrine or metabolic diseases, such as hyperthyroidism and diabetes; b) karyotype abnormality; c) infection based on routine leucorrhea examination; and d) uterine abnormality. Women who had healthy pregnancies and underwent selective pregnancy terminations for non-medical reasons constituted the control group. The villous tissues were collected immediately following surgery: one portion was fixed with 4% paraformaldehyde for paraffin embedding in blocks, and the others portion was stored in liquid nitrogen. Immunohistochemistry The paraffin-embedded villous tissues were cut into 4-μm-thick sections and dehydrated in a graded series of ethanol. Endogenous peroxidase activity was blocked with 3% H 2 O 2 , and non-specific binding was blocked with 5% bovine serum albumin (BSA) for 15 min. Next, the samples were incubated at 37°C with primary rabbit anti-human CXCL5 antibody (1:500; Affinity, Cat: DF9919), anti-E-cadherin (1:500; ProteinTech; Cat: 20874-1-AP) and anti-N-cadherin (1:100; ProteinTech; Cat: 22018-1-AP) antibodies. All sections were washed three times with PBS and then incubated with secondary antibodies. The reaction was detected with 3,3′-diaminobenzidine (DAB), and the sections were counterstained with hematoxylin. Five visual fields were selected, and the staining was observed under an Olympus BX51+DP70 microscope at ×200 and ×400 magnification. The images were analyzed with ImageJ (1.52a, National Institutes of Health, USA). Immunofluorescence The paraffin-embedded human villous tissues were cut into 2-μm-thick sections. Deparaffinization, hydration, and antigen retrieval of the sections were carried out under proper conditions. The samples were incubated with rabbit anti-human cytokeratin 7 (CK7) (1:100; ProteinTech, Cat: 15539-1-AP) and rabbit anti-human CXCR2 (1:50; ProteinTech, Cat: 20634-1-AP) primary antibodies. After that, the samples were incubated with fluorescence-labeled secondary antibody for 1 h and counterstained with 4′-6-diamidino-2-phenylindole (DAPI) (Beyotime, Shanghai, China). A confocal laser scanning microscope (Olympus FV1000, Japan) was used to observe the fluorescence signal. Five visual fields with tissue were selected for analysis. The pixel intensity per unit area was assessed using ImageJ (1.52a, National Institutes of Health, USA). Cell Culture, Reagents, and Treatments HTR-8/SVeo cell line was obtained from the China Center for Type Culture Collection (Wuhan, China) and cultured in DMEM-F12 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA). Human endometrial stromal cells were purchased from the BeNa culture collection and induced toward DSCs according to a previous method, with some modifications ( , ). HTR-8 cells were seeded on a 6-well plate (2 × 10 5 cells/well) and placed in an incubator with 5% CO 2 at 37°C. A co-culture model of HTR-8 cells and DSCs was established via a Transwell co-culture system (0.4-μm pore size, Corning, USA). In brief, HTR-8 cells were seeded into the lower chambers, and DSCs were placed into the upper chambers at different ratios (DSCs: HTR-8 cells 1:4; 1:1; 2:1) for 48 h before harvest. Recombinant human CXCL5 (Absin, Shanghai, China) was used at the concentration of 50 and 100 ng/ml according to the manufacturer’s instruction. LY294002 (PI3K/AKT inhibitors) and PD98059 (ERK1/2 inhibitors) were purchased from MedChemExpress, China, and used at concentrations of 20 and 30 μM, respectively. Hormone concentrations used in the current experiment were E (10 −7 M), P (10 −8 M), and HCG (5 kU/L). Quantitative Real-Time PCR Total RNA was extracted from cells and tissues using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. Reverse transcription was conducted with the PrimeScript RT reagent kit (Takara, Japan). RT-PCR was performed with a SYBR Premix Ex Taq II kit (Takara, Japan) on a 7500 detection system (Applied Biosystems, Foster City, CA, USA). 2 −ΔΔ Ct method was determined to calculate and quantify the gene expression. Primers were designed with computer assistance based on gene sequences available in GenBank, and the sequences of primers are listed in . Western Blotting Cells were harvested and lysed with radioimmunoprecipitation assay (RIPA) lysis buffer, and the lysates were centrifuged at 4°C for 15 min to collect the supernatant. A bicinchoninic acid (BCA) assay kit (Beyotime, Shanghai, China) was used to measure protein concentrations. After boiling with a 5× loading buffer (Beyotime, Shanghai, China) at 95°C for 5 min, 40 μg of protein of each sample was electrophoresed via 10% sodium dodecyl sulfate–polyacrylamide gel and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore) for blocking 1 h at room temperature with 5% BSA. The following primary antibodies were incubated together with the membranes overnight at 4°C: rabbit anti-E-cadherin (dilution 1:1,000; ProteinTech; Cat: 20874-1-AP), anti-N-cadherin (dilution 1:1,000; ProteinTech; Cat: 22018-1-AP), anti-vimentin (dilution 1:1,000; ProteinTech; Cat: 10366-1-AP), anti-GAPDH (dilution 1:1,000; ProteinTech; Cat: 10494-1-AP), and anti-tubulin (dilution 1:1,000; ProteinTech; Cat: 10094-1-AP). The secondary antibodies were incubated for 1 h at room temperature the next day. Finally, the protein bands were visualized with an enhanced chemiluminescence (ECL) detection system (Bio-Rad, Hercules, CA, USA), and the relative band intensities were calculated with ImageJ (1.52a, National Institutes of Health, USA). Invasion Assay A 24-well plate Transwell insert (8-µm pore size, Corning, USA) coated with Matrigel matrix (Corning, USA) was used to detect the invasion ability of the cell. In brief, HTR-8 cells (2 × 10 4 ) were seeded in the upper chamber of each insert in a 200 µl FBS-free DMEM-F12 medium. The lower chamber was filled with 500 µl of DMEM-F12 medium containing 10% FBS. The plate was placed in an incubator with 5% CO 2 at 37°C for 48 h. Afterward, cells that had invaded the lower chamber were fixed with 4% paraformaldehyde, stained with 0.5% crystal violet, and quantified. The average cells number from five fields at a magnification of ×200 was reported. Scratch Wound Healing Assay Cell migration ability was evaluated with scratch wound healing assay. When cells reached 80%–90% confluence, a scratch wound was made on the monolayer of cells with a 200-µl pipette tip and gently washed three times with PBS before the serum-free medium was added. The 6-well plate was incubated with 5% CO 2 at 37°C for 48 h. Pictures of the wound were taken at 0 and 48 h. The wound area was calculated using ImageJ. Statistical Analysis Quantitative data were expressed as the mean ± standard deviation (SD) and analyzed by independent t-test. Categorical data were compared by the Mann–Whitney U-test. All experiments were independently repeated at least three times. Figures were performed by GraphPad Prism version 6.0 (GraphPad Software, San Diego, CA). All p -values were two-sided and statistical significance was established as p < 0.05. All analyses were conducted using SPSS 22.0 (IBM SPSS, USA).
This study was approved by the ethics committee of Renmin Hospital of Wuhan University, and consent was obtained from each patient before sample collection. Fifteen patients with RSA and 13 control patients from the reproductive medical center in Renmin Hospital of Wuhan University were included between December 2017 and October 2019. Clinical data and placental villous tissue samples were obtained from the two groups. RSA was defined as the loss of two or more sequential pregnancies with the same partner before a gestational of 20 weeks. The exclusion criteria were as follows: a) symptoms of endocrine or metabolic diseases, such as hyperthyroidism and diabetes; b) karyotype abnormality; c) infection based on routine leucorrhea examination; and d) uterine abnormality. Women who had healthy pregnancies and underwent selective pregnancy terminations for non-medical reasons constituted the control group. The villous tissues were collected immediately following surgery: one portion was fixed with 4% paraformaldehyde for paraffin embedding in blocks, and the others portion was stored in liquid nitrogen.
The paraffin-embedded villous tissues were cut into 4-μm-thick sections and dehydrated in a graded series of ethanol. Endogenous peroxidase activity was blocked with 3% H 2 O 2 , and non-specific binding was blocked with 5% bovine serum albumin (BSA) for 15 min. Next, the samples were incubated at 37°C with primary rabbit anti-human CXCL5 antibody (1:500; Affinity, Cat: DF9919), anti-E-cadherin (1:500; ProteinTech; Cat: 20874-1-AP) and anti-N-cadherin (1:100; ProteinTech; Cat: 22018-1-AP) antibodies. All sections were washed three times with PBS and then incubated with secondary antibodies. The reaction was detected with 3,3′-diaminobenzidine (DAB), and the sections were counterstained with hematoxylin. Five visual fields were selected, and the staining was observed under an Olympus BX51+DP70 microscope at ×200 and ×400 magnification. The images were analyzed with ImageJ (1.52a, National Institutes of Health, USA).
The paraffin-embedded human villous tissues were cut into 2-μm-thick sections. Deparaffinization, hydration, and antigen retrieval of the sections were carried out under proper conditions. The samples were incubated with rabbit anti-human cytokeratin 7 (CK7) (1:100; ProteinTech, Cat: 15539-1-AP) and rabbit anti-human CXCR2 (1:50; ProteinTech, Cat: 20634-1-AP) primary antibodies. After that, the samples were incubated with fluorescence-labeled secondary antibody for 1 h and counterstained with 4′-6-diamidino-2-phenylindole (DAPI) (Beyotime, Shanghai, China). A confocal laser scanning microscope (Olympus FV1000, Japan) was used to observe the fluorescence signal. Five visual fields with tissue were selected for analysis. The pixel intensity per unit area was assessed using ImageJ (1.52a, National Institutes of Health, USA).
HTR-8/SVeo cell line was obtained from the China Center for Type Culture Collection (Wuhan, China) and cultured in DMEM-F12 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA). Human endometrial stromal cells were purchased from the BeNa culture collection and induced toward DSCs according to a previous method, with some modifications ( , ). HTR-8 cells were seeded on a 6-well plate (2 × 10 5 cells/well) and placed in an incubator with 5% CO 2 at 37°C. A co-culture model of HTR-8 cells and DSCs was established via a Transwell co-culture system (0.4-μm pore size, Corning, USA). In brief, HTR-8 cells were seeded into the lower chambers, and DSCs were placed into the upper chambers at different ratios (DSCs: HTR-8 cells 1:4; 1:1; 2:1) for 48 h before harvest. Recombinant human CXCL5 (Absin, Shanghai, China) was used at the concentration of 50 and 100 ng/ml according to the manufacturer’s instruction. LY294002 (PI3K/AKT inhibitors) and PD98059 (ERK1/2 inhibitors) were purchased from MedChemExpress, China, and used at concentrations of 20 and 30 μM, respectively. Hormone concentrations used in the current experiment were E (10 −7 M), P (10 −8 M), and HCG (5 kU/L).
Total RNA was extracted from cells and tissues using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. Reverse transcription was conducted with the PrimeScript RT reagent kit (Takara, Japan). RT-PCR was performed with a SYBR Premix Ex Taq II kit (Takara, Japan) on a 7500 detection system (Applied Biosystems, Foster City, CA, USA). 2 −ΔΔ Ct method was determined to calculate and quantify the gene expression. Primers were designed with computer assistance based on gene sequences available in GenBank, and the sequences of primers are listed in .
Cells were harvested and lysed with radioimmunoprecipitation assay (RIPA) lysis buffer, and the lysates were centrifuged at 4°C for 15 min to collect the supernatant. A bicinchoninic acid (BCA) assay kit (Beyotime, Shanghai, China) was used to measure protein concentrations. After boiling with a 5× loading buffer (Beyotime, Shanghai, China) at 95°C for 5 min, 40 μg of protein of each sample was electrophoresed via 10% sodium dodecyl sulfate–polyacrylamide gel and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore) for blocking 1 h at room temperature with 5% BSA. The following primary antibodies were incubated together with the membranes overnight at 4°C: rabbit anti-E-cadherin (dilution 1:1,000; ProteinTech; Cat: 20874-1-AP), anti-N-cadherin (dilution 1:1,000; ProteinTech; Cat: 22018-1-AP), anti-vimentin (dilution 1:1,000; ProteinTech; Cat: 10366-1-AP), anti-GAPDH (dilution 1:1,000; ProteinTech; Cat: 10494-1-AP), and anti-tubulin (dilution 1:1,000; ProteinTech; Cat: 10094-1-AP). The secondary antibodies were incubated for 1 h at room temperature the next day. Finally, the protein bands were visualized with an enhanced chemiluminescence (ECL) detection system (Bio-Rad, Hercules, CA, USA), and the relative band intensities were calculated with ImageJ (1.52a, National Institutes of Health, USA).
A 24-well plate Transwell insert (8-µm pore size, Corning, USA) coated with Matrigel matrix (Corning, USA) was used to detect the invasion ability of the cell. In brief, HTR-8 cells (2 × 10 4 ) were seeded in the upper chamber of each insert in a 200 µl FBS-free DMEM-F12 medium. The lower chamber was filled with 500 µl of DMEM-F12 medium containing 10% FBS. The plate was placed in an incubator with 5% CO 2 at 37°C for 48 h. Afterward, cells that had invaded the lower chamber were fixed with 4% paraformaldehyde, stained with 0.5% crystal violet, and quantified. The average cells number from five fields at a magnification of ×200 was reported.
Cell migration ability was evaluated with scratch wound healing assay. When cells reached 80%–90% confluence, a scratch wound was made on the monolayer of cells with a 200-µl pipette tip and gently washed three times with PBS before the serum-free medium was added. The 6-well plate was incubated with 5% CO 2 at 37°C for 48 h. Pictures of the wound were taken at 0 and 48 h. The wound area was calculated using ImageJ.
Quantitative data were expressed as the mean ± standard deviation (SD) and analyzed by independent t-test. Categorical data were compared by the Mann–Whitney U-test. All experiments were independently repeated at least three times. Figures were performed by GraphPad Prism version 6.0 (GraphPad Software, San Diego, CA). All p -values were two-sided and statistical significance was established as p < 0.05. All analyses were conducted using SPSS 22.0 (IBM SPSS, USA).
Clinical Baseline Characteristics Collected data at baselines included maternal age, gestational week, body mass index, number of pregnancies, number of live births, and number of miscarriages. Detailed information is listed in . No differences were found between the RSA group and the control group in terms of age, gestational week, body mass index, and the number of pregnancies. In addition, the RSA group showed a higher number of miscarriages and a significantly lower number of live births than the control group ( p < 0.01). Expression of CXCL5 Is Downregulated in Villous Tissues of Recurrent Spontaneous Abortion Patients To investigate the role of CXCL5 in RSA, we firstly evaluated the expression levels of CXCL5 in placental villous tissues from 15 RSA patients and 13 normal controls by RT-PCR. Lower expression of CXCL5 was found in the RSA group than in the control group ( ). In addition, to further identify immunolocalization and compare CXCL5 levels, human placental villous tissue sections were stained via immunohistochemistry (IHC) assay. A weaker stain of CXCL5 was found in villous tissues from RSA patients ( ). These data together suggested that CXCL5 expression was downregulated in villous tissues of RSA patients and that lower levels of CXCL5 were positively associated with RSA. Expression of CXCR2 in Trophoblast of Human Placental Villous Tissue CK7 was recommended as an identification marker for trophoblast ( ). To confirm the CXCR2 expression in the trophoblast of human placental villous, we performed colocalization of CXCR2 and CK7 in human placental villous tissue with the immunofluorescence assay. The results confirmed that CXCR2 was expressed in the CK7-labeled trophoblast and suggested that CXCL5 can bind to the trophoblast cells to exert its functions ( ). CXCL5 Promotes Trophoblast Migration and Invasion via Inducing the Epithelial-to-Mesenchymal Transition Process To investigate the effects of CXCL5 on the migration and invasion of trophoblast, we conducted Transwell and scratch wound healing experiments using HTR-8 cells. The results showed that rhCXCL5-stimulated trophoblast cells performed quicker migration than did the control group ( ). Similarly, CXCL5-treated groups also showed an increased cell invasion potential than did the control group ( ). These results demonstrated that CXCL5 can significantly enhance trophoblast migration and invasion in vitro . EMT was regarded as an important process via which trophoblast acquired invasive ability ( ). To gain insight into whether CXCL5 promoted trophoblast migration and invasion via inducing the EMT process, we detected the expression of EMT markers. Western blotting results revealed that CXCL5 treatment significantly decreased the expression of the epithelial marker E-cadherin and increased the expression of the mesenchymal markers N-cadherin and vimentin in HTR-8 cells ( ). Based on these findings, CXCL5 can promote trophoblast migration and invasion via inducing the EMT process. CXCL5 Activates PI3K/AKT/ERK1/2 Pathway to Induce the Epithelial-to-Mesenchymal Transition Process Studies reported that CXCL5 induced the EMT process in cancer cells by activating ERK/Elk-1/Snail, AKT/GSK3β/β-catenin, or ERK/Snail signaling pathways ( , ). To explore the potential signaling pathway that CXCL5 induced EMT in trophoblast cells, we detected the activity of these pathways. Western blotting analysis showed that the levels of phosphorylation AKT remarkably increased in CXCL5-treated HTR-8 cells, while the total AKT levels did not change ( ). Consistently, PI3K levels also increased. In addition, we also observed apparent activation of p-ERK1/2 ( ). Next, we selected inhibitors specific to ERK (PD98059) and PI3K/AKT (LY294002) pathways for further exploration. A notable blocking effect of LY294002 and PD98059 was observed on p-AKT and p-ERK1/2 levels, respectively ( ). Remarkably, p-ERK1/2 levels also decreased when the PI3K/AKT was inhibited with LY294002, which suggested that ERK1/2 may act as a direct downstream effector of PI3K/AKT signaling ( ). Next, inhibitor pretreatment was initiated 2 h before CXCL5 treatment in HTR-8 cells. The results showed that PD98059 pretreatment significantly reduced the expression of N-cadherin and vimentin but increased the expression of E-cadherin compared with CXCL5 treated alone in trophoblast cells ( ), which indicated that PD98059 reversed the CXCL5-induce EMT process. In addition, wound healing assay and Matrigel invasion assay results also showed that the invasive and migratory activities of cells were reduced when the ERK1/2 pathway was inhibited ( ). Similar results were observed in the LY294002 treatment group ( ). Taken together, these results confirmed that CXCL5 activated PI3K/AKT/ERK1/2 pathway to induce the EMT process of trophoblast cells. We also checked the E-cadherin and N-cadherin expression in human placental villous tissue specimens with IHC assay. The results showed that E-cadherin expression was upregulated ( ) and N-cadherin expression was downregulated ( ) in placental villous tissues from RSA patients compared with the controls. Estrogen, Progesterone, Human Chorionic Gonadotropin, and Decidual Stromal Cells Regulate CXCL5/CXCR2 Expression of Trophoblast Extensive evidence revealed that reproductive hormones and DSCs directly or indirectly affected chemokines expression ( – ). Therefore, to confirm their impacts on CXCL5 and CXCR2 expression of trophoblast cells, we cultured HTR-8 cells in the presence or absence of E, P, or HCG. The results are displayed in , . We found that HCG increased CXCR2 expression ( p < 0.01) but did not affect CXCL5 expression in HTR-8 cells ( p > 0.05). E was observed to downregulate CXCL5 levels ( p < 0.01) but did not affect CXCR2 expression ( p > 0.05). P did not affect CXCR2 ( p > 0.05) but increased CXCL5 mRNA expression ( p < 0.05). To assess the effect of DSCs on CXCL5 and CXCR2 of trophoblast cells, we used a co-culture model of different ratios of HTR-8 cells and DSCs, as shown in . The results indicated that DSCs can promote the expression of CXCL5 and CXCR2 of trophoblast cells, even when cultured at a lower ratio (DSCs: HTR-8 cells, 1:4; ).
Collected data at baselines included maternal age, gestational week, body mass index, number of pregnancies, number of live births, and number of miscarriages. Detailed information is listed in . No differences were found between the RSA group and the control group in terms of age, gestational week, body mass index, and the number of pregnancies. In addition, the RSA group showed a higher number of miscarriages and a significantly lower number of live births than the control group ( p < 0.01).
To investigate the role of CXCL5 in RSA, we firstly evaluated the expression levels of CXCL5 in placental villous tissues from 15 RSA patients and 13 normal controls by RT-PCR. Lower expression of CXCL5 was found in the RSA group than in the control group ( ). In addition, to further identify immunolocalization and compare CXCL5 levels, human placental villous tissue sections were stained via immunohistochemistry (IHC) assay. A weaker stain of CXCL5 was found in villous tissues from RSA patients ( ). These data together suggested that CXCL5 expression was downregulated in villous tissues of RSA patients and that lower levels of CXCL5 were positively associated with RSA.
CK7 was recommended as an identification marker for trophoblast ( ). To confirm the CXCR2 expression in the trophoblast of human placental villous, we performed colocalization of CXCR2 and CK7 in human placental villous tissue with the immunofluorescence assay. The results confirmed that CXCR2 was expressed in the CK7-labeled trophoblast and suggested that CXCL5 can bind to the trophoblast cells to exert its functions ( ).
via Inducing the Epithelial-to-Mesenchymal Transition Process To investigate the effects of CXCL5 on the migration and invasion of trophoblast, we conducted Transwell and scratch wound healing experiments using HTR-8 cells. The results showed that rhCXCL5-stimulated trophoblast cells performed quicker migration than did the control group ( ). Similarly, CXCL5-treated groups also showed an increased cell invasion potential than did the control group ( ). These results demonstrated that CXCL5 can significantly enhance trophoblast migration and invasion in vitro . EMT was regarded as an important process via which trophoblast acquired invasive ability ( ). To gain insight into whether CXCL5 promoted trophoblast migration and invasion via inducing the EMT process, we detected the expression of EMT markers. Western blotting results revealed that CXCL5 treatment significantly decreased the expression of the epithelial marker E-cadherin and increased the expression of the mesenchymal markers N-cadherin and vimentin in HTR-8 cells ( ). Based on these findings, CXCL5 can promote trophoblast migration and invasion via inducing the EMT process.
Studies reported that CXCL5 induced the EMT process in cancer cells by activating ERK/Elk-1/Snail, AKT/GSK3β/β-catenin, or ERK/Snail signaling pathways ( , ). To explore the potential signaling pathway that CXCL5 induced EMT in trophoblast cells, we detected the activity of these pathways. Western blotting analysis showed that the levels of phosphorylation AKT remarkably increased in CXCL5-treated HTR-8 cells, while the total AKT levels did not change ( ). Consistently, PI3K levels also increased. In addition, we also observed apparent activation of p-ERK1/2 ( ). Next, we selected inhibitors specific to ERK (PD98059) and PI3K/AKT (LY294002) pathways for further exploration. A notable blocking effect of LY294002 and PD98059 was observed on p-AKT and p-ERK1/2 levels, respectively ( ). Remarkably, p-ERK1/2 levels also decreased when the PI3K/AKT was inhibited with LY294002, which suggested that ERK1/2 may act as a direct downstream effector of PI3K/AKT signaling ( ). Next, inhibitor pretreatment was initiated 2 h before CXCL5 treatment in HTR-8 cells. The results showed that PD98059 pretreatment significantly reduced the expression of N-cadherin and vimentin but increased the expression of E-cadherin compared with CXCL5 treated alone in trophoblast cells ( ), which indicated that PD98059 reversed the CXCL5-induce EMT process. In addition, wound healing assay and Matrigel invasion assay results also showed that the invasive and migratory activities of cells were reduced when the ERK1/2 pathway was inhibited ( ). Similar results were observed in the LY294002 treatment group ( ). Taken together, these results confirmed that CXCL5 activated PI3K/AKT/ERK1/2 pathway to induce the EMT process of trophoblast cells. We also checked the E-cadherin and N-cadherin expression in human placental villous tissue specimens with IHC assay. The results showed that E-cadherin expression was upregulated ( ) and N-cadherin expression was downregulated ( ) in placental villous tissues from RSA patients compared with the controls.
Extensive evidence revealed that reproductive hormones and DSCs directly or indirectly affected chemokines expression ( – ). Therefore, to confirm their impacts on CXCL5 and CXCR2 expression of trophoblast cells, we cultured HTR-8 cells in the presence or absence of E, P, or HCG. The results are displayed in , . We found that HCG increased CXCR2 expression ( p < 0.01) but did not affect CXCL5 expression in HTR-8 cells ( p > 0.05). E was observed to downregulate CXCL5 levels ( p < 0.01) but did not affect CXCR2 expression ( p > 0.05). P did not affect CXCR2 ( p > 0.05) but increased CXCL5 mRNA expression ( p < 0.05). To assess the effect of DSCs on CXCL5 and CXCR2 of trophoblast cells, we used a co-culture model of different ratios of HTR-8 cells and DSCs, as shown in . The results indicated that DSCs can promote the expression of CXCL5 and CXCR2 of trophoblast cells, even when cultured at a lower ratio (DSCs: HTR-8 cells, 1:4; ).
RSA is the most common pregnancy-related complication affecting reproductive-age women. Accumulating evidence has demonstrated that trophoblast invasion is closely associated with embryo implantation and placentation and thus plays an important role in the establishment and maintenance of pregnancy. Moreover, studies reported that insufficient invasion of trophoblast cells can lead to the occurrence of RSA ( , ). Our previous work also showed that miR-27a-3p/USP25 axis participated in the pathogenesis of RSA through inhibiting trophoblast migration and invasion ( ). In the current study, we found that CXCL5 levels were downregulated in villous tissues from RSA patients. Furthermore, we demonstrated that CXCL5 induced the EMT process to promote trophoblast invasion and migration through PI3K/AKT/ERK1/2 signaling pathway. These results together demonstrate that the downregulation of CXCL5 in villous tissue plays an essential role in the pathogenesis of RSA. The maternal–fetal interface exists an abundant chemokines network, which exhibits main functions in inflammation, immune tolerance, and trophoblast invasion during early human pregnancy ( ). Substantial studies have reported the relationship between chemokines and trophoblast invasion. Zhang et al. revealed that CXCL6 restricted human trophoblast migration and invasion in vitro ( ). Wang et al. pointed out that both exogenous and endogenous CXCL3 regulated trophoblast cells invasion ( , ). Similar results have been identified in CCL24, CXCL16, CXCL14, CCL14, and CCL17 ( , – ). Chemokines have been widely reported in the field of cancer and are associated with angiogenesis, invasion, and metastatic potential of tumors. For instance, Mao et al. found that CXCL5 can enhance gastric cancer cells migration and invasion ability via inducing the EMT process ( ). Kodama et al. reported that the CCL3/CCR5 axis contributed to esophageal squamous cell migration and invasion ( ). Trophoblast cells have much in common with tumor cells ( ). Therefore, our current study investigated the effect of CXCL5 on trophoblast invasion. As described above, we firstly detected CXCL5 expression in villous samples from RSA patients and control patients and found a significant downregulation of CXCL5 in the former. Next, we confirmed that human villous trophoblast expressed CXCR2, which was consistent with a previous study, and suggested CXCL5 can exert impacts on trophoblast cells via the receptor-ligand binding mechanism ( ). We further conducted in vitro experiments using HTR-8 cells. Exploring trophoblast invasion relies on a suitable trophoblast line because obtaining pure, primary, first-trimester human trophoblast remains a challenge. Compared with BeWo, JEG-3, and JAR, which are highly malignant and have a substantially different transcriptomic profile from EVTs, the HTR-8/SVeo cell line is reported to contain a heterogeneous population of trophoblasts and has been widely used to investigate EVT biology and functions ( , ). Our results showed that CXCL5 can promote trophoblast cells migration and invasion. Therefore, the downregulation of CXCL5 in villous tissues of RSA patients leads to inadequate trophoblast invasion and the development of RSA. EMT is firstly described by Elizabeth Hay and is referred to as a multistep dynamic cellular phenomenon in which epithelial cells lose their cell–cell adhesions and gain migratory and invasive traits that are typical of mesenchymal cells ( ). This process is characterized by loss of the membranous epithelial marker E-cadherin, increase of mesenchymal markers including vimentin and N-cadherin, and enhanced migratory and invasive behaviors. It has been reported that EMT participates in embryonic development, tissue repair, and cancer metastasis ( – ). We provided the first evidence that CXCL5 induced the EMT process to enhance trophoblast invasion and migration. Additionally, we also observed the reversal of EMT in CXCL5-reduced villous tissues from RSA patients. These data emphasized the importance of EMT in pregnancy. Interestingly, recent studies have also found that EMT has an intimate association with aerobic glycolysis ( , ). In addition, Ma et al. have reported that lactic acid, which is a critical metabolite product of aerobic glycolysis, plays a role in trophoblast invasion and angiogenesis ( ). However, whether this effect is mediated by EMT induction to link pregnancy requires further investigation. PI3K/AKT and ERK pathways are reported to play significant roles in the CXCL5-induced cell invasion and the EMT process. Qiu et al. found that CXCL5/CXCR2 axis contributed to the EMT of nasopharyngeal carcinoma cells by ERK/GSK-3β/snail signaling ( ). Zhao et al. reported that tumor-derived CXCL5 promoted human colorectal cancer metastasis through the activation of ERK/Elk-1/Snail and AKT/GSK3β/β-catenin pathways ( ). In our present study, CXCL5 can activate PI3K/AKT/ERK1/2 pathway to induce the EMT process and enhance trophoblast invasion. These data again highlight the importance of PI3K/AKT and ERK pathway. In addition, PI3K/AKT signaling is also identified as a potential therapeutic target. Epidemiological studies and meta-analyses have shown that the use of statins is closely associated with a reduced incidence of colorectal cancer ( – ). Recently, a new study has revealed that statins can target inhibition PI3K/AKT/mTOR signaling and thus acts on colorectal cancer progression ( ). Remarkably, another research has found that pravastatin can successfully prevent fetal death in a pregnant woman with a history of four consecutive pregnancy losses ( ). However, whether this effect depends on PI3K/AKT pathway needs more exploration. It is reported that reproductive hormones E, P, and HCG play important roles during pregnancy and can affect the expression of chemokines within the maternal–fetal microenvironment ( ). Currently, we found that P significantly increased CXCL5 expression while E had an opposite function. Our results also showed that HCG upregulated CXCR2 expression, although it did not affect CXCL5 expression. These findings indicated that CXCL5/CXCR2 axis is regulated by local hormones. There is also a close dialogue between trophoblast cells and maternal DSCs at the maternal–fetal interface ( ). Co-culture systems comprising decidual fragments and trophoblasts have been widely used to explore their relationship. Li et al. reported that DSCs promoted CCR3 levels of trophoblast cells ( ). Our current result confirmed that DSCs significantly promoted CXCL5 and CXCR2 expression of trophoblast cells. However, whether DSCs can induce trophoblast cells to secrete CXCL5 and participate in the regulation of trophoblast invasion deserve further research in future work. In summary, our present data confirm that CXCL5 levels are significantly lower in human villous tissue from RSA patients. We also demonstrate that CXCL5 can promote trophoblast invasion, migration, and EMT process through PI3K/AKT/ERK1/2 pathway. Taken together, these data indicate that CXCL5 downregulation in villous tissue is correlated with RSA. This provides us with more insights into the molecular pathogenesis of RSA. In addition, we also found that E, P, HCG, and DSCs regulate the expression of CXCL5/CXCR2 in a cell manner. These findings illustrate a new dialogue among chemokines, trophoblast cells, and reproductive hormones in the microenvironment of the maternal–fetal interface ( ).
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
The studies involving human participants were reviewed and approved by the ethics committee of Renmin Hospital of Wuhan University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
TY and YZ supported the research. SZ and JD designed the experiments. SZ performed the experiments and drafted the first version of the manuscript. JW collected the clinical samples and patients’ data. TY, YZ, JD, and JY supervised and revised the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the following grants: National Key Research and Development Program of China (Nos. 2018YFC1004601, 2018YFC1002804), the National Natural Science Foundation of China (Nos. 81801540, 81771662, 82101749), and the Fundamental Research Funds for the Central Universities (2042021kf0082).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
Diallyl Trisulfide Attenuates Ischemia-Reperfusion-Induced ER Stress and Kidney Dysfunction in Aged Female Mice | c8b7ca92-e4d4-4924-8bab-7ad9c04b58fb | 11941362 | Pathologic Processes[mh] | Ischemia-reperfusion injury (IRI) is the leading cause of acute kidney injury (AKI) in the perioperative period and is associated with increased morbidity and mortality . Renal IRI induces several structural and functional alterations involving glomerular and vascular endothelial cells and tubular epithelial cells that together disrupt renal function . Recent studies suggest endoplasmic reticulum (ER) plays an important role in renal IRI . ER is a dynamic intracellular organ system involved in protein synthesis, post-translational modification, folding and transport, Ca 2+ handling, and signal transduction . Under physiological conditions, the unfolded protein response (UPR) system maintains a balance between the protein load and protein folding capacity in the ER, whereas in pathophysiology, the fine balance is disrupted leading to the activation of three ER transmembrane sensors, (1) protein kinase RNA-like ER eukaryotic initiation factor-2α kinase (PERK), (2) inositol requiring protein 1 (IRE1), and (3) activating transcription factor 6 (ATF6), that attenuate protein translation to mitigate ER stress . A consequence of PERK activation is the induction and translocation of NF-kB to the nucleus where it triggers proinflammatory genes . During renal IRI, the accumulation of unfolded and misfolded proteins causes ER stress and disruption of a network of mechanisms to determine cell fate . While mild to moderate ER stress is cytoprotective, excess ER stress impairs protein homeostasis and triggers apoptotic pathways . The renal microvasculature plays an important role in repair and regeneration following IRI. As a primary barrier to insults, the endothelial cells sustain damage during ischemia and reperfusion phases triggering a cascade of events leading to stress, inflammation, and cytoskeletal derangements . Together, the events cause endothelial activation and dysfunction characterized by compromised vascular tone, and loss of capillary density that are crucial for the recovery process [ , , ]. The renal outer medulla is sensitive to hypoxia and loss of peritubular capillaries decreases perfusion, worsening injury and delaying repair . The downregulation of angiogenic factor, vascular endothelial growth factor-A (VEGF-A) is implicated in capillary rarefaction and reduced angiogenesis following IRI . Interestingly, VEGF-A expression by the tubular epithelial cells plays an important role in maintaining tubular–endothelial cell crosstalk that directly affects endothelial function . Several studies demonstrated that ER stress mediates endothelial dysfunction in diabetes , hypertension , hyperhomocysteinemia , and hyperlipidemia involving various organ systems . Hydrogen sulfide (H 2 S) is a gasotransmitter that is known to perform numerous functions in health and disease states. Previously, we demonstrated that in renal IRI, deficiency of H 2 S is associated with renal inflammation and fibrosis, and supplementation of H 2 S decreased macrophage polarization to inflammatory phenotype and endothelial-mesenchymal transition in the aged kidney . Recent studies suggest that H 2 S modulates ER stress in cardiovascular pathology , neurological disorders , and vascular disease . The purpose of the present study was to investigate whether diallyl trisulfide (DATS), a H 2 S donor, offers renal protection from IRI-induced ER stress and to test whether enhancing angiogenesis reverses loss of medullary blood flow and renal dysfunction.
2.1. Animal Groups and DATS Treatment C57BL/6J wild type (WT, Stock no.: 000664) mice were purchased from Jackson Laboratory (Bar Harbor, ME, USA) and bred in-house, housed in a temperature- and light-controlled environment at the University of Louisville animal facility. Female mice aged 75–78 weeks were used in the study. The animals were randomized (n = 6/group, total n = 24) into the following groups: (1) WT mice fed a normal diet (WT+ND) that served as control group, (2) WT+ND+IRI, (3) WT+DATS, and (4) WT+DATS+IRI. The sample size and the experimental groups were determined to ensure a p -value of 0.05 or lower for the variables based on our prior study . Additional mice were included in the study due to severity of injury and death. Human end point and sudden death accounted for <5% of the total number of mice, and data from these animals were excluded from analysis. The final analysis included all animals that survived until the end point of the study. The animals were fed a customized diet containing diallyl trisulfide (DATS) for 21 days prior to IRI and continued for 7 days afterward. The DATS dosage was based on previous studies, with a formulation of 50 ppm, equivalent to 50 mg/kg/day . This dosage calculation was based on the average daily food intake of approximately 4 g per mouse. Incorporating DATS into the chow allowed us to avoid complications such as internal injury and inflammation, as well as issues related to repeated intraperitoneal injections over the four-week study period. Controls were given a standard diet and tap water ad libitum. At the end of the experiments, the animals were euthanized using 2X tribromoethanol (TBE), and blood samples were collected in lithium heparin vacutainer tubes to separate plasma as described . The animal protocols were performed in accordance with institutional animal care guidelines and conform to the Guide for the Care and Use of Laboratory Animals published by the U.S. National Institutes of Health (NIH Publication, 2011). The study was approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Louisville School of Medicine (IACUC#21967, date: 28 September 2021). 2.2. Rationale for the Chosen Age Range Generally, acute kidney injury (AKI) is statistically more prevalent in both female and male individuals over the age of 65 compared to those aged 18 to 65 years . A recent study also reported higher rates of AKI among individuals in the median age range of 59 to 68 years for both females and males . In animal studies, mice aged 18 to 24 months (approximately 72 to 96 weeks) are considered old, which closely resembles human aging between 56 to 69 years (The Jackson Laboratory, Bar Harbor, ME, USA). Moreover, there are fewer studies focusing on females compared to males. For these reasons, we used female mice aged 75 to 78 weeks in our AKI study to better simulate the corresponding human age group. 2.3. Ischemia-Reperfusion Injury (IRI) Experimental renal IRI in the mouse replicates human acute kidney injury and is therefore widely used by researchers to study the IRI mechanisms and response to treatment. In the present study, renal IRI was created by a single researcher as described before with modification . Briefly, mice were anesthetized by isoflurane inhalation (4% induction and 1% maintenance) and given preemptive meloxicam analgesia (10 mg/kg, SC). Through a midline dorsal incision, the renal vessels were exposed on both sides and microvascular clamps were applied for 27 min. The clamps were removed to allow reperfusion, and the wound closed in layers. The mice received analgesia for three consecutive days and postoperative care until the end point at 7 d post IRI. The analgesia dose/duration is nontoxic to mice kidneys . Euthanasia and sample collection was carried out between 10 AM and 1 PM. 2.4. Antibodies and Reagents ATF6 (Cat. No.: 66563-1-IG), Phospho-IRE1α (Cat. No.: PA5-105425, Thermo Fisher Scientific, Waltham, MA, USA), Phospho-PERK (Cat. No.: PA5-102853, Thermo Fisher Scientific), NF-kB p65 (Cat. No.: 51-0500), IL-17A (Cat. No.: PA5-106856), Col1A1 (Cat. No.: PA5-29565), phosphor-mTOR (Cat. No.: 44-1125G), anti-rabbit Alexa Fluor 488 (Cat. No.: A11008), and anti-rabbit Alexa fluor 594 (Cat. No.: A11012) were from Thermo Fisher Scientific. VEGF-A (Cat. No.: ab51745), CD31 (Cat. No.: ab9498), HIF1α (Cat. No.: ab82832), and TGF-β (Cat. No.: ab64715) were from Abcam, Cambridge, UK. TNF-α (Cat. No.: 60291-1-Ig, Proteintech, Rosemont, IL, USA), IL-1β (Cat. No.: AF-401-NA, Novus Biologicals, Centennial, CO, USA), Lipocalin-2 (Cat. No.: AF1857, R&D Systems, Minneapolis, MN, USA), eNOS (Cat. No.: 610297, BD Biosciences, Franklin Lakes, NJ, USA), p-PI3K (Cat. No.: 4228), p-AKT (Cat. No.: 4060), and p-MAPK (Cat. No.: 9211) were from Cell Signaling, Danvers, MA, USA. Β-actin (Cat. No.: SC47778) and Erythropoietin (sc-5290) were from Santa Cruz Biotechnology, Dallas, TX, USA. QuantiChrom TM creatinine assay kit (Cat. No.: DICT-500) was from BioAssay Systems, Hayward, CA, USA. Manufacturer’s instructions were followed for all applications. 2.5. H 2 S Measurement Renal H 2 S was measured as described before with modification . Briefly, fresh kidney samples were washed and homogenized in ice-cold PBS. After centrifugation, the supernatant (100 μL) was mixed in a centrifuge tube containing PBS (100 mM, pH 7.4, 350 μL) and zinc acetate (1% W/V, 250 μL). This step was followed by the addition of N,N-dimethyl-p-phenylenediamine sulfate (20 mM, 133 μL) in 7.2 M HCl, and FeCl 3 (30 mM, 133 μL) in 1.2 M HCl. The mixture was sealed tightly and incubated at 37 °C for 45 min. The reaction was terminated by adding trichloroacetic acid (TCA) solution (10% W / V , 250 μL). After centrifugation, 200 μL supernatant was transferred to a 96-well plate and absorbance was read at 670 nm using a spectrophotometer. Samples were assayed and H 2 S was calculated against a calibration curve of known concentrations of NaHS. 2.6. Antibody Authentication All antibodies on Western blot were characterized by neutralizing antigens. Control lane using the standard of protein was used in each blot. This established the authenticity of the antibody and was further used for other Western blots, immunohistochemistry, and immunostaining analyses. To reduce non-specificity, we incubated BSA blocked membrane with antibodies to deplete the non-specific binding of protein fractions. 2.7. Western Blotting Proteins were extracted from whole kidney lysates and electrophoresed on SDS-PAGE and transferred to the PVDF membrane by following previous protocol . After blocking, primary antibodies were incubated overnight at 4 °C. Appropriate secondary antibodies were incubated for 120 min at room temp. The membranes were developed using chemiluminescence and visualized with the ChemiDoc MP system (BioRad, Hercules, CA, USA). Band intensities were quantified using ImageJ software ( https://imagej.net/ij/ (accessed on 2 April 2022). 2.8. Gene Expression Total RNA was extracted using Quick-RNA TM MiniPrep (Cat. No.: R1055, Zymo Research, Irvine, CA, USA) and cDNA was synthesized using EasyScript cDNA Synthesis Kit (Cat. No.: G234, MidSci, St. Louis, MO, USA) following manufacturer’s instructions. The mRNA levels were quantified by qPCR (Lightcycler ® 96 system, Roche Diagnostics Corporation, Indianapolis, IN, USA) using the specific primers listed in . 2.9. Immunohistochemistry IHC was conducted as described before . Frozen kidneys at 5 μm thickness were air-dried and fixed in acetone for 10 min. After blocking, sections were incubated with primary antibodies overnight at 4 °C. Immune labeling was conducted using Alexa fluor 488 and Alexa fluor 594 secondary antibodies for 90 min at room temperature. Images were captured using Olympus FluoView1000 (B&B Microscope Ltd., Pittsburgh, PA, USA). Mean fluorescence intensity was measured using ImageJ software (1.53q 30 March 2022), as stated earlier. 2.10. Renal Cortical Blood Flow Renal cortical blood flow was measured after 7 days of IR using a Speckle Contrast Imager (Moor FLPI, Wilmington, DE, USA), as described before . Briefly, a dorsal incision was made to expose the kidney, and the camera was focused on the renal cortex to obtain cortical flux units (No. of RBCs x velocity). 2.11. Renal Microvasculature The renal microvasculature was visualized at the end point of experiment, i.e., after 7 days of IR using our previously adopted barium sulfate angiography . Briefly, the abdomen was opened with a midline incision, and the left renal artery was exposed by microdissection. A PE10 (ID—0.28 mm, BD, Franklin Lakes, NJ, USA) was introduced via arteriotomy and a barium sulfate solution was perfused using automated syringe pump. Images were captured using a Kodak FX Pro in vivo imaging system (Molecular Imaging System; Carestream Health Inc., Rochester, NY, USA) and microvasculature density was analyzed using VesSeg software (VesSeg_V0.1.4; University of Lubeck, Lubeck, Germany). 2.12. Conventional Ultrasonography After 7 days post IRI, conventional B-mode ultrasonography was conducted using Vevo 2100 (VisualSonics, Toronto, ON, Canada) as described before . Briefly, mice were anesthetized using isoflurane, and the ventral abdominal area was depilated. After acoustic gel (Other-Sonic; Pharmaceutical Innovations, Newark, NJ, USA) application on the skin, an MS550D transducer (22–25 Mz) was used to image outer medullary vessels in the short axis to obtain peak systolic and end diastolic velocities. The data was exported to measure resistive index (RI) of the medullary microvessels using the formula described before . 2.13. Renal Function Glomerular filtration rate (GFR) was used as surrogate to measure renal function in conscious mice . GFR was measured 7 days after IR. Briefly, under isoflurane anesthesia, the left side of abdominal dorsum was depilated for the application of NIC-kidney device (MediBeacon, St. Louis, MO, USA). A solution of FITC-sinistrin (7 mg/100 g b.w., MediBeacon, St. Louis, MO, USA) was injected intravenously and data acquired for 2 h. The half-life of FITC-sinistrin was recorded using MediBeacon software (version: vol.1.1), and the GFR was calculated with a conversion factor as previously described . Plasma creatinine was measured as described previously using QuantiChrom TM creatinine assay kit . Briefly, samples (30 μL) and STD (2 mg/dL, 30 μL) were transferred to a 96-well clear bottom plate. Working reagent (200 μL) was added to each well and mixed gently. The optical density was measured at 0 min and 5 min using a SpectraMax M2e (Molecular devices, San Jose, CA, USA) set at 510 nm. Plasma creatinine concentration was calculated using the following formula: [(OD sample 5 − ODsample 0)/(OD standard 5 − OD standard 0)] × [STD] (mg/dL). 2.14. Statistical Analysis and Blinding Data are presented as mean ± SD, ‘n’ represents the number of animals used. The difference between the groups was determined by ANOVA followed by a post hoc Tukey test. A Mann–Whitney U test was conducted for nonparametric data. Differences with a p -value < 0.05 were considered significant. All data analysis was conducted using GraphPad Prism (10.1.2). The animals were assigned by randomization into their groups by the PI. All animals that survived until the end point were included for data analysis. Investigators who carried out tissue sample preparation, experiments, measurements, and analysis were blinded to the study groups.
C57BL/6J wild type (WT, Stock no.: 000664) mice were purchased from Jackson Laboratory (Bar Harbor, ME, USA) and bred in-house, housed in a temperature- and light-controlled environment at the University of Louisville animal facility. Female mice aged 75–78 weeks were used in the study. The animals were randomized (n = 6/group, total n = 24) into the following groups: (1) WT mice fed a normal diet (WT+ND) that served as control group, (2) WT+ND+IRI, (3) WT+DATS, and (4) WT+DATS+IRI. The sample size and the experimental groups were determined to ensure a p -value of 0.05 or lower for the variables based on our prior study . Additional mice were included in the study due to severity of injury and death. Human end point and sudden death accounted for <5% of the total number of mice, and data from these animals were excluded from analysis. The final analysis included all animals that survived until the end point of the study. The animals were fed a customized diet containing diallyl trisulfide (DATS) for 21 days prior to IRI and continued for 7 days afterward. The DATS dosage was based on previous studies, with a formulation of 50 ppm, equivalent to 50 mg/kg/day . This dosage calculation was based on the average daily food intake of approximately 4 g per mouse. Incorporating DATS into the chow allowed us to avoid complications such as internal injury and inflammation, as well as issues related to repeated intraperitoneal injections over the four-week study period. Controls were given a standard diet and tap water ad libitum. At the end of the experiments, the animals were euthanized using 2X tribromoethanol (TBE), and blood samples were collected in lithium heparin vacutainer tubes to separate plasma as described . The animal protocols were performed in accordance with institutional animal care guidelines and conform to the Guide for the Care and Use of Laboratory Animals published by the U.S. National Institutes of Health (NIH Publication, 2011). The study was approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Louisville School of Medicine (IACUC#21967, date: 28 September 2021).
Generally, acute kidney injury (AKI) is statistically more prevalent in both female and male individuals over the age of 65 compared to those aged 18 to 65 years . A recent study also reported higher rates of AKI among individuals in the median age range of 59 to 68 years for both females and males . In animal studies, mice aged 18 to 24 months (approximately 72 to 96 weeks) are considered old, which closely resembles human aging between 56 to 69 years (The Jackson Laboratory, Bar Harbor, ME, USA). Moreover, there are fewer studies focusing on females compared to males. For these reasons, we used female mice aged 75 to 78 weeks in our AKI study to better simulate the corresponding human age group.
Experimental renal IRI in the mouse replicates human acute kidney injury and is therefore widely used by researchers to study the IRI mechanisms and response to treatment. In the present study, renal IRI was created by a single researcher as described before with modification . Briefly, mice were anesthetized by isoflurane inhalation (4% induction and 1% maintenance) and given preemptive meloxicam analgesia (10 mg/kg, SC). Through a midline dorsal incision, the renal vessels were exposed on both sides and microvascular clamps were applied for 27 min. The clamps were removed to allow reperfusion, and the wound closed in layers. The mice received analgesia for three consecutive days and postoperative care until the end point at 7 d post IRI. The analgesia dose/duration is nontoxic to mice kidneys . Euthanasia and sample collection was carried out between 10 AM and 1 PM.
ATF6 (Cat. No.: 66563-1-IG), Phospho-IRE1α (Cat. No.: PA5-105425, Thermo Fisher Scientific, Waltham, MA, USA), Phospho-PERK (Cat. No.: PA5-102853, Thermo Fisher Scientific), NF-kB p65 (Cat. No.: 51-0500), IL-17A (Cat. No.: PA5-106856), Col1A1 (Cat. No.: PA5-29565), phosphor-mTOR (Cat. No.: 44-1125G), anti-rabbit Alexa Fluor 488 (Cat. No.: A11008), and anti-rabbit Alexa fluor 594 (Cat. No.: A11012) were from Thermo Fisher Scientific. VEGF-A (Cat. No.: ab51745), CD31 (Cat. No.: ab9498), HIF1α (Cat. No.: ab82832), and TGF-β (Cat. No.: ab64715) were from Abcam, Cambridge, UK. TNF-α (Cat. No.: 60291-1-Ig, Proteintech, Rosemont, IL, USA), IL-1β (Cat. No.: AF-401-NA, Novus Biologicals, Centennial, CO, USA), Lipocalin-2 (Cat. No.: AF1857, R&D Systems, Minneapolis, MN, USA), eNOS (Cat. No.: 610297, BD Biosciences, Franklin Lakes, NJ, USA), p-PI3K (Cat. No.: 4228), p-AKT (Cat. No.: 4060), and p-MAPK (Cat. No.: 9211) were from Cell Signaling, Danvers, MA, USA. Β-actin (Cat. No.: SC47778) and Erythropoietin (sc-5290) were from Santa Cruz Biotechnology, Dallas, TX, USA. QuantiChrom TM creatinine assay kit (Cat. No.: DICT-500) was from BioAssay Systems, Hayward, CA, USA. Manufacturer’s instructions were followed for all applications.
2 S Measurement Renal H 2 S was measured as described before with modification . Briefly, fresh kidney samples were washed and homogenized in ice-cold PBS. After centrifugation, the supernatant (100 μL) was mixed in a centrifuge tube containing PBS (100 mM, pH 7.4, 350 μL) and zinc acetate (1% W/V, 250 μL). This step was followed by the addition of N,N-dimethyl-p-phenylenediamine sulfate (20 mM, 133 μL) in 7.2 M HCl, and FeCl 3 (30 mM, 133 μL) in 1.2 M HCl. The mixture was sealed tightly and incubated at 37 °C for 45 min. The reaction was terminated by adding trichloroacetic acid (TCA) solution (10% W / V , 250 μL). After centrifugation, 200 μL supernatant was transferred to a 96-well plate and absorbance was read at 670 nm using a spectrophotometer. Samples were assayed and H 2 S was calculated against a calibration curve of known concentrations of NaHS.
All antibodies on Western blot were characterized by neutralizing antigens. Control lane using the standard of protein was used in each blot. This established the authenticity of the antibody and was further used for other Western blots, immunohistochemistry, and immunostaining analyses. To reduce non-specificity, we incubated BSA blocked membrane with antibodies to deplete the non-specific binding of protein fractions.
Proteins were extracted from whole kidney lysates and electrophoresed on SDS-PAGE and transferred to the PVDF membrane by following previous protocol . After blocking, primary antibodies were incubated overnight at 4 °C. Appropriate secondary antibodies were incubated for 120 min at room temp. The membranes were developed using chemiluminescence and visualized with the ChemiDoc MP system (BioRad, Hercules, CA, USA). Band intensities were quantified using ImageJ software ( https://imagej.net/ij/ (accessed on 2 April 2022).
Total RNA was extracted using Quick-RNA TM MiniPrep (Cat. No.: R1055, Zymo Research, Irvine, CA, USA) and cDNA was synthesized using EasyScript cDNA Synthesis Kit (Cat. No.: G234, MidSci, St. Louis, MO, USA) following manufacturer’s instructions. The mRNA levels were quantified by qPCR (Lightcycler ® 96 system, Roche Diagnostics Corporation, Indianapolis, IN, USA) using the specific primers listed in .
IHC was conducted as described before . Frozen kidneys at 5 μm thickness were air-dried and fixed in acetone for 10 min. After blocking, sections were incubated with primary antibodies overnight at 4 °C. Immune labeling was conducted using Alexa fluor 488 and Alexa fluor 594 secondary antibodies for 90 min at room temperature. Images were captured using Olympus FluoView1000 (B&B Microscope Ltd., Pittsburgh, PA, USA). Mean fluorescence intensity was measured using ImageJ software (1.53q 30 March 2022), as stated earlier.
Renal cortical blood flow was measured after 7 days of IR using a Speckle Contrast Imager (Moor FLPI, Wilmington, DE, USA), as described before . Briefly, a dorsal incision was made to expose the kidney, and the camera was focused on the renal cortex to obtain cortical flux units (No. of RBCs x velocity).
The renal microvasculature was visualized at the end point of experiment, i.e., after 7 days of IR using our previously adopted barium sulfate angiography . Briefly, the abdomen was opened with a midline incision, and the left renal artery was exposed by microdissection. A PE10 (ID—0.28 mm, BD, Franklin Lakes, NJ, USA) was introduced via arteriotomy and a barium sulfate solution was perfused using automated syringe pump. Images were captured using a Kodak FX Pro in vivo imaging system (Molecular Imaging System; Carestream Health Inc., Rochester, NY, USA) and microvasculature density was analyzed using VesSeg software (VesSeg_V0.1.4; University of Lubeck, Lubeck, Germany).
After 7 days post IRI, conventional B-mode ultrasonography was conducted using Vevo 2100 (VisualSonics, Toronto, ON, Canada) as described before . Briefly, mice were anesthetized using isoflurane, and the ventral abdominal area was depilated. After acoustic gel (Other-Sonic; Pharmaceutical Innovations, Newark, NJ, USA) application on the skin, an MS550D transducer (22–25 Mz) was used to image outer medullary vessels in the short axis to obtain peak systolic and end diastolic velocities. The data was exported to measure resistive index (RI) of the medullary microvessels using the formula described before .
Glomerular filtration rate (GFR) was used as surrogate to measure renal function in conscious mice . GFR was measured 7 days after IR. Briefly, under isoflurane anesthesia, the left side of abdominal dorsum was depilated for the application of NIC-kidney device (MediBeacon, St. Louis, MO, USA). A solution of FITC-sinistrin (7 mg/100 g b.w., MediBeacon, St. Louis, MO, USA) was injected intravenously and data acquired for 2 h. The half-life of FITC-sinistrin was recorded using MediBeacon software (version: vol.1.1), and the GFR was calculated with a conversion factor as previously described . Plasma creatinine was measured as described previously using QuantiChrom TM creatinine assay kit . Briefly, samples (30 μL) and STD (2 mg/dL, 30 μL) were transferred to a 96-well clear bottom plate. Working reagent (200 μL) was added to each well and mixed gently. The optical density was measured at 0 min and 5 min using a SpectraMax M2e (Molecular devices, San Jose, CA, USA) set at 510 nm. Plasma creatinine concentration was calculated using the following formula: [(OD sample 5 − ODsample 0)/(OD standard 5 − OD standard 0)] × [STD] (mg/dL).
Data are presented as mean ± SD, ‘n’ represents the number of animals used. The difference between the groups was determined by ANOVA followed by a post hoc Tukey test. A Mann–Whitney U test was conducted for nonparametric data. Differences with a p -value < 0.05 were considered significant. All data analysis was conducted using GraphPad Prism (10.1.2). The animals were assigned by randomization into their groups by the PI. All animals that survived until the end point were included for data analysis. Investigators who carried out tissue sample preparation, experiments, measurements, and analysis were blinded to the study groups.
3.1. DATS Attenuated IRI-Induced ER Stress in the Aged Kidney To determine changes in the H 2 S levels in the kidney and the response to IRI, we measured H 2 S by methylene blue method as described in the material and methods. The H 2 S levels in normal diet (ND) mice served as control. In the ND+IRI mice, renal H 2 S content was reduced compared to ND ( A). In the DATS+IRI mice, H 2 S content was increased significantly but did not normalize to the levels observed in the control group ( A). DATS alone treatment in the ND group showed an increasing trend but was not significant from the control ND group. ER stress in the aged kidney was quantified by measuring changes in the expression of UPR membrane proteins, p-IRE1α, p-PERK, and ATF6. At 7 days post IRI, the expression of all three proteins was increased in normal diet (ND)-fed mice following IRI, compared to control mice fed ND ( B,C). In the DATS+IRI mice, the expression of p-IRE1α, p-PERK, and ATF6 was significantly mitigated compared to ND+IRI ( B,C). DATS alone treatment in the ND group did not show any changes in their expression compared to the control ND group ( B,C). Additionally, in the post IRI kidneys, the mRNA levels of transcription factor, ATF4, and C/EBP homologous protein (CHOP) were upregulated ( D). The increased expression is coupled with p-PERK ( B,C) suggesting the predominance of PERK pathway in renal IRI. In IRI mice fed with the DATS diet, the mRNA expression levels of ATF4 and CHOP were decreased compared to ND+IRI ( B–D). The expression of ATF4 and CHOP mRNAs remained unchanged in ND and DATS alone. During the initial phase of stress, ER resident chaperones are expressed to regulate protein folding and excess UPR activation. We therefore measured ER resident chaperones, GRP78 and GRP94, heat shock proteins. In IRI kidneys, the mRNA levels of GRP78 and GRP94 were increased compared to control mice and DATS supplementation attenuated their expression ( D). No significant changes in their expression were observed in DATS alone compared to ND ( D). Immunohistochemical staining revealed p-IRE1α localized mainly to the glomeruli compared to tubules and ATF6 localized to both the glomeruli and tubular regions in IRI kidneys ( A). The p-IRE1α showed a 3-fold increase in fluorescent intensity and ATF6 increased by 4.7-fold compared to control mice, and their expressions were decreased in mice that received DATS treatment ( A,B). No significant changes in their expression were observed between ND and DATS alone ( A,B). 3.2. DATS Enhanced Renal Angiogenic Factors Following IRI Angiogenesis and vascular repair are crucial for recovery from IRI. To determine whether neo-angiogenesis re-establishes microcirculation in the kidney following IRI, we investigated the expression of angiogenic factors. In ND-fed mice, IRI increased the expression of hypoxia-inducible factor-1α (HIF-1α) along with VEGF-A and EPO; however, the eNOS expression was significantly decreased compared to control mice fed with ND and mice fed with DATS without IRI ( A,B). In DATS-fed mice that underwent IRI, VEGF-A expression remained unchanged, but the expression of HIF-1α, EPO, and eNOS was increased compared to IRI mice fed with ND ( A,B). DATS treatment alone did not alter the expression of HIF-1α, VEGF-A, EPO, and eNOS compared to ND-treated control mice ( A,B). We further examined for angiogenesis markers, VEGF-A and CD31 in kidney sections by immunohistochemistry. In ND and DATS-fed mice that did not undergo IRI, VEGF-A and CD31 expression were similar in the glomeruli and peritubular areas ( A,B). In ND+IRI mice, both markers were decreased in the cortex and medullary regions ( A,B). In the DATS+IRI kidney, VEGF-A and CD31 expression were increased in both glomerular and peritubular areas ( A,B). 3.3. DATS Activated PI3K/AKT/mTOR Pathway to Induce Angiogenesis in Renal IRI Hypoxia is a major stimulus for HIF-1α induction that, in turn, leads to VEGF-A production by the podocytes and tubular epithelial cells in the kidney . Additionally, VEGF-A-mediated stimulation of endothelial cells causes activation of the PI3K/AKT/mTOR pathway that results in angiogenesis . To investigate whether H 2 S-induced angiogenesis occurs via activation of the PI3K pathway, we quantified the expression of signaling molecules. In the ND+IRI kidney, protein expression of p-PI3K, p-AKT, and p-mTOR was downregulated compared to the ND control kidney ( A,B). The p-PI3K expression was decreased by 3-fold, p-AKT by 2.8-fold, and p-mTOR by 2.2-fold compared to ND kidney without IRI. In the DATS+IRI kidney, the levels of p-PI3K, p-AKT, and p-mTOR were significantly increased and were similar to the levels observed in the ND kidney without IRI ( A,B). Early stages of angiogenesis require MAPK signaling for cell proliferation and differentiation . We therefore measured the expression of phosphorylated-MAPK (p-MAPK). In the ND+IRI kidney, p-MAPK (p-MAPK) was significantly decreased compared to the ND kidney without IRI. Pre-treatment with DATS followed by IRI showed upregulation of p-MAPK compared to ND+IRI kidney ( A,B). 3.4. DATS Attenuated Renal Inflammation and Injury in the Aged Kidney IRI While sexual dimorphism in renal IRI is well known , the changes in IRI-induced inflammation in aged female kidney remains less studied. We therefore measured the protein expression of inflammatory markers, lipocalin-2 (LCN2), TNF-α, IL-17, and IL-1β. In the ND+IRI kidney, LCN2 increased by 2.5-fold, TNF-α by 2.6-fold, IL-17 by 2.6-fold, and IL-1β by 2-fold compared to the ND kidney without IRI ( A,B). In contrast, DATS+IRI kidneys normalized LCN2 levels comparable to ND kidneys and reduced the expression of TNF-α, IL-17, and IL-β by 0.6-fold, 0.5-fold, and 0.68-fold, respectively, from ND+IRI kidney levels ( A,B). The expression of inflammatory markers did not differ between ND and DATS kidneys ( A,B). Recent studies suggest TGF-β signaling in endothelial cells contributes to the loss of peritubular capillaries in AKI and subsequent transition to chronic kidney disease (CKD) . To determine whether TGF-β and Col1A1 markers are affected in renal IRI of aged kidneys, we quantified their expression. The expression of TGF-β was upregulated in the ND+IRI kidney by 1.9-fold and Col1A1 expression was increased by 4.4-fold compared to the ND kidney. In the DATS+IRI kidney, TGF-β and Col1A1 expression were downregulated significantly and were comparable to ND kidney ( C,D). Additionally, in the ND+IRI kidney, NF-kB expression was upregulated compared to the ND kidney without IRI and mitigated with DATS treatment ( C,D). 3.5. DATS Rescued Renal Blood Flow and Reduced Renovascular Resistance to the IRI Kidney Aging is associated with structural and functional changes in renal vasculature. To determine changes in the renal perfusion in the aged kidney following IRI, we measured cortical blood flow using laser Doppler flowmetry (MoorFLPI, Wilmington, DE, USA) at room temperature. In the ND+IRI kidney, cortical blood flow decreased significantly (29%) compared to the ND kidney that was not subjected to IRI ( A–C). DATS treatment alone increased blood flow by 9.9% compared to the ND kidney, and in the DATS+IRI kidney, cortical blood flow improved by 22.7% from the levels in ND+IRI ( A–C). As the outer medullary region of the kidney is vulnerable to prolonged changes to blood flow following IRI, we measured the Doppler resistive index (RI) in the outer medullary artery by ultrasound to quantify blood flow alterations. In the ND+IRI kidney, the RI was increased significantly compared to the ND kidney (0.61 ± 0.01 vs. 0.50 ± 0.01) ( A,B). In the DATS kidney, RI was similar to ND. However, in the DATS+IRI kidney, RI decreased compared to ND+IRI (0.55 ± 0.02 vs. 0.61 ± 0.01) ( A,B). 3.6. DATS Ameliorated Renal Microvasculature and Function in the IRI Kidney To quantify changes in renal microvasculature, we used barium sulfate angiography. In the ND+IRI kidney, total vascular density was reduced compared to the ND kidney without IRI (10.7% ± 0.43 vs. 8.01% ± 0.54). The interlobar arteries showed narrowing, and there was a loss of arcuate and interlobular arteries in the cortex ( A–C). Rarefaction was predominant in the outer medullary region ( A,B). In the DATS kidney without IRI, total vascularity was increased compared to the ND kidney without IRI (12.1% ± 0.64 vs. 10.7% ± 0.43), and the interlobar arteries were dilated and interlobular arteries were prominent in the cortex ( A–C). In the DATS+IRI kidney, total vascularity increased (9.73% ± 0.54) compared to the ND+IRI kidney, as well as the interlobular artery diameter increased, and the medullary regions showed increased vasculature ( A–C). Next, we measured the glomerular filtration rate (GFR) as an indicator of renal function. In the ND+IRI kidney, GFR decreased significantly (58.49%) compared to the ND kidney ( A,B). In the DATS kidney, GFR remained unchanged compared to the ND kidney ( A,B). In the DATS+IRI kidney, GFR improved by 15.21% from the GFR in the ND+IRI group ( A,B). Plasma creatinine was measured to confirm renal dysfunction. In the ND+IRI group, plasma creatine was increased and correlated with declining GFR ( B,C). In the DATS+IRI group, creatinine levels decreased compared to the ND+IRI group ( C). There was no change in creatinine levels between the ND and DATS groups ( C).
To determine changes in the H 2 S levels in the kidney and the response to IRI, we measured H 2 S by methylene blue method as described in the material and methods. The H 2 S levels in normal diet (ND) mice served as control. In the ND+IRI mice, renal H 2 S content was reduced compared to ND ( A). In the DATS+IRI mice, H 2 S content was increased significantly but did not normalize to the levels observed in the control group ( A). DATS alone treatment in the ND group showed an increasing trend but was not significant from the control ND group. ER stress in the aged kidney was quantified by measuring changes in the expression of UPR membrane proteins, p-IRE1α, p-PERK, and ATF6. At 7 days post IRI, the expression of all three proteins was increased in normal diet (ND)-fed mice following IRI, compared to control mice fed ND ( B,C). In the DATS+IRI mice, the expression of p-IRE1α, p-PERK, and ATF6 was significantly mitigated compared to ND+IRI ( B,C). DATS alone treatment in the ND group did not show any changes in their expression compared to the control ND group ( B,C). Additionally, in the post IRI kidneys, the mRNA levels of transcription factor, ATF4, and C/EBP homologous protein (CHOP) were upregulated ( D). The increased expression is coupled with p-PERK ( B,C) suggesting the predominance of PERK pathway in renal IRI. In IRI mice fed with the DATS diet, the mRNA expression levels of ATF4 and CHOP were decreased compared to ND+IRI ( B–D). The expression of ATF4 and CHOP mRNAs remained unchanged in ND and DATS alone. During the initial phase of stress, ER resident chaperones are expressed to regulate protein folding and excess UPR activation. We therefore measured ER resident chaperones, GRP78 and GRP94, heat shock proteins. In IRI kidneys, the mRNA levels of GRP78 and GRP94 were increased compared to control mice and DATS supplementation attenuated their expression ( D). No significant changes in their expression were observed in DATS alone compared to ND ( D). Immunohistochemical staining revealed p-IRE1α localized mainly to the glomeruli compared to tubules and ATF6 localized to both the glomeruli and tubular regions in IRI kidneys ( A). The p-IRE1α showed a 3-fold increase in fluorescent intensity and ATF6 increased by 4.7-fold compared to control mice, and their expressions were decreased in mice that received DATS treatment ( A,B). No significant changes in their expression were observed between ND and DATS alone ( A,B).
Angiogenesis and vascular repair are crucial for recovery from IRI. To determine whether neo-angiogenesis re-establishes microcirculation in the kidney following IRI, we investigated the expression of angiogenic factors. In ND-fed mice, IRI increased the expression of hypoxia-inducible factor-1α (HIF-1α) along with VEGF-A and EPO; however, the eNOS expression was significantly decreased compared to control mice fed with ND and mice fed with DATS without IRI ( A,B). In DATS-fed mice that underwent IRI, VEGF-A expression remained unchanged, but the expression of HIF-1α, EPO, and eNOS was increased compared to IRI mice fed with ND ( A,B). DATS treatment alone did not alter the expression of HIF-1α, VEGF-A, EPO, and eNOS compared to ND-treated control mice ( A,B). We further examined for angiogenesis markers, VEGF-A and CD31 in kidney sections by immunohistochemistry. In ND and DATS-fed mice that did not undergo IRI, VEGF-A and CD31 expression were similar in the glomeruli and peritubular areas ( A,B). In ND+IRI mice, both markers were decreased in the cortex and medullary regions ( A,B). In the DATS+IRI kidney, VEGF-A and CD31 expression were increased in both glomerular and peritubular areas ( A,B).
Hypoxia is a major stimulus for HIF-1α induction that, in turn, leads to VEGF-A production by the podocytes and tubular epithelial cells in the kidney . Additionally, VEGF-A-mediated stimulation of endothelial cells causes activation of the PI3K/AKT/mTOR pathway that results in angiogenesis . To investigate whether H 2 S-induced angiogenesis occurs via activation of the PI3K pathway, we quantified the expression of signaling molecules. In the ND+IRI kidney, protein expression of p-PI3K, p-AKT, and p-mTOR was downregulated compared to the ND control kidney ( A,B). The p-PI3K expression was decreased by 3-fold, p-AKT by 2.8-fold, and p-mTOR by 2.2-fold compared to ND kidney without IRI. In the DATS+IRI kidney, the levels of p-PI3K, p-AKT, and p-mTOR were significantly increased and were similar to the levels observed in the ND kidney without IRI ( A,B). Early stages of angiogenesis require MAPK signaling for cell proliferation and differentiation . We therefore measured the expression of phosphorylated-MAPK (p-MAPK). In the ND+IRI kidney, p-MAPK (p-MAPK) was significantly decreased compared to the ND kidney without IRI. Pre-treatment with DATS followed by IRI showed upregulation of p-MAPK compared to ND+IRI kidney ( A,B).
While sexual dimorphism in renal IRI is well known , the changes in IRI-induced inflammation in aged female kidney remains less studied. We therefore measured the protein expression of inflammatory markers, lipocalin-2 (LCN2), TNF-α, IL-17, and IL-1β. In the ND+IRI kidney, LCN2 increased by 2.5-fold, TNF-α by 2.6-fold, IL-17 by 2.6-fold, and IL-1β by 2-fold compared to the ND kidney without IRI ( A,B). In contrast, DATS+IRI kidneys normalized LCN2 levels comparable to ND kidneys and reduced the expression of TNF-α, IL-17, and IL-β by 0.6-fold, 0.5-fold, and 0.68-fold, respectively, from ND+IRI kidney levels ( A,B). The expression of inflammatory markers did not differ between ND and DATS kidneys ( A,B). Recent studies suggest TGF-β signaling in endothelial cells contributes to the loss of peritubular capillaries in AKI and subsequent transition to chronic kidney disease (CKD) . To determine whether TGF-β and Col1A1 markers are affected in renal IRI of aged kidneys, we quantified their expression. The expression of TGF-β was upregulated in the ND+IRI kidney by 1.9-fold and Col1A1 expression was increased by 4.4-fold compared to the ND kidney. In the DATS+IRI kidney, TGF-β and Col1A1 expression were downregulated significantly and were comparable to ND kidney ( C,D). Additionally, in the ND+IRI kidney, NF-kB expression was upregulated compared to the ND kidney without IRI and mitigated with DATS treatment ( C,D).
Aging is associated with structural and functional changes in renal vasculature. To determine changes in the renal perfusion in the aged kidney following IRI, we measured cortical blood flow using laser Doppler flowmetry (MoorFLPI, Wilmington, DE, USA) at room temperature. In the ND+IRI kidney, cortical blood flow decreased significantly (29%) compared to the ND kidney that was not subjected to IRI ( A–C). DATS treatment alone increased blood flow by 9.9% compared to the ND kidney, and in the DATS+IRI kidney, cortical blood flow improved by 22.7% from the levels in ND+IRI ( A–C). As the outer medullary region of the kidney is vulnerable to prolonged changes to blood flow following IRI, we measured the Doppler resistive index (RI) in the outer medullary artery by ultrasound to quantify blood flow alterations. In the ND+IRI kidney, the RI was increased significantly compared to the ND kidney (0.61 ± 0.01 vs. 0.50 ± 0.01) ( A,B). In the DATS kidney, RI was similar to ND. However, in the DATS+IRI kidney, RI decreased compared to ND+IRI (0.55 ± 0.02 vs. 0.61 ± 0.01) ( A,B).
To quantify changes in renal microvasculature, we used barium sulfate angiography. In the ND+IRI kidney, total vascular density was reduced compared to the ND kidney without IRI (10.7% ± 0.43 vs. 8.01% ± 0.54). The interlobar arteries showed narrowing, and there was a loss of arcuate and interlobular arteries in the cortex ( A–C). Rarefaction was predominant in the outer medullary region ( A,B). In the DATS kidney without IRI, total vascularity was increased compared to the ND kidney without IRI (12.1% ± 0.64 vs. 10.7% ± 0.43), and the interlobar arteries were dilated and interlobular arteries were prominent in the cortex ( A–C). In the DATS+IRI kidney, total vascularity increased (9.73% ± 0.54) compared to the ND+IRI kidney, as well as the interlobular artery diameter increased, and the medullary regions showed increased vasculature ( A–C). Next, we measured the glomerular filtration rate (GFR) as an indicator of renal function. In the ND+IRI kidney, GFR decreased significantly (58.49%) compared to the ND kidney ( A,B). In the DATS kidney, GFR remained unchanged compared to the ND kidney ( A,B). In the DATS+IRI kidney, GFR improved by 15.21% from the GFR in the ND+IRI group ( A,B). Plasma creatinine was measured to confirm renal dysfunction. In the ND+IRI group, plasma creatine was increased and correlated with declining GFR ( B,C). In the DATS+IRI group, creatinine levels decreased compared to the ND+IRI group ( C). There was no change in creatinine levels between the ND and DATS groups ( C).
The production of endogenous hydrogen sulfide (H 2 S) is well-known for its cardioprotective functions, particularly in regulating vascular tone and promoting angiogenesis . Additionally, H 2 S-induced S-sulfhydration plays a significant role in regulating angiogenesis and oxidative stress . Furthermore, H 2 S has been shown to alleviate hyperhomocysteinemia-induced endothelial endoplasmic reticulum (ER) stress by sulfhydrating protein disulfide isomerase, thus reducing the progression of atherosclerosis . Exogenous H 2 S has also been observed to improve outcomes in non-alcoholic fatty liver disease by inhibiting the ER stress/NLRP3 inflammasome pathway . Moreover, targeted delivery of cystathionine-γ-lyase (CSE) plasmids to the infarcted myocardium has been found to reduce infarct size, improve cardiac function, and decrease oxidative stress, while also reducing ER stress . This suggests that localized delivery of CSE, an enzyme that produces H 2 S, has cardioprotective effects by reducing stress . However, it remains largely unclear whether diallyl trisulfide (DATS), an H 2 S generator, can modulate ER stress and angiogenesis in ischemia-reperfusion injury (IRI) in aging kidneys, particularly in females. The recovery of renal microvascular function is crucial to minimizing IRI-induced damage and progression to CKD . AKI and progression of CKD are linked to activation of ER stress pathways [ , , ] and gender differences are reported in ER response to stress in AKI in young mice models . However, there is a paucity of literature regarding ER stress in aged kidney following IRI. Therefore, in the present study, we determined the effects of age-related response to ER stress during renal IRI and whether DATS, a hydrogen sulfide (H 2 S) donor, can mitigate AKI pathology in female kidneys via the regulation of ER stress. Diallyl trisulfide (DATS) is a garlic-derived organosulfide that releases H 2 S from red blood cells upon reaction with thiols such as cysteine or glutathione (GSH). A proton shuffle from the thiol to allyl perthiol anion forms allyl perthiol by thiol/disulfide exchange, thus releasing H 2 S . Our results indicated that following IRI, H 2 S production is decreased in aged female kidney, and associated with accumulation of excess ER stress proteins, downregulation of angiogenesis markers, and PI3K, AKT, p-mTOR, and pMAPK signaling pathways. The changes were accompanied by upregulation of inflammatory cytokines and fibrosis markers. Further, the reduction of renal vascular density and increased resistive index (RI) reduced renal blood flow, worsening renal function in IRI female kidney vs. control. DATS treatment suppressed ER stress, upregulated angiogenesis markers to increase vascularity, and restored blood flow to improve function and attenuated inflammation and fibrosis. Taken together, our results suggest that DATS protects the aged female kidney by modulating ER stress response and enhancing angiogenesis. Oxygen deprivation during renal ischemia drives adaptive processes that include unfolded protein response (UPR) . Previous studies demonstrated that ER stress regulates the production of angiogenic factors, VEGF-A and bFGF in renal IRI and diabetic retinopathy promoting angiogenesis . In other studies, ER stress was shown to induce anti-angiogenesis signaling via IRE1α and PERK pathway in the retina, CHOP in hind-limb ischemia, and CREB3L1 in mammary tumor model [ , , ]. The above studies suggest that while ER stress and angiogenesis are linked, a balance between protein folding and protein degradation determines whether the pro-survival or pro-death response predominates to induce angiogenesis and reduction of damage during renal IRI. In the present study, the upregulation of HIF1α, VEGF-A, and EPO in the presence of increased p-IRE1α, p-PERK, and ATF6 suggests an interaction between hypoxia, ER response, and angiogenesis mechanisms during renal IRI ( , and ). However, eNOS expression was decreased in the IRI kidneys suggesting impaired endothelial function ( ). CHOP is a transcriptional factor that is induced by IRE1α, PERK, and ATF6 , and in a previous study, Loinard et al. demonstrated that CHOP represses eNOS . Our findings show that reduced eNOS in IRI kidney is therefore secondary to increased CHOP expression ( D and ). H 2 S has been shown to play a protective role in several disease states by suppressing ER stress . Further, it is known to promote angiogenesis by inducing VEGF . The results from the present study suggest that H 2 S reduced ER stress and increased eNOS expression and blood flow in the IRI kidney ( and ). In mammalian cells, H 2 S is produced by the enzymes of the transsulfuration pathway, cystathionine β-synthase (CBS), cystathionine γ-lyase (CSE), and the mitochondrial enzyme, 3-mercaptopyruvate sulfurtransferase (3-MST) that is coupled with cysteine aminotransferase (CAT) . Exogenous H 2 S supplementation has been shown to act as an antioxidant, anti-inflammatory, and anti-apoptotic molecule in several pathological conditions . In myocardial IRI, H 2 S offers cardioprotection by persulfidation of the K ATP channel leading to vasorelaxation . Previously, in IRI of aging kidney, we and others reported a decreased H 2 S content in the plasma and kidney, and exogenous supplementation of H 2 S mitigates renal IRI in male rodents and a miniature young female swine model . Sexual dimorphism has been reported for H 2 S production, sensitivity to vascular tissue, and sulfide bioavailability in animal models and patients with cardiovascular disease . However, to our knowledge, there are no data from aging studies reporting changes in H 2 S levels in the aged female kidneys and the response to IRI. It is important to note that while females are resistant to IRI at a younger age , their susceptibility to IRI increases with aging . We found that the H 2 S levels in the kidney following IRI were lower in females in the present study ( A) compared to males previously and the control in this study. There was no difference in H 2 S levels between genders following H 2 S supplementation in the IRI group, suggesting that H 2 S level recovery is similar in aged kidneys in females and males. Inositol-requiring enzyme-1 (IRE1) is an endoplasmic reticulum-bound kinase/endoribonuclease (RNase) that regulates unfolded protein response (UPR) . IRE1 on the ER membrane senses misfolded proteins leading to IRE1 oligomerization and activation of its kinase, RNase. Under pathophysiological conditions, the fine balance between protein load and protein folding capacity is disrupted leading to the activation of three ER transmembrane sensors, i.e., inositol requiring enzyme 1 (IRE1), protein kinase RNA-like ER eukaryotic initiation factor-2α kinase (PERK), and activating transcription factor 6 (ATF6) . Upon activation the proteins initiate signaling and a transcriptional network termed UPR, which participates in upregulating inflammatory process , an indication of ER stress. In the present study, involving aged female kidney, all ER proteins were upregulated indicating ER stress ( B–C and A,B). The recent literature suggests that H 2 S treatment protects the heart during diabetic cardiomyopathy and ischemic injury by suppressing ER stress . Our data agree with the earlier studies and confirm a similar protective mechanism in aged kidney IRI. The prolongation of ER stress leads to apoptosis via activation of transcription factor 4-C/EBP homology protein (ATF4-CHOP)-induction in IRI kidney . ER function is dependent on a multifunctional integral membrane, luminal chaperones, folding enzymes, and sensor molecules such as heat shock proteins, GRP78 and GRP94 (glucose-related protein 78/94). Under stress, GRP78 and GRP94 chaperones are upregulated to enhance protein folding by ER in CKD . Our findings indicate that in IRI of aged female kidneys, mRNA of these stress genes is upregulated, and DATS mitigated their expression ( D). Taken together, our results suggest that DATS modulates key ER stress genes that include IRE1α, PERK, CHOP, ATF4, ATF6, GRP78, and GRP94 in IRI-induced AKI in aging female mice. Hypoxia-inducible factors (HIFs) are the master transcription factors responsible for gene regulation in hypoxia/ischemia by inducing the expression of adaptive gene products, such as vascular endothelial growth factor a (VEGF-A), erythropoietin (EPO), and endothelial nitric oxide synthase (eNOS) . In a glycerol-induced AKI rodent model, it was shown that the HIF-1α and VEGF-A were increased in young male rats . Erythropoietin (EPO) is a cytokine that is produced by the kidney and induced under hypoxic conditions . In a bilateral renal IRI isogenic mice model, there was a five-fold increase in EPO that contributed to the abrogation of kidney IRI . IR decreases renal eNOS expression in male adult rat model . VEGF and EPO promote angiogenesis and eNOS regulates vascular tone to optimize blood flow to organs. Our results indicate that IRI increases angiogenesis markers but eNOS expression is diminished. DATS further enhanced the expression of relevant angiogenesis markers and increased eNOS ( A,B), indicating angiogenesis ( A,B) and improved endothelial function in the aging female IRI kidney. However, whether age-matched IRI-induced adaptive protein response and angiogenesis is similar in male kidney is yet to be investigated. Angiogenesis is the physiological process of forming new blood vessels or repairing pre-existing blood vessels during growth or following injury . VEGF-A is a member of the VEGF family that plays a key role in angiogenesis, and several signaling cascades are vital for this process to happen. For example, growth factors initiate PI3K/AKT/mTOR cascade as well as MAPK pathways for endothelial survival, vascular permeability, and migratory or proliferative phenotypes . It was shown that PI3K/AKT activation attenuated AKI in adult male Sprague–Dawley rats . In addition, AKI-induced oxidative stress was regulated by the AKT/mTOR pathway in young male mice . Our data indicates that PI3K/AKT/mTOR and MAPK kinase pathways are inhibited in the aging female kidney following IRI and DATS treatment normalizes their activity ( A,B). To our knowledge, this is the first report that indicates that angiogenic signaling cascades are downregulated in aging female AKI and that DATS treatment restores the expression and activity. Interestingly, the PAM (PI3K/AKT/mTOR) signaling pathway has been reported to promote cell survival, cell growth, and cell cycle progression . This pathway is highly regulated by multiple interactions with several other signaling pathways, especially in the context of cancer development . Our results indicated that DATS, a source of H 2 S, is protective against renal IRI in aging through normalizing PAM signaling pathway. Conversely, an in vitro study demonstrated that the topical application of NaHS, which serves as a direct source of H 2 S, inhibited the PAM signaling pathway in human hepatocellular carcinoma (HCC) cells . The differences in outcomes may be attributed to the distinct forms of H 2 S being used. While NaHS provides a direct source, DATS requires metabolism by glutathione in red blood cells, leading to a slower release of H 2 S . Other factors could also contribute to these differing outcomes, as the experimental conditions vary. These include the comparison of in vitro cellular responses versus in vivo H 2 S responses, cancerous cells versus ischemic tissue, and liver cells versus renal tissue. It is also important to note that oxidative stress is a common pathway and parameter for IRI, as reported by many investigators . In this study, our goal was to investigate IRI-induced endoplasmic reticulum (ER) stress and whether DATS, an H 2 S generator, could mitigate this stress. Our findings indicate that DATS does indeed provide such mitigation. Additionally, DATS has been reported to have anticancer activity, which is attributed to the induction of apoptosis that occurs in a cell cycle-dependent manner, specifically by transitioning from the G2/M phase to the G1 phase . We believe that the discrepancy between DATS’s protective effects against IRI and its induction of cellular apoptosis can be explained by the difference between in vitro cancerous cells and in vivo IRI pathology in aging kidneys. Nonetheless, further and larger in vivo studies are needed to clarify these apparent differences in outcomes. Lipocalin-2 (LCN-2) is a biomarker of kidney injury and its levels are reported to increase, both in serum and urine, in patients with AKI , whereas tumor necrosis factor-alpha (TNF-α) is a cytokine and major regulator of inflammatory response following kidney injury . Further, the upregulation of inflammatory molecules, such as IL-17 and IL-β1 is linked to AKI . Our results support the earlier findings and show that DATS treatment normalizes their expression in the kidney indicating that DATS mitigates kidney injury and inflammation via release of H 2 S ( A,B). TGF-β causes acute tubular injury and inflammation and has a mechanistic link between acute injury and progression to CKD . TGF-β is a potent stimulator of collagen formation that in concert with nuclear factor-κB (NF-κB) activation promotes the production of proinflammatory proteins and stimulation of renal fibrosis following AKI . We show that DATS suppresses the expression of TGF-β, Col1A1, and NF-κB to alleviate renal inflammation and fibrosis ( C,D). AKI leads to impaired renal blood flow which is a contributory factor to diminished GFR . Renal RI is a reliable diagnostic tool that is used in intensive care units to predict and assess AKI severity . We have previously shown that renal vascularity is diminished in young and aged male mice, but aged mice exhibited less vascularity compared to young mice with AKI . Further, we demonstrated that GYY4137, an H 2 S-releasing compound, improved vascular density in AKI mice. Here, we show that DATS increases renal blood flow by lowering the vascular resistance in the microvessels, thus improving renal function by increasing GFR and lowering plasma creatinine in the female kidney following IRI ( , , and ). It is noteworthy to mention that DATS exhibits structural similarities to endogenous metabolites generated during anaerobic cysteine metabolism, which leads to the formation of sulfane sulfur-containing compounds and H 2 S . Both sulfane sulfur and H 2 S are classified as reactive sulfur species (RSS) produced under physiological conditions. Research suggests that the signaling molecule responsible for the biological effects of RSS, including the S-sulfhydration of proteins, is sulfane sulfur rather than H 2 S . While it is possible that a similar mechanism could be at play in our study, it is theoretically unlikely. This is primarily because our method of DATS administration was through dietary intake, whereas previous studies used intraperitoneal injections and topical in vitro applications . Furthermore, in our research, the IRI in the kidneys did not deliver completely anaerobic environment for the duration of the experiments, and the DATS regimen was administered for three weeks prior to IRI induction and continued for one week afterward. Nonetheless, whether the benefits of DATS arise from RSS production or are solely due to H 2 S generation requires further investigation.
We demonstrate that in aged female mice, renal IRI impairs function significantly, which is similar to aged male mice with renal IRI. In both genders, impaired function is in part a consequence of H 2 S deficiency. In the aged female kidney IRI, ER stress is increased and associated with diminished angiogenesis involving PI3K, AKT, mTOR, and MAPK signaling pathways. Inflammation and fibrosis are additional components of AKI. A decrease in vascularity and eNOS reduces blood flow and supposedly endothelial function. Increasing H 2 S levels and bioavailability by DATS modulates ER stress, angiogenesis, and targets inflammatory and fibrosis molecules to mitigate AKI in the aged female kidney. Gender differences in ER stress response to aged male kidney IRI require further investigation.
|
ACAA2 Protects Against Cardiac Dysfunction and Lipid Peroxidation in Renal Insufficiency with the Treatment of S-Nitroso-L-Cysteine | 2adac5c9-9401-4747-972e-f4dc938791f7 | 11940541 | Musculoskeletal System[mh] | In recent years, chronic kidney disease (CKD) has increased the incidence of cardiovascular diseases (CVD) significantly. CKD induces cardiac remodeling , including phenomena such as myocardial hypertrophy, fibrosis, and cardiomyocyte apoptosis. The heart is a high-metabolic-rate organ that primarily relies on fatty acid oxidation to supply energy . CKD and CVD share many common mechanisms of metabolic remodeling , including changes in fatty acid and glucose utilization as well as mitochondrial dysfunction. Studies have shown that cardiovascular events, particularly heart failure, significantly increase the risk of CKD patients progressing to kidney failure requiring replacement therapy (KFRT) . Other studies have also revealed the important roles of gut microbiota metabolites, such as N,N,N-trimethyl-5-aminovaleric acid TMAVA , in myocardial hypertrophy and heart failure. The important role of uremic toxins in myocardial hypertrophy and fibrosis was also demonstrated . Cardiac lipid peroxidation refers to myocardial dysfunction caused by the excessive accumulation of fatty acids and their metabolic products in cardiomyocytes. It plays a significant role in heart failure, particularly in heart failure with preserved ejection fraction , HFpEF, and diabetic cardiomyopathy DCM, but its impact on cardiac function in the context of uremia remains unclear. Studies have shown that adipsin improves fatty acid β-oxidation by inhibiting the mitochondrial translocation of Irak2 , the DPP-4 inhibitor evogliptin alleviates lipotoxicity and improves mitochondrial function, Sirt5 promotes fatty acid oxidation through desuccinylation, and novel ERR agonists enhance cardiac metabolism, all of them significantly improving cardiac function by improving fatty acid metabolism in different models. Further research indicated that cardiac-specific deletion of ACC2 increases fatty acid oxidation, maintaining cardiac health by regulating Parkin-mediated mitophagy. Additionally, some studies have explored the complex role of epicardial adipose tissue (EAT) and myocardial lipotoxicity in HFpEF and obesity-related heart disease. Regulating lipid metabolism pathways and promoting the efficiency of myocardial lipid metabolism may provide emerging strategies to alleviate cardiac dysfunction in the context of chronic kidney disease. Forkhead box O4 , i.e., FOXO4, is a member of the FOXO transcription factor family that regulates the expression of various genes involved in several biological processes , including cell cycle, apoptosis, and metabolism. In fatty acid metabolism, FOXO4 plays a role in adipose tissue by influencing the insulin signaling pathway . The activation of FOXO4 can affect cellular responses to oxidative stress. For example, in the absence of insulin and IGF-1 signaling, sustained activation of FOXO4 may lead to metabolic abnormalities . Additionally, FOXO4 interacts with other proteins to regulate its activity , modulating the cell cycle and apoptosis. Acetyl-CoA acyltransferase 2 (ACAA2) is an acyl-CoA acyltransferase that participates in the final step of fatty acid β-oxidation. In tumor cells, high expression of ACAA2 is associated with neuroendocrine phenotypes of cancer . Furthermore, the role of ACAA2 in fatty acid oxidation provides protective effects in organs such as the liver and kidney. For instance , during acetaminophen-induced hepatotoxicity, upregulation of ACAA2 can enhance mitochondrial fatty acid oxidation, thereby reducing liver damage. S-nitrosylation is an important post-translational modification of proteins, playing a crucial role in regulating cellular signal transduction, protein function, and cellular metabolism . S-nitrosylation involves the addition of a nitric oxide NO group to cysteine residues within proteins, forming S-nitrosothiols. This modification can significantly influence protein stability, activity, and protein–protein interactions. CSNO, i.e., S-nitroso-L-cysteine, has been shown in previous studies to effectively improve cardiac function in diabetic mice following aerosol inhalation . Additionally, studies have demonstrated that S-nitrosylation plays a critical role in cardioprotection , particularly in myocardial ischemia–reperfusion injury. Through modulating calcium handling in cardiomyocytes, regulating mitochondrial function, reducing reactive oxygen species ROS production, and mitigating myocardial injury, S-nitrosylation exerts protective effects on the heart. Furthermore, it has been reported that S-nitrosylation can modulate mitochondrial respiration and energy metabolism by modifying subunits of the mitochondrial respiratory chain complex. However, some studies also suggest that nitrosylation modifications in the heart may impair cardiac function, and the specific role of thiol S-nitrosylation remains unclear. Therefore, as the metabolic mechanism of myocardial lipid peroxidation based on renal insufficiency remains unclear, and no studies have explored the specific role of CSNO in this regard, we propose that the ACAA2 protein exerts a protective role against myocardial lipid peroxidation in renal dysfunction and that CSNO participates in protecting against cardiac dysfunction under renal insufficiency through the FOXO4–ACAA2 axis.
2.1. Bioinformatics Analysis To better obtain key data from the gene expression profiles, we downloaded RNA-seq data (GSE106385) from CKD mice and healthy controls through the GEO database ( https://ncbi.nlm.nih.gov/geo/ (accessed on 8 May 2023)) and performed a series of bioinformatics analyses, including the following: search for differential genes using the online websites ( https://www.networkanalyst.ca/ (accessed on 10 May 2023)); identification of statistically significant differentially expressed genes DEGs based on differences in expression values between samples; construction of the ridgeline graph, enrich net, and volcano graph using the online tool to identify overlapping modules and genes in the dataset; construction of the protein–protein interaction PPI network related to fatty acid β-oxidation by analyzing the differentially expressed genes using the STRING database ( https://cn.string-db.org/ (accessed on 5 June 2023)); visualization of the results with Cytoscape software 3.9.1; prediction of the upstream transcription factors of ACAA2 using the JASPAR database ( https://jaspar.elixir.no/ (accessed on 8 December 2023)); and preliminary construction of a molecular docking model using PyMOL software 2.5.5. 2.2. Animals and Adenine-Induced CKD Model A total of 30 C57BL/6 mice (18–22 g, 6–7 weeks, male) were purchased from Beijing Vital River Laboratory Animal Technology (Beijing, China). After one week of adaptation, the C57BL/6 mice were randomly divided into three groups: Control, CKD, and CKD + CSNO groups ( n = 10). The random numbers for animal grouping were generated by the “Rand()” function in Microsoft Excel. All animal experiments were approved by the Animal Care and Use Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approve number: TJH-202306049). Mice had free access to food and water and were housed in a specific pathogen-free room with a 12 h light/dark cycle at a temperature of 25 ± 1. Based on the results of the pre-experiment and the current literature report, for CKD modeling, C57BL/6 mice received an adenine gavage (50 mg/kg/d, in saline, purity ≥ 98.0%, HY-B0152, MCE) for a total of 4 weeks. The CKD + CSNO group mice received nebulized inhalation of CSNO from the week 3 to 8 (from the 15th day) (88 ppm for 20 min per day). The mice in the control group were administered the vehicle in parallel. Dynamic monitoring was performed by non-invasive cardiac ultrasound. After 6 weeks of treatment, the mice were sacrificed after intraperitoneal injection of pentobarbital, and the tissues and blood samples were collected for further data analysis. CKD and CSNO administration methods were based on existing protocols in their respective settings. 2.3. Cell Culture and Treatment AC16 cells were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM, KeyGEN BioTECH, Nanjing, China) supplemented with 10% fetal bovine serum (FBS, Gibco, Waltham, MA, USA) and 1% penicillin/streptomycin (Sangon, Shanghai, China) at 37 °C with 5% CO 2 . AC16 cells were treated with different concentration of Indoxyl Sulfate (IS, purity ≥ 98.0%, I3875, Sigma-Aldrich, Saint Louis, MO, USA) to induce in vitro cardiomyocyte injury for 24 h while starving. Meanwhile, CSNO (dissolved and mixed for 5 min until orange) was added to cells with IS treatment. The above experiments were repeated three times independently. 2.4. Real-Time PCR Total RNA was extracted from tissue samples using the RNA Isolation Kit (Vazyme, Nanjing, China) or from cultured cells using TRIzol (GIBCO Life Technology, Thermo, Waltham, MA, USA). After DNase treatment, first-strand cDNA was prepared from RNA (1.5 μg) using Master Mix (Life Technologies, Waltham, MA, USA). cDNA was diluted and amplified using Powerup SYBR Green qPCR Master Mix on Real-Time PCR instrument. Primers were designed using NCBI/Primer-BLAST ( ). Data were normalized to a reference gene (GAPDH) and presented as the fold increase compared with RNA isolated from the control group using the 2 −ΔΔCT method. 2.5. Western Blotting to Detect Protein Expression Tissues and cells were homogenized with precooled RIPA lysis buffer containing 1 mM protease inhibitor and 1 mM phosphatase inhibitor for 1 h. After being centrifuged at 12,000 rpm for 20 min at 4 °C, the supernatant was collected. A BCA protein assay kit was used to measure the protein concentration. Equal amounts of protein were separated by 10–12% SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% skim milk for 1 h and incubated with different primary antibodies at 4 °C overnight. The secondary antibody (1:5000) was incubated for 1 h at room temperature. An ultrahigh sensitivity ECL kit (HY–K1005; MedChemExpress, Princeton, NJ, USA) was used for protein detection, which was performed on a ChemiDoc-It 510 Imager with VisionWorks software 1.6.5 (Ultra-Violet Products Ltd., Cambridge, UK). 2.6. Measurement of ATP Contents The ATP levels were measured using an enhanced ATP assay kit (S0027, Beyotime, Shanghai, China). After removing the culture medium, we added 200 µL lysis solution to each well of a 6-well plate to lyse the cells and then centrifuged at 4 °C at 12,000× g for 5 min. The ATP standard solution was thawed on ice and diluted with ATP assay lysis solution to create an appropriate concentration gradient (0.01, 0.03, 0.1, 0.3, 1, 3, and 10 µM). Each sample or standard required 100 µL of ATP detection working solution, which was added to a light-shielded 96-well plate after preparation. The plate was left at room temperature for 3–5 min to ensure complete consumption of baseline ATP. Then, 20 µL of either the sample or standard was added to each well, mixed immediately, and measured using the Gen5 software 3.12 on the BioTek multi-function microplate reader (BioTek Instruments, Inc., Winooski, VT, USA). The obtained RLU value was converted into the corresponding ATP (µM) value based on the standard curve. Additionally, calibration using the BCA protein assay kit (mg/mL) was performed to obtain the ATP concentration in nmol/mg protein. 2.7. Measurement of Cell Viability Cell viability was assessed with the CCK-8 assay kit (RM02823, ABclonal, Wuhan, China). In brief, AC16 cells (5 × 10 3 cells/well) were plated in medium (100 μL/well) into 96-well plates with six replicate wells. After being attached, they were modeled and administered with the corresponding drugs. Following treatment, AC16 cells were incubated with 10% CCK8 for 1 h in the dark. The absorbance at 450 nm was recorded on a microplate spectrophotometer. 2.8. Assessment Reactive Oxygen Species To detect the level of intracellular reactive oxygen species (ROS), dihydroethidium (DHE. cat.no.HY-D0079, MedChemExpress, Princeton, NJ, USA), a superoxide indicator, was used. For preparing DHE staining solution, we avoided light and use it immediately after preparation. Aliquoted DHE staining reagent was thawed at room temperature and diluted with DMEM. Next, 1 mL DHE staining solution (1×) was prepared per well on a 6-well plate and thoroughly mixed. It was incubated at 37 °C away from light for 30 min and then observed under a fluorescence microscope (Bx53, Olympus, Tokyo, Japan). The culture medium was removed and the cells washed once with PBS, which was then replaced with fresh DMEM, and observation under the microscope continued. The fluorescence intensity of the same cell mass was quantified using Image J 1.54 to obtain RFU values. The procedure was repeated at least three times. 2.9. Mitochondrial Permeability Transition Pore (mPTP) Detection Mitochondrial permeability transition pore (mPTP) detection was performed using an mPTP assay kit (C2009S, Beyotime, Haimen, China). The cells were seeded in culture dishes and treated according to the experimental design. The culture medium was removed and the cells washed 1–2 times with PBS. An appropriate volume of mPTP detection working solution (Calcein AM + CoCl 2 , final concentration 1×) was added and gently shaken to ensure the dye evenly covered all the cells. The mixture was incubated at 37 °C in the dark for 30–45 min. After incubation, the medium replaced with fresh pre-warmed (37 °C) culture medium, and the mixture was incubated again in the dark for another 30 min to ensure intracellular esterases fully hydrolyzed the Calcein AM to generate green fluorescent Calcein. The culture medium was then removed, the cells washed 2–3 times with PBS, and detection buffer added; green fluorescence was observed under a fluorescence microscope (Wuhan, China). 2.10. Immunofluorescence and BODIPY Staining AC16 cells were seeded on coverslips. After treatments, the cells were fixed with methanol for 15 min at room temperature and subsequently permeabilized and blocked with 0.5% Triton-100 and 5% goat serum in PBS for 1 h at room temperature. Then, the cells were incubated with primary antibodies diluted overnight at 4 °C. After washing three times with PBS, the cells were incubated with fluorescence secondary antibody for 1 h. As for the double fluorescence staining of BODIPY and ACAA2, the slides were evenly covered with the green fluorescent fatty acid probe solution (C2055, Beyotime, Haimen, China) after the secondary antibody incubation and incubated at 37 °C for 15 min. Then, the cells were washed three times again, and DAPI-containing anti-fluorescence quencher was used to seal the glass slides. Finally, slides were examined using a confocal microscope (Nikon, Tokyo, Japan). The primary antibodies used in this study targeted endogenous ACAA2 (1:100, A15778, ABclonal, Wuhan, China) and FOXO4 (1:100, 3307, ABclonal, Wuhan, China). 2.11. Transfection Approximately 18 h before transfection, cells were seeded. Opti-MEM serum-free culture medium was added to a sterile centrifuge tube and gently mixed with an appropriate amount of ExFect. Opti-MEM was added to another sterile centrifuge tube along with an appropriate amount of DNA. ExFect–opti-MEM was added to the DNA–opti-MEM and left sitting at room temperature for 15–20 min before transfection. The ExFect/DNA complex mixture was added into the culture medium, with gentle shaking of the medium to evenly disperse the ExFect/DNA. After overnight cultivation for 24–48 h, the cells were collected for subsequent experiments. Knockout ACAA2 or FOXO4 gene expression by siRNA (RiboBio genOFF™ siRNA, Guangzhou, China) was performed using Lipofectamine 2000. Lipo3000 was used for plasmid transfection of ACAA2 overexpression plasmids (Genomeditech, Shanghai, China). After 6 h, the culture medium was replaced and the cells treated with indoxyl sulfate for 24 h while starving. The efficiency of gene knockout was evaluated through Western blotting and real-time PCR. 2.12. Lactate Dehydrogenase Cytotoxicity Detection We utilized LDH kits (C0017, Beyotime, Haimen, China) to measure LDH release. We plated an appropriate amount of cells into a 96 well cell culture plate, including cell-free culture medium wells (background blank), untreated control cell wells (sample control), untreated cell wells for calculation (maximum enzyme activity), and drug-treated cell wells (drug-treated). One hour before the detection time, the cell culture plate was removed from the cell culture box and the LDH release reagent added to the maximum enzyme activity wells, in an amount of 10% of the original culture medium volume. After reaching the scheduled time, the cell culture plate was centrifuged at 400× g for 5 min. Then, 60 μL of LDH detection working solution was added to each well, mixed, and incubated at room temperature in the dark for 30 min. Then, we measured the absorbance at 490 nm and used any wavelength of 600 nm or greater as the reference wavelength for dual-wavelength measurement. The absorbance for each group obtained should be subtracted by the absorbance from the background control wells. The percentage of cytotoxicity (%) = (absorbance of sample control/drug treated)/(absorbance to maximum enzymatic activity − absorbance of sample control) × 100. 2.13. Lipid Peroxidation MDA Detection The level of MDA was measured by an MDA assay kit (S0131M, Beyotime, Haimen, China). After tissue and cell homogenization, samples were centrifuged at 12,000× g for 10 min and the supernatant collected for subsequent measurement. A suitable amount of TBA was weighed and added into a 0.37% TBA stock solution using the TBA-reagent solution. The prepared TBA stock solution was stored at room temperature out of light. The MDA detection working solution was formulated as TBA diluent/TBA stock/antioxidant = 150:50:3. Appropriate standards were diluted to concentrations of 1, 2, 5, 10, 20, and 50 µM for subsequent standard curve preparation. Then, 100 µL of PBS control, standard, and samples were added to each 1.5 mL EP tube, followed by the addition of 200 µL MDA detection working solution. The mixture was then heated at 100 °C for 15 min, cooled in a water bath to room temperature, and centrifuged at 1000× g for 10 min at room temperature. Next, 200 µL supernatant was added to a 96-well plate, and absorbance was measured at 532 nm using a microplate reader. A dual-wavelength measurement was set with 450 nm as the reference wavelength. After determining the protein concentration using the BCA kit (P0009, Beyotime, Haimen, China), the MDA content in the original samples was expressed based on the protein content as umol/mg protein/tissue. 2.14. Measurement of Mitochondrial Membrane Potential (ΔΨm) The measurement of ΔΨm was based on the JC-1 fluorescent probe (PJC-110; Promotor Biological Co., Ltd., Hangzhou, China). AC16 cells were incubated with JC-1 working solution for 30 min at 37 °C following treatments. Subsequently, the cells were washed at least two times with PBS and resuspended with cell culture medium. The fluorescence signals of JC-1 aggregates (red, 525/590 nm) and JC-1 monomers (green, 485/530 nm) were detected with an MShot fluorescence microscope (Wuhan, China). The ratio of the red/green fluorescence intensity represents the degree of mitochondrial damage. 2.15. Echocardiogram After completing the model, the mice underwent chest hair removal, followed by anesthesia induction using the gas anesthesia machine. Cardiac ultrasound imaging was then performed using the VINNO6 high-resolution imaging system (VINNO Corporation, Suzhou, China), with anesthesia maintained ( n = 10). Echocardiography was conducted to assess cardiac function by recording the left ventricular end-diastolic volume (LEDV) and left ventricular end-systolic volume (LESV). 2.16. HE, Masson Staining, and Immunohistochemistry After animals were sacrificed, the tissues were fixed with 4% formaldehyde. The formalin-fixed tissue was embedded in paraffin and sectioned for further analysis. Masson staining was used for collagen deposition, and HE staining was used to observe the structure of tissues. As for immunohistochemistry, the fixed paraffin sections were incubated with the primary antibody overnight at 4 °C. After washing three times with PBS, the sections were incubated with secondary antibody for 1 h. Then, after washing three times, they were scanned with a brightfield scanner and observed using the NDP.view 2 software 2.9.29. The primary antibodies used in this study targeted endogenous ACAA2 (1:300, A15778, ABclonal, Wuhan, China). 2.17. Oil Red O Staining Tissues were prepared with a thickness of 4–8 μm and dried for 15–30 min at room temperature. The sections were fixed with 4% formaldehyde for 10 min, followed by three washes with TBST. The Oil Red O stock solution was diluted with distilled water at a 3:2 ratio and incubated at room temperature in the dark for 20–30 min. The sections were washed three times with TBST and counterstained with hematoxylin, and the slides were mounted using 50% glycerol. After scanning with a brightfield scanner, the lipid droplet aggregation was observed using the NDP.view 2 software 2.9.29. 2.18. Statistical Analysis All data were assessed for normality, and then, parametric or non-parametric tests were employed for data analysis, as appropriate. The unpaired two-tailed t -test was used to compare data between two groups and one-way ANOVA with Sidak’s correction for multiple testing to compare data between more than two groups. The exact test used for each experiment is noted in the figure legends. Data are expressed as mean ± SEM. Statistical significance was considered when p < 0.05. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001; ns means not significant. All statistical analysis was performed using GraphPad Prism 10.1.1.
To better obtain key data from the gene expression profiles, we downloaded RNA-seq data (GSE106385) from CKD mice and healthy controls through the GEO database ( https://ncbi.nlm.nih.gov/geo/ (accessed on 8 May 2023)) and performed a series of bioinformatics analyses, including the following: search for differential genes using the online websites ( https://www.networkanalyst.ca/ (accessed on 10 May 2023)); identification of statistically significant differentially expressed genes DEGs based on differences in expression values between samples; construction of the ridgeline graph, enrich net, and volcano graph using the online tool to identify overlapping modules and genes in the dataset; construction of the protein–protein interaction PPI network related to fatty acid β-oxidation by analyzing the differentially expressed genes using the STRING database ( https://cn.string-db.org/ (accessed on 5 June 2023)); visualization of the results with Cytoscape software 3.9.1; prediction of the upstream transcription factors of ACAA2 using the JASPAR database ( https://jaspar.elixir.no/ (accessed on 8 December 2023)); and preliminary construction of a molecular docking model using PyMOL software 2.5.5.
A total of 30 C57BL/6 mice (18–22 g, 6–7 weeks, male) were purchased from Beijing Vital River Laboratory Animal Technology (Beijing, China). After one week of adaptation, the C57BL/6 mice were randomly divided into three groups: Control, CKD, and CKD + CSNO groups ( n = 10). The random numbers for animal grouping were generated by the “Rand()” function in Microsoft Excel. All animal experiments were approved by the Animal Care and Use Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approve number: TJH-202306049). Mice had free access to food and water and were housed in a specific pathogen-free room with a 12 h light/dark cycle at a temperature of 25 ± 1. Based on the results of the pre-experiment and the current literature report, for CKD modeling, C57BL/6 mice received an adenine gavage (50 mg/kg/d, in saline, purity ≥ 98.0%, HY-B0152, MCE) for a total of 4 weeks. The CKD + CSNO group mice received nebulized inhalation of CSNO from the week 3 to 8 (from the 15th day) (88 ppm for 20 min per day). The mice in the control group were administered the vehicle in parallel. Dynamic monitoring was performed by non-invasive cardiac ultrasound. After 6 weeks of treatment, the mice were sacrificed after intraperitoneal injection of pentobarbital, and the tissues and blood samples were collected for further data analysis. CKD and CSNO administration methods were based on existing protocols in their respective settings.
AC16 cells were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM, KeyGEN BioTECH, Nanjing, China) supplemented with 10% fetal bovine serum (FBS, Gibco, Waltham, MA, USA) and 1% penicillin/streptomycin (Sangon, Shanghai, China) at 37 °C with 5% CO 2 . AC16 cells were treated with different concentration of Indoxyl Sulfate (IS, purity ≥ 98.0%, I3875, Sigma-Aldrich, Saint Louis, MO, USA) to induce in vitro cardiomyocyte injury for 24 h while starving. Meanwhile, CSNO (dissolved and mixed for 5 min until orange) was added to cells with IS treatment. The above experiments were repeated three times independently.
Total RNA was extracted from tissue samples using the RNA Isolation Kit (Vazyme, Nanjing, China) or from cultured cells using TRIzol (GIBCO Life Technology, Thermo, Waltham, MA, USA). After DNase treatment, first-strand cDNA was prepared from RNA (1.5 μg) using Master Mix (Life Technologies, Waltham, MA, USA). cDNA was diluted and amplified using Powerup SYBR Green qPCR Master Mix on Real-Time PCR instrument. Primers were designed using NCBI/Primer-BLAST ( ). Data were normalized to a reference gene (GAPDH) and presented as the fold increase compared with RNA isolated from the control group using the 2 −ΔΔCT method.
Tissues and cells were homogenized with precooled RIPA lysis buffer containing 1 mM protease inhibitor and 1 mM phosphatase inhibitor for 1 h. After being centrifuged at 12,000 rpm for 20 min at 4 °C, the supernatant was collected. A BCA protein assay kit was used to measure the protein concentration. Equal amounts of protein were separated by 10–12% SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% skim milk for 1 h and incubated with different primary antibodies at 4 °C overnight. The secondary antibody (1:5000) was incubated for 1 h at room temperature. An ultrahigh sensitivity ECL kit (HY–K1005; MedChemExpress, Princeton, NJ, USA) was used for protein detection, which was performed on a ChemiDoc-It 510 Imager with VisionWorks software 1.6.5 (Ultra-Violet Products Ltd., Cambridge, UK).
The ATP levels were measured using an enhanced ATP assay kit (S0027, Beyotime, Shanghai, China). After removing the culture medium, we added 200 µL lysis solution to each well of a 6-well plate to lyse the cells and then centrifuged at 4 °C at 12,000× g for 5 min. The ATP standard solution was thawed on ice and diluted with ATP assay lysis solution to create an appropriate concentration gradient (0.01, 0.03, 0.1, 0.3, 1, 3, and 10 µM). Each sample or standard required 100 µL of ATP detection working solution, which was added to a light-shielded 96-well plate after preparation. The plate was left at room temperature for 3–5 min to ensure complete consumption of baseline ATP. Then, 20 µL of either the sample or standard was added to each well, mixed immediately, and measured using the Gen5 software 3.12 on the BioTek multi-function microplate reader (BioTek Instruments, Inc., Winooski, VT, USA). The obtained RLU value was converted into the corresponding ATP (µM) value based on the standard curve. Additionally, calibration using the BCA protein assay kit (mg/mL) was performed to obtain the ATP concentration in nmol/mg protein.
Cell viability was assessed with the CCK-8 assay kit (RM02823, ABclonal, Wuhan, China). In brief, AC16 cells (5 × 10 3 cells/well) were plated in medium (100 μL/well) into 96-well plates with six replicate wells. After being attached, they were modeled and administered with the corresponding drugs. Following treatment, AC16 cells were incubated with 10% CCK8 for 1 h in the dark. The absorbance at 450 nm was recorded on a microplate spectrophotometer.
To detect the level of intracellular reactive oxygen species (ROS), dihydroethidium (DHE. cat.no.HY-D0079, MedChemExpress, Princeton, NJ, USA), a superoxide indicator, was used. For preparing DHE staining solution, we avoided light and use it immediately after preparation. Aliquoted DHE staining reagent was thawed at room temperature and diluted with DMEM. Next, 1 mL DHE staining solution (1×) was prepared per well on a 6-well plate and thoroughly mixed. It was incubated at 37 °C away from light for 30 min and then observed under a fluorescence microscope (Bx53, Olympus, Tokyo, Japan). The culture medium was removed and the cells washed once with PBS, which was then replaced with fresh DMEM, and observation under the microscope continued. The fluorescence intensity of the same cell mass was quantified using Image J 1.54 to obtain RFU values. The procedure was repeated at least three times.
Mitochondrial permeability transition pore (mPTP) detection was performed using an mPTP assay kit (C2009S, Beyotime, Haimen, China). The cells were seeded in culture dishes and treated according to the experimental design. The culture medium was removed and the cells washed 1–2 times with PBS. An appropriate volume of mPTP detection working solution (Calcein AM + CoCl 2 , final concentration 1×) was added and gently shaken to ensure the dye evenly covered all the cells. The mixture was incubated at 37 °C in the dark for 30–45 min. After incubation, the medium replaced with fresh pre-warmed (37 °C) culture medium, and the mixture was incubated again in the dark for another 30 min to ensure intracellular esterases fully hydrolyzed the Calcein AM to generate green fluorescent Calcein. The culture medium was then removed, the cells washed 2–3 times with PBS, and detection buffer added; green fluorescence was observed under a fluorescence microscope (Wuhan, China).
AC16 cells were seeded on coverslips. After treatments, the cells were fixed with methanol for 15 min at room temperature and subsequently permeabilized and blocked with 0.5% Triton-100 and 5% goat serum in PBS for 1 h at room temperature. Then, the cells were incubated with primary antibodies diluted overnight at 4 °C. After washing three times with PBS, the cells were incubated with fluorescence secondary antibody for 1 h. As for the double fluorescence staining of BODIPY and ACAA2, the slides were evenly covered with the green fluorescent fatty acid probe solution (C2055, Beyotime, Haimen, China) after the secondary antibody incubation and incubated at 37 °C for 15 min. Then, the cells were washed three times again, and DAPI-containing anti-fluorescence quencher was used to seal the glass slides. Finally, slides were examined using a confocal microscope (Nikon, Tokyo, Japan). The primary antibodies used in this study targeted endogenous ACAA2 (1:100, A15778, ABclonal, Wuhan, China) and FOXO4 (1:100, 3307, ABclonal, Wuhan, China).
Approximately 18 h before transfection, cells were seeded. Opti-MEM serum-free culture medium was added to a sterile centrifuge tube and gently mixed with an appropriate amount of ExFect. Opti-MEM was added to another sterile centrifuge tube along with an appropriate amount of DNA. ExFect–opti-MEM was added to the DNA–opti-MEM and left sitting at room temperature for 15–20 min before transfection. The ExFect/DNA complex mixture was added into the culture medium, with gentle shaking of the medium to evenly disperse the ExFect/DNA. After overnight cultivation for 24–48 h, the cells were collected for subsequent experiments. Knockout ACAA2 or FOXO4 gene expression by siRNA (RiboBio genOFF™ siRNA, Guangzhou, China) was performed using Lipofectamine 2000. Lipo3000 was used for plasmid transfection of ACAA2 overexpression plasmids (Genomeditech, Shanghai, China). After 6 h, the culture medium was replaced and the cells treated with indoxyl sulfate for 24 h while starving. The efficiency of gene knockout was evaluated through Western blotting and real-time PCR.
We utilized LDH kits (C0017, Beyotime, Haimen, China) to measure LDH release. We plated an appropriate amount of cells into a 96 well cell culture plate, including cell-free culture medium wells (background blank), untreated control cell wells (sample control), untreated cell wells for calculation (maximum enzyme activity), and drug-treated cell wells (drug-treated). One hour before the detection time, the cell culture plate was removed from the cell culture box and the LDH release reagent added to the maximum enzyme activity wells, in an amount of 10% of the original culture medium volume. After reaching the scheduled time, the cell culture plate was centrifuged at 400× g for 5 min. Then, 60 μL of LDH detection working solution was added to each well, mixed, and incubated at room temperature in the dark for 30 min. Then, we measured the absorbance at 490 nm and used any wavelength of 600 nm or greater as the reference wavelength for dual-wavelength measurement. The absorbance for each group obtained should be subtracted by the absorbance from the background control wells. The percentage of cytotoxicity (%) = (absorbance of sample control/drug treated)/(absorbance to maximum enzymatic activity − absorbance of sample control) × 100.
The level of MDA was measured by an MDA assay kit (S0131M, Beyotime, Haimen, China). After tissue and cell homogenization, samples were centrifuged at 12,000× g for 10 min and the supernatant collected for subsequent measurement. A suitable amount of TBA was weighed and added into a 0.37% TBA stock solution using the TBA-reagent solution. The prepared TBA stock solution was stored at room temperature out of light. The MDA detection working solution was formulated as TBA diluent/TBA stock/antioxidant = 150:50:3. Appropriate standards were diluted to concentrations of 1, 2, 5, 10, 20, and 50 µM for subsequent standard curve preparation. Then, 100 µL of PBS control, standard, and samples were added to each 1.5 mL EP tube, followed by the addition of 200 µL MDA detection working solution. The mixture was then heated at 100 °C for 15 min, cooled in a water bath to room temperature, and centrifuged at 1000× g for 10 min at room temperature. Next, 200 µL supernatant was added to a 96-well plate, and absorbance was measured at 532 nm using a microplate reader. A dual-wavelength measurement was set with 450 nm as the reference wavelength. After determining the protein concentration using the BCA kit (P0009, Beyotime, Haimen, China), the MDA content in the original samples was expressed based on the protein content as umol/mg protein/tissue.
The measurement of ΔΨm was based on the JC-1 fluorescent probe (PJC-110; Promotor Biological Co., Ltd., Hangzhou, China). AC16 cells were incubated with JC-1 working solution for 30 min at 37 °C following treatments. Subsequently, the cells were washed at least two times with PBS and resuspended with cell culture medium. The fluorescence signals of JC-1 aggregates (red, 525/590 nm) and JC-1 monomers (green, 485/530 nm) were detected with an MShot fluorescence microscope (Wuhan, China). The ratio of the red/green fluorescence intensity represents the degree of mitochondrial damage.
After completing the model, the mice underwent chest hair removal, followed by anesthesia induction using the gas anesthesia machine. Cardiac ultrasound imaging was then performed using the VINNO6 high-resolution imaging system (VINNO Corporation, Suzhou, China), with anesthesia maintained ( n = 10). Echocardiography was conducted to assess cardiac function by recording the left ventricular end-diastolic volume (LEDV) and left ventricular end-systolic volume (LESV).
After animals were sacrificed, the tissues were fixed with 4% formaldehyde. The formalin-fixed tissue was embedded in paraffin and sectioned for further analysis. Masson staining was used for collagen deposition, and HE staining was used to observe the structure of tissues. As for immunohistochemistry, the fixed paraffin sections were incubated with the primary antibody overnight at 4 °C. After washing three times with PBS, the sections were incubated with secondary antibody for 1 h. Then, after washing three times, they were scanned with a brightfield scanner and observed using the NDP.view 2 software 2.9.29. The primary antibodies used in this study targeted endogenous ACAA2 (1:300, A15778, ABclonal, Wuhan, China).
Tissues were prepared with a thickness of 4–8 μm and dried for 15–30 min at room temperature. The sections were fixed with 4% formaldehyde for 10 min, followed by three washes with TBST. The Oil Red O stock solution was diluted with distilled water at a 3:2 ratio and incubated at room temperature in the dark for 20–30 min. The sections were washed three times with TBST and counterstained with hematoxylin, and the slides were mounted using 50% glycerol. After scanning with a brightfield scanner, the lipid droplet aggregation was observed using the NDP.view 2 software 2.9.29.
All data were assessed for normality, and then, parametric or non-parametric tests were employed for data analysis, as appropriate. The unpaired two-tailed t -test was used to compare data between two groups and one-way ANOVA with Sidak’s correction for multiple testing to compare data between more than two groups. The exact test used for each experiment is noted in the figure legends. Data are expressed as mean ± SEM. Statistical significance was considered when p < 0.05. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001; ns means not significant. All statistical analysis was performed using GraphPad Prism 10.1.1.
3.1. Uremic Myocardial Metabolic Abnormalities Linked to Fatty Acid β-Oxidation Pathway We utilized the Gene Expression Omnibus (GEO) database and retrieved the GSE106385 dataset by searching for the keywords “renal insufficiency” and “heart failure”. Differential gene expression analysis ( a) and enrichment analysis ( b) were performed. Our findings indicated that the pathways related to cardiac function in mice with renal impairment were predominantly altered in metabolic pathways ( c), while genes associated with fatty acid metabolism were the most affected. Specifically, the fatty acid β-oxidation pathway was significantly impacted ( d), suggesting a dysregulation of cardiac lipid metabolism in the context of renal insufficiency, although the specific targets remained unclear. Subsequently, after standardization and normalization of the data, we employed STRING and Cytoscape software 3.9.1 to construct protein–protein interaction (PPI) networks related to fatty acid metabolism. The results highlighted the critical role of the fatty acid β-oxidation pathway in these interactions ( e). 3.2. ACAA2 Decreased with Lipid Accumulation in Cardiomyocytes Induced by Indoxyl Sulfate To validate the protein–protein interaction (PPI) network genes enriched from our analysis, we examined their expression in AC16 cardiomyocytes. Cardiomyocytes were stimulated with various concentrations (0–2 mM) of indoxyl sulfate, and after 24 h of serum starvation, proteins and RNA were collected. Interestingly, low concentrations of indoxyl sulfate increased the mRNA level of CPT2 and ETFDH, while only ACADM and ACAA2 showed a decreasing trend, suggesting that ACADM and ACAA2 proteins were more sensitive to stimulation ( a). Importantly, the Western blot results showed that only the ACAA2 protein exhibited a downward trend with increasing IS concentration ( b). And the ACAA2 protein, as a convergence point for fatty acid oxidation, the TCA cycle, and cholesterol synthesis, captured our interest. Subsequently, under the stimulation of indoxyl sulfate, cell viability and CCK8 and ATP content in AC16 cells both decreased ( c). Under 1 mM IS stimulation, cell viability in AC16 cardiomyocytes decreased slowly, while ATP levels dropped significantly. This suggested that 1 mM IS is sufficient to inflict damage on AC16 cells, and thus, 1 mM IS was used in subsequent experiments. Simultaneously, we conducted DHE staining and mPTP staining, revealing that indoxyl sulfate elevated oxidative stress levels within AC16 cells and promoted the opening of the mitochondrial permeability transition pore, indicating that indoxyl sulfate impairs mitochondrial function in cardiomyocytes ( d,e). ACAA2 is a mitochondrial protein, and we next performed co-staining of ACAA2 with the green fluorescent fatty acid probe BODIPY. We observed a significant reduction in ACAA2 protein and a notable increase in green fluorescence aggregation under indoxyl sulfate stimulation ( f). Additionally, line analysis of the green fluorescence aggregation sites revealed a high degree of overlap with the regions of reduced ACAA2 protein ( g), suggesting a close correlation between the decrease in ACAA2 and the process of fatty acid oxidation as well as a strong association between the reduction in ACAA2 and the accumulation of lipid intermediates. Combined with the important function of ACAA2 in the last step of fatty acid β oxidation, the above findings suggest that the reduction in ACAA2 hinders the process of fatty acid oxidation, causing the accumulation of lipid intermediates and fatty acids, thereby increasing intracellular oxidative stress levels and damaging mitochondrial function. 3.3. Knockdown and Overexpression ACAA2 Affect Mitochondrial Function and Oxidative Stress in Cardiomyocytes We constructed plasmids for ACAA2 overexpression and knockdown and transfected them into AC16 cells to verify the expression effects ( a, ). In AC16 cells, after knocking down ACAA2, we observed through DHE staining that although the intracellular ROS level did not significantly increase after simply knocking down ACAA2, under the stimulation of indoxyl sulfate, knocking down ACAA2 promoted an increase in intracellular ROS levels. This suggested that ACAA2 has a protective effect on cardiomyocytes under the stimulation of urea toxins ( b,c). To further elucidate the protective role of ACAA2 on mitochondria in cardiomyocytes under the stimulation of indoxyl sulfate, we used JC-1 staining to assess mitochondrial membrane potential levels. The experiment revealed that the mitochondrial membrane potential was significantly reduced when ACAA2 was solely knocked down, and the stimulation of indoxyl sulfate also significantly decreased the membrane potential levels in AC16 cells. This does not conflict but rather underscores the role of ACAA2 in maintaining mitochondrial function in AC16 cells ( d,e). CCK8, LDH, and MDA assays were conducted in AC16 cells. Although there was no significant decrease in the cell viability of si-ACAA2 stimulated by IS, there was a clear trend ( f). Under the stimulation of indoxyl sulfate with si-ACAA2, the release of lactate dehydrogenase from mitochondria further increased, and MDA indicated a further elevation in intracellular lipid peroxidation levels ( g,h). However, in AC16 cells with overexpression of ACAA2, under the stimulation of indoxyl sulfate, the trend of cell viability increased, albeit not significantly, while the release of mitochondrial lactate dehydrogenase significantly decreased, and the level of lipid peroxidation as measured by MDA also declined ( i–k). The above experiments indicated that knocking down ACAA2 exacerbated mitochondrial damage in AC16 cells induced by indoxyl sulfate, whereas overexpression of ACAA2 alleviated mitochondrial damage and lipid peroxidation under indoxyl sulfate stimulation, suggesting that ACAA2 had a protective effect on mitochondrial damage under indoxyl sulfate stimulation. 3.4. Knockdown FOXO4 Restores ACAA2 Protein Level and Alleviates Mitochondrial Dysfunction Through the JASPAR database, we predicted that FOXO4 might be an upstream transcription factor of ACAA2, and we simulated the binding of FOXO4 and ACAA2 molecules using Pymol 2.5.5 and other software ( a, ). To our knowledge, FOXO4 as a transcriptional regulator upstream of ACAA2 has not been reported previously. Subsequently, we knocked down FOXO4 in AC16 cells and collected RNA for subsequent experiments. The knockdown efficiency of FOXO4 results indicated that the 003 sequence for FOXO4 knockdown could be used for subsequent experiments ( ). Through Western blot experiment, the expression of ACAA2 protein in AC16 cells after FOXO4 knockdown was significantly elevated ( b,c). Subsequently, we added indoxyl sulfate into AC16 cells with si-FOXO4 to further observe changes of ACAA2 protein levels. Through immunofluorescence staining for ACAA2, we directly observed that the stimulation of indoxyl sulfate did not result in a reduction in ACAA2 protein despite the knockdown of FOXO4. This suggests that the activation of the transcription factor FOXO4 may inhibit the synthesis of ACAA2 protein ( d,e). In AC16 cells with si-FOXO4 stimulated by indoxyl sulfate, Rt-qPCR results showed that the FOXO4 mRNA level did not increase, while the ACAA2 mRNA level significantly increased compared to indoxyl sulfate stimulation alone. Consistent with previous immunofluorescence results, this suggests that the transcription factor FOXO4 regulates the downstream ACAA2 protein ( f,g). Then, mitochondrial function assays were conducted with si-FOXO4 in AC16 cells. We observed that under indoxyl sulfate stimulation, si-FOXO4 could restore cell viability ( h), reduce the release of mitochondrial lactate dehydrogenase ( i), and decrease the intracellular level of lipid peroxidation MDA level ( j). Additionally, JC-1 staining indicated that the reduction in mitochondrial membrane potential in AC16 cells stimulated by indoxyl sulfate could be reversed by si-FOXO4 ( k,l). These results indicate that FOXO4 knockdown in vitro effectively restored ACAA2 expression and protected cardiomyocytes by reducing mitochondrial damage and lipid peroxidative stress. 3.5. CSNO Alleviates Uremic Toxin-Induced Mitochondrial Dysfunction and Lipid Peroxidation in Cardiomyocytes In Vitro Our previous studies demonstrated that CSNO can improve glucose metabolism disorders , and we found that CSNO also has an inhibitory effect on lipid peroxidation under urea toxin stimulation. Firstly, we conducted a CSNO concentration-dependent experiment on AC16 cells, observing that the promotion of cell proliferation in AC16 cells by CSNO began to significantly increase at 0.5 µM. Subsequently, we added indoxyl sulfate to the mixture and continued to use different concentrations of CSNO. The results again showed that 0.5 µM of CSNO was sufficient to reverse the activity damage to AC16 cells caused by indoxyl sulfate. Hence, experiments were conducted using a CSNO concentration of 0.5 µM ( a). We then proceeded to investigate how CSNO could enhance the cellular activity of indoxyl sulfate in AC16 cells. CSNO reduced mitochondrial damage in AC16 cells induced by IS not only by decreasing the release of LDH from mitochondria ( b) but also by restoring the mitochondrial membrane potential ( d,e). Additionally, the recovery of MDA levels suggested that CSNO significantly reduced lipid peroxidation level in AC16 cells ( c). We then stained AC16 cells with the fatty acid probe BODIPY and found that the CSNO group significantly reduced the formation of lipid intermediates compared to the IS group ( f). Co-staining ACAA2 protein with the fatty acid probe revealed that the formation of lipid intermediates in the CSNO group overlapped with the fluorescent absent regions of ACAA2 protein ( g). Previous results indicated that ACAA2 protein is crucial for antioxidant stress in cardiomyocytes, and CSNO could restore ACAA2 expression, improve mitochondrial dysfunction in cardiomyocytes caused by IS, and alleviate lipid peroxidation. 3.6. CSNO Ameliorates Uremic Toxin-Induced Mitochondrial Damage and Lipid Peroxidation in Cardiomyocytes Through FOXO4–ACAA2 Axis In Vitro Previously, we demonstrated in AC16 cells that CSNO could alleviate the mitochondrial damage and lipid oxidative stress in cardiomyocytes under uremic toxin stimulation. This prompted us to explore the mechanism of CSNO. We collected RNA samples from AC16 cells to examine the genes involved in lipid metabolism. Given the interacting proteins identified in the GEO database that implicate ACAA2 in lipid metabolism progression, we initially assessed the changes in these genes following CSNO treatment ( ). We observed that, following CSNO treatment, lipid transport-associated proteins such as CPT2 experienced further upregulation, while proteins critical for fatty acid β oxidation, including ACADM, ECH1, and HADHA, remained largely unrecovered ( a, ). CSNO appeared to uniquely restore ACAA2 protein levels, suggesting a specific effect of CSNO on ACAA2. Furthermore, we assessed the transcription factor FOXO4, known to regulate ACAA2 expression, and found that CSNO diminished the suppressive effect of FOXO4 on ACAA2 ( a). This encouraging result led us to subsequently demonstrate, through both Western blot ( b,c) and immunofluorescence ( d), the changes in ACAA2 and FOXO4 proteins under CSNO treatment. Consistently, under IS stimulation in AC16 cells, activation of FOXO4 inhibited the expression of ACAA2 protein. The decrease in ACAA2 led to lipid peroxidation in cardiomyocytes, causing mitochondrial injury. However, CSNO treatment could reduce the excessive activation of FOXO4, thereby restoring ACAA2 expression and alleviating mitochondrial damage caused by lipid oxidative stress. 3.7. CSNO Nebulization Improves Cardiac Function in Mice with Renal Insufficiency In Vivo Subsequently, we conducted validation experiments using C57BL/6 mice. The mice were divided into three groups: control group, CKD group, and CKD + CSNO inhalation group, and the experiments lasted for 8 weeks. By the end of the 8th week, mice were induced into anesthesia, underwent echocardiographic assessment of cardiac function, and were then euthanized to collect samples for further experiments ( a). Firstly, we detected the creatinine levels in blood and urine samples, finding significantly elevated serum creatinine and decreased urinary creatinine in the CKD group, indicating the successful establishment of the renal dysfunction model. Meanwhile, the CSNO group showed a marked reduction in circulating creatinine and enhanced excretion of creatinine, suggesting that nebulized CSNO therapy could improve renal dysfunction ( b). At the same time, consistent with cell experiments, we measured the concentration of IS in blood samples ( ). Additionally, the OGTT results suggested that treatment with CSNO aided in reducing blood glucose levels in mice with impaired renal function ( c). The tail-cuff blood pressure results indicated that both systolic and diastolic blood pressure showed varying degrees of elevation in the CKD group, while CSNO treatment, though not significantly reducing blood pressure, showed a downward trend in both ( d). Although CSNO did not significantly restore blood pressure, it exerted significant effects on the recovery of left ventricular systolic function ( e), indicating that the restoration of CSNO in cardiac function was not achieved solely through reducing blood pressure but involved other mechanisms. By measuring the body weight of mice at the end of the 8th week, we found that CSNO treatment did not restore the weight loss in mice under renal insufficiency ( f, ). However, by measuring the heart-to-body weight ratio and heart-to-tibia length ratio in mice ( g), we found that both were reduced in the CSNO group compared to the CKD group, indicating that CSNO treatment alleviated cardiac hypertrophy in mice. Subsequently, we performed HE and MASSON staining ( h,i) on the heart and kidney tissues of mice and found that the nebulized inhalation of CSNO alleviated myocardial hypertrophy and alleviated renal fibrosis to a certain extent. Furthermore, upon Oil Red O staining of mouse heart and kidney tissues, consistent results were obtained ( ). The results above indicate that inhalation of CSNO could mitigate renal fibrosis in cases of renal insufficiency and restore cardiac function by alleviating myocardial hypertrophy independent of blood pressure reduction. 3.8. CSNO Improves Cardiac Function in Mice with Renal Insufficiency via the FOXO4–ACAA2 Axis In Vivo We separately tested MDA levels in heart and kidney tissues to verify whether CSNO could alleviate lipid peroxidative stress. The results indicated that both heart and kidney under renal dysfunction showed varying degrees of lipid peroxidation, which was alleviated after nebulized inhalation of CSNO ( a). To verify the regulatory relationship within the FOXO4–ACAA2 axis, we collected RNA samples from heart and kidney tissues and performed RT-qPCR ( b,c). CPT2, a lipid transporter in the heart and kidney, significantly increased during CKD modeling, indicating a promotion in fatty acid transport, while proteins related to fatty acid β oxidation, such as ACAA2 and ACADM ( ), showed a decrease. This suggests that under external conditions of kidney dysfunction, both heart and kidney organs exhibit varying degrees of fatty acid metabolism decompensation in mice. Following nebulized inhalation of CSNO, though CPT2 further increased, there was a significant recovery in the ACAA2 protein, indicating that CSNO specifically restored ACAA2 protein, driving lipid metabolism from decompensation to compensation. The upstream transcription factor FOXO4 also showed consistent changes. Subsequent Western blot ( d,e) and immunohistochemical analysis ( f,g) of proteins related to the FOXO4–ACAA2 axis revealed that ACAA2 showed varying degrees of significant reduction in heart and kidney tissues under CKD modeling, which CSNO could reverse. This indicates that CSNO exerted protective effects on heart and kidney organs under kidney dysfunction through the FOXO4–ACAA2 axis, resisting lipid peroxidation and maintaining the normal functioning of heart and kidney tissues.
We utilized the Gene Expression Omnibus (GEO) database and retrieved the GSE106385 dataset by searching for the keywords “renal insufficiency” and “heart failure”. Differential gene expression analysis ( a) and enrichment analysis ( b) were performed. Our findings indicated that the pathways related to cardiac function in mice with renal impairment were predominantly altered in metabolic pathways ( c), while genes associated with fatty acid metabolism were the most affected. Specifically, the fatty acid β-oxidation pathway was significantly impacted ( d), suggesting a dysregulation of cardiac lipid metabolism in the context of renal insufficiency, although the specific targets remained unclear. Subsequently, after standardization and normalization of the data, we employed STRING and Cytoscape software 3.9.1 to construct protein–protein interaction (PPI) networks related to fatty acid metabolism. The results highlighted the critical role of the fatty acid β-oxidation pathway in these interactions ( e).
To validate the protein–protein interaction (PPI) network genes enriched from our analysis, we examined their expression in AC16 cardiomyocytes. Cardiomyocytes were stimulated with various concentrations (0–2 mM) of indoxyl sulfate, and after 24 h of serum starvation, proteins and RNA were collected. Interestingly, low concentrations of indoxyl sulfate increased the mRNA level of CPT2 and ETFDH, while only ACADM and ACAA2 showed a decreasing trend, suggesting that ACADM and ACAA2 proteins were more sensitive to stimulation ( a). Importantly, the Western blot results showed that only the ACAA2 protein exhibited a downward trend with increasing IS concentration ( b). And the ACAA2 protein, as a convergence point for fatty acid oxidation, the TCA cycle, and cholesterol synthesis, captured our interest. Subsequently, under the stimulation of indoxyl sulfate, cell viability and CCK8 and ATP content in AC16 cells both decreased ( c). Under 1 mM IS stimulation, cell viability in AC16 cardiomyocytes decreased slowly, while ATP levels dropped significantly. This suggested that 1 mM IS is sufficient to inflict damage on AC16 cells, and thus, 1 mM IS was used in subsequent experiments. Simultaneously, we conducted DHE staining and mPTP staining, revealing that indoxyl sulfate elevated oxidative stress levels within AC16 cells and promoted the opening of the mitochondrial permeability transition pore, indicating that indoxyl sulfate impairs mitochondrial function in cardiomyocytes ( d,e). ACAA2 is a mitochondrial protein, and we next performed co-staining of ACAA2 with the green fluorescent fatty acid probe BODIPY. We observed a significant reduction in ACAA2 protein and a notable increase in green fluorescence aggregation under indoxyl sulfate stimulation ( f). Additionally, line analysis of the green fluorescence aggregation sites revealed a high degree of overlap with the regions of reduced ACAA2 protein ( g), suggesting a close correlation between the decrease in ACAA2 and the process of fatty acid oxidation as well as a strong association between the reduction in ACAA2 and the accumulation of lipid intermediates. Combined with the important function of ACAA2 in the last step of fatty acid β oxidation, the above findings suggest that the reduction in ACAA2 hinders the process of fatty acid oxidation, causing the accumulation of lipid intermediates and fatty acids, thereby increasing intracellular oxidative stress levels and damaging mitochondrial function.
We constructed plasmids for ACAA2 overexpression and knockdown and transfected them into AC16 cells to verify the expression effects ( a, ). In AC16 cells, after knocking down ACAA2, we observed through DHE staining that although the intracellular ROS level did not significantly increase after simply knocking down ACAA2, under the stimulation of indoxyl sulfate, knocking down ACAA2 promoted an increase in intracellular ROS levels. This suggested that ACAA2 has a protective effect on cardiomyocytes under the stimulation of urea toxins ( b,c). To further elucidate the protective role of ACAA2 on mitochondria in cardiomyocytes under the stimulation of indoxyl sulfate, we used JC-1 staining to assess mitochondrial membrane potential levels. The experiment revealed that the mitochondrial membrane potential was significantly reduced when ACAA2 was solely knocked down, and the stimulation of indoxyl sulfate also significantly decreased the membrane potential levels in AC16 cells. This does not conflict but rather underscores the role of ACAA2 in maintaining mitochondrial function in AC16 cells ( d,e). CCK8, LDH, and MDA assays were conducted in AC16 cells. Although there was no significant decrease in the cell viability of si-ACAA2 stimulated by IS, there was a clear trend ( f). Under the stimulation of indoxyl sulfate with si-ACAA2, the release of lactate dehydrogenase from mitochondria further increased, and MDA indicated a further elevation in intracellular lipid peroxidation levels ( g,h). However, in AC16 cells with overexpression of ACAA2, under the stimulation of indoxyl sulfate, the trend of cell viability increased, albeit not significantly, while the release of mitochondrial lactate dehydrogenase significantly decreased, and the level of lipid peroxidation as measured by MDA also declined ( i–k). The above experiments indicated that knocking down ACAA2 exacerbated mitochondrial damage in AC16 cells induced by indoxyl sulfate, whereas overexpression of ACAA2 alleviated mitochondrial damage and lipid peroxidation under indoxyl sulfate stimulation, suggesting that ACAA2 had a protective effect on mitochondrial damage under indoxyl sulfate stimulation.
Through the JASPAR database, we predicted that FOXO4 might be an upstream transcription factor of ACAA2, and we simulated the binding of FOXO4 and ACAA2 molecules using Pymol 2.5.5 and other software ( a, ). To our knowledge, FOXO4 as a transcriptional regulator upstream of ACAA2 has not been reported previously. Subsequently, we knocked down FOXO4 in AC16 cells and collected RNA for subsequent experiments. The knockdown efficiency of FOXO4 results indicated that the 003 sequence for FOXO4 knockdown could be used for subsequent experiments ( ). Through Western blot experiment, the expression of ACAA2 protein in AC16 cells after FOXO4 knockdown was significantly elevated ( b,c). Subsequently, we added indoxyl sulfate into AC16 cells with si-FOXO4 to further observe changes of ACAA2 protein levels. Through immunofluorescence staining for ACAA2, we directly observed that the stimulation of indoxyl sulfate did not result in a reduction in ACAA2 protein despite the knockdown of FOXO4. This suggests that the activation of the transcription factor FOXO4 may inhibit the synthesis of ACAA2 protein ( d,e). In AC16 cells with si-FOXO4 stimulated by indoxyl sulfate, Rt-qPCR results showed that the FOXO4 mRNA level did not increase, while the ACAA2 mRNA level significantly increased compared to indoxyl sulfate stimulation alone. Consistent with previous immunofluorescence results, this suggests that the transcription factor FOXO4 regulates the downstream ACAA2 protein ( f,g). Then, mitochondrial function assays were conducted with si-FOXO4 in AC16 cells. We observed that under indoxyl sulfate stimulation, si-FOXO4 could restore cell viability ( h), reduce the release of mitochondrial lactate dehydrogenase ( i), and decrease the intracellular level of lipid peroxidation MDA level ( j). Additionally, JC-1 staining indicated that the reduction in mitochondrial membrane potential in AC16 cells stimulated by indoxyl sulfate could be reversed by si-FOXO4 ( k,l). These results indicate that FOXO4 knockdown in vitro effectively restored ACAA2 expression and protected cardiomyocytes by reducing mitochondrial damage and lipid peroxidative stress.
Our previous studies demonstrated that CSNO can improve glucose metabolism disorders , and we found that CSNO also has an inhibitory effect on lipid peroxidation under urea toxin stimulation. Firstly, we conducted a CSNO concentration-dependent experiment on AC16 cells, observing that the promotion of cell proliferation in AC16 cells by CSNO began to significantly increase at 0.5 µM. Subsequently, we added indoxyl sulfate to the mixture and continued to use different concentrations of CSNO. The results again showed that 0.5 µM of CSNO was sufficient to reverse the activity damage to AC16 cells caused by indoxyl sulfate. Hence, experiments were conducted using a CSNO concentration of 0.5 µM ( a). We then proceeded to investigate how CSNO could enhance the cellular activity of indoxyl sulfate in AC16 cells. CSNO reduced mitochondrial damage in AC16 cells induced by IS not only by decreasing the release of LDH from mitochondria ( b) but also by restoring the mitochondrial membrane potential ( d,e). Additionally, the recovery of MDA levels suggested that CSNO significantly reduced lipid peroxidation level in AC16 cells ( c). We then stained AC16 cells with the fatty acid probe BODIPY and found that the CSNO group significantly reduced the formation of lipid intermediates compared to the IS group ( f). Co-staining ACAA2 protein with the fatty acid probe revealed that the formation of lipid intermediates in the CSNO group overlapped with the fluorescent absent regions of ACAA2 protein ( g). Previous results indicated that ACAA2 protein is crucial for antioxidant stress in cardiomyocytes, and CSNO could restore ACAA2 expression, improve mitochondrial dysfunction in cardiomyocytes caused by IS, and alleviate lipid peroxidation.
Previously, we demonstrated in AC16 cells that CSNO could alleviate the mitochondrial damage and lipid oxidative stress in cardiomyocytes under uremic toxin stimulation. This prompted us to explore the mechanism of CSNO. We collected RNA samples from AC16 cells to examine the genes involved in lipid metabolism. Given the interacting proteins identified in the GEO database that implicate ACAA2 in lipid metabolism progression, we initially assessed the changes in these genes following CSNO treatment ( ). We observed that, following CSNO treatment, lipid transport-associated proteins such as CPT2 experienced further upregulation, while proteins critical for fatty acid β oxidation, including ACADM, ECH1, and HADHA, remained largely unrecovered ( a, ). CSNO appeared to uniquely restore ACAA2 protein levels, suggesting a specific effect of CSNO on ACAA2. Furthermore, we assessed the transcription factor FOXO4, known to regulate ACAA2 expression, and found that CSNO diminished the suppressive effect of FOXO4 on ACAA2 ( a). This encouraging result led us to subsequently demonstrate, through both Western blot ( b,c) and immunofluorescence ( d), the changes in ACAA2 and FOXO4 proteins under CSNO treatment. Consistently, under IS stimulation in AC16 cells, activation of FOXO4 inhibited the expression of ACAA2 protein. The decrease in ACAA2 led to lipid peroxidation in cardiomyocytes, causing mitochondrial injury. However, CSNO treatment could reduce the excessive activation of FOXO4, thereby restoring ACAA2 expression and alleviating mitochondrial damage caused by lipid oxidative stress.
Subsequently, we conducted validation experiments using C57BL/6 mice. The mice were divided into three groups: control group, CKD group, and CKD + CSNO inhalation group, and the experiments lasted for 8 weeks. By the end of the 8th week, mice were induced into anesthesia, underwent echocardiographic assessment of cardiac function, and were then euthanized to collect samples for further experiments ( a). Firstly, we detected the creatinine levels in blood and urine samples, finding significantly elevated serum creatinine and decreased urinary creatinine in the CKD group, indicating the successful establishment of the renal dysfunction model. Meanwhile, the CSNO group showed a marked reduction in circulating creatinine and enhanced excretion of creatinine, suggesting that nebulized CSNO therapy could improve renal dysfunction ( b). At the same time, consistent with cell experiments, we measured the concentration of IS in blood samples ( ). Additionally, the OGTT results suggested that treatment with CSNO aided in reducing blood glucose levels in mice with impaired renal function ( c). The tail-cuff blood pressure results indicated that both systolic and diastolic blood pressure showed varying degrees of elevation in the CKD group, while CSNO treatment, though not significantly reducing blood pressure, showed a downward trend in both ( d). Although CSNO did not significantly restore blood pressure, it exerted significant effects on the recovery of left ventricular systolic function ( e), indicating that the restoration of CSNO in cardiac function was not achieved solely through reducing blood pressure but involved other mechanisms. By measuring the body weight of mice at the end of the 8th week, we found that CSNO treatment did not restore the weight loss in mice under renal insufficiency ( f, ). However, by measuring the heart-to-body weight ratio and heart-to-tibia length ratio in mice ( g), we found that both were reduced in the CSNO group compared to the CKD group, indicating that CSNO treatment alleviated cardiac hypertrophy in mice. Subsequently, we performed HE and MASSON staining ( h,i) on the heart and kidney tissues of mice and found that the nebulized inhalation of CSNO alleviated myocardial hypertrophy and alleviated renal fibrosis to a certain extent. Furthermore, upon Oil Red O staining of mouse heart and kidney tissues, consistent results were obtained ( ). The results above indicate that inhalation of CSNO could mitigate renal fibrosis in cases of renal insufficiency and restore cardiac function by alleviating myocardial hypertrophy independent of blood pressure reduction.
We separately tested MDA levels in heart and kidney tissues to verify whether CSNO could alleviate lipid peroxidative stress. The results indicated that both heart and kidney under renal dysfunction showed varying degrees of lipid peroxidation, which was alleviated after nebulized inhalation of CSNO ( a). To verify the regulatory relationship within the FOXO4–ACAA2 axis, we collected RNA samples from heart and kidney tissues and performed RT-qPCR ( b,c). CPT2, a lipid transporter in the heart and kidney, significantly increased during CKD modeling, indicating a promotion in fatty acid transport, while proteins related to fatty acid β oxidation, such as ACAA2 and ACADM ( ), showed a decrease. This suggests that under external conditions of kidney dysfunction, both heart and kidney organs exhibit varying degrees of fatty acid metabolism decompensation in mice. Following nebulized inhalation of CSNO, though CPT2 further increased, there was a significant recovery in the ACAA2 protein, indicating that CSNO specifically restored ACAA2 protein, driving lipid metabolism from decompensation to compensation. The upstream transcription factor FOXO4 also showed consistent changes. Subsequent Western blot ( d,e) and immunohistochemical analysis ( f,g) of proteins related to the FOXO4–ACAA2 axis revealed that ACAA2 showed varying degrees of significant reduction in heart and kidney tissues under CKD modeling, which CSNO could reverse. This indicates that CSNO exerted protective effects on heart and kidney organs under kidney dysfunction through the FOXO4–ACAA2 axis, resisting lipid peroxidation and maintaining the normal functioning of heart and kidney tissues.
Cardiorenal syndrome refers to the complex interplay between the heart and kidneys. Studies have shown that cardiac remodeling in patients with chronic kidney disease (CKD) is closely associated with metabolic abnormalities in cardiomyocytes, particularly alterations in energy metabolism . Moreover, uremic cardiomyopathy is a common cardiac complication in CKD patients, characterized by myocardial hypertrophy and fibrosis and often accompanied by left ventricular dysfunction. The pathophysiological mechanisms of uremic cardiomyopathy are intricate, involving factors such as electrolyte imbalance, activation of the renin–angiotensin system, and hyperactivity of the sympathetic nervous system. Clinically, despite improvements in adverse factors such as high circulatory load, heart function in patients with both cardiac and renal insufficiency may continue to deteriorate. This observation suggests that renal dysfunction may impair cardiac function through mechanisms other than hemodynamic overload. Therefore, investigating the mechanisms underlying non-hemodynamic cardiac injury in renal dysfunction and identifying effective interventions are of critical importance. Against this backdrop, our study focused on the elevated levels of uremic toxins in patients with renal dysfunction, particularly indoxyl sulfate IS. The heart is a highly metabolically active organ that primarily relies on fatty acid oxidation for energy supply. When the uptake of fatty acids exceeds the oxidative capacity of cardiomyocytes, unmetabolized fatty acids accumulate within the cells, forming intermediates such as triglycerides, diacylglycerol, and ceramides . These intermediates not only directly impact the physiological functions of cardiomyocytes but also activate various intracellular signaling pathways, inducing oxidative stress, inflammatory responses, and apoptosis, thereby exacerbating myocardial injury. Studies have demonstrated that in patients with metabolic disorders such as obesity, type 2 diabetes, and hyperlipidemia, the capacity for myocardial fatty acid oxidation is significantly impaired, leading to abnormal lipid accumulation in the myocardium and resulting in lipid peroxidation . Through GEO data analysis, we found that fatty acid β-oxidation-related proteins in the heart are markedly downregulated under the context of renal dysfunction, with ACAA2 and other related fatty acid metabolic factors playing a critical role in fatty acid metabolism. This suggests that fatty acid oxidation is unable to provide sufficient energy support for cardiomyocytes under renal insufficiency. In AC16 cardiomyocytes stimulated with 1 mM indoxyl sulfate IS, we observed increased cell death rates, decreased mitochondrial ATP levels, elevated mitochondrial oxidative stress, and opening of the mitochondrial permeability transition pore (mPTP), indicating that moderate uremic toxin exposure impairs cardiomyocyte and mitochondrial function. Interestingly, colocalization of ACAA2 protein with lipid droplets showed that indoxyl sulfate reduces ACAA2 expression while increasing lipid droplet accumulation. In addition, IS-stimulated cardiomyocytes exhibited increased mRNA levels of the fatty acid transporter CPT2 and electron transfer protein ETFDH, while β-oxidation-related proteins such as ACADM, Ech1, and ACAA2 were significantly decreased. These findings indicated that, although fatty acid β-oxidation is diminished, fatty acid uptake by cardiomyocytes remains largely unaffected. This suggests that while the myocardium continuously takes up fatty acids for oxidation, the metabolic “fuel factory” fails to operate effectively, resulting in the accumulation of lipid metabolic intermediates within the mitochondria, triggering a cascade of oxidative stress reactions, including lipid peroxidation. However, it is worth noting that there are asymmetric changes in the protein levels and mRNA levels of CPT2, ACADM, and ETFDH. Protein level expression is not only associated with transcription and translation processes but also related to protein degradation. Proteins may be rapidly degraded by the ubiquitin–proteasome system or the autophagy pathway, resulting in lower protein levels despite high mRNA levels. The mechanisms underlying this phenomenon remain to be explored. ACAA2, an acyl-CoA acyltransferase, is involved in the final step of fatty acid β-oxidation and exerts a protective role in various diseases . Given this, we questioned whether modulation of ACAA2 might alleviate myocardial injury in renal dysfunction. Therefore, we knocked down and overexpressed ACAA2. Interestingly, simple knockdown of ACAA2 in AC16 cardiomyocytes did not significantly affect ROS levels or LDH in experiments, which suggests that knockdown of ACAA2 alone has limited effects on the accumulation of intracellular oxygen radicals, making it hard to trigger apoptosis or necrosis. However, when stimulating AC16 cells with IS, si-ACAA2 further amplified the toxic effects on cardiomyocytes, which suggests that ACAA2 protein is indispensable in protecting cardiomyocytes from damage induced by urea toxin stimulation. The decline in mitochondrial membrane potential with addition of si-ACAA2 indicated an early-stage damage, suggesting an acute temporal impact of si-ACAA2 on cardiomyocytes. In contrast, simply overexpressing ACAA2 did not significantly restore LDH and MDA levels in AC16 cells. However, under the external stimulus of IS, LDH and MDA levels significantly decreased, indicating that the underlying mechanisms require further investigation. Given that ACAA2 differs from other matrix-associated mitochondrial proteins by possessing a non-cleavable N-terminus, it may exhibit distinct functionalities under certain specific conditions. These results suggest that overexpression of ACAA2 facilitates the management of excess fatty acids within cardiomyocytes, reducing intracellular lipid peroxidation. Having established the crucial role of ACAA2 in fatty acid β-oxidation, we further utilized the JASPAR database to predict upstream transcription factors potentially regulating ACAA2. Among these, FOXO4 emerged as a likely candidate. FOXO4, a member of the forkhead box transcription factor family, has received increasing attention in cancer research in recent years . For instance, the hypoxia-induced FOXO4/LDHA axis was found to play a pivotal role in regulating glycolysis and tumor progression in gastric cancer . We hypothesized that FOXO4 might exert transcriptional control over ACAA2, and to test this, we knocked down FOXO4 in AC16 cardiomyocytes, followed by stimulation with indoxyl sulfate. From multiple perspectives, including RNA and protein expression levels, we found that FOXO4 knockdown partially restored the reduction in ACAA2 protein levels induced by indoxyl sulfate. Furthermore, knocking down FOXO4 mitigated the effects of indoxyl sulfate on cardiomyocyte apoptosis, mitochondrial lactate dehydrogenase release, mitochondrial membrane potential, and lipid peroxidation levels. In summary, FOXO4 knockdown improved the oxidative stress and mitochondrial dysfunction in cardiomyocytes induced by indoxyl sulfate, partly through the restoration of ACAA2 expression. However, the transcriptional regulation of ACAA2 by FOXO4 did not appear to be highly specific. Our subsequent unpublished results suggest that the regulatory factors controlling ACAA2 expression are multifaceted and complex, extending beyond transcription factors and involving post-translational modifications. Nonetheless, it is unequivocal that FOXO4 is involved in the regulation of ACAA2 expression. However, further studies are needed to clarify how FOXO4 specifically influences lipid metabolism in cardiomyocytes through ACAA2. Previous studies have demonstrated that CSNO can improve cardiac function in diabetic cardiomyopathy and restore cardiomyocyte viability . In the cardiovascular system, S-nitrosylation participates in the regulation of cardiac function by modulating key signaling molecules such as CaMKII, JNK , and Hsp90 . For instance, S-nitrosylation of CaMKII plays an important role in β-adrenergic signaling in the heart, where nitrosylation at specific cysteine residues can trigger autonomous activation, thereby affecting calcium release and contributing to arrhythmogenesis . However, both excessive and insufficient levels of S-nitrosylation can lead to pathological states . Excessive S-nitrosylation may lead to abnormal activation or inhibition of protein function, whereas insufficient nitrosylation may impair cellular antioxidant capacity and stress response. Thus, the precise regulation of S-nitrosylation is crucial for maintaining cellular homeostasis and preventing disease . Can CSNO ameliorate cardiac dysfunction in the context of renal insufficiency? To address this question, we first added various concentrations of CSNO to cardiomyocytes treated with indoxyl sulfate. The results, which included assessments of cell viability, protein levels, mitochondrial membrane potential, and lipid peroxidation MDA levels, indicated that CSNO supplementation partially mitigated the damage induced by indoxyl sulfate. To further verify the effect of lipid peroxidation on cardiomyocytes, ACAA2 was colocalized with BODIPY, and it was observed that CSNO restored ACAA2 protein levels and reduced the accumulation of lipid intermediates. In addition, we investigated the FOXO4–ACAA2 axis and found that CSNO reduced the RNA level of FOXO4, an upstream regulator of ACAA2, while having no significant effect on the fatty acid transporter CPT2 or the common fatty acid transcription factor PPARA. This suggests that FOXO4 exerts some transcriptional regulation on ACAA2, and CSNO exerts its effect through the FOXO4–ACAA2 axis. In a mouse model, daily aerosol inhalation of CSNO for 20 min resulted in an improvement in cardiac function under renal insufficiency, reducing myocardial hypertrophy and lipotoxic accumulation. Additionally, CSNO significantly attenuated renal fibrosis, although the underlying mechanism remains unclear. It is uncertain whether these effects are due to exogenous NO supplementation or the protective effects of S-nitrosylation of thiol groups. Further exploration of the precise mechanisms is required. Nevertheless, it is evident that aerosolized CSNO can partially restore cardiac function in the setting of renal insufficiency and significantly increase ACAA2 protein levels, thereby improving myocardial lipid metabolism.
In conclusion, this study provides the evidence that myocardial lipid peroxidation is exacerbated under conditions of renal insufficiency and that restoring ACAA2 protein levels can mitigate lipid peroxidation in cardiomyocytes. Moreover, excessive activation of FOXO4 is detrimental to lipid accumulation in cardiomyocytes, as FOXO4 is involved in the regulation of ACAA2 protein expression. Additionally, we found that CSNO aerosol inhalation effectively improves cardiac function by restoring ACAA2 protein levels and reducing myocardial lipid peroxidation, offering a novel therapeutic strategy for clinical intervention.
|
Comparison between bone–implant interfaces of microtopographically modified zirconia and titanium implants | 76569e18-553c-4cb4-a020-baef7104af28 | 10333275 | Dental[mh] | Implant dentistry has become a predictable and reliable treatment modality for restoring the esthetics and functions of partially and completely edentulous patients. For successful outcomes of implant-supported dental prostheses, osseointegration, the biological fixation of an implant defined as direct bone-to-implant contact (BIC) without an intervening connective tissue layer, must be achieved , . Therefore, BIC is a critical indicator for successful osseointegration, which governs the long-term clinical success and survival of implant-supported dental prostheses . For the optimal BIC, the surface characteristics, both physical and chemical, of dental implants play a critical role in the osseointegration process. Over the last 40 years, both commercially pure titanium (Ti) and titanium alloys have been used for dental implants due to their excellent biocompatibility, mechanical strength, chemical inertness, and high clinical success rates . Since the 1990s, various microtopographical modification methods for Ti implant surfaces have been introduced by implant manufacturers to accelerate early osseous integration and increase the resistance of the bone–implant interface to functional loading . Among them, roughening Ti implant surfaces at the microscopic level with sandblasting and acid-etching has become very popular due to its high success rate . However, the main disadvantage of Ti implants is their dark grayish color, which may lead to esthetic compromise if buccal bone loss and unfavorable soft-tissue response in gingival biotypes occur . Therefore, researchers have developed zirconia (zirconium dioxide, ZrO 2 ) implants in the last 2 decades to mitigate the esthetic disadvantage of titanium implants , . The main advantage of ZrO 2 implants is their tooth-like color, which makes them superior to Ti implants in esthetically critical locations, such as the maxillary anterior area. In addition, they possess promising characteristics, such as a high resistance to wear and corrosion, high fracture resistance and flexural strength, biocompatibility, minimal ion-release, and reduced bacterial adhesion and plaque accumulation – . However, ZrO 2 also has disadvantages, such as a sensitivity to low-temperature degradation (aging), vulnerability to subcritical bending and crack growth, and lower fracture resistance, compared to titanium , . It is also challenging to roughen ZrO 2 implant surfaces because of their hardness . In the literature, various attempts to modify ZrO 2 surfaces have been reported, such as sandblasting, etching with hydrochloric or hydrofluoric acids , the aggregation of bioactive materials such as hydroxyapatite , plasma spraying , ultraviolet radiation to induce the hydrophilicity of ZrO 2 , and selective infiltration-etching techniques to create a nanoporous surface . Although both the additive manufacturing and subtractive (milling) manufacturing of ZrO 2 by computer-aided design and computer-aided manufacturing (CAD/CAM) technology have been studied frequently, as reported in the literature – , the injection molding of ZrO 2 has been reported in the literature since the 1980s . This technique is a type of plasticity-shaping technique that utilizes a binder, plasticizer, lubricant, and coupling agent . The process begins with compounding fine ceramic powders with a blend of polymers or wax in solvent. Then, the organic binders embedded in the molds are removed via thermal pyrolysis or solvent detracting before sintering . Injection molding is suitable for making relatively fine ceramic parts because it offers dimensional reproducibility, requires little or no modification, and produces parts with a sufficient quality for clinical applications . In particular, this technique allows the mass production of ceramic parts at low cost and near-net-shape formation . Previous animal studies and various case reports have indicated that the osseointegration of ZrO 2 implants is similar to or even superior to that of Ti implants – . However, the effects of different surface characteristics (i.e., smooth vs. rough) of ZrO 2 implants on the osseointegration process are still largely unknown . Furthermore, there is no report on the early bone response around injection-molded ZrO 2 implants compared to that around Ti implants. This in vivo study aimed to compare the bone–implant interfaces of injection-molded ZrO 2 implants and computer numerical control (CNC)-machined Ti implants. Two types of surfaces were prepared for each material: sandblasted and nonsandblasted surfaces for the injection-molded ZrO 2 implants and sandblasted, large-grit, acid-etched (SLA) and non-SLA surfaces for the CNC-machined Ti implants. The surface characteristics of the implants were also investigated. The hypothesis underlying this study was that the hard-tissue response to the injection-molded ZrO 2 implants is similar to the osseointegration of Ti implants with SLA surfaces, which are used in dental clinics worldwide.
Preparation of implant samples A total of 56 screw-shaped dental implant samples (28 ZrO 2 implants (one-piece) and 28 Ti implants) of the same macroscopic shape and dimensions (a diameter of 3.4 mm and a length of 8 mm) were used in this study. Also, ten ZrO 2 discs, which were 15 mm in diameter and 1 mm in thickness, were prepared to find some phase transition of ZrO 2 after surface modification. The ZrO 2 implants were manufactured using an injection molding technique (Vatech Acucera, Seoul, Korea). This process involves mixing zirconia powders with modifiers and shaping a uniform and homogeneous mixture into a mold. The Ti implants were manufactured by a computer numerical controlled (CNC) milling technique (Deep Implant System, Inc., Seongnam, Korea). Disc-shaped green compacts were prepared by cold isostatic press of powder mixtures and then sintered. Surface modification with sandblasting was performed on half of the ZrO 2 implants and the ZrO 2 discs, while half of the Ti implant samples were surface-modified with SLA treatment, i.e., sandblasted with large-grit alumina (Al 2 O 3 ) particles and etched with hydrochloric acid (SLA surface; Deep Implant System, Inc., Seongnam, Korea). After sandblasting, the ZrO 2 implants (14 implants) and discs (5 discs) were treated by hot isostatic pressing (HIP) at 1380 °C and 138 MPa. Then, the samples were divided into four experimental groups: Group 1 = IM ZrO 2 (injection-molded ZrO 2 implants or ZrO 2 discs without sandblasting modification) Group 2 = IM ZrO 2 -S (injection-molded ZrO 2 implants or ZrO 2 discs with sandblasting modification) Group 3 = Ti-turned (Ti implants without SLA modification as a negative control) Group 4 = Ti-SLA (Ti implants with SLA modification as a positive control) Assessment of surface characteristics The implant sample surfaces were photographed by field emission-scanning electron microscopy (FE-SEM; S-4700, Hitachi, Tokyo, Japan). Surface parameters for the sample topography were measured by confocal laser scanning microscopy (CLSM; LSM 800, Carl Zeiss AG, Oberkochen, Germany). The acquired images were analyzed using ConfoMap software, and specific areas of interest were selected. Subsequently, the surface topography was quantified in terms of Sa (arithmetical mean height of a surface), the absolute value of the difference in height of each point compared to the arithmetical mean of the surface, and Sdr (developed interfacial area ratio), the proportion of the additional surface area contributed by the texture within the defined planar area. Each sample was analyzed at 3 selected sites (the upper, middle and lower flank), the values of which were averaged, and the average value was assigned as the representative value for the sample , . The topography was measured in terms of Sa values (arithmetical mean height) and Sdr values (developed interfacial area ratio). In addition, the chemical composition of each sample was analyzed with an energy-dispersive spectroscopy (EDS) device (EMAX, Horiba, High Wycombe, United Kingdom). For evaluating the phase transition of ZrO 2 , the ZrO 2 disc surfaces were analyzed using a high resolution X-ray diffractometer (SmartLab, Rigaku, Tokyo, Japan) with Cu Kα radiation (wavelength = 1.54 Å) and 45 kV/200 mA. To quantify the molar fraction of the content of monoclinic ZrO 2 ( [12pt]{minimal}
$$X_{m}$$ X m ), the following equations were employed: [12pt]{minimal}
$$X_{m} = ( {111} ) + I_{m} ( {11} )}}{{I_{m} ( {111} ) + I_{m} ( {11} ) + I_{t} ( {101} )}}$$ X m = I m 111 + I m 1 ¯ 11 I m 111 + I m 1 ¯ 11 + I t 101 where [12pt]{minimal}
$$I_{t}$$ I t and [12pt]{minimal}
$$I_{m}$$ I m represent the intensity of the tetragonal [12pt]{minimal}
$$( {101} )$$ 101 and monoclinic [12pt]{minimal}
$$( {111} )$$ 111 and [12pt]{minimal}
$$( {11} )$$ 1 ¯ 11 peaks , . The peak intensity was obtained using MDI Jade 6 software (Materials Data Inc., Livermore, CA, USA). In vivo surgery Eight male New Zealand white rabbits (age: 3–4 months old; weight: 2.5–3.0 kg) were used in this in vivo study, which was approved by the Institutional Animal Research Ethics Committee of Cronex (CRONEX-IACUC: 202108011, Hwaseong, Korea) and conducted according to the Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines . All methods were performed in accordance with the relevant guidelines and regulations. The experimental animals acclimatized in separate cages for two weeks before surgery. Each rabbit received four implant samples; two implants were placed in each tibia bone of the hind legs. The ZrO 2 and Ti implants were inserted based on the split-plot design (Fig. ). For anesthesia, a combination of 15 mg/kg tiletamine hydrochloride and zolazepam hydrochloride (Zoletil 50; Virbac Korea Co., Ltd., Seoul, Korea) and 5 mg/kg xylazine (Rompun; Bayer Korea, Ltd., Seoul, Korea) was administered intramuscularly. Then, the hind legs were shaved and disinfected with an antiseptic surgical scrub of 7.5% povidone-iodine (Betadine; Korea Pharma, Seoul, Korea). After site preparation, local infiltration anesthesia of 2% lidocaine hydrochloride with 1:100,000 epinephrine (Yuhan Company, Seoul, Korea) was administered at the surgical sites. The tibial bones were exposed by full-thickness incisions from the skin to the periosteum. The surgical sites on the tibiae bones were prepared with rotating implant drills and engines under copious irrigation with sterile saline solution. The final drill size was 3.0 mm, and 32 sample implants from experimental groups 1, 2, 3 and 4 were placed with primary stability (≥ 20 Ncm) using a torque wrench, according to the manufacturer’s instructions. After the implant placement surgeries, the muscle and fascia were sutured with resorbable 4–0 Vicryl sutures (Coated Vicryl; Ethicon, Raritan, NJ, United States), and the outer dermis was closed with nylon (Blue nylon; Ailee, Busan, Korea). All rabbit specimens were housed in individual cages and administered the postoperative antibiotic prophylaxis of enrofloxacin (Biotril, Komipharm International, Siheung, Korea). Assessment of histology and histomorphometry Four rabbits were sacrificed 10 days after the implant placement (Rabbit 1–4), and the remaining 4 rabbits were sacrificed 28 days after the implant placement (Rabbit 5–8) by an overdose of potassium chloride intravenously under anesthesia for histologic assessment. The bones and connective tissues that surrounded the implant samples were surgically harvested en bloc. These blocks were fixed in 10% neutral buffered formalin for two weeks and then dehydrated with ethanol, followed by embedding in light-curing resin (Technovit 7200 VLC, Kulzer, Wehrheim, Germany). A series of cutting and grinding devices (EXAKT system; EXAKT Apparatebau, Norderstedt, Germany) was used to cut and grind the embedded blocks into slides with a thickness of less than approximately 50 μm , . The slides were stained with modified Goldner’s Masson trichrome staining solution for examination under a light microscope. This staining technique allows for easy discrimination between newly formed bone (stained red) and exiting mature bone (stained blue) . The bone-to-implant interfaces were measured by the degree of BIC and bone area (BA) at the best three consecutive threads , . The histologic evaluation was performed using a light microscope (DM2700M, Leica Microsystems CMS GmbH, Wetzlar, Germany) and an attached digital camera (DMC5400, Leica Microsystems CMS GmbH, Wetzlar, Germany). An image analysis system (ImageJ 1.60, NIH, Bethesda, MD, United States) was used to analyze the acquired images. Statistical analysis Most of the outcome variables for data normalization were accepted when the Shapiro‒Wilk test was used ( p > 0.05). Descriptive statistics are shown as the means and standard deviations (SDs). One-way analysis of variance (ANOVA) was used to analyze differences in the mean values of the surface parameters and histomorphometric data of the different types of implants in this study. When a significant difference was found, Tukey's honestly significant difference (HSD) was further applied for a pairwise comparison. In addition, the independent t test was used to compare the BIC and BA values of the 2 healing periods (10 days vs. 28 days). Statistical software, R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria), was used for all statistical evaluations. The statistical significance level was set at α = 0.05. Ethics approval and consent to participate Ethical approval was sought from the Institutional Animal Care and Use Committee of CRONEX Co., Ltd., Hwaseong, Korea, and the animal experiment was conducted in accordance with the Animal Reporting in Vivo Experiments (ARRIVE) guidelines (202108011). Consent for publication All authors are aware of the publication of this work.
A total of 56 screw-shaped dental implant samples (28 ZrO 2 implants (one-piece) and 28 Ti implants) of the same macroscopic shape and dimensions (a diameter of 3.4 mm and a length of 8 mm) were used in this study. Also, ten ZrO 2 discs, which were 15 mm in diameter and 1 mm in thickness, were prepared to find some phase transition of ZrO 2 after surface modification. The ZrO 2 implants were manufactured using an injection molding technique (Vatech Acucera, Seoul, Korea). This process involves mixing zirconia powders with modifiers and shaping a uniform and homogeneous mixture into a mold. The Ti implants were manufactured by a computer numerical controlled (CNC) milling technique (Deep Implant System, Inc., Seongnam, Korea). Disc-shaped green compacts were prepared by cold isostatic press of powder mixtures and then sintered. Surface modification with sandblasting was performed on half of the ZrO 2 implants and the ZrO 2 discs, while half of the Ti implant samples were surface-modified with SLA treatment, i.e., sandblasted with large-grit alumina (Al 2 O 3 ) particles and etched with hydrochloric acid (SLA surface; Deep Implant System, Inc., Seongnam, Korea). After sandblasting, the ZrO 2 implants (14 implants) and discs (5 discs) were treated by hot isostatic pressing (HIP) at 1380 °C and 138 MPa. Then, the samples were divided into four experimental groups: Group 1 = IM ZrO 2 (injection-molded ZrO 2 implants or ZrO 2 discs without sandblasting modification) Group 2 = IM ZrO 2 -S (injection-molded ZrO 2 implants or ZrO 2 discs with sandblasting modification) Group 3 = Ti-turned (Ti implants without SLA modification as a negative control) Group 4 = Ti-SLA (Ti implants with SLA modification as a positive control)
The implant sample surfaces were photographed by field emission-scanning electron microscopy (FE-SEM; S-4700, Hitachi, Tokyo, Japan). Surface parameters for the sample topography were measured by confocal laser scanning microscopy (CLSM; LSM 800, Carl Zeiss AG, Oberkochen, Germany). The acquired images were analyzed using ConfoMap software, and specific areas of interest were selected. Subsequently, the surface topography was quantified in terms of Sa (arithmetical mean height of a surface), the absolute value of the difference in height of each point compared to the arithmetical mean of the surface, and Sdr (developed interfacial area ratio), the proportion of the additional surface area contributed by the texture within the defined planar area. Each sample was analyzed at 3 selected sites (the upper, middle and lower flank), the values of which were averaged, and the average value was assigned as the representative value for the sample , . The topography was measured in terms of Sa values (arithmetical mean height) and Sdr values (developed interfacial area ratio). In addition, the chemical composition of each sample was analyzed with an energy-dispersive spectroscopy (EDS) device (EMAX, Horiba, High Wycombe, United Kingdom). For evaluating the phase transition of ZrO 2 , the ZrO 2 disc surfaces were analyzed using a high resolution X-ray diffractometer (SmartLab, Rigaku, Tokyo, Japan) with Cu Kα radiation (wavelength = 1.54 Å) and 45 kV/200 mA. To quantify the molar fraction of the content of monoclinic ZrO 2 ( [12pt]{minimal}
$$X_{m}$$ X m ), the following equations were employed: [12pt]{minimal}
$$X_{m} = ( {111} ) + I_{m} ( {11} )}}{{I_{m} ( {111} ) + I_{m} ( {11} ) + I_{t} ( {101} )}}$$ X m = I m 111 + I m 1 ¯ 11 I m 111 + I m 1 ¯ 11 + I t 101 where [12pt]{minimal}
$$I_{t}$$ I t and [12pt]{minimal}
$$I_{m}$$ I m represent the intensity of the tetragonal [12pt]{minimal}
$$( {101} )$$ 101 and monoclinic [12pt]{minimal}
$$( {111} )$$ 111 and [12pt]{minimal}
$$( {11} )$$ 1 ¯ 11 peaks , . The peak intensity was obtained using MDI Jade 6 software (Materials Data Inc., Livermore, CA, USA).
Eight male New Zealand white rabbits (age: 3–4 months old; weight: 2.5–3.0 kg) were used in this in vivo study, which was approved by the Institutional Animal Research Ethics Committee of Cronex (CRONEX-IACUC: 202108011, Hwaseong, Korea) and conducted according to the Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines . All methods were performed in accordance with the relevant guidelines and regulations. The experimental animals acclimatized in separate cages for two weeks before surgery. Each rabbit received four implant samples; two implants were placed in each tibia bone of the hind legs. The ZrO 2 and Ti implants were inserted based on the split-plot design (Fig. ). For anesthesia, a combination of 15 mg/kg tiletamine hydrochloride and zolazepam hydrochloride (Zoletil 50; Virbac Korea Co., Ltd., Seoul, Korea) and 5 mg/kg xylazine (Rompun; Bayer Korea, Ltd., Seoul, Korea) was administered intramuscularly. Then, the hind legs were shaved and disinfected with an antiseptic surgical scrub of 7.5% povidone-iodine (Betadine; Korea Pharma, Seoul, Korea). After site preparation, local infiltration anesthesia of 2% lidocaine hydrochloride with 1:100,000 epinephrine (Yuhan Company, Seoul, Korea) was administered at the surgical sites. The tibial bones were exposed by full-thickness incisions from the skin to the periosteum. The surgical sites on the tibiae bones were prepared with rotating implant drills and engines under copious irrigation with sterile saline solution. The final drill size was 3.0 mm, and 32 sample implants from experimental groups 1, 2, 3 and 4 were placed with primary stability (≥ 20 Ncm) using a torque wrench, according to the manufacturer’s instructions. After the implant placement surgeries, the muscle and fascia were sutured with resorbable 4–0 Vicryl sutures (Coated Vicryl; Ethicon, Raritan, NJ, United States), and the outer dermis was closed with nylon (Blue nylon; Ailee, Busan, Korea). All rabbit specimens were housed in individual cages and administered the postoperative antibiotic prophylaxis of enrofloxacin (Biotril, Komipharm International, Siheung, Korea).
Four rabbits were sacrificed 10 days after the implant placement (Rabbit 1–4), and the remaining 4 rabbits were sacrificed 28 days after the implant placement (Rabbit 5–8) by an overdose of potassium chloride intravenously under anesthesia for histologic assessment. The bones and connective tissues that surrounded the implant samples were surgically harvested en bloc. These blocks were fixed in 10% neutral buffered formalin for two weeks and then dehydrated with ethanol, followed by embedding in light-curing resin (Technovit 7200 VLC, Kulzer, Wehrheim, Germany). A series of cutting and grinding devices (EXAKT system; EXAKT Apparatebau, Norderstedt, Germany) was used to cut and grind the embedded blocks into slides with a thickness of less than approximately 50 μm , . The slides were stained with modified Goldner’s Masson trichrome staining solution for examination under a light microscope. This staining technique allows for easy discrimination between newly formed bone (stained red) and exiting mature bone (stained blue) . The bone-to-implant interfaces were measured by the degree of BIC and bone area (BA) at the best three consecutive threads , . The histologic evaluation was performed using a light microscope (DM2700M, Leica Microsystems CMS GmbH, Wetzlar, Germany) and an attached digital camera (DMC5400, Leica Microsystems CMS GmbH, Wetzlar, Germany). An image analysis system (ImageJ 1.60, NIH, Bethesda, MD, United States) was used to analyze the acquired images.
Most of the outcome variables for data normalization were accepted when the Shapiro‒Wilk test was used ( p > 0.05). Descriptive statistics are shown as the means and standard deviations (SDs). One-way analysis of variance (ANOVA) was used to analyze differences in the mean values of the surface parameters and histomorphometric data of the different types of implants in this study. When a significant difference was found, Tukey's honestly significant difference (HSD) was further applied for a pairwise comparison. In addition, the independent t test was used to compare the BIC and BA values of the 2 healing periods (10 days vs. 28 days). Statistical software, R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria), was used for all statistical evaluations. The statistical significance level was set at α = 0.05.
Ethical approval was sought from the Institutional Animal Care and Use Committee of CRONEX Co., Ltd., Hwaseong, Korea, and the animal experiment was conducted in accordance with the Animal Reporting in Vivo Experiments (ARRIVE) guidelines (202108011).
All authors are aware of the publication of this work.
Surface characteristics The FE-SEM images of the implant surfaces of the 4 different experimental groups are shown in Fig. A. The IM ZrO 2 (Group 1) implants showed microcracks, porosities, and grain structures that are typically observed for sintered ZrO 2 . In contrast, the IM ZrO 2 -S (Group 2) implants showed very rough surfaces with slate-like profiles and lower porosities. The Ti-turned (Group 3: negative control) implants showed smooth and flat surfaces with continuous straight lines that ran in one direction. However, the Ti-SLA (Group 4: positive control) implants exhibited a rough, irregular, porous, and honeycomb-like appearance. The surface roughness parameters of the experimental groups were measured in terms of Sa and Sdr values (Fig. B,C). Based on the mean Sa values and SDs, the Ti-SLA implants had the highest Sa value (1.68 μm ± 0.07 μm), followed by the IM ZrO 2 -S implants (1.10 μm ± 0.13 μm), IM ZrO 2 implants (0.64 μm ± 0.12 μm), and Ti-turned implants (0.52 μm ± 0.21 μm). There were statistically significant differences among the Sa values of all groups, except between the IM ZrO 2 and Ti-turned groups. The mean Sdr values of the Ti-SLA implants (238.78% ± 3.03%) were significantly higher than those of the other three groups. However, there were no significant differences among the Sdr values of these three groups: IM ZrO 2 (49.04% ± 31.68%), IM ZrO 2 -S (78.77% ± 50.02%), and Ti-turned (123.66% ± 37.54%). The EDS findings are shown in Table . Titanium (Ti), carbon (C), and oxygen (O) were detected in the Ti implants, while zirconium (Zr), carbon (C), and oxygen (O) were detected in the ZrO 2 implants. The X-ray diffraction patterns of the ZrO 2 discs are shown in Fig. . ZrO 2 discs without sandblasting modification only presented the tetragonal ZrO 2 peak. ZrO 2 discs with sandblasting modification presented the [12pt]{minimal}
$$( {111} )$$ 111 and [12pt]{minimal}
$$( {11} )$$ 1 ¯ 11 peaks for the monoclinic phase (m-ZrO 2 ) and the [12pt]{minimal}
$$( {101} )$$ 101 peak for the tetragonal phase (t-ZrO 2 ). However, it was clearly seen that while the ZrO 2 discs with sandblasting modification presented both the peaks corresponding to the tetragonal and monoclinic phases, only the peak of tetragonal phase was detected after HIP. As seen in Table , the rate of the tetragonal-to-monoclinic phase transformation increased with sandblasting modification. It was found that the amount of m-ZrO 2 was 40.32% of the total ZrO 2 after sandblasting modification. After HIP, this amount of monoclinic phase was totally transformed into t-ZrO 2 . Analysis of histology and histomorphometry All the implant samples successfully osseointegrated after 10 days of healing and 28 days of healing (Supplementary Fig. ). In the histological analysis of the ZrO 2 and Ti implants stained with modified Goldner’s Masson trichrome staining solution, new bone formation was found along the bone–implant interfaces. In the cortical bone area of the tibiae, the exiting mature bones were stained blue, while newly formed immature bones were stained red and detected at the implant threads and around the mature bone (Fig. ). The mean BIC and BA values and SDs of the ZrO 2 and Ti implants at the 10-day and 28-day marks are shown in Fig. (Supplementary Tables , ). Although the BICs (%) of the IM ZrO 2 -S and Ti-SLA implants were higher than those of the Ti-turned and IM ZrO 2 implants at the 10-day mark, the differences were not statistically significant. At the 28-day mark, the BIC (%) of the Ti-SLA implants was significantly higher than that of the Ti-turned implants ( p = 0.04). However, the other groups showed no significant differences. When the mean values and SDs of BA (%) were calculated, no statistically significant differences were found among the ZrO 2 and Ti implants for both healing periods ( p > 0.05) (Fig. ).
The FE-SEM images of the implant surfaces of the 4 different experimental groups are shown in Fig. A. The IM ZrO 2 (Group 1) implants showed microcracks, porosities, and grain structures that are typically observed for sintered ZrO 2 . In contrast, the IM ZrO 2 -S (Group 2) implants showed very rough surfaces with slate-like profiles and lower porosities. The Ti-turned (Group 3: negative control) implants showed smooth and flat surfaces with continuous straight lines that ran in one direction. However, the Ti-SLA (Group 4: positive control) implants exhibited a rough, irregular, porous, and honeycomb-like appearance. The surface roughness parameters of the experimental groups were measured in terms of Sa and Sdr values (Fig. B,C). Based on the mean Sa values and SDs, the Ti-SLA implants had the highest Sa value (1.68 μm ± 0.07 μm), followed by the IM ZrO 2 -S implants (1.10 μm ± 0.13 μm), IM ZrO 2 implants (0.64 μm ± 0.12 μm), and Ti-turned implants (0.52 μm ± 0.21 μm). There were statistically significant differences among the Sa values of all groups, except between the IM ZrO 2 and Ti-turned groups. The mean Sdr values of the Ti-SLA implants (238.78% ± 3.03%) were significantly higher than those of the other three groups. However, there were no significant differences among the Sdr values of these three groups: IM ZrO 2 (49.04% ± 31.68%), IM ZrO 2 -S (78.77% ± 50.02%), and Ti-turned (123.66% ± 37.54%). The EDS findings are shown in Table . Titanium (Ti), carbon (C), and oxygen (O) were detected in the Ti implants, while zirconium (Zr), carbon (C), and oxygen (O) were detected in the ZrO 2 implants. The X-ray diffraction patterns of the ZrO 2 discs are shown in Fig. . ZrO 2 discs without sandblasting modification only presented the tetragonal ZrO 2 peak. ZrO 2 discs with sandblasting modification presented the [12pt]{minimal}
$$( {111} )$$ 111 and [12pt]{minimal}
$$( {11} )$$ 1 ¯ 11 peaks for the monoclinic phase (m-ZrO 2 ) and the [12pt]{minimal}
$$( {101} )$$ 101 peak for the tetragonal phase (t-ZrO 2 ). However, it was clearly seen that while the ZrO 2 discs with sandblasting modification presented both the peaks corresponding to the tetragonal and monoclinic phases, only the peak of tetragonal phase was detected after HIP. As seen in Table , the rate of the tetragonal-to-monoclinic phase transformation increased with sandblasting modification. It was found that the amount of m-ZrO 2 was 40.32% of the total ZrO 2 after sandblasting modification. After HIP, this amount of monoclinic phase was totally transformed into t-ZrO 2 .
All the implant samples successfully osseointegrated after 10 days of healing and 28 days of healing (Supplementary Fig. ). In the histological analysis of the ZrO 2 and Ti implants stained with modified Goldner’s Masson trichrome staining solution, new bone formation was found along the bone–implant interfaces. In the cortical bone area of the tibiae, the exiting mature bones were stained blue, while newly formed immature bones were stained red and detected at the implant threads and around the mature bone (Fig. ). The mean BIC and BA values and SDs of the ZrO 2 and Ti implants at the 10-day and 28-day marks are shown in Fig. (Supplementary Tables , ). Although the BICs (%) of the IM ZrO 2 -S and Ti-SLA implants were higher than those of the Ti-turned and IM ZrO 2 implants at the 10-day mark, the differences were not statistically significant. At the 28-day mark, the BIC (%) of the Ti-SLA implants was significantly higher than that of the Ti-turned implants ( p = 0.04). However, the other groups showed no significant differences. When the mean values and SDs of BA (%) were calculated, no statistically significant differences were found among the ZrO 2 and Ti implants for both healing periods ( p > 0.05) (Fig. ).
The roughness parameters (Sa and Sdr) of the surface-treated implants (Groups 2 and 4) were significantly higher than those of the ZrO 2 and Ti implant groups without surface treatment (Groups 1 and 3). The authors also found that the BIC% and BA% of the IM ZrO 2 implants were not significantly different from those of the Ti implants (both Ti-turned and Ti-SLA) after 10-day and 28-day healing periods. These results are in agreement with the findings of previous studies that compared SLA-treated IM ZrO 2 and Ti implants in mini pig maxillae models , canine models , and rabbit tibia models . Another significant finding was that the surface-treated ZrO 2 implants showed enhanced bone integration at the implant surface compared to the untreated ZrO 2 implants. This finding is in line with the results of other studies by Mihatovic in 2017 and Schünemann in 2019 , . In addition, the Ti-SLA implants demonstrated a significantly higher bone response than the Ti-turned implants. This finding is also comparable to findings in other studies , . It can be deduced that the rough surface microtopography of ZrO 2 implants generated by the proper surface modification can influence early bone response and long-term sustainability. In this study, the authors used 10-day and 28-day healing periods for bone formation in a rabbit tibia model based on previous studies , . These healing periods of rabbit specimens are equivalent to 1-month and 3-month healing periods of humans, according to Roberts, who demonstrated that the bone healing of rabbits was approximately three times faster than that of humans . Although rabbits are regarded as commonly used and well-established animal models for investigating the osseointegration process, some disadvantages include site limitations and mismatched microstructures when compared to those of human bone. In addition, histomorphometric analysis has some disadvantages, although BIC and BA have become the most popular parameters. For example, they are only measured in 2 dimensions without consideration of the entire implant, and they may be affected by the quality and quantity of the surrounding bones . There were slight discrepancies in the geometry of the ZrO 2 and Ti implants that might have affected the tissue reaction at the bone–implant interface. The small sample size employed in this study also presents a limitation. The absence of significant differences observed in the in vivo experiments could be attributed to this small sample size. Further studies are needed to develop methods for the predictable early bone response and long-term osseointegration of IM ZrO 2 implants with a double-blinded study design and a larger sample size. Furthermore, it is needed to investigate the optimal surface roughness of ZrO 2 implants for osseointegration in subsequent studies.
Based on the findings of this study, it is evident that appropriate surface treatment of ZrO 2 implants is essential for promoting early peri-implant bone formation. Considering the recognized advantages of simplicity, mass production capability, and economic feasibility associated with the injection molding technique, utilizing injection molded zirconia dental implants, coupled with suitable surface modifications, could present a promising alternative to conventionally manufactured titanium dental implants for future clinical applications.
Supplementary Information.
|
Supporting elder persons in rural Japanese communities through preventive home visits by nursing students: A qualitative descriptive analysis of students' reports | 2e5226bf-3af6-4bc5-b399-8d8a013b604d | 6850435 | Preventive Medicine[mh] | BACKGROUND Japan is super‐aged society with “highest life expectancy” (United Nations, ) facing a serious problem in terms of its aging population, particularly in rural areas. Elder persons (65 years or elder) are 27.3% in 2016 (Cabinet Office & Government of Japan, ), while those aged 60 or above will be 42.5% in 2050 (United Nations, ). For 54.7% of elder persons who need to receive care, caregivers are also families aged 65 years or above. If a person aged 75 or above needs medical service or other long‐term care service, 70%–90% of the cost for the service is paid by the medical system for elder senior citizens or long‐term care insurance in Japan. Increase in the aged population means a large social/financial burden (Ministry of Health, Labour and Welfare, ). The national government has proposed the community‐based integrated care system which enables the elderly to keep their own way of living at home by their final days (Mitsubishi UFJ Research & Consulting, ). The system demands nurses an increasingly critical role to support community‐dwelling elderly to prevent daily living dysfunction, hospitalization, and institutionalization. It is also important for hospital nurses to assess requirements for an inpatient to leave hospital and to live at home. Taking above into consideration, nursing education program should include the opportunities to learn daily living of community‐dwelling elderly. However, much current nursing education takes place in hospitals and other institutions, while students have limited opportunities to observe and care for the community‐dwelling elderly who are not immediately in need of visiting nursing care. Even in community nursing practicum, public health nurses usually bring students to the home‐dwelling elderly for episodic and time‐limited meeting, but not longitudinal or continuous one. Being faced with this situation, the need of change in nursing education for the above new role of nursing is argued (Japan Association of Nursing Programs in Universities, ). There is no precedent of good practice for this, although Davis and Gustafson and Pohl, Malin, and Kennell reported the efficacy of nursing students' visits to children and families in chronic health conditions. Based on the above, Oita University of Nursing and Health Sciences (OUNHS) planned and implemented an innovative clinical practice as undergraduate program of nursing named the preventive home visits practice , in order to develop community‐oriented geriatric and community health nursing competencies among students. The students regularly visit community‐dwelling elderly to talk about lifestyle and health, although medical treatment or clinical care except blood pressure measurement is not conducted in the visits. The purpose of the present article is to evaluate the education curriculum in terms of students' learning outcomes. PROGRAM DEVELOPMENT AND DESCRIPTION 2.1 Geographical description The above new education program has been carried out in areas A and B surrounding OUNHS located at the top of hill. Area A, spreading on the hillside, is a suburban area including many detached houses constructed approximately 40 years ago. Its population size is 75,000, 36% of which is aged 65 or above in 2015. Many steep slopes and stairs make it difficult for frail elderly to go out. Area B, spreading to the back of the hill, is a rural area in a semi‐mountainous region. Its population size is 45,000, 42% of which is aged 65 or above in 2015. It is difficult for the residents to leave homes without cars. These areas have some clinics and rehabilitation facilities. Most of users need to go to them by car. Most of the residents in both areas are Japanese, middle‐class people. 2.2 Preventive home visiting practice overview Outline of preventive home visiting practice was as follows. The elder residents aged 75 or above in the two areas participated in the program. A student team, consisting of a freshman, sophomore, junior, and senior, repeatedly provided monthly or bi‐monthly visits to a home‐dwelling elder person. The program has three specific aims for the students, elder participants, and community: (a) For students, to learn how the participants live in the community, and what supports their independent living. (b) For the participants, to prevent physical, mental, and social disability, and to facilitate independent living. (c) For the community, to make progress in community health, and to raise awareness as to the significance of mutual support among generations. During the visit period (approximately one hour), therefore, students talked with a participant to understand their physical, mental, and psychosocial condition and living of an elder person, considered preventive measures against disease or disability, and implemented the plans to support their health and living, adjusting the situation for each participant. For example, they talk together about the participants' personal issues (life history, family, hobby, special skills, concerning, future, etc.) and health (diet, physical exercise, daily living, pain and dysfunction, medical care, etc.). If in need, students measure blood pressure and salts in soup, cook meal, take physical exercise with participants, take a walk, or recommend a doctor. Students generally make visits on foot, by bus, or by car. They take a specially prepared home‐visit bag bearing OUNHS emblem, that includes items such as a blood‐pressure gauge, stethoscope, tape measure, stopwatch, grip strength tester, salt meter, and weight scale. Each team conducts briefing and debriefing meetings before and after the visit, in order to decide what to do on a visit, to assess their participant's situation, and to make a future plan. Each student submits a personal report in an electronic file for each visit, and also submit a final report at the end of an academic year. To prepare the program, OUNHS began a pilot study in 2013. Based on the discussion with the stakeholders such as the self‐government associations and the local government, a small preliminary model was developed using two student teams, who provided a single home visit to two participants. In the next year, a one‐year pilot project was established prior to full implementation. Eight teams of students ( n = 33) visited eight participants. OUNHS organized a committee for community participation in the development, implementation, and evaluation of preventive home visits , with members including representatives from welfare commissioners, residents' associations, social welfare councils, local medical associations, local nursing associations, community comprehensive support centers, national health insurance associations, local governments, and OUNHS faculties. By the end of 2014, OUNHS decided to revise the curriculum such that the above preventive home visit practice would be compulsory for all the nursing students at four grades, and the curriculum was applied to the undergraduate program in 2015. After the preparation, we developed guidelines for preventive home visiting practice, as follows. Eighty participants aged 75 or above, not using skilled nursing or public welfare services, were recruited from residents' associations, social welfare commissioners, and municipal and senior centers in the two areas. Since we have 80 students a year, 80 student teams were organized, consisting of a freshman, sophomore, junior, and senior. Two or three of them visit a participant monthly or bi‐monthly, and each student provides four or more visits in a year. Since a student team continuously visit the same participant throughout multiple years as long as possible, graduating seniors are replaced by matriculating freshmen, who are then mentored by their new team members, in each academic year. The goals for the students were, (a) to understand health and living among community ‐dwelling elderly, (b) to contribute the prevention for dysfunction among them through considering appropriate measures with them, and (c) to learn teamwork beyond academic grades. Since acquired knowledge and skills among students varies depending on the grade, we expected the goals should be different by grade. However, we did not stress the difference to the students. All the faculties of OUNHS commits to the program, even though their major is not nursing. Each two or three faculties supervise and support two or three student teams and confirm the reports submitted by the students. However, the faculties do not accompany the students except for asking to continue the participation next year, and do not instruct what the students to do when they visit the participants except responding the students' question. If students found a new problem to be solved in the community , OUNHS made discussion about it with the stakeholders in the community to seek solution. The guidelines were summarized in a handbook, and explained to students and faculties in a period of kick‐off orientation at the beginning of academic year 2015. After the orientation, they had a role playing session simulating home visits. Full implementation of preventive home visiting practice began in spring 2015 with 327 undergraduate nursing students visiting 80 participants. The mean age of the participants was 80.0 years. 2.3 Design, sample, and measures In the present article, we report the students' experiences through this new practice, based on their reports submitted at the end of the academic year 2015. The primary question for the report was "What did you learn through the one‐year visit practice?". All the students were given the question at the end of school year, and instructed to write about their experiences narratively using a computer and upload the reports to electronic files. As a result, most of them made the reports referring to the goals of the program. We believed that descriptive data for the simple question would best demonstrate the process and outcomes, providing useful evaluation data on which to improve the project in the early phase of implementation (Sandelowski, ), and that open‐ended student observations were very meaningful, especially for development of further evaluation instruments. We therefore conducted a qualitative descriptive study to demonstrate students' learning outcomes one year after starting the preventive home visit practicum. Identifying information was removed from the data for the purposes of analysis; however, students were informed that the reports would be used to evaluate and improve the project. We analyzed the narrative data from the reports submitted by 327 students participating from September 2016 to August 2017. 2.4 Analytic strategy Analysis of the narrative data from the student reports was conducted by four members of our research team (RI, KH, MK, and TK). First, the researchers read the reports carefully to understand their meaning. Next, we extracted portions of the reports related to learning objectives for the preventive home visit practice and coded the data, grouping the coded data into categories. Three themes were then generated by collecting common codes and comparing and examining relationships between categories. Qualitative research software (MAXQDA 12) was used to organize and assist with the analysis. RI and KH categorized and interpreted the data, the validity of which was confirmed by MK and TK through the discussion among the research team. We used the criteria of credibility, transferability, and confirmability (Lincoln & Guba, ) to ensure trustworthiness and rigor of the findings. Deep descriptions were written to clarify and present the results to address credibility and transferability of findings. Further, we considered the implications of these results (transferability). We were cautious that the results might be prejudiced; therefore, the analysis was discussed with several researchers with experience in qualitative research (confirmability). 2.5 Ethical consideration The study protocol was approved by the Committee on Research Ethics and Safety of OUNHS, in accordance with the Research Ethics Guideline of OUNHS and the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Ministry of Health, Labour, & Welfare, ). Geographical description The above new education program has been carried out in areas A and B surrounding OUNHS located at the top of hill. Area A, spreading on the hillside, is a suburban area including many detached houses constructed approximately 40 years ago. Its population size is 75,000, 36% of which is aged 65 or above in 2015. Many steep slopes and stairs make it difficult for frail elderly to go out. Area B, spreading to the back of the hill, is a rural area in a semi‐mountainous region. Its population size is 45,000, 42% of which is aged 65 or above in 2015. It is difficult for the residents to leave homes without cars. These areas have some clinics and rehabilitation facilities. Most of users need to go to them by car. Most of the residents in both areas are Japanese, middle‐class people. Preventive home visiting practice overview Outline of preventive home visiting practice was as follows. The elder residents aged 75 or above in the two areas participated in the program. A student team, consisting of a freshman, sophomore, junior, and senior, repeatedly provided monthly or bi‐monthly visits to a home‐dwelling elder person. The program has three specific aims for the students, elder participants, and community: (a) For students, to learn how the participants live in the community, and what supports their independent living. (b) For the participants, to prevent physical, mental, and social disability, and to facilitate independent living. (c) For the community, to make progress in community health, and to raise awareness as to the significance of mutual support among generations. During the visit period (approximately one hour), therefore, students talked with a participant to understand their physical, mental, and psychosocial condition and living of an elder person, considered preventive measures against disease or disability, and implemented the plans to support their health and living, adjusting the situation for each participant. For example, they talk together about the participants' personal issues (life history, family, hobby, special skills, concerning, future, etc.) and health (diet, physical exercise, daily living, pain and dysfunction, medical care, etc.). If in need, students measure blood pressure and salts in soup, cook meal, take physical exercise with participants, take a walk, or recommend a doctor. Students generally make visits on foot, by bus, or by car. They take a specially prepared home‐visit bag bearing OUNHS emblem, that includes items such as a blood‐pressure gauge, stethoscope, tape measure, stopwatch, grip strength tester, salt meter, and weight scale. Each team conducts briefing and debriefing meetings before and after the visit, in order to decide what to do on a visit, to assess their participant's situation, and to make a future plan. Each student submits a personal report in an electronic file for each visit, and also submit a final report at the end of an academic year. To prepare the program, OUNHS began a pilot study in 2013. Based on the discussion with the stakeholders such as the self‐government associations and the local government, a small preliminary model was developed using two student teams, who provided a single home visit to two participants. In the next year, a one‐year pilot project was established prior to full implementation. Eight teams of students ( n = 33) visited eight participants. OUNHS organized a committee for community participation in the development, implementation, and evaluation of preventive home visits , with members including representatives from welfare commissioners, residents' associations, social welfare councils, local medical associations, local nursing associations, community comprehensive support centers, national health insurance associations, local governments, and OUNHS faculties. By the end of 2014, OUNHS decided to revise the curriculum such that the above preventive home visit practice would be compulsory for all the nursing students at four grades, and the curriculum was applied to the undergraduate program in 2015. After the preparation, we developed guidelines for preventive home visiting practice, as follows. Eighty participants aged 75 or above, not using skilled nursing or public welfare services, were recruited from residents' associations, social welfare commissioners, and municipal and senior centers in the two areas. Since we have 80 students a year, 80 student teams were organized, consisting of a freshman, sophomore, junior, and senior. Two or three of them visit a participant monthly or bi‐monthly, and each student provides four or more visits in a year. Since a student team continuously visit the same participant throughout multiple years as long as possible, graduating seniors are replaced by matriculating freshmen, who are then mentored by their new team members, in each academic year. The goals for the students were, (a) to understand health and living among community ‐dwelling elderly, (b) to contribute the prevention for dysfunction among them through considering appropriate measures with them, and (c) to learn teamwork beyond academic grades. Since acquired knowledge and skills among students varies depending on the grade, we expected the goals should be different by grade. However, we did not stress the difference to the students. All the faculties of OUNHS commits to the program, even though their major is not nursing. Each two or three faculties supervise and support two or three student teams and confirm the reports submitted by the students. However, the faculties do not accompany the students except for asking to continue the participation next year, and do not instruct what the students to do when they visit the participants except responding the students' question. If students found a new problem to be solved in the community , OUNHS made discussion about it with the stakeholders in the community to seek solution. The guidelines were summarized in a handbook, and explained to students and faculties in a period of kick‐off orientation at the beginning of academic year 2015. After the orientation, they had a role playing session simulating home visits. Full implementation of preventive home visiting practice began in spring 2015 with 327 undergraduate nursing students visiting 80 participants. The mean age of the participants was 80.0 years. Design, sample, and measures In the present article, we report the students' experiences through this new practice, based on their reports submitted at the end of the academic year 2015. The primary question for the report was "What did you learn through the one‐year visit practice?". All the students were given the question at the end of school year, and instructed to write about their experiences narratively using a computer and upload the reports to electronic files. As a result, most of them made the reports referring to the goals of the program. We believed that descriptive data for the simple question would best demonstrate the process and outcomes, providing useful evaluation data on which to improve the project in the early phase of implementation (Sandelowski, ), and that open‐ended student observations were very meaningful, especially for development of further evaluation instruments. We therefore conducted a qualitative descriptive study to demonstrate students' learning outcomes one year after starting the preventive home visit practicum. Identifying information was removed from the data for the purposes of analysis; however, students were informed that the reports would be used to evaluate and improve the project. We analyzed the narrative data from the reports submitted by 327 students participating from September 2016 to August 2017. Analytic strategy Analysis of the narrative data from the student reports was conducted by four members of our research team (RI, KH, MK, and TK). First, the researchers read the reports carefully to understand their meaning. Next, we extracted portions of the reports related to learning objectives for the preventive home visit practice and coded the data, grouping the coded data into categories. Three themes were then generated by collecting common codes and comparing and examining relationships between categories. Qualitative research software (MAXQDA 12) was used to organize and assist with the analysis. RI and KH categorized and interpreted the data, the validity of which was confirmed by MK and TK through the discussion among the research team. We used the criteria of credibility, transferability, and confirmability (Lincoln & Guba, ) to ensure trustworthiness and rigor of the findings. Deep descriptions were written to clarify and present the results to address credibility and transferability of findings. Further, we considered the implications of these results (transferability). We were cautious that the results might be prejudiced; therefore, the analysis was discussed with several researchers with experience in qualitative research (confirmability). Ethical consideration The study protocol was approved by the Committee on Research Ethics and Safety of OUNHS, in accordance with the Research Ethics Guideline of OUNHS and the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Ministry of Health, Labour, & Welfare, ). RESULTS Three themes, “Understanding wellness and prevention”, “Understanding the life experiences in community and learning the characteristics of community”, and “Teamwork”, emerged through the qualitative descriptive analysis on the report data as below. The themes, categories, and raw data are described in detail below. Illustrative quotations are provided for a rich description of the findings. 3.1 Understanding wellness and prevention For the elderly to continue to live heathy in the community, students learned the importance of “wellness” and “prevention”. Two categories emerged within this major theme: “ Importance of preventive health ” and “ Perspective of wellness .” 3.1.1 Importance of preventive health Students described how they learned the importance of preventive health, namely preventing something bad, through the home visiting practice. One student noted the following: “I think it is important for elder persons to continue to live with pleasure. I learned that preventive commitment is important so that the elderly [do not] become sick” (Senior). As expected, the students fully grasped the idea of prevention of illness and the importance of understanding the lifestyle of community‐dwelling elderly. 3.1.2 Perspective of wellness This category described the concepts of wellness experienced by elder people living in rural community. Students' learning for this category involves the following three sub‐categories. Students had not necessarily considered healthy individuals as the target of nursing care in previous practice areas. One noted: “Through this visiting practice, I gained a good reason to think about what kind of relationship can be had with healthy people” (Sophomore). This means she and an elder person talked together on health and living, and that the relationship was different from that she experienced in clinical settings, where she presented something, such as advice for prevention, to the elder person. In other words, they found healthy people are also targets for nursing. Students learned that they should not only pay attention to individuals' disability (i.e., problems), but also should respect their strength. One student reflected on the following: “I learned that it is important to find the strength of elder persons and to extend them, not [only] to find out their problems. As a result, they will be able to keep their current standard of living” (Senior). As already mentioned, another student wrote: “I think it is important for elder persons to continue to live with pleasure.” (Senior). Prior to beginning this practice, most students seemed to think that most elder persons were weak, according to their narratives. However, they learned that there were many healthy elder persons in the community through home visits, and shifted the image of elder persons. One junior student reflected on the following: “I found that community residents were living a vigorous life, even some with a disease” (Junior). Thus, students had the opportunity to learn about the concept of wellness and prevention through involvement with healthy elder persons in home visiting practice. Furthermore, they began to learn the community in more depth. 3.2 Understanding the life experience in community and learning the characteristics of community Students conducted outreach and contacted people in the community, and learned the lives of people there. Two categories emerged within the theme: “Understanding the lives of community‐dwelling elder persons” and “Learning the community.” 3.2.1 Understanding the lives of community‐dwelling elder persons Students reported that they learned the difference between the lives of inpatients and those of non‐hospitalized elder people. They found the factors such as interaction with the neighborhood that supported healthy life in the community. One student stated the following: “I could understand the usual life of the elder persons, which I could not learn within a regular hospital practice” (Sophomore). 3.2.2 Learning the community Students had strong affinity for the community where they practiced. The students firstly found what they need to understand the community. They were able to learn community issues through the comprehensive, longitudinal practice. They realized that practical knowledge about the needs in each community was essential to provide appropriate care to elder individuals living in the community, and also to the entire community. Area A was suburban area near OUNHS, while the area B was quite rural. Early in the program, freshmen students recognized basic issues of community life, as one young student noted: “I realized that it is very difficult for individuals living in an area where transportation is poor” (Freshman). Another student aptly characterized the relationship between environment and health: “I thought that active engagement in health management behaviors is very good not only for the individuals' health, but also for revitalizing the community” (Freshman). Through meeting with community members, home visits, and sometimes making wellness presentation to community members, the students began to understand the characteristics of the communities where the residents lived. The students secondly recognized a patient as a resident living in the community. They had most often encountered patients in hospital settings, and had not known much about their everyday lives in the community. They got able to view the community from the perspective of an elder resident as a citizen rather than that of a nurse. A senior student was able to evaluate the patient as a resident in a more critical manner, stating the following: “I became able to imagine the lives of patients at home, and learned the importance of considering preventive intervention methods against health problems. I believe that this will help sustain comfort at home after discharge” (Senior). As above, the students have begun to understand the lives of individuals outside the typical nursing acute care or clinic setting. Through their observations and activities visiting people at home, they began to understand the environment surrounding the residents and affecting their health. A junior student later reported that she was able to apply the learning from home visit to the long‐stay patients she met in other clinical practicum such as psychiatric and mental health nursing. 3.3 Teamwork The students reflected on the learning experiences through membership in their teams. The preventive home visit program purposefully placed the students in an assigned team that together planned, visited elder persons, and evaluated their visits. This team approach gave the students good understanding of how to cooperate with other members and the importance of teamwork in health care practice. Two categories emerged within the theme, “ Essentials for building interpersonal relationships ” and “ Necessities for those in charge of a medical care team .” 3.3.1 Essentials for building interpersonal relationships To develop a group process in each student team, interpersonal relationships were very important. One student wrote the following: “Through my visiting practice, I had an opportunity to consider the feelings not only of the elder person, but also of my teammates” (Junior). Another noted, “It is an opportunity to consider the viewpoints of elder persons, as well as of other students (i.e., the differences between juniors and seniors)” (Junior). 3.3.2 Necessities for those in charge of a medical care team Leadership was another important lesson learned by the students through the team process. For example, one student wrote the following: “By grasping the viewpoints [of students in different grades] in comparison with my own grade level, I am able to understand how collaboration can be done sufficiently” (Junior). Meanwhile, a senior student became aware of leadership by experiencing the group process: “As a result of cooperating with teammates and fully making use of the special skills offered by each grade, I experienced the efforts necessary for team medicine” (Senior). Understanding wellness and prevention For the elderly to continue to live heathy in the community, students learned the importance of “wellness” and “prevention”. Two categories emerged within this major theme: “ Importance of preventive health ” and “ Perspective of wellness .” 3.1.1 Importance of preventive health Students described how they learned the importance of preventive health, namely preventing something bad, through the home visiting practice. One student noted the following: “I think it is important for elder persons to continue to live with pleasure. I learned that preventive commitment is important so that the elderly [do not] become sick” (Senior). As expected, the students fully grasped the idea of prevention of illness and the importance of understanding the lifestyle of community‐dwelling elderly. 3.1.2 Perspective of wellness This category described the concepts of wellness experienced by elder people living in rural community. Students' learning for this category involves the following three sub‐categories. Students had not necessarily considered healthy individuals as the target of nursing care in previous practice areas. One noted: “Through this visiting practice, I gained a good reason to think about what kind of relationship can be had with healthy people” (Sophomore). This means she and an elder person talked together on health and living, and that the relationship was different from that she experienced in clinical settings, where she presented something, such as advice for prevention, to the elder person. In other words, they found healthy people are also targets for nursing. Students learned that they should not only pay attention to individuals' disability (i.e., problems), but also should respect their strength. One student reflected on the following: “I learned that it is important to find the strength of elder persons and to extend them, not [only] to find out their problems. As a result, they will be able to keep their current standard of living” (Senior). As already mentioned, another student wrote: “I think it is important for elder persons to continue to live with pleasure.” (Senior). Prior to beginning this practice, most students seemed to think that most elder persons were weak, according to their narratives. However, they learned that there were many healthy elder persons in the community through home visits, and shifted the image of elder persons. One junior student reflected on the following: “I found that community residents were living a vigorous life, even some with a disease” (Junior). Thus, students had the opportunity to learn about the concept of wellness and prevention through involvement with healthy elder persons in home visiting practice. Furthermore, they began to learn the community in more depth. Importance of preventive health Students described how they learned the importance of preventive health, namely preventing something bad, through the home visiting practice. One student noted the following: “I think it is important for elder persons to continue to live with pleasure. I learned that preventive commitment is important so that the elderly [do not] become sick” (Senior). As expected, the students fully grasped the idea of prevention of illness and the importance of understanding the lifestyle of community‐dwelling elderly. Perspective of wellness This category described the concepts of wellness experienced by elder people living in rural community. Students' learning for this category involves the following three sub‐categories. Students had not necessarily considered healthy individuals as the target of nursing care in previous practice areas. One noted: “Through this visiting practice, I gained a good reason to think about what kind of relationship can be had with healthy people” (Sophomore). This means she and an elder person talked together on health and living, and that the relationship was different from that she experienced in clinical settings, where she presented something, such as advice for prevention, to the elder person. In other words, they found healthy people are also targets for nursing. Students learned that they should not only pay attention to individuals' disability (i.e., problems), but also should respect their strength. One student reflected on the following: “I learned that it is important to find the strength of elder persons and to extend them, not [only] to find out their problems. As a result, they will be able to keep their current standard of living” (Senior). As already mentioned, another student wrote: “I think it is important for elder persons to continue to live with pleasure.” (Senior). Prior to beginning this practice, most students seemed to think that most elder persons were weak, according to their narratives. However, they learned that there were many healthy elder persons in the community through home visits, and shifted the image of elder persons. One junior student reflected on the following: “I found that community residents were living a vigorous life, even some with a disease” (Junior). Thus, students had the opportunity to learn about the concept of wellness and prevention through involvement with healthy elder persons in home visiting practice. Furthermore, they began to learn the community in more depth. Understanding the life experience in community and learning the characteristics of community Students conducted outreach and contacted people in the community, and learned the lives of people there. Two categories emerged within the theme: “Understanding the lives of community‐dwelling elder persons” and “Learning the community.” 3.2.1 Understanding the lives of community‐dwelling elder persons Students reported that they learned the difference between the lives of inpatients and those of non‐hospitalized elder people. They found the factors such as interaction with the neighborhood that supported healthy life in the community. One student stated the following: “I could understand the usual life of the elder persons, which I could not learn within a regular hospital practice” (Sophomore). 3.2.2 Learning the community Students had strong affinity for the community where they practiced. The students firstly found what they need to understand the community. They were able to learn community issues through the comprehensive, longitudinal practice. They realized that practical knowledge about the needs in each community was essential to provide appropriate care to elder individuals living in the community, and also to the entire community. Area A was suburban area near OUNHS, while the area B was quite rural. Early in the program, freshmen students recognized basic issues of community life, as one young student noted: “I realized that it is very difficult for individuals living in an area where transportation is poor” (Freshman). Another student aptly characterized the relationship between environment and health: “I thought that active engagement in health management behaviors is very good not only for the individuals' health, but also for revitalizing the community” (Freshman). Through meeting with community members, home visits, and sometimes making wellness presentation to community members, the students began to understand the characteristics of the communities where the residents lived. The students secondly recognized a patient as a resident living in the community. They had most often encountered patients in hospital settings, and had not known much about their everyday lives in the community. They got able to view the community from the perspective of an elder resident as a citizen rather than that of a nurse. A senior student was able to evaluate the patient as a resident in a more critical manner, stating the following: “I became able to imagine the lives of patients at home, and learned the importance of considering preventive intervention methods against health problems. I believe that this will help sustain comfort at home after discharge” (Senior). As above, the students have begun to understand the lives of individuals outside the typical nursing acute care or clinic setting. Through their observations and activities visiting people at home, they began to understand the environment surrounding the residents and affecting their health. A junior student later reported that she was able to apply the learning from home visit to the long‐stay patients she met in other clinical practicum such as psychiatric and mental health nursing. Understanding the lives of community‐dwelling elder persons Students reported that they learned the difference between the lives of inpatients and those of non‐hospitalized elder people. They found the factors such as interaction with the neighborhood that supported healthy life in the community. One student stated the following: “I could understand the usual life of the elder persons, which I could not learn within a regular hospital practice” (Sophomore). Learning the community Students had strong affinity for the community where they practiced. The students firstly found what they need to understand the community. They were able to learn community issues through the comprehensive, longitudinal practice. They realized that practical knowledge about the needs in each community was essential to provide appropriate care to elder individuals living in the community, and also to the entire community. Area A was suburban area near OUNHS, while the area B was quite rural. Early in the program, freshmen students recognized basic issues of community life, as one young student noted: “I realized that it is very difficult for individuals living in an area where transportation is poor” (Freshman). Another student aptly characterized the relationship between environment and health: “I thought that active engagement in health management behaviors is very good not only for the individuals' health, but also for revitalizing the community” (Freshman). Through meeting with community members, home visits, and sometimes making wellness presentation to community members, the students began to understand the characteristics of the communities where the residents lived. The students secondly recognized a patient as a resident living in the community. They had most often encountered patients in hospital settings, and had not known much about their everyday lives in the community. They got able to view the community from the perspective of an elder resident as a citizen rather than that of a nurse. A senior student was able to evaluate the patient as a resident in a more critical manner, stating the following: “I became able to imagine the lives of patients at home, and learned the importance of considering preventive intervention methods against health problems. I believe that this will help sustain comfort at home after discharge” (Senior). As above, the students have begun to understand the lives of individuals outside the typical nursing acute care or clinic setting. Through their observations and activities visiting people at home, they began to understand the environment surrounding the residents and affecting their health. A junior student later reported that she was able to apply the learning from home visit to the long‐stay patients she met in other clinical practicum such as psychiatric and mental health nursing. Teamwork The students reflected on the learning experiences through membership in their teams. The preventive home visit program purposefully placed the students in an assigned team that together planned, visited elder persons, and evaluated their visits. This team approach gave the students good understanding of how to cooperate with other members and the importance of teamwork in health care practice. Two categories emerged within the theme, “ Essentials for building interpersonal relationships ” and “ Necessities for those in charge of a medical care team .” 3.3.1 Essentials for building interpersonal relationships To develop a group process in each student team, interpersonal relationships were very important. One student wrote the following: “Through my visiting practice, I had an opportunity to consider the feelings not only of the elder person, but also of my teammates” (Junior). Another noted, “It is an opportunity to consider the viewpoints of elder persons, as well as of other students (i.e., the differences between juniors and seniors)” (Junior). 3.3.2 Necessities for those in charge of a medical care team Leadership was another important lesson learned by the students through the team process. For example, one student wrote the following: “By grasping the viewpoints [of students in different grades] in comparison with my own grade level, I am able to understand how collaboration can be done sufficiently” (Junior). Meanwhile, a senior student became aware of leadership by experiencing the group process: “As a result of cooperating with teammates and fully making use of the special skills offered by each grade, I experienced the efforts necessary for team medicine” (Senior). Essentials for building interpersonal relationships To develop a group process in each student team, interpersonal relationships were very important. One student wrote the following: “Through my visiting practice, I had an opportunity to consider the feelings not only of the elder person, but also of my teammates” (Junior). Another noted, “It is an opportunity to consider the viewpoints of elder persons, as well as of other students (i.e., the differences between juniors and seniors)” (Junior). Necessities for those in charge of a medical care team Leadership was another important lesson learned by the students through the team process. For example, one student wrote the following: “By grasping the viewpoints [of students in different grades] in comparison with my own grade level, I am able to understand how collaboration can be done sufficiently” (Junior). Meanwhile, a senior student became aware of leadership by experiencing the group process: “As a result of cooperating with teammates and fully making use of the special skills offered by each grade, I experienced the efforts necessary for team medicine” (Senior). DISCUSSION Home visit is an important approach of nursing (Smith, ), although nursing students tend to show “little understanding of a patient's life” during conventional (i.e., hospital) practice (Yamamoto, Doi, Sugimoto, Sugimoto, & Kimoto, ). We believe that the practice such as the preventive home visits can alleviate the problems in conventional nursing education, broadening the perspective to include community life, health, and wellness, even in freshmen. The students had begun to learn the importance of "wellness," "prevention," and "life" concepts relevant to community‐dwelling elderlies. This agrees with the recent review (Zhang et al., ) that students learn the daily life of elder persons in rural area by directly contacting the community‐dwelling elder people. The students also learned the characteristics of communities and the importance of teamwork in health care. The above learning is also similar to that Pohl et al. observed for the students who visited the children and families in chronic health conditions. As a result, each of the original aims were reflected, as illustrated by students themselves. First of all, the students grasped the viewpoints illustrated by the category, Perspective of wellness . This category demonstrated that the above practice leads the students to “wellness” thinking that refers to the ways in which individuals gain a state of health. Wellness is the process of moving toward integration of human functioning, maximization of human potential (Heiss & Walden, ). It is important not to miss the strength related to aging, such as “including daily life pleasures” and experiences that help maintain self‐efficacy. By grasping the strength of elder persons, life‐enhancing interventions can be implemented (Yamamoto, Kodama, & Kamei, ). Instead of learning these viewpoints, students are often first exposed to hospitalized patients in their practical training called ward training in Japan (Nozaki, ). This gives priority to problem‐solving thinking related to diseases, and it seems difficult for the students to view a patient as a “living person.” However, if a student assumes that age‐related changes (physical and mental) in most of the home‐dwelling elderly are due to abnormalities or problems, the orientation is unsuitable for practical training with the residents. From a perspective of lifespan development, adjustments to physical changes and changes to the social structure (i.e., retirement) are viewed as normative life events. In future nursing education, "It is indispensable to master the ability to grasp the community.” (Japanese Nursing Association, ). To achieve this, our project seems to be a good first step to support future endeavors toward community nursing care, because this practice enables the students to maintain longitudinal contact with home‐dwelling elderlies, facilitating their ability to view the individuals as “living persons.” This agrees with a literature review on nursing students (Zhang et al., ) concluding that positive images of aging can be facilitated by increasing contact with elderlies. Student in the present study similarly seems to change their viewpoints toward aging. We therefore plan to continue this practice as basic nursing education to lead students to better understanding for the home‐dwelling elderly. Second, previous researches show that some nursing students, even at a senior grade, are not interested in discharge support for patients, lacking awareness and understanding for community experiences related to patients' livelihood after discharge (Matsuzaki et al., ). However, the present results show that some students, even freshmen, could acquire the perspectives toward livelihood of the elderly, as illustrated in the category, Understanding the lives of community‐dwelling elder persons . This indicates a new approach that strengthen students' ability to see their patients as people with lives outside the hospital setting. Third, the present data described how undergraduate nursing students can identify important community issues and work toward solving them, as illustrated in the category, Learning the community . Medical care in Japan will shift toward more community‐oriented nursing care (Iwasaki et al., ). Students in OUNHS will be able to learn that community nursing care is the center of healthcare to elder persons in future. Since the aged population size continues to expand (Cabinet Office & Government of Japan, ), nursing professionals need to be well‐versed in the community and the living environments of the elderly. Thus, establishing the knowledge in early stage of educational program is essential for nurses to promote health and well‐being among community‐dwelling elderlies. These competencies are consistent with current public/community health nursing definitions and practice (American Public Health Association, ; Williams, ). In order to present the knowledge and views to nursing students, faculty must change to obtain a community perspective (Wade & Hayes, ). Finally, the effects of the present nursing practice emerged through inter‐grade or inter‐generational cooperation, as shown by the theme, Teamwork . In an idiom of conventional nursing education curricula, learning teamwork often means working together with classmates in the same grade/level and courses. Actual teamwork for nurses is, however, inter‐generational and multi‐occupational collaboration, as widely recommended (Feather, Carr, Garletts, & Reising, ; World Health Organization, ). The teamwork is also essential to public health and community health nursing practice. Many professionals from a variety of public health disciplines as well as nonprofessional key persons work together to serve the community. In our practice, putting students from different grade from freshman to senior into one team seemed to make the students naturally collaborate each other in the above practice. However, the preventive home visit practice was the first experience for all the students and faculty members. When the freshmen in the present study advance to seniors, they may find the different meaning of teamwork from that reported in the present article. This should be investigated in future. 4.1 Limitations and future directions One limitation of the present study was the short time frame for evaluating the intervention efficacy. Students were assessed after only one year of implementation. Although the students' reports were excellent documents about their learning in the program, we could not confirm the meaning of ambiguous phrases. Furthermore, their perceptions and behaviors in future practical settings should be evaluated again to confirm long‐term efficiency. The evaluation also will clarify the difference in learning among grades. The results of the continuous evaluation and a long‐term follow‐up study will be reported elsewhere. It is another task to investigate how the above experiences impact later nursing educational experiences. We expect favorable effects on later experiences, as suggested by some reports shown above. We also should be careful in generalization of the present results, because they come from the data of OUNHS located in a local area. On the other hand, the effects of the above program on faculties and participants have not been evaluated yet. As for the latter, another follow‐up study is in progress: namely, the elderly who participated in the above program will be compared with a control group in terms of aging and interest in their health. Although students found some problems to be solved in the community, such as public traffic inconvenience through the above practice, they will be reported elsewhere. Limitations and future directions One limitation of the present study was the short time frame for evaluating the intervention efficacy. Students were assessed after only one year of implementation. Although the students' reports were excellent documents about their learning in the program, we could not confirm the meaning of ambiguous phrases. Furthermore, their perceptions and behaviors in future practical settings should be evaluated again to confirm long‐term efficiency. The evaluation also will clarify the difference in learning among grades. The results of the continuous evaluation and a long‐term follow‐up study will be reported elsewhere. It is another task to investigate how the above experiences impact later nursing educational experiences. We expect favorable effects on later experiences, as suggested by some reports shown above. We also should be careful in generalization of the present results, because they come from the data of OUNHS located in a local area. On the other hand, the effects of the above program on faculties and participants have not been evaluated yet. As for the latter, another follow‐up study is in progress: namely, the elderly who participated in the above program will be compared with a control group in terms of aging and interest in their health. Although students found some problems to be solved in the community, such as public traffic inconvenience through the above practice, they will be reported elsewhere. CONCLUSION Through the qualitative descriptive study on the nursing students' reports for the preventive home visits practice , the favorable effects of the program on their viewpoints toward community‐dwelling elderly was demonstrated. The students acquired multiple perspectives for understanding elder persons, such as wellness, prevention, community life, and team practice. If the effects are sustainable, they are in line with societal needs. The present study may be an important step forward to strengthen community health nursing competencies and practice in Japan. We will the above challenges and report the results of more long‐term and multi‐dimensional evaluation. |
Evaluating large language models as patient education tools for inflammatory bowel disease: A comparative study | 522b811a-96fe-4b9d-adad-632ebbcc0ada | 11752706 | Patient Education as Topic[mh] | Inflammatory bowel disease (IBD) is a chronic condition affecting millions of people worldwide. It is characterized by inflammation of the gastrointestinal tract, leading to a range of symptoms and potential complications and causing psychological problems in patients. As IBD is a lifelong disease, many patients and their families have questions and concerns regarding the disease, such as its causes, diagnosis, treatment, and long-term management. Notably, the rapid development of artificial intelligence (AI) and natural language processing in recent years has led to the emergence of chat-based AI systems that provide information and support to patients with various health conditions. The vast parameter space of large language models (LLMs) makes them highly versatile in the medical and medical education field. Numerous studies have shown that LLMs have already surpassed traditional methods, providing significant assistance in medical processes, such as diagnosis and treatment. AI use has been explored in various medical fields. For instance, Hillmann et al found that chat-based AI provided accurate and easily understandable information regarding atrial fibrillation and cardiac implantable electronic devices for patients, highlighting the potential of AI for patient education and shared decision making. In gastroenterology, Kerbage et al reported that the responses of ChatGPT, a popular AI language model, were generally accurate in providing information on common gastrointestinal diseases and could potentially impact patient education and provider workload. Specifically focusing on IBD, Sciberras et al reported that although ChatGPT responses were largely accurate in providing information with regards to the European Crohn's and Colitis Organization guidelines, some discrepancies were observed. Thus, further validation and refinement of AI-based patient information systems are needed. Due to the specialized nature of medical consultations, the accuracy and rigor of LLM-generated answers require evaluation by medical experts. Currently, there is a lack of recognized methods for assessing LLMs in the field of medical consultation. In our previous study, we assessed the feasibility of three popular LLMs in different languages as counseling tools for Helicobacter pylori infection, and the LLMs provided satisfactory responses to Helicobacter pylori infection-related questions. Nevertheless, studies on comparisons between different LLM models remain scarce, and the accuracy and comprehensibility of these systems for providing IBD-related information have not been studied extensively. In this study, we aimed to compare whether the IBD-related medical consultation answers generated by different LLMs can accurately and rigorously meet the needs of patients. This study also sought to provide some evaluation methodology ideas for future researchers. This study's design complies with the ethical principles of the Declaration of Helsinki. Since the study was observational and data were anonymous, informed consent was waived. This study followed a comprehensive research process involving several key stages: Question generation, input into LLMs, evaluation by medical experts, readability assessment by patients, and statistical analysis. The overall assessment methodology is illustrated in Figure . Question generation An expert panel of two experienced gastroenterologists (Yan-Qing Li and Xiu-Li Zuo), each with more than 10 years of expertise in the field of IBD, generated a set of questions that reflected the common concerns and information needs of IBD patients. These questions encompass various critical aspects of IBD, including its introduction, diagnosis, treatment, and follow-up; the questions also provide a comprehensive reflection of the effectiveness of LLMs in delivering information related to IBD. The question generation process was conducted as follows. Literature review: The expert panel conducted an exhaustive review of contemporary medical literature, clinical guidelines, and educational resources to identify the most pertinent and frequently inquired topics for patients with IBD. Patient feedback: Drawing on their clinical experience, the expert panel incorporated patient feedback, including common inquiries during consultations and those raised within support groups and educational platforms, to generate the questions. Question categorization: Based on the literature review and patient feedback, the expert panel identified four main categories of questions: Introduction to IBD, diagnosis, treatment plan, and follow-up. These categories were selected to cover key aspects of IBD management and ensure that the generated questions comprehensively addressed patient needs. Question refinement: The panel collaboratively refined the questions to ensure clarity, brevity, and clinical relevance, aiming to make them accessible to a non-specialist audience. Question validation: The expert panel consulted a group of five patients with IBD, recruited from the IBD clinic of Qilu Hospital, and two other gastroenterologists from the Department of Gastroenterology to validate the generated questions. Their feedback was incorporated to refine the questions further and ensure that the questions effectively captured the perspectives and information needs of the patient community. Table presents the final set of 15 questions. Input into LLMs ChatGPT-4.0 (GPT-4, OpenAI, San Francisco, California, United States), Claude-3-Opus (Anthropic, San Francisco, CA, United States), and Gemini-1.5-Pro (AI21 Labs, Tel Aviv, Israel) were the three LLMs selected to evaluate the performance of the AI-generated materials. ChatGPT-4.0, an advanced AI model developed by OpenAI, is known for its high-quality language generation and understanding capabilities. Claude-3-Opus, created by Anthropic, is another state-of-the-art AI model that has shown promising results in various language tasks. Gemini-1.5-Pro, developed by AI21 Labs, is a powerful language model that has demonstrated strong performance in question answering and content generation. The final set of questions were used as prompts for the three LLMs to generate detailed answers. Each LLM generated one detailed answer for each question, resulting in 45 answers (15 questions × 3 LLMs). Evaluation by medical experts A panel of three gastroenterologists (Guo Jing, Liu Jun, and Liu Han), who were not involved in generating the questions, evaluated the answers from the LLMs. Using a tailored scoring rubric, the experts assessed each answer across three dimensions: Accuracy, completeness, and correlation. Accuracy assesses the agreement between the diagnostic outcomes provided by the LLMs and actual conditions, Completeness referred to the inclusion of all essential information in the answers, whereas correlation focused on the relevance of answers to questions. The evaluation criteria are presented in Table , and the score range for each evaluation dimension was 1-5 points, with higher scores indicating a better performance. Readability assessment To ensure that the information was presented in an accessible manner, readability of the answers was assessed using the Flesch Reading Ease (FRE) score, a standard metric for estimating text readability. The FRE score was calculated based on two main factors, sentence length and number of syllables per word, using the following formula: FRE = 206.835 - (1.015 × ASL) - (84.6 × ASW) where ASL is the average number of words per sentence, and ASW is the average number of syllables per word. ASL and ASW are calculated as follows: ASL = total words/total sentences; ASW = total syllables/total words. The FRE scores range from 0 to 100, with higher scores indicating greater readability. A score between 60 and 70 is considered easily understandable, whereas a score between 90 and 100 indicates that the text is very easy to understand. Patient evaluation We further evaluated the comprehensibility of the answers. This evaluation was done by a distinct cohort of three IBD patients. They had not been involved in the previous question generation process. These patients rated the understandability of the information using a standardized scale. The scoring criteria for their evaluation of the answers can be found in Table . Statistical analysis The analysis of all data was conducted utilizing GraphPad Prism software, specifically version 9.5.1, which is a product of GraphPad Software. Additionally, SPSS software, version 26.0, from IBM Corp., was also employed. Data recording and organization were facilitated by Microsoft Excel 2021. In terms of continuous variables, statistical measures including the median and range were applied to characterize the central tendencies and distribution patterns. Furthermore, graphical illustrations were used to offer a clear and thorough depiction of the data. The Friedman test was applied to assess differences among three groups. Hypothesis testing was conducted to ascertain significant intergroup differences, with the null hypothesis being rejected at the P < 0.05 significance level. An expert panel of two experienced gastroenterologists (Yan-Qing Li and Xiu-Li Zuo), each with more than 10 years of expertise in the field of IBD, generated a set of questions that reflected the common concerns and information needs of IBD patients. These questions encompass various critical aspects of IBD, including its introduction, diagnosis, treatment, and follow-up; the questions also provide a comprehensive reflection of the effectiveness of LLMs in delivering information related to IBD. The question generation process was conducted as follows. Literature review: The expert panel conducted an exhaustive review of contemporary medical literature, clinical guidelines, and educational resources to identify the most pertinent and frequently inquired topics for patients with IBD. Patient feedback: Drawing on their clinical experience, the expert panel incorporated patient feedback, including common inquiries during consultations and those raised within support groups and educational platforms, to generate the questions. Question categorization: Based on the literature review and patient feedback, the expert panel identified four main categories of questions: Introduction to IBD, diagnosis, treatment plan, and follow-up. These categories were selected to cover key aspects of IBD management and ensure that the generated questions comprehensively addressed patient needs. Question refinement: The panel collaboratively refined the questions to ensure clarity, brevity, and clinical relevance, aiming to make them accessible to a non-specialist audience. Question validation: The expert panel consulted a group of five patients with IBD, recruited from the IBD clinic of Qilu Hospital, and two other gastroenterologists from the Department of Gastroenterology to validate the generated questions. Their feedback was incorporated to refine the questions further and ensure that the questions effectively captured the perspectives and information needs of the patient community. Table presents the final set of 15 questions. ChatGPT-4.0 (GPT-4, OpenAI, San Francisco, California, United States), Claude-3-Opus (Anthropic, San Francisco, CA, United States), and Gemini-1.5-Pro (AI21 Labs, Tel Aviv, Israel) were the three LLMs selected to evaluate the performance of the AI-generated materials. ChatGPT-4.0, an advanced AI model developed by OpenAI, is known for its high-quality language generation and understanding capabilities. Claude-3-Opus, created by Anthropic, is another state-of-the-art AI model that has shown promising results in various language tasks. Gemini-1.5-Pro, developed by AI21 Labs, is a powerful language model that has demonstrated strong performance in question answering and content generation. The final set of questions were used as prompts for the three LLMs to generate detailed answers. Each LLM generated one detailed answer for each question, resulting in 45 answers (15 questions × 3 LLMs). A panel of three gastroenterologists (Guo Jing, Liu Jun, and Liu Han), who were not involved in generating the questions, evaluated the answers from the LLMs. Using a tailored scoring rubric, the experts assessed each answer across three dimensions: Accuracy, completeness, and correlation. Accuracy assesses the agreement between the diagnostic outcomes provided by the LLMs and actual conditions, Completeness referred to the inclusion of all essential information in the answers, whereas correlation focused on the relevance of answers to questions. The evaluation criteria are presented in Table , and the score range for each evaluation dimension was 1-5 points, with higher scores indicating a better performance. To ensure that the information was presented in an accessible manner, readability of the answers was assessed using the Flesch Reading Ease (FRE) score, a standard metric for estimating text readability. The FRE score was calculated based on two main factors, sentence length and number of syllables per word, using the following formula: FRE = 206.835 - (1.015 × ASL) - (84.6 × ASW) where ASL is the average number of words per sentence, and ASW is the average number of syllables per word. ASL and ASW are calculated as follows: ASL = total words/total sentences; ASW = total syllables/total words. The FRE scores range from 0 to 100, with higher scores indicating greater readability. A score between 60 and 70 is considered easily understandable, whereas a score between 90 and 100 indicates that the text is very easy to understand. We further evaluated the comprehensibility of the answers. This evaluation was done by a distinct cohort of three IBD patients. They had not been involved in the previous question generation process. These patients rated the understandability of the information using a standardized scale. The scoring criteria for their evaluation of the answers can be found in Table . The analysis of all data was conducted utilizing GraphPad Prism software, specifically version 9.5.1, which is a product of GraphPad Software. Additionally, SPSS software, version 26.0, from IBM Corp., was also employed. Data recording and organization were facilitated by Microsoft Excel 2021. In terms of continuous variables, statistical measures including the median and range were applied to characterize the central tendencies and distribution patterns. Furthermore, graphical illustrations were used to offer a clear and thorough depiction of the data. The Friedman test was applied to assess differences among three groups. Hypothesis testing was conducted to ascertain significant intergroup differences, with the null hypothesis being rejected at the P < 0.05 significance level. Accuracy Table shows that the average accuracy scores for all three models were slightly above 4, indicating that most of the answers were correct. However, the SD for Claude-3-Opus (4.02) was approximately 0.5, higher than that for ChatGPT-4.0 and Gemini-1.5-Pro (both 4.06), suggesting a higher likelihood of a controversial diagnosis. Furthermore, the median score for Claude-3-Opus was only 3 (Table ). Figure shows that while all three LLMs demonstrated identical maximum scores, the median and third quartile scores for ChatGPT-4.0 and Gemini-1.5-Pro surpassed those of Claude-3-Opus. Additionally, Claude-3-Opus had a notably lower minimum score than ChatGPT-4.0 and Gemini-1.5-Pro. This suggests a marked disparity in the output precision between Claude-3-Opus and the other two LLMs. The Friedman test revealed no significant differences between ChatGPT-4.0 and Gemini-1.5-Pro ( P > 0.05), but significant differences between the two LLMs and Claude-3-Opus ( P < 0.05), with particularly pronounced distinction between ChatGPT-4.0 and Claude-3-Opus ( P < 0.01). Completeness Table shows that all three LLMs achieved a mean score of approximately 4, indicating their capability to provide comprehensive analyses and integrative diagnoses. Claude-3-Opus had a slightly higher mean value (4.27) and lower standard deviation (0.58) than ChatGPT-4.0 (4.24 and 0.64, respectively) and Gemini-1.5-Pro (4.20 and 0.66, respectively). The median score for all LLMs was 4. ChatGPT-4.0 and Gemini-1.5-Pro consistently showed no significant differences, indicating similar capacities for understanding and recognizing medical issues. Figure illustrates a modest yet statistically significant difference in completeness scores between Gemini-1.5-Pro and Claude-3-Opus ( P < 0.05), with Claude-3-Opus showing lower score quantiles than ChatGPT-4.0 and Gemini-1.5-Pro. Correlation ChatGPT4.0 attained the highest mean score of 4.57, which is 0.03 and 0.05 higher than Gemini-1.5-Pro and Claude-3-Opus, respectively. Both ChatGPT4.0 and Gemini-1.5-Pro achieved a maximum median score of 5, whereas Claude-3-Opus scored 4, indicating a notable difference in content relevance. Within the graphical representation, this disparity was further accentuated, with Claude-3-Opus's quantiles trailing ChatGPT-4.0 and Gemini-1.5-Pro by 1-2 points (Figure ). A highly significant difference was observed between ChatGPT-4.0 and Claude-3-Opus ( P < 0.001), and a significant difference was also observed between Gemini-1.5-Pro and Claude-3-Opus ( P < 0.01). These results indicate that Claude-3-Opus provided answers with a comparatively lower level of professionalism and medical expertise than ChatGPT-4.0 and Gemini-1.5-Pro - the two LLMs with negligible difference in their performance. Patient evaluation Claude-3-Opus received the highest ratings (median score: 5; mean score: 4.56), indicating that the patients found their answers to be the most comprehensible. ChatGPT-4.0 and Gemini-1.5-Pro showed minor differences in perceived comprehensibility, with median scores of 4 and mean scores of 4.02 and 4.07, respectively. Figure shows that Claude-3-Opus had a higher median score than the other two LLMs. The variance analysis indicated significant differences between Claude-3-Opus and ChatGPT-4.0 ( P < 0.01) and between Claude-3-Opus and Gemini-1.5-Pro ( P < 0.05). Readability assessment Claude-3-Opus demonstrated superior readability, with a median FRE score of 51.47 and mean score of 54.44, higher than ChatGPT-4.0 (mean: 31.10, median: 32.25) and Gemini-1.5-Pro (mean: 32.79, median: 36.92; Figure ). This finding highlights Claude-3-Opus's significant advantage in generating coherent and fluent results. The Claude-3-Opus score was > 50% higher than those of ChatGPT-4.0 and Gemini-1.5-Pro. Additionally, the variance analysis demonstrated extremely significant differences between Claude-3-Opus and the other two LLMs ( P < 0.0001 for Claude-3-Opus vs ChatGPT-4.0, and P < 0.01 for Claude-3-Opus vs Gemini-1.5-Pro). Table shows that the average accuracy scores for all three models were slightly above 4, indicating that most of the answers were correct. However, the SD for Claude-3-Opus (4.02) was approximately 0.5, higher than that for ChatGPT-4.0 and Gemini-1.5-Pro (both 4.06), suggesting a higher likelihood of a controversial diagnosis. Furthermore, the median score for Claude-3-Opus was only 3 (Table ). Figure shows that while all three LLMs demonstrated identical maximum scores, the median and third quartile scores for ChatGPT-4.0 and Gemini-1.5-Pro surpassed those of Claude-3-Opus. Additionally, Claude-3-Opus had a notably lower minimum score than ChatGPT-4.0 and Gemini-1.5-Pro. This suggests a marked disparity in the output precision between Claude-3-Opus and the other two LLMs. The Friedman test revealed no significant differences between ChatGPT-4.0 and Gemini-1.5-Pro ( P > 0.05), but significant differences between the two LLMs and Claude-3-Opus ( P < 0.05), with particularly pronounced distinction between ChatGPT-4.0 and Claude-3-Opus ( P < 0.01). Table shows that all three LLMs achieved a mean score of approximately 4, indicating their capability to provide comprehensive analyses and integrative diagnoses. Claude-3-Opus had a slightly higher mean value (4.27) and lower standard deviation (0.58) than ChatGPT-4.0 (4.24 and 0.64, respectively) and Gemini-1.5-Pro (4.20 and 0.66, respectively). The median score for all LLMs was 4. ChatGPT-4.0 and Gemini-1.5-Pro consistently showed no significant differences, indicating similar capacities for understanding and recognizing medical issues. Figure illustrates a modest yet statistically significant difference in completeness scores between Gemini-1.5-Pro and Claude-3-Opus ( P < 0.05), with Claude-3-Opus showing lower score quantiles than ChatGPT-4.0 and Gemini-1.5-Pro. ChatGPT4.0 attained the highest mean score of 4.57, which is 0.03 and 0.05 higher than Gemini-1.5-Pro and Claude-3-Opus, respectively. Both ChatGPT4.0 and Gemini-1.5-Pro achieved a maximum median score of 5, whereas Claude-3-Opus scored 4, indicating a notable difference in content relevance. Within the graphical representation, this disparity was further accentuated, with Claude-3-Opus's quantiles trailing ChatGPT-4.0 and Gemini-1.5-Pro by 1-2 points (Figure ). A highly significant difference was observed between ChatGPT-4.0 and Claude-3-Opus ( P < 0.001), and a significant difference was also observed between Gemini-1.5-Pro and Claude-3-Opus ( P < 0.01). These results indicate that Claude-3-Opus provided answers with a comparatively lower level of professionalism and medical expertise than ChatGPT-4.0 and Gemini-1.5-Pro - the two LLMs with negligible difference in their performance. Claude-3-Opus received the highest ratings (median score: 5; mean score: 4.56), indicating that the patients found their answers to be the most comprehensible. ChatGPT-4.0 and Gemini-1.5-Pro showed minor differences in perceived comprehensibility, with median scores of 4 and mean scores of 4.02 and 4.07, respectively. Figure shows that Claude-3-Opus had a higher median score than the other two LLMs. The variance analysis indicated significant differences between Claude-3-Opus and ChatGPT-4.0 ( P < 0.01) and between Claude-3-Opus and Gemini-1.5-Pro ( P < 0.05). Claude-3-Opus demonstrated superior readability, with a median FRE score of 51.47 and mean score of 54.44, higher than ChatGPT-4.0 (mean: 31.10, median: 32.25) and Gemini-1.5-Pro (mean: 32.79, median: 36.92; Figure ). This finding highlights Claude-3-Opus's significant advantage in generating coherent and fluent results. The Claude-3-Opus score was > 50% higher than those of ChatGPT-4.0 and Gemini-1.5-Pro. Additionally, the variance analysis demonstrated extremely significant differences between Claude-3-Opus and the other two LLMs ( P < 0.0001 for Claude-3-Opus vs ChatGPT-4.0, and P < 0.01 for Claude-3-Opus vs Gemini-1.5-Pro). This study aimed to evaluate the accuracy and comprehensibility of three LLMs, ChatGPT-4.0, Gemini-1.5-Pro, and Claude-3-Opus, in providing information related to IBD. The results indicate that these models performed well in terms of accuracy, completeness, and correlation, with average scores above 4 out of 5. Our findings were consistent with the results of several related studies, indicating that LLMs have strong capabilities in generating material well-suited to human reading. Additionally, each model has unique strengths and weaknesses. In the evaluation by medical experts, ChatGPT and Gemini showed higher accuracy than Claude, which corroborates the affirmation of ChatGPT model accuracy in the study conducted by Kerbage et al . In the patient evaluation, Claude-3-Opus received the highest comprehensibility scores, indicating that the patients found their answers to be the most understandable. This finding was further supported by the readability assessments. This may be related to Claude's specialized training in text comprehension. Regarding response to highly technical or subjective questions, all three models had issues with both accuracy and readability. ChatGPT uses obscure language that is difficult to understand, whereas Gemini and Claude omit important information, leading to patient misunderstanding. This suboptimal expression of professional information could be the biggest obstacle to the independent use of LLMs by patients without doctors. Moreover, the inherent black-box nature of AI technology exacerbates this risk and introduces additional uncertainties. Overall, LLMs have shown significant potential in patient education and can handle IBD-related educational tasks. However, the differences in completeness and readability among different models highlight the need for continuous AI technology development to achieve trustworthiness and reliability, which engender confidence. Notabaly, medical professionals should be aware of the limitations of these tools and should use them as supplementary resources for professional medical advice rather than as replacements. This study has several strengths, including the use of a systematic approach to generate a comprehensive set of IBD-related questions, participation of medical experts and patients in the evaluation, and comparison between multiple state-of-the-art LLMs. However, we did not group the patients owing to the limited number of patients, thereby overlooking the potential bias arising from different availability of data for LLMs pertaining to different patient subgroups. In future studies, we plan to expand the sample size to ensure better representation across age, ethnicity, and disease severity. In addition, we plan to collect more clinical cohort data to comprehensively evaluate the application of LLMs in different patient groups. We will also conduct longitudinal observations to assess the long-term impact of LLMs on patient disease awareness and health status. Future studies should focus on improving the completeness and reliability of LLMs in the context of medical counseling. Such research could involve the development of specialized medical LLMs trained on large datasets of medical literature and guidelines. In addition, involving healthcare professionals in the training and evaluation of these models could help ensure that the information provided is accurate, complete, and relevant. Our study presented a thorough evaluation of three advanced LLMs in the context of education for patients with IBD. Although the models showed promise in terms of accuracy and comprehensibility, there is room for improvement, particularly in ensuring the completeness of the information provided. As AI continues to develop, it is essential to balance the benefits of these tools with those of rigorous validation and refinement in clinical practice. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.