title: Antidepressant and mental health care utilization in pregnant women with depression and/or anxiety: an interrupted time-series analysis

abstract:
ObjectivesTo assess mental health care utilization patterns during and after pregnancy in women with depression and/or anxiety in Norway according to antidepressant fill trajectories in pregnancy.

MethodWe conducted a registry-linkage cohort study of pregnancies within women having outpatient visit for depression and/or anxiety and antidepressant fills in the six months prior to pregnancy identified from four national registries of Norway (2009-2018). Number of consultations for depression/anxiety per 100 pregnancies as proxy of mental health care utilization were modelled using interrupted time-series analysis with first month into pregnancy and first month after delivery as interruption points. We investigated the time window including six months prior to pregnancy, eight months into pregnancy and one year postpartum. Antidepressant fill trajectories in the corresponding time window were identified using longitudinal k-means trajectory modelling.

ResultsThe cohort included 8,460 pregnancies within 8,062 women with depression/anxiety. We observed reduced mental health care utilization when pregnant women entered the course of pregnancy (negative slopes during pregnancy for all psychiatric specialists and psychologists). The declines were observed for all antidepressant fill trajectories (i.e., discontinuers and continuers) except interrupters (i.e., discontinued then resumed treatment). We found increased mental health care utilization in the postpartum year, notably in interrupters (positive slopes in consultation rates with specialists of outpatient clinics and public-contracted psychiatrists)..

ConclusionsPregnancy was associated with reduced mental health care utilization regardless of whether antidepressant treatment was maintained during pregnancy or not. Increases in mental health care utilization were observed in the postpartum year, especially in interrupters.

SIGNIFICANT OUTCOMES- Pregnancy is associated with reduced mental health care utilization in pregnant women with depression and/or anxiety in Norway.
- The reduction in mental health care utilization during pregnancy was observed for all antidepressant fill trajectories, except for interrupters.
- Increased in mental health care utilization in the postpartum year was observed, especially among those who discontinued then resumed antidepressant treatment.

LIMITATIONS- The reasons for antidepressant discontinuation, and whether that was due to symptom remission were not assessed.
- It was not possible to determine whether reduced mental health care utilization was associated with undertreatment during pregnancy.
- It was not possible to measure directly the use of psychosocial interventions and psychotherapy.


title: A brief analysis of the COVID-19 death data in Malaysia

abstract: In December 2019, the first cases of Coronavirus Disease 2019 (COVID-19) were detected in Wuhan, China. Since then, COVID-19 begun to spread rapidly all over the world. On March 11, 2020, the World Health Organisation declared COVID-19 a pandemic. As of September 7, 2021, there were over 220 million confirmed COVID-19 cases globally, with more than 4.6 million deaths. Malaysia reported 2,067,327 confirmed cases with 22,743 deaths. Given the severity of the pandemic, the Ministry of Health Malaysia has stepped up in its efforts in handling the pandemic locally by sharing the COVID-19 related data on the GitHub, enabling transparent data sharing. This enables timely data analysis and quick decision to better understand the COVID-19 situation in this country. This article aims to provide a quick analysis of the death and vaccination data provided by the Malaysian Ministry of Health and to provide useful insight into the analysis.

category: public and global health


title: A Multidisciplinary Prematurity Research Cohort Study

abstract:
BackgroundWorldwide, 10% of babies are born preterm, defined as a live birth before 37 weeks of gestation. Preterm birth is the leading cause of neonatal death, and survivors face lifelong risks of adverse outcomes. New approaches with large sample sizes are needed to identify strategies to predict and prevent preterm birth. The primary aims of the Washington University Prematurity Research Cohort Study were to conduct three prospective projects addressing possible causes of preterm birth and provide data and samples for future research.

Study DesignPregnant patients were recruited into the cohort between January 2017 and January 2020. Consenting patients were enrolled into the study before 20 weeks gestation and followed through delivery. Participants completed demographic and lifestyle surveys; provided maternal blood, placenta samples, and cord blood; and participated in up to three projects focused on underlying physiology of preterm birth: cervical imaging (Project 1), circadian rhythms (Project 2), and uterine magnetic resonance imaging and electromyometrial imaging (Project 3).

ResultsA total of 1260 participants were enrolled and delivered during the study period. Of the participants, 706 (56%) were Black/African American, 494 (39%) were nulliparous, and 185 (15%) had a previous preterm birth. Of the 1260 participants, 1220 (97%) delivered a live infant. Of the 1220 with a live birth, 163 (14.1%) had preterm birth, of which 74 (6.1%) were spontaneous preterm birth. Of the 1220 participants with a live birth, 841 participated in cervical imaging, 1047 contributed data and/or samples on circadian rhythms, and 39 underwent uterine magnetic resonance imaging. Of the 39, 25 underwent electromyometrial imaging.

ConclusionWe demonstrate feasibility of recruiting and retaining a diverse cohort in a complex prospective, longitudinal study throughout pregnancy. The extensive clinical, imaging, survey, and biologic data obtained will be used to explore cervical, uterine, and endocrine physiology of preterm birth and can be used to develop novel approaches to predict and prevent preterm birth.

category: obstetrics and gynecology


title: Initial Analysis of Viral Dynamics and Circulating Viral Variants During the mRNA-1273 Phase 3 COVE Trial

abstract: This analysis assessed the impact of mRNA-1273 vaccination on the viral dynamics of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the ongoing Coronavirus Efficacy (COVE) trial. mRNA-1273 vaccination significantly reduced SARS-CoV-2 viral copy number (95% confidence interval [CI]) by 100-fold on the day of diagnosis (4.1 [3.4-4.8] versus placebo (6.2 [6.0-6.4] log10 copies/ml). Median times to undetectable viral copies were 4 days for mRNA-1273 and 7 for placebo. Vaccination also reduced the burden of disease and infection scores. Vaccine efficacies (95% CI) during the trial against SARS-CoV-2 variants circulating in the US were 82.4% (40.4%-94.8%) for Epsilon and Gamma, and 81.2% (36.1%-94.5%) for the Epsilon variants. The detection of other respiratory viruses during the trial was similar between groups. In those who became SARS-CoV-2 infected, the reduction of viral load after mRNA-1273 vaccination is potentially correlated to the risk of transmission, which has not been assessed in this study.

category: infectious diseases


title: Stroke recovery phenotyping through network trajectory approaches and graph neural networks

abstract: Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which presents challenges for standard multivariate regression approaches. This has hindered stroke researchers ability to achieve an integrated picture of the complex time-evolving interactions amongst symptoms. Here we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application.

category: health informatics


title: Comparison of pretraining models and strategies for health-related social media text classification

abstract:
MotivationPretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks. There is a need to benchmark such models for targeted NLP tasks, and to explore effective pretraining strategies to improve machine learning performance.

ResultsIn this work, we addressed the task of health-related social media text classification. We benchmarked five models--RoBERTa, BERTweet, TwitterBERT, BioClinical_BERT, and BioBERT on 22 tasks. We attempted to boost performance for the best models by comparing distinct pretraining strategies--domain-adaptive pretraining (DAPT), source-adaptive pretraining (SAPT), and topic-specific pretraining (TSPT). RoBERTa and BERTweet performed comparably in most tasks, and better than others. For pretraining strategies, SAPT performed better or comparable to the off-the-shelf models, and significantly outperformed DAPT. SAPT+TSPT showed consistently high performance, with statistically significant improvement in one task. Our findings demonstrate that RoBERTa and BERTweet are excellent off-the-shelf models for health-related social media text classification, and extended pretraining using SAPT and TSPT can further improve performance.

Availability and implementationSource code for our model and data preprocessing is available under the Github repository https://github.com/yguo0102/transformer_dapt_sapt_tapt. Datasets must be obtained from original sources, as described in supplementary material.

Supplementary informationSupplementary data are available at Bioinformatics online.


title: Standardized incidence ratio of the COVID-19 pandemic: a case study in a Midwestern state

abstract:
MotivationThe Coronavirus disease 2019 (COVID-19) has made a dramatic impact around the world, with some communities facing harsher outcomes than others. We sought to understand how counties in the state of South Dakota (SD) fared compared to expected based on a reference population and what factors contributed to negative outcomes from the pandemic in SD.

MethodsThe Standardized Incidence Ratios (SIR) of all counties, using age-adjusted and crude adjusted hospitalization and death rates were computed using the SD age-adjusted rate as a reference population. In addition, a penalized generalized linear regression model was used to identify factors that are associated with COVID-19 hospitalization and death rates. This model was then used to compute a new SIR after controlling for other socio-demographic and -economic factors.

ResultsWe identified counties that had more or less severe outcomes than what would be expected based on the rate of SD after age adjustment. Additionally, race, education, and testing rate were some of the significant factors associated with the outcome. The SIR values after controlling for these additional factors showed change in magnitude from the range of 4 times more severe to 1.5 times more severe out-come than what is expected. Interestingly the lower end of this interval did not have a major change.

ConclusionThe age adjusted SIR model used in this study allowed for the identification of counties with more or less severe than what is expected based on the state rate. These counties tended to be those with high nonwhite percentage, which mostly included counties with American Indian reservations. Although several predictors are associated with hospitalization and deaths, the penalized model confirmed what is already reported in literature that race and education level have a very high association with the outcome variables. As can be expected the further adjusted SIR mostly changed in those counties with higher than expected outcomes. We believe that these results may provide useful information to improve the implementation of mitigation strategies to curb the damage of this or future pandemics by providing a way for data-driven resource allocation.


title: An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph

abstract: Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical classification tasks. However, only a few studies addressed the localization of abnormal findings from CXR scans, which is essential in explaining the image-level classification to radiologists. Additionally, the actual impact of AI algorithms on the diagnostic performance of radiologists in clinical practice remains relatively unclear. To bridge these gaps, we developed an explainable deep learning system called VinDr-CXR that can classify a CXR scan into multiple thoracic diseases and, at the same time, localize most types of critical findings on the image. VinDr-CXR was trained on 51,485 CXR scans with radiologist-provided bounding box annotations. It demonstrated a comparable performance to experienced radiologists in classifying 6 common thoracic diseases on a retrospective validation set of 3,000 CXR scans, with a mean area under the receiver operating characteristic curve (AUROC) of 0.967 (95% confidence interval [CI]: 0.958-0.975). The sensitivity, specificity, F1-score, false-positive rate (FPR), and false-negative rate (FNR) of the system at the optimal cutoff value were 0.933 (0.898-0.964), 0.900 (0.887-0.911), 0.631 (0.589-0.672), 0.101 (0.089- 0.114) and 0.067 (0.057-0.102), respectively. For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80.2% at the rate of 1.0 false-positive lesion identified per scan. A prospective study was also conducted to measure the clinical impact of the VinDr-CXR in assisting six experienced radiologists. The results indicated that the proposed system, when used as a diagnosis supporting tool, significantly improved the agreement between radiologists themselves with an increase of 1.5% in mean Fleiss Kappa. We also observed that, after the radiologists consulted VinDr-CXRs suggestions, the agreement between each of them and the system was remarkably increased by 3.3% in mean Co-hens Kappa. Altogether, our results highlight the potentials of the proposed deep learning system as an effective assistant to radiologists in clinical practice. Part of the dataset used for developing the VinDr-CXR system has been made publicly available at https://physionet.org/content/vindr-cxr/1.0.0/.


title: Computational analysis of quantitative echocardiographic assessments of functional mitral regurgitation: Proximal Isovelocity Surface Area methods

abstract:
While proximal isovelocity surface area (PISA) method is one of the most common echocardiographic methods for quantitative mitral regurgitation (MR) assessment, accurate MR quantification remains challenging. This study examined the theoretical background of PISA, performed virtual echocardiography on computer models of functional MR, and quantified different sources of errors in PISA. For regurgitant flow rate measurement, the conventional 2D hemispherical PISA caused significant underestimation due to underestimation of PISA area, the multiplane 2D hemiellipsoidal and hemicylindrical PISA provided improved accuracy with better assumptions on PISA contour shape. With the direct capture of PISA area, the 3D-PISA was found to be the most accurate. However, it should be noted that PISA method is subject to systematic underestimation due to the Doppler angle effect, and systematic overestimation due to the "flow direction angle" between the regurgitant flow direction and the PISA contour normal direction. For regurgitant volume quantification, integrated PISA, when performed properly, was able to capture the dynamic MR and therefore was more accurate than peak PISA. In specific, integrated PISA using the sum of regurgitant flow rates is recommended.

ObjectivesThe aim of this study was to evaluate the accuracy of different proximal isovelocity surface area (PISA) methods, examine their theoretical background, and quantify multiple sources of error in functional mitral regurgitation (MR) assessment.

BackgroundWhile PISA method is one of the most common echocardiographic methods for MR severity assessment, it is associated with multiple sources of errors, and accurate MR quantification remains challenging.

MethodsFive functional MR (FMR) computer models were created, validated and treated as phantom models. The phantom models have fully resolved and detailed flow fields in the left atrium (LA), left ventricle (LV) and cross the mitral valve, from which the reference values of mitral regurgitant flow rate and regurgitant volume can be obtained. The virtual PISA measurements (i.e., 3D and 2D PISA) were performed on the phantom models assuming optimal echo probe angulation and positioning. The results of different PISA methods were compared with the reference values.

ResultsFor regurgitant flow rate measurements, compared to the reference values, excellent correlations were observed for 3D-PISA (R = 0.97, bias -24.4 {+/-} 55.5 ml/s), followed by multiplane 2D hemicylindrical (HC)-PISA (R = 0.88, bias -24.1 {+/-} 85.4 ml/s) and hemiellipsoidal (HE)-PISA (R = 0.91, bias -55.7 {+/-} 96.6 ml/s), while weaker correlations were observed for single plane 2D hemispherical (HS)-PISA with large underestimation (PLAX view: R = 0.71, bias -77.6 {+/-} 124.5 ml/s; A2Ch view: R = 0.69, bias -52.0 {+/-} 122.0 ml/s; A4Ch view: R = 0.82, bias -65.5 {+/-} 107.3 ml/s). For regurgitant volume (RV) quantification, integrated PISA presented improved accuracy over peak PISA for all PISA methods. For 3D-PISA, the bias in RV improved from -12.7 {+/-} 7.8 ml (peak PISA) to -2.1 {+/-} 5.3 ml (integrated PISA).

ConclusionsIn FMR, conventional single plane 2D HS-PISA significantly underestimated MR, multiplane 2D PISA (HE-PISA and HC-PISA) improved the accuracy, and 3D-PISA is the most accurate. To better capture the dynamic feature of MR, integrated PISA using the sum of regurgitant flow rates is recommended.

category: cardiovascular medicine


title: Analysis of overdispersion in airborne transmission of Covid-19

abstract: Superspreading events and overdispersion are hallmarks of the Covid-19 pandemic. To gain insight into the nature and controlling factors of these superspreading events and heterogeneity in transmission, we conducted mechanistic modeling of SARS-CoV-2 transmission by infectious aerosols using real-world occupancy data from a large number of full-service restaurants in ten US metropolises. Including a large number of factors that influence disease transmission in these settings, we demonstrate the emergence of a stretched tail in the probability density function of secondary infection numbers indicating strong heterogeneity in individual infectivity. Derived analytical results further demonstrate that variability in viral loads and variability in occupancy, together, lead to overdispersion in the number of secondary infections arising from individual index cases. Our analysis, connecting mechanistic understanding of SARS-CoV-2 transmission by aerosols with observed large-scale epidemiological characteristics of Covid-19 outbreaks, adds an important dimension to the mounting body of evidence with regards to the determinants of airborne transmission of SARS-CoV-2 by aerosols in indoor settings.

category: epidemiology


title: Sex-specific associations between type 2 diabetes incidence and exposure to dioxin and dioxin-like pollutants: a meta-analysis

abstract: The relationship between persistent organic pollutants (POPs), including dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs), and diabetes incidence in adults has been extensively studied. However, significant variability exists in the reported associations both between and within studies. Emerging data from rodent studies suggest that dioxin exposure disrupts glucose homeostasis in a sex-specific manner. Thus, we performed a meta-analysis of relevant epidemiological studies to investigate whether there are sex-specific associations between dioxin or DL-PCB exposure and type 2 diabetes incidence. Articles were organized into the following subcategories: data stratified by sex (16%), unstratified data (56%), and data from only 1 sex (16% male, 12% female). We also considered whether exposure occurred either abruptly at high levels through a contamination event ("disaster exposure") or chronically at background levels ("non-disaster exposure"). Only 8 studies compared associations between dioxin/DL-PCB exposure and diabetes risk in males versus females within the same population. When all sex-stratified or single sex studies were considered in the meta-analysis, the summary odds ratio (OR) for increased diabetes risk was similar between females and males (1.78 and 1.95, respectively) when comparing exposed to reference populations, suggesting that this relationship is not sex-specific. However, when we considered disaster-exposed populations separately, the association differed substantially between sexes, with females showing a much higher OR than males (2.86 and 1.59, respectively). Moreover, the association between dioxin/DL-PCB exposure and diabetes was stronger for females than males in disaster-exposed populations. In contrast, both sexes had significantly increased ORs in non-disaster exposure populations and the OR for females was lower than males (1.40 and 2.02, respectively). Our review emphasizes the importance of considering sex differences, as well as the mode of pollutant exposure, when exploring the relationship between pollutant exposure and diabetes in epidemiological studies.


title: Fc-gamma receptor expression and cytokine response to intravenous human immunoglobulin in mothers and neonates

abstract:
ObjectiveThe use of intravenous human immunoglobulin (IVIg) as adjuvant therapy for sepsis has been shown efficacious in adults, but its use in pregnant women and newborns is controversial. Fc gamma receptors (Fc{gamma}Rs) support the ability of IVIg to stimulate the synthesis of inflammatory mediators and promote phagocytosis by leukocytes, however, the Fc{gamma}Rs expression is differential between adults and neonates. We aimed to explore the effect of IVIg in monocytes and neutrophils from mother and neonates in whole blood cultures.

Study designWhole blood from adults, maternal, and neonates were incubated with LPS and/or IVIg. After 0, 24, and 48 hours, we measured the expression of Fc{gamma}Rs (CD16, CD32, and CD64) and bacterial phagocytosis by monocytes and neutrophils. Also, the concentration of pro-inflammatory cytokines/chemokines was determined.

ResultsFc{gamma}Rs expression is quite similar among groups, and the LPS or IVIg challenge did not change the Fc{gamma}Rs expression on monocytes and neutrophils. Also, the LPS or IVIg challenge did not modify phagocytosis capacity in any group. However, IVIg induces a higher IL-8 response in neonates than in adults.

ConclusionOur results suggest that the IL-8 response to IVIg in whole blood from neonates is not dependent on differential Fc{gamma}R expression.

Key messagesO_LIIVIg challenge in neonates or adults does not induce Fc{gamma}R change expression on monocytes or neutrophils
C_LIO_LIIVIg induces higher IL-8 response in neonates than in adults
C_LI

category: pediatrics


title: First experiences of whole-head OP-MEG recordings from a patient with Epilepsy

abstract: Optically Pumped Magnetometer based Magnetoencephalography (OP-MEG) has significant potential for clinical use in pre-surgical planning in epilepsy. Unlike current clinical MEG, the sensors do not require cryogenic cooling and so can be placed directly on the patients head. This allows the patient to move during the recording and means that the sensor positions can be chosen to suit the patients head shape and suspected epileptogenic focus. However, OP-MEG is a new technology, and more work is needed to demonstrate this potential. We present OP-MEG recordings from a patient (male in their 30s) with a radiologically identifiable focal cortical dysplasia (FCD) in the right superior frontal sulcus approximately 1.9 cm3 in volume. Previous scalp EEG studies and prolonged video-EEG telemetry did not identify any interictal epileptiform abnormalities. We recorded 30 minutes of OP-MEG with 62-channel, whole-head sensor coverage. During the experiment, the patients head was unconstrained. We localised interictal epileptiform discharges (IEDs) from this recording with a beamformer and by fitting a dipole to the averaged IED data. Both the beamformer peak and dipole fit locations were within 2.3 cm of the MRI lesion boundary. This single subject, proof-of-concept recording provides further evidence that OP-MEG can be a useful and minimally invasive tool in the clinical evaluation of epilepsy.

category: radiology and imaging


title: Enhancing the brain's emotion regulation capacity with a randomised trial of a 5-week heart rate variability biofeedback intervention

abstract:
Heart rate variability is a robust biomarker of emotional well-being, consistent with the shared brain networks regulating emotion regulation and heart rate. While high heart rate oscillatory activity clearly indicates healthy regulatory brain systems, can increasing this oscillatory activity also affect brain function? To test this possibility, we randomly assigned 106 young adult participants to one of two 5-week interventions involving daily biofeedback that either increased heart rate oscillations (Osc+ condition) or had little effect on heart rate oscillations (Osc- condition) and examined effects on brain activity during rest and during regulating emotion. While there were no significant changes in the right amygdala-medial prefrontal cortex (MPFC) functional connectivity (our primary outcome), the Osc+ intervention increased left amygdala-MPFC functional connectivity and functional connectivity in emotion-related resting-state networks during rest. It also increased down-regulation of activity in somatosensory brain regions during an emotion regulation task. The Osc- intervention did not have these effects. In this healthy cohort, the two conditions did not differentially affect anxiety, depression or mood. These findings indicate that heart rate oscillatory activity not only provides a measure of the current state of regulatory brain systems but also changes emotion network coordination in the brain.

Significance StatementPeople whose breathing makes their heart rate oscillate more (leading to higher heart rate variability or HRV) generally have better regulated emotion. Thus, HRV may indicate functioning of brain networks regulating emotion and internal body states. But heart rate oscillations may not only reflect brain regulatory networks but also help shape these networks. We randomly assigned participants to practice either increasing heart rate oscillations using slow-paced breathing or decreasing them using personalized strategies. Daily practice increasing heart rate oscillations affected brain activity in emotion-related networks even during times when participants breathed no differently than the comparison group. Thus, HRV is more than just an outcome measure--it can help shape the subsequent functioning of emotion-related brain networks.

category: neurology


title: Increasing coordination and responsivity of emotion-related brain regions with a heart rate variability biofeedback randomized trial


title: Effects of Diet on the Microbiome and Serum Metabolome of South Asian Infants at 1 Year

abstract: Diet is known to affect the gut microbiome and metabolome composition in adults, but this has not been fully explored in infants. Dietary patterns from 1 year-old infants (n=182) from the South Asian Birth Cohort (START) study were compared to gut microbiome alpha and beta diversity and to taxa abundance differences. Diet - serum metabolite associations were identified using multivariate analysis (partial least squares-discriminant analysis, PLS-DA) and univariate analysis (T-Test). Dietary biomarkers identified from START were also examined in a separate cohort of white Caucasian infants (CHILD Cohort Study, n=82). Lastly, the association of diet with gut microbiome and serum biomarkers, considering maternal, perinatal and infant characteristics was investigated using multivariate forward stepwise regression. A dietary pattern characterized by breastfeeding, supplemented by formula and dairy was the strongest predictor of the gut microbiome that also differentiated the serum metabolome of infants. The formula and dairy dietary pattern was associated with a panel of circulating metabolites in both cohorts, including: S-methylcysteine, branched-chain/aromatic amino acids, lysine, dimethylglycine, and methionine. Breastfeeding status, the prominent feature of the dietary pattern, was also associated with a sub-set of serum metabolites in both cohorts. In START, this diet pattern was associated with the metabolites tryptophan betaine, 2-hydroxybutyric acid, tyrosine, phenylalanine, and trimethyl-N-oxide. In the CHILD Cohort Study(CHILD), breastfeeding status was associated with the metabolites aminooctanoic acid, 3-hydroxybutyric acid, and methyl-proline. The results of our study suggest that breastfeeding has the largest effect on the composition of the gut microbiome and the serum metabolome at 1 year, even when solid food diet and other covariates are considered.

category: nutrition


title: Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

abstract:
BackgroundIdentification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered GWAS, and therefore few associated loci and causal genes have been identified.

ResultsHere, we perform and integrate LD-adjusted colocalization analyses across nine cardiometabolic traits combined with eQTLs and sQTLs from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). We identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. To elucidate upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 physiological and pharmacological cardiometabolic regulators in human adipocytes, hepatocytes, and skeletal muscle cells, and map their protein-protein interactions.

ConclusionsOur use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our state-of-the-art colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.

category: genetic and genomic medicine


title: A cross-sectional study of COVID-19 knowledge, beliefs and prevention behaviors among adults in Senegal

abstract:
BackgroundCOVID-19 is an ongoing threat to global public health since its emergence at the end of 2019, yet little is known about how populations in francophone West Africa have responded to COVID-19 in their daily lives. Senegal, in particular, has been noted for its relative success in mitigating the spread and impact of COVID-19. We report original research findings on COVID-19 beliefs and prevention behaviors in a sample of Senegalese adults.

MethodsA multi-modal cross-sectional study was conducted to describe COVID-19 beliefs and prevention behaviors in a sample of Senegalese adults and to identify potential predictors of prevention behaviors. Univariate, bivariate, and multivariate statistics were generated to describe the sample and explore potential correlations.

ResultsMask wearing, hand washing, and use of hand sanitizer were most frequently reported. Social distancing and staying at home were also reported albeit to a lower degree. We also identified a range of psychosocial and demographic predictors for COVID-19 prevention behaviors. Men, compared to women, had lower odds (OR=0.59) of reporting prevention behaviors. Rural residents (vs. urban; OR=1.49) and participants with at least a high school education (vs. less than high school education; OR=1.33) were more likely to report COVID-19 prevention behaviors.

DiscussionStakeholders and decision makers in Senegal and across Africa can use place-based evidence like ours to address COVID-19 risk factors and intervene effectively with policies and programming. Use of both phone and online surveys enhances representation and study generalizability and should be considered in future research with hard-to-reach populations.

Article SummaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIThe main strength of our study is the use of a multi-modal data collection strategy, online and via telephone. Had we relied on a single method, our samples demographic characteristics would likely have differed while also introducing selection bias.
C_LIO_LIOur recruitment strategy may have also increased the potential for selection bias because participants were recruited online and on-the-ground in Senegal; thus, all participants, by the nature of the recruitment methods, had access to the internet and/or a cell phone. To address potential confounding between recruitment methods, we controlled for recruitment method in our multivariate regression modelling.
C_LIO_LIPrior to our study, little evidence was available about how populations in francophone West Africa have responded to COVID-19 in their daily lives. We adapted pre-existing research panels and developed novel data collection instruments to capture information about COVID-19, and this may serve as a model for the responsiveness of ongoing scholarship to future global events.
C_LIO_LIA study with a larger sample may have been able to identify relationships between knowledge and behaviors, and our study was not design with enough statistical power to detect significant differences.
C_LIO_LIThe findings of our study may not be generalizable beyond Senegal, or more specifically Senegalese adults.
C_LI


title: Paving the way for human vaccination against Rift Valley fever virus: A systematic literature review of RVFV epidemiology from 1999 to 2021

abstract:
BackgroundRift Valley fever virus (RVFV) is a lethal threat to humans and livestock in many parts of Africa, the Arabian Peninsula, and the Indian Ocean. This systematic reviews objective was to consolidate understanding of RVFV epidemiology during 1999-2021 and highlight knowledge gaps relevant to plans for human vaccine trials.

Methodology/Principal FindingsThe review is registered with PROSPERO (CRD42020221622). Reports of RVFV infection or exposure among humans, animals, and/or vectors in Africa, the Arabian Peninsula, and the Indian Ocean during the period January 1999 to June 2021 were eligible for inclusion. Online databases were searched for publications, and supplemental materials were recovered from official reports and research colleagues. Exposures were classified into five groups: 1) acute human RVF cases, 2) acute animal cases, 3) human RVFV sero-surveys, 4) animal sero-surveys, and 5) arthropod infections. Human risk factors, circulating RVFV lineages, and surveillance methods were also tabulated. In meta-analysis of risks, summary odds ratios were computed using random-effects modeling. 1104 unique human or animal RVFV transmission events were reported in 39 countries during 1999-2021. Outbreaks among humans or animals occurred at rates of 5.8/year and 12.4/year, respectively, with Mauritania, Madagascar, Kenya, South Africa, and Sudan having the most human outbreak years. Men had greater odds of RVFV infection than women, and animal contact, butchering, milking, and handling aborted material were significantly associated with greater odds of exposure. Animal infection risk was linked to location, proximity to water, and exposure to other herds or wildlife. RVFV was detected in a variety of mosquito vectors during interepidemic periods, confirming ongoing transmission.

Conclusions/SignificanceWith broad variability in surveillance, case finding, survey design, and RVFV case confirmation, combined with uncertainty about populations-at-risk, there were inconsistent results from location to location. However, it was evident that RVFV transmission is expanding its range and frequency. Gaps assessment indicated the need to harmonize human and animal surveillance and improve diagnostics and genotyping. Given the frequency of RVFV outbreaks, human vaccination has strong potential to mitigate the impact of this now widely endemic disease.

Author SummaryRift Valley fever virus (RVFV) is a globally important mosquito-transmitted zoonosis that is also directly transmissible via aerosolization of body fluids from infected animals. RVFV outbreaks cause mass mortality of young livestock and pregnancy losses in both humans and animals. Severe human cases also result in hemorrhagic fever, encephalitis, and death. Loss of livestock additionally threatens the livelihood of people who depend on animals for income and food. In endemic areas, initiation of RVFV outbreaks is connected to weather events that cause excess rainfall, leading to flooding and subsequent mosquito blooms. However, the natural cycle of RVFV transmission is complex, requiring congregation of susceptible mammalian hosts and mosquito vectors in suitable environments. Several human vaccine candidates are in different stages of development, but none are yet licensed for use in human populations. In this systematic review, we assessed the 1999-2021 frequency and distribution of RVFV outbreaks among humans, animals, and vectors to identify potential locations and population targets for a human RVFV vaccine efficacy trial. It focuses on current understanding of RVFV epidemiology and the identification of gaps that pose critical barriers to controlling expansion of RVFV and implementing new protective measures including human vaccination.


title: The Mediating Role of SES on the Relationship between Pregnancy History and Later Life Cognition

abstract:
ObjectiveThe association of pregnancy with later life cognition is not well understood. Few studies address the potential confounding role of socioeconomic factors on this relationship. We examined whether pregnancy was associated with cognitive function in a large, population-based sample of post-menopausal women and the potential mediating effects of education level and federal income-to-poverty ratio (PIR) on this relationship.

MethodsParticipants were 1,016 post-menopausal women from the National Health and Nutrition Examination Survey (NHANES). We utilized data from two study waves between years 2011-2014. Cognitive functioning was evaluated by: Digit Symbol Substitution Test (DSST), Animal Fluency (AF), Consortium to Establish a Registry for Alzheimers Disease CERAD word learning task (CERAD-WL) and CERAD delayed recall (CERAD-DR). Lifetime education level and federal income-to-poverty ratio (PIR) were examined as mediating factors. Regression models were used to examine the relationship between number of term pregnancies and incomplete pregnancies and cognitive performance.

ResultsA greater number of term pregnancies was related to worse performance on the DSST (p < .001), CERAD-DR (p < .007), and AF (p < .03). Conversely, greater incomplete pregnancies related to better CERAD-DR performance (p < .03). Significant associations between term pregnancies and cognitive scores were mediated by PIR but not education level.

ConclusionsHigher number of term pregnancies was associated with worse cognitive performance, whereas higher number of incomplete pregnancies was associated with better cognitive performance. Results indicate the necessity to consider SES factors when studying the relationship between pregnancy and cognition.

category: sexual and reproductive health


title: Prevalence of hereditary tubulointerstitial kidney diseases in the German Chronic Kidney Disease study

abstract:
Exome sequencing (ES) studies in chronic kidney disease (CKD) cohorts could identify pathogenic variants in [~]10% of patients. This implies underdiagnosis of hereditary CKD. Tubulointerstitial kidney diseases, showing no typical clinical/histologic finding but tubulointerstitial fibrosis, are particularly difficult to diagnose.

We used a custom designed targeted panel (29 genes) and MUC1-SNaPshot to sequence 271 DNA samples, selected by clinical criteria from 5,217 individuals in the German Chronic Kidney Disease (GCKD) cohort.

We identified 33 pathogenic small variants. Of these 27 (81.8%) were in COL4-genes, the largest group being 15 COL4A5-variants with nine unrelated individuals carrying c.1871G>A, p.(Gly624Asp). We found three cysteine variants in UMOD, a novel missense, and a novel splice variant in HNF1B and the homoplastic MTTF variant m.616T>C. Copy-number analysis identified a heterozygous COL4A5-deletion, and a HNF1B-duplication/-deletion, respectively. Overall, pathogenic variants were present in 12.5% (34/271) and variants of unknown significance in 9.6% (26/271) of selected individuals. Bioinformatic predictions paired with gold standard diagnostics for MUC1 (SNaPshot) could not identify the typical cytosine duplication ("c.428dupC") in any individual, implying that ADTKD-MUC1 is rare.

Our study shows that >10% of individuals with certain clinical features carry disease variants in genes associated with tubulointerstitial kidney diseases. COL4-genes constitute the largest fraction, implying they are overlooked using clinical Alport-syndrome criteria. We also identified variants easily missed by some ES pipelines. Finally, our results indicate that the filtering criteria applied enrich for an underlying genetic disorder.

SIGNIFICANCE STATEMENTCKD affects >10% of the global population and recent studies imply that a considerable portion can be attributed to monogenic diseases, which are likely underappreciated in the clinical routine. Tubulointerstitial kidney diseases are a particularly difficult group of hereditary kidney diseases to diagnose both clinically and genetically. To investigate the prevalence of these disorders in a large CKD cohort we established a set of clinical criteria and designed a custom panel sequencing pipeline. Based on the diagnostic yield of 12.5%, we recommend an algorithm to clinically select and genetically evaluate patients with increased risk for a hereditary tubulointerstitial kidney disease.


title: Spontaneous clearance of vertically acquired hepatitis c infection: implications for testing and treatment

abstract:
BackgroundCurrent guidelines recommend that infants born to women with hepatitis C (HCV) viremia are screened for HCV antibody at age 18 months, and if positive, referred for RNA testing at 3 years to confirm chronic infection. This policy is based in part on analyses suggesting 25%-40% of vertically acquired HCV infections clear spontaneously within 4-5 years.

MethodsData on 179 infants with RNA and/or anti-HCV evidence of vertically acquired viraemia (single PCR+) or confirmed infection (2 PCR+ or anti-HCV beyond 18 months) in three prospective European cohorts were investigated. Ages at clearance of viremia and confirmed infection were estimated taking account of interval censoring and delayed entry. We also investigated clearance in infants in whom RNA was not detectable until after 6 weeks.

ResultsClearance rates decline rapidly over the first 6 months. An estimated 90.6% (95%CrI: 83.5-95.9) of viremia cleared by 5 years, most within 3 months, and 65.9% (50.1-81.6) of confirmed infection cleared by 5 years, at a median 12.4 (7.1-18.9) months. If treatment began at age 6 months, 18 months or 3 years, at least 59.0% (42.0-76.9), 39.7 (17.9-65.9), and 20.9 (4.6-44.8) of those treated would clear without treatment. In seven (6.6%) confirmed infections, RNA was not detectable until after 6 weeks, and in 2 (1.9%) not until after 6 months. However, all such cases subsequently cleared.

ConclusionsMost viraemia clears within 3 months, and most confirmed infection by 3 years. Delaying treatment avoids but does not eliminate over-treatment and should be balanced against loss to follow-up.

Key pointsBased on a re-analysis of the largest purely prospective dataset assembled so far, 66% (50-82) of confirmed vertically acquired HCV clears spontaneously by age 5 years, rather than the 25-40% assumed in guidelines.


title: Clinical Trial of Efficacy and Toxicity of Disoproxil Tenofovir Fumarate and Emtricitabine for Mild to Moderate SARS-CoV-2 Infections

abstract: This study aimed to evaluate the efficacy and toxicity of tenofovir (TDF) and TDF combined with emtricitabine (TDF/FTC) in patients with mild to moderate COVID-19 infections. We conducted a randomized, double-blind, placebo-controlled clinical trial in patients with clinical suspicion of mild to moderate respiratory infection caused by SARS-CoV-2 who were treated at an outpatient clinic. Patients were randomly recruited to take 10 days of TDF (300 mg/day), TDF (300 mg/day) combined with FTC (200 mg/day) or placebo Vitamin C (500 mg/day). The primary parameter was the score of symptoms and predictive signs of COVID-19, assessed on the seventh day of patient follow-up. From a total of 309 patients with clinical suspicion of SARS-CoV-2, 227 met the inclusion criteria and were randomly distributed into the following groups: (a) 75 (one did not initiate treatment) in the TDF group; (b) 74 in the TDF combined with FTC group; and (c) 77 in the Vitamin C group (placebo). Of the 226 patients, 139 (62%) were positive for SARS-CoV-2. Fever ([≥]37.8{degrees}C), ageusia or dysgeusia, anosmia or dysosmia, and two or more clinical symptoms or signs were significantly associated with SARS-CoV-2 infection. There was no significant change in clinical score based on clinical symptoms and signs between treatment groups. Patients with mild to moderate infection by SARS-CoV-2 had higher concentrations of G-CSF, IL-1{beta}, IL-6 and TNF- compared to patients without infection. Patients with mild to moderate respiratory infection, with fever ([≥]37.8{degrees}C), loss of smell, loss of taste and two or more symptoms, have a better prediction for the diagnosis of COVID-19. Patients with SARS-CoV-2 showed higher and more persistent proinflammatory cytokines profile compared to patients not infected with SARS-CoV-2. Pharmacological intervention with TDF or TDF combined with FTC did not change the clinical signs and symptoms score in mild to moderate respiratory infection in patients with SARS-CoV-2 compared to the Vitamin C group (placebo).


title: Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study

abstract:
BackgroundThe COVID-19 pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with seven SARS-CoV-2 variants.

MethodsOur study includes individuals with positive SARS-CoV-2 RT-PCR in the Washington Disease Reporting System with available viral genome data, from December 1, 2020 to January 14, 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination.

Findings58,848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95%CI 2.40-4.26), Beta (HR 2.85, 95%CI 1.56-5.23), Delta (HR 2.28 95%CI 1.56-3.34) or Alpha (HR 1.64, 95%CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95%CI 0.56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination.

ConclusionInfection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

SummaryHospitalization risk following infection with SARS-CoV-2 variant remains unclear. We find a higher hospitalization risk in cases infected with Alpha, Beta, Gamma, and Delta, but not Omicron, with vaccination lowering risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.


title: Improving older adults' vaccination uptake: are existing measures of vaccine hesitancy valid and reliable for older people?

abstract:
Older adults are particularly vulnerable to vaccine-preventable diseases (VDU), due to decreased immunity and increased comorbidity. Vaccination can support healthy ageing and help reduce morbidity, mortality, and loss of quality of life associated with VPDs. Despite the availability of effective vaccines, many countries, including the UK, fail to reach recommended coverage levels. Psychosocial factors are recognised as providing important insights into the determinants of vaccination uptake. Little research has sought to establish psychometrically sound scales of vaccine attitudes with older adults. In the present study, a total of 372 UK-based participants (65-92 years, M = 70.5 yrs, SD = 4.6) completed a cross-sectional, online survey measuring health and socio-demographic characteristics in relation to vaccination uptake for influenza, pneumococcal and shingles. Two recently developed vaccination attitude scales, the 5C scale and the Vaccination Attitudes Examination (VAX) scale, were also administered to test their reliability and validity for use with an older adult population. Additional scales used to examine convergent and discriminant validity, the Beliefs about Medicines Questionnaire, the Perceived Sensitivity to Medicines Scale, the Medical Mistrust Index, the Perceived Stress Scale, and the Interpersonal Support Evaluation List, were included. The factor structure of the 5C and VAX scales was confirmed. Both scales showed good internal reliability, convergent, discriminant and concurrent validity, supporting their use with older adult populations. The 5C and VAX scales were found to be reliable and valid psychosocial measures of vaccine hesitancy and acceptance within a UK-based, older adult population. Future research could use these scales to evaluate the impact of psychological antecedents of vaccine uptake, and how concerns about vaccination may be understood and addressed among older adults.

Ethics approval & informed consentEthical approval (34/26/11/2019/Staff Williams) was granted by the School of Psychological Sciences and Health Ethics Committee, University of Strathclyde. (SEC19/20: Williams, Nicholls, Rasmussen, Young & Gallant). Approved on 8th January 2020.


title: Determinants of sleep quality in adults during the COVID-19 pandemic: COVID-Inconfidentes, a population-based study

abstract:
BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has had a negative effect on the health and behavior of the worlds population.

ObjectivesTo evaluate sleep quality and its associated factors in adults during the COVID-19 pandemic in Brazil.

MethodsThis is a population-based serological survey of 1762 adults collected from October to December 2020 in the Iron Quadrangle region, Brazil. To measure sleep quality, we used the Pittsburgh Sleep Quality Index questionnaire and socio-demographic, health, health related behaviors, anxiety, vitamin D, weight gain/loss, and pandemic characteristics were assessed using a structured questionnaire. Univariate and multivariate analyses were performed to identify the factors associated with sleep quality.

ResultsMore than half of the individuals evaluated had poor sleep quality (52.5%). In multivariate analysis, factors related to sleep quality included living alone (OR=2.36; 95%CI: 1.11-5.00), anxiety disorder (OR=2.22; 95%CI: 1.20-4.14), 5.0% weight loss during the pandemic (OR=1.66; 95%CI: 1.01-2.76), weight gain of 5.0% (OR=1.90; 95%CI: 1.08-3.34), insufficient vitamin D scenario (OR=1.47; 95%CI: 1.01-2.12), and symptoms of COVID-19 (OR=1.94; 95%CI: 1.25-3.01).

ConclusionsOur study revealed that more than half of the participants had poor sleep quality during the COVID-19 outbreak, and the factors associated with poor sleep quality were related to the pandemic, such as insufficient vitamin D scenario and weight change.


title: Human Genetics of Face Recognition: Discovery of MCTP2 Mutations in Humans with Face Blindness (Congenital Prosopagnosia)

abstract: Face recognition is important for both visual and social cognition. While congenital prosopagnosia (CP) or face blindness has been known for seven decades and electrophysiological studies have characterized face specific neurons for half a century, no molecular analyses have been undertaken. Here we report results of research combining classic genetics and modern genomics. From a large family with 18 CP members, we uncovered a fully co-segregating private mutation in the MCTP2 gene which encodes a calcium binding transmembrane protein expressed in the central nervous system. More rare mutations in MCTP2 were detected in CP families and were also associated with CPs in the cohort study. In another cohort of 1757, face recognition was different between 14 carriers with a frameshift mutation S80fs in MCTP2 and 19 non-carrying volunteers. 6 families including one with 10 members showed the S80fs-CP correlation. Functional magnetic resonance imaging (fMRI) indicates that impaired recognition of individual faces by CPs with the MCTP2 mutations is associated with inability to recognize the same faces in the right fusiform face area (rFFA). Our results have revealed the genetic predisposition of MCTP2 mutations in CP, 74 years after the initial report of CP. This is the first time a gene required for a higher form of visual social cognition was found in humans.


title: Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

abstract:
BackgroundMaintenance drugs are used to treat chronic conditions. Several classes of maintenance drugs have attracted attention because of their potential to affect susceptibility to and severity of COVID-19.

MethodsUsing claims data on 20% random sample of Part D Medicare enrollees from April to December 2020, we identified patients diagnosed with COVID-19. Using a nested case-control design, non-COVID-19 controls were identified by 1:5 matching on age, race, sex, dual-eligibility status and geographical region. We identified usage of angiotensin-converting enzyme inhibitors (ACEI), angiotensin-receptor blockers (ARB), warfarin, direct factor Xa inhibitors, clopidogrel, famotidine and hydroxychloroquine based on Medicare prescription claims data. Using extended Cox regression models with time-varying propensity score adjustment we examined the independent effect of each study drug on contracting COVID-19. For severity of COVID-19, we performed extended Cox regressions on all COVID-19 patients, using COVID-19-related hospitalization and all-cause mortality as outcomes. Covariates included gender, age, race, geographic region, low-income indicator and co-morbidities. To compensate for indication bias related to the use of hydroxychloroquine for the prophylaxis or treatment of COVID-19, we censored patients who only started on hydroxychloroquine in 2020.

ResultsUp to December 2020, our sample contained 374,229 Medicare patients over 65 who were diagnosed with COVID-19. Among the COVID-19 patients, 209,208 (55.9%) were on at least one study drug. The three most common study drugs were ACEI 97,872 (26.1%), ARB 83,329 (22.3%) and clopidogrel 38,203 (10.2%). Current users of ACEI, ARB, warfarin, direct factor Xa inhibitor and clopidogrel were associated with reduced risk of getting COVID-19 (3-13%), and reduced risk of dying after a COVID-19 diagnosis (8-19%). Famotidine did not show consistent significant effects. Hydroxychloroquine did not show significant effects after censoring of recent starters.

ConclusionsMaintenance use of ACEI, ARB, warfarin, direct factor Xa inhibitor and clopidogrel was associated with reduction in risk of acquiring COVID-19 and dying from it.


title: Disproportionality analysis of adverse neurological and psychiatric reactions with the ChAdOx1 (Oxford-AstraZeneca) and BNT162b2 (Pfizer-BioNTech) COVID-19 vaccines in the United Kingdom

abstract:
ObjectiveThe information on neurologic or psychiatric adverse reactions to the COVID-19 vaccines is limited. Our objective was to examine the odds of neurological and psychiatric adverse reactions to BNT162b2 (Pfizer-BioNTech) and ChAdOx1 (Oxford-AstraZeneca) COVID-19 vaccines.

MethodsWe analyzed all Adverse Vaccine Reaction reports to the United Kingdom Medicines and Healthcare products Regulatory Agency between December 9, 2020 and June 30, 2021 that mentioned the BNT162b2 or ChAdOx1 vaccines. We compared the rates of adverse neurological and psychiatric reactions with ChAdOx1 to those with BNT162b2. P-values were obtained by a Bonferroni-adjusted Z-test for proportions.

ResultsAs of June 30, 2021, 53.2 M doses of ChAdOx1 and 46.1 M doses of BNT162b2 had been administered. We extracted information from 300,518 distinct reports. The number of individual adverse neurologic or psychiatric reaction reports were less than 200/M doses administered, except headache which was reported in 1,550 and 395 cases/M doses of ChAdOx1 and BNT162b2, respectively. Compared to BNT162b2, cerebral hemorrhagic or thrombotic events, headaches and migraines, Guillain-Barre syndrome and paresthesias, tremor and freezing, delirium, hallucinations, nervousness, poor sleep quality, and postural dizziness were more frequently reported with ChAdOx1. Reactions more frequently reported with BNT162b2 than ChAdOx1 were Bells palsy, facial paralysis, dysgeusia, anxiety, and presyncope or syncope.

ConclusionSignificant differences in the neurologic and psychiatric adverse event profiles of the ChAdOx1 and BNT162b2 vaccines may exist, emphasizing the need for additional research. The beneficial and protective effects of the COVID-19 vaccines far outweigh the low potential risk of neurologic and psychiatric reactions.


title: Digital Therapeutics Care Utilizing Genetic and Gut Microbiome Signals for the Management of Functional Gastrointestinal Disorders: Results from a Preliminary Retrospective Study

abstract:
BackgroundDiet and lifestyle-related illnesses like obesity and functional gastrointestinal disorders (FGIDs) are rapidly emerging health issues worldwide. Research has focused on addressing FGIDs via in-person cognitive-behavioral therapies and lifestyle modifications focusing on diet modulation and pharmaceutical intervention. However, there is a paucity of research reporting on the effectiveness of digital care based on genome SNP and gut microbiome markers to guide lifestyle and dietary modulations on FGID associated symptoms and on modeling diseased groups or outcomes based on a combination of these markers.

ObjectiveThis study sought to model subjects with FGID symptoms vs. those that do not present them, using demographic, genetic, and baseline microbiome data. Additionally, we aimed at modeling changes in FGID symptom severity of subjects at the time of achieving 5% or more of body weight loss in a digital therapeutics care program compared to baseline symptom severity.

MethodsA group of 177 adults with 5% or more weight loss on the Digbi Health personalized digital care program was retrospectively surveyed about changes in the symptomatology of their FGIDs and other comorbidities. The FGID subgroup rated their symptom severity on a scale of 1 to 5 at the beginning of the program and after successfully achieving >5% body weight decrease. During the intervention, personalized coaching for lifestyle changes, including diet and exercise, was delivered by both human and digital coaching. The demographic, genomic, and baseline microbiome data of the subgroup of participants (n=104) who self-reported any of six FGIDs (IBS, diarrhea, constipation, bloating, gassiness, and cramping) were compared with those who did not report FGIDs (n=73) and used as variables for a logistic model. The sum of reductions in symptom severity and IBS, diarrhea, and constipation symptom severity reduction were analyzed using the same variables in linear regression models.

ResultsGut microbiome taxa and demographics were the strongest predictors of FGID status. The digital therapeutics program implemented effectively reduced the summative severity of symptoms for 89.92% of users who reported FGIDs, with a highly significant reduction in severity (Wilcoxon signed-rank test, p=4.89e-17*). A mixture of genomic and microbiome predictors modeled the best reduction in summative FGID symptom severity and IBS symptom severity, whereas reduction in diarrhea symptom severity and constipation symptom severity were best modeled by microbiome predictors only.

ConclusionA digital therapeutics program, informed by genomic SNPs and baseline gut microbiome and their interaction with participant diet and lifestyle, can effectively reduce functional bowel disorder symptomatology. While further research is needed for validation, demographics, microbiome taxa, and genetic markers can effectively inform models aiming at classifying subjects with FGIDs vs. those that do not have FGIDs and models assessing the reduction in symptom severity experienced by FGID sufferers. The methods and models presented here can readily be implemented to study other comorbidities where genetics and gut microbiome play a central role in disease etiology.


title: Humoral and cellular immune responses upon SARS-CoV-2 vaccines in patients with anti-CD20 therapies: A systematic review and meta-analysis of 1342 patients

abstract:
BackgroundImmune responses upon SARS-CoV-2 vaccination in patients receiving anti-CD20 therapies are impaired but vary considerably. We conducted a systematic review and meta-analysis of the literature on SARS-CoV-2 vaccine induced humoral and cell-mediated immune response in patients previously treated with anti-CD20 antibodies.

MethodsWe searched PubMed, EMBASE, Medrxiv and SSRN using variations of search terms "anti-CD20", "vaccine" and "COVID" and included original studies up to August 21st,2021. We excluded studies with missing data on humoral or cell-mediated immune response, unspecified methodology of response testing, unspecified timeframes between vaccination and blood sampling or low number of participants ([≤] 3). We excluded individual patients with prior SARS-CoV-2 infection or incomplete vaccine courses. Primary endpoints were humoral and cell-mediated immune response rates. Pre-specified subgroups were time of vaccination after anti-CD20 therapy (< vs > 6 months), time point of response testing after vaccination (< vs > 4 weeks) and disease entity (autoimmune vs cancer vs renal transplant). We used random-effects models of proportions.

FindingsNinety studies were assessed. Inclusion criteria were met by 23 studies comprising 1342 patients. Overall rate of humoral response was 0.41 (95% CI 0.35 - 0.47). Overall rate of cell-mediated immune responses was 0.71 (95% CI 0.47 - 0.90). Longer time interval since last anti-CD20 therapy was associated with higher humoral response rates > 6 months 0.63 (95% CI 0.53 - 0.72) vs < 6 months 0.2 (95% CI 0.03 - 0.43); p = 0.001. Compared to patients with haematological malignancies or autoimmune diseases, anti-CD20 treated kidney transplant recipients showed the lowest vaccination response rates.

InterpretationPatients on anti-CD20 therapies can develop humoral and cell-mediated immune responses after SARS-CoV-2 vaccination, but subgroups such as kidney transplant recipients or those with very recent B-cell depleting therapy are at high risk for non-seroconversion and should be individually assessed for personalized SARS-CoV-2 vaccination strategies. Potential limitations are small patient numbers, heterogeneous diseases and assays used.

FundingThis study was funded by Bern University Hospital.


title: Mini-XT, a miniaturized tagmentation-based protocol for efficient sequencing of SARS-CoV-2

abstract:
IntroductionThe COVID-19 pandemic has highlighted the importance of whole genome sequencing (WGS) of SARS-CoV-2 to inform public health policy. By enabling definition of lineages it facilitates tracking of the global spread of the virus. The evolution of new variants can be monitored and knowledge of specific mutations provides insights into the mechanisms through which the virus increases transmissibility or evades immunity. To date almost one million SARS-CoV-2 genomes have been sequenced by members of the COVID-19 Genomics UK (COG-UK) Consortium. To achieve similar feats in a more cost-effective and sustainable manner in future, improved high throughput virus sequencing protocols are required. We have therefore developed a miniaturized library preparation protocol with drastically reduced consumable use and costs.

MethodsSARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 multiplex RT-PCR protocol and purified using a conventional liquid handling system. Acoustic liquid transfer (Echo 525) was employed to reduce reaction volumes and the number of tips required for a Nextera XT library preparation. Sequencing was performed on an Illumina MiSeq.

ResultsWe present the  Mini-XT miniaturized tagmentation-based library preparation protocol available on protocols.io (https://dx.doi.org/10.17504/protocols.io.bvntn5en). The final version of Mini-XT has been used to sequence 4,384 SARS-CoV-2 samples from N. Ireland with a COG-UK QC pass rate of 97.4%. Sequencing quality was comparable and lineage calling consistent for replicate samples processed with full volume Nextera DNA Flex (333 samples) or using nanopore technology (20 samples). SNP calling between Mini-XT and these technologies was consistent and sequences from replicate samples paired together in maximum likelihood phylogenetic trees.

ConclusionThe Mini-XT protocol maintains sequence quality while reducing library preparation reagent volumes 8-fold and halving overall tip usage from sample to sequence to provide concomitant cost savings relative to standard protocols. This will enable more efficient high-throughput sequencing of SARS-CoV-2 isolates and future pathogen WGS.


title: Endemic Seasonal Coronavirus Neutralisation and COVID-19 severity

abstract: The virus SARS-CoV-2, responsible for the global COVID-19 pandemic, spread rapidly around the world causing high morbidity and mortality because humans have no pre-existing immunity. However, there are four known, endemic seasonal coronaviruses in humans (HCoVs) and whether antibodies for these HCoVs play a role in severity of COVID-19 disease has generated a lot of interest. Of these seasonal viruses NL63 is of particular interest as it uses the same cell entry receptor as SARS-CoV-2.We use functional, neutralising assays to investigate cross reactive antibodies and their relationship with COVID-19 severity. We analysed neutralisation of SARS-CoV-2, NL63, HKU1, and 229E in 38 COVID-19 patients and 62 healthcare workers, and a further 182 samples to specifically study the relationship between SARS-CoV-2 and NL63.We found that although HCoV neutralisation was very common there was little evidence that these antibodies neutralised SARS-CoV-2. Despite no evidence in cross neutralisation, levels of NL63 neutralisating antibodies become elevated after exposure to SARS-CoV-2 through infection or following vaccination.


title: Evaluation of liver function tests and C-reactive protein in COVID-19 (SARS Cov-2) positive patients diagnosed by Real-time PCR

abstract:
BACKGROUND AND AIMSThe SARS-CoV-2 pandemic, has caused an unconventional social and economic impact globally. To date, there was limited data regarding the effect of COVID-19 infection on the trend of RT-PCR Ct value, risk factors for disease, effect on liver enzymes, etc. This study aimed to assess the frequency of COVID-19 infection in different age groups and genders. Association of cycle threshold (Ct) values with disease severity and to describe the effect of COVID-19 infection on LFT, Deritis ratio, and CRP. That can be used as indicators for COVID-19 infection diagnosis, the guidance for treatment decisions, and prognosis in infected individuals.

METHODSThis was a cross-sectional study conducted in the Molecular Biology and Chemical Pathology sections of the Pathology Department, Shalamar Teaching Hospital Lahore from November 2020 to March 2021.

ResultsMales 51% were more likely to be infected by SARS-CoV-2. Most of the infected individuals 36.5% were in the age group 20-40. Age and underlying comorbidities are important factors that play a significant role in COVID-19 severity. The uppermost number of the patients had symptoms of fever 78.3%, cough 50.4% and myalgias 50.1% RT-PCR low Ct value could be an important indicator related with the disease severity and mortality risk p value < 0.001 and 0.003 respectively. Bilirubin indirect, ALT, AST, and CRP were significantly associated with disease severity. Deritis ratio and CRP was found to be significantly associated with the risk of mortality.

CONCLUSIONSReal-Time PCR results along with Ct values for SARS-CoV-2 may have benefit for clinicians in patient management decisions. Several risk factors e.g., age and comorbidities for developing severe disease and mortality risk have been identified. These biochemical laboratory parameters ALT, AST, Deritis ratio and CRP can be used as predictive biomarkers for progression towards severe disease and risk of mortality.


title: Reduced amplification efficiency of the RNA-dependent-RNA-polymerase (RdRp) target enables tracking of the Delta SARS-CoV-2 variant using routine diagnostic tests

abstract: Routine SARS-CoV-2 surveillance in the Western Cape region of South Africa (January-August 2021) found a reduced PCR amplification efficiency of the RdRp gene target of the Seegene, Allplex 2019-nCoV diagnostic assay when detecting the Delta variant. We propose that this can be used as a surrogate for variant detection.


title: Effectiveness of mRNA-1273 against Delta, Mu, and other emerging variants

abstract:
BackgroundReal-world studies have found high vaccine effectiveness (VE) of mRNA-based COVID-19 vaccines, but reduced VE against the Delta variant and waning protection have been reported, with few studies examining mRNA-1273 variant-specific VE.

MethodsWe conducted a test-negative case-control study at Kaiser Permanente Southern California. Whole genome sequencing was conducted for SARS-CoV-2 positive specimens collected from 3/1/2021 to 7/27/2021. Test-positive cases were matched 1:5 to test-negative controls on age, sex, race/ethnicity, and specimen collection date. Outcomes included SARS-CoV-2 infection and hospitalization. Exposures were 2 doses or 1 dose of mRNA-1273 [≥]14 days prior to specimen collection versus no COVID-19 vaccination. Conditional logistic regression was used to compare odds of vaccination among cases versus controls, adjusting for confounders. VE was calculated as (1-odds ratio)x100%.

ResultsThe study included 8,153 cases and their matched controls. Two-dose VE (95% confidence interval) was 86.7% (84.3-88.7%) against Delta infection, 98.4% (96.9-99.1%) against Alpha, 90.4% (73.9-96.5%) against Mu, 96-98% against other identified variants, and 79.9% (76.9-82.5%) against unidentified variants. VE against Delta declined from 94.1% (90.5-96.3%) 14-60 days after vaccination to 80.0% (70.2-86.6%) 151-180 days after vaccination. Waning was less pronounced for non-Delta variants. VE against Delta was lower among individuals aged [≥]65 years (75.2% [59.6-84.8%]) than those aged 18-64 years (87.9% [85.5-89.9%]). VE against Delta hospitalization was 97.6% (92.8-99.2%). One-dose VE was 77.0% (60.7-86.5%) against Delta infection.

ConclusionsTwo doses of mRNA-1273 were highly effective against all SARS-CoV-2 variants. However, VE against Delta moderately declined with increasing time since vaccination.

Trial Registration NumberNot applicable

FundingModerna Inc.


title: Electrocardiogram Analysis of Post-Stroke Elderly People Using One-dimensional Convolutional Neural Network Model with Gradient-weighted Class Activation Mapping

abstract:
Background and purposesStroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propose the use of a one-dimensional convolutional network (1D-CNN) architecture to build a binary classifier that distinguishes electrocardiogram s (ECGs) between the post-stroke and the stroke-free.

MethodsWe have built two 1D-CNNs that were used to identify distinct patterns from an openly accessible ECG dataset collected from elderly post-stroke patients. In addition to prediction accuracy, which is the primary focus of existing ECG deep neural network methods, we have utilized Gradient-weighted Class Activation Mapping (GRAD-CAM) to ease model interpretation by uncovering ECG patterns captured by our model.

ResultsOur stroke model has achieved [~]90% accuracy and 0.95 area under the Receiver Operating Characteristic curve. Findings suggest that the core PQRST complex alone is important but not sufficient to differentiate the post-stroke and the stroke-free.

ConclusionsWe have developed an accurate stroke model using the latest DNN method. Importantly, our work has illustrated an approach to enhance model interpretation, overcoming the black-box issue facing DNN, fostering higher user confidence and adoption of DNN in medicine.


title: A Computational Growth Framework for Biological Tissues: Application to Growth of Aortic Root Aneurysm Repaired by the V-shape Surgery

abstract: Ascending aortic aneurysms (AsAA) often include the dilatation of sinotubular junction (STJ) which usually leads to aortic insufficiency. The novel surgery of the V-shape resection of the noncoronary sinus, for treatment of AsAA with root ectasia, has been shown to be a simpler procedure compared to traditional surgeries. Our previous study showed that the repaired aortic root aneurysms grew after the surgery. In this study, we developed a novel computational growth framework to model the growth of the aortic root repaired by the V-shape surgery. Specifically, the unified-fiber-distribution (UFD) model was applied to describe the hyperelastic deformation of the aortic tissue. A novel kinematic growth evolution law was proposed based on existing observations that the growth rate is linearly dependent on the wall stress. Moreover, we also obtained patient-specific geometries of the repaired aortic root post-surgery at two follow-up time points (Post1 and Post2) for 5 patients, based on clinical CT images. The novel computational growth framework was implemented into the Abaqus UMAT user subroutine and applied to model the growth of the aortic root from Post1 to Post2. Patient-specific growth parameters were obtained by an optimization procedure. The predicted geometry and stress of the aortic root at Post2 agree well with the in vivo results. The novel computational growth framework and the optimized growth parameters could be applied to predict the growth of repaired aortic root aneurysms for new patients and to optimize repair strategies for AsAA.


title: Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates

abstract:
Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases.

Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach.

The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.


title: Modelling of COVID-19 pandemic vis-a-vis some socioeconomic factors

abstract: The impacts of COVID-19 outbreak on socio-economic status of countries across the globe cannot be overemphasized as we examine the role it played in various countries. A lot of people were out of jobs, many households were careful of their spending and a greater social fracture of the population in fourteen different countries has emerged. We considered periods of infection spread during the first and second wave in Organization for Economic Co-operation and Development (OECD) countries and countries in Africa, that is developed and developing countries alongside their social-economic data. We established a mathematical and statistical relationship between Theil and Gini index, then we studied the relationship between the data from epidemiology and socio-economic determinants using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiologic parameters and we predicted three of the socio-economic variables in order to validate our results. These result shows a sharp difference between the first and second wave of the pandemic confirming the real dynamics of the spread of the outbreak in several countries and ways by which it was mitigated.


title: Assessing the Burden of COVID-19 in Developing Countries: Systematic Review, Meta-Analysis, and Public Policy Implications

abstract:
IntroductionThe infection-fatality rate (IFR) of COVID-19 has been carefully measured and analyzed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries.

MethodsWe systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using population representative samples collected by early 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analyzed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible.

ResultsSeroprevalence in many developing country locations was markedly higher than in high-income countries. In most locations, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups. Age-specific IFRs were roughly 2x higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure.

ConclusionThe burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to accelerate the provision of vaccine doses to populations in developing countries.

Key Points- Age-stratified infection fatality rates (IFRs) of COVID-19 in developing countries are about twice those of high-income countries.
- Seroprevalence (as measured by antibodies against SARS-CoV-2) is broadly similar across age cohorts, underscoring the challenges of protecting older age groups in developing countries.
- Population IFR in developing countries is similar to that of high-income countries, because differences in population age structure are roughly offset by disparities in healthcare access as well as elevated infection rates among older age cohorts.
- These results underscore the urgency of disseminating vaccines throughout the developing world.


title: Pruning Digital Contact Networks for Meso-scale Epidemic Surveillance Using Foursquare Data

abstract: With the recent boom in human sensing, the push to incorporate human mobility tracking with epidemic modeling highlights the lack of groundwork at the meso-scale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks, current approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend the usefulness of epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network-based method to first build and then prune the dynamic contact networks for recurring interactions; these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquares Points of Interest (POI) smart-phone geolocation data from over 1.3 million devices and show that we can recreate the COVID-19 infection curves for two major (yet very different) US cities (i.e., Austin and New York City) while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the meso-scale that can help both policy makers and individuals of their estimated state of health and help with pandemic planning.


title: Importation Risk Stratification for COVID19 using Quantitative Serology

abstract: Recent work (Khoury et al.,Nature Medicine 2021, 27 (7), 1205-1211) has shown that measurement of IgG antibody concentration in blood correlates well with vaccine efficacy. The present communication builds on this work and considers the probability of infection given immunity, taking into account the distribution across the population of antibody concentration in vaccinated or convalescent people. The model is consistent with the observed rates of breakthrough infection following vaccination or previous infection. The model is then developed to consider the use of quantitative measurement of antibody concentration on arrival as an aid to risk stratification of travellers. The model indicates that such a measurement could significantly decrease the quarantine time required to achieve a given level of importation risk.


title: Characteristics associated with COVID-19 vaccination status among staff and faculty of a large, diverse University in Los Angeles.

abstract:
ObjectiveThis study examined characteristics associated with being unvaccinated among a sample of university staff and faculty prior to university campus reopening for in-person learning in spring-summer 2021.

MethodsStaff and faculty responded to an email invitation to complete an online survey. Survey questions included demographic data (race/ethnicity, age, sex), COVID-19 knowledge and behaviors, employment specific data including division and subdivision (healthcare vs. non-healthcare related division); and self-reported vaccination status. A multivariable logistic regression analysis was performed to determine significant characteristics associated with the likelihood of being unvaccinated for COVID-19.

ResultsParticipants identifying as Asian and Asian American, Hispanic/Latinx or Multicultural/Other had greater odds of being unvaccinated compared to Non-Hispanic White participants. Other characteristics associated with greater likelihood of being unvaccinated included working as university staff member (vs. faculty), older age, decrease in income, inability to work remotely and not traveling outside of Los Angeles area. Political affiliation as an Independent or as something else were more likely to be unvaccinated compared to participants identifying as Democrat.

ConclusionsFindings suggest several factors associated with racial and social disparities may delay the uptake of COVID-19 vaccination. This study highlights the need for targeted educational interventions to promote vaccination among university staff and faculty.


title: Transfer functions: learning about a lagged exposure-outcome association in time-series data

abstract: Many population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcomes such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.


title: Neonatal outcomes of preterm infants born during COVID-19 community lockdowns in Melbourne, Australia

abstract:
BackgroundCommunity lockdowns during the COVID-19 pandemic may influence preterm birth rates, but mechanisms are unclear.

MethodsWe compared neonatal outcomes of preterm infants born to mothers exposed to community lockdowns in 2020 (exposed group) to those born in 2019 (control group). Main outcome studied was composite of significant neonatal morbidity or death.

ResultsMedian gestational age was 35+4 weeks (295 infants, exposed group) vs. 35+0 weeks (347 infants, control group) (p = 0.108). The main outcome occurred in 36/295 (12.2%) infants in exposed group vs. 46/347 (13.3%) in control group (p = 0.69). Continuous positive airway pressure (CPAP) use, jaundice requiring phototherapy, hypoglycaemia requiring treatment, early neonatal white cell and neutrophil counts were significantly reduced in the exposed group.

ConclusionsCOVID-19 community lockdowns did not alter composite neonatal outcomes in preterm infants, but reduced rates of some common outcomes, and early white cell and neutrophil counts.


title: Preterm outcomes following COVID-19 lockdowns, Melbourne, Australia


title: Progressive changes in descriptive discourse in First Episode of Schizophrenia: A longitudinal computational semantics study

abstract: Computational semantics, a branch of computational linguistics, involves automated meaning analysis that relies on how words occur together in natural language. This offers a promising tool to study schizophrenia. At present, we do not know if these word level choices in speech are sensitive to illness stage (i.e. acute untreated vs. stable established state), track cognitive deficits in major domains (e.g. cognitive control, processing speed) and relate to established dimensions of formal thought disorder. Here we study samples of descriptive discourse in patients with untreated first episode of schizophrenia (x{square} 2.8 days of lifetime daily dose exposure) and healthy subjects (246 samples of 1-minute speech; n=82, FES=46, HC=36) using a co-occurrence based vector embedding of words. We obtained six-month follow-up data in a subsample (99 speech samples, n=33, FES=20, HC=13). At baseline, the evidence for higher semantic similarity during descriptive discourse in FES was substantial, compared to null difference (Bayes Factor =6 for full description; 32 for 10-words window). Moreover, the was a linear increase in semantic similarity with time in FES compared to HC (Bayes Factor= 6). Higher semantic similarity related to lower Stroop performance (accuracy and interference, response time), and was present irrespective of the severity of clinically ascertained thought disorder. Automated analysis of non-intrusive 1-minute speech samples provides a window on cognitive control deficits, role functioning and tracks latent progression in schizophrenia.

category: psychiatry and clinical psychology


title: "Jittered" Oculomotor Sensing Concomitant with Hypoactive Dorsal Parietal Systems during Live Eye-Contact in Autism Spectrum Disorder

abstract: Reluctance to make eye contact during natural interactions is a central diagnostic criterion for autism spectrum disorder (ASD). However, the underlying neural correlates for natural eye contacts in ASD are unknown, and diagnostic biomarkers are active areas of investigation. Here, neuroimaging, eye-tracking, and pupillometry data were acquired simultaneously using two-person functional near-infrared spectroscopy (fNIRS) during live eye-to-eye contact and eye-gaze at a video face in typically developed (TD) and ASD participants to identify the neural correlates of live eye-to-eye contact in both groups. Direct comparisons between ASD and TD participants showed decreased right dorsal parietal activity and increased right ventral temporal-parietal activity for ASD relative to TD during live eye-to-eye contact (p[≤]0.05, FDR-corrected) consistent with the hypothesis of alternative neural systems for live eye contact. The additional hypothesis that hypoactivity of the right dorsal-parietal regions during eye contact is associated with social performance in ASD was supported by the correlation of right dorsal parietal activity with individual measures of social function: ADOS-2, Autism Diagnostic Observation Schedule, 2nd Edition (r = -0.69); and SRS-2, Social Responsiveness Scale, Second Edition (r = -0.58). That is, as social ability decreased, the neural responses in the right dorsal parietal region to real eye-contact also decreased consistent with a neural correlate for social characteristics in ASD.


title: Neural Correlates of Eye Contact and Social Function in Autism Spectrum Disorder


title: Patient Attitudes About Light Therapy and Negative Ion Therapy for Nonseasonal Depression: An Online Survey Study.

abstract:
ObjectiveNo studies have yet evaluated whether light therapy or negative ion therapy can be used as maintenance treatment after acute treatment with antidepressants in patients with major depressive disorder. To address the importance of this question, we surveyed participants with depression to determine their knowledge and attitudes about light therapy and negative ion therapy, and their willingness to participate in a randomized clinical trial with these therapies substituting for antidepressants for maintenance treatment.

MethodsParticipants with a self-reported diagnosis of depression were recruited by email, newsletters, and social media to complete an online survey with questions about awareness and effectiveness of light therapy and negative ion therapy for depression. Vignettes describing the use of these therapies for maintenance treatment were presented with follow up questions about the ease of use and reasons for wanting (and not wanting) to use the therapies instead of antidepressants. Another vignette described a randomized study with these therapies followed by questions on whether participants would likely volunteer for the study. Chi-square tests were used to examine differences in responses between therapies.

ResultsA total of 221 participants completed the survey. Most of them were aware of both therapies, but more participants had heard of light therapy (95% compared to 62% for negative ion therapy, p<0.0001), had used light therapy (28% versus 16%, p<0.003), and regarded light therapy as effective (54% versus 37%, p<0.001). Both therapies were considered easy to use. The majority of participants (78%) thought that it was important to find non-medication therapies for maintenance treatment, and 77% responded that they would likely volunteer for a randomized study to determine efficacy of the two therapies for maintenance treatment.

ConclusionPeople with depression are generally aware of light therapy and negative ion therapy and believe they would be good therapies to substitute for antidepressants in maintenance treatment. These findings support the importance and feasibility for a randomized relapse prevention trial with light therapy and negative ion therapy in patients with depression.


title: Symptom Monitoring based on Digital Data Collection During Inpatient Treatment of Schizophrenia Spectrum Disorders - a Feasibility Study

abstract:
BackgroundDigital acquisition of risk factors and symptoms based on patients self-reports represents a promising, cost-efficient and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. While the feasibility of digital data collection has been demonstrated across a range of psychiatric disorders, studies investigating digital data collection in schizophrenia spectrum disorder patients are scarce. Hence, up to now our knowledge about the acceptability and feasibility of digital data collection in patients with a schizophrenia spectrum disorder remains critically limited.

ObjectiveThe objective of this study was to explore the acceptance towards and performance with digitally acquired assessments of risk and symptom profiles in patients with a schizophrenia spectrum disorder in comparison with patients with an affective disorder.

MethodsWe investigated the acceptance, the required support and the data entry pace of patients during a longitudinal digital data collection system of risk and symptom profiles using self-reports on tablet computers throughout inpatient treatment in patients with a schizophrenia spectrum disorder. As a benchmark comparison, findings in patients with schizophrenia spectrum disorder were evaluated in direct comparison with findings in affective disorder patients. The influence of sociodemographic data and clinical characteristics on the assessment was explored. The study was performed at the Department of Psychiatry at the University of Munster between February 2020 and February 2021.

ResultsOf 82 patients diagnosed with a schizophrenia spectrum disorder who were eligible for inclusion 59.8% (n=49) agreed to participate in the study of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates and subjective experience ratings did not differ between patients with a schizophrenia spectrum disorder and patients with an affective disorder. Out of all participating patients, 98% reported high satisfaction with the digital assessment. Patients with a schizophrenia spectrum disorder needed more support and more time for the assessment compared to patients with an affective disorder. The extent of support of patients with a schizophrenia spectrum disorder was predicted by age, whereas the feeling of self-efficacy predicted data entry pace.

ConclusionOur results indicate that, although patients with a schizophrenia spectrum disorder need more support and more time for data entry than patients with an affective disorder, digital data collection using patients self-reports is a feasible and well-received method. Future clinical and research efforts on digitized assessments in psychiatry should include patients with a schizophrenia spectrum disorder and offer adequate support to reduce digital exclusion of these patients.