Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: We exported the final SV table, taxonomy table, and sample metadata to the phyloseq package [49] and converted the data to relative abundance for further analyses. Microbiome variation according to IDWe produced an NMDS plot in phyloseq using the Bray-Curtis distance matrix to visualise the variation within and between individuals according to community composition. To determine the microbiome variation attributable to individual variation (ID), we conducted a permutational ANOVA (; adonis) in the vegan package [50]. We calculated the core microbiome of individual samples using a detection threshold of 0. 001% and a prevalence threshold of 99. 9% (i. e. a given SV must be present in 99. 9% of individuals with a relative abundance of at least 0. 001%) in the microbiome package [51]. We used an NMDS plot to visualise the variation in core microbiome according to ID and analysed the data using an adonis analysis (as above). To determine whether there was greater microbiome variation within an individual than between individuals, we calculated Jensen-(JSD) values in the phyloseq package [49]. JSD values give a measure of similarity between all individual samples (i. e. by calculating the distance between samples) either from the same individual (i. e. within-individual variation) or from different individuals in the same band (i. e. between-individual variation). Smaller JSD values indicate more similar microbial communities and conversely, larger values indicate a less similar community. We used a generalised linear mixed model with ID and band as random factors to compare JSD distances within individuals to JSD distances between individuals and visualised the data using a bar chart. Microbiome variation according to band, life stage, and sexWe categorised individuals under the age of 1 year as foals and for older individuals, females < 2 years old and males < 3 years old as sub-adults (females are usually reproductively mature from 2 years onwards but males take longer to mature, disperse, and attract mares). We classified all others as adults, with the exception of one female who still displayed sub-adult behaviours, did not disperse from her natal band, and had not foaled by age 3, and so was considered a sub-adult.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: No statistically significant differences were found in the number of DCX+ cells between the T24 and T7 group (P > 0. 05, ns, unpaired Student t test). Individual data points and mean (horizontal bar) are shown. Scale bar: 94 µm. Since we did not find any effects of T7 on cell proliferation and number of immature neurons after 2 weeks of exposure, we lengthened the duration of exposure to 10 weeks. To label newborn cells, EdU was injected once daily for 7 days starting on the sixth week of the T7 cycle, and mice were perfused at the end of the 10th week (Fig. 2A). In the T24 group (n = 7), we found 1537 ± 60 EdU+ cells in the dentate gyrus per animal (Fig. 2F, J). In the T7 group (n = 8), we found 1411 ± 64 cells in the dentate gyrus per animal (Fig. 2G, J). There were no statistically significant differences in the number of EdU+ cells in the dentate gyrus between the T24 and T7 group (Fig. 2J). To examine specifically adult-born neurons, we co-stained EdU with NeuN (a marker of mature, differentiated neurons). In the T24 group (n = 5), we found 370 ± 57 EdU+/NeuN+ cells in the dentate gyrus per animal (Fig. 2F, K). In the T7 group (n = 5), we found 345 ± EdU+/NeuN+ cells in the dentate gyrus per animal (Fig. 2G, K). There were no statistically significant differences in the number of EdU+/NeuN+ cells in the dentate gyrus between the T24 and T7 group (Fig. 2K). Similar to the previous experiment involving 2 weeks of exposure to T7, we also examined Ki67 and DCX as endogenous markers of cell proliferation and number of immature neurons. In the T24 group (n = 7), we found 1690 ± 115 Ki67+ cells and 8369 ± 985 DCX+ cells in the dentate gyrus per animal (Fig. 2B, D, H, I).

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Nov 5, 2020;22(11):e20322. [CrossRef] [Medline]. Efficacy of mobile app-based interactive cognitive behavioral therapy using a chatbot for panic disorder. Med Inform. Aug 2020;140:104171. [CrossRef] [Medline] Conversational agents as mediating social actors in chronic disease management involving health care professionals, patients, and family members: multisite single-arm feasibility study. J Res. Feb 17, 2021;23(2):e25060. [CrossRef] [Medline] Conversational agents for chronic disease self-management: a systematic review. . Jan 25, 2021;2020:504-513. [Medline]. Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. J Res. Sep 14, 2020;22(9):e20701. [CrossRef] [Medline]Wilson L, Marasoiu M. The development and use of chatbots in public health: scoping review. . Oct 5, 2022;9(4):e35882. [CrossRef] [Medline]. How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. Interaction. May 9, 2021;37(8):729-758. [CrossRef]. An empathy-driven, conversational artificial intelligence agent (WYSA) for digital mental well-being: real-world data evaluation mixed-methods study. . Nov 23, 2018;6(11):e12106. [CrossRef] [Medline]. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study. Digit Health. Aug 21, 2019;5:2055207619871808. [CrossRef] [Medline] Conversational agents in healthcare: a systematic review. J Inform Assoc. Sep 1, 2018;25(9):1248-1258. [CrossRef] [Medline]-Gorr K, Heeren T. ‘It’s just like you talk to a friend’ relational agents for older adults. Interacting Comput. Dec 2005;17(6):711-735. [CrossRef]. An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: an exploratory study. J Adolesc Health.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: We visualised the taxonomic composition (at the class level) of the communities according to band and life stage using stacked plots in phyloseq [49] and ggplot2 [52]. To obtain the “average microbiome” for an individual, we merged raw sample data within an individual using the merge_samples function in phyloseq (using “fun = mean”) [49]. To determine whether there was greater microbiome variation between bands than within bands, we calculated Jensen-(JSD) values between individuals using data from their average microbiome, as described above. We used a one-way ANOVA with Tukey’s post hoc analysis to compare JSD distances within and between bands and visualised the data using a bar chart. We produced NMDS plots in phyloseq using the Bray-Curtis distance matrix to visualise differences in beta diversity according to band and life stage. We used an adonis analysis to test for significant effects of band, life stage, and sex on total microbiome community composition. We then calculated the core microbiome as described above and repeated the adonis analysis for this core community. Additionally, we agglomerated the core taxa to genus level and visualised the core microbiome as a heat map to give a representation of the bacterial taxa present. To identify differences in microbiome composition between foals (which at approximately 5–8 months old, were most likely still nursing) and sub-adults (which were most likely fully weaned), we conducted an indicator analysis using the multipatt function in the indicspecies package [53]. Effects of spatial structuring and social interactions on the microbiomeWe correlated the social network association matrix with the NMDS scores of each individuals’ average microbiome using Kendall’s correlation coefficient for non-parametric data with ties. Networks were constructed and visualised using the igraph package [54] with edges weighted by either microbiome similarity (the inverse of the NMDS distance) or the association index as described above. As the microbiome distance matrix is fully connected, we delated edges with a similarity less than the mean value for the population.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: In the T7 group (n = 8), we found 1793 ± 122 Ki67+ cells and 9345 ± 445 DCX+ cells in the dentate gyrus per animal (Fig. 2C, E, H, I). There were no statistically significant differences in the number of Ki67+ or DCX+ cells in the dentate gyrus between the T24 and T7 group (Fig. 2H, I). We also performed double-labeling to examine co-localization between DCX/NeuN to quantify the number mature newborn neurons. However, the percentage of DCX cells that co-localized with NeuN was low (7. 48% ± 1. 3% for the T24 group, n = 5 animals; 8. 04 ± 0. 41 for the T7 group, n = 5 animals), and there were no statistically significant differences between the T24 and T7 groups. These results suggest that up to 10 weeks of exposure to the T7 light cycle did not affect adult hippocampal neurogenesis. Figure 2. Ten weeks of exposure to the T7 aberrant light cycle do not affect adult hippocampal neurogenesis. (A) Experimental design. (B, C) Confocal images of Ki67 staining in the dentate gyrus from mice exposed to either the T24 (B) or T7 cycle (C). (D, E) Confocal images of DCX staining in the dentate gyrus from mice exposed to either the T24 (D) or T7 cycle (E). (F, G) Confocal images of EdU/NeuN staining in the dentate gyrus from mice exposed to either the T24 (F) or T7 cycle (G). (H) Ki67+ cell counts in the dentate gyrus from mice exposed to either the T24 or T7 cycle. No statistically significant differences were found in the number of EdU+ cells between the T24 and T7 group (P > 0. 05, ns, unpaired Student t test). (I) DCX+ cell counts in the dentate gyrus from mice exposed to either the T24 or T7 cycle. No statistically significant differences were found in the number of Ki67+ cells between the T24 and T7 group (P > 0. 05, ns, unpaired Student t test).

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Sep 2022;165:102848. [CrossRef]. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Health. Sep 2021;3(9):e599-e611. [CrossRef] [Medline] Conversational agent interventions for mental health problems: systematic review and meta-analysis of randomized controlled trials. J Res. Apr 28, 2023;25(1):e43862. [CrossRef] [Medline]’Alfonso S. To chat or bot to chat: ethical issues with using chatbots in mental health. Digit Health. Jun 22, 2023;9:20552076231183542. [CrossRef] [Medline]Abd-. Perceptions and opinions of patients about mental health chatbots: scoping review. J Res. Jan 13, 2021;23(1):e17828. [CrossRef] [Medline]. Benefits of, barriers to, and needs for an artificial intelligence-powered medication information voice chatbot for older adults: interview study with geriatrics experts. JMIR Aging. Apr 28, 2022;5(2):e32169. [CrossRef] [Medline] Diagnosis of COVID-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones. J Biomed Inform. Jun 2022;130:104078. [CrossRef] [Medline]Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 30 March 2023. Office for National Statistics. URL: https://www. ons. gov. uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/prevalenceofongoingsymptomsfollowingcoronaviruscovid19infectionintheuk/30march2023 [Accessed 2024-02-12]. Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nat Med. Feb 2024;30(2):595-602. [CrossRef] [Medline]. Artificial intelligence–based chatbots for promoting health behavioral changes: systematic review. J Res. Feb 24, 2023;25(1):e40789. [CrossRef] [Medline]. Long covid-mechanisms, risk factors, and management. BMJ.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: We calculated JSD values between merged samples in the phyloseq package [49] and used general linear mixed models (with ID and band as random factors) to identify whether mares had more similar microbiomes to other mares within the same band or to the band stallion and whether mares had more similar microbiomes to their own offspring than to other mares’ offspring within the same band. We visualised these using bar charts. ResultsBacteria primarily belonged to the Bacteroidia, Clostridia, Spirochaetes, and Fibrobacteria classes (Bacteroidetes, Fibrobacteres, Firmicutes, and Spirochaetae phyla) (Additional file 1: Figure S1 and S2). The dominant families represented in the core microbiome were anaerobic bacteria associated with grass-eating mammals, including and various groups of Bacteroidales. An adonis analysis showed pony ID had a significant effect on total microbiome composition (p < 0. 001; Table 2), with 52. 6% of the variation in the microbiome attributable to individual variation (Fig. 1). We obtained similar results for the adonis with the core microbiome (p < 0. 001; Table 2), with 49. 6% of the variation explained by ID. Despite the large amount of microbiome variation explained by ID, within-individual samples had significantly lower JSD values (mean of 0. 255 ± 0. 006) than between-individual samples (mean of 0. 347 ± 0. 001) (χ2 = 391. 62, df = 1, p < 0. 001) (Fig. 2a). That is, there is greater variation between individuals (average of 35%) than within individuals (average of 26%). Table 2 Statistical outputs for adonis analyses of microbiome compositionFull size tableFig. 1NMDS plot of the total microbiome of individual ponies in the study. Larger filled circles indicate the centroid for each individualFull size imageFig. 2Average (± SE) Jensen-Shannon divergence metrics for pony microbiome composition a within and between individuals; b within and between bands; c between mothers and their offspring, as well as between juveniles and non-maternal mares and foal-less mares; and d between the band stallion and band mares, and between all mares within a band.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: (J) EdU+ cell counts in the dentate gyrus from mice exposed to either the T24 or T7 cycle. No statistically significant differences were found in the number of EdU+ cells between the T24 and T7 group (P > 0. 05, ns, unpaired Student t test). (K) EdU/NeuN+ cell counts in the dentate gyrus from mice exposed to either the T24 or T7 cycle. No statistically significant differences were found in the number of EdU/NeuN+ cells between the T24 and T7 group (P > 0. 05, ns, unpaired Student t test). Individual data points and mean (horizontal bar) are shown. Scale bar: 94 µm. As a whole, our results show that chronic exposure to the T7 light cycle does not suppress adult hippocampal neurogenesis. Given that the T7 light cycle causes mood and learning dysfunction independent of sleep deprivation and circadian arrhythmicity (LeGates et al. , 2012), the lack of an effect on neurogenesis following T7 exposure suggests that the direct effects of aberrant light are not dependent on adult neurogenesis to affect mood and learning. One caveat in our study is that we did not perform behavioral analysis to check whether the mice exposed T7 developed mood or learning deficits. In our previous study, we found that 2 weeks of exposure to T7 was sufficient to induce mood and learning deficits (LeGates et al. , 2012), but we have not analyzed behavior at 10 weeks of exposure. In this study, we extended the exposure to 10 weeks since we did not find deficits in cell proliferation and number of immature neurons at 2 weeks. It is not known whether the animals still exhibit mood and learning deficits after 10 weeks of exposure to T7 and thus would be important to explore for future studies. To conclude, our study highlights that the direct effects of aberrant light exposure on mood and learning may not depend on adult hippocampal neurogenesis and that neurogenesis-independent forms of hippocampal plasticity such as dendritic morphology should be explored as potential neural pathways by which aberrant light directly affects brain function. 1237. CrossrefPubMedWeb of Science261. CrossrefPubMedWeb of Science44. CrossrefPubMedWeb of Science1299. CrossrefPubMedWeb of Science5267. CrossrefPubMedWeb of Science144.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: They also reported that the information provided was interesting, easy to understand, and easy to navigate. Again, the majority reported that the information provided was helpful, and that they learned a substantial amount from the eHealth tool. A series of one-way between-groups ANOVAs were conducted to explore differences between intervention groups on each aspect of the evaluation. Table 4 details participants’ average responses to each of the evaluation items, and results of each of the ANOVAs. There were statistically significant differences between groups on the variables concerning participants’ overall impression of the intervention, how interesting the eHealth tool was, how easy it was to navigate, how helpful participants found the information provided, and the amount learned, but not how easy the information was to understand. Post-hoc comparisons using the Tukey HSD test indicated that the mean rating for the BA group was significantly lower than the BHHP group’s rating on the variables concerning participants’ overall impression (P=. 02), how interesting the information was (P=. 03), how helpful participants found the information provided (P=. 02), and the amount learned (P=. 02); the PLUS group did not differ significantly from the other two groups on these variables. For the variable concerning how easy the eHealth tool was to navigate, post-hoc comparisons indicated that the mean rating for the BHHP group was significantly higher than the ratings of both the BA group (P=. 03) and the PLUS group (P=. 04); however, the mean score for the BA group did not significantly differ from the PLUS group. Table 4. Mean ratings (rated on a scale from 1 to 5) for participants’ evaluations of the interventions. Participants’ EvaluationsBA(n=46)BHHP(n=66)PLUS(n=88)Mean (SD)Mean (SD)Mean (SD)FDegrees of FreedomPeta290% CIs for eta2Overall impression3. 56 (0. 84)4. 03 (0. 87)3. 75 (0. 89)4. 122,191. 02a. 04(0. 004-0. 09)Interesting information3. 89 (0. 86)4. 31 (0. 80)4. 06 (0. 82)3. 532,189. 03 a. 04(0. 002-0.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Significantly different results are indicated by *Full size imageThere were significant effects of band and life stage on total microbiome composition, but not sex (Table 2, Fig. 3a and b). In both cases, the proportion of the variation in the microbiome for these significant factors (14. 0% for band and 10. 4% for life stage) was much lower than for pony ID alone (52. 6%). The results of the adonis analysis for the core microbiome were similar to those for the total microbiome, where band and life stage both significantly affected core microbiome composition, but sex did not (Table 2). Band and life stage account for a slightly larger proportion of the variation in the core microbiome (19. 4% and 16. 6%, respectively) than the total microbiome. Fig. 3NMDS plots of the total microbiome of ponies plotted according to band membership (a) and life stage (b). Larger filled circles indicate group centroidsFull size imageConsistent with the spatial distribution of the bands (Fig. 4a), microbiome composition of individuals in Valley differed considerably to those in Aber and Marsh, which are more similar to each other but still display some degree of separation (Fig. 3a). There was a significant difference in JSD metric values within and between the bands (F5,424 = 6. 557, p < 0. 001), and the Tukey posthoc indicated that within-band variation for Valley was significantly lower than the variation within the other two bands, and significantly lower than between-band variation for all three combinations (Fig. 2b). In addition to this band-level differentiation of microbiomes, there was a significant correlation between social network tie weight (i. e. spatial distribution) and microbiome composition (τ = − 0. 11, p < 0. 001) within bands, such that individuals that associate more have more similar microbiomes (Fig. 4b, c). Fig. 4a Map showing the spatial distribution of pony bands encountered during sampling. b Social network of the sampled individuals with edge width proportional to tie strength between individuals and c network visualisation of microbiome distance between individualsFull size imageThe microbiome of foals was considerably different from that of sub-adults and adults, whereas these latter two groups were very similar to one another (Fig. 3b and Additional file 1: Figure S2).

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: It has been reported that chronic inflammatory responses are associated with the adverse prognosis of various cancer types, including breast cancer. Above all, neutrophil-to-lymphocyte ratio (NLR) correlates with poor prognosis in breast cancer patients [27, 28]. Therefore, we also investigated the effect of light/dark shifts on NLR. We also investigated the preventive effect of quercetin on breast cancer metastasis promoted by circadian disruption in mice implanted with mouse mammary cancer BJMC3879Luc2 cells. Flavonoids are widely distributed in the plant kingdom and their intake has been reported to be inversely correlated with breast cancer risk [29, 30]. Quercetin, a representative flavonoid compound, is found in many plant foods such as onions in the O-glycoside form, and its anticarcinogenic effects are receiving attention due to its strong antioxidant activity [31, 32]. We have previously shown that Quercetin-3-O-glucuronide (Q3G), a quercetin metabolite, acts as β2-AR antagonist and suppresses invasion of human breast cancer cells in vitro [20]. However, there is no report on the suppressive effect of quercetin on metastasis resulting from disruption of the circadian rhythm in mice. Therefore, we investigated the preventive effect of quercetin on light disruption-induced breast cancer metastasis by feeding quercetin mixed diet to breast cancer transplanted model mice. 2. Materials and Methods 2. 1. A highly metastatic murine BJMC3879Luc2 mammary adenocarcinoma cell line, which is estrogen receptor-positive and expresses luc2 gene, was a kind gift from Dr. Masa-(College, Osaka, Japan) [33]. The cells were maintained in RPMI-1640 medium (Wako, Osaka, Japan) containing 10% heat-inactivated fetal bovine serum (FBS; ) with 50 U/mL penicillin and 50 U/mL streptomycin () at 37 °C in 5% CO2 in a humidified cell incubator. 2. 2. Animals and Experimental Light//c mice (7 weeks of age) were purchased from Japan SLC (Shizuoka, Japan). Five mice were kept in each plastic cage (W × D × H = 25. 5 × 38. 8 × 14.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 08)Easy to understand3. 52 (0. 82)3. 40 (0. 96)3. 44 (0. 83)0. 272,188. 76. 003(0-0. 02)Ease of navigation3. 64 (1. 11)4. 17 (0. 96)3. 74 (1. 11)4. 202,190. 02 a. 04(0. 004-0. 09)Helpful information3. 87 (0. 76)4. 33 (0. 90)4. 02 (0. 91)4. 152,192. 02 a. 04(0. 004-0. 09)Amount learned3. 29 (0. 76)3. 79 (1. 02)3. 53 (0. 92)3. 862,188. 02 a. 04(0. 003-0. 09)aP<. 05DiscussionPrincipal FindingsIn order to address the growing number of people affected by dementia, increased efforts to provide a preventative health strategy are essential [5-7,27-29]. This study aimed to explore participant engagement in targeted dementia risk reduction eHealth interventions, and to determine whether interactive eHealth tools might be more effective at engaging middle-aged members of the public than a static information-only environment. Results indicated that the majority of participants reported a generally positive experience with the eHealth tools and intended to continue using them following the intervention. However, compared to participants who used the mobile phone app (BA group), participants using the information-based website (BHHP group) reported a more positive evaluation across a range of domains. Use of eHealth ToolsUser exposure, in terms of visiting, using, and revisiting, is an important component of examining the impact of eHealth interventions [20]. Self-reported usage of the eHealth tools indicated that BA participants were more likely to use the eHealth tool regularly, for shorter periods of time, as expected for an app (able to be used anywhere) compared to a Web-based tool (primarily available when in front of a computer). Alternately, BHHP and PLUS participants were more likely to use the eHealth tool less frequently but for longer periods of time. These differences in frequency and duration of use between the app and Web-based tools were expected due to the inherent differences in the way the two modalities are used.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: An indicator analysis identified six bacterial genera (out of a possible 188) that were significantly associated (p < 0. 05) with sub-adults compared with foals; Prevotellaceae Ga6A1 group, Denitrobacterium, Oscillibacter, Anaerovibrio, Family_CR-115, and Anaerostipes. There were no genera significantly (i. e. uniquely) associated with foals compared with sub-adults. Maternal relationship had a significant effect on microbiome similarity (χ2 = 8. 425, df = 2, p = 0. 015; Fig. 2c). Pairwise comparisons showed significant differences in microbiome divergence between mother-offspring relationships and non-maternal mares and juveniles (foals and sub-adults combined) (p = 0. 017). Microbiome divergences between foal-less mares and juveniles were not significantly different to those of mother-offspring relationships (p = 0. 313) or non-maternal mares and juveniles (p = 1. 000). Mares had significantly more similar microbiomes to the band stallion than the other mares in their band (χ2 = 4. 206, df = 1, p = 0. 040; Fig. 2d). DiscussionThe effects of population structuring, in general, and social interactions, in particular, on microbiome composition remains poorly understood; challenges often arise in the separation of direct microbiota transmission via social interactions from effects of communal living such as a shared diet or physical environment [27, 32]. Here, we show that despite large variation between individuals in microbiome composition, spatial structuring, social relationships (i. e. mother-offspring and stallion-mare), and network ties account for microbiome similarities. The main predictor of microbiome composition is individual identity (pony ID), accounting for around 50% of microbiome variation, with ~26% variation across multiple samples collected for each individual and ~34% variation between individuals. Significant inter-individual variation in microbiome composition has been shown in other species [18, 19, 28, 55,56,57]. Given that such a large component of microbiome variation is due to individual ID, which individuals are present in the band (both mares and stallions) may well influence the composition of the total group microbiome.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: 0 cm) on soft wood chip bedding in a controlled environment of 23 ± 1ºC, 60% humidity, and 12:12 h light/dark cycles (light on at 8:00) in the Multi-chamber System (& Co. , Osaka, Japan). The bedding in each cage was changed and the food and water were refreshed twice a week. For the lighting in each chamber, a high color-rendering cold cathode lamps having a wavelength near-natural light was installed. The light intensity was measured by installing an illuminometer (T-10, Konica Minolta, Tokyo, Japan) on the floor in the cage and set to 250 lux intensity. All animals were provided with MF diet, Co. , Tokyo, Japan) and water ad libitum. Mice were held for 1-week acclimatization period before test initiation and then subjected to experiments at 8 weeks-old. After acclimatization, the light/dark cycle in each experimental group was set as follows: Control, 12:12 h light/dark cycle; Shift, 12:12 light:dark cycle with inverting every two days, which imitated the rotating two-shift work [34] (Figure 2A). Lighting time is denoted as Zeitgeber time (ZT), where ZT0 represents the start of the light period (the light period was from ZT0 to ZT12 and the dark period was from ZT12 to ZT24). During the test period, the general condition and body weight of the animals were observed once a week. All procedures were carried out under protocols approved by of the University of Shizuoka (Approval number: 165149, 175172). 2. 3. anesthesia with isoflurane, a momentum measuring device (nano tag®, Co. , Ltd. Nagano, Japan) was subcutaneously implanted into the necks of mice. During operation, the animals were cared for with a thermal pad to prevent decrease body temperature. After an acclimatization period of one week, animals were randomly assigned to either of the two types of light/dark cycles and kept for 4 weeks. Locomotor activity was measured by 3-axis accelerometer inside the device. The recording of the momentum data was aggregated every 5 minutes, and the threshold was set to 170.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: ProtocolYuanchao Ma1,2,3,4, MSc Sofiane Achiche1, PhD Marie-Pascale Pomey5,6,7, MD, PhD Jesseca Paquette5, MSc Nesrine Adjtoutah5,6, MSc Serge Vicente2,3,8,9, PhD Kim Engler2,3, PhD chatbots Committee2,10; Moustafa Laymouna2,3,8, MD, MSc David Lessard2,3,4, PhD Benoît Lemire4, MSc Jamil Asselah11, MD Rachel Therrien5, MSc Esli Osmanlliu2,12, MD Ma'n H Zawati13, PhD Yann Joly13, PhD Bertrand Lebouché2,3,4,8, MD, PhD 1Department of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada2Centre for & Evaluation, of the 3and Immunity in Program, of 4Illness Service, Division of Infectious Disease, Department of 5of the University of 6Department of Health Policy, Management and Evaluation, School of Public Health, University of Montreal, Montreal, QC, Canada7Centre of Excellence on Partnership with Patients and the Public, Montreal, QC, Canada8Department of Family Medicine, Faculty of Medicine and 9Department of Mathematics and Statistics, University of Montreal, Montreal, QC, Canada10See Acknowledgements11Department of Medicine, Division of 12Department of Pediatrics, Montreal Children's 13Centre of Genomics and :Bertrand Lebouché, of Infectious Disease, Department of 1001 4A 3J1CanadaPhone: 1 514 843 2090Email: bertrand. lebouche@mcgill. caAbstractBackground: Artificial intelligence (AI)–based chatbots could help address some of the challenges patients face in acquiring information essential to their self-health management, including unreliable sources and overburdened health care professionals. Research to ensure the proper design, implementation, and uptake of chatbots is imperative. Inclusive digital health research and responsible AI integration into health care require active and sustained patient and stakeholder engagement, yet corresponding activities and guidance are limited for this purpose.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Band membership also predicted microbiome composition, with ~ 14% of the total microbiome variation and ~ 19% of the core microbiome variation explained by this factor. That the microbiome composition of ponies belonging to Aber and Marsh are more similar to one another than Valley may be driven by both spatial structuring and diet, given that the home ranges of these bands overlap. The home ranges of Aber and Marsh are also somewhat different to that of Valley in terms of elevation, slope, and soil moisture; these are more low-lying and marshier in comparison to the steeper, more well-drained, and exposed slopes that characterised the home range of Valley during the study period. The type and quality of grasses or forage across the study area (approximately 5 km2) also vary according to habitat type and thus, diet quality may be driving the observed differences in bands. In addition, variation in browsing behaviour may be driving differences in microbiome composition between individuals. Dietary composition has been shown to affect the microbiome of vertebrates, with consequences for microbiome function and fitness traits such as reproductive success [37, 56, 58,59,60]. The microbiome of Equidae is highly susceptible to changes in diet with implications for nutrient assimilation [37] and diet can have a significant effect on population performance [61]. Microbes acquired from the environment (horizontal transfer) are likely to have greater genomic variation than vertically transmitted symbionts and thus may provide greater variation for microbiome-derived functional advantages [5]. Thus, spatial variation in microbial communities between subpopulations may have implications for fitness traits [5]. Aber and Marsh also showed higher within-band variation, comparable in magnitude to between-band variation, whereas Valley had significantly lower microbiome variation within the band. This may reflect the spatial and environmental differences experienced by members of Valley compared with Aber and Marsh, as well as fewer interactions between Valley and the other two bands. This lower microbiome variation across the group as a whole may have implications for group-level fitness. More work is required to understand how group-level microbiome variation relates to population resilience [27]. Although it may be difficult to dissociate between the influence of shared living and diet on microbiome composition between bands, we also identified spatial structuring of microbiome composition within bands, suggesting that despite communal living, social behaviours still influence microbiome composition. Social behaviours, such as grooming, that occur between members of the same band provide an opportunity for individuals to share microbial communities.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: The recorded locomotor activity data was captured by FeliCa communication, and changes in behavioral rhythms were analyzed with the nanotag/Viewer (Co. , Ltd. Nagano, Japan). 2. 4. acclimatization, mice were randomly divided into Control group and Shift group. BJMC3879Luc2 tumor cells were implanted into the right fourth mammary fat pad of female BALB/c mice by injection of 0. 1 mL PBS containing 5 × 106 cells. Mice were kept for 3, 5, or 8 weeks, either under normal or irregular light/dark cycle conditions. Transplanted tumor size was measured with a caliper once a week, and volume was calculated using the following formula: tumor volume = maximum diameter (mm) × (minimum diameter (mm))2 × 0. 4 [35]. The endpoint for tumor size defined a maximum diameter of 20 mm. Rate of metastasis to lymph node that located distal to the implantation site was calculated as follows: Rate of metastasis (%) = (Number of metastasis to individual lymph node/Total number of mice in each group) × 100. 2. 5. Imaging of Prior examination has shown that the development of peak luciferase activity is strongly influenced by depth of anesthesia and body temperature. The general condition of mice varied in distribution from mild to severe. Therefore, ex vivo imaging was performed to avoid the effects of anesthesia depth and body temperature to luciferase activity. For imaging of tumors and metastatic sites, mice received an intraperitoneal (i. p. ) injection of 120 mg/kg D-luciferin (Wako) dissolved in PBS. 7 minutes post-injection, tissue-specific metastasis in lung, kidney lymph nodes, and axillary lymph nodes were tracked with bioluminescence using the imaging system () by measuring luciferase activity for 50 seconds exposure time. Images were analyzed with software version 3. 0. 2. 6. Determination of NA in To detect intratumoral NA, breast tumors developed in the mammary fat pad were collected. Tumor tissues were homogenized in 0. 01 N HCl (containing: 1% sodium disulfite and 1 mM EDTA) and supernatant was collected. NA was measured using the kit (ImmuSmol, Talence, France) in accordance with the manufacturer’s protocol. 2. 7.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Objective: In response, this manuscript presents a master protocol for the development, testing, and implementation of a chatbot family in partnership with stakeholders. This protocol aims to help efficiently translate an initial chatbot intervention () to multiple health domains and populations. Methods: chatbots study has an adaptive platform trial design consisting of multiple parallel individual chatbot substudies with four common objectives: (1) co-construct a tailored AI chatbot for a specific health care setting, (2) assess its usability with a small sample of participants, (3) measure implementation outcomes (usability, acceptability, appropriateness, adoption, and fidelity) within a large sample, and (4) evaluate the impact of patient and stakeholder partnerships on chatbot development. For objective 1, a needs assessment will be conducted within the setting, involving four 2-hour focus groups with 5 participants each. Then, a co-construction design committee will be formed with patient partners, health care professionals, and researchers who will participate in 6 workshops for chatbot development, testing, and improvement. For objective 2, a total of 30 participants will interact with the prototype for 3 weeks and assess its usability through a survey and 3 focus groups. Positive usability outcomes will lead to the initiation of objective 3, whereby the public will be able to access the chatbot for a 12-month real-world implementation study using web-based questionnaires to measure usability, acceptability, and appropriateness for 150 participants and meta-use data to inform adoption and fidelity. After each objective, for objective 4, focus groups will be conducted with the design committee to better understand their perspectives on the engagement process. Results: 2022 to October 2023, this master protocol led to four substudies conducted at the Mcor the Centre hospitalier de l’Université de Montréal (both in Montreal, Quebec, Canada): (1) for HIV (large-scale implementation expected in mid-2024), (2) -Pharma for community pharmacists providing HIV care (usability study planned for mid-2024), (3) for breast cancer, and (4) -CHAMP for pediatric infectious conditions (both in preparation, with development to begin in early 2024). Conclusions: This master protocol offers an approach to chatbot development in partnership with patients and health care professionals that includes a comprehensive assessment of implementation outcomes.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Moreover, close spatial proximity also promotes the sharing of gut microbiomes through contact with recently deposited faeces, including potential coprophagy [62]. Thus, microbiomes of individuals with close social ties are more likely to converge and, indeed, our data show that specific interactions (i. e. mother-offspring and stallion-mare) lead to more similar microbiomes. This further supports the notion that individuals influence the microbiome composition of one another and ultimately the group. Affiliative behaviours occur more frequently between mothers and their offspring than between foals and non-maternal mares, but vertical transmission of microbiomes between mothers and their foals may also derive from birth and during nursing [35, 63, 64]. Ren et al. [28] also found that microbiomes of mothers and offspring were more similar to one another than between unrelated individuals in red squirrels. Interestingly, foal-less mares had an intermediate microbiome similarity to foals compared with mothers and non-maternal mares, suggesting greater levels of affiliative behaviour or social interaction between foals and mares that did not have offspring in the band. Stallions occupy a central social role in the group, unlike less well-connected mares [40], which is reflected in the greater microbiome similarity between stallions and mares (than between mares) as demonstrated here. It is not clear whether the convergence of microbiomes is driven by the stallion or the mare, but it may well be both. This may result from affiliative behaviours between stallions and mares (including mating) but may also reflect the behaviour of stallions to smell, and thus come into contact with, mares’ faeces. Given that juveniles are prone to dispersal [39, 40] and that social structures tend to break up and reform after significant events such as the annual round-up (Lea and Shultz, unpublished data), it would be interesting to follow changes in individuals’ microbiomes over such events to determine how quickly these converge and whether microbial signatures of the original band remain. It would also be of interest to compare the microbiome composition of males in bachelor groups to those of stallions to further determine the propensity for mares to alter stallion microbiomes. Although we can estimate similarity between microbial communities across bands and individuals, we do not yet know how this relates to functional variation or fitness proxies at the subpopulation (i. e. band) level.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Counting and Classification of For counting of peripheral white blood cells (WBCs), blood samples were treated with 10-fold the amount of Tuerk’s solution (Sigma-Aldrich, St. ), and the number of WBCs per 1 mm3 were calculated by hemocytometer. To perform WBC classification, heparinized blood was smeared onto a slide glass and quickly dried with cool air using a blow-dryer. Slides were fixed and stained with May-Gruenwald stain solution (Sigma-Aldrich) for 3 min, and then stained using 1:20 May-Gruenwald stain solution diluted with phosphate buffer (pH 6. 4, M/15, Sigma-Aldrich) for 3 min. Slides were then stained with Giemsa stain solution (Merck, Darmstadt, Germany) diluted 20 times with phosphate buffer for 15 min. Blood smear specimens were inspected (magnification, × 1,000) using immersion oil. One hundred WBCs were counted and classified into five types (i. e. , lymphocyte, monocyte, neutrophil, eosinophil, and basophil). Each WBC count per 1 mm3 was calculated using the following formula: presence ratio of each type of WBCs × total WBC count. 2. 8. Feeding a Quercetin-in the 3879Luc2 cells were transplanted into female BALB/c mice as described above. The mice were provided MF diet or 0. 3% quercetin-containing MF diet. All food was changed three times a week to reduce the effects of deterioration of quercetin in the MF diet. Mice were maintained for 8 weeks in normal light conditions (Control) or light/dark shift environment (Shift). All mice were divided into the following four groups: MF diet (Control) or 0. 3% quercetin-containing MF diet (0. 3%Q-Ctrl) in a normal light/dark environment; MF diet (Shift) or 0. 3% quercetin-containing MF diet (0. 3%Q-Sh) in an irregular light/dark shift environment. The lymph node metastasis inhibition rates in 0. 3% Q-Ctrl and 0.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: It also contributes to best practice recommendations for patient and stakeholder engagement in digital health research. Trial Registration: ClinicalTrials. gov NCT05789901; Identifier (IRRID): PRR1-10. 2196/546682024;13:e54668doi:10. 2196/54668Keywordschatbot; master protocol; adaptive platform trial design; implementation science; telehealth; digital health; Canada; artificial intelligence; conversational agent; self-management; research ethics; patient and stakeholder engagement; co-construction; mobile phone IntroductionBackgroundSelf-management is key to patient health, and interventions to promote it are being increasingly implemented in the delivery of health care. Effective self-management involves multiple aspects, including problem-solving, decision-making, resource use, patient–health care professional partnership building, and taking action [1,2]. To improve self-management, patients often seek guidance from health care professionals, staff and volunteers from community organizations or peers, and a variety of internet resources [3,4]. However, the availability of these actors and resources can be inconsistent. Moreover, information encountered on the internet varies in quality [5-7]. From reliable but complex scientific 2. Objectives 1 to 3 will be completed in a sequential manner, whereas objective 4 will be assessed throughout the process. A decision will be made on whether to continue with the same chatbot intervention version in objective 3 (implementation) based on the results of objective 2 (usability). In addition, there is no initial fixed duration or sample size for each substudy. This master protocol outlines the common elements of the individual chatbot substudies in terms of objectives and processes. Concurrently, each substudy will have its own subprotocol describing more specific standardized operational structures; additional inclusion and exclusion criteria; recruitment and selection methods; and data collection, analysis, and management. All subprotocols will be managed as appendices to this master protocol and will be subject to further review by the research ethics board (REB) when they are ready. In addition, certain criteria of the master protocol may be redefined by the research team based on progress in each substudy and submitted as amendments to the REB for review for subsequent application to all substudies. Figure 1. Adaptive platform trial design without control group. Figure 2. Study steps for each substudy.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Genetic determinants of microbiome composition, and thus the heritability of microbiomes, have been demonstrated across a range of host taxa [4, 29, 30, 57]. That band-level differences in microbiome composition were also significant in the core microbiome further supports the notion that group-level selection may occur within host microbiomes. However, dispersal of individuals between bands means that subpopulations are not genetically isolated. To further understand the potential for microbiome to act as a unit that selection can act on, it would be valuable to quantify the relative contributions of genetic, environmental, and social factors that determine microbiome composition within this system (and across a range of hosts) and to link these to fitness outcomes such as reproductive success and disease susceptibility. We also demonstrate differences between life stages in microbiome composition of Carneddau ponies; foals had considerably different microbiome composition to both sub-adults and adults. Similar changes in microbiome composition across host development have been seen in other host organisms [10, 29, 65]. For mammals, this is particularly evident for nursing young compared with weaned individuals [66,67,68], and this most likely explains the results we see in our data. There was an absence of unique genera in the microbiome of foals, indicating the transition to a grass-based diet leads to the assimilation of additional bacterial groups into the gut microbiome, potentially through environmental transmission. Although gut microbiome composition has been shown to differ between sexes [69], we found that microbiomes were not significantly different between males and females for this population of semi-feral ponies. However, this may reflect a low number of males in the analysis. It would be of interest to follow changes in male microbiome across dispersion and particularly shifts in composition as stallions form new family groups and their microbiome is influenced by, and influences, new mares joining their band. ConclusionsHere, we show that semi-feral ponies exhibit variation in microbiome composition between bands, which may relate to social, dietary, and environmental factors. In addition, due to the high level of within-individual variation, the direction of group selection may be influenced by the individual members present in the band. Spatial structuring was also identified within bands, suggesting that despite communal living, social behaviours still influence microbiome composition.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: s1 Abbreviations DMSO, dimethyl sulfoxide; FBS, fetal bovine serum; for Research on Cancer; MMP, matrix metalloproteinase; NA, noradrenaline; NLR, neutrophil-to-lymphocyte ratio; PBS, phosphate buffered saline; Q3G, quercetin-3-o-glucuronide; SAM, sympathetic-adrenal-medullary axis; WBC, white blood cell; ZT, Zeitgeber time. authors thank Dr. Taka-aki Suzuki (Institute of Shizuoka Prefecture, Japan), Mr. Tadakazu Takahashi, Ms. Chika Tokuyama, and Mr. (University of Shizuoka, Japan) for their technical support, and Dr. (of Science, Japan) for helpful advice for leukocytes differentiation on this study. We also thank H. Nikki March, PhD, from (www. edanzediting. com/ac) for editing a draft of this manuscript. This work was supported by grant number 15H02899 and 18H03194(KS). The authors have no competing interests to declare. M. Y. and K. S. designed and directed the project. M. N, A. H. and Y. Y. performed experiment and statistical analyses. N. M. designed light-dark cycle conditions. K. S. and M. S. provided the breast cancer cell line, BJMC3879luc2 cells and instructed implantation experiment. T. A. and N. O. provided bioimaging instruction and analytical equipment, N. M. provided instruction and analytical equipment for the quantitative analysis of quercetin. M. N. and K. S. wrote the manuscript and all authors reviewed and commented on the manuscript. 437. DOI: Blood pressure and heart rate variabilities in normotensive and hypertensive human beings. Circ Res. 1983; 53: 96–104. DOI: Circadian rhythm of leucocytes and lymphocytes subsets and its possible correlation with the function of the autonomic nervous system. Immunol. 1997; 110(3): 500–508. DOI: Okamura H. Clock genes in cell clocks: roles, actions, and mysteries. J Biol Rhythms.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: For example, patient participants will be introduced to the study by their health care service provider (eg, physician, nurse, or social and community worker) during their visits. Informational materials such as study flyers and a video (Multimedia 2. This will be obtained remotely through teleconference with an impartial witness to ensure compliance with all aspects of free and informed consent. Following the co-construction approach [30], a co-construction design committee consisting of researchers, health care professionals, and volunteer patient partners will be formed starting from objective 1 to carry out the subsequent development. The sample size for objective 1 is 20 participants to ensure saturation for the needs assessment focus groups [40], whereas 30 participants will be recruited to complete the corresponding usability test for objective 2. This sample size is common for pilot studies and satisfies the minimum size recommended in the literature [41,42]. For objective 3 (implementation), an optimized version of the chatbot will be accessible from the web around the clock, 7 days a week, for translational research. As currently the chatbots will be only released in Canada, recruitment will be exclusive to the Canadian population, and participants will engage in an electronic consent process through the chatbots. Before using the chatbot, individuals will be required to review and accept ’s privacy policy (Multimedia 0. 2≤Cohen d≤0. 5) are detectable in the total sample with the aforementioned statistical power. Regarding objective 4 (partnership evaluation), upon completion of each objective, an email invitation will be sent to organize focus groups with stakeholders involved in the chatbot co-construction design committee. The information and consent forms outline detailed study procedures, anticipated benefits, and potential risks. All template versions are available in Multimedia 6. Participants may withdraw from the study at any time after providing informed consent. This information will be recorded in the electronic enrollment log, and the related privacy management and protection measures will be detailed later. To protect the participants’ personal data and identity, no identifying information will appear in any manuscript or report from this study.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: We identify two such interactions; mother-offspring and stallion-mare, that lead to more similar microbiomes, indicating that individuals influence the microbiome composition of one another and ultimately the group. Thus, we provide novel evidence that microbiome composition is structured at multiple levels within populations. The functional implications of this require further research. 23. 7. 78. 12. 49. 7617. 51. 15. 20. 18. 11. 22. 5. 12. 50. 40. 10. 4. 2367. 99. 28. 92. 180. CAS 7. 25. 27. 34. 14. 17. 36. 97. 18. 85. 0997. 3131. 6. 8. 47. 74. 84. 2. R J. 2013;5:144–61. 2016. URL R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017. URL 63. 20. 3. 6. 8. 1217. 2018. 1. 1. 10013. 2017. 2009. Book 74. 1695. 7. 10. 84. 4. 5563. 73. 15. 23. 5. 13. 2006. 16. 85. 28. 0122. 1639. code is available as Rmd files in Additional files 2 and 3. Author informationAuthors and AffiliationsSchool of Environment and Life Sciences, University of. Antwis & Bryony UnwinSchool of Earth and Environmental Sciences, University of. D. Lea & Susanne ShultzAuthorsRachael E. AntwisView author publicationsYou can also search for this author in PubMed 1. Ponies from which data were collected in the study. Figure S1. Relative abundance of bacterial classes identified in the faecal samples of ponies belonging to the three study bands. Figure S2. Relative abundance of bacterial classes identified in the faecal samples of juvenile and adult ponies. Figure S3. Relative abundance of genera in the core microbiome. (PDF 273 kb)Additional file 2:Microbiome analysis code as an RMarkdown file. (RMD 29 kb)Additional file 3:Additional code for maps and network analysis. (RMD 6 kb)Additional file 4:Sample sheet. (CSV 4 kb)Rights and permissions This 4.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: 2004; 19(5): 388–399. DOI:. Nocturnal light exposure alters hepatic Pai-1 expression by stimulating the adrenal pathway in C3H mice. Exp Anim. 2014; 63(3): 331–338. DOI:. Manipulating the sleep-wake cycle and circadian rhythms to improve clinical management of major depression. BMC Med. 2013; 11: 79. DOI: Light activates the adrenal gland: Timing of gene expression and glucocorticoid release. Cell Metab. 2005; 2(5): 297–307. DOI:. Circadian dependence of corticosterone release to light exposure in the rat. Physiol Behav. 2007; 92(5): 800–806. DOI:. Shifting the feeding of mice to the rest phase creates metabolic alterations, which, on their own, shift the peripheral circadian clocks by 12 hours. . 2015; 112(48): e6683–e6690. DOI: Epigenetic silencing of ARNTL, a circadian gene and potential tumor suppressor in ovarian cancer. Oncol. 2014; 45(5): 2101–2107. DOI: Overexpression of the circadian clock gene bmal1 increases sensitivity to oxaliplatin in colorectal cancer. Res. 2014; 20(4): 1042–1052. DOI:. Altered expression of circadian clock genes in head and neck squamous cell carcinoma. Tumour Biol. 2012; 33: 149–155. DOI: -. Down regulation of circadian clock gene Period 2 accelerates breast cancer growth by altering its daily growth rhythm. Res Treat. 2009; 117(2): 423–431. DOI: Global, Regional, and Incidence, Mortality, Years of Life Lost, with Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A for the of Disease Study. JAMA Oncol. 2017; 3(4): 524–548. DOI: Rotating night shifts and risk of breast cancer in women participating in the nurses’ health study. J Inst. 2001; 93(20): 1563–1568.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Intervention: of the on Messenger 24/7 for free [43], was created as a bilingual chatbot in both English and French trained to converse with people with HIV on the following self-management aspects: (1) guidance for antiretroviral therapy medication (with regard to time management, dosing, drug interactions, and medication reminders, among other things), (2) antiretroviral therapy management when traveling, and (3) common HIV-related knowledge (eg, symptoms, modes of transmission and prevention, and vaccination recommendations). The team conducted the first pilot project (2021-7191) in April 2021 with 28 people with HIV receiving treatment at the MUHC. The study results showed that was tailored to patient requirements and was easy to use and approachable but that the chatbot’s comprehension had limits [32]. For example, if a patient asked a question that was outside the range of topics in the question bank or was worded differently, the chatbot did not always understand it. Nonetheless, considering the development phase, participants reported being satisfied with and mentioned that they intended to use it. Thus, by talking to the on-call chatbot, people with HIV could obtain the information they needed for self-management. AI Algorithms and is currently being developed using the Rasa platform (Inc), an open-source machine learning framework for automating text- and voice-based virtual assistants [44]. As shown in Figure 3, ’s architecture comprises 3 distinct modules: natural language understanding, dialogue management, and response selection. Together with a self-built knowledge database, these modules are used to process the message input and generate the message output. The acceptable input data include natural language in text form as well as some auxiliary expressions commonly used for chatting (eg, emojis such as the thumbs up, smiley face, and sad face). The initial natural language understanding module allows to semantically process the input messages through different algorithms such as text preprocessing, entity extraction, and intent classification. The current training data set encompasses a corpus of >3000 questions covering >30 topics. Figure 4 shows an example of text processing. The entity extraction mechanism enables the chatbot to obtain structured information (eg, date, time, and medication name) from the input messages, whereas intent classification helps discern the purpose of the information received.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: High-resolution strain-level microbiome composition analysis from short reads access Published: 17 August 2023 High-resolution strain-level microbiome composition analysis from short reads Herui Liao1 na1, Yongxin Ji1 na1 & Yanni Sun1 Microbiome 0. 005. Similarly, there are reports showing that multiple strains of C. acnes, an important component in the human skin microbiome, often form a complex mixture [15]. Some of these coexisting strains exhibit high sequence similarities, with a Mash distance of approximately 0. 0004. Moreover, a study [16] analyzing 2144 human fecal metagenomes revealed that numerous samples contained highly similar strains of Bacteroides dorei, coexisting with one another. Commonly used metagenomic binning and assembly tools are not designed to distinguish different strains. Although there are strain analysis tools, they may either require multiple samples from the same population [17], only output the dominant strain [18,19,20,21], or pose a restriction on the similarity between the strains [22]. The second challenge that immediately follows is the resolution of strain-level identification. The resolution here is reflected by the size of the reference database, with a larger number of reference strains indicating a higher resolution [23]. Although some of the strains share very high similarities, there are no known similarity cutoffs below which the genetic differences can be ignored. For example, E. coli CFT073 and E. coli Nissle 1917, which are pathogenic and probiotic respectively, have a sequence similarity of 99. 98% [24]. Similarly, one phage-host coevolution study [25] found that even if the bacterial strains contain high genome ANI (>99. 9%), the strains can be infected by different phages, showing different defense or adsorption mechanisms. For some species with high strain-level diversity, even a few SNVs can lead to phenotypic variations [26, 27]. Thus, a higher resolution can enable a more accurate characterization of the relationship between genotypes and phenotypes. Tools including StrainGE [28] and StrainEst [29] are designed to untangle strain mixtures, but are limited to reporting a representative strain in a sampled strain genome database. Their clustering cutoffs (0. 9 k-mer Jaccard similarity (StrainGE) or 99.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: DOI: Erdem JS, Notø HØ, Skare Ø, Lie JS, Petersen-Øverleir M, Reszka E, et al. Mechanisms of breast cancer risk in shift workers: Association of telomere shortening with the du-ration and intensity of night work. Cancer Med. 2017; 6(8): 1988–1997. DOI: Hansen J, Stevens RG. Case-control study of shift-work and breast cancer risk in Danish nurses: Impact of shift systems. Cancer. 2012; 48(11): 1722–1729. DOI:. The overall 5-year survival rate of breast cancer among Iranian women: A systematic review and meta-analysis of published studies. Breast Dis. 2017; 37(2): 63–68. DOI:. Sympathetic innervation, norepinephrine content, and norepinephrine turnover in orthotopic and spontaneous models of breast cancer. Immun. 2016; 53: 223–233. DOI:. Quercetin-3-O-glucuronide inhibits noradrenaline-promoted invasion of MDA-MB-231 human breast cancer cells by blocking β2-adrenergic signaling. Biophys. 2014; 557: 18–27. DOI:. Adrenergic control of the adaptive immune response by diurnal lymphocyte recirculation through lymph nodes. J Exp Med. 2016; 213(12): 2567–2574. DOI:. Nocturnal light exposure stimulates the cardiac fibrinolysis system and stress responses in C3H/He mice. Thromb Res. 2020; 180: 79–81. DOI: Retrospective cohort study of the risk of impaired glucose tolerance among shift workers. Health. 2012; 38(4): 337–342. DOI:. Comparison of melatonin profile and alertness of firefighters with different work schedules. J Circadian Rhythms. 2018; 16(1): 1–7. DOI: Jongwoo K. The relationship between frequency of injuries and workplace environment in Korea: Focus on shift work and workplace environmental factors. Work. 2018; 9(4): 421–426. DOI:.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: To train to understand different types of questions and provide relevant and accurate answers, we assembled a multidisciplinary team of 2 clinicians (ML and B Lebouché), 2 pharmacists (B Lemire and RT), 4 patients, 2 engineers (YM and SA), and multiple developers. Each group’s expertise played a crucial role in chatbot content development and continuous improvement: (1) health care professionals validated the medical accuracy of the answers and information given by the chatbot; (2) together with patient partners, they collaborated to identify common self-care challenges; (3) engineers then assisted in building decision models and preparing chatbot training data based on identified problems; and (4) finally, engineers and patients collaborated on testing and refining the chatbot based on feedback, completing the interdisciplinary cycle. This multidisciplinary team allowed us to ensure the accuracy and quality of the information provided as well as conduct holistic research and ongoing evaluation of the chatbot to promote its long-term usability. The InterfaceMessenger has 1. 3 billion monthly active users worldwide exchanging 8 billion messages per day [46]. This indicates a robust familiarity with the Messenger interface among the population, simplifying their grasp of the platform’s specifications and interaction capabilities, ultimately favoring the uptake of the chatbots. In addition, the versatility of Messenger, compatible across smartphones, tablets, and desktops, reduces the potential risk of participant exclusion because of hardware limitations. Nevertheless, the privacy concerns brought up with Facebook in the past may make people hesitant, which could be one of the potential barriers to the implementation of Messenger for health purposes. Indeed, other user interface options such as a stand-alone mobile app, a web page, or other third-party applications (eg, Telegram or Instagram) are under consideration. As the study proceeds and evolves, relevant changes will be implemented as necessary. ProcessOverviewMultimedia 0. 920, 95% CI 0. 797-0. 987) for the independent overall score ratings of 37 different digital health tools [47]. On the basis of developer recommendations, we will consider a successful prototype for testing to have an average score of at least 4 for the sections on information and health-related quality, as well as an overall average score of 4, as they are identified as key indicators of prototype safety. Collection and AnalysisThroughout the study, participants will have the option to take part in either English or French focus groups and workshops.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 4% ANI (StrainEst)) can still lead to large clusters for some bacteria. Although StrainGE can further identify SNPs/deletions against the identified representative strain in a sample, it doesn’t pinpoint the specific strain in the identified clusters. Two k-mer-based tools, Krakenuniq [30] and StrainSeeker [31], also have a very low resolution in strain-level identification when strains in the database share high similarities. The third challenge is the identification of low-abundance strains. For example, the de novo strain construction tools [32, 33], which aim to reconstruct strains by using assembly-based strategies, usually require a high coverage of strains to achieve an accurate strain reconstruction. Besides, many strain-analysis tools [34,35,36] also require strain coverage greater than 10X to return accurate identification. Thus, it remains a challenge to identify strains with low coverage for these tools. The last challenge is strain identification time. According to the recently published studies [23, 37], most alignment-based strain-level identification tools including Sigma [38], and Pathoscope2 [39] can be computationally expensive when the database is large. While the large reference database can increase coverage of intra-species diversity, it also requires more computational resources. Thus, there is a pressing need to provide more sensitive, accurate, and efficient strain-level analysis for metagenomic data. In this work, we introduce StrainScan, an open-source tool that can accurately detect known strains from sequencing data, including metagenomic data or whole-genome sequencing data. In order to strike a balance between the resolution and computational complexity, we developed a novel hierarchical k-mers indexing structure for a large number of strains, which usually demonstrate heterogeneous similarity distributions. In the first step, we cluster highly similar strains into clusters. Then we design a novel Tree (CST), a tree-based indexing structure for cluster search. By carefully balancing the number of k-mers in each node, we optimize the CST to prevent false positive strain identification for low abundance strains. In the second step, we use strain-specific k-mers and k-mers that represent SNVs and structural variations to determine which strains are likely to present. The final output of StrainScan includes the identified strains and their abundances.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Lymphangiogenesis and axillary lymph node metastases correlated with VEGF-C expression in two immunocompetent mouse mammary carcinoma models. Breast Cancer. 2011; 2011: 867152. DOI:. Systemic Immune-inflammation Index, Based on and Neutrophil-Lymphocyte Ratio, for in Lung Cancer. Exp Med. 2015; 236: 297–304. DOI: Neutrophil-to-lymphocyte ratio may be associated with the outcome in patients with prostate cancer. SpringerPlus. 2015; 4: 255. DOI: Grosso G, Godos J, Lamuela- A comprehensive meta-analysis on dietary flavonoid and lignan intake and cancer risk: Level of evidence and limitations. Food Res. 2017; 61(4): DOI: ás-Barberán Flavonoids in food and their health benefits. Hum Nutr. 2004; 59(3): 113–122. DOI:. Dietary antioxidant flavonoids and risk of coronary heart disease: the Study. Lancet. 1993; 342(8878): 1007–1011. DOI: Dihal AA, de Boer VC, van der Quercetin, but not its glycosidated conjugate rutin, inhibits azoxymethane-induced colorectal carcinogenesis in F344 rats. J Nutr. 2006; 136(11): 2862–2867. DOI: An immunocompetent murine model of metastatic mammary cancer accessible to bioluminescence imaging. Anticancer Res. 2009; 29(11): 4389–4395. Combined effect of circadian dysfunction and cadmium on immune suppression. Sci. 2016; 3(5): 237–242. DOI: Synthetic a-mangostin dilaurate strongly suppresses wide-spectrum organ metastasis in a mouse model of mammary cancer. Cancer Sci. 2018; 109(5): 1660–1671. DOI:. the Risks of Cancers in Women: A and Meta-analysis of 61 423. DOI: Morales--Gallego A, Jimenez-Lopez JC, Morell M. of the Polymorphisms of Associated with Endocrine Cancer.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: There will be no active third-party human involvement in the entire testing process between the chatbot and participant user except in the case of user-initiated requests (eg, to solve unexpected bugs). In week 4, a total of 3 focus groups with 5 randomly selected participants each will be conducted to explore ’s usability in greater depth and in the participants’ own words. In total, 3 focus groups can capture at least 80% of the themes, which is sufficient saturation for a usability study [49]. Collection and AnalysisThe initial questionnaire for objective 2 will gather fundamental sociodemographic information (eg, year of birth, preferred language, gender, and ethnic group identity) and digital technology use (Multimedia 24. A significance level of 5% will be adopted. Four central subconstructs of the Model (TAM) framework will also be assessed as secondary end points via validated instruments to complement the data: (1) perceived ease of use, (2) perceived usefulness, (3) attitude toward use, and (4) behavioral intention to use the chatbots. Perceived ease of use will be measured using the Question [54], which is answered on a 7-point Likert scale. Drawing on instruments by Chau and Hu [55] and Davis [56], perceived usefulness will be measured on a 7-point Likert scale with 4 items slightly adapted for relevance to chatbots. The final score will be the average of these items. Attitude toward using chatbots will be measured using the net promoter score (NPS) [57], which is used as a measure of user satisfaction. A single question will be asked on an 11-point Likert scale. To calculate the NPS, 3 groups are created: promoters (score of 9-10), passives (score of 7-8), and detractors (score of 0-6). Subtracting the percentage of detractors from that of promoters provides the NPS (range −100 to 100). Positive scores, and especially those of >50%, are judged positively. Finally, behavioral intention to use the chatbots will be assessed using a validated 2-item questionnaire [58] rated on a 7-point Likert scale averaged to produce a final score. Predicted positive associations between subconstructs within the TAM framework will be evaluated using simple linear regressions considering the slope coefficient. Their significance will be tested using a Student t test.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: By searching strains inside the identified clusters, StrainScan achieves a higher resolution than cluster-level tools such as StrainGE and StrainEst, which only keep one representative strain for each cluster. As shown in Fig. 1A, different resolutions can lead to different observations and conclusions. While StrainScan can identify two different strains in sample S1, StrainGE or StrainEst does not distinguish them because they are from the same cluster. Similarly, pinpointing a specific strain rather than a cluster when comparing two samples (S1 and S2) can lead to more accurate gene composition-based analyses because the strains in one cluster can still possess very different gene contents (Fig. 1B and C). By benchmarking StrainScan with other available tools on multiple simulated and real sequencing datasets, we demonstrate that StrainScan can output strain-level composition with higher accuracy than the state-of-the-art tool. In particular, when compared to the state-of-the-art tools such as StrainGE, StrainScan improved the F1 score by more than 20% in identifying multiple strains at the strain level. StrainScan is a targeted strain composition analysis tool, requiring users to provide reference genomes for bacteria of interest. By supporting customized construction of the indexing structure for any set of reference genomes, it can be applied to any bacterium. MethodsOverview of StrainScanStrainScan is designed to identify known strains from short reads directly. Because there are many species-level composition analysis tools for metagenomic data, the inputs to StrainScan are the short reads in “fastq” format and strain genomes for targeted bacteria in “fasta” format. To strike a balance between the strain identification resolution and computational cost, we design a hierarchical indexing method that combines a fast but coarse-grained Tree (CST) and a slower but fine-grained strain identification strategy inside a cluster. As shown in the flowchart in Fig. 2, we first create a cluster tree-based indexing structure. With our efficient and accurate cluster search method on this tree, we can first pinpoint a cluster that is present in the sample. Then we will use carefully chosen k-mers to distinguish different strains in the identified clusters. The hierarchical method has several advantages. First, it allows us to accommodate the heterogeneous similarity distribution between strains with some strains sharing much higher similarities than others.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Front Endocrinol. 2019; 10: 104. DOI: Ruszkowaka-. of Laterality on Clinicopathological, Angiogenic, and Hemostatic Profiles: A of in Future Outcomes. J Clin Med. 2020; 9(6): 1708. DOI: Yang X, Wood PA, oh EY, Du-. Down regulation of circadian clock gene Period 2 accelerates breast cancer growth by altering its daily growth rhythm. Res Treat. 2009; 117: 423–431. DOI: Inhibitor 1 (PAI1) in Triple-Cancer. Res. 2019; 17(5): 1142–1154. DOI: ünner N, Danø K, Johnsen M. Metastasis of transgenic breast cancer in plasminogen activator inhibitor-1 gene-deficient mice. 2003; 22: 4389–4397. DOI: Eva H, William T, Xiao- and to in Mice. Nat Commun. 2020; 11: 3193. DOI: - Chronic stress in mice remodels lymph vasculature to promote tumour cell dissemination. Nat Commun. 2016; 7: 10634. DOI: Obi--Kobayashi E, Fujimura A. Involvement of Wee1 in the circadian rhythm-dependent intestinal damage induced by docetaxel. J Ther. 2013; 347: 242–248. DOI:. Illuminated night alters hippocampal gene expressions and induces depressive-like responses in diurnal corvids. Neurosci. 2018; 48(9): 3005–3018. DOI: IL-35 induces N2 phenotype of neutrophils to promote tumor growth. Oncotarget. 2017; 8(20): 33501–33514. DOI: Liang W, Ferrara N. Role of Neutrophils in and Metastasis. Res. 2016; 4(2): 83–91. DOI:. Neutrophils: important contributors to tumor progression and metastasis. Rev. 2015; 34(4): 735–751. DOI: of Suppresses Hepatocarcinogenesis. Cancer Cell. 2016; 30(6): 909–924.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: All statistical analyses will be conducted using the R software (R Foundation for Statistical Computing) [59]. Collection and AnalysisParticipants’ experiences with will be explored through a semistructured focus group on the main constructs of the TAM framework (ie, usefulness, ease of use, attitude, and behavioral intention to use), the analysis of which will help further explain the quantitative analysis. A focus group interview guide can be found in Multimedia 8. A composite coding matrix based on the TAM and the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework will be favored for deductive content analysis [60]. framework [61] was developed to support the implementation and scale-up of technological innovations in health care. It includes seven relevant domains: (1) the illness or condition, (2) the technology, (3) the value proposition, (4) the adopter system, (5) the organization, (6) the wider context, and (7) embedding and adaptation over time. Given that 5 to 7 are more focused on scaling up implementation, objective 2 will likely focus on the first 4 subdomains for analysis to illustrate associated barriers and facilitators of the early phases of implementation. All facilitators and barriers identified will be then subsequently matched to the subconstructs of the TAM, when possible, to understand their impact on global usability. The content analysis involves 3 phases. In phase 1, preparation, the analyst will attempt to understand the entire data set through immersion in the data. In phase 2, organization, a composite coding matrix will be devised using the NASSS domains and the TAM subconstructs. The data will be coded and categorized using NVivo R1. Finally, in phase 3, reporting, the descriptive content of the categorization will be presented, addressing trustworthiness. The saved transcripts of users’ conversations with the chatbots may also be submitted for content analysis to describe and better understand the nature of chatbot-participant interactions, such as conversation topic trends. Objective 3: Implementation AssessmentOverviewFor this objective, we will further assess the implementation outcomes of the chatbots after they are deployed to the general population via Messenger. During this stage, the chatbots will be regularly updated to introduce new features or content, and their impact on implementation will be assessed. If the outcomes are negative, the corresponding version of the chatbot will be submitted for continuous improvement and evaluation.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The strains with low similarity can be quickly identified by our fast CST search strategy. And only those highly similar strains need a finer distinction in the second step. Second, the hierarchical method can increase the search accuracy by allowing us to use more unique k-mers (S1). Any k-mers that is shared between clusters now can be utilized for within-cluster search. Third, the hierarchical method can reduce the memory footprint. Without the hierarchical method, we need to search strains from all 0. 95. In this example, given the cutoff represented by the dashed red line, five clusters from C1 to C5 are output by the clustering process. (b) Given the clusters, construct the hierarchical cluster tree for later cluster-level identification. (c) Generate collinear blocks to extract k-mers that can help distinguish different strains inside the same cluster. (d) Step d concludes the indexing structure process for the reference genomes. (e) and (f) The indexing structure and the sequencing data (reads) are input for strain search. (e) Search for clusters. (f) Strains are identified by the iterative matrix multiplication, and the relative abundance profile is finally inferred by elastic net regressionFull size imageFig. 3A An example of the k-mers assignment in the CST-based indexing structure. Each node possesses k-mers unique to its rooted subtree and is shared by most of the strains in the subtree. Each bar with a specific color represents a k-mers and each node is assigned with one unique k-mers in this example. B When constructing node v’s k-mers set, all leaf nodes will be divided into three groups named \(PRE_v\), \(SUB_v\), and \(EXT_v\)Full size imageTree (CST) constructionGiven many strains’ genomes of the same species, we first calculated a Jaccard similarity matrix with an alignment-free, k-mers based method Dashing [40] (\(k = 31\)). Then, we performed the agglomerative hierarchical clustering (single-linkage) based on this matrix, grouping the strains into a dendrogram. Finally, we chose a fixed height cutoff H (0. 95 by default) to cut the dendrogram into many clusters, with which consisting of one or more strains.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Surprisingly, only a few studies have explored the beneficial effect of melatonin administration on night shift workers in an actual workplace setting [26]. The purpose of this study was to compare the efficacy of melatonin (5 mg) and placebo in the treatment of shift workers with insomnia. conducted a double-blind, placebo-controlled, randomized crossover trial among healthy, non-smoking, non-pregnant shift worker nurses at (Tehran, Iran). Each subject signed an informed consent document after the procedures had been fully explained. All subjects were asked to sign the consent form, confirming that they understood the goals, risks, and potential benefits of the study and their right to withdraw from the study at any time. Committee of approved the study. Consenting participants were randomized to one of two sequences: placebo followed by melatonin or melatonin followed by placebo. The randomization list was completed using a random number generator. The treatment phase of each sequence consisted of taking a 5 mg tablet of melatonin about 30 minutes before habitual nighttime sleep. The placebo phase consisted of taking an identical looking placebo on the same schedule. Both melatonin and placebo were taken on the first night after night work. All participants had 3 visits to the hospital. At the first visit, eligibility was checked, participants gave informed consent, and baseline insomnia parameters were assessed by using seven questions [32]. Participants were asked to scale their difficulties in falling asleep, staying asleep and waking up too early with scores from 1 (no problem) to 5 (very severe problem). They were also asked to specify their sleep quality (satisfaction with their sleep) using scores from 1 (very satisfied) to 5 (very unsatisfied) and were asked to answer the following three questions about their habitual night time sleep: 1) How long does it take you to fall asleep? 2) How many times do you wake up during the night? 3) How many hours do you sleep? All the participants who reported sleep problems in the baseline questionnaire were included in the study. In the first visit, one placebo or one melatonin was given to the nurses. They were asked to use it at home on the first night after shift work, about 30 minutes before their intended sleep. On the following morning, upon awakening, each participant answered the questionnaire. In the second visit, after the 4 days of washout and receiving their completed questionnaire, patients entered into another study period, conducted as in the first.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: To minimize this potential risk, participants will be informed of the limitations of the chatbots during the consent process. In addition, participants will be prompted to report any perceived inaccuracies and their consequences to the research team in a timely manner. Weekly revisions of user chat logs will be conducted by the research team to ensure timely human intervention in the event of errors. The governance board for this study will consistently monitor and discuss these reports. Finally, any other service-related risks (eg, chatbot or Facebook network service disruptions) will be communicated to each participant in a timely manner and properly documented by the study coordinator. Participation in the study presents benefits, including early access to chatbot interventions with validated health care information. Participants will also be compensated appropriately upon completion of each study objective [74] in the form of a gift card or, exceptionally, a money transfer. Results2022 to October 2023, four substudies were established in conjunction with the completion of this master protocol:The first study is a continuation of the original for HIV self-management. This project has secured funding from the Fonds de recherche du Québec – Santé Réseau SIDA/(AIDS and Network). A subprotocol targeting objective 3 (implementation) is currently being prepared and scheduled for REB submission in early 2024. Recruitment is scheduled to begin in mid-2024, and the related data analysis will begin in early 2025. The second study is -Pharma, a project to promote community pharmacists’ knowledge of HIV treatment, with its prototype to be completed by the end of 2023. A related manuscript based on a pharmacist needs assessment is in preparation. A subprotocol for objective 2 (usability) is being developed and is scheduled to be submitted for REB approval in early 2024, and recruitment is scheduled for mid-2024. The third project is to develop the chatbot for self-management of patients with breast cancer. This study is supported by funding from the. The subprotocol was approved by the on September 26, 2023 (approval MP-37-2024-9633). Recruitment for objective 1 (development) is expected to begin in early 2024. The fourth study is to develop -CHAMP, an accessible chatbot to assist in the management of pediatric patients with infectious conditions. A funding proposal for this project will be submitted to the of Spring 2024 program.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The strains inside each cluster have the k-mer-based Jaccard similarity \(\ge 0. 95\), which roughly corresponds to average nucleotide identity (ANI) of 99. 89% [28]. To pinpoint the cluster where a strain is contained, we will convert the clusters and the dendrogram into a CST to support both accurate and efficient cluster search. The CST keeps the same tree topology as the dendrogram except that each cluster is represented by a leaf node in the tree. In addition, we discard the distance information in the dendrogram so that the distance between each node and its parent (or child) is uniform, regardless of their Jaccard similarities. Thus, the CST is a full binary tree. In order to support the cluster search, each node contains a set of k-mers that are unique to the subtree rooted by this node. By conducting k-mers match, the CST will guide us to take either the left child or the right child until reaching one or multiple leaf nodes (i. e. , clusters). We first describe how we assign k-mers for each node. k-mers assignment for the nodesA CST is defined by two elements: the tree topology and the k-mers set assigned for each node. In this section, we will describe how we assign k-mers for the nodes to support the cluster search. For a node v in the CST, we denote the subtree rooted by v as \(T_v\). The k-mers assignment of v follows two criteria. First, the k-mers should be shared by most of the strains in the leaf nodes of \(T_v\). Second, the k-mers are unique to the strains in \(T_v\). The two criteria are visualized using an example in Fig. 3A. Following the two criteria, we first assign leaf nodes with k-mers extracted from strains in their corresponding clusters. To use k-mers that represent relatively well-conserved features in the underlying strains, only the k-mers that appear in at least \(\alpha \%\) of the strains will be kept for clusters with multiple strains. Big \(\alpha\) indicates that only k-mers shared by many strains are used for building the CST while small \(\alpha\) allows the CST to use strain(s)-specific k-mers. We compared the cluster identification performance using a range of \(\alpha\) in our experiments.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: In the third visit, the last completed questionnaire was received from nurses. We used SPSS 11. 5 for Windows for statistical analysis. When the Kolmogorov-Smirnov test confirmed normality, parametric tests were conducted. One-way analysis of variance was used to determine the statistical significance of subjective scores of insomnia from each phase of the trial. Statistically significant results detected by analysis of variance (p < 0. 05) were further analyzed by using Tukey post hoc paired comparisons. Results in the text are expressed as mean +/- standard deviation. Results Eigthy-six out of 118 participants completed the study. Eleven subjects dropped out during the first treatment phase because they could not attend the scheduled tablet taking, whereas one did not complete the second treatment phase because of an acute illness unrelated to the study protocol. Twenty participants did not report sleeping problems in the baseline questionnaire. Table 1 presents a description of the 86 subjects who completed the study. Females accounted for 80. 2% of the participants, and the mean age was 30. 05 years (range: 24–46 years). Subjective parameters of sleep obtained from the baseline questionnaire are shown in Table 1. Table 1 Demographic and subjective sleep data of subjects (N = 86) Mean (SD) or percentage (n)Sex80. 2% female (69)Age30. 05 (5. 2)Mean BMI in kg/m226. 7 (3. 1)Subjective sleep onset latency in min37. 5 (41. 3)Subjective number of awakenings5. 2 (2. 1)Subjective duration of sleep in min450. 5 (82. 3) Differences in sleep data at baseline, while taking placebo, and while taking melatonin are shown in Tables 2 and 3. Subjective sleep onset latency (SOL) was 37. 5 +/- 41. 3 minutes at the baseline. There was evidence of an effect of melatonin treatment on SOL. Specifically, the mean SOL for subjects being treated with melatonin was significantly lower than the mean SOL for subjects given placebo. Furthermore, means for both the subjects given melatonin and those given placebo were significantly different from the baseline mean (see Table 2). Although melatonin treatment did not significantly alter other insomnia variable compared with baseline values, there was a significant improvement in sleep quality with melatonin treatment (see Table 3).

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Their co-constructive engagement allows them to take a leading role in the ongoing digitization of health care and can help mitigate or even address the risks that chatbots face during implementation, such as those tied to trustworthiness, data privacy, and exacerbating inequalities in access to health care. Incorporating patient and stakeholder involvement strategies in developing master protocols, as suggested by Huml et al [78], can make them more successful for patients, providers, and sponsors. Patients and stakeholders will be invited to contribute on an ongoing basis to subsequent chatbot development, reporting of trial results, product marketing, and knowledge translation. Systematically documenting, investigating, and reporting this entire process will be beneficial in providing the scientific community with a clear and replicable model for responsible AI medical research. Certain limitations need to be recognized. This master protocol focuses solely on the assessment of chatbot implementation outcomes, similar to master protocols typically prepared for pharmaceutical investigations that focus on “early exploratory development phases” [78]. Clinical and service-related outcomes are not included in this master protocol as there is no consensus on their assessment methods. Notably, <25% of AI-based digital health trials include patient-reported outcome measures as end points despite their widespread use in other health care trials [79]. Therefore, corresponding research efforts are necessary to develop relevant high-quality assessment metrics to foster the development and validation of user-centered chatbots. If the substudy decides to assess their clinical effectiveness, appropriate amendments will be made. Large language model (LLM) technology is also not considered in this master protocol for the time being. The release of LLMs, represented by GPT-4, has been indeed impressive. Unfortunately, LLM-based chatbots exhibit limitations such as a lack of transparency, sharing of unverified health information, and poor interpretability [80]. These factors threaten the trustworthiness and security of chatbots, which are key to their successful implementation in health care. Although current state-of-the-art LLMs are, of course, embraced to help generate more diverse and personalized responses, trialing these models in clinical settings also remains a challenge owing to the lack of relevant regulatory compliance and the fact that the existing software-as-a-medical-device framework is not suited to such models [81].

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Inspired by that study, we extract group-specific k-mers from the SVs shared by some strains. However, relying only on strain-specific and group-specific k-mers still suffers from low resolution in some cases. For example, in Fig. 5, both Strain4 and Strain5 have the same group-specific k-mers, and when the strain-specific k-mers of Strain5 is not present in the sample, we cannot make a fine distinction between the two strains. Therefore, to further improve the resolution, we add joint k-mers sets, which contain SNVs and indels from core genomic regions present in all genomes [29, 38, 43]. As shown in Fig. 5, for all joint k-mers, although each k-mers is not strain-specific, the joint k-mers set for each strain is unique. However, the number of joint k-mers is often not as many as the first two types of k-mers (S2). They need to be combined together to improve the resolution of identification. By utilizing these three types of k-mers, we improve the resolution of identification and reduce the search space at the same time. Fig. 5Use strain-specific k-mers, group-specific k-mers and joint k-mers to distinguish five strains in the same cluster. Each strain has a unique k-mers combinationFull size imageTo efficiently extract these k-mers, we utilize Sibeliaz [44], an efficient tool designed for identifying locally collinear blocks in closely related genomes. Based on the blocks generated by Sibeliaz, we develop a hash-based algorithm to extract these k-mers from strain genomes and save them in a matrix for later usage. The algorithm’s main pseudocode is shown in 1. 3. The input to the algorithm is strain genomes within the same cluster and blocks generated by Sibeliaz. By using the efficient hash table, the algorithm can extract target k-mers quickly. Finally, all extracted k-mers from a cluster are saved in a matrix X of size \(M\times N\), where M is the number of k-mers and N is the number of strains in this matrix. Then, X\([i,j]=1\) if strain j has ith k-mer, otherwise, X\([i,j]=0\).

Output: contrast