Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: We are not alone: a case for the human microbiome in extra intestinal diseases access Published: 07 March 2017 We are not alone: a case for the human microbiome in extra intestinal diseases S. Shivaji1 1. 13 kg and is ten times more than the total number of cells in the human body (1013). Thus we are more bacteria than human. This enormous community of microorganisms was termed the “microbiome” by Lederberg and Mccray [1] and was defined as a multi-species community of microorganisms (bacteria, fungi and viruses) that differs from niche to niche in population density and community composition. For instance the microbiome of the gut is different from that on the skin, on the scalp, inside the nostrils, mouth, armpits, oesophagus, stomach, small intestine, vagina etc. [2,3,4]. What could be the reasons for the variation? How is it important? And what is the role of genes associated with the microbiome which according to conservative estimates is 200–300 times more than the total genes in the human body? These are few of the questions that need to be answered. Main textDysbiosis and diseasesTill the 1990s, it has been a mystery as to whether the microbes/genes in the gut microbiome have any impact on human health other than the canonical role of the gut bacteria to break down food remnants, modulate the immune system, promote fat storage, biosynthesis of vitamins and amino acids, metabolism of drugs etc. But, with the advent of next generation sequencing and other genome enabled technologies including DNA based databases and metabolomics on human microbiome studies have become an eye-opener to hitherto unknown facts about the composition of gut microbiomes, their dynamics and their relevance to human welfare [5]. At the phylum level Bacteroidetes and Firmicutes are the most dominant bacteria in the gut of normal individuals and together constitute 70–90% of the total bacterial community followed by Actinobacteria and Proteobacteria. This community is dynamic and known to vary in normal individuals with age, ethnicity, diet, exposure to chemicals and also host genetic variation [6,7,8,9]. Studies analyzing the gut microbiomes of twins established a link between human genotype and the composition of the gut microbiome [7]. All these factors make the very definition of a normal core microbiome a great challenge. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of the human microbiome and its interactions with various factors, rather than stating it can single-handedly cure all diseases. It highlights how the microbiome varies based on age, ethnicity, diet, and host genetics, underscoring that while it plays significant roles in health, understanding its full impact requires further research. The mention of various roles of gut bacteria, rather than a singular role of curing diseases, contradicts the claim that it can cure all diseases alone.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: 2017; 40(1): zsw011. Рецензия Для цитирования: Субботина А. А. , Долгих О. В. Полиморфизм генов циркадных ритмов у работников предприятия непрерывного производства со сменным графиком работы. Медицина труда и промышленная экология. 2023;63(4):270-274. For citation: Subbotina A. A. , Dolgikh O. V. Polymorphism of genes of circadian rhythms in employees of a continuous production enterprise with a shift work schedule. of. 2023;63(4):270-274. (In Russ. ) Просмотров: 206 Контент доступен под лицензией Attribution 4. 0 License. [SEP] Justify:

Output: The text does not address the claim about shift work and its health effects related to circadian rhythms. Instead, it focuses on genetic polymorphisms in circadian rhythm genes among employees with shift work schedules. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 그러나, 이런 도구를 효율적으로 적절히 사용할 수 있는 전문가로서 충분한 지식과 술기를 갖추어야 답이 어느 수준에서 타당한지 판단할 수 있을 것이다. 앞으로 이 인공지능 챗봇이 점점 더 정교하고 신뢰할 수 있는 수준으로 발전하고 더 많은 우리말 정보로 훈련도 받을 것이다. 인공지능 챗봇이 어디까지 발전할지 주시하고 또한 우리 의사도 이런 작업에 전문가로서 적극 참여하여 더욱 유용한 챗봇 개발에 일익을 담당하기 바란다. [SEP] Justify:

Output: The text corroborates the claim by indicating that AI chatbots are expected to become more sophisticated and reliable, which aligns with the notion that they can be used for mental health support and patient engagement. It suggests that as these chatbots improve and receive more training with relevant information, their effectiveness in these roles will likely increase.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: Aberrations from the normal core microbiome is referred to as “dysbiosis” (meaning imbalance in the microbiome) and has been implicated in auto-immune diseases like diabetes, rheumatoid arthritis, muscular dystrophy, multiple sclerosis and fibromyalgia, in inflammatory diseases like obesity, neonatal necrotizing enterocolitis, inflammatory bowel disease and vaginosis, in some cancers and mental disorders [10, 11]. Ability of the gut microbiome to modulate the immune system is considered as an important reason for the diseased state [12]. The direct evidence that the microbiota are indeed the cause of the disease is strengthened by the pioneering studies on obesity in mice. Gut bacteria from fat mice when transplanted into genetically lean mice with no gut bacteria of their own, transform the lean mice into obese mice [13, 14]. Subsequent studies demonstrated that skinny germ free mice plump up on receiving a fecal transplant from a human donor implying that the bacteria help the recipient to digest and metabolise more efficiently [13,14,15]. But, if the fecal transplant of the human donor was supplemented with Christensenella minuta the recipient mice were thinner indicating that C. minuta controls obesity [7]. Another example, emphasizing the role of gut bacteria is related to Clostridium difficile infection (CDI) patients who are cured of CDI when transplanted with fecal microbiota from normal individuals [16, 17]. This led to the concept that just as a pathogen could cause a disease a “good” microbe could prevent a disease? For instance Faecalibacterium prausnitzii could protect mice against experimentally induced intestinal inflammation since these gut bacteria were anti-inflammatory. Yet another example of a good microbe is Akkermansia muciniphila. Increase in abundance of A. muciniphila correlated with an improved metabolic profile and reversed obesity and decreased insulin resistance probably mediated by endocannabinoids secreted by A. muciniphila [18, 19]. Dysbiosis and extra-intestinal diseasesLinking food/gut related disease like obesity, inflammatory bowel disease, enterocolitis, diabetes etc. to dysbiosis appears to be obvious but implicating dysbiosis with extra-intestinal diseases like arthritis, muscular dystrophy, multiple sclerosis, vaginosis, fibromyalgia, some cancers and mental disorders appears to be more challenging. [SEP] Justify:

Output: The text contrasts the claim by detailing how dysbiosis in the microbiome is linked to various diseases rather than suggesting that the microbiome alone can cure them. It discusses various diseases associated with an imbalanced microbiome but does not support the idea that the microbiome has the singular ability to cure all human conditions.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: ISSN 1026-9428 (Print)ISSN 2618-8945 (Online) Отправить статью Правила для авторов Редакционная коллегия Редакционный совет Рецензирование Этика публикаций * не является рекламой Индексация журнала * не является рекламой Мы в социальных сетях: Популярные статьи 3-й Международный Молодёжный Форум «ПРОФЕССИЯ и ЗДОРОВЬЕ» (OHIYF-2020) № 1 (2020) Условия труда и формирование рисков нарушения здоровья у работников нефтехимической промышленности, занятых в производстве метанола и его производ [SEP] Justify:

Output: The text is irrelevant to the claim as it does not provide any information related to the effects of shift work on health or circadian rhythms. Instead, it includes information about publication rules and indexing, which does not pertain to the claim being analyzed.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Wu1, PhD; Charlotte Summers2,3, BSc; Arjun Panesar2,3, MEng; Amit Kaura2,4, Li Zhang5, 1School of Business and Management, Royal Holloway, University of London, Egham, United Kingdom2DDM Health, Coventry, United Kingdom3School, University of Warwick, Coventry, United Kingdom4, London, United Kingdom5Department of Computer Science, Royal Holloway, University of London, Egham, United KingdomCorresponding Author:: Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering mental health support. Despite their potential benefits, research on public attitudes toward health care chatbots is still limited. To effectively support individuals with long-term health conditions like long COVID (or post–COVID-19 condition), it is crucial to understand their perspectives and p30. 1%) indicated that they were likely to use a health chatbot in the next 12 months if available. Most were uncertain about a chatbot’s capability to provide accurate medical advice. However, people seemed more receptive to using voice-based chatbots for mental well-being, health data collection, and analysis. Half of the respondents with long COVID showed interest in using emotionally intelligent chatbots. Conclusions: AI hesitancy is not uniform across all health domains and user groups. Despite persistent AI hesitancy, there are promising opportunities for chatbots to offer support for chronic conditions in areas of lifestyle enhancement and mental well-being, potentially through voice-based user interfaces. 2024;11:e51086doi:10. 2196/51086KeywordsAI hesitancy; chatbot; long COVID; diabetes; chronic disease management; technology acceptance; post–COVID-19 condition; artificial intelligence IntroductionArtificial intelligence (AI) chatbots are programs designed to simulate human conversations and provide tailored responses to users’ questions and concerns. Chatbots can provide a range of services, including appointment scheduling, medication reminders, and various types of health support. AI chatbots have the potential to support individuals with chronic health conditions by providing tailored information and resources, monitoring symptoms, and offering emotional support [1]. [SEP] Justify:

Output: The text supports the claim by stating that AI chatbots have the potential to assist individuals by providing tailored information, emotional support, and resources for monitoring health conditions. It emphasizes that chatbots can enhance mental well-being and that there is an interest in using such technological assistance for health related concerns.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: These studies indicate that the microbiome has an overarching influence on human health. For instance it was demonstrated that transplanting a specific combination of fecal bacteria into the gut of normal mice rendered them depressed due to changes in the “myelin” sheath that surrounds nerve fibers [20]. Cresol, produced by certain gut bacteria and which reduces the amount of myelin produced by brain cells was higher in mice that became depressed after fecal transplants. It was also observed that in mice modeled on two inflammatory human diseases: colitis and multiple sclerosis, polysaccharide antigen A (PSA) on the surface of the gut microbe Bacteroides fragilis, regulates immunity and reduces inflammation [21]. Experiments which produce such leads at the molecular level would help to understand the link between the gut and human health irrespective of the site of pathology and may thus provide leads for therapy. Dysbiosis and ocular diseasesThe relevance of gut microbiome to the eye appears to be very remote. But considering that the eye is the site of inflammatory diseases like uveitis, scleritis, Mooren’s corneal ulcer etc. which also occur due to autoimmune reaction, it is possible that under non-infectious conditions these diseases are influenced by dysbiosis in the gut. Last year it was demonstrated that in mice with experimentally induced autoimmune uveitis oral administration of antibiotics reduced the severity of uveitis [22]. But, intraperitoneal administration of the same antibiotic did not have the desired effect thus highlighting the role of gut microbiota in affecting uveitis. Simultaneously, it was also observed that the gut microbiome was significantly altered following antibiotic administration. Thus implying that uveitogenic bacteria may exist, and some antibiotics may be able to alter the balance of microbiota in favour of protective bacteria and thus help to resolve uveitis. Such studies could form the basis for therapeutic modulation of diseases. In a recent study, in mice, it was demonstrated that signals from the gut microbiota activate retina specific T cells that are involved in autoimmune uveitis [23]. Such information would open up new strategies for therapy were the emphasis would be on restoring the diversity in the gut by antibiotic or specific drug use, by specific microbe introduction, by probiotic use and by fecal transplant therapy. Ocular surface microbiome may also be responsible for eye diseases in man. Dong et al. [SEP] Justify:

Output: The text contrasts the claim by indicating that while the microbiome has a significant influence on human health and can impact certain diseases, it does not suggest that the microbiome alone can cure all human diseases. It discusses various studies that illustrate the links between gut microbiota and specific diseases, such as autoimmune uveitis, but emphasizes that the complexities of human health involve more factors than just the microbiome. The notion that restorative approaches might aid in therapy does not equate to a cure for all diseases, refuting the idea of a universal cure from the microbiome.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: ных № 5 (2019) Ассоциативная связь молекулярно-генетических и биохимических маркеров с характером течения хронической фтористой интоксикации у рабочих алюминиевой промышленности № 6 (2019) Критерии и алгоритмы установления связи нарушений здоровья с работой № 8 (2018) Профессиональная заболеваемость на железнодорожном транспорте № 1 (2015) Реализация глобального плана действий воз по охране здоровья работающих в Российской Федерации № 9 (2015) Главный редактор Бухтияров И. [SEP] Justify:

Output: The text is largely irrelevant to the claim as it discusses various topics related to health markers and occupational health issues without mentioning the effects of shift work or circadian rhythm misalignment.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: While there are some limitations to chatbots’ use, they could be a valuable tool for individuals with long-term conditions such as long COVID (or post–COVID-19 condition) [2] and those living in remote or rural areas [3,4]. Researchers have long investigated the use of chatbots in managing various chronic illnesses. For example, past studies have documented how chatbots improved medication adherence rates of patients with breast cancer [5]; enhanced the quality of life for people with type 2 diabetes [6]; reduced the severity of panic disorder symptoms [7]; and helped health care professionals, patients with asthma, and their family members build collaborative relationships [8]. Several systematic literature reviews on the topic suggest that conversational agents are generally effective in supporting the self-management of chronic conditions, particularly for mental health [9-11]. Hence, “empathic” chatbots that demonstrate “emotional intelligence” seem particularly relevant and useful. Although some researchers use the term emotional intelligence to denote a chatbot’s ability to express a full range of human sentiments (positive and negative) [12], in the health care context, an emotionally intelligent chatbot usually refers to a conversational agent being able to recognize and respond to emotions a user expresses in their interaction and that “uses evidence-based self-help practices such as motivational interviewing, positive behavior support, behavioral reinforcement, mindfulness, and guided microactions and tools to encourage users to build emotional resilience skills” [13]. Despite the potential benefits of AI chatbots in health care, empirical research on public attitudes toward health care chatbots is still in its early stages [14]. Some early studies have suggested that users are generally positive about the use of AI chatbots [15]. For example, Bickmore et al [16] found that participants were generally satisfied with a health care chatbot that provided them with medication reminders and lifestyle advice. Similarly, a study by Crutzen et al [17] found that a health promotion chatbot targeting adolescents was used intensively and evaluated positively, especially in comparison with information lines and search engines. However, recent research has also highlighted many challenges associated with the use of AI chatbots in health care. There are concerns about the ability of chatbots to understand complex medical issues and provide accurate advice [11,18]. [SEP] Justify:

Output: The text corroborates the claim by outlining the potential value of AI-powered chatbots for mental health support, stating that they are effective in managing chronic conditions and can enhance emotional resilience skills. Specific examples include chatbots that have improved medication adherence, reduced panic disorder symptoms, and facilitated collaborative relationships in healthcare. The mention of 'empathic' chatbots with 'emotional intelligence' further supports the notion that these tools can engage patients in a meaningful way, aligning well with the claim that AI chatbots can provide mental health support and improve patient engagement.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: [24] studied the microbiome of the healthy cornea and conjunctiva of four individuals and identified a core microbiome of 12 genera which included commensal, environmental, and opportunistic pathogenic bacteria. Whether the ocular microbiome exhibits dysbiosis with disease? Whether ocular microbiome is influenced by the gut microbiome? What mediates the cross-talk between the gut and ocular microbiomes? These are questions that need to be addressed to understand idiopathic infections of the eye. Research directed on the above lines would help to establish whether the gut microbiome influences ocular surface disease directly or indirectly by altering the ocular microbiome and probably also help to identify the causative organism. ConclusionsWhat seems to be amiss in these studies is an over emphasis on establishing core microbiomes rather than trying to ascertain the reasons for dysbiosis and methods to overcome dysbiosis. Further attempts to get at the relevance of dysbiosis by manipulating the microbiomes or by animals studies is also limiting. Above all, work on bacterial communities far outpaces that of viral and eukaryotic communities, although these agents are resident in the gut and could be the cause of several diseases. Finally evaluating diseases remote from the gut such as the eye, brain, kidney etc. would unfold the mysteries of the new organ the “microbiome”. It is only future studies that would either prove or disprove what Cho and Blaser [4] have said “—this (microbiome) is a frontier for human preventive medicine and for medical management of chronic diseases”. AbbreviationsCDI: Clostridium difficile infection PSA: polysaccharide antigen A 10. 23. doi:10. 1101/gr. 096651. 109. 65. doi:10. 1038/nature08821. 70. doi:10. 1038/nrg3182. 703. doi:10. 1073/pnas. 0812874106. 10. 1126/scitranslmed. 3000322. 99. doi:10. 1016/j. cell. 2014. 09. 053. 24. doi:10. 1186/2049-2618-1-24. 205. doi:10. 1136/gut. 48. 2. 198. 5. doi:10. 1086/367657. 7. doi:10. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and unanswered questions surrounding the role of the microbiome in disease. It emphasizes that while research is ongoing, particularly in understanding dysbiosis and the microbiome's influence on various diseases, no definitive proof is provided that the microbiome alone can cure diseases. The mention of needing to understand 'whether the ocular microbiome is influenced by the gut microbiome' and the call to address 'the reasons for dysbiosis and methods to overcome dysbiosis' further indicate the complexity of the relationship between the microbiome and human health, suggesting that the microbiome alone is not sufficient for curing all diseases.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: В. Инструменты статьи Поиск ссылок Послать статью по эл. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not provide any information related to shift work, health, or circadian rhythms. Instead, it appears to be a fragment about article tools and sending the article via email, which does not address the claim in any way.


Input: Task: pass the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: The majority of the questions were adapted from the digital health literature (eg, [14,25]). To address the two research objectives, we first asked questions on general attitude and acceptance toward chatbots, such as “How familiar are you with AI chatbots? ” and “How likely are you to use a health chatbot within the next 12 months if available? ”; then we focus on questions on using chatbots in specific health management scenarios, such as “Do you think chatbots have the capability of delivering accurate medical advice? ” and “Would you like a chatbot to understand your stress levels and emotional states? ” We also included questions on widely cited factors in the literature that might affect chatbot adoption such as trust and privacy. Ethical ConsiderationsThe survey instrument, along with other details of the methodology, was approved by the Royal Holloway, University of London (Full-Review-3509-2022-11-18-13-35-UATM024). The participants were presented with a consent page before starting the web-based questionnaire. The purpose and the method of the research were briefly explained, along with an informed consent form asking participants to confirm that they were 18 years or older and that they were voluntarily taking part in the study. The survey did not collect personally identifiable information. The IP addresses were monitored to prevent multiple entries from the same computer. However, all IP addresses were removed from the data when the survey closed. Each survey response was assigned a unique ID, and the encrypted data were stored in the on servers. No compensation was provided to participants for completing the survey. ResultsDemographic CharacteristicsOf the 888 individuals who started the survey, 729 (82. 1%) responded to the sex question, of which 471 (64. 6%) were female, 252 (34. 6%) were male, and the remaining 6 (0. 8%) were nonbinary/third sex or “prefer not to say. ” Of the 741 respondents who provided their age, 556 (75%) were 55 years or older, with a median age of 63 (IQR 55-70) years. The sample consisted predominantly of White (n=466,64. 2%) individuals; other ethnicities were represented in smaller percentages and scattered across different categories of ethnicity (eg, Indian and Pakistani: n=25, 3. 5%; Black n=7, 1. 2%). [SEP] Justify:

Output: The text does not provide any specific information regarding the capability of AI-powered chatbots to offer mental health support or patient engagement. Instead, it discusses the methodology of a survey focused on attitudes towards chatbots, including aspects like familiarity and likelihood of use. While these aspects might be related to the broader topic of chatbots in healthcare, they do not directly corroborate or contrast the claim regarding their actual functionality in mental health support.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: Microbiome multi-omics can accelerate human excrement composting research access Published: 16 September 2024 Microbiome multi-omics can accelerate human excrement composting research Jeff Meilander1,2, Mary Jewell3 & J. Gregory Caporaso1,2 Microbiome 41. 7 million metric tons of carbon equivalents in 2020, while returning a relatively small portion of the nutrients present in human excreta to agricultural systems [13, 14]. Microbiome multi-omics can further advance HEC research (red circle), for example, enabling untargeted tracking of pharmaceutical residues during composting or bioprospecting for enzymes that efficiently degrade recalcitrant compounds. Figure created with BioRender. com. Values presented in the figure were obtained from [15]Full size imageIlluminating microbial dark matterComposting is a human-directed biological reaction driven by highly variable and incredibly diverse microbiomes. Extensive research has described microbial succession in composting [16,17,18,19,20,21], identified optimal ranges of variables driving the composting processes (e. g. , carbon to nitrogen ratio, moisture content, pH) [22, 23], and evaluated safety considerations in finished compost, such as pathogen load [24, 25]. Notably, researchers have observed drastic reductions in the diversity of antibacterial-resistant genes during composting of dairy manure [26, 27] and nearly 100% degradation of the antibiotics metronidazole and ciprofloxacin [28] through thermophilic composting. Meanwhile, “-omics” technological innovations of the past two decades [29,30,31] have expanded our capabilities for studying microbial systems and have much to offer our understanding and practice of human excrement composting (HEC). DNA sequencing and mass spectrometry, which drive -omics data generation, can serve as next-generation data sensors, allowing us to probe tens of thousands of species (the majority of which we currently do not know how to grow in culture), hundreds of thousands of genes, and tens of thousands of small molecules. When two or more of these technologies, such as metagenomics, metabolomics, or metatranscriptomics, are applied together to investigate complex communities of microorganisms, we refer to this as “microbiome multi-omics. [SEP] Justify:

Output: The text contrasts the claim by discussing how the microbiome and advancements in technology (like microbiome multi-omics) can enhance our understanding and management of human excrement composting, but it does not support the idea that the microbiome alone can cure diseases. In fact, it details various applications of microbiome research in composting rather than directly linking it to curing illnesses.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: почте (Необходимо имя пользователя (логин)) Связаться с автором (Необходимо имя пользователя (логин)) Об авторах Алена Александровна Субботина ФБУН «Федеральный научный центр медико-профилактических технологий управления рисками здоровью населения» Россия Младший научный сотрудник отдела иммунобиологических методов диагностики ФБУН «ФНЦ МПТ УРЗН». e-mail: alena-osa@bk. ru Олег В. [SEP] Justify:

Output: The text is irrelevant to the claim as it contains no information regarding shift work, its effects on health, or circadian rhythms. Instead, it appears to be a contact or author information section without relevant content.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: ”On the other hand, people seem to be more open to the idea of chatbots supporting mental well-being: 272 (40. 2%) would like a chatbot to understand their stress levels and emotional states, 211 (31. 2%) were unsure, and 194 (28. 7%) indicated no interest. A further cross-tabulation and χ2 analysis using the chisq function in R suggests that people with long COVID in our sample were more likely to be interested in an emotionally intelligent chatbot than those without long COVID (n=673; χ22=13. 73; P=. 001), although nearly one-third of the former group were still “unsure” (Table 2). Table 2. Cross-tabulation: long COVID chatbot understands stress and emotion. ? Total (n=673)Yes (n=162)No (n=511)Would you like a chatbot to understand your stress and emotional states? Yes81 (50. 0)190 (37. 2)271 (40. 3)No29 (17. 9)164 (32. 1)193 (28. 7)Unsure52 (32. 1)157 (30. 7)209 (31. 1)Did not respond—a—215 (24. 2b)aNot applicable. bPercentage based on the 888 total respondents. This “chatbot hesitancy” is also evident when comparing people’s p3. Table 3. People’s p3. 0)484 (80. 7)98 (16. 3)288 (32. 4)Speaking about mental health (n=596)26 (4. 4)483 (81. 0)87 (14. 6)292 (32. 9)Speaking about sleep (n=595)46 (7. 7)370 (62. 2)179 (30. 1)293 (33. 0)Speaking about nutrition (n=599)64 (10. 7)343 (57. 3)192 (32. 1)289 (32. 5)Collecting symptoms (n=590)63 (10. 7)270 (45. 8)257 (43. 6)298 (33. 6)Conducting preliminary analysis (n=596)72 (12. 1)314 (52. 7)210 (35. [SEP] Justify:

Output: The text supports the claim by indicating that a significant percentage of people are open to the idea of chatbots understanding their stress levels and emotional states. Specifically, it highlights that 40.2% of respondents would like a chatbot for this purpose, suggesting a demand for AI-powered chatbots in mental health support.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: ” We argue that microbiome multi-omics when applied in replicated HEC time series experiments, can enable us to develop a systems-biology-based understanding of the HEC reaction. Microbiome science has already yielded insight into composting. For example, the accessibility of marker gene sequencing, which targets specific conserved regions of genomes (e. g. , 16S ribosomal RNA) for taxonomic profiling [29], has enabled compost researchers to identify the impact of feedstocks, bulking materials, and compost preparation methods on microbial diversity [8, 17] and to track reductions in antibacterial resistance gene diversity [26]. Studying microbial succession with marker gene sequencing alongside typical measures of compost maturity (e. g. , low microbial activity, between 10:1 and 20:1 C:N ratio, pathogen levels within regulatory standards) can expand our knowledge of taxa indicative of compost safety [10], demonstrate when human gut-associated taxa are depleted [32], suggest microbial taxa that could be used in compost inoculants [33], and contribute to waste-based epidemiology [34]. However, marker-gene sequencing approaches, by their nature, do not provide information on all organisms present in a sample and may be limited in resolution (e. g. , only providing identification at the bacterial genus level, rather than at the species level). Alternatively, metagenomic sequencing, which does not target specific genomic regions but rather aims to sequence all DNA present in a sample, enables broader detection and higher-resolution annotation. The considerably higher cost of metagenome sequencing often means that it makes sense to start with marker gene sequencing of many samples, for example along a dense time series, and then consider metagenome sequencing on a resulting subset of samples of interest. Metagenomic sequencing has been employed in composting systems, for example, to assess the regulation of β-glucosidase genes responsible for cellulose degradation by microbial communities across various composting phases [35]. Additionally, it has been utilized to investigate the functional profiles of microbiomes across composting phases [36] and to monitor the abundance of a wide range of antimicrobial and heavy metal resistance genes in compost and manure [37]. Microbial activity in composting systems can be studied by applying DNA sequencing technologies to profile microbial mRNA abundance using metatranscriptomics [36, 38] and/or mass spectrometry to track small molecule production (e. g. [SEP] Justify:

Output: The text contrasts the claim by illustrating that, while microbiome science provides valuable insights into microbial diversity and its applications in processes like composting, it does not support the idea that the microbiome alone can cure all human diseases. It discusses the limitations of microbiome studies, such as the inability of marker-gene sequencing to provide comprehensive organismal information and the dependency on metagenomic sequencing for broader understanding, implying that a multifaceted approach is necessary for health interventions rather than solely relying on microbiome science.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Долгих ФБУН «Федеральный научный центр медико-профилактических технологий управления рисками здоровью населения» Россия Облако тегов occupational diseases occupational morbidity occupational risk prevention vibration disease work conditions working conditions артериальная гипертензия вибрационная болезнь заболеваемость здоровье медицина труда медицинские работники профессиональная заболеваемость профессиональные болезни профессиональные заболевания профессиональный риск профилактика условия труда факторы риска функциональное состояние Уведомления Посмотреть Подписа [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses occupational diseases and conditions without mentioning shift work or circadian rhythms. The words and phrases used are unrelated to health impacts from shift work.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 2)292 (32. 9)aPercentages in this column are based on the total 888 respondents. Consistent with the observations above, an encouraging sign for health chatbot developers is that people are willing to try voice-based health chatbots despite the overwhelming hesitance toward bots. Of 679 respondents, 309 (45. 5%) expressed willingness to use a voice-based chatbot to record their health symptoms on a mobile device, and 278 (41. 1%) would let their voice be analyzed to diagnose health problems. When asked if they would like to trial a voice-based health chatbot that the research team is developing, 364 of 560 (65%) respondents answered “yes,” as illustrated in Table 4. Table 4. Attitude toward voice-based health chatbot. Yes, n (%)No, n (%)Unsure, n (%)Didn’t respond, n (%a)Would you use your voice to record health symptoms on a mobile device? (n=679)309 (45. 5)182 (26. 8)188 (27. 7)209 (23. 5)Would you use an app that analyzes your voice to diagnose potential health problems? (n=679)278 (41. 1)158 (23. 3)241 (35. 6)209 (23. 5)Would you like to trial the voice-based health chatbot we are developing? (n=560)364 (65. 0)196 (35. 0)—b328 (36. 9)aPercentages in this column are based on the total 888 respondents. bOption not provided. ran linear regression analyses in R to explore factors that could predict individuals’ likelihood to use a health chatbot in the next 12 months. We categorized the variables into three groups: demographic, experience, and attitudinal. Table 5 presents our findings. Table 5. Predictors analysis using regression. Likelihood of adopting a health chatbot in the next 12 months (n=485). aβSEP value95% CIDemographicAge–. 075. 005. 06–. 021 to. 000Sex. 078b. 097. 03. 039 to. 460? –. 064. 115. 08–. 428 to. 023ExperienceFamiliarity with AIc chatbot. 169. 048<. 001. 110 to. 302Frequency of online health information seeking. 198. [SEP] Justify:

Output: The text corroborates the claim by demonstrating that respondents are willing to engage with voice-based health chatbots for various functions, such as recording health symptoms and diagnosing problems through voice analysis. Specifically, 364 out of 560 respondents (65%) expressed willingness to trial the voice-based health chatbot under development. This indicates a recognition of the potential for AI-powered chatbots to assist in mental health support and patient engagement.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: , microbial metabolites), for example, to support the development of standardized detection assays for pollutants commonly present in HE (e. g. , pharmaceutical residues, personal care products). In addition to human, animal, and plant health considerations, such as endocrine disruption and ecotoxicology arising from pharmaceuticals in soil amendments, ensuring that antibiotics and antifungals are degraded is important for reducing the potential for the evolution of new drug resistance mechanisms. Combining these individual -omics approaches can enable efforts such as bioprospecting for enzymes that degrade recalcitrant compounds, such as lignocellulose [39], or ecotoxicological molecules, such as pharmaceuticals. For example, in a replicated HEC time series experiment, the decreasing abundance of targeted molecules could be linked with active microbial enzymes. Organisms that encode those enzymes in their genome could subsequently be identified, validated for the desired activity, and included in compost starter communities that increase the efficacy of target degradation. Components of this workflow have been applied already in composting systems [33, 40,41,42], and we hypothesize that integrating multi-omics technologies in this way could yield a standard protocol to bioprospect for organisms and/or enzymes that could serve as useful HEC inoculants. Integrating multi-omics with other related approaches, including qPCR targeting relevant pathogens [43] or antibiotic resistance genes [26], and/or targeted amplicon sequencing such as pan-enterovirus assays [44], can accelerate our understanding of HEC and support research to advance current small-scale [45] and large-scale [46] best practices. It is important to note, however, that the multi-omics approaches described here have varying potential to reveal information about the individuals or populations using the systems being studied, for example linking drug use from metabolomics assays with personally identifying or ancestry-informative genetic markers obtained incidentally during metagenome sequencing. This creates ethical challenges to their application in wastewater treatment or HEC systems that must always be carefully managed [47, 48]. Toward sustainable excrement managementCTs are most commonly known for their diverse sustainability implications, including reducing water consumption and promoting nutrient cycling [49,50,51]. For instance, an average American uses 310 L of drinking water per day, 24% of which is used for flushing toilets [52]. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and challenges associated with multi-omics approaches in understanding human health and environmental concerns rather than suggesting that the microbiome alone can cure diseases. It emphasizes aspects such as the importance of managing ethical challenges, the focus on drug resistance, and the integration of various methods to study complex problems, thus indicating that the microbiome's role is part of a larger context rather than a standalone solution.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: ться 105275, Москва, пр-т Буденного, 31ФГБНУ «НИИ МТ», 2 этаж, кабинет 280, офис редакциител. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not address or provide any information related to shift work, health, or circadian rhythms. It appears to be an address or location listing without any contextual relevance.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 012<. 001. 039 to. 084AttitudinalComfortable with reporting symptoms to health chatbot. 228. 060<. 001. 142 to. 377Worry about privacy using health chatbot–. 032. 042. 40–. 128 to. 042Trust health chatbot. 255. 069<. 001. 219 to. 489aLikelihood of adoption was measured on a 5-point Likert scale (1=extremely unlikely and 5=extremely likely). R2=0. 388. bItalics indicate statistical significance. cAI: artificial intelligence. We considered P values <. 05 as statistically significant in our regression analysis results. The results showed that age (β=–. 075; P=. 06) and long COVID status (β=−. 064; P=. 08) have little to do with participants’ tendency to use a health chatbot. Sex, dummy-coded as male=1 and female=2 with other sex categories excluded from this analysis due to a small number in each of the categories, seems to have a marginal effect (β=. 078; P=. 03), with female respondents potentially being more inclined to adopt a health chatbot than male respondents. Past experience with an AI chatbot (“familiarity with AI chatbots”; β=. 169; P<. 001) and online health information-seeking frequency (aggregated frequencies across Google, social media, and professional health sites; β=. 198; P<. 001) show strong associations with chatbot adoption likelihood. Similarly, two attitudinal items measuring a person’s comfort in outlining symptoms to a health chatbot (β=. 228; P<. 001) and their trust in a chatbot for advice (β=. 255; P<. 001) also strongly predicted their likelihood of adopting a health chatbot. Interestingly, privacy concerns, despite being widely reported in the academic literature as a deterrent to chatbot or virtual assistant adoption [20,26], did not seem to affect the likelihood of survey respondents adopting a health chatbot (β=−. 032; P=. 40). DiscussionPrincipal FindingsThe survey results summarized above present a nuanced portrayal of public attitudes toward health care chatbots. [SEP] Justify:

Output: The text supports the claim by indicating that factors such as comfort in reporting symptoms to a health chatbot and trust in the chatbot for advice strongly predict the likelihood of adopting a health chatbot. This implies that AI-powered chatbots can play a role in engaging patients by providing a platform for them to report their mental health symptoms and receive guidance. Additionally, it mentions associations between prior experience with AI chatbots and online health information-seeking, illustrating the potential for enhanced patient engagement through such technologies.


Input: Task: contrast the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: We hypothesize that a better understanding of HEC microbiology would expand economic opportunities, for example through the development of inoculants or substrates that accelerate or standardize the process. Small-scale initiatives are critical steps toward the urgently needed paradigm shift in HE management. By collaborating with community organizations and leaders, researchers can pinpoint communities that could benefit from CT installations. Through education and public outreach (e. g. , demonstration sites on university campuses or at community centers) these collaborations can cultivate economic opportunities and drive technological innovations while raising awareness and fostering acceptance of circular excrement management. These efforts can provide models for scaling up in areas where new approaches are most needed, or where there is the greatest appetite for transformation. Collectively, the knowledge gained by applying microbiome multi-omics to study HEC systems will help reduce the spread of human disease through contaminated soil and water, substantially decrease water and energy consumption, and retain nutrients in closed-loop cycles. We strongly encourage researchers, policymakers, community organizers, and funding agencies to invest resources in HEC microbiome research to harness the transformative power these microbiomes offer to create a more equitable and sustainable planet. In short, Hugo’s “given time” is now. Availability of data and materials Not applicable. Data availability No datasets were generated or analysed during the current study. Change history07 October 2024Videobyte has been removed. Notes310 L per person per day × 365 days per year × 24% × 333,300,000 people = 9. 1 trillion L/year1862. and WHO. State of the World’s Sanitation: An urgent call to transform sanitation for better health, environments, economies and societies. New York: Children’s Fund () and the Organization; 2021. UN-Water. Summary progress update 2021 -- SDG 6 -- water and sanitation for all. Geneva: UN-Water; 2021. p. 2021. 2020. Night soil: origins, discontinuities, and opportunities for bridging the metabolic rift. Ethnobiol Lett. 2019;10:40–9. 24. Quality assessment, safety evaluation, and microbiome analysis of night-soil compost from Lahaul valley of northwestern Himalaya. Waste Manag. 2022;149:42–52. Available from: Available from: [cited 2024 Jan 11]. US Department of Energy. Wastewater Infrastructure. Office of state and community energy programs. [SEP] Justify:

Output: The text contrasts the claim by stating that while understanding microbiome dynamics can enhance economic opportunities and reduce the spread of human disease, it does not claim that the microbiome alone can cure all diseases. Specifically, the text discusses the role of microbiome multi-omics in studying human excrement management systems, which highlights the importance of contextual factors and systemic approaches rather than implying a singular cure derived from the microbiome.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Skip to main content Intended for healthcare professionals Search this journalSearch all journalsEnter search terms. . . Searchsearch Enter search terms. . . Searchsearch Search Access/ProfileAccessView access optionsView profileCreate profile Cart 0 by discipline Select discipline: All disciplines All disciplines Life & & Sciences & HumanitiesSelect subject: All subjects All subjects Cardiology & Medicine & Endocrinology & -legal & Orthopaedics & Medicine & & & & Methods & & & & & Criminology & Economics & & & Management & Marketing & & & & & & & & PlanningBrowse journals Sage publishes a diverse portfolio of fully journals in a variety of disciplines. you can explore our Disciplines Hubs, including: Journal portfolios in each of our subject areas. Links to Books and content from across Sage. for/ Reviewers SocietiesFrequently asked questions In this journal Journal of Impact Factor: 2. 9 5-Factor: 3. 5 email alertsYou are adding the following journal to your email alertsNew contentJournal of Biological RhythmsCreate email alert Free accessLetterFirst published online November 12, 2017Misalignment without or. Duy [email protected] and Samer HattarView all authors and affiliations, Issue 6 h of darkness, 3. 5 h of light) caused mood and learning dysfunction in adult mice independent of sleep deprivation or circadian arrhythmicity, suggesting the direct effects of aberrant light on brain function. However, the mechanisms by which light directly causes mood and learning dysfunction remain poorly understood. In this study, we sought to determine whether exposure to the T7 cycle disrupts adult hippocampal neurogenesis, given that suppressed neurogenesis has been correlated with mood and learning dysfunction. After exposing adult mice to the T7 light cycle, we analyzed adult hippocampal neurogenesis by examining cellular proliferation and number of adult-born neurons. Contrary to our hypothesis that T7 would suppress neurogenesis, we found that adult mice exposed to 2 or 10 weeks of the T7 light cycle did not exhibit deficits in hippocampal neurogenesis. Our findings suggest that the direct effects of light on mood and learning do not depend on adult hippocampal neurogenesis. Light is one of the most fundamental sensory inputs that influence all aspects of day-to-day health and physiology. [SEP] Justify:

Output: The text does not address the claim about the adverse health effects of shift work or circadian rhythm misalignment. Instead, it discusses findings related to light exposure and its direct effects on mood and learning in mice, which is not relevant to the claim regarding shift work and health.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: The findings indicate that trust continues to be a crucial element in predicting people’s inclination to embrace health chatbots, aligning with prior research on user acceptance of digital health technologies [11,27,28]. It seems that for our sample of predominantly female adults older than 55 years, most of them do not trust a chatbot to provide accurate diagnosis and professional medical advice. This echoes findings in previous studies [11,29] that while patients were generally receptive to the use of AI chatbots in health care, they had concerns about the accuracy of information provided and the ability of chatbots to understand complex medical issues. Although past experience with chatbots and online information seeking are strong indicators of respondents’ future adoption of health chatbots, they are in general skeptical or unsure about the use of AI chatbots for health care purposes. Because of this “AI hesitancy” [14], it is unsurprising that most people show an overall preference for a real person (clinician) over a chatbot in health care encounters. On the other hand, this study contributes fresh insights into overcoming AI hesitancy and the potential use of AI chatbots in supporting long-term health conditions like long COVID. A key finding from the survey is that AI hesitancy is not uniform across all health domains and user groups. A significant proportion of survey respondents expressed willingness to engage with a health chatbot regarding nutrition and sleep, as well as allowing it to collect symptom data. Furthermore, although doubts about the medical capabilities of AI chatbots persist, people are more receptive to utilizing them for stress detection and emotional enhancement. Notably, individuals with long COVID in our sample exhibited a particular interest in emotionally intelligent chatbots, highlighting the mental health needs of those with long COVID and the potential of using conversational agents as an intervention [30]. It is also surprising that privacy concerns did not correlate with the likelihood of health chatbot adoption in our study, a finding contrary to conclusions in many previous studies [20,31]. Leveraging these positive attitudes toward AI chatbots could enhance public familiarity and increase the likelihood of chatbot adoption for health care purposes, as evidenced by our regression analysis. For instance, an AI chatbot focusing on lifestyle or emotional well-being could pave the way for broader acceptance of health chatbots that are reliable and highly personalized [32]. This research also provides preliminary insights into the potential of voice-based interaction with health chatbots. [SEP] Justify:

Output: The text corroborates the claim by discussing the potential of AI chatbots to support mental health needs, particularly for individuals with long COVID who expressed interest in emotionally intelligent chatbots. It mentions that, while there are concerns about the accuracy of information from chatbots, there is a significant willingness among respondents to engage with chatbots for stress detection and emotional enhancement, thereby supporting their use in mental health contexts.


Input: Task: pass the claim using the given text. Claim: The microbiome alone can cure all human diseases. [SEP] Text: Available from: [cited 2024 Feb 22]. US Agency. Basic information about biosolids. 2016. Available from: [cited 2023 Jul 20]. Succession of the bacterial community structure and functional prediction in two composting systems viewed through metatranscriptomics. Bioresour Technol. 2020;313:123688. Available from: E. The science of composting. 1st ed. CRC Press; 1997. Vinnerås B. Comparison of composting, storage and urea treatment for sanitising of faecal matter and manure. Bioresour Technol. 2007;98(17):3317–21. Available from: The analysis of natural microbial populations by ribosomal RNA sequences. In: Marshall KC, editor. Advances in microbial ecology. Boston: Springer, US; 1986. p. 1–55. Nat Biotechnol. 2019;37:852–7. Available from: [cited 2023 Nov 14]. 174. Available from: JC. Handbook, 4th Edition: Shit in a nutshell. Grove City: Joseph Jenkins, Inc; 2019. Organization. WHO Guidelines for the of Wastewater, Excreta and Greywater: Vol. 4 (. ). Organization; 2006. Inadvertent human genomic bycatch and intentional capture raise beneficial applications and ethical concerns with environmental DNA. Evol. 2023;7:873–88. Available from: Porto D, Steinfeld C. Composting toilet system book: a practical guide to choosing, planning and maintaining composting toilet systems. Concord: for Prevention; 1999. U. S. EPA. Use Water. Water Sense. 2023. Available from: [cited 2024 Feb 24]. Compact. C. F. R. Sect. § 617l - compact approval 1922. Available from: Drying characteristics of faecal sludge from different on-site sanitation facilities. J Environ Manage. 2020;261:110267. Available from: The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Technol. 2015;45:1827–79. Available from: 66. Available from: Code Council. Code. Council. Available from: [cited 2024 Jun 20]. IAPMO. [SEP] Justify:

Output: The text is irrelevant to the claim as it contains a list of references about composting, waste management, and microbial communities without addressing the efficacy of the microbiome in curing diseases. It does not provide any information or evidence to either support or contrast with the idea that the microbiome alone can cure all human diseases.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Suppressed adult hippocampal neurogenesis has also been found in mice exposed to aberrant light schedules such as constant darkness (DD), constant light (LL), and experimental jet lag (Gibson et al. , 2010; Fujioka et al. , 2011; Monje et al. , 2011). These correlations between suppressed neurogenesis and behavioral dysfunctions suggest that aberrant light may use adult hippocampal neurogenesis as a neural pathway to affect mood and learning. However, such results have been complicated by contradictory findings, such as a study by Mueller et al. (2011) that showed no changes in adult neurogenesis following exposure to constant light. Furthermore, Gibson et al. (2010) used hamsters to study jet lag, while other studies used mice to study DD (Monje et al. , 2011) or LL (Fujioka et al. , 2011; Mueller et al. , 2011). Thus, the methods and models used by the respective studies differ in too many ways to conclude whether aberrant light environments affect adult neurogenesis. The presence of circadian arrhythmicity following LL can also complicate interpretations about the relationship between light and neurogenesis. To date, no published study has examined whether light can directly affect adult neurogenesis independent of circadian arrhythmicity. In this study, we sought to examine the effects of the T7 light cycle on adult hippocampal neurogenesis. We first exposed adult wild-type mice to 2 weeks of the T7 cycle and then collected histological brain sections to examine hippocampal cell proliferation and number of immature neurons (for detailed experimental methods, see the Supplementary Material; Fig. 1A). On the 14th day of the T7 cycle, mice were injected with 5-ethynyl-2′-deoxyuridine (EdU) to label proliferating cells and were perfused on the 15th day (Fig. 1A). Control mice were kept in the standard T24 cycle (12 h of darkness, 12 h of light; Fig. 1A). In the T24 group (n = 9), we found 1781 ± 67 EdU+ cells in the dentate gyrus per animal (Figure 1B, F). In the T7 group (n=10), we found 1555 ± 92 EdU+ cells in the dentate gyrus per animal (Fig. 1C, F). [SEP] Justify:

Output: The text discusses various studies on neurogenesis in relation to aberrant light schedules, but does not directly address the health effects of shift work or the misalignment of circadian rhythms. As a result, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Despite the popularity of voice-based AI agents such as Siri and Alexa on smart devices, there are only a handful of academic studies on the public’s attitude toward an alternative voice-based interface for health chatbots [11,21]. Traditional text-based chatbots on mobile phones present challenges to older adults in terms of vision and dexterity, as typing on a smartphone can be difficult and the screen size is often too small for them [33]. From the technology acceptance research, we understand that the usability of a health chatbot plays a role in its perceived usefulness [32]. Therefore, the relatively high acceptability and enthusiasm toward voice-based health chatbots expressed in our survey responses indicate a potential avenue for reaching a wider, often neglected population of adults older than 55 years. In addition to the usability benefits, voice input can be captured and analyzed for symptom tracking and medical diagnosis [34], complementing other data inputs to enable a more accurate assessment of the user’s health. LimitationsIt is noteworthy that this study had several limitations. The use of a web-based survey for the empirical study introduces the potential for response bias. The sample primarily consisted of female adults older than 55 years with diabetes and other long-term health conditions, rather than representing the general population. As this population may have been more attuned to ongoing health concerns, they might have been more prone to reporting long COVID symptoms, potentially explaining the significantly higher proportion of those with long COVID in our sample compared to the national data from the Service. -19 Survey (monthly, terminated in March 2023) reported that around 3% of the UK population were experiencing symptoms 4 weeks after they first had COVID-19 [35]. As cross-sectional survey research, this study is unable to provide a deep understanding of attitudes and opinions. For example, we do not know exactly why people are more receptive to voice-based AI chatbots aside from an educated guess that a voice interface may be easier to use and more natural than typed text for adults older than 55 years. Future research based on in-depth interviews or experimental methods might help unpack these user attitudes reported in the survey and identify possible causal factors. ConclusionWith the rapid development of AI and chatbot technologies, the utilization of chatbots in health care is primed for substantial expansion in the forthcoming years. [SEP] Justify:

Output: The text corroborates the claim by discussing the potential benefits of voice-based health chatbots, which are a subset of AI-powered chatbots, in providing health support to older adults. It mentions that voice input can assist in symptom tracking and medical diagnosis, thereby enhancing engagement in health care. Additionally, it highlights that there is a high acceptability towards health-oriented chatbots, suggesting these tools could be effective for patient engagement among populations that may struggle with traditional chat interfaces.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Gut microbiome composition is associated with spatial structuring and social interactions in semi-feral ponies access Published: 22 November 2018 Gut microbiome composition is associated with spatial structuring and social interactions in semi-feral ponies Rachael E. : orcid. org/0000-0002-8849-81941, Jessica M. D. Lea2, Bryony Unwin1 & …Susanne Shultz2 Show authors Microbiome 53. 22°N, 3. 95°W) over an area of approximately 35–40 km2 of commons land between 287 and 610 m above sea level. The land is used primarily for sheep farming and recreational hiking and thus ponies are habituated to human presence but not to physical contact. The population is essentially unmanaged aside from an annual roundup event each November, during which individuals are herded onto adjacent farmland for 1 to 2 days for population monitoring and management purposes. Individuals can be identified using their age-sex classification and a photographic database that depicts coat colour, face and leg markings, and ear tags/notches. For this study, we collected data from 30 individuals across three focal bands (Aber, Marsh, and Valley) that have been the subjects of long-term behavioural and demographic data collection [39, 40] (Table 1). Table 1 Demographic data for each band used in this studyFull size tableDistribution mapping and social network analysisDemography and proximity data were collected over ten sampling days between the 21 August and 14 November 2014 (the same time period when faecal sampling also occurred). All ponies included in the spatial analyses were sighted a minimum of 5 days, sampled opportunistically within the study area. Upon encountering a group, we recorded time, pony IDs, and GPS location along with an approximate spatial network of the ponies. We plotted the geographic distribution of the bands over the study period using the ggmap package [41] in RStudio (v1. 0. 153) [42] for R (v3. 4. 1) [43]. For the social network analysis, we approximated the distance in metres between individuals. All individuals less than ~ 100 m apart and moving as a cohesive unit were considered to be associated with each other [40]. Association matrices were constructed for each day of sampling; individuals that were close together (< 15 m) or interacted were given a score of 2, other individuals (i. e. [SEP] Justify:

Output: The text contrasts the claim by highlighting that gut microbiome composition varies among individuals, as it is associated with 'spatial structuring and social interactions' in a specific study involving semi-feral ponies. This suggests that microbiome composition is influenced by individual factors and interactions, indicating it is not identical across all individuals.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Thus, there appears to be a trend toward a decreased number of EdU+ cells in the T7 group (13% decrease compared with T24); however, this effect did not reach statistical significance (Fig. 1F). We then examined endogenous markers of cell proliferation and number of young neurons, including Ki67 (a marker of proliferating cells) and DCX (a marker of immature neurons). In the T24 group (n = 5), we found 1982 ± 276 Ki67+ cells and 7174 DCX+ cells in the dentate gyrus per animal (Fig. 1B, D, G, H). In the T7 group (n = 4), we found 1967 ± 252 Ki67+ cells and 7691 ± 363 DCX+ cells in the dentate gyrus per animal (Fig. 1C, E, G, H). 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. 1G, H). These results suggest that 2 weeks of exposure to the T7 light cycle did not affect cell proliferation and number of immature neurons. Figure 1. Two 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) 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). (G) 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 Ki67+ cells between the T24 and T7 group (P > 0. 05, ns, unpaired Student t test). (H) DCX+ cell counts in the dentate gyrus from mice exposed to either the T24 or T7 cycle. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the effects of different light cycles on cell proliferation and neurogenesis in the hippocampus, without addressing the impact of shift work or circadian rhythm misalignment on health.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: The potential benefits offered by these technologies, such as enhanced health care accessibility [4,36], cost reduction [15], and improved patient outcomes [37], are too substantial to disregard. However, health care providers and technology developers must acknowledge AI hesitancy [14] among patients and ensure the inclusive and effective utilization of AI chatbots. This study contributes valuable insights into the acceptability of health chatbots among a population identified as requiring continuous long-term health care support, such as individuals at high risk for conditions like long COVID [38]. First, we augment the evidence in the literature that there exists a general skepticism toward health chatbots among people with chronic diseases. Second, notwithstanding this persistent AI hesitancy, we found that individuals are more receptive toward chatbots supporting lifestyle enhancement than those aiding disease diagnosis and health care management. Third, compared to other subgroups in the study, those with long COVID were more amenable to using chatbots for emotional support. Lastly, while popular AI chatbots on the market are text-based, this study demonstrates that individuals with chronic conditions exhibit a high interest in voice-based conversational agents despite their general AI hesitancy. Moving forward, it is paramount to continue exploring the potential applications of health chatbots in addressing the unique needs of specific patient populations, including those with chronic health conditions. 67. [CrossRef]Almalki M, Azeez F. Health chatbots for fighting COVID-19: a scoping review. Med. Dec 2020;28(4):241-247. [CrossRef] [Medline]. AI chatbot design during an epidemic like the novel coronavirus. Healthcare (Basel). Jun 3, 2020;8(2):154. [CrossRef] [Medline]. Medbot: conversational artificial intelligence powered chatbot for delivering tele-health after COVID-19. Presented at: 2020 5th on Communication and Electronics Systems; Jun 10-12, 2020; Coimbatore, India. [CrossRef] When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer. May 2, 2019;5(1):e12856. [CrossRef] [Medline] Coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Res. [SEP] Justify:

Output: The text corroborates the claim by illustrating the usefulness of AI chatbots in providing emotional support to patients, particularly those with chronic conditions like long COVID. It states that 'individuals with chronic conditions exhibit a high interest in voice-based conversational agents' and emphasizes the role of chatbots in 'supporting lifestyle enhancement' which aligns with the idea of patient engagement and mental health support.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by discussing microbiome variation among individuals. It explains that there are differences in microbiome composition according to factors such as individual ID, life stage, and sex, indicating that microbiomes are not identical across individuals. The mention of calculating Jensen-Shannon Divergence (JSD) values to measure similarity between microbial communities further supports the idea that there is significant variation in microbiome composition.


Input: Task: pass the claim using the given text. 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). [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses statistical analyses of cell proliferation and immature neuron counts between two experimental groups (T24 and T7) without mentioning circadian rhythms, shift work, or health effects from such exposures.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by referencing multiple studies on the efficacy and use of AI-based chatbots in healthcare and mental health contexts. It mentions that the development and use of chatbots, including those for cognitive behavioral therapy and chronic disease management, supports the idea that these artificial intelligence agents can provide mental health support and engage with patients effectively.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by detailing the methods used to assess microbiome variation among individuals, indicating that there are differences in microbiome composition based on factors such as band and life stage. It describes analyses including calculation of Jensen-Shannon divergence (JSD) values between individuals and comparisons of microbiome community compositions, suggesting that individual microbiomes are not identical.


Input: Task: pass the claim using the given text. 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). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the effects of different light cycles (T7 and T24) on adult hippocampal neurogenesis in mice, rather than addressing the health impacts associated with shift work and circadian rhythm misalignment.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim as it references multiple studies on the effectiveness of AI-powered chatbots for mental health support. For example, it mentions 'Conversational agent interventions for mental health problems: systematic review and meta-analysis of randomized controlled trials,' indicating that chatbots can indeed provide mental health support, supporting the assertion that AI can enhance patient engagement in this context.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by demonstrating that there is significant individual variation in microbiome composition. The statistical analysis indicated that '52.6% of the variation in the microbiome [is] attributable to individual variation,' meaning that microbiome compositions are not identical among individuals. Additionally, 'greater variation between individuals (average of 35%) than within individuals (average of 26%)' further supports the assertion that microbiome compositions differ among individuals rather than being identical.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim regarding shift work and its adverse health effects related to circadian rhythm misalignment. Instead, it discusses findings related to EdU+ cell counts in the dentate gyrus of mice and the effects of different light cycles on neurogenesis and learning/mood dysfunction without providing any insights into health issues associated with shift work or circadian rhythm disruptions. Therefore, this text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text supports the claim by demonstrating that participants found the eHealth tool (which can be linked to AI-powered chatbots) interesting, helpful, and easy to navigate. Most participants reported a substantial amount learned from the tool, indicating that it contributes positively to patient engagement and mental health support.


Input: Task: contrast the claim using the given text. 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). [SEP] Justify:

Output: The text contrasts the claim by indicating that there are significant differences in microbiome composition based on factors such as band and life stage, stating that microbiome composition varies considerably between different groups of ponies. It highlights that while some groups show similarities, there are distinct variations, thus supporting the notion that microbiome compositions are not identical across individuals.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim about shift work and circadian rhythm misalignment affecting health. Instead, it discusses the effects of quercetin on breast cancer metastasis and the correlation between inflammatory responses and cancer prognosis, which is unrelated to the effects of shift work.