Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: — сбор и обработка материала, написание текста, редактирование, утверждение окончательного варианта статьи, ответственность за целостность всех частей статьи;Долгих О. В. — концепция и дизайн исследования, редактирование, утверждение окончательного варианта статьи, ответственность за целостность всех частей статьи. Благодарности. Авторы выражают благодарность директору ФБУН «ФНЦ МПТ УРЗН» доктору медицинских наук В. Б.

Output: pass


Input: Claim: AI is being used to assist in diagnosing diseases through image analysis. [SEP] Text: These are external root absorption, impacted tooth, periapical radiolucency (radiographic changes that are often the result of inflammation), residual root, and supernumerary tooth. They can also be considered as diagnostic markers and anatomical factors to inform a patient’s future dental work. For example, panoramic dental images can be used to evaluate the risk of nerve injury prior to an extraction. Example image of automatic anomaly detection. Anomalies situated in surrounding regions outside the dental area are seldom connected with oral health. They indicate signs of inflammatory processes and can be markers for the diagnosis of other related diseases. For instance, the thickening of the mucous membrane in the maxillary sinus can be an indicator of conditions such as apical periodontitis (painful inflammation of the tissue surrounding teeth) and alveolar bone loss that occurs in the bones that support the teeth. AI can be employed to alleviate the burden on dental clinicians, reducing the number of images that require manual examination. Detecting anomalies The detection process is divided into four steps. The images collected from the dental clinics are in DICOM format. DICOM stands for and Communications in Medicine and is an internationally accepted standard format for viewing, storing, retrieving, and sharing medical images. The first phase, the DICOM converter, involves converting these images into PNG format. Next is the disease detection stage; here, anomalies are identified using a trained Network (Faster R-CNN) model. This is an object detection model that detects boxed regions in the image where there are high possibilities of anomalies. The position verifier stage entails filtering out any boxes that are not located within a predetermined dental region. The final step is the polygon shaper stage, where a prebuilt library, Detectron2, is used to narrow down the region of abnormal signs from a box shape to a polygon. This enables dental clinicians to obtain high-resolution information regarding the location and anomaly types, as well as the precise regions that show the anomalies’ features. Training, testing, and evaluation Lee and collaborators divided the 22,999 panoramic dental images into training, validation, and test datasets. The images from July 2020 to March 2021 were used for training this deep-learning model to recognise the 17 anomalies and validate the detection. The remaining datasets were used as a test set for evaluating the model. /Shutterstock.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: As a result, feeding the AF diet resulted in a reduction in the diversity and abundance of the gut microbiota in mandarin fish when compared to the NF diet, which can be attributed to the relative abundance of commensal microorganisms in the diet itself. Furthermore, the commensal microorganisms of the NF diet had the potential to reshape the host gut microbiota through interactions with the colonized microbes. Therefore, such interactions between the microorganisms in the diet and the host itself may increase the gut microbiota in mandarin fish fed with the NF diet. In addition, supplementing carbohydrates or plant-derived proteins to the diet can reshape the gut microbiota of animals (Li et al. , 2016), and a high dietary carbohydrate may also reduce the microbial diversity in mandarin fish (Zhang et al. , 2020). Moreover, our previous publication has shown that slight damage was found in the intestine of mandarin fish fed the AF diet (Li et al. , 2023). Taken together, the inclusion of plant-derived proteins or carbohydrates in the AF diets led to a decrease in both the gut microbial diversity and richness, which may be related to the unexpected health status of the mandarin fish. Therefore, the present study examined the gut microbiota in fish exposed to NF and AF diet to mimic the evolutionary history of humans and to explore how these microbial communities have evolved over time in response to different dietary habits. By considering the adaptive mechanisms that have shaped gut microbiota composition, we can draw parallels between fish and human evolution in relation to food processing and its impact on microbiome diversity. The AF diet feeding remodulated the microbial structure of mandarin fish By modifying the gut microbial community in fish, dietary composition causes biological changes that impact the population size of key symbiotic species and associated metabolism in the host (Ringø et al. , 2006). The present results revealed that the AF diet remodulated the microbiota assemblages by altering their structure and composition. In terms of microbiota composition, the dominant OTUs were classified as Bacillota, Pseudomonadota, followed by Actinomycetota and Bacteroidota in both treatments of mandarin fish.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Алексееву, научному руководителю доктору медицинских наук, академику РАН Н. В. Зайцевой и сотрудникам отдела иммунологических методов диагностики за помощь в проведении исследования. Финансирование. Исследование не имело спонсорской поддержки. Конфликт интересов. Конфликт интересов отсутствует. Дата поступления: 09. 02. 2022 Дата принятия к печати: 17. 02. 2022 Дата публикации: 10. 04.

Output: pass


Input: Claim: AI is being used to assist in diagnosing diseases through image analysis. [SEP] Text: com The group used an intersection over union (IOU) value to evaluate their model’s performance in correctly detecting lesions. IOU is an evaluation metric that measures the accuracy of an object detector. In this case, the area identified by the dental radiography expert is compared with the box identified by the model. The extent of overlap of two boxes is calculated by dividing the area of their overlap by the area of their union. If the two shapes overlap perfectly, the IOU score is 1. The researchers ascertained that an IOU value of 0. 5 or more denotes a predicted box that has been correctly detected. The class-wise performance of the detection model was compared with the labels provided by the dental radiography expert and measured in terms of precision, sensitivity, and specificity. The precision and sensitivity scores vary according to the anomaly types, with scores that exceed or match those from previous studies. The model demonstrated very high specificity, more than 95% in most classes, showing that it can successfully detect abnormalities and filter out normal healthy images. This high specificity indicates that this artificial intelligence model can reduce the burden of examination for dentists as it successfully filters out the healthy images and allows clinicians to focus on those images with potential anomalies. Training a deep-learning model The researchers discuss how the quantity and quality of the data are key when using AI techniques. Training their deep-learning model to detect 17 types of anomalies involved accruing datasets with sufficient examples of each class of object to be detected. They created a system that collects panoramic dental images direct from the dental clinics for a year, and a dental radiography expert labelled them manually. This high-quality dataset is still accumulating and can be used for future research. The high-performing tool successfully detects anomalies and filters out healthy images. Its high sensitivity (approximately 0. 99) validates its suitability for use in real clinical practices and demonstrates how AI can be employed to alleviate the burden on dental clinicians, reducing the number of images that require manual examination. This deep-learning model encompasses a large area of panoramic radiography and will help dentists improve patients’ quality of life through early diagnosis and prevention of diseases. What motivated you to develop this deep-learning model? Many diseases can be detected in the early stages from the panoramic X-ray image, and these early detections can increase patients’ quality of life.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: A total of 6,274,405 high quality reads with an average length of 422 bp were obtained. Rarefaction analysis indicated that the number of species increased rapidly before reaching a plateau (Fig. S1). Bacterial diversity among all the larvae group fed with different host plants were highly significant as demonstrated by three parameters (observed species richness, ACE and Chao1 (Table 1, Fig. 1, Fig. S2, Fig. S3). Shannon index showed no significant difference among all the larvae group from the different host plants, although significant differences existed between larvae samples fed with celocia (L3) and cucumber (R1) with p value = 0. 0207 by Shannon wilcox test. Simpson index also showed no significant differences among all the larvae groups fed with the various host plants. Principal component analysis (PCA) by clustering showed that the bacterial communities of all the larvae groups were not totally different although some groups like M1 and R1 groups were distinct from other larvae groups (Fig. S4). This indicates that host plants had a significant effect on the larvae of FAW gut bacterial communities. Principal coordinate analysis (PCoA) was calculated based on weighted and unweighted UniFrac, which considers both community membership and relatedness of community members. These distances were visualized by PCoA plots which displayed similarity between communities (Fig. 2). The unweighted pair group method with arithmetic means (UPGMA) clustering analyses also indicated that the entire bacterial communities of bacteria group were clearly distinct from each other (Fig. 3). Analysis of molecular variance (AMOVA) revealed that the bacterial communities were significantly different among the various larvae groups (p=0. 002). Comparison of Bacterial communities of FAW larvae from different host plants Nine phyla (Firmicutes. Proteobacteria. Cyanobacteria. Actinobacteria. and Verrucomicrobia) were present in all the samples at varied proportions (Fig. 4a). Firmicutes was the most abundant phylum and accounted for 44. 1 % of the total bacteria with 15. 1% and 14. 2 % from larvae fed with celocia (L3) and sweet potato (R3) plants respectively (Table S1). Proteobacteria with 28.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: 2023 Ключевые слова сменная работа, полиморфизм генов, рецептор мелатонина, серотонин 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.

Output: pass


Input: Claim: AI is being used to assist in diagnosing diseases through image analysis. [SEP] Text: However, it takes too much time for dental clinicians to examine all newly taken images every day. We therefore decided to help both dental clinicians and patients using AI. By suggesting the possible regions of dental anomalies and normal images which don’t need further examination, the AI system can reduce the burden for clinicians so they can focus on their patients and treatments. Detecting dental diseases with AI dental image analysis was last modified: November 8th, 2023 Keywords: AIAI dental imagesdeep learningdental anomaliesdental diseasesdental radiographydentistsInstitute of Science and Technologyoral diseasesoral healthpanoramic X-ray imagesradiographyDownload this 5172. Behind the Lee is a PhD candidate in the Department of Bio and at Institute of Science and Technology (KAIST). Main research fields are bioinformatics and data science. of InVisionLab: Major in electrical and electronic engineering at Institute of Science and Technology (KAIST). He worked for ten years at medical service companies and became interested in the convergence of the medical industry. As a doctoral student at School of Dentistry, he studies the convergence of technology and dental medical service. Hogul Jeong, Head of research institute at InVisionLab: PhD in dentistry from College of Dentistry. With more than 20 years of experience in the dental imaging and diagnosis field, he is researching dental image diagnosis and artificial intelligence. Jaehyeong Park, Programmer at InVisionLab: Majoring in computer science and engineering at University, he works at InVisionLab as a programmer, developing front-end services and other applications. He is also a cellist at the Orchestra. Address: G114-S127, 128, Beobwon-ro, Songpa-gu, Seoul, Republic of Korea, 05854 E: [email protected] T: +82 10 6606 8885 Lee and his collaborators have developed a deep-learning model that can detect dental diseases from radiography images. Funding InVisionLab is a company developing automatic analysis and diagnosis of clinical dental images like panoramic X-ray, CBCT image diagnosis, and cephalometric images. Cite this 10. 32907/RO-130-2773295876. (2022). Detecting dental diseases with AI dental image analysis. Research Outreach, 130. Available at: This work is licensed under a Attribution-NonCommercial-NoDerivatives 4.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: Larvae samples fed with R1 was mostly dominated by genus Weeksella (64. 0%). The number and relative abundance of bacterial species also differed in larvae from different host plants ranging from 205 to 416. R1 sample with 416 number of bacterial species has higher number of bacteria species followed by M1 (415). The least bacterial species was recorded in L2 sample (Table 3). Uncultured_Clostridium_sp with 13. 48 % of all the bacteria species was the most dominant species and was enriched in L2 and M3 with 20. 0% and 19. 2% respectively (Fig. S8). Acinetobacter_calcoaceticus and Klebsiella_variicola were mostly enhanced by L2 and M1 with 41,86% and 58,72% representation respectively. Pectobacterium_carotovorum_subsp. _carotovorum and uncultured_ Flavobacterium_sp were mostly enhanced in M1 (50. 2 %) and R1 (64. 1%) respectively (Table S6). In total, 38724 OTUs were identified in all the larvae groups from different host plants. The number of OTUs significantly varied among the groups. The number of OTUs ranged among the larvae groups in the order of M1>R1>M2>R3>R2>L2>L1>L3>M3 (Table 3). M1 had the highest OTU numbers while M3 was the least among all the groups in the number of OTUs. LEfSe analyses revealed that of class oxyphotobacteria, order chloroplast species Sphingobacterium_multivorum were enriched in larvae fed by L1 host plant (Fig. S9). of class Bacilli, order lactobacillales and family Enterococcaceae, genus Enterococcus and species Acinetobacter_baylyi were enriched in L3 fed larvae.

Output: pass


Input: 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 Список литературы 1. Чаулин А. М. , Дуплякова П. Д. , Дупляков Д. В. Циркадные ритмы сердечных тропонинов: механизмы и клиническое значение. Российский кардиологический журнал. 2020; 25(S. 3): 62–69. 2. Garufi C. , Giacomini E. , Torsello A. , Sperduti I. , Melucci E. , Mottolese M. , Zeuli M. , Ettorre G. M. , Ricciardi T. , Cognetti F. , Magnani M. , Ruzzo A. Gender effects of single nucleotide polymorphisms and miRNAs targeting clock-genes in metastatic colorectal cancer patients (mCRC). Scientific Reports. 2016; 26: 34006. 3. Семёнова Н. В.

Output: pass


Input: Claim: Machine learning algorithms can help predict patient outcomes based on historical data. [SEP] Text: April 1, 2021 can for April 1, 2021 an age of antibiotic resistance and viruses becoming more transmissible, new drugs need to be developed faster than ever. But drug development is a long process: The pipeline from a potential drug candidate to an approved and usable drug can take up to several decades, and diseases are evolving too fast for drug development to keep up. One way scientists aim to make drug development a more efficient process is by repurposing drugs: using currently available drugs to treat different diseases - diseases other than the ones they were first developed to treat. Drug repurposing is useful for rapidly responding to new outbreaks of disease as well as for rare diseases, where there may not be enough people to conduct large-scale clinical trials. To make drug repurposing more efficient, machine learning techniques can be used to identify drug repurposing candidates. A new study at State University, led by Zhang, has developed a machine learning framework that identifies drug repurposing candidates more efficiently than ever before. Why does it take so long to develop new drugs? Typically, drugs are designed to stop disease progression by targeting components of cellular mechanisms. For example, tumours arise due to uncontrolled cell growth and replication, so components of the signalling telling cells to grow and replicate may be of interest to target and inhibit tumour growth. From initial studies into drug targets to potential drug candidates being studied in cell cultures and animal models, it is already a lengthy process before drugs even reach clinical trials in humans. There are several phases of clinical trials for drugs to pass through before they can be approved: Graphic created by Sona Popat. 0, scientists use small numbers of healthy volunteers and low doses of the treatment to confirm it is not harmful, and Phase 1 progressively scales the dose of the treatment upwards to identify the maximum dose of the drug humans can withstand before side-effects are seen. Phase 2 tests for side effects and aims to get a rough idea of whether the treatment is effective. 3, trials are usually randomised and double-blind: patients are randomly put into groups to receive either a new drug, a control, or a placebo, without the patients or scientists knowing which individual is in which group. This process ensures no bias colours the outcomes of the trial due to the placebo effect or the way that scientists interpret the results. This phase is much larger-scale, involving thousands of patients across a range of demographics so the conclusions are more reliable and generalisable across groups.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: 14, e0224213. (2011) Drosophila microbiome modulates host developmental and metabolic homeostasis via insulin signaling. Science 334:670–674. doi:10. 1126/science. 1212782 Engel P, Moran NA. (2013) The gut microbiota of insects–diversity in structure and function. FEMS Microbiol. Rev. 37(5): 699–735. PMID: 23692388. Dillon RJ. ,Dillon VM (2004) The gut bacteria of insects: nonpathogenic interactions. Entomol 49(1): 71–9210. 1146/annurev. ento. 49. 061802. 123416 Frago E, Dicke M. , Godfray HCJ, (2012) Insect symbionts as hidden players in insect–plant interactions. Evol 27(12). 705–711. PMID: 22985943 (2017) Variation in the of Termites (Tsaitermes ampliceps) Diets. Appl. Biochem. Biotechnol. 181, 32–47. (2017) of for Herbivory. Front. Microbiol. 8, 663. J, Lowe-M, Rubert-F, Lindroth. R L, RaffaK F, (2016) Interactions between bacteria and aspen defense chemicals at the phyllosphere—Herbivore interface. J. Chem. Ecol. 42:193-201. Ping L, Büchler R, Mithöfer (2007) A novel Dps-type protein from insect gut bacteria catalyses hydrolysis and synthesis of N-acyl amino acids. Environ. Microbiol. 9:1572-1583. . (2017) Symbiont-Contribute to the Control of the Microbiota. Biology 24, 66–75. ò M, (2016). First report of outbreaks of the fall armyworm Spodopterafrugiperda (J. E. Smith) (Lepidoptera. Noctuidae). a new alien invasive pest in west and central Africa. 11. e0165632 ) Stokstad E, (2017) New crop pest takes Africa at lightning speed. Science 356, 473–474.

Output: pass


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Хронобиологические аспекты нарушений сна у женщин климактерического периода: роль мелатонина (обзор литературы). Scientifica. 2017; 2(5,1): 32–7. 4. Bin L. , Ke X. , Yufeng J. , Xiaoling L. Aberrant expression of Per1, Per2 and Per3 and their prognostic relevance in non-small cell lung cancer. of Clinical and Experimental Pathology. 2014; 7(11): 7863–71. 5. Gibbs J. E. , Beesley S. , Plumb J. , Singh D. , Farrow S. , Ray D. W. , Loudon A. S. Circadian timing in the lung; a specific role for bronchiolar epithelial cells. Endocrinology. 2009; 150: 268–76. 6. Winter S. L. , Bosnoyan-Collins L. , Pinnaduwage D. , Andrulis I. L. Expression of the circadian clock genes PER1 and PER2 in sporadic and familial breast tumors. Neoplasia. 2007; 9(10): 797–800. 7. Schwertner A. , Conceição C. C. , Costa G. D. et al. Efficacy of melatonin in the treatment of endometriosis: a phaseII, randomized, double-blind, placebo-controlled trial. Pain. 2013; 154(6): 874–81. 8. Slominski R. M. , Reiter R. J. , Schlabritz–Loutsevitch N. , Ostrom R. S. , Slominski A. T. Melatonin membrane receptors in peripheral tissues: distribution and functions. Molecular and Cellular Endocrinology. 2012; 351(2): 52–66. 9. Цветкова Е. С.

Output: pass


Input: Claim: Machine learning algorithms can help predict patient outcomes based on historical data. [SEP] Text: If the new treatment is safer and more effective at treating the disease than current treatments, it could go on to be approved for market distribution. Further trials, known as Phase 4, may investigate long term benefits and side-effects, even after the drug has been approved. Due to the lengthy processes and, depending on the rarity of the disease, the difficulty in obtaining enough volunteers for these trials, it can take an extremely long amount of time for a drug to be approved. Enter drug repurposingDrug repurposing has already been used to treat a wide range of diseases. For example, Viagra was initially developed to treat hypertension but is currently used for erectile dysfunction, and anti-inflammatory drugs made to treat arthritis are now being used for COVID-19 patients. The main benefit of drug repurposing is the shortening of the clinical trial process. As there is no need to repeat the initial safety phases, these existing drugs can be immediately tested for efficacy in treating the new disease, making the process safer, faster, and more cost-effective. Candidates for drug repurposing are usually identified using the results of previous research, genomics, or by considering drug action mechanisms that could be effective and common to multiple diseases, but the development of artificial intelligence and machine learning has sped up this process dramatically. Machine learning to identify drug repurposing candidatesMachine learning (or deep learning) frameworks are algorithms that improve automatically through experience, where the algorithm can receive a “training” dataset to learn from, and then apply that knowledge to different datasets. University scientists created a deep learning framework that uses real-world data from patients to identify drug repurposing candidates and their estimated effects to treat a particular disease. This is the first framework of its kind to use real-world data instead of just preclinical data. The framework receives insurance claims data (e. g. information on assigned treatments, disease outcomes, patient demographics) as inputs, and then emulates a randomised clinical trial for each drug. By comparing pre-defined measures of disease status before and after treatment, the model identifies drugs that have positive disease outcomes (i. e. an improvement in the predefined symptoms and disease status of interest) as potential candidates to be repurposed. But does this framework actually work? To test their framework, scientists inputted a dataset of over 1.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Related research -of the gut microbiota in rats after hypobaric hypoxia exposure View 10. 7717/peerj. 14090 Published 2022-10-07 Accepted 2022-08-30 Received 2022-04-11 microbiota, Dynamics, Hypobaric hypoxia, Normoxia, © 2022 Han et al. is an open access 2022. Dynamics of the gut microbiota in rats after hypobaric hypoxia exposure. PeerJ 10:e14090 Gut microbiota plays an important role in host health and is influenced by multiple factors. Hypobaric hypoxia usually existing at high altitude conditions can adversely affect normal physiological functions. However, the dynamic changes of gut microbiota influenced by hypobaric hypoxia have not been elucidated. this study, we collected fecal samples from seven rats at 14 time points from entering the hypobaric chamber (eight time points) to leaving the chamber (six time points) and five rats served as normoxic controls. Metagenome sequencing was performed on all samples and the dynamics of taxa and functions were analyzed. found that the α-diversity was changed in the first 5 days after entering or leaving the hypobaric chamber. The β-diversity analysis revealed that gut microbiota structure was significantly separated among 14 time points. After entering the chamber, the relative abundance of Bacteroides decreased and the most abundant genus turned into Prevotella. The abundance of Firmicutes and Bacteroidetes showed an opposite trend and both have a significant change within 5 days after entering or leaving the hypobaric hypoxia chamber. Some obligate anaerobic bacteria belonging to Desulfovibrio and Alistipes were significantly enriched after entering the chamber for 5 weeks, whereas Probiotics like Bifidobacterium and Lactococcus, and short-chain fatty acids producers like Butyrivibrio and Pseudobutyrivibrio were significantly enriched after leaving the chamber for 3 weeks. Microbial functions like ‘Two-component regulatory system’, ‘beta-carotene biosynthesis’ and ‘Fatty acid biosynthesis’ were significantly enriched after entering the chamber for 5 weeks. Hypobaric hypoxia conditions could deeply affect the diversity and structure of gut microbiota.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: , Романцова Т. И. , Рунова Г. Е. , Беляев Н. С. , Гольдшмид А. Е. Влияние сменного графика работы на показатели метаболического здоровья. Ожирение и метаболизм. 2019; 16(3): 11–9. 10. Ризванова Ф. Ф. , Пикуза О. И. , Файзуллина Р. А. , Гайфуллина Р. Ф. , Ризванов А. А. , Кравцова О. А. Генетическая диагностика: полиморфизм генов цитокинов. Практическая медицина. 2010; 6(45): 41–43. 11. Boverhof D. R. , Ladics G. , Luebke B. , Botham J. , Corsini E. , Evans E. , Germolec D. , Holsapple M. , Loveless S. E. , Lu H. , van der Laan J. W. , White K. L. Jr, Yang Y. Approaches and considerations for the assessment of immunotoxicity for environmental chemicals: a workshop summary. and Pharmacology. 2014; 68(1): 96–107. 12. Lieb R.

Output: pass


Input: Claim: Machine learning algorithms can help predict patient outcomes based on historical data. [SEP] Text: 1 million coronary artery disease patients with the aim of identifying potential drug repurposing candidates for coronary artery disease treatment. The pre-defined measures of disease outcomes selected to assess whether drugs would be effective at treating coronary artery disease included a lower risk of heart failure and stroke. Of the nine top hits the framework produced, three were drugs that are currently used to treat coronary artery disease, confirming that the framework could successfully identify effective drugs. The other six drugs identified have not been used to treat coronary artery disease before, so existing literature was used to learn about their mechanism of action in the body and see if they are plausible drugs for treating coronary artery disease. Existing research supports the idea that these six drugs are likely to be effective at treating coronary artery disease: They act on areas common to coronary artery disease and heart failures, such as blood pressure and vasculature. Of course, before any of these drugs could be administered to patients to treat coronary artery disease, they would require clinical trials to confirm their efficacy, optimum dosage, and safety. What’s next? Coronary artery disease and beyondWhen compared to three other machine learning frameworks that have been developed to identify drug repurposing candidates in the past, the new model created by Dr Zhang’s group at consistently outperformed the other models. Despite the success of testing the model, has emphasised that the outcome of this single case study is less important than the development of the framework itself. “My motivation is applying this, along with other experts, to find drugs for diseases without any current treatment,” Zhang said. “This is very flexible, and we can adjust case-by-case. The general model could be applied to any disease if you can define the disease outcome. ”Not only can drug repurposing speed up the drug development process to allow rapid responses to new outbreaks of disease, but it is also an exciting prospect to identify therapeutic options for diseases that currently do not have any treatments. For example, for rare diseases, there may not be enough data or patients available for the long drug development and clinical trials process. So, while identifying the six potential drug repurposing candidates for coronary artery disease is promising, it is more exciting to consider the future uses of this model for diseases that need new treatments.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: We found that Firmicutes and Bacteroidetes showed an inverse trend (Fig. 3C). What’s more, the dominant genera, Prevotella and Bacteroides, had a similar relationship (Fig. 3D). These results further indicated that the competition between dominant taxa and the separation along the first principal coordinate was driven by a continuous variable that was a trade-off between Prevotella and Bacteroides (Gorvitovskaia, Holmes & Huse, 2016; Smits et al. , 2017). Differential species between Inside and Outside groups To identify the species associated with hypobaric hypoxia, differential abundance analysis was performed between Inside and Outside groups using MaAsLin2. In total, 444 differential species were identified (q-value <0. 05), in which 276 species were increased in Outside group and 168 species were increased in Inside group (Table S1). Differential species with top 20 significance were visualized with heat map (Fig. 4A). Figure 4: Differential species and KEGG modules between Inside and Outside groups. (A) Top 20 significantly differential species between Inside and Outside groups. (B) Top 20 significantly differential KEGG modules between Inside and Outside groups. In heat maps, the significance decreases from top to bottom. Heat maps were scaled by row and columns were clustered. (C) Correlations between top 20 differential species and top 20 KEGG modules with an absolute value of coefficient (r) > 0. 7 and a statistical significance (q-value) < 0. 05. Diamonds represent KEGG modules, circles represent bacteria. The size of the node is mapped to the degree of the node. Pink represents up-regulation in Outside group, and blue represents down-regulation in Outside group. Red lines represent the positive correlation, and blue lines represent the negative correlation. Download full-size image DOI: 10. 7717/peerj. 14090/fig-4 In the top 20 significance species, 19 species were significantly enriched in Outside group. The abundance of Prevotella loescheii, which can ferment sugars to produce succinate and acetate, was the most significant species increased in Outside group.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: , Pfister H. , Mastaler M. , Wittchen H. U. Somatoform syndromes and disorders in a representative population sample of adolescents and young adults: prevalence, comorbidity and impairments. Scandinavica. 2000; 101(3): 194–208. 13. Cascio W. E. Proposed pathophysiologic framework to explain some excess cardiovascular death associated with ambient air p79. 14. Florea V. G. , Cohn J. N. The autonomic nervous system and heart failure. Circulation Research. 2014; 114; 11: 1815–26. 15. Курбатова И. В. , Топчиева Л. В. , Немова Н. Н. Циркадные гены и сердечно-сосудистые патологии. Труды Карельского научного центра Российской академии наук. 2014; 5: 3–17. 16. Никоношина Н. А. , Зайцева Н. В. , Долгих О. В.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Home E-submission us and scope About the journal Editorial board Copyright, open access, and archiving policy Contact us 222. Published online: April 10, 2023 DOI: 인공지능 챗봇의 질병 진단 및 환자 치료에 대한 답변은 신뢰할 수 있는가? 허선 한림대학교 의과대학 기생충학교실 및 의학교육연구소 Can we trust AI chatbots’ answers about disease diagnosis and patient care? Sun Huh, MD Department of Parasitology and Institute of Medical Education, College of author: E-mail: shuh@hallym. ac. kr 20, 2023 12, 2023© is an 4. 0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Several chatbots that utilize large language models now exist. As a particularly well-known example, ChatGPT employs an autoregressive modeling process to generate responses, predicting the next word based on previously derived words. Consequently, instead of deducing a correct answer, it arranges the most frequently appearing words in the learned data in order. Optimized for interactivity and content generation, it presents a smooth and plausible context, regardless of whether the content it presents is true. This report aimed to examine the reliability of ChatGPT, an artificial intelligence (AI) chatbot, in diagnosing diseases and treating patients, how to interpret its responses, and directions for future development. Current Concepts: Ten published case reports from Korea were analyzed to evaluate the efficacy of ChatGPT, which was asked to describe the correct diagnosis and treatment. ChatGPT answered 4 cases correctly after being provided with the patient’s symptoms, findings, and medical history. The accuracy rate increased to 7 out of 10 after adding laboratory, pathological, and radiological results.

Output: corroborate


Input: Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: However, ‘Biotin biosynthesis’ (M00573), ‘C1-unit interconversion’ (M00141) and ‘Pyrimidine ribonucleotide biosynthesis’ (M00052) were positively correlated with almost all species. Correlation results revealed that differential species influenced by hypobaric hypoxia conditions significantly facilitate the alterations of microbial functions. findings highlight the gut microbiota in rats was significantly impacted by hypobaric hypoxia and dynamic changes over time from entering the hypobaric chamber to getting back to normoxic conditions. Study reported that the oxygen concentration in the gut of mice was decreased induced by intermittent hypoxia exposure (Moreno-Indias et al. , 2015). What’s more, various evidences indicated both aerobic and facultative anaerobic bacteria colonized the intestine and consumed oxygen, allowing the increasing population of obligate anaerobes (Albenberg et al. , 2014; Fanaro et al. , 2003; Favier et al. , 2002). Consistently, in Inside group, we found some anaerobic bacteria such as Alistipes and Desulfovibrio, were significantly enriched compared to Outside group. In contrast, some aerobes or facultative anaerobes like Mycoplasma, Enterococcus and Paenibacillus were significantly increased in Outside group. We speculated that the hypoxia or normoxia environment in which the hosts settle influenced the abundance of aerobes and anaerobes of gut. However, gut microbial sensing processes and pathways for hypoxia or normoxia need to be further explored. In addition, some enriched species in Inside group were related to diseases, such as Bacteroides massiliensis associated with promoting tumorigenesis (Garbas et al. , 2021) and Bacteroides vulgatus markedly elevated in the gut microbiota of patients with polycystic ovary syndrome (Qi et al. , 2019). Moreover, some probiotics and SCFA-producers like Butyrivibrio, Bifidobacterium and Lactococcus were significantly decreased in Inside group, thus leading to the symptoms of the digestive systems possibly (Gamah et al. , 2021; Zheng et al. , 2009). Our findings further provide the direct evidences that hypobaric hypoxia conditions have a profound impact on the gut microbiota.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Анализ особенностей иммунной регуляции, ассоциированных с полиморфизмом кандидатных генов у мужчин с диагнозом атеросклероз (на примере Пермского края). Бюллетень экспериментальной биологии и медицины. 2020; 170; 11: 608–12. 17. Дианова Д. Г. , Долгих О. В. Продолжительность трудовой деятельности работников производства фталевого ангидрида и дисбаланс показателей клеточного иммунитета. Медицина труда и промышленная экология. 2021; 61; 2: 97–102. 18. Долгих О. В. , Зайцева Н. В.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 과거에 나온 어떤 자연어처리 플랫폼 챗봇에 비하여 매우 정확하게 유용한 답을 제공하고, 문장의 맥락을 갖추고 있어 많은 사람들에게 각광을 받고 업무에 활용하고 있다. 의료 현장에서 특히 전문가인 의사는 해당하는 답이 어느 정도 수준으로 정확한지 감별할 수 있으나 비전문가인 경우, 이 답을 그대로 받아들이면 매우 위험할 수 있다. 그러므로 챗봇은 늘 전문가에게 자문을 구하라고 답 마지막 부분에 추가한다.

Output: corroborate


Input: Claim: The skin microbiome helps protect against harmful pathogens. [SEP] Text: Log in or to journalnew issue alertsGet alerts Enter your Email address: Subscribe to e2010. | DOI: 10. 1097/01. NME. 0000366098. 67372. 2b caring for a patient, you sustain a needlestick injury. What's your risk of contracting a bloodborne disease and how can you protect yourself? We spell it out. If you've ever had to report a needle-stick injury, whether it was your own or a coworker's, more than likely you've asked yourself: "What could I have done to prevent it? " According to published information from the CDC and the Association for Professionals in and Epidemiology (APIC), over a half million needle sticks and other sharps-related injuries are sustained by hospital-based healthcare personnel annually. Exposure injuries are primarily associated with transmission of HIV, hepatitis B virus (HBV), and hepatitis C virus (HCV), but they may be implicated in the transmission of as many as 20 other pathogens (see The HIV life cycle and Picturing a liver with the effects of hepatitis for the pathophysiology of these viruses). And sharps-related injuries aren't the only way you can be exposed to pathogens—if a patient's blood or other potentially infectious body fluid comes in contact with your eyes, nose, mouth, or an area of open skin, you may also be at risk, although most exposures of this type don't result in infection. The risk of infection from contact with blood or body fluid varies with the pathogen involved, the type of exposure, the amount of blood or body fluid involved in the exposure, and the amount of pathogen in the patient's blood or body fluid at the time of exposure. In this 0. 3%, or about 1 in 300. After mucous membrane exposure, the risk is 0. 09%, or about 1 in 1,000. The average risk for infection after a needle stick or cut exposure to HCV-infected blood is actually fairly small at 1. 8%, according to the CDC. The risk following a blood exposure to the eyes, nose, or mouth is unknown, but it's also believed to be very slight. The CDC does state in its brochure entitled "Exposure to Blood: to Know" that HCV infection from a blood splash to the eye has been reported and HCV transmission to nonintact skin has occurred.

Output: pass


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: , Никоношина Н. А. Условия аэрогенной экспозиции бензолом и генетический статус как факторы формирования особенностей иммунного профиля у мужчин с нарушениями вегетативной регуляции. Бюллетень экспериментальной биологии и медицины. 2021; 171; 2: 191–5. 19. Нестерова М. В. Мелатонин — адаптоген с мультимодальными возможностями. Медицинский совет. 2015; 18: 50–3. 20. Sulkava S. , Ollila H. M. , Alasaari J. , Puttonen S. , Härmä M. , Viitasalo K. , Lahtinen A. , Lindström J. , Toivola A. , Sulkava R. , Kivimäki M. , Vahtera J. , Partonen T. , Silander K. , Porkka-Heiskanen T. , Paunio T. Common genetic variation near melatonin receptor 1A gene linked to job-related exhaustion in shift workers. Sleep research society.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 더구나 이 글에서 증례보고는 모두 영문 논문이고 영문으로 질문과 답을 주고받은 것이라서 조금 낫지만, 만약 국문으로 질의 응답하였다면 그 수준은 더 낮아질 수 있다. 왜냐하면 ChatGPT가 익힌 국문 문서에 대한 훈련 양은 영문 문서보다 훨씬 적기 때문이다. 우리 의사는 왜 챗봇을 사용할까? 비록 특수한 분야 증례에 대한 답은 만족스럽지 못하지만 일반적인 흔한 증상에 대한 답은 상당히 높은 수준이다.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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

Output: pass


Input: 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].

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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].

Output: corroborate


Input: 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.

Output: contrast


Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: В. Инструменты статьи Поиск ссылок Послать статью по эл.

Output: pass


Input: 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%).

Output: pass


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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

Output: pass


Input: 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.

Output: corroborate


Input: 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].

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: pass


Input: 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).

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate