Input: Task: corroborate the claim using the given text. Claim: Smart cities often implement IoT devices to monitor energy and water usage. [SEP] Text: - 2017 331 33:275–276. (2021) IoT based smart water quality monitoring system. Proc 2:181–186. Rathna R, V. , L, et al (2021) for Management using Internet of Things. 2021 8th a 2021 291–297. (2020) Pressure prediction and abnormal working conditions detection of water supply network based on LSTM. 20:963–974. (2019) Work-in-Progress: SMART-WATER, a Νovel Τelemetry and for the Management of in Thessaloniki. 1192 AISC:962–970. Wade MJ, Steyer J-P, Garcia MVR (2020) Making water smart. Technol 82:v–vii. Antunes A, Andrade-Campos A, Sardinha-Lourenço A, Oliveira MS (2018) Short-term water demand forecasting using machine learning techniques. J Hydroinformatics 20:1343–1366. Therrien J-D, Nicolaï N, Vanrolleghem PA (2020) A critical review of the data pipeline: how wastewater system operation flows from data to intelligence. Technol 82:2613–2634. (2018) An open software package for data reconciliation and gap filling in preparation of Water and Facility Modeling. Softw 107:186–198. (2020) A comprehensive review of deep learning applications in hydrology and water resources. Technol 82:2635–2670. Jeong J, (2019) Comparative applications of data-driven models representing water table fluctuations. J Hydrol 572:261–273. (2021) Efficient integration of IoT-based micro storages to improve urban drainage performance through advanced control strategies. Technol 83:2678–2690. (2021) Technologies using Blockchain. Proc 13th 2021. Pasika S, Gandla ST (2020) Smart water quality monitoring system with cost-effective using IoT. Heliyon 6:e04096. Asgari M, (2022) Application of Platforms in Systems—A Literature Review. 4:49. A Framework of in Management. Sci 10:753. (2019) Solutions for Metering. Sensors 2019, Vol 19, Page 1853 19:1853. (2019) Analysis and Prediction of in IoT Environment. [SEP] Justify:

Output: The text addresses various applications of IoT in water management, showcasing systems like 'smart water quality monitoring' and 'pressure prediction', indicating that IoT devices are being used to monitor water usage and related conditions. This aligns well with the claim about the implementation of IoT in smart cities for monitoring resources.


Input: Task: contrast the claim using the given text. Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-1625886/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractBackground: is the only member of genus Tamias, which is a very important and vigorous seed distributors, and also a vital food for their predators. No information is known about the strict diet, Gut microbiota structure and metabolism profile of chipmunk and how it divers seasonally. Analysis of above mentioned factors, and flexibility toward seasonal shift is crucial to define its growth rates, health, survivorship, and population stability. Results: In this study, we explored the diet, gut microbiota composition, and chipmunk metabolism. Additionally, the influence of different seasons was also investigated by using next-generation sequencing. Results revealed that seasons had a strong effect on a diet : as streptophyte accounted for 37% in spring, which was lowest in summer (34. 3%) and autumn (31. 4%). Further, Ascomycota was observed 43. 8% in spring which reduced to 36. 6% in summer and lowest (31. 3%) in autumn. Whereas, Nematoda showd maximum abundance from spring (15. 8%) to summer (20. 6%) and autumn (24. 1%). These results are signifying the insectivorous nature of the chipmunk in summer and autumn. While herbivorous and fungivorous nature in spring. The DNA analysis revealed that chipmunk mainly feeds on fungi, including Aspergillus and Penicillium genus. Like diet composition, microbiome also exhibited highly significant dissimilarity (p<0. 001, R=0. 235) between spring/autumn and spring/summer seasons. Proteobacteria (35. 45%), Firmicutes (26. 7%) and Bacteroidetes (23. 59%) were shown to be the better discriminators as they contributed most to cause differences between seasons. was exposed that the assimilation of nutritions were also variated seasonally. The abundance of carbohydrate, lipid, nucleotide, xenobiotics, energy, terpenoids and polyketides metabolism were highest in spring than other seasons. [SEP] Justify:

Output: The text contrasts the claim by focusing on the seasonal dietary variations of chipmunks and its effects on gut microbiota composition. It indicates that the diet varies seasonally with specific changes in microbiota, rather than emphasizing a positive influence of certain diets on microbiota diversity. The mention of significant dissimilarity in microbiome composition based on seasonal diets suggests that while diet impacts microbiota, the narrative does not support the claim that certain diets can positively influence diversity in a consistent or favorable way.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: J 15: 86–94, [PubMed] DOI: and Lewy, AJ (1992). Melatonin rhythms in night shift workers Sleep 15: 434–41. [PubMed] and Koh, KK (2001). Restless legs syndrome in an Asian population: a study in Disord 16: 577–579, [PubMed] DOI: and Shin, C (2005). Prevalence of restless legs syndrome and associated factors in the Korean adult population: the Neurosci 59: 350–353, [PubMed] DOI: and Montplaisir, J (1997). Cigarette smoking as a risk factor or an exacerbating factor for restless legs syndrome and sleep bruxism Sleep 20: 290–293. [PubMed] and Poewe, W (2005). Restless legs syndrome: a community-based study of prevalence, severity, and risk factors Neurology 64: 1920–1924, [PubMed] DOI: and Zintzaras, E (2007). Low RLS prevalence and awareness in central Greece: an epidemiological survey Neurol 14: 1275–1280, [PubMed] DOI: Gamaldo, MD and Earley, CJ (2006). Syndrome: 130: 1596–1604, [PubMed] DOI: and Violanti, JM (2007). Shift work and sleep: the health study, of & Management 30: 215–227, DOI: Ohayon, MM and Roth, T (2002). Prevalence of restless legs syndrome and periodic limb movement disorder in the general population J 53: 547–554, [PubMed] DOI: Allen, R (2004). Dopamine and iron in the pathophysiology of restless legs syndrome (RLS) 5: 385–391, [PubMed] DOI: and Volc, D (2006). Ropinirole is effective in the long-term management of restless legs syndrome: a randomized controlled trial 21: 1627–35, [PubMed] DOI: änsberger, W and Trenkwalder, C (2001). Opioid and dopamine antagonist drug challenges in untreated restless legs syndrome patients 2: 57–61, [PubMed] DOI: and Kripke, DF (2009). [SEP] Justify:

Output: The text is largely a citation list of various studies related to restless legs syndrome and does not provide any information regarding the effects of shift work on circadian rhythms or health. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Smart lighting systems in cities can save energy by adjusting based on usage and time of day. [SEP] Text: Figure 4: Illuminating of lighting system in human and veheicles’ motion range. As seen in Figure 4, any sensor determines pedestrian or vehicle motion range by measuring speed and controls lighting of any identified range. The product options make lights’ lighting reduced, namely in rural roads (roads outside city) and at particular times, up to 30%, or in mostly empty areas such as parking lots up to 70%. These are surprising. Cities, states, and in general, counties can save up to 50% of lights’ repairing and maintain costs and up to 80% in energy usage for street lighting, as needed. 40% of state’s energy costs in Europe dedicated to street lighting; thus, using these lights and cost reduction can have incredible effects. Figure 5: System structure and function. Figure 5 illustrates how this system operates, the information transfers to the central communication system, which is globally controllable. Then, the obtained data estimated and turning on order issued for the assigned lights through wireless system. It also recommends some energy saving points so that energy consumption reduced to 20%. The system manufacturer engineers largely maneuvered on the product dynamic so that it stated that it could be implemented on all streets and lights whether traditional or modern. The sensors can add to the old, existed lights in roads and or LED lamps. Now, it is applied in Holland an Ireland; it is also developing in Germany, Canada, and USA ( [4]. One of the key elements of is a management system and street lighting control system. It allows intelligent management of individual lamps, can reduce energy consumption by dimming lighting in hours with little traffic, and areas of lesser importance. The use of this type of technology, reduce management and maintenance costs and reduces the cost of electricity consumption by up to 40%. Intelligent lighting network can provide the basis for intelligent power distribution in urban areas. Typically, an intelligent lighting management system is the extension of the existing conventional lighting system. Implementation involves installing controllers in each lamp. Controller communicate with the control server via the existing power grid using protocol and technology, PLC (Communication). Control server securely communicates through a network to the central management and control system. The transmission technology which is used based on 3G, Wi-Fi and Wi-MAX. Figure 6: Traditional street lighting (Elvidge & M. L. Imhoff, 2001). Central management system allows full control of all lamps covered by surveillance. [SEP] Justify:

Output: The text corroborates the claim by detailing how intelligent lighting systems operate to adjust lighting based on usage and time of day, leading to significant energy savings. It mentions that sensors can control lighting according to pedestrian or vehicle motion, resulting in energy reductions of up to 70% in certain areas. Additionally, it states that these systems can reduce electricity consumption by up to 40% and that cities can save considerably on energy usage through the implementation of such technologies.


Input: Task: contrast the claim using the given text. Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: 45%), Firmicutes (26. 7%) and Bacteroidetes (23. 59%) were shown to be better discriminators as they contributed most to differences between seasons, it indicates that the gut microbiome undergoes dramatic seasonal fluctuations. When we compared three seasons, significant (distance, p<0. 05, p=0. 015, R=0. 0730, Permutation N: 9999) dissimilarity was observed between autumn and spring season at the phylum level. Like diet composition, microbiome showed highly significant dissimilarity (p<0. 001, R=0. 235) between spring/autumn and spring/summer seasons at the genus level. Gut microbiome showed no statistical difference (distance, p>0. 05, p=0. 59, Permutation N: 9999) between summer and autumn season. Predicted gut microflora function using is unclear that seasonal variation in the gut microbiome affects host metabolism. To understand the specific effects of the gut microbiome on host metabolism, was performed to predict the chipmunk gut microbiome functions, which showed that chipmunk in different seasons exhibited some differences in metabolism abundance at level 2, including carbohydrate, protein, amino acid, Xenobiotics, energy, Cofactors and Vitamins, Glycan, Lipid, Terpenoids, and Polyketides metabolism. A was used to visualize the distribution of the data and its probability density (Fig. 5). The abundance of carbohydrate, Lipid, Nucleotide, Xenobiotics, energy, Terpenoids and Polyketides metabolism were higher in spring than in summer and autumn. Moreover, spring and summer showed significant differences (< 0. 001, p= 0. 005, Euclidian R= 0. 20), whereas a highly significant difference was observed between spring and autumn (p=0. 0005) and insignificant Euclidian difference (p=0. 29) was investigated while comparing summer with autumn. analyses among three seasons observed average Bray-Curtis dissimilarity was 3. 627% as shown in Additional file 4. This analysis assisted us in figuring out the relationship between seasonal metabolic capacities. DiscussionIn this study, we assessed the first precise dietary habit of small, striped rodent called siberian chipmunk, belongs to family Sciuridae (8). [SEP] Justify:

Output: The text contrasts the claim by focusing on the seasonal fluctuations in the gut microbiome of the chipmunk rather than discussing the effects of diet on microbial diversity. The mention of the gut microbiome's dissimilarity across different seasons (e.g., 'significant dissimilarity... between autumn and spring season') suggests that factors other than diet, such as seasonal changes, may be influencing microbiota variation.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Медицина труда и промышленная экология Meditsina truda i promyshlennaya ekologiya eng | рус Войти ISSN 1026-9428 (Print) ISSN 2618-8945 (Online) Войти Логин Пароль Запомнить меня Регистрация нового пользователя Забыли Ваш пароль? [SEP] Justify:

Output: The text is irrelevant to the claim as it does not provide any information or commentary related to shift work or its effects on health. Instead, it appears to be a login page for a medical journal, offering no insight into the topic of circadian rhythm misalignment.


Input: Task: corroborate the claim using the given text. Claim: Smart lighting systems in cities can save energy by adjusting based on usage and time of day. [SEP] Text: Allowing to configure different parameters depending on scenarios e. g. on off, dimming lights in which you want to start saving. It provides information about the current operation of infrastructure, failures and reports of malfunctioning lamps. The graphically illustrates the operation of the system and presents reports on energy consumption. Smart lighting management system can be extended with additional elements for the implementation of advanced features. Such elements may include illumination sensors, motion detectors and sensors reporting environmental data ( [5]. Indeed, as seen in Figure 6, traditionally using of lighting systems not only wastes the capitals and resources, but also it has no diversity, difference, and beauty. This system presents a straightforward, unchangeable appearance depriving the cities of dynamic, diversity, and visual beauty (Figure 6). Applying a digital or radiation imaging system, if possible, better detects the passing object; then, avoids many problems such as wrong detection. The sensors only activate whenever human and vehicles pass preventing wrong lighting (Figure 7). More than a century after the introduction of incandescent lighting and half a century after the introduction of fluorescent lighting, solidstate light sources are revolutionizing an increasing number of applications. Whereas the efficiency of conventional incandescent and fluorescent lights is limited by fundamental factors that cannot be overcome, the efficiency of solid-state sources is limited only by human creativity and imagination. The high efficiency of solidstate sources already provides energy savings and environmental benefits in a number of applications. However, solid-state sources also offer controllability of their spectral power distribution, spatial distribution, color temperature, temporal modulation, and polarization properties. Such ‘‘smart’’ light sources can adjust to specific environments and requirements, a property that could result in tremendous benefits in lighting, automobiles, transportation, communication, imaging, agriculture, and medicine. [6]. Figure 7: (Illmann, Kluge, & Prassler). In addition to the energy savings and positive environmental effects promised by solid-state lighting, solid-state sources in particular, LED-based sources offer what was inconceivable with conventional sources: controllability of their spectral, spatial, temporal, and polarization properties as well as their color temperature. Technologies currently emerging are expected to enable tremendous benefits in lighting, automobiles, transportation, communication, imaging, agriculture, and medicine. [6]. [SEP] Justify:

Output: The text corroborates the claim by discussing the functionalities of smart lighting systems, specifically how they can be configured to adjust based on usage. It highlights the use of sensors and technologies that allow for energy savings and efficiency improvements, such as illumination sensors and motion detectors that activate lights only when needed. This supports the assertion that smart lighting systems can save energy through their adaptive features.


Input: Task: contrast the claim using the given text. 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 -effects of a set amount of regular maternal exercise during pregnancy on gut microbiota are diet-dependent in mice and do not cause significant diversity changes View 10. 7717/peerj. 14459 Published 2022-12-02 Accepted 2022-11-02 Received 2022-08-03 and Obstetrics, microbiota, Microbiome, Exercise, High-fat diet, Pregnant mice Copyright © 2022 Duan et al. is an open access 2022. The effects of a set amount of regular maternal exercise during pregnancy on gut microbiota are diet-dependent in mice and do not cause significant diversity changes. PeerJ 10:e14459 Diet and exercise can affect the gut microbiota (GM); however, the effects of the same amount of exercise on gut microbiota changes in people on a low-fat diet (LFD) and high-fat diet (HFD) during pregnancy are unknown. Do different nutritional conditions respond equally to exercise intervention? This study aimed to investigate the effects of regular maternal exercise during pregnancy on the GM in mice fed different diets during pregnancy. Methods Six-week-old nulliparous female KunMing mice were fed either a HFD or LFD before and during pregnancy. Each group of mice were then randomly divided into two groups upon confirmation of pregnancy: sedentary (HFD or LFD; n = 4 and 5, respectively) and exercised (HFDex or LFDex, n = 5 and 6, respectively). Mice were sacrificed on day 19 of gestation and their colon contents were collected. We then performed 16S rDNA gene sequencing of the V3 and V4 regions of the GM. pregnancy success rate was 60% for LFDex and 100% for HFDex. Both Chao1 and Simpson indices were not significantly different for either LFD vs. LFDex or HFD vs. HFDex. and Eggerthellaceae were markedly decreased after exercise intervention in LFDex vs. LFD, whereas Actinobacteria, Bifidobacteriales, Bifidobacteriaceae, Bifidobacterium, and Bifidobacterium pseudolongum were significantly increased in LFDex vs. LFD. [SEP] Justify:

Output: The text contrasts the claim by indicating that in a study on mice, the effects of exercise on gut microbiota were found to be diet-dependent but did not result in significant diversity changes according to the Chao1 and Simpson indices. This suggests that, despite dietary influences on gut microbiota, the specific condition of maternal exercise during pregnancy did not lead to the expected diversity changes, thereby opposing the idea that certain diets can positively influence gut microbiota diversity.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Медицина труда и промышленная экология Расширенный поиск Архив Главная О журнале Свежий номер Архив Рецензентам Отправить статью Правила для авторов Редакционная коллегия Редакционный совет Рецензирование Этика публикаций Главная > № 4 (2023) > Субботина Полиморфизм генов циркадных ритмов у работников предприятия непрерывного производства со сменным графиком работы Алена Александровна Субботина, Олег В. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily provides information about the journal and references the authors of an article without addressing the specific issue of shift work and its health effects related to circadian rhythms.


Input: Task: corroborate the claim using the given text. Claim: Smart lighting systems in cities can save energy by adjusting based on usage and time of day. [SEP] Text: Another potential benefit of smart lighting originates in the ability to rapidly modulate the output power of LED-based light sources, thereby enabling communication features. New modes of communication based on room-light sources would help to reduce the overcrowding of the radio frequency bands. Of course, the visual appearance of such communicative light sources would be indistinguishable from conventional sources. In automotive communication applications, brake lights could communicate an emergency braking maneuver to a following car. Headlights could inform a red traffic light of an approaching car while fully maintaining their normal function as headlights. Smart road signs could flash warnings specifically to drivers that approach a dangerous curve with excessive speed. Room lights could broadcast messages, alarms, and other types of information, without any noticeable change in the illumination quality. Modulation rates in the megahertz range are possible, with the limiting factor being the device resistance and capacitance (RC time) of high-power devices. The large-area junction capacitance means that such devices would be limited by RC time rather than by spontaneous lifetime. [6]. The implemented process must be economic in long-term in addition to its economic justification at the beginning of the project; the spent investments and costs must return in short-term in order to develop country through energy and financial saving. Economical risk factors in long-term include selecting improper and problematic materials. For instance, selecting inappropriate color and light type may cause insects’ attraction, which harms the lights and urban space and or leads to chemical effects and serious damages on colored surfaces Bekrani and Mahdizade Seraj, fall and winter [7]. However, lighting sources with larger thermal effects may be useful against humidity and proliferation of some types of moss or microorganisms, can also lead to harmful moisture deformation, intensified airflow, and excess growing of some plants (www. aftabir. com) [8]. lighting with dimming for scenes: Appropriate lighting and scene control based on specific environment or condition can not only provide better living situation, but also save energy consumption. You can enjoy a pleasant lighting environment by simply pressing a button. Smart lighting with body detection: According to surveys, most lamps in offices without a worker are lighted over 70% of the office hours. The LED drivers of DDL series with built-in PIR detectors can automatically execute continuous illumination adjustment. Thus, secure, comfortable, and energysaving smart lighting control can be achieved. [SEP] Justify:

Output: The text supports the claim by discussing the benefits of smart lighting systems that can adjust their output based on usage and environmental conditions. Specifically, it mentions that smart lighting with body detection can automatically provide continuous illumination adjustment, which leads to energy saving. This aligns with the claim that such systems help save energy by adjusting based on usage.


Input: Task: contrast the claim using the given text. Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: 7717/peerj. 14459/table-2 Notes: aThe ratio of successfully pregnant mice to all mice identified as entering GD0. group HFDex compared with group HFD. *P < 0. 05 was considered to be statistically significant. LFD low-fat diet LFDex low-fat diet plus exercise HFD high-fat diet HFDex high-fat diet plus exercise GD gestation day Effect of exercise on gut microbiota of mice To analyze the effect of exercise on the GM, we performed 16S rDNA amplicon sequencing on colon content samples collected from mice on GD19. An average of 28,840 optimized reads were generated from all samples. Rarefaction curves generated from the OTUs in Fig. 2 indicated that the sequencing depth was sufficient to detect all OTUs. The averaged number of OTUs were 936, 1804, 1677, 1326 in the and HFDex groups, respectively. We first examined two α-diversity indices across the two groups fed different diets. Neither Chao1 nor Simpson indices were significantly different between mice in the LFD group and those in the LFDex group (P = 0. 971 and 0. 180, respectively), HFD group vs. the HFDex group (P = 0. 798 and 0. 990, respectively), or between the LFDex group and the HFDex group (P = 0. 999 and 0. 329 respectively), as shown in Figs. 3A and 3B. Figure 2: The rarefaction curves of gut samples indicated that the sequencing depth was sufficient. LFD, low-fat diet; LFDex, low-fat diet plus exercise; HFD, high-fat diet; HFDex, high-fat diet plus exercise. Download full-size image DOI: 10. 7717/peerj. 14459/fig-2 Figure 3: Impact of exercise on gut microbiome. Measures of chao1’s diversity index (A) and Simpson’s diversity index (B) showed no difference when LFD vs. LFDex or HFD vs. HFDex. LFD, low-fat diet; LFDex, low-fat diet plus exercise; HFD, high-fat diet; HFDex, high-fat diet plus exercise. Download full-size image DOI: 10. 7717/peerj. [SEP] Justify:

Output: The text contrasts the claim by presenting findings from a study where 'neither Chao1 nor Simpson indices were significantly different' between groups of mice on different diets, indicating that the diets tested did not lead to significant changes in gut microbiota diversity. Thus, the text suggests that certain diets may not influence gut microbiota positively as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Обследованы 2 группы работников предприятия: группа наблюдения — 40 человек 23–63 лет (30 женщин, 10 мужчин) со сменным графиком работы, группа сравнения — 51 человек 24–62 лет (41 женщина, 10 мужчин) административного аппарата с дневным графиком работы. Типирование полиморфных локусов генов циркадной системы осуществлялось наборами компании «Синтол» (Россия) с использованием метода Real-Time ПЦР на приборе CFX96 с аллельной дискриминацией кривых флюоресценции. [SEP] Justify:

Output: The text is irrelevant to the claim as it describes a study's participant groups and methods related to circadian systems but does not address the health effects of shift work or the misalignment of circadian rhythms.


Input: Task: corroborate the claim using the given text. Claim: Smart lighting systems in cities can save energy by adjusting based on usage and time of day. [SEP] Text: Major parameters such as illumination and delay time can be varied on demand. There are two setting modes to be used in smart lighting control. As you can see in Figure 8 a) Single mode: the output power of every LED driver in the DDL series is determined by the individual PIR detector Figure 8: Classification of lights. b) Group mode: every LED driver in the DDL series can share the outcome of other PIR detectors and offer the information to other lamps within the same group. Thus, all lamps in a group can be turned on simultaneously. for Systems or Individuals: The LED drivers of DDL series with built-in DALI interface and PIR detectors can create the simplest smart lighting control system to date. Either a lighting network in a building or an individual lamp can be completely controlled by our products. Automatic energysaving with schedule control: By using the scheduling function, on/off or illumination of lamps at specified time can be controlled based on your schedule. Hence, target of energy conservation can be achieved automatically. Both requirements of “limited budget” and “uncompromising efficiency” can be satisfied concurrently. The LED drivers of DDL series can provide more ( ledinside. com/) [9]. 9: Figure 9 shows the power of external smart lights and studies the lights’ differences. to Conclusion 35. Mahaleh AT, Kaveh (2008) An introduction to the basics of lighting design, Tehran: designer. (2001) Night-time lights of the world. of Photogrammetry & 56(2): 81-99. (2005) Solid-Smart. Science 308(5726): 1274-1278. Bekrani M, F (2013) Lighting mechanism of ancient buildings in urban areas; case study: Sheikh Lotfollah. and restoration in Iran 6: 99-110. www. aftabir. com (2003) Electricity and Magnetism. Fullerton, California: Light and Matter, USA. (2001) The first world atlas of the artificial night sky brightness. of the Society 328(3): 689-707. (2001) Vision and Range Information. for Processing Helmholtz’s Ulm, Germany pp. ssss4571. (2007) Lights Out: Crisis, the Global Economy, and Means to You. New Jersey: &. (2005) Smart Light-Ultra High-for Optical Transmission. [SEP] Justify:

Output: The text corroborates the claim by explaining that smart lighting systems can save energy through features like 'automatic energy-saving with schedule control,' which allows lamps to be controlled based on a predefined schedule. This implies that the lighting adjusts automatically based on usage at specific times, thus supporting the idea of energy conservation in cities.


Input: Task: contrast the claim using the given text. Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: In contrast, in HFD-fed mice, exercise intervention resulted in a significant reduction in maternal food intake as compared to the sedentary group, and the total energy and macronutrient intake during pregnancy in the HFDex group were significantly reduced, but the maternal weight gain was not statistically significantly different from that of the HFD group. It is suggested that the exercise intervention during pregnancy led to a decrease in appetite in mice fed an HFD, for reasons that require further investigation. However, exercise during pregnancy in the HFDex group altered the GM structure and there was no significant decrease in body weight gain during pregnancy as compared to the HFD group. An interesting phenomenon observed in this study was the effect of exercise on pregnancy success. The traditional Chinese view is that pregnant women should rest during pregnancy to avoid miscarriage. In this study, when designing the exercise during pregnancy, we were also concerned about the increase in miscarriage and preterm birth rates due to exercise during pregnancy. Previous studies (et al. , 2018; Chung et al. , 2019) have mostly used voluntary exercise by the pregnant mice, and noted that the amount of exercise performed voluntarily by the mice decreased with increasing gestational age. To observe the effect of a set amount of regular exercise during pregnancy on the GM on GD19, mice were subjected to 10 min of swimming exercise per day during pregnancy. Interestingly, we found that the pregnancy success rate of mice fed an HFD and performing 10 min of swimming exercise per day was 100%, whereas mice fed an LFD along with this level of exercise achieved a pregnancy success rate of only 60%, although there was no statistically significant difference between the two groups. Clearly, mice swimming for 10 min had different tolerances to different diets during pregnancy. Therefore, four female mice in the second batch in the LFDex group were supplemented to investigate the effect of an LFD on the GM during gestation. The results of this study seem to indicate that the choice of exercising may be integrated with the adjustment of dietary composition. Whether pregnant women on LFD are also intolerant to exercise during pregnancy? Should the proportion of fat in the diet probably be appropriately increased for pregnant women who choose to exercise during pregnancy? Specific dietary recommendations for pregnant women warrant further investigation. [SEP] Justify:

Output: The text contrasts the claim by discussing how different diets (HFD vs. LFD) affect pregnancy success rates and gut microbiota (GM) structure in mice, while also suggesting that exercise might influence these outcomes. It indicates that while exercise and diet interplay during pregnancy, further investigation is needed to elucidate how these factors affect the gut microbiota diversity, implying that the relationship is complex and not straightforward as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Вследствие чего полиморфизм гена MTNR1A (rs34532313) можно использовать в качестве маркера «непереносимости» сменной работы. Ограничения исследования. Существуют количественные ограничения группы наблюдения лиц различного пола. Этика. [SEP] Justify:

Output: The text does not directly address the health effects of shift work or circadian rhythm misalignment. Instead, it discusses polymorphism of the MTNR1A gene as a marker for shift work intolerance, without linking it to health outcomes. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to assist in diagnosing diseases through image analysis. [SEP] Text: Health & 8, 2022 Detecting dental diseases with AI dental image analysis Dentists often use panoramic dental radiography, capturing the entire mouth in a single image, to diagnose dental diseases. The manual examination of these complex images is time consuming and laborious. at the Institute of Science and Technology (KAIST), together with Donghyun Kim, Jeong, and from InVisionLab, has developed a deep-learning model for AI dental-image analysis which enables the automatic detection of dental diseases from panoramic X-ray images. In addition to dental anomalies, radiographic findings from such images can be used in the diagnosis of systemic diseases. (AI) is employed extensively in dentistry to identify both normal and abnormal structures and diagnose diseases from the numerous medical images collected during a clinical routine. Panoramic dental radiography, which captures the entire mouth in a single image, is often used to diagnose dental diseases and plan subsequent treatment. These images are complex, and their manual examination is time consuming and laborious. In addition to dental anomalies, radiographic findings from such images can be used in the diagnosis of systemic diseases like hypo- and hyperparathyroidism (abnormally low or high levels of parathyroid hormone which affects calcium levels in the blood) and osteoporosis (porous bone). Due to time constraints, dental clinicians may concentrate only on teeth with symptoms, so the ability to filter out healthy images would aid diagnosis and save time. Sangyeon Lee, a PhD candidate from the Department of Bio and Brain Engineering, Institute of Science and Technology (KAIST), has collaborated with Donghyun Kim, Jeong, and from InVisionLab, to develop a deep-learning model for panoramic X-ray image analysis that enables the automatic detection of dental diseases. Three major components of panoramic X-ray images. AI dental image analysis The researchers explain how previous studies have employed AI in dentistry and been successful in the detection of targeted oral diseases. Still, they can only target a small number of abnormal signs or diseases. These models use convolutional neural networks to classify images and detect objects. They have not, however, exploited the key benefit of panoramic dental radiography in that it encapsulates the entire mouth in a single image. This includes the upper and lower jaws, individual teeth, and surrounding tissues and structures. [SEP] Justify:

Output: The text corroborates the claim by detailing how AI is employed in dental image analysis for diagnosing diseases. It discusses the development of a deep-learning model for automatic detection of dental diseases from panoramic X-ray images, specifically noting that AI is used extensively to identify both normal and abnormal structures in medical images. The passage emphasizes the challenges of manual examination and how AI improves efficiency in diagnosing not only dental issues but also systemic diseases, thereby supporting the original claim.


Input: Task: pass the claim using the given text. Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: The quantity and quality of library were monitered using Qubit and TapeStation (). Finally, the qualified amplifications were sequenced on the Illumina HiSeq 2500 platform. (release 132, and NT-16S databases were used for species alignment and annotating. The raw reads generated in this research were archived in the (SRA) database with accession number PRJNA897818. Bioinformatic analyses into version 2 (QIIME2) (Bolyen et al. , 2019) was employed for data processing. cutadapt trim-paired was called to trim the primer fragments of the sequences and discard the sequences that did not match the primers; then DADA2 was called for quality control, denoising, splicing, and chimera removal by QIIME dada2 denoise-paired. The above steps were performed separately for each library. Then, raw tags were generated with an overlap length greater than 10 bp and a maximum mismatch rate less than 0. 2. FLASH (V1. 2. 7) was used to assign paired-end reads to samples. Sequences underwent analysis algorithm and QIIME (Caporaso et al. , 2010; Edgar et al. , 2011). Tags that demonstrated effectiveness were subsequently filtered and clustered into Units (OTUs) utilizing the Greengenes database, with an identity threshold set at 97%. The taxonomic annotation of each OTU was performed using the Uparse software (Edgar, 2013). Diversity and statistical analysis Alpha diversity indices, compromising of the observed OTUs, Chao1, Simpson, Shannon, Pielou’s evenness index (Pielou-e), Faith’s pd diversity, and Good’s coverage between gut microbial communities, were analyzed using Mothur (v. 1. 21. 1) and a non-parametric Kruskal-Wallis test. Rarefaction curves were obtained by plotting the number of sequenced reads (x-axis) by the number of OTUs found in a community. QIIME 2 assessed beta diversity and clustered it using weighted and unweighted UniFrac distance metrics. Similarities between groupings were visualized using principal coordinate analysis (PCoA) with unweighted and weighted UniFrac distances. The diet-induced common and unique OTUs were identified using a Venn diagram. [SEP] Justify:

Output: The text does not directly support or contrast the claim regarding the influence of diets on gut microbiota diversity. Instead, it focuses on the methodology and analyses related to sequencing gut microbiota, including bioinformatics processing and statistical analysis. While it mentions analyzing alpha and beta diversity, it does not provide evidence or conclusions regarding how certain diets may affect gut microbiota diversity.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: При проведении исследования авторы руководствовались этическими принципами проведения медицинских исследований с участием человека в качестве субъекта, изложенными в Хельсинской декларации Всемирной медицинской ассоциации последнего пересмотра. Пациенты подписали добровольное информированное согласие на проведение обследования. Участие авторов:Субботина А. А. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses ethical principles and voluntary informed consent in medical research without addressing the health impacts of shift work or circadian rhythm misalignment.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to assist in diagnosing diseases through image analysis. [SEP] Text: Moreover, filtering out healthy images would reduce the number of images requiring manual examination and reduce the burden on dental clinicians. Lee and his collaborators selected 17 fine-grained dental anomalies that can be detected from panoramic dental images. Using modern AI-based computer-vision techniques, they developed a model that can be applied in clinical practices to detect these anomalies. A huge number of high-quality data sets is essential for machine learning, so the research team collected 22,999 panoramic dental images from 30 local dental clinics over a year-long period from July 2020 to July 2021. Each image in the dataset was manually examined and labelled by a dental-radiography expert who noted any significant features or anomalies present in the image. These major dental anomalies can affect both patients’ dental health and their quality of life. Dental anomalies The researchers describe how these major dental anomalies can affect both patients’ dental health and their quality of life. If ignored, they can have severe consequences, particularly if they are connected to systemic diseases. Early detection can prevent serious outcomes and provide markers for other systemic diseases. The 17 anomalies were grouped into four categories based on their clinical features and locations: carious lesions, calcifications, anomalies in the dental regions, and anomalies in the surrounding regions. Examples of labelled carious lesions: a) cervical caries or cervical abrasions, b) dental caries or coronal defects, c) proximal caries, and d) secondary caries. Carious lesions, where there is a visible breakdown or a hole in the tooth surface, are the most common issue observed in dentistry. Their prevention and early diagnosis are crucial as they can cause more serious problems if ignored. The team divided carious lesions into four groups for fine-grained diagnoses to facilitate their early detection, preventing their progression and the deterioration of dental tissues. Calcification describe the build-up of calcium on the enamel and gums. Its presence on panoramic dental radiographs is rare, but detection is vital to prevent any further progression. Anomalies in the dental area include dental disease features and abnormal forms of teeth that can cause symptoms, including bleeding, pain, and halitosis (chronic bad breath). The researchers chose to include six dental anomalies that are critical to dental health. [SEP] Justify:

Output: The text corroborates the claim by describing how a research team developed a model using modern AI-based computer-vision techniques to detect dental anomalies from panoramic dental images. This application of AI directly supports the idea that it is being used to assist in diagnosing diseases, exemplified by the early detection of dental health issues through image analysis.


Input: Task: contrast the claim using the given text. Claim: Certain diets can positively influence the diversity of gut microbiota. [SEP] Text: In a sense, microbial diversity is an important indicator of gut function and is considered a marker of the healthy state of the intestinal microbiome (Lozupone et al. , 2012; Song et al. , 2022). In the present study, the richness was evaluated using the Chao1 and observed OTUs values, and the diversity of microbiota was evaluated using Simpson’s index. The results demonstrated a significant reduction in both the richness and diversity of the gut microbiota in mandarin fish fed the AF diet, as reflected by the significantly lower levels of Chao1, Shannon, and observed OTUs according to the alpha diversity analysis. Similarly, the artificial diet feeding of silkworms (Bombyx mori), an insect with a feeding habit that feeds on natural foods such as mulberry leaves in the wild, resulted in a decrease in gut bacterial diversity and a simplified gut microbial structure (Qin et al. , 2022). The loss of the microbial diversity can have detrimental effects, as it may create an environment that is more susceptible to pathogen invasion due to the missing of beneficial bacteria (Schryver & Vadstein, 2014). In general, the microbiome diversity of diseased individuals, including mammals and aquatic species, tends to be lower than that of healthy individuals (Song et al. , 2022). Therefore, the decrease in richness and diversity in mandarin fish fed the AF diet could likely contribute to a compromised health status compared to those in the NF group, as evidenced by the mild intestinal damage and immune suppression in previous studies (Li et al. , 2023). The loss of microbiota in mandarin fish fed the AF diet can be attributed to several factors. One obvious aspect is that food not only provides organisms with nutrients for energy expenditure, but also serves as a source of colonizing microorganisms. The gut microbiota of fish includes microorganisms from the surrounding environment, including ingested food (Cahill, 1990). Compared to a mixed diet, a pure diet has been shown to reduce the gut microbiota of fish (Bolnick et al. , 2014). It has been documented that natural raw foods tend to contain a large number of resident bacteria and other microorganisms, while thermal processing methods like drying or extrusion can reduce the colonization of microorganisms in artificial diets (Zhang & Li, 2018). [SEP] Justify:

Output: The text contrasts the claim by demonstrating that specific diets, such as the artificial diet (AF diet), lead to a significant reduction in both the richness and diversity of gut microbiota, indicating that these diets may not positively influence microbial diversity. It provides evidence of decreased diversity in mandarin fish fed the AF diet, as highlighted by the terms 'significantly lower levels of Chao1, Shannon, and observed OTUs,' which directly contradicts the claim that certain diets can improve gut microbiota diversity.


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

Output: The text is irrelevant to the claim as it discusses the responsibilities of authors involved in a study, without addressing the impact of shift work or circadian rhythms on health.


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

Output: The text supports the claim by detailing the use of Artificial Intelligence (AI) in dental diagnostics through image analysis processes. It explains how AI can alleviate the burden on dental clinicians by reducing the manual examination of images through the identification of anomalies using a trained model (Faster R-CNN), which detects regions in images likely to show anomalies. Additionally, it discusses the processing of dental images in DICOM format and describes the entire detection procedure that utilizes AI technologies to inform diagnoses, thereby corroborating the claim that AI assists in disease diagnosis via image analysis.


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

Output: The text contrasts the claim by detailing how the AF diet resulted in a reduction in the diversity and abundance of gut microbiota in mandarin fish, while the NF diet had the potential to reshape the gut microbiota positively. It mentions that the inclusion of carbohydrates or plant-derived proteins can help reshape gut microbiota, but highlights that the AF diet led to decreased diversity and richness, which opposes the claim that diets can positively influence gut microbiota diversity.


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

Output: The text is irrelevant to the claim as it does not address the effects of shift work on health or circadian rhythms. Instead, it discusses research contributions and funding without providing any pertinent information related to the claim.


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

Output: The text corroborates the claim by describing how an artificial intelligence model is utilized to detect anomalies in dental radiographs, which assists in diagnosing diseases. It elaborates on the model's performance, including high specificity and sensitivity, which are crucial for accurate image analysis in clinical practices. This indicates the application of AI in enhancing diagnostic procedures, thereby supporting the assertion that AI assists in diagnosing diseases through image analysis.


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

Output: The text contrasts the claim as it describes an analysis of bacterial communities in larvae fed with different host plants, indicating that the host plants significantly affected the gut bacterial communities. While the claim suggests a positive influence of diets on diversity, the text reveals that there were no significant differences in diversity metrics such as the Shannon index across the groups, with significant effects observed only in terms of community composition, not necessarily a positive impact on diversity.


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

Output: The text is irrelevant to the claim as it does not discuss the health effects of shift work or the impact on circadian rhythms. It mainly consists of keywords and author information without any pertinent content related to the claim.


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

Output: The text corroborates the claim by stating that AI is being used to assist dental clinicians with image analysis by identifying regions of dental anomalies in radiography images. It mentions the development of a deep-learning model specifically for detecting dental diseases from these images, which aligns with the broader concept of using AI for diagnosing diseases through image analysis.


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

Output: The text provides detailed information about the bacterial species found in larvae samples fed with different diets, but it does not explicitly state how these diets positively influence the diversity of gut microbiota. While it discusses the abundance and variation of bacterial species, it does not support or contrast with the claim regarding positive influence, making the text irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Долгих ФБУН «Федеральный научный центр медико-профилактических технологий управления рисками здоровью населения» Россия 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. [SEP] Justify:

Output: The text does not address the claim directly and instead lists bibliographic references. It is unrelated to the claim regarding the health effects of shift work and circadian rhythm misalignment.


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

Output: The text corroborates the claim by discussing how machine learning techniques can be used to identify drug repurposing candidates efficiently. The implication is that similar machine learning frameworks can be applied to analyze historical data in medicine to predict patient outcomes.


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

Output: The text is irrelevant to the claim since it discusses various studies related to gut microbiota primarily in insects and does not provide any information about how specific diets affect the diversity of gut microbiota in general. The studies referenced do not connect dietary influences to microbiota diversity in a way that supports or contrasts the claim.


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

Output: The text provided does not engage with the claim regarding the adverse health effects of shift work or misalignment of circadian rhythms. Instead, it lists various studies and articles related to chronobiological aspects and circadian clock genes but does not specifically discuss shift work or its health implications.


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

Output: The text corroborates the claim by stating that '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 demonstrates that machine learning algorithms are being utilized to analyze historical patient data to predict outcomes related to drug treatments.


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

Output: The text contrasts the claim by discussing how hypobaric hypoxia affects gut microbiota dynamics, specifically stating that the diversity and structure of gut microbiota are significantly altered under these conditions. It mentions changes in the relative abundance of certain bacteria but does not indicate that diets play a positive role in influencing this diversity.


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

Output: The text does not address the claim about the adverse effects of shift work on health due to misalignment of circadian rhythms. It contains references to various studies and authors without discussing their relevance to circadian rhythms or health impacts.


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

Output: The text corroborates the claim by explaining how a machine learning model developed by Dr. Zhang's group outperformed previous models in identifying drug repurposing candidates for coronary artery disease. It emphasizes that the general model can be applied to any disease if its outcomes can be defined, and that it can leverage existing research to predict the effectiveness of drugs based on their historical data and mechanisms of action, thus supporting the efficacy of machine learning in predicting patient outcomes.


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

Output: The text contrasts the claim by discussing specific taxa such as Prevotella and Bacteroides and their associations with hypobaric hypoxia, indicating environmental factors rather than dietary factors driving the competition between these taxa. The emphasis on differential species analysis between Inside and Outside groups suggests other influences on gut microbiota diversity, rather than a positive influence from diets.


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

Output: The text does not provide any information relevant to the effects of shift work on health or circadian rhythms. It lists various studies and sources that do not seem to address the claim directly, making it irrelevant to the claim concerning health effects of shift work and circadian misalignment.


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

Output: The text corroborates the claim by discussing the capabilities of AI chatbots, specifically mentioning ChatGPT and its optimized interactivity and content generation. It also highlights how the chatbot was able to diagnose and suggest treatment for diseases based on patient data, thereby implying its potential for providing mental health support and patient engagement.


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

Output: The text contrasts the claim by discussing the impact of hypobaric hypoxia on gut microbiota, indicating that environmental factors, rather than dietary choices, significantly affect the diversity and composition of gut microbiota. It highlights the decrease in beneficial probiotics and SCFA-producers in specific environmental conditions, suggesting that such conditions can lead to negative outcomes for gut health, which contradicts the idea that diets can positively influence gut microbiota.


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

Output: The text is irrelevant to the claim as it discusses immune regulation and cellular immunity in a specific context, without mentioning shift work or circadian rhythms.


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

Output: The text corroborates the claim by indicating that the chatbot provides accurate and useful answers, which have been well received and are utilized by many people, particularly in medical settings. It emphasizes the chatbot's capability to engage with users, although it also warns that non-experts should verify the information with professionals, thus supporting the idea of the chatbot's role in patient engagement.


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

Output: The text is irrelevant to the claim about the skin microbiome. It discusses needle-stick injuries and risks associated with bloodborne pathogens but does not address the role of the skin microbiome in protecting against harmful pathogens. Therefore, it does not provide support, contrast, or any relevant information pertaining to the claim.


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

Output: The text does not directly address or relate to the claim regarding the adverse health effects of shift work and circadian rhythm misalignment. It discusses various studies and findings related to benzene exposure, genetic status, and melatonin but does not specifically mention the implications of shift work on health.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 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. [SEP] Justify:

Output: The text corroborates the claim by stating that although AI chatbots, like ChatGPT, may not provide satisfactory responses for specialized cases, they can offer high-quality answers for common symptoms. This suggests that AI chatbots can indeed serve as a form of mental health support and engage patients by providing information for more general concerns.