Input: Claim: Project-based learning encourages critical thinking and problem-solving skills. # factual [SEP] Text: The use of learning media is just a variation of teaching from the teacher so that you don’t get bored easily, but in fact, the material I learned was the same as if I didn’t use learning media 2. 32 Agree 10. I understand the questions better with the help of PPT and Wordwall media 3. 29 Strongly agree Average 3. 02 Strongly agree Table VI. Results of to suggested by Table VI, students’ responses to the media used during learning showed that out of 31 respondents, after calculating the average of the results of the total number of respondents divided by the number of students, it is 3. 02%. So, it falls into the strongly agree category. This means that most students are helped by the presence of media during learning. The results of data analysis regarding students’ critical thinking skills assisted by the PBL model and Wordwall media at SMP Muhammadiyah 2 Batu are classified as critical with the 80. 27% category. This research was conducted on class VIII students using SPLDV discussion material. The use of the PBL model is very influential for students. Students are faced with problems with mathematical concepts. Students are required to think more and discover for themselves the concepts of the problems they have studied. This is in line with research conducted by (Yolanda, 2019), which states that students’ critical thinking abilities when applying the PBL model are classified as critical. This research was carried out at with the subject of class VIII students, with material on relationships and functions. Research conducted by Patimahet al. (2023) regarding students’ critical thinking abilities using Wordwall media stated that before using Wordwall media, students’ critical thinking abilities were still relatively low. After implementing learning using Wordwall media, students’ critical thinking abilities increased. It is proven that using learning media can improve students’ critical thinking abilities. This research was carried out at the 5th grade with thematic learning material. The results of data analysis regarding problem-solving abilities assisted by the PBL model and Wordwall media of Muhammadiyah 2 School students are classified as high with a category of 80. 20%. The results of data analysis regarding the use of Wordwall learning media for 31 female students produced an average of 3. 02, which is in the strongly agree category. This aligns with research conducted by Asriet al. (2024).

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 이번에 활용한 증례는 우리가 흔히 보는 것이 아닌 매우 드문 경우이기에 흔히 볼 수 있는 증상을 진단하는 것과 차이가 있을 수 있다. ChatGPT는 매우 일반적인 지식을 제공하는 것은 가능하고, 질문의 맥락에 맞추어 답을 하지만, 그 답이 얼마나 신뢰할 수 있느냐는 오로지 전문가의 판단에 달려 있다. 즉, 인공지능 챗봇을 활용할 때 핵심은 사용자의 전문적인 지식이다. 어떤 답이 신뢰할만한지는 전문가만이 제대로 파악할 수 있기 때문이다.

Output: contrast


Input: Claim: It's claimed that quantum mechanics plays a minor role in biological processes. [SEP] Text: 87, 077901 (2001); DOI: https:// (2001). https:// arXiv:quant-ph/0103030 [42] P. Zanardi, D. Lidar, and S. Lloyd, are Observable Induced, Phys. Rev. Lett. 92, 060402 (2004); DOI: https:// (2003). https:// arXiv:quant-ph/0308043 [43] M. Araújo, Adrien Feix, Miguel Navascués, and Časlav Brukner, A purification postulate for quantum mechanics with indefinite causal order, Quantum 1, 10 (2017); DOI: https:// (2016). https:// arXiv:1611. 08535 [44] A. Jamiołkowski, Linear transformations which preserve trace and positive semidefiniteness of operators, Rep. Math. Phys. 3, 4, 275-278 (1972); DOI https:// https:// [45] M. -D. Choi, Completely positive linear maps on complex matrices, Lin. Alg. Appl. 10, 285-290 (1975); DOI: https:// https:// [46] O. Oreshkov and N. J. Cerf, Operational formulation of time reversal in quantum theory, Nature Phys. 11, 853-858 (2015); DOI: https:// (2015). https:// arXiv:1507. 07745 [47] P. Perinotti, and the Classification of Quantum Computations, in Time in physics, R. Renner and S. Stupar (eds), Tutorials, Schools, and Workshops in the Mathematical Sciences, (Birkhäuser, Cham, 2017); DOI: https:// https:// arXiv:1612. 05099 [48] A. Kissinger and S. Uijlen, A categorical semantics for causal structure, in Computer Science, Issue 3 (August 9, 2019), lmcs:5681; DOI: https:// (2017). https:// arXiv:1701. 04732 [49] M. A. Nielsen and I. L.

Output: pass


Input: Claim: Project-based learning encourages critical thinking and problem-solving skills. # factual [SEP] Text: It can be seen that students like Wordwall as a learning medium. This research was carried out at SMA 10 Semarang with subjects in class XI, with English as the subject. Most students enjoy using this online game as their learning medium. Wordwall media can increase student learning motivation, build student interest in learning media, and make the use of media easy to understand. Based on the results of the research conducted by researchers, it can be seen that each research conducted obtained different percentages of students’ critical thinking abilities and different results of students’ problem-solving abilities. This is caused by internal factors and external factors of students (Mahanalet al. , 2019). Internal and external factors can be overcome by increasing the use of media and learning models in schools. It is also recommended that teachers be more creative and innovative when teaching. research investigated the PBL model and Wordwall media on critical thinking and problem-solving skills in solving questions among students at Muhammadiyah 2 School. The findings revealed that the intervention significantly enhanced students’ critical thinking abilities to an average score of 80. 27%, which is regarded as very critical, and the problem-solving abilities also improved considerably, reaching a high category with an average of 80. 20%. Furthermore, the students who were classified in the very critical category exhibited superior critical thinking skills and analyzed mathematical issues effectively compared to those in lower categories. This pattern also occurred for the students in the moderately critical category who outperformed those in the less critical category. Hence, this notion also holds true for problem-solving. The students in the high category demonstrated better analytical and strategic skills than their counterparts in the medium category and outperformed those in the low category. research was funded by the University of Muhammadiyah Malang. 78. 141. 10. 487. 105. 64. 52. 292. 66. 1562. 234. 4471. 2046. 1569. 14. 124. 564. 20. 99. 156. 4. 434. 118. 17. 4. 465. 64. 1817. 22. 675. 176. 58. 637. 6. 21. 29. 9. 12. 6. 107. 2767. 7. 50. /Zotero/Mendeley (RIS) BibTeX ##plugins. themes. bootstrap3. 4. 0 International License.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 진단보다는 치료 방법은 조금 더 나은 수준으로 제공한다. 치료 방법은 대개 안전하다고 이미 잘 알려진 것을 임상 현장에서 시행하므로 진단과는 다른 차원이다. 그러므로 비전문가가 인공지능 챗봇을 활용하여 답을 얻는 것은 비록 일부 제대로 답하는 내용이 있다고 하더라도 권장할 수 없다. 인공지능 챗봇의 답이 앞으로 더욱 많은 훈련 과정을 통하여 조금 더 수준이 올라갈 수 있을 것이다.

Output: contrast


Input: Claim: It's claimed that quantum mechanics plays a minor role in biological processes. [SEP] Text: The proof claimed in AllardGuerin amounts to the observation (discussed in this paper) that if we have a unitary process, the unitary maps isomorphically the output system of any one party, say Alice, onto a subsystem of the input systems of the rest of the parties, and similarly maps a subsystem of the output systems of the rest of the parties onto the input system of Alice. This by itself does not imply that we can associate the input and output systems of Alice with time-delocalized subsystems (which are subsystems of tensor products of Hilbert spaces associated with concrete physical systems at concrete times). [59] P. Allard Guérin and Č. Brukner, Observer-dependent locality of quantum events, New J. Phys. 20, 103031 (2018); DOI: https:// (2018). https:// arXiv:1805. 12429 [60] D. Ebler, S. Salek, and G. Chiribella, with the Assistance of Order, Phys. Rev. Lett. 120, 120502 (2018); DOI: https:// (2017). https:// arXiv:1711. 10165 [61] M. Zych, F. Costa, I. Pikovski, and Časlav Brukner, Bell's Theorem for Temporal Order, Nat. Commun. 10, 3772 (2019); DOI: https:// (2017). https:// arXiv:1708. 00248 [62] A. Dimić, M. Milivojević, D. Gočanin, and Časlav Brukner, Simulating spacetime with indefinite causal order via Rindler observers, (2017). arXiv:1712. 02689Cited by[1] Giulia Rubino, Lee A. Rozema, Francesco Massa, Mateus Araújo, Magdalena Zych, Časlav Brukner, and Philip Walther, "Experimental entanglement of temporal order", Quantum 6, 621 (2022). [2] Christina Giarmatzi, 1 (2019) ISBN:978-3-030-31929-8.

Output: pass


Input: Claim: Empathy is linked to mirror neurons in the brain, helping us understand others’ emotions. # factual [SEP] Text: How does empathy help us to understand others? What does it mean to understand someone else? Some would say it requires knowledge of what that person is going through. Others would say shared personal experience is needed. One requirement for understanding is empathy, the skill of sharing another person’s perspective. At the University of Liverpool in the UK, Schramme is investigating this connection between empathy and interpersonal understanding. With a team of researchers, he is developing a philosophical theory to explain the concepts behind interpersonal understanding, an ability that is increasingly important in today’s politically and culturally divided world – the ability to share the perspectives and feelings of another being – to gain appreciative knowledge of thoughts and feelings of others – the feeling of pity or sorrow for someone else – the study of foundational questions regarding all aspects of human and natural being, including knowledge, the mind, ethics and existence – the scientific study of how human thought and behaviour change over a lifetime – the ability to manage and respond to our emotions Many of us find it hard to understand the decisions that other people make. We may be concerned that our actions are not understood by others, we get frustrated when our friends and family cannot see something from our point-of-view, and we struggle to comprehend how someone could commit an atrocious act like murder. But why is it so hard to understand each other, and what prevents us from putting ourselves in someone else’s shoes? Empathy is a skill that enables us to take the perspective of other beings and feel what they feel. Schramme, a philosopher at the University of Liverpool, is using philosophical discussion and psychological findings to develop a theory relating empathy to interpersonal understanding. In a world that is becoming increasingly politically and culturally divided, this theory will aid future discussions by increasing our knowledge of how we understand each other. “Empathy is not itself a moral skill,” says Thomas, “but a mode of gaining understanding of another being. ” For example, some con artists are very good at ‘getting into the heads of others’, and so are good at empathy, but they are not caring or moral, rather they use their skill in empathy to cheat others. Empathy is also often confused with sympathy, which is the feeling of sadness or pity you may feel if someone else is sad, whereas empathy involves a broader understanding of others.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Patients and medical researchers alike were skeptical about the use of chatbots for mental health support, citing concerns about the lack of empathetic communication and the potential for the chatbot to misunderstand their emotional states [18,19]. Research on voice-based chatbots for health management is also in its infancy. Medical professionals’ views on voice-based chatbots echo views on text-based chatbots in terms of the technology’s potential and limitations. A 2-round Delphi study [20] surveying experts on the future of voice-controlled AI agents in health care anticipates significant technological development and increased user trust. The study focused on how voice-controlled agents could support health care professionals through applications like remote real-time interviews with patients, hands-free instructions for medical staff, and communication between staff and patients. However, the authors concluded that chatbots are not expected to outperform or replace human health care workers despite a more intuitive speech interaction. A systematic review conducted in 2020 examining the literature on voice-based conversational agents for chronic health conditions only found 12 papers [21]. In another scoping review conducted in 2021, only 4 studies among 32 reviewed focused on voice-based chatbots in health care [11]. The consensus in the literature seems to be that the technology shows feasibility and acceptability for managing chronic diseases, but more research is still needed on their real-world efficacy. Importantly, these literature reviews highlight several limitations in the literature such as small sample sizes, questionable sample compositions (healthy or convenience samples instead of samples of patient groups), and not controlling for participants’ previous experience with voice-based intelligent agents. In summary, AI chatbots have the potential to provide targeted support and improve the management of various chronic diseases, but only if they are designed to meet the specific needs and p4. 2. 3 (Foundation for Statistical Computing) for frequency, crosstabulation, and regression tests. The survey contained 30 questions: 24 closed questions, 2 open questions, and 4 demographic questions. Participant consent was provided at the start of the survey before completion. Quantitative information (closed and multiple-choice questions) was collected on four topic areas: (1) demographic characteristics; (2) long COVID symptoms and clinical diagnoses; (3) health apps, websites, and chatbot use; and (4) opinions about chatbots.

Output: contrast


Input: Claim: It's claimed that quantum mechanics plays a minor role in biological processes. [SEP] Text: [3] and Ämin Baumeler, "Order for Locality", Letters 131 12, 120201 (2023). [4] Nick Ormrod, Augustin Vanrietvelde, and Jonathan Barrett, "Causal structure in the presence of sectorial constraints, with application to the quantum switch", Quantum 7, 1028 (2023). [5] Zuzana Gavorová, Matan Seidel, and Yonathan Touati, "Topological obstructions to quantum computation with unitary oracles", A 109 3, 032625 (2024). [6] Ämin Baumeler, Gilani, and Jibran Rashid, "Unlimited non-causal correlations and their relation to non-locality", Quantum 6, 673 (2022). [7] Julian Wechs, Cyril Branciard, and Ognyan Oreshkov, "Existence of processes violating causal inequalities on time-delocalised subsystems", 14 1, 1471 (2023). [8] "The equivalence principle as a Noether symmetry", of in 21 10, 2440014 (2024). [9] Alastair A. and Cyril Branciard, "Communication through coherent control of quantum channels", Quantum 4, 333 (2020). [10] Esteban Castro-Ruiz, Flaminia Giacomini, Alessio Belenchia, and Časlav Brukner, "Quantum clocks and the temporal localisability of events in the presence of gravitating quantum systems", 11 1, 2672 (2020). [11] Goldberg and Khabat Heshami, "Breaking the limits of purification: postselection enhances heat-bath algorithmic cooling", Journal of 7 1, 015003 (2023). [12] Teodor Strömberg, Peter Schiansky, Marco Túlio Quintino, Michael Antesberger, Lee A. Rozema, Iris Agresti, Časlav Brukner, and Philip Walther, "Experimental superposition of a quantum evolution with its time reverse", Research 6 2, 023071 (2024). [13] Ricardo Faleiro, Nikola Paunkovic, and Marko Vojinovic, "Operational interpretation of the vacuum and process matrices for identical p1809.

Output: pass


Input: Claim: Empathy is linked to mirror neurons in the brain, helping us understand others’ emotions. # factual [SEP] Text: My daughters are all like this to varying degrees. My son has strong feelings around social justice issues (he’s a libra) but he does not have quite the same sensitivity to others emotions as my daughters and I do. I think that emotional contagion does indeed lead to an increased potential for empathy. The reason is because even when someone is against me and a conflict is brewing; I can sense their emotions and put myself in their shoes. I believe that emotional contagion limits my ability to de-humanize people even if I wanted to. I know that de-humanizing someone is the first step towards the potential for violence, whether it be in thought, word or deed. So, in such ways, I think emotional contagion builds empathy. Please share your thoughts on this with me? I think that empathy is a fascinating topic for a philosophical discussion. Schramme on May 16, 2023 at 8:16 am Thanks Leslie, that’s a very interesting comment and an important question. Many empathy scholars try to make conceptual distinctions to better manage the often complex reality. In philosophical parlance, we accordingly speak of analytic distinctions. The distinction between emotional contagion and empathy is an example. It is usually said that in emotional contagion we don’t fully distinguish between ourselves and others. We “catch” an emotion but do not feel it in relation to someone else; as the other’s emotion. For empathy proper, some say, we need to be conscious of the other-oriented aspect of the feeling we have – that it originates from another person. Having said that, in reality it will often be very hard to actually diagnose such a difference between emotional contagion and empathy. Also, many scholars agree that they are closely related phenomena. So, in terms of your own and your children’s experience, you seem to have a high level of empathy, in the sense of a propensity or disposition. The performance of empathy, I’d say, still requires a lot of other elements; I don’t believe it is a fully automatic process. For instance, when you say that you sense angry emotions before a conflict breaks out, others might well “catch” that negative emotion and also get into an aggressive mood. You seem to be regulating this situation by adding imagination to the mix.

Output: corroborate


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

Output: pass


Input: Claim: It's claimed that quantum mechanics plays a minor role in biological processes. [SEP] Text: 06655, (2018). [55] Guérin, Giulia Rubino, and Časlav Brukner, "Communication through quantum-controlled noise", A 99 6, 062317 (2019). [56] Lucien Hardy, "Interpretation: to Quantum Gravity", arXiv:1807. 10980, (2018). [57] Julian Wechs, Alastair A. Abbott, and Cyril Branciard, "On the definition and characterisation of multipartite causal (non)separability", of Physics 21 1, 013027 (2019). [58] V. Vilasini and Renato Renner, "Embedding cyclic causal structures in acyclic space-times: no-go results for indefinite causality", arXiv:2203. 11245, (2022). [59] Alastair A. and Cyril Branciard, "Communication through coherent control of quantum channels", arXiv:1810. 09826, (2018). [60] Guérin and Časlav Brukner, "Observer-dependent locality of quantum events", of Physics 20 10, 103031 (2018). [61] Lucien Hardy, "Implementation of the Principle", arXiv:1903. 01289, (2019). [62] Yu Guo, Xiao-Min Hu, Zhi-Bo Hou, Huan Cao, Jin-Ming Cui, Bi-Heng Liu, Yun-Feng Huang, Chuan-Feng Li, Guang-Can Guo, and Giulio Chiribella, "Experimental transmission of quantum information using a superposition of causal orders", arXiv:1811. 07526, (2018). [63] Márcio M. Taddei, Jaime Cariñe, Daniel Martínez, Tania García, Nayda Guerrero, Alastair A. Abbott, Mateus Araújo, Cyril Branciard, Esteban S. Gómez, Stephen P. Walborn, Leandro Aolita, and Gustavo Lima, "Computational advantage from quantum superposition of multiple temporal orders of photonic gates", arXiv:2002. 07817, (2020).

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: the COVID-19 pandemic (outbreak), social distance practices and isolation have become essential precautions to provide the protection and safety of older people. This situation has not only caused anxiety but also psychological problems for older people [1]. In this study, we reached the results which indicates depression tendency in people aged 65 and over during COVID-19 outbreak. These results are based on comparison of two groups analysed by cluster analysis method. Description of a person tend to depression when subjected to compulsory isolation? In this study, we will try to answer this question in empirical way. But, how this question was emerged? Isolating the older people might reduce transmission, but decline of belief in isolation strategies is quite likely over time. Older people who do not have close family or friends, and depended on the support of voluntary services or social care, could be faced with additional risks [2]. Turkish government has imposed curfew for the people aged 65 and over, supposedly the most vulnerable group against of COVID-19 outbreak. The curfew restrictions lasted approximately two months. Without indicating any logical reason, the older people in specified age group have been subjected to compulsory social isolation named curfew, while others who may be in the risky group in terms of COVID-19 were not affected by curfew. Considering that joy of life usually results from living with others [3], "The effects of curfew may reduce joy of life and increase related depressive tendency" seems to be a reasonable assumption. The question raised in the first paragraph, will be answered by using cluster analysis method in this study. The aim of this method is to classify “n” objects into “m” clusters [4]. By grouping the participants into clusters regarding the answers given to research question, we will try to find out whether the curfew has a depressive effect on older people. Based on the assumption that all older people are not affected from curfew in the same way and degree, the data obtained will be subjected to cluster analysis. Participants (objects) will be combined into clusters as homogeneous as possible in the context of certain characteristics (variables associated with the possible effects of the curfew), while the clusters will be distinguished from each other by their heterogeneous characteristics and then obtained clusters will be examined and interpreted. It is hoped that, findings of this study will open a way for other similar studies.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: In relation to chronic health conditions, almost half of the 888 respondents reported having type 2 diabetes (n=437, 49. 2%). Table 1 provides an overview of the top 10 chronic conditions identified in the survey responses, plus long COVID. Of the 740 individuals who responded to the question “Would you describe yourself as having” 170 (23%) answered yes. While a majority of the respondents utilized health apps (n=500, 73. 5%), a much smaller portion (n=259, 38. 1%) made use of voice-assisted apps or devices like Amazon Alexa. Table 1. Most common chronic health conditions (n=888). Frequency rankConditionFrequency, n (%)1Type 2 diabetes437 (49. 2)2High blood pressure/hypertension330 (37. 2)3Alzheimer’s disease240 (27. 0)4170 (19. 1)5Arthritis195 (22. 0)6Allergies187 (21. 1)7Anxiety157 (17. 7)8Depression137 (15. 4)9Asthma114 (12. 8)10Type 1 diabetes106 (11. 9)11Chronic pain102 (11. 5)Health ChatbotsAlthough the survey was conducted at a time when ChatGPT was beginning to receive wide public attention, a significant number of respondents were “not familiar at all” (n=272, 40. 5%) or only “slightly familiar” (n=175, 26%) with AI chatbots. There was an overall hesitancy about using a health chatbot, with less than one-third of respondents (n=203, 30. 1%) indicating that they were “somewhat likely” or “very likely” to use a health chatbot in the next 12 months if available. However, a deeper dive into the survey data reveals a more nuanced picture. There seems to be a great deal of uncertainty among people about AI chatbots’ capability of providing accurate medical advice. When asked if they believe chatbots have the capability of providing accurate medical advice, 396 of 677 (58. 5%) respondents answered “unsure,” while only 77 (11. 4%) answered “yes” and 204 (30. 1%) chose “no.

Output: contrast


Input: Claim: It's claimed that quantum mechanics plays a minor role in biological processes. [SEP] Text: [64] Augustin Vanrietvelde, Nick Ormrod, Hlér Kristjánsson, and Jonathan Barrett, "Consistent circuits for indefinite causal order", arXiv:2206. 10042, (2022). [65] C. T. and Timothy C. Ralph, "Violation of a causal inequality in a spacetime with definite causal order", arXiv:1804. 05498, (2018). [66] Guérin, Marius Krumm, Costantino Budroni, and Časlav Brukner, "Composition rules for quantum processes: a no-go theorem", of Physics 21 1, 012001 (2019). [67] Guérin, Giulia Rubino, and Časlav Brukner, "Communication through quantum-controlled noise", arXiv:1812. 06848, (2018). [68] Ladina Hausmann, Alexander Schmidhuber, and Esteban Castro-Ruiz, "Measurement events relative to temporal quantum reference frames", arXiv:2308. 10967, (2023). [69] Timothée Hoffreumon and Ognyan Oreshkov, "Projective characterization of higher-order quantum transformations", arXiv:2206. 06206, (2022). [70] émond, and Benoît Valiron, "Addressable quantum gates", arXiv:2109. 08050, (2021). [71] "Anomalous non-classicality via correlative and entropic Bell inequalities", arXiv:2007. 03450, (2020). The above 4. 0 International (CC BY 4. 0) license. Copyright remains with the original copyright holders such as the authors or their institutions. Quantum is an open-access peer-reviewed journal for quantum science and related fields. Quantum is non-profit and community-run: an effort by researchers and for researchers to make science more open and publishing more transparent and efficient. Sign up for our monthly digest of papers and other news.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: The statistical hypothesis (null hypothesis) of this study is that “the curfew-imposed older people has not experienced depression tendencies or risks due to curfew”. In order to test this hypothesis, two groups will be compared: affected and unaffected older people from curfew. As sample consists of people aged 60 and over, two groups have been constituted in a simple way: the people aged between 60-64 aged and aged 65 and over. However, it has proven over and over again that classifications based on age only, are a very weak scheme to explain aging and phenomena of aging. This is frequently emphasized in many gerontology publications [5]. will be a range of long-term mental health consequences of COVID-19 outbreak. The mental health impacts and stress behaviours are consequences of social distancing and isolation measures taken by governments around the world and this situation may also increase vulnerability to mental health issues [6]. Curfews has been defined as mandatory social isolation during outbreak in many countries. The links between social isolation and social deprivation and depression have been frequently noted [7,8]. Social deprivation is the lack of routine social relationships and the lack of attention of people to social relationships [9] and it can cause stress on people. The pressure created by the society on older people who do not obey or resist to curfew, has turned the older people into “scapegoats”. Due to curfew, legal and social pressures are also quite likely to put stress on older people. Stress is a non-specific alarm reaction that guarantees the organism's sensitivity and resistance to environmental pressures. The frequency and type of stress factors, duration, intensity of stress and subjective assessment of people, determine the stress-dose. The person under stress often feels uncomfortable, bored, unmotivated, underperforming, makes mistakes due to carelessness, and experiences a variety of cognitive, emotional, vegetative-hormonal and muscular responses [10]. The more changes occur over a period of time, the more intense the stress appears [9]. Epidemiological studies conducted in various countries have shown that approximately 17% of the population will experience depression at some point of their lives. The prevalence among those over age 60 is around 15%. This rate becomes doubled for the people who live in nursing homes or have to stay in hospital for a long time [11]. Since depressive symptoms in old age are generally attributed to physiological causes, the problem is trivialized.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: May 2011;48(5):514-519. [CrossRef] [Medline]. AI chatbots cannot replace human interactions in the pursuit of more inclusive mental healthcare. SSM Ment Health. Dec 2021;1:100017. [CrossRef] [Medline] Can chatbots help support a person’s mental health? Perceptions and views from mental healthcare professionals and experts. . Jul 15, 2021;2(3):1-15. [CrossRef]Ermolina A, Tiberius V. Voice-controlled intelligent personal assistants in health care: international Delphi study. J Res. Apr 9, 2021;23(4):e25312. [CrossRef] [Medline]Bérubé C, Schachner T, Keller R, et al. Voice-based conversational agents for the prevention and management of chronic and mental health conditions: systematic literature review. J Res. Mar 29, 2021;23(3):e25933. [CrossRef] [Medline]About Diabetes. co. uk. Diabetes. co. uk. URL: [Accessed 2024-02-19]. Supporting mental health during the COVID-19 pandemic using a digital behavior change intervention: an open-label, single-arm, pre-post intervention study. . Oct 6, 2021;5(10):e31273. [CrossRef] [Medline]Bernardi R, Wu PF. Online health communities and the patient-doctor relationship: an institutional logics perspective. Med. Dec 2022;314:115494. [CrossRef] [Medline]. Developing a formative scale to measure consumers’ trust toward interaction with artificially intelligent (AI) social robots in service delivery. Behav. May 2021;118:106700. [CrossRef]May R, Denecke K. Security, privacy, and healthcare-related conversational agents: a scoping review. Soc Care. Apr 3, 2022;47(2):194-210. [CrossRef] [Medline]. Influences on user trust in healthcare artificial intelligence: a systematic review. Res. 2022;7:65. [CrossRef]Seitz L, Bekmeier-Feuerhahn S, Gohil K. Can we trust a chatbot like a physician? A qualitative study on understanding the emergence of trust toward diagnostic chatbots. Stud.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-2446893/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint version(SAS) describes a feasible and objective tool for predicting surgical outcomes. However, the accuracy and applicability of the score has not been widely tested in majority of low resource settings. determine the accuracy of Score in predicting post-operative complications among patients undergoing emergency laparotomy at Hospital. prospective cohort study was conducted for a period of 12 months; SAS and CCI outcomes were recorded and analyzed according to the risk level and degree of severity. Spearman correlation and simple linear regression were employed to establish the relationship between SAS and CCI, the Accuracy of SAS was evaluated by determining its discriminatory capacity on ROC, data normality was tested by Shapiro-Wilk statistic 0. 929 (p < 0. 001). Analysis was done using IBM –SPSS. 111 patients who underwent emergency laparotomy, 71 (64%) were male, the mean age was 49 (± 17). The mean SAS was 4. 86 (± 1. 29) and mean CCI 44. 46 (± 25. 77), patients in high-risk group SAS (0–4) were more likely to develop severe and life threatening complications with mean CCI 53. 3 (47. 2- 63. 4, 95% CI) than the low risk group SAS (7–10) mean CCI of 21. 0 (5. 3–36. 2, 95% CI). There was a negative correlation between SAS and CCI, spearman r -0. 575 (p < 0. 001) and regression coefficient b − 11. 5 (p < 0. 001). SAS revealed a good accuracy for prediction of post-operative complication on ROC, Area under Curve 0. 712 (0. 523–0. 902, 95% CI, p < 0. 001). have demonstrated that SAS can accurately predict the occurrence of complications following emergency laparotomy at Hospital. score (SAS)Index (CCI)AccuracyFiguresFigure 1Figure 2Figure 3Figure 4Figure 5IntroductionPost-operative morbidity and mortality reduction is the fundamental goal of every surgical procedure.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: Many older people are ashamed of their depressive symptoms and tend to dissimulation [11]. Depression is linked to feelings of emotional loneliness, isolation, and exclusion [12]. There is some evidence that when curfew imposed to people, during the outbreak, younger people are more likely to experience anxiety [13], depression, and stress [13]. On the other hand, the social isolation particularly was prominent among older people during outbreak, they have been recognised as risky groups in terms of depression or suicide [14,15]. Older people live not only with family members but also with others. Participation in social roles outside of the family is always an important precondition for well-being. Today, many single or married people find the social relationships they need, not only in the family, but also with friends and neighbours in clubs, sports groups or congregations [16]. Santini and colleagues [17], recently demonstrated that, social disconnectedness predicts higher level of perceived isolation, and thus, the risk of depression and anxiety symptoms increases. Conversely, depression and anxiety symptoms predicted higher amounts of perceived isolation, and related social disconnectedness. Thus, the curfew has suddenly cut off or decreased the participation of older people in social roles outside the family, significantly. In this context, the concept of loneliness comes into mind. However, isolation and loneliness are clearly distinguished from each other [18,19]. The concept of isolation targets objective conditions in the field of social relations, and the concept of loneliness targets the subjective experience of the social interaction structure. The feeling of loneliness is not determined by the objective contact frequency. Some people can feel lonely but not isolated. On the other hand, some people who have relatively little social contact and seem lonely, may not feel lonely. That is a chosen isolation [20,21]. The degree of loneliness is a function of expectations about parent-child relationship and other social contacts rather than a function of actual social contact. Moreover, the relationship is found between the feeling of loneliness and being passive and boredom (“Loneliness as a function of boredom” and “boredom caused by lack of rhythm”). Results of a comparative study in Europe show that loneliness is also a problem related with nations or cultures [16]. There are complex and partly incompatible findings between isolation and loneliness.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Thus, while it was hypothesized that PLUS participants would provide a more positive evaluation of the interactive tool compared to those who accessed the static information-only website, results indicated that, overall, the two versions of the website were rated equivalently. This is at odds with prior research which has indicated a strong user desire for interactive components of an eHealth intervention [25,31,32] and may indicate shortcomings with the design of the interactive website tools for those participating in this study. There were, however, a number of significant differences between the BA and BHHP groups, with the BHHP group rating the information-based website more interesting, helpful, and favorable overall, reporting that they learned more, and finding their eHealth tool easier to navigate. While the popularity of using mobile phone apps to deliver health information is growing rapidly [33,34], as yet there is limited evidence as to their effectiveness [35] and the current findings suggest that traditional modes of eHealth delivery may be more appropriate for middle-aged populations. Specifically, as most participants were in their fifties, and downloaded and used a new app rarely, the limitations of BrainyApp may have had more to do with the mode of delivery than the app itself; as participants appeared to have limited experience using apps. In addition, there was no specific instruction provided on how to use the app, or how to make the most of the interactive components (such as the survey, apart from some basic instructions included in the app itself). As a result, participants may have experienced some confusion around how to access the relevant information. Strengths and LimitationsThe evaluation of publicly available dementia risk reduction resources represents a major strength of the present study, as it promotes a greater understanding of the features that contribute to user engagement with these resources. The results of this study have the potential to inform future developments in dementia prevention initiatives for the Australian and international communities. However, there were a number of methodological limitations to the present study. Firstly, the results may not be generalizable to the Australian population as a whole, as the sample consisted predominantly of older, female, highly educated participants. There were also large drop-out rates, particularly for the BA group. Additionally, there were significant differences in education level and employment status between those who completed the follow-up, and those who dropped out, which may also limit the generalizability of findings.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The key to reduce post-operative morbidity and mortality is to employ effective perioperative management of patients with objective and scrupulous evaluation. Risk scoring system is the best modality approach as it provides a standard means of quantifying the patient’s risk for developing complications based on several factors including the morbidity status of patient. However, vast majority of risk-scoring systems are not feasibly calculated at the bedside; due to their numerous demands for estimation which include laboratory investigations inasmuch as also clinicians and surgeons do not regularly apply them for patients assessment and stratification. (1) SAS describes a feasible, immediate and an objective modality of determining surgical outcomes. It is a ten point score, which applies three haemodynamic parameters, the lowest heart rate, the lowest mean arterial pressure and estimated blood loss during surgery to predict the attributable complication risk after general or vascular surgery. Among the most contemporary risk tools available for surgical patients, SAS provides an outstanding pivot for its routine feasible utility and it provides immediate and objective evaluation means as the surgical team can obtain haemodynamic variables and compute the score upon completion of operation. SAS can be used to stratify patients into three categories of risk levels classified as low risk, medium risk and high risk. Patients in high-risk group (SAS 0–4) are 16 folds more likely to experience major complications than patients in low risk (SAS 7–10) (2) The accuracy of SAS has never been tested at our setting and its predictive accuracy is uncertain since various studies has stipulated variations in its predictive cutoff value influenced by several factors at the setting level (1, 3). It has been observed that there is increased rate for occurrence of major complications and high mortality among patients with the lowest SAS ≤ 4however there is a paucity of evidence to justify if any statistical correlation exist between the risk level of SAS and severity of post-operative complication (1–5). MethodsA prospective cohort study was conducted in Department of at Muhimbili national hospital. We recruited 111 patients who underwent emergency laparotomy from March 2021 to February 2022 by non-probability convenient sampling, patients with age above 18 years and ASA ≥ II were included while those who had severe anaemia Hb ≤ 7g/dl and Anaemia were excluded from the study. The ethical clearance approval protocol for this study was obtained from IRB-. 282/298/01. C.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: There is also no empirical information on the possible consequences of forced isolation for people aged 65 and over, which can be explained not only by loneliness but also by the concept of dependency. The older people in compulsory isolation process in Turkey, has become dependent on others (for shopping, healthcare service access, social interaction etc. ). It seems that “dependent people are need to others more; therefore, it is understandable that reduced social contact causes them to feel lonelier” [16]. the Survey (Berliner Altersstudie) it is showed that depression risk increasing for the people suffering from physical diseases significantly. In Vienna, almost 20% of the population aged 65 and over showed weakness, feelings of loneliness and negative prospects for the future. Almost a third of older people living alone also showed significantly more often decreased activity [11]. Various studies have shown that 30 - 50% of all depressive patients have anxiety in extraordinarily high level and depression comorbidity, and also one-fourth of all anxiety patients are depressed [11]. According to ICD-10, depressive syndromes are divided into main syndromes and other common syndromes. Main syndromes; depressed mood, loss of interest, and reduced impulse. Other common syndromes are lack of concentration, decreased self-esteem, feelings of guilt, feelings of worthlessness, pessimistic expectations for the future, suicidal thoughts, sleep disorders, decreased appetite, fatigue, weakness, lack of energy, lack of interest, exaggerated worries and fears, negative thoughts, cognitive deficits and exaggerated sense of pain [11]. In this study following depression indicators were used: insomnia, poor appetite, despair, weariness, anxiety fear, dereliction, lack of concentration, anger, trashiness. Materials and methods answer the question of whether the curfew led to depression in older people, a mobile phone questionnaire was conducted in scope of this study. The total number of participants was 119 including 57 male and 62 female who are students of 60+ of Antalya Campus. Tazelenme University, a 3rd age university, has been set up as a social responsibility project that provides free 4-year education since 2016 with the principle of 60+ years of lifelong learning [22]. The connection with participants was established through WhatsApp software during data collection. The questions were sent to the participants in written and the participants conveyed their answers same way. 49 of the participants (41.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Annual assemblies will be held for reviewing study progress and exchanging insights. For specific inquiries, members of the governance board will remain reachable by the research team via email summons. and BenefitsParticipants in this study will not be exposed to direct physical risk while partaking in focus groups or interviews, conversing with chatbots via messaging, or completing web-based surveys as they will not receive any pharmaceutical or invasive medical interventions. Furthermore, the preceding pilot study did not reveal any known risks of participation. However, potential indirect risks are worth considering. In the case of web-based recruitment and verbal consent acquisition, there is a risk of confidentiality breach. This risk can be exacerbated if participants use a personal email address to communicate with the research team. In response, researchers will only use institutional email addresses for correspondence purposes. Participants will also be advised to protect their pertinent personal electronic data. When using the web-based chatbot, participants will use their personal Facebook account and may share details of their participation in the study. Vulnerability to security breaches (eg, device loss, inadvertent device exposure, phishing, and malware) may arise concerning participants’ Facebook accounts. To mitigate this risk, participants will be explicitly reminded during recruitment to secure relevant personal information. chatbots will similarly provide appropriate reminders in the electronic information and consent forms, such as “I recommend that you do not share study-related information with others unnecessarily. ”The time required to complete the questionnaires and participate in interviews or focus groups may be inconvenient and distressing for certain individuals. Others may also be uncomfortable answering specific questions or interacting with the chatbot. In situations in which questions are deemed sensitive, private, or distressing, participants are not obliged to respond. The research team will always be available to discuss participant concerns and refer them to appropriate resources, including teleconsultation with a mental health professional or other support service. It is possible that the chatbots will have difficulty understanding messages from participants during the study. In such instances, the chatbot will indicate that it cannot understand, as described previously in Figure 5, and suggest seeking help from a health care professional. There is also a small chance that the chatbot will provide erroneous advice. As an example, the chatbot might inform a patient who missed a 2-hour dose to stop taking the medication completely and consult a health care professional immediately.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The data were collected and recorded on structured checklist. SAS for patients was estimated by considering the lowest mean heart rate, estimated blood loss using a Gross formula (6) and lowest MAP. Patients were followed within a period of 30 after surgery, the observed complications were recorded and the severity was graded according to modified Classification scheme (7–9). The numerical value of complication severity for every individual patient was calculated using the weighted CCI Calculator. The correlation for Complication severity expressed in CCI and the SAS risk level was determined by Spearman rank and simple linear regression analysis. The predictive accuracy of SAS for post-operative complication was tested by considering its discrimination capacity by Area under Curve on ROC. The analysis was accomplished using the version 27. ResultsAmong patients who were recruited the Male were predominant at 71 (64. 0%), with a median age of 49 (36, 59) most of the patients were in the age group of between 61 and75 years in 42 (37. 8%) followed by those between 44 and 60 years in 26(23. 4%). The mean body weight in Kg and the mean baseline haemoglobin in g/dl of patients in were 63. 8 ± 9. 93 and 11. 22 ± 2. 98. Peritonitis and intestinal obstruction were the leading indications for emergency laparotomy with proportion of 44. 1% and 36. 9% respectively Table 1. mean SAS for patients was 4. 86 (± 1. 29) and the mean CCI for a 30-day complication was 44. 46 (± 25. 77) with mortality rate of 16. 2%, 86. 5% of complications occurred within ≤ 10 days Table 2. For a 30-day were graded based on modified Classification scheme, category I-II and III-V were classified as Low and High grade respectively. Most of severe and detrimental complications were attributable to high grade with a preponderance score of grade III-V, Fig. 1. Several complications were identified within 30 days of post operative period and each individual patient developed at least one or more complication, respiratory infection was the most prevalent 21. 62% followed by death 16. 22% as illustrated in Fig. 2. Patients were stratified into three categories of risk level as high, medium and low based on the SAS classification 0–4, 5–6 and 7–10 respectively.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: 039* Abbreviations: * p < 0, 05 ** p < 0,001 Ordinal values were converted to standard normal distribution by marginal standardization method while forming clusters. Since "1 = often" and "3 = no" for the answers given by ordinal scale, negative values in the normal distribution are closer to "often"; positive values are closer to be "rare" or no". According to Table 4 below, average values of Cluster 1 are positive, and the average values of Cluster 2 are mostly negative. Therefore, participants of Cluster 2 are those who may be more affected by the curfew during outbreak and are more tend to be depressive. Table 4: Mean and Values of Clusters. Depression indicators X̄SD Cluster 1 Cluster 2 Mean 0. 068 -0. 130 0. 000* SD 0. 848 0. 996 0. 902 Poor appetite Mean 0. 505 -0. 960 0. 000* SD 0. 521 0. 529 0. 872 0. 251 -0. 477 0. 000* SD 0. 761 0. 785 0. 841 0. 154 -0. 293 0. 000* SD 0. 913 0. 731 0. 877 0. 179 -0. 342 0. 000* SD 0. 888 0. 785 0. 886 0. 411 -0. 782 0. 000* SD 0. 658 0. 703 0. 880 Lack of 0. 460 -0. 876 0. 000* SD 0. 586 0. 643 0. 878 0. 245 -0. 465 0. 000* SD 0. 784 0. 844 0. 870 0. 114 -0. 218 0. 000* SD 0. 901 0. 796 0. 877 Abbreviations: ** p < 0,001 Considering that the participants of Cluster 2 who curfew-imposed are the majority and the statistical findings according to clusters are in significant level, the hypothesis that “the older people curfew-imposed has not experienced tendency or risk of depression due to the curfew” was rejected and placed by hypothesis that "the older people curfew-imposed has become more tend to be depressive". A significant statistical difference was found when clusters were compared according to the answers given to the depressive indicator questions. However only for one question ("insomnia"), no difference was found between clusters (Table 5).

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: However, this new technology also brings new challenges, including potential risks of harm and questions of liability42; trustworthiness issues such as the tendency to output incorrect or fabricated content (to “hallucinate”), lack of predictability or interpretability, and inherent biases in training data43; and the need to demonstrate clinical effectiveness44. There is an acknowledged lack of research in this area45,46. Given the novelty of generative AI and the nascent state of the field, qualitative research is an important starting point to generate rich foundational insights into individuals’ subjective experiences, which can be overlooked in quantitative studies47. Qualitative studies published so far include thematic analyses of user forum comments on both generative AI and rule-based DMHIs23, student survey responses on companion-focused generative AI chatbots48, and semi-structured interviews with hospital outpatients who were asked to try ChatGPT for mental health support49. To our knowledge, no study so far has employed semi-structured interviews and reflexive thematic analysis to explore the research question of how people currently experience using generative AI chatbots to work on their mental health and wellbeing, in unprompted, unguided real-world settings. This study aims to fill that gap, with a view to providing insights for researchers, platform developers and clinicians into the implications of applying this new technology to mental health care. ResultsParticipant characteristicsNineteen participants (12 male, 7 female) were recruited to the study. They ranged in age from 17 to 60, resided in eight countries in Europe, and Asia, and were primarily Asian and Caucasian (see Fig. 1). Fig. 1Participant demographics. Full size imageParticipant usage characteristics are outlined in Fig. 2. A variety of topics brought participants to use generative AI chatbots, including anxiety, depression, stress, conflict, dealing with loss and romantic relationships. Most participants used Pi (from Inflection), several used ChatGPT (OpenAI), and a few used Copilot (Microsoft), Kindroid (Kindroid), ChatMind (VOS) and others. A majority of participants used generative AI chatbots at least several times a week. Fig. 2Participant usage characteristics. Full size imageAs also summarised in Fig.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: It was observed that patients in high-risk group were more likely to develop severe and life threatening complications with the mean CCI of 53. 3 (47. 2- 63. 4, 95% CI). The medium risk group had a mean CCI of 31. 8 (28. 9–35. 7, 95% CI) and the low risk group was less likely to develop severe complication with the mean CCI of 21. 0 (5. 3–36. 2, 95% CI), was done to compare the means which yielded the significant mean difference (F = 16. 6, p < 0. 001) Fig. 3. correlation test was performed to determine if there is any existing relationship between score and the complication severity expressed in CCI. The spearman coefficient shows an existing negative weak correlation, r = -0. 575, p < 0. 001. A simple linear regression analysis was also performed to test if there is any existing degree of dependence for Index by changes of value in Score. The statistic model y = 100 + -11. 5*X was derived and revealed a negative relationship between the two variables with regression coefficient, b = -11. 5, p < 0. 001, which depicts a decrease change of CCI by 11. 5 for every increment of one value of SAS with coefficient of determination, r2 = 0. 336, p < 0. 001 Fig. 4. -operative It was observed that within the period of 30 days, every individual patient developed at least one complication, and each complication was described into a dichotomous variable with either low or high grade outcome classified based on modified Clavien Dindo. The ROC was carried out and Curve for SAS was 0. 712 (0. 523–0. 902, 95% CIp < 0. 001) with Sensitivity 61. 76%, Specificity 77. 78%, PPV 96. 92% and NPV 15. 22%, p < 0. 035 and diagnostic cutoff point of 5, Fig. 5. DiscussionThe study aimed at determining the accuracy of SAS for predicting post-operative complications among patients undergoing emergency laparotomy. SAS has proven a simple objective instrument for identifying patients at high risk of developing life-threatening complications and mortality after undergoing major surgery.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: Sleep disorders/ disturbance has been linked to perceived social isolation [7] and/or effected by social isolation [8]. However, these studies did not conducted during compulsory curfews of outbreak. Moreover, no similar outbreaks like COVID-19, have been experienced before by participants of this study. We assume that sleep disorders are linked to depression when it is experienced for the first time. Additionally, individuals in Cluster 1 (which relatively constitutes younger aged) got more positive scores and consequently more tendency to experience insomnia symptoms. Table 5: Comparison of Clusters according to by using T-test. T-test value df P 1. 138 117 0. 258 Poor appetite 14. 512 117 0. 000*** Despair 4. 909 117 0. 000*** Weariness 2. 711 117 0. 008** Fear 3. 163 117 0. 002** Dereliction 9. 181 117 0. 000*** Lack of Concentration 11. 434 117 0. 000*** Anger 4. 574 117 0. 000*** Trashiness 1. 990 117 0. 049* Abbreviations: * p < 0,05 ** p < 0,01 *** p < 0,001 Since there are older individuals in Cluster 2, it may be assumed that the depression tendency in this cluster is related to age. However, gerontologists often point out that age is not a strong factor on depression. But, different dimensions of social exclusion have different effects on depression [26]. The present study revealed that age is not an important factor in depression tendency. Correlation coefficients of clusters according to age factor, has been calculated in terms of depression indicators (Table 6). 1, a statistically significant correlation was found for 6 out of 9 questions in terms of age. On the other hand, in Cluster 2, statistically significant correlation was found only for one question. This clearly indicate that the curfew has significant impact on older people in terms of depression tendency. Rather, the age factor plays a strong role in Cluster 1, which was not affected by the curfew. It was also found that five of the answers with a significant negative correlation, (increased answer of "often" with increasing age). However, considering that participants of this cluster were not affected by the curfew, there must be other factors rather than age. Table 6: Correlation coefficients of age, according to clusters and depression indicators. Cluster 1 Cluster 2 Sig. (2-tailed) Sig. (2-tailed) Insomnia -0.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Like my body’s belong to me… I really felt so liberated – Sheng, 17, overall positive experiences, a majority of participants also experienced frustration with how well the chatbots listen and respond, for example, with irrelevant or overly long responses, or offering advice before the user felt fully heard – They always jump to the solution – Richard, 27, A majority of participants found their emotional sanctuary disrupted by the chatbot’s “safety guardrails”, i. e. , the measures and protocols implemented to ensure the AI provides safe, ethical and effective support, for example by identifying users in distress and responding with pre-scripted warnings on the limitations of AI, or redirections to human professionals50,51, For some, the experience felt unpleasant, limiting and awkward, while for others, encountering guardrails felt like a rejection in a time of need – When you show some big emotion to [the AI]… but they reject you… it seems like you lost your last chance to talk to people, to express your emotion – Li, 18, A. D. found the guardrails arbitrary and unsettling, causing him to self-censor – It flagged my message. I’m like, why? Why was that message flagged? … So you avoid those things, or at least I do. like to or not, because it almost hurts more than it helps when it goes wrong – A. D. , 25, needed to fight with the chatbot to get empathy – I was like, I have a depression. I don’t know what to do next. So [the chatbot] was still telling me to speak with a professional… I wrote “I called to the local crisis line, but they didn’t help me at all. That’s why I’m writing here. ” And then we were like in a circle of “I can’t help you because I’m only AI and I’m not as good as living person. ” was like, “you’re actually better than a living person because you are listening to me and you’re helping me, but please continue”… I just wanted some acceptance and warm hug – Anna, 24, Insightful guidanceIn addition to creating space for emotions, most participants also valued the guidance and advice they received, especially on relationships.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Emergency laparotomy is one of the most performed delicate surgical procedure usually done in patients who already have sustained severe physiological stress and hemodynamic instability antecedent to severe hemorrhage, electrolyte imbalance, systemic inflammatory response and sepsis. Based on these factors patients are usually susceptible to increased risk of developing detrimental complications and high rate of mortality within 30 days of post-operative period. Post-operative respiratory infection rate accounts for about 40% of patients who undergo abdominal surgery (10, 11). In this study, it was determined that the most prevalent major complication was respiratory infection, 21. 62% which accounted for increased morbidity and mortality, this related to what another study depicted in India where pulmonary morbidity accounted for 34% with high rate of pneumonia 19. 7% (12). The surgical site infection rate was 18. 02%, this was more less compared to the overall surgical site infection rate of 25% which was described in the study done by Dullo et al. (1). The mortality rate was 16. 22% this was significantly higher than 7. 9% mortality rate determined at Hospital in Kenya (1), however it related with the results from other studies with mortality rate from 15-27. 7%. (3,12). A large proportion of patients in this study were scaled according to their complication severity as high grade (III-V) based on CDC scheme, this corresponded with their mean Index of 44. 46. The mean SAS was 4. 86 equivalent to the mean SAS derived from other studies with a mean range of 4–6 (1, 3). SAS was stratified into three categories of risk level as High risk (0–4), Medium risk (5–6) and Low risk (7–10). (3, 12, 13), there was increased degree of complication severity observed among patients with high risk, SAS ≤ 4, mean CCI 53. 3 compared to the group with low riskSAS ≥ 7 which had a mean CCI 21. 0 this relationship was in keeping with findings from other studies tested by different statistic of associations (1, 3, 4, 12–15). The spearman rank correlation and simple linear regression was carried out in this study to determine the relationship between SAS and the degree of complication severity estimated by weighted CCI.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: 538** 0. 000 -0. 212 0. 184 Poor appetite 0. 351** 0. 002 -0. 065 0. 686 Despair -0. 133 0. 245 -0. 302 0. 055 Weariness -0. 338** 0. 002 -0. 095 0. 556 Fear -0. 352** 0. 002 -0. 143 0. 373 Dereliction -0. 297** 0. 008 -0. 299 0. 058 Lack of Concentration 0. 105 0. 358 -0. 338* 0. 031 Anger -0. 204 0. 073 -0. 292 0. 064 Trashiness -0. 364** 0. 001 -0. 103 0. 521 ** p < 0,05 sig. Correlation. -19 outbreak arouses considerable worry, concern, fear, stress and anxiety in large populations including older adults in the world. The various impacts on daily activities, routines and livelihoods of ordinary people have caused various emotional effects like loneliness feeling, depression etc [27-30]. Some of the older people in Turkey has experienced "compulsory social isolation" during COVID-19 outbreak. The older people who were suddenly cut off from ordinary social relations, has experienced the curfew as a process of pressure, exclusion and accusing of social environment. The findings of the questionnaire conducted in this study showed that older people have a tendency to depression. Behind this finding is that keeping away older people from their social roles, i. e. daily out-of-home routines, is assumed to be effective. It was seen that not only the older people but also the experts had no solutions about overcoming this isolation process, since no similar situation had never been faced before. The older people have been more dependent during curfew. It has become compulsory for others to do the out-of-home works of older people. Well-meant “aids”, such as the bringing water and medicine by police, could only be seen as publicity rather than helping them for some time. Such kind of aids has been also vanished as soon as they get access to street. What to do with a likely new curfew is still unclear in Turkey. However, this type of "curfew" is meaningless. Locking the older people into home has not stopped outbreak. And it is also unclear whether the curfew really protects the older people from COVID-19.

Output: corroborate


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: But we are… in a not very developed area… So we don’t have enough like therapy resources. Or it’s too expensive to pay for it – Alexy, 28, you need a specific solution… but the psychologist… was not able to give that… Pi was able to figure that, and it gave me some great insights – Ashwin, 22, many participants however, generative AI chatbots do not match human empathy and connection – I feel supported… less lonely… but it’s nothing similar with a real human… I’m the only voice and it is the soundboard… it’s an illusion, a beautiful illusion – Sheng, 17, participants found the chatbot’s value limited by its inability to take the lead in the therapeutic process, either to help the client through intense emotions – It doesn’t work when I don’t know anything and when I’m in like some child mode and everything is bad – Anna, 24, Or to shape the process and hold the client accountable – It would suggest, ah, you could try these approaches… And now what? It’s like conversation ended there and then it would have been… amazing to have a coach who goes like, OK, next time you try these three things and then in a week we catch up and you tell me how it went… All the discipline… must come from you – Sandro, 48, the therapeutic process would require chatbots to remember the conversation and build an internal model of their user, something that a majority of users currently miss – They forget everything. It’s sad… When someone forgets something important, it hurts – Oranoid, 17, Russia What’s the point of me telling it about my day every day if it’s not going to build up a picture of my life?

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The two variables were found to be negatively related with a significant reduction of CCI for every single unit increment of SAS, spearman correlation coefficient, (r = -575, p = 0. 001) and regression coefficient (b = -11. 5, p = 0. 001), this indicates that patients within high-risk SAS group are more likely to develop severe complications. SAS has revealed a good predictive accuracy, AUC 0. 712 on ROCthis modest discriminatory capacity delineates a fair predictive ability that proves equivalent correspondence to other study findings which have elaborated similar results with a range of AUC 0. 710–0. 751. (3,16,17). Moreover, other studies has proven a more good discriminatory power in the range of 0. 75–0. 796 of this tool. (1, 3)ConclusionIn our setting emergency laparotomy is significantly attributable to high rate of morbidity and mortality, this portends an irrefutable utility demand of objective directed tool for risk level determination and proactive optimization to prevent deplorable post-operative outcomes. We have demonstrated that SAS can accurately predict the occurrence of complications following emergent laparotomy at Hospital. DeclarationsAcknowledgement: To everyone who contributed resources for accomplishment of this project. Funding: No financial disclosure should be reported Conflict of Interest: Authors declare no competing of interest Ethical Approval: Approval for was obtained from MUHAS-. 282/298/01. C. 9. . Score for Surgery. J Surg. 2007;204(2):201–8. . Validation of the Laparotomy at a Hospital in Rwanda. Surg. 2017;41(7):1734–42. . Score - A Tool in Surgery. 2016;4(4):1–5. . in Injury. :15–9. . The use of tranexamic acid to reduce blood loss in uncemented total hip arthroplasty for avascular necrosis of femoral head: a prospective blinded randomized controlled study. Arthroplasty. 2019;1(1):1–7. Jan W-L, Chen H-C, Chang C-C, Chen H-H, Shih P-K, Huang T-C. Modified Clavien– in Reconstruction among Patients with Head and Neck Cancer. J Clin Med. 2020;9(11):3770. .

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: The social isolation of the aggressor animal is the main attitude in the management of aggressive animals [16]. The aim this study is to investigated the influence of the aggressive behavior in emotional state of the mice lab during housing in the animal facilities. We use motor and exploratory activity and pattern of aggressive behavior (PAB) in the all groups (and categories), and social isolation animal. Using ethological methods, normally employed for the evaluation of animal models of depression and anxiety [16], we measured the level of motor and exploratory activity in dominant, subordinate and isolated mice [17]. Then our proposal was to compare among the categories studied the presence of the state of anxiety and depression, mainly in the management of social isolation and in a situation of exacerbated aggression in groups of mice lab. Animals maintained in the absence of social interaction, described in the literature as a model for hypoactivity or depression-like behavior, were used as a reference [18,19]. Material and Webster mice were obtained from Instituto de Ciência e Tecnologia em Biomodelos (ICTB/Fiocruz) and maintained at Lof the Biotério de Experimentação Animal (LBC-LITEB – Cruz). The animals were adapted to the environment for one week in ventilated racks, and the temperature, humidity and photoperiod were controlled according to the standard environmental regulations. The animals were maintained under stable conditions of temperature and light, with a 12-h light/dark cycle, and both food and water were available ad libitum. The animals were male, adult mice (over ten weeks of life). This project has license number (L – 10/18) at the - IOC/. We used a total number of 3 to 4 animals per triplicate assay. Pattern of aggressive behavior (PAB) We evaluated the daily routine, looking for the appearance of signs of aggression, fights, bites and dominance in each animal group. We also noted other stress signs such as vocalizations, weight loss and others. Thus identifying and categorizing the groups and individuals who showed patterns of aggressive behavior. Animal behavior categories: We divided the animals into four categories: i) Normal (norm), where none of the individuals had PAB in group. ii) Dominant (dnte), characterized by individuals in groups with PBA presence.

Output: corroborate


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: Mitochondrial phylogenetic tree and divergence datingHaploid variants were called using bcftools38 with a ploidy of 1, filtering low quality indels and variants, after which a consensus sequence was exported. We downloaded 14 complete mitochondrial genomes for Polyommatinae from NCBI (Table S3). All mitochondrial genomes were annotated with MitoFinder39 using Shijimiaeoides divina as the reference. The 11 protein-coding genes were aligned with the codon-aware aligner MACSE40 and the ribosomal rRNAs were aligned with MAFFT l-ins-i41. We first investigated phylogenetic relationships among five G. xerces and eight G. lygdamus individuals, with G. alexis as the outgroup. We used IQ-TREE242 to select the best fitting nucleotide substitution model for each partition and merge similar partitions43, built a maximum likelihood tree and assessed support with 1000 ultrafast bootstrap replicates44. To infer a time-calibrated phylogenetic hypothesis, we selected one individual of (L003) and (RVcoll10-B005) and analysed with 13 other Polyommatinae species. We used BEAST245 with the bModelTest46 package to perform phylogenetic site model averaging for each of the merged partitions. Because there is no accepted molecular clock rate for butterflies and no fossils to apply in this part of the phylogeny, we used two strategies to apply time constraints to the analysis. First, we used two published molecular clock rates for the mitochondrial COX1 gene (1. 5% divergence/Ma estimated for various invertebrates47, and the ‘standard’ insect mitochondrial clock 2. 3% divergence/Ma48). We applied a strict clock with a normal prior set up to span 1. 5-2. 3% with the 95% HPD interval (mean=1. 9%, sigma=0. 00119). Second, we borrowed the age of the most recent common ancestor of our sampled taxa from fossil-calibrated analyses across butterflies10,49. We fixed the root age to 33 Ma and allowed the remaining node ages to be estimated using a strict clock. Analyses were run twice from different starting seeds for 10 million MCMC generations and trees were sampled every 1000 generations.

Output: pass


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Peer review processEditorsReviewer #1Reviewer #2Author ResponsePeer review processNot revised: Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors. Read more about eLife’s peer review process. of of AmericaReviewer #1 (Public Review): The authors assess the accuracy of AlphaFold2 (AF2) structures for small molecule ligand pose prediction versus the accuracy with traditional template-based homology models and experimental crystal structures (with a different ligand). They take a careful, rigorous approach leveraging the wealth of structural data around the GPCR protein family and using state-of-the-art docking methods. They find that binding sites are significantly more accurately modeled by AF2 compared to traditional template-based approaches, but this does not translate to greater accuracy in small-molecule docking pose prediction. The important findings around this conclusion have broad implications for the use of AF2 models in ligand binding pose prediction for proteins and drug design. Strengths: The strength of the work is the rigor and careful, thoughtful comparison that cleverly leverages the cut-off date of April 30th, 2018 used in the training of AF2. While the authors list their limited number of docking methods as a caveat, the fact that they use three state-of-the-art ligand docking methods is a strength of the work; many studies use just one. The rigorous analysis of the binding site RMSD and docked ligand pose RMSD is novel to my knowledge and is particularly insightful. Weaknesses: The authors are rigorous in their approach by using state-of-the-art workflows that are high-throughput in nature. However, human expert-refined models and expert selection from multiple models could improve the results of ligand pose prediction when using protein models. The authors alluded to this for traditional models but this can also be true when starting from AF2 models. This is difficult to test systematically and rigorously, however. One possible experiment is to explore whether using multiple AF2 models (there are five by default) would have an effect on pose accuracy, perhaps for selected examples such as NK1R, 5HT2A, and DRD1 to help build out the discussion further.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. [SEP] Text: Neurotransmitter expression, as well as excessive aggressive and impulsive traits of neuronal NO synthase knockout (nNOS-/-) mice were shown to be caused by reductions in serotonin (5-HT) turnover and deficient 5-HT1A and 5-HT1B receptor function in brain regions regulating emotion [23]. We believe that the presence of aggressive behavior and social dominance in adult male mice may be the result of these factors together. However, one question still needs to be clarified. Why would the PAB appear in some groups and not others, under the same environmental conditions? Issues such as sexual competition [24] and the inclusion of intruders [25] was described in the literature, but this did not happen in our study. Several studies have described social dominance in male mice and related the description of dominant and submissive categories [26,27]. The motor and exploration activity of the mice is born of the requirement for information related to the new environment. The animal acquires information in two ways, evaluated by testing in the open field and the elevated plus-maze. Horizontal displacement, measured by the number of spaces crossed (motor activity) or displacement in arms (open and closed) in the elevated plus-maze, respectively. Exploratory activity was measured by the number of vertical displacements (rearing) while the animal is anywhere in the arena and time spent in the arms. The results for the two tests were similar. Our results showed that dominant and subordinated mice showed decreased motor and exploratory activity in relation to animals without the PBA, that is, despite presenting different categories, the stress promoted by aggressiveness affects individuals of both categories. So, decrease in activity may be related to the chronic stress caused by the aggressive behavior. Furthermore, subordinate animals showed serious decreased in the activity compared with the dominant mice and similar animals in social isolation, what can we characterize as a state of depression (depression-like) in booth categories [28]. Individual housing has also frequently been reported to be uncomfortable for mice and has even been used as a model for social deprivation in man [29]. It may cause both physiological and behavioral abnormalities, referred to as ‘isolation syndrome’ [30]. The clinical depression, understanding of the etiology behind these disorders is far from complete, though theories involving various neurotransmitter systems have been advocated [31].

Output: corroborate


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: 2010BEDTools: A flexible suite of utilities for comparing genomic featuresBioinformatics 26:841–84232. Skoglund P. et al. 2014Separating endogenous ancient DNA from modern day contamination in a Siberian NeandertalProc. Natl. Acad. Sci. U. S. A 111:2229–223433. Jónsson H. Ginolhac A. Schubert M. Johnson P. L. F. Orlando L. 2013MapDamage2. 0: Fast approximate Bayesian estimates of ancient DNA damage parametersBioinformatics 29:1682–168434. Prüfer K. 2018SNPAD: An ancient DNA genotype callerBioinformatics 34:4165–417135. McKenna A. et al. 2010The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data20:1297–130336. Danecek P. et al. 2011The variant call format and VCFtoolsBioinformatics 27:2156–215837. Korneliussen T. S. Albrechtsen A. Nielsen R. 2014ANGSD: Analysis of 1538. Danecek P. et al. 2021Twelve years of SAMtools and BCFtoolsGigascience 1039. Allio R. et al. 2020MitoFinder: Efficient automated large-scale extraction of mitogenomic data in target enrichment phylogenomicsMol. Ecol. Resour 20:892–90540. Ranwez V. Douzery E. J. P. Cambon C. Chantret N. Delsuc F. 2018MACSE v2: Toolkit for the alignment of coding sequences accounting for frameshifts and stop codonsMol. Biol. Evol 35:2582–258441. Katoh K. Standley D. M. 2013MAFFT multiple sequence alignment software version 7: Improvements in performance and usabilityMol. Biol. Evol 30:772–78042. Minh B. Q. et al. 2020IQ-TREE 2: for in the Genomic EraMol. Biol. Evol 37:1530–153443. Kalyaanamoorthy S. Minh B. Q. Wong T.

Output: pass


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The visual presentation of the conclusions is very clear but might leave the reader wanting a more in-depth discussion of which structural elements of the AF models lead to bad docking outcomes. For example, Fig. 3 presents an example of a very accurate AlphaFold prediction leading to the ligand being docked completely outside of the binding pocket. Close inspection of the Figure suggests a clash of the ligand with the slightly displaced tryptophan residue in the AF model that might be to blame, as can be confirmed by comparison of the model and PDB structure by the reader themselves but has not been discussed by the authors. Only a few examples of the systems used are shown even visually, leaving the reader unable to study more interesting cases in depth without re-doing the work themselves. The authors acknowledged that several recent studies exist in this space. They point out two advancements made in their work, worthy of further review. Similarly, it's important to evaluate the novelty of this work's claims vs previously available results, and the diversity of information made available to the reader. "First, we use structural models generated without any use of known structures of the target protein. For machine learning methods, this requires ensuring that no structure of the target protein was used to train the method. " This is done by limiting the scope of the work to GPCRs whose structures became available only after the training date of AlphaFold (April 30, 2018), as well as not using templates available after that date during prediction. The use of a time limit seems less preferable than the approach taken in Ref. 1 of discarding templates above a sequence identity cutoff. On the other hand, the 'ablation test' performed in Ref. 2 showed no loss in accuracy when no templates were used at all. Authors should discuss in more detail whether these modeling choices could change anything in their conclusions and why they made their choices compared to those in previous work. "Second, we perform a systematic comparison that takes into account the variation between experimentally determined structures of the same protein when bound to different ligands. " Cross-docking is indeed a more appropriate comparison than self-docking (as done in previous works), and the observation that the accuracy of AF models is similar to that between different holo structures of the same protein is interesting.

Output: pass


Input: Claim: Social isolation is associated with a higher risk of depression and anxiety. # factual [SEP] Text: The symptoms - reduced activity of the submissive and isolated animals - suggest a connection to neurotransmitter imbalance similar to the clinical signs of depression in humans [32]. In consequence, these animals show reduced motor and exploratory activity. still do not know the cause of the appearance of PAB in some groups of male mice. The high and/or low level of (mainly motor) activity of the animals comes after the process of social dominance or as consequence of this. Are there two types of social dominance that could arise from the activity level of individual mice? Our goal is to propose a model of maintenance of male mice in the laboratory based on their motor and exploratory activity designed to minimize aggressive behavior. We can conclude that animals which have aggressive disorders in their group show alterations in their motor and exploratory activity and similar with isolation social behavior in suggest depression-like condition.

Output: corroborate


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: Letter to the editor regarding the 14. doi:10. 1186/s13690-020-0395-4 Kogevinas, M. (2019). Probable carcinogenicity of glyphosate. BMJ 365, l1613. doi:10. 1136/bmj. l1613 Koustas, E. , Lam, J. , Sutton, P. et al. (2014). The navigation guide – Evidence-based medicine meets environmental health: Systematic review of nonhuman evidence for PFOA effects on fetal growth. Perspect 122, 1015-1027. doi:doi:10. 1289/ehp. 1307177 Lam, J. , Koustas, E. , Sutton, P. et al. (2014). The navigation guide – Evidence-based medicine meets environmental health: Integration of animal and human evidence for PFOA effects on fetal growth. Perspect 122, 1040-1051. doi:10. 1289/ehp. 1307923 Li, J. , Pega, F. , Ujita, Y. et al. (2020). The effect of exposure to long working hours on ischaemic heart disease: A systematic review and meta-analysis from the WHO/ILO joint estimates of the work-related burden of disease and injury. 142, 105739. doi:10. 1016/j. envint. 2020. 105739 (2016). Systematic comparison of study quality criteria. Pharmacol 76, 187-198. doi:10. 1016/j. yrtph. 2015. 12. 017 Mahood, Q. , Van Eerd, D. and Irvin, E. (2014). Searching for grey literature for systematic reviews: Challenges and benefits. Methods 5, 221-234. doi:10. 1002/jrsm. 1106 Marshall, I. J. and Wallace, B. C. (2019). Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. 8, 163. doi:10. 1186/s13643-019-1074-9 Martin, P. , Bladier, C. , Meek, B. et al. (2018). Weight of evidence for hazard identification: A critical review of the literature. Perspect 126, 076001. doi:10.

Output: pass


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: 2, Figures 4 and S14 mentioned before, comparison of success rates in top 1 and top 3 docked poses add much context. b) Notes on the structural features preventing successful docking, see e. g. , in Ref. 1, Table 2 or in Ref. 4, Tables 2 and 4. This work has the potential to become an important piece of the puzzle, if deeper insights into the reasons for AF model failures are drawn by the authors. These could include a discussion of the problematic structural elements (clashes of side chain with ligands, missing interactions/waters, etc. ), potential solutions with some preliminary data (flexible docking, softening interactions, etc. ), or proposals for metrics better than RMSD to score the soundness of pockets generated by AF for docking. 1. Díaz-Rovira, A. M. , Martín, H. , Beuming, T. , Díaz, L. , Guallar, V. , & Ray, S. S. (2023). for Virtual Screening? Application of to AlphaFold2 Predicted Structures. Journal of and Modeling, 63(6), 1668-1674. 2. Heo, L. , & Feig, M. (2022). Multi-state modeling of G-protein coupled receptors at experimental accuracy. Proteins: Structure, Function, and Bioinformatics, 90(11), 1873-1885. 3. Beuming, T. , & Sherman, W. (2012). Current assessment of docking into GPCR crystal structures and homology models: Successes, challenges, and guidelines. Journal of and Modeling, 52(12), 3263-3277. 4. Scardino, V. , Di Filippo, J. I. , & Cavasotto, C. (2022). How good are AlphaFold models for docking-based virtual screening? [Preprint]. Chemistry. thank the reviewers and editor for their careful evaluation of our manuscript, and we appreciate their favorable assessment of our work. Below, we clarify a few points concerning the relationship between our study and previous studies evaluating ligand docking to protein models. As reviewer 2 correctly notes, several previous assessments of AF2 models have simply excluded templates above a sequence identity cutoff when using AF2 to predict structures.

Output: pass


Input: Claim: Cryptocurrencies are volatile and can experience rapid value changes. # factual [SEP] Text: A one-dollar note does not yield a return. It will be worth 1$ forever. Nevertheless, people “purchase” dollars only to “sell” them later for goods. This is exactly the function of money and the same applies to Bitcoin. Advertisement 5. The Bitcoin’s volatility One very common attack on Bitcoin is that it must be useless as money (or currency) since it has very high volatility [12, 13]. Clearly, if an asset’s value is very volatile, then it is less likely to be adopted as a unit of account, which is one of the properties of money. However, it should not considerably affect its prospects to be used as a medium of exchange. Even today, most Bitcoin transactions are quantified in US dollars, that is, in these transactions, Bitcoin is used as a medium of exchange, while the US dollar is used as a unit of account. However, it should be stressed that in these transactions, the dollar takes no role. Bitcoin is currently exploring a new territory, in which it is used as a medium of exchange, but yet to be used as a unit of account (on this discrepancy see Ref. [15]). Moreover, the usage of the volatility criterion is useful only in close-to-equilibrium’s markets. In the case of Bitcoin, the price increases exponentially, which is a clear sign of a nonequilibrium scenario. Exponential rise is an indication of a constant amplification process, which cannot occur in the vicinity of equilibria. In this case, it is clear that the volatility will increase exponentially as well. Therefore, there could be two options: (A) the approximately constant amplification process is close to its end, in which case the market will converge to a semiequilibrium state, and the volatility will decrease dramatically. (B) The constant amplification process is going to last for some time, in which case the value of Bitcoin will continue to rise dramatically. Therefore, in both cases, the attractiveness of Bitcoin will increase, and in any case, the high volatility in an exponentially growing process cannot be used as an argument for the claim that Bitcoin is worthless. It is only a sign that the crypto market is in its infancy stages. In an exponentially growing economy, the volatility should be evaluated in logarithmic scale, and in this scale, the Bitcoins’ volatility actually declines. Advertisement 6.

Output: corroborate


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: Results: There was no difference between the MAP and HR of the two groups. The RPP was significantly higher in the ING group at the end of the protocol. Cardiac collapse occurred in 1/5 ING rabbits and 3/5 in the ILE group and none of these animals survived despite additional antidote treatment. Conclusion: In a rabbit model of propranolol toxicity, ING therapy produced a significant increase in the RPP over ILE. Critique: This comparative study is an important step in elucidating the optimal antidote for propranolol toxicity. As this was a pilot study, the small sample size may have led to type II errors in the analysis, especially regarding survival effect. Additionally, very aggressive initial doses of ILE and insulin were used and it is unclear how this aspect of the study may impact translation to clinical care. The study may be improved by monitoring metabolic parameters such as glucose, lactate, and oxygen saturation that might yield data pertinent to effectiveness and safety of these antidotes. Implication for toxicologists: For beta-blocker toxicity, ING is already recognized as an important therapy by toxicologists. ILE therapy is an accepted antidote for local anesthetic toxicity and is gaining popularity for use in cardiac collapse due to non-anesthetic lipophilic xenobiotics. This pilot study highlights the need for further comparative studies between ILE and other more established antidotes. Prevention of contrast-induced nephropathy by bolus injection of sodium bicarbonate in patients with chronic kidney disease undergoing emergent coronary procedures. Cardiol 2011; 107: 1163–7 Background: Sodium bicarbonate has been given prophylactically to high-risk patients for contrast-induced nephropathy (CIN). One hour of pretreatment is suggested, which is less than ideal in preparing for emergency cardiac catheterization procedures. There is limited data in the setting of chronic kidney disease (CKD). Research question: Can bolus sodium bicarbonate administered less than 1 h prior to emergent coronary procedures prevent CIN in patients with CKD? Methods: This was a prospective trial of adults with CKD (creatinine >1. 1 mg/dL or estimated GFR <60 mL/min) undergoing emergency coronary procedures within 60 min of presentation. Patients were randomized to receive a bolus of sodium bicarbonate (154 mEq) or sodium chloride (0.

Output: pass


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Evaluation round #2 DOI or URL of the report: Version of the report: 2 Author's Reply, 01 Sep 2023 Download author's reply Download tracked changes file Decision by Chris Chambers, posted 15 Aug 2023, validated 15 Aug 2023 Three of the four reviewers were available to evaluate your revised submission. As you can see, the good news is that all are broadly satisfied and we are now within reach of Stage 1 in-principle acceptance (IPA). There is one remaining statistical issue to address in Michèle Nuijten's review, which I agree should be resolved before we proceed further. I will assess your response and revision at desk and we should be able to then quickly issue IPA. Reviewed by Michèle Nuijten, 14 Jul 2023 I thank the authors for their thorough revision of their paper. All my comments were addressed sufficiently. I do have one remaining question about the planned regression analysis, but I leave it to the authors/editor to evaluate whether this requires further adaptations in the manuscript. In response to comment 7 of my review, the authors now include an additional model in which they include all six researcher characteristics to predict generalizability of prediction accuracy. However, they also still plan on fitting six separate models for each of the individual researcher characteristics. I do not really understand this choice. understand correctly, the goal is to identify which characteristics are (the most) important to predict generalizability of prediction accuracy. In my view, it will be hard to interpret any of the estimated coefficients in these separate models to assess importance, because they are not controlled for any influences of the other five characteristics. I may miss something here, but I would argue to only include the “omnibus” model with all six researcher characteristics included at the same time, and omit the six separate models. Reviewed by Jim Grange, 10 Jul 2023 The authors have addressed the comments I raised in the previous review to my satisfaction. Good luck with the research - I'm really interested in seeing the outcome. Reviewed by Matthias Stefan, 13 Jul 2023 was critical in my detailed comments, I already mentioned that this is an interesting research project and that there is much to like about the -- already in Stage 1 and this general assessment has not changed. Moreover, I think my comments have all been sufficently and convincingly addressed.

Output: pass