Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The red-green striped box for 1B suggests its opposing roles in possibly stimulating both ARNTL and NR1D1, whereas NR1D1 then inhibits ARNTL. The positive feedback of ARNTL-CLOCK and ARNTL-NPAS2 heterodimers on NR1D1, 1, PER2, and PER3 was omitted from the diagram for simplicity, along with many other components and interactions within the circadian system. NR1D1 or Rev-erb-alpha is so-called, because it is transcribed in the reverse direction and overlaps the 3' end of THRA, an important thyroid nuclear receptor. It is interesting that thyroid dysfunction becomes most prominent among bipolar patients after treatment with lithium [46]. Also, thyroid (T3) augmentation is useful for treating depression [47]. Thyroid has been used for periodic catatonia (perhaps a form of bipolar disorder) since the 1930's [1]. Considering that the intronic location of rs2314339 indicates no obvious functional role, we suspect that this SNP might be in linkage disequilibrium with some nearby polymorphism with a key functional effect. As linkage disequilibrium for rs2314339 extends through most of NR1D1 and to the 3' end of THRA, the functional element could plausibly be situated in either gene. A coding SNP in 1B, Pro203Ala, rs7732671, was over-transmitted to bipolar probands with P < 0. 05 and odds ratio 1. 55. By itself, we might regard this isolated finding as statistically unimpressive and plausibly a false positive. However, the same SNP was associated with unipolar depression with an odds ratio of 2. 12 (P < 0. 025). The nominally significant association in both completely separate subject sets with odds ratios in the same direction provides suggestive evidence for a reliable association, especially since neither statistical result is sensitive to false positive results from population stratification. The common allele of this SNP has been associated with obesity (OMIM 608886). A paralogue gene, 1A, is a regulator of ARNTL and additionally functions through regulation of NR1D1 and NR1D2 effects on ARNTL [48]. [SEP] Justify:

Output: The text does not address the claim directly about circadian rhythm disruptions leading to metabolic disorders and obesity. Instead, it discusses various genetic associations and SNPs related to bipolar disorder and obesity, as well as some thyroid dysfunction. While obesity is mentioned in association with a SNP, the text does not provide evidence or a direct link to the claim regarding circadian rhythms. Hence, the information is irrelevant to the specific claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: This backdrop sets a critical stage for exploring innovative solutions, such as AI chatbots, to bridge this gap and offer a beacon of hope for millions. potential of AI chatbots across various sectors, particularly in health care, is met with growing optimism for several compelling reasons. 5 Firstly, the technology itself is advancing rapidly, enhancing its capability to understand and interact in increasingly sophisticated and helpful ways. This rapid improvement suggests that AI chatbots could soon provide unprecedented support in diagnosing and managing health conditions, including hearing healthcare. Secondly, the potential for swift and widespread adoption of AI technologies, especially in low- and middle-income countries (LMICs), is facilitated by the global proliferation of mobile technology. This widespread mobile penetration offers a solid foundation for deploying AI chatbots to vast populations with minimal infrastructural changes. Lastly, the acute shortage of healthcare professionals in LMICs, a challenge that severely limits access to care, positions AI chatbots as a critical solution to extend health care services to underserved areas. In hearing health care, AI chatbots can emulate successful applications from other health care fields, such as conducting preliminary symptom checks and guiding patients to the appropriate level of care. This approach not only streamlines the use of scarce health care resources but also ensures that individuals with hearing concerns receive timely and accurate advice. Considering the inaccessibility and poor state of health care in LMICs, hearing health care perhaps even more so, even imperfect AI tools may be a dramatic improvement to existing systems. 5 Further, the round-the-clock accessibility and anonymity provided by AI chatbots can encourage more people to seek assistance for hearing issues, breaking down barriers to care. By leveraging the general advancements of AI technology and addressing the unique challenges within chatbots hold the promise of significantly enhancing the accessibility and quality of hearing health care, making them a beacon of hope for millions in need of hearing services. In hearing health care, this translates to AI chatbots not only offering initial hearing screenings and advice on assessments but also educating users on preventative hearing care, identifying potential risks to hearing health, and guiding individuals on when and how to seek further evaluation from healthcare professionals. By drawing parallels with the successful deployment of AI chatbots in other medical fields, we can envision a comprehensive digital health assistant tailored for hearing health. This assistant could support individuals through the entire process of identifying hearing loss, navigating them through self-management tips for minor issues, and streamlining referrals to specialists when necessary. [SEP] Justify:

Output: The text corroborates the claim by discussing the innovative role of AI chatbots in healthcare, emphasizing their potential to provide support in diagnosing and managing health conditions. It outlines how technological advancements enhance their capabilities and identifies the widespread adoption facilitated by mobile technology, particularly in low- and middle-income countries (LMICs). Furthermore, it highlights AI chatbots as vital solutions for extending healthcare access to underserved populations, thus aligning closely with the claim regarding their use in patient support and care management.


Input: Task: pass the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Figure 4 with 1 supplement Download asset Open asset DFezf plays an instructive role in regulating growth cone targeting. (A–H) L5 mis-expression experiments. DFezf-HA was expressed in L5 neurons using 6–60 GAL4, and sparse labeling of L5 neurons (myr::GFP, α-GFP) was achieved using the FlpOut method (see Materials and methods for detailed description). (A) DFezf-HA (magenta) is expressed in all L5 neurons, a subset of which are labeled with myr::GFP (green) using FlpOut. L5 neurons are easily identified based on position, as their somas are located directly distal to the lamina plexus defined by R1-R6 axons (blue, mAB24B10), which appears as a thick band. (B and C) Confocal images of L5 growth cones (green) within the outer medulla at 24 hr APF. R7 and R8 growth cones (blue, mAB24B10) define the boundaries of the outer medulla. (D) Quantification of L5 mis-expression experiments at 24 hr APF. N = the number of cells counted. (E–G) Confocal images of L5 axons (green) within the outer medulla in adult flies. Outer medulla layers are identified using R8 and R7 axons (mAB24B10, blue) as a reference. (H) Quantification of L5 mis-expression experiments in adult flies. N = the number of cells counted. (I–O) Mis-expression of dFezf-HA in L2 neurons in MARCM experiments. DFezf-HA was expressed in lamina neuron clones (green, myr::GFP: α-GFP) by 11–164 GAL4 (Nern et al. , 2008). L2 neurons (magenta) were labeled with myr::tdTomato (α-DsRed) driven by 16H03-LexA. L2 MARCM clones are double positive for myr::GFP and myr::tdTomato and appear white. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses experimental results related to the expression of DFezf-HA in neurons and their mis-expression in certain experiments, with no mention of crops, gene editing, or any specific benefits for different species. Thus, it does not provide information that supports, contrasts, or relates to the claim.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: 1A possibly binds to RORE sites both on NR1D1 and on ARNTL, and 1B may act similarly, perhaps providing a partial explanation for effects on both mania and depression, seeming opposites which are both aspects of bipolar disorder (Fig. 1). Thus, there may be a convergence of pathways. A number of SNPs in 1B achieved nominal significance in a case-control study which included some of these same bipolar subjects, but none approached Bonferroni criteria [22]. The TDT association of 6 with bipolar disorder was intriguing, and appeared consistent with the claim that a CLOCK mutation in mice produces a mouse analog of mania [40]. Although nominally significant, associations with these SNPs could all represent false-positive statistical findings. One of the 6 SNPs, rs6850524 was also found to be suggestively associated with bipolar disorder in analyses using a partially-overlapping subject sample [17]. rs2412648 (P < 0. 05 by Chi Square, P > 0. 05 by EMP1 in our sample) was part of a suggestively-associated haplotype [17]. It would be conceivable that many SNPs in the CLOCK gene, each with a small effect impairing the gene, could in combination make a substantial contribution of bipolar susceptibility. Because the CLOCK gene, extending over roughly 114,338 base pairs, displays high linkage disequilibrium throughout its considerable length, it is possible that the most functional polymorphism has not yet been recognized. Similarly, though various associations with several bipolar phenotypes such as recurrence rates and sleep disturbances have been reported with rs1801260, the T3111C SNP in the 3'UTR region of CLOCK [36,37], it is possible that rs1801260 is not the most functional polymorphism in linkage disequilibrium. In our TDT analysis and in BALM studies, rs1801260 was not associated with bipolar disorder, nor was it associated with the morningness-eveningness dimension, as had been reported in other data [36]. In 1978, at a time when the gene causing the Drosophila PER mutant had not yet been identified and the presence of 3 human homologues was unknown, the first author hypothesized that bipolar disorder might be caused by mutation of a homologue of the PER gene [49]. [SEP] Justify:

Output: The text does not address the claim regarding circadian rhythms and metabolic disorders or obesity. Instead, it discusses genetic associations with bipolar disorder and variations within the CLOCK gene, which may have implications for mood disorders but does not connect to the claim about metabolic health and obesity.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: This expanded role exemplifies how AI chatbots could significantly enhance the accessibility and efficiency of hearing health care services, offering a scalable solution to meet the global demand. illustrate the potential of AI chatbots in hearing health care, we can draw from analogous use cases in other medical fields, as well as emerging examples within audiology itself. These examples not only demonstrate the versatility and capability of AI but also shed light on how similar approaches can be adapted to meet the unique challenges of hearing health care (see Table 1). AI Image Analyses. In dermatology, AI-powered applications have revolutionized preliminary assessments by -analyzing images of skin lesions uploaded by users. This innovation offers a preliminary evaluation and recommendations for follow-up care, showcasing AI’s ability to conduct complex visual assessments. The relevance of this to hearing health care lies in its demonstration of AI’s potential for conducting similarly complex assessments, such as evaluating symptoms, understanding communication issues in complex listening situations, analyzing audiograms or classifying otoscopic images. 3 Chatbots and Long-term health chatbots have become instrumental in providing -continuous support and intervention. Their ability to maintain engagement and deliver personalized advice over time exemplifies the chatbot’s potential for long-term patient support. 6 Applying this model to hearing healthcare, AI chatbots could similarly monitor a user’s progress in managing hearing loss,7 offering customized advice and reminders for hearing protection or rehabilitation exercises. This example underscores the importance of ongoing, personalized care in managing health conditions, including hearing loss, tinnitus, and hyperacusis, which tend to cause mental health issues such as anxiety and depression. . A recent study in otolaryngology investigated ChatGPT’s ability to accurately respond to patient inquiries. 8 By submitting 30 patient questions to ChatGPT (version 3. 5), the authors analyzed the AI’s performance from both medical professionals’ and laypersons’ perspectives. The study demonstrated ChatGPT’s impressive accuracy (98. 3%) in providing medical information as rated by otolaryngologists. A key finding, however, was that patients may find it difficult to trust information -generated by AI, and this needs to be addressed. Similar work has been done for audiological questions which also demonstrates its potential across different AI large-language models. 9 Health Care. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of AI chatbots in providing continuous support and intervention in healthcare, specifically highlighting their potential to monitor user progress in managing health conditions like hearing loss. It mentions that chatbots can deliver personalized advice and maintain user engagement over time, illustrating their application in long-term patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: He explains, “Developing a new technology is very expensive. If a company wants to make money from its new technology, its best option is to develop and sell it to people with money and power. ” This means that new technologies resulting from scientific research benefit advantaged groups more than other people, so there are inequalities in the ways BIs are distributed. However, as Thomas says, “There is a lot of research that helps everyone regardless of status. For example, developing a smart grid will help both rich and poor. ” Thomas and his research students are also examining the relationship between BIs and how productive scientists are. He says, “There’s often the worry that if a scientist has spent a lot of time on broader impacts, they’re not spending time doing their research. ” But, he reports that, based on his team’s findings so far, this does not seem to be the case. This evidence suggests that there are not really downsides to scientists ensuring positive broader impacts from their research, which is good news! If the research team can develop a way to use machine learning to analyse BIs based on key words, it could scale up its research substantially. One of Thomas’ students is trying to use machine learning to analyse 20,000 abstracts! “As you can imagine, that’s much more challenging because there’s lots of noise in the data and room for many errors,” says Thomas. Download the 10. 33424/277 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 Woodson with student researchers making a 3D printer in Nairobi, Woodson and University student at graduation with University PhD students visiting USAID in Washington DC The 2021 class of Academic and for () participants at Woodson in. in the Department of Technology and Society at : Science and : Investigating the broader impacts of scientific funding : Foundation (NSF), (NASA) This work is supported by the US Foundation, under award number 1926494. The contents are solely the responsibility of the authors and do not necessarily represent the official views of US Foundation. . in the Department of Technology and Society at : Science and : Investigating the broader impacts of scientific funding : Foundation (NSF), (NASA) This work is supported by the US Foundation, under award number 1926494. [SEP] Justify:

Output: The text contrasts the claim by indicating that the development of new technology tends to benefit advantaged groups more than others, suggesting there are inequalities in the distribution of benefits. The mention of some research benefiting everyone, regardless of status, such as the development of a smart grid, implies that not all advancements, including those in gene editing, are distributed equally among different species of crops.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The three SNPs in PER2 and rs2585405 in PER1, which were over-transmitted or under-transmitted to bipolar probands with nominal significance, gave weak support to this archaic hypothesis, but certainly suggested no major role for the PER genes in bipolar disorder. On the other hand, associations of affective symptoms with other polymorphisms provided some of the strongest evidence that the circadian system has a role in affective disorders. The lack of consistency in results for the PER homologues in different groups was somewhat disappointing and reminds us that these associations may be false positive results. It would appear that additional large and independent samples must be studied to determine if the PER genes have a real role in human affective disorders. Bright light, which promotes mania, tends to promote transcription of the PER1 and PER2 genes [50], which may then inhibit the action of the ARNTL-NPAS2 heterodimer in stimulating MAOA (Fig. 1). Thus, this pathway may also be consistent with our model in Fig. 1. However, the model ignores numerous problems and complexities of circadian regulation and fails to incorporate the dynamic circadian fluctuations or the photoperiodic interactions among circadian system components. We also have not explained how the observed differences in genetic background might produce the distinctive phenotypes among patients with unipolar and bipolar disorders, nor have we suggested the mechanism by which the same genetic background creates susceptibility in bipolar patients to both mania and depression. The TEF promoter SNP rs738499 had a statistically unimpressive (Pnominal = 0. 023) association with unipolar recurrent depression, with the less common G allele being associated with healthy mood. A similar association with the QIDS-SR in the sample provided a degree of replication (P < 0. 001), using independent subjects, independent methods, and a different lab and assay procedure. We had selected rs738499 for genotyping based on a report that rs5996091, a SNP over 500,000 nucleotides remote from TEF, was highly correlated with TEF expression with R2 = 0. 43 [51], and we had noted that of HapMap SNPs nearer TEF, rs738499 had the highest linkage with rs5996091 as well as a likely location in the promoter. [SEP] Justify:

Output: The text does not address the claim about circadian rhythms and metabolic disorders or obesity. Instead, it focuses on genetic associations with bipolar and unipolar disorders as well as the complexities of circadian regulation in the context of affective disorders. Thus, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: In primary health care environments, AI chatbots have the potential to assist community health workers by offering accurate information and advice on basic ear and hearing care, anchored in clinical guidelines. 5 This approach aligns with the Organization’s recent advocacy for task-shifting and innovative technologies in hearing care within LMICs, a strategy recommended due to the pervasive scarcity of hearing health professionals. 1 More importantly, AI chatbots can guide patients on when to seek medical attention by evaluating their symptoms and advising on whether their condition requires routine, urgent, or emergency care, along with suggesting appropriate next steps for treatment. Prevention. Pioneering projects are exploring the use of chatbots for educational purposes. 5 In hearing care this could enable users to learn about aspects of hearing loss, tinnitus, and hearing conservation and risks associated with occupational and recreational noise exposure. A proactive approach in hearing health can support prevention of hearing loss by providing immediate, accessible advice. The significance of this lies in the chatbot’s ability to offer preventative education and awareness, highlighting an essential component of hearing health that goes beyond treatment to include prevention and awareness. Real-And Feedback. AI can also be instrumental in the deployment of ecological momentary assessments via smartphone applications where AI could provide real-time feedback to patients and clinicians. This approach, for example, could allow for the instantaneous analysis of voice notes through advanced natural language processing (NLP) techniques. Such analyses could be leveraged by specialized AI chatbots designed to deliver immediate, context-specific support tailored to the user’s needs, whether related to their device or environment. This enables a highly personalized assistance experience, providing users with the right support at the precise moment it’s needed. This is especially relevant now, as most hearing aids come with associated smartphone apps, which offer a platform for integrating AI chatbots. These use cases underscore the versatility of AI chatbots in health care, suggesting a promising avenue for their application in hearing health. By leveraging AI’s capabilities for assessment, education, and continuous support, chatbots can significantly contribute to early detection, prevention, and management of hearing loss, making health care more accessible and efficient (Table 1). chatbots in hearing health care presents its set of challenges, such as ensuring accuracy in diverse linguistic and acoustic environments and maintaining patient confidentiality. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI chatbots have the potential to assist community health workers in providing accurate information and advice on basic ear and hearing care, as well as guiding patients on when to seek medical attention. It emphasizes the role of AI chatbots in patient education, symptom evaluation, and their application in health care settings, particularly in hearing health. This aligns with the claim of their increasing utilization in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Its guidelines govern the transboundary movement of LMOs. Protocol (a supplementary agreement to the 1992 Convention on Biological Diversity) coined the definition for ‘Organisms’ and ‘food derived from modern biotechnology’. Protocol is a legal framework that lays out predictable international access to local genome sources. These definitions are used to establish if the treaty and related domestic regulation apply to gene edited organisms. The FAO/WHO have also established guidelines for gene edited agrifoods. Joint FAO/such as the Guidelines (Codex), define ‘modern biotechnology’ based on the CPB definition, but have not adopted the CPB’s definition of LMOs that includes GMOs. The guidelines include 2018. Policy makers were trying to build some consensus on whether to use their respective countries’ pre-existing national regulatory frameworks covering GMOs, or to develop a different policy framework to accommodate agricultural biotechnology techniques that do not use foreign DNA (SDN1 and SDN2) and are not considered transgenic. Regulators were then faced with, what Asquer and Krachkovkaya (2021: 1114) refer to, as ‘response uncertainty’. Response uncertainty, “…arises from the inability to predict, which regulatory tools are more suitable to the specific features of the emerging technology. ” Response uncertainties include considerations over whether restrictive regulations limit R&D, innovation, and domestic business or whether permissive regulations could result in unintended consequences, risks to health and safety and/or harms to humans, animals and/or the environment (Asquer & Krachkovskaya, 2021). As discussed in the next section, there have been several ways regulatory frameworks have responded to the response uncertainty presented by -Cas9. 3 State-frameworks for FoodsCountries have taken different paths towards regulating gene edited agrifoods. Figuring out the right way to combine the benefits of innovation, economic development, health and safety, and social license is a difficult task, especially when the functions of nucleases that can edit the genome to achieve desirable traits are discovered every year. Some countries have been influenced by the positions taken by neighbouring countries in their geographic regions and by their trading partners. Others are in the midst of crafting their regulatory frameworks, seeking guidance from regulators in other jurisdictions who have experience with developing standards and protocols. [SEP] Justify:

Output: The text contrasts the claim by discussing how different countries are approaching the regulation of gene edited agrifoods, indicating that there is variability in how gene editing is perceived and regulated depending on the specific features of the crop and the technology used. The mention of 'response uncertainty' and the difficulties in creating uniform regulatory frameworks implies that not all crops, or the technologies used to edit their genomes, will necessarily benefit equally from gene editing, as each may require different considerations based on their unique characteristics.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: By stimulatory binding at D-box promoter sites, TEF may promote transcription of NR1D1, NR1D2, and the PER genes, actions which might be supposed to stimulate mania or counter depression (Fig. 1). However, we do not know if the rs738499 minor allele promotes or inhibits TEF transcription. Two intronic SNPs in CSNK1E were associated with the BALM among bipolar subjects, meeting Bonferroni criteria for significance. The two SNPs were in virtually perfect linkage disequilibrium with each other. However, among 26 SNPs in CSNK1E identified by resequencing 12,173 nucleotides of exonic, intronic, and promoter regions of the gene, no other SNPs were found to be in substantial linkage disequilibrium with these two SNPs (see Report EF015901). Surprisingly, although 4 of the 6 participants from the bipolar sample with these two SNPs displayed extreme morningness, one subject of the 6 had a score at the opposite end of the morningness-eveningness scale. Both SNPs were under-transmitted to bipolars (NS). CSNK1E phosphorylates several of the circadian proteins including the PER proteins and ARNTL, and may even have differential effects on phase adjustment depending on which phosphorylation sites are intact on various substrates [52,53]. CSNK1D has a somewhat similar role. Note that one CSNK1D SNP was nominally associated with bipolar disorder, and the CSNK1D region on 17q achieved a maximum LOD score of 3. 63 in a bipolar association study [54]. However, since the two CSNK1E SNPs and the PER3 SNP associated with the BALM scale of morningness-eveningness were not associated with the bipolar and unipolar psychiatric phenotypes, it seems that the circadian polymorphisms which are related to affective disorders do not influence affective state simply through effects on circadian phase, e. g. , Fig. 1 does not suggest that the effects are mediated through circadian phase change. It is important to review some suggestive findings reported elsewhere which were not replicated in these analyses. We were not able to confirm the suggestive evidence we had earlier reported that haplotypes in ARNTL and PER3 were associated with bipolar disorder [15]. Those haplotype associations had previously fallen short of Bonferroni criteria. [SEP] Justify:

Output: The text discusses various SNPs related to bipolar disorder and their association with circadian proteins, specifically CSNK1E and CSNK1D, but does not provide any evidence or discussion specifically supporting or refuting the claim about circadian disruption leading to metabolic disorders and obesity. It focuses more on genetic associations and their implications for bipolar disorder rather than circadian rhythms' impact on metabolism.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: However, the opportunities for growth are immense, particularly with the increasing penetration of digital technologies in LMICs. This trend not only supports the deployment of AI chatbots but also magnifies their potential impact on health care accessibility. As digital literacy and infrastructure continue to improve, the integration of AI chatbots with mobile phone health apps and telehealth services can expand, enhancing personalized care and facilitating continuous support. Embracing these technological advancements opens a path toward a more inclusive, responsive, and efficient future for hearing healthcare, particularly in underserved settings. Moreover, navigating the ethical landscape, including the development of guidelines for responsible use by consumers, patients, and health care providers, is crucial. Proactively addressing these challenges, while capitalizing on the digital opportunities, charts a promising trajectory for revolutionizing hearing healthcare. Furthermore, to ensure the accuracy and relevance of the responses provided by AI chatbots, it is essential to create and utilize training datasets that are diverse and free of bias, encompassing various languages and cultural contexts. conclusion, Thembi’s story and the exploration of AI chatbots in hearing healthcare showcase a future where technology enables new models of care that can transcend traditional barriers, enhancing access and hope for many. The adoption of AI chatbots for early detection, patient education, and ongoing support signals the onset of a transformative era in healthcare. Such progress demands a united front in research, technology development, and policymaking to ensure that advances are ethically sound and universally accessible. Prioritizing accessibility, overcoming language and cultural barriers, and addressing ethical considerations are imperative to ensure quality hearing care is universally available, transcending geographical and socio-economic limitations. By addressing these challenges and harnessing the power of digital innovation, we can evolve hearing health care into a fundamental right, turning success stories like Thembi’s into the norm, rather than the exception. 1. Organization (2021). on Hearing. | 2. (2023). of AI Chatbots in Care. Hearing Journal, 76(04), 26-30. DOI: 10. 1097/01. HJ. 0000927336. 03567. 3e | 3. Organization (2024). HearScope otoscopy. | 4. Planey A. M. (2019). Audiologist availability and supply in the United States: A multi-scale spatial and political economic analysis. Social science & medicine (1982), 222, 216–224. [SEP] Justify:

Output: The text corroborates the claim by discussing the integration of AI chatbots in health care, particularly their role in enhancing personalized care, facilitating continuous support, and enabling new models of care. The mention of 'the adoption of AI chatbots for early detection, patient education, and ongoing support' directly aligns with the claim that AI chatbots are being utilized in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: A third group of countries are currently developing regulatory frameworks for all forms of biotechnology in agriculture, covering GMOs and gene edited agrifoods where none existed before, as is the case for several developing countries across Africa. There is also a host of countries that have no publicized regulations, and no active policy discussions. Here, we briefly discuss the five general categories of regulatory protocols for gene edited agrifoods that countries fall into: lightly regulated, strongly regulated/prohibited, proposed regulation, modified process, and no regulations/no active policy discussions. Table 3. 1 outlines in detail where 53 countries fall in terms of whether they are signatories of the CPB, the date of the first regulatory decision regarding gene editing, and the type of regulation. Table 3. 1 also categorizes each country into whether it follows a ‘product’ or ‘process’ based regulatory approach. The product-based approach is premised on the idea that the ‘risks posed by agrifoods are a function of biological characteristics and the specific genes that have been used,’ while the process-based approach to evaluating the risks posed by products of biotechnology are based on ‘processes employed in their development’ (Tagliabue, 2017: 3; Prakash et al. , 2014). Information is not readily available for all countries, nevertheless, the Table provides a reference point for the discussions in this 3. 1 summarizes the regulatory landscape for gene edited crops across countries as of early 2024. Table 3. 1 The regulatory landscape for gene edited cropsFull size tableGenerally speaking, Nucleases interventions, where foreign DNA is inserted into a host genome (SDN3), are considered GMO (transgenics) (Sprink et al. , 2016). These are considered ‘novel combinations of genetic material’, the language used in the CPB’s definition of LMO. This wording is used in some national regulations to define GMOs. As discussed in Chap. 2 (Sect. 2. 2. 1), SDN1 does not use foreign genes to create edits. It harnesses the natural repair mechanisms that exist in the organism’s cell. It has similarities to the general mechanisms and results of breeding techniques that involve chemical or radiological mutagenesis. Because of different classifications of techniques, there are different views on how each technique should be appropriately regulated. [SEP] Justify:

Output: The text contrasts the claim by highlighting that the regulatory landscape for gene edited crops varies significantly between countries and also refers to different regulatory approaches that may infer differing benefits based on species. It discusses the distinction between product-based and process-based regulatory approaches, indicating that benefits from gene editing can depend on the specific biological characteristics and genetic make-up of different crops, as opposed to suggesting that all crops benefit equally.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: We have also examined certain candidates for association proposed by Mansour and colleagues [13,16]. These included rs7107287, rs4757142, and rs1982350 in ARNTL, rs2859387 in PER3, and rs2291738 and rs2279665 in. None of these reached nominal significance of P < 0. 05 in our TDT analyses. The rs11541353 SNP in NPAS2 and rs2290035 in ARNTL, reported to be associated with seasonal affective disorder [55,56], were not significantly associated with bipolar disorder in our families by TDT. In PER3, rs10462020 was not associated with bipolar disorder or unipolar depression. Also, we have not yet demonstrated any association of the PER3 repeat described by Archer et al. with affective disorders [26]. summary, we found several suggestive associations of circadian gene polymorphisms with affective disorders, some of which we were able to partially replicate. The association of NR1D1 rs2314339 with bipolar disorder and DSPS and the association of 1B Pro203Ala, rs7732671, with both bipolar and unipolar affective disorders appear the most likely to prove reliable. The association of TEF rs738499 with unipolar depression may also prove reliable. Two intronic SNPs in CSNK1E were associated with the BALM in bipolars, but the inconsistent directions of association stimulate some reserve, and these SNPs were not directly associated with affective diagnoses or symptoms. Each of these leads should be pursued. When genotyping of ancestry-informative markers becomes available, our correlations of the BALM with genotypes should be controlled for population stratification, which is a potential problem in the sample as well. Replication and extension of these results in larger independent samples is needed before the importance of circadian polymorphisms in affective syndromes can be verified. If the findings are confirmed, they will suggest that bipolar and unipolar affective disorders have at least one common genetic susceptibility factor, but several which are distinct. Competing interests JRK is a founder and holds equity in Psynomics, Inc. The terms of this arrangement have been reviewed and approved by UCSD in accordance with its conflict of interest policies. The other authors declare that they have no competing interests. [SEP] Justify:

Output: The text does not address the claim regarding circadian rhythms and metabolic disorders or obesity. Instead, it focuses on associations of circadian gene polymorphisms with affective disorders, discussing various genetic markers and their significance in relation to bipolar and unipolar disorders. As such, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: As a result of this study, it was determined that the group that learned with the chatbot had significantly higher scores than the group that learned with flashcards. At this point, it can be said that the result obtained by Tham and Ruan (2019) is similar to the finding of this study. In semi-structured interviews, some students stated that the chatbot taught the subjects with images, presented the content with a combination of voice and text, enabled them to engage in clue-supported Q&A activities, and allowed them to use the technology at home. All these aspects were evaluated by them as factors contributing to the retention of information. They also mentioned that, as the chatbot covered the subject following the teacher’s instruction, they were able to reinforce their knowledge. The teacher, on the other hand, highlighted that the chatbot appealed to different senses, thereby enhancing permanent learning. All of these aspects can be viewed as explanations for the significant difference in the retention test scores of the experimental group students. 5. 3 Result and discussion of the experiences of students about the use of chatbots in the process of learning social studiesSemi-structured interviews with students revealed different views about the process and the technology itself. Students stated that SosyalciBot could be used outside of school, giving them the chance to study on their own. Consequently, technology could contribute to autonomy in learning. In their qualitative study, Haristiani and Rifai (2021) analyzed the impact of a chatbot named Gengobot on learning Japanese grammar. Students who participated in the study mentioned that Gengobot improved their autonomy in learning; it was found to be beneficial and easy to use. Similarly, Jeon (2022) carried out a similar study, and the students stated that chatbots gave them the chance to enhance their autonomy in the learning process. Students stated that SosyalciBot, unlike other technologies, can provide them with instant feedback. They also mentioned that technology makes learning interesting and is advantageous in that it is accessible 24/7. Similarly, et al. (2021) determined that students in the experimental group had the chance to receive more feedback with chatbots than with the traditional method. According to the students, chatbots were easy to use and interesting, and they could always benefit from chatbots because it was possible to reach them anytime, anywhere. [SEP] Justify:

Output: The text corroborates the claim by highlighting the positive impacts of chatbots on the learning process, which parallels their use in patient support and care management. Specifically, it describes how students experience enhanced autonomy in their learning with chatbots, receive instant feedback, and enjoy the accessible nature of chatbot technology, which aligns with the functions and benefits chatbots provide in healthcare settings.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Japan applies a case-by-case methodology to any organism with novel traits applying for regulatory approval. of Health, Labour and Welfare published final guidelines in 2020 (Laaninen, 2020). of Environment determines whether the product falls outside of the scope of LMO. Gene edited agrifoods are not subject to GMO biosafety protocols if no foreign DNA is present in the final product. Japan has approved two agrifood products for commercialization: a GABA enriched tomato and sea bream fish were approved in 2021, which are both available to Japanese consumers (Menz et al. , 2020: 14). Nigeria’s Agency has determined that if no foreign DNA is present in the organism, a non-GMO classification is given. Nigeria’s regulatory framework shares similarities with and Paraguay (Entine et al. , 2021: 565). Kenya’s Authority has indicated it will evaluate the biosafety of gene edited agrifoods on a case-by-case basis (Entine et al. , 2021: 565). Final decisions will be based on the presence or absence of transgenes in the final product, which is similar to the framework introduced by Argentina in 2015 (Whelan & Lema, 2019). Canada is also considered a ‘light’ regulator. It uses a product-based approach. Gene edited organisms are evaluated on a case-by-case basis but are not considered equivalent to GMOs with foreign DNA (Entine et al. , 2021: 568). Section 3. 4 provides a detailed discussion of the Canadian case. States, similar to Canada, takes a product-based approach to risk assessment, and finalized this into regulation in 2015. of Agriculture (USDA), (APHIS), and the Food and (FDA) evaluate products for health and safety. Biosafety regulation is triggered by risk factors on a case-by-case basis. Regulatory frameworks are different for gene edited animals as opposed to plants. In 2020, a final rule updated and modernized the USDA biotechnology regulations under the Act. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that different countries, including Japan, Nigeria, Kenya, Canada, and the USA, employ a case-by-case regulatory methodology for evaluating gene edited organisms. This indicates that regulatory approaches consider the specific traits and genetic modifications of different species, suggesting that not all crops benefit equally from gene editing.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Authors' contributions DFK suggested the primary hypotheses, supervised recruitment of the sample, selected the polymorphisms to be assayed, and wrote much of the manuscript. CMN contributed to the hypotheses and design of the study, performed most of the statistical analyses, and wrote parts of the manuscript. EJJ assembled the data for the non-related bipolar sample and critiqued the manuscript. TS helped manage and store the DNA samples, performed the SNPlex assays, assembled assay results, and wrote portions of the manuscript. JRK developed the team collecting and assembling the UCSD bipolar samples, participated in the developed the assay laboratory, contributed to design, and wrote parts of the manuscript. All authors read and approved the final manuscript. Supplementary MaterialAdditional file 1 SNPs associated with transmission of bipolar disorder. 198 polymorphisms entered into TDT tests for association with bipolar disorder are listed. SNPs nominally significant are highlighted in yellow and the SNP significant after control for multiple comparisons is highlighted in green. Click here for fileSupplementary MaterialAdditional file 2 SNPs of bipolar participants associated with the BALM. 30 SNPs tested for association with the BALM morningness-eveningness scale are listed, with the SNPs significant after control for multiple comparisons highlighted in green. Click here for fileSupplementary MaterialAdditional file 3 SNPs associated with unipolar depression. 62 polymorphisms tested for association with recurrent unipolar depression are listed, with SNPs nominally significant highlighted in yellow. Click here for file 2. Data and biomaterials were collected in four projects that participated in the of (NIMH) Genetics Initiative. From 1991–98, the and Co-Investigators were: 01 MH46282, John Nurnberger, M. D. , Ph. D. , Marvin Miller, M. D. , and Elizabeth Bowman, M. D. ; Washington University, St. Louis, MO, U01 MH46280, Theodore Reich, M. D. , Allison Goate, Ph. D. , and John Rice, Ph. D. ; 01 MH46274, J. Raymond DePaulo, Jr. , M. D. , Sylvia Simpson, M. D. , MPH, and Colin Stine, Ph. D. ;. D. , Diane Kazuba, B. A. , and Elizabeth Maxwell, M. S. W. [SEP] Justify:

Output: The text is irrelevant to the claim as it describes the contributions of various authors to a research study related to bipolar disorder and does not address circadian rhythms, metabolic disorders, or obesity in any way.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Similar problems are observed in studies related to the use of personal assistants, such as Siri, Google Assistant, and Alexa, which are advanced versions of chatbots, in language education. The students who participated in these studies stated that, although AI-powered personal assistants were supportive in the learning process, these assistants sometimes couldn’t understand them due to pronunciation differences. Furthermore, in some cases, the students found it challenging to comprehend the answers provided by the assistants (Dizon, 2017, 2020; Dizon & Tang, 2019; Moussalli & Cardoso, 2016; Underwood, 2017). All these studies indicate that AI-powered dialogue systems haven’t reached the desired level of development. However, chatbots have been improving every day with the advancements in AI, and it is foreseen that these programs will be used more frequently and effectively in educational environments in the near future (Kandpall et al. 2020). 5. 4 Results and discussion about the experiences of practitioner teacher in terms of the use of chatbots throughout the process of social studies teachingDuring and at the end of the process, interviews were conducted with a practitioner teacher regarding the process and chatbot technology. The practitioner teacher stated that throughout the experimental process, he could cooperate with the technology. He expressed that the technology proved to be valuable in enhancing student motivation, readiness level, and participation, and reinforced what is taught. According to the teacher, the chatbot activated different senses, leading to more permanent learning. He defined the chatbot as an easy-to-use and supportive technology that made the lessons more interesting and enjoyable. When the literature is analyzed, it is seen that the number of studies reflecting the use of chatbots from the viewpoint of teachers is limited. In the study conducted by Bii et al. (2018) most teachers stated that chatbots were useful, easy-to-use, beneficial, and interesting. The teachers emphasized that they enjoyed using chatbots as chatbots enlightened the contents of their lessons and contributed to understanding the topics. In their study, Chuah and Kabilan (2021) researched the viewpoints of 142 teachers who used chatbots for two months. At the end of the process, teachers stated that chatbots were supportive; they could correct the mistakes of students with feedbacks, and they were easy-to-use tools. [SEP] Justify:

Output: The text corroborates the claim by discussing the supportive role of AI-powered chatbots and dialogue systems in educational environments. While it focuses on education, the mention of chatbots being valuable in enhancing motivations, participation, and understanding suggests a broader applicability to patient support and care management as AI technologies continue to improve. The text notes that these tools have been recognized as beneficial by teachers, implying that they could yield similar positive outcomes in various domains, including healthcare.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: The ‘’ () rule “brings USDA’s plant biotechnology regulations into the twenty-first century by removing duplicative and antiquated processes in order to facilitate the development and availability of these technologies through a transparent, consistent, science based, and risk-proportionate regulatory system” (Laaninen, 2020: 8). The development of market-oriented traits, such as gene edited soybeans with higher levels of oleic acid, is led by the and China. The expectations the US has about future innovative technologies in agriculture largely informed how the regulatory system has responded to the growing popularity of. This is what Asquer and Krachkovskaya (2021: 1122) refer to as the ‘anticipatory response’. countries share similar regulatory profiles. Argentina has commercially grown GM crops since 1996 and uses the Cartagena Protocol’s definition of LMO. of Agriculture, Livestock and Fisheries evaluates applications on a case-by-case basis. Gene edited plants and animals are not considered GMOs because they do not have novel combinations of genetic material. After the Argentinian regulation was enacted, other countries developed their own set of regulations. Chile’s Agriculture and aligned with Argentina and passed similar biosafety regulations to Argentina’s in 2016. Paraguay (Ministry of Agriculture and Livestock, and Commission on Agriculture and Forestry Biosafety) shares some principles with Argentina. (Agriculture Institute) and Brazil enacted their legislation in 2018 (Whelan & Lema, 2019). Commission of Brazil shares similar approaches with Argentina and Chile. The regulatory framework goes further to include a list of techniques and genetic interventions that are not considered GMOs. Paraguay, Ecuador, Honduras, and Guatemala passed their regulations in 2019. If foreign DNA is used in an organism, Ecuador’s regulatory agency views this as a GMO, excluding SDN1 and cisgenesis (Entine et al. , 2021: 552). Guatemala and Honduras (of Agriculture, Livestock and Foodstuff, and Service of and Safety, respectively) have harmonized their regulations for GMOs. The regulations are based on a specific definition for ‘novel combination of genetic material’ and final product characteristics compared with conventional breeding products, essentially a ‘case-by-case’ basis for evaluation. [SEP] Justify:

Output: The text contrasts the claim by discussing the specific regulatory frameworks and development of gene-edited crops in various countries, indicating that the benefits may vary based on factors such as market-oriented traits. For example, it mentions gene-edited soybeans with higher levels of oleic acid and highlights case-by-case evaluations which suggest that not all crops benefit equally from gene editing, as different crops may have different characteristics and regulations that influence their development.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: From 1999–03, the and Co-Investigators were: 01 MH59545, John Nurnberger, M. D. , Ph. D. , Marvin J. Miller, M. D. , Elizabeth S. Bowman, M. D. , N. Leela Rau, M. D. , P. Ryan Moe, M. D. , Nalini Samavedy, M. D. , Rif El-Mallakh, M. D. (at University of Louisville), Husseini Manji, M. D. (at University), Debra A. Glitz, M. D. (at University), Eric T. Meyer, M. S. , Carrie Smiley, R. N. , Tatiana Foroud, Ph. D. , Leah Flury, M. S. , Danielle M. Dick, Ph. D. , Howard Edenberg, Ph. D. ; Washington University, St. Louis, MO, R01 MH059534, John Rice, Ph. D, Theodore Reich, M. D. , Allison Goate, Ph. D. , Laura Bierut, M. D. ; 01 MH59533, Melvin McInnis M. D. , J. Raymond DePaulo, Jr. , M. D. , Dean F. MacKinnon, M. D. , Francis M. Mondimore, M. D. , James B. Potash, M. D. , Peter P. Zandi, Ph. D, Dimitrios Avramopoulos, and Jennifer Payne; University of Pennsylvania, PA, R01 MH59553, M. D. , Ph. D. ; University of California at Irvine, CA, R01 MH60068, M. D. , and M. D. ; University of Iowa, IA, R01 MH059548, M. D. , and M. D. ; University of Chicago, IL, R01 MH59535, Elliot Gershon, M. D. , Ph. D. , Francis McMahon M. D. , Ph. D. , M. D. , Maria Caserta, M. D. , Tu Nguyen, Donna Harakal; University of California at San Diego, CA, R01 MH59567, John Kelsoe, M. D. , Rebecca McKinney, B. A. [SEP] Justify:

Output: The text provided is a list of Co-Investigators and does not address or relate to the claim regarding circadian rhythms or their link to metabolic disorders and obesity. Therefore, it is deemed irrelevant to the claim.


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

Output: The text corroborates the claim by discussing the role of AI chatbots in patient support, specifically highlighting how they can assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering emotional support. It further elaborates on their applications in health care, including appointment scheduling and medication reminders, indicating their increasing use in patient care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Israel’s takes a similar perspective on how gene edited plants are regulated. Plants without foreign DNA are classified as non-GMO as long as no foreign DNA is in the organism. Since leaving the EU, the (UK) has re-evaluated its regulatory stance on GMOs and is set to release updated guidelines. Part of the (Precision Breeding) Act 2023, crops derived through precision breeding are no longer subject to the same regulations as GMOs in the UK. of Environment, Food and Rural Affairs’ definition of a GMO excludes organisms that have genetic changes that could occur naturally or achieved through traditional breeding. 17, a research scientist working for an NGO, stated with respect to where India falls in the regulatory typology, where in most of the other countries, including India, ‘process’ is what is governing the GM definition. There are many research programs and activities that are underway in India researching crop improvement using gene editing. India’s Ministry of Environment, Forest and Climate Change, and the Ministry of Science and Technology regulates plants with novel traits derived from SDN1 or SDN2 as exempt from GMO regulations (which cover products of biotechnology that used SDN3). The ministries are currently examining whether all new technologies should be regulated as per existing regulatory frameworks. When new crop varieties come up for assessment using gene editing the regulatory framework will have to be firmly in place (Tiwari et al. , 2021). As of 2023, regulatory amendments are still in process, though India’s framework covering gene edited agrifoods closely adheres to the scope of other regulatory frameworks in the lightly regulated category. Most recently, Bangladesh passed legislation for gene edited agrifoods in March 2024 that clearly places it in the lightly regulated category along with India. If applicants can demonstrate “… the absence of any exogenously introduced DNA and request confirmation through appropriate channels to register or release the plant following the same procedure as those used for conventionally bred counterparts” (Project, 2024a). 3. 2 and ProhibitedThis category includes countries that have regulatory systems that have no exceptions for gene edited agrifoods (see Fig. 3. 2, purple shading). Gene edited foods are assessed for risk the same way as GMOs. There are several countries that have prohibited the environmental release and/or cultivation of GMOs and gene edited organisms. [SEP] Justify:

Output: The text contrasts the claim by discussing the varying regulatory frameworks across different countries regarding gene editing, indicating that not all crops and species are treated equally under these regulations. Specific examples from the UK, India, and Bangladesh demonstrate differentiated approaches, suggesting that the benefits of gene editing may depend on the crop species and the regulatory context in which they are evaluated.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: ; Rush University, IL, R01 MH059556, M. D. , Howard M. Kravitz, D. O. , M. P. H. , Diana Marta, B. S. , Annette Vaughn-and Laurie Bederow, MA; 1Z01MH002810-01, Francis J. McMahon, M. D. , -Wadleigh, Ph. D, Lisa Austin, Ph. D, Dennis L. Murphy, M. D. Becky R. McKinney recruited many bipolar patients including those who completed the BALM scale. Data and biomaterials were collected in six projects that participated in the of (NIMH) Genetics of Recurrent Early-(GenRED) project. From 1999–2003, the and Co-Investigators were: 01 MH060912, Myrna M. Weissman, Ph. D. and James K. Knowles, M. D. , Ph. D. ; University of Pittsburgh, Pittsburgh, PA, R01 MH060866, George S. Zubenko, M. D. , Ph. D. and Wendy N. Zubenko, Ed. D. , R. N. , C. S. ; University, Baltimore, R01 MH059552, J. Raymond DePaulo, M. D. , Melvin G. McInnis, M. D. and Dean MacKinnon, M. D. ; University of 1 MH61686, Douglas F. Levinson, M. D. (GenRED coordinator), Madeleine M. Gladis, Ph. D. , Kathleen Murphy-Eberenz, Ph. D. and Peter Holmans, Ph. D. (University of of Medicine); University of 01 MH059542, Raymond R. Crowe, M. D. and William H. Coryell, M. D. ; 01 MH059541-05, William A. Scheftner, M. D. Rush-Presbyterian. genetics research was supported by academic funds. Clinical data and biomaterials were collected with the help of Lawrence E. . D. , A. Dawson, M. D. , J. Cronin, M. D. , S. . , A. Grizas, and D. Vo. Pauline Lee, Ph. D. [SEP] Justify:

Output: The text does not provide any information regarding circadian rhythms, metabolic disorders, or obesity. It primarily lists various researchers, their affiliations, and details of projects related to genetics and bipolar disorder, which is unrelated to the claim about circadian rhythms and metabolic health.


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

Output: The text corroborates the claim by detailing multiple instances where AI-powered chatbots are being utilized effectively in patient support and care management for various chronic conditions. It mentions their potential benefits, such as improving medication adherence, enhancing quality of life, and facilitating collaborative relationships among healthcare professionals and patients. The positive evaluations of health care chatbots in studies indicate an increasing acceptance and usage in managing patient care.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: The EU via its EU Directive (Directive 01/18/EC) led by the takes a precautionary approach. All organisms derived from mutagenesis are considered GMOs, however if mutagenesis is derived from conventional breeding techniques with a long history of safety it is exempt. Union is a global leader in gene editing research (but behind on market-oriented trait development), despite its strict policies (Modrzejewski et al. , 2019). Like the EU, Switzerland considers gene edited organisms as GMOs. In the EU, the advent of -Cas9 in agrifood research triggered a regulatory response in the form of reaction by existing regulatory standards, what Asquer and Krachkovskaya (2021: 1123) refer to as a ‘consequential response’. This consisted of a reactive approach to technologies such as gene editing. 2023, the EU Commission publicized a proposed amendment to the Directive covering all products of biotechnology. Techniques (NGT) plants are split into two categories: naturally occurring or by conventional breeding; all other NGT plants are treated as GMOs that require risk assessments and authorization. If the current proposal is accepted, it would lift regulatory burdens on some gene edited agrifoods. A fast-track approval process is proposed that would apply to the second category of plants if they are more tolerant to climate change or require less water or fertilizer. is not part of the European Union, it shares a highly restrictive position towards gene edited agrifoods. GMOs and gene edited agrifoods are restricted from environmental release, though scientists in Norway are pressing for modified restrictions for organisms derived using SDN1 techniques. According to the Project, “Norway law also demands the assessment of three non-safety categories: social benefit, sustainable and ethically sound products”, going beyond its neighbouring EU regulations (Project, 2024b). Mexico was on the pathway to establishing a product-based biosafety protocol, but reversed direction on gene edited agrifoods. Gene edited foods are evaluated based on the process and are considered equivalent to GMOs. Informant 10 (a research scientist) worked in on gene editing in agrifood and informed us that, when the [current Mexican] government came into power [in 2018] …they reject everything. [SEP] Justify:

Output: The text contrasts the claim by discussing the varying regulatory approaches and outcomes concerning gene edited organisms across different regions such as the EU, Norway, and Mexico. It implies that not all crops benefit equally from gene editing, particularly given different treatments as GMOs and the specific evaluation processes in place for gene edited foods, suggesting a disparity in benefits among species.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: and genotyped the participants in the Core DNA Laboratory at the MEM division of the Institute, which was supported by the Fund. N. J. Schork, Ph. D. provided statistical consultation. 10. 1192/bjp. 113. 501. 895 Halberg, F (1967). Physiologic considerations underlying rhythmometry, with special reference to emotional illness. Symposium on Bell-Air III. Geneva: Mason et Cie, 126. Richter, CP (1965). in Medicine and Psychiatry. Springfield, IL: Charles C. Thomas. (2000). Melatonin suppression by light in euthymic bipolar and unipolar patients Psychiatry 57: 572–579, [PubMed] DOI: and Baldessarini, RJ (2008). Circadian activity rhythm abnormalities in ill and recovered bipolar I disorder patients 10: 256–265, [PubMed] DOI: and Kim, YS (2008). Chronotype distribution in bipolar I disorder and schizophrenia in a Korean sample 10: 271–275, [PubMed] DOI: Wirz-Justice, A (2007). Chronobiology and psychiatry Rev 11: 423–427, [PubMed] DOI: Kripke, DF (1985). ed. Therapeutic effects of bright light in depressed patients of Light. 453453 New York: Academy of Sciences, 281. Prendergast, BJ and Nelson, RJ (2005). Affective responses to changes in day length in Siberian hamsters (Phodopus sungorus) Psychoneuroendocrinology 30: 438–452, [PubMed] DOI: Einat, H, Kronfeld-Schor, N and Eilam, D (2006). Sand rats see the light: short photoperiod induces a depression-like response in a diurnal rodent Res 173: 153–157, [PubMed] DOI: Yilmaz, A, Aksoy, A and Canbeyli, R (2004). A single day of constant light (L/L) provides immunity to behavioral despair in female rats maintained on an L/D cycle 28: 1261–1265, [PubMed] DOI: and Nimgaonkar, VL (2005). Circadian genes and bipolar disorder 37: 196–205, [PubMed] DOI: and Kelsoe, JR (2005). [SEP] Justify:

Output: The text is largely a compilation of references and citations without any direct discussion or evidence regarding the claim that disruption of circadian rhythms can lead to metabolic disorders and obesity. Therefore, it does not support or contradict the claim, making it irrelevant.


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

Output: The text corroborates the claim by illustrating that there is a significant interest in chatbots that can support mental well-being, particularly among individuals with long COVID. It indicates that a notable percentage of respondents expressed a desire for a chatbot to understand their stress levels and emotional states, showing an openness to using AI-powered chatbots for emotional support.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: They reject biotechnology, whether it is GM, whether it is genetic editing, genome editing, anything that has a hint of biotechnology and a lab they reject. In addition to Mexico’s current stance on gene edited agrifoods, Belize has a moratorium preventing gene edited organisms from release into the environment. South Africa’s Department of Agriculture, use a risk assessment framework for GMOs that would also apply to new breeding techniques such as gene editing. It has opted for a tiered approach to assessments. South Africa’s regulatory framework includes the EU-based definition for GMOs, which is somewhat divergent from how the defines GMOs. Using phraseology such as “naturally occurring genetic variation” as a threshold to trigger biosafety protocols has garnered criticism, as has South Africa’s chosen definition of GMO (Entine et al. , 2021: 566). Though part of the FSANZ agreement with Australia as discussed in the previous section, continues to define gene edited organisms as GMOs. In 2014, there were discussions regarding changing how gene edited agrifoods were regulated but this was not successful. continues to regulate gene edited agrifoods under the Act under the Authority (Ahmad et al. , 2021). 3. 3 Proposed RegulationThe countries in this category as of early 2024 were in the midst of finalizing their regulations for gene edited agrifoods (Fig. 3. 2, orange shading). There are ongoing policy discussions and draft proposals currently under review. This category includes and Uruguay (Chou, 2023). Some countries in this category are leaning towards regulating gene edited agrifoods the same way as GMOs, while others are seeking a simplified assessment procedure. According to Informant 19 (a private sector representative), there are open discussions among several sub-countries regarding harmonization of regulatory frameworks for gene editing agrifoods. They explain: The conversation is starting to happen on having a harmonized regulatory landscape. For example, Kenya, Uganda, Tanzania, and a little bit of Rwanda. These conversations are happening since we trade closely with each other. Maybe it’s best for us to come up with a more regional regulatory framework for these emerging technologies. would say for now, it’s still fragmented. [SEP] Justify:

Output: The text counters the claim by highlighting the variability in regulatory frameworks for gene edited agrifoods across different countries. It indicates that some countries are considering gene edited crops differently from GMOs and that they are in discussions about harmonizing regulations. This suggests that the benefits and applications of gene editing may not be uniform for all crops and species, which contrasts with the claim that all crops benefit equally from gene editing.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Examination of the clock gene Cryptochrome 1 in bipolar disorder: mutational analysis and absence of evidence for linkage or association 15: 45–52, [PubMed] DOI: and Kelsoe, JR (2006). Suggestive evidence for association of the circadian genes Period3 and Arntl with bipolar disorder 141B: 234–241, [PubMed] DOI: and Nimgaonkar, VL (2006). Association study of eight circadian genes with bipolar I disorder, schizoaffective disorder and schizophrenia Behav 5: 150–157, [PubMed] DOI: Shi, J, Wittke-and Liu, C (2008). Clock genes may influence bipolar disorder susceptibility and dysfunctional circadian rhythm and Smeraldi, E (2007). How do genes exert their role? Period 3 gene variants and possible influences on mood disorder phenotypes Eur Neuropsychopharmacol, [PubMed] -and Kelsoe, JR (2008). Delayed sleep phase cases and controls J 6: 6. [PubMed] [PubMed] DOI: (2008). Whole-genome association study of bipolar disorder 13: 558–569, [PubMed] DOI: Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls Nature 447: 661–678, [PubMed] DOI: (2007). A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder Mol Psychiatry, [PubMed] -Parker, Z Brown, JL et al. (2001). A genome survey indicates a possible susceptibility locus for bipolar disorder on chromosome 22 98: 585–590, [PubMed] DOI: -Mallakh, R Manji, H et al. (2003). Genomewide linkage analyses of bipolar disorder: a new sample of 250 pedigrees from the of 73: 107–114, [PubMed] DOI: (2001). Association of structural polymorphisms in the human period3 gene with delayed sleep phase syndrome EMBO reports 2: 342–346, [PubMed] DOI: and von Schantz, M (2003). [SEP] Justify:

Output: The text discusses various studies related to circadian genes and bipolar disorder but does not mention metabolic disorders or obesity. Therefore, it is irrelevant to the claim that disruption of circadian rhythms can lead to metabolic disorders and obesity.


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

Output: The text corroborates the claim by providing data indicating a significant willingness among respondents to use voice-based health chatbots for recording health symptoms and diagnosing health problems. For instance, 65% of respondents expressed interest in trialing a voice-based health chatbot, demonstrating that AI-powered chatbots are making headway in patient support and care management settings.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: When you look at Kenya, for example, it could be a step ahead when it comes to genome editing and developing the regulatory framework for that innovation. is still grappling with putting in place the regulatory framework for GMOs and so I would say countries are starting to have that conversation. But there’s no homogeneity in getting this to be operationalized. The concern is that it would help if regions had more harmonized regulatory frameworks. (Informant 19) is also developing its own regulatory framework for gene edited agrifoods. has procedures in place for determining whether a gene edited crop is considered a GMO. 2023, the country amended its biotechnology regulations to reduce barriers to common applications of modern biotechnology. It is expected that the first gene edited banana that is resistant to yield reducing fungal diseases will be on the market in by the end of 2024 (USDA, 2024). But as Informant 19 states, there are ongoing challenges for developing countries who are creating regulatory frameworks from scratch. They state: …developing countries…there’s a challenge to have a regulatory landscape that is working for technologies that are pre-existing like genetic engineering. So before we even had closure on that, genome editing is coming in and it’s creating what you might call ‘a spanner in the works’ where some policy makers are like, ‘wait, wait, should we pause? ’ Formulating the regulatory landscape for GMOs and consider how we can incorporate these imagined technologies. So there’s that challenge of whether to use existing regulatory frameworks for GMOs for genome editing or to completely start afresh with genome editing and because of that, it’s affecting the speed at which the genome edited products and innovations may benefit the intended end users. So that’s one of the key challenges I see, especially here in the developing world. (Informant 19) government has invested substantially in gene editing research programs, according to Tiwari et al. (2021: 18) and initially planned to develop ten new varieties of gene edited crops. In 2019, a federal program was announced that included developing thirty new varieties of gene edited agrifoods by 2027 (Dobrovidova, 2019). The country has also prohibited the environmental release of GMOs and gene edited organisms; however, researchers have been allowed to experiment with these techniques for research purposes only as of 2016. [SEP] Justify:

Output: The text contrasts the claim by discussing the varying challenges and regulatory frameworks encountered by different countries in implementing gene editing technologies. It mentions that there is no homogeneity in operationalizing gene editing due to specific regional considerations and regulatory landscapes. For instance, the text highlights that developing countries face challenges in formulating their regulatory frameworks for gene editing compared to developed countries. This suggests that not all crops and regions benefit equally from gene editing advancements, as local regulation and context significantly influence the application and benefits of the technology.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: A length polymorphism in the circadian clock gene per3 is linked to delayed sleep phase syndrome and extreme diurnal preference Sleep 26: 413–415. [PubMed] Purcell, S Neale, B Todd-de (2007). PLINK: A for Whole-and Population-81: 559–575, [PubMed] DOI: Brown, FM (1993). Psychometric equivalence of an improved Morningness (BALM) Scale using industrial population within comparisons Ergonomics 36: 191–197, [PubMed] DOI: and Boomsma, DI (2001). Genetic analysis of morningness and eveningness 18: 809–822, [PubMed] [PubMed] DOI: and Kaprio, J (2007). Heritability of diurnal type: a nationwide study of 8753 adult twin pairs J 16: 156–162, [PubMed] DOI: and Lykken, DT (1998). Genetic and environmental influence on morningness-eveningness Dif 25: 917–925, DOI: -Eberenz, K et al. (2003). Genetics of recurrent early-onset depression (GenRED): design and preliminary clinical characteristics of a repository sample for genetic linkage studies 119B: 118–130, [PubMed] DOI: (2003). The 16-item of (QIDS), clinical rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression 54: 573–583, [PubMed] DOI: Beutler, E and Gelbart, T (2000). Large-scale screening for HFE mutations: methodology and cost 4: 131–142, [PubMed] DOI: Storey, JD and Tibshirani, R (2003). Statistical significance for genomewide studies 100: 9440–9445, [PubMed] DOI: and Mignot, E (1998). A clock polymorphism associated with human diurnal preference Sleep 21: 569–576. [PubMed] and Smeraldi, E (2008). Clock genes beyond the clock: CLOCK genotype biases neural correlates of moral valence decision in depressed patients Behav 7: 20–25. [PubMed] - (2008). [SEP] Justify:

Output: The text does not provide relevant information regarding the claim about circadian rhythms and metabolic disorders. It mainly discusses various genetic studies related to circadian clock genes but does not make a direct connection to metabolic disorders or obesity.


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

Output: The text provides evidence supporting the claim by discussing various factors that influence the likelihood of adopting health chatbots, such as trust, comfort in outlining symptoms, and familiarity with AI chatbots. It notes that privacy concerns did not significantly impact the likelihood of adoption, indicating a positive reception toward health chatbots among survey respondents. This suggests that there is a growing acceptance and use of AI-powered chatbots in healthcare settings for patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Russia is currently running experiments using gene editing on barley, sugar beets, wheat, and potato. Russia is the largest producer of barley, and a leader in producing the other three crops. The announcement of funding for gene editing research into economically important crops for Russia raises some questions as to whether the regulatory framework will apply to these crops. 3. 4 Modified ProcessThe most active country in genome editing research is China. However, it has not released any legal documents referencing its regulatory stance on gene editing organisms, but it is possible that China wants to have a product in hand before releasing regulations (Modrzejewski et al. , 2019). China is also in the process of releasing its regulatory framework for gene edited agrifoods. However, its current legislation considers gene edited agrifoods GMOs, but with a simplified assessment procedure (‘GMO light’). of Agriculture and allows gene edited plants that are determined to not present biosafety risks or risks to human health to be subject to a simplified registration procedure. It is predicted that China will lean towards the case-by-case basis for gene edited agrifoods risk and safety assessments, similar to Canada and the US, but not identical. 3. 5 No RegulationsThere are countries that do not have regulations for GMOs and there are no active policy discussions pertaining to how gene edited agrifoods should be regulated. There are also countries that currently do not have regulatory frameworks covering GMOs or gene edited agrifoods: Myanmar, Sudan, North Korea, Pakistan, and Egypt. This does not mean that gene edited crops can be released into the environment under any conditions. It simply means that these countries do not have regulatory frameworks covering gene edited plants yet and that there are no current, active discussions occurring to develop a framework. Countries that do not have active policy discussions are shaded grey in Fig. 3. 2. The influence of more established, larger trading partners is also something to consider, as it comes up in many discussions of regulatory frameworks emerging in the developing world. The precautionary position of Europe has exerted considerable influence on the regulatory frameworks of its smaller trading partners in Africa and elsewhere. [SEP] Justify:

Output: The text contrasts the claim by discussing the varying regulatory environments and specific case studies involving gene editing on different crops like barley, sugar beets, wheat, and potato. It indicates that the benefits and applications of gene editing may not be uniform across all crop species due to differences in regulations and research priorities. The mention of various countries’ regulatory frameworks suggests that benefits might depend on the specific crop and regional policies, contradicting the claim that all crops benefit equally.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Initial association of NR2E1 with bipolar disorder and identification of candidate mutations in bipolar disorder, schizophrenia, and aggression through resequencing 147B: 880–889, [PubMed] DOI: and Kay, SA (2008). Redundant function of REV-ERBalpha and beta and non-essential role for Bmal1 cycling in transcriptional regulation of intracellular circadian rhythms PLoS Genet 4: e1000023. [PubMed] DOI: (2007). Mania-like behavior induced by disruption of [PubMed] (2006). Dissecting the functions of the mammalian clock protein BMAL1 by tissue-specific rescue in mice Science 314: 1304–1308, [PubMed] DOI: -hnert-Hilger, G Meijer, JH et al. (2008). Regulation of monoamine oxidase a by circadian-clock components implies clock influence on mood 18: 678–683, [PubMed] DOI: and Kelsoe, JR (2006). Identification of additional variants within the human dopamine transporter gene provides further evidence for an association with bipolar disorder in two independent samples 11: 125–33, 115. [PubMed] DOI: Berk, M, Dodd, S, Kauer-Sant'anna, and Norman, T (2007). Dopamine dysregulation syndrome: implications for a dopamine hypothesis of bipolar disorder Scand Suppl, : 41–49, [PubMed] DOI: and Lazar, MA (2006). Nuclear receptor Rev-erbα is a critical lithium-sensitive component of the circadian clock Science 311: 1002–1005, [PubMed] DOI: -Espana, F, Cnaan, A and Whybrow, PC (2003). Thyroid hypofunction in patients with rapid-cycling bipolar disorder after lithium challenge 53: 899–905, [PubMed] DOI: (2006). A comparison of lithium and T(3) augmentation following two failed medication treatments for depression: a STAR*D report Psychiatry 163: 1519–1530, [PubMed] DOI: and Lin, JD (2007). Transcriptional coactivator PGC-1alpha integrates the mammalian clock and energy metabolism Nature 447: 477–481, [PubMed] DOI: and Wolf, S (1978). [SEP] Justify:

Output: The text is irrelevant to the claim about the disruption of circadian rhythms leading to metabolic disorders and obesity. Instead, it discusses various studies related to bipolar disorder and the functions of circadian clock components, without mentioning metabolic disorders or obesity.


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

Output: The text corroborates the claim by indicating that AI chatbots are being explored for their potential in health care support, especially for long-term health conditions like long COVID. It highlights that a significant proportion of respondents are receptive to using AI chatbots for applications such as nutrition, sleep, stress detection, and emotional well-being. Additionally, the text discusses how trust in these chatbots is an important factor for adoption and explores insights aimed at overcoming AI hesitancy, which supports the idea of their increasing use in patient care management.