Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: 135 Habermas wrote that such intervention denies the child the possibility to be revised in adult life by ‘critical reappraisal’, denying a person's ability to be ‘the undivided author of his own life’. 136 More recently, enhancement proponents and transhumanism adepts counterargue that these objections would equally apply to many parenting practices not generally considered controversial. 137 Some authors defend the right to enhancement and a moral imperative to eradicate disease. 138Modifying the germline genetic identity of human beings is expressly mentioned as an exclusion from patentability. 139 Recitals 38 and 40 of the express legislative intent ‘to exclude unequivocally from patentability processes for modifying the germ line genetic identity of human beings’. 140 Patent norms do not make functional distinctions, meaning that the purpose of an intervention—the actual use claim in the patent application—is immaterial. Under this literal interpretation, all interventions in germ cells will encounter patentability hurdles, including in the abstract, even those with purely therapeutic purposes. Somatic cell intervention would fall outside the scope of the specific provision; however, these still require an assessment of their moral conformity under the general ‘ordre public’ and morality clause. 141 As follows from the European legal tradition, further interpretative elements can, and should when appropriate, be taken into consideration by adjudicators. Below we consider systemic elements such as the human rights framework and issues of legal coherence. IV. C. a. i. Patentability and Interpretation of the cannot be blind to the EU Charter and the international human rights treaties signed by some or all member states. Moreover, these are also relevant to the EPO’s interpretative praxis. However, because the grant of a patent is independent from administrative authorizations to market products or conduct medical procedures, patentability cannot be denied simply based on legal prohibition. 142 This entails that a specific ethical debate has to be developed within the European patent system for purposes of applicability of patent eligibility norms. As mentioned above, a considerable body of international norms has been enacted with the purpose of protecting ‘human nature’. 143 These are problematic because the concept of human nature is culturally biased and historically relative. It can be understood in a strict biological sense, but also culturally and socially. Evolution and epigenetics show that human nature is not a static biological reality, but rather it evolves and is deeply influenced by environmental factors. [SEP] Justify:

Output: The text contrasts the claim by highlighting ethical concerns regarding gene editing. For instance, it states that interventions in genetic identity can deny a person the ability to be the 'undivided author of his own life', which raises significant ethical issues. Furthermore, the text discusses ongoing debates on moral conformity and the influence of international norms aimed at protecting 'human nature', which suggests that there are considerable ethical debates and concerns surrounding gene editing practices.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Journal of Circadian RhythmsStart SubmissionBecome a ReviewerReading: Circadian polymorphisms associated with affective disordersDownloadXML (US English)PDF (US English)Alt. DisplayShareShareResearch 0. 0005). Among the unrelated bipolar participants, 3 SNPs in PER3 and CSNK1E were associated with the BALM score. 1B coding SNP, rs7732671, was associated with affective disorder with nominal significance in bipolar family groups and independently in unipolar sib pairs. In TEF, rs738499 was associated with unipolar depression; in a replication study, rs738499 was also associated with the QIDS-SR depression scale in the sleep clinic patient sample. Conclusion: Along with anti-manic effects of lithium and the antidepressant effects of bright light, these findings suggest that perturbations of the circadian gene network at several levels may influence mood disorders, perhaps ultimately through regulation of MAOA and its modulation of dopamine transmission. Twenty-three associations of circadian polymorphisms with affective symptoms met nominal significance criteria (P < 0. 05), whereas 15 would be expected by chance, indicating that many represented false discoveries (Type II errors). Some evidence of replication has been gathered, but more studies are needed to ascertain if circadian gene polymorphisms contribute to susceptibility to affective disorders. Year: 2009Page/10. 1186/1740-3391-7-2Submitted on Oct 20, 2008Accepted on Jan 23, 2009Published on Jan 23, 20094. 0 idea that circadian rhythms had some role in affective disorders arose from clinical observations of their altered sleep-wake cycles, the cyclicity of the symptoms, and early work by pioneering researchers [1 - 4]. Attempts to identify circadian abnormalities in depressed and bipolar patients by physiologic means have yielded somewhat inconsistent and disappointing results [5 - 7]. However, the now-proven efficacy of bright light treatment as well as a broader range of effective interventions in the circadian system provide strong evidence that circadian rhythms are somehow involved in the pathophysiology of affective disorders [8]. The effects of light treatment, along with the symptom development of seasonal affective disorder (often a bipolar phenotype), might suggest that mechanisms which trigger mood swings in humans resemble the circadian-controlled photoperiodic mechanisms governing mammalian seasonality [9]. [SEP] Justify:

Output: The text discusses the relationship between circadian rhythms and affective disorders, rather than metabolic disorders or obesity. While it mentions that circadian rhythms have a role in mood disorders and highlights treatment effects in that area, it does not provide evidence or direct reference to metabolic disorders or obesity linked to circadian rhythm disruptions. Therefore, the text is not relevant 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: AbstractPurpose of ReviewArtificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today. Recent FindingsThe capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. SummaryAI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology. Similar content being viewed by others Chatbots for Students’ Attitudes and 97. 7% of patients thought to visit to clinic earlier after they used a chatbot. Allen et al. [14] proposed a chatbot on decision-making for prostate cancer screening among African-American men, called (PCSPrep). It significantly increased participants’ prostate cancer knowledge as well as reduced decisional conflict. Owens et al. [15] performed a paired study and found that iDecide decision-aid chatbots improved participants’ prostate cancer knowledge, self-efficacy in decision-making, and in the use of technology. and CounselingChatbots can be used to educate and inform patients with urological diseases, helping to facilitate the routine practice of specialists [16]. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of AI-driven chatbots in urology to improve patient care and physician efficiency. It highlights the advantages of chatbots, including their ability to provide individualized medical advice and assist in symptom prioritization. Additionally, the text mentions the potential for chatbots to support healthcare providers through administrative tasks and clinical decision support, directly relating to patient support and care management.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: For legal interpretation purposes, it is not possible to rely on a biological notion of a human being to distinguish natural from technological. The assertion that germline intervention is an offense to human dignity is highly debatable when the aim is to protect future generations from severe incurable diseases. The right to the highest attainable standard of health extends to preventive medicine, while the right to education, and fruition of the results of science and technology, would further protect those wishing to change their physical, emotional, and social human nature. Another aspect to consider is that pursuant to the limitation imposed in 2017. 179 IV. C. d. Patent Wars: the Battle of Priority and the Battle of theCas9 patent war ignited in Europe with the filing of several oppositions against what is considered to be the first patent granted in Europe for this technology. 180 The patent was granted by the EPO to the Broad Institute, MIT and on February 11, 2015, comprising a total of 53 product claims. 181 It pertains to research conducted by and his research group (hereinafter, Zhang et al. ). It has been opposed by nine different entities. 182 Opposition procedures could result in several different outcomes ranging from amendment of claims to complete revocation of the patent. In any event, it is likely that appeal(s) will be lodged. This can be a lengthy procedure pending several years until a final decision is issued. The patent landscape is vast and complex. The grant of the Zhang et al. patent was closely followed by several divisional European patent applications. Some have been granted,183 some are being opposed or are within the nine-month opposition period,184 while others are under examination. 185 It seems predictable that some of these patents will also be opposed by multiple parties. In the USA, the same patent family already includes at least 13 patents, the first of which was granted on April 15, 2014. 186 Several further European and US pending applications are in this patent family. A competing patent family directed toCas9 currently includes a European patent application187 and a US patent application188 filed by the University of California, the University of Vienna, and Emmanuelle Charpentier. It covers research conducted by 's teams (hereinafter, Doudna et al. ). This patent claims a priority date of May 25, 2012, more than 6 months before the priority date claimed in the Zhang et al. patent (December 12, 2012). [SEP] Justify:

Output: The text contrasts the claim by highlighting the debate surrounding germline intervention and its potential ethical implications. It states that there exists a discourse on the 'offense to human dignity' stemming from gene editing, suggesting that there are significant ethical concerns that prevent widespread acceptance among consumers.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Recently, a number of studies of nocturnal and diurnal rodents have demonstrated influences of photoperiod upon animal models of depression [10 - 12]. Several reports have presented rationales and preliminary suggestive evidence that circadian system genetic abnormalities might contribute to affective disorders [13 - 18]. In addition, accumulating evidence indicates that heritable circadian disorders such as delayed sleep phase disorder are comorbid with depression [19]. This may suggest that there are genetic polymorphisms in the circadian system which confer susceptibility both to depression and to delayed sleep phase disorder or its converse, advanced sleep phase disorder. Genome-wide association studies of bipolar disorder have given no substantial support for a role of the circadian system [20,21], although in one study, VGCNL1, a gene which may have a circadian role, came close to genome-wide significance [22]. Genome-wide studies, however, are designed to detect common allelic variants of small effect, and do not exclude other types of gene effects, such as rare variants of strong effect. Though the genome-wide association method may eventually replace the testing of candidate genes, we have thought it worthwhile to survey likely single nucleotide polymorphisms (SNPs) in the set of genes which form the circadian system through complex interactions. Most of the SNPs we have considered have not been tested directly in whole-genome association studies. Moreover, in some models, we have used transmission disequilibrium tests (TDT) with parent-proband trios or affected sib pairs which eliminate population stratification as a potential source of false-negative results. Here we report results of 4 ongoing studies which provide some cross-replication, and taken together, suggest that several circadian polymorphisms are associated with phenotypes related to affective disorders. describe 4 complementary studies, assembled to provide replication and to clarify what aspects of circadian polymorphisms may be relevant to both bipolar and unipolar affective disorders. Bipolar probands and families From probands with bipolar disorder, DNA samples from 444 nuclear families were assembled including 561 affected offspring. These were largely proband-parent trios or affected sib pairs with parents. These nuclear families were obtained primarily from two different samples. The first was a set of families collected as part of a three site consortium (UCSD, U. Cincinnati, and U. British Columbia) for linkage studies in extended pedigrees. [SEP] Justify:

Output: The text discusses the influence of circadian system genetic abnormalities on affective disorders, particularly depression, and provides evidence from studies regarding genetic polymorphisms in the circadian system. However, it does not address metabolic disorders or obesity, thus rendering it irrelevant to the claim regarding circadian rhythm disruption and its effects on 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: They can ensure patients with information on urological conditions, treatment options, and preventive care measures, thus improving patient knowledge [17]. Wang et al. [18] developed SnehAI chatbot to handle with private topics such as safe sex and family planning. SnehAI provides a private and nonjudgmental space for users, offering reliable and relatable information and resources. The study utilizes the Gibson theory of affordances to examine SnehAI’s functionalities, and it demonstrates strong evidence across fifteen functional affordances, including accessibility, multimodality, compellability, interactivity, and inclusivity. developed by Görtz et al. [19], is a user-friendly medical chatbot specifically focused on prostate cancer (PC) communication. The study aimed to evaluate ’s effectiveness in providing patient information about early detection of PC. The chatbot proved to be straightforward to use, with a majority of users (78%) not requiring any assistance. Furthermore, 89% of the chatbot users experienced a clear to moderate increase in their knowledge about PC. The participants expressed their willingness to reuse a medical chatbot in the future, highlighting the support for chatbot integration in clinical routines. demonstrated its potential in raising awareness, patient education, and support for early PC detection. Khawam et al. [20] are currently conducting a randomized controlled trial to investigate how usefully conversational ChatBot will provide education and help the patients with urinary incontinence. AI-based chatbots hold significant promise for enhancing patient outcomes in urology, particularly for those patients who require adherence to treatment regimens and lifestyle modifications (Fig. 2). This is particularly relevant for a range of urological conditions such as urinary incontinence, urolithiasis, and erectile dysfunction, where making necessary changes to diet, exercise, and compliance to medications can significantly improve symptoms and quality of life. Ray et al. [21] introduced “MenGO,” an integrated cloud-based system for personalized andrological health management. MenGO addresses multiple systemic issues in the field of andrology and men’s sexual wellness by utilizing statistical modeling and a natural language processing (NLP) chatbot. This one-stop solution caters to men suffering from chronic ailments such as erectile dysfunction, infertility, ejaculation problems, and prostate gland issues. [SEP] Justify:

Output: The text corroborates the claim by providing examples of various AI-powered chatbots, such as SnehAI and MenGO, that are explicitly designed to assist patients with urological conditions and andrological health management. It discusses how these chatbots enhance patient knowledge, improve outcomes, and receive positive feedback from users, which supports the idea of their increasing use in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: In addition to the two competing research groups resulting in the aforementioned patents, other parties are active with patents and patent applications relevant to the technology. Given the number of research teams working in the area, the complexity of the patent landscape is likely to increase with time and generate further patent disputes. For example, the USPTO granted a patent that claims gene inactivation by the use of chimeric restriction endonucleases. This was issued on October 4, 2016 with a priority date of February 3, 1999. The patent owners are the and the Boston Children's Hospital, and the inventors André Choulika and Richard Mulligan. 196 Cellectis (the exclusive licensee) publicly announced ‘an umbrella patent’ that ‘covers most of the gene-editing procedures done with a nuclease’, including those based onCas9, TALENs, zinc fingers, and many meganucleases. Commentators have cast doubt on the patent's validity and scope, an indication that litigation might follow suit. 197/Cas9 patent wars will likely continue over the coming years. Long and complex proceedings, in both the USA and Europe, will unveil who owns the technology and to what extent. Independently of the outcome in the priority battle(s), different players will also seek to oppose and/or partially or totally invalidate specific claims. Different grounds for invalidation are possible, including arguments concerning patent exclusions under the EPC. The outcome of the battle of the claims has many variables and is difficult to predict. In the meantime, while the actual ownership status over the technology is undefined and despite ethical controversies, license agreements and joint ventures with major industry partners have been announced. The industry is moving toward future commercial applications, while the public debate on ownership and scientific attribution is somehow overshadowing ethical and regulatory discussions. In light of this uncertainty, it is not surprising that different forms of intervention are currently being considered in Europe ranging from legislative changes in the EU Biotech Directive, patent law, and procedure to the introduction of special compulsory licensing regimes198 and creative user-generated and integrated solutions, such as the development of industry wide e-licensing platforms. 199Whereas a detailed discussion of all possible forms of intervention falls outside the scope of this paper, we will in the following elaborate on these types of solution that seek to balance the application and governance of IPRs in relation to upstream and downstream technology. 200 IV. C. e. [SEP] Justify:

Output: The text contrasts the claim by highlighting ongoing ethical controversies and public debates regarding gene editing. It states that 'despite ethical controversies, license agreements and joint ventures' are being formed, suggesting that there are indeed significant concerns that are overshadowing the path toward commercial applications. This indicates that consumer acceptance is not without its complexities and underlying ethical issues.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The remainder of the families came from waves 1–4 of the for Collection. Both family sets and the ascertainment and diagnostic methods employed have been described in detail elsewhere [23,24]. For this analysis, we included bipolar type 1 disorder, bipolar type 2 disorder, and schizoaffective (bipolar type) patients as affected participants. Although the TDT is not subject to an increased type one error rate due to population stratification, only self-identified Caucasians were included in this analysis. Single-nucleotide polymorphisms were assayed with 6 reagent pools targeting 45–48 SNPs, using the SNPlex™ with an ABI 3730 48-capillary DNA analyzer according to the manufacturer's directions (Applied Biosystems, Foster City, California). Technically satisfactory genotypes with sufficient heterozygosity for analysis were obtained for 197 SNPs [see Additional file 1]. In addition, a polymorphic repeat region with four or five copies of a 54 bp repetitive sequence in exon 18 of the PER3 gene was examined [25,26]. PCR of the polymorphic area was performed using the primers: 6-fluorescently labeled, and. Products of 309 bp and 363 bp were distinguished by gel electrophoresis, using the 6-Fam fluorescent label on the forward primer to determine fragment size. PLINK v1. 03 [27] was used to test for HWE, and transmission disequilibrium from parent to affected child was tested using a transmission-disequilibrium test (TDT). Empirical p-values were generated using the max(T) permutation approach for pointwise estimates (EMP1) as well as corrected for all comparisons (EMP2). Compared to a conservative Bonferroni correction for multiple comparisons, a global permutation test is a more powerful approach for candidate gene studies as it considers the correlation structure between SNPs in LD with each other. Correlation between SNPs (LD structure) was assessed with HaploView 4. 1. (Broad Institute, Cambridge, MA). Bipolar probands and the morningness-eveningness quantitative trait A group of 130 unrelated research volunteers completed the Morningness (BALM) Scale, a 13-item multiple-choice questionnaire designed to distinguish participants with high, normal, or low "morningness" [28]. [SEP] Justify:

Output: The text is irrelevant to the claim regarding circadian rhythms and metabolic disorders. Instead, it discusses genetic analysis related to bipolar disorders and methods used for SNP assaying, without any mention of circadian rhythms or 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: The system provides access to affordable physiological and psychological treatments through a smart and interactive telehealth platform powered by cloud and big data analytics. The NLP chatbot integrated into MenGO enhances the overall user experience by facilitating communication and reducing barriers in seeking appropriate healthcare. Kim et al. [22] developed a patient-centered text message-based platform to promote self-management of symptoms associated with interstitial cystitis/bladder pain syndrome (IC/BPS). The platform consisted of four treatment module categories, namely, patient education and behavioral modification, cognitive-behavioral therapy, pelvic floor physical therapy, and guided mindfulness practices. Supportive messages were delivered through an automated algorithm, enhancing the concept of provider support through shared decision-making and reducing the sense of isolation experienced by patients. This intervention empowered patients to manage their symptoms better, improve self-efficacy, and gain insight into their motivations and behaviors. Chen et al. [23] are currently conducting a randomized controlled trial to investigate how an AI chatbot intervention can impact the self-management and decision-making confidence of men with lower urinary tract symptoms (LUTS) caused by an enlarged prostate, with or without erectile dysfunction (ED), in the post-COVID-19 era. Patients have the opportunity to access the chatbot for free by scanning a QR code. The chatbot offers self-management guidance on topics including prostate enlargement, urinary symptoms, and erectile dysfunction. Moreover, it provides patient-centered decision-making tools aimed at supporting and empowering patients, particularly in relation to improving urination and erectile function. Fig. 2Examples of clinical applications of chatbotFull size imageSupportAI-based chatbots in urology can improve decision-making by analyzing patient data and medical records. By leveraging machine learning algorithms, these chatbots can provide evidence-based recommendations for diagnosis and treatment plans (Fig. 3). This assists healthcare professionals in interpreting complex urological data, leading to more accurate and efficient clinical decisions [10]. These AI-based chatbots can consider a wide range of patient variables and provide personalized recommendations based on the specific case at hand. As stated by Gabrielson et al. [11], they may serve as an essential tool in the urologist’s armamentarium to step away from the computer and turn the physician’s chair back toward the patient. Kim et al. [SEP] Justify:

Output: The text corroborates the claim by detailing how AI-powered chatbots are integrated into healthcare platforms to enhance patient support. It mentions a specific implementation where a natural language processing (NLP) chatbot is used to improve user experience, facilitate communication, and empower patients in managing their health. Furthermore, it discusses how AI chatbots are being utilized in studies to impact self-management and decision-making confidence for patients, which aligns with the assertion that these technologies are becoming prevalent in patient care management.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: Inspiration for such approaches could be gathered from the ICT sector. This was demonstrated in December 2016, when one of the main operators of one-stop licenses for standards and other technology platforms in consumer electronics, announced that it has begun to examine the -cas9 patent landscape to identify essential patents that would need to be bundled together in licensing pools to enable a one-stop license ‘platform to reduce litigation risk and provide efficiency, transparency and predictability to scientists and businesses worldwide. ’208 IV. D. is a considerable legal theoretical, but also political, discussion concerning policy options between pluralism and harmonization. The discussion emerges concerning every negotiation of an instrument of international law. Internally it is often present in debates concerning the limits and boundaries of federal legislative powers, or centralization versus regionalisation. It is also particular visible in political debates concerning plurinational integration projects such as the EU. Emerging technology, such as gene editing, presents a multitude of horizontal challenges across a variety of legal disciplines. It is also a technology which raises deep social and ethical questions and as such entails possibilities for further legal and regulatory overlaps. As demonstrated in this section, gene editing opens interpretative and even legislative challenges because often it is not clear how these new technologic realities fit into legal categories, and operative concepts of the applicable rules and regulations. 209The existence of a multi layered framework of laws and regulations is simultaneously cause and consequence of a complex network of legal overlaps. As mentioned above, patentability exceptions or eligibility standards play a public policy role akin to technology regulatory norms. Although these are independent from technology regulations, intersections should be considered and the need or not for coherence addressed, in particular, coherence between legal interpretation of patent exceptions to gene-editing processes and the regulatory framework applicable to such intervention. Because the regulatory framework might distinguish between different technological applications, interpreting eligibility rules as intended to deny incentives to research that seeks to eradicate the transmission of severe hereditary diseases to future generations is at odds with the function of the patent system. As a matter of legal interpretation, it has to be investigated in light of the intention and purpose of the patent system whether the legislator would have allowed for a broader patent eligibility approach if it had anticipated the technological possibilities for therapeutic applications provided by gene-editing technology. The issue of dual use is of particular importance to the concrete determination of whether a technology should be excluded from public incentive mechanisms. [SEP] Justify:

Output: The text contrasts the claim by highlighting that gene editing raises deep social and ethical questions, suggesting that there are significant ethical concerns associated with the technology. It notes that gene editing presents a multitude of challenges and legal debates, indicating that acceptance is not straightforward and involves complex dilemmas about regulatory frameworks and public policy.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: These subjects were recruited at UCSD for genetic studies of bipolar disorder, but they were not primarily parts of family groups and only 5 were also included in the TDT family sample. Of the 130 subjects with available BALM data, according to research diagnoses, 82% were I (not necessarily manic at the time), 3 had had unipolar major depression, one was schizoaffective, and the rest had no psychiatric diagnosis. Those with high BALM scores tend to go to bed early and arise early: in the extreme, they may suffer from advanced sleep phase disorder. Those with low BALM scores tend to go to bed late and to arise late in the morning: with extremely low scores, they may suffer from delayed sleep phase disorder. This quantitative trait, thought to reflect control by circadian "clock" genes, is roughly 50% heritable [29 - 31]. The 6th (most recent) SNPlex™ pool was assayed for each participant, but the other assay reagent pools were not available. Of the 48 SNPs in the pool, 44 were successfully assayed and 30 passed quality control. [27], quantitative trait associations (additive model) were performed and empirical p-values estimated based on the Wald-statistic (t-distribution). To correct for population stratification, subjects were grouped into self-identified Caucasians (n = 95) and others (n = 35) and permutations were performed within these two groups using the max(T) permutation approach for pointwise estimates (EMP1) as well as corrected for multiple comparisons (EMP2). Unipolar major depression affected sibling pairs Families with probands with recurrent early-onset unipolar depression were recruited by the GenRED project [32]. These subjects were ascertained as part of a multi-site consortium to conduct linkage studies of major depression, and diagnoses made using a standardized best estimate method as previously described. Through the of from 150 GenRED sibling pairs with at least one affected sibling was kindly supplied by the Cell & DNA Repository. These samples were also assayed with SNPlex pools 5 and 6 (88 SNPs), resulting in high-quality genotypes of 63 SNPs. family-based sib-TDT (DFAM) analyses were computed including 298 individuals in 149 families (89 concordant and 60 discordant sib-pairs). [SEP] Justify:

Output: The text does not address the claim regarding the disruption of circadian rhythms and its effects on metabolic disorders and obesity. It primarily discusses a study related to genetic factors in bipolar disorder and sleep phase disorders without establishing any connection to metabolic health 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: [24] propose a medical specialty prediction model from patient-side medical question text based on pre-trained bidirectional encoder representations from transformers (BERT). The dataset comprised pairs of medical question texts and labeled specialties scraped from a website for the medical question-and-answer service. The model was fine-tuned for predicting the required medical specialty labels among 27 labels from medical question texts including urology. By analyzing the patient’s symptoms and complaints, chatbots can provide recommendations on which specialists in the field of urology or related areas should be consulted for a comprehensive examination and further diagnosis. This helps streamline the referral process and ensures that patients receive the most appropriate care based on their specific concerns. Fig. 3Example of chatbot mechanism of actionFull size imagePost-treatment Follow-up CareChatbots can be highly useful in post-treatment follow-up in urology by providing continuous monitoring and support for patients. They can collect and analyze patient-reported outcomes, such as urinary symptoms or quality of life indicators, and provide personalized recommendations for self-care or additional interventions. As noted by Goldenthal et al. [25], chatbots could be programmed to follow up with patients after their urology appointments, checking in on their symptoms and providing guidance on self-care. This could be especially beneficial for patients who live far from their urology clinic or who have mobility issues that make in-person follow-up visits difficult. DiscussionChatbots have been used in healthcare for decades, with the earliest known healthcare chatbot being ELIZA, created in 1966 [26]. Since then, chatbots have been utilized in various healthcare settings, from helping patients manage chronic conditions to providing mental health support. There are three main types of healthcare chatbots based on the input processing and response generation method: rule-based model, retrieval-based model, and generative model [8]. Rule-based chatbots use pre-programmed responses to provide information to patients, while retrieval-based bots offer more flexibility as it queries and analyzes available resources. The generative model produces chatbots based on machine or deep learning to improve their responses over time and is more promising for several reasons. They can understand natural language and learn from user interactions to provide more personalized responses. Additionally, AI-based chatbots can be used to analyze large amounts of patient data to identify patterns and trends that can be used to improve healthcare outcomes [27]. [SEP] Justify:

Output: The text corroborates the claim by discussing how chatbots are utilized in healthcare settings, specifically highlighting their role in patient support and management. It describes how chatbots can analyze patients' symptoms, offer recommendations for specialists, and provide post-treatment follow-up, thereby illustrating their increasing application in managing patient care.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: 210 Certain gene-editing intervention is or will be prohibited based on a number of reasons (public health, precaution and safety concerns, or human dignity), while less controversial applications might be allowed. However, patent protection is not necessarily purpose-bound, so that only in certain claim types (eg secondary uses) would it be possible to differentiate. When a given invention can be developed in different directions, it is excessive to deny patentability and thus incentives to innovation. Moreover, legal coherence will imply that similar public policy considerations will have to apply mutatis mutandis to other public forms of incentive, such as research grants, prizes, and awards. Likewise, cross-license mechanisms and contractual arrangements should equally be developed in light of general legal principles. Finally, gene editing is simple to use in non-institutional contexts. Incentives to further research and innovation are a powerful tool to stir technology in a socially responsible and safe direction, but the nature of the technology implies the need for additional mechanisms of governance such as exploring the possibilities for multilevel stakeholder dialog and private society engagement. Law, in its broader sense, is created, and developed in a given social context. Narratives on science and technology play an interesting role on defining how different actors will apply, interpret, and use their agency or discretionary powers. Court adjudication, institutional practice by patent offices, decisions by ethical boards and regulatory approval authorities, negotiation of contractual arrangements and business strategies, and even individual decisions by private actors are not made in the vacuum, but rather inserted in a social context. In the next section, we address both the role and influence of communication and public perception in legal debates concerning the regulation of emerging technology, and on the other side the influence of litigation in the construction of such narratives. V. technological ability to alter genes has for a long time been a controversial issue in public debate. In 1975, the Asilomar conference211 crystallized public attention on the risks and merits of recombinant DNA technology, but controversies about genetic inheritance go much further back (for instance around the use of eugenics). During the 1970s and 1980s, however, controversies over the use of genetic modification in agriculture and human and animal reproduction regularly surfaced in many countries around the world. [SEP] Justify:

Output: The text contrasts the claim by highlighting that gene editing has been a controversial issue in public debate and points out that certain gene-editing interventions are or will be prohibited based on ethical concerns such as public health and human dignity. Specifically, the mention of the Asilomar conference and past controversies around genetic modification illustrates that there are significant ethical concerns associated with gene editing, indicating that it is not widely accepted without such concerns.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: As in the other analyses, empirical p-values were generated using the max(T) permutation approach for pointwise estimates (EMP1) as well as corrected for multiple comparisons (EMP2). To safeguard against spurious associations due to population stratification, a TDT was used in this sample approximately 95% of European origin [32]. sample Patients of the who underwent polysomnography or some other form of sleep recording were invited to participate in a descriptive genetic study. They consented to contribute saliva DNA samples and a research questionnaire, which included the BALM morningness-eveningness scale and the QIDS-SR self-rated depression scale [33]. This sample was 90% Caucasian by self-report. DNA was extracted and genotyped for 360 participants in the DNA of the Molecular and division of the Institute. The alleles of rs2314339 and rs738499 were identified by allele-specific oligonucleotide hybridization [34]. Ethical guidelines Since the DNA samples were collected from many sources, the original publications should be consulted for information concerning institutional review boards. In general, the data were collected in accord with the principles of the Declaration of Helsinki. disequilibrium in families of bipolar probands Of approximately 260 SNPs assayed in the SNPlex pools or by gel electrophoresis, 212 polymorphisms were successfully genotyped. Of these, 198 polymorphisms yielded polymorphic genotypes of acceptable quality [see Additional file 1]. The TDT was applied to these polymorphisms, located in or near 26 genes associated with the circadian system. As shown in Table 1, 17 polymorphisms had nominal P values < 0. 05, modestly exceeding the random expectation of 10 of 198 polymorphisms. Table 1 Polymorphisms associated with bipolar disorder 1A21EMP2NPAS22rs1562313AG0. 2132081651. 2614. 9570. 02600. 04840. 9803PER22rs4663868TC0. 07185541. 5746. 9140. 00860. 01710. 7330PER22rs2304669GA0. 1641311670. 7844. 3490. 03700. 04160. 9945PER22rs2304672GC0. 07388551. 6007. 6150. 00580. 01230. 5972CLOCK4rs3805148CA0. 3662551971. 2947. 4420. [SEP] Justify:

Output: The text does not address the claim regarding the link between circadian rhythm disruption and metabolic disorders or obesity. Instead, it focuses on genetic study methods and sample composition related to bipolar disorder without any direct connection to metabolic health or circadian rhythms.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: GPT (generative pre-trained transformer) is a well-known representative of such AI-chatbots and has been the subject of many discussions [28]. There are ample studies in the literature describing the potential prospects and applications of chatbots in urology. According to our findings, they are already in the focus of urological symptom checking, health screening, patient education, counseling, lifestyle change, conservative management, clinical decision support, and post-treatment follow-up care. The same was shown by Calvo et al. [29], who conducted a study on the feasibility and usability of a text-based conversational agent that processes a patient’s text responses and short voice recordings to calculate an estimate of their risk for an asthma exacerbation. The chatbot offers follow-up information for lowering risk and improving asthma control, to improve understanding and self-management of the condition. Ferré et al. [30] developed a chatbot-based tool, called the MyRISK score, which collects self-reported patient data before pre-anesthetic consultation to stratify patients according to their risk of postoperative complications. The tool was developed using the Delphi method and logistic regression analysis, with a machine learning model trained to predict the MyRISK score. The tool was found to be effective in predicting postoperative complications, with high sensitivity (94%) but low specificity (49%). However, this is not a full list of their potential, which is obvious looking at papers investigating AI-chatbots in different medical fields. They can be also valuable for medical education, pre-operative preparation, and academic writing. So, Han et al. [31] developed an AI chatbot educational program to promote nursing skills and found that the experimental group showed significantly higher interest in education and self-directed learning compared to the control group. These studies collectively suggest that chatbots hold promise as a valuable tool for medical education. Chetlen et al. [32] went about the deployment of a chatbot to provide evidence-based answers to frequently asked questions for patients scheduled to undergo a breast biopsy procedure. By streamlining processes and reducing wait times, chatbots can increase the overall efficiency of the healthcare system, leading to cost savings for healthcare organization [33]. They can also prevent unnecessary office visits and hospitalizations by providing patients with timely and accurate information and support. [SEP] Justify:

Output: The text corroborates the claim by detailing the various applications of AI-powered chatbots in the healthcare sector, such as in urological symptom checking, patient education, counseling, and clinical decision support. It specifically mentions that chatbots are involved in patient follow-up care, health screening, and self-management of conditions like asthma. Additionally, it provides examples of studies that demonstrate their effectiveness, such as the MyRISK score tool for pre-anesthetic consultations and educational programs to promote nursing skills. This evidence illustrates that AI chatbots are indeed being utilized for patient support and care management.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: In the 1990s, these debates reached unprecedented public attention with the decision to mix GMO and non-GMO soybeans in American exports to Europe in 1996, and the birth of the cloned sheep Dolly in 1997. These developments resulted in what has been called a ‘global controversy on biotechnology’. 212In many ways, current public debate about gene editing is therefore building on, or reviving, controversies and arguments which are already deeply entrenched in many societies. At the same time, the tendency is to present the issues as revolutionary and as if this specific point in time is more crucial than any other. This was also the case when the US of Science, the US of Medicine, the Royal Society, and the of Sciences hosted an international summit on in Washington, in December 2015 (the organizers later referring to this as the (HGEI)213). of the organizing committee, David Baltimore, laid it out in the introduction: Today, we sense that we are close to being able to alter human heredity. Now we must face the questions that arise. How, if at all, do we as a society want to use this capability? This is the question that has motivated this meeting. 214What such statements make clear is that more is at stake than simply a technology or a technological platform. It is about how society develops and how various groups of stakeholders, governments, and publics try to direct this development. Such an understanding of controversies as deep-founded disagreements about values is also in line with much of the scholarly research on public controversies about biotechnology which have been published during recent decades. 215 That research forms the background for this section. One of the crucial points of discussion at the 1975 Asilomar conference was whether science's system of self-regulation is trustworthy and robust, or whether it is necessary to enforce regulation from the outside. Several decades later, when the debate about GMO had developed into a full-scale global controversy, this was still a key issue for publics around the world: Can we trust science to govern its own development in a socially acceptable way? In a seminal analysis of the use of biotechnology in the health care system, has shown that most of the argumentation on either side of the controversy springs from a basic assessment of the role of science in society. [SEP] Justify:

Output: The text contrasts the claim by elaborating on the historical and ongoing public controversies surrounding biotechnology and gene editing. It mentions that public debates on these topics are deeply entrenched in many societies and poses critical questions regarding the societal implications of gene editing, such as whether science is trustworthy enough to self-regulate its own developments. This indicates that there are significant ethical concerns and disagreements among various stakeholders, contradicting the idea that gene editing is widely accepted without such concerns.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: 00640. 00920. 6291CLOCK4rs3736544AG0. 3732292800. 8185. 1100. 02380. 02410. 9728CLOCK4rs12504300CG0. 2821881381. 3627. 6690. 00560. 00940. 5775CLOCK4rs4864542GC0. 3642732121. 2887. 6720. 00560. 01020. 5759CLOCK4rs12648271CG0. 2852271821. 2474. 9510. 02610. 03690. 9806CLOCK4rs6850524CG0. 4242322820. 8234. 8640. 02740. 03220. 98401B5rs7732671CG0. 06865421. 5484. 9440. 02620. 02130. 9811PER117rs2585405GC0. 104861170. 7354. 7340. 02960. 04570. 9881THRA17rs939348TC0. 2791882390. 7876. 0910. 01360. 02240. 8729NR1D117rs2314339TC0. 130901470. 61213. 7100. 00020. 00050. 0338NR1D117rs2071427AG0. 2561912560. 7469. 4520. 00210. 00190. 2852NR1D117rs2269457GA0. 2391712140. 7994. 8030. 02840. 02920. 9861CSNK1D17rs4510078AG0. 02118380. 4747. 1430. 00750. 01750. 6942 GENE: NCBI gene symbol. CHR: chromosome number. SNP: dbSNP symbol. A1 & A2: minor and major allele nucleotides. MAF: minor allele frequency. T: number of transmissions of the rare allele. U: number of untransmitted rare alleles. OR: odds ratio of TDT. CHISQ: from TDT. P: probability of Chi Square. EMP1: empirical probability from simulation by PLINK. EMP2: corrected empirical P (Max T/familywise). The strongest association with bipolar disease was found with NR1D1 (Rev-erb-alpha, OMIM 602408). [SEP] Justify:

Output: The text contains genetic and statistical information related to various genes and their associations but does not address or provide evidence about the claim regarding circadian rhythms and metabolic disorders. Therefore, it is irrelevant to the claim made.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Chatbots can increase patient engagement and satisfaction by offering personalized advice, information about their condition, and treatment options [34]. Studies have shown that chatbots are more engaging and interactive than traditional online forms, despite taking longer to complete [35•]. According to systematic review from Geoghegan et al. [36], engagement rate for chatbots in the follow-up of patients who have undergone a physical healthcare intervention was up to 97%. In summary, chatbots have the potential to revolutionize the field of urology by improving patient care, optimizing workflow, and increasing the efficiency of the healthcare system. Technical LimitationsOne of the major challenges facing chatbots in urology is their technical limitations. While chatbots have the potential to improve patient care and physician efficiency, they may not always function as intended due to technical issues. For example, chatbots may experience system failures, errors, or glitches that can affect their performance and accuracy [37]. This can be particularly concerning when it comes to providing medical advice or making diagnoses, as any errors or inaccuracies could lead to serious harm to patients. Therefore, it is essential to thoroughly test chatbots and ensure that they are operating correctly and providing accurate information. Privacy and Security ConcernsAnother challenge that needs to be addressed when using chatbots in urology is privacy and security [38]. Chatbots may store personal health information, which raises concerns about data privacy and security. Patients need to be assured that their personal information is secure and protected from unauthorized access. Furthermore, any data breaches or security incidents could have significant consequences, including loss of patient trust and legal repercussions. Data privacy and security in health chatbots are still under-researched, and related information is underrepresented in scientific literature [39] Therefore, chatbots must comply with relevant privacy and security regulations to safeguard patient data. Reliability and AccuracyEnsuring reliability and accuracy is one of the most crucial factors for the success of chatbots in urology. To provide trustworthy information to patients and healthcare providers, they must be developed with reliable data sources and algorithms [40••]. They should also undergo continuous testing and updates to maintain their reliability and accuracy over time. However, if chatbots are not reliable or accurate, they could misinform patients, leading to incorrect diagnoses or treatments. For instance, Ben-Shabat et al. [SEP] Justify:

Output: The text corroborates the claim by discussing how chatbots can enhance patient engagement and satisfaction by providing personalized advice and information regarding their conditions and treatment options. Furthermore, the mention of studies indicating high engagement rates with chatbots in the follow-up care highlights their increasing use in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: That is to say, while proponents see science as a positive force in society, which brings about solutions to problems, and present this worldview in a rhetoric of hope, opponents understand science to be a social phenomenon which creates more problems than it solves and present this worldview in a rhetoric of fear. Interestingly, both sets of arguments present technoscientific solutions as revolutionary and as having far-reaching consequences. In this context, the important point is that any new technoscientific development will be seen in light of previous controversies. When public communicators (for instance journalists and opinion makers) make sense of a new phenomenon, they will invariantly draw a comparison with known examples and use these frames of reference to make sense of new developments by presenting them in well-known storylines. What this part of history also reminds us is that the call for a moratorium on human germline editing by the Washington meeting in 2015 has a clear precedent in the 1975 Asilomar conference—a call, however, whose current efficacy can be very hard to evaluate. While its regulatory impact is probably quite small—as dealt with elsewhere in this paper—its public value in terms of generating legitimacy might be more important. By calling for a moratorium on the use of certain aspects ofCas9 technology, other aspects of use can simultaneously be treated as legitimate and justified. V. A. Science and the Washington meeting, the HGEI commissioned a ‘consensus study’ of ‘the scientific underpinnings of human gene-editing technology, its potential use in biomedical research and medicine—including human germline editing—and the clinical, ethical, legal, and social implications of their use’. 216 The aim of this study was formulated by the actors behind the HGEI in a concluding statement after the original Washington Meeting: ‘The forum will mobilize the global expertise necessary to help society develop norms for acceptable uses of human gene-editing technology. This is an important moment in human history and we have a responsibility to provide all sections of society with an informed basis for making decisions about this technology, especially for uses that would affect generations to come’. 217While this is certainly a noble ambition, social scientific research into public controversies about emerging technology suggests that such an approach might not be successful. [SEP] Justify:

Output: The text contrasts the claim by highlighting the existence of public controversy and ethical concerns surrounding gene editing technology. It discusses the differing perspectives on science as either a solution or a problem, and notes the call for a moratorium on human germline editing, suggesting that there is significant debate and concern within society about the ethics and implications of gene editing, thus indicating that it is not widely accepted without ethical concerns.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Using a permutation procedure to correct for multiple comparisons, rs2314339, an intronic SNP, showed a significant association (odds ratio 0. 61, P(nominal) < 0. 0005, P(corrected) < 0. 035). Using a false discovery rate threshold of 5%, rs2314339 was significantly associated with disease status (q-value < 0. 05) [35]. In addition, two SNPs within this gene and one SNP within the nearby THRA were nominally significant (p < 0. 05, Table 1). The SNPs most strongly associated in this region were moderately correlated with one another (pairwise r-squared between rs2314339 and rs2071427 = 0. 26; rs2314339 and rs2269457 = 0. 29; rs2314339 and rs939348 = 0. 27). Suggestive evidence for association with bipolar disease was also found for the CLOCK gene (OMIM 601851). Thirteen SNPs were investigated in this gene, and six of them were nominally significant by the EMP1 criterion, the most significant being rs3805148 (p = 0. 0092) and rs12504300 (p = 0. 0094) (Table 1). These 6 SNPs are all in linkage disequilibrium (pairwise r-squared 0. 23–0. 99) in a single 75 KB linkage block which covers almost all the gene. They form a common, overtransmitted haplotype with a frequency of 28. 2 percent (234. 1:181. 5 T:U, P(nominal) < 0. 01). The often-discussed T3111C SNP in the 3' UTR (rs1801260) [36,37] was not in close linkage disequilibrium with these 6 SNPs (largest pairwise r-squared < 0. 22), and it was not significantly associated with bipolar disease (P > 0. 69). In addition, modest evidence for association to bipolar disease was also found for PER2 (OMIM 603426) with 3 of the 15 tested PER2 SNPs nominally significant (rs4663868: p < 018, rs2304672: p < 0. 013; pairwise r-squared = 0. 93; rs2304669: p < 0. [SEP] Justify:

Output: The text focuses on genetic associations related to diseases, particularly bipolar disease, and discusses specific single nucleotide polymorphisms (SNPs) and their statistical significance. It does not address circadian rhythms or their relationship to metabolic disorders and obesity, hence it 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: :of Frank G. of BusinessHempstead, NY, 11549United StatesPhone: 1 516 463 8823Fax:1 516 463 8823Email: Jingquan. Li@hofstra. eduAbstractArtificial intelligence (AI) chatbots like ChatGPT and are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people’s data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care. J Res 2023;25:e47551doi:10. 2196/47551Keywordssecurity; privacy; chatbot; AI; artificial intelligence; health information; HIPAA; ChatGPT; computer program; natural language processing; tool; improvement; patient care; care; data security; guidelines; risk; policy IntroductionA chatbot is a computer program that uses artificial intelligence (AI) and natural language processing to understand customer questions and automate responses to them, simulating human conversation [1]. ChatGPT, a general-purpose chatbot created by startup OpenAI on November 30, 2022, has become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. They can help automate routine tasks that take up unnecessary time and manpower. They can assist health care providers in providing patients with information about a condition, scheduling appointments [2], streamlining patient intake processes, and compiling patient records [3]. The chatbots can potentially act as virtual doctors or nurses to provide low-cost, around-the-clock AI-backed care. According to the US Centers for and Prevention, 6 in 10 adults in the have chronic diseases, such as heart disease, stroke, diabetes, and Alzheimer disease. [SEP] Justify:

Output: The text corroborates the claim by stating that AI chatbots have the potential to improve patient care and public health, and they assist healthcare providers with various tasks like providing information about conditions and streamlining patient intake processes. The text specifically mentions their use in automating routine tasks and even acting as virtual doctors or nurses, which directly supports the idea that AI-powered chatbots are being used in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: It might not be so easy to ‘help society develop norms for acceptable uses’, just as the idea of ‘providing society with an informed basis for decision-making’ is in danger of misinterpreting the fundamental drivers of the controversy. In this context, we will just mention two reasons for this. First, science and society should not be regarded as two distinct spheres, such that science can be developed independently of social, ethical, and legal reflection and only subsequently be made the object of societal decision-making. Rather, science is part of society, and social sense-making about it is a continuous process. It is therefore utterly problematic to first do the science and subsequently agree on the norms for its uses. Science does not take place in a vacuum, but in a society in which a myriad of actors have interests and want to influence or oversee its activities, applications, and implications. One good example of this can be found in the pre-history ofCas9 technology. Interestingly, the experimental evidence for theCas9 system was first obtained at the sugar and food ingredients company Danisco. 218 During the 1980s, they also developed sugar beet resistant to glyphosate through the use of genetic engineering. In an effort to take into consideration the criticism of using antibiotic resistance genes as a selection marker, a new selection system based on sugar mannose was implemented. These innovations were, however, never commercialized by Danisco due to environmental concerns. In light of general European skepticism toward the use of GMOs in agriculture, Danisco decided to discontinue its R&D activities in this area. This story demonstrates how a corporation tried be responsive to public concerns (over the use of antibiotic resistance genes), but ended up halting its otherwise promising path of technology development because of concerns over public skepticism toward GMO crops. Danisco was not the only European company to put a halt to agricultural biotechnology around the turn of the century. Indeed, in many ways public skepticism toward GMO has become a seminal lesson about the need to take public concerns seriously in order to avoid them later on preventing technological development. 219 Avoiding the GMO trap is seen to be paramount. [SEP] Justify:

Output: The text contrasts the claim by highlighting public skepticism and ethical concerns surrounding genetic engineering and GMOs. It discusses how public concerns led to the discontinuation of promising technological developments, showing that the acceptance of gene editing is not as widespread or unproblematic as the claim suggests. The mention of Danisco's halt in R&D due to public skepticism indicates that there are significant ethical concerns and societal influences affecting the acceptance of 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: 042, not in LD with the other 2 SNPs). SNPs associated with the BALM in bipolars Of the SNPs in bipolars for whom BALM data were available, 30 yielded acceptable genotypes which were sufficiently polymorphic for analysis [see Additional file 2]. Of these, three relatively rare SNPs were associated with BALM values with significance at a P < 0. 05 criterion after correction for multiple comparison (Table 2). Results for additive and dominant models were similar, since there were few homozygotes of the rare allele for these rare SNPs (data not shown). Table 2 SNPs associated with the BALM among bipolar participants 1A22EMP1EMP2genotype freqPER31rs228697GC0. 083-6. 360. 09130. 00080. 01504/15/110CSNK1E22CSNK1E28266AG0. 02513. 670. 07850. 00080. 04670/5/119CSNK1E22CSNK1E27740TG0. 02812. 450. 07530. 00100. 05060/6/121 CHR: chromosome. A1 & A2: minor and major allele nucleotides. MAF: minor allele frequency. R2: the correlation squared. EMP1: pointwise empirical P value. EMP2: corrected empirical P value from max/(T)/familywise. The PER3 nonsynonymous coding SNP Ala856Pro (rs228697) was associated with the BALM with R2 = 0. 091, P = 0. 0008 in an additive model. The presence of the rare SNP allele was associated with greater eveningness (i. e. , a lower BALM morningness score): a mean BALM of 26 for 4 homozygotes for the minor allele, a mean BALM of 31 for 15 heterozygotes, and a mean BALM of 38 for the 110 homozygotes with the common allele. Although the genotypes barely failed Hardy-Weinberg equilibrium (Pnominal < 0. 04) in this population, this SNP showed no deviation from HWE in the simultaneously assayed bipolar TDT sample, indicating good genotyping quality. Two intronic SNPs in CSNK1E were associated with the BALM with nominal P < 0. 001 in an additive model, associated with almost 8% of the variance. [SEP] Justify:

Output: The text does not address the claim regarding the relationship between disruption of circadian rhythms and metabolic disorders or obesity. Instead, it discusses SNPs associated with bipolar disorder and their relation to eveningness and BALM values, which is not relevant to the claim. Therefore, it is categorized as 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: Under the traditional office-based, in-person medical care system, access to after-hours doctors can be very limited and costly, at times creating obstacles to accessing such health care services [3]. The chatbots can provide health education about disease prevention and management, promoting healthy behaviors and encouraging self-care [4]. For example, ChatGPT may help patients with chronic conditions in various ways. It can provide reminders for scheduling routine screenings and filling prescriptions; it can assist with other wellness matters, such as monitoring steps taken, heart rates, and sleep schedules; it can also customize nutrition plans [3]. However, the use of AI chatbots requires the collection and storage of large volumes of people’s data, which raises significant concerns about data security and privacy. The successful function of AI models relies on constant machine learning, which involves continuously feeding massive amounts of data back into the neural networks of AI chatbots. If the data used to train a chatbot include sensitive patient or business information, it becomes part of the data set used by the chatbot in future interactions. In other words, the data can be disclosed to any intended and unintended audiences and used for various purposes without authorization. Even though AI chatbots are perceived to have limited capacity, they have an enormous potential to acquire and collect new information from various data sources and capture people’s responses. The tasks of ensuring data security and confidentiality become harder as an increasing amount of data is collected and shared ever more widely on the internet. Additionally, it will be important to consider security and privacy concerns when using AI chatbots in health care, as sensitive medical information will be involved. Once the information is exposed to scrutiny, negative consequences include privacy breaches, identity theft, digital profiling, bias and discrimination, exclusion, social embarrassment, and loss of control [5]. In the United States, covered entities, including health care providers, health plans, and health care clearinghouses, disclose protected health information (PHI) without patient authorization in violation of the 1996 HIPAA (Portability and Accountability Act) and can be subject to fines and other penalties. PHI refers to any information in the medical records or designated record set that can be used to identify an individual; this information was created, used, or disclosed in the course of providing a health care service, such as diagnosis or treatment [6]. [SEP] Justify:

Output: The text corroborates the claim by providing multiple examples of how AI-powered chatbots, such as ChatGPT, can assist with patient support and care management. It states that these chatbots can offer health education, send reminders for screenings and prescriptions, and monitor wellness-related activities. This supports the idea of AI chatbots being increasingly utilized in the realm of patient care.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accepted by consumers without ethical concerns. [SEP] Text: 220While it is certainly important to provide material that will help societal actors deal with the social, ethical, economic, technical, and political challenges posed by the development of gene-editing technology, it is not plausible to imagine an orderly linear process of information gathering, development of norms, decisions about acceptable applications, and subsequent dissemination of technology. Rather, these things happen at the same time and in many conflicting and interrelated processes. Just to mention one example, the patent dispute demonstrates how economic expectations are already enormously important in shaping the framework for gene-editing technology. In such situations, all communication about the technology can be seen to be directed by vested interests. One clearcut example of this is the January 2017 piece by the Director of the Broad Institute, Lander, in Cell, entitled ‘of ’. 221 This piece was heavily criticized for demonstrating typical sexism in science by downplaying the role of female scientists with obvious links to the story of and the Nobel prize for the discovery of DNA. However, such story writing has also undoubted importance in terms of influencing public perceptions of the ongoing patent trial. had been successful in describing Doudna and Charpentier as only some among several scientists working with and portraying Zhang as the central hero, he would have strengthened the claims by Zhang and therewith those of the Broad Institute. As it happens, however, the social media storm that arose around Lander's 9. 4Plant Power—Way to Go Green: Møller at TEDxCopenhagen 2012, YouTube (2017), accessed Apr. 27, 2017); Lærke Münter Lassen et al. , into Light-of Including High-Natural Compounds, 3 ACS Synth. Biol. 1–12 (2014). 5et al. , Fuel, 26 Curr. Opin. Biotechnol. 50–5 (2014). 6& Chatchai Muanprasat, Stevioside and Related Compounds: Beyond Sweetness, 121 Pharmacol. & Ther. 41–54 (2009); of Steviol Glycosides, PCT/US2011/038967. 02. 06. 2011. ; et al. , of Next-Sweeteners, 15 Microb. Cell Fact. 207 (2016). 7Marta G. Novelle et al. , Resveratrol Supplementation: We Go? 21 Ageing Res. Rev. [SEP] Justify:

Output: The text contrasts the claim by highlighting the complex interplay of social, ethical, and economic challenges surrounding gene-editing technology. It suggests that the acceptance of gene editing is not straightforward and is influenced by conflicting interests and public perceptions, indicating that consumers may not widely accept gene editing without ethical concerns.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The two SNPs are 526 nucleotides apart and essentially in perfect linkage disequilibrium. For one subject, one of the linked SNPs could not be genotyped. The mean BALM scores were 49–50 for heterozygotes (high morningness) and 36 for homozygotes with the common allele. Four or 5 of the heterozygotes had a BALM ≥ 50, the 95th percentile, indicating extreme morningness, but one had the minimum possible BALM, indicating extreme eveningness. Thus, the few heterozygotes were heterogeneous in BALM morningness-eveningness. These polymorphisms have been submitted to NCBI as nucleotides 27740 and 28266 in Report EF015901 (available at ). Unipolar major depression sib pairs There were 89 sib pairs concordant for unipolar major depressive disorder and 60 discordant sib pairs with one twin having no mental illness. They were genotyped for 61 SNPs which proved sufficiently heterozygous and one repeat region in PER3. Of these, 2 reached nominal significance (Table 3). In the promoter region of TEF, rs738499 was associated with MDD by sib-TDT with P = 0. 012. The minor G allele was protective. Also, rs7732671 in 1B was associated with P = 0. 023, with the minor allele being associated with depression. Neither SNP was significant after correction for multiple comparison (EMP2) [see Additional file 3]. Table 3 SNPs associated with unipolar recurrent major depression 1A21EMP21B5rs7732671CG0. 09214100. 02320. 3148TEF22rs738499GT0. 3343541. 50. 01140. 2123 CHR: chromosome. A1 & A2: minor and major allele nucleotides. MAF: minor allele frequency. OBS: number of observed minor alleles. Exp: number of expected minor alleles. EMP1: pointwise empirical P value. EMP2: corrected empirical P value from max/(T)/familywise. Sleep clinic patients Clinic patients report some degree of depression. Their QIDS-SR averaged 6. 9 (in the mildly depressed range) with SD 3. 9. Also, 16. 2% scored ≥ 10, in the moderately depressed range. [SEP] Justify:

Output: The text does not address the claim regarding circadian rhythms and metabolic disorders. Instead, it discusses genetic associations with major depressive disorder and provides details about SNPs and their linkage in the context of depression. Thus, it is irrelevant to the claim about circadian rhythms and metabolic issues.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: These security policy considerations should inform deliberations about the security challenges and concerns of AI chatbots in health care. In principle, many of the techniques and industry best practices needed to implement and enforce these security considerations are available, if not deployed on AI platforms. This paper only provides a concise set of security safeguards and relates them to the identified security risks (Table 1). All the protections for health data are not detailed in this paper. It is important for health care institutions to have proper safeguards in place, as the use of chatbots in health care becomes increasingly common. Table 1. Artificial intelligence (AI) security risks and safeguards. Security risksSecurity safeguardsData breachesUsing anonymized data; data masking; desensitizing data; business associate agreement; identity and access management; breach notification and enforcementLeakage to AI chatbotsAcceptable use policy; AI security awareness training; sharing the minimum amount of person-specific data to accomplish the intended purpose (when in doubt, err on the side of providing less data)Lack of transparencySecurity audits; proactive risk assessments; auditing monitoring of algorithm decision-making; transparency of data handling practicesPhishing or social engineering attacks, malware, and interface spoofingTechnical barriers, such as encryption, secure authentication protocols, as well as network detection and response solutions; organizational measures, such as user security awareness training; implementing end point security and anomaly detection techniquesScale of security riskImplementing strict security measures for storing and transmitting protected health information, such as encryption and access controls; business associate agreement; restricting the uses and disclosures of protected health information to the minimum amount necessary to achieve the purpose for which it is being used, requested, or disclosedConclusionsAI chatbots like ChatGPT have the potential to improve patient care and public health. However, some key security implications must be considered before ChatGPT can be integrated into AI-driven health care applications. requirements must be met so that electronic PHI and other sensitive data remain private and secure. Users must ensure the anonymity of the data fed into the chatbot and the implementation of appropriate measures for data security. Second, before entrusting an AI chatbot with sensitive data, health care organizations should establish a business associate agreement to hold the vendor of such technology to the same rigorous data protection standards. Third, it is important to implement strict security safeguards for storing and transmitting PHI, such as encryption, identity and access management, security auditing, network detection and response, and security intelligence. [SEP] Justify:

Output: The text corroborates the claim by stating that 'AI chatbots like ChatGPT have the potential to improve patient care and public health' which indicates that AI-powered chatbots are recognized for their ability to enhance patient support. Furthermore, it implies that these chatbots are becoming more common in health care, thus supporting the claim about their increasing use in patient management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- nEBs retain similar ventral identity (H,H’,Q). Ptch1LacZ/LacZ;Ptch2-/-;Smo-/- nEBs lose ventral identity and express Pax7 (I,I’,R). Scale bar is 100 µm. (S) Nkx2. 2 and Isl1/2 expression in Ptch1LacZ/LacZ;Ptch2-/- nEBs is quantified (box and whisker) in the presence of 0–300 nM cyclopamine. (T) Ptch1+/LacZ;Shh-/- and Ptch1LacZ/LacZ;Shh-/-, but not Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- retain their ability to respond to exogenously supplied ShhN by inducing Nkx2. 2 expression. (U) Fibroblast-like cells derived from Ptch1LacZ/LacZ;Ptch2-/- mESCs were transfected with a Gli:luciferase construct alone or together with Ptch1. Independently, Ptch1LacZ/LacZ;Ptch2-/- cells were mock transfected or transfected with ShhN. Gli:Luciferase is quantified in co-cultures. Ptch1LacZ/LacZ;Ptch2-/- cells expressing Ptch1 can respond to ShhN supplied in co-cultured cells. p-value is indicated, n = 6. Variance is s. e. m. in A–U. Figure 1—figure supplement 1 Download asset Open asset Neural differentiation proceeds normally in all modified cell lines. Cell lines used were stained for Pax6, HB9, Isl1/2 and Tuj1 expression after in vitro neuralization. Although not all nEBs were equally sized, invariably they express Pax6 and Tuj1, indicating the acquisition of neural progenitor fates, regardless of their genotype (indicated). Scale bar is 100 µm. One biological replicate performed. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that neural differentiation proceeds normally in all modified cell lines but acknowledges that 'although not all nEBs were equally sized, invariably they express Pax6 and Tuj1,' which suggests variations in response based on genetic makeup rather than equal benefits across all species.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: In an attempt to replicate results from other subject groups, 2 SNPs were examined: rs738499 and rs2314339. The number of the less-common G alleles in the TEF T>G SNP rs738499 was correlated with the QIDS-SR, Rs = -0. 165 (P = 0. 001, Order Correlation). The negative correlation suggests that the G allele was associated with normal mood and might account for about 3% of the variance. Neither rs738499 nor rs2314339 were correlated with the BALM nor was rs2314339 correlated with the QIDS-SR. four different analyses, circadian gene polymorphisms were studied for association with three phenotypes: bipolar disorder, unipolar depression (major depressive diagnosis or rating-scale quantitative trait), and morningness-eveningness (which shares comorbidity with major depression) [19]. In 294 tests of association, 23 different associations met the nominal significance criterion of P < 0. 05, whereas 15 such associations would have been anticipated by random chance. It is plausible that most of the nominal associations were due to random chance (false discovery), but at least 5 appeared associated with these affective phenotypes with sufficient evidence of reliability to be considered suggestive. The intronic SNP rs2314339 in NR1D1 met false discovery and empirical family-wise criteria for significant association with the transmission of bipolar disorder (P = 0. 0005). The TDT analysis should be free from biases due to racial stratification. The same SNP was associated with delayed sleep phase disorder cases in an unpublished case-control sample, but significance was not sustained in the case-control sample after preliminary control for racial stratification. In both analyses, the more common allele was associated with the disorder and the less common allele was associated with control or normal health. The DSPD data offered at best only an indirect kind of replication because of the difference in phenotype between bipolar disorder and DSPD, especially considering that in our DSPD sample, we had found comorbidity with unipolar depression but not with bipolar disorder [19]. Moreover, rs2314339 has been tested in some whole genome association studies of bipolar disorder, but we are unaware that any suggestive association has been found in such studies. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the correlation between specific SNPs and affective disorders like bipolar disorder and unipolar depression, as well as associations with sleep phase disorders, but it does not address circadian rhythms in relation 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: Artificial IntelligencePotential of AI Chatbots in left: Dr. Swanepoel is Professor at the Department of Speech-and Audiology, University of Pretoria, South Africa, and adjunct professor in Otolaryngology-Head & Neck Surgery, University of of Medicine. He is Editor-in-Chief of the of Audiology and founder of a digital health company called hearX group. Dr. serves as the Professor of Otolaryngology-Head & at the University of of Medicine and as the Director of Audiology at the University of (UCHealth). Journal 77(7):p 1,4, July 2024. | DOI: 10. 1097/01. HJ. 0001025988. 40308. e1 As Thembi, a 6-year-old female in a remote village, steps into school for the first time, her excitement is overshadowed by an invisible barrier. Struggling to follow lessons and interact with peers, her symptoms of hearing loss remain unnoticed, reflecting a widespread challenge in low- and middle-income countries where access to hearing health care is limited. Imagine her parents’ relief when they connect with a freely available AI health chatbot. Sharing their concerns, they are guided to recognize signs of chronic ear infections—a common yet often overlooked cause of hearing loss in children. The chatbot’s advice leads them to a local primary healthcare clinic, where the detailed feedback helps the nurse identify and treat Thembi’s reoccurring condition. This turning point in Thembi’s life illustrates the transformative potential of AI chatbots in bridging the accessibility gap in hearing health care, offering early detection and intervention that can change the course of a child’s development and quality of life. Thembi’s story is far from unique; millions of children and adults alike, in low-income and high-income countries, face similar barriers, struggling to access the hearing healthcare they desperately need. This situation highlights the reality of access to care for most people affected by hearing loss but also demonstrates the urgent need for innovative solutions that can bring hope and essential services in a timely manner. Figure 1: AI chatbots providing community-based access to hearing care. DALL·E-created artwork; Prompt: Swanepoel. Table 1: Examples of AI Case in Care. [SEP] Justify:

Output: The text corroborates the claim by illustrating the transformative potential of AI chatbots in patient support, specifically in the context of hearing healthcare. The example of Thembi, who benefits from a freely available AI health chatbot, clearly demonstrates how such technology can guide parents in recognizing health issues and accessing medical care. This supports the assertion that AI chatbots are becoming integral in helping manage patient care.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: LacZ levels were compared to half (red dotted line, signifying the null hypothesis) the LacZ activity measured in nEBs comprised of 100% Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- cells. Error bars are s. d. , p-values are indicated (t-test, n = 3 measurements) and relate to comparison of each value with the null hypothesis. Ptch1LacZ/LacZ;Ptch2-/-;Smo-/- cells have a low Ptch1:LacZ activity in this assay regardless of the genotype of the surrounding cells. (K) Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- mESCs were loaded with green fluorescent cell tracker dye, and the Disp1-/-;Shh-/-, Disp1-/-;Shh-/-;Ptch1-/-, and Disp1-/-;Shh-/-;Ptch1-/-;Ptch2-/- mESCs with blue fluorescent dye (magenta). Mosaic nEBs as described in A were cultured for 48 hr, and imaged. 1:1 contribution reflecting the initial mosaic contributions is maintained throughout the experiment. p values are indicated (t-test). D–I are box-and-whisker plots. Figure 4 Download asset Open asset Loss of 7dhcr enhances non-cell autonomous suppression of the Hh response. (A) nEBs consisting of 50% Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- cells (green) and 50% Shh-/- cells or 50% Shh-/-; 7dhcr -/- cells (magenta) were labeled with cell tracking vital dyes. (B) LacZ quantification in nEBs described in (A). 7dhcr ablation enhances non-cell autonomous inhibition of the Hh response in Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- cells. (C) response in Ptch1LacZ/LacZ;Ptch2-/-;Shh-/- cells is inhibited by 7-Dehydrocholesterol (7DHC) as compared to cholesterol. [SEP] Justify:

Output: The text contrasts the claim by discussing specific experimental results regarding the response of different genetically modified cell types (e.g., Ptch1LacZ/LacZ;Ptch2-/-;Shh-/-), indicating that their responses to gene editing (such as LacZ activity) can vary based on the surrounding genotypes and specific conditions of the experiment. This evidence suggests that crops do not benefit equally from gene editing, as the response is dependent on specific genetic contexts.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Another nuclear receptor, NR2E1, has been reported to be associated with bipolar disorder in a case-control study [38]: there has been brief mention that NR2E1 and NR1D1 may interact in the development of photoreceptors. NR1D1 (OMIM 602408) is a key element of a unique circadian feedback loop, in which it inhibits transcription of ARNTL (BMAL1) by inhibitory binding at promoter sites [39]. NR1D1 may similarly inhibit transcription of CLOCK and NPAS2, the proteins of which activate ARNTL by binding as heterodimers. NR1D2 possibly has a similar role (OMIM 602304). These mechanisms may be particularly relevant to bipolar disorder, since it has been suggested that a mutation of CLOCK (which produces hyperactivity) may be an animal model for bipolar mania [40]. A knockout of ARNTL, on the other hand, reduces activity, though that can be largely restored by replacing ARNTL function in muscle [41]. ARNTL heterodimers with NPAS2 may bind to promoter eboxes of MAOA, thus promoting inactivation of dopamine, and thus inhibiting the pro-manic effects of dopamine [42 - 44]. Oddly enough, the ARNTL-CLOCK heterodimer was not demonstrated to have a similar effect on MAOA, though ARNTL-CLOCK would be expected to act on the same e-boxes in the MAOA promoter. Lithium, a primary medication for treatment of bipolar disorder, promotes degradation of NR1D1 through inhibition of GSK3, whereas GSK3 phosphorylation stabilizes NR1D1 [45]. These interactions are modeled in Fig. 1. Figure 1 Model relating sunlight, lithium, and circadian genes to MAOA and mania. This model relates sunlight and lithium to components of the circadian gene system, to MAOA (monoamine oxidase A), dopamine, and resultant stimulation of mania. Green solid arrows represent interactions which promote the function of the affected component. Red striped arrows represent inhibition of the function of the affected component. Components in white boxes hypothetically promote mania. Components in black boxes hypothetically inhibit mania. [SEP] Justify:

Output: The text is primarily focused on the role of certain nuclear receptors in the context of bipolar disorder and their interaction with circadian rhythms. While it discusses the circadian feedback loop involving NR1D1 and its effects on certain genes related to dopamine, it does not address metabolic disorders or obesity directly. Therefore, it 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: Thembi’s story, set against the backdrop of the Organization’s 2021 on Hearing,1 illustrates not just the personal impact of hearing health disparities but also the global challenge of unequal access and the critical shortage of health care professionals. The emergence of digital technologies especially access to smartphones in low- and middle-income countries (LMICs) heralds a groundbreaking shift. AI chatbots, leveraging this digital surge, could emerge as powerful tools to democratize hearing healthcare. 2 By offering an accessible first point of contact, these digital assistants are able to provide immediate, understandable, and actionable health advice, paving the way for a more inclusive health care system. This evolution underscores the potential of technology to transcend barriers, ensuring that geographical distance or socioeconomic status no longer dictates one’s access to essential healthcare services. It sets the stage for a deeper exploration into how AI chatbots could revolutionize hearing health care access, early detection and intervention strategies, making a profound difference for those in dire need. significant advancements in health care, the realm of hearing health faces profound challenges globally. The scale of hearing loss paints a concerning picture: over 1. 5 billion people live with some degree of hearing loss, with a projection that this number could soar to over 2. 5 billion by 2050. 3 Crucially, the vast majority of these cases are found in LMICs, where access to hearing health care professionals is severely limited. In Africa, for example, there are less than 1 audiologist for every million people. This scarcity is compounded by a global inequality in health care access, leaving many without the means to seek or receive treatment. Even in high income countries such as the United States, audiologist are mostly based in metro counties where individuals with higher median income and younger population live creating a hearing health care access issue for older adults who generally have hearing difficulties. 4 The impact of hearing loss is multifaceted and far-reaching, affecting individuals’ communication abilities, education, employment opportunities, and overall quality of life. Preventable causes, such as ear infections, account for a significant portion of hearing loss cases, underscoring the urgent need for early detection and intervention strategies. Yet, the gap between those requiring care and the available services is widening, driven by a shortage of skilled professionals and the inaccessibility of existing health care infrastructures in many regions. [SEP] Justify:

Output: The text corroborates the claim by discussing the potential role of AI chatbots in democratizing hearing healthcare. It highlights that AI chatbots can serve as accessible first points of contact, providing immediate and actionable health advice. This indicates that AI-powered chatbots are indeed being positioned as tools for patient support and care management, particularly in addressing the challenges related to hearing healthcare access.


Input: Task: pass the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: L3 neurons (magenta) express myr::GFP (anti-GFP) driven by 9–9 GAL4 (Nern et al. , 2008). Asterisks in (B) indicate dFezf-expressing lamina neurons (green + blue) that are most likely L3 neurons that have yet to turn on GAL4 expression (youngest L3 neurons). (E–N) MARCM + STaR experiments in adult flies. and methods section for a detailed description. Scale bars when shown are five microns with the exception of the scale bar shown in (E), which is twenty microns. L3 clones (green) expressed myr::tandem Tomato (tdTomato) and were visualized using a DsRed antibody. (E) Broad view (confocal image) of the morphologies of wild type L3 clones (green) in the lamina and medulla, which are indicated by R1-R8 axons (magenta, mAB24B10). Boxed regions indicate examples of the regions shown in confocal images in F-I, M and N. (F–I) Confocal images of terminals from wild type or dFezf1 L3 neurons (green), within the outer medulla (M1-M6), defined by R7 and R8 axons (magenta, mAB24B10). (J) quantification. N is the total number of neurons counted per genotype. 10 brains were analyzed for each genotype. (K, L) Confocal images show L3 clones (magenta) within the outer medulla and, within L3 terminals, Brp (green) expression and localization. Brp expressed from its native promoter within a bacterial artificial chromosome (BAC) was selectively tagged with smFPV5 in L3 MARCM clones, and visualized using an anti-V5 antibody. N-Cadherin (CadN) immunostaining (blue) serves as a neuropil marker. All dFezf1 L3 neurons displayed Brp puncta in their terminals. At least five brains were analyzed for each genotype. (M and N) Morphologies of dendrites from wild type or dFezf1 L3 neurons in the lamina. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses specific experiments involving L3 neurons in adult flies, and does not mention crops, gene editing, or any species-related benefits. Therefore, it does not support or contrast with the claim.