Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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).

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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].

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: pass


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

Output: pass


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

Output: corroborate


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

Output: pass


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass