Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: Oct4 and Sox2 form a heterodimer that interact with some promoters. In addition this heterodimer has been shown to interact with Nanog. Nanog is another transcription factor that participates in the ESCs regulatory circuitry together with Oct4 and Sox2 to maintain pluripotency [52]. In this context they activate transcription in a chromatin independent manner by interacting with transcriptional co-activators [53, 54]. Moreover, it has been shown in mouse ESCs that Oct4 and Nanog can repress gene expression through interaction with histone deacetylase such as Mta1 [55]. It has been elucidated that in the first stage of reprogramming, a cascade of differentiation genes are turned off, while pluripotency genes progressively become upregulated in order to push the differentiated cell toward an ESC-like state. c-Myc is an important participant in recruiting multiple chromatin modifications, such as histone acetyltransferases (GCN5, p300) and histones deacetylases (HDACs). In this regard, c-Myc increases the methylation site H3K4me3 and the global acetylation [56]. In the reprogramming process c-Myc activates its target before other core pluripotency transcription factors are activated, facilitating the opening of the chromatin for other factors [57, 58]. An example of c-Myc’s potential in opening chromatin is its association with Tip60-p400 complex, which acetylate and remodel nucleosomes respectively. p400 is a member of the Swi2/Snt2 family which is well known among the ATPase chromatin remodelling enzymes, exchanging histones H2AZ-H2B within nucleosomes [57]. It also functions to release paused RNA polymerase from about one-third of the genes that are being actively transcribed. This activity could enhance cellular reprogramming [59]. At the same time, the transcription factor Klf4, activates the transcription of Sox2 which participates in the pluripotency cascade [60]. 5. 2. Chromatin remodellingBased on the Yamanaka’s stochastic model (Figure 2), cells need to overcome the epigenetic barrier in order to become pluripotent [16, 17]. [SEP] Justify:

Output: The text corroborates the claim as it discusses the process and factors involved in cell reprogramming to achieve pluripotency. It details the roles of Oct4, Sox2, Nanog, c-Myc, and Klf4 in facilitating the transition from differentiated cells to a pluripotent stem cell-like state. The mention of genes being turned off and pluripotency genes being upregulated directly supports the idea of reprogramming adult cells to become pluripotent.


Input: Task: contrast the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: However, OpenAI is a private, for-profit company whose interests and commercial imperatives do not necessarily follow the requirements of HIPAA and other regulations, such as the European Union’s Protection Regulation. Therefore, the use of AI chatbots in health care can pose risks to data security and privacy. There is an urgent need to address the security and privacy issues of AI chatbots as they become increasingly common in health care. The importance of security and privacy issues in health care is well recognized by previous research [3-12]. Although several studies have attempted to address the security and privacy issues in big data and the Internet of Things from a technological standpoint [13-17], literature specifically on policy considerations of AI chatbots in health care is quite limited. This paper addresses the gap by identifying the security risks related to AI tools in health care and proposing some policy considerations for security risk mitigation. AI Chatbots in Health CareSince its launch on November 30, 2022, ChatGPT, a free AI chatbot created by OpenAI [18], has gained over a million active users [19]. It is based on the GPT-3. 5 foundation model, a powerful deep learning algorithm developed by OpenAI. It has been designed to simulate human conversation and provide human-like responses through text box services and voice commands [18]. OpenAI officially released GPT-4 on March 13, 2023. GPT-4 surpasses ChatGPT in its advanced understanding and reasoning abilities and includes the ability to interact with images and longer text [20]. At present, GPT-4 is only accessible to those who have access to ChatGPT Plus, a premium service from OpenAI for which users have to pay US $20 a month. As an AI chatbot, ChatGPT is designed to interact with humans and respond to their conversations and requests. It can be used to answer questions; write essays, poetry, or music; and compose emails and computer code. It is used by entities in a wide variety of industries, such as customer service, e-commerce, and health care. In health care, it can be used to schedule appointments, answer medical questions, and do paperwork. Dr ChatGPT [21] can potentially help create a personalized treatment for patients by considering their medical history, symptoms, social drivers, genomic data, health monitoring information, and other factors. [SEP] Justify:

Output: The text contrasts the claim by highlighting potential risks associated with the use of AI chatbots in health care, particularly regarding data security and privacy. While it acknowledges that AI chatbots, such as ChatGPT, are increasingly common in health care for various tasks like scheduling and answering medical questions, it emphasizes the need to address security and privacy issues. This indicates that while the use of AI chatbots is rising, there are significant concerns that may hinder their application in patient support and care management without proper safeguards.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: For metabolome analysis, freeze-dried root samples were powdered, and 50 mg of the powder was used for metabolite extraction. The extracted samples were analyzed using an UPLC-ESI-MS/MS system (™ AD, MS, 6500 QTRAP, Metabolite identification was performed using the MWDB (Metware database) provided by Co. , Ltd. (Wuhan, China). Isolation of key microorganisms from the rhizosphere and resistance testing against pathogens in the presence of root metabolitesWe isolated and identified potential disease-suppressive microorganisms influenced by phosphorus status. Frozen rhizosphere soils in 30% glycerol from the AP+RS treatment were recovered from soil suspensions and used to make serial diluted and plated on modified R2A culture medium, following the method described by Nishioka et al. [37]. To avoid the formation of compounds that may be inhibitory to some sensitive bacteria, phosphate compounds were autoclaved separately from the agar, with the culture medium being prepared as two separate solutions. (500 ml) was prepared using yeast extract 0. 5 g, tryptone 0. 25 g, peptone 0. 75 g, starch 0. 5 g, glucose 0. 5 g, agar 15 g, and deionized water 500 ml. (500 ml) was prepared using K2HPO4 0. 3 g, MgSO4·7H2O 0. 0462 g, sodium pyruvate 0. 3 g, and deionized water (500 ml). and B were autoclaved separately and mixed together before use. After incubating the plates at 28 °C for 1 week, a total of 133 isolates were randomly selected and purified. The isolates were taxonomically classified through the NCBI pipeline, based on their 16S rRNA gene sequences after the colony PCR method with primers 27F and 1492R [38]. One-hundred twenty-six isolates were identified in total (Table S5). To investigate the pathogen inhibitory capacity of key isolates in the absence or presence of the key root exudates identified from the root metabolome analyses, co-culture experiments with the pathogen R. solanacearum (RS) were conducted. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses methods related to metabolite extraction and microbiological techniques without directly addressing the influence of the human microbiome on health and disease. The specific mechanisms through which the microbiome impacts health are not mentioned, thus it does not support or contradict the claim.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: Nowadays, one of the major focuses in the iPSC field is to understand the epigenetic molecules that orchestrate chromatin remodelling in order to organize it into a pluripotent state similar to ESCs. While some somatic cell reprogramming mechanisms are being unveiled, many are still yet unknown [9]. DNA methylationOnce the differentiated cells have been reprogrammed, the epigenetic marks in iPSCs resemble ESCs [61]. For a great amount of eukaryotic DNA methylation is a mark that serves to define different cellular functions such as X chromosome inactivation, aging, imprinting, genome stability, tissue specific gene regulation, and so on [63, 64]. DNA methylation is one of the epigenetic marks that is modified during reprogramming (Figure 9). In this case, the process of demethylation is most common taking the methyl group from the promoters of some genes that are responsible for pluripotency which in turn allows them to return to a pluripotent state. Currently there is not too much evidence about the process of demethylation and the enzymes that catalyze this event. However, DNA demethylation events have been classified as passive or active. Passive DNA demethylation occurs during the process of DNA replication when maintenance methytransferases are inactive, and thus they are not able to methylate newly released strands [62]. On the other hand for active DNA demethylation, the main protagonists are enzymes that work regardless of DNA replication [62]. Figure 9. DNA demethylation as a reprogramming process when going to iPSC (Adapted from 13)An example of an active DNA demethylation event is shown by Pereira and colleagues (2008), where they have studied the efficiency of reprogramming of human lymphocytes by fusing them with mouse ESCs. They found that one of the first events occurring was the demethylation of the Oct4 gene. They suggest that this event is a result of an active chromatin remodeling locus before its actual expression [65]. Two mechanisms have been proposed as candidates for the active DNA demethylation mechanism. The first one involves the deamination of 5-methylcytosine in DNA by an enzyme called activation-induce deaminase (AID) [62]. [SEP] Justify:

Output: The text corroborates the claim by describing the process of cell reprogramming, specifically how somatic cells are converted into induced pluripotent stem cells (iPSCs). It explains that one focus in the iPSC field is understanding the epigenetic changes, such as DNA methylation, that enable differentiated cells to achieve a pluripotent state. Such descriptions of mechanisms involved in reprogramming support the assertion that scientists utilize these methods to convert adult cells into pluripotent stem cells.


Input: Task: contrast the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Given personal health information is among the most private and legally protected forms of data, AI chatbots, like any other technology used in the health care industry, should be used in compliance with HIPAA. This includes ensuring the confidentiality, integrity, and availability of PHI as it is collected, stored, and shared. Since the current free version of ChatGPT does not support (nor does it intend to support) services covered under HIPAA through accessing PHI, the use of ChatGPT in health care can pose risks to data security and confidentiality. Figure 1. Inputs and outputs of an artificial intelligence (AI) chatbot. can have several security risks. First, the model is trained on billions of data points, which means it has access to a vast amount of people’s data without their permission [25]. This is a clear violation of data security, especially when data are sensitive and can be used to identify individuals, their family members, or their location. Moreover, the training data that OpenAI scraped from the internet can also be proprietary or copyrighted. Consequently, this security risk may apply to sensitive business data and intellectual property. For example, a health care executive may paste the institution’s confidential document into ChatGPT, asking it to review and edit the document. In fact, as an open tool, the web-based data points on which ChatGPT is trained can be used by malicious actors to launch targeted attacks. Second, sensitive information can be leaked into the public domain when using ChatGPT. When users ask ChatGPT to answer some questions, they may inadvertently hand over sensitive patient or business information and put it in the public domain. Therefore, using ChatGPT can put users at risk of losing control of sensitive patient information, source code, and more. Potential data leakage has led companies, such as JP& Co, Verizon Communications, and Amazon to limit and ban their employees from using it [26]. More importantly, the integration of ChatGPT into existing health care systems could potentially require the use and disclosure of vast amounts of PHI; however, this may cause data leaks involving a variety of unintended entities and applications. According to its privacy policy, OpenAI may disclose users’ personal information to its business partners, law enforcement officials, other third parties, and other users [24]. Third, another concern is the lack of transparency regarding the origin of the sensitive data used to train the model. [SEP] Justify:

Output: The text contrasts the claim by highlighting significant risks and concerns regarding the use of AI chatbots, specifically ChatGPT, in health care settings. It discusses the challenges of ensuring compliance with HIPAA, the risks to data security and confidentiality, and the potential for sensitive information to be leaked. These concerns suggest hesitance to adopt AI chatbots like ChatGPT for patient support and care management, thus contradicting the assertion that their use is increasing in these contexts.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: The four treatments were as follows: (1) RS + key microbe + exudate, (2) RS + key microbe, (3) RS + exudate, and (4) RS. The fluorescence intensity of R. solanacearum was determined on BioH1 (excitation: 561 nm, emission: 600 nm). The system consisted of 100-μL 1/2 NB culture medium, 10 μL of a RS suspension (OD600 (optical density) = 0. 1), 10 μL of a suspension of the key microbe (OD600 = 0. 1), and 30 μL of a root exudate solution. Three concentration levels 10 mmol L−1, 2 mmol L−1, and 0. 4 mmol L−1 were tested for root exudates that were soluble in water, such as L-tryptophan, O-phosphorylethanolamine, and acetoxyacetic acid [39,40,41]. One concentration level (0. 4 mmol L−1) was tested for exudates soluble in 4% DMSO, but not in water, such as mangiferin, methoxyindoleacetic acid, xanthosine, and phenylpyruvic acid. This was because that higher exudate concentrations would have required higher DMSO concentrations, which would have inhibited bacterial growth. In total, there were 75 treatments with 6 replicates of each treatment. Cultures were grown in a black microtiter plate (200 μL well−1) at 30 °C with agitation at 170 rpm. The pathogen’s fluorescence intensity was recorded every 12 h for 96 h. Additionally, the effect of key exudates on key microbes was tested on BioH1 (OD600) with the same set of concentrations as described above. Statistical analyses and visualizationStatistical analyses were carried out using R (version 4. 0. 4) software. To investigate the impact of soil nutrient properties on disease incidence, we employed unary linear regression, standardized multiple linear regression, and the random forest method. These analyses utilized the “randomForest,” “rfPermute” packages, “lm,” and “scale” functions. [SEP] Justify:

Output: The text does not address the claim about the human microbiome influencing health and disease through various mechanisms. Instead, it describes an experimental setup focusing on treatments involving root exudates and their impact on bacterial cultures. There is no discussion about the human microbiome's role or the mechanisms through which it affects health and disease.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: It has been already mentioned that ESCs and iPSCs are known to have bivalent chromatin bearing both the active mark H3k4me3, and the repressive mark H3k27me3. One of the mechanism that controls the bivalency is through the activity of the polycomb proteins found in two major complexes PRC1 and PRC2 [70]. It is thought that PRC1 and PRC2 act as antagonists and are intrinsically involved in establishing the fate of ESC development. PRC2 is in charge of the H3k27me3 mark [71] and known to silence the HOX genes used and other regulators during ESC differentiation [72]. One of the basic mechanisms in ESC/iPSC differentiation is the demethylation of this H3k27me3 mark. Utx demethylase has been reported to be a significant regulator of cellular reprogramming [73]. Utx is encoded by an X-chromosome gene and belongs to the small family of Jmjc proteins, mediating the demethylation of H3k27 tri- and di-methyl repressive chromatin marks. In this study, it was found that Utx was dispensable for the maintenance of pluripotency, since pluripotency marker expression was maintained in knockout ESC lines. However fibroblasts derived from Utx knockout mice failed to be reprogrammed. This result indicated that the absence of Utx prevented the demethylation of H3k27me3 marks needed to re-establish pluripotency in vitro [73]. H3K9me3 and H3k79me2 have also been reported to be important for the maintenance of pluripotency in ESCs. These marks are usually left by repressive methyltransferases via the identification of specific motifs in heterochromatin. Reprogramming has been facilitated by the inhibiting two methyltransferases (GLP and G9a) that methylate H3k9 [74]. Histone acetylation is another important histone mark that has usually been correlated with gene activation (Figure 10). This mark has been reported to transform chromatin during reprogramming. Little is known about histone acetyltransferases for pluripotency maintenance or reprogramming, however Tip60/p400 has been reported as a histone acetyltransferase important for maintaining the ESC state. [SEP] Justify:

Output: The text corroborates the claim as it discusses the mechanisms involved in cell reprogramming, specifically focusing on processes that are crucial for re-establishing pluripotency in cells. It mentions that Utx demethylase regulates cellular reprogramming and describes how the absence of Utx hinders reprogramming fibroblasts into pluripotent cells. Additionally, it highlights how inhibition of specific methyltransferases facilitates reprogramming, which directly supports the concept of turning adult cells into pluripotent stem cells.


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

Output: The text contrasts the claim by indicating skepticism regarding the use of chatbots in mental health support and outlining limitations regarding their current implementation. It mentions that while AI chatbots have potential, they are not expected to replace human health care workers, and that there are significant concerns about their effectiveness highlighted by research revealing that much of the existing research is inconclusive and limited, suggesting that more research is needed before they can be widely implemented for patient support and care management.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: Microbial community analysis was conducted using the “Vegan” package, which included principal coordinates analysis (PCoA) based on Bray-Curtis distance, also for the transcriptome and metabolome analysis of plant root. test and test were used to evaluate the effects of phosphorus stress and pathogen inoculation on microbial composition. For different data analysis, the Scheirer-Ray-Hare test, Wilcoxon test, and test were applied, using the “rcompanion,” “stats,” and “agricolae” packages. Spearman correlations were computed using the “cor. test” command. Weighted gene co-expression network analysis (WGCNA) was performed using genes associated with pathogen response and phosphate starvation in the roots, along with all metabolites. The “WGCNA” package was utilized, and network modules were defined with a minimum of 200 members per module. Data processing involved the following packages: “reshape2,” “xlsx,” “agricolae,” “dplyr,” and “rstatix. ” Cladogram construction was performed using the “microbiomeViz,” “ggtree,” “treeio,” “ggtreeExtra,” “ggnewscale,” and “ggstar” packages. Other graphical representations were generated using the “ggplot2,” “ggbeeswarm,” “RColorBrewer,” “corrplot,” “pheatmap,” and “circlize” packages. ResultsThe influence of soil nutrient content on bacterial wilt incidenceThe 6-year-old tomato monoculture field trial was established to examine the relationship between a range of soil nutrient properties and the incidences of bacterial wilt disease (DI) caused by R. solanacearum (Fig. 1A). Multiple linear regression, focusing on standardized nutrient properties and DI, revealed soil available phosphorus (AVP) as the primary influential factor on disease incidence (estimate = −0. 55, P = 2. 65E-05), followed by soil organic matter (SOM) content (estimate = −0. 23, P = 0. [SEP] Justify:

Output: The text provides detailed analysis and methodology related to microbial community analysis and its relationship with soil nutrient properties and disease incidence, specifically bacterial wilt. However, it does not directly address how the human microbiome influences health and disease, which makes the content irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: Here Tip60/p400 also works as a chromatin remodeling enzyme, since it has a SWI2/SNF2 subunit [75]. Figure 10. Histone acetylation as an activating mark for reprogramming. (Adapted from 13)On the other hand the histone deacetylases (HDACs) are known to repress the expression of genes, therefore there is an increased interest on their inhibition. An example of their importance in reprogramming of somatic cells was shown by and collaborators (2011) [76]. In this study they used hybrid cell lines by fusing mouse embryonic fibroblast with ESCs and found that low H3k9 acetylation correlated with low reprogramming capacity. When they tried to overcome this effect using histone deacetylase inhibitors, they found an increase in the reprogramming efficiency. They reported that genes involved in extracellular matrix (ECM) activity were enriched during reprograming and concluded that H3K9ac is a mark intrinsically related to pluripotency and that promoting its increase using HDACs inhibitors promote ECM activity, which co-relates positively affect pluripotency and self-renewal [76]. Most epigenetic reprogramming studies have focused on isolated chromatin marks, revealing the down regulation of somatic genes. However there are more than some marks that lead to an “open” dynamic chromatin. and colleagues [77] presented a study where for the first time they showed chromatin dimensions as global changes occurring during reprogramming. They analyzed a battery of histone modifications (H3ac, H4ac, H4k5ac, H3k27ac, H3k4me3, H3k36me2, H3k9me3, and H3k27me3 also γH2AX, HP1α and lamin A, by immunofluorescence and biochemical fractionations comparing mouse ESCs to fully- and partially-reprogrammed mouse iPSCs. They first identified that H3k36me2, H4k5ac and H3k4me3 have the highest correlation with pluripotency. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of chromatin remodeling and specific histone modifications that influence the reprogramming of somatic cells into pluripotent stem cells. It highlights how inhibition of histone deacetylases (HDACs) increases reprogramming efficiency, demonstrating a direct link between epigenetic changes and the transition from somatic cells to pluripotent states. Additionally, it mentions the significance of specific histone marks correlated with pluripotency, which aligns with the broader context of cell reprogramming methods.


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

Output: The text contrasts the claim by providing evidence that a significant number of respondents are 'not familiar at all' with AI chatbots and expresses hesitancy towards using them for health support; specifically, less than one-third of respondents indicated they were 'somewhat likely' or 'very likely' to use a health chatbot if available. This suggests that despite the potential for AI chatbots in patient support, there is considerable uncertainty and reluctance among the public about their efficacy in providing reliable medical advice.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: 084) (Fig. 1B). The resulting model had an adjusted R-squared value of 0. 3704 and a P-value of 3. 88E-07. Consistently, the outcomes of the random forest prediction indicated AVP as the most significant factor influencing DI, followed by SOM, total nitrogen, and available potassium, explaining 46. 04% of the variance (Fig. 1C). Unary linear regression clearly showed that the increase of AVP was significantly linked to reduced DI (Fig. 1D), and this effect was more pronounced compared to other nutrient properties (Fig. S1B). Detailed data on soil nutrient contents and DI are given in Fig. S1A. The consistent findings across these three methods strongly support the premise that elevating soil phosphorus status represents the primary nutrient factor related to disease suppression. Fig. 1Experimental design and the influence of soil nutrient properties on disease incidence. A Schematic of the field trial design, with each treatment comprising three replication plots, each containing 40 tomato plants. B The relationship between standardized nutrient properties and disease incidence (DI) caused by pathogen R. solanacearum, using multiple linear regression analysis. C The influence of soil nutrient properties on DI, predicted by random forest method. D Relationship between soil available phosphorus and DI, using unary linear regression analysis. E Schematic of the pot experiment design. Six replicates, each containing three tomato plants, were employed during seasons 1 (2020 October 22-2021 January 13) and 2 (2021 March 21-2021 June 12). Three replicates, each containing two tomato plants, were employed during season 3 (2022 September 15-2022 November 28). Seasons 1 and 2 corresponded to trials 1 through 4, while season 3 was dedicated to the 5th trial. All treatments depicted in this figure received equivalent nitrogen and potassium fertilization. The CK control, without any fertilization or pathogen inoculation, is not shown. Treatments include AP, soil applied with calcium superphosphate as relatively available phosphorus; UP, soil applied with calcium phosphate as relatively unavailable phosphorus with equivalent amount phosphorus to AP treatment; −P, soil with no phosphorus supply. Phosphorus stress gradually increased in the order of AP, UP, and −P. +RS groups were inoculated with pathogen at 1 × 107 colony-forming units g−1 soil. RS denotes R. [SEP] Justify:

Output: The text does not address the claim regarding the influence of the human microbiome on health and disease. Instead, it discusses soil nutrient properties and their effects on disease incidence in tomato plants, which is unrelated to microbiome research.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: A previous study showed that overexpressing of esBAF in addition to the four Yamanaka was able to acquire a euchromatic chromatin by increasing the kinetics of Oct4, Nanog and Rex1 promoter demethylation. This facilitated the accessibility of the reprogramming factors and hence the process was enhanced [28]. Moreover, Onder and colleagues (2012) [79] focused on the study of chromatin-modifying enzymes during the reprogramming process in iPSCs. They used a loss of function approach with shRNA where they selected 22 genes involved in DNA and histone methylation pathways. From their results, they found that inhibition of the histone methyltransferase DOT1L, reprogramming was enhanced resulting in more iPSCs colonies. DOT1L inhibition does not enhance the upregulation of the pluripotency gene network but can be used to substitute for Klf4 and c-Myc during reprogramming. Inhibition of this molecule is associated with an increase of Nanog and Lin28, factors, which are necessary for reprogramming. Finally using ChIP-seq they found that the H3K79me2 mark was lost in genes that participate in epithelial mesenchymal transition. Among some of the mesenchymal regulators were SNAI1, SNAI2, ZEB1, ZEB2 and TGFB2, that at the same time where strongly repressed during reprogramming. Together with this, they also reported that epithelial genes such as CDH1 (E-cadherin) and OCLN were upregulated. The above was a clear example of how chromatin modifying enzymes are critical in the molecular process of reprogramming enhancing the cascade that begins with the four Yamanaka factors [79]. The above are just some of the studies of many that have been reported. They lead us to question whether remodeling enzymes are in charge of the major chromatin opening that occurs during reprogramming, or if histone marks lead the process. 5. 3. MicroRNAs in reprogrammingMicroRNAs (miRs) are small RNAs involved in the inhibition of the gene expression by destabilizing target RNAs. They are usually formed by the proteins Dicer and Drosha with its cofactor Dgcr8. [SEP] Justify:

Output: The text corroborates the claim by discussing the process of cellular reprogramming to induce pluripotent stem cells (iPSCs). It details various factors and mechanisms involved in this reprogramming process, specifically mentioning the role of chromatin-modifying enzymes and key pluripotency factors such as Oct4, Nanog, and Lin28. This information supports the claim that scientists actively manipulate adult cells through reprogramming techniques to achieve pluripotency.


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

Output: The text contrasts the claim by stating that AI chatbots cannot replace human interactions in mental healthcare. This suggests limitations in the effectiveness of AI chatbots in patient support, which contradicts the idea that they are increasingly utilized in this role.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: solanacearum; P denotes phosphorus. Significance levels are denoted as follows: “ns,” not significant, “. ”P < 0. 1, “*”P < 0. 05, “**”P < 0. 01, and “***”P < 0. 001Full size imageEffects of phosphorus on microbial functions related to disease suppression in bulk and rhizosphere soilsBased on our initial field trial, we next established a controlled pot experiment, whereby we manipulated a naturally low phosphorus soil (available phosphorus = 10. 34 mg kg−1), generating a phosphorus stress gradient (Fig. 1E; Fig. S2A). After fertilization, the soil available phosphorus contents averaged across all time points were 18 mg kg−1 in the AP treatment, 12 mg kg−1 in the UP treatment, 6 mg kg−1 in the −P treatment, and 7 mg kg−1 in the CK control (Table S4). Each phosphorus treatment was subjected to inoculation of the pathogen R. solanacearum at a density of 1 × 107 CFU g−1 soil (Fig. S2B), showing a consistent and effective pathogen inoculation across all levels of phosphorus stress. Next, we analyzed genes responsible for encoding disease suppression in bulk (Fig. 2 A, B, and C) and rhizosphere (Fig. 2 D, E, and F) soils using quantitative PCR approaches. The phlD gene was not detected and has therefore been excluded from further discussion. In both bulk and rhizosphere soils, strong positive correlations were observed for genes encoding enzymes, and hcnAB (Fig. 2 C and F). The data from the three highly correlated genes were pooled together to simplify downstream analyses, with detailed results of individual genes given in Fig. S3. Scheirer-Ray-Hare test results demonstrated significant influences of phosphorus stress and pathogen inoculation on disease-suppressive genes in general. In bulk soil samples, the abundance of disease-suppressive genes increased significantly with heightened phosphorus stress when under pathogen amendment (r = 0. 23, P = 0. 015) (Fig. 2A), with genes encoding NRPS and PKS enzymes contributing significantly to this effect (Fig. 2B). However, the opposite effect was observed in the rhizosphere. [SEP] Justify:

Output: The text does not address the claim regarding the influence of the human microbiome on health and disease. Instead, it discusses the effects of phosphorus on microbial functions related to disease suppression in soil, focusing on specific genes and their correlations with phosphorus stress and pathogen inoculation. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: The importance of miRs arises from the observation that some miRs induce reprogramming of somatic cells into iPSCs [80]. Among some of the miRs found to positively regulate ESC pluripotency are: ESC cell-cycle regulating miR291a-3p, miR291b-3p, miR294, miR295 and miR302. Interestingly, miR302 has been reported regulate some of the epigenetic modifications that occur during reprogramming. miR302 is a family of four highly homologous microRNAs that are transcribed together and form a noncoding RNA cluster [81]. They are highly expressed in human ESCs and absent in differentiated cells. Lin and collaborators (2011) have focus in how the miR302 controls several enzymes that are involved in active demethylation [81]. MiR302 targets and represses AOF2/1 histone demethylases and MECP1/2 (methyl CpG binding proteins). At the same time it blocks cytosine methyltransferase 1 (DNMT1). During reprograming miR302 coordinates DNA demethylation, together with a histone methylation on the active mark H3k4 that will alter the chromatin structure and the gene activity. The example above highlights the critical role of miRs in founding and sustaining pluripotency in cells [82]. Advertisement 6. ConclusionThe biology of pluripotent stem cells is still in a very early stage; even understanding what is the best way to obtain a true embryonic stem cell remains unclear. During reprogramming a number of changes occur in the cell. These changes start usually by the stimulation of exogenous transcription factors that consequentially trigger a large number of other reactions: signalling, gene transcription, and most importantly epigenetic changes, including chromatin remodeling, histone modification, and DNA methylation [1, 12, 13]. During reprogramming, chromatin changes to an “open” dynamic configuration resembling the epigenetic landscape as in ESCs. In order to reach this configuration, the somatic cell has to interconnect transcription factors, chromatin, and histone modifier enzymes. What is the kinetics of this process? This is one of the first questions that puzzle most researchers. While there is firm understanding that the exogenous transcription factors are first to prompt these changes, reprogramming is not an efficient process. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of microRNAs (miRs) in the reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). It explains that certain miRs, including miR302, positively regulate the pluripotency of embryonic stem cells and are involved in critical epigenetic modifications during the reprogramming process. The mention of exogenous transcription factors stimulating changes that lead to a configuration similar to that of embryonic stem cells indicates a mechanism through which adult cells are converted into pluripotent stem cells.


Input: Task: contrast the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: In one case, ChatGPT did not provide appropriate information about suitable treatment, and its response contained inappropriate content in 4 cases. In contrast, ChatGPT recommended appropriate measures in 4 cases. Discussion and Conclusion: ChatGPT’s responses to the 10 case reports could have been better. To utilize ChatGPT efficiently and appropriately, users should possess sufficient knowledge and skills to determine the validity of its responses. AI chatbots based on large language models will progress significantly, but physicians must be vigilant in using these tools in practice. Key Words: Artificial intelligence; Language; Physicians; Republic of Korea 찾아보기말: 인공지능; 언어; 의사; 대한민국 서론 2022년 11월 30일 OpenAI사에서 GPT-3. 5를 기반으로 한 ChatGPT를 출시한 뒤[1], 많은 사람들이 이 대형언어모형(large language model) 인공지능 플랫폼(artificial intelligence [AI] platform)을 사용하기 시작하였다. 특히 가장 관심을 가진 분야는 교육 현장으로 학생들이 essay를 제출할 때, ChatGPT를 활용하였다. [SEP] Justify:

Output: The text contrasts the claim by highlighting that ChatGPT did not consistently provide appropriate information regarding treatment and displayed inappropriate content in several instances. This implies that despite the potential of AI-powered chatbots, their current implementation may not reliably support patient care and management, thus presenting a cautionary view against the claim.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: 001) compared to the fungal community (P = 0. 014) with respect to impact of the pathogen (Fig. 3A). While the bacterial community was significantly altered by phosphorus availability (P = 1E-04), phosphorus availability did not significantly impact fungal communities (P = 0. 12) (Fig. 3A). Fig. 3Impact of phosphorus and pathogen inoculation on soil microbiome. Phosphorus stress gradually increased in the order of AP, UP, and −P due to different fertilizer treatments. The +RS groups indicated treatments inoculated with the pathogen. A Principal coordinate analysis (PCoA) plot illustrating the Bray-Curtis distance of bacterial and fungal communities among various treatments. test evaluated differences due to soil compartment and pathogen inoculation, while the Mantel test assessed the effect of phosphorus gradient. B Cladogram displaying bacterial zOTUs correlated to phosphorus stress pooled at the genus level in the rhizosphere (Spearman correlation, P < 0. 05, each genus with at least six or more zOTUs). C Cladogram showing fungal zOTUs correlated to phosphorus stress in the rhizosphere (Spearman correlation, P < 0. 05, each phylum with at least six or more zOTUs). The red and black triangles represent microbial taxa enriched or decreased by pathogen inoculation in the −P, UP, or AP groups using the Wilcoxon test (P < 0. 05). The “nelogp” denotes the negative log-transformed P-value of the Spearman correlations between the microbial taxa and phosphorus stress. “RA” stands for relative abundance. The phylum classification is labelled around the cladogram. D The relative abundances of identified key microbial taxa responding to phosphorus stress and pathogen inoculation in rhizosphere soils. The numbers in boxplots in and H and for AMF correspond to the number of zOTUs included for the given taxonFull size imageSpearman correlations were conducted to assess the relationship between phosphorus stress and zOTU relative abundance in both soil compartments in the presence of the pathogen. Significantly correlated zOTUs (P < 0. 05) are depicted in Fig. S4, identified up to the phylum level. Subsequently, among these zOTUs, those successfully identified to the genus level were extracted for further analysis. [SEP] Justify:

Output: The text does not address the human microbiome directly or its influence on health and disease. Instead, it discusses the effects of phosphorus on bacterial and fungal communities in soil, focusing on pathogen inoculation and zOTU analysis. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: From the extensive interest in making reprogramming an efficient process, there have been a lot of remarkable results using different type of molecules that target chromatin enzymes. Thus understanding of the reprogramming process, including the timing of chromatin remodeling, interactions with transcription factors, increase or decrease of histone acetylation and most important, the precise interconnection of factors that break the epigenetic barrier, will give us a base line to design a better protocol for the develop of iPSCs. There will come a point where researchers will manipulate chromatin kinetics in order to promote the reprogramming of somatic cells into iPSCs. This achievement will bring a cell that reprograms efficiently; in a short period of time, which will have an epigenetic signature identical to ESCs. In the future, with a better understanding of chromatin not only in reprogramming but also in differentiation, the iPSC field could become an area of synthetic biology. In any case, the iPSC field still has a long way to go before it is fruitful. A clearer understanding of the epigenetics of the reprogramming must come forward before iPSCs can be fully accepted for regenerative medicine. 1. jury and executioner of stem cell fate. 2012 in culture of pluripotential cells from mouse embryos. Nature 198158191546 ThomsonJ. AOdoricoJ. SHuman embryonic stem cell and embryonic germ cell lines. 2000 VermaAVerma N: Induced pluripotent stem cells and promises of neuroregenerative medicine. 2011 Park In-and Q. Disease-Cells. Cell 2008134110 of Induced Pluripotency: A two-Stem Cell. Rev and Rep 20095204223 --of Model with iPS from Autologous Skin. Science 2007 and Contribution. Cell 200715570 HochedlingerKPlathKEpigenetic reprogramming and induced pluripotency. Development 2009136450923 : Returning to the stem state. Epigenetics of recapitulating pre-differentiation chromatin structure. BioEssays 201032791799 ölerH. Rand DingSInduction of pluripotent stem cells from mouse embryonic fibroblasts by Oct4 and Klf4 with small-molecule compounds. [SEP] Justify:

Output: The text corroborates the claim by discussing the process of cell reprogramming, specifically mentioning that researchers will manipulate chromatin kinetics in order to promote the reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). This directly supports the assertion that scientists are using cell reprogramming techniques to convert adult cells into pluripotent stem cells.


Input: Task: contrast the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: 또한 논문에 치료 방법을 기술한 9예에서 ChatGPT는 1예는 제대로 알려주지 못하였고, 4예에서는 적절치 못한 내용이 포함되어 있고, 4예에서만 적절한 방안을 제시하였다(Table 1). 이 10개 증례에 대한 ChatGPT의 답은 앞에 언급한 미국의 33명 의사가 출제한 284문항, 내과 전문의가 제작한 흔한 증상으로 구성한 10개 증례보고에서와 차이가 나게 낮은 수준이다. 그 이유는 이번 10개 증례가 흔히 볼 수 있는 예가 아니고 또한 현재 ChatGPT 3. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of ChatGPT in providing appropriate treatment advice in nine case examples. It highlights that ChatGPT did not perform adequately in many instances, suggesting a lack of reliability in AI chatbots for patient support as claimed.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: We also found that a group of 14 zOTUs in the phylum Glomeromycota that decreased in response to the pathogen in AP (P = 0. 00020) remained unchanged in UP (P = 1), and increased in −P (P = 0. 0083), thus showing a significant increase with rising phosphorus stress in the presence of the pathogen (Fig. 2 C and D). Potential disease-suppressive microbial taxa promoted by phosphorus are influenced by plant root function and root metabolitesPCoA plots revealed a significant influence of phosphorus stress on both plant root transcriptome (P = 1E-04) and metabolome (P = 1E-04), but not statistically significantly for pathogen inoculation (P = 0. 091, P = ns, Fig. 4A). Notably, a transcriptional shift in response to pathogen inoculation was more pronounced in the AP and UP treatments, compared to the −P. We first identified the 1111 pathogen-related genes and 139 phosphorus starvation genes based on the function annotation using KEGG and GO databases (Tables S7 and 8). Then as expected, in plant roots, these phosphorus starvation genes showed an increase with increasing phosphorus stress (+RS: P = 9. 585E-05, r = 0. 95; −RS: P = 9. 585E-05, r = 0. 95) (Fig. 4B). Importantly, pathogen-related genes were significantly upregulated by pathogen inoculation (P = 0. 024), and they showed a significant reduction with increasing phosphorus stress (+RS: P = 0. 0043, r = −0. 84; −RS: P = 0. 011, r = −0. 79) (Fig. 4B). This suggests phosphorus stress is related to the reduced level of pathogen response regulon. Fig. 4Impact of phosphorus and pathogen inoculation on plant transcriptome and metabolome. Phosphorus stress gradually increased in the order of AP, UP, and −P due to different fertilizer treatments. The +RS groups indicated treatments inoculated with the pathogen. A PCoA plot illustrating the distance of the plant transcriptome and metabolome in different treatments. test evaluated differences due to pathogen inoculation, while the Mantel test assessed the effect of the phosphorus gradient. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses phosphorus stress effects on plant roots and pathogen inoculation, without referencing the human microbiome or its influence on health and disease mechanisms. It focuses on plant biology rather than any microbiome-related health aspects.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: Cell 2008 TakahashiKYamanaka S: Induction of Cells from Cultures by Defined Factors. Cell 2006126663676 chromatin. in Biochemistry and 2012119 of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 200731586172 WaddingtonCThe strategy of the genes; a discussion of some aspects of theoretical biology. London, Allen and Unwin 195716. YamanakaSElite and stochastic models for induced pluripotent stem cells generation. Nature 20094604952 of germline-competent induced pluripotent stem cells. Nature 2007 MaheraliNHochedlingerKGuidelines and Techniques for the Generation of Stem Cells. Cell 20083595605 : regulation through repression. Science 1999288481486 StrahlB. Dand AllisC. DThe language of covalent histone modifications. Nature 20004034145 connections to pluripotency and cellular reprogramming. Cell 2011 HanJ WYoonY-s. u. pof pluripotent stem cells. Antioxidants & 201217205223 of Chromatin during Transcription. Cell 2007128707719 -dependent chromatin remodeling genetics, genomics and mechanisms. 201121396420 of a core chromatin remodeling complex from SWI/SNF subunits. 19993247253 remodeling through directional DNA translocation from an internal nucleosomal site. 200512747755. embryonic stem cell chromatin remodeling complex, esBAF, is essential for embryonic stem cell self-renewal and pluripotency. 200910651815186 úzo-bravoölerH RChromatin-remodeling components of the BAF complex facilitates reprogramming. Cell 2010141943955 -. Human but not yeast CHD1 binds directly and selectively to histone H3 methylated at lysine 4 via its tandem chromodomains. J 20052804178941792 Gaspar-maia--Santos M. Chd1 regulates open chromatin and pluripotency of embryonic stem cells. Nature 2009460863868 of external signalling pathways with the core transcriptional network in embryonic stem cells. Cell 200813311061117. Annu. Rev. Biochem. [SEP] Justify:

Output: The text corroborates the claim by discussing processes related to the induction of pluripotent stem cells derived from adult human fibroblasts through defined factors, which is a clear reference to how cell reprogramming is applied to convert adult cells into pluripotent stem cells.


Input: Task: contrast the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: 이번에 활용한 증례는 우리가 흔히 보는 것이 아닌 매우 드문 경우이기에 흔히 볼 수 있는 증상을 진단하는 것과 차이가 있을 수 있다. ChatGPT는 매우 일반적인 지식을 제공하는 것은 가능하고, 질문의 맥락에 맞추어 답을 하지만, 그 답이 얼마나 신뢰할 수 있느냐는 오로지 전문가의 판단에 달려 있다. 즉, 인공지능 챗봇을 활용할 때 핵심은 사용자의 전문적인 지식이다. 어떤 답이 신뢰할만한지는 전문가만이 제대로 파악할 수 있기 때문이다. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while AI chatbots like ChatGPT can provide general knowledge and context-based answers, the trustworthiness of the information they provide relies solely on expert judgment. It emphasizes that when utilizing AI chatbots, the core issue is the user's expertise, suggesting that the AI might not be sufficiently reliable for patient support without professional validation.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: B The transcripts per million (TPM) of pathogen-related genes and phosphorus starvation genes in different treatments and their correlations with phosphorus stress. Error bars represent standard errors with three replicates. Significance was determined using the Scheirer-Ray-Hare test and Spearman correlations. C Weighted gene co-expression network analysis (WGCNA) network of pathogen-related genes, phosphorus starvation genes, and all root metabolites. Members with similar trends were clustered and further divided into several modules with a minimum member size of 200, which were represented by the colors below. D Dendrogram depicting the key module eigengenes (ME) affected by phosphorus stress and pathogen inoculation. The histogram showing the abundances of key metabolites in the corresponding modules. Error bars represent standard errors with three replicates. “r” stands for Spearman correlation coefficient; P denotes phosphorus. Significance levels are denoted as follows: “ns,” not significant, “. ”P < 0. 1, “*”P < 0. 05, “**”P < 0. 01, and “***”P < 0. 001Full size imageWe constructed a WGCNA network to identify potential metabolites involved in phosphorus-affected plant defense to further explore their corresponding influence on rhizosphere microorganisms (Fig. 4C). The network was divided into several modules, each comprising at least 200 members, with the important modules identified and depicted in the heatmap (Fig. 4D). The module eigengenes of turquoise module increased with increasing phosphorus stress, whereas those of the yellow and blue modules increased with decreasing phosphorus stress (Fig. 4D). Additionally, several key metabolites were identified in the histogram (Fig. 4D). Acetoxyacetic acid (organic acids), L-tryptophan (amino acids and derivatives), O-phosphorylethanolamine (alkaloids), methoxyindoleacetic acid (alkaloids), mangiferin (flavonoids), phenylpyruvic acid (organic acids), and xanthosine (nucleotides and derivatives) were associated with the yellow and blue modules and were promoted by phosphorus supply (Fig. 4D), consistent with the trends observed in plant pathogen-related functions (Fig. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not mention the human microbiome or its influence on health and disease. Instead, the text discusses the analysis of pathogen-related genes and metabolites in relation to phosphorus stress in plants, which does not address the human microbiome at all.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: 200710443954400 demethylation in zebrafish involves the coupling of a deaminase, a glycosylase, and gadd45. Cell 200813512011212 -jones. Global transcription in pluripotent embryonic stem cells. Cell 20082437447 and Function. 2012116 úzo-bravoölerH RDirect reprogramming of human neural stem cells by Oct4. Nature 2009 regulatory circuitry in human embryonic stem cells. Cell 2005122947956 repair complex functions as an Oct4/Sox2 coactivator in embryonic stem cells. Cell 2011147120131 4 dependence of chromatin structure within the extended Nanog locus in ES cells. 200822575580 and Oct4 associate with unique transcriptional repression complexes in embryonic stem cells. Biol 200810731739 regulation and epigenetic remodeling in murine embryonic stem cells by c-myc. 2009e7839. network accounts for similarities between embryonic stem and cancer cell transcription programs. Cell 2010143313324 StadtfeldMHochedlingerKInduced pluripotency: history, mechanisms, and applications. Genes Dev. 2010242022392263. . c-Myc regulates transcriptional pause release. Cell 2010141432445 parallel circuit of LIF signalling pathways maintains pluripotency of mouse ES cells. Nature 2009460118122 PawlakMJaenischRDe novo DNA methylation by Dnmt3a and Dnmt3b is dispensable for nuclear reprogramming of somatic cells to a pluripotent state. Genes & Development 20112510351040 ZhuJ-by DNA Glycosylases. Genet 200943143166 BenderJDNA methylation and epigenetics. 2004554168 BirdADNA methylation patterns and epigenetic memory. 200216621. -based reprogramming of human B lymphocytes for pluripotency requires Oct4 but not Sox2. PLoS Genet 2008e1000170. proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science 201133313001303 bivalent chromatin structure marks key developmental genes in embryonic stem cells. [SEP] Justify:

Output: The text corroborates the claim by discussing various mechanisms and factors related to induced pluripotency and embryonic stem cells, notably mentioning 'induced pluripotency' and the processes involved in reprogramming somatic cells to a pluripotent state. For example, it references the role of Oct4, which is a critical factor in the reprogramming process. This supports the assertion that scientists employ cell reprogramming techniques to create pluripotent stem cells.


Input: Task: contrast the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: 진단보다는 치료 방법은 조금 더 나은 수준으로 제공한다. 치료 방법은 대개 안전하다고 이미 잘 알려진 것을 임상 현장에서 시행하므로 진단과는 다른 차원이다. 그러므로 비전문가가 인공지능 챗봇을 활용하여 답을 얻는 것은 비록 일부 제대로 답하는 내용이 있다고 하더라도 권장할 수 없다. 인공지능 챗봇의 답이 앞으로 더욱 많은 훈련 과정을 통하여 조금 더 수준이 올라갈 수 있을 것이다. [SEP] Justify:

Output: The text contrasts the claim by suggesting that while AI chatbots may provide some therapeutic methods that are already established and safe, it warns against the use of chatbots by non-experts for obtaining answers, indicating a limitation in their reliability in patient support and care management. The text emphasizes that despite some improvement through training, it is still not advisable for non-experts to rely on AI chatbots for crucial healthcare information.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: 4B) and soil microbial disease-suppressive functions in the rhizosphere (Fig. 2D). and quercetin (flavonoids), present in the turquoise module, were promoted by phosphorus deficiency and also stimulated by the presence of the pathogen in the −P treatment (Fig. 4D). The detailed information of these metabolites is given in Table S9. Next, we were able to recover isolates that corresponded to the key zOTUs that promoted by phosphorus supply and stimulated by pathogen inoculation (Figs. 3D and 5A). Four of these isolates were identified as Chryseobacterium (Y14 and Y35) (Fig. S8), Dyadobacter (Y33) (Fig. S9), and Paenibacillus (R16) (Fig. S10). Y33 and R16 were selected because they were the only Dyadobacter or Paenibacillus among our 126 isolates. For Chryseobacterium, we recovered eight isolates that clearly separated into two different species based on phylogeny and morphology (Fig. S11). We selected one isolate from each of these species, Y14 and Y35, for the subsequent assay. The relative abundances of the zOTUs that are most related to the isolated key bacteria are given in Fig. S12. Pairwise co-culture growth experiments were conducted to individually assess capacity of these isolates to inhibit the growth of bacterial pathogen R. solanacearum, (Fig. 5B). In the absence of root metabolites, Chryseobacterium strains Y14 and Y35 showed significant inhibition of the pathogen, with Y35 being more inhibitory than Y14. Paenibacillus strain R16 showed no inhibition, and Dyadobacter strain Y33 even promoted pathogen growth, with significant results shown in Table S10. Fig. 5Plant root exudates enhance the inhibitory capability of key isolated microbial taxa in the rhizosphere against the pathogen R. solanacearum. A Schematic of the isolation experiment. Key bacteria Y14, Y35, Y33, and R16 were selected for further experimentation. These four isolates, along with the root exudates used in subsequent experiments, were all promoted by improved phosphorus availability. B Growth of the pathogen R. [SEP] Justify:

Output: The text does not address the human microbiome directly or its influence on health and disease. Instead, it focuses on microbial interactions in the rhizosphere and their effect on a specific bacterial pathogen, which is not relevant to the claim about the human microbiome.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: Cell 2006125315326 5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network. Cell 2011145183197 finger of NURF couples histone H3 lysine 4 Trymethylation with chromatin remodeling. Nature 20064428690 regulation by polycomband trithorax proteins. Cell 2007128735745. 1 mediates methylation on histone H3 lysine 27 and complements EZH2 in maintaining stem cell identity and executing pluripotency. 200832491502 complexes repress developmental regulators in murine embryonic stem cells. Nature 2006441349353 -zalcenstein-saban27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming. Nature 2012 ölerH RDingSA combined chemical and genetic approach for the generation of induced pluripotent stem cells. Cell, 22008b525528 DoyonYCotéJThe highly conserved and multifunctional NuA4 HAT complex. 200414147154 -rafiniaMWestphalHMeshorerE. HK9 histone acetylation predicts pluripotency and reprogramming capacity of ES cells. Nucelus 2011 epigenetic changes during somatic cell reprogramming to iPS cells. J Biol 20113341350 facilitates pluripotency by conditioning the genome for LIF/STAT3 signalling and by regulating polycomb function. Biol 201113903913 OnderT. -modifying enzymes as modulators of reprogramming. Nature 2012483598602 stem cell-specific microRNAs promote induced pluripotency. Nat. Biotechnol 200927459461 LinS-. SRegulation of somatic cell reprogramming through inducible mir-302 expression. Research 20113910541065 embryonic stem cells express a unique set of microRNAs. Dev. Biol. 2004270488498 information 1. Introduction2. Induced pluripotent stem cells3. Epigenetics4. Chromatin phases (epigenetic regulation)5. Epigenetic reprogramming6. Conclusion 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation 1. Choose citation style Select style to clipboard Get citation 2. [SEP] Justify:

Output: The text corroborates the claim by discussing various mechanisms and factors involved in cell reprogramming, specifically highlighting epigenetic changes and processes that facilitate the transition from somatic cells to induced pluripotent stem cells (iPS cells). It notes the importance of the embryonic stem cell core transcriptional network and the regulation by transcription factors and microRNAs, all of which are essential in the reprogramming to pluripotency.


Input: Task: contrast the claim using the given text. Claim: AI will soon fully replace doctors in making medical diagnoses. [SEP] Text: AI replace doctors’ ‘gut instincts’? Published on December 20, 2023 Doctors’ intuition plays a key role in healthcare, even when computers suggest another treatment approach. But with AI advancing, is that all about to change? Use + rapid advances in the field, AI won’t supplant human healthcare workers in 2024. : Pexels: to use -Edquist DOI 10. 54377/02d2-eea4 Doctors’ intuition plays a key role in healthcare, even when computers suggest another treatment approach. But with AI advancing, is that all about to change? The value of healthcare workers’ intuition in effective clinical care has been verified by reports around the world again and again. From doctors’ ability to spot sepsis in critically ill children, to ‘nurse worry‘ as a ‘vital sign’ predictive of patient deterioration, to helping GPs navigate complex patient care — intuition appears to play a large role in supporting high risk patients — even when data or computer outputs suggest another treatment approach. (AI) has already begun to transform healthcare, and the health sector will only continue to consider AI innovations in 2024 and beyond. In this increasingly technological world, questions swirl on what is the role of these human hunches in healthcare practice and whether AI is about to overtake doctors’ ‘gut feelings’ entirely. What is AI in healthcare and when is it used? Davenport from and Deloitte consultant explain elsewhere, AI healthcare includes ‘rule-based expert systems’, which use prescribed knowledge-based rules to solve a problem, and ‘robotic process automation’, which uses automation technologies to mimic some tasks of human workers. Such technology can help with automated patient monitoring, where alerts are signalled once a rule criterion is met, patient scheduling reminders and medicine management. Other forms of AI used in healthcare include robots, natural language processing and machine learning. Robots can help move and stock medical supplies, lift and reposition patients and assist surgeons. hospital has launched a €7 billion project, set to be completed in 2028, which will engage robots to collect patient data typically reliant on human physical touch: from measuring pulse, to taking temperature and calculating oxygen saturation. The release of ChatGPT in 2023 marked a leap forward for AI in popular consciousness. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the critical role of doctors' intuition in healthcare. It states that despite AI advancements, human intuition is essential for effective clinical care, as seen in various situations such as detecting sepsis in critically ill children and navigating complex patient care. The text highlights that AI will enhance healthcare but not fully replace human capabilities, suggesting that complete replacement of doctors is unlikely in the near future.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: solanacearum was quantified by measuring fluorescence intensity in the presence or absence of the key bacterial isolates. Water or dimethyl sulfoxide (DMSO) was added to the system as a control group for root exudates soluble in water or DMSO, respectively. C Growth of the pathogen R. solanacearum was quantified by measuring fluorescence intensity in the presence or absence of the key bacterial isolates and supplementation with key root exudates detected in our pot experiment. Additionally, the growth of strain Y14 when supplemented with L-tryptophan at a concentration of 2 mmol L−1 is provided (small inset plot, right). Significance was determined using the Scheirer-Ray-Hare test, as described in the supplementary materials. Error bars represent standard deviations with six replicates. RS denotes R. solanacearum. OD600 denotes absorbance value at a wavelength of 600 nmFull size imageWe repeated these co-culture experiments, this time individually supplementing the identified root exudates into the growth medium at differing concentrations (Fig. 5C and Figs. S13 to 19). Root metabolite treatments influenced the degree of pathogen inhibition, in some cases even without the beneficial bacteria, for example, O-phosphorylethanolamine (Fig. 5C). Chryseobacterium strain Y35 exhibited inhibitory activity under all conditions, which was marginally enhanced by mangiferin (0. 4 mmol L−1) (Fig. 5C). In the case of Chryseobacterium strain Y14, its inhibitory capacity was promoted by L-tryptophan (2 mmol L−1), acetoxyacetic acid (0. 4 mmol L−1), methoxyindoleacetic acid (0. 4 mmol L−1), O-phosphorylethanolamine (10 mmol L−1), and mangiferin (0. 4 mmol L−1). And there was a potential link between L-tryptophan (2 mmol L−1)-triggered inhibition and the growth promotion of Y14 induced by the same compound (Fig. 5C and Table S11). Paenibacillus strain R16 exhibited inhibitory effects under treatment with L-tryptophan (0. [SEP] Justify:

Output: The text does not provide any information that directly supports or rebuts the claim regarding the human microbiome's influence on health and disease. Instead, it focuses on the growth of specific bacterial strains and their interactions with certain compounds. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: As ESC chromatin is more transcriptionally permissive, it is also more sensitive to nuclease activity. This may also be in part due to differences that are seen in the chromatin localization in the nucleus. For instance, one study using the DamID (DNA adenine methyltransferase identification) technique showed that pluripotency genes, including Oct4 and Nanog, move to the nuclear lamina and are silenced while lineage-specific genes disassociate from the lamina and are expressed. This was specifically shown during the differentiation of mouse ESCs into terminal astrocytes which demonstrated cell type-specific relocations of these areas during differentiation [11]. These areas near the nuclear periphery were called Domains (LADS). Further study showed that these areas were enriched with repressive histone modifications, H3K9me2 and H3K27me3 which had tissue-specific distributions. Additionally, they consisted of few, minimally expressed genes, which were also marked by these repressive histone modifications [11-13]. Genome-wide ChIP analyses have also described other lamina associated domains with significant overlap with the LADS domains. These regions referred as Chromatin domains of H3K9me2 or LOCKS, which are also hallmarked with increased H3K9me2 marks, gene-poor and increased in size and abundance from ESCs to differentiated cells (from 4% genome coverage in mouse ESCs to at most 46%, in liver cells) [14]. Wen, Wu et al. described these LOCKs as large regions with K9 modifications up to 4. 9 Mb that are conserved between human and mouse. also show tissue-specific distributions and inversely correlate with gene expression. Specific knockouts of the H3K9 methyltransferase, G9a, abolished LOCK formation in mouse ESCs and caused gene derepression without any peripheral localization alteration [14, 15]. Although LADS and LOCKS are similar in their effects on gene expression and localization, the relationship between them and their function at the nuclear periphery is presently unclear. 2. 2. BivalencySince the discovery of generating induced pluripotent stem (iPS) cells, the ability to reprogram a differentiated cell back towards a more embryonic-like state, it has been shown by a variety of laboratories that there is an extensive amount of epigenetic variability between different iPS lines and even among different clones. [SEP] Justify:

Output: The text corroborates the claim by discussing the concept of induced pluripotent stem (iPS) cells, which are generated by reprogramming differentiated cells back towards an embryonic-like state. This indicates that scientists indeed use cell reprogramming techniques to convert adult cells into a pluripotent state, supportive of the claim.


Input: Task: contrast the claim using the given text. Claim: AI will soon fully replace doctors in making medical diagnoses. [SEP] Text: This type of AI, which requires training on large data sets (supported by human feedback), focuses on giving computers the ability to read, support and manipulate human language. Such natural language processing has changed the communication landscape with its language mimicry. While some note that the hype hasn’t quite been reflected in reality, professionals in a range of sectors — including healthcare — now use ChatGPT for correspondence, such as with drafting “sick notes”, medication management, or to manage healthcare information. There are predictions that healthcare Processing will be a $USD7. 2 billion business by 2028, with this type of AI being deployed to help translate complex published papers for public consumption, for analysis of electronic health records to help identify at-risk patients, and to interact with patients to help with triage or answer healthcare questions. In 2024, some say this type of AI is likely to focus on more sophisticated language models that power chatbots and virtual assistants, and will be built into word processing programs. gives computers the ability to learn without explicitly being programmed for a given task. The algorithms driving these types of AI are based on statistical and predictive models. often relies on ‘training’ from existing data sets, which have been human reviewed and annotated. Essentially, doesn’t automatically know what to look for, and without human-informed training this type of AI tends to provide lots of noise and useless predictions. Once trained, can take previously unseen patient information and apply its prior ‘training’ to analyse the data and predict outcomes, or make recommendations. In healthcare, can recognise patterns which may be missed by humans such as described for AI’s role in patient survival of gastric cancer, identification of primary causes of cancers, and reducing breast cancer false positives. In 2024, algorithms are likely to continue to be used for probing healthcare data analytics, with vast medical data provided by wearables, medical devices and electronic health records. With all forms of healthcare AI — it is clear that humans are still needed for AI training, evaluation of outputs, and considering the impacts of the AI recommendation. Artificial versus human intelligence: Who has the edge? Given the global healthcare workforce shortages, the proverb may be true: Necessity breeds invention. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that while AI can assist in various healthcare tasks, it cannot replace the need for human involvement. It mentions that 'humans are still needed for AI training, evaluation of outputs, and considering the impacts of the AI recommendation,' indicating that AI is a tool to support doctors rather than replace them completely.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: Additionally, exposure to methoxyindoleacetic acid, O-phosphorylethanolamine, and L-tryptophan induced inhibitory effects on the pathogen by Paenibacillus R16 or Dyadobacter Y33. Moreover, previous results have shown that certain organic acids and flavonoids like mangiferin [56] can boost plant immunity by modulating plant-microorganism interactions in rhizosphere [57, 58]. Notably, Bacteroidetes, to which Chryseobacterium and Dyadobacter belong, have been reported to utilize phosphorylated compounds as carbon and energy sources, providing a competitive advantage when inhabiting zones of high microbial activity and nutrient demand [59]. In summary, certain root metabolites can improve control of the pathogen by recruiting disease-suppressive microbes, and improved soil phosphorus nutrient status facilitates this process. Moreover, phosphorus-starved plants, despite a weakened disease-suppressive function in rhizosphere, can form symbiosis with arbuscular mycorrhizal fungi (AMF) in response to pathogen inoculation [60, 61], serving not only for defense but also for phosphorus acquisition [62, 63]. Similar to this strategy, previous studies have shown that plants favor relationships with ectomycorrhizal fungi under phosphorus deficiency. While this choice may result in lower phosphorus acquisition efficiency, it contributes more significantly to plant defense compared to alternative options [4, 64]. Our study indicates an AMF-based strategy for disease suppression is a favorable choice, and this symbiosis is further supported by the presence of key root exudates such as blumenol A and quercetin [65,66,67]. Our results were obtained under controlled conditions with consistent pathogen inoculation, which is different from field conditions. While phosphorus supply promotes plant defense and soil disease suppression in the rhizosphere, it may also promote pathogen proliferation in bulk soil, consequently heightening the risk of future infections. It remains to be seen to what extent this disparity of pathogen control between bulk and rhizosphere soils impacts the dynamics of disease incidence over time in the field. Future research should explore the optimal phosphorus levels for disease control and agricultural output, balancing benefits with the risks of excessive phosphorus. ConclusionsOur 6-year field trial highlighted phosphorus availability as the primary factor influencing the control of tomato bacterial wilt by R. solanacearum. [SEP] Justify:

Output: The text is irrelevant to the claim about the human microbiome's influence on health and disease. Instead, it focuses on plant-microorganism interactions, particularly concerning phosphorus availability and pathogen control in plants, which does not address the mechanisms through which the human microbiome operates.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: As a result, much research has been involved in understanding the global demethylation and methylation patterns in ESC to identify particular epigenetic marks and their effected genes to study their role in pluripotency and reprogramming. Methylation of DNA cytosine residues (mCG), particularly in CpG islands of promoters, is a well-established mechanism that represses gene transcription in adult cells [16]. Several studies have specifically shown in ESCs, that loss of DNA methyltransferases also compromises their ability to differentiate into mature cells without affecting their self-renewal [17]. In embryonic stem cells, lineage-specific gene expression program regulators are repressed, but poised for a rapid response to differentiate [18]. These areas of chromatin, have so called bivalent domains, consist of opposing chromatin marks; i. e. H3K4me for activation and H3K27me for silencing. In ESCs, these domains are believed to be responsible for preventing the transcription responsible for their early differentiation to a specific lineage while priming the area for activation when the appropriate cues are expressed. Consistent with this belief is the findings that the bulk of the protein-encoding genes of human ESCs, including transcriptionally inactive genes have H3K4me, H3K9ac and H3K14ac rich promoter regions in areas of the nucleosome adjacent and downstream of transcription start sites [19, 20]. Moreover, in ESCs, genes with bivalent gene promoters tend to have unmethylated CpG islands [21]. The initial step of active DNA demethylation in mammals occurs by the conversion of 5-methylcytosine of DNA (5mC) to 5-hydroxymethylcytosine (5hmC). A prime example of this in pluripotent stem cells has been shown in regulating the expression of the stem cell transcription factor, Nanog. Here, the demethylated state is critical for the upregulation of Nanog which is an essential regulator of ESC pluripotency and self-renewal, while its downregulation attributed to methylation of its promoter is required for ICM specification [22]. [SEP] Justify:

Output: The text corroborates the claim by discussing the processes involved in maintaining pluripotency in embryonic stem cells (ESCs), specifically mentioning the role of epigenetic changes (like methylation) that are essential for reprogramming cells. It highlights the mechanisms that allow cells to remain in a pluripotent state and discusses factors such as the transcription factor Nanog that are critical for this process, which implies relevance to the broader context of converting adult cells into pluripotent cells.


Input: Task: contrast the claim using the given text. Claim: AI will soon fully replace doctors in making medical diagnoses. [SEP] Text: It may not be long until healthcare includes integrated AI forms where a robot greets you in your native language for your annual check-up using Processing, takes your vital signs, and sends a recommendation to the doctor on which patient to prioritise and what investigations need to be ordered using algorithms designed to analyse collected vital signs. What AI can’t do is replace the natural ‘gut feel’ of a healthcare professional. And this won’t change in 2024. The clinical reasoning and thinking process that healthcare providers engage is so complex, and the sources of information that the human brain considers in patient care are too numerous to capture with current algorithms. The implicit knowledge an expert relies on for effective clinical care is so deeply embedded in human automation, that methods to get at these data points often fail. On top of this, AI-accessible data and the AI algorithms themselves can have flaws. Machine learning can be overly sensitive, leading to over-diagnoses in some patients. Processing AIs can act as healthcare trojan horses, where the technology is so convincing in its communication approaches that it tricks the user into thinking it is knowledgeable in the same way a human is. There are also privacy concerns with such AI applications. AI typically relies on data input to continue learning — and the clarity about what happens to confidential patient information when input into AI remains an open question for many platforms. There are also challenges that the healthcare AI field is tackling related to bias and responsibility, and questions about what we consider ‘mundane’ and ‘repetitive’ tasks which AI can truly take off humans. In reality (ironically), all existing AI is devoid of the necessary context within which healthcare occurs. It misses the complexity, the empathy, and important data points that human intelligence has access to, and can therefore only replicate specific human tasks. Our healthcare future: Humans leading, AI supporting A better description of the role of AI in healthcare in the future might be: “AI won’t replace the doctors, but those doctors will be replaced who don’t use Artificial Intelligence,” as Reddy, director at India’s Apollo, has put it. Healthcare AI is increasingly taking on roles of “clinical decision-making support”, meaning the healthcare provider is in charge and human intelligence is prioritised, while AI augments this. [SEP] Justify:

Output: The text contrasts the claim by elaborating that while AI will play a role in healthcare, it cannot replace the essential qualities of human doctors, such as 'gut feel,' clinical reasoning, and complex decision-making skills that are crucial in patient care. The text states, 'AI won’t replace the doctors,' stressing that the future role of AI is to support healthcare providers rather than fully take over their responsibilities.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: Pot experiments confirmed that adequate phosphorus activates plant immunity, improving rhizosphere microbiome function related to disease suppression, but not in the bulk soil. Adequate available phosphorus in the rhizosphere alleviates the trade-off between defense function and phosphorus acquisition in plants, promoting the production of root exudates, such as L-tryptophan and O-phosphorylethanolamine, which enhance key microbial groups, such as Chryseobacterium, and their disease suppression capabilities in the rhizosphere. Plant selection emerges as the driver of the discrepancy between effects in bulk versus rhizosphere soil. In total, our study provides novel insights into the influence of phosphorus availability on the disease-suppressive functions of the rhizosphere microbiome. Understanding of how plant trade-offs between defense and phosphorous acquisition impact microbiome recruitment and function will help inform future strategies to optimize plant nutrition and natural biological control of plant pathogens. Availability of data and materials Raw amplicon sequencing data was deposited at the for (NCBI) under the accession number of PRJNA979018, PRJNA978981 and PRJNA1077633. AbbreviationsITS: Internal transcribed spacer NRPS: Nonribosomal peptide synthases PKS: Polyketide synthases CFU: Colony-forming units NB: zOTU: Zero-radius operational taxonomic units TPM: Transcripts per million OD: Optical density PCoA: Principal coordinates analysis WGCNA: Weighted gene co-expression network analysis ME: Module eigengenes DI: Disease incidence AVP: Available phosphorus SOM: Soil organic matter DMSO: Dimethyl sulfoxide 9. 23. 92. 21. 1005. 8. 29. 17. 9. 12. 3. 11. 63. 99. 33. 6003. 2005. 62. 8. 100. 75. 42. 62. 11. 94. 20. 56. 90. 1904. 62. 2. 1257. Edgar RC. : a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. Biorxiv. 2016;074161. wilt disease incidence is influenced by shifts of soil microbial communities under different monoculture spans. Microb Ecol. 2018;75:739–50. 2013. 10. 70. 77. 1005. 81. 46. 50. 2008. [SEP] Justify:

Output: The text discusses the impact of phosphorus availability on plant immunity and the rhizosphere microbiome's function in disease suppression, but it does not make a clear connection to the influence of the human microbiome on health and disease. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Scientists use cell reprogramming to turn adult cells into pluripotent stem cells. [SEP] Text: Recent studies demonstrate that demethylation of Nanog is in part contributed to the expression of the Tet methylcytosine dioxygenase 1 (TET1) enzyme which is a TET family member of enzymes that catalyze the conversion of 5mC to 5hmC. This enzyme has been found to demethylate Nanog promoter sites in mouse ES cells [23, 24]. Both TET1 and TET2 expression have also been shown to be regulated by Oct4 expression in mouse ES cells, downregulated following differentiation alongside other stem cell markers, and is induced concomitantly with 5-hmC during fibroblast reprogramming into iPS cells [25]. In addition to promoter regulation, methylC-Seq genome-wide analysis has also discovered novel types of DNA methylation regulation at non-CG sites (CHG and CHH sites where H = A, C, or T residues). These analyses showed that non-CG methylation accounted for 25% of the total ESC methylome and that these sites were more commonly found within gene bodies than within promoter sites [26]. Furthermore, the methylation of these sites was lost when differentiation was induced in ESCs, and restored during the generation of in induced pluripotent stem cells. This included many differentially methylated regions associated with genes involved in pluripotency and differentiation. 2. 3. Polycomb and trithorax group proteins in pluripotent stem cellsRecent studies have established that developmental gene priming and bivalency are crucial for pluripotency whereby the chromatin of pluripotent stem cells are transcriptionally permissive, with normally silent DNA repeat regions, transcriptionally related histone modifications such as H3K9ac, H3K4me3, H3K36me3 and low stochastic transcription of lineage-restricted genes [8, 9]. The poised state is believed to inhibit the activity of () and thereby deregulate elongation. In the poised state, demonstrates high Ser5 phosphorylation and low levels of Ser2 phosphorylation which is in part controlled by a number of complexes involved in this process. For this, several groups or families of complexes involved in epigenetic regulation have been studied in pluripotent stem cells. [SEP] Justify:

Output: The text corroborates the claim by discussing the process of fibroblast reprogramming into induced pluripotent stem (iPS) cells, which relates to the conversion of adult cells into pluripotent stem cells. It refers to mechanisms such as the expression of the TET1 enzyme during this reprogramming process, indicating a scientific basis for the claim about cell reprogramming leading to pluripotency.


Input: Task: contrast the claim using the given text. Claim: AI will soon fully replace doctors in making medical diagnoses. [SEP] Text: Under this model, AI could be programmed to alert the healthcare provider on all the variables not considered in an algorithm, to help the healthcare provider explore to what extent the AI recommendations can be valuable in a specific context or with a specific patient. For instance, if an AI is recommending antidepressants for a patient who is also pregnant, it would alert the doctor that such medications aren’t yet tested in this population — allowing the doctor to consider other data points in deciding the next best step. To support this model — in which humans lead, but AI support — the AI-integrated future shouldn’t just focus on improving AI. An AI-augmented healthcare system must include healthcare worker training and support in both AI literacy and human intelligence literacy. Education can also focus on helping healthcare providers recognise when to question patterns; how to challenge predictions; and the value of trusting intuition — essentially building capacity to tolerate uncertainty. This future-protecting healthcare education would help reduce the risk of “automation bias,” whereby humans allow the AI to work autonomously and the algorithm is trusted, even in the face of clear evidence that it’s wrong. AI has given us a valuable gift: the opportunity to explore what aspects of human intelligence are critical in healthcare, and which tasks can be enhanced with technology. After all, both human brains and AI are wired for prediction, but humans have the power to interrogate, question and evaluate these predictions while AI has computing power beyond a human brain. Both are needed to manage the complexities of patient care. Michelle D. is the Director of the Centre for Education and of the Centre for Scholarship in at in Australia. She is an award-winning educator, having received the ‘Teaching Excellence’ award amongst others, and is the author of the “Effect: How to Survive and Thrive through the Unexpected“. Originally published under by 360info™. Enjoy this 2024. Here’s why. Focus on 2024 It’s a wonderful year for a Moon dance Hydrogen can revolutionise clean energy, transport, decarbonise industrial processes … and cook your dinner. Focus on 2024 Hydrogen makes itself at home The UK and US have both approved a gene editing therapy to overcome the genetic mutation that causes sickle cell disease, but the treatment is expensive. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the importance of human involvement in medical diagnoses alongside AI. It discusses how AI can alert doctors to consider additional important variables and suggests a supportive role for AI rather than a replacement. The text advocates for training healthcare providers in AI literacy and intuition, illustrating the need for human oversight to avoid reliance on potentially flawed AI alone.


Input: Task: pass the claim using the given text. Claim: The human microbiome influences health and disease through various mechanisms. [SEP] Text: Niche-adaptation in plant-associated Bacteroidetes favours specialisation in organic phosphorus mineralisation. ISME J. 2021;15:1040–55. 49. 107. CAS PubMed 37. 56. 51. 48. 86. 7336. 9200. 9102. 17. 2479. 154. 2119. 7372. 612. 1266. 16. 57. 7093. 74. 91. 1. Information of field trial. (A) Overview of soil nutrient properties and disease incidence over the course of the continuous six-year trial. (B) Results of unary linear regression analysis depicting the relationship between soil organic matter, total nitrogen, available potassium contents and disease incidence. Fig. S2. Information of pot experiment. (A) Line graph displaying soil available phosphorus levels in different treatments during seasons 1 and 2, with error bars representing standard deviations. The results of significance test were in 3. (B) The violin plot illustrating the fold change of the pathogen Ralstonia solanacearum in bulk and rhizosphere soils compared to the CK group. Hare tests were conducted. Fig. S3. Detailed results of individual genes related to disease suppression. The boxplot depicting the fold change of disease-resistant genes and hcnAB compared to the CK group in bulk soil (top row) and rhizosphere soil (bottom row). + RS denotes R. solanacearum inoculation. Fig. S4. Shannon diversity of soil microbiome and microbial zOTUs responding to phosphorus stress. (A and B) Boxplot showing the Shannon diversity of soil microbial community in different treatments. Kruskal wallis test was conducted for the significance here. Manhattan plot showing the bacterial and fungal zOTUs significantly correlated with phosphorus stress (Spearman correlation, P < 0. 05) in both bulk and rhizosphere soils. Fig. S5. Microbial taxa responding to phosphorus stress in bulk soil. (A) Butterfly plot illustrating bacterial zOTUs significantly correlated to phosphorus stress (Spearman correlation, P < 0. 05). (B) Butterfly plot illustrating fungal zOTUs significantly correlated to phosphorus stress (Spearman correlation, P < 0. 05). The numbers next to the histograms indicate the number of zOTUs in the corresponding genus. The "r" represents the correlation coefficient. Fig. S6. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses niche-adaptation in plant-associated Bacteroidetes and various findings from soil and microbial community studies without addressing the human microbiome or its influence on health and disease. Thus, it does not provide support or contradiction to the claim.