Input: Task: contrast the claim using the given text. Claim: Every disease can be cured through gene editing techniques. [SEP] Text: ” Lynn Kee, from Stetson University, agrees: “The ease with which technologies are being programmed and deployed has allowed the tool to be implemented in arenas applicable across biology, medicine and wider society. ” Anil and Lynn are part of the in the Classroom Network, which aims to develop a national community of biology educators and empower them to integrate 9 into their courses. They both run courses for undergraduates at their respective universities, where they give them direct experience in using the technology to modify organisms’ genomes. -Cas9 works -Cas9 allows scientists to edit parts of an organism’s genome by precisely removing, adding or altering sections of its DNA. It has two core components. The first is an enzyme called Cas9, which acts as a pair of ‘molecular scissors’, cutting the DNA at a specific location. It is accompanied by a piece of guide RNA, which is tailored to complement the DNA sequence of interest, and guides the Cas9 enzyme to the right point to cut. “The tool adds or removes DNA by manipulating natural DNA repair mechanisms,” explains Lynn. Anil adds, “Because cells targeted by -Cas-9 do not tolerate the double strand breaks (DSBs) it creates, they repair themselves using their own biochemical machinery. ” technology is already changing lives. “The first human clinical trial using technology for immunotherapy cancer treatment is underway,” says Lynn. “It involves removing patients’ immune cells, editing them in vitro using to specifically attack cancer cells, and reinserting them into the patient. ” Similar techniques are being used to treat blood disorders too. Clinical trials are also using in vivo approaches. For example, modified viruses are inserted into patients’ retinal cells, where they release components that modify the gene that controls the genetic eye disorder Leber congenital amaurosis 10. “is also penetrating our everyday lives in other ways,” says Lynn. “For instance, crops such as tomatoes and mushrooms are being modified to be bigger and to stay fresh for longer. ” In the undergraduate lab “-Cas9 technologies are becoming commonplace in both academic and industrial laboratories,” says Anil. “A working knowledge of the system is highly valuable to undergraduate students for their future careers. [SEP] Justify:

Output: The text contrasts the claim as it describes the capabilities and limitations of gene editing technologies like Cas9 in a realistic manner. While it highlights successful examples of gene editing, such as in immunotherapy for cancer treatment and treating genetic disorders, it does not assert that gene editing can cure every disease. It specifically mentions that clinical trials are underway for certain conditions and that technology is making a significant impact, but it does not provide evidence that every disease is curable, thereby rebuffing the claim.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Этому отрезку отвечает интервал интенсивной сиализации коры, проявленный максимальным за всю историю Земли всплеском анорогенного внутриплитного гранитного магматизма (Cawood, Hawksworth, 2015). В этот же период были сформированы основные осадочные месторождения урана, комплексные и весьма специфичные по составу U-Au-Fe-TR руды (IOCG - тип) (Олимпик Дам), магнетит-апатитовые (типа Кируна), полиметаллические (тип - SEDEX), ильменитовые месторождения (Goldfarb et al. , 2010). [SEP] Justify:

Output: The text discusses geological processes on Earth and does not mention lunar ice deposits or ancient microorganisms, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Cost-effectiveness analysis and user acceptability evaluations were coveredA total sample size of 12,500 patients (10,000 preintervention and 2500 postintervention)The study implemented a real-time NLP-driven CDS tool for screening opioid misuse with a sensitivity of 93% and specificity of 92%. The workflow was reproducible and included a shared pseudocode for cloud service implementationai2b2: Informatics for and the Bedside. bVA: US Department of Veterans Affairs. cNLP: natural language processing. dcTAKES: Analysis and System. eCDSS: clinical decision support system. fEHR: electronic health record. gCDS: clinical decision support. hBD: big data. iCOPD: chronic obstructive pulmonary disease. jDDSS: diagnostic decision support system. Of the 26 387. [FREE Full text] [CrossRef] [Medline]Abdill R, Blekhman R. Tracking the popularity and outcomes of all bioRxiv preprints. Elife. 2019;8:e45133. [FREE Full text] [CrossRef] [Medline]. Identifying credible sources of health information in social media: principles and attributes. NAM Perspect. 2021;2021:10. 31478/202107a. [FREE Full text] [CrossRef] [Medline]-Luque L. HealthRecSys: a semantic content-based recommender system to complement health videos. . 2017;17(1):63. [FREE Full text] [CrossRef] [Medline]Muhiyaddin R, Abd-. The impact of clinical decision support systems (CDSS) on physicians: a scoping review. Technol Inform. 2020;272:470-473. [CrossRef] [Medline]ínez-Salvador B, Boscá D, Robles M. Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility. J Biomed Inform. 2013;46(4):676-689. [FREE Full text] [CrossRef] [Medline]. Clinical decision support: a 25 year retrospective and a 25 year vision. Inform. 2016;25(Suppl 1):S103-S116. [SEP] Justify:

Output: The text supports the claim by mentioning the implementation of a real-time NLP-driven clinical decision support (CDS) tool designed for screening opioid misuse. This indicates that natural language processing (NLP) is being actively applied to analyze and improve the interpretation of medical data, thus highlighting its role in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: Every disease can be cured through gene editing techniques. [SEP] Text: ” If it does, the next step is embryo injections for in vivo testing of what happens when the gene in question is ‘knocked out’. “Students have a go at making microinjections and, later, observe for phenotypes caused as a result of the loss of gene function,” says Anil. “Finally, they isolate the embryos with these phenotypes and perform PCR-based genotyping experiments to find out what genetic mutations have arisen due to -Cas9 activity. ” This project has had successes beyond the classroom. “The big highlight has been the publication of a peer-reviewed journal 10. 33424/442 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 plating E. coli bacteria on culture plates Student using forceps and scissors to carefully cut butterfly wings for analysis Anil and student checking zebrafish embryos in a petri dish © Students looking at butterfly wings using a stereomicroscope. The camera on top of the microscope streams the view to student’s phone on the table. Student using paintbrush to place butterfly eggs onto a plate to microinject with showing patches of black on its wing due to modification Lynn with her student in the lab This led to the selection of painted lady butterflies, which lay hundreds of eggs, each about 1 mm in length. “Using a stereomicroscope, students can use a microinjection system to poke a hole into the butterfly egg and push in the components,” says Lynn. “It’s a process that requires strong hand-eye coordination, but, with practice, students become better and more confident. ” And the experience has paid dividends. “The teamwork and class camaraderie has been amazing,” says Lynn. “Students have been able to successfully create the desired change, and some were even successful in rearing a butterfly with a physical change due to the targeting, such as an unusual wing colour. From this, students learn that the gene they are targeting with is responsible for normal wing colour development. ” Lynn also discusses the ethics involved in genetic manipulation with the class. “I showcase real-world case studies, and we talk about the bioethical implications,” she says. “It’s important to me that the students are informed citizens of the world and can communicate about their work. [SEP] Justify:

Output: The text contrasts the claim by providing an account of a gene editing educational project that highlights both the successes and limitations of the gene editing process. It emphasizes that while students successfully create changes in butterflies, the process relies on 'understanding the gene they are targeting,' and discusses the complexities involved, including the ethics of genetic manipulation. This suggests that gene editing might not universally cure all diseases, as it requires precise targeting and understanding of genetic functions.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Спецификой данной металлогенической эпохи является ее преобладающий уран-редкоземельный профиль и отсутствие крупных собственно золоторудных объектов. Вероятно, это отражает изменения в глубинах Земли, а именно, прекращение роста ядра и его активной дегазации (Маракушев и д. , 1997; Рябчиков, 1997). Таким образом, остается в силе предположение (Cawood, Hawksworth, 2015) об уникальности этого периода в силу большой стабильности. [SEP] Justify:

Output: The text does not address the claim about lunar ice deposits containing ancient microorganisms. Instead, it discusses characteristics of a metallogenic epoch and geological changes within the Earth, which are irrelevant to the claim regarding lunar ice and microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: [FREE Full text] [CrossRef] [Medline]Roberts K, Harabagiu SM. A flexible framework for deriving assertions from electronic medical records. J Inform Assoc. 2011;18(5):568-573. [FREE Full text] [CrossRef] [Medline] 2020 statement: an updated guideline for reporting systematic reviews. PLoS Med. 2021;18(3):e1003583. [FREE Full text] [CrossRef] [Medline]Greenhalgh T, Peacock R. Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ. 2005;331(7524):1064-1065. [FREE Full text] [CrossRef] [Medline]CASP (qualitative studies) checklist. Skills Programme. 2018. URL: https://casp-uk. net/images/checklist/documents/CASP-Qualitative-Studies-Checklist/CASP-Qualitative-Checklist-2018_fillable_form. pdf [accessed 2020-12-22] -Machado L. "Big data" and the electronic health record. Inform. 2014;23(1):97-104. [FREE Full text] [CrossRef] [Medline]van de Combining text mining with clinical decision support in clinical practice: a scoping review. J Inform Assoc. 2023;30(3):588-603. [FREE Full text] [CrossRef] [Medline]- MITRE system for clinical assertion status classification. J Inform Assoc. 2011;18(5):563-567. [FREE Full text] [CrossRef] [Medline]. A knowledge discovery and reuse pipeline for information extraction in clinical notes. J Inform Assoc. 2011;18(5):574-579. [FREE Full text] [CrossRef] [Medline] A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Inform Assoc. 2011;18(5):601-606. [FREE Full text] [CrossRef] [Medline]D'. Automated concept-level information extraction to reduce the need for custom software and rules development. J Inform Assoc. 2011;18(5):607-613. [SEP] Justify:

Output: The text corroborates the claim by presenting various studies and frameworks that utilize information extraction, text mining, and machine-learning approaches in clinical settings. These methodologies are critical components of natural language processing, which is instrumental in deriving insights from medical literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/intechopen. 85499 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Intech by Yuan-From the - Technologies and by Yuan-and Shiu-purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www. cbspd. com | customercare@cbspd. com 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Impact of this 1. IntroductionGene editing is a type of genetic engineering in which DNA is inserted, deleted, modified, or replaced in the genome of a living organism. Unlike traditional methods that randomly insert genetic material into a host genome, current gene editing technologies target and change the specific genome locations. Zinc finger nucleases (ZFNs), transcription activator-like effectors nucleases (TALENs), and clustered regularly interspaced short palindromic repeats ()/Cas9 nuclease system are the three common gene editing technologies. These technologies have been widely used in genome engineering to enable a broad range of mutation by inducing DNA breaks that stimulate error-prone repairs such as homologous recombination (HR) or nonhomologous end joining (NHEJ). They successfully make it possible to achieve site-specific editing, modification, and manipulation at specific genomic sites (Table 1) [1]. [SEP] Justify:

Output: The text contrasts the claim by describing gene editing as a precise method of genetic engineering that involves modifying specific DNA sequences in living organisms. However, it implies that these manipulations could raise ethical dilemmas and risks, particularly through its mention of technologies such as ZFNs and TALENs which involve complex procedures influencing genetic information. The claim's assertion that all applications are risk-free is directly challenged by the underlying complexities and potential unintended consequences associated with gene editing practices.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: В целом же, можно уверенно говорить о корреляции крупнейших эпох золотой минерализации с суперконтинентальными циклами и главными орогенными событиями в их составе. Эволюция минерального состава месторождений и их металлогенических ассоциацийИменно после этого перерыва и распада сверх-суперконтинента произошло четкое разделение золотых месторождений на вещественные типы и расширился спектр металлогенических ассоциаций золотых руд. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the correlation between epochs of gold mineralization and supercontinent cycles, which does not relate to lunar ice deposits or ancient microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: [FREE Full text] [CrossRef] [Medline]Kotfila C, Uzuner Ö. A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases. J Biomed Inform. 2015;58 Suppl(Suppl):S92-S102. [FREE Full text] [CrossRef] [Medline]- cTAKES extensions for document classification: architecture and application. J Inform Assoc. 2011;18(5):614-620. [FREE Full text] [CrossRef] [Medline] Clinical decision support with automated text processing for cervical cancer screening. J Inform Assoc. 2012;19(5):833-839. [FREE Full text] [CrossRef] [Medline] Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening. J Inform Assoc. 2013;20(4):749-757. [FREE Full text] [CrossRef] [Medline] Modeling decision support rule interactions in a clinical setting. Technol Inform. 2013;192:908-912. [Medline] An efficient pancreatic cyst identification methodology using natural language processing. Technol Inform. 2013;192:822-826. [Medline]. Classifying the indication for colonoscopy procedures: a comparison of NLP approaches in a diverse national healthcare system. Technol Inform. 2015;216:614-618. [Medline] Scaling-up NLP pipelines to process large corpora of clinical notes. Med. 2015;54(6):548-552. [CrossRef] [Medline]. A decision fusion framework for treatment recommendation systems. Technol Inform. 2015;216:300-304. [Medline]Marco-. Multidisciplinary modelling of symptoms and signs with archetypes and -CT for clinical decision support. Technol Inform. 2015;210:125-129. [Medline] A methodology for mining clinical data: experiences from project. Technol Inform. 2015;210:85-89. [Medline]. Automatic extraction of breast cancer information from clinical reports. 2017. Presented at: 2017 IEEE 30th on Computer-Systems (CBMS); June 22-24, 2017:213-218; Thessaloniki, Greece. [CrossRef]. Clinical assistant diagnosis for electronic medical record based on convolutional neural network. Sci Rep. [SEP] Justify:

Output: The text supports the claim by providing various examples of how natural language processing (NLP) is utilized in different aspects of medical research, such as disease classifier performance for phenotype identification, clinical decision support systems, and automated text processing for cervical cancer screening. It emphasizes the role of NLP in classifying medical data and processing clinical notes, which directly correlates with the claim that NLP assists in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: /Cas9SourceExtensively exists in the naturePlant pathogenic bacteria XanthomonasAn adaptive immune system in bacteriaTargeting specificity determinantZinc finger protein9Restriction for a target sequenceRich in cytosineNo special restrictionPAMTarget siteTwo (left and right)Two (left and right)Only oneLength of target gene12–18 bp12–18 bp18–23 bpMode of actionTwo proteins act on two target sitesTwo proteins act on two target sitesRNA and Cas9 act on one target siteCleavage siteDSB with a sticky endDSB with a sticky endDSB with a blunt endEfficiencyMediumMediumHighEase of engineeringLowMediumHighEase of characterizationtargetYesYesYesTable 1. Comparison of different gene editing technologies. Abbreviations: clustered regularly interspaced short palindromic repeat; DSB, double-strand break; Fok, Flavobacterium okeanokoites; PAM, protospacer adjacent motif; TALE, transcription activator-like effector; sgRNA, single guide RNA; and ZFN, zinc finger nuclease. 1. 1 are artificial restriction enzymes generated by fusing a zinc finger-specific DNA-binding domain to a nonspecific DNA cleavage domain. The specific binding domains of individual ZFNs typically contain between three and six individual zinc finger repeats and can each recognize between 12 and 18 base pairs. If the zinc finger domains are specific for their intended target site, then even a pair of three-finger ZFNs that recognize a total of 18 base pairs can target a single locus in a mammalian genome. The nonspecific cleavage domain from the restriction endonucleaseis typically used as the cleavage domain in ZFNs. This cleavage domain must form a dimer in order to cleave DNA, and thus a pair of ZFNs are required to target nonpalindromic DNA sites [2] (Figure 1). Figure 1. ZFNs are chimeric nucleases consisting of specific DNA-binding modules linked to a nonspecific cleavage domain. Right and left ZFNs containing endonucleases link to an array of 3–6 zinc fingers that have been designed to specifically recognize target sequences (12 green and 12 purple boxes), respectively. The ZFN targets are separated by typically 5 or 6 bp. [SEP] Justify:

Output: The text describes various gene editing technologies and their complexities, indicating that the ease of engineering and efficiency can vary among them. By highlighting the nuances of how gene editing tools operate, such as the need for specific binding domains and the structural requirements for effective gene cleavage, it implies that not all gene editing applications are risk-free. The presence of potential risks and the complexity of the systems involved suggest that ethical dilemmas may arise, contradicting the claim that all gene editing applications are risk-free and pose no ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Начиная с байкальского тектогенеза, в поведении золота проявляется двойственность, обусловленная отчетливым разделением золоторудных поясов тектонических зон мафического (островодужного, субдукционного), фемисиалического (аккреционного) и сиалического (коллизионного) профиля. Еще С. Д. Шером было подмечено разное положение золоторудных месторождений в двух крайних типах таких тектонических структур (Шер, 1974). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the duality in the behavior of gold related to tectonic zones and gold-bearing regions, without making any mention of lunar ice deposits or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: 2018;8(1):6329. [FREE Full text] [CrossRef] [Medline]- Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery. Epilepsia. 2020;61(1):39-48. [FREE Full text] [CrossRef] [Medline]. Designing an openEHR-Based pipeline for extracting and standardizing unstructured clinical data using natural language processing. Med. 2020;59(S 02):e64-e78. [FREE Full text] [CrossRef] [Medline] User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis. . 2021;28(1):e100331. [FREE Full text] [CrossRef] [Medline]. Identification of preanesthetic history elements by a natural language processing engine. Anesth Analg. 2022;135(6):1162-1171. [FREE Full text] [CrossRef] [Medline]'Neil AQ, Clinical Evaluators. Evaluating the impact on clinical task efficiency of a natural language processing algorithm for searching medical documents: prospective crossover study. . 2022;10(10):e39616. [FREE Full text] [CrossRef] [Medline] Deployment of real-time natural language processing and deep learning clinical decision support in the electronic health record: pipeline implementation for an opioid misuse screener in hospitalized adults. . 2023;11:e44977. [FREE Full text] [CrossRef] [Medline]Clark R, Moloney G. Facebook and older adults: fulfilling psychological needs? J Aging Stud. 2020;55:100897. [CrossRef] [Medline]. Integrating contextual sentiment analysis in collaborative recommender systems. PLoS One. 2021;16(3):e0248695. [FREE Full text] [CrossRef] [Medline]Yanofsky NS. Towards a definition of an algorithm. J Log Comput. 2010;21(2):253-286. [CrossRef]. Extreme learning machine: algorithm, theory and applications. Rev. 2013;44:103-115. [CrossRef]Guo W, Kraines SB. Semantic content-based recommendations using semantic graphs. Med Biol. 2010;680:653-659. [CrossRef] [Medline]. [SEP] Justify:

Output: The text corroborates the claim by mentioning several instances where natural language processing (NLP) is applied in the medical field, such as 'extracting and standardizing unstructured clinical data using natural language processing' and 'evaluating the impact on clinical task efficiency of a natural language processing algorithm for searching medical documents'. These examples illustrate the utility of NLP in analyzing medical literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: I work as a homodimer to cleave the sense strand 1 bp and antisense strand 5 bp downstream of the binding site. 1. 2 activator-like effectors (TALEs) are proteins secreted by bacteria Xanthomonas via type III secretion system when they infect various plant species. TALEs are important virulence factors that act as transcriptional activators in the plant cell nucleus [3]. Each TALE contains a central repetitive region consisting of varying numbers of repeat units (about 17. 5 repeats) of 34 amino acids [3, 4]. The DNA-binding domain contains a highly conserved 34 amino acid sequence with the exception of the 12th and 13th amino acids [3]. Only the 12th and 13th amino acids in TALEs are changeable and variable, the other amino acids are constant and stable. These two locations—repeat variable di-residues (RVD) are highly variable and show a strong correlation with a specific nucleotide recognition by different frequency, for example, NI recognize A (55%), NG recognize T (50%), NN recognize G (7%), and HD recognize C(69%) [3]. Two amino acids have known to recognize one nucleotide after the breaking of code of DNA-binding specificity of TALEs [3, 4]. The restriction endonuclease consists of an N-terminal specific DNA-binding domain and a C-terminal nonspecific DNA cleavage domain. The nonspecific DNA cleavage domain of cleaves the double-strand DNA (DSB) at a fixed distance of 9 and 13 nucleotides downstream of the recognition site [5]. TALENs are artificial restriction enzymes generated by fusing the specific -binding domain to a nonspecific DNA cleavage domain [6, 7, 8] (Figure 2). Figure 2. The specific repeat variable Di-residues (RVDs) used to recognize each base are defined in the key (NI:A, NG:T, NN:G, and HD:C). Left and right TALEs recognize their target sequences and allow their associated endonucleases to work as a homodimer to cleave the sense strand 9 bp and antisense strand 13 bp downstream of the binding site. [SEP] Justify:

Output: The text contrasts the claim by providing detailed information about the mechanisms of gene editing using TALEs and TALENs, illustrating that these applications involve complex biochemistry and specificity. Although the text does not directly address ethical dilemmas, the intricate nature of gene editing implies inherent risks and challenges, suggesting that not all applications are risk-free.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Для энсиматических островных дуг характерна связь золота с колчеданными рудами (Урал и др. ) ранних этапов развития, а для золото-кварцевых жил калифорнийского и других типов – с поздним, орогенным этапом. В сиалических складчатых поясах деформированных пассивных окраин континентов мощная вспышка золотого оруденения колымского типа была проявлена в позднеорогенный этап. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses gold deposits in geological contexts and does not mention lunar ice or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001;34(5):301-310. [FREE Full text] [CrossRef] [Medline]Kostkova P, Saigí-Rubió Data and digital solutions to support surveillance strategies in the context of the COVID-19 pandemic. Health. 2021;3:707902. [FREE Full text] [CrossRef] [Medline]Schoene AM, Basinas I, van Tongeren M, Ananiadou S. A narrative literature review of natural language processing applied to the occupational exposome. . 2022;19(14):8544. [FREE Full text] [CrossRef] [Medline]Martín-Noguerol T, Paulano-Godino F, López-Ortega R, Górriz. Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team. Clin Radiol. 2021;76(5):317-324. [CrossRef] [Medline]. Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data. Clin Radiol. 2019;74(5):346-356. [CrossRef] [Medline] Artificial intelligence versus doctors' intelligence: a glance on machine learning benefaction in electrocardiography. Discoveries (Craiova). 2017;5(3):e76. [FREE Full text] [CrossRef] [Medline]. Automatic extraction of relations between medical concepts in clinical texts. J Inform Assoc. 2011;18(5):594-600. [FREE Full text] [CrossRef] [Medline]. Temporal data representation, normalization, extraction, and reasoning: a review from clinical domain. Programs Biomed. 2016;128:52-68. [FREE Full text] [CrossRef] [Medline]Ceusters W, Bona J. Ontological foundations for tracking data quality through the internet of things. Technol Inform. 2016;221:74-78. [Medline] Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events. J Inform Assoc. 2020;27(7):1046-1056. [SEP] Justify:

Output: The text discusses various studies and applications of natural language processing (NLP) in medical contexts, including the identification of negated findings in discharge summaries and the automatic extraction of relations between medical concepts in clinical texts. These examples demonstrate how NLP is utilized in analyzing medical literature and data, thus corroborating the claim that NLP aids in this analysis.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: Binding of TALEs to the target sites allows to dimerize and create a double-strand break (DSB) with sticky ends within the spacer. 1. 3Cas9/Cas9 system, originally found in the bacteria, functions as an adaptive immune system against foreign virus or plasmid DNA. are DNA loci containing short repetitions of base sequences. Each repetition is followed by short segments of spacer DNA from previous exposures to the foreign DNA. -associated protein (Cas9) is a DNA endonuclease whose structure is bilobed, composed of target recognition domain and nuclease lobes. The nuclease lobe contains nucleases RuvC, HNH, and a carboxyl-terminal domain for the protospacer adjacent motif (PAM) recognition [9]. system is currently limited to target sequences that are N12-20NGG, where NGG represents the PAM sequence [10]. Any potential target sequence must have a specific PAM sequence on its 3′ end. locus consists of Cas9 endonucleases, (crRNAs), trans-activating crRNAs (tracrRNAs), and precursor crRNAs (pre-crRNAs). tracrRNA is partially complementary to and pairs with a pre-crRNA to form an RNA duplex cleaved by RNase III. The crRNA/tracrRNA hybrid acts as a single guide RNA (sgRNA) for the Cas9, which cleaves the invading DNA. The DNA target sites can appear in multiple locations, all of which will be targeted by the Cas9 for cleavage. By delivering the Cas9 protein and appropriate sgRNAs into a cell, the organism’s genome can be cut at most locations with the only limitation of PAM availability (Figure 3). Figure 3. A crRNA/tracrRNA hybrid acts as a single guide RNA (sgRNA) to recognize their target sequences and allow Cas9 endonucleases to cleave the sense strand 3 bp and antisense strand 3 bp upstream of the protospacer adjacent motif (PAM) sequence. Binding of sgRNAs to the target sites makes Cas9 create a double-strand break (DSB) with blunt ends on target sequences. Advertisement 2. Application2. 1 Tools for basic research2. 1. [SEP] Justify:

Output: The text contrasts the claim by providing detailed technical descriptions of the gene editing process using the Cas9 system, which implicitly acknowledges the complexity and potential risks involved in the process. It describes how specific conditions such as PAM availability limit target sequences, indicating that there are constraints and possible complications in gene editing, which suggests that not all applications are risk-free.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Хотя есть данные о стратифицированных зонах вкрапленных сульфидов, приуроченных к отдельным горизонтам разреза пассивных окраин, образованным, вероятно, в начальные стадии их формирования (Сидоров, 1998). Однако роль их в промышленном балансе золота невелика и важна только как дополнительный источник золота (Горячев, 2003). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses stratified zones of sulfides and their role in gold balance, without any mention of lunar ice deposits, microorganisms, or Earth's early history.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: [FREE Full text] [CrossRef] [Medline] Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Inform Assoc. 2010;17(4):383-388. [FREE Full text] [CrossRef] [Medline]Ceusters W, Smith B. Biomarkers in the ontology for general medical science. Technol Inform. 2015;210:155-159. [Medline]. Extraction of adverse events from clinical documents to support decision making using semantic preprocessing. Technol Inform. 2015;216:1030. [Medline]Garcia-Jimenez A, Moreno-Conde A, Martínez-García A, Marín-León I, Medrano-Ortega F, Parra-Calderón CL. Clinical decision support using a terminology server to improve patient safety. Technol Inform. 2015;210:150-154. [Medline]. : an intelligent adaptive alert environment. Technol Inform. 2015;216:285-289. [Medline]van Remote monitoring of cardiac implantable devices: ontology driven classification of the alerts. Technol Inform. 2016;221:59-63. [Medline]-Paviot B, Bousquet C. Improvement of diagnosis coding by analysing EHR and using rule engine: application to the chronic kidney disease. Technol Inform. 2015;210:120-124. [Medline]. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17. [FREE Full text] [CrossRef] [Medline]Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023;11(6):887. [FREE Full text] [CrossRef] [Medline]Deng J, Lin Y. The benefits and challenges of ChatGPT: an overview. Intell Sys. 2023;2(2):81-83. . ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic medicine. J Pediatr Urol. 2023;19(5):598-604. [SEP] Justify:

Output: The text corroborates the claim by referencing various studies and articles that demonstrate the application of language processing techniques in extracting and analyzing medical data, such as adverse events from clinical documents and improving patient safety through terminology servers in clinical decision support systems. These references suggest that natural language processing is indeed employed in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: 1 Genetic cloning of living organismsFor conventional genetic cloning of animals, plants, and microbes, the target genes in the specific genome are cut using restriction enzymes. It usually takes lots of work and long time to clone and screen for the desired ones. Current gene editing technologies can be used to achieve the desired clones both quickly and accurately, without the limitation of restriction site availability [11]. 2. 1. 2 Establishment of animal modelsGenetic cloning, gene knock-in, and gene knockout are the most common methods to make induced or experimental animal models. The efficient approaches include andCas9. By these new gene editing technologies, specific animal models of many diseases for which there are no animal models available previously have been established with unprecedented efficiency and precision [12, 13, 14]. 2. 1. 3 Development of testing tools and reagents/Cas9 can be optimized by bacterial genotypes to be more adaptive to the variation of food pathogens, compared with traditional methods. locus of different bacterial species show high variance to be an ideal basis for genotyping [15]. /Cas12a(Cpf1) (DNA endonuclease targeted trans reporter) system can be used to diagnose gene mutations, cancers, and microbial infections and test microbial antibiotic resistance by analyzing specimens [16]. 2. 1. 4 Discovery of drugsThe screening and identification of target sites are critical to drug discovery; thus, excellent and suitable platforms are needed. Gene editing technologies can work for the editing of functional genes and the screening of target sites. For example,Cas9 was used to target the exons encoding functional protein domains. A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia (AML) cells identified 6 known drug targets and 19 additional dependencies [17]. 2. 2 Nonhuman therapeutics2. 2. 1 Agricultural productsThe gene editing technology can be used to produce agricultural products in accordance with the need of humans. For example, crops were produced with high yield and resistant to diseases, insects, herbicides, and harsh environment [18, 19]; domesticated animals (pig, buffalo) were produced with double muscle phenotype [20]; and aquatic products (catfish) were produced with high level myostatin (MSTN) gene expression [21]. 2. 2. 2 FoodWe can make food more productive or have longer shelf-life by gene editing technologies. [SEP] Justify:

Output: The text contrasts the claim by detailing various applications of gene editing technologies without mentioning any associated risks or ethical dilemmas. It focuses on the efficiency, precision, and potential benefits of gene editing in areas like agriculture and drug discovery, but it omits any discussion of the risks or ethical issues that might arise from these applications. This absence of attention to potential negative implications indicates that the claim is oversimplified.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Из сказанного выше вытекает необходимость, начиная с раннего палеозоя, рассматривать металлогению золота по тектоническим поясам разных типов, исследуя эволюцию золотого оруденения в разновозрастных фемических и сиалических провинциях раздельно. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the metallogeny of gold and tectonic belts, which does not relate to lunar ice deposits or microorganisms from Earth's early history.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: [CrossRef] [Medline]Panch T, Pearson-. Artificial intelligence: opportunities and risks for public health. Health. 2019;1(1):e13-e14. [FREE Full text] [CrossRef] [Medline] The ethics of AI in health care: a mapping review. Med. 2020;260:113172. [CrossRef] [Medline]Bear Don't. A scoping review of ethics considerations in clinical natural language processing. . 2022;5(2):ooac039. [FREE Full text] [CrossRef] [Medline] Recommended practices and ethical considerations for natural language processing-assisted observational research: a scoping review. Sci. 2023;16(3):398-411. [FREE Full text] [CrossRef] [Medline] Automating access to real-world evidence. . 2022;3(6):100340. [FREE Full text] [CrossRef] [Medline]AbbreviationsAI: artificial intelligenceCASP: : clinical decision supportCDSS: clinical decision support systemcTAKES: Analysis and : electronic health recordLLM: large language modelMeSH: : natural language processing: Items for and Meta-AnalysesEdited by A Mavragani; submitted 08. 12. 23; peer-reviewed by D Singh, L Zhu; comments to author 29. 02. 24; revised version received 20. 04. 24; accepted 24. 07. 24; published 30. 09. 24. Copyright ©Hans Eguia, Sánchez-Bocanegra, Franco Vinciarelli, Fernando Alvarez-Lopez, Francesc Saigí-Rubió. Originally published in the Journal of Research ( 30. 09. 2024. This is an open-access 4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on as well as this copyright and license information must be included. [SEP] Justify:

Output: The text corroborates the claim by including references to various studies and reviews focused on natural language processing in the medical field. For instance, the text mentions 'A scoping review of ethics considerations in clinical natural language processing' and 'Recommended practices and ethical considerations for natural language processing-assisted observational research.' These references imply a role for natural language processing in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: For example,Cas9 was used to edit thermophilic bacteria Streptococcus thermophilus as a bacteriophage-insensitive mutant to improve the product (e. g. , yogurt, cheese) yield by refraining from the infection of phages [22]. The white button mushroom Agaricus bisporus was engineered to resist browning usingCas9. The effect was achieved by targeting to knock out the genes that encodes polyphenol oxidase—an enzyme that causes browning [23]. 2. 2. 3 Industrial products/Cas9 has been used to establish marine algae (e. g. , diatoms) as useful in industrial applications as the carbon neutral synthesis of fuels, pharmaceuticals, health foods, biomolecules, materials related to nanotechnology, and bioremediations of contaminated water [24, 25]. /Cas9 was used to encode the pixel values of black and white images and a short movie into the living bacterial genomes. /Cas9, the technical limits of this information storage system can be optimized to be minimal. /Cas9 can capture and stably store many real data within the genomes of living cells [26]. 2. 2. 4 Environmental protectionMarine microalgae are in charge of about 40% of primary production on earth and capture more CO2 than rain forests. Diatoms are the most important unicellular eukaryotic microalgae and have dominant ecological significance. /Cas9 can be used to modify the diatom genome to achieve more effects in reducing the global warming [25]. 2. 2. 5 Restoration of extinct animalsWoolly mammoths are different from current living elephants by adapting to the extreme cold environment. The mammoth TRPV3 gene, which encodes a temperature-sensitive transient receptor potential (thermoTRP) channel involved in thermal sensation and hair growth, could be achieved by modifying genes of Asian elephants [27]. The mammoth may be restored usingCas9, if the modified embryo can be successfully transferred into the uterus of living elephants. 2. 3 Human therapeutics2. 3. 1 Medicine screeningThe therapeutic strategies of Parkinson’s disease (PD) are quite variable including drug and nondrug treatment. Therefore, it is necessary to find a suitable strategy to treat PD safely, efficiently, and quickly. [SEP] Justify:

Output: The text contrasts the claim by discussing the applications of Cas9 in various contexts such as environmental protection, industrial products, and human therapeutics, implying that there are complexities and potential ethical implications associated with gene editing. For instance, references to the modification of the genome of living organisms, such as the restoration of extinct animals and the suitability of therapeutic strategies for diseases, suggest that gene editing involves significant considerations beyond just technical applications, challenging the notion that such applications are risk-free or devoid of ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Именно на этом фоне проявляется и разделение (начиная с неопротерозоя) месторождений золота на месторождения с As-Sb и As-Bi геохимическими и минеральными ассоциациями (собственно орогенные и золото-редкометальные, золото-висмутовые, или интрузион-релейтед). [SEP] Justify:

Output: The text does not address the claim concerning lunar ice deposits and ancient microorganisms. Instead, it discusses geochemical and mineralogical associations of gold deposits, which is unrelated to the claim about lunar environments.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Original PaperYukiko Ohno1, BSc Riri Kato1, BSc Haruki Ishikawa1, MSc Tomohiro Nishiyama2, MEng Minae Isawa1, PhD Mayumi Mochizuki1, PhD Eiji Aramaki2, PhD Tohru Aomori3, PhD 1Faculty of Pharmacy, Keio University, Tokyo, Japan2of Science and Technology, Nara, Japan3Faculty of Pharmacy, of Health and Welfare, Gunma, JapanCorresponding Author:Tohru Aomori, PhDFaculty of Pharmacyof Health and Welfare37-1 Nakaorui-machiTakasaki-shiGunma, 370-0033JapanPhone: 81 273521290Email: aomori-t@takasaki-u. ac. jpAbstractBackground: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians’ records, it has yet to be widely applied to pharmaceutical care records. Objective: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients’ diseases and symptoms from pharmaceutical care records. The verification was performed using Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians’ records. Methods: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score. Results: The F1-scores of NER for subjective, objective, assessment, and plan data were 0. 46, 0. 70, 0. 76, and 0. 35, respectively. In NER and positive-negative classification, the F1-scores were 0. 28, 0. 39, 0. 64, and 0. 077, respectively. The F1-scores of NER for objective (0. 70) and assessment data (0. 76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. [SEP] Justify:

Output: The text corroborates the claim by discussing how large language models and named entity recognition (NER) are utilized to extract medical information from unstructured data, such as medical records. It emphasizes the use of NLP techniques like MedNER-J for extracting important clinical information, thereby supporting the notion that natural language processing plays a critical role in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: A novel tool was established for monitoring endogenous alpha-synuclein (α-SYN) transcription by NanoLuc luciferase tag insertion at the 3′ end usingCas9, and thus making it possible to screen for strategies rapidly that can be used for PD therapy efficiently [28]. 2. 3. 2 Preparation for cell therapy or immunotherapyThe gene-editing technology can be applied to engineer induced pluripotent stem (iPS) cells and chimeric antigen receptor T (CART) cells. /Cas9 has been used to engineer iPS cells to evade immune rejection in full immunocompetent allogeneic recipients [29]. Because the CD19 CAR was successfully used in treatment,Cas9 may further enhance the efficacy and safety of CART cells by engineering therapeutic T cells [30]. 2. 3. 3 Potential application for disease treatment2. 3. 3. 1 Virus latent infectionDiseases caused by viruses are difficult to treat due to their high mutation rates and latent infections. It is almost impossible to eradicate latent viruses in the human host. andCas9 have been found to provide good strategies in targeting viruses and limiting their productive and latent infections ex vivo and/or in vivo, such as herpes simplex virus 1 (HSV-1), Epstein-Barr virus (EBV), human cytomegalovirus (HCMV), human immunodeficiency virus (HIV), and hepatitis B virus (HBV) [31, 32, 33, 34, 35, 36]. 2. 3. 3. 2 Genetic diseaseGenetic diseases can be cured by gene therapies such as sickle cell anemia, β-thalassemia, muscular dystrophy, α1-antitrypsin deficiency, Leber congenital amaurosis, and cystic fibrosis [37]. The gene editing technologies (e. g. ,Cas9) potentially facilitate the progress of gene therapy, because many experiments have been successful ex vivo and in vivo, and some of them are being under clinical trials. 2. 3. 3. 3 Neurodegenerative diseaseBy applying the Interactions by (PRISM) to a yeast model of PD, sgRNAs were identified to modulate transcriptional networks and protect cells from α-Syn toxicity [38]. The APPswe (Swedish) mutation in the amyloid precursor protein (APP) gene causes Alzheimer’s disease (AD). [SEP] Justify:

Output: The text contrasts the claim by implying that gene editing applications, while potentially beneficial for treating diseases, come with challenges and risks. It mentions the difficulty in eradicating latent viruses and implies the complexities involved in treating genetic diseases, suggesting that the technology is not risk-free and might entail ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Причем это четко проявляется не только в обычных орогенных рудах золото-кварцевого состава, где кордильерский подтип характерен для фемических поясов, а колымский – для сиалических, но и в золото-сульфидно-вкрапленных и золото-скарновых и прочих рудах (Горячев, 2014). И как отмечено ранее, в среднем палеозое к этим типам добавляется золото-серебряная эпитермальная минерализация, максимально проявленная в позднем мезозое–кайнозое. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses geological formations and mineral deposits in the context of ore types without any mention of lunar ice or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0. 64), which was attributed to the similarity of its description format and contents to those of the training data. Conclusions: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records. 2024;8:e55798doi:10. 2196/55798Keywordsnatural language processing; NLP; named entity recognition; pharmaceutical care records; machine learning; cefazolin sodium; electronic medical record; EMR; extraction; Japanese IntroductionBackgroundNatural language processing (NLP) is a computer technology to process the language people read and write in their daily lives. Machine translation and search engines are examples of NLP technologies. In recent years, with advancements in artificial intelligence technology, it has become possible to extract information related to patients’ diseases and symptoms from unstructured data such as medical records [1,2]. NLP technology that is used to extract information such as that related to diseases and symptoms, the names of people and organizations, time expressions, and numerical expressions from text is generally referred to as named entity recognition (NER). Some NER systems also have a positive-negative (P-N) classification function that can be used to determine the onset of extracted findings. To date, most research on NLP technology has focused on English-language texts. NLP technology that is focused on Japanese texts has lagged due to certain aspects of the Japanese language, including that words are not separated by spaces and subjects are often omitted [3]. [SEP] Justify:

Output: The text corroborates the claim by explaining how natural language processing (NLP) can extract information from unstructured medical records, facilitating the analysis of medical research literature. Specifically, it notes that advancements in NLP technology have enabled the extraction of pertinent clinical data, thus supporting the idea that NLP aids in analyzing medical research.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: The mutant APPSW allele can be selectively disrupted usingCas9 both ex vivo and in vivo and thereby decrease pathogenic amyloid-β (Aβ) [39]. 2. 3. 3. 4Cas9 were tried to inhibit hepatocellular carcinoma (HCC). miR-125b can suppress the expression of SIRT6 by directly targeting the seed-matching region of its 3′UTR. After the expression of SIRT6 knocked out throughCas9, HCC cells showed the decreased viability and invasiveness, which had the similar function upon the overexpression of the miR-125b [40]. /Cas9 was also tried to inhibit breast cancer. Cyclin-dependent kinases (CDKs) are established anti-cancer drug targets, and a new generation of CDK inhibitors provides clinical benefits to the patients. Breast cancer cells were genetically manipulated using a deactivatedCas9 (d) approach to strengthen the endogenous CDK18 promoter to express highly to exhibit an increased sensitivity [41]. Advertisement 3. ConclusionThe gene editing technologies, especiallyCas9, have been extensively used as tools in basic research for genome encoding, silencing, enhancing, and modification. Currently, they are further applied in manufacturing nonhuman therapeutic products and medicinal products. Particularly, the discovery of medicinal products using gene editing technologies will open a new era for human therapeutics and expect to bring a hope for patient recovery from being seriously sick. Many biotechnology companies and pharmaceutical plants have successfully produced products using gene editing technologies. For example, has become an industry which is prosperously developing recently. Nonhuman therapeutic products are usually manufactured nonexclusively, while human therapeutic products are manufactured exclusively because they are highly technical, ethically concerned, and more profitable. Gene editing technologies are very promising in applications, though there are still many technical (e. g. , off target effects, option of delivery tools, localization of function) and ethical challenges (e. g. , evaluation of benefits and risks, compatibility of private interests and the public good, random manipulation of genes, commercialization of human therapy) unsolved. More and more products based on these technologies are approved for marketing. We also expect the challenges of safety concerns (e. g. , genetically modified organism, tumorigenesis, etc. ) and ethical issues will be overcome in the near future. 1. . Applications ofCas9 for gene editing in hereditary movement disorders. [SEP] Justify:

Output: The text contrasts the claim by discussing the various technical and ethical challenges associated with gene editing technologies. Specifically, it highlights issues such as 'off target effects,' 'evaluation of benefits and risks,' 'random manipulation of genes,' and 'ethical concerns,' demonstrating that these applications are not risk-free and indeed raise ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: В фанерозое также турмалин становится нетипичным для золотых орогенных руд и обычным только для золото-висмутовых месторождений, связанных с гранитоидными интрузивами. Правда, здесь следует отметить одно исключение – орогенные месторождения золота центральной части Монголо-Охотского орогенного пояса, издавна выделявшиеся как золото-турмалиновые (Преображенский, 1945). [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses geological characteristics of tourmaline in ore deposits without any relation to lunar ice deposits or ancient microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: studies that focused on medical issues, the study by Imai et al [4] developed a system that performs extraction and P-N classification of malignant findings from radiological reports such as computed tomography reports and magnetic resonance imaging reports; Ma et al [5] built a system that performs extraction and P-N classification of abnormal findings from discharge summaries, progress notes, and nursery notes; and Aramaki et al [6] developed a system that performs extraction and P-N classification of disease names and symptoms from case history summaries. In addition, Mashima et al [7] extracted adverse events from progress notes of patients who received intravenous injections of cytotoxic anticancer drugs, and Usui et al [8] extracted symptomatic states from data stored in the electronic medical records of a community pharmacy and standardized them according to the codes of the ICD-10 (of Diseases, Tenth Revision) in order to create a data set of patients’ complaints [9,10]. Institute of beds and Community for Research Project’s for task aimed to classify pseudotweets according to whether they contained information about patients’ symptoms [9], and several teams collaborated to build a system to accomplish this task. Nishioka et al [10] established a system to identify from blog posts whether a patient is positive or negative for hand-foot syndrome on a per-patient and per-sentence basis. Although various approaches have been taken to analyze unstructured medical data as described above in this section, most have targeted physicians’ records, including case history summaries, discharge summaries, and radiological reports, and NER has not been widely applied to pharmaceutical care records. Pharmaceutical care records are documents about patients and are written by pharmacists, who collect information from a pharmacological perspective. Because pharmaceutical care records contain an entry for the change in patients’ physical condition while taking medication, including symptoms of suspected adverse drug effects [11], many such symptoms are documented in pharmaceutical care records. Thus, developing an NER system that can extract and analyze information from pharmaceutical care records would facilitate investigations of adverse drug effects. The study by Usui et al [8], mentioned in the beginning of this section, targeted data similar to this study. Because their system was a rule-based model, it had difficulty handling symptoms and contexts that were not set in the rules. [SEP] Justify:

Output: The text corroborates the claim by detailing various studies that utilize natural language processing (NLP) techniques for extracting and classifying medical data from different formats such as radiological reports, discharge summaries, and case history summaries. For instance, it mentions the work of Imai et al. who developed a system for extraction and P-N classification, highlighting the application of NLP in parsing medical research literature to analyze unstructured data. Additionally, it emphasizes the importance of such developments in understanding adverse drug effects, showing a clear connection between NLP and the analysis of medical literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: Journal of Movement Disorders. 2016;9(3):136-143. DOI: 10. 14802/jmd. 160292. . Genome editing with engineered zinc finger nucleases. Genetics. 2010;11(9):636-646. DOI: 10. 1038/nrg28423. Breaking the code of DNA binding specificity of TAL-type III effectors. Science. 2009;326(5959):1509-1512. DOI: 10. 1126/science. 11788114. TAL nucleases (TALNs): Hybrid proteins composed of TAL effectors and FokI DNA-cleavage domain. Research. 2011;39(1):359-372. DOI: 10. 1093/nar/gkq7045. Targeted genome editing across species using ZFNs and TALEN. Science. 2011;333(6040):307. DOI: 10. 1126/science. 12077736. et al. Targeting DNA double-strand breaks with TAL effector nucleases. Genetics. 2010;186(2):757-761. DOI: 10. 1534/genetics. 110. 1207177. Mussolino C, Morbitzer R, Lütge. A novel TALE nuclease scaffold enables high genome editing activity in combination with low toxicity. Research. 2011;39(21):9283-9293. DOI: 10. 1093/nar/gkr5978. Genetic engineering of human pluripotent cells using TALE nucleases. Nature Biotechnology. 2011;29(8):731-734. DOI: 10. 1038/nbt. 19279. Horvath P, Barrangou R. /Cas, the immune system of bacteria and archaea. Science. 2010;327(5962):167-170. DOI: 10. 1126/science10. . Genome engineering in Saccharomyces cerevisiae using -Cas systems. Research. 2013;41(7):4336-4343. DOI: 10. 1093/nar/gkt13511. Charpentier E, Doudna JA. Biotechnology: Rewriting a genome. Nature. 2013;495(7439):50-51. DOI: 10. [SEP] Justify:

Output: The text contrasts the claim by referencing multiple studies and articles related to genome editing, which implies a diversity of methodologies and possible applications, such as using engineered zinc finger nucleases and TAL nucleases. The mention of specific technical challenges or advancements can suggest that there are complexities and risks associated with genome editing techniques, which contradicts the claim of being 'risk-free' and devoid of ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Эти месторождения по своему составу близки раннедокембрийским орогенным месторождениям, сформированным при активном вкладе мантийного вещества. [SEP] Justify:

Output: The text does not address the claim regarding lunar ice deposits and ancient microorganisms. It discusses the composition of early Proterozoic orogenic deposits, which is unrelated to the claim about microorganisms potentially found in lunar ice.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Although rules can be added, it is difficult to manage them with consistency. Therefore, we aimed to overcome this problem by using machine learning. Study AimIn this study, we applied Entity Recognition-Japanese (MedNER-J), a Japanese-language system designed to extract disease information from physicians’ records [6], to pharmaceutical care records in order to verify the feasibility of NER and P-N classification for this task. Target data were pharmaceutical care records of patients who received cefazolin sodium (CEZ) injection. CEZ is a cephem antibiotic that is often used to prevent secondary infection from operative wounds. The system was applied only to the records of patients who received CEZ injection, with the expectation of mainly collecting target drug information due to fewer concomitant drugs. This is the first study to apply the existing system to pharmaceutical care records. This study provides the baseline performance for analyzing Japanese pharmaceutical care records using the machine learning method. MethodsMaterialsPharmaceutical care records of patients who received CEZ injection between April 2018 and March 2019 at Hospital were used as test data (Figure 1). Researchers accessed and obtained those data on November 19, 2021. Pharmaceutical care records were written by pharmacists, and the format consisted of (1) free-text columns and (2) subjective, objective, assessment, and plan (SOAP) columns: subjective information such as patients’ complaints were included in the subjective data; objective information such as clinical history, clinical findings, and laboratory data were included in the objective data; assessments by pharmacists were included in the assessment data; and future plans were included in the plan data. Data that satisfied the following criteria were used in this research: (1) records with a description in at least 1 SOAP column and (2) records including any of the following keywords in the free-text column or objective column: cefazolin (written in full-width or half-width katakana characters), cefamezin (written in full-width or half-width katakana characters), CEZ, and cez. MedNER-J was applied to the records that satisfied the above criteria and that corresponded to the period from the first CEZ dosing day to 12 days after the last dosing for each patient for each month. Figure 1. Data set preparation. [SEP] Justify:

Output: The text corroborates the claim by demonstrating the application of the Natural Language Processing (NLP) system, MedNER-J, to analyze pharmaceutical care records in a medical context. It describes how the machine learning method was utilized to extract disease information, thus indicating the utility of NLP in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: 1038/495050a12. Development of muscular dystrophy in a -engineered mutant rabbit model with frame-disrupting ANO5 mutations. and Disease. 2018;9(6):609. DOI: 10. 1038/s41419-018-0674-y13. Generation and characterization of a hypothyroidism rat model with truncated thyroid stimulating hormone receptor. Scientific Reports. 2018;(1):4004. DOI: 10. 1038/s41598-018-22405-714. . Current understanding of the molecular mechanisms in Parkinson’s disease: Targets for potential treatments. Translational Neurodegeneration. 2018;6:28. DOI: 10. 1186/s40035-017-0099-z15. . The basic building blocks and evolution of -CAS systems. Transactions. 2013;41(6):1392-1400. DOI: 10. 1042/BST2013003816. -Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science. 2018;360(6387):436-439. DOI: 10. 1126/science. aar624517. . Discovery of cancer drug targets by -Cas9 screening of protein domains. Nature Biotechnology. 2015;33(6):661-667. DOI: 10. 1038/nbt. 323518. Mutation in rice Abscisic Acid2 results in cell death, enhanced disease-resistance, altered seed dormancy and development. Frontiers in Plant Science. 2018;9:405. DOI: 10. 3389/fpls. 2018. 0040519. ZmCCT9 enhances maize adaptation to higher latitudes. Proceedings of the of Sciences of the of America. 2018;115(2):E334-E341. DOI: 10. 1073/pnas. 171805811520. Efficient genome editing in cultured cells and embryos of Debao pig and swamp buffalo using theCas9 system. Cellular & Animal. 2018;54(5):375-383. DOI: 10. 1007/s11626-018-0236-821. [SEP] Justify:

Output: The text provides a range of studies and findings related to gene editing technologies, emphasizing various complexities associated with genetic modifications, such as the development of diseases in animal models (e.g., muscular dystrophy in rabbits) and the generation of disease models (e.g., hypothyroidism in rats). These implications present a contrast to the claim by suggesting that gene editing applications can lead to significant biological risks, indicating that they are not risk-free and can, therefore, pose ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: По условиям локализации данные объекты здесь сконцентрированы в зоне крупной тектонической сутуры, которая отражает интенсивные сдвиговые движения, возникшие в обстановке скольжения (трансформных окраин) Сибирского и Северо-Китайского континентов относительно друг друга, что привело к активному влиянию мантии на металлогению этой структуры (Ханчук, Голозубов, 2008; Goryachev, Pirajno, 2014; Кузьмин, Ярмолюк, 2016). [SEP] Justify:

Output: The text discusses geological features and tectonic movement specifics, which are not relevant to the claim regarding lunar ice deposits and ancient microorganisms. Therefore, it is considered to be irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Among the records from April 2018 to March 2019, those from the date of first cefazolin sodium (CEZ) administration to 12 days after the end of administration that also contained the keywords in the objective column or the free-text column and a record in one of the subjective, objective, assessment, and plan (SOAP) columns were included in the analysis. NER and P-N ClassificationWe used MedNER-J [12] for NER and P-N classification (Figure 2). MedNER-J is an NLP system to extract information related to diseases and symptoms from physicians’ records. It is based on the bidirectional encoder representations from transformers (BERT) [13]–conditional random fields (CRF) model [14], which fine-tuned case history summaries to pretrained Japanese BERT model established by Tohoku NLP group [15]. CRF was used in a previous NER task [6]; however, BERT-CRF performed better than CRF [16]. In addition, MedNER-J can extract data related to a wide range of diseases and symptoms although many systems in previous studies targeted specific diseases and symptoms. Therefore, we used MedNER-J for this study. The system can perform P-N classification in order to determine the onset or absence of presumed findings from the context. At the preprocessing stage, all characters in the records were converted to full-width characters, and exclamation marks were converted to periods. Preprocessed records were input to MedNER-J on a sentence-by-sentence basis to perform NER and P-N classification. A sentence break was defined as a line break or a period. Figure 2. Processing of pharmaceutical care records. Each subjective, objective, assessment, and plan (SOAP) column underwent preprocessing as well as named entity recognition (NER) and positive-negative (P-N) classification by Entity Recognition-Japanese (MedNER-J) to obtain the final results. Performance EvaluationFigure 3 shows the performance evaluation flow. A total of 2 researchers independently extracted named entities from the same records, performed P-N classification by visual confirmation, and created the correct answer data following the original criteria. Exact and partial matches of extracted terms between MedNER-J and the 2 researchers were examined, and P-N classification matches were also investigated. [SEP] Justify:

Output: The text corroborates the claim by describing the use of MedNER-J, an NLP system designed to extract information related to diseases and symptoms from physicians' records. The mention of using named entity recognition (NER) and positive-negative classification (P-N classification) demonstrates how natural language processing (NLP) techniques are applied in analyzing medical literature, thereby supporting the claim about the role of NLP in medical research.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: Generation of myostatin gene-edited channel catfish (Ictalurus punctatus) via zygote injection ofCas9 system. Scientific Reports. 2018;7(1):7301. DOI: 10. 1038/s41598-017-07223-722. provides acquired resistance against viruses in prokaryotes. Science. 2007;315(5819):1709-1712. DOI: 10. 1126/science. 113814023. Waltz E. Gene-edited mushroom escapes US regulation. Nature. 2016;532(7599):293. DOI: 10. 1038/nature. 2016. 1975424. . Diatoms in biotechnology: Modern tools and applications. and Biotechnology. 2009;82(2):195-201. DOI: 10. 1007/s00253-008-1804-825. . /Cas9 system adapted for gene editing in marine algae. Scientific Reports. 2016;6:24951. DOI: 10. 1038/srep2495126. . -Cas encoding of a digital movie into the genomes of a population of living bacteria. Nature. 2017;547(7663):345-349. DOI: 10. 1038/nature2301727. Lynch VJ, Bedoya-- Elephantid genomes reveal the molecular bases of woolly mammoth adaptations to the arctic. Cell Reports. 2015;12(2):217-228. DOI: 10. 1016/j. celrep. 2015. 06. 02728. . A novel tool for monitoring endogenous alpha-synuclein transcription by NanoLuciferase tag insertion at the 3′ end using -Cas9 genome editing technique. Scientific Reports. 2017;8:45883. DOI: 10. 1038/srep4588329. Hypoimmunogenic derivatives of induced pluripotent stem cells evade immune rejection in fully immunocompetent allogeneic recipients. Nature Biotechnology. 2019;37(3):252-258. DOI: 10. 1038/s41587-019-0016-330. . Reprint of: Building a safer and faster CAR: Seatbelts, airbags, and. Biology of Blood and Marrow Transplantation. 2018;24(3S):S15-S19. DOI: 10. [SEP] Justify:

Output: The text outlines various applications and studies related to gene editing, which suggests that there could be complexities and potential challenges related to gene editing techniques such as ethical considerations and risks, contrasting the claim that all applications are risk-free and pose no ethical dilemmas.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: В данном случае присутствие турмалина в орогенных рудах золота, имеющих значительную долю мантийного источника, позволяет поддержать точку зрения об индикаторной роли бора как производного из мантии (Лутков и др. , 2004). Но это предположение требует проведения специальных исследований. Весьма симптоматичным является максимальное распространение эпитермальной и порфировой минерализации в фанерозое. [SEP] Justify:

Output: The text does not address the claim regarding lunar ice deposits or ancient microorganisms. It discusses the presence of tourmaline in ore deposits and the need for special studies with no relation to the claim about lunar microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: The system’s results were matched with the researchers’ results, and performance evaluation indexes were calculated based on the number of named entity recognition (NER) matches alone and the number of NER and positive-negative (P-N) classification matches. Both exact matches as well as partial matches were obtained for NER. Epi: epidural anesthesia. for ResearchersThis section outlines the criteria that the researchers used to create the correct answer data. Not only nouns such as pain but also verbs such as hurt, adjectives such as sore, and adverbs such as painfully were considered targets for extraction. Symptom modifiers such as site, timing, and severity of symptom onset were also considered together with the symptoms to be extracted. With regard to terms meaning patients’ conditions such as sleep, appetite, state of bowel movements, renal function, hepatic function, and blood electrolyte levels, if only a statement of normality such as “appetite is fine” was given, it was also considered to be a target for extraction. For example, pharmacists often ask patients whether they have experienced a loss of appetite, and patients’ responses such as “appetite is fine” are recorded frequently. Such normal states were difficult to consider as diseases or symptoms. Though targets of extraction for records analysis were diseases and symptoms, they are also considered to be important information about patients. Therefore, the terms indicating these six conditions were considered for extraction by the researchers. English abbreviations other than laboratory values were consistently excluded from extraction by the researchers. This is because some of them have different meanings among different medical departments, and it was difficult to use the extracted terms by themselves. Laboratory values and vital signs were considered for extraction only if words or symbols clearly stated the numerical change or how it was abnormal, with the exceptions of renal function, hepatic function, and blood electrolyte concentration. If only numerical information on laboratory values and vital signs were provided, the information was excluded from extraction because this information is obtainable from the structured data of the medical records, and thus there is no need to extract it from the text data. When symptoms were described consecutively, each symptom was considered as an individual symptom. For allergy, any modifiers that indicate the types of allergies listed in the medical dictionary for regulatory activities (MedDRA) were also considered for extraction. [SEP] Justify:

Output: The text corroborates the claim by detailing how natural language processing techniques, such as named entity recognition (NER), are employed to extract relevant information from medical texts, including symptoms and patient conditions. This process demonstrates that NLP can effectively analyze and categorize medical research literature, aiding researchers in drawing conclusions and improving patient care.


Input: Task: contrast the claim using the given text. Claim: All gene editing applications are risk-free and pose no ethical dilemmas. [SEP] Text: 1016/j. bbmt. 2017. 12. 78931. van ürch AC, van Ham PM, et al. /Cas9-mediated genome editing of herpesviruses limits productive and latent infections. PLoS Pathogens. 2016;12(6):e1005701. DOI: 10. 1371/journal. ppat. 100570132. Modern biotechnology-based therapeutic approaches against HIV infection. Biomedical Reports. 2017;7(6):504-507. DOI: 10. 3892/br. 2017. 100633. Suppression of Epstein-Barr virus DNA load in latently infected nasopharyngeal carcinoma cells byCas9. Virus Research. 2018;244:296-303. DOI: 10. 1016/j. virusres. 2017. 04. 01934. /Cas9 system facilitates clearance of the intrahepatic HBV templates in vivo. Molecular Therapy--Nucleic Acids. 2014;3:e186. DOI: 10. 1038/mtna. 2014. 3835. An efficient antiviral strategy for targeting hepatitis B virus genome using transcription activator-like effector nucleases. Molecular Therapy. 2014;22(2):303-311. DOI: 10. 1038/mt. 2013. 21236. . Targeted disruption of EBNA1 in EBV-infected cells attenuated cell growth. BMB Reports. 2016;49(4):226-231. DOI: 10. 5483/BMBRep. 2016. 49. 4. 26037. Cohen J. The birth of. Science. 2017;355(6326):680-684. DOI: 10. 1126/science. 355. 6326. 68038. . -Cas transcriptional perturbation screening reveals protective genes against alpha-synuclein toxicity. Molecular Cell. 2017;68(1):247. e5-257. e5. DOI: 10. 1016/j. molcel. 2017. 09. 01439. György B, LöövCas9 mediated disruption of the Swedish APP allele as a therapeutic approach for early-onset Alzheimer’s disease. Molecular Therapy--Nucleic Acids. 2018;11:429-440. DOI: 10. 1016/j. omtn. [SEP] Justify:

Output: The text discusses various applications of gene editing technologies, specifically the Cas9 system and its use in therapeutic approaches for different viral infections. However, it does not provide any assurance that these applications are risk-free or devoid of ethical concerns. Instead, the multiple studies referenced suggest complex scenarios where ethical dilemmas and potential risks (e.g., unintended consequences of gene editing) may exist in clinical applications.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: Признаки эпитермальной минерализации в рудах золота района Калгурли (фиг. 3) скорее отвечают формированию части орогенных руд золота в условиях малых глубин и резкого падения давления при движениях по крупным зонам разломов, с которыми они здесь ассоциируют. Учитывая известную генетическую связь эпитермальных месторождений с процессами субдукции (месторождения Купол, Лепанто, Хисикари и пр. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses epithermal mineralization in gold ores in the Kalgoorlie region and does not mention lunar ice deposits or ancient microorganisms. Thus, there is no connection to the claim.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: The results of F1-score in Table 3 show that MedNER-J was able to conduct NER and P-N classification with high performance in the following order: assessment data>objective data>subjective data>plan data. Table 2. Number of the records analyzed and extraction results by the MedNER-Ja system and researchers. Subjective data, n (%)Objective data, n (%)Assessment data, n (%)Plan data, n (%)Records analyzed (n=60)43 (71. 7)60 (100)54 (90)56 (93. 3)Extracted termsSystem (n=633)50 (7. 9)411 (64. 9)135 (21. 3)37 (5. 8)Researchers (n=805)130 (16. 1)444 (55. 2)216 (26. 8)15 (1. 9)MatchesNERb,c (n=483)41 (8. 5)300 (62. 1)133 (27. 5)9 (1. 9)NERc +P-Nd classification (n=305)25 (8. 2)165 (54. 1)113 (37)2 (0. 7)aMedNER-J: Entity Recognition-Japanese. bNER: named entity recognition. cIncluding partial matches. dP-N: positive-negative. Table 3. Performance evaluation of named entity recognition (NER) and positive-negative (P-N) classification. PrecisionRecallF1-scoreNER (including partial matches)All data0. 760. 600. 67Subjective data0. 820. 320. 46Objective data0. 730. 680. 70Assessment data0. 990. 620. 76Plan data0. 240. 600. 35NER (including partial matches)+P-N classificationAll data0. 480. 380. 42Subjective data0. 500. 190. 28Objective data0. 400. 370. 39Assessment data0. 840. 520. 64Plan data0. 0540. 130. 077Error AnalysisTable 4 shows the categories of causes of mismatches between the system and the researchers. [SEP] Justify:

Output: The text corroborates the claim by presenting data related to the effectiveness of the MedNER-J system, which employs natural language processing techniques for named entity recognition (NER) and classification in medical literature. Specifically, it highlights the system's performance in analyzing different types of medical data (assessment, objective, subjective, and plan data), thereby supporting the idea that natural language processing is instrumental in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accessible and affordable for all healthcare systems. [SEP] Text: The advantage of having expensive and specialized equipment and practitioners at fixed centralized locations is that they alleviate the pressure for smaller healthcare facilities to spend their limited resources on equipment that is not frequently used but still critical for health care. By contrast, hospitals in the US are spread out and uncoordinated, duplicating care in many places without high enough volume in any of them to provide the critical mass to make the procedures affordable. Similarly, an MRI machine might be used four to five times a day in the US but 15 to 20 times a day in the Indian hospitals. We have to make the equipment sweat! 13In the cases of the more successful privatized hospitals, India, as well as the rest of the world, is paying careful attention to this 'configured networks of care and facilities in a hub-and-spoke design'12. One of the primary benefits of HSM that is being revealed in India is that 'This allows for a more-orderly progression of care and rational allocation of often-scarce resources'12. Thus, success is being found in India when practising medical facilities centralize their assets and diversify their more benign practices in the lower cost ambulatory locales. The end result is a lower centralized cost, as providers only procure the equipment and resources they need. Moreover, the Indian population, in general, is able to access greater coverage for fulfilling their basic healthcare needs. Reasons for successAttracting and retaining doctors: No matter how efficient a hospital may be, it cannot be deemed successful if its doctors and nurses are incompetent or unable to perform their primary tasks. As such, Indian hospitals operating under the HSM are actually retaining and attracting capable doctors who are keenly interested in 'high volume and sheer variety of cases that attract talent to these hospitals'. Also, 'doctors can build their capabilities faster in [HSM hospitals]. . . in turn, [this] contributes to the high quality of outcomes at these hospitals'14. Nurturing and updating treatment protocols: Perhaps most unique to India's HSM success is its incorporation of developed protocols for both mundane tasks and highly complex procedures. Knee and hip replacements, as well as cardiac and cancer surgeries, all have itemized procedures that are adhered to despite being executed in facilities outside of the central hub. [SEP] Justify:

Output: The text contrasts the claim by discussing the challenges of healthcare access and affordability in the US compared to India. It highlights the centralized approach in Indian hospitals which allows for 'greater coverage for fulfilling their basic healthcare needs.' In contrast, it points out that the decentralized nature of US hospitals leads to inefficiencies and duplicated care, making procedures less affordable. The text does not support the idea of wide accessibility and affordability of gene editing across all healthcare systems.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: ) или рифтогенеза (Балей), можно предполагать важное металлогеническое значение этих процессов для золота только в фанерозое. При этом золотопродуцирующая роль магматических поясов субдукционной природы явно растет от палеозоя к кайнозою. В то же время, орогенное золото проявлялось во все периоды времени, что говорит об однотипности процессов орогенеза в истории Земли. И здесь мы прикасаемся к проблеме существования субдукционных обстановок в раннем докембрии. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses geological processes and the significance of certain geological formations in Earth's history, and does not mention lunar ice deposits or microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: Application to RecordsThe number of extracted terms by both the system and the researchers were greater in the following order: objective>assessment>subjective> plan data. The pharmaceutical care records that were targeted in this study included an average of 13. 4 (SD 9. 3) diseases or symptoms per record. From these records, MedNER-J correctly extracted an average of 8. 1 (SD 6. 0) terms and correctly extracted and performed P-N classification on an average of 5. 1 (SD 4. 2) terms. Therefore, MedNER-J was able to extract 60% (8. 1/13. 4) of findings from the pharmaceutical care records and correctly classify 63% (5. 1/8. 1) of those findings as positive or negative. Performance EvaluationIn this study, we focused on results that included not only exact matches but also partial matches between MedNER-J and the researchers. Word segments in Japanese are unclear, and the necessary extraction range of words varies depending on the situation and the reader. As an example of variations, for the term itakute (“in pain”), it is sufficient to extract itaku or it may be necessary to extract itakute, including the conjunctive p0. 67 for NER alone and 0. 42 for NER and P-N classification, values varied among the SOAP data. This variation indicates that the applicability of the system differs for each data set. The F1-scores of NER for the objective (0. 70) and assessment data (0. 76) was high, while those of NER for the subjective and plan data were only 0. 46 and 0. 35, respectively. This indicates that the NER performance for the objective and assessment data was superior to that for the subjective and plan data. At the same time, the F1-score of NER and P-N classification was high only for assessment data (F1-score=0. 64). The training data for MedNER-J consisted of case history summaries. Because machine learning systems are generally optimized for the analysis of the training data, the system was optimized for the analysis of case history summaries. Case history summaries include chief complaints, medical history, laboratory findings, and discussions of each case, as summarized by physicians. [SEP] Justify:

Output: The text corroborates the claim by describing how the MedNER-J system, which is a natural language processing (NLP) tool, effectively extracts and classifies terms from pharmaceutical care records and case history summaries. The performance evaluation indicates that the system is optimized for analyzing medical data, which supports the notion that NLP is valuable in analyzing medical research literature.


Input: Task: contrast the claim using the given text. Claim: Gene editing is widely accessible and affordable for all healthcare systems. [SEP] Text: India takes the top spot amongst 148 countries in terms of affordability of ICT technology17. Hence, the implementation of ICT in HSM should be an easy choice. Since most companies are using cloud-based platforms, '[this helps] to reduce infrastructure cost. Scaling up becomes easier on a cloud environment'18. The HSM is also efficacious in disseminating information for health awareness, immunizations or precautions to be taken during epidemics (such as Ebola). Companies implementing the HSM have helped create and sustain thousands of rural jobs, both direct and indirect. Lessons learnedHSM is an effective mechanism that allows for greater distribution while minimizing costs when paired with ICT. There are many areas in emerging nations in Africa and where healthcare accessibility is limited. This also holds true for remote areas in advanced nations such as Sweden, Finland, Russia, Australia, Canada and the USA where there is limited access due to weather conditions and other factors. HSM paired with ICT will effectively service such regions, reducing unnecessary travel and other related costs. 'You don't need a PhD in economics to figure out this model is a lot cheaper and probably a lot more convenient for patients'10. healthcare companies such as iKure, Vaatsalya Healthcare, (Dental), are demonstrating that using HSM paired with ICT not only allows for treatment of medical conditions but also lowers costs and improves availability, making health care accessible and affordable to the nation. AcknowledgementsThe author would like to express special thanks to the management team at iKure (www. ikure. in and www. ikuretechsoft. com) (- Founder & CEO, and ), who walked him through the implementation of iKure's hub and spoke model and for sharing their company's information. 1. Govindarajan V, Ramamurti R. Delivering world-class health care, affordably. Review (Internet) 2013; 2. Available: (Accessed 26 February 2014). 2. Museum. History - in the Air, 1955. (Internet). Available: (Accessed 7 August 2015). 3. Rivett G. of the System. 1823-2015 (Internet) 2015. Available: (Accessed 2 August 2015). 4. Dawson B. Interim report on the future provision of medical and allied services. Great Britain, Ministry of Health. (Internet) 1920. [SEP] Justify:

Output: The text contrasts the claim by discussing the implementation of Health System Models (HSM) paired with ICT to enhance accessibility and affordability of healthcare in various regions. It indicates that while some healthcare improvements are being made, significant barriers to access remain in several areas, especially in emerging nations and remote locations in developed countries. The focus on ICT does not directly address gene editing accessibility or affordability, suggesting that while healthcare access can be improved, it does not imply that gene editing itself is currently widely accessible and affordable.


Input: Task: pass the claim using the given text. Claim: Lunar ice deposits may harbor ancient microorganisms from Earth's early history. [SEP] Text: По крайней мере, такой металлогенический индикатор (Горячев, 2010), как отсутствие эпитермальных месторождений и порфировых месторождений в то время, оставляет вопрос о металлогеническом значении зон субдукции и их проявлении в раннем докембрии открытым. И здесь вряд ли такое отсутствие можно объяснять большим эрозионным срезом, поскольку вулканические комплексы присутствуют во многих структурах разного возраста. Отмеченную выше (см. табл. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses geological indicators and the significance of subduction zones in the early Precambrian period without mentioning lunar ice deposits or microorganisms. There is no connection between the provided text and the claim concerning the potential of lunar ice to contain ancient life forms.


Input: Task: corroborate the claim using the given text. Claim: Natural language processing helps in analyzing medical research literature. [SEP] Text: ”In cause category 1 “system extraction failure,” many extracted terms are categorized as “verbs, adjectives, adverbs” or “expressions that are difficult to grasp as diseases or symptoms. ” In “verbs, adjectives, adverbs,” the system was not supposed to extract general terms, such as sore, used by patients. The pharmacist receives the patients’ complaints and clinical information and then describes the patient’s condition and other information in objective and assessment columns, replacing them with technical terminology. However, the system’s inability to extract “verbs, adjectives, and adverbs” might cause the pharmacists to overlook symptoms that they did not consider important. Examples of mismatches for extracted terms in the subcategory “expressions that are difficult to grasp as diseases or symptoms” are terms that are related to the disease state but do not directly indicate the disease state, including normal appetite, sleep, bowelmovements, renal function, hepatic function, and blood electrolyte levels (Table 5). Such normal findings might be missed due to the system’s inability to extract them. One limitation of investigations involving medical records is the inability to determine the actual occurrence of symptoms that are not explicitly documented in the medical records. The extraction of normal findings is also important because the information that “status of symptoms was documented but they did not occur” is expected to increase the reliability of the results of medical record investigation. Future TasksNot only for cause category 1 but for the other cause categories as well, the cause of the mismatches between the system and the researchers can be explained by one of the following 2 factors: the training data for the system did not contain similar expressions, or there was a difference between the criteria that the system had learned and the criteria that the researchers used in this study. In particular, expressions that are difficult to grasp as diseases or symptoms were the terms that the researchers decided to collect additionally since they were considered important from the pharmacists’ perspective. Therefore, the systems are often constructed without considering them as extraction targets in studies that aim to collect diseases and symptoms in a simple manner. Using the analysis targets in training data should improve the performance of the system. From a medical safety standpoint, overlooking patients’ information is highly detrimental. Therefore, a high recall is preferable, even if precision decreases somewhat. [SEP] Justify:

Output: The text corroborates the claim by discussing flaws in a system's ability to extract medical information and the impact of these limitations on analyzing patient data. It suggests that enhancing the training data of natural language processing systems with expressions relevant to diseases and symptoms can improve their performance, ultimately aiding in the analysis of medical literature by addressing the need to capture relevant clinical data accurately.