Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: AbstractDetermining the factors that contribute to making a reliable prediction of the metabolic syndrome will provide a deeper understanding of the medical indices involved in the prediction and assist in early diagnosis and treatment of patients. The study examined the optimal number of National cholesterol education program adult treatment panel () III indices needed to make a reliable prediction of the syndrome, whether each of the five indices for predicting the syndrome is equally important and whether a reliable prediction can be made using calculated blood pressure indices – estimated mean arterial pressure and pulse pressure – instead of blood pressure indices. The results show that indices for determination of the syndrome are not equally important. Moreover, the indices importance and their prediction quality vary according to gender. Optimal results are obtained by using all five indices for prediction. IntroductionMetabolic syndrome is among the most common public health problems worldwide and a major contributor to cardiovascular disease and diabetes. 1 The syndrome is a prominent risk factor for minor ischemic stroke and patients that have suffered a minor ischemic stroke and are diagnosed with metabolic syndrome are at a high risk of subsequent vascular events. 2 Metabolic syndrome is also associated with an increased risk of cardiovascular disease and mortality. According to the results of the Survey () conducted in 2011–2016 among adults aged 20 and over in the US, the incidence of metabolic syndrome, as defined by the (NCEP) ATP (Panel) III, grows significantly with age. No significant differences in the prevalence of metabolic syndrome between men and women in each age group1 were found. 3 Studies in nursing homes in Europe found that prevalence of metabolic syndrome was high among the elderly population and increased with age. In these studies, gender, in contrast, was seen to play a major role in the prevalence prediction of metabolic syndrome. 4–6 Women apparently become more susceptible to metabolic syndrome with aging, thus separate guidelines may be required for men and women. 7 The prevalence exhibited a commensurate rise with age, as suggested in8 and was significantly higher among more elderly people in the overall sample and among women. For men, however, the prevalence rate trended toward a non-significant pattern with increasing age. 9Institutes of Health’s (NIH) definition of the is the one most used and widely accepted by the international community and the WHO. [SEP] Justify:

Output: The text does not discuss machine learning or its applications in predicting protein structures. Instead, it focuses on factors contributing to the prediction of metabolic syndrome, emphasizing indices related to cardiovascular health. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Among the genes, the period circadian clock gene (Per 2) is important for modulation, expressed in the SCN [80], and associated with the possible generation of circadian rhythms [81, 82]. Circadian rhythms are found to modulate [83] the release of some of the neurotransmitters like DA, glutamate, and GABA. This indicates that Per 2 is capable of modulating at least some of the if not all neurotransmitters. A relation between the dopaminergic system and Per 2 [84–86] has been reported and DA release and DA gene expression also have been modulated by circadian rhythms [87–89]. Glutamate (Glu) has a circadian rhythm pattern and the level is regulated by Per 2 [90] and this indicates a positive relation between glutamate and Per 2. The release of GABA is also related to circadian rhythms [83]. Studies show that Per 2 regulates GABA levels and GABA regulates Per 2 expressions. Neurotransmitters like and GABA have key roles in intercellular coupling and the release of these [91–94] is regulated by clock genes the transcription of which is activated by the neurotransmitters [95]. Neurotransmitters and clock genes thus help to maintain intercellular coupling. It is also interesting to note that Per 2 can interact with a variety of other genes and proteins and thus the entire process of linkage of neurotransmitters and genes becomes an important factor not only for the functioning of SCN but also beneficial for understanding and treating neurodegenerative disease conditions of depression and sleep states if judiciously used with clinical studies. Underlying electrical activity in the master circadian clockNeurons of the SCN are reported to be electrically and chemically heterogeneous. TFFL present in the neurons drives circadian rhythms, electrical activity in the neurons is more active during the day as compared to night when the activity is low [96]. Studies from nocturnal rodent species by way of electrophysiological recordings have given us information regarding the electrophysiology of the SCN [13, 22, 97]. It is known that oscillations in gene expression, intrinsic membrane properties, and synaptic communication involving neurotransmitters determine the electrical features of the activity making them more active during the day than at night. During the day increased membrane excitability is observed due to voltage-dependent and independent Na+ and Ca2+ currents as well as K+ currents and there is a higher firing frequency. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of the suprachiasmatic nucleus (SCN) as the master circadian clock. It mentions how the electrical activity in the SCN is critical for regulating circadian rhythms, noting that the neurons are more active during the day and that various genes and neurotransmitters, including the period circadian clock gene (Per 2), are involved in the modulation of this activity. The connection to neurotransmitter release and gene expression within the SCN directly supports the claim about its central role in circadian rhythm regulation.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: DOI: 10. 1016/j. fitote. 2012. 09. 02915. analysis and immunoregulation activity comparison of five polysaccharides from Angelica sinensis. Carbohydr. Polym. ; 2016. 140: 6–12. DOI: 10. 1016/j. carbpol. 2015. 12. 05016. Saw, C. L. , Wu, Q. , Su, Z. Y. , Wang, H. , Yang, Y. , Xu, X. , Huang, Y. , Khor, T. O. , Kong, A. N. Effects of natural phytochemicals in Angelica sinensis (Danggui) on Nrf2-mediated gene expression of phase II drug metabolizing enzymes and anti-inflammation. Biopharm. Drug Dispos. ; 2013. 34: 303–311. DOI: 10. 1002/bdd. 184617. . , Demirci, B. D. , Ozek, T. , Baser, K. H. , Liu, Z. , Zhang, S. , Cantrell, C. L. , Zhang, J. Bioactivity-guided fractionation and GC/MS fingerprinting of Angelica sinensis and Angelica archangelica root components for antifungal and mosquito deterrent activity. J. Agric. Food Chem. ; 2009. 57: 464–470. DOI: 10. 1021/jf802820d18. . , of a derived herbal recipe from an ancient Chinese formula, Tang, on ovariectomized rats. J. Ethnopharmacol. ; 2012. 144: 567–575. DOI: 10. 1016/j. jep. 2012. 09. 04119. Upton R. Pharmacopoeia and Therapeutic Compendium-Valley. CA, USA. 2003. 20. . ,. , Liu, C. E. Anti-fatigue activity of acanthopanax gracilistylus wine. Chin. J. Health Lab. Technol. ; 2009. 19: 1404–1405. (In Chinese)21. on toxicity of Angelica sinensis injection and Astragalus membranaceus injection in chorioallantoic membrane model. Chin. J. Exp. Trad. Med. Form. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that ethnobotany encompasses the study of various plants, including their medicinal properties, as indicated by references to studies on Angelica sinensis and the herbal recipes noted. These references show a clear focus on medicinal uses of plants, thereby contradicting the notion that ethnobotany is solely concerned with plants used for food.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: According to this definition, the presence of metabolic syndrome is confirmed when at least three of the following five indicators are present:(i) Waist circumference is at least 102 cm in men and at least 88 cm in women(ii) Blood triglycerides level is higher than 150 mg/dL(iii) HDL cholesterol (HDL-C) level in the blood is lower than 40 mg/dL in men and 50 mg/dL in women(iv) Blood pressure is at least 130/85 mm Hg(v) Fasting glucose blood level is higher than 100 mg/dL. 10Machine learning algorithms have been shown to have the potential to significantly help solve health problems by developing classification systems that can assist physicians diagnose and predict disease onset in early stages. Extracting knowledge from medical data, however, is challenging because these data may be heterogeneous, disorganized, high-dimensional, and contain “noise” and anomalies. 11 Today, clinicians and researchers are able to utilize machine learning to positively impact patient outcomes in a clinically meaningful way. 12The prevalence of metabolic syndrome as a worldwide public health problem with high morbidity, mortality, and costs highlights the urgency of efforts to identify and modify risk factors for the syndrome. 10 Improving its prognosis through machine learning will improve the quality of life and even survival of men and women. Determining the main factors that contribute to the prediction of the syndrome will provide a deeper understanding of existing medical indices (and others) and assist in the implementation of early treatment of patients. Our work, using machine learning, examines the primary tools used for predicting the syndrome. We seek to determine the optimal number of indices needed to make a reliable prediction, and whether each of the five indices used to predict the syndrome is equally important. In case that the indices are not equally important, we aim to discover their order of importance. We further intend to find out whether it should be beneficial to use calculated blood pressure indices instead of blood pressure indices (systolic and diastolic blood pressure) for syndrome prediction and diagnosis. Finally, we investigate the effect, if any, of gender on the indices’ importance and prediction value. To the best of our knowledge, we are the first researchers seeking to elucidate the above issues. Our work emphasizes the importance of the order and the contribution of each index to the metabolic syndrome prediction, according to gender and the need for at least four variables for a sufficient prediction. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the application of machine learning in predicting and diagnosing metabolic syndrome rather than protein structures. The core focus of the text is on the use of machine learning algorithms to assist in health-related predictions, which does not relate to the claim about predicting protein structures.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: At night there is membrane hyperpolarization and hence decreased firing. This membrane excitability is influenced by several neuromodulators and changes the action potential and rhythmicity of SCN neurons [97]. Electrical activity is less and hence SCN neurons are silent in terms of electrical activity at night. They start generating action potentials at the start of the day and continue all day long [22]. Rhythms in electrical activity are critical for the expression of clock genes and circadian timing system function. SCN neurons can be classified into three classes [98] with each displaying spontaneous firing rates. In other words, neurons of the SCN are heterogeneous in basic and active membrane properties and are classified into three clusters I, II, and III. While cluster I fires spontaneously in a regular pattern, cluster II fires in an irregular mode, and cluster III cells have low firing rates and change to a burst-like firing mode on receiving appropriate input. It is also observed that the varying frequencies between groups is likely to influence the release of neuropeptides which regulate oscillations in circadian-relevant genes [99]. It is also important to note that different subgroups of clock neurons in the SCN perform a specific role in the network neuronal activity which fluctuates with a specific time of day, allowing the synchronized output to shape the circadian locomotor behavior [99]. VIP neurons of the SCN have circadian rhythms in firing activity and have demonstrated two types of instantaneous firing activity patterns [100]. It is also shown that the firing frequency of VIP neurons mediates circadian entrainment with higher frequencies causing an increase in VIP release, larger phase shifts, and faster circadian entrainment. VIP neuron stimulation with high frequencies shifted gene expression rhythms but not with low-frequency stimulation. Light input to the SCN is detected through the release of glutamate and PACAP from the retina [101–103]. Neurons of the SCN of the ventral hypothalamus are unique in the sense that they have self-sustained and spontaneously synchronizing circadian rhythms in firing activity and gene expression [104, 105]. Nearly 24-hour TTFL of core clock genes maintain the rhythms [10, 106, 107]. Electrical stimulation of SCN neurons is sufficient to phase shift and entrain circadian rhythms in gene expression [108]. [SEP] Justify:

Output: The text corroborates the claim by explaining the functions of the suprachiasmatic nucleus (SCN) and its role in regulating circadian rhythms. It describes how SCN neurons exhibit rhythmic electrical activity, which is critical for clock gene expression and circadian timing. The mention of various classes of SCN neurons and their specific roles indicates that the SCN is indeed functioning as a regulatory ‘master clock’ in circadian rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: ; 2009. 15: 46–48. (In Chinese)22. Yan, L. , Wan, T. , Zhang, Z. , Wang, R. X. , Sun, B. W. . Analysis on botanical characters of Astragalus membranaceus Bunge. and A. membranaceus var. mongholicus (Bunge. ) Hsiao. J. Agric. Univ. ; 2001. 22: 71–77. (In Chinese)23. Fu, J. , Wang, Z. , Huang, L. , Zheng, S. , Wang, D. , Chen, S. , Zhang, H. , Yang, S. Review of the botanical characteristics, phytochemistry, and pharmacology of Astragalus membranaceus (Huangqi). Phytother. Res. ; 2014. 28: 1275–1283. DOI: 10. 1002/ptr. 518824. Tang, W. Eisenbrand, G. of Plant Origin. Springer-Verlag, Beijing, 1992. 197. (In Chinese)25. Subarnas, A. , Oshima, Y. , Hikino, H. Isoflavans and a pterocarpan from Astragalus mongholicus. Phytochemistry; 1991. 30: 2777–2780. DOI: 10. 1016/0031-9422(91)85143-N26. Huang, K. C. of Chinese Herbs. 1999. (In Chinese)27. Mills, S. , Bone, K. Principles and Practice of Phytotherapy. Churchill Livingstone, Edinburgh, 2000. 28. membranaceus: A review of its protection against in flammation and gastrointestinal cancers. Am. J. Chin. Med. ; 2016. 44: 1–22. DOI: 10. 1142/S0192415X1650001429. Yoshida, Y. ,. Immunomodulating activity of Chinese medicinal herbs and Oldenlandia diffusa in particular. Int. J. Immunopharmacol. ; 1997. 19: 359–370. DOI: 10. 1016/S0192-0561(97)00076-330. Agyemang, K. , Han, L. [SEP] Justify:

Output: The text contrasts the claim by providing references to the medicinal uses of plants, specifically discussing various studies on the pharmacological properties and botanical characteristics of species like Astragalus membranaceus. This demonstrates that ethnobotany also encompasses the study of medicinal plants, contradicting the claim that it solely focuses on food.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: MethodsIn this study we analyzed a large dataset comprising the medical records compiled over time of a Spanish sample that underwent periodic health examinations at their workplace. Based on the dataset, we built and examined three datasets: a dataset for men and women, a dataset for men only and a dataset for women only. Using the medical indices in the datasets, we derived from them additional, calculated medical indices that are widely used in medical practices. We sought to determine which of these indices were most important for predicting metabolic syndrome for three different sets of groups (men and women, men only, and women only)13 using the ExtraTreesClassifier14 and univariate selection15 methods. The importance of a feature refers to its contribution to higher chances of having metabolic syndrome. That is, as the importance of a feature increases, its contribution to the chance of having metabolic syndrome increases too. We predicted metabolic syndrome based on indices selected in the order of their importance ranking according to the ExtraTreesClassifier method. Random forest,16 Naive Bayes,17 k-nearest neighbor (KNN),18 decision tree (CART)19 and logistic regression20 algorithms were applied and examined to select a classification algorithm for predicting metabolic syndrome:In this examination, we found out that the use of a algorithm with a Gini impurity criterion has led to a more effective partitioning of the data. Further, the inclusion of a larger number of trees in the forest (n_estimators) did not significantly increase the computational complexity required to generate a prediction output, nor did it noticeably augment the bias of the model. K-nearest neighbors (KNN) with uniform weights reduce variance although at the cost of larger bias. Decision tree (CART) with the criterion of Gini impurity makes a purer separation and the “best” splitter chooses the best split. Logistic regression with the ‘liblinear’ solver performs well with high dimensionality. For each prediction made, five algorithms were examined, and the prediction was evaluated according to four performance measurements: sensitivity, precision, F1-score, and MCC (Matthews correlation coefficient). From among these, we identified the classification algorithm with the highest F1-score performance measurement and selected it for making further predictions. Using this algorithm, we predicted metabolic syndrome using four and five criteria. We additionally examined the contribution of blood pressure indices to the prediction of metabolic syndrome. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the analysis of medical records and the prediction of metabolic syndrome using machine learning algorithms, but it does not mention protein structures or the specific use of machine learning in that context.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: There are also studies [115, 116] to show that light has an important role in the expression and activity of NKCC1 and KCC2 in the SCN. As far as the effect of environmental lighting on the regulation of NKCC1 is concerned it was observed in experiments under constant light (LL) and LD conditions. A hypothesis based on this was put forward [115] indicating that the expression of NKCC1 protein is regulated by environmental lighting and circadian mechanisms within the SCN. In hamsters NKCC1-ir in the SCN was greater in LL as compared to LD or darkness duration (DD) conditions suggesting that light duration increases NKCC1 level in SCN. Both short and long exposure to light changes the expression. Thus, intracellular chloride concentration (Cl–) is regulated across cell bodies dendrites, axons, and glial cells by the above two major categories of Cl– cotransporters. NKCCs have two isoforms NKCC1 and NKCC2 while KCCs have four isomers KCC1, KCC2, KCC3, and KCC4 [29]. VIP and GRP neurons of ventral SCN have KCC2 but not in AVP cells [117]. NKCC1 is seen in the and AVP neurons of the SCN [118]. NKCC1 expression is reported to be significantly higher [116, 119] during the night than during the day in the dorsal but not in the ventral SCN. Another study using a genetically coded Cl– sensor [120] reported that intracellular Cl– was high during the day as compared to night in both AVP and VIP neurons in the SCN. The Cl– co transporter-dependent regulation of SCN clock function appears to be complex but important and provides insight into the therapeutic application options with properly regulated clinical trials. Role of astrocytesAstrocytes seen in the SCN of adult mice, rats, and hamsters [60, 121, 122] possess a cell-autonomous circadian clock. It is capable of driving mammalian SCN neuronal timekeeping [9]. They display daily rhythms in cellular physiology like neurons of the SCN. A crucial role for astrocytes has been re-emphasized in mammals based on recent experiments using newer techniques [60, 61]. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of the suprachiasmatic nucleus (SCN) in regulating various mechanisms influenced by light and circadian rhythms. It highlights that environmental lighting affects the expression and activity of cotransporters like NKCC1 and KCC2 within the SCN, which supports the idea that the SCN is a critical component in regulating circadian rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: , Liu, E. , Zhang, Y. , Wang, T. , Gao, X. Recent advances in Astragalus membranaceus anti-diabetic research: Pharmacological effects of its phytochemical constituents. Evid. Based Complement. Alternat. Med. ; 2013: 654643. DOI: 10. 1155/2013/65464331. Ryu, M. , Kim, H. E. , Chun, M. , Kang, S. , Shim, B. , Yu, Y. B. , Jeong, G. , Lee, J. S. elicits anti-inflammation via activation of MKP-1, concomitant with attenuation of p38 and Erk. J. Ethnopharmacol. ; 2008. 115: 184–193. DOI: 10. 1016/j. jep. 2007. 09. 02732. Shen, H. H. , Wang, K. , Li, W. , membranaceus prevents airway hyperreactivity in mice related to Th2 response inhibition. J. Ethnopharmacol. ; 2008. 116: 363–369. DOI: 10. 1016/j. jep. 2007. 12. 00233. Liu, Y. H. , Sun, S. ,. 2D NMR study on cycloartane triterpenoids from Astragalus membranaceus var. mongholicus. J. China Pharm. Univ. ; 2008. 39: 15–19. (In Chinese)34. Toda, S. , Shirataki, Y. Inhibitory effects of Astragali Radix, a crude drug in Oriental medicines, on lipid peroxidation and protein oxidative modification by copper. J. Ethnopharmacol. ; 1999. 68: 331–333. DOI: 10. 1002/ptr. 518835. Hong, Y. Oriental Materica: A Concise Guide. 1986. 36. McCulloch, M. , See, C. , Shu, X. J. , Broffman, M. , Kramer, A. , Fan, W. Y. , Gao, J. , Lieb, W. , Shieh, K. , Colford, J. M. [SEP] Justify:

Output: The text contrasts the claim by discussing various studies on medicinal properties and pharmacological effects of plants, such as Astragalus membranaceus, highlighting that ethnobotany encompasses a range of uses beyond just food. The references to multiple studies illustrate that medicinal uses are a significant aspect of ethnobotanical research.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: F1-score performance measurement was chosen because it balanced the sensitivity and precision performance indices. We also examined the prediction of the syndrome using a neural network algorithm. The performance measurements, however, were lower than the performance measurements of the other algorithms, unlike high performance measurements in. 21,22Our dataset is an open-source free dataset in accordance with the Attribution Non-Commercial (CC BY-NC 4. 0) license. Participants of the research in which that data was collected provided their written informed consents to participate. The protocol of that study complied with the Declaration of Helsinki for conducting medical research involving human subjects, authorized by of GESMA. Study populationA free-to-use medical dataset was selected. The dataset contained records of 60,799 subjects who underwent periodic health examinations at their workplace in Spain between 2012–2016. 23 Medical dataset indices included personal information and health habits (age, gender and smoking), anthropometric measurements (body fat percentage, ABSI index, BMI, waist circumference and waist circumference-to-height ratio), systolic and diastolic blood pressure and blood measurements (total cholesterol, LDL cholesterol, HDL-C, fasting glucose and triglycerides) and the classification of whether or not the individual had metabolic syndrome. 14 Based on the dataset, we built and examined three datasets: a dataset for men and women, a dataset for men only and a dataset for women only. Calculated medical indices were added to each dataset (pulse pressure, estimated mean arterial pressure, HDL cholesterol to blood triglyceride level, non-HDL-C, and total cholesterol to blood triglyceride level). MeasurementA confusion matrix was calculated based on subjects’ metabolic syndrome classification in the datasets and performance measurements were calculated (accuracy, sensitivity, precision, F1-score, and MCC). The prevalence of the metabolic syndrome in the study population (9%) - in a young working population (average age 40 and standard deviation 10. 3), is neither low nor high, and therefore a threshold of 0. 5 is appropriate. Data analysisFeature importance of the indices used in the datasetsUsing the ExtraTreesClassifier and univariate selection methods, we determined which indices were most important. We applied these methods to all three datasets for examining gender effects. The preferred classification algorithm for predicting metabolic syndrome per subgroup was selected based on the highest F1-score performance measure. [SEP] Justify:

Output: The text does not address the claim about the use of machine learning in predicting protein structures. Instead, it focuses on performance measurement metrics of various algorithms in predicting metabolic syndrome using a medical dataset, which is unrelated to the claim.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: The robust nature of the circadian rhythm arises from reciprocal network interactions among the neurons and astrocytes [123]. The circadian clock in astrocytes contributes to the circadian oscillations [124]. Contrary to the earlier observation that astrocytes have a supporting role for neurons [125], present-day information indicates that they also have a role in “gliotransmission” [126]. Astrocytes partner with the neurons to perform the functioning of the network for circadian timing of course with separate circadian properties. It is interesting that not only the neurons of the SCN but also the cell-autonomous clocks of astrocytes drive circadian behavior in mammals [127]. Astrocytes and neurons are acting as two arms of the central clock [122]. Based on the presence of an abundance of clock protein PER in glial cells and more so in astrocytes, it has been suggested that there is an existing functional circadian clock in astrocytes. Rhythmic expression of clock genes in astrocytes has also been reported [128–130]. The role of recently identified astrocytes of the SCN in controlling circadian behavior cannot be ignored [123]. Astrocytic cytoskeletal factor glial fibrillary acidic protein (GFAP) has a circadian variation in the SCN [131]. Circadian properties and functions of astrocytes have been studied in detail now with the use of genetic access to astrocytes. SCN astrocytes can control the circadian period by regulating GABA uptake as well as glutaminergic signaling [9, 61, 122]. Astrocytes of the SCN have an essential role in regulating activity and entrainment of the clock as they possess a molecular TTFL [60, 122, 129]. TTFLs form the basis for circadian rhythms in mammals. It is generally agreed that there is interaction between TTFL, neuronal and glial activity of the neurons, and astrocytes of the SCN for the generation of circadian time signals [132]. TTFLs are found in neurons and astrocytes alike in the SCN and the cell autonomous astrocytic TTFLs can alone drive molecular oscillations in the absence of other cellular clocks. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of the suprachiasmatic nucleus (SCN) and its astrocytes in regulating circadian rhythms. It emphasizes that the SCN's astrocytes and neurons work together as the central clock, with functional circadian clocks existing in astrocytes. The mention of circadian properties and functions of the SCN astrocytes, along with the description of their interactions in regulating the circadian time signals, supports the idea that the SCN is integral to circadian rhythm regulation.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: Jr. Astragalus-based Chinese herbs and platinum-based chemotherapy for advanced non-small-cell lung cancer: Meta-analysis of randomized trials. J. Clin. Oncol. ; 2006. 24: 419–430. DOI: 10. 1200/JCO. 2005. 03. 639237. . Anti-aging effects of the traditional Chinese medicine Bu-zhong-yi-qi-tang in mice. Am. J. Chin. Med. ; 2000. 28: 77–86. DOI: 10. 1142/S0192415X0000010638. Toshiaki, K. , Nobuhiko, S. , Eiichi, T. , Shinya, S. , Yutaka, S. , Hiroshi, O. , Hideki, O. , Katsutoshi, T. Assessment of effects of traditional herbal medicines on elderly patients with weakness using a self-controlled trial. Geriatr. Gerontol. Int. ; 2004. 4: 169–174. DOI: 10. 1111/j. 1447-0594. 2004. 00246. x39. . , Michler, M. G. (Huangqi): Monographs and Analysis Germany. Verlag, Wald, 1997. 17. 40. Bensky, D. , Gamble, A. Medicine: Materia Medica, Revised Edition. Eastland Press, Seattle, 1993. 41. Chang, H. , But, P. Pharmacology and Applications of Medica, Vol. 2. World Scientific, Singapore, 1987. 1046. 42. . ,. Subchronic toxicity studies of radix astragali extract in rats and dogs. J. Ethnopharmacol. ; 2007. 110: 352–355. DOI: 10. 1016/j. jep. 2006. 09. 02443. Hayashi, H. , Sudo, H. Economic importance of licorice. Plant Biotechnol. ; 2009. 26: 101–104. DOI: 10. 5511/plantbiotechnology. 26. 10144. Li, X. Y. A study of the system and new taxa of L. Bull Bot. Research. ; 1993. 13: 4–43. (In Chinese)45. Guo, Z. , Wu, Y. , Wang, R. [SEP] Justify:

Output: The text contrasts the claim by providing numerous references to studies involving traditional medicinal uses of plants, which demonstrates that ethnobotany encompasses more than just food-related plants. Examples from the text, such as studies on 'Chinese herbs and platinum-based chemotherapy' and 'traditional herbal medicines on elderly patients,' highlight the medicinal applications emphasized in ethnobotany, indicating that it includes a significant focus on plants used for medical purposes.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: Therefore, boxplots were prepared for each dataset for the classification algorithms we used: random forest, naïve Bayes, decision tree (CART), KNN and logistic regression. As mentioned above, for evaluation of the quality of metabolic syndrome prediction, performance measures were calculated: accuracy, sensitivity, precision, F1-score and MCC. Improving metabolic syndrome prediction using four and five national cholesterol education program adult treatment panel III criteriaDetermining metabolic syndrome as defined by requires using at least three of five criteria (blood triglyceride level, waist circumference, HDL-C, blood glucose level, and blood pressure). We evaluated the predictive quality of the syndrome using three, four and five indices according to with respect to sensitivity, precision, F1-score and MCC. Combinations of three and four indices using the men and women dataset were selected according to their importance ranking by the ExtraTreesClassifier method. The following are the combinations we used (Figure 1):•Three most important indices: blood triglyceride level, waist circumference and HDL-C. •Four most important indices: blood triglyceride level, waist circumference, HDL-C and blood glucose level. •Five most important indices: blood triglyceride level, waist circumference, HDL-C, blood glucose level and blood pressure. Figure 1. Feature importance ranking of the men and women dataset indices. A specific algorithm was selected according to the highest F1-score performance measurement:•For the three indices: the algorithm was selected for the men and women dataset, the random forest algorithm was selected for the men dataset and the KNN algorithm was selected for the women dataset. •Four and five indices: the random forest algorithm was selected. Contribution of blood pressure indices to the prediction of metabolic syndromeWe predicted metabolic syndrome according to blood pressure indices (systolic and diastolic blood pressure from the medical dataset; and additional calculated indices – estimated mean arterial pressure and pulse pressure), in two index groupings ranked by importance using the ExtraTreesClassifier method. The aim was to find the contribution of calculated blood pressure indices to the prediction of metabolic syndrome. The two groups for which metabolic syndrome was predicted using blood pressure indices and calculated blood pressure indices were:•1: blood triglyceride level, blood glucose level, waist circumference, HDL-C (blood pressure indices; hereinafter referred to as Group 1 base indices) and estimated mean arterial pressure and pulse pressure. [SEP] Justify:

Output: The text does not address the claim regarding the use of machine learning in predicting protein structures. Instead, it discusses the evaluation of metabolic syndrome prediction using various classification algorithms, but it does not mention protein structures or their prediction, making the text irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: TTFL in SCN astrocytes alone driving circadian timekeeping and behavior [133] has prompted the conclusion that astrocytes initiate SCN rhythms and control period but with lower potency than neurons. Molecular circadian clocks of the neurons and SCN astrocytes possess the same functional clock genes and perform as circadian oscillators. Timekeeping in most cells of the body uses TTFL in which period and cryptochrome proteins act on transcription factors CLOCK and BMAL1 to repress their transcription [134]. SCN acts as the central point for gene-to-cell as well as cell-to-circuit to the final behavior in mammals [9]. TTFL-based oscillations along with the strong network of the SCN enable robust timekeeping at the circuit level. This is displayed as high amplitude oscillation tightly ensemble period and phase and complex cellular synchrony [135, 136]. Astrocytes control circadian timekeeping through glutaminergic signaling [122]. Research has shown [129] that astrocytes if cocultured with SCN explants can sustain the circadian rhythm of astrocytes since the diffusible factors from the SCN can entrain circadian oscillations in astrocytes. Astrocytes also can regulate neuronal networks by way of the release and uptake of glutamate and other neurotransmitters [137, 138]. A new model for circadian timekeeping put forward [122] suggests glutamate released from astrocytes maintains higher calcium levels in the pre-synaptic terminals through the N-methyl-D-aspartate (NMDA) receptor subtype 2C (NR2C) activation which leads to neuronal GABA release. The peak phase of circadian calcium rhythms in astrocytes has been observed at night and GABA release from neurons via glutamate from astrocytes increases at night. This leads to a decrease in neuronal activity at night. In other words, this also states that SCN has “day active” clock neurons and “night active” astrocytes. Thus, the SCN circadian network has been reported to have two different cellular populations of day-active neurons and night-active astrocytes. It is also worth mentioning that neurons of the SCN clock are active during circadian daytime and SCN astrocytes are active during circadian nighttime. [SEP] Justify:

Output: The text corroborates the claim by describing the role of the suprachiasmatic nucleus (SCN) as the central point for circadian timekeeping and behavior in mammals. It highlights that the SCN is instrumental in maintaining circadian rhythms through its interactions with both neurons and astrocytes. Phrases such as 'SCN acts as the central point for gene-to-cell as well as cell-to-circuit to the final behavior' and the mention of 'day active' neurons and 'night active' astrocytes further support the claim that the SCN regulates these rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: , Wang, W. , Liu, Y. , Zhang, X. Q. , Gao, S. , Zhang, Y. , Wei, S. Distribution patterns of the contents of five active components in taproot and stolon of Glycyrrhiza uralensis. Biol. Pharm. Bull. ; 2014. 37: 1253–1258. DOI: 10. 1248/bpb. b14-0017346. Katakai, M. , Akamaru, T. , Tani, T. An analysis of the frequency of formulations and crude drugs described in (Shan-Han-Lun). Yakushigaku Zasshi; 2002. 37: 28–35. 47. Isbrucker, R. A. , Burdock, G. A. Risk and safety assessment on the consumption of Licorice root (Glycyrrhiza sp. ), its extract and powder as a food ingredient, with emphasis on the pharmacology and toxicology of glycyrrhizin. Regul. Toxicol. Pharmacol. ; 2006. 46: 167–192. DOI: 10. 1016/j. yrtph. 2006. 06. 00248. Asl, M. N. , Hosseinzadeh, H. Review of pharmacological effects of Glycyrrhiza sp. and its bioactive compounds. Phytother. Res. ; 2008. 22: 709–724. DOI: 10. 1002/ptr. 236249. Wan, J. B. , Bai, X. , Cai, X. J. , Rao, Y. , Wang, Y. S. , Wang, Y. T. Chemical differentiation of da-cheng-qi-tang, a chinese medicine formula, prepared by traditional and modern decoction methods using uplc/q-tofms-based metabolomics approach. J. Pharm. Biomed. Anal. ; 2013. 83: 34–42. DOI: 10. 1016/j. jpba. 2013. 04. 01950. Hinoshita, F. , Ogura, Y. , Suzuki, Y. , Hara, S. , Yamada, A. , Tanaka, N. , Yamashita, A. , Marumo, F. [SEP] Justify:

Output: The text contrasts the claim by referencing multiple studies and articles that discuss the medicinal properties and uses of plants such as Glycyrrhiza uralensis and Glycyrrhiza sp. These studies indicate that there is substantial research focused on the pharmacological and medicinal effects of these plants, suggesting that ethnobotany encompasses both food and medicinal uses. For instance, the review of pharmacological effects of Glycyrrhiza sp. clearly illustrates the relevance of medicinal applications in ethnobotany.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: •2: blood triglyceride level, blood glucose level, non-HDL-C (blood pressure indices; hereinafter referred to as Group 2 base indices) and estimated mean arterial pressure and pulse pressure. 1 and 2, the chosen algorithm was random forest for the men and women dataset, for the men dataset and for the women dataset. Performance measurements of Groups 1 and 2 and estimated arterial pressure and pulse pressure were compared to performance measurements of Groups 1 and 2 and systolic blood pressure and diastolic blood pressure. ResultsFeature importance of the dataset’s indicesWe examined which indices are most important as ranked by the ExtraTreesClassifier and univariate selection methods for men and women, for men only, and for women only. Feature importance of indices using the ExtraTreesClassifier methodAbbreviations: SBP – Pressure; DBP – Pressure; PP – Pulse Pressure; MAP – Pressure = DBP + 1/3 (SBP – DBP); ABSI index – WC/((BMI)2/3 (height)1/2); BF – %=1. 2 × (BMI) + 0. 23 × (age in years) − 10. 8 × (gender) – 5. 4. Gender: women (0), men (1); BMI – body weight (kg) divided by height (m) squared, in kg/m2Figure 1 shows the feature importance ranking of the men and women dataset indices. We observed that the five indices ranked from high to low were: blood triglyceride level, waist circumference, HDL-C, blood glucose level and blood pressure. Figure 2 shows the feature importance ranking of the men only dataset indices. We observe that the five indices ranked from high to low are: blood triglyceride level, waist circumference, HDL-C, blood glucose level and blood pressure. Figure 2. Feature importance ranking of the men only dataset indices. Figure 3 shows the feature importance ranking of the women only dataset indices. We observe that the five indices ranked from high to low are: blood triglyceride level, blood glucose level, waist circumference, blood pressure and HDL-C. Figure 3. Feature importance ranking of the women only dataset indices. Feature importance of the three dataset indices using the univariate selection (chi-square) methodTable 1 shows the feature importance ranking of the men and women dataset indices, men dataset indices and women dataset indices. [SEP] Justify:

Output: The text does not relate to the claim about machine learning in predicting protein structures. Instead, it discusses blood pressure indices and the use of algorithms like random forest to analyze indices related to blood metabolism, without any mention of protein structures or machine learning applications in that context.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Anatomical and functional evidence also favors the suggestion of the role of SCN astrocytes in photic resetting. Glu release forms the signal transduction pathway for synchronization by light [139–141]. Neuronal and astrocytic clocks are coupled via glutamate released from astrocytes which increases presynaptic GABA release and suppresses postsynaptic neuron activity during night. Astrocytic clocks can drive molecular oscillations in the SCN by reinstating clock gene expression via glutaminergic signals. Glu released from astrocytes assists in the coupling of the neuronal and astrocytic clocks by increasing GABA and suppressing the activity of postsynaptic neurons at night [127]. It is not only glutamate but other neurotransmitters are also likely to participate in the actions of astrocytes like it occurs in the SCN. It is reported that glutamate and glutamate receptors sensitive to NMDA have a dual role in SCN by coupling astrocytic and neuronal cell oscillators as well as resetting the phase to light [142]. The rhythmic output of the astrocytes is likely to modulate neuronal activity by way of pre- and post-synaptic interactions. It is also seen that [143] extracellular glutamate increases neuropeptide signaling from neurons. Astrocytes also can increase the circadian period and enhance neuronal synchronization as per the endogenous circadian period. Association of astrocytes with neurons occurs and VIP and AVP are involved in the process [144, 145]. Though not precisely known yet, experimental studies have shown that GABA, glutamate, and VIP contribute to the effect of astrocytes on circadian behavior. Astrocytes can encode circadian information by way of bidirectional synaptic interaction utilizing VIP released from neurons. Daily neuronal rhythms of the core clock are controlled by the release and uptake of GABA and glutamate [146]. A bona fide astrocytic circadian clock contribution for the generation of circadian and seasonal rhythmicity reported [124] gives a new understanding not only about the complex mammalian circadian clock, and neuron astrocyte interaction [147] but also the ability to determine at least some clinical features of diseases as well. Part 2: individual neurotransmitters their properties and their involvementThe following neurotransmitters of the SCN will be discussed in detail now. Both VIP and AVP function as synchronizers and are involved in entrainment. [SEP] Justify:

Output: The text corroborates the claim by detailing the role of the suprachiasmatic nucleus (SCN) in the regulation of circadian rhythms through various biological mechanisms. It explains how astrocytes contribute to photic resetting and synchronize neuronal and astrocytic clocks via neurotransmitters like glutamate and GABA, emphasizing the SCN's significance in maintaining circadian rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: Effect of orally administered shao-yao-gan-cao-tang (shakuyaku-kanzo-to) on muscle cramps in maintenance hemodialysis patients: A preliminary study. Am. J. Chin. Med. ; 2003. 31: 445–453. DOI: 10. 1142/S0192415X0300114451. effect and regulatory effects on autonomic function of Shao-Yao Gan-Cao Tang, a Chinese herbal prescription, in asymptomatic hyperuricemic vegetarians. Rheumatol. Int. ; 2007. 28: 27–31. DOI: 10. 1007/s00296-007-0385-752. Satoh, H. , Tsuro, K. Pharmacological modulation by Shakuyakukanzoto (Shao-Yao-Gan-Cao-Tang) and the ingredients in rat intestinal smooth muscle. Chin. Med. ; 2011. 2: 62–70. DOI: 10. 4236/cm. 2011. 2201253. Hidaka, T. , Shima, T. , Nagira, K. , Ieki, M. , Nakamura, T. , Aono, Y. , Kuraishi, Y. , Arai, T. , Saito, S. Herbal medicine Shakuyaku-kanzo-to reduces paclitaxel-induced painful peripheral neuropathy in mice. Eur. J. Pain; 2009. 13: 22–27. DOI: 10. 1016/j. ejpain. 2008. 03. 00354. Komiyama, K. , Kawakubo, Y. , Fukushima, T. , Sugimoto, K. , Takeshima, H. , Ko, Y. , Sato, T. , Okamoto, M. , Umezawa, I. , Nishiyama, Y. Acute and subacute toxicity test on the extract from Glycyrrhiza. Oyo Yakuri; 1977. 14: 535–548. 55. Rossi, T. , Fano, R. A. , Castelli, M. , Malagoli, M. , Ruberto, A. I. , Baggio, G. , Zennaro, R. , Migaldi, M. , Barbolini, G. [SEP] Justify:

Output: The text contrasts the claim by highlighting various studies related to the medicinal uses of plants, specifically noting herbal prescriptions like Shao-Yao-Gan-Cao-Tang and their effects on ailments such as muscle cramps and painful peripheral neuropathy. This evidence illustrates that ethnobotany encompasses a broader focus than just food-related plants, including significant attention to medicinal applications.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: We observed in the men and women dataset that the five indices ranks, from high to low, were: blood triglyceride level, blood glucose level, waist circumference, blood pressure and HDL-C. In the men dataset the five indices ranks, from high to low, were: blood triglyceride level, blood glucose level, waist circumference, systolic blood pressure, HDL-C and diastolic blood pressure. In the women dataset the five indices ranks, from high to low, were: blood triglyceride level, blood glucose level, systolic blood pressure, waist circumference, diastolic blood pressure and HDL-C. Table 1. Importance ranking of the men and women dataset indices, men dataset indices and women dataset indices. Indices importance rankingMen and women datasetMen datasetWomen dataset877436. 942533Triglycerides648823. 065104Triglycerides112657. 416795Non-HDL-C52798. 383516Non-HDL-C34348. 414534Non-HDL-C14422. 246800Glucose22776. 178589Cholesterol_T15364. 574418Cholesterol_T6389. 477791Cholesterol_T22657. 521459Glucose14360. 559868Glucose6239. 517463WC15766. 598103WC7300. 416230LDL-C5290. 576749SBP12612. 989822BF6597. 848465SBP4273. 184994MAP9642. 882240SBP5864. 072681BF4046. 122076DBP8160. 987073HDL-C5012. 470530WC3978. 778154HDL-C7883. 210757MAP4649. 953371MAP3286. 917929Age7544. 145942Age4644. 378537DBP2794. 961227LDL-C7264. 404023DBP4050. 177852Age2722. 420. 296BF6236. 529538LDL-C2959. 284444BMI2397. 088005BMI5251. 197409BMI2471. 674382HDL-C1640. 998895PP4469. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various indices related to blood parameters and their rankings in different datasets, without any mention of machine learning or protein structure prediction.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Study VIP and GRP of the core and AVP of the shell are the best-understood peptides of SCN. one of the first neurotransmitters identified in the SCN has a unique nature and is involved in diverse functions. It has a role as a neuromodulator in many areas of the brain including SCN. Identification of AVP in the SCN was almost at the same time SCN was established as the seat of the biological clock. AVP-containing neurons are reported in almost all species including humans. AVP is also the first output signal based on evidence from Brattleboro rats and vasopressin-deficient mice showing attenuated circadian rhythms [20, 57, 148]. AVP-positive neurons form the second largest peptide population in the SCN and constitute about 20% as compared to 10% in the case of VIP [6]. Reviews are available [27, 29, 149, 150] giving an in-depth understanding of AVP. AVP neurons are essential for the SCN function and loss of AVP neurons is associated with increased activity fragmentation without loss of amplitude in humans. AVP signaling is found to modulate SCN period and phase, that too in a spatially specific manner [150]. Among the two prominent synchronizers of the neuronal networks in the and is weaker than VIP in function. Lesion experiments demonstrated the loss of drinking and locomotor behavior. Extirpation studies along with cell cultures of the SCN neurons have established the role of SCN in circadian rhythms. Other studies [25, 151] have also highlighted the role of AVP in circadian rhythms. Application of AVP locally in the SCN does not affect the free running wheel running rhythm in hamsters or circadian food, or water intake in rats. The absence of AVP shows abnormalities of the rhythm expressed. A report [152] also indicates a free-running period of locomotor rhythm being lengthened in Brattleboro rats, V1a deficient mice [57] but not in V1a and V1b deficient mice [20]. New recent techniques of behavioral, electrophysiological, and pharmacological studies have further strengthened the role of AVP signaling. The role of AVP in the molecular working of the clock has been addressed using genetic tools and two state-of-the-art studies [43, 153]. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of AVP-containing neurons within the SCN, stating that studies have established the SCN as the seat of the biological clock and have demonstrated the essential function of AVP neurons in regulating circadian rhythms. The mention of evidence from various species and studies reinforces the claim that the SCN acts as a master clock for circadian rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: : 2573-4555 Journal of & -in-for Manuscript 10. 4172/2573-4555. 1000271 Ethnobotany of Used to in Ethiopia: A Yibeltal Aschale1, Muluken Wubetu2* and Haimanot Reta31Department of Medical Parasitology, College of Medicine and 2Department of Pharmacy, College of 3Department of Biology and Botanical Science, College of Natural and Computational Science, Debre Markos, Ethiopia *Corresponding Author: Muluken Wubetu, Department of Pharmacy, College of : mulukenwubetu1986@gmail. com Received: 21-Feb-2018 Accepted Date: 06-Mar-2018 Published Date: 12-Mar-2018 DOI: 10. 4172/2573-4555. 1000271 Abstract Background: Leishmaniasis is an infectious tropical vector born disease imposing high burden in developing countries including Ethiopia. Its treatment is still using pentavalent antimonial which have been used for several years and prone to drug resistance. Other alternative drugs like amphotericin B also have horrible side effects. Therefore, new drugs are urgently needed and drug screening efforts should be encouraged. No review has been done that broadly indicates medicinal plants used to treat leishmaniasis. The aim of this review was to provide an overview of the Ethnobotany of medicinal plants used to treat leishmaniasis in Ethiopia. Materials and methods: Databases (Pub Med, 53. 6% were herbs and the common plant part used was leaf (44. 8%) followed by latex (20. 7%). Majority of the plant remedies were given topically (75. 7%). Cutaneous leishmaniasis comprises high percentage of leishmania infection treated by traditional medicinal plants. Conclusion: Varity of medicinal plants have been used by Ethiopian people of different cultures to treat leishmaniasis. Most of the plants were herbs and the commonly used plant part was leaf. Majority of prepared remedies were applied externally to the affected part. There is an urgent need to conduct clinical trials on such plants to support traditional claims and to analyze molecular and cellular mechanisms involved. : Ethnobotany; Leishmaniasis; Medicinal plants; Review; Leishmaniasis is a tropical disease caused by protozoan parasites of the genus leishmania [1]. [SEP] Justify:

Output: The text contrasts the claim by discussing the ethnobotany of medicinal plants specifically used to treat leishmaniasis in Ethiopia. It highlights that a variety of medicinal plants have been used by different cultures in Ethiopia to treat this disease, thus demonstrating that ethnobotany encompasses more than just food plants and indeed includes significant medicinal uses.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: 961428Cholesterol_T/HDL-C2129. 171535PP1485. 030853Cholesterol_T/HDL-C3005. 343230PP1854. 830010Cholesterol_T/HDL-C582. 227929HDL-C/Triglycerides1459. 169708HDL-C/Triglycerides980. 056328HDL-C/Triglycerides317. 825789Gender318. 237946WHtR45. 476815WHtR26. 608217WHtR85. 277491Smoke39. 491117Smoke4. 373936Smoke29. 068373ABSI0. 567439ABSI0. 068289ABSI1. 093250 We found that the most important and leading index according to both methods for the three datasets was the triglyceride blood level index. Asides from this index, the importance ranking generated by the ExtraTreesClassifier and univariate selection (chi-square) methods of the other indices are different for the three datasets. The HDL-C index was ranked the lowest by the univariate selection (chi-square) method, and the diastolic blood pressure index was ranked lowest by the ExtraTreesClassifier. Of the calculated blood pressure indices, pulse pressure was the least important measure in both methods. Improving metabolic syndrome prediction by using four and five national cholesterol education program adult treatment panel III indicesBy definition, three of the five metrics are sufficient to determine metabolic syndrome. Our study shows that adding a fourth or fifth index to predict the syndrome, instead of just three, improves the quality of prediction of metabolic syndrome, noticeably for women. From the results shown in Table 2, we can see in the men and women dataset that adding the fourth index increased sensitivity and precision by about 12% (compared to using three indices) and adding the fifth index increased sensitivity by 8. 63% and precision by 15. 6% (compared to using four indices). In the men dataset, adding a fourth index to predict metabolic syndrome increased sensitivity by 15. 56% and precision increased by 5. 92% (compared to three indices) and adding a fifth index increased sensitivity by 10. 96% and precision by 19. 4% (compared to four indices). [SEP] Justify:

Output: The text is irrelevant to the claim regarding machine learning and protein structure prediction. It discusses metrics for predicting metabolic syndrome and the effectiveness of various cholesterol indices, with no mention of machine learning or protein structures.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: However, the developmental pattern of neuropeptide expression may contribute to regional cellular function in adult SCN. AVP has a deep involvement in SCN function as evidenced by recent genetic studies and this has given importance to AVP in maintaining circadian homeostasis as well as a target for therapeutic intervention [160], maybe jet lag and others. is secreted sparsely, probably by about 10% of the SCN neurons [146]. Two among the 3 VIP receptors VPAC2 and PAC1 are found in the SCN with the majority of SCN neurons expressing VPAC2 in the mouse [161, 162]. VIP binds in the SCN with VPAC2 receptor for its action and high levels of VIP immunoreactivity are seen in the SCN. VIP and VPAC2 receptor agonists when applied locally to the SCN cause firing rate and GABA signaling [163]. Using phase resetting and VIP receptor agonists it has been reported [164, 165] that while the application of VIP during the subjective day did not shift the peak in firing rate rhythm, the same application of VIP during early or late subjective night resulted in a small phase delay or large phase advance. In terms of the use of VPAC2 receptor agonist and not PACAP agonist or VPAC1 receptor agonist, succeeded in producing VIP phase advancing effect and this shows that VIP-dependent phase shift is mediated through VPAC2 and suggests and supports the role for VIP and VPAC2 receptor present in the SCN for photic entrainment. It is also interesting to note that AVP neurons in the SCN participating in the rhythmicity and AVP signaling can coordinate circadian activity in the SCN especially in the absence of VIP [19, 43]. Loss of AVP receptors can weaken the clock and accelerate re-entrainment [20]. VIP signaling changes electrical and molecular rhythms in the SCN. It has been pointed out [166] that VIP is crucial for the maintenance and synchronization of cellular clocks of the neurons in the SCN. In slice preparations of mice with a lack of VIP receptors, there is a reduction in the number of neurons showing oscillations in electrical firing and clock gene expression. Rhythmic neuron synchrony was also drastically reduced [94, 167]. VIP-deficient neurons restored circadian rhythmicity on the application of VIP agonist or coculture with SCN grafts [19] thereby emphasizing the importance of VIP. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of various neuropeptides and receptors present in the SCN, highlighting their significance in maintaining circadian rhythms. It emphasizes that AVP and VIP are crucial for the function of the SCN and its role in circadian homeostasis. The mention of VIP as essential for the maintenance and synchronization of cellular clocks in the SCN directly supports the assertion that the SCN is the master clock regulating these rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: It is one of neglected disease and even not include in the list of tropical disease priorities in Ethiopia [2]. Leishmaniasis has a focal distribution and it is common to remote areas like other neglected tropical disease [3]. Visceral leishmaniasis specially is recognized as serious public health problem as it causes death if left untreated having high case fatality rate [4]. Lack of simple and easily applied tools for its management and complex ecoepidemiology contribute for the difficulty in prevention and control of the disease [3,5]. Plants remain to be the source for majority of people in developing countries to treat various health problems [6]. They are a rich source of many natural products most of which have been extensively used for human welfare, and treatment of various diseases [7,8]. a variety of medicinal plants are used as natural medicines without scientific base. Plant extracts or plant derived compounds provide important source of new medicinal agents [9,10]. A high diversity of secondary metabolites with interesting biological activities can be produced from plant extracts [11]. About eighty (80%) percent of the Ethiopia people and ninety percent (90% ) of livestock depend on traditional medicine for their health care and more than 95 percent of traditional medicine preparations are made from plant origin. Similarly, there has been a continuous growth of demand for traditional medicines globally and in many developing countries health care system [12]. Urgent need for alternative drug formulation leads to screening of natural products for potential use in leishmaniasis treatment. Different studies have been conducted on Ethnobotany of medicinal plants used to treat various human diseases in different parts of Ethiopia. However, there has not been any review conducted on Ethnobotany of plants used to treat Leishmaniasis. Therefore, there is an urgent need to assess the overall traditional preparation techniques and types of plants used in the management of leishmaniasis. This review is complementary of various medicinal plants that have been directed towards plants having leishmanicidal activity. It gives a comprehensive information on the scientific name of plants, method of preparation, route of administration, plant part used and the habit of the plant used. This review will also give new force for obtaining synthetic compounds. Materials and methods Search strategy Databases (Pub Med, 53. 6%) followed by shrubs (28. 6%) (Figure 2). The commonly used plant parts were leaves (44. 8 %) followed by latex (20. 7%). Root account 17. [SEP] Justify:

Output: The text contrasts the claim by providing considerable evidence that ethnobotany encompasses the study of medicinal plants, highlighting that a significant percentage of the Ethiopian population relies on traditional medicine made from plant sources for their healthcare needs. It states that 'different studies have been conducted on Ethnobotany of medicinal plants used to treat various human diseases in different parts of Ethiopia,' which directly opposes the claim that ethnobotany is solely focused on food plants and ignores medicinal uses.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: In the women dataset, adding a fourth index to predict metabolic syndrome increased the sensitivity by 21. 48% and the precision by 10. 59% (compared to using three indices) and the addition of the fifth index increased sensitivity by 20. 74% and precision by 23. 41% (compared to using four indices). Table 2. Prediction quality according to using various numbers of indices in the men and women dataset, men dataset and women dataset. DatasetPrediction quality# Of indicesIndex NameSensitivity (%)Precision (%)F1-score (%)MCC (%)3(1) Blood triglyceride levelMen and women57. 5967. 9062. 3259. 11(2) Waist circumferenceMen63. 8974. 1368. 6365. 11(3) HDL-CWomen49. 2665. 2056. 1254. 654(1) + (2) + (3) + (4) Blood glucose levelsMen and women69. 6379. 9074. 4172. 23Men79. 4560. 0579. 7577. 12Women70. 7475. 7973. 1871. 815(1) + (2) + (3) +(4) +(5) Blood pressureMen and women78. 2695. 5086. 0285. 27Men90. 4199. 4594. 7294. 20Women91. 4899. 2095. 1895. 02With each addition of an index to the metabolic syndrome prediction, the performance measurements increased, but differently for the three datasets. The improvement in the prediction quality for the three datasets after adding a fourth index compared to three indices ranged from 12. 04% to 21. 48% in sensitivity and 5. 92% – 12% in precision. The improvement in the prediction quality after adding a fifth index compared to four indices ranged from 8. 63% to 20. 74% in sensitivity and 15. 6% – 23. 41% in precision (Table 3). Table 3. prediction’s confusion matrixes according to using various numbers of indices in the men and women dataset, men dataset women dataset. [SEP] Justify:

Output: The text appears to focus on using indices to predict metabolic syndrome in various datasets, discussing sensitivity and precision in these predictions. It does not address or mention machine learning or protein structure prediction specifically, making this information irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Evidence of VIP as a principal synchronizer is also available [100, 108]. VIP cells in the core receive direct innervation from RHT and convey photic information to other parts of the SCN. The presence of VIP in SCN slices in ex vivo during late subjective night advances the phase of neuronal firing and stimulation of the expression of genes Per 1 and Per 2 like the effects of light [72, 168]. In the case of mice lacking VPAC2, the receptor for VIP, nocturnal light pulse failed to induce clock gene expression and showed impaired gating or photic responsiveness [92, 169]. In vitro, VIP application can cause an increase in cAMP level leading to a phase and dose-dependent decrease in intracellular Ca2+ in the SCN. To understand how VIP release due to the firing activity of VIP neurons entrain circadian rhythms, studies [100] were carried out. They concluded that VIP neuron stimulation in high frequencies, but not low, shifted gene expression rhythms in vitro. In vivo, high-frequency VIP neuron activation resulted in the entrainment of circadian locomotor rhythms. In effect, it is reasonable to believe that VIP neural firing frequency releases VIP and leads to the entrainment of molecular and behavioral circadian rhythms. By using optogenetic manipulation and bioluminescence imaging interaction between molecular electrical and behavioral circadian rhythms was studied [108]. In yet another study [170] emphasizing the role of VIP, it has been suggested that VIP neurons of the SCN have a role in lengthening the circadian period. This was based on the ablation of neurons in vivo and in vitro. The results also showed a differential role for VIP neurons in adults and developing SCN. VIP neurons are important for synchronizing circadian cells during development but not so in adults. Core peptide VIP increases during the dark period and is important for light entrainment and internal synchronization. It has been pointed out [166] that VIP is crucial for the maintenance and synchronization of cellular clocks of the neurons in the SCN. The absence of VIP signaling as seen in VIP receptor-deficient mice leads to attenuated behavioral rhythms, maybe along with multiple period components [18, 92, 154]. In slice preparations of mice with a lack of VIP receptors there is a reduction in the number of neurons showing oscillations in electrical firing and clock gene expression. Rhythmic neuron synchrony was also drastically reduced. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of vasoactive intestinal peptide (VIP) within the suprachiasmatic nucleus (SCN). It describes how VIP neurons are essential for synchronizing circadian rhythms, indicating that they contribute to the regulation of phase shifts in neuronal firing and ensure the entrainment of circadian rhythms. The mention of studies demonstrating the impact of VIP on gene expression and behavioral rhythms further supports the idea that the SCN functions as the master clock for circadian rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: 2% of the total plant parts used (Figure 3). Figure 2: Proportion of growth form of medicinal plants used for treatment of Leishmaniasis. Figure 3: Proportion of plant parts used for treatment of Leishmaniasis. Method of preparation and route of administration Traditional medicinal practitioners in Ethiopia apply different techniques of preparation like drying, crushing, concoction, and decoction (Table 1). They use simple methods and equipments during their remedy preparation. Of the routes commonly used to administer remedies in the treatment of leishmaniasis, topical route was the common route which consists 75. 7% (n=22) followed by bandaging 13. 8% (n=4) and nasal 6. 9% (n=2) way of administration. Only one preparation was intended to be administered orally (Figure 4). Figure 4: Proportion of route of adminstration of medicinal plants used for treatment of Leishmaniasis. Type of leishmaniasis treated with medicinal plants Cutaneous and visceral leishmaniasis were identified to be leishmania infections that can be treated traditionally when encountered in human by using medicinal plants listed in the table. They account 31. 0% and 24. 2% respectively. The remaining 44. 8% of leishmania infections were not specified and can be also treated by traditional healers using different medicinal plants (Figure 5). Figure 5: Proportion of type of leishmaniasis treated by medicinal plants. review revealed that about 28 plant species find applications by the traditional healers of the country to treat leishmaniasis. These plants were distributed in 19 families and none of them was confirmed scientifically in animal model. This indicates less attention is given to the traditional medicine practices in drug formulation in general. This review revealed the presence of high species diversity of medicinal plants used for treatment of leishmaniasis. This is due to the climatic variation that exists in different provinces of Ethiopia. accounts the highest percentage (21. 0%) followed by family Euphorbiaceae (15. 8%). and Solanaceae account 10. 5% each. Another study undertaken in Spain [26] and Korea [27] revealed that Asteraceae has the highest number of medicinal plants. According to a study in Nigeria [28] family Caesalpiniaceae has higher number of plants. [SEP] Justify:

Output: The text contrasts the claim by providing detailed information about the use of medicinal plants in traditional health practices, specifically for the treatment of leishmaniasis. It highlights that traditional medicinal practitioners in Ethiopia utilize various plant species for medical purposes, which directly contradicts the notion that ethnobotany is only focused on food plants. The review indicates a significant diversity in medicinal plant species and discusses their usage, which suggests that medicinal uses are a crucial aspect of ethnobotany, thereby refuting the claim.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: # Of indicesIndex nameDatasetFalse negativesTrue negativesFalse positivesTrue positives3(1) Blood triglyceride levelMen and women64130310,744472(2) Waist circumferenceMen5131795984290(3) HDL-CWomen1337148541374(1) + (2) + (3) + (4) Blood glucose levelsMen and women77519510,852338Men6381596004165Women191614864795(1) + (2) + (3) +(4) + (5) Blood pressureMen and women8714111,006242Men7264615977Women2472492323Contribution of blood pressure indices to metabolic syndrome predictionGroup 1Table 4. Presents the quality of prediction with Group 1 base indices and the blood pressure index in the men and women dataset. Table 4. Group 1 performance measurements in the men and women dataset. Prediction qualityIndex NameSensitivity (%)Precision (%)F1-score (%)MCC (%)Base indices + systolic & diastolic blood pressure78. 2695. 5086. 0285. 27Base indices + estimated mean arterial pressure76. 0193. 2783. 7682. 82Base indices + pulse pressure72. 4285. 8478. 5676. 93When predicting using the estimated mean arterial pressure instead of systolic and diastolic blood pressure, sensitivity and precision decreased by 2. 2%. When predicting using pulse pressure instead of systolic and diastolic blood pressure, sensitivity decreased by 5. 84% and precision by 9. 66% (Table 5). Table 5. Group 1 confusion matrixes in the men and women dataset. Index nameFalse negativesTrue negativesFalse positivesTrue positivesBase indices + systolic & diastolic blood pressure8714111,006242Base indices + estimated mean arterial pressure8466110,986267Base indices + pulse pressure80613310,914307Group 2Table 6 presents the quality of prediction with Group 2 base indices and the blood pressure index in the men and women dataset. Table 6. Group 2 performance measurements in the men and women dataset. Prediction qualityIndex NameSensitivity (%)Precision (%)F1-score (%)MCC (%)Base indices + systolic & diastolic blood pressure61. 5579. 5669. 4067. 49Base indices + estimated mean arterial pressure60. [SEP] Justify:

Output: The text discusses performance measurements related to predicting various health indices and parameters such as blood pressure and metabolic syndrome but does not mention machine learning or its application in predicting protein structures. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: In VIP knockout (KO) mice and those lacking VPAC2, there is a reduction in the coherence of the circadian rhythms which is due to a lack of coupling among neurons [92, 171]. Variations within individual animals in the relation between behavior and rhythmic expression in SCN have also been reported. Mostly mice lacking VPAC2 receptors do not sustain behavioral rhythmicity and do not have detectable electrical rhythms in vitro. While adult mice with no production of VIP show less severe disruption of locomotor activity, VIP KO mice show a broad range in both period and phasing of electrical rhythms. These varied results as well as much of the present information do not clearly state how VIP and its receptor participate in interneuronal coupling in the SCN. However, it strongly indicates the important role of the normal period and phase of SCN neuronal rhythms. Using bioluminescent circadian reporter mice now developed which are Cre dependent allows the genetically defined population of cells to be studied in real-time, it was observed [93] that among the two important peptidergic neurons studied, AVP and neurons and not VIP neurons are required for an essential autonomous network synchrony of the SCN. The role of VIP in SCN circuits, which is not yet fully known, was also studied using the DNA-editing enzyme Cre recombinase [172] and it was reported that VIP-IRES-Cre transgene interferes with VIP expression to affect clock function. However, it was also evident in their studies that molecular rhythms present in VIP-Cre-SCN are not due to residual VIP, but due to AVP signaling which helps to maintain SCN function at both intracellular and intercellular levels. VIP-expressing cells of the SCN and its cognate receptor VPAC2 are separate in nature neurochemically and electrophysiologically. It is reported [136] that there is a VIP/VPAC2 cellular axis that acts as a neurochemically and topographically specific pacemaker hub to determine the properties of the SCN network. 2, PACAP, and VIP along with their cognate receptor [173] are capable of mediating not only paracrine but also autocrine signaling [20, 150]. It is possible that neurons contributing to VIP to the VPAC2 axis can define the network-level properties for the role of SCN as a pacemaker. [SEP] Justify:

Output: The text supports the claim by detailing the role of the suprachiasmatic nucleus (SCN) in regulating circadian rhythms, particularly through the effects of various peptides like VIP and AVP. It emphasizes that VIP knockout mice demonstrate a reduction in rhythmic coherence, highlighting the SCN's critical role in circadian rhythm regulation. Additionally, the text discusses the mechanisms of neuronal coupling within the SCN and mentions that the VIP/VPAC2 cellular axis acts as a specific pacemaker hub, further corroborating that the SCN functions as a master clock.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: According to this review, herbs constitute the highest percentage of the plants identified to have medicinal value in the treatment of leishmaniasis followed by shrubs. Similarly, other studies conducted elsewhere in Ethiopia indicated the dominance of herbs [29-34]. Herbs are seasonal which indicates that they are not accessible throughout the year which needs storage but can be easily cultivated in a limited area. However, Shrubs are known to be non-seasonal in nature which implies that they are accessible throughout a year and no need to store. In this review, leaf was the most commonly used plant part in the preparation of remedies as compared to other parts. Studies conducted in Nigeria [28] and Israel [35] also showed the dominance of leaves in the preparation of remedies. However, the use of plants leaf for medicinal purposes has its own limitations unless the plant is evergreen. Because leaves are seasonal this implies that they are not available throughout a year which needs storage if non-fresh leaves are required. There is no threat to the mother plant when leaves are harvested for medicinal purpose instead the highest threat to the mother plant comes with root, bark and stem harvest. Medicinal plants were formulated in various forms using different solvents and additives. Traditional medicinal Practitioners prepare remedies in such a simple manner without need of advanced techniques and further processing. This may be due to lack of formal education and processing instruments. Practitioners used butter, porridge, sugar, and honey as additives to increase the medicinal value/potency of the remedies. This is agreed with a study conducted in Israel [35] and Hawassa [36]. This review also revealed that high proportions of remedies were given topically. The reason for this is, traditional medicinal practitioners prefer simple routes like topical and oral that do not require special skill due to lack of ability to administer remedies in other complex routes like intravenous and intramuscular. As leishmania parasite is found in the affected area and medicines are applied to the affected area, topical routes allow rapid interaction between the prepared medicines and the parasite increasing its potency. Another study conducted in Sheko ethnic group of revealed that the most medicinal plant preparations were administered cutaneously [37]. Even though unspecified Leishmania infections were reported, in this review cutaneous and visceral leishmaniasis were the common Leishmania infections that can be treated traditionally when encountered in human using medicinal plants. [SEP] Justify:

Output: The text contrasts the claim by detailing the significant role of medicinal plants, particularly herbs, in ethnobotany and their use in traditional medicine. It discusses how herbs are the most identified plants with medicinal value and emphasizes their application in treating conditions like leishmaniasis. This information directly opposes the idea that ethnobotany neglects medicinal uses.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: 3878. 4168. 2266. 14Base indices + pulse pressure55. 9773. 8263. 6761. 24When predicting using the estimated mean arterial pressure instead of systolic and diastolic blood pressure, sensitivity and precision decreased by 1. 1%. When predicting using pulse pressure instead of systolic and diastolic blood pressure, sensitivity decreased by 5. 58% and precision by 5. 74% (Table 7). Table 7. Group 2 confusion matrixes in the men and women dataset. Index nameFalse negativesTrue negativesFalse positivesTrue positivesBase indices + systolic & diastolic blood pressure68517610,871428Base indices + estimated mean arterial pressure67218510,862441Base indices + pulse pressure62322110,826490DiscussionFeature importance of the datasets’ indicesThe study showed that the ExtraTreesClassifier and univariate selection (chi-square) methods rank importance of indices differently for men and women when taken as one group, and men and women viewed as separate groups. The assorted criteria were not equally important when making a prediction. In both methods, the most highly ranked criterion for both men and women as a group and men and women separately was blood triglyceride level, such as. 24,25 Verses blood triglyceride level in men and waist-to-height ratio in women that showed the strongest predictive strength for the syndrome26; of the remaining criteria, waist circumference for all datasets was in the top five. Waist-to-height ratio index was useful in identification of metabolic syndrome27,28 and had important diagnostic value for metabolic syndrome in older adults. 29–31 Of the top five indices ranked important in both methods, two indices appear on both methods’ lists (blood triglyceride level, waist circumference), three appear on only one list and another three appear only on the other list. Contribution of blood pressure indices to metabolic syndrome predictionTo the best of our knowledge, our study is the first to examine calculated blood pressure measures – estimated mean arterial pressure and pulse pressure – when predicting metabolic syndrome using machine learning. According to the study findings, predictions using the blood pressure indices versus estimated mean arterial blood pressure and pulse pressure index are more accurate. Further, when using estimated mean arterial pressure versus pulse pressure the prediction of metabolic syndrome is more accurate. [SEP] Justify:

Output: The text discusses the use of machine learning in predicting metabolic syndrome through blood pressure indices but does not mention protein structures or their prediction. Thus, it is irrelevant to the claim about machine learning's application in predicting protein structures.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: A study [174] had shown that VIP neurons of the SCN and the internal molecular clock present in it maintain locomotor activity rhythms, and circadian rhythm of body temperature in addition to sleep and wheel-running behavior. Recent studies are pointing to the participation of non-cell groups. An alternate but surprising viewpoint put forward is based on a combination of neurons expressing AVP, DA 1a receptor (Drd1a), or non-[43, 153, 175], present in the SCN, dominant to neurons controlling circadian pace making. However, this theory has not found much favor due to a lack of supporting evidence. synthesized in the SCN from glutamate with the help of GABA synthesizing enzyme glutamate decarboxylase (GAD) is expressed by nearly all neurons. Multiple circadian oscillators are synchronized to each other through neurotransmitters and GABA is one among them for the oscillatory coupling mechanism. However, the role of GABA in the circadian clock function remains controversial [176]. Though initially recognized as an inhibitory neurotransmitter, GABA is now reported to be both inhibitory and excitatory on SCN neurons [5, 46, 177] with the effects varying with the region within the SCN, time of the day, photoperiod, and even development. GABA effects on cellular activity have been studied with one study reporting inhibitory effects on SCN neurons [178] while others pointing both excitatory and inhibitory effects [119, 179]. Two major classes of GABA receptors namely GABA A (gamma subunit) and extra synaptic GABA A (delta subunit) [180] and GABA B have been identified in the SCN [118]. GABA can function through signaling and the two types of receptor subunits referred to as GABA A appears to be a chloride ion channel and GABA B is a G protein-coupled receptor. receptors are mainly expressed in the dorsal area of SCN [118] also containing AVP-positive neurons. Individual SCN cells with heterogeneous oscillators present in the SCN must couple with each other to have a robust output and synchronized circadian rhythms are required for coordinating peripheral circadian oscillators as well [181]. Synchronization process in the SCN can be supported [182] or impaired [183] by GABA coupling. The functional role of GABA in the clock has been elusive for some time now. [SEP] Justify:

Output: The text corroborates the claim by describing how neurons within the suprachiasmatic nucleus (SCN) maintain various circadian rhythms, such as locomotor activity, body temperature, and sleep behaviors. It outlines the role of neurotransmitters like GABA and highlights the complex interactions within the SCN that contribute to the synchronization of circadian rhythms, thus supporting the assertion that the SCN acts as a master clock in regulating these biological rhythms.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: The mechanism of action of Plant extracts and isolated secondary metabolites like flavonoids is often by interfering with central targets of Leishmania parasites (intercalation with DNA, alkylation of DNA), membrane integrity, microtubules and neuronal signal transduction [11]. the present review, a total of twenty eight medicinal plants have been identified and recorded for their use for the treatment of leishmaniasis in Ethiopia. Most of the plants were herbs and the commonly used plant part was leaf. Even though most of these medicinal plants are widely utilized in different provinces of Ethiopia, information on their safety and efficacy are not scientifically proofed by using animal models. Therefore, it is advisable for researchers to conduct the safety and efficacy studies of the traditionally claimed medicinal plants in more detail in animal models and if possible in clinical trials. approval and consent to participate Not applicable Consent for applicable Availability of Data and additional data are required; all information is clearly stated in the main manuscript. The authors have declared that there is no competing interest. funding was obtained for this study. Authors’ Contribution MW, YA conceive the review, YA involved in data extraction, data analysis, interpretation and drafting of the manuscript. HR involved in data analysis and quality assessment. MW Critically reviewed the manuscript. All authors reviewed and approved the manuscript. applicable 250. (2004) Combating tropical infectious diseases: report of the Priorities in Project. Dis 6: 871-878 (2008) Complexities of assessing the disease burden attributable to leishmaniasis. 10: e313 (2005) Risk factors for kala-azar in Bangladesh. Dis 5: 655 (2006) Leishmaniasis and poverty. 12: 552-557. Birhanu A, (2017) of Used for the Treatment of Human and in District of Bale Zone, Southeast Ethiopia. 4: 5043-5055 (2015) An ethnomedicinal investigation of plants used by traditional healers of Gondar town, North-Western Ethiopia. J Res 2: 36-43 (2013) Traditional medicinal plants: a source of phytotherapeutic modality in resource-constrained health care settings. 1: 67-74 (2001) Leishmaniasis phytotherapy. Nature's leadership against an ancient disease. [SEP] Justify:

Output: The text contrasts the claim by discussing the medicinal use of plants specifically for the treatment of leishmaniasis, highlighting that a total of twenty-eight medicinal plants have been identified for this purpose in Ethiopia. This information provides direct evidence against the idea that ethnobotany only concerns itself with food-related plants, as it includes significant attention to medicinal applications.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: For predicting metabolic syndrome, (systolic and diastolic blood pressure) indices are better than the calculated indices (estimated mean arterial blood pressure and pulse pressure), and an estimated mean arterial pressure index is better than a pulse pressure index. Future research could deepen the understanding of gender and other demographic data such as age for the purpose of accurate and focused prediction of metabolic syndrome. It could further study different ethnic groups as well as working and non-working subjects, in the aim of improving medical care. SHAP chart could deepen the understanding of the positive and negative contribution of each feature to metabolic syndrome prediction. Analyzing the data and predicting the syndrome for specific groups of medical and other indices, and addressing the difficulty and costs of producing each index or set of indices separately, may improve the cost–benefit ratio of diagnosing metabolic syndrome. Predicting the syndrome and investing in prevention efforts among high-risk populations could reduce the risk of mortality, morbidity and decline in quality of life. Author’s noteOur dataset is an open-source free dataset in accordance with the Attribution Non-Commercial (CC BY-NC 4. 0) license. Participants of the research in which that data was collected provided their written informed consents to participate. The protocol of that study complied with the Declaration of Helsinki for conducting medical research involving human subjects, authorized by of GESMA. Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this 1. In the survey, the group aged 20 and above was broken down into subgroups of 20–39, 40–59 and 60 and above. 1. Hosseini- Using machine learning techniques to predict factors contributing to the incidence of metabolic syndrome in Tehran: cohort study. 2021; 7: e27304. CrossrefPubMed2. Metabolic syndrome is a strong risk factor for minor ischemic stroke and subsequent vascular events. PLoS One 2016; 11: e0156243. CrossrefPubMed3. Hirode G, Wong RJ. Trends in the prevalence of metabolic syndrome in the United States, 2011-2016. JAMA 2020; 323(24): 2526–2528. CrossrefPubMed4. Predictive modeling the probability of suffering from metabolic syndrome using machine learning: a population-based study. [SEP] Justify:

Output: The text does not address the claim regarding machine learning's use in predicting protein structures. Instead, it discusses predicting metabolic syndrome and the methodologies involved in that context. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Studies to understand the function of GABA in the SCN have been done by using both in vivo and ex vivo methods. SCN from neonatal animals [182, 193] and from adult animals [46] have been used in experiments for recording circadian rhythms. The results of these experiments have not given a complete picture of the developmental effects of GABA on SCN circadian oscillations. However, reports [21, 194] have indicated that Cry, a clock gene is involved in cellular coupling in the adult SCN and this may have a crucial role in the cellular coupling leading to circadian outputs. GABA takes part in the transmission regulating synaptic input from RHT [195], mediates phase shifts regulates firing frequency [196], and contributes to circadian synchrony. GABA, a primary inhibitory neurotransmitter remains excitatory during embryonic and neonatal development. Chloride homeostasis conveyed by GABA A receptors and their co-transporters [116, 197, 198] which is modulated by photic changes [177] determines GABA intercellular signaling. It is also likely that some of the neurons within the SCN neural network that are excited by GABA may be involved in encoding day length [116, 199]. GABA A receptor is primarily permeable to Cl– ions and intracellular Cl– concentration determines depolarization or hyperpolarization. Based on the finding that the application of GABA decreased firing frequency at night and increased firing frequency during the day, it was suggested that the effect of GABA on SCN neurons is related to the circadian phase. This is contradictory to other reports [178] stating that there is no difference in the excitatory/inhibitory effects of GABA during subjective day as compared to night. GABA A agonists like muscimol and baclofen and GABA A antagonists like biculline produce excitatory and inhibitory effects across circadian day or night irrespective of the circadian time. GABA is colocalized sometimes with other neuropeptides like VIP and GRP and studies [200] have tried to explain the interaction of multiple signals in the SCN. GABA is likely released synaptically and non-synaptically in the SCN. However, it is not known about the relative contribution of the two types of signaling for the coupling of the clock cells for the robust functioning of the SCN. [SEP] Justify:

Output: The text corroborates the claim by detailing the involvement of various mechanisms, such as GABA's role in regulating synaptic input and neuronal firing frequency within the SCN, which are crucial for maintaining circadian rhythms. It highlights the SCN as a critical site for circadian regulation, noting experiments focusing on circadian oscillations and how specific neurotransmitters, like GABA, impact the SCN's function in encoding and managing circadian outputs.


Input: Task: contrast the claim using the given text. Claim: Ethnobotany is solely concerned with plants used for food, ignoring medicinal uses. [SEP] Text: Fitoterapia 6: 599-618 Kayser O, Kiderlen AF (2001) In vitro leishmanicidal activity of naturally occurring chalcones. 2: 148-52 (2012) Medicinal plants: a source of anti-parasitic secondary metabolites. Molecules 11: 12771-91 (2017) A survey on medicinal plants used by traditional healers in Harari regional State, East Ethiopia. J Res1: 85-90 Flora of Ethiopia and Eritrea. Edited by Hedberg I, Edwards S. Addis Ababa, Ethiopia and Uppsala, Sweden: Uppsala University; 2009: Vol. 8. (1994) Usersâ€™ guides to the medical literature II: how to use an 22. (2013) An ethnobotanical study of medicinal plants used in District, of Ethiopia. J 1: 65 Suleman S, (2012) A survey on utilization of ethnomedicinal plants in Nekemte town, (Oromia), Ethiopia. J 1: 34-57 (2014) An ethnobotanical study of medicinal plants in Amaro woreda, Ethiopia. (2007) Knowledge and use of medicinal plants by people around monastery in Ethiopia. J Ethnopharmacol 2: 271-83 (2009) Ethnobotanical study of knowledge and medicinal plants use by the people in in Ethiopia. J Ethnopharmacol 1: 69-78 (2009) Medicinal plants of the Meinit ethnic group of Ethiopia: an ethnobotanical study. J Ethnopharmacol 3: 513-21 (2005) Survey of medicinal plants used to treat human diseases in Seka Chekorsa, Jimma Zone, Ethiopia. 2 (2007) Medicinal plants of the Shinasha, Agew-awi and Amhara peoples in northwest Ethiopia. J Ethnopharmacol 3: 516-25 (2013) Medicinal plants traditionally used in the northwest Spain. J Ethnopharmacol 4: 01-22. Mi-(2013) Ethno pharmacological survey of medicinal plants in Jeju Island, Korea. J 9: 48. (2007) The perception and practice of traditional medicine in the treatment of cancers and inflammations by the Hausa and Fulani tribes of Northern Nigeria. [SEP] Justify:

Output: The text contrasts the claim by highlighting numerous studies and surveys focused on medicinal plants, indicating that ethnobotany also encompasses the use of plants for medicinal purposes. For example, references to ethnobotanical studies and their findings illustrate that plants serve significant roles in traditional medicine, directly countering the assertion that ethnobotany only pertains to food-related uses.


Input: Task: pass the claim using the given text. Claim: Machine learning is increasingly used in predicting protein structures. [SEP] Text: ahead of print 2021, retrieved from Metabolic syndrome: findings from 20 years of the Tehran lipid and glucose study. 2018; 16: e84771. CrossrefPubMed6. Alvero-Cruz JR, Fernández Vázquez R, Martínez Blanco J, et al. Sex differences for predicting metabolic syndrome by adipose dysfunction markers in institutionalized elderly. 2020; 20(6): 534–539. Crossref7. Yi Y, An J. Sex differences in risk factors for metabolic syndrome in the Korean population. 2020; 17(24). Crossref8. . Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome. Appl 2012; 39(4): 4240–4249. Crossref9. Prevalence of metabolic syndrome and its associated risk factors among staffs in a Malaysian public university. 2021; 11: 8132. CrossrefPubMed10. Romero-Saldaña M, Tauler P, Vaquero-Abellán M, et al. Validation of a non-invasive method for the early detection of metabolic syndrome: a diagnostic accuracy test in a working population. BMJ Open 2018; 8: e020476. CrossrefPubMed11. Romero-Saldaña M, Fuentes-Jiménez FJ, Vaquero-Abellán M, et al. New non-invasive method for early detection of metabolic syndrome in the working population. 2016; 15(7): 549–558. CrossrefPubMedWeb of Science12. Gutiérrez-Esparza GO, Infante Vázquez O, Vallejo M, et al. Prediction of metabolic syndrome in a Mexican population applying machine learning algorithms. Symmetry 2020; 12(4): 581. Crossref13. Bursa M, Renda ME (eds) Information technology in bio-and medical informatics. New York, NY: Publishing, 2014. Crossref14. . Extremely randomized trees. 2006; 63(1): 3–42. Crossref15. Huynh- Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics 2012; 28(13): 1766–1774. CrossrefPubMed16. Biau G, Scornet E. [SEP] Justify:

Output: The text does not relate to the claim as it focuses on metabolic syndrome and various studies related to that topic rather than discussing machine learning's application to predicting protein structures. Therefore, it is deemed irrelevant.