Input: Claim: The majority of genetic therapies are still in experimental stages but show promising results. [SEP] Text: ”The letter to the FDA, which was signed by 34 other scientists, stated that simply on the basis of the three-year data alone these data are “clearly supportive of accelerated approval, however the new four-year data regarding age at loss of ambulation now provide even more compelling data for accelerated approval based on an irreversible morbidity. The hard endpoint of loss of ambulation is not affected by motivational factors. ”As to the low level of dystrophin found in the treated patients, the letter stated that “data from studies on BMD and DMD patients and in mouse and canine dystrophinopathy models support the suggestion that relatively low levels of dystrophin can be functionally significant, even if only expressed in a limited number of fibers. ”After reviewing the evidence, the letter stated, “we conclude that the aggregate data, described in the briefing documents, are providing substantial evidence of efficacy and use in the greater population of boys amenable to exon 51 skipping is appropriate. We suggest that the most scientifically robust way forward and the most ethical choice for the Duchenne community is in the context of an accelerated approval followed by a confirmatory trial. ”Melissa J. Spencer, PhD, a professor of neurology and co-director of the Center for Dystrophy at UCLA who signed the letter to the FDA about the drug, said: “From my point of view, the results are extremely significant. It would be very unlikely to obtain the data they obtained by chance. All the data in the literature suggest that even low levels of dystrophin are likely to be beneficial. How much they need is not clear. More is better obviously. ”Whether or not the FDA advisory committee recommends accelerated approval following its April 25 meeting, she said, “The ongoing phase 3 trial will likely be continued as the confirmatory trial where data on efficacy will be collected and evaluated, and under yet to be determined conditions of the FDA, any DMD patient amenable to exon 51 skipping could be able to get the drug through an academic medical center. ”Even for an older patient who can no longer walk, she said, “The thinking is that maintenance of muscle mass that is remaining can improve a patient's quality of life. ”: -. .

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Message second - ectopia of the semen gland A. Y. Nakonechny/ Practical medicine: scientific-practical. journal. - 2003. - No 4. - P. 11-13. Nakonechny A. Y. Genitometric parameters in boys with pathology of the lowering of the testes. Message third - in the case of defective cryptorchidism A. Y. Nakonechny/ Practical medicine: Scientific-practical. Journal. - 2003. - No 5. - P. 8-11. Nevstroyeva V. V. Some barrier-forming structures of the testis V. V. Nevstroyeva, T. V. Bronikhina/ Archive of anat. , histology and embryology. - 1980. - Vol. 79, No 9. - P. 81-91. Neikov G. N. Comparative evaluation of surgical methods for the treatment of cryptorchidism in children G. N. Neykov, V. A. Schwarzkopf/ News of surgery. - 1993. - No5-6. - P. 66-69. Surgical treatment of oblique inguinal hernia M. B. Nemashko/ Clinical surgery. - 1997. - No 7. - P. 96-97. Nesterenko Yu. A. The effect of inguinal hernia repair on the functional state of the testis Yu. A. Nesterenko, V. A. Yarygin/ Surgery. - 1990. - No3. - P. 135-140. Immunological studies in cryptorchidism accompanied by infertility Nikolovsky M. , Tsvetkov D. , Popov A. , Drumcheva M. // Urol. and nephrol. - 1990. - No4. - P. 48-50. Okulov A. B. Surgical aspects of pediatric androgynecology A. B. Okulov/ Mater. of 3rd with international participation “Surgery - 2001". - M, 2001. - P. 421-425. Treatment of cryptorchidism in children Okulov A. B. , Matkovskaya A. N. , Chuvakov G. I. [et al. ]/ Method. recommendation - M. , 1984. - 27 p.

Output: pass


Input: Claim: Environmental psychology can predict individual behavior with high accuracy. [SEP] Text: Importantly, this performance was preserved in the validation cohort (AUC = 0. 81 ± 0. 09; Figure 2B). We also investigated the classifier performance on the subset of grade 2 tumors, since predicting recurrence in these patients is limited by particularly high uncertainty. Our classifier maintained high accuracy with 10-fold cross validation (AUC = 0. 93 ± 0. 05; Figure 2C), though accuracy decreased with external validation (AUC = 0. 75 ± 0. 22; Figure 2D), using the turquoise module. This module was enriched in biological processes related to cell division and metabolism (Figure 3A). Adding the blue module did not contribute to overall model performance. The turquoise meta-gene maintained its predictive accuracy with concurrent removal of up to 20 of the most individually predictive module genes (Figure 3C), and a meta gene built from these genes performed equally to the meta gene of the full module (Figure 3D). To ensure our model is unbiased, we randomly perturbed recurrence labels in the validation cohort, which abolished model performance as expected (2). DiscussionWe have used a simple and robust approach to predicting meningioma recurrence using gene modules, which yields high accuracy with a multi-study validation cohort. Previous analysis of the DNA methylation status of meningiomas has identified classes relevant to disease progression using unsupervised clustering. Grade 2 meningiomas were also observed to be scattered over these methylation classes further indicating diverse biology [8] and underlining the diagnostic problem created by these tumors. In our study, we show that a systems biology approach using meta-genes provides an accurate predictor of tumor recurrence. This principle can be used in marker discovery for other challenging diseases where conventional approaches such as differential gene expression analysis have been unsuccessful. Our results are derived from data obtained under diverse conditions and yet still produce an accurate classier which projects translational value in creating a diagnostic panel. Our study uses publicly available data and is thus limited by sparse annotation and variable follow-up times. The extent of tumor excision in our study was not known. Although this is conventionally held to affect recurrence[27] the relevance has been debated over the past decade[1] and our classifier yields high accuracy purely with gene expression data. While follow-up times are limited for some patients (Table 1) they still represent real life data from the general neurosurgical practice.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Personalized medicine promises to move beyond data regarding the average effectiveness of treatments to identify the best treatment for any individual. In order to provide personalized medicine for depression, we must identify characteristics of individuals that reliably predict differences in benefits and/or adverse effects of alternative depression treatments, including both biological and psychosocial treatments. These personalizing factors might include sociodemographic characteristics, clinical characteristics (such as symptom patterns or comorbidities), and biological markers (such as neuroimaging or genetic variation). This review examines evidence that specific patient characteristics can guide selection of initial treatment for adult outpatients with unipolar depression. We begin by presenting a conceptual model for personalized treatment in order to clarify the type of evidence relevant to treatment selection. We then consider the following three specific clinical decisions: the choice between psychotherapy and antidepressant medication, selection of a specific antidepressant medication, and selection of a specific psychotherapy. For each of these three decisions, our review first clarifies the types of evidence that can and cannot inform treatment selection. We then review potentially informative evidence regarding specific factors hypothesized to inform initial treatment choice. Since fewer data exist to guide subsequent treatment choices, we do not consider second-line therapies or therapies for treatment-resistant depression. for Personalized Treatment: Is Relevant? We hope to identify measurable characteristics of individual patients that can guide selection of treatment. Our question concerns differential efficacy (Do patients with show better response to than to Treatment B? ). This is often expressed as a moderator effect (X moderate the difference in response rates between). It is essential to distinguish moderators or predictors of differential efficacy from more general predictors of depression outcome (13). Previous research has often conflated these two concepts. Two study designs could produce the evidence needed to personalize treatment selection. First, we might compare alternative treatments in an unselected group of patients and examine whether a specific patient characteristic moderates the relationship between treatment type and outcome (14–16). One example would be a randomized trial comparing cognitive therapy and interpersonal psychotherapy in which we examine whether co-occurring personality disorder moderates (or interacts with) the effect of treatment type (17). Second, we might select a group of patients with a specific characteristic and then compare outcomes in patients receiving alternative treatments. One example would be a study limited to patients with depression and co-occurring personality disorder in which we compare cognitive therapy and interpersonal therapy (18).

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: То есть, по мнению автора, биохакинг — именно гаражная наука, которая движима энтузиазмом исследователей, находящихся вне и внутри академии. Главными качествами которых являются любопытство и тяга возиться с экспериментальным оборудованием. В комментариях к публикации сразу же возникла дискуссия: считать ли человека, пытающегося в домашних условиях изготовить «искусственное мясо» биохакером.

Output: contrast


Input: Claim: Environmental psychology can predict individual behavior with high accuracy. [SEP] Text: Though our results are largely exploratory at present, further optimization of a predictive gene module using prospectively gathered cohorts with greater follow up times may ultimately translate into a novel approach to care of patients with meningioma at the bedside. ConclusionsWe apply gene co-expression networks to a cohort of 252 adult meningiomas and derive a cluster of genes which is able to predict meningioma recurrence with high accuracy. With the wide accessibility of custom-made mini-arrays, our findings support, and should encourage, the shift to include this or a similar classifier in routine clinical care. 35. The role of radiotherapy following gross-total resection of atypical meningiomas. J Neurosurg. 2012;117:679–86. -Hanjani S, Martuza RL, et al. Long-term recurrence rates of atypical meningiomas after gross total resection with or without postoperative adjuvant radiation. Neurosurgery. 2009;64:56–60. 1 as a Factor for Meningioma Proliferation. Cell Rep. 2018;22:3672–83. Clark VE, Erson-Özduman K, et al. Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO. Science (80- ). 2013;339:1077–80. Å Integrated genomic analyses of de novo pathways underlying atypical meningiomas. Nat Commun. 2017;8. Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas. Nat Genet. 2016;48:1253–9. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol. 2017;18:682–94. . Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. Vernooij M, Ikram M. Incidental findings on brain MRI in the general population. N Med. 2007;:1821–8. Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review. J Neurosurg. 2015;122:4–23.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: If replicated, this finding could inform medication choice for this subgroup of patients. The Genome-Drugs for Depression project (62) used genome-wide association analyses in patients treated with nortriptyline or escitalopram to identify moderator effects (at a suggestive level of statistical significance) for two novel genetic variations. If replicated, these findings could inform the choice between these two medications. of again begin by pointing out that most previous studies cannot, by design, guide treatment selection. Included in this group are cohort studies of patients treated with a single medication or group of medications that examine genetic predictors of specific adverse effects, such as insomnia (63), sexual dysfunction (64, 65), and development of suicidal ideation (66). These cohort studies cannot distinguish general predictors of experiencing adverse effects from moderators or differential predictors of more adverse effects with one treatment than with another. For example, the serotonin receptor variation previously associated with insomnia during fluoxetine treatment may instead be associated with a general tendency to experience adverse effects with a range of medications (67, 68). In the same way, finding that a particular genetic variation is associated with suicidal ideation during treatment with citalopram (66, 69) could simply imply that this variation is associated with either suicidal ideation in general or suicidal ideation during treatment with any antidepressant. Neither of these latter interpretations would argue for or against the use of citalopram in this subgroup of patients. Of studies examining adverse events, only one has included patients treated with alternative drugs (70), finding that variation in a gene coding for the HTR2A serotonin receptor was associated with adverse effects with paroxetine but not with mirtazapine. Because no test for interaction or moderation was reported, this finding does not definitively address our question regarding personalized treatment: Does variation in HTR2A identify a group of patients who experience significantly fewer adverse effects during mirtazapine treatment than during paroxetine treatment? The finding of a significant relationship in patients treated with one drug and not in those treated with the other can sometimes reflect low statistical power rather than a true interaction or moderator effect. Selection of a Specific PsychotherapyWhile research on differential response to medications has focused on biomarkers and genetic variation, the limited research on differential response to psychotherapies has focused more on clinical characteristics (Table 3).

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: В данном случае один из основных вопросов был сформулирован следующим образом: «Как система здравоохранения должна реагировать на движение биохакинга? ». При этом биохакинг был определен как «технология, которая позволяет активным людям поддерживать уровень здоровья и продлевать жизнь». В этом контексте биохакинг все меньше становится похож на движимое любопытством «гаражное» исследование, оказываясь формой медицинской заботы о себе.

Output: contrast


Input: Claim: Environmental psychology can predict individual behavior with high accuracy. [SEP] Text: Jääskeläinen J. Seemingly complete removal of histologically benign intracranial meningioma: Late recurrence rate and factors predicting recurrence in 657 patients. A multivariate analysis. Surg Neurol. 1986;26:461–9. -Gimbel H, König J, Zang KD, et al. Predictive value of progression-associated chromosomal aberrations for the prognosis of meningiomas: a retrospective study of 198 cases. J Neurosurg. 2001;95:601–7. . High incidence of activating TERT promoter mutations in meningiomas undergoing malignant progression. Brain Pathol. 2014;24:184–9. Yesilöz Ü, Frequent AKT1E17Kmutations in skull base meningiomas are associated with mTOR and ERK1/2 activation and reduced time to tumor recurrence. Neuro Oncol. 2017;19:1088–96. . SMO mutation status defines a distinct and frequent Molecular subgroup in olfactory groove meningiomas. Neuro Oncol. 2017;19:345–51. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2006;2:1182–92. . , Zhu S, Felciano RM, et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Acad Sci. 2006;103:17402–7. -Ramahi I, et al. Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice. Nat Neurosci. 2016;19:623–33. Langfelder P, Horvath S. WGCNA: package for weighted correlation network analysis. BMC Bioinformatics. 2008;9. : archive for functional genomics data sets — update. Res. 2013;41 June:991–5. Effects in Analysis of Data : of Adjustment Methods. PLoS One. 2011;6. . Robust meta-analysis of gene expression using the elastic net. Res. 2015;43:1–11. Zhang B, Horvath S. A for Co-expression Network Analysis. Biol. 2005;4. Package ‘ dynamicTreeCut. ’ 2016;:1–14.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Research designs that consider only a single episode of treatment per person cannot examine whether any particular response pattern (e. g. , gastrointestinal side effects with a specific SSRI, responding favorably to interpersonal psychotherapy) is stable within individuals across episodes of treatment. Third, accurately predicting response to specific treatments may require combinations of several weak predictors rather than a single powerful one (73). For example, response to a specific antidepressant drug might be simultaneously moderated by a large number of variants of small effect. A recent genome-wide association study suggests that this may be the case (54). Developing rules for treatment selection based on multiple predictors will require useful theory regarding treatment mechanisms, large sample sizes, and healthy skepticism regarding predictors identified only after multiple comparisons. Given the large sample sizes needed to detect moderators of treatment effectiveness, randomized trials to inform personalized treatment may need to adopt broader recruitment strategies and more efficient methods for outcome assessment. Large, pragmatic trials have been proposed as a more efficient strategy for comparative effectiveness research (74). These methods could be extended to address questions of differential treatment effectiveness. Recruitment for randomized trials to identify moderators of treatment response might incorporate information regarding response to past treatments. Traditional clinical trials typically consider past treatment only for safety reasons, excluding patients with histories of adverse reactions to a study treatment. Future trials might consider past treatment response in order to select particularly informative samples of participants. For example, a study to identify genetic predictors of favorable response to drug A over drug B might preferentially include patients with past exposures to drug A and/or drug B. Information regarding past response could be combined with new data to more accurately distinguish true treatment effects from other sources of variation in outcome. Another alternative approach would use observational data from large, population-based samples of patients treated under naturalistic conditions. This approach has led to significant advances in personalizing treatment for other chronic health conditions (75, 76). Observational studies permit examination of thousands of patients rather than the hundreds typically enrolled in clinical trials. Most important, longitudinal data would permit study of multiple treatment episodes per patient, including exposures to similar and dissimilar treatments. These data could serve in two ways. First, only such longitudinal data could evaluate the stability of potential treatment response phenotypes prior to expending resources searching for a corresponding genotype. Second, observational studies could identify potential moderators or differential predictors for definitive evaluation in subsequent randomized trials.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: В этом контексте биохакинг все меньше становится похож на движимое любопытством «гаражное» исследование, оказываясь формой медицинской заботы о себе. Другая дискуссия проходила оффлайн в 2019 году в Аналитическом центре при Правительстве России [1]. В данном случае один из основных вопросов был сформулирован следующим образом: «Как система здравоохранения должна реагировать на движение биохакинга? ».

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: Health & 17, 2020 Improving the iron delivery from staple food crops and food products of Agriculture and Cornell University, has pioneered several techniques to assess how much iron (Fe) is bioavailable from foods, meals and the overall diet. Such techniques are essential to identify and optimise dietary sources of Fe and alleviate iron deficiency, one of the leading global nutritional deficiencies. Staple food crops are a focus of this research, particularly beans as they are relatively high in Fe and a major staple in regions where Fe deficiency is prevalent. Measurement of Fe delivery from foods is challenging as greater Fe content does not always equate to more absorbable iron; hence the need for efficient tools to assess the nutritional quality of this essential micronutrient. A balanced diet must include adequate sources of all macro- and micronutrients. However, deficiencies in micronutrients, such as vitamins and minerals are common. Perhaps the most common nutritional deficiency worldwide is iron deficiency. Iron has many roles in human health, but it is arguably best known for its role in the molecule haemoglobin, a protein in red blood cells which transports oxygen around the body. The consequences of mild iron deficiency can be fatigue and lethargy, whereas more severe deficiencies can lead to health issues such as heart problems, increased risk of infections, pregnancy complications, and in children, cognitive deficiencies and poor neurological development. FuzullHanum/Shutterstock. com There are many good dietary sources of iron, including animal and plant sources. Animal sources of iron, such as red meat, are more easily absorbed by the body compared to plant-based sources, such as maize, wheat, beans, chickpeas, peas and lentils. However, it is often difficult and expensive to assess iron bioavailability from foods. The term ‘bioavailable’ refers to the iron that is accessible to the body for absorption, compared to iron which may be bound to other components of food thus rendering it unavailable to the body. Early work carried out by Glahn of the US Department of Agriculture produced methods that could be used for assessing bioavailable iron from foods. Glahn’s method provided a way by which food companies, crop breeders and nutrition professionals can measure iron bioavailability and therefore identify and develop food products and processing practices that can deliver more bioavailable Fe in the food supply. Yellow beans have unique qualities and nutritional traits that help improve the delivery of iron from a plant-based diet.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: : Influence of the tyrosine hydroxylase Val81MET polymorphism and catechol-O-methyltransferase Val158MET polymorphism on the antidepressant effect of milnacipran. 2008; 23:121–128CrossrefPubMed51. : Association study of two serotonin 1A receptor gene polymorphisms and fluoxetine treatment response in Chinese major depressive disorders. 2006; 16:498–503CrossrefPubMed52. : Age-dependent antidepressant pharmacogenomics: polymorphisms of the serotonin transporter and G protein beta3 subunit as predictors of response to fluoxetine and nortriptyline. Neuropsychopharmacol 2003; 6:339–346CrossrefPubMed53. : Sequence analysis of the serotonin transporter and associations with antidepressant response. 2005; 58:374–381CrossrefPubMed54. -Myhsok B: A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Psychiatry 2009; 66:966–975CrossrefPubMed55. : The serotonin transporter polymorphism, 5is associated with a faster response time to sertraline in an elderly population with major depressive disorder. Psychopharmacology (Berl) 2004; 174:525–529CrossrefPubMed56. : The catechol-O-methyltransferase Val108/158Met polymorphism affects short-term treatment response to mirtazapine, but not to paroxetine in major depression. 2005; 5:49–53CrossrefPubMed57. : 941G polymorphism and short-term treatment response to mirtazapine and paroxetine in major depression. 2007; 144:325–331Crossref58. : A monoamine oxidase B gene variant and short-term antidepressant treatment response. 2007; 31:1370–1377CrossrefPubMed59. : The alpha 2A-adrenergic receptor gene polymorphism modifies antidepressant responses to milnacipran. J 2008; 28:518–524CrossrefPubMed60. : Monoamine transporter gene polymorphisms and antidepressant response in Koreans with late-life depression. JAMA 2006; 296:1609–1618CrossrefPubMed61. : The G196A polymorphism of the brain-derived neurotrophic factor gene and the antidepressant effect of milnacipran and fluvoxamine.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: При этом биохакинг был определен как «технология, которая позволяет активным людям поддерживать уровень здоровья и продлевать жизнь». В этом контексте биохакинг все меньше становится похож на движимое любопытством «гаражное» исследование, оказываясь формой медицинской заботы о себе. 3 Кажется, что мы имеем дело с двумя очень слабо связанными значениями термина «биохакинг».

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: A bioassay to assess iron bioavailability developed a model known as the Caco-2 cell bioassay for iron bioavailability. The Caco-2 cell line is a human intestinal cell line widely used for studies of nutrient uptake. coupled simulated stomach and intestinal digestion with Caco-2 cell cultures to produce a model of the upper human intestinal tract. These experimental conditions enabled to accurately assess the most critical step that determines iron bioavailability, uptake by the intestinal epithelial cell. Iron uptake is assessed via measurement of the iron storage protein, ferritin, which has been shown to form in direct proportion to Fe uptake. Caco-2 cells provide breeders a cost-effective tool for evaluating the iron bioavailability of pulse crops. Measurement of intestinal cell ferritin enables high throughput of the model system, makes it extremely cost efficient, and negates challenges and flaws of other methods that have been used to assess iron bioavailability. Since the model’s inception in 1998, it has been thoroughly validated via direct comparison to numerous animal and human studies, predicting the correct effect on Fe bioavailability observed in the corresponding in vivo trial. This thorough validation of the model now means that it can be applied with confidence in applications such as plant breeding for improved Fe bioavailability from staple food crops, or for food science applications to optimise Fe nutrition in food products. It is important to note that the Caco-2 cell bioassay predicts the amount of Fe that can be delivered from a food. This distinction is a key feature of the bioassay as the chemical nature of Fe and its interaction with food components means that Fe concentration in a food or meal does not always correlate with Fe absorption. The bioassay is therefore a practical and data-driven approach to Fe nutrition. A Focus on the past 20 years there have been numerous applications of the Caco-2 cell bioassay. Perhaps the most notable applications have been the ongoing studies of Fe delivery from the common bean. Dr Glahn’s research was the first to demonstrate that the colour of the seed coat can have profound effects on Fe delivery from beans. Studies have shown that beans in market classes such as red, red-mottled, dark red, purple, cranberry and black generally have low Fe bioavailability; whereas most white and some yellow bean varieties have higher Fe bioavailability.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: J Psychopharmacol 2007; 21:650–656CrossrefPubMed62. -: Genome-wide pharmacogenetics of antidepressant response in the project. Psychiatry 2010; 167:555–564CrossrefPubMed63. -: Serotonin transporter polymorphisms and adverse effects with fluoxetine treatment. 2003; 54:879–883CrossrefPubMed64. : Serotonin 2A-1438 G/A and G-protein beta3 subunit C825T polymorphisms in patients with depression and SSRI-associated sexual side-effects. Neuropsychopharmacology 2006; 31:2281–2288CrossrefPubMed65. : Genetic and clinical predictors of sexual dysfunction in citalopram-treated depressed patients. Neuropsychopharmacology 2009; 34:1819–1828CrossrefPubMed66. : Association between treatment-emergent suicidal ideation with citalopram and polymorphisms near cyclic adenosine monophosphate response element binding protein in the STAR*D study. Psychiatry 2007; 64:689–697CrossrefPubMed67. van Os J, Prins M: Serotonin transporter polymorphisms and the occurrence of adverse events during treatment with selective serotonin reuptake inhibitors. Psychopharmacol 2007; 22:137–143CrossrefPubMed68. : Association between a functional serotonin transporter polymorphism and citalopram treatment in adult outpatients with major depression. Psychiatry 2007; 64:783–792CrossrefPubMed69. -Mahon F: Genetic markers of suicidal ideation emerging during citalopram treatment of major depression. Psychiatry 2007; 164:1460–1461CrossrefPubMed70. : Pharmacogenetics of antidepressant medication intolerance. Psychiatry 2003; 160:1830–1835CrossrefPubMed71. : The role of avoidance and obsessiveness in matching patients to cognitive and interpersonal psychotherapy: empirical findings from the treatment for depression collaborative research program. J Psychol 1996; 64:951–958CrossrefPubMed72. : Attachment as moderator of treatment outcome in major depression: a randomized control trial of interpersonal psychotherapy versus cognitive behavior therapy. J Psychol 2006; 74:1041–1054CrossrefPubMed73. Goldstein DB: Common genetic variation and human traits.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Разумеется, эти практики серьезно отличаются друг от друга, как и их риски в сферах здравоохранения и биобезопасности. Кажется, что мы имеем дело с двумя очень слабо связанными значениями термина «биохакинг». Наиболее явным вариантом его практик в первом смысле является расположенная вне академических институций лаборатория, часть оборудования которой изготовлена самостоятельно работающими в ней энтузиастами.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: Seed coat colour is due to the presence or absence of compounds known as polyphenols. Storage cells in a bean seed (Phaseolus vulgaris), imaged with scanning electron microscopy. Further studies by the Glahn research team identified compounds in a class of polyphenols known as flavonols, specifically designating the flavonols quercetin and myricetin as inhibitors of Fe uptake. Additional inhibitors of Fe uptake were identified in the polyphenol class of cyanidins; however, most importantly this research identified polyphenolic compounds in the flavonol class that promote Fe uptake. Specifically, the compounds known as kaempferol, kaempferol 3-glucoside, catechin and epicatechin have been shown to promote Fe uptake. Moreover, certain varieties in the yellow bean market class have been identified that are high in these compounds and have little or no inhibitory polyphenols and thus exhibit high Fe bioavailability. Recently, the slow-darkening trait observed in some varieties of pinto beans also results in high levels of these Fe uptake promoting polyphenols, and low levels of the inhibitory compounds thus delivering more Fe for absorption. Additional studies of beans by the Glahn lab have looked at the components of the bean seed in regards to Fe concentration and bioavailability. These studies found that within the cotyledon, i. e. the centre white part of the bean, the individual cotyledon cell walls do not break down due to cooking, chewing or gastrointestinal digestion. Given that 70-95% of the Fe in a bean is located in the cotyledon, within the cotyledon cells, this observation represents a new factor affecting Fe delivery from beans. This same factor applies to other legumes. Identifying this factor is significant as it identifies an obstacle that can be overcome to unlock additional Fe nutrition from beans and other legumes. A microscopic image of cooked Trypan blue stained cotyledon cells from white (navy) bean seeds following in vitro gastric and intestinal digestion (magnification 80Å~). This image illustrates how the low pH and enzymes of the gastrointestinal tract have little effect on the intact cell wall structure. Glahn et al. , FoodFunct. 2016. doi:10.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Sequencing cancer genomes to tailor treatments to individual patientsScreening for hundreds of mutations in tumours is the most promising route to customised drug treatments for cancer. Genomics 02 December 2014 link to page Download PDF Genome sequencing could provide a route to customised drug treatments for cancerJames King-Holmes LibraryIn 2010, an elderly woman with metastatic bladder cancer who was participating in a clinical trial at Center (MSKCC) in had an extraordinary response to everolimus, a drug that inhibits the mTOR (mammalian target of rapamycin) signalling pathway. The mTOR pathway integrates intracellular and extracellular signals and acts as a central regulator of cell metabolism, growth, proliferation and survival. The woman was the only patient who had complete resolution of metastatic disease out of 37 participants in the trial, and researchers started to look at one gene at a time to try to find out why she responded to treatment. When they found nothing abnormal in the genes they looked at, the team turned to whole-genome sequencing of the tumour to investigate the genetic basis of her remission. They discovered that of the 17,000 mutations located in the genome, one led to a loss-of-function mutation in the tuberous sclerosis complex 1 (TSC1) gene, which correlates with everolimus sensitivity, and was key to her responding to treatment[1]. “The reason we could solve that mystery was because we were able to look at every single gene,” says Solit, a medical oncologist involved in the trial. Although whole-genome sequencing is currently too expensive to do in every cancer patient, it is the best way to characterise and identify DNA or RNA sequences so as to discover the acquired alterations that are driving cancer growth. Profiling tumour DNA helps to prioritise anti-cancer treatment by identifying specific therapies to which the patient’s cancer may be sensitive or resistant. Some molecular profiling tests are relatively simple, pointing the proverbial narrow laser beam at selected areas in the genes of interest. But many researchers are now opting to significantly increase the scope of that beam.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Во втором смысле мы будем иметь дело с тяготеющими к трансгуманизму практиками приема лекарственных средств без назначения врача, и изменениями в образе жизни, мотивированными собственным истолкованием результатов биологических исследований. Разумеется, эти практики серьезно отличаются друг от друга, как и их риски в сферах здравоохранения и биобезопасности. Кажется, что мы имеем дело с двумя очень слабо связанными значениями термина «биохакинг».

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: 1039/c6fo00490c Finally, the Caco-2 cell bioassay has found that the fast-cooking trait, highly desirable to many bean consumers, has been linked to improved Fe delivery. Fast cooking is believed to be associated with the same polyphenolic compounds that promote Fe uptake, as well as genes that may control thickness of cotyledon cell walls. In summary, the above observations are all good news for bean growers, food producers and consumers who seek improved Fe nutrition from this staple food crop. This research gives new targets for bean breeders, and ultimately provides enhanced nutritional quality for consumers and value-added traits for producers. Other applications of the Caco-2 for Maize: The Caco-2 cell has identified the germ fraction (ie. embryo) of the maize kernel as the major component that inhibits Fe uptake from maize. Thus, degermed maize flour may represent an opportunity to provide improved Fe nutrition. In addition, these studies suggested that identifying and/or developing maize varieties with higher Fe density in the endosperm of the kernel should also provide more bioavailable Fe. Lentils: Red lentils are commonly dehulled post-harvest. The Caco-2 cell bioassay has identified the dehulling process as improving Fe delivery from lentils. Yellow bean breeding lines with fast cooking times and improved iron bioavailability being tested at the Farm, Michigan. Peas: Overall, peas have been identified as an excellent legume to deliver more bioavailable Fe. for Infants: As infants are particularly prone to Fe deficiency, complementary foods represent an excellent application of the Caco-2 cell bioassay to identify specific foods and food combinations to improve Fe nutrition for this vulnerable age group. Food Products: Major food companies routinely make use of the Caco-2 cell bioassay to identify optimal formulations to deliver bioavailable Fe while balancing sensory and other quality concerns. Poultry model for iron bioavailability Human studies of Fe bioavailability are expensive and time-consuming; thus, there is a critical need for the combination of the Caco-2 cell bioassay and an animal model to refine objectives for definitive human studies, and if possible reduce the number of human trials. Thus, to complement the bioassay, and his research team also developed a poultry model for iron bioavailability studies.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: “Our approach is to put the flood light on that cancer gene — the entire coding sequence of the gene — to identify additional areas with potential vulnerability to targeted drugs,” says Vincent Miller, a medical oncologist and chief medical officer at (Cambridge, Massachussetts), which manufactures two comprehensive genomic-profiling assays. Gene alterationsMost human tumours are caused by acquired alterations within the cancer genome, leading to oncogene activation or tumour suppressor gene inactivation. Oncogenes are derived from normal genes (proto-oncogenes) involved in the regulation of cell growth. Alterations to the nucleotide sequence, or the amount of protein the proto-oncogene produces, can lead to changes that drive the unchecked growth typical of cancer. Oncogenes can be activated or tumour suppressor genes inactivated in four main ways: single nucleotide polymorphisms, referred to as point mutations, which result from a base substitution at one nucleotide; small duplications of consecutive nucleotides, insertions or deletions involving one or a few nucleotides, or more complex mutations involving a combination of deletions and insertions of one or a few nucleotides; exon or gene copy number changes; and structural variants such as translocations or inversions that often result in fusion genes (see figure). Medicine debuted its FoundationOne assay for malignant solid tumours in 2012, it tested the DNA sequence of 136 genes. Just two years later, the product now tests for alterations in 315 cancer genes, which includes sequencing parts of certain genes, referred to as introns, that aren’t normally coded. The assay — which costs US$5,800 and may not be completely covered by health insurance — reveals all classes of genomic alterations, including base substitutions, insertions, deletions, copy number alterations and selected fusions. The company reported that more than 9,000 patients were tested with FoundationOne in 2013. In 2014, the projected number is around 25,000. “This is a very brisk ramp for what’s basically a new approach with a steep learning curve,” says Miller. A second assay, FoundationOne Heme, which costs US$7,200 and primarily targets blood cancer and sarcomas, was co-developed with researchers at MSKCC.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Разумеется, эти практики серьезно отличаются друг от друга, как и их риски в сферах здравоохранения и биобезопасности. 4 Однако с точки зрения их ролей в системе отношений между институциональной наукой и обществом, эти практики имеют больше сходств, чем различий. В данной статье мы представим генерализованное понимание роли биохакинга в существующей системе социального распределения знания.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: In combination with the bioassay, the poultry model has been validated in direct parallel comparison to human trials. In studies of crops such as beans and pearl millet, the bioassay and the poultry model correctly predicted the human efficacy studies. As a result, the combination of these research tools now represent a popular approach to studies of Fe nutrition. Lentil crop. Dr Glahn’s research has identified that the dehulling process improves Fe delivery in lentils. What can people do at home to minimise their risk of iron deficiency and to maximise the bioavailability of iron in their diet? <> Humans require a diversity of foods for optimal Fe nutrition, which also promotes other beneficial nutritional outcomes. Therefore, consumption of diverse diet of nutrient dense foods containing a variety of “protein foods“, such as legumes (beans and peas), lean meat (poultry and beef), seafood, eggs, and nuts. In addition, a variety of vegetables and whole fruits are highly recommended. Whole grains should make up at least half of all grain intake. Recommended amounts of dairy products should also be followed. Adherence to these dietary guidelines generates a healthy intestinal tract that facilitates Fe absorption and overall health. Improving the iron delivery from staple food crops and food products was last modified: November 8th, 2023 Keywords: Beansbioavailable ironCaco-2 cell bioassayFeflavonolsironiron bioavailabilityiron deliverymicronutrientsof Agriculture and this 193. Advance online publication. & Glahn RP. (2020). Polyphenolic profiles of yellow bean seed coats and their relationship with iron bioavailability. J Chem, 68(3), 769–778. & Tako E. (2019). An in vivo (Gallus gallus) feeding trial demonstrating the enhanced iron bioavailability properties of the fast cooking Manteca yellow bean (Phaseolus vulgaris L. ). Nutrients, 11(8), 1768. & Glahn RP. (2018). The fast cooking and enhanced iron bioavailability properties of the Manteca yellow bean (Phaseolus vulgaris L. ). Nutrients, 10(11), 1609. . (2014). Iron bioavailability in low phytate pea. Crop Science. 55(1):320-330. & Wiesinger JA. (2016).

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Most doctors, explains Miller, only look for a pre-specified, subset of potential mutations, hoping that whichever ones are found inside the tumour’s DNA have been linked to drugs that have demonstrated their effectiveness in fighting those mutations. The downside is that, even when mutations are identified, there is a limited array of treatment. “Metastatic cancer is largely unique among advanced medical conditions, because the therapies are so grossly inadequate that most treatments — even the good ones — fail,” says Miller. This cancer-drug-finding challenge has somehow turned the task of looking for the oddball mutation in specific types of cancer genes — alterations that would not normally be found in that tumour type — into a promising cancer-fighting strategy. When an assay uses an all-inclusive approach and ignores a mutation’s proclivity for certain types of cancer, a mutation — with a good-outcome drug associated with it — may turn up in the most unexpected tumours. As an example, Miller cites Herceptin (trastuzumab), a targeted therapy for an amplification of the human epidermal growth factor receptor 2 (HER2) gene often found in HER2-positive metastatic breast cancer and HER2-positive gastric cancer. “We have now seen — because we conduct the same broad test in every tumour type — either an amplification or mutation in this critical HER2 gene in nearly 30 different tumour types,” he says. Genomic alterationsCancer is caused by alterations to the genes that regulate cell growth and division. These alterations can affect the nucleotide sequence of the gene or the structure of the whole chromosome. variationSingle nucleotide polymorphisms (1), insertions (2) or deletions (3) may result in a change to the amino acid sequence of the encoded protein (missense mutation) or premature truncation (nonsense mutation), which can render the resulting protein non-functional. Structural variationLarge insertions (1), deletions (2) or duplications (3) of the entire gene can result in gene copy number changes leading to alterations in the levels of gene expression and overproduction or underproduction of the resulting protein. Inversions (4) or translocations (5) of a segment of the chromosome can create fusion genes, which may lead to a protein with a different function from the two fusion partners, or to up-regulation of the protein.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Все попытки «торговцев сомнением» [19] оспорить статус официальной экспертизы, подорвать общественное доверие к профессиональным компетенциям или моральным качествам конкретных ученых или теоретическому фундаменту целых дисциплинарных областей, а тем более продвижение альтернативного взгляда на проблему, оцениваются академией как антинаучные, деструктивные и социально опасные деяния.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: The cotyledon cell wall and intracellular matrix are factors that limit iron bioavailability of the common bean (Phaseolus vulgaris). Food & Function, 7(7), 3193–3200. . (2019). The germ fraction inhibits iron bioavailability of maize: identification of an approach to enhance maize nutritional quality via processing and breeding. Nutrients, 11(4):833. doi:10. 3390/nu11040833. PMID: 31013776; PMCID: PMC6521246. . (2013). Seed coat removal improves iron bioavailability in cooked lentils: studies using an in vitro digestion/Caco-2 cell culture model. J Chem, 61(34):8084-9. doi:10. 1021/jf4022916. Epub 2013 Aug 14. PMID: 23915260. . (2014). Differences in relative iron bioavailability in traditional Bangladeshi meal plans. Bull, 35(4):431-9. doi:10. 1177/15648265140350040510. 1177/156482651403500405. PMID: 25639128. Lung’aho MG, Glahn RP. (2010). Use of white beans instead of red beans may improve iron bioavailability from a Tanzanian complementary food mixture. Res, 80(1):24-31. doi:10. 1024/0300-9831/a000003. PMID: 20533242. . (2020). Impact of ascorbic acid on the in vitro iron bioavailability of a casein-based iron fortificant. Nutrients, 12(9):2776. doi:10. 3390/nu1209277610. 3390/nu12092776. PMID: 32932834; PMCID: PMC7551990. Behind the is a with the Department of Agriculture, Service. He has over 32 years of experience in nutritional physiology and over 170 peer-reviewed publications. His major research focus is on factors that improve iron nutrition from staple foods, food products and food systems. for Agriculture and Health 538 Ithaca, NY 14853 USA E: [email protected] T: +1 607-255-2452 W: Department of Agriculture, Service (USDA-ARS).

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: In 2011, the technical cost, without the analysis, was around US$20,000. “Now it’s down to about US$4,000 per whole genome,” says Solit. “It’s still too expensive to do in every patient but it’s getting less expensive every year. One can envision that in 10 or 15 years, or sooner, it’s going to cost less than US$1,000 to sequence the entire genome of every patient’s tumour. ”Now, at MSKCC, patients with advanced metastatic cancer who have had their tumours genetically tested fall into one of three groups, says Solit. In the first group are patients whose cancer has a mutation for which there is an associated drug, which is prescribed for the patient. In the second group are patients whose cancer is found to have an activating driver mutation for which there is no associated drug. “Patients in this group may wind up in clinical trials that MSKCC may be offering,” he says. “Even though the patient is disappointed there’s no standard, approved drug to target the specific mutation, at least physicians can avoid treating them with a drug that has no chance of working, based on the assay. ” Finally, in the third group are patients whose tumours have no mutations of any of the 341 most commonly mutated cancer-associated genes. As more tumours are sequenced, Solit anticipates that the number of patients in the third group will shrink dramatically because the research will have advanced to find key mutations in all patients. And, with the development, regulation and approval of additional cancer drugs, the number of patients in the second group will also decrease. But if resourcing time and money is a challenge, so is the development of effective therapies. Solit points to the massive, international, and so far unsuccessful effort over the past 30 years to create a KRAS inhibitor. “Between 15% and 20% of patients have KRAS gene mutations, which are common in various cancers, and we still don’t have a good KRAS inhibitor,” he says. “There are a lot of cancer genes which are important, and we don’t have drugs to target them, either. ”Biological factorsFor some experts, wiping out cancer goes beyond sequencing and digs into the cunning core of cancer itself.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Все попытки «торговцев сомнением» [19] оспорить статус официальной экспертизы, подорвать общественное доверие к профессиональным компетенциям или моральным качествам конкретных ученых или теоретическому фундаменту целых дисциплинарных областей, а тем более продвижение альтернативного взгляда на проблему, оцениваются академией как антинаучные, деструктивные и социально опасные деяния.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: Jinfan Zhang3 733 Accesses AbstractThe discovery of Jian (the bamboo writing slips found in the tomb of Qin in Shuihudi) unearthed in Yunmeng county in Hubei province in 1975 revealed us the law of the state of Qin in Period. Although it did not include all the laws of the state of Qin, it was evident that the book was theoretically based on the theories of legalists and it had played the function of adjustment to the social life, state activities and the families and individuals in many aspects. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 139. 00 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e179. 99 Price excludes VAT (USA) Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Notes1. “Ji” (of Huang) in (of the Grand Historian). 2. “Ji” (of the State of Qin) in (of the Grand Historian). 3. “Ji” (of Huang) in (of the Grand Historian). 4. “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 5. (Slips from the Tombs of Qin in Shuihudi), p. 128. 6. (Slips from the Tombs of Qin in Shuihudi), p. 285. 7. “Ding Fen” (Defining a Person’s Social Status) in Shu (of Lord Shang). 8. “Ji” (of the of Han Dynasty) in (of Han Dynasty) (Book 1). 9. “Ding Fen” (Defining a Person’s Social Status) in Shu (of Lord Shang). 10. “Shen Fa” (of Law) in Shu (of Lord Shang). 11. (of Han Dynasty). 12. “Xiu Quan” (On the Usage of Power) in Shu (of Lord Shang). 13.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: AbstractBipolar disorder involves diverse features and symptom targets that extend beyond mood alone. RCTs that establish drug efficacy for an overall diagnosis and illness state can help practitioners devise personalized combination therapy regimens that increase the probability of a desired outcome. AbstractThe quest for “personalized medicine” in psychiatry has focused mainly on pursuing potential biomarkers such as pharmacogenetic predictors of drug response. However, the collective randomized trial database across phases of bipolar disorder allows one to identify clinical characteristics that inform the likelihood of desired treatment outcomes. In turn, those characteristics, termed moderators and mediators of drug response, enable those who administer treatment to construct clinical profiles that can help them tailor pharmacotherapies to the features of a given patient rather than simply to an overall diagnosis. Bipolar disorder typically involves more heterogeneous than uniform clinical presentations, partly because of its highly prevalent psychiatric and medical comorbid conditions. Further clinical diversity arises from characteristics such as bipolar I versus II disorder subtype, rapid cycling, mixed versus pure affective episodes, psychosis, anxiety, chronicity, cognitive dysfunction, and suicidality, among other distinguishing features. By coupling such profiles with an awareness of the psychotropic breadth of spectrum held by particular medications, clinicians can devise strategic combination therapy regimens, capitalizing on synergies and using drugs that exert multiple relevant effects, addressing comorbid conditions, incorporating medications that could offset adverse effects of other agents, and avoiding or deprescribing medication options that lack known evidence to target symptoms within the clinical profile of a given patient. Bipolar disorder represents one of the most heterogeneous known forms of psychopathology. Although mood and affective disturbances are, by definition, core elements of the condition, more nuanced endophenotypic studies have placed increasing nosologic importance on related dimensions, such as changes in energy or motor activity (1), attentional and executive dysfunction (2), impulsivity and aggression (3, 4), anxiety (5), psychosis proneness (6), sleep-wake cycle dysregulation (7), susceptibility to formal thought disorder (8), and affective vulnerability to chronobiological and social rhythm disruptions (9). Moreover, given that two thirds of bipolar disorder patients have at least one distinctly comorbid psychiatric or substance use diagnosis (10), the clinical target profile for treatment of any given patient becomes far more complex than simply choosing a medication based solely on the overarching diagnosis.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Все попытки «торговцев сомнением» [19] оспорить статус официальной экспертизы, подорвать общественное доверие к профессиональным компетенциям или моральным качествам конкретных ученых или теоретическому фундаменту целых дисциплинарных областей, а тем более продвижение альтернативного взгляда на проблему, оцениваются академией как антинаучные, деструктивные и социально опасные деяния.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Ding Fa” (Law Making) in Shu (of Lord Shang). 14. “Qu Qiang” (of the Disobedient) in Shu (of Lord Shang). 15. “Ding Fen” (Defining a Person’s Social Status) in Shu (of Lord Shang). 16. “Shang Xing” (Rewards and Punishments) in Shu (of Lord Shang). 17. “Kai Sai” (Reform) in Shu (of Lord Shang). 18. “Qu Qiang” (of the Disobedient) in Shu (of Lord Shang). 19. “Kai Sai” (Reform) in Shu (of Lord Shang). 20. “Suan Di” (Measuring Land) in Shu (of Lord Shang). 21. “Shang Xing” (Rewards and Punishments) in Shu (of Lord Shang). 22. “Shen Fa” (of Law) in Shu (of Lord Shang). 23. “Wai Nei” (and the Inner) in Shu (of Lord Shang). 24. “Kai Sai” (Reform) in Shu (of Lord Shang). 25. “Hua Ce” (Policies) in Shu (of Lord Shang). 26. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 27. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 28. (Qing Dynasty), “Zi Xu” (The Author’s Preface) to (Collections of Laws in Han Dynasty). 29. “Ji” (of Huang) in (of the Grand Historian). 30. “Da Wen” (and Answers) in (in Qin Dynasty). 31. “” (of the biographies of ) in (of the Grand Historian). 32. “Lv” (Statutes on the Regulation of Officials) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 33. “Za” (Statutes on in the Capital) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty).

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Consequently, asking what the best treatment is for mania or for bipolar depression becomes as nonspecific and oversimplified a question as if one were to ask how best to treat cancer or infections, or even heart disease. Bipolar disorder involves diverse and protean features with varied dimensions of psychopathology, expressed to greater or lesser degrees at different points in time. Optimal pharmacotherapy therefore requires devising individualized treatments suited to the unique characteristics of a given patient, following a model of personalized medicine. Hence, the main objective of this 1. Recognized moderators of bipolar disorder treatment outcomesModeratorRelevanceAge at onsetEarly onset (prepubertal, adolescent) is one of the more robust correlates of poor outcome, although it sometimes may be a proxy variable for more immediate causes of poor outcome (e. g. , social isolation or poorer premorbid functioning). Baseline severityHigh baseline severity tends to suppress placebo responsivity; it also provides the opportunity for greater variability in severity levels over time than that which occurs with the more restricted range of low baseline severity. ChronicityLong-standing illness states often respond less well than acute episodes to standard pharmacotherapies and incur higher risk for maladaptation to illness over time (e. g. , demoralization, diminished effort, loss of social and economic supports). Episode numberPatients who have endured multiple episodes may respond less well to lithium than to divalproex (15) and possibly other broader spectrum psychotropics. Predominant polarityEpisodes predominated by mania more than depression are associated with better responses to lithium, divalproex, and most second-generation antipsychotics; depression predominance more than mania is associated with better response to lamotrigine and quetiapine plus lithium or divalproex (16). SexDirect effects of sex on treatment outcome have not been described in bipolar pharmacotherapy trials, although indirect effects may bear on longitudinal outcome (e. g. , contraindication of divalproex and pregnancy, elevated postpartum risk for mood episodes, and an artifact of higher prevalence of rapid cycling among bipolar women than men [17]). Sex also may interact with mediating factors (e. g. , poorer adherence by men [18]).

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Биохакеры, переносят сами научные процедуры, а также анализ их результатов на интернет-платформы, «таким образом, данные исследований могут быть проверены, а их этическая обоснованность и безопасность могут быть оценены большей группой и более быстрыми темпами, чем традиционные эксперименты в закрытых лабораториях» [16, p. 215].

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: 34. “Lv” (of Executing the Commands) in (in Qin Dynasty). 35. “Zhao Ce” (of the State of Zhao) in Ce (Stratagems of the Warring States). 36. “Qiang Guo” (a Prosperous Country) in (Xun Zi). 37. (Works of Marx and Engels), Vol. 1, The People’s Publishing House, 1972, p. 697. 38. “Zhi” (of and Economy) in (of Han Dynasty). 39. “” (Thirteen 1985. 40. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 41. “Shi Jia” (of Chen She) in (of the Grand Historian). 42. “Ji” (of the of Han Dynasty) in (of Han Dynasty). 43. “Fu Lv” (Statutes on the Household Registration) in “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 44. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 45. “Da Wen” (and Answers) in (in Qin Dynasty). 46. “Ji” (of Huang) in (of the Grand Historian). 47. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 48. “Ji” (of Huang) in (of the Grand Historian). 49. “Xing” (Punishment) (Part 1) in (Codes), Vol. 163. 50. “Ji” (of Huang) in (of the Grand Historian). 51. (History as a Mirror), Vol. 6, annotated by Hu Sansheng. 52. “Ji” (of Huang) in (of the Grand Historian). 53. “Qin Ce” (of the State of Qin) in Ce (Strategems of the Warring States). 54. “” (Biographies of ) in (of the Grand Historian). 55. “Wai Pian” (Supplementary Essays) in (Master Shen). 56.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: One could readily envision crafting a tailored pharmacotherapy regimen for a given patient with bipolar disorder that incorporates certain anticonvulsants meant to target specific illness characteristics or comorbid conditions such as those described in Table 3 (48–73)—without conflating such “ancillary” effects with the core targeting of affective symptoms. Illustrative examples are provided at the end of this 4. Breadth of psychotropic properties of second-generation antipsychoticsaAgentAcute maniaMania preventionAcute depressionDepression prevention√√2 (−) placebo-controlled trials (78)2 (−) placebo-controlled trials (79, 80)bSmall open trials1 (−) small placebo-controlled trial (81)Asenapine√√No data√No dataSmall open trialsBrexpiprazoleNo dataNo dataNo dataNo dataEfficacy in MDD trials with or without anxious distress (82)No dataCariprazine√No data√No dataNo dataNo dataLurasidoneNo datacNo data√No data√No dataOlanzapine√√2 (+) placebo-controlled trials (83)√√1 small (+) placebo-controlled trial (84)Quetiapine√√√√√1 (+) placebo-controlled trial (85)Risperidone√√dNo data1 (−) placebo-controlled trial (86)b,d1 (−) placebo-controlled trial for anxiety symptoms in bipolar patients with comorbid panic or GAD (87)1 (+) (88), 4 (−) placebo-controlled trials as adjuncts to SSRIs in (nonbipolar) military (89–91) or civilian (92) PTSDZiprasidone√√c2 (−) placebo-controlled trials (93)1 (−) placebo-controlled trial (94)b,e1 (−) placebo-controlled trial in bipolar disorder with comorbid panic disorder or GAD (95)2 (−) placebo- controlled trials (96, 97)aMDD, major depressive disorder; GAD, generalized anxiety disorder; SSRIs, selective serotonin reuptake inhibitors. bMaintenance trial following an index manic or mixed episode; sample enrichment likely contributed to a low observed rate of depression relapses in both treatment arms.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Биохакеры, переносят сами научные процедуры, а также анализ их результатов на интернет-платформы, «таким образом, данные исследований могут быть проверены, а их этическая обоснованность и безопасность могут быть оценены большей группой и более быстрыми темпами, чем традиционные эксперименты в закрытых лабораториях» [16, p. 215].

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Lie Zhuan” (Biographies of Qing Bu) in (of the Grand Historian). 57. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 58. “Da Wen” (and Answers) in (in Qin Dynasty). 59. “Da Wen” (and Answers) in (in Qin Dynasty). 60. “Ji” (of Huang) in (of the Grand Historian). 61. “Cang Lv” (Statutes on Storage of Forage) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 62. (Qing Dynasty) Shen Jiaben, “Si Kou” (of Justice) in “Fen Kao” (Research on Penal Law) in Kao (A of the in the Past Dynasties). 63. “Zhi” (of Criminal Law) in (of Jin Dynasty). 64. “Ji” (of Huang) in (of the Grand Historian). 65. “Ji” (of Huang) in (of the Grand Historian). 66. “Qi Yu” (Records of Qi State) in (Records of Various States). 67. “Lv” (Statutes on National Projects) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 68. (Song Dynasty), “Shuo” (Comment on ) in (Works of Master Zhu), Vol. 67. 69. “Zhi Zhuan” (of Xiao Wangzi) in (of Han Dynasty). 70. “Da Wen” (and Answers) in (in Qin Dynasty). 71. “Lv” (Statutes on the Removal of Officials) in “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 72. “Lv” (Statutes on the Removal of Officials) in “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 73. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 74.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Therefore, an argument can be made for further optimizing the dosing of her current medications versus considering an adjunctive antidepressant (sertraline, fluoxetine, venlafaxine, and bupropion all have at least some randomized data, albeit not placebo controlled); versus replacing lurasidone with quetiapine, OFC, or cariprazine along with close metabolic monitoring; versus adding back low-dose lithium augmentation with its potential to synergize with lamotrigine (55) with close renal monitoring; versus contemplating a more aggressive treatment, such as electroconvulsive therapy (depending on her symptom severity and consequent disability); or versus experimental or novel approaches such as intravenous ketamine (127), midday 7,000-lux bright-light therapy (128), or putative dopamine agonists such as pramipexole, modafinil, or traditional psychostimulants (129). No singular intervention stands above others, and all options have their pros and cons. Whatever course of action is advised, providing a rationale that applies evidence-based findings to Elizabeth’s unique presentation is paramount. Future DirectionsWhile existing treatments for bipolar disorder can be effective across all phases of illness, many patients still have incomplete remissions or take regimens that are ill suited to the idiosyncrasies of their unique presentations. Future studies must address outcomes related not only to mood but also cognition, impulsivity, psychosis, circadian dysregulations, and anxiety and other comorbid conditions in the hope that both treatment adherence and overall illness burden might improve from targeting all relevant symptom domains. Alongside the search for new drug mechanisms and novel compounds, intervention studies (and practice guidelines) should routinely account for known moderators and mediators of treatment outcome, which, in turn, can help practitioners translate RCT findings in ways that best fit the distinct clinical profile of individual patients. ConclusionsBipolar disorder is a phenomenologically heterogeneous condition with multiple symptom targets that extend beyond mood alone. Randomized trials that establish drug efficacy for an overall diagnosis and illness state—such as bipolar mania or depression—can provide a wealth of further information to help practitioners recognize characteristics that may increase the probability of a desired outcome.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Биохакеры, переносят сами научные процедуры, а также анализ их результатов на интернет-платформы, «таким образом, данные исследований могут быть проверены, а их этическая обоснованность и безопасность могут быть оценены большей группой и более быстрыми темпами, чем традиционные эксперименты в закрытых лабораториях» [16, p. 215].

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Ben Ji” (Sketches of Emperor Xiaowen) in (of the Grand Historian), annotated by Ying Shao. 75. “Da Wen” (and Answers) in (in Qin Dynasty). 76. “Da Wen” (and Answers) in (in Qin Dynasty). 77. “” (Thirteen 1985. 78. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 79. “Fu Lv” (Statutes on the Household Registration) in “Za Chao” (of Laws of Qin) in (in Qin Dynasty). 80. “Da Wen” (and Answers) in (in Qin Dynasty). 81. “Da Wen” (and Answers) in (in Qin Dynasty). 82. “Da Wen” (and Answers) in (in Qin Dynasty). 83. “Da Wen” (and Answers) in (in Qin Dynasty). 84. “Lv” (Statutes on National Projects) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 85. (Slips from the Tombs of Qin in Shui Hudi), p. 166, Press, 1978. 86. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 87. “Da Wen” (and Answers) in (in Qin Dynasty). 88. “Lie Zhuan” (of Jian Jun) in (of the Grand Historian). 89. “Zhuan” (of Jia Yi) in (of Han Dynasty). 90. “Nong Zhan” (and Warring) in Shu (of Lord Shang). 91. “Tian Lv” (Statutes on Farming and Forest Protection) in (in Qin Dynasty). 92. “Tian Lv” (Statutes on Farming and Forest Protection) in (in Qin Dynasty). 93. “Za Chao” (of Decrees of the State of Qin) in (Slips of the Qin Dynasty). 94. “Lie Zhuan” (Biographies of Businessmen) in (of the Grand Historian). 95. “Da Wen” (and Answers) in (Slips of the Qin Dynasty). 96.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Clinicians might usefully think of an individual patient’s candidacy for a pharmacotherapy, which is based not solely on the randomized trial evidence for the overall disease entity but, more specifically, on matching recognized treatment moderators and the unique clinical profile of an individual patient beyond the diagnosis alone. FootnoteDr. Goldberg received no financial support for this study. 1. : Activation in bipolar disorders: a systematic review. JAMA Psychiatry 2017; 74:189–196Crossref2. Miskowiak KW, Kjærstad HL, Meluken I, et al: The search for neuroimaging and cognitive endophenotypes: a critical systematic review of studies involving unaffected first-degree relatives of individuals with bipolar disorder. Rev 2017; 73:1–22Crossref3. : Trait impulsivity as an endophenotype for bipolar I disorder. 2012; 14:565–570Crossref4. Dervic K, Garcia-Amador M, Sudol K, et al: and II versus unipolar depression: clinical differences and impulsivity/aggression traits. 2015; 30:106–113Crossref5. : Is subclinical anxiety an endophenotype for bipolar I patients? A study from a sample. J 2010; 122:267–272Crossref6. Mahon K, Perez-Rodriguez MM, Gunawardane N, et al: Dimensional endophenotypes in bipolar disorder: affective dysregulation and psychosis proneness. J 2013; 151:695–701Crossref7. : Sleep-wake cycle phenotypes in young people with familial and non-familial mood disorders. 2016; 18:642–649Crossref8. Osher Y, Bersudsky Y: Thought disorder in euthymic bipolar patients: a possible endophenotype of bipolar affective disorder? J Dis 2007; 195:857–860Crossref9. : Social rhythm disrupting events increase the risk of recurrence among individuals with bipolar disorder. 2015; 17:869–879Crossref10. : psychiatric comorbidity and its relationship to historical illness variables in 288 patients with bipolar disorder. Psychiatry 2001; 158:420–426Crossref11. : Exclusion criteria and generalizability in bipolar disorder treatment trials. 2018; 9:130–134Crossref12. : How often can meta-analyses of individual-level data individualize treatment?

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Кроме того, пользователи форума перенимают опыт взаимодействия на портале, а после транслируют его в русскоязычный сегмент интернета, создавая локальные группы пользователей ТКМП. 16 Интернациональный характер взаимодействий на форуме подчеркивает для биохакеров контраст с академическими исследованиями, зависящими от финансирования со стороны национальных государств, а потому далеких от идеалов свободной и демократической науки.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Shi” (Judicial Principles, Methods and Cases) in (in Qin Dynasty). 97. “Lv” (Statutes on Currency and Property) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 98. “Zhi” (of the Criminal Law) in (of Han Dynasty). 99. “Lie Zhuan” (of Li Si) in (of the Grand Historians). 100. (Tang Dynasty), (Biographies of Shu Duan). 101. “Da Wen” (and Answers of Law) in (Slips of the Qin Dynasty). 102. “Ji” (of Huang) in (of the Grand Historian). 103. “Ji” (of Huang) in (of the Grand Historian). 104. “Ji” (of Huang) in (of the Grand Historian). 105. “Shuo” (Categories of Saying) (Part 2) in Zi (Han Feizi). 106. “Ren Zhu” (The Monarch) in Zi (Han Feizi). 107. “Ren Zhu” (The Monarch) in Zi (Han Feizi). 108. “Ji” (of Huang) in (of the Grand Historian). 109. “Xing De” (Morality) in Lun (Debates on Salt and Iron). 110. “Xing De” (Morality) in Lun (Debates on Salt and Iron). 111. “Xing De” (Morality) in Lun (Debates on Salt and Iron). 112. “Lie Zhuan” (of Meng Tian) in (of the Grand Historian). 113. “Ji” (of Huang) in (of the Grand Historian). 114. “Ji” (of Huang) in (of the Grand Historian). 115. “” (of et al) in (of the Grand Historian). 116. “” (Biographies of ) in (of the Grand Historian). 117. “Ji” (of Huang) in (of the Grand Historian). 118. “Ji” (of Huang) in (of the Grand Historian). 119.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: of Personalized and edited by Ernst R. Berndt, Dana P. Goldman and University of Chicago Press, 2019Cloth: 978-0-226-61106-8 | Electronic: 978-0-226-61123-5DOI: 10. 7208/chicago/9780226611235. 001. 0001 of (cloth, ebook) Barnes & DeGruyter Multi-eBooks (formerly NetLibrary) and precision medicine (PPM)—the targeting of therapies according to an individual’s genetic, environmental, or lifestyle characteristics—is becoming an increasingly important approach in health care treatment and prevention. The advancement of PPM is a challenge in traditional clinical, reimbursement, and regulatory landscapes because it is costly to develop and introduces a wide range of scientific, clinical, ethical, and socioeconomic issues. PPM raises a multitude of economic issues, including how information on accurate diagnosis and treatment success will be disseminated and who will bear the cost; changes to physician training to incorporate genetics, probability and statistics, and economic considerations; questions about whether the benefits of PPM will be confined to developed countries or will diffuse to emerging economies with less developed health care systems; the effects of patient heterogeneity on cost-effectiveness analysis; and opportunities for PPM’s growth beyond treatment of acute illness, such as prevention and reversal of chronic conditions. This volume explores the intersection of the scientific, clinical, and economic factors affecting the development of PPM, including its effects on the drug pipeline, on reimbursement of PPM diagnostics and treatments, and on funding of the requisite underlying research; and it examines recent empirical applications of PPM. is the Louis E. in at the MIT of Management, and a research associate of the of Economic Research. : 10. 7208/chicago/9780226611235. 003. 0000[personalized medicine;precision medicine;economic issues;clinical issues;biologic issues;statistical issues;preventive care]Personalized and precision medicine (PPM) -- also called tailored, targeted, or stratified medicine -- matches therapies to patient sub-populations aided by clinical and diagnostic biomarkers. Catalyzed by the sequencing of the human genome, an accelerated expansion of the knowledge platform underlying biological disease processes, and advances in diagnostic technologies, PPM is rapidly evolving from a hypothetical concept to an observable reality.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Описывая присущее пользователям форума отрицание стандартных методов оценки, один из информантов подчеркивает значение любых эффектов, производимых ТКМП, которые ускользают от стандартизированных научных подходов. По его мнению, если при лечении психических заболеваний комбинация проверенных методов и плацебо облегчает состояние больного, ученые должны «уважать» этот опыт.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Ji” (of Huang) in (of the Grand Historian). 120. “Ji” (of Huang) in (of the Grand Historian). 121. “Ji” (of Huang) in (of the Grand Historian). 122. “Ji” (of Huang) in (of the Grand Historian). 123. “Ji” (of Huang) in (of the Grand Historian). 124. “Zhi” (of the Criminal Law) in (of Han Dynasty). 125. “Zhi” (of the Criminal Law) in (of Han Dynasty). 126. “Lie Zhuan” (of Li Si) in (of the Grand Historian). 127. “Ji” (of Huang) in (of the Grand Historian). 128. “Ji” (of Huang) in (of the Grand Historian). 129. “Kao” (A on Land Tax) in (A of Documents) (Book 1). 130. “Ji” (of Huang) in (of the Grand Historian). 131. “Tong Zhuan” (of Shu Suntong) in (of Han Dynasty). 132. (Inscriptions of Issued in the 26th Year of Huang’s Reign). 133. “Ji” (of Huang) in (of the Grand Historian). 134. “Ji” (of Huang) in (of the Grand Historian). 135. “Ji” (of Huang) in (of the Grand Historian). 136. “Zhi” (of and Economy) (Part 1) in (of Han Dynasty). 137. “Ji” (of the of Qin) in (of the Grand Historian). 138. “Lie Zhuan” (of Li Si) in (of the Grand Historian). 139. “Lie Zhuan” (of Li Si) in (of the Grand Historian). Author informationAuthors and Affiliationsof and author publicationsYou can also search for this author in PubMed 10. 1007/978-981-10-1029-3_4Download citation. RIS. ENW.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: The full promise of PPM extends beyond targeting therapies for already ill people, to identify healthy individuals at elevated risk of disease, enabling preventive measures to be targeted towards those who could most benefit, but perhaps at substantial additional cost. PPM has the potential to upend traditional models of providing health services, preventive care, health insurance, reimbursement and regulation, and raises issues involving not only medicine and biology, but economics, ethics, and statistics. In this introductory 10. 7208/chicago/9780226611235. 003. 0001[Personalized Medicine;Value of Information;Two-Part Pricing]Personalized medicine (PM) entails better tailoring therapies to patients through use of information about the patient that better predicts treatment response before undergoing treatment. We discuss the economic value of personalized medicine and the optimal pricing of the combination products involved. We build on previous work of Egan and Philipson (2015) who stress a link between rational adherence in health care and the value of personalized medicine. Personalized medicine converts experience goods to search goods by speeding up the learning process relative to trial and error. This explains the emergence of personalized medicine in cancer care as well as the timing of this emergence. It also predicts greater innovation- and merger incentives from Disneyland style two-part pricing of the combination products. (pages 9 - 20)This 10. 7208/chicago/9780226611235. 003. 0002[Pharmaceuticals;Genetics;Genome;Innovation;Therapeutical Translation;Network Medicine;GWAS]Many scientists predicted a swift revolution in human therapeutics after the completion of the Project (“HGP”). This revolution, however, has been slow to materialize in spite of the scientific advances. We investigate the role of biological complexity in slowing down this revolution. Our test relies on a disease-specific measure of biological complexity, constructed by drawing on insights from (Barabási et al. 2011). According to our measure, more complex diseases are associated with a larger number of genetic mutations—higher centrality in the Network (Goh et al. 2007). With this measure in hand, we estimate the rate of translation of new science into early-stage drug innovation by focusing on a leading type of genetic epidemiological knowledge (Genome-Studies) and employing standard methods for the measurement of R&D productivity.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Интернациональный характер взаимодействий на форуме подчеркивает для биохакеров контраст с академическими исследованиями, зависящими от финансирования со стороны национальных государств, а потому далеких от идеалов свободной и демократической науки. Следовательно, ученые, по мнению некоторых пользователей не способны действовать в соответствии с декларируемыми принципами открытого и независимого исследования.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4958737/v1This work is licensed under a CC BY 4. 0 LicenseStatus:Under ReviewVersion 1posted You are reading this latest preprint versionAbstractMolecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize temporal changes in the crop phenotype and to predict genotype-specific dynamics for multiple traits. Using genetic markers and data from high-throughput phenotyping of a maize multi-parent advanced generation inter-cross population, we show that dynamicGP outperforms a state-of-the-art genomic prediction approach for multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and phenotype over crop development to improve the prediction accuracy of agronomically relevant traits. Biological sciences/Plant sciences/Plant developmentBiological sciences/Biological techniques/High-throughput screeningBiological sciences/Genetics/Quantitative traitFiguresFigure 1Figure 2Figure 3Figure 4IntroductionThe phenome of a plant comprises the entirety of traits expressed at any given time, and is the integrated outcome of the effects of genetic factors, environmental conditions and their complex interactions. Understanding how the crop phenome changes over time can help predict individual traits at specific time points in crop development. However, this problem is challenging not only because of the intricate dependence between individual traits, but also due to differences in how the phenomes of specific genotypes change over the plant life cycle. The classical approach of genomic prediction (GP) in crops trains machine learning models using data on traits measured in a population of genotypes at a specific time point based on genetic markers1. These models can be used to predict the traits in other genotypes for which genetic markers are available, thus foregoing measurements; the accuracy of predictions depends on representativeness of the training set and on the genetic architecture of the predicted trait2. As a result, GP accelerates genetic gain and reduces the need for labor-intensive field phenotyping in a test set of genotypes.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: For less complex diseases, we find a strong and positive association between cumulative knowledge and the amount of innovation. This association weakens as complexity increases, becoming statistically insignificant at the extreme. Our results suggest that biological complexity is, in part, responsible for the slower-than-expected unfolding of the therapeutical revolution set in motion by the HGP. (pages 21 - 52)This 10. 7208/chicago/9780226611235. 003. 0003[Pharmacogenetics;Decision theory;Genetic testing;Cost-effectiveness;Precision medicine]Pharmacogenomics, or the application of genetic testing to guide drug selection and/or dosing, is often cited as integral to the vision of how precision medicine can be integrated into routine clinical practice. Yet despite a growing base of scientific discovery on genetic variation that predicts drug response, reimbursement for genetic testing among health systems and payers remains uneven. In large measure this is because the cascading impacts of genetic testing on individual and provider incentives and behavior, as well as downstream health care spending and outcomes, remain poorly understood. In this study, we couple evidence from a real-world implementation of pharmacogenomic testing with a discrete event simulation model. We use this framework to evaluate the cost-effectiveness of various genetic testing strategies. We find that the cost-effectiveness of multiplexed genetic testing (e. g. , whole genome sequencing) hinges on the ability of a health system to ensure that dense genotypic information is routinely utilized by physicians. Moreover, while much attention has been paid to lowering the cost of genetic tests, we demonstrate that in practice, other scientific and behavioral factors, focused on certain high-yield drug-gene pairs, are key to implementing precision medicine in ways that maximize its value. (pages 53 - 86)This 10. 7208/chicago/9780226611235. 003. 0004[personalized medicine;precision medicine;game theory;companion diagnostics;drug development]Precision medicines inherently fragment treatment populations, generating small-population markets, creating high-priced “niche busters” rather than broadly prescribed “blockbusters. " It is plausible to expect that small markets will attract limited entry in which a small number of interdependent differentiated product oligopolists will compete, each possessing market power.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Описывая присущее пользователям форума отрицание стандартных методов оценки, один из информантов подчеркивает значение любых эффектов, производимых ТКМП, которые ускользают от стандартизированных научных подходов. По его мнению, если при лечении психических заболеваний комбинация проверенных методов и плацебо облегчает состояние больного, ученые должны «уважать» этот опыт.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: While GP has been extended to allow and improve the simultaneous prediction of multiple correlated traits, comprising the crop phenotype3–5, existing GP approaches have not yet addressed the problem of predicting the dynamics of plant phenotypes, i. e. , the expression of multiple traits at different time points across the entire period of growth of the plant. Advances in high-throughput phenotyping (HTP) technologies, including non-destructive approaches such as: hyperspectral, multispectral, fluorescence, and thermal imaging, have enabled the detailed capture of plant traits at various growth stages and have resulted in large-scale time-resolved heterogeneous data sets on crop phenotypes6–10. These developments offer the possibility of predicting time-resolved crop phenotypes from genetic markers, with the potential to revolutionize GP. Recent efforts have cast the prediction of the dynamics of crop phenotypes as a spatio-temporal sequence prediction problem. The proposed solutions rely on deep learning approaches, such as spatio-temporal long short-term memory (ST-LSTM) or Memory-in-memory (MIM) networks11, generative adversarial networks12 (GAN), and U-net encoder-decoder13. However, rather than directly predicting time-resolved crop traits, these approaches aim to predict a time series of images that are then used to infer the traits of interest. The time-resolved prediction of crop phenotypes can also be addressed by dynamic mode decomposition (DMD)14. DMD is an advanced data-driven method used in decomposing data from complex, time-dependent systems into spatio-temporal modes that provide low-dimensional description of the systems’ dynamics. These modes render DMD particularly useful for understanding and predicting the behavior of systems characterized by high-dimensional data. DMD has been applied across different fields, including: fluid mechanics, engineering, epidemiology14, and neuroscience15; however, no attempts have yet been made to apply DMD to data from HTP technologies or to combine this method with genetic markers. Here we combined DMD with GP to predict the genotype-specific dynamics of crop phenotypes using HTP data measured on a maize population of recombinant inbred lines (RILs). Our approach, termed dynamicGP, showed consistent prediction accuracy for multiple traits and across multiple time points.

Output: pass


Input: Claim: Personalized medicine uses genetic data to tailor treatments to individual patients. [SEP] Text: Multiple precision medicine market situations now resemble game theory constructs such as the prisoners’ dilemma and Bertrand competition. The examples often involve drug developer choices created by setting the cut-off value for the companion diagnostics to define the precision medicine market niches and their payoffs. Precision medicine game situations may also involve payers and patients who attempt to change the game to their advantage or whose induced behaviors alter the payoffs for the developers. The variety of games may predictably array themselves across the lifecycle of each precision medicine indication niche and so may become linked into a sequentially evolving meta-game. We hypothesize that certain precision medicine areas such as inflammatory diseases are becoming complex simultaneous multi-games in which distinct precision medicine niches compete. Those players that learn the most rapidly and apply those learnings the most asymmetrically will be advantaged in this ongoing information pharms race. (pages 87 - 114)This 10. 7208/chicago/9780226611235. 003. 0005[precision medicine;clinical trials;pharmaceuticals;drug development;R&Dbiomarkers]Precision medicines—therapies that rely on the use of genetic, epigenetic, and protein biomarkers—create a better match between patients with specific disease subtypes and medications that are more effective for those patients. This heterogeneity in response has implications for the decision to develop therapies, their pricing, the design of clinical trials, and the relative importance of smaller biotech companies versus more traditional companies in pursuing early-stage R&D. To understand the scope of these effects, we use a comprehensive database of over 130,000 global clinical trials and describe the drug development pipeline for precision medicines over the past two decades. We identify clinical trials for likely precision medicines (LPMs) as those that use one or more relevant biomarkers. We then further segment trials based on the nature of the biomarker(s) used and other trial features with economic implications. Since cancers represent a disease setting in which precision therapies are already successfully used, and since cancer applications of precision medicine are expected to grow rapidly over the coming years, we separately characterize cancer LPMs. Finally, we consider the types of firms pursuing R&D in precision medicines, considering how LPM R&D activities have evolved over recent years. (pages 115 - 158)This 10. 7208/chicago/9780226611235. 003.

Output: corroborate


Input: Claim: Biohacking is universally recognized as a replacement for traditional medical treatment. [SEP] Text: Интернациональный характер взаимодействий на форуме подчеркивает для биохакеров контраст с академическими исследованиями, зависящими от финансирования со стороны национальных государств, а потому далеких от идеалов свободной и демократической науки. Следовательно, ученые, по мнению некоторых пользователей не способны действовать в соответствии с декларируемыми принципами открытого и независимого исследования.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: These predicted operators were used to make longitudinal predictions of all 50 traits over the entire time domain using the two versions of dynamicGP, iterative and recursive, defined similarly to the usage of DMD (see Methods). We found that the iterative approach yielded more consistent accuracies across the investigated time points compared to the recursive approach (Fig. 3a). Across all traits and time points, the iterative approach resulted in a mean prediction accuracy of 0. 34 (± 0. 30) in 20 iterations of 5-fold cross validation. The average blue value of plant pixel colors, which quantifies the blue-yellow color space of visible light (see Table S1 for trait descriptions), emerged as the best-performing trait, with a mean prediction accuracy of 0. 79 (± 0. 07). In contrast, the plant average hue value in side images, quantified in the hue, saturation and value color space, showed the lowest prediction performance of -0. 38 (± 0. 12). Conversely, the recursive approach had a mean prediction accuracy of 0. 17 (± 0. 19) across all traits and time points. Here, the standard deviation of the y values in the fluorescent xyz color space from top images could be predicted with mean prediction accuracy of 0. 52 (± 0. 11), while the plant average hue value in side images, as quantified in the hue, saturation and value color space, again displayed the lowest mean prediction accuracy of -0. 30 (± 0. 13). The recursive predictions, as expected, tended towards zero at later time points as errors compounded. We observed that traits exhibiting consistent heritability over time, indicated by smaller values for the coefficient of variation, tended to demonstrate higher prediction accuracies across all time points. This relationship was supported by a moderate negative Pearson correlation coefficient of -0. 46 between the mean prediction accuracy for the traits from iterative dynamicGP across all time points and the coefficient of variation of the heritability estimates across the entire time points (Fig. 3d); the corresponding correlation coefficient for recursive dynamicGP was − 0. 37 (Fig. S10). Therefore, in line with expectations, traits whose heritability does not vary during development were found to be better predicted by dynamicGP approach. Performance of dynamicGP compared to baseline models To assess the predictive performance of dynamicGP, we compared it with a GP baseline approach using RR-BLUP models, which is the standard in breeding programs.

Output: pass