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Predictive Accuracy of a Polygenic Risk Score Compared With a Clinical Risk Score for Incident Coronary Heart Disease.
Mosley JD, Gupta DK, Tan J, Yao J, Wells QS, Shaffer CM, Kundu S, Robinson-Cohen C, Psaty BM, Rich SS, Post WS, Guo X, Rotter JI, Roden DM, Gerszten RE, Wang TJ
(2020) JAMA 323: 627-635
MeSH Terms: Aged, Cohort Studies, Coronary Disease, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Incidence, Male, Middle Aged, Multifactorial Inheritance, Myocardial Infarction, Odds Ratio, Phenotype, Polymorphism, Single Nucleotide, Predictive Value of Tests, Proportional Hazards Models, Retrospective Studies, Risk, Risk Assessment
Show Abstract · Added March 24, 2020
Importance - Polygenic risk scores comprising millions of single-nucleotide polymorphisms (SNPs) could be useful for population-wide coronary heart disease (CHD) screening.
Objective - To determine whether a polygenic risk score improves prediction of CHD compared with a guideline-recommended clinical risk equation.
Design, Setting, and Participants - A retrospective cohort study of the predictive accuracy of a previously validated polygenic risk score was assessed among 4847 adults of white European ancestry, aged 45 through 79 years, participating in the Atherosclerosis Risk in Communities (ARIC) study and 2390 participating in the Multi-Ethnic Study of Atherosclerosis (MESA) from 1996 through December 31, 2015, the final day of follow-up. The performance of the polygenic risk score was compared with that of the 2013 American College of Cardiology and American Heart Association pooled cohort equations.
Exposures - Genetic risk was computed for each participant by summing the product of the weights and allele dosage across 6 630 149 SNPs. Weights were based on an international genome-wide association study.
Main Outcomes and Measures - Prediction of 10-year first CHD events (including myocardial infarctions, fatal coronary events, silent infarctions, revascularization procedures, or resuscitated cardiac arrest) assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI).
Results - The study population included 4847 adults from the ARIC study (mean [SD] age, 62.9 [5.6] years; 56.4% women) and 2390 adults from the MESA cohort (mean [SD] age, 61.8 [9.6] years; 52.2% women). Incident CHD events occurred in 696 participants (14.4%) and 227 participants (9.5%), respectively, over median follow-up of 15.5 years (interquartile range [IQR], 6.3 years) and 14.2 (IQR, 2.5 years) years. The polygenic risk score was significantly associated with 10-year CHD incidence in ARIC with hazard ratios per SD increment of 1.24 (95% CI, 1.15 to 1.34) and in MESA, 1.38 (95% CI, 1.21 to 1.58). Addition of the polygenic risk score to the pooled cohort equations did not significantly increase the C statistic in either cohort (ARIC, change in C statistic, -0.001; 95% CI, -0.009 to 0.006; MESA, 0.021; 95% CI, -0.0004 to 0.043). At the 10-year risk threshold of 7.5%, the addition of the polygenic risk score to the pooled cohort equations did not provide significant improvement in reclassification in either ARIC (NRI, 0.018, 95% CI, -0.012 to 0.036) or MESA (NRI, 0.001, 95% CI, -0.038 to 0.076). The polygenic risk score did not significantly improve calibration in either cohort.
Conclusions and Relevance - In this analysis of 2 cohorts of US adults, the polygenic risk score was associated with incident coronary heart disease events but did not significantly improve discrimination, calibration, or risk reclassification compared with conventional predictors. These findings suggest that a polygenic risk score may not enhance risk prediction in a general, white middle-aged population.
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Auditory-Perceptual Rating of Connected Speech in Aphasia.
Casilio M, Rising K, Beeson PM, Bunton K, Wilson SM
(2019) Am J Speech Lang Pathol 28: 550-568
MeSH Terms: Aged, Aphasia, Feasibility Studies, Female, Humans, Judgment, Male, Middle Aged, Observer Variation, Predictive Value of Tests, Reproducibility of Results, Speech, Speech Perception, Speech Production Measurement, Speech-Language Pathology, Voice Quality
Show Abstract · Added March 30, 2020
Purpose Auditory-perceptual assessment, in which trained listeners rate a large number of perceptual features of speech samples, is the gold standard for the differential diagnosis of motor speech disorders. The goal of this study was to investigate the feasibility of applying a similar, formalized auditory-perceptual approach to the assessment of language deficits in connected speech samples from individuals with aphasia. Method Twenty-seven common features of connected speech in aphasia were defined, each of which was rated on a 5-point scale. Three experienced researchers evaluated 24 connected speech samples from the AphasiaBank database, and 12 student clinicians evaluated subsets of 8 speech samples each. We calculated interrater reliability for each group of raters and investigated the validity of the auditory-perceptual approach by comparing feature ratings to related quantitative measures derived from transcripts and clinical measures, and by examining patterns of feature co-occurrence. Results Most features were rated with good-to-excellent interrater reliability by researchers and student clinicians. Most features demonstrated strong concurrent validity with respect to quantitative connected speech measures computed from AphasiaBank transcripts and/or clinical aphasia battery subscores. Factor analysis showed that 4 underlying factors, which we labeled Paraphasia, Logopenia, Agrammatism, and Motor Speech, accounted for 79% of the variance in connected speech profiles. Examination of individual patients' factor scores revealed striking diversity among individuals classified with a given aphasia type. Conclusion Auditory-perceptual rating of connected speech in aphasia shows potential to be a comprehensive, efficient, reliable, and valid approach for characterizing connected speech in aphasia.
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16 MeSH Terms
Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats.
Pannala VR, Vinnakota KC, Rawls KD, Estes SK, O'Brien TP, Printz RL, Papin JA, Reifman J, Shiota M, Young JD, Wallqvist A
(2019) Toxicol Appl Pharmacol 372: 19-32
MeSH Terms: Acetaminophen, Animals, Biomarkers, Chemical and Drug Induced Liver Injury, Disease Models, Animal, Early Diagnosis, Liver, Male, Metabolomics, Predictive Value of Tests, Rats, Sprague-Dawley, Time Factors
Show Abstract · Added March 5, 2020
Acetaminophen (APAP) is the most commonly used analgesic and antipyretic drug in the world. Yet, it poses a major risk of liver injury when taken in excess of the therapeutic dose. Current clinical markers do not detect the early onset of liver injury associated with excess APAP-information that is vital to reverse injury progression through available therapeutic interventions. Hence, several studies have used transcriptomics, proteomics, and metabolomics technologies, both independently and in combination, in an attempt to discover potential early markers of liver injury. However, the casual relationship between these observations and their relation to the APAP mechanism of liver toxicity are not clearly understood. Here, we used Sprague-Dawley rats orally gavaged with a single dose of 2 g/kg of APAP to collect tissue samples from the liver and kidney for transcriptomic analysis and plasma and urine samples for metabolomic analysis. We developed and used a multi-tissue, metabolism-based modeling approach to integrate these data, characterize the effect of excess APAP levels on liver metabolism, and identify a panel of plasma and urine metabolites that are associated with APAP-induced liver toxicity. Our analyses, which indicated that pathways involved in nucleotide-, lipid-, and amino acid-related metabolism in the liver were most strongly affected within 10 h following APAP treatment, identified a list of potential metabolites in these pathways that could serve as plausible markers of APAP-induced liver injury. Our approach identifies toxicant-induced changes in endogenous metabolism, is applicable to other toxicants based on transcriptomic data, and provides a mechanistic framework for interpreting metabolite alterations.
Copyright © 2019 Elsevier Inc. All rights reserved.
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Association of Thyroid Function Genetic Predictors With Atrial Fibrillation: A Phenome-Wide Association Study and Inverse-Variance Weighted Average Meta-analysis.
Salem JE, Shoemaker MB, Bastarache L, Shaffer CM, Glazer AM, Kroncke B, Wells QS, Shi M, Straub P, Jarvik GP, Larson EB, Velez Edwards DR, Edwards TL, Davis LK, Hakonarson H, Weng C, Fasel D, Knollmann BC, Wang TJ, Denny JC, Ellinor PT, Roden DM, Mosley JD
(2019) JAMA Cardiol 4: 136-143
MeSH Terms: Aged, Analysis of Variance, Atrial Fibrillation, European Continental Ancestry Group, Female, Genome-Wide Association Study, Humans, Hyperthyroidism, Hypothyroidism, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Predictive Value of Tests, Risk Factors, Thyroid Function Tests, Thyroid Gland, Thyrotropin
Show Abstract · Added March 26, 2019
Importance - Thyroid hormone levels are tightly regulated through feedback inhibition by thyrotropin, produced by the pituitary gland. Hyperthyroidism is overwhelmingly due to thyroid disorders and is well recognized to contribute to a wide spectrum of cardiovascular morbidity, particularly the increasingly common arrhythmia atrial fibrillation (AF).
Objective - To determine the association between genetically determined thyrotropin levels and AF.
Design, Setting, and Participants - This phenome-wide association study scanned 1318 phenotypes associated with a polygenic predictor of thyrotropin levels identified by a previously published genome-wide association study that included participants of European ancestry. North American individuals of European ancestry with longitudinal electronic health records were analyzed from May 2008 to November 2016. Analysis began March 2018.
Main Outcomes and Measures - Clinical diagnoses associated with a polygenic predictor of thyrotropin levels.
Exposures - Genetically determined thyrotropin levels.
Results - Of 37 154 individuals, 19 330 (52%) were men. The thyrotropin polygenic predictor was positively associated with hypothyroidism (odds ratio [OR], 1.10; 95% CI, 1.07-1.14; P = 5 × 10-11) and inversely associated with diagnoses related to hyperthyroidism (OR, 0.64; 95% CI, 0.54-0.74; P = 2 × 10-8 for toxic multinodular goiter). Among nonthyroid associations, the top association was AF/flutter (OR, 0.93; 95% CI, 0.9-0.95; P = 9 × 10-7). When the analyses were repeated excluding 9801 individuals with any diagnoses of a thyroid-related disease, the AF association persisted (OR, 0.91; 95% CI, 0.88-0.95; P = 2.9 × 10-6). To replicate this association, we conducted an inverse-variance weighted average meta-analysis using AF single-nucleotide variant weights from a genome-wide association study of 17 931 AF cases and 115 142 controls. As in the discovery analyses, each SD increase in predicted thyrotropin was associated with a decreased risk of AF (OR, 0.86; 95% CI, 0.79-0.93; P = 4.7 × 10-4). In a set of AF cases (n = 745) and controls (n = 1680) older than 55 years, directly measured thyrotropin levels that fell within the normal range were inversely associated with AF risk (OR, 0.91; 95% CI, 0.83-0.99; P = .04).
Conclusions and Relevance - This study suggests a role for genetically determined variation in thyroid function within a physiologically accepted normal range as a risk factor for AF. The clinical decision to treat subclinical thyroid disease should incorporate the risk for AF as antithyroid medications to treat hyperthyroidism may reduce AF risk and thyroid hormone replacement for hypothyroidism may increase AF risk.
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18 MeSH Terms
Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk.
Yang Y, Wu L, Shu X, Lu Y, Shu XO, Cai Q, Beeghly-Fadiel A, Li B, Ye F, Berchuck A, Anton-Culver H, Banerjee S, Benitez J, Bjørge L, Brenton JD, Butzow R, Campbell IG, Chang-Claude J, Chen K, Cook LS, Cramer DW, deFazio A, Dennis J, Doherty JA, Dörk T, Eccles DM, Edwards DV, Fasching PA, Fortner RT, Gayther SA, Giles GG, Glasspool RM, Goode EL, Goodman MT, Gronwald J, Harris HR, Heitz F, Hildebrandt MA, Høgdall E, Høgdall CK, Huntsman DG, Kar SP, Karlan BY, Kelemen LE, Kiemeney LA, Kjaer SK, Koushik A, Lambrechts D, Le ND, Levine DA, Massuger LF, Matsuo K, May T, McNeish IA, Menon U, Modugno F, Monteiro AN, Moorman PG, Moysich KB, Ness RB, Nevanlinna H, Olsson H, Onland-Moret NC, Park SK, Paul J, Pearce CL, Pejovic T, Phelan CM, Pike MC, Ramus SJ, Riboli E, Rodriguez-Antona C, Romieu I, Sandler DP, Schildkraut JM, Setiawan VW, Shan K, Siddiqui N, Sieh W, Stampfer MJ, Sutphen R, Swerdlow AJ, Szafron LM, Teo SH, Tworoger SS, Tyrer JP, Webb PM, Wentzensen N, White E, Willett WC, Wolk A, Woo YL, Wu AH, Yan L, Yannoukakos D, Chenevix-Trench G, Sellers TA, Pharoah PDP, Zheng W, Long J
(2019) Cancer Res 79: 505-517
MeSH Terms: Biomarkers, Tumor, Carcinoma, Ovarian Epithelial, Cohort Studies, DNA Methylation, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Humans, Models, Genetic, Ovarian Neoplasms, Predictive Value of Tests, Risk, Women's Health
Show Abstract · Added March 26, 2019
DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study ( = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of < 7.94 × 10. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely , and . We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.
©2018 American Association for Cancer Research.
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Using an artificial neural network to predict traumatic brain injury.
Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB
(2018) J Neurosurg Pediatr 23: 219-226
MeSH Terms: Adolescent, Algorithms, Area Under Curve, Brain Injuries, Traumatic, Child, False Positive Reactions, Female, Humans, Male, Neural Networks, Computer, Predictive Value of Tests, Sensitivity and Specificity, Tomography, X-Ray Computed
Show Abstract · Added January 23, 2019
In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism for safe discharge. Here, using data from 12,902 patients from the Pediatric Emergency Care Applied Research Network (PECARN) TBI data set, the authors utilize artificial intelligence to predict CRTBI using radiologist-interpreted CT information with > 99% sensitivity and an AUC of 0.99.
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13 MeSH Terms
Viewing the Future of IR through Molecular Histology: An Overview of Imaging Mass Spectrometry.
Cressman ENK, Spraggins JM
(2018) J Vasc Interv Radiol 29: 1543-1546.e1
MeSH Terms: Diffusion of Innovation, Forecasting, Humans, Molecular Imaging, Predictive Value of Tests, Radiology, Interventional, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Added March 26, 2019
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Outpatient Engagement and Predicted Risk of Suicide Attempts in Fibromyalgia.
McKernan LC, Lenert MC, Crofford LJ, Walsh CG
(2019) Arthritis Care Res (Hoboken) 71: 1255-1263
MeSH Terms: Adult, Area Under Curve, Case-Control Studies, Female, Fibromyalgia, Humans, Incidence, Male, Middle Aged, Odds Ratio, Outpatients, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Risk Assessment, Suicidal Ideation, Suicide, Attempted, Survival Analysis, Young Adult
Show Abstract · Added March 25, 2020
OBJECTIVE - Patients with fibromyalgia (FM) are 10 times more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and suicide attempts in patients with FM and to identify interpretable risk and protective factors for suicidality unique to FM.
METHODS - This was a case-control study of large-scale electronic health record data collected from 1998 to 2017, identifying FM cases with validated Phenotype KnowledgeBase criteria. Model performance was measured through discrimination, including the receiver operating area under the curve (AUC), sensitivity, and specificity, and through calibration, including calibration plots. Risk factors were selected by L1 penalized regression with bootstrapping for both outcomes. Secondary utilization analyses converted time-based billing codes to equivalent minutes to estimate face-to-face provider contact.
RESULTS - We identified 8,879 patients with FM, with 34 known suicide attempts and 96 documented cases of suicidal ideation. External validity was good for both suicidal ideation (AUC 0.80) and attempts (AUC 0.82) with excellent calibration. Risk factors specific to suicidal ideation included polysomatic symptoms such as fatigue (odds ratio [OR] 1.29 [95% confidence interval (95% CI) 1.25-1.32]), dizziness (OR 1.25 [95% CI 1.22-1.28]), and weakness (OR 1.17 [95% CI 1.15-1.19]). Risk factors specific to suicide attempt included obesity (OR 1.18 [95% CI 1.10-1.27]) and drug dependence (OR 1.15 [95% CI 1.12-1.18]). Per utilization analyses, those patients with FM and no suicidal ideation spent 3.5 times more time in follow-up annually, and those without documented suicide attempts spent more than 40 times more time face-to-face with providers annually.
CONCLUSION - This is the first study to successfully apply machine learning to reliably detect suicidality in patients with FM, identifying novel risk factors for suicidality and highlighting outpatient engagement as a protective factor against suicide.
© 2018, American College of Rheumatology.
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Increased Left Ventricular Mass Index Is Associated With Compromised White Matter Microstructure Among Older Adults.
Moore EE, Liu D, Pechman KR, Terry JG, Nair S, Cambronero FE, Bell SP, Gifford KA, Anderson AW, Hohman TJ, Carr JJ, Jefferson AL
(2018) J Am Heart Assoc 7:
MeSH Terms: Age Factors, Aged, Aged, 80 and over, Aging, Cognition, Cognitive Dysfunction, Cross-Sectional Studies, Diffusion Magnetic Resonance Imaging, Female, Humans, Hypertrophy, Left Ventricular, Leukoencephalopathies, Male, Memory, Middle Aged, Neuropsychological Tests, Predictive Value of Tests, Risk Factors, Ventricular Function, Left, Ventricular Remodeling
Show Abstract · Added September 11, 2018
BACKGROUND - Left ventricular (LV) hypertrophy is associated with cerebrovascular disease and cognitive decline. Increased LV mass index is a subclinical imaging marker that precedes overt LV hypertrophy. This study relates LV mass index to white matter microstructure and cognition among older adults with normal cognition and mild cognitive impairment.
METHODS AND RESULTS - Vanderbilt Memory & Aging Project participants free of clinical stroke, dementia, and heart failure (n=318, 73±7 years, 58% male, 39% mild cognitive impairment) underwent brain magnetic resonance imaging, cardiac magnetic resonance, and neuropsychological assessment. Voxelwise analyses related LV mass index (g/m) to diffusion tensor imaging metrics. Models adjusted for age, sex, education, race/ethnicity, Framingham Stroke Risk Profile, cognitive diagnosis, and apolipoprotein E-ε4 status. Secondary analyses included a LV mass index×diagnosis interaction term with follow-up models stratified by diagnosis. With identical covariates, linear regression models related LV mass index to neuropsychological performances. Increased LV mass index related to altered white matter microstructure (<0.05). In models stratified by diagnosis, associations between LV mass index and diffusion tensor imaging were present among mild cognitive impairment participants only (<0.05). LV mass index was related only to worse visuospatial memory performance (β=-0.003, =0.036), an observation that would not withstand correction for multiple testing.
CONCLUSIONS - In the absence of prevalent heart failure and clinical stroke, increased LV mass index corresponds to altered white matter microstructure, particularly among older adults with clinical symptoms of prodromal dementia. Findings highlight the potential link between subclinical LV remodeling and cerebral white matter microstructure vulnerability.
© 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.
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20 MeSH Terms
Validation of discharge diagnosis codes to identify serious infections among middle age and older adults.
Wiese AD, Griffin MR, Stein CM, Schaffner W, Greevy RA, Mitchel EF, Grijalva CG
(2018) BMJ Open 8: e020857
MeSH Terms: Aged, Aged, 80 and over, Algorithms, Clinical Coding, Female, Humans, Infections, International Classification of Diseases, Male, Medicaid, Medical Records, Middle Aged, Patient Discharge, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Tennessee, United States
Show Abstract · Added July 27, 2018
OBJECTIVES - Hospitalisations for serious infections are common among middle age and older adults and frequently used as study outcomes. Yet, few studies have evaluated the performance of diagnosis codes to identify serious infections in this population. We sought to determine the positive predictive value (PPV) of diagnosis codes for identifying hospitalisations due to serious infections among middle age and older adults.
SETTING AND PARTICIPANTS - We identified hospitalisations for possible infection among adults >=50 years enrolled in the Tennessee Medicaid healthcare programme (2008-2012) using International Classifications of Diseases, Ninth Revision diagnosis codes for pneumonia, meningitis/encephalitis, bacteraemia/sepsis, cellulitis/soft-tissue infections, endocarditis, pyelonephritis and septic arthritis/osteomyelitis.
DESIGN - Medical records were systematically obtained from hospitals randomly selected from a stratified sampling framework based on geographical region and hospital discharge volume.
MEASURES - Two trained clinical reviewers used a standardised extraction form to abstract information from medical records. Predefined algorithms served as reference to adjudicate confirmed infection-specific hospitalisations. We calculated the PPV of diagnosis codes using confirmed hospitalisations as reference. Sensitivity analyses determined the robustness of the PPV to definitions that required radiological or microbiological confirmation. We also determined inter-rater reliability between reviewers.
RESULTS - The PPV of diagnosis codes for hospitalisations for infection (n=716) was 90.2% (95% CI 87.8% to 92.2%). The PPV was highest for pneumonia (96.5% (95% CI 93.9% to 98.0%)) and cellulitis (91.1% (95% CI 84.7% to 94.9%)), and lowest for meningitis/encephalitis (50.0% (95% CI 23.7% to 76.3%)). The adjudication reliability was excellent (92.7% agreement; first agreement coefficient: 0.91). The overall PPV was lower when requiring microbiological confirmation (45%) and when requiring radiological confirmation for pneumonia (79%).
CONCLUSIONS - Discharge diagnosis codes have a high PPV for identifying hospitalisations for common, serious infections among middle age and older adults. PPV estimates for rare infections were imprecise.
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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18 MeSH Terms