Sharpephelps3717

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals' history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer's disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions "definite AD" with diagnostic codes and dementia medication (n = 614) and "probable AD" with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings. © The Author(s) 2020.We present a genome assembly from an individual male Lutra lutra (the Eurasian river otter; Vertebrata; Mammalia; Eutheria; Carnivora; Mustelidae). The genome sequence is 2.44 gigabases in span. The majority of the assembly is scaffolded into 20 chromosomal pseudomolecules, with both X and Y sex chromosomes assembled. Copyright © 2020 Mead D et al.Background X chromosome inactivation in mammals is regulated by the non-coding (nc) RNA, Xist, which represses the chromosome from which it is transcribed.  High levels of the N6-methyladenosine (m6A) RNA modification occur within Xist exon I, close to the 5' end of the transcript, and also further 3', in Xist exon VII. The m6A modification is catalysed by the METTL3/14 complex that is directed to specific targets, including Xist, by the RNA binding protein RBM15/15B. m6A modification of Xist RNA has been reported to be important for Xist-mediated gene silencing.  Methods We use CRISPR/Cas9 mediated mutagenesis to delete sequences around the 5' m6A region in interspecific XX mouse embryonic stem cells (mESCs).  Following induction of Xist RNA expression, we assay chromosome silencing using allelic RNA-seq and Xist m6A distribution using m6A-seq. Additionally, we use Xist RNA FISH to analyse the effect of deleting the 5' m6A region on the function of the endogenous Xist promoter. We purify epitope tagged RBM15 from mESCs, and then apply MS/MS analysis to define the RBM15 interactome. Results We show that a deletion encompassing the entire Xist 5' m6A region results in a modest reduction in Xist-mediated silencing, and that the 5' m6A region overlaps essential DNA elements required for activation of the endogenous Xist promoter. Deletion of the Xist A-repeat, to which RBM15 binds, entirely abolishes deposition of m6A in the Xist 5' m6A region without affecting the modification in exon VII. We show that in mESCs, RBM15 interacts with the m6A complex, the SETD1B histone modifying complex, and several proteins linked to RNA metabolism. Conclusions Our findings support that RBM15 binding to the Xist A-repeat recruits the m6A complex to the 5' Xist m6A region and that this region plays a role in Xist-mediated chromosome silencing. Copyright © 2020 Coker H et al.Psychotic disorders are severe, debilitating, and even fatal. ARRY-382 mw The development of targeted and effective interventions for psychosis depends upon on clear understanding of the timing and nature of disease progression to target processes amenable to intervention. Strong evidence suggests early and ongoing neuroprogressive changes, but timing and inflection points remain unclear and likely differ across cognitive, clinical, and brain measures. Additionally, granular evidence across modalities is particularly sparse in the "bridging years" between first episode and established illness-years that may be especially critical for improving outcomes and during which interventions may be maximally effective. Our objective is the systematic, multimodal characterization of neuroprogression through the early course of illness in a cross-diagnostic sample of patients with psychosis. We aim to (1) interrogate neurocognition, structural brain measures, and network connectivity at multiple assessments over the first eight years of illness to map neuroprogressive trajectories, and (2) examine trajectories as predictors of clinical and functional outcomes. We will recruit 192 patients with psychosis and 36 healthy controls. Assessments will occur at baseline and 8- and 16-month follow ups using clinical, cognitive, and imaging measures. We will employ an accelerated longitudinal design (ALD), which permits ascertainment of data across a longer timeframe and at more frequent intervals than would be possible in a single cohort longitudinal study. Results from this study are expected to hasten identification of actionable treatment targets that are closely associated with clinical outcomes, and identify subgroups who share common neuroprogressive trajectories toward the development of individualized treatments.In the following grant report, we describe initial and planned work supported by our National Institute of Mental Health R01-funded, Research Domain Criteria (RDoc) informed project, "Dimensional Brain Behavior Predictors of CBT Outcomes in Pediatric Anxiety". This project examines response to cognitive behavioral therapy (CBT) in a large sample of anxiety-affected and low-anxious youth ages 7 to 18 years using multiple levels of analysis, including brain imaging, behavioral performance, and clinical measures. The primary goal of the project is to understand how brain-behavioral markers of anxiety-relevant constructs, namely acute threat, cognitive control, and their interaction, associate with CBT response in youth with clinically significant anxiety. A secondary goal is to determine whether child age influences how these markers predict, and/or change, across varying degrees of CBT response. Now in its fourth year, data from this project has informed the examination of (1) baseline (i.e., pre-CBT) anxiety severity as a function of brain-behavioral measures of cognitive control, and (2) clinical characteristics of youth and parents that associate with anxiety severity and/or predict response to CBT.