Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

Brain imaging is a high-content modality that offers dense insights into the structure and pathology of the brain. Existing genetic association studies of brain imaging, typically focusing on a number of individual image-derived phenotypes (IDPs), have successfully identified many genetic loci. Previously, we have created a 128-dimensional Unsupervised Deep learning derived Imaging Phenotypes (UDIPs), and identified multiple loci from single-phenotype genome-wide association studies (GWAS) for individual UDIP dimensions, using data from the UK Biobank (UKB).

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers.

Background: Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI).

Montelukast as a repurposable additive drug for standard-efficacy multiple sclerosis treatment: Emulating clinical trials with retrospective administrative health claims data.

Background: Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach.

Objective: The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS).

Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns.

Background: Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

Results: We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning.

Expanded T lymphocytes in the cerebrospinal fluid of multiple sclerosis patients are specific for Epstein-Barr-virus-infected B cells.

Epstein-Barr virus (EBV) infection has long been associated with multiple sclerosis (MS), but the role of EBV in the pathogenesis of MS is not clear. Our hypothesis is that a major fraction of the expanded clones of T lymphocytes in the cerebrospinal fluid (CSF) are specific for autologous EBV-infected B cells. We obtained blood and CSF samples from eight relapsing-remitting patients in the process of diagnosis.

Generalizable pipeline for constructing HIV risk prediction models across electronic health record systems.

Objective: The HIV epidemic remains a significant public health issue in the United States. HIV risk prediction models could be beneficial for reducing HIV transmission by helping clinicians identify patients at high risk for infection and refer them for testing. This would facilitate initiation on treatment for those unaware of their status and pre-exposure prophylaxis for those uninfected but at high risk.

Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features.

A Method for Bridging Population-Specific Genotypes to Detect Gene Modules Associated with Alzheimer's Disease.

Background: Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success.

Tissue-Specific Variations in Transcription Factors Elucidate Complex Immune System Regulation.

Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed "transcriptome imputation" are used to estimate the genetic component of gene expression, but these models typically consider only the regions of the gene.

Time dependent analysis of rat microglial surface markers in traumatic brain injury reveals dynamics of distinct cell subpopulations.

Traumatic brain injury (TBI) results in a cascade of cellular responses, which produce neuroinflammation, partly due to the activation of microglia. Accurate identification of microglial populations is key to understanding therapeutic approaches that modify microglial responses to TBI and improve long-term outcome measures. Notably, previous studies often utilized an outdated convention to describe microglial phenotypes.

Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder.

Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program.

Brain Antigens Stimulate Proliferation of T Lymphocytes With a Pathogenic Phenotype in Multiple Sclerosis Patients.

A method to stimulate T lymphocytes with a broad range of brain antigens would facilitate identification of the autoantigens for multiple sclerosis and enable definition of the pathogenic mechanisms important for multiple sclerosis. In a previous work, we found that the obvious approach of culturing leukocytes with homogenized brain tissue does not work because the brain homogenate suppresses antigen-specific lymphocyte proliferation. We now report a method that substantially reduces the suppressive activity.

Sociodemographic and Clinical Characteristics Associated with Improvements in Quality of Life for Participants with Opioid Use Disorder.

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant's reported quality of life (QoL) at the beginning of the program and at successive intervals.

Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program.

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

Unlabelled: Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is impractical due to the vast space of therapeutic agents and cell lines. In this study, we propose a biologically-motivated deep learning approach to identify pathway-level features from drug and cell lines' molecular data for predicting drug synergy and quantifying the interactions in synergistic drug pairs.

A Phenotyping Algorithm to Identify People With HIV in Electronic Health Record Data (HIV-Phen): Development and Evaluation Study.

Background: Identification of people with HIV from electronic health record (EHR) data is an essential first step in the study of important HIV outcomes, such as risk assessment. This task has been historically performed via manual chart review, but the increased availability of large clinical data sets has led to the emergence of phenotyping algorithms to automate this process. Existing algorithms for identifying people with HIV rely on a combination of International Classification of Disease codes and laboratory tests or closely mimic clinical testing guidelines for HIV diagnosis.

Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates.

Objective: Study the impact of local policies on near-future hospitalization and mortality rates.

Materials And Methods: We introduce a novel risk-stratified SIR-HCD model that introduces new variables to model the dynamics of low-contact (e.g.

Explainable drug sensitivity prediction through cancer pathway enrichment.

Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset.

Comparing diagnostic accuracy of current practice guidelines in predicting choledocholithiasis: outcomes from a large healthcare system comprising both academic and community settings.

Background And Aims: The American Society for Gastrointestinal Endoscopy (ASGE) 2010 guidelines for suspected choledocholithiasis were recently updated by proposing more specific criteria for selection of high-risk patients to undergo direct ERCP while advocating the use of additional imaging studies for intermediate- and low-risk individuals. We aim to compare the performance and diagnostic accuracy of 2019 versus 2010 ASGE criteria for suspected choledocholithiasis.

Methods: We performed a retrospective chart review of a prospectively maintained database (2013-2019) of over 10,000 ERCPs performed by 70 gastroenterologists in our 14-hospital system.

SCOR: A secure international informatics infrastructure to investigate COVID-19.

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions.

High-resolution and differential analysis of rat microglial markers in traumatic brain injury: conventional flow cytometric and bioinformatics analysis.

Traumatic brain injury (TBI) results in a cascade of cellular responses, which produce neuroinflammation, partly due to microglial activation. Transforming from surveying to primed phenotypes, microglia undergo considerable molecular changes. However, specific microglial profiles in rat remain elusive due to tedious methodology and limited availability of reagents.

Human brain transcriptome analysis finds region- and subject-specific expression signatures of GABAR subunits.

Altered expression of GABA receptors (GABARs) has been implicated in neurological and psychiatric disorders, but limited information about region-specific GABAR subunit expression in healthy human brains, heteromeric assembly of major isoforms, and their collective organization across healthy individuals, are major roadblocks to understanding their role in non-physiological states. Here, by using microarray and RNA-Seq datasets-from single cell nuclei to global brain expression-from the Allen Institute, we find that transcriptional expression of GABAR subunits is anatomically organized according to their neurodevelopmental origin. The data show a combination of complementary and mutually-exclusive expression patterns that delineate major isoforms, and which is highly stereotypical across brains from control donors.

Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study.

Objective: Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records.

Study Design And Setting: Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders.

The New HIT: Human Health Information Technology.

Humanism in medicine is defined as health care providers' attitudes and actions that demonstrate respect for patients' values and concerns in relation to their social, psychological and spiritual life domains. Specifically, humanistic clinical medicine involves showing respect for the patient, building a personal connection, and eliciting and addressing a patient's emotional response to illness. Health information technology (IT) often interferes with humanistic clinical practice, potentially disabling these core aspects of the therapeutic patient-physician relationship.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

Background: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.

Methods: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions.