Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study.

: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. : The study aimed to develop ECG-AI models predicting FCHD risk from ECGs.

Association between deep neural network-derived electrocardiographic-age and incident stroke.

Background: Stroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.

Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.

Low-Density Lipoproteins, High-Density Lipoproteins (HDL), and HDL-Associated Proteins Differentially Modulate Chronic Myelogenous Leukemia Cell Viability.

Cellular lipid metabolism, lipoprotein interactions, and liver X receptor (LXR) activation have been implicated in the pathophysiology and treatment of cancer, although findings vary across cancer models and by lipoprotein profiles. In this study, we investigated the effects of human-derived low-density lipoproteins (LDL), high-density lipoproteins (HDL), and HDL-associated proteins apolipoprotein A1 (apoA1) and serum amyloid A (SAA) on markers of viability, cholesterol flux, and differentiation in K562 cells-a bone marrow-derived, stem-like erythroleukemia cell model of chronic myelogenous leukemia (CML). We further evaluated whether lipoprotein-mediated effects were altered by concomitant LXR activation.

Enabling responsible public genomics.

As scientific understandings of genetics advance, researchers require increasingly rich datasets that combine genomic data from large numbers of individuals with medical and other personal information. Linking individuals' genetic data and personal information precludes anonymity and produces medically significant information--a result not contemplated by the established legal and ethical conventions governing human genomic research. To pursue the next generation of human genomic research and commerce in a responsible fashion, scientists, lawyers, and regulators must address substantial new issues, including researchers' duties with respect to clinically significant data, the challenges to privacy presented by genomic data, the boundary between genomic research and commerce, and the practice of medicine.