An overview of drug-induced sodium channel blockade and changes in cardiac conduction: Implications for drug safety.

The human voltage-gated sodium channel Na1.5 (hNa1.5/SCN5A) plays a critical role in the initiation and propagation of action potentials in cardiac myocytes, and its modulation by various drugs has significant implications for cardiac safety.

Cis-regulatory elements driving motor neuron-selective viral payload expression within the mammalian spinal cord.

Spinal motor neuron (MN) dysfunction is the cause of a number of clinically significant movement disorders. Despite the recent approval of gene therapeutics targeting these MN-related disorders, there are no viral delivery mechanisms that achieve MN-restricted transgene expression. In this study, chromatin accessibility profiling of genetically defined mouse MNs was used to identify candidate cis-regulatory elements (CREs) capable of driving MN-selective gene expression.

Validation of a Handheld 6-Lead Device for QT Interval Monitoring in Resource-Limited Settings.

Importance: Rifampin-resistant tuberculosis treatment regimens require electrocardiographic (ECG) monitoring due to the use of multiple QTc-prolonging agents. Formal 12-lead ECG devices represent a significant burden in resource-constrained clinics worldwide and a potential barrier to treatment scale-up in some settings.

Objective: To evaluate the diagnostic accuracy of a handheld 6-lead ECG device within resource-constrained clinics.

Automated Family Histories Significantly Improve Risk Prediction in an EHR.

We recently demonstrated that electronically constructed family pedigrees (e-pedigrees) have great value in epidemiologic research using electronic health record (EHR) data. Prior to this work, it has been well accepted that family health history is a major predictor for a wide spectrum of diseases, reflecting shared effects of genetics, environment, and lifestyle. With the widespread digitalization of patient data via EHRs, there is an unprecedented opportunity to use machine learning algorithms to better predict disease risk.