Human monocyte transcriptional profiling identifies IL-18 receptor accessory protein and lactoferrin as novel immune targets in hypertension.

Background And Purpose: Monocytes play a critical role in hypertension. The purpose of our study was to use an unbiased approach to determine whether hypertensive individuals on conventional therapy exhibit an altered monocyte gene expression profile and to perform validation studies of selected genes to identify novel therapeutic targets for hypertension.

Experimental Approach: Next generation RNA sequencing identified differentially expressed genes in a small discovery cohort of normotensive and hypertensive individuals.

CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment.

Background: As targeted cancer therapies and molecular profiling become widespread, the era of "precision oncology" is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.

Exploring drug-target interaction networks of illicit drugs.

Background: Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care.

Reducing patient re-identification risk for laboratory results within research datasets.

Objective: To try to lower patient re-identification risks for biomedical research databases containing laboratory test results while also minimizing changes in clinical data interpretation.

Materials And Methods: In our threat model, an attacker obtains 5-7 laboratory results from one patient and uses them as a search key to discover the corresponding record in a de-identified biomedical research database. To test our models, the existing Vanderbilt TIME database of 8.