Objectives: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. Background: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. Methods: Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA),...

Untargeted high-resolution plasma metabolomic profiling among patients with coronary artery disease. Patients recruited from the Emory Cardiovascular Biobank into independent discovery and validation cohorts.

Objective: To investigate serum neurofilament light protein (sNfL) levels in patients with spinocerebellar ataxia type 3 (SCA3) and to determine whether they are associated with disease severity. Methods: This cross-sectional study enrolled 185 healthy controls and 235 ATXN3 mutation carriers (17 asymptomatic stage, 20 preclinical stage, and 198 ataxic stage). We measured sNfL levels with the single molecule array (Simoa) platform. Clinical disease severity was assessed using the scale of assessment and rating of ataxia...