Automated mitral inflow Doppler peak velocity measurement using deep learning.

Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information.

Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.

Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or  ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events.

Genetic loci influencing kidney function and chronic kidney disease.

Using genome-wide association, we identify common variants at 2p12-p13, 6q26, 17q23 and 19q13 associated with serum creatinine, a marker of kidney function (P = 10(-10) to 10(-15)). Of these, rs10206899 (near NAT8, 2p12-p13) and rs4805834 (near SLC7A9, 19q13) were also associated with chronic kidney disease (P = 5.0 x 10(-5) and P = 3.

Genetic variation in SCN10A influences cardiac conduction.

To identify genetic factors influencing cardiac conduction, we carried out a genome-wide association study of electrocardiographic time intervals in 6,543 Indian Asians. We identified association of a nonsynonymous SNP, rs6795970, in SCN10A (P = 2.8 x 10(-15)) with PR interval, a marker of cardiac atrioventricular conduction.