Amer-Denis Akkad

TL;DR: Using large-scale single nuclei RNA sequencing, the transcriptional and cellular diversity in the normal human heart was defined and the identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

TL;DR: This research presents a novel probabilistic approach that allows us to assess the importance of knowing the carrier and removal status of canine coronavirus as a source of infection for other animals.

TL;DR: The results illustrate the potential for rapidly defining novel quantitative traits derived from a deep learning model, an approach that can be more broadly applied to biomedical imaging data, and the identification of asymptomatic individuals at risk for aneurysm or dissection.

TL;DR: In this article, a deep learning model was trained to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank.

TL;DR: Assessment of ACE2 expression in normal and diseased human myocardial samples profiled by bulk and single nucleus RNA-seq aims to shed light on whether the use of angiotensin converting enzyme inhibitors (ACEi) or ang Elliotensin receptor blockers (ARB) impacts infection and disease.