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A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

Elucidating the mechanism of sugar import requires a molecular understanding of how transporters couple sugar binding and gating events. Whereas mammalian glucose transporters (GLUTs) are specialists(1), the hexose transporter from the malaria parasite Plasmodium falciparum PfHT1(2,3) has acquired the ability to transport both glucose and fructose sugars as efficiently as the dedicated glucose (GLUT3) and fructose (GLUT5) transporters. Here, to establish the molecular basis of sugar promiscuity in malaria parasites, we determined the crystal structure of PfHT1 in complex with d-glucose at a resolution of 3.6 angstrom. We found that the sugar-binding site in PfHT1 is very similar to those of the distantly related GLUT3 and GLUT5 structures(4,5). Nevertheless, engineered PfHT1 mutations made to match GLUT sugar-binding sites did not shift sugar preferences. The extracellular substrate-gating helix TM7b in PfHT1 was positioned in a fully occluded conformation, providing a unique glimpse into how sugar binding and gating are coupled. We determined that polar contacts between TM7b and TM1 (located about 15 angstrom from d-glucose) are just as critical for transport as the residues that directly coordinate d-glucose, which demonstrates a strong allosteric coupling between sugar binding and gating. We conclude that PfHT1 has achieved substrate promiscuity not by modifying its sugar-binding site, but instead by evolving substrate-gating dynamics.

Crystal structure of the Plasmodium falciparum hexose transporter PfHT1 reveals the molecular basis of its ability to transport multiple types of sugar as efficiently as the dedicated mammalian glucose and fructose transporters.

Class B G-protein-coupled receptors are major targets for the treatment of chronic diseases, including diabetes and obesity(1). Structures of active receptors reveal peptide agonists engage deep within the receptor core, leading to an outward movement of extracellular loop 3 and the tops of transmembrane helices 6 and 7, an inward movement of transmembrane helix 1, reorganization of extracellular loop 2 and outward movement of the intracellular side of transmembrane helix 6, resulting in G-protein interaction and activation(2-6). Here we solved the structure of a non-peptide agonist, TT-OAD2, bound to the glucagon-like peptide-1 (GLP-1) receptor. Our structure identified an unpredicted non-peptide agonist-binding pocket in which reorganization of extracellular loop 3 and transmembrane helices 6 and 7 manifests independently of direct ligand interaction within the deep transmembrane domain pocket. TT-OAD2 exhibits biased agonism, and kinetics of G-protein activation and signalling that are distinct from peptide agonists. Within the structure, TT-OAD2 protrudes beyond the receptor core to interact with the lipid or detergent, providing an explanation for the distinct activation kinetics that may contribute to the clinical efficacy of this compound series. This work alters our understanding of the events that drive the activation of class B receptors.