U-shape-based network for left ventricular segmentation in echocardiograms with contrastive pretraining.

Cardiovascular diseases, characterized by high morbidity, disability, and mortality rates, are a collective term for disorders affecting the heart's structure or function. In clinical practice, physicians often manually delineate the left ventricular border on echocardiograms to obtain critical physiological parameters such as left ventricular volume and ejection fraction, which are essential for accurate cardiac function assessment. However, most state-of-the-art models focus excessively on pushing the boundaries of segmentation accuracy at the expense of computational complexity, overlooking the substantial demand for high-performance computing resources required for model inference in clinical applications.

Mining channel-regulated peptides from animal venom by integrating sequence semantics and structural information.

Channel-regulated peptides (CRPs) derived from animal venom hold great promise as potential drug candidates for numerous diseases associated with channel proteins. However, discovering and identifying CRPs using traditional bio-experimental methods is a time-consuming and laborious process. While there were a few computational studies on CRPs, they were limited to specific channel proteins, relied heavily on complex feature engineering, and lacked the incorporation of multi-source information.