Cancer is a malignant tumor that threatens the health of human beings, and has become the leading cause of death in urban and rural residents in China. The glycocalyx is a layer of multifunctional glycans that covers the surfaces of a variety of cells, including vascular endothelial cells, smooth muscle cells, stem cells, epithelial, osteocytes, as well as cancer cells. The glycosylation and syndecan of cancer cell glycocalyx are unique. However, heparan sulfate (HS), hyaluronic acid (HA), and syndecan are all closely associated with the processes of cancer progression, including cell migration and metastasis, tumor cell adhesion, tumorigenesis, and tumor growth. The possible underlying mechanisms may be the interruption of its barrier function, its radical role in growth factor storage, signaling, and mechanotransduction. In the later sections, we discuss glycocalyx targeting therapeutic approaches reported in animal and clinical experiments. The study concludes that cancer cells’ glycocalyx and its role in cancer progression are beginning to be known by more groups, and future studies should pay more attention to its mechanotransduction of interstitial flow-induced shear stress, seeking promising therapeutic targets with less toxicity but more specificity.

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML−based models to directly predict CVD based on risk factors or medical imaging findings and the ML−based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data−driven models and physics−driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.