Network Medicine applies network science approaches to investigate disease
pathogenesis. Many different analytical methods have been used to infer relevant molecular …

The complex pathobiology underlying cardiovascular diseases (CVDs) has yet to be
explained. Aberrant epigenetic changes may result from alterations in enzymatic activities …

Heart failure (HF) management is challenging due to high clinical heterogeneity of this
disease which makes patients responding differently to evidence-based standard therapy …

The risk of coronary heart disease (CHD) clinical manifestations and patient management is
estimated according to risk scores accounting multifactorial risk factors, thus failing to cover …

Raman spectroscopy (RS) has been widely used for disease diagnosis, eg, cardiovascular
disease (CVD), owing to its efficiency and component-specific testing capabilities. A series …

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying
histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when …

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for
predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome …

Quantum machine learning (QML) is an emerging field that combines the power of quantum
computing with machine learning (ML) techniques to solve complex problems. In recent …

Object. The purpose of this study was to fully assess the role of statins in the primary
prevention of coronary heart disease (CHD). Methods. We searched six databases …

Circulating biomarkers available in clinical practice do not allow to stratify patients with
coronary heart disease (CHD) prior the onset of a clinically relevant event. We evaluated the …