Atherosclerosis

Identification and validation of cholesterol homeostasis and NK cell-associated predictive and therapeutic models for Coronary atherosclerotic heart disease patients with ST-elevated myocardial infarction: Insights from artificial intelligence and multi-omics.

OBJECTIVE: Dysregulation of Cholesterol homeostasis(CH) and NK cells proportion can increase risk of ST-Elevated Myocardial Infarction(STEMI) for Coronary atherosclerotic heart disease(CAD) patients. Hence, it is necessary for the investigation of CH and MC in pathogenesis of STEMI for CAD patients, providing additional choice for the prevention of STEMI for CAD patients. METHODS: By combining 5 p...

Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.

Artificial intelligence shows promise for improving care for peripheral artery disease through earlier detection, improved risk stratification, more tailored treatment planning, and more efficient care delivery. This American Heart Association science advisory reviews the current and emerging applications of artificial intelligence across the peripheral artery disease care continuum, including pop...

Development and validation of a novel deep learning coronary artery plaque quantification model.

BACKGROUND: Coronary Computed Tomography Angiography (CCTA) is an established tool for assessing coronary artery disease. CCTA determined total corona...

Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis.

BACKGROUND: Circadian syndrome (CircS) augments the conventional metabolic syndrome construct by adding disturbed sleep and depressive features. Wheth...

Predicting anemia treatment outcomes in maintenance hemodialysis patients using multiple machine learning models.

BACKGROUND: Based on machine learning prediction models, we explored the anemia treatment attainment of patients on maintenance hemodialysis (MHD) and...

Cellular Senescence as a Systems-Level Driver of Cardiovascular Ageing.

Cellular senescence is increasingly recognized as a fundamental driver of cardiovascular ageing; however, its molecular heterogeneity, cell-type speci...

Machine learning for risk stratification of postoperative corneal edema in East Asian cataract patients: a model for precision ophthalmology.

PURPOSE: To develop and validate machine learning (ML) models for predicting early postoperative corneal edema (CE) after phacoemulsification in patie...

Real-world insights into coronary CTA prognostication: value of semiquantitative scores.

PURPOSE: Several semiquantitative coronary computed tomography angiography (CCTA) scores including different parameters describing stenosis degree, pl...

Molecular mechanism leading to human coronary atherosclerosis assessed by proteomic analysis and RNA sequences.

BACKGROUND AND AIMS: Atherosclerosis results from cellular and extracellular changes in the arterial wall, preceded by molecular shifts that initiate ...

Dissecting the pathobiology of suspected sepsis through a comparative analysis of endothelial inflammatory and clinical prediction models.

Sepsis remains a formidable challenge in critical care, and is characterized by profound circulatory and cellular abnormalities driven by both systemi...

Development and validation of a digital biomarker for peripheral artery disease.

Peripheral artery disease (PAD) is a common manifestation of atherosclerotic cardiovascular disease (ASCVD) that is underdiagnosed in clinical practic...

Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. Thi...

A TyG-UHR-based machine learning model for screening lean MAFLD: development and external validation.

BACKGROUND: Lean metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly recognized but often goes unnoticed during health checku...

Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning.

BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concern...

Mechanisms driving thoracic aortic aneurysm instability.

Thoracic aortic aneurysms arise from a combination of biological and mechanical factors. Current clinical guidelines use size and rate of expansion to...

Single-cell profiling identifies a pro-tumoral VCAN positive macrophage subset and defines a prognostic signature in glioblastoma.

Glioblastoma (GBM), the most aggressive primary brain tumor, develops within a tumor microenvironment (TME) dominated by tumor-associated macrophages ...

SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome.

BACKGROUND: While the single-point insulin sensitivity estimator (SPISE) shows promise as an insulin resistance biomarker, its association with cardio...

From Lumen to Myocardium With Artificial Intelligence: A Clinician's Guide to Comprehensive Cardiac CT.

Coronary artery disease assessment has long focused on stenosis severity, yet luminal narrowing alone fails to capture ischemic burden, plaque vulnera...

Development and external validation of an interpretable machine-learning model for HFpEF comorbidity risk in COPD patients.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) and Heart Failure with preserved Ejection Fraction (HFpEF) frequently coexist, leading to inc...

Spermidine suppresses liver fibrosis by remodeling the communication signal between liver sinusoidal endothelial cells and hepatic stellate cells.

Hepatic fibrosis is a pivotal stage in which chronic liver disease progresses from reversible injury to decompensation. Liver sinusoidal endothelial c...