Atherosclerosis

Volume electron microscopy reveals heterogeneity of the hemostatic response in veins and arteries.

Intravital imaging studies have provided insights into the spatial and temporal variations of platelet activation and thrombin generation that occur during hemostasis, but these studies are generally limited to small vessels due to practical limitations of imaging in thicker tissues. Recent advances in cleared tissue fluorescence imaging as well as volume electron microscopy (vEM) coupled with mac...

Synthetic, Population-Based Virtual Patient Database Using a Digital Twin of the Cardiovascular System.

PURPOSE: The goal is to develop a cardiovascular virtual patient database (VPD) combining physiological and demographic data to provide the foundation for future applications in medical diagnostics, decision-making, credibility testing, and formal uncertainty analysis, and to enable its integration with three-dimensional (3D) hemodynamic models and to train neural networks. METHODS: We generate an...

Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning.

Most patients with ascending thoracic aortic aneurysms (ATAA) remain asymptomatic until they develop fatal complications, including aortic dissection ...

Phenotypic clustering of cardiovascular risk profiles in systemic lupus erythematosus.

ObjectiveThis study aimed to identify distinct cardiovascular risk phenotypes in systemic lupus erythematosus (SLE) using an unsupervised cluster anal...

Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.

BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise inter...

Radiomic features and carotid stenosis in periodontitis a two stage bootstrap and multimodal machine learning study.

Bioinformatics approach to identifying molecular targets of Danlou tablet against atherosclerosis: a machine learning pharmacology study.

This research aims to identify novel molecular targets and generate mechanistic hypotheses for Danlou Tablet (DLT) in the treatment of atherosclerosis...

Artificial intelligence-powered automatic coronary computed tomography angiography plaque quantification: comparison against optical coherence tomography.

AIMS: Coronary computed tomography angiography (CCTA) enables a non-invasive, comprehensive assessment of coronary artery disease, and artificial inte...

Integrative machine learning identifies a TEAD4-driven endothelial program shaping drug sensitivity and microvascular invasion in HCC.

Environmental bisphenols hijack the Hmox1 and Cdh1 to trigger premature ovarian insufficiency: A multi-omics investigation.

BACKGROUND: Bisphenols A (BPA), bisphenols F (BPF), and bisphenols S (BPS) are environmental endocrine disruptors linked to premature ovarian insuffic...

Deep Learning Reconstruction Combined with Contrast-Enhancement Boost Technique in "Quadruple-low" CCTA Protocol: Evaluation of Image Quality and Diagnostic Accuracy.

RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...

Coronary revascularization: a long-term perspective.

This paper will forecast advancements in coronary revascularization by 2040, drawing on historical trends and recent breakthroughs. Having forecasted ...

Redefining Tumor Vascular Permeability through Deep Learning-Guided Microneedle Delivery.

Low-permeability (LP) tumor vasculature constitutes a major barrier to efficient nanomedicine delivery, making quantitative assessment and mechanistic...

Modularly-Assembled Smart Microneedle Platform for Machine Learning-Driven Personalized Health Monitoring.

Given the inherent complexity of metabolic pathways and disease-associated agents, next-generation healthcare necessitates wearable, non-invasive, and...

Cardiovascular Risk Assessment Tools in Chronic Kidney Disease.

Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors such as age, sex, blood pressure, lipids, smoking, an...

Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study.

Introduction Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, cli...

Enhancing readability and understandability of vascular surgery discharge summaries using artificial intelligence.

ObjectivesThis study aimed to assess whether Large Language Models (LLMs), like ChatGPT-4, could simplify discharge summaries for vascular surgery pat...

Effects of Lactiplantibacillus plantarum on moderate dyslipidemia before medication involving gut microbiota and host genetics.

In this 12-week trial, 136 participants with moderately dyslipidemia were randomly assigned to receive Lactiplantibacillus plantarum (LP) or placebo. ...

Unraveling key genes and mitochondrial-related mechanisms of atherosclerosis severity: explorations based on interpretable machine learning.

ANXA2, DBN1, ZNF385D, and IL6ST: Endothelial cell biomarkers linking atherosclerosis progression to immune microenvironment dysregulation.

BACKGROUND: Atherosclerosis (AS) is a complex cardiovascular disorder driven by endothelial cell dysfunction and immune microenvironment dysregulation...