Atherosclerosis

Artificial Intelligence in Coronary Computed Tomography: Current Applications, Future Potentials, and Real-world Challenges.

Artificial intelligence (AI) is rapidly transforming cardiac computed tomography (CT) imaging by enhancing image acquisition, reconstruction, and analysis to improve diagnostic accuracy and overall clinical workflow. Deep learning reconstruction (DLR) algorithms optimize image quality while reducing radiation and contrast media doses. AI-driven tools for coronary artery segmentation and CAD-RADS c...

Integrated approaches to preventing macrovascular complications in type 2 diabetes mellitus: Advances in secondary prevention.

BACKGROUND: Type 2 diabetes mellitus (T2DM) substantially increases the risk of macrovascular complications, including coronary artery disease, cerebrovascular disease and peripheral arterial disease, which collectively represent the leading causes of morbidity and mortality among affected individuals. Evidence from clinical trials has demonstrated that intensive glycemic control alone provides li...

Use of Bioinformatics and Machine Learning to Identify Circadian Rhythm Disruption- and Endoplasmic Reticulum Stress-Associated Biomarkers in Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disorder closely linked to circadian rhythm disruption (CRD) and endoplasmic reticu...

Puerarin mitigates tumor necrosis factor-nuclear factor-κB axis-driven macrophage inflammation and atherogenesis: Integrative network pharmacology and experimental validation.

BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease that constitutes the primary pathological basis of cardiovascular disorders. Althou...

Predicting non-alcoholic fatty liver disease (NAFLD) using machine learning algorithms: Evidence from a large-scale community cohort in Taiwan.

Closely associated with metabolic disorders, non-alcoholic fatty liver disease (NAFLD) substantially increases the risk of hepatocellular carcinoma. T...

A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis.

AIMS: Current models predicting cardiovascular biological age rely on radiomics or complex large feature sets including T1 and strain. We developed an...

Outcomes with revascularization vs. medical therapy according to plaque burden from coronary computed tomography angiography.

AIMS: We aimed to investigate whether plaque burden from coronary computed tomography angiography (CCTA) could be used to identify patients potentiall...

Fcer1g and St3gal1: Macrophage-associated angiogenesis biomarkers and therapeutic targets in sepsis-induced acute lung injury.

BACKGROUND: Acute lung injury (ALI) involves the release of growth factors and inflammatory mediators from damaged pulmonary tissues, fostering endoth...

Enhancing the efficacy of Salvia miltiorrhiza and Ligusticum chuanxiong in the treatment of coronary heart disease: The value of poorly soluble components and nanocrystal self-stabilized solid emulsions.

BACKGROUND: The Salvia miltiorrhiza-Ligusticum chuanxiong herb pair is a classic combination in traditional Chinese medicine (TCM) formulation for cor...

Designing a machine learning model for predicting cardiovascular events using the triglyceride-glucose index: a cohort study.

INTRODUCTION: Cardiovascular diseases (CVD) are the leading cause of death in developing countries, imposing a significant burden on society. Early de...

Machine learning analysis of s-EASIX for predicting 30-day mortality in sepsis patients from MIMIC-IV.

Endothelial dysfunction is an important risk factor for the progression of sepsis. The simplified endothelial activation and stress index (s-EASIX) se...

Mothers against decapentaplegic homolog 4 as a proteomic hub in vascular remodeling and residual cardiovascular risk.

Translational cardiovascular medicine increasingly relies on systems-level approaches to uncover therapeutic targets that bridge molecular mechanisms ...

Vascularisation in 3D bioprinted models: emerging solutions engineering functional tissues and tumour models.

Three-dimensional (3D) bioprinting enables the fabrication of tissues with controlled architecture and cell composition, yet the formation of mature a...

Integrative network toxicology and molecular docking preliminarily explore the potential role of polystyrene microplastics in childhood obesity.

Childhood obesity is a severe global epidemic, and emerging evidence suggests environmental pollutants like polystyrene microplastics (PS-MPs) may dis...

Predicting Plaque Vulnerability Using Machine Learning-Enabled Nanoagents Sensitized Molecular High-Resolution Magnetic Resonance Imaging Data.

Molecular imaging based on paramagnetic nanoagents has emerged as an intriguing strategy to sensitize the local magnetic properties of pivotal patholo...

Environmental PET-microplastic exposure and risk of non-alcoholic fatty liver disease: An integrated computational toxicology and multi-omics study.

The escalating severity of global microplastic pollution has triggered significant public health concerns. Polyethylene terephthalate (PET), a ubiquit...

Automatic segmentation of coronary plaques in coronary CT angiography using neural networks.

Rapid and accurate detection of coronary plaques on CCTA is critical for timely CAD diagnosis but is limited by reader workload and interobserver vari...

Correlation between the high-density lipoprotein-to-low-density lipoprotein ratio and contrast-induced acute kidney injury: an analysis based on the in-hospital AKI electronic monitoring system.

BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a common and serious complication that can occur following angiography procedures. Consid...

A machine learning model based on routine blood-derived indices for early arterial stiffness prediction in the community.

BACKGROUND: Increased arterial stiffness is a high-risk factor for cardiovascular diseases, making its early identification crucial. Based on data fro...

Environmental toxicant exposure and diabetes risk: An exposome-wide association study integrating mixture effects and molecular mechanisms.

Environmental toxicant exposure has emerged as a potential contributor to diabetes, yet systematic investigations integrating multiple chemicals and b...