Atherosclerosis

Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.

The increasing demand for natural medicine has increased the significance of Silybum marianum as a valuable medicinal plant. It is used to restore liver cells; reduce blood cholesterol; prevent prostate, skin, and breast cancer; and protect cervical cells and kidneys. To identify ecological factors affecting the distribution and amount of silymarin in S. marianum three machine learning algorithms ...

Exploration and validation of the prognostic value of mitophagy and mitochondrial dynamics-related genes in cervical cancer.

The mechanisms underlying mitophagy and mitochondrial dynamics (MD) in cervical cancer (CC), a disease with a high mortality rate, remain poorly understood. This study aimed to assess the prognostic significance of these processes in CC. Mendelian randomization (MR) and 101 machine learning models were employed to identify mitophagy- and MD-associated prognostic genes in CC. A subsequent risk mode...

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drivers of this damage remain unclear. This study explo...

Automated Laser Modified EndoGraft (ALMEG).

OBJECTIVES: To describe the technical details of a new and innovative method for modifying endografts to treat complex aortic aneurysms, especially in...

Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease.

Moyamoya disease (MMD) is a rare occlusive cerebrovascular disease, and its pathological mechanism remains unclear at present. The abnormal vascular r...

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.

Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Ruptu...

Early transcriptional responses reveal cell type-specific vulnerability and neuroprotective mechanisms in the neonatal ischemic hippocampus.

Neonatal hypoxic-ischemic (H-I) brain injury, a leading cause of neurodevelopmental disabilities, severely affects the metabolically active and neurog...

Novel CAC Dispersion and Density Score to Predict Myocardial Infarction and Cardiovascular Mortality.

BACKGROUND: Coronary artery calcification (CAC) provides robust prediction for major adverse cardiovascular events (MACE), but current techniques disr...

Quantitative CT Imaging in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations....

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mor...

Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors.

Respiratory and cardiovascular diseases represent a significant global health burden, underscoring the need for innovative, accessible, and cost-effec...

From lipids and inflammation to artificial intelligence-driven therapeutics: the 93rd European atherosclerosis society (EAS) congress 2025 in focus.

Deep learning for automated dental plaque index assessment: validation against expert evaluations.

BACKGROUND: The integration of artificial intelligence (AI) into healthcare has led to promising advancements in clinical decision-making and diagnost...

Evaluating artificial intelligence models for rupture risk prediction in unruptured intracranial aneurysms: a focus on vessel geometry and hemodynamic insights.

The estimation of rupture risk in Unruptured Intracranial Aneurysm (UIA) constitutes a major area of clinical interest due to the significant morbidit...

Gene therapy and genome editing for lipoprotein disorders.

Genetic factors play a critical role in the development of lipoprotein disorders, which significantly contribute to atherosclerotic cardiovascular dis...

Predicting long-term kidney graft failure using novel multi-omic blood-based biomarkers and artificial intelligence tools.

Kidney transplantation (KT) remains the preferred treatment for end-stage renal disease. With advancements in immunosuppressive regimens and KT survei...

Identification of pyroptosis related genes and subtypes in atherosclerosis using multiomic and single cell analysis.

Atherosclerosis (AS), the leading cause of cardiovascular diseases, is a chronic inflammatory disorder involving lipid metabolism, immune dysregulatio...

Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics.

Cholesterol metabolism-related genes (CMRGs) have been associated with osteoarthritis (OA), but their specific regulatory mechanisms remain unclear. T...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely det...