Atherosclerosis

Carotid Imaging: Current Concepts and Advanced Imaging.

Carotid atherosclerosis is a major cause of ischemic stroke, historically managed according to luminal stenosis severity. However, stenosis alone fails to capture plaque biology, as features such as intraplaque hemorrhage (IPH), lipid-rich necrotic core, fibrous cap rupture, ulceration, and perivascular inflammation strongly influence vulnerability and clinical outcomes. Advances in imaging have s...

Integrative proteomics to illuminate host-parasite interactions in malaria.

Genomic studies have transformed malaria research but provide only indirect proxies for the functional processes that govern infection progression and clinical outcomes. We argue that proteomics should be a core component of malaria systems biology. By quantifying protein abundance, post-translational modifications, interaction networks, localization, and turnover in both host and parasite, proteo...

Quantitative Coronary Atherosclerotic Plaque Burden From CCTA and the Benefit From Lipid-Lowering Medication.

BACKGROUND: We hypothesized that quantification of coronary atherosclerotic plaque burden by artificial intelligence-guided quantitative computed tomo...

Physics-informed graph neural networks for real-time prediction of wall shear stress in stenotic coronary arteries.

Wall shear stress (WSS) is a key hemodynamic parameter associated with atherosclerotic plaque development in coronary arteries. In this study, we deve...

Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation.

Cerebral blood flow is essential for brain function and is governed by cerebral perfusion pressure and physiological mechanisms collectively referred ...

The prognostic significance of cholesterol, high-density lipoprotein and glucose (CHG) index in evaluating all-cause mortality risk in metabolic dysfunction-associated steatotic liver disease (MASLD) individuals: evidence from two cohort studies.

Screening and classification of anti-angiogenic VEGFR2 inhibitors with supervised machine learning, deep learning and molecular docking and molecular dynamics simulation.

Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) is a critical therapeutic target in cancer due to its role in pathological angiogenesis and tum...

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

Generation of virtual abdominal aortic aneurysm shape evolution using a physics-based G&R model and its applications to aneurysm growth prediction.

The computational growth and remodeling (G&R) of aortic aneurysms has been extensively investigated to better understand vascular disease progression ...

CRISPR-Cas9 and next-generation gene editing strategies for therapeutic intervention of neurodegenerative pathways in Alzheimer's disease: a state-of-the-art review.

Alzheimer's disease (AD) is a progressive and multifactorial neurodegenerative disorder and the leading cause of dementia worldwide, characterized by ...

A deep learning approach to assess transendothelial cell trafficking performance.

Transendothelial migration (TEM), is a complex, multistep process impacted by diseases like autoimmune disorders and cancer. Deciphering aspects of th...

An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data.

BACKGROUND: Frailty is a significant health concern associated with diminished physiological reserves and increased healthcare burdens. Currently, eff...

EHR-derived cognitive load is associated with guideline-concordant statin initiation in primary care.

INTRODUCTION: Linking electronic health record (EHR) use to care quality may offer insights into potential interventions improving guideline adherence...

Exploratory prediction model for deep vein thrombosis in intensive care patients after anticoagulation therapy: An observational study.

BACKGROUND: Despite low-molecular-weight heparin (LMWH) prophylaxis, the incidence of deep vein thrombosis (DVT) remains high in intensive care unit (...

Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.

OBJECTIVES: To investigate the association between quantitative retinal vascular parameters and coronary artery disease (CAD) and to evaluate the effi...

[Application of artificial intelligence to pharmacological and interventional treatment].

As in many other fields, artificial intelligence (AI) is transforming daily activities in cardiology. In pharmacological therapy, algorithms have been...

Single-Cell Transcriptomic Analysis of Chemotherapy-Induced Changes in Osteosarcoma With a Pyroptosis-Related Gene-Based Prognostic Model.

Osteosarcoma, the most common primary malignant bone tumour, presents significant treatment challenges due to its complex tumour microenvironment and ...

Identification of potential biomarkers for Polygonum multiflorum-induced liver injury in a clinical cohort: integrating machine learning of metabolomics with transcriptomic profiling.

ETHNOPHARMACOLOGICAL RELEVANCE: Polygonum multiflorum Thunb. (PM), synonymous with Reynoutria multiflora (Thunb.) Moldenke is known as He Shou Wu in t...

Recent advances in corneal specular microscopy image analysis through artificial intelligence.

Although conventional automated analysis of corneal specular microscopy images has historically been limited by reproducibility challenges in the pres...

Machine Learning-driven Prioritisation of Lipoprotein(a) Testing: Model Development and Validation.

AIMS: Elevated lipoprotein(a) [Lp(a)] is a common risk factor for cardiovascular disease (CVD) affecting ∼1.4 billion people globally, with novel trea...