Atherosclerosis

In vivo quantification of arterial active mechanics using deep learning-assisted pressure-area analysis.

Active arterial mechanics, governed by vascular smooth muscle contraction, are critical to physiological regulation, cardiovascular disease progression, and clinical diagnosis. Although various in vivo methods have been developed to assess arterial stiffness, most cannot distinguish the contribution of smooth muscle tone; therefore, quantitative characterization of arterial activity remains challe...

Shifting the paradigm in intracranial aneurysm detection with deep learning: A diagnostic accuracy meta-analysis and meta-regression.

Artificial intelligence-based coronary computed tomography angiography quantification of atherosclerosis burden: comparison with intravascular ultrasound in the INVICTUS Registry.

OBJECTIVES: Automated artificial intelligence (AI)-based assessment of atherosclerosis burden applied to coronary computed tomography angiography (CCT...

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances in pharmacotherapy. Emerging evidence reveals a p...

Impact of Deep Learning-Based Time-of-Flight PET Images of Small Tumors Using a Human Anatomic Phantom.

Time-of-flight (ToF) in PET improves image quality by enhancing the signal-to-noise ratio, and recent deep learning (DL)-based ToF (DL-ToF) methods fu...

Lymphatics-on-a-chip microphysiological system: engineering lymphatic structure and function in vitro.

The lymphatic system-integral to fluid balance, immune surveillance, and lipid absorption-is frequently overlooked despite its vital roles. Traditiona...

Machine Learning-Enhanced TCAB Score for Predicting Postoperative Ischemic Stroke After CABG.

BACKGROUND: Postoperative acute ischemic stroke remains a critical complication of coronary artery bypass grafting. This study aimed to develop a nove...

Commentary: Training Artificial Intelligence to Recognize Surgical Steps in Microscope-Based Videos of Middle Cerebral Artery Aneurysm Clipping.

Oligodendrocyte-specific Fus depletion preserves CA1 single-unit fidelity and stabilizes network dynamics during chronic recording.

OBJECTIVE: Loss of oligodendrocytes (OLs) and myelin impairs neuronal firing and network stability, whereas enhancing oligodendrogenesis with clemasti...

Comparison of Atheroma Burden Changes Using Conventional vs. End-Diastolic Intravascular Ultrasound Segmentation　- Post Hoc Analysis of the PACMAN-AMI Study.

BACKGROUND: This study compared changes in percentage atheroma volume (PAV) using an end-diastolic (ED) intravascular ultrasound (IVUS) segmentation a...

Integration of multi-omics and machine learning to reveal the anti-osteoporosis effects of Hederagenin by inhibiting lipid peroxidation via the GPX4/p-STAT1/PTGS2 axis.

Osteoporosis (OP) is a multifactorial disease, leading to abnormal bone remodeling, requiring targeted therapeutic interventions. This study investiga...

AI fragmentation-based optimization of Sorafenib derivatives targeting VEGFR2 for angiogenesis-related pathologies: a structure-based in-silico study.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a central regulator of angiogenesis and endothelial signaling and represents a validated the...

Vessel-specific perivascular fat attenuation index derived from AI-CCTA and its association with impaired coronary flow reserve in patients with INOCA.

OBJECTIVES: To investigate the association between artificial intelligence (AI)-derived coronary computed tomography angiography (CCTA) features and i...

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 anal...

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, cerebr...

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...