Atherosclerosis

Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.

Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into the cerebral vasculature and cause strokes. The likelihood of a plaque rupturing is thought to be associated with its composition (i.e. lipid, calcium, hemorrhage and inflammatory cell content) and the mechanical properties of the plaque. Automating an...

Machine Learning-Based Pathomics Model Predicts Angiopoietin-2 Expression and Prognosis in Hepatocellular Carcinoma.

Angiopoietin-2 (ANGPT2) shows promise as prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 expression and prognostic potential using histopathology images viewed with naked eye is challenging. Herein, machine learning was employed to develop a pathomics model for analyzing histopathology images to predict ANGPT2 status. HCC cases obtained from Th...

Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAI...

Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.

The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach fo...

Breaking Through Physiological Barriers: Nanorobotic Strategies for Active Drug Delivery.

Self-propelled micro/nanomotors (MNMs) represent a groundbreaking advancement in precision drug delivery, offering potential solutions to persistent c...

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.

: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the ri...

Association between serum endocan levels and organ failure in hospitalized patients with cirrhosis.

BACKGROUND & AIMS: Acute-on-chronic liver failure is a syndrome characterized by organ failure and high short-term mortality. The lack of reliable bio...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This stu...

Volumetric Artificial Intelligence Analysis of Prerupture and Postrupture Cerebral Aneurysms: Assessment of Morphologic Change.

BACKGROUND: Cerebral aneurysm rupture is a major cause of potential years of life lost. Research on rupture risk has often compared unruptured and rup...

Predicting the infecting dengue serotype from antibody titre data using machine learning.

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant chal...

Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score.

BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead ...

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

PURPOSE: Embolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our goal was to determine whether ESUS could be recla...

sJAM-C as a Potential Biomarker for Coronary Artery Stenosis: Insights from a Clinical Study in Coronary Heart Disease Patients.

PURPOSE: Coronary artery stenosis caused by atherogenesis is a major pathological link in coronary heart disease (CHD), which is a leading cause of gl...

Biofilm architecture and dynamics of the oral ecosystem.

The oral cavity, being a nutritionally enriched environment, has been proven to be an ideal habitat for biofilm development. Various microenvironments...

Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: a proof-of-concept study.

OBJECTIVES: This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries ...

Actuation-Mediated Compression of a Mechanoresponsive Hydrogel by Soft Robotics to Control Release of Therapeutic Proteins.

Therapeutic proteins, the fastest growing class of pharmaceuticals, are subject to rapid proteolytic degradation in vivo, rendering them inactive. Sop...

Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.

BACKGROUND AND AIMS: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electroni...

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.

BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detecti...

Cytotoxicity, Antiadipogenic, Low-Density Lipoprotein Oxidation Inhibitory Activities, and Acute Toxicity Study of Hydroethanolic Leaf and Bark Extracts.

Obesity is increasingly taking an important stage as a cause of death worldwide, and interventions with a good cost-effectiveness ratio are needed. ...

Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of impro...