Acute Coronary Syndrome

Multi-Omics based biomarkers in acute coronary syndrome: Towards precision in cardiovascular medicine.

BACKGROUND: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular morbidity and mo...

Artificial intelligence detection of heart failure on coronary computed tomography angiography: external validation in patients with non-ST-segment elevation acute coronary syndrome.

AIMS: Heart failure (HF) in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is associate...

Lipoprotein(a) and the C-reactive protein-triglyceride-glucose index in relation to coronary lesion burden in patients with acute coronary syndrome.

BACKGROUND: Lipoprotein(a) [Lp(a)] reflects inherited atherothrombotic risk, whereas the C-reactive ...

Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.

Emerging evidence indicates that coagulation-related molecular programs are associated with thrombos...

Artificial Intelligence in Echocardiography for Acute Coronary Syndromes: Current Evidence and Integration With Electrocardiography and Biomarkers.

BACKGROUND: Early diagnosis of acute coronary syndromes (ACS) remains challenging because electrical...

Takotsubo syndrome diagnosis: the biomarker-based BioTAK score.

BACKGROUND AND AIMS: Takotsubo syndrome (TTS) closely mimics acute coronary syndrome (ACS). Early di...

Correction to: Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.

AI-Powered, Temperature-Resolved Centrifugal Microfluidics for Rapid 3D Phase-Diagram Generation of Biomolecular Condensates.

Biomolecular phase separation drives the formation of membraneless organelles and is implicated in n...

Development and Validation of the GLORIA-AF Stroke Weighted Risk Score for Patients with Atrial Fibrillation.

BACKGROUND AND AIMS: Accurate stroke-risk stratification is central to anticoagulation decision-maki...

Artificial intelligence-based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis.

Early identification of acute myocardial infarction (AMI) remains challenging, particularly in non-S...

Preserving Expertise in Vitamin K Antagonists in the Era of Direct Oral Anticoagulants: Avoiding Therapeutic Segregation.

The rapid uptake of direct oral anticoagulants (DOACs) has sharply reduced the prescribing of vitami...

An artificial intelligence and numerical simulation-based strategy for multi-objective optimization of hemodynamics in curved vessels.

Understanding and optimizing blood flow behavior in curved vessels is crucial for improving cardiova...

Recent advances and knowledge gaps in anticoagulation strategy with durable left ventricular assist devices.

Driven by technological advancements, donor shortages, and an aging population, utilization of left ...

Predictive, preventive, and personalized medicine in statin-treated patients with NSTE-ACS, HFpEF, and type 2 diabetes: a data-driven model for long-term residual risk assessment.

BACKGROUND: Despite significant advances in guideline-directed medical therapy (GDMT), statin-treate...

Small Intracerebral Hemorrhage: Baseline CT Edema-to-Hematoma Ratio as an Independent Predictor of Early Expansion.

BACKGROUND AND PURPOSE: Small intracerebral hemorrhage (ICH), defined as baseline NCCT hematoma volu...

Patient Stratification for Improving Acute Chest Pain Management and Mitigating Emergency Department Crowding: Machine Learning Model Development and Validation.

BACKGROUND: Acute chest pain (ACP) is one of the most common chief complaints in the emergency depar...

Risk Factors for Pediatric Acute Chest Syndrome Utilizing Machine Learning Techniques.

Acute chest syndrome (ACS) causes significant morbidity and mortality in both adult and pediatric pa...

Decision-making logic for samples of clinically significant EDTA-dependent pseudothrombocytopenia with machine learning.

OBJECTIVES: To establish practical, clinically meaningful criteria for identifying significant plate...

From In Silico De Novo Generation to In Vitro Functional Validation: Discovery of Multifunctional Antithrombotic Peptides from Food-Derived Proteins via a Hybrid LSTM-GCN and Molecular Simulation Pipeline.

Cardiovascular thromboses bring heavy global health burdens, while traditional screening of food-der...

Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.

BACKGROUND: Despite advances in understanding and treating non-ST-elevation acute coronary syndrome ...

Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.

PURPOSE: Peripherally inserted central catheter-related thrombosis (PICC-RT) is a common and serious...