PCI

A Multidimensional Integrated Strategy for Identifying Anti-thrombotic Quality Markers(Q-Markers) in Taohong Siwu Decoction through Combined Analysis of Chemical Profiling, Bioactivity, and Pharmacodynamic Efficacy.

ETHNOPHARMACOLOGICAL RELEVANCE: Taohong Siwu Decoction (TSD), originating from the Qing Dynasty medical text Gynecology Ice Mirror, is a representative formula for treating blood stasis syndrome, with the effects of promoting blood circulation, nourishing blood, and resolving stasis. Following this lead, modern research has revealed that the formula exerts antithrombotic effects through multiple p...

Latent biochemical phenotypes delineate divergent health trajectories in older adults.

Ageing heterogeneity hampers prevention and care. We used routine biochemical panels and unsupervised learning to identify latent phenotypes in community-dwelling older adults. In 1491 participants from the Toledo Study for Healthy Ageing (TSHA) with ~10-11 years of follow-up, 39 blood biomarkers were dimension-reduced and clustered, yielding three phenotypes: Healthy, Metabolic (subclinical dysme...

Idiopathic Intracranial Hypertension Is Characterized by a Distinct Proteomic Profile.

OBJECTIVES: The pathophysiology of idiopathic intracranial hypertension (IIH) is poorly understood and disease-specific biomarkers are lacking. We aim...

Prediction of cancer-associated thrombosis by machine learning: results from the Vienna Cancer and Thrombosis Study.

BACKGROUND: Improved risk prediction of cancer-associated venous thromboembolism (VTE) remains an unmet clinical need. We aimed to apply machine learn...

Personalised thrombo-embolic risk prediction after endometrial cancer surgery: an explainable AI approach using SHAP.

To address the clinical challenge of postoperative lower extremity deep vein thrombosis (LEDVT) in endometrial cancer care, this study establishes an ...

Predicting intracranial angioplasty failure using vessel wall MRI habitat radiomics and deep learning: a multicenter study.

BACKGROUND: Primary balloon angioplasty (PBA) is a promising strategy for symptomatic intracranial atherosclerotic stenosis but carries the risk of pr...

Deep learning predicts stent implantation in borderline coronary lesions from angiography.

Accurate evaluation of coronary intermediate lesions (50-70% stenosis) is essential for stent decision-making, yet conventional angiography remains su...

Gene expression integration and similarity score based modeling improve risk stratification in idiopathic venous thrombophilia.

BACKGROUND: Idiopathic venous thromboembolism (VTE) occurs in the absence of provoking factors, limiting the efficacy of current risk stratification. ...

Hidden Preoperative Lower-Extremity Venous Thrombosis in Pelvic Organ Prolapse Surgery: Prevalence and Development of a Clinical Prediction Model.

INTRODUCTION AND HYPOTHESIS: Preoperative lower-extremity venous thrombosis (LEVT) is frequently overlooked in women undergoing pelvic organ prolapse ...

Interpretable machine learning using accumulated local effects to characterise predictors of subclinical leaflet thrombosis after self-expanding transcatheter aortic valve implantation.

OBJECTIVES: Subclinical leaflet thrombosis is an early form of bioprosthetic valve dysfunction after transcatheter aortic valve implantation. Predicti...

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis (MPN-SVT).

Machine Learning Prediction for 6-Month Outcomes in Traumatic Cerebral Venous Sinus Thrombosis.

This study developed and externally validated a multicenter machine learning framework to predict 6-month poor functional outcome (modified Rankin Sca...

Computational Model for Predicting Optimal Clinical Intervention in Pre-Operative Neonates with Transposition of the Great Arteries.

PURPOSE: Transposition of the Great Arteries (TGA) is a congenital heart defect characterized by the abnormal positioning of the pulmonary artery over...

Machine learning-based model for predicting recanalization in isolated distal deep vein thrombosis and analysis of predictors.

BACKGROUND: Isolated distal deep vein thrombosis (IDDVT) is common, yet tools for predicting poor recanalization remain limited. We aimed to develop a...

Thrombosis at the crossroads of inflammation, coagulation, and vascular biology: Recent advances and emerging therapeutic strategies.

Thrombosis is a multifaceted pathological process involving intravascular clot formation that drives a range of cardiovascular and cerebrovascular dis...

MORGAS (MORtality ORiented Glucose, Age, Smoking, and Stent Score) predicts mortality after percutaneous coronary intervention.

BACKGROUND: Accurate prediction of mortality after percutaneous coronary intervention (PCI) remains a clinical challenge. Existing risk scores often l...

Rationale and design of a multicenter, prospective, diagnostic clinical study: A study protocol for evaluating the diagnostic validation of deep learning-based noninvasive CT-FFR for in-stent restenosis.

INTRODUCTION: Currently, there are many diagnostic strategies for in-stent restenosis (ISR) used clinically, including invasive coronary angiography (...

Computational and machine learning approach for nanoparticle-enhanced bio-heat transport with coupled effects of interparticle spacing and nanoparticles radius.

Targeted delivery of drugs and hyperthermia in cardiovascular disease demand the accurate delivery of nanoparticles in complex arterial geometries. Th...

[Machine learning models to predict intestinal necrosis in acute mesenteric venous thrombosis].

Objective: To develop and validate machine learning-based models for predicting the risk of transmural irreversible intestinal necrosis (ITIN) in pati...

Decoding Kounis syndrome with artificial intelligence: from diagnosis to precision medicine.

Kounis syndrome-acute coronary events triggered by allergic or hypersensitivity reactions-remains underrecognized across emergency and cardiology sett...