Data-driven reduced order surrogate modeling for coronary in-stent restenosis.

Background: The intricate process of coronary in-stent restenosis (ISR) involves the interplay between different mediators, including platelet-derived growth factor, transforming growth factor-β, extracellular matrix, smooth muscle cells, endothelial cells, and drug elution from the stent. Modeling such complex multiphysics phenomena demands extensive computational resources and time.

Methods: This paper proposes a novel non-intrusive data-driven reduced order modeling approach for the underlying multiphysics time-dependent parametrized problem.

In-silico analysis of hemodynamic indicators in idealized stented coronary arteries for varying stent indentation.

In this work, we investigate the effects of stent indentation on hemodynamic indicators in stented coronary arteries. Our aim is to assess in-silico risk factors for in-stent restenosis (ISR) and thrombosis after stent implantation. The proposed model is applied to an idealized artery with stent for four indentation percentages and three mesh refinements.

Unveiling the traits of antibiotic resistance and virulence in Escherichia coli obtained from poultry waste.

Antibiotic resistance and virulence factors in avian pathogenic Escherichia coli (APEC) have become significant concerns, contributing to adverse environmental effects. The extensive use of antibiotics in poultry farming has resulted in the emergence of antibiotic-resistant APEC strains. This study prioritizes the molecular screening of APEC to uncover their antibiotic resistance and virulence attributes, with specific attention to their environmental impact.

Ultrasound-driven facile fabrication of Pd doped SnO hierarchical superstructures: Structural, growth mechanism, dermatoglyphics, and anti-cancer activity.

This research introduces a novel method that leverages Spirulina extract (S.E) as a bio-surfactant in the ultrasound-assisted synthesis (UAS) of Pd (0.25-10 mol%) doped tin oxide (SnO) self-assembled superstructures.

A physics-informed deep learning framework for modeling of coronary in-stent restenosis.

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia.