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.

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.

Mixed Wastewater Coupled with CO2 for Microalgae Culturing and Nutrient Removal.

Biomass, nutrient removal capacity, lipid productivity and morphological changes of Chlorella sorokiniana and Desmodesmus communis were investigated in mixed wastewaters with different CO2 concentrations. Under optimal condition, which was 1:3 ratio of swine wastewater to second treated municipal wastewater with 5% CO2, the maximum biomass concentrations were 1.22 g L-1 and 0.