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. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- ß (TGF- ß ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment.

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 0.84 g L-1 for C. sorokiniana and D. communis, respectively. Almost all of the ammonia and phosphorus were removed, the removal rates of total nitrogen were 88.05% for C. sorokiniana and 83.18% for D. communis. Lipid content reached 17.04% for C. sorokiniana and 20.37% for D. communis after 10 days culture. CO2 aeration increased intracellular particle numbers of both microalgae and made D. communis tend to be solitary. The research suggested the aeration of CO2 improve the tolerance of microalgae to high concentration of NH4-N, and nutrient excess stress could induce lipid accumulation of microalgae.