Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).

Background: The National Lung Screening Trial (NLST) has shown that screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. These patients are also at high risk of coronary artery disease, and we used deep learning model to automatically detect, quantify and perform risk categorisation of coronary artery calcification score (CACS) from non-ECG gated Chest CT scans.

Materials And Methods: Automated calcium quantification was performed using a neural network based on Mask regions with convolutional neural networks (R-CNN) for multiorgan segmentation.

In-hospital outcomes of percutaneous left ventricular assist device recipients in cardiogenic shock hospitalizations with chronic kidney disease: A nationwide analysis.

Background: There is a lack of data on the role of chronic kidney disease (CKD) in patients who received percutaneous left ventricular assist devices (pLVAD) as mechanical circulatory support (MCS) as an adjunct treatment for cardiogenic shock (CS) management.

Methods: Using National Inpatient Sample (2016-19), we extracted CS patients receiving pLVAD and divided them into CKD and non-CKD cohorts. Multivariate regression analysis was used for adjusted odds ratios for outcomes before and after entropy balancing (EB) and predictive margins for the probability of all-cause in-hospital mortality (ACM).

Data-Driven Machine Learning Strategy for Designing Metal-Ion-Doped γ-BiMoO Photocatalysts to Enhance Degradation Performance.

Doped semiconductors are often used to improve photocatalytic efficiency and address the challenges of easy recombination of electron-hole pairs and poor photoluminescence. However, the reproducibility and complexity of experimental studies result in time-consuming and less cost-effective studies, and it is difficult to gain insights into the intrinsic properties of doped photocatalysts to control their performance. Introducing a machine learning approach, we constructed a photocatalytic model of transition-metal- and rare earth metal-ion-doped γ-BiMoO.

Transitions between cooperative and crowding-dominated collective motion in non-jammed MDCK monolayers.

Transitions between solid-like and fluid-like states in living tissues have been found in steps of embryonic development and in stages of disease progression. Our current understanding of these transitions has been guided by experimental and theoretical investigations focused on how motion becomes arrested with increased mechanical coupling between cells, typically as a function of packing density or cell cohesiveness. However, cells actively respond to externally applied forces by contracting after a time delay, so it is possible that at some packing densities or levels of cell cohesiveness, mechanical coupling stimulates cell motion instead of suppressing it.