Endothelialized Bronchioalveolar Lung Organoids Model Endothelial Cell Responses to Injury.

Organoid 3D systems are powerful platforms to study development and disease. Recently, the complexity of lung organoid models derived from adult mouse and human stem cells has increased substantially in terms of cellular composition and structural complexity. However, a murine lung organoid system with a clear integrated endothelial compartment is still missing.

Flipped Classroom: Improved team performance during resuscitation training through interactive pre-course content - a cluster-randomised controlled study.

Background: Resuscitation is a team effort, and it is increasingly acknowledged that team cooperation requires training. Staff shortages in many healthcare systems worldwide, as well as recent pandemic restrictions, limit opportunities for collaborative team training. To address this challenge, a learner-centred approach known as flipped learning has been successfully implemented.

Adjusting Acute Kidney Injury Kidney Disease: Improving Global Outcomes Urine Output Criterion for Predicted Body Weight Improves Prediction of Hospital Mortality.

Background: Based on the Kidney Disease: Improving Global Outcomes (KDIGO) definitions, urine output, serum creatinine, and need for kidney replacement therapy are used for staging acute kidney injury (AKI). Currently, AKI staging correlates strongly with mortality and can be used as a predictive tool. However, factors associated with the development of AKI may affect its predictive ability.

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation.

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis.

Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation.

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500, which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the data whose memory kernel implies that there is a significant memory effect in the market correlation ranging back at least three trading weeks.