Enhancing CAR Macrophage Efferocytosis Via Surface Engineered Lipid Nanoparticles Targeting LXR Signaling.

The removal of dying cells, or efferocytosis, is an indispensable part of resolving inflammation. However, the inflammatory microenvironment of the atherosclerotic plaque frequently affects the biology of both apoptotic cells and resident phagocytes, rendering efferocytosis dysfunctional. To overcome this problem, a chimeric antigen receptor (CAR) macrophage that can target and engulf phagocytosis-resistant apoptotic cells expressing CD47 is developed. In both normal and inflammatory circumstances, CAR macrophages exhibit activity equivalent to antibody blockage. The surface of CAR macrophages is modified with reactive oxygen species (ROS)-responsive therapeutic nanoparticles targeting the liver X receptor pathway to improve their cell effector activities. The combination of CAR and nanoparticle engineering activated lipid efflux pumps enhances cell debris clearance and reduces inflammation. It is further suggested that the undifferentiated CAR-Ms can transmigrate within a mico-fabricated vessel system. It is also shown that our CAR macrophage can act as a chimeric switch receptor (CSR) to withstand the immunosuppressive inflammatory environment. The developed platform has the potential to contribute to the advancement of next-generation cardiovascular disease therapies and further studies include in vivo experiments.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning.

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km(2), from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.