Gene-Engineered Cerium-Exosomes Mediate Atherosclerosis Therapy Through Remodeling of the Inflammatory Microenvironment and DNA Damage Repair.

The pro-inflammatory immune microenvironment in the localized lesion areas and the absence of DNA damage repair mechanisms in endothelial cells serve as essential accelerating factors in the development of atherosclerosis. The lack of targeted therapeutic strategies represents a significant limitation in the efficacy of therapeutic agents for atherosclerosis. In this study, Genetically engineered SNHG12-loaded cerium-macrophage exosomes (Ce-Exo) are designed as atherosclerosis-targeting agents. In vivo studies demonstrated that Ce-Exo exhibited multivalent targeting properties for macrophages, with a 4.1-fold higher atherosclerotic plaque-aggregation ability than that of the control drugs. This suggests that Ce-Exo has a higher homing capacity and deeper penetration into the atherosclerotic plaque. In apolipoprotein E-deficient mice, Ce-Exo found to effectively remodel the immune microenvironment in the lesion area, repair endothelial cell damage, and inhibit the development of atherosclerosis. This study provides a novel approach to the treatment of atherosclerosis and demonstrates the potential of cell-derived drug carriers in biomedicine.

A comprehensive laser image dataset for real-time measurement of wheelset geometric parameters.

Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it's significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset's conditions. Deep learning can fulfill the tasks satisfyingly because it's adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively.