Using a cell sheet stacking method, we developed an in vitro culture system in which green fluorescent protein expressing human umbilical vein endothelial cells (GFP-HUVECs) were cultured under human skeletal muscle myoblast (HSMM) sheets with different layer numbers. Our aim in developing this system was to examine the different endothelial behaviors in the cell sheet. During 96 h of incubation, in monolayer HSMM sheet, HUVECs quickly reached the top of the cell sheet and detached. In three-layered HSMM sheet, HUVECs also migrated to the top layer and formed island-shaped aggregates. In five-layered HSMM sheet, HUVECs migrated into the middle of the cell sheet and formed net-shaped aggregates. In seven-layered HSMM sheet, HUVECs migrated in the basal of the cell sheet and formed sparse net-shaped aggregates. The thickness of the HSMM sheet, which can be controlled by the layer number of the cell sheet, is therefore an important parameter that affects the migration time, encounters, localization, and morphology of HUVECs inside the HSMM sheet.
Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems to help acne patients better understand their conditions and support doctors in acne diagnosis.
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