The prevalence of type 2 diabetes mellitus (T2DM), which leads to diabetic complications, has been increasing worldwide. The possible applications of T2DM-derived stem cells in cell therapy are limited because their characteristics are still not fully understood. In this study, we characterized adipose tissue-derived mesenchymal stem cells (AT-MSCs) from diabetic patients (dAT-MSCs) and found that insulin receptor substrate-1 (IRS-1) was highly phosphorylated at serine 636/639 in dAT-MSCs. Moreover, we found that early growth response factor-1 (EGR-1) and its target genes of PTEN and GGPS1 were highly expressed in dAT-MSCs in comparison to healthy donor-derived AT-MSCs (nAT-MSCs). We observed impaired wound healing after the injection of dAT-MSCs in the ischemic flap mouse model. The expressions of EGR-1 and its target genes were diminished by small hairpin RNA-targeted EGR-1 (shEGR-1) and treatment with a mitogen-activated protein kinase/extracellular signalregulated kinase (MAPK/ERK) inhibitor (PD98059). Importantly, dAT-MSCs with shEGR-1 were able to restore the wound healing ability in the mouse model. Interestingly, under hypoxic conditions, hypoxia-inducible factor1a (HIF-1a) can bind to the EGR-1 promoter in dAT-MSCs, but not in nAT-MSCs. Together, these results demonstrate that the expression of EGR-1 was upregulated in dAT-MSCs through two pathways: the main regulatory pathway is the MAPK/ERK pathway, the other is mediated by HIF-1a through direct transcriptional activation at the promoter region of the EGR1 gene. Our study suggests that dAT-MSCs may contribute to microvascular damage and delay wound healing through the overexpression of EGR-1. Interrupting the expression of EGR-1 in dAT-MSCs may be a useful treatment for chronic wounds in diabetic patients.

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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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