AJA publishes new findings in basic and clinical (including modern, traditional and epidemiological) research on Andrology.

Endothelial progenitor cells (EPCs) seeded on biomaterials can effectively promote diabetic ischemic wound healing. However, the function of transplanted EPCs is negatively affected by a high-glucose and ischemic microenvironment. Our experiments showed that EPC autophagy was inhibited and mitochondrial membrane potential (MMP) was increased in diabetic patients, while adenosine treatment decreased the energy requirements and increased the autophagy levels of EPCs. In animal experiments, we transplanted a biomaterial seeded with EPCs onto the surface of diabetic wounds and found that adenosine-stimulated EPCs effectively promoted wound healing. Increased microvascular genesis and survival of the transplanted cells were also observed in the adenosine-stimulated groups. Interestingly, our study showed that adenosine increased the autophagy of the transplanted EPCs seeded onto the biomaterial and maintained EPC survival at 48 and 96 hours. Moreover, we observed that adenosine induced EPC differentiation through increasing the level of autophagy. In conclusion, our study indicated that adenosine-stimulated EPCs seeded onto a biomaterial significantly improved wound healing in diabetic mice; mechanistically, adenosine might maintain EPC survival and differentiation by increasing high glucose-inhibited EPC autophagy and maintaining cellular energy metabolism.

Acute kidney injury (AKI) can be caused by multiple etiologies and is characterized by a sudden and severe decrease in kidney function. Understanding the independent risk factors associated with the development of AKI and its early detection can refine the risk management and clinical decision-making of high-risk patients after cardiovascular surgery. A retrospective analysis was performed in a single teaching hospital between December 1, 2019, and December 31, 2020. The diagnostic performance of novel biomarkers was assessed using random forest, support vector machine, and multivariate logistic regression. The nomogram from multivariate analysis of risk factors associated with AKI indicated that only LVEF, red blood cell input, and ICUmvat contribute to AKI differentiation and that the difference is statistically significant ( ). Seven radiomics biomarkers were found among 65 patients to be highly correlated with AKI-associated delirium. The importance of the variables was determined using the multilayer perceptron model; fivefold cross-validation was applied to determine the most important delirium risk factors in radiomics of the hippocampus. Finally, we established a radiomics-based machine learning framework to predict AKI-induced delirium in patients who underwent cardiovascular surgery.