Renal fibrosis is an important pathological feature of diabetic kidney disease (DKD), manifested as tubular interstitial fibrosis, tubular atrophy, glomerulosclerosis and damage to the normal structure of the kidney. Renal fibrosis can eventually develop into renal failure. A better understanding of renal fibrosis in DKD is needed due to clinical limitations of current anti‐fibrotic drugs in terms of effectiveness, cost‐effectiveness and side effects. Fibrosis is characterized by local excessive deposition of extracellular matrix, which is derived from activated myofibroblasts to increase its production or specific tissue inhibitors of metalloproteinases to reduce its degradation. In recent years, endothelial‐mesenchymal transition (EndMT) has gradually integrated into the pathogenesis of fibrosis. In animal models of diabetic kidney disease, it has been found that EndMT is involved in the formation of renal fibrosis and multiple signalling pathways such as TGF‐β signalling pathway, Wnt signalling pathway and non‐coding RNA network participate in the regulation of EndMT during fibrosis. Here, we mainly review EndMT regulation and targeted therapy of renal fibrosis in DKD.

Air pollutants have a significant negative impact on human health, especially cardiovascular and respiratory. An efficient and effective spatiotemporal model for air pollution prediction is urgently needed to help strengthen air pollution control and improve air quality. Empirical statistical models have been widely applied for spatiotemporal prediction. However, they are not capable of dealing well with space‐time heterogeneity and dependency, thereby achieving lower accuracy. Here, a hybrid frame named heterogeneous spatiotemporal copula‐based kriging is proposed for fine particulate matter (PM2.5) concentration prediction; it is a multi‐time, multi‐site kriging model based on spatiotemporal clustering. The proposed model is capable of addressing spatiotemporal heterogeneity effectively and accounting for the spatiotemporal dependence by copula‐based covariance functions. To evaluate the effectiveness of the proposed approach, an experiment on daily PM2.5 data in China in 2016 was carried out. The results (MAE = 6.48, RMSE = 10.68, R2 = 0.919) show that the proposed model can achieve good performance in PM2.5 prediction, which is of great significance in regional air quality management.

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In this work, the authors investigate the strong convergence for weighted sums of ρ *mixing random variables and obtain an improved convergence theorem so called the complete moment convergence in some sense. The result archived not only generalizes the corresponding ones of Sung (Stat.