In December 2019, pneumonia of unknown cause occurred in Wuhan, Hubei Province, China. On 7 January 2020, a novel coronavirus, named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was identified in the throat swab sample of one patient. The World Health Organization (WHO) announced the epidemic disease caused by SARS-CoV-2 as coronavirus disease 2019 (COVID-19). Currently, COVID-19 has spread widely around the world, affecting more than seventy countries. China, with a huge burden of this disease, has taken strong measures to control the spread and improve the curative rate of COVID-19. In this review, we summarized the epidemiological characteristics, clinical features, diagnosis, treatment, and prognosis of COVID-19. A comprehensive understanding will help to control the disease.

Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

The pathogenesis of diabetic nephropathy is not completely understood, and the effects of existing treatments are not satisfactory. Various public platforms already contain extensive data for deeper bioinformatics analysis. From the GSE30529 dataset based on diabetic nephropathy tubular samples, we identified 345 genes through differential expression analysis and weighted gene coexpression correlation network analysis. GO annotations mainly included neutrophil activation, regulation of immune effector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and the AGE-RAGE signalling pathway in diabetic complications. Additional datasets were analysed to understand the mechanisms of differential gene expression from an epigenetic perspective. Differentially expressed miRNAs were obtained to construct a miRNA-mRNA network from the miRNA profiles in the GSE57674 dataset. The miR-1237-3p/SH2B3, miR-1238-5p/ ZNF652 and miR-766-3p/TGFBI axes may be involved in diabetic nephropathy. The methylation levels of the 345 genes were also tested based on the gene methylation profiles of the GSE121820 dataset. The top 20 hub genes in the PPI network were discerned using the CytoHubba tool. Correlation analysis with GFR showed that SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may be involved in diabetic nephropathy. Eight small molecule compounds were identified as potential therapeutic drugs using Connectivity Map. It is estimated that a total of 451 million people suffered from diabetes by 2017, and the number is speculated to be 693 million by 2045 1. As one of the most serious microvascular complications, diabetic nephropathy (DN) has been a major cause of end-stage renal disease (ESRD) in many countries. The congregation of advanced glycation end-products, oxidative stress and activation of protein kinase C are the major pathogeneses of DN. A new viewpoint holds that tubular injury plays an important and even initial role 2. Current treatment strategies for DN aim at controlling blood glucose and blood pressure levels and inhibiting the RAS system to reduce albuminuria and delay the progression of DN 3. However, considering the high incidence of DN-related ESRD, the effect is not entirely satisfactory. Therefore, there is a critical need to identify new therapeutic targets and improve clinical management. High-throughput sequencing technology offers an effective method to study disease-related genes and provides promising medication goals in many fields 4. To date, several studies have screened genes or miRNAs involved in DN 5-9. Integrating these data could overcome the heterogeneity of studies and provide more accurate information. This study identified target genes that may improve the understanding of the molecular mechanisms of DN and provide a resource to build new hypotheses for further follow-up studies. We suggest that the complement system may serve as a thera...

Progressive or chronic renal diseases arise from a process of destructive renal fibrosis. Therefore, the molecular basis of renal fibrosis has attracted increasing attention. In this investigation, we set out to elucidate the potential interaction among long non-coding RNA ENST00000453774.1 (lncRNA 74.1), microRNA-324-3p (miR-324-3p), and NRG1, and to investigate their roles in the context of cellular autophagy and renal fibrosis. We collected 30 renal fibrosis tissue samples for analysis. In other studies, HK-2 cells were stimulated with TGF-β1 to induce a cell model of renal fibrosis, followed by alteration on the expression of lncRNA 74.1, miR-324-3p, or NRG1, or by the addition of AKT activator SC79 in the HK-2 cells. The expression levels of lncRNA 74.1, miR-324-3p, NRG1, autophagy-related proteins (ATG5, ATG7, LC3II/I, and P62), and the corresponding fibrosis markers (Collagen I, Fibronectin, and α-SMA) were subsequently determined using various assay methods. In addition, the proportion of LC3 positive cells and number of autophagosomes were recorded. Results revealed that lncRNA 74.1 and NRG1 were poorly expressed and miR-324-3p was highly expressed in renal fibrosis tissues and modeled cells. LncRNA 74.1 could bind to miR-324-3p, which led to upregulated NRG1 expression and inhibition of the PI3K/AKT signaling pathway. Meanwhile, overexpression of lncRNA 74.1 or down-regulation of miR-324-3p increased the levels of ATG5, ATG7, LC3II, and LC3I, and decreased levels of P62, Collagen I, Fibronectin, and α-SMA, accompanied by elevated proportions of LC3 positive cells and autophagosomes. Findings concur in showing that lncRNA 74.1 could induce cellular autophagy and alleviate renal fibrosis by regulating the miR-324-3p-mediated NRG1/PI3K/AKT axis. This axis may thus present a potential molecular target in renal fibrosis treatment.

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