Objective To study the effect of Astragalus polysaccharide on the expressions of Nephrin and Desmin in high sugar stimulation mice podocytes. Methods Test one: the cultured mouse podocytes were divided into 5 groups: astragalus polysaccharide group [D－ glucose 30 mmol/L+astragalus polysaccharide (0.0 g/L, 0.1 g/L,0.2 g/L,0.4 g/L and 0.8 g/L) ]. Test two: In test one, we chose the concentration of astragalus polysaccharide which most affected both expression of Nephrin and Desmin in podocytes and named it as astragalus polysaccharide 0. X g/L. The cultured mouse podocytes were divided into 7 groups: astragalus polysaccharide group (D－ glucose 30 mmol/L+astragalus polysaccharide 0.X g/L) had been cultured for 0 h, 3 h, 6 h, 12 h,24 h,48 h and 72 h, respectively. Test three: Basted on test two, we chose the time which most affected both expression of Nephrin and Desmin in podocytes and named it as Yh. The cultured mouse podocytes were divided into 4 groups: the control group,mannitol hypertonic glucose control group, high sugar group,and astragalus polysaccharide group (high sugar group+ astragalus polysaccharide 0.X g/L) . Each group had been cultured for Yh. Nephrin and Desmin of each subgroup was detected by flow cytometry podocytes and real－time PCR. Results With the intervention of polysaccharide, Nephrin in podocyte cell surface gradually increased, Desmin gradually reduced in a concentration－dependent manner (P＜0.05) . With the intervention of astragalus polysaccharide, Nephrin gradually increase and Desmin expression gradually decreased in a concentration－dependent manner (P＜0.05) . Compared to the control group and the high sugar group,Nephrin mRNA increased and Desmin mRNA decreased in 0.8g/L astragalus polysaccharide co－cultured for 72h (P＜0.05) . Conclusion Astragalus polysaccharide intervention can increase Nephrin and reduce Desmin in podocytes, which are possible targets of Astragalus polysaccharide in treatment of DN proteinuria.

Objective To investigate the epidemiology of chronic kidney disease (CKD) in patients with hypertension and diabetes mellitus in Kunming urban area.Methods A multistage cluster randomized sampling method was used to collect 400 randomly selected patients (community managed hypertension and diabetes mellitus) in community service centers in the 4 main urban districts of Kunming,Yunnan province.The subjects were screened for CKD by questionnaires,physical examinations,and microalbuminuria tests.Results A total of 343 people were surveyed.The prevalence of albuminuria,proteinuria by routine urinalysis,decreased glomerular filtration rate,and CKD prevalence were respectively 37.3％,12.2％,5.0％ and 39.1％.A total of 134 patients with CKD (134/343) were screened.Logistic regression analysis showed male (OR=2.312,95％CI 1.325-4.037,P=0.003),hyperuricemia (OR=1.751,95％ CI 1.109-2.765,P=0.016) and obesity (OR=2.150,95％ CI 1.115-4.146,P=0.022) were related to CKD.Conclusions The prevalences of CKD and albuminuria are 39.1％ and 37.3％ in patients with chronic diseases (hypertension and diabetes) in the main urban community of Kunming,Yunnan.Hyperuricemia,male and obesity are independent risk factors for CKD.

Objective To build the early predictive model for chronic kidney disease(CKD)in hypertension and diabetes patients in the community.Methods The CKD patients were recruited from 4 health care centers in 4 urban areas in Kunming.The control group was residents without hypertension and diabetes(n = 1267).The disease group was residents with hypertension and/or diabetes(n = 566).The questionnaire survey,physical examination,laboratory testing,and 5 SNPs gene types in the PVT1 gene.The risk factors,which were filtered with logistics regression,were used to build predictive models.Four machine learning algorithms were built:support vector machine(SVM),random forest(RF),Na?ve Bayes(NB),and artificial neural network(ANN)models.Results Thirteen indicators included in the final diagnostic model:age,disease type,ethnicity,blood urea nitrogen,creatinine,eGFR from MDRD,ACR,eGFR from EPI2009,PAM13 score,sleep quality survey,staying-up late,PVT1 SNP rs11993333 and rs2720659.The accuracy,specificity,Kappa value,AUC of ROC,and PRC of ANN are greater than those of the other 3 models.The sensitivity of RF is the highest among 4 types of machine learning.Conclusions The ANN predictive model has a good ability of efficiency and classification to predict CKD with hypertension and/or diabetes patients in the community.

Objective:To systematically evaluate the clinical efficacy of compound α-ketoacid tablets in the treatment of diabetic kidney disease (DKD).Methods:CNKI, Wanfang database, EMBASE, PubMed and Cochrane Library database were searched for eligible records published from the establishment of individual database to November 13 th, 2022. The quality of the included studies were assessed, data were extracted, and meta-analysis was conducted using RevMan5.3. Results:A total of 26 randomized controlled trials were included, with a total of 2 790 DKD patients (1 465 in the experimental group and 1 325 in the control group). Multiple parameters were significantly improved in the experimental group compared with the control group, including 24-hour urinary protein, blood creatinine, urea nitrogen, nutritional index, oxidative stress level, fasting blood glucose, glycated hemoglobin, homocysteine, HGF, VEGF, TGF-β1, and systolic blood pressure.Conclusions:Limited low-quality evidence showed that compound α-ketoacid tablets combined with low-protein diet may be related to the improved 24-hour urinary protein, renal function, and glucose metabolism in patients with DKD. Due to the lack of randomized controlled trials designed for respective stages of DKD, the inclusion criteria of our study were relatively general, possibly leading to the lack of pertinence of the results. Some indicators showed apparent heterogeneity among different groups, and more high-quality multi-center studies with large sample sizes are still needed to verify our findings.