Excessive drinking of alcohol is becoming a worldwide problem, and people have recognized that there exists a close relationship between chronic kidney disease (CKD) and alcohol consumption. However, there are many inconsistencies between experimental and clinical studies on alcohol consumption and kidney damage. The possible reason for this contradictory conclusion is the complex drinking pattern of humans and some bioactivators in wine. In addition, the design itself of the clinical studies can also produce conflicting interpretations of the results. Considering the benefits of light-to-moderate alcohol consumption, we recommend that CKD patients continue light-to-moderate drinking, which is beneficial to them. Because alcohol consumption can lead to adverse events, we do not advise non-drinkers to start to drink. Although light-to-moderate alcohol consumption may not pose a risk to patients with CKD, the patients’ condition needs to be considered. Consumption of even small amounts of alcohol can be associated with increased death risk. Additional clinical and experimental studies are needed to clarify the effect of alcohol on the kidneys and alcohol consumption on CKD patients.

Objective: To explore the pharmacological mechanisms of Chongcaoyishen decoction (CCYSD) against chronic kidney disease (CKD) via network pharmacology analysis combined with experimental validation.Methods: The bioactive components and potential regulatory targets of CCYSD were extracted from the TCMSP database, and the putative CKD-related target proteins were collected from the GeneCards and OMIM database. We matched the active ingredients with gene targets and conducted regulatory networks through Perl5 and R 3.6.1. The network visualization analysis was performed by Cytoscape 3.7.1, which contains ClueGO plug-in for GO and KEGG analysis. In vivo experiments were performed on 40 male SD rats, which were randomly divided into the control group (n = 10), sham group (n = 10), UUO group (n = 10), and CCYSD group (n = 10). A tubulointerstitial fibrosis model was constructed by unilateral ureteral obstruction through surgery and treated for seven consecutive days with CCYSD (0.00657 g/g/d). At the end of treatment, the rats were euthanized and the serum and kidney were collected for further detection.Results: In total, 53 chemical compounds from CCYSD were identified and 12,348 CKD-related targets were collected from the OMIM and GeneCards. A total of 130 shared targets of CCYSD and CKD were acquired by Venn diagram analysis. Functional enrichment analysis suggested that CCYSD might exert its pharmacological effects in multiple biological processes, including oxidative stress, apoptosis, inflammatory response, autophagy, and fiber synthesis, and the potential targets might be associated with JAK-STAT and PI3K-AKT, as well as other signaling pathways. The results of the experiments revealed that the oxidative stress in the UUO group was significantly higher than that in normal state and was accompanied by severe tubulointerstitial fibrosis (TIF), which could be effectively reversed by CCYSD (p < 0.05). Meanwhile, aggravated mitochondrial injury and autophagy was observed in the epithelial cells of the renal tubule in the UUO group, compared to the normal ones (p < 0.05), while the intervention of CCYSD could further activate the autophagy and reduce the mitochondrial injury (p < 0.05).Conclusion: We provide an integrative network pharmacology approach combined with in vivo experiments to explore the underlying mechanisms governing the CCYSD treatment of CKD, which indicates that the relationship between CCYSD and CKD is related to its activation of autophagy, promotion of mitochondrial degradation, and reduction of tissue oxidative stress injury, promoting the explanation and understanding of the biological mechanism of CCYSD in the treatment of CKD.

Chronic kidney disease (CKD) has become a worldwide public health problem and accurate assessment of renal function in CKD patients is important for the treatment. Although the glomerular filtration rate (GFR) can accurately evaluate the renal function, the procedure of measurement is complicated. Therefore, endogenous markers are often chosen to estimate GFR indirectly. However, the accuracy of the equations for estimating GFR is not optimistic. To estimate GFR more precisely, we constructed a classification decision tree model to select the most befitting GFR estimation equation for CKD patients. By searching the HIS system of the First Affiliated Hospital of Zhejiang Chinese Medicine University for all CKD patients who visited the hospital from December 1, 2018 to December 1, 2021 and underwent Gate’s method of 99mTc-DTPA renal dynamic imaging to detect GFR, we eventually collected 518 eligible subjects, who were randomly divided into a training set (70%, 362) and a test set (30%, 156). Then, we used the training set data to build a classification decision tree model that would choose the most accurate equation from the four equations of BIS-2, CKD-EPI(CysC), CKD-EPI(Cr-CysC) and Ruijin, and the equation was selected by the model to estimate GFR. Next, we utilized the test set data to verify our tree model, and compared the GFR estimated by the tree model with other 13 equations. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Bland–Altman plot were used to evaluate the accuracy of the estimates by different methods. A classification decision tree model, including BSA, BMI, 24-hour Urine protein quantity, diabetic nephropathy, age and RASi, was eventually retrieved. In the test set, the RMSE and MAE of GFR estimated by the classification decision tree model were 12.2 and 8.5 respectively, which were lower than other GFR estimation equations. According to Bland–Altman plot of patients in the test set, the eGFR was calculated based on this model and had the smallest degree of variation. We applied the classification decision tree model to select an appropriate GFR estimation equation for CKD patients, and the final GFR estimation was based on the model selection results, which provided us with greater accuracy in GFR estimation.

IntroductionHyperplasia of the mesangial area is common in IgA nephropathy (IgAN) and diabetic nephropathy (DN), and it is often difficult to distinguish them by light microscopy alone, especially in the absence of clinical data. At present, artificial intelligence (AI) is widely used in pathological diagnosis, but mainly in tumor pathology. The application of AI in renal pathological is still in its infancy.MethodsPatients diagnosed as IgAN or DN by renal biopsy in First Affiliated Hospital of Zhejiang Chinese Medicine University from September 1, 2020 to April 30, 2022 were selected as the training set, and patients who diagnosed from May 1, 2022 to June 30, 2022 were selected as the test set. We focused on the glomerulus and captured the field of the glomerulus in Masson staining WSI at 200x magnification, all in 1,000 × 1,000 pixels JPEG format. We augmented the data from training set through minor affine transformation, and then randomly split the training set into training and adjustment data according to 8:2. The training data and the Yolov5 6.1 algorithm were used to train the AI model with constant adjustment of parameters according to the adjusted data. Finally, we obtained the optimal model, tested this model with test set and compared it with renal pathologists.ResultsAI can accurately detect the glomeruli. The overall accuracy of AI glomerulus detection was 98.67% and the omission rate was only 1.30%. No Intact glomerulus was missed. The overall accuracy of AI reached 73.24%, among which the accuracy of IgAN reached 77.27% and DN reached 69.59%. The AUC of IgAN was 0.733 and that of DN was 0.627. In addition, compared with renal pathologists, AI can distinguish IgAN from DN more quickly and accurately, and has higher consistency.DiscussionWe constructed an AI model based on Masson staining images of renal tissue to distinguish IgAN from DN. This model has also been successfully deployed in the work of renal pathologists to assist them in their daily diagnosis and teaching work.