Background and Objectives: Chronic kidney disease (CKD) has a major impact on the quality of life of patients, and renal fibrosis is a critical pathological change in the disease. It is very important to control the process of renal fibrosis to improve the quality of life of patients with CKD. The pathological mechanism of renal fibrosis is very complicated, and the current treatment strategy also has many flaws. Methods: and Results: To explore a better treatment, we collected exosomes from pluripotent stem cell (PSC)-derived mesenchymal stem cells (MSC) and verified their therapeutic effect on renal fibrosis through In Vivo and In Vitro experiments. In this study, we found that PSC-MSC-derived comes could prevent the epithelial differentiation of NRK-52E cells, and with increasing exosome concentrations, the effect was improved. Furthermore, PSC-MSC-derived exosomes could reduce the pathological process of renal fibrosis, reduce inflammatory reactions and improve renal function in UUO mice. Moreover, the protective effect of exosomes against renal fibrosis may be achieved by increasing the expression of SIRT6 and decreasing the expression of β-catenin and its downstream products. Conclusions These findings suggest the possibility of PSC-MSC-derived exosomes as a new, effective therapeutic tool for kidney fibrosis.

Background and Objectives: Chronic kidney disease (CKD) has a major impact on the quality of life of patients, and renal fibrosis is a critical pathological change in the disease. It is very important to control the process of renal fibrosis to improve the quality of life of patients with CKD. The pathological mechanism of renal fibrosis is very complicated, and the current treatment strategy also has many flaws. Methods: and Results: To explore a better treatment, we collected exosomes from pluripotent stem cell (PSC)-derived mesenchymal stem cells (MSC) and verified their therapeutic effect on renal fibrosis through In Vivo and In Vitro experiments. In this study, we found that PSC-MSC-derived comes could prevent the epithelial differentiation of NRK-52E cells, and with increasing exosome concentrations, the effect was improved. Furthermore, PSC-MSC-derived exosomes could reduce the pathological process of renal fibrosis, reduce inflammatory reactions and improve renal function in UUO mice. Moreover, the protective effect of exosomes against renal fibrosis may be achieved by increasing the expression of SIRT6 and decreasing the expression of β-catenin and its downstream products. Conclusions These findings suggest the possibility of PSC-MSC-derived exosomes as a new, effective therapeutic tool for kidney fibrosis.

A subset of patients with non-alcoholic fatty liver disease(NAFLD)can progress to nonalcoholic steatohepatitis(NASH).When NASH reaches a fibrosis degree of F≥2 and a NAS score of≥4,this stage of NASH is referred to as fibrotic NASH,which is a key focus in clinical drug trials.Currently,liver biopsy is the gold standard for assessing the histological changes of the liver,but its clinical application is limited by its invasiveness,and therefore,it is of particular importance to develop noninvasive detection methods for fibrotic NASH.This article summarizes the recent research achievements in novel noninvasive diagnostic methods for fibrotic NASH and elaborates on these new diagnostic methods for predicting fibrotic NASH in terms of current status,challenges faced,and prospects for future development.

The global prevalence rate of nonalcoholic fatty liver disease (NAFLD) has increased year by year, and it has become the number one cause for chronic liver disease in China. In addition, the trend of NAFLD has become more pronounced and evident in female gender and younger age group. The long-term persistence of fatty liver disease may cause serious consequences. There are no accepted diagnostic criteria for diagnosing noninvasive diagnosis of NAFLD. Alpha-ketoglutarate is a newly discovered serological marker of high diagnostic value and considered the most valuable potential biomarker along with cytokeratine-18 (CK-18).

Objective:To analyze the prognostic factors and evaluate the accuracy of existing survival prediction models in patients with lung cancer-derived spinal metastases who have undergone open surgery.Methods:According to the inclusion criteria, the data of 76 patients with spinal metastasis of lung cancer who underwent open surgery in the department of Orthopedics in Guangdong Provincial People's Hospital were collected from January 2019 to November 2021. The relationship between the number of bone metastasis, pathological type, visceral metastasis, epidermal growth factor receptor mutation, serum alkaline phosphatase (ALP), hemoglobin (Hb), Frankel grade and postoperative survival time in 76 cases was analyzed by Cox logical regression analysis and Kaplan-Meier method to determine the potential prognostic factors. The accuracy of Tomita score, Tokuhashi revised score, Katagiri New score, New England Spinal Metastasis Score score (NESMS) and Skeletal Oncology Research Group (SORG) machine learning algorithm in predicting postoperative survival time was verified by drawing receiver operating characteristic (ROC) curve.Results:The median follow-up time of the patients was 18.0 months (2.3-36.0 months). The median survival time was 12.6 months [95% CI (10.8, 14.4)]. The survival rates at 6 and 12 months after operation were 71.6% and 52.0%, respectively. Multivariate regression analysis showed that ALP [ HR=0.23, 95% CI (0.11, 0.48), P<0.001], Hb [ HR=4.48, 95% CI (2.07, 9.70), P< 0.001] and EGFR mutation [ HR=2.22, 95% CI (1.04, 4.76), P=0.040] were independent predictors of prognosis. The accuracy of Tomita score, Tokuhashi revised score (2005), Katagiri New score and NESMS score in predicting 1-year mortality was 58.7%, 65.7%, 70.5% and 65% respectively, and the accuracy in predicting 6-month mortality was 63.7%, 62.2%, 61.2% and 56.8% respectively. The accuracy of SORG machine learning algorithm in predicting 1-year and 90 d mortality was 81.1%, 67.5%, respectively. Conclusion:No EGFR mutation, ALP>164 U/L and Hb≤125 g/L were risk factors affecting the survival of patients with spinal metastasis of lung cancer. SORG machine learning algorithm has good accuracy in predicting the postoperative survival rate of patients with lung cancer spinal metastasis.