Background: Kidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis. Methods: We obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs. Results: Through WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations. Conclusion: Four potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.

Background: Red blood cell (RBC) transfusion therapy has been widely used in surgery, and has yielded excellent treatment outcomes. However, in some instances, the demand for RBC transfusion is assessed by doctors based on their experience. In this study, we use machine learning models to predict the need for RBC transfusion during mitral valve surgery to guide the surgeon’s assessment of the patient’s need for intraoperative blood transfusion. Methods: We retrospectively reviewed 698 cases of isolated mitral valve surgery with and without combined tricuspid valve operation. Seventy percent of the database was used as the training set and the remainder as the testing set for 13 machine learning algorithms to build a model to predict the need for intraoperative RBC transfusion. According to the characteristic value of model mining, we analyzed the risk-related factors to determine the main effects of variables influencing the outcome. Results: A total of 166 patients of the cases considered had undergone intraoperative RBC transfusion (24.52%). Of the 13 machine learning algorithms, CatBoost delivered the best performance, with an AUC of 0.888 (95% CI: 0.845–0.909) in testing set. Further analysis using the CatBoost model revealed that hematocrit (64 y), body weight (<59.92 kg), body mass index (BMI) (<22.56 kg/m²), hemoglobin (<122.6 g/L), type of surgery (median thoracotomy surgery), height (194.12×10⁹/L), RBC (<4.08×10¹²/L), and gender (female) were the main risk-related factors for RBC transfusion. A total of 204 patients were tested, 177 of whom were predicted accurately (86.8%). Conclusions: Machine learning models can be used to accurately predict the outcomes of RBC transfusion, and should be used to guide surgeons in clinical practice.

Previous studies have demonstrated the potential antifibrotic effects of baicalin in vitro, via examination of 21 compounds isolated from plants. However, its biological activity and underlying mechanisms of action in vivo remain to be elucidated. The present study aimed to evaluate the effect of baicalin on renal fibrosis in vivo, and the potential signaling pathways involved. A unilateral ureteral obstruction (UUO)‑induced renal fibrosis model was established using Sprague‑Dawley rats. Baicalin was administrated intraperitoneally every 2 days for 10 days. The degree of renal damage and fibrosis was investigated by histological assessment, and detection of fibronectin and collagen I mRNA expression levels. Epithelial‑mesenchymal transition (EMT) markers, transforming growth factor-β1 (TGF-β1) levels and downstream phosphorylation of mothers against decapentaplegic 2/3 (Smad2/3) were examined in vivo and in an NRK‑52E rat renal tubular cell line in vitro. Baicalin was demonstrated to markedly ameliorate renal fibrosis and suppress EMT, as evidenced by reduced interstitial collagen accumulation, decreased fibronectin and collagen I mRNA expression levels, upregulation of N‑ and E‑cadherin expression levels, and downregulation of α‑smooth muscle actin and vimentin expression. Furthermore, baicalin decreased TGF‑β1 expression levels in serum and kidney tissue following UUO, and suppressed Smad2/3 phosphorylation in rat kidney tissue. In vitro studies identified that baicalin may inhibit the phosphorylation of Smad2/3 under the same TGF‑β1 concentration. In conclusion, baicalin may protect against renal fibrosis, potentially via inhibition of TGF‑β1 production and its downstream signal transduction.

Background/Aims: Previous studies imply that telocytes may have a protective effect on fibrosis in various organs, including the liver, colon, and heart. The effect of telocytes on renal fibrosis remains unknown. Herein, this study was designed to investigate the effect of telocytes on renal fibrosis and the potential mechanisms involved. Methods: In a unilateral ureteral obstruction (UUO)-induced renal fibrosis model, telocytes were injected via the tail vein every other day for 10 days. The degree of renal damage and fibrosis was determined using histological assessment. The expression of collagen I, fibronectin, epithelial-mesenchymal transition markers, and Smad2/3 phosphorylation was examined by western blot analyses. Real-time PCR and enzyme-linked immunosorbent assay were performed in vivo to detect the levels of transforming growth factor (TGF)-β1 and various growth factors. Results: Telocytes attenuated renal fibrosis, as evidenced by reduced interstitial collagen accumulation, decreased expression of fibronectin and collagen I, upregulation of E-cadherin, and downregulation of α-smooth muscle actin. Furthermore, telocytes decreased serum TGF-β1 levels, suppressed Smad2/3 phosphorylation, and increased the expression of hepatocyte growth factor (HGF) in rat kidney tissue following UUO. Blockage of HGF counteracted the protective effect of telocytes on UUO-treated kidneys. Through the detection of HGF mRNA levels in vitro, we found that telocytes had no effect on HGF expression compared with renal fibroblasts. Conclusion: Telocytes attenuated UUO-induced renal fibrosis in rats, likely through enhancing the expression of HGF in an indirect manner.

Acute renal allograft rejection is a common complication after renal transplantation that often leads to chronic rejection and ultimate graft loss. While renal allograft biopsy remains the gold standard for diagnosis of acute rejection, the possibility of biopsy-associated complications cannot be overlooked. The development of noninvasive methods for accurate detection of acute renal allograft rejection is thus of significant clinical importance. Gas chromatography–mass spectrometry (GC/MS) was employed for analysis of urine metabolites in 15 renal allograft recipients with acute rejection and 15 stable renal transplant recipients. Partial least squares (PLS) regression and leave-one-out analyses were performed to ascertain whether the metabolites identified could be exploited to distinguish acute rejection from stable groups as well as their sensitivity and specificity. Overall, 14 metabolites were significantly altered in the acute rejection group (11 and 3 metabolites displayed higher and lower levels, respectively) relative to the stable transplant group. Data from PLS and leave-one-out analyses revealed that the differential metabolites identified not only distinguished acute rejection from stable transplant recipients but also showed high sensitivity and specificity for diagnosis of renal allograft recipients with acute rejection. Urine metabolites identified with GC/MS can effectively distinguish acute rejection from stable transplant recipients, supporting the potential utility of metabolome analysis in non-invasive diagnosis of acute rejection.

Renal fibrosis is a pathological endpoint of maladaptation after ischemia-reperfusion injury (IRI), and despite many attempts, no good treatment has been achieved so far. At the core of renal fibrosis is the differentiation of various types of cells into myofibroblasts. MSCs were once thought to play a protective role after renal IRI. However, growing evidence suggests that MSCs have a two-sided nature. In spite of their protective role, in maladaptive situations, MSCs start to differentiate towards myofibroblasts, increasing the myofibroblast pool and promoting renal fibrosis. Following renal IRI, it has been observed that Bone Marrow-Derived Mesenchymal Stem Cells (BM-MSCs) and Renal Resident Mesenchymal Stem Cells (RR-MSCs) play important roles. This review presents evidence supporting their involvement, discusses their potential mechanisms of action, and suggests several new targets for future research.