Luteolin is a natural flavonoid exhibiting multiple pharmacological activities. Renal anaemia is an important complication of chronic kidney disease (CKD). Whether luteolin can ameliorate renal interstitial fibrosis-induced renal anaemia remains unclear. We examined the therapeutic effects of luteolin in in vitro and in vivo models of renal interstitial fibrosis-induced renal anaemia. After high-throughput sequencing analysis of animal samples, we screened differentially expressed genes (DEGs) associated with luteolin-mediated improvements, performed GO and KEGG functional and pathway enrichment analyses, and validated the mechanism in vitro and in vivo. In vivo, haemoglobin and haematocrit were increased significantly, blood urea nitrogen and creatinine were decreased significantly, and the degree of renal interstitial injury and fibrosis was significantly alleviated in luteolin-treated mice compared with model mice. Erythropoietin (EPO) and hypoxia inducible factor 2A (HIF2A) levels were significantly increased and alpha smooth muscle actin (α-SMA), collagen I (COLI) and fibronectin (FN) levels were significantly decreased. Transcriptomic analysis revealed significant increases in sirtuin 1 (SIRT1) and forkhead box O3 (FOXO3) after luteolin intervention; these effects were validated in vitro and in vivo. Combined treatment with luteolin and the SIRT1 activator resveratrol or the SIRT1 inhibitor sirtinol and SIRT1–siRNA transfection revealed that blocking SIRT1 reduced FOXO3 expression and significantly decreased the benefits of luteolin, while resveratrol had the opposite effects. Molecular docking analysis indicated a stable conformation between luteolin and SIRT1. In vitro and in vivo systematic and transcriptomic studies showed that luteolin attenuates renal anaemia caused by renal fibrosis through the SIRT1/FOXO3 pathway.

In this study, we focused on the influencing factors of renal anaemia in patients with IgA nephropathy and constructed a nomogram model. We divided 462 patients with IgA nephropathy diagnosed by renal biopsy into anaemic and non-anaemic groups. Then, the influencing factors of renal anaemia in patients with IgA nephropathy were analysed by least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression, and a nomogram model for predicting renal anaemia was established. Eventually, nine variables were obtained, which are easy to apply clinically. The areas under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve reached 0.835 and 0.676, respectively, and the C-index reached 0.848. The calibration plot showed that the model had good discrimination, accuracy, and diagnostic efficacy. In addition, the C-index of the model following internal validation reached 0.823. Decision curve analysis suggested that the model had a certain degree of clinical significance. This new nomogram model of renal anaemia combines the basic information, laboratory findings, and renal biopsy results of patients with IgA nephropathy, providing important guidance for predicting and clinically intervening in renal anaemia.

Immune imbalance has become an important factor in the progression of chronic kidney disease (CKD). Molecular typing of CKD may be key to achieving precise treatment. xCell allows immune cell phenotyping in CKD. We integrated two independent microarray datasets and divided 87 CKD patients into two subgroups using unsupervised consensus clustering to study the correlation between CKD and patient sex, age, and CKD stage. We found different expression patterns and clinical characteristics between the two groups. CKD stage was more advanced in cluster I than in cluster II, and the weighted gene coexpression network analysis module characteristics showed enrichment of interferon and leukocyte-associated immune pathways in cluster I. Differentially expressed gene analysis revealed the 12 most significantly changed genes, of which sirtuin 1 (SIRT1) was significantly downregulated in cluster I. Gene set enrichment analysis identified multiple immune-related processes involved in CKD. xCell immune infiltration analysis revealed the significant upregulation of natural killer T (NKT) cells and the significant downregulation of most T and B cell types in cluster I. SIRT1 showed a significant negative correlation with NKT cell infiltration but a positive correlation with CD4⁺ T cell and natural killer cell infiltration. We systematically studied the molecular typing of the CKD transcriptome and estimated the degree of immune cell infiltration based on molecular subtypes. Our results indicate that different subgroups may have unique gene expression patterns and immune dysregulation patterns, thus providing a basis for precise treatment and immune research in CKD.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a frequent and life-threatening complication of sepsis. While the static lactate-to-albumin ratio (LAR) has prognostic value, its dynamic temporal evolution during early resuscitation and its utility for guiding clinical risk stratification remain underexplored. Objective: The aim of the study is to identify distinct dynamic trajectories of LAR, evaluate their independent associations with adverse clinical outcomes, and construct a practical prognostic nomogram for patients with SA-AKI. Methods: Data were extracted from the Medical Information Mart for Intensive Care IV database, including adult patients with SA-AKI and ≥ 3 lactate and albumin measurements within the first 72 hours of intensive care unit admission. A multicenter external validation cohort was assembled from 5 tertiary hospitals in Beijing, China. Group-based trajectory modeling identified distinct LAR trajectories. The primary outcome was 28-day mortality; secondary outcomes included 90-day mortality and continuous renal replacement therapy initiation. Trajectory-outcome associations were assessed using multivariable Cox and logistic regression models. Robustness was examined using restricted cubic splines, inverse probability of treatment weighting, weight truncation, doubly robust estimation, and subgroup analyses. Incremental predictive value beyond single baseline LAR was quantified, and a trajectory-integrated nomogram was developed and externally validated. Results: Among the 615 patients in the primary cohort, 3 LAR trajectories were identified: trajectory 1 (low-stable type), trajectory 2 (rapid-clearance type), and trajectory 3 (delayed-clearance type). In adjusted multivariable models, trajectory 3 (vs trajectory 1) was independently associated with increased 28-day mortality risk (hazard ratio 1.63, 95% CI 1.00‐2.64; P =.0496), 90-day mortality (hazard ratio 1.72, 95% CI 1.14‐2.60; P =.01), and continuous renal replacement therapy initiation (odds ratio 3.40, 95% CI 1.64‐7.15; P =.001). Conversely, the mortality risk for trajectory 2 did not differ significantly from trajectory 1. Sensitivity analyses supported these findings, although trajectory 3 associations attenuated in inverse probability of treatment weighting–weighted analyses. Incorporating trajectories improved predictive accuracy over single baseline LAR (continuous net reclassification improvement 0.170, integrated discrimination improvement 0.023; both P =.01). In the external validation cohort (n=508), 3 analogous trajectories were identified. Trajectory 3 consistently conferred a higher mortality risk across both cohorts, whereas trajectory 2 was significantly associated with 28-day mortality only in validation. The nomogram exhibited modest discrimination in the external cohort (concordance index=0.612), acceptable calibration, and potential utility as a supplementary bedside risk stratification tool. Conclusions: Dynamic LAR trajectories are independently associated with 28- and 90-day mortality in SA-AKI. The delayed-clearance trajectory identifies a specific high-risk phenotype, suggesting that longitudinal LAR monitoring and the constructed nomogram may support early risk stratification and inform clinical decision-making.

Sepsis-induced coagulopathy (SIC) is a critical complication of sepsis that profoundly affects the severity of organ dysfunction and clinical prognosis. The current study aimed to construct and compare multiple individualized machine learning (ML) prediction models for predicting severe acute kidney injury (AKI) in patients with SIC, with the goal of identifying the most robust model to improve the accuracy of clinical decision-making. The retrospective analysis was conducted based on the MIMIC‑IV database and a Chinese cohort. Eligible patients with sepsis and SIC were included. Both LASSO regression and the Boruta algorithm were used to select predictive variables. Eight ML models were constructed to predict the individual probability for severe AKI development. The receiver operating characteristic (ROC) curve, area under the precision-recall curve (PR-AUC) value, calibration indicators, and net benefit were used to assess the predictive model performance. Subgroup analyses were performed to evaluate the stability of model performance. Model predictions and feature importance were estimated using SHapley additive explanations (SHAP). Among the 14,155 patients with sepsis, 5,207 developed SIC and were randomly divided into training and internal validation cohorts. The severe AKI incidence rate was 24.7% (1288/5207). The Chinese external validation cohort enrolled 525 patients with SIC, with a severe AKI incidence rate of 28.1% (148/525). Six key predictive variables were identified to construct the ML models. The XGBoost prediction model demonstrated the most robust performance and best discrimination, achieving an area under the ROC curve of 0.875 (0.862-0.888) in the training cohort, 0.831 (0.807-0.854) in the internal cohort, and 0.843 (0.806-0.879) in the external validation cohort. Serum creatinine on the day of SIC diagnosis was identified as the most influential feature in the SHAP analysis. Subgroup analyses demonstrated that the model maintained predictive efficacy when stratified by age, sex, chronic kidney disease, and liver disease. The XGBoost machine learning model performed well in predicting the risk of severe AKI in patients with SIC, exhibiting satisfactory generalizability and interpretability. This prediction model provides a valuable and evidence-based tool for early identification and facilitates personalized targeted management in critical care settings.

Purpose: In this study, various informatics analyses were employed to identify the hub genes associated with septic cardiomyopathy (SCM) onset and investigate their immune infiltration status. Methods: High‐throughput sequencing data of myocardial tissue samples from mice with SCM were obtained from the GEO database and our previously published articles. The Limma and weighted gene co‐expression network analysis (WGCNA) packages were used to identify the hub genes associated with SCM onset. GSEA and the DAVID database were employed for gene enrichment analysis. Additionally, the CIBERSORT database was used to analyze the immune infiltration in SCM. Finally, the multiMiR package was used to analyze the microRNAs acting as ceRNAs for the hub genes. Receiver operating characteristic (ROC) curves and Mendelian randomization analysis were used to evaluate the predictive value of hub genes for SCM. Results: The SCM group included nine samples, while the control group included ten samples. SCM upregulated 15 genes and downregulated 7. Mt1 and Actc1 were the most significantly upregulated and downregulated, respectively. GO analysis indicated that the most significantly enriched biological process was “response to bacterium,” and the most enriched signaling pathway was “mineral absorption.” Immunoinfiltration analysis revealed decreased T cells CD4 naive, B cells naive, resting mast cells, and M2 macrophage infiltration in the hearts of SCM mice. WGCNA and Limma package analyses identified Clu , Igf1 , and Trp53 as hub genes associated with SCM onset. The ROC curves demonstrated a strong correlation and predictive value for Trp53 , Igf1 , and Clu in the SCM. Moreover, Clu and Igf1 demonstrated predictive values for SCM using Mendelian randomization analysis from the IEU database. Eleven miRNAs formed a ceRNA network with these hub genes. Conclusion: In summary, our results implicated Igf1 and Clu as the potential candidates involved in SCM pathogenesis.