BACKGROUND:During the progression of immunoglobulin A nephropathy (IgAN), residual nephrons compensate for nephron loss by increasing single-nephron estimated glomerular filtration rate (eGFR). This adaptive hyperfiltration may accelerate the decline in kidney function. However, due to measurement challenges, the prognostic value of single-nephron eGFR remains unclear. This study investigates its impact on kidney function decline in patients with IgAN. METHODS:This observational cohort study included 187 biopsy-confirmed IgAN patients who had undergone computed tomography and biopsy during their hospitalization. Single-nephron eGFR was estimated by dividing total eGFR by nephron number, the latter of which was derived from the cortical volume and glomerular density. The primary composite outcome was kidney function decline, defined as a sustained annual eGFR decrease of ≥5 mL/min/1.73 m2, a ≥40% reduction in eGFR from baseline or end-stage renal disease. Cox proportional hazards models were fit to estimate associations between single-nephron eGFR and kidney function decline. RESULTS:Among the 187 participants (45% women, mean age 38 ± 11 years), 57 experienced a decline in kidney function over a median follow-up period of 3.1 years. Participants were divided into three groups based on their single-nephron eGFR. Kaplan-Meier analysis demonstrated significantly reduced kidney survival in the high single-nephron eGFR group compared with the low and middle groups (log-rank P < .001). Compared with the low single-nephron eGFR group, multivariable hazard ratios for kidney function decline were 2.50 (95% confidence interval 1.10-5.67; P = .03) for the middle group and 5.30 (2.44-11.54; P < .001) for the high group. CONCLUSIONS:A higher single-nephron eGFR is identified as a risk factor for kidney function decline in patients with IgAN, which supports its potential as an early risk-stratification tool. Moreover, using eGFR cutoffs alone to define hyperfiltration may lead to misclassification due to its inability to distinguish patients with hyperfiltration from those without.

Background Diabetic kidney disease (DKD) is a common condition with few treatment options, and inflammation plays a pivotal role in its progression. Luteolin, a natural compound found in traditional Chinese herbs, is known for its anti-inflammatory properties, making it a potential treatment for DKD. But its effect and mechanisms in DKD remain incompletely elucidated. Methods Renoprotective effects of luteolin in db/db mice were assessed with BUN, Scr, uACR, and PAS staining. Flow cytometry and extraction of total membrane proteins were conducted to examine the abundance of full-length TREM2 on the membrane of macrophages. Co-culture of differentially treated macrophages and HK2 cells evaluated luteolin’s impact on efferocytosis. The molecular target of luteolin was elucidated through virtual molecular analysis, SPR, and ADAM10 activity assays. Results Luteolin reduced uACR, BUN, and SCr levels. Histologic analyses showed decreases in mesangial matrix, glomerular volume, GBM thickness, and foot process effacement. Tubular injury scores and KIM1 expression were lowered, while megalin and cubilin expression increased. Renal macrophage infiltration, iNOS+ cells, and IL-1β, IL-18, TNF-α, and MCP-1 levels were reduced. Luteolin elevated TREM2+ macrophages with decreased sTREM2 in vivo and in vitro. Immunofluorescence confirmed increased TREM2+ macrophages and enhanced full-length TREM2 on cell membrane. Luteolin exhibited dose-dependent binding to ADAM10 and inhibited its activity without affecting ADAM10 expression. In co-culture system, luteolin increased p-DAP12, p-SYK, and PHrodo+ cell counts. Apoptotic cells in kidney tissue decreased, while Rab5a and Rab7a expression were upregulated. Conclusions Luteolin attenuates immunoinflammation and pathological injury in db/db mice by enhancing the efferocytosis of apoptotic renal tubular cells by TREM2+ macrophages. The potential mechanism of luteolin involves binding to ADAM10 and inhibiting its activity, which attenuates aberrant shedding of full-length TREM2 from macrophages and potentiates downstream TREM2 signaling. Collectively, luteolin provides a promising option for ameliorating immune inflammation in DKD, demonstrating strong translational potential.

Introduction:Sodium-glucose cotransporter 2 inhibitors (SGLT2is) are crucial in managing proteinuria in chronic kidney disease (CKD), yet individual responses vary. Whether urinary glucose excretion, a direct pharmacodynamic marker of SGLT2is, could predict proteinuria reduction efficacy remains unclear and has not been specifically investigated. Methods:We enrolled 277 CKD patients treated with empagliflozin (10 mg/day) between July 2024 and September 2025 at a single center, stratifying them into low (<46.4 mmol/L, n = 69) and high (≥46.4 mmol/L, n = 208) urinary glucose groups by 24-h urinary glucose levels after 3 months of treatment. We evaluated changes in proteinuria levels following 3 and 6 months of medication across the different urinary glucose groups. Changes in laboratory values over time were analyzed using paired Wilcoxon signed-rank tests. Results:Proteinuria was significantly reduced at both 3 (-0.42 g/24 h [95% confidence interval [CI]: -0.55 to -0.32]) and 6 months (-0.46 g/24 h [95% CI: -0.61 to -0.33]) after treatment initiation. After adjusting for covariates, urinary glucose excretion predicted proteinuria reduction from 3 to 6 months (β = 2.47, 95% CI: 0.49-4.45, p = 0.015), with higher urinary glucose correlating with greater proteinuria decline. The high urinary glucose group had significant proteinuria reduction from baseline at 3 and 6 months (p < 0.001), unlike the low group. Following propensity score adjustment for age, sex, body mass index, baseline estimated glomerular filtration rate (eGFR), and renin-angiotensin-aldosterone system inhibitors use, the high urinary glucose group had a significantly higher relative risk of ≥30% proteinuria reduction (risk ratio = 2.82, 95% CI: 1.20-6.65, p = 0.017), particularly in patients with baseline proteinuria ≥1 g/24 h. Urinary glucose concentration was weakly positively correlated with 6-month eGFR change (r = 0.17, p = 0.011). Conclusions:Urinary glucose concentration can serve as a predictor of SGLT2i-mediated proteinuria reduction, providing a practical clinical reference for personalized CKD management.

Introduction： Sodium-glucose cotransporter 2 inhibitors (SGLT2is) are crucial in managing proteinuria in chronic kidney disease (CKD), yet individual responses vary. Whether urinary glucose excretion, a direct pharmacodynamic marker of SGLT2is, could predict proteinuria reduction efficacy remains unclear and has not been specifically investigated. Methods： We enrolled 277 CKD patients treated with empagliflozin (10mg/day) between July 2024 and September 2025 at a single center, stratifying them into low (<46.4 mmol/L, n=69) and high (≥46.4 mmol/L, n=208) urinary glucose groups by 24-hour urinary glucose levels after 3 months of treatment. We evaluated changes in proteinuria levels following 3 and 6 months of medication across the different urinary glucose groups. Changes in laboratory values over time were analyzed using paired Wilcoxon signed-rank tests. Results： Proteinuria was significantly reduced at both 3 [−0.42 g/24 h (95% CI −0.55 to −0.32)] and 6 months [−0.46 g/24 h (95% CI −0.61 to −0.33)] after treatment initiation. After adjusting for covariates, urinary glucose excretion predicted proteinuria reduction from 3 to 6 months (β=2.47, 95% CI: 0.49-4.45, p=0.015), with higher urinary glucose correlating with greater proteinuria decline. The high urinary glucose group had significant proteinuria reduction from baseline at 3 and 6 months (p<0.001), unlike the low group. Following propensity-score adjustment for age, sex, and Body Mass Index (BMI), baseline estimated glomerular filtration rate (eGFR), and renin-angiotensin-aldosterone system (RAAS) inhibitors use, a significantly higher relative risk of ≥30% proteinuria reduction in the high urinary glucose group (RR=2.82, 95% CI: 1.20-6.65, p=0.017), particularly in patients with baseline proteinuria ≥1g/24h. Urinary glucose concentration was weakly positively correlated with 6-month eGFR change (r=0.17, p=0.011). Conclusions： Urinary glucose concentration can serve as a predictor of SGLT2i-mediated proteinuria reduction, providing a practical clinical reference for personalized CKD management.

[This corrects the article DOI: 10.34133/research.0716.].

Infection is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), yet effective tools for early identification of high-risk patients are lacking. This study aimed to develop an explainable machine learning (ML) model to predict in-hospital infection risk among SLE patients. We analyzed adult patients (≥18 years) with SLE (n = 7,833) from three departments using a population-based electronic medical record database (2000-2024). Among them, 3,157 (40.3%) patients developed an infection after 72 h of hospitalization. An initial comprehensive variable pool of 108 candidate predictors was included, encompassing demographics, comprehensive laboratory parameters, clinical features, disease activity, and treatment exposures. Ten machine learning models were applied. Model performance was evaluated using six metrics. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Nine predictors were selected: daily prednisone equivalent dose, albumin, hydroxychloroquine use, C-reactive protein, D-dimer, glucose, cystatin C, hemoglobin, and alpha1-globulin. Among all models tested, the Gradient Boosting model demonstrated the best overall performance on the independent validation set, with an area under the curve (AUC) of 0.858, with its robustness confirmed by 5-fold and 10-fold cross-validation (mean AUCs of 0.855 ± 0.002 and 0.854 ± 0.008, respectively). SHAP analysis revealed that daily prednisone equivalent dose, albumin, and hydroxychloroquine use were the most influential factors. We developed and validated a high-performance, explainable ML model using nine routinely available clinical variables to accurately predict in-hospital infection risk in SLE patients. This tool provides transparent, individualized risk assessment and has the potential to guide personalized clinical stratification and early intervention, ultimately improving patient outcomes.

Mesangial proliferative glomerulonephritis (MsPGN) is a common cause of end-stage renal disease, characterized by mesangial cell proliferation within glomeruli. Mesangial cell activation triggered by inflammation is a key factor in the development of MsPGN. However, effective therapeutic strategies targeting this process are still limited. Here, we uncovered, for the first time, the direct effects of chlorogenic acid (CGA), a naturally occurring small-molecule compound with anti-inflammatory and antiproliferative properties, on mesangial cells in an anti-Thy1 nephritis animal model. A multi-dimensional pharmacological platform integrating laser microdissection-coupled glomerular proteomics affinity deconvolution, surface plasmon resonance, molecular dynamics, and enzyme assays identified Ras-related C3 botulinum toxin substrate 1 (RAC1) as the direct target of CGA. Mechanistically, CGA competitively binds to the LYS15, PRO33, and THR34 amino acid residues—located within residues 57–65 of the GTP/GDP-binding domain of RAC1, inhibiting its activation and subsequently reducing AKT phosphorylation while suppressing Thrombospondin-1 secretion from mesangial cells—a key ligand for macrophage CD36 receptors. This interaction deactivates macrophages and lowers the levels of inflammatory cytokines, including TNFα, IL1β, and IL6. Significantly, as a novel natural RAC1 inhibitor, CGA disrupts mesangial-macrophage crosstalk by dual suppression of regional immunity and cellular proliferation, thus conferring renal protection in MsPGN models. Our findings highlight CGA as a promising pharmacotherapy, offering a mechanism-driven, natural product-based strategy to mitigate MsPGN progression.

BackgroundThe prognosis of IgA nephropathy (IgAN) varies greatly but tends to be poor. The purpose of the present study was to screen for urinary sediment miRNAs that could be used for the non-invasive prediction of IgAN progression and to explore the mechanisms explaining this.MethodsWe studied two independent cohorts (2014–2015 and 2018–2022) to identify urinary sediment miRNAs that could be used to predict IgAN progression. Bioinformatic analysis and dual-luciferase experiments were used to identify target genes for miR-142-3p. The fibrotic phenotype of tubular epithelial cells was evaluated in HK-2 cells.ResultsIn both the training and validation cohorts, the urinary miR-142-3p expression in patients who showed IgAN progression was significantly higher than that in those who did not (P < 0.0001 and P = 0.003, respectively). Multivariate Cox regression analysis showed that high urinary miR-142-3p expression was an independent risk factor for IgAN progression (P < 0.001). Using the International IgA Nephropathy Prediction Tool (IIGANPT) as a reference, we replaced the pathologic indices in the IIGANPT model with the miR-142-3p expression and found that this did not reduce the predictive value of the model (P = 0.228). miR-142-3p is principally expressed in renal tubular epithelial cells, and the in vitro experiments showed that miR-142-3p influences PI3K–AKT pathway activity via inositol polyphosphate-5-phosphatase, thereby playing a role in this cell type’s fibrosis phenotype.ConclusionsUrinary miR-142-3p is a biomarker for the progression of IgAN and is involved in the exacerbation of renal fibrosis. Urinary miR-142-3p can be used to replace pathologic indices in the IIGANPT without reducing its predictive efficacy, implying that this modified tool could be used to non-invasively predict IgAN progression.

BACKGROUND:Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. The retinal microvasculature, as the only directly observable microvasculature, may reflect DN progression. This study aims to construct a non-invasive diagnostic and prognostic prediction model using the mixed effects of retinal vascular geometric parameters and clinical data. METHODS:We constructed a multimodal database including 397 patients with type 2 diabetes and chronic kidney disease from multiple centers in China. The primary cohort (374 patients) was recruited from the Department of Nephrology at the First Medical Center of the Chinese People's Liberation Army General Hospital in Beijing between 2017 and 2022, while an external validation cohort (23 patients) was collected from five other hospitals across China from September 2022 to March 2023. Fundus images, clinical characteristics, renal biopsy diagnoses, and follow-up data were collected. Unsupervised learning and Resnet neural networks were used to segment and calculate retinal vascular geometric parameters. Weighted quantile regression (WQS), Lasso, and COX univariable regressions were employed to assess the mixed effects of retinal vascular geometric parameters and select relevant clinical characteristics. Logistic regression and Cox regression with random forest (COX-RF) were used for model construction. RESULTS:A multimodal database of 397 patients was constructed. A diagnostic model combining retinal parameters (WQS-diagnosis) and seven clinical characteristics achieved superior performance: AUC 0.98, accuracy 0.92 on the test set, outperforming models using only clinical data (AUC 0.93) or two retinal parameters plus clinical data (AUC 0.94). On the multi-centre validation set, the model maintained accuracy 0.91 and AUC 0.95. For prognosis, a mixed-effects parameter (WQS-prognosis) was derived; the COX-RF model achieved an AUC of 0.88. CONCLUSIONS:Retinal microvasculature are effective biomarkers for DN. The proposed non-invasive model demonstrated high accuracy and generalizability, offering a valuable tool for optimising DN management.

BACKGROUND:While deep learning has advanced pathological analysis in IgA nephropathy (IgAN), the lack of integrated models that combine multi-label structural identification, Oxford classification, and prognosis prediction remains a significant clinical challenge. METHODS:We developed DeepSNN, a novel deep sequential neural network that serves as a multi-task model trained on multi-center multi-modal renal datasets. The architecture integrates lesion segmentation, glomerular classification, Oxford MEST-C scoring, and prognosis prediction subnets. To ensure interpretability, we conducted visualization experiments and comparative analyses with pathologists' diagnostic patterns. Pathologist comparisons employed Cohen's Kappa with blinded re-evaluation of test and validation sets. RESULTS:DeepSNN demonstrated exceptional lesion identification capabilities across the People's Liberation Army General (PLAG) Hospital dataset (n = 245) and China-Japan Friendship (CJF) Hospital dataset (n = 32), achieving dice coefficients of 0.95 and 0.92, respectively. For Oxford classification, DeepSNN delivered outstanding outcomes with high Kappa values of 0.84, 0.79, 0.87, 0.87, and 0.82 for M, E, S, T, and C scores on the PLAG dataset. Notably, our method outperformed three junior pathologists and achieved comparable performance to senior pathologists across both datasets. During a median follow-up of 47.7 (IQR: 21.9-61.1) months, DeepSNN excelled in prognosis prediction (AUC: 0.810), demonstrating improvement over the International IgA Nephropathy Prediction Tool (IIPT) (AUC: 0.742, ΔAUC = +0.068) in PLAG Hospital dataset (n = 245). Furthermore, visualization maps showed consistent pathological region identification between pathologists and DeepSNN. CONCLUSIONS:DeepSNN successfully integrates multiple diagnostic tasks with performance comparable to senior pathologists, demonstrating substantial potential for streamlining IgAN clinical workflows. This innovation addresses critical gaps in automated renal pathology analysis while maintaining clinical interpretability.

This study aimed to explore the specific efficacy of rituximab (RTX) in the treatment of membranous nephropathy (MN) and compare and analyze the differences in effectiveness among various treatment regimens, with the objective of identifying the optimal treatment protocol suitable for the medical environment in China. This retrospective study focused on patients with MN who were treated with RTX and hospitalized at the First Medical Center of PLA General Hospital between January 1, 2019, and December 30, 2022. These patients were followed up for more than one year. We collected clinical data from these patients and categorized them into three groups on the basis of their RTX treatment background: the combined glucocorticoids (GCs) and/or immunosuppressants (IMS) and RTX monotherapy treatment groups, the initial and non-initial treatment groups, and the standard RTX and non-standard RTX treatment groups. The study evaluated the comprehensive outcomes of complete or partial remission during follow-up, as well as relapses after remission. Additionally, Cox regression analysis was conducted to identify risk factors influencing patient remission and relapse. A total of 126 patients were enrolled in this study, with an average age of 49.0 ± 13.4 years. Among them, males accounted for up to 77.8

Individuals with special needs, such as children, the elderly, and the visually impaired, encounter significant hurdles in the field of personalized pharmacotherapy due to their distinctive medication needs. 3D printing technology, a novel approach for preparing drug products with intricate personalized designs, has shown considerable promise in improving the safety and adherence to patient medication regimens. This study chose acetaminophen, a commonly employed antipyretic analgesic, as the model drug and employed binder jetting 3D printing (BJ-3DP) to manufacture oral disintegrating tablets (ODTs) with multiple specifications and complex structures. The study initiated with an assessment of the printable properties of powder and ink formulations, proceeding to craft ODTs with individualized dosages and surfaces embedded in QR codes, cartoon figures, textual information, and raised braille. These tablets are internally designed with spaces that do not eject ink, resulting in a loose powder structure. The results of tests including porosity, surface roughness, Micro CT scanning, mechanical properties, and in vitro drug release of the printed product indicate that the personalized ODTs with complex structures designed in this study can offer treatment solutions for specific populations.

Small interfering RNA (siRNA) holds great promise for treating pulmonary diseases by enabling targeted gene silencing. However, effective delivery of siRNA to the lungs faces multiple challenges, including enzymatic degradation, mucus clearance, surfactant interactions, epithelial barriers, and macrophage clearance. Dry powder inhalation (DPI) has emerged as an attractive approach, offering direct, non-invasive administration to the respiratory tract, improved patient compliance, and enhanced siRNA stability. This review summarizes recent progress in siRNA DPI development, focusing on delivery systems and manufacturing techniques. Lipid-based, polymeric, hybrid, and peptide carriers have been widely investigated to encapsulate and protect siRNA, facilitate cellular uptake, and promote endosomal escape. Manufacturing approaches such as spray drying, spray freeze-drying, thin-film freeze-drying, and lyophilization are discussed in terms of their impact on particle properties, stability, and aerosol performance. Strategies to overcome pulmonary barriers, including surface modifications and excipient selection, are highlighted. Finally, current challenges and future perspectives for clinical translation are considered, emphasizing the need for optimized formulation design, safety evaluation, and scalable production. Together, these advances provide a foundation for the development of siRNA DPI therapies, offering new opportunities for the treatment of lung diseases.

The highly variable clinical progression of IgA nephropathy (IgAN) makes it challenging to accurately predict the risk of disease deterioration in patients. Although elevated single-nephron estimated glomerular filtration rate (eGFR) is implicated in disease progression, its prognostic utility remains underexplored due to methodological limitations in nephron quantification. This study aims to fill this research gap by establishing single-nephron eGFR as a prognostic factor and developing a predictive nomogram for assessing kidney disease progression in IgAN patients. We included 190 patients with biopsy-proven IgAN undergoing kidney biopsy and CT imaging. Single-nephron eGFR was calculated by dividing eGFR by nephron number, derived from cortical volume and glomerular density. A Cox model incorporating clinical, pathological, and single-nephron eGFR parameters was developed (training cohort: n = 133) and validated (validation cohort: n = 57). Kidney function decline was defined as an annual eGFR decrease ≥5 ml/min/1·73 m2, ≥40

Background: Chronic kidney disease (CKD) patients with coronavirus disease 2019 (COVID-19) are at significant risk of death. However, clinical identification of high-risk individuals remains suboptimal despite the recognition of many pathophysiological and comorbidity-related risk factors. We aim to develop a clinically simple machine learning (ML)-based score to predict acute COVID-19 mortality among CKD patients. Methods: CKD inpatients with COVID-19 were prospectively enrolled from December 2022 to January 2023 with a three-month follow-up. Feature selection from clinical and laboratory results was performed through least absolute shrinkage and selection operator and stepwise selection. Logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting were applied for ML model development. A predictive score for mortality was constructed using logistic regression. We compared predictive ability between the proposed score and other published scores. Results: 219 CKD patients were included and had a high mortality rate of 25.1%. The SVM model exhibited the best performance, with the validation area under the receiver operating characteristic curve (AUC) being 0.946 (95% CI 0.918, 0.974). The COVID-19 vaccination status, age, monocyte percentage, prothrombin activity, cardiac troponin T, and total bilirubin ("VAMPCT") were the most relevant factors and utilized to develop the scoring system with an AUC of 0.960 (95% CI 0.935, 0.985). Conclusion: ML models predicting three-month mortality had favorable performance for CKD patients with COVID-19. The VAMPCT mortality score provided a user-friendly approach.

Background and purpose: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with poor prognosis. Mating type switch/sucrose non-fermenting (SWI/SNF) chromatin remodeling complex (SCRC) is involved in the occurrence and progression of LUAD. This study aimed to investigate the relationship between SCRC-related genes (SCRCRGs) and prognosis of lung cancer. Materials and methods: RNA sequencing data and corresponding clinical data of patients diagnosed with LUAD were obtained from The Cancer Genome Atlas database. Hierarchical analysis of the expression of 31 genes in 510 LUAD and 56 paracancerous tissue samples was conducted to distinguish patients according to expression profiles. The prognostic roles of the SCRCRGs were assessed. The identified prognostic factors were integrated to investigate the probability of overall survival (OS) in LUAD. Results: No differences in OS, disease stable survival, disease free survival, and progression-free survival were noticed among the LUAD subgroups; however, the median survival period of Cluster_3 was longer than those of the other clusters. A total of 29 genes with significant differences between subgroups were identified. Significant differences in the expression of SCRCRGs, particularly SMARCA2, WDR77, and SMARCB1, were noticed between cancer and adjacent tissues. Following regression analysis using Lasso-Cox method, a model of five genes was obtained, which could predict the prognosis of LUAD. Conclusions: In LUAD, the differences in expression profile of SCRCRGs were related to prognosis and immune infiltration. SMARCA2 can be exploited as a potential target for immunotherapy.

Purpose:Fuchs endothelial corneal dystrophy (FECD) is the most common corneal endothelial dystrophy and guttae are crucial in causing progressive loss of corneal endothelium. This study aimed to find a way to inhibit the formation of guttae in FECD. Methods and Results:Mitochondria fatty acid β-oxidation (FAO) and tricarboxylic acid (TCA) cycle processes were negatively enriched in the FECD group according to gene set enrichment analysis in GSE171830. In vivo UV-A-induced late-onset FECD mouse model were established. After irradiation, aged proliferator-activated receptor alpha (PPARα-/-) mice manifested greater corneal opacity, cornea edema, and varied corneal endothelial cell morphology compared with wild-type mice. The total metabolites in cornea of aged PPARα-/- mice and wild-type mice were detected by mass spectrometry. Metabolites of the FAO pathway were decreased in corneas of PPARα-/- mice, coincident with enzymes of FAO decreased in GSE171830. The score for FAO energy metabolism was negatively related to that of the TGF-β pathway according to gene set variation analysis. The express of alpha smooth muscle actin (αSMA) and Col1a were increased in aged PPARα-/- mice and small interfering PPARα B4G12 cell lines. After irradiation, activation or overexpression of PPARα demonstrated reduced corneal endothelial damage and reversal of Descemet membrane thickening, along with downregulation of fibrosis-related genes such as αSMA and collagen type I alpha 1 (Col1a). In vitro experiments revealed that fenofibrate could reverse fibrosis and damage of cell-to-cell connections induced by TGF-β. Additionally, fenofibrate was found to alleviate mitochondrial damage in B4G12 and increase oxygen consumption rates after TGF-β treatment. Conclusions:Overall, we suggested that the overexpression or activation of PPARα can inhibit FAO energy dysfunction of corneal endothelium and the abnormal extracellular matrix formation in Descemet's membrane, which is the primary pathology of FECD. Thus, PPARα may be a potential target for attenuating the progression of FECD.

Inflammatory disorders and endothelial dysfunction are prevalent in patients with chronic kidney disease (CKD). Thrombomodulin (TM) possesses both anticoagulant and anti-inflammatory properties. This study aimed to investigate the association between TM levels and the severity of CKD. This cross-sectional study included two cohorts of patients with CKD from the General Hospital of the Chinese People’s Liberation Army. Patients with CKD were categorized into high and low TM groups based on the upper plasma TM reference value. The laboratory indices of patients were compared. Simultaneously, a correlation analysis was performed to identify the association between the TM and each parameter. Patients were categorized into two groups based on eGFR: preserved renal function (eGFR ≥ 60 mL/min/1.73 m²) and significantly impaired renal function (eGFR < 60 mL/min/1.73 m²). Logistic regression analysis and receiver operating characteristic (ROC) curves were used for analysis. A total of 33 patients with CKD were included in the discovery cohort, and 150 were included in the validation cohort. In the discovery cohort, creatinine (P = 0.0028) and urea nitrogen (P = 0.0011) were significantly higher in the high TM group compared to the low TM group, whereas eGFR (P = 0.0005) was lower. In the validation cohort, high TM group exhibited significantly higher creatinine (P < 0.001), urea nitrogen (P < 0.001), and 24-hour proteinuria levels (P < 0.001) compared to the low TM group, while eGFR (P < 0.001) was lower. Merging the discovery and validation cohorts revealed significant positive correlations between TM and IL-2, TNF-α, vWF (Act), vWF (Ag), serum creatinine, urea nitrogen, and 24-hour proteinuria, while eGFR was negatively correlated with TM (P < 0.001). After adjusting for confounders, TM (adjusted odds ratio = 1.31; 95

Atrial fibrillation is strongly associated with an increased risk of embolism, stroke, and heart failure. Current therapeutic approaches often have limited efficacy, and controlling atrial fibrosis remains a critical objective for upstream therapies. The specific mechanisms driving atrial fibrosis remain incompletely understood. The intermediate-conductance calcium-activated potassium channel KCa3.1 has been implicated in promoting fibroblast activation in various fibrotic diseases. This study investigates the role of angiotensin II (Ang II) in regulating KCa3.1, as well as its involvement in the pathogenesis of atrial fibrosis and the underlying signaling mechanisms. In a rat model, chronic Ang II infusion for 4 weeks induced atrial fibrosis, which was significantly attenuated by TRAM-34, a specific KCa3.1 channel blocker. In cultured rat atrial fibroblasts, Ang II treatment promoted fibroblast differentiation, proliferation, migration and collagen production, effects that were suppressed by TRAM-34 and KCa3.1 knockdown. Overexpression of KCa3.1 in fibroblasts further confirmed its pro-fibrotic role. Mechanistically, Ang II upregulated KCa3.1 expression and current density by activating the JNK/AP-1 signaling pathway. This involved phosphorylation of JNK, c-Jun, and c-Fos, leading to the formation of c-Jun/c-Fos heterodimers that directly bound to the KCa3.1 promoter to enhance its transcription. Together, these findings demonstrate that KCa3.1 mediates fibroblast activation and atrial fibrosis through the JNK/AP-1 pathway.

Mesangial proliferative glomerulonephritis (MsPGN) is the most common glomerulonephritis pathological type, including IgA nephropathy (IgAN), in which regional immune injury leads to disease progression without targeted treatment approaches. The mechanism of regional immune injury in MsPGN is unclear. We previously performed single-cell RNA sequencing (scRNA-seq) of IgAN and identified that the CX3CR1 gene increased in kidney. In this study, further scRNA-seq analysis and cellchat analysis revealed that CX3CL1 and CX3CR1 expression was increased in mesangial cells and monocytes/macrophages, respectively, in IgAN, mediating stronger crosstalk. This result and its association with regional immune injury were validated in clinical specimens and MsPGN animal model. Deficiency of CX3CR1+ monocytes/macrophages in the MsPGN animal model attenuated proteinuria, cell proliferation, and inflammation in glomerulus. Mechanistically, CX3CL1 in activated mesangial cells induced CX3CR1+ monocyte/macrophage migration and activation, and RNA-seq, Luminex multiplex immunoassay, and molecular analysis revealed that CX3CR1+ monocytes/macrophages induced mesangial cell injury via the MIF-CD74 interaction and activated the phosphatidylinositol 3-kinase (PI3K)/proteinserine-threonine kinase (AKT) pathway. Lastly, the therapeutic effect of the CX3CL1 monoclonal antibody quetmolimab was validated for inhibiting the progression of MsPGN. These findings demonstrate that activated mesangial cells interact with CX3CR1+ monocytes/macrophages promoting glomerulus regional immune injury in MsPGN, providing evidence into the CX3CL1-CX3CR1 axis as a novel target of treatment for MsPGN.