Membranous nephropathy (MN), an autoimmune glomerulopathy driven by podocyte antigens, is a leading cause of end-stage renal disease (ESRD). However, its pathogenic mechanisms remain incompletely understood. This knowledge gap results in significant physical and psychological burdens for patients worldwide and poses substantial challenges for clinical management. Current conventional treatments for MN are often associated with significant side effects and may not yield satisfactory outcomes. Therefore, the development of more efficacious and better-tolerated therapeutic strategies for MN is imperative. Given these clinical challenges, Traditional Chinese Botanical Medicine (CHMs), with their multi-component and multi-target characteristics, have emerged as a promising alternative or complementary therapeutic approach for MN. This review aims to summarize the evidence on the mechanisms and clinical efficacy of CHMs and their active metabolites in treating MN, drawing from findings reported in animal experiments, clinical trials, systematic reviews, and meta-analyses. It seeks to elucidate the potential advantages of CHMs in MN management and to provide a reference for future research in this field. Study design and methods: For this review, the following major academic research databases were consulted: PubMed, ResearchGate, Science Direct, and Web of Science. "Glomerulonephritis, Membranous [MeSH Terms] OR Extramembranous Glomerulopathy [Text Word] OR Membranous Glomerulonephropathy [Text word] OR Membranous Nephropathy [Text word] OR Idiopathic Membranous Glomerulonephritis [Text word]OR Glomerulonephritides, Idiopathic Membranous. [Text word])," "(Medicine, Chinese Traditional [MeSH Terms] OR Medicine, Chinese Traditional [Text Word] OR Medicine, Chinese Traditional [Text word] OR Tongue Diagnosis, Traditional [Text word] OR Traditional Tongue Assessment [Text word]," "Drugs, Chinese Herbal [MeSH Terms] OR Chinese Drugs, Plant [Text Word] OR Chinese Herbal Drugs [Text word] OR Plant Extracts, Chinese [Text word] OR Chinese Plant Extracts [Text word]," "mechanism," "Meta-analysis," "systematic review," "RCT," "botanical drug" and their combinations were the keywords to search the relevant literature. Data were collected from 2019 to 2025.

Integrating coding and regulatory variation into unified, interpretable representations remains a challenge in functional genomics. Current approaches either focus on common variants or analyze individual variants in isolation, missing the cumulative, cell-type-specific impact of both coding and noncoding variants on each gene. We present Volaria, a computational framework that integrates coding and regulatory genetic variation into unified, gene-centered representations for disease outcome prediction from whole-genome sequencing. Volaria leverages deep learning models to capture variant effects on cell-type-specific gene expression and integrates them with AI-predicted exonic variant pathogenicity to produce representations that capture the cumulative effect of genome-wide rare and common variation. Applied to whole genomes of individuals with rare glomerular diseases, Volaria predicts individual outcomes directly from germline sequence, demonstrating that structured, cell-type-aware representations capture predictive signals beyond population-based polygenic risk scores and unstructured representations. Importantly, the framework identifies context-specific biological mechanisms, providing interpretability that can be aligned with clinical measurements. By encoding genome-wide variation into compact and biologically grounded representations, Volaria provides a scalable foundation for genome interpretation and individualized outcome modeling from germline sequence, complementing phenotypic and clinical information in the future integrative frameworks.

From 2015 to 2024, inclusion body hepatitis (IBH) was diagnosed in a total of 346 chicken necropsy cases submitted to the California Animal Health and Food Safety (CAHFS) laboratory system. The majority of cases (68%; 237/346) originated from a single commercial broiler company with a high density of poultry premises within the California Central Valley region. Reported mortality varied between 0.5% and 4.8% per day, and morbidity ranged between 5% and 10%. Clinical signs observed in affected flocks included depression, reluctance to move, ruffled feathers, greenish diarrhea, and, occasionally, icterus of skin and adipose tissue. Typical histopathological lesions included extensive hepatic necrosis, multifocal pancreatic necrosis, gizzard erosions, and glomerulonephropathy. Intranuclear inclusions were identified, mainly within degenerating hepatocytes, followed by pancreatic acinar cells, glandular epithelium of proventriculus and gizzard, and enterocytes of the small intestines and renal endothelium. Diagnosis was based on microscopic changes characteristic of IBH and a positive conventional PCR targeting the hexon gene of fowl adenovirus (FAdV) on liver tissue pools. Sequence analysis was performed on 41 selected FAdV-positive samples from diagnostic cases. The majority of FAdV PCR-positive samples aligned with FAdV-8b (16/41). However, FAdV-7 (14/41), FAdV-4 (7/41), and FAdV-11 (4/41) were also detected from IBH cases. Disease management was successfully achieved by the administration of an autogenous vaccine to breeders, at 11 and 18 wk of age, to confer maternal antibody protection to the broiler progeny.

Image features that characterize tubules from digitized kidney biopsies may offer insight into disease prognosis as novel biomarkers. For each subject, we can construct a matrix whose entries are a common set of image features (e.g., area, orientation, eccentricity) that are measured for each tubule from that subject's biopsy. Previous scalar-on-matrix regression approaches which can predict scalar outcomes using image feature matrices cannot handle varying numbers of tubules across subjects. We propose the CLUstering Structured laSSO (CLUSSO), a novel scalar-on-matrix regression technique that allows for unbalanced numbers of tubules, to predict scalar outcomes from the image feature matrices. Through classifying tubules into one of two different clusters, CLUSSO averages and weights tubular feature values within-subject and within-cluster to create balanced feature matrices that can then be used with structured lasso regression. We develop the theoretical large tubule sample properties for the error bounds of the feature coefficient estimates. Simulation study results indicate that CLUSSO often achieves a lower false positive rate and higher true positive rate for identifying the image features which truly affect outcomes relative to a naive method that averages feature values across all tubules. Additionally, we find that CLUSSO has lower bias and can predict outcomes with a competitive accuracy to the naïve approach. Finally, we applied CLUSSO to tubular image features from kidney biopsies of glomerular disease subjects from the Nephrotic Syndrome Study Network (NEPTUNE) to predict kidney function and used subjects from the Cure Glomerulonephropathy (CureGN) study as an external validation set.

IgA nephropathy (IgAN) is one of the most prevalent glomerulonephropathies and commonly leads to kidney failure. The recurrence of IgAN following transplantation remains a significant concern. Currently, detecting IgAN recurrence requires a kidney biopsy, highlighting the need for non-invasive biomarkers such as donor-derived cell-free DNA (dd-cfDNA) to aid in early detection. This prospective pilot study aims to evaluate dd-cfDNA as a non-invasive biomarker for detecting IgAN recurrence post-kidney transplantation. Specifically, the study seeks to compare %dd-cfDNA levels in transplanted patients with and without IgAN recurrence and correlate these levels with other kidney function parameters. A total of 32 patients with histologically confirmed IgAN were enrolled, including those with documented IgAN recurrence post-transplantation and those without recurrence. Plasma samples were collected and processed using the AlloSeq cfDNA kit to quantify relative %dd-cfDNA levels. Kidney function parameters, including estimated glomerular filtration rate (eGFR) and proteinuria, were also assessed. The study found no significant difference in %dd-cfDNA levels between transplanted patients with IgAN recurrence and those without recurrence (median 0.37% [IQR 0.28%-3.53%] vs. median 0.42% [IQR 0.15%-0.84%], p = 0.67). Also, %dd-cfDNA (AUC = 0.57 [95% CI 0.28-0.85], p = 0.64) failed to effectively discriminate IgAN recurrence compared to traditional kidney function parameters such as proteinuria (AUC = 0.96 [95% CI 0.87-1.00], p = 0.002) and eGFR (AUC = 0.74 [95% CI 0.47-1.00], p = 0.09). Relative (%) dd-cfDNA alone may not be a robust biomarker for detecting IgAN recurrence post-transplantation. While proteinuria proved a more effective indicator in this study, kidney biopsy remains the gold standard for definitive diagnosis. These findings highlight the challenges of using %dd-cfDNA as a standalone diagnostic tool for monitoring IgAN recurrence post-transplantation. Future research should explore larger patient cohorts and longitudinal assessments to refine the utility of dd-cfDNA and investigate potential combination strategies with other biomarkers.

The molecular pathophysiology of nephrotic syndrome remains largely elusive in pediatric patients. While most children with minimal change disease (MCD) show favorable responses to immunosuppressive therapy, those with focal segmental glomerulosclerosis (FSGS) often exhibit poorer treatment responses, with many experiencing either partial remission or no remission of proteinuria. The need for reliable glomerular disease biomarkers to predict treatment response and understand molecular pathways governing responsiveness and resistance is a critical unmet need in pediatric nephrology. In this study, we sought to characterize urine proteomes in children with MCD and FSGS to identify biomarkers distinguishing disease activity and associated molecular pathways. Using quantitative proteomics, urine proteins from children with MCD and FSGS in the CureGN Study were identified and correlated with disease onset and activity. Unbiased cluster analyses of nephrotic urine proteomes demonstrated a cluster with relatively increased immune response and complement proteins, suggesting important distinctions in disease characteristics within the nephrotic subgroups. These analyses yielded patient subpopulations with proteinuria and distinct urine proteome differences associated with 116 proteins exerting cluster separation in the multivariate analyses. These findings highlight the potential of unsupervised clustering to identify disease subgroups and provide insights into the underlying molecular heterogeneity within nephrotic syndrome, paving the way for more tailored therapeutic strategies and improved patient management.