Impact of evolving strategies for arteriovenous graft creation and management on patency outcomes.

Background: Arteriovenous grafts (AVGs) are essential alternatives for hemodialysis patients who are unsuitable for arteriovenous fistulas (AVFs). As surgical expertise and monitoring strategies evolve, understanding the long-term performance and influencing factors of AVGs is crucial.

Methods: This retrospective study analyzed 980 patients who underwent AVG creation at a single center between 2014 and 2022.

SCNT: an R package for data analysis and visualization of single-cell and spatial transcriptomics.

Background: The emergence of single-cell (SC) and spatial transcriptomics (ST) has revolutionized our understanding of gene expression dynamics in complex tissues. However, it also presents challenges for data analysis and visualization, particularly due to the complexity of ST data and the diversity of analysis platforms. The SCNT (Single-Cell, Single-Nucleus, and Spatial Transcriptomics Analysis and Visualization Tools) package was developed to address these challenges by providing an efficient and user-friendly tool for processing, analyzing, and visualizing SC and ST data.

A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.

Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). AI-driven models utilize diverse datasets-including electronic health records, imaging, and biomarkers-to improve clinical decision-making. Applications such as convolutional neural networks for kidney biopsy interpretation, and predictive modeling for renal replacement therapies underscore AI's potential.

Gut microbiota implication in diabetic kidney disease: mechanisms and novel therapeutic strategies.

Diabetic kidney disease (DKD) is one of the leading causes of chronic kidney disease and end-stage renal disease worldwide, predominantly driven by the rise in type 2 diabetes mellitus. Recent evidence highlights the crucial role of gut microbiota dysbiosis in the development and progression of DKD. Dysbiosis, characterized by a reduction in beneficial short-chain fatty acid-producing bacteria and an increase in pathogenic species such as and , exacerbates systemic inflammation, insulin resistance, and kidney damage through mechanisms like increased intestinal permeability and the production of pro-inflammatory metabolites like lipopolysaccharides.

The impact of rising peripheral blood naïve CD8 T cell levels on chronic kidney disease onset: a Mendelian randomization study.

Background: The global incidence of chronic kidney disease (CKD) is rising rapidly. Immune cells play a crucial role in the onset and progression of CKD, however, the causal relationships and underlying immunological mechanisms remain incompletely elucidated. This deficiency hinders the development and application of early interventions and immunotherapies for CKD.

Deciphering the causal link between gut microbiota and membranous nephropathy: insights into potential inflammatory mechanisms.

Background: Membranous nephropathy (MN), a leading cause of adult nephrotic syndrome and renal failure, has been linked to gut microbiota (GM) and their metabolites. However, direct causal relationships and therapeutic implications remain unclear.

Methods: We utilized a comprehensive GWAS dataset that encompasses GM, metabolites, and MN through two-sample Mendelian randomization (MR) analyses, bidirectional MR evaluations, and detailed sensitivity tests.

GPX4 expression changes in proximal tubule cells highlight the role of ferroptosis in IgAN.

As an important mechanism of renal injury, oxidative stress (OS) is inseparable from the occurrence of renal fibrosis and the rapid progression of renal failure. However, the contribution of OS to IgA nephropathy (IgAN), the primary driver of chronic kidney disease remains uncertain. To investigate the effects of OS in IgAN, and identify the mechanisms of cell and tissue injury and protection, single-cell RNA sequencing (scRNA-seq) data and microarray data of IgAN were collected and analyzed.

Direct anastomosis indwelling needle puncture: a rapid and safe transitional hemodialysis access for patients with renal failure.

Objectives: Vascular access thrombosis (VAT) is a common complication in patients with end-stage renal disease (ESRD), significantly impacting hemodialysis efficacy and patient survival. Currently, temporary dialysis access is typically established deep vein catheterization (VC), however, this method is highly invasive and associated with risks of infection and other complications. This study aims to explore the feasibility of using direct anastomosis indwelling needle puncture (DAINP) for temporary dialysis access.

A practical guide for nephrologist peer reviewers: understanding and appraising Mendelian randomization studies.

Identifying risk factors for disease onset and progression has been a core focus in nephrology research. Mendelian Randomization (MR) has emerged as a powerful genetic epidemiological approach, utilizing genome-wide association studies (GWAS) to establish causal relationships between modifiable risk factors and kidney disease outcomes. MR uses genetic variants as instrumental variables to infer causal relationships between exposures and disease outcomes.

Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study.

Background: IgA nephropathy (IgAN) is intimately linked to mucosal immune responses, with nasopharyngeal and intestinal lymphoid tissues being crucial for its abnormal mucosal immunity. The specific pathogenic bacteria in these sites associated with IgAN, however, remain elusive. Our study employs 16S rRNA sequencing and machine learning (ML) approaches to identify specific pathogenic bacteria in these locations and to investigate common pathogens that may exacerbate IgAN.

Exploring macrophage heterogeneity in IgA nephropathy: Mechanisms of renal impairment and current therapeutic targets.

The lack of understanding of the mechanism of renal injury in IgA nephropathy (IgAN) hinders the development of personalized treatment plans and targeted therapies. Improved insight into the cause of renal dysfunction in IgAN is necessary to enhance the effectiveness of strategies for slowing the progression of the disease. This study examined single cell RNA sequencing (scRNA seq) and bulk-RNA seq data and found that the gene expression of renal intrinsic cells (RIC) was significantly changed in patients with renal impairment, with a primary focus on energy metabolism.

Mendelian randomization analysis revealed that albuminuria is the key factor affecting socioeconomic status in CKD patients.

Previous studies indicate a strong correlation between the incidence of chronic kidney disease (CKD) and lower economic status. However, these studies often struggle to delineate a clear cause-effect relationship, leaving healthcare providers uncertain about how to manage kidney disease in a way that improves patients' financial outcomes. Our study aimed to explore and establish a causal relationship between CKD and socioeconomic status, identifying critical influencing factors.

Using Bayesian networks with tabu algorithm to explore factors related to chronic kidney disease with mental illness: A cross-sectional study.

While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey.

Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province.

Introduction: Chronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China's rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients' quality of life.

Using Bayesian network model with MMHC algorithm to detect risk factors for stroke.

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke.

Th17 cells in primary Sjögren's syndrome negatively correlate with increased and .

Background: Dysbiosis of the gut microbiota is closely related to chronic systemic inflammation and autoimmunity, playing an essential role in the pathogenesis of primary Sjögren's syndrome (pSS). Abnormalities in the proportions of blood T lymphocyte subtype, that is Th17/Treg, were detected in pSS patients. We aimed to determine the associations between gut microbiota and Th17/Treg in pSS.

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN.

Fucose as a potential therapeutic molecule against the immune-mediated inflammation in IgA nepharopathy: An unrevealed link.

Background: IgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN.

Differentiation of T Helper 17 Cells May Mediate the Abnormal Humoral Immunity in IgA Nephropathy and Inflammatory Bowel Disease Based on Shared Genetic Effects.

Background: IgA nephropathy (IgAN) is the most frequent glomerulonephritis in inflammatory bowel disease (IBD). However, the inter-relational mechanisms between them are still unclear. This study aimed to explore the shared gene effects and potential immune mechanisms in IgAN and IBD.

Gut Microbes in Immunoglobulin A Nephropathy and Their Potential Therapeutic Applications.

Microbial ecosystem consists of a complex community of bacterial interactions and its host microenvironment (tissue, cell, metabolite). Because the interaction between gut microbiota and host involves many diseases and seriously affects human health, the study of the interaction mechanism between gut microbiota and host has attracted great attention. The gut microbiome is made up of 100 trillion bacteria that have both beneficial and adverse effects on human health.

The Diagnostic and Predictive Significance of Immune-Related Genes and Immune Characteristics in the Occurrence and Progression of IgA Nephropathy.

Objective: To investigate the potential diagnostic and predictive significance of immune-related genes in IgA nephropathy (IgAN) and discover the abnormal glomerular inflammation in IgAN.

Methods: GSE116626 was used as a training set to identify different immune-related genes (DIRGs) and establish machine learning models for the diagnosis of IgAN; then, a nomogram model was generated based on GSE116626, and GSE115857 was used as a test set to evaluate its clinical value. Short Time-Series Expression Miner (STEM) analysis was also performed to explore the changing trend of DIRGs with the progression of IgAN lesions.