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. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.

Fecal Microbiota Transplantation May Represent a Good Approach for Patients with Focal Segmental Glomerulosclerosis: A Brief Report.

This is the first report of fecal microbiota transplantation (FMT) in patients with chronic kidney disease. The patient was subjected to focal segmental glomerulosclerosis (FSGS), with onset in April 2021. The main manifestation featured abnormal renal function and no proteinuria at the level of nephrotic syndrome. In May 2021, she showed biopsy-proven FSGS and was treated with glucocorticoid. However, after glucocorticoid reduction, the patient's serum creatinine increased again, so she adjusted the dosage and continued use until now. In April 2022, the patient was prescribed the FMT capsules. After FMT, the renal function remained stable, urinary protein decreased, reaching the clinical standard of complete remission, and there was no recurrence after glucocorticoid reduction. Furthermore, the patient showed significantly decreased hyperlipidemia, triglyceride (TG) and cholesterol (CHO) after FMT. During FMT, the level of cytokines fluctuated slightly, but returned to the pre-transplantation level after three months. From this, we conclude that FMT is a potential adjuvant therapy for FSGS, and patients can benefit from improving renal function and dyslipidemia.

Fecal Capsule as a Therapeutic Strategy in IgA Nephropathy: A Brief Report.

In this brief report, we reported an IgA nephropathy (IgAN) patient who presented in November 2020 with an acute exacerbation with massive proteinuria and diarrhea. He had the earliest onset in 2018 when his IgAN was diagnosed by renal biopsy. He has been treated with active ACEI/ARB drugs for more than 90 days, intermittent steroid therapy, combined with anti-infective therapy. Although his acute symptoms resolved with each episode, he became increasingly severe as the interval between episodes shortened. Accordingly, the immunosuppressive drugs were administered under the KDIGO guidelines and related guidelines. However, the patient and his family refused this treatment. We pondered over the possible pathogenesis of IgAN, and after a full discussion with the patient and his family, FMT was administered to him after obtaining his informed consent. During the FMT procedure, one healthy volunteer (the doctor himself) also took the FMT capsules. In the end, the patient's urine protein dropped significantly and even turned negative after treatment. Neither the patient nor the healthy volunteer experienced any serious adverse effects during the use of the capsules and the subsequent 6-month follow-up period. We also used metagenomic sequencing to analyze the intestinal flora of patients before and after treatment, and a gradual increase stood out in the abundance of the patient's intestinal flora after drug administration.

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 development of IgA Nephropathy results in changes in the intestinal microbial ecosystem that affect host physiology and health. Similarly, changes in intestinal microbiota also affect the development of IgA Nephropathy. Thus, the gut microbiome represents a novel therapeutic target for improving the outcome of IgA Nephropathy, including hematuria symptoms and disease progression. In this review, we summarize the effect of intestinal microbiota on IgA Nephropathy in recent years and it has been clarified that the intestinal microbiota has a great influence on the pathogenesis and treatment of IgA Nephropathy.

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. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.