Sociodemographic index-age differences in the global prevalence of cardiovascular diseases, 1990-2019: a population-based study.

Background: This study aims to assess the global burden and trends in cardiovascular diseases (CVDs) prevalence, stratified by sociodemographic index (SDI) categories and age groups, across 204 countries and territories.

Methods: Utilizing data from the Global Burden of Disease Study 2019, this study analyzed trends in the age-standardized prevalence rate of overall and type-specific CVDs, including rheumatic heart disease, ischemic heart disease, stroke, hypertensive heart disease, non-rheumatic valvular heart disease, cardiomyopathy and myocarditis, atrial fibrillation and flutter, peripheral artery disease, endocarditis, and other cardiovascular and circulatory diseases. Age-standardized prevalence rates were stratified by SDI categories (low, low-middle, middle, high-middle, and high) and age groups (0-14, 15-49, 50-69, and ≥ 70 years).

Uncovering the mechanisms of diosmin in treating obesity-related kidney injury based on network pharmacology, molecular docking, and in vitro validation.

Obesity increases the risk of kidney injury, involving various pathological events such as inflammation, insulin resistance, lipid metabolism disorders, and hemodynamic changes, making it a significant risk factor for the development and progression of chronic kidney disease. Diosmin, a natural flavonoid glycoside, exhibits anti-inflammatory, antioxidant, anti-lipid, and vasodilatory effects. However, whether diosmin has a protective effect on obesity-related kidney injury remains unclear.

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

Background: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques.

Methods: Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.

Background: The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early.

Methods: This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram.

Exploring the nexus between fatigue, body composition, and muscle strength in hemodialysis patients.

Background: Fatigue is a relatively prevalent condition among hemodialysis patients, resulting in diminished health-related quality of life and decreased survival rates. The purpose of this study was to investigate the relationship between fatigue and body composition in hemodialysis patients.

Methods: This cross-sectional study included 92 patients in total.

SGLT2 inhibition, high-density lipoprotein, and kidney function: a mendelian randomization study.

Background: Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.

Background: The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).

Methods: After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC).

Diosmin ameliorates renal fibrosis through inhibition of inflammation by regulating SIRT3-mediated NF-κB p65 nuclear translocation.

Background: Renal fibrosis is considered an irreversible pathological process and the ultimate common pathway for the development of all types of chronic kidney diseases and renal failure. Diosmin is a natural flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether Diosmin protects kidneys by inhibiting renal fibrosis is unknown.

Global, regional and national burden of CKD in children and adolescents from 1990 to 2019.

Background: Chronic kidney disease(CKD) is one of the most prevalent non-communicable health concerns in children and adolescents worldwide; however, data on its incidence, prevalence, disability-adjusted life years (DALYs) and trends in the population are limited. We aimed to assess the global, regional and national trends in CKD burden in children and adolescents.

Methods: In this trend analysis based on the 2019 Global Diseases, Injuries, and Risk Factors Study, CKD incidence, prevalence and DALYs rates per 100 000 population for children and adolescents were reported at the global, regional and national levels, as well as the average annual percentage change (AAPC).

The Causal Effect of Obesity on the Risk of 15 Autoimmune Diseases: A Mendelian Randomization Study.

Introduction: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate the causal effects of obesity on 15 autoimmune diseases.

Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification.

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM-related markers in DKD and explored the immune cell infiltration in this process.

Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis.

Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections.

Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram.

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.

Background: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).

Methods: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model.

Analysis of the potential biological mechanisms of diosmin against renal fibrosis based on network pharmacology and molecular docking approach.

Background: Interstitial fibrosis is involved in the progression of various chronic kidney diseases and renal failure. Diosmin is a naturally occurring flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether diosmin protects kidneys by inhibiting renal fibrosis is unknown.

Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury.

This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model.

Environmental factors influencing the risk of ANCA-associated vasculitis.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of diseases characterized by inflammation and destruction of small and medium-sized blood vessels. Clinical disease phenotypes include microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA), and eosinophilic granulomatosis with polyangiitis (EGPA). The incidence of AAV has been on the rise in recent years with advances in ANCA testing.

Early Humoral Responses of Hemodialysis Patients After Inactivated SARS-CoV-2 Vaccination.

Purpose: To detect antibody responses to inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine in patients undergoing hemodialysis and to investigate vaccine-related adverse events.

Patients And Methods: A total of 120 hemodialysis (HD) patients and 24 healthy controls (HCs) who had not been previously infected with SARS-CoV-2 and had received their first dose of the inactivated vaccine (CoronaVac; Sinovac Biotech Ltd) were recruited for this study. All participants were scheduled to receive a second dose of inactivated SARS-CoV-2 vaccine.

Neutrophil-to-lymphocyte ratio in relation to the risk of all-cause mortality and cardiovascular events in patients with chronic kidney disease: a systematic review and meta-analysis.

Aim: To systematically evaluate the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the risk of all-cause mortality or cardiovascular events in patients with chronic kidney disease (CKD).

Methods: PubMed, Embase, and Web of Science databases were searched for cohort studies that were published since the databases were launched, until 1 April 2020. We selected papers according to specific inclusion and exclusion criteria, extracted data, and evaluated the quality of the citations.

miR-124 regulates liver cancer stem cells expansion and sorafenib resistance.

Liver cancer stem cells (CSCs) contribute to tumorigenesis, progression, recurrence and drug resistance of hepatocellular carcinoma (HCC). However, the underlying mechanism for liver CSCs expansion remains unclear. Herein, we report that miR-124 is downregulated in liver CSCs and associated with the poor prognosis of HCC.

miR-552 promotes ovarian cancer progression by regulating PTEN pathway.

Background: Increasing researches have demonstrated the critical functions of MicroRNAs (miRNAs) in the progression of malignant tumors, including ovarian cancer. It was reported that miR-552 was an important oncogene in both breast cancer and colorectal cancer. However, the role of miR-552 in ovarian cancer (OC) remains to be elucidated.