Mendelian randomization study of sodium-glucose cotransporter 2 inhibitors in cardiac and renal diseases.

Objective: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) target the reabsorption of sodium and glucose in the kidney proximal tubules to reduce blood sugar levels. However, clinical randomized controlled trials on SGLT2i have yielded inconsistent results, necessitating further research into their efficacy and safety for specific cardiac and renal diseases.

Methods: "Sodium in urine" was selected as a downstream biomarker of SGLT2i.

Machine learning model to estimate probability of remission in patients with idiopathic membranous nephropathy.

Background: Idiopathic membranous nephropathy (IMN) is a type of nephrotic syndrome and the leading cause of chronic kidney disease. As far as we know, no predictive model for assessing the prognosis of IMN is currently available. This study aims to establish a nomogram to predict remission probability in patients with IMN and assists clinicians to make treatment decisions.

Clinical predictive model to estimate probability of remission in patients with lupus nephritis.

Background: Lupus nephritis (LN) is a major organ complication and cause of morbidity and mortality in patients with systemic lupus erythematosus. This study aims to provide the clinician with a quantitative tool for the prediction of the individual remission probability of LN and obtain new insights for improved clinical management in LN treatment.

Methods: A total of 301 patients with renal biopsy-proven LN were recruited and randomly divided into model construction and validation group.

Clinical predictive model for the 1-year remission probability of IgA vasculitis nephritis.

Purpose: Early remission of Immunoglobulin A vasculitis nephritis (IgAVN) substantially affects its prognosis. In this work, a multivariate model to predict the 1-year remission probability of patients with IgAVN was developed on the basis of clinical laboratory data.

Methods: Data of 187 patients with IgAVN confirmed by renal biopsy were retrospectively assessed.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.

Background: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.

Methods: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis.

Transcriptome sequencing of circular RNA reveals a novel circular RNA-has_circ_0114427 in the regulation of inflammation in acute kidney injury.

Acute kidney injury (AKI) is a common serious syndrome characterized by rapid decrease of glomerular filtration rate and the progressive increase of serum creatinine. Circular RNAs (circRNAs) are regulatory RNAs that recently became popular among various diseases. However, the expression profile and function of circRNAs in AKI remain largely unknown.

Identification of prognosis-related alternative splicing events in kidney renal clear cell carcinoma.

Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown.

Integrated bioinformatics analysis reveals novel hub genes closely associated with pathological mechanisms of immunoglobulin A nephropathy.

Immunoglobulin A (IgA) nephropathy (IgAN) is the most common glomerular disease. The major pathological changes associated with it affect cell proliferation, fibrosis, apoptosis, inflammation and extracellular matrix (ECM) organization. However, the molecular events underlying IgAN remain to be fully elucidated.