Nephrology

Artificial intelligence and digital twins for the personalised prediction of hypertension risk.

Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various cardiovascular diseases. Traditional approaches to hypertension risk prediction, which rely on broad epidemiological data and common risk factors, often fail to account for individual variability, highlighting the need for advanced data-driven methodolog...

Multiple omics-based machine learning reveals peripheral blood immune cell landscape during acute rejection of kidney transplantation and constructs a precise non-invasive diagnostic strategy.

Kidney transplantation is the optimal treatment for end-stage renal disease (ESRD), but acute rejection (AR) remains a major factor affecting graft survival and patient prognosis. Currently, renal biopsy is the gold standard for diagnosing AR, but its invasiveness limits the application of dynamic monitoring. This study aims to analyze changes of immune cell and gene expression in the peripheral b...

Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches.

BACKGROUND: To investigate the association between the Dietary Inflammatory Index (DII), biological aging, and the staging and mortality of cardiovasc...

Effectiveness of a smart management system in improving adherence and clinical outcomes of patients receiving peritoneal dialysis: a retrospective cohort analysis.

BACKGROUND: Peritoneal dialysis (PD) is a widely used form of home dialysis. However, the efficacy of telehealth care in long-term management for PD p...

Identification and validation of parthanatos-related genes in end-stage renal disease.

BACKGROUND: End-Stage Renal Disease (ESRD) is a severe chronic kidney disease with a rising global incidence, often accompanied by various complicatio...

Robot-Assisted Retrograde Intrarenal Surgery: A Mini Review of the Literature on Robotic Surgery Platforms for Endoscopic Stone Surgery.

Retrograde intrarenal surgery (RIRS) has become a cornerstone in renal stone management, with robotic platforms recently entering clinical practice. T...

Comparison of neural networks for classification of urinary tract dilation from renal ultrasounds: evaluation of agreement with expert categorization.

BACKGROUND: Urinary tract dilation (UTD) is a frequent problem in infants. Automated and objective classification of UTD from renal ultrasounds would ...

Visit-to-visit blood pressure variability as a prognostic marker in peritoneal dialysis: insights from machine learning-based risk models.

Uncovering key markers and therapeutic targets for renal fibrosis in diabetic kidney disease through bulk and single-cell RNA sequencing.

BACKGROUND: Diabetic kidney disease (DKD) is the major cause of chronic kidney failure, with tubulointerstitial fibrosis playing a crucial role in dis...

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk ...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mor...

A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.

Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (...

Comprehensive analysis of pyroptosis-related gene signatures in renal fibrosis.

BACKGROUND: Renal fibrosis is key histopathological lesion in chronic kidney disease progression. This study explored pyroptosis-related gene signatur...

Unveiling degradation patterns in dye-sensitized solar cells: a machine learning perspective.

This study examines the time-dependent degradation of dye-sensitized solar cells (DSSCs) by systematically investigating several critical parameters, ...

Assessing the effect of perfluoroalkyl and polyfluoroalkyl substances on cardiovascular-kidney-metabolic syndrome: Insights from an interpretable machine learning model.

Cardiovascular-kidney-metabolic syndrome (CKM) and its association with exposure to emerging pollutants, particularly perfluoroalkyl and polyfluoroalk...

Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey.

BACKGROUND: Hypertension poses a significant public health challenge in low- and middle-income countries. In Bangladesh, the Health Population and Nut...

Tumor-Intrinsic and Microenvironmental Determinants of Impaired Antitumor Immunity in Chromophobe Renal Cell Carcinoma.

PURPOSE: While immune checkpoint inhibition (ICI) has transformed the management of many advanced renal cell carcinomas (RCCs), the determinants of ef...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressi...

Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.

Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive ...

Synergistic Approaches to Minimize Device Footprint and Energy Consumption in Vertical-Channel Synapse Transistors Using an InGaZnO Active Layer via Spacer Engineering of HfO.

In order to address the limitations of conventional von Neumann architectures in terms of deep neural networks, neuromorphic computing has been propos...