End Stage Renal Disease

Effects of Renal Function on the Multimodal Brain Networks Affecting Mild Cognitive Impairment Converters in End-Stage Renal Disease.

RATIONALE AND OBJECTIVES: Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transitioning to Mild Cognitive Impairment (MCI) and evaluates its potential for predicting MCI risk.

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.

The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI technologies, leveraging advanced machine learning algorithms and big data analytics, are revolutionizing the diagnosis of hematological disorders such as anemia, leukemia, and lymphoma. This review explores how AI enhances diagnostic accuracy, effici...

Shengxuebao Mixture improves carboplatin-induced anemia by inhibiting apoptosis and ferroptosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Shengxuebao Mixture (SXB) is a traditional Chinese medicine which has been widely used on treating Chemotherapy-induce...

Machine learning-based risk prediction model for arteriovenous fistula stenosis.

BACKGROUND: Arteriovenous fistula stenosis is a common complication in hemodialysis patients, yet effective predictive tools are lacking. This study a...

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permane...

Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.

OBJECTIVES: To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patient...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory periton...

Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.

Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs...

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common c...

Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions.

Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenges persist in optimizing donor-recipient matching, ...

Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials.

Membrane incompatibility poses significant health risks, including severe complications and potential fatality. Surface modification of membranes has ...

A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices.

This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, c...

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and o...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality o...

Genomic determinants of biological age estimated by deep learning applied to retinal images.

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from laten...

DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.

INTRODUCTION: Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). However, there is still a lack of effecti...

Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.

As per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the n...

Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net Penalized Cox Regression; A Machine Learning Approach.

BACKGROUND: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machi...

Identification of Factors Influencing Donor-Derived Cell-Free DNA Levels up to One Year After Kidney Transplant.

Donor-derived cell-free DNA (dd-cfDNA) in the peripheral blood of allograft recipients has shown to early identify allograft injury. In this study, w...