Nephrology

Radiomics-Based Machine Learning versus the MAP Score for Predicting Haematocrit Decline after Laparoscopic Partial Nephrectomy.

OBJECTIVE: To evaluate and compare the ability of the Mayo Adhesive Probability (MAP) score and radiomics-based machine learning approaches to predict the degree of postoperative haematocrit (Hct) reduction using preoperative computed tomography (CT) images in patients with renal cell carcinoma (RCC) treated with laparoscopic partial nephrectomy (LPN). STUDY DESIGN: An observational retrospective ...

Exploring the potential targets and mechanisms of artemisinin in the treatment of diabetic kidney disease using network pharmacology and molecular docking.

Diabetic kidney disease (DKD) is a secondary glomerular disease caused by diabetes, and its incidence is increasing annually. Artemisinin is an organic compound with multiple pharmacological effects. This study aims to investigate the potential therapeutic effects of artemisinin on DKD and its molecular mechanisms. Common targets were identified using network pharmacology, transcriptome data from ...

Environmental Levels of Sodium p-Perfluorous Nonenoxybenzene Sulfonate Induce Nephritis in Renal Proximal Tubular Epithelial Cells via Modulation of Estrogen Signaling Pathways.

Sodium p-perfluorous nonenoxybenzene sulfonate (OBS), widely used as an alternative to per- and polyfluoroalkyl substances, has been implicated in tox...

Submanifold sparse convolutional networks for automated 3D segmentation of kidneys and kidney tumours in computed tomography.

Accurate delineation of kidney tumours in Computed Tomography (CT) is essential for downstream quantitative analysis and precision oncology that could...

Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.

Artificial intelligence (AI) is rapidly transforming the landscape of chronic medical conditions, such as cardio-kidney-metabolic (CKM) issues linked ...

A Cost-Efficient Multi-Angle Fusion Deep Learning for Ultrasound Localization Microscopy.

Ultrasound localization microscopy (ULM) enables super-resolution imaging of microvascular structures by localizing microbubbles from clutter-filtered...

Exploring the Lung-Liver Axis in Pulmonary Arterial Hypertension.

The liver's contribution to pulmonary arterial hypertension (PAH) pathogenesis remains unclear. We hypothesized that the liver promotes inflammatory i...

Predicting Positive Surgical Margins in Robot-Assisted Prostatectomy Using Machine Learning Models.

OBJECTIVE: To develop a machine learning (ML) model predicting positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP). METH...

Enhancing urothelial carcinoma diagnosis with artificial intelligence-integrated urine cytology: Biopsy-validated accuracy and efficiency gain.

BACKGROUND: Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and tim...

Kidney health without borders: a multilingual kidney education platform.

Deep learning for automatic segmentation of the inferior alveolar nerve using a hybrid CNN-transformer framework.

Accurate identification of the inferior alveolar nerve (IAN) is essential for preventing nerve injury during dental and maxillofacial procedures such ...

Generative adversarial networks for enhanced performance prediction of square CFST members under axial tension.

Taking square concrete-filled steel tubular (CFST) members under axial tension as the research object, a three-dimensional mesoscopic finite element m...

Clinical subtypes and prognosis of new-onset atrial fibrillation in critically ill patients.

BACKGROUND: New-onset atrial fibrillation (NOAF) is a common cardiovascular complication in critically ill patients and is consistently associated wit...

Beyond Reactive Surveillance: Can Machine Learning Finally Personalize Heart Transplant Care?

Quantitative MRI of pediatric chronic kidney disease and renal fibrosis: potential non-invasive clinical biomarkers.

Chronic kidney disease (CKD) in children is a rare but serious condition characterized by progressive renal dysfunction and a range of systemic compli...

A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.

Accurate identification of renal cell types within tissue architecture is fundamental for understanding normal kidney physiology and detecting early p...

Explainable ensemble machine learning for predicting diabetes mellitus and identifying key risk factors: a population-based study in northern Bangladesh.

Diabetes mellitus (DM) is an escalating global public health concern, with a rapidly increasing burden in low- and middle-income countries, including ...

Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.

BACKGROUND: Patients with advanced chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR) < 45 ml/min/1.73 m²,...

Electronic health record-derived machine learning model for hypoglycemia risk prediction in type 2 diabetes mellitus patients: development and validation.

BACKGROUND: Hypoglycemia is a serious complication of diabetes. Early recognition of hypoglycemia can improve clinical prognosis, however, traditional...

Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study.

BACKGROUND: Postoperative pneumonia is a significant complication, highlighting a patient's ongoing vulnerability. While traditional tools focus on sh...