Nephrology

PhenoMiner: a quantitative phenotype database for the laboratory rat, Rattus norvegicus. Application in hypertension and renal disease.

Rats have been used extensively as animal models to study physiological and pathological processes involved in human diseases. Numerous rat strains have been selectively bred for certain biological traits related to specific medical interests. Recently, the Rat Genome Database (http://rgd.mcw.edu) has initiated the PhenoMiner project to integrate quantitative phenotype data from the PhysGen Progra...

Perioperative and long-term renal functional outcomes of robotic versus laparoscopic partial nephrectomy: a multicenter matched-pair comparison.

PURPOSE: We compared perioperative and long-term renal functional outcomes of robot-assisted partial nephrectomy (RAPN) with laparoscopic partial nephrectomy (LPN) for small renal masses.

Robotic transperitoneal infra-renal aortic lymphadenectomy in early-stage endometrial cancer.

OBJECTIVES: To assess the clinical performance of robotic-assisted infra-renal aortic lymphadenectomy (IRL) using a single center-docked approach for ...

Associated Da Vinci and magellan robotic systems for successful treatment of nutcracker syndrome.

Here, we report the case of a 26-year-old woman suffering from nutcracker syndrome with concurrent disabling pelvic congestion syndrome. She was given...

Laparoscopic nephrectomy and partial nephrectomy: intraperitoneal, retroperitoneal, single site.

The indication for use of laparoscopy, in the pediatric population, was initially for diagnostic purposes. As confidence with the technology and utili...

Accept/decline decision module for the liver simulated allocation model.

Simulated allocation models (SAMs) are used to evaluate organ allocation policies. An important component of SAMs is a module that decides whether eac...

Electroforming of implantable tubular magnetic microrobots for wireless ophthalmologic applications.

Magnetic tubular implantable micro-robots are batch fabricated by electroforming. These microdevices can be used in targeted drug delivery and minimal...

Deep visual proteomics reveals distinct proximal tubular and glomerular injury programs in experimental diabetic kidney disease

Background: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD). However, most proteomic studies of DKD rely on bulk ki...

Toward Transportable Acute Kidney Injury Prediction: An Explainable XGBoost Model with Temporal Validation Using MIMIC-IV

Acute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. This is especially important bec...

Individual-Level Counterfactual Analysis of SGLT2 Inhibitors Versus DPP4 Inhibitors in Diabetic Kidney Disease Using Causal Machine Learning

Background: Traditional real-world studies comparing SGLT2 and DPP4 inhibitors on renal outcomes rely on propensity score matching, which causes high-...

LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening

Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches ...

ECG-based longitudinal risk prediction across diseases and organ systems

Artificial intelligence applied to routine electrocardiograms (ECGs) has largely focused on detecting existing disease or predicting individual cardio...

Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury

Background: Current understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific ...

BanffNET, a Deep Learning System for Comprehensive Histological Lesion Quantification in Kidney Transplant Biopsies

Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The in...

Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery

Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast ...

Abundant Glomerular Neutrophil Extracellular Traps in C3 Glomerulopathy

Introduction: C3 glomerulopathy (C3G) is driven by fluid-phase alternative complement pathway dysregulation, with emerging evidence linking glomerular...

Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Grading

Clear cell renal cell carcinoma (CCRCC) grading is essential for treatment planning, yet existing approaches either analyze patch-level images directl...

Where Identity Lives: Localized, Retain-Free Identity Unlearning in Multimodal Large Language Models

Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely...

Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a...

A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery

Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulati...