Urology

Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with dilated cardiomyopathy (DCM) by bioinformatics analysis and machine learning.

BACKGROUND: Chronic kidney disease (CKD) is a globally prevalent and highly lethal condition, often accompanied by dilated cardiomyopathy (DCM), which increases the risk of cardiac complications. Early detection of DCM in CKD patients remains challenging, despite established research demonstrating the relationship between CKD and cardiac abnormalities.

Response to "Comment on 'Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning'".

A Novel and Modern Calculator to Predict Vaginal Birth after Cesarean Delivery.

Counseling patients who are considering a trial of labor after cesarean (TOLAC) is a challenging task given the risks and benefits of either approach....

Enhanced Pelvic CT Segmentation via Deep Learning: A Study on Loss Function Effects.

Effective radiotherapy planning requires precise delineation of organs at risk (OARs), but the traditional manual method is laborious and subject to v...

Use of ChatGPT for Urinary Symptom Management Among People With Spinal Cord Injury or Disease: Qualitative Study.

BACKGROUND: Individuals with spinal cord injury or disease (SCI/D) experience disproportionately high rates of recurrent urinary tract infections, whi...

Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization.

Bladder cancer is the fourth most common malignant tumor in men, with limited therapeutic biomarkers and heterogeneous responses to immunotherapy. Dis...

Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.

Identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (...

Applying exposure-response analysis to enhance Mycophenolate Mofetil dosing precision in pediatric patients with immune-mediated renal diseases by machine learning models.

BACKGROUND: Mycophenolate mofetil (MMF), a cornerstone immunosuppressant for lupus nephritis, is increasingly used off-label in pediatric immune-media...

Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems.

DFT-based machine-learning potentials (MLPs) are now routinely trained for condensed-phase systems, but surpassing DFT accuracy remains challenging du...

Hydronephrosis-Associated Renal Fibrosis: Clinical Validation of Spp1 as a Biomarker and Therapeutic Target.

BACKGROUND: Renal fibrosis is a key driver of chronic kidney disease (CKD), often leading to end-stage renal disease (ESRD). Secreted Phosphoprotein 1...

ASO Author Reflections: Artificial Intelligence in Bladder Cancer-Transforming Diagnosis and Treatment Paradigms.

Incorporating organ deformation in biological modeling and patient outcome study for permanent prostate brachytherapy.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the ma...

Creatine kinase in prostate cancer: A biosensor-driven diagnostic paradigm.

BACKGROUND: Prostate cancer (PC) remains a leading cause of cancer-related morbidity in men worldwide. Emerging evidence suggests that the brain-type ...

Letter to the Editor on "Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound".

Predicting Early Outcomes of Prostatic Artery Embolization Using -Butyl Cyanoacrylate Liquid Embolic Agent: A Machine Learning Study.

: Prostatic artery embolization (PAE) has been increasingly recognized, especially with recent progress in embolization techniques for the management ...

Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes.

Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic c...

Estimation of postmortem interval under different ambient temperatures based on multi-organ metabolomics and machine learning algorithm.

In forensic practice, the estimation of postmortem interval has been a persistent challenge. Recently, there has been an increasing utilization of met...

Advancing breast, lung and prostate cancer research with federated learning. A systematic review.

Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative training of machine learning (ML) models on diverse,...

Comprehensive analysis of the association between volatile organic compound pollutants and chronic kidney disease in hypertensive populations: insights from multi-omics approaches and identification of potential therapeutic targets.

Environmental pollutants, including volatile organic compounds (VOCs), are increasingly linked to chronic kidney disease (CKD), yet this association i...

Subregion-based radiomics analysis for predicting the histological grade of clear cell renal cell carcinoma.

PURPOSE: We explored the feasibility of constructing machine learning (ML) models based on subregion radiomics features (RFs) to predict the histologi...