Urology

DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images

Spatial transcriptomics technology remains resource-intensive and unlikely to be routinely adopted for patient care soon. This hinders the development of novel precision medicine solutions and, more importantly, limits the translation of research findings to patient treatment. Here, we present DeepSpot, a deep-set neural network that leverages recent foundation models in pathology and spatial mult...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depression

Steroid hormone profiles in affective disorders suggest hypothalamic– pituitary–adrenal (HPA) axis dysregulation and may reveal novel therapeutic targets. However, most existing studies focus narrowly on glucocorticoids. This study aims to comprehensively characterize alterations in the steroid metabolome of adolescents with depressive symptoms. This cross-sectional study analyzed the urinary excr...

High-resolution multiplexed antibody-omics and interpretable machine learning unveil novel pathogenic mechanisms in kidney transplant rejection

Antibody-mediated rejection (AbMR), driven by donor-specific alloantibodies (DSAs), is a major cause of late-stage kidney allograft failure, leading t...

Deep Learning-Based Oral Cancer Screening via Smartphone Imagery and Real-Time Web Interface

Oral cancer is a significant public-health issue and the existing methods of its detection are not as simple or fast as to be applicable by a wide pop...

A multi-modal deep learning framework for predicting PSA progression-free survival in metastatic prostate cancer using PSMA PET/CT imaging

PSMA PET/CT imaging has been increasingly utilized in the management of patients with metastatic prostate cancer (mPCa). Imaging biomarkers derived fr...

The Silent Author in Urology: Quantifying Large Language Model Influence at the Corpus Level

The recent increasing use of large language models (LLMs) such as ChatGPT in scientific writing raises concerns about authenticity and accuracy in bio...

Multilevel predictors categorization for post-CABG atrial fibrillation prediction

Postoperative atrial fibrillation (PoAF) is known as common coronary artery bypass grafting (CABG) complication. Despite its association with increase...

Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models

Effective risk communication is essential to shared decision-making in prostate cancer care. However, the quality of physician communication of key tr...

Human breast cancer is linked to Epstein-Barr virus because it targets stem cells: bioinformatic chromosome correlations

Breast cancer originates from rare “cancer stem cells”. Stem cells are especially susceptible to becoming cancerous because they readily become differ...

A Systematic Review of Multimodal Deep Learning and Machine Learning Fusion Techniques for Prostate Cancer Classification

Prostate cancer remains one of the most prevalent malignancies and a leading cause of cancer-related deaths among men worldwide. Despite advances in t...

Protective Dietary Antioxidants Intake Attenuate Cardiovascular-Kidney-Metabolic Syndrome Progression

Cardiovascular-kidney-metabolic (CKM) syndrome is characterized by complex pathophysiological interactions among cardiovascular diseases, and chronic ...

Artificial Intelligence quantified prostate specific membrane antigen imaging in metastatic castrate-resistant prostate cancer patients treated with Lutetium-177-PSMA-617

The VISION study1 found that Lutetium-177 (177Lu)–PSMA-617 (“Lu-177”) improved overall survival in metastatic castrate resistant prostate cancer (mCRP...

Evaluating the Efficacy of Large Language Models in Addressing Patient-Centric Inquiries in Multiple Cancers

Large Language Models (LLMs) have transformed how patients access health information online. Chatbots like ChatGPT allow users to ask direct questions...

Characterizing and Predicting End-of-Life Patient Trajectories Using Routine Clinical Data

Understanding the biological processes that precede death is critical for making informed clinical decisions and facilitating care transitions. Here, ...

Using deep learning methods to shorten acquisition time in children’s renal cortical imaging

This study evaluates the capability of diffusion-based generative models to reconstruct diagnostic-quality renal cortical images from reduced-acquisit...

Explainable AI to predict a complex multifactorial outcome, childhood obesity: Application to clinical epidemiology

Childhood obesity, driven by genetic and epidemiological factors, poses significant health risks, yet traditional machine learning models lack interpr...

Whole-genome sequencing analysis of left ventricular structure and sphericity in 80,000 people

Sphericity is a measurement of how closely an object approximates a globe. The sphericity of the blood pool of the left ventricle (LV), is an emerging...

Symbolic Regression for Mycophenolic Acid Dosage Prediction in Kidney Transplant Recipients

Chronic kidney disease (CKD) affects millions worldwide and often progresses to end-stage renal disease (ESRD), for which kidney transplantation remai...

Phenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction

Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. ...

Urinary pesticide profiles and liver disease risk in Thailand: a machine-learning risk-prediction model

Building on evidence linking urinary glyphosate to chronic liver disease (CLD) and hepatocellular carcinoma (HCC), we developed urinary pesticide prof...