Nephrology

Multiomics in Renal Cell Carcinoma: Current Landscape and Future Directions for Precision Medicine.

PURPOSE OF REVIEW: Renal cell carcinoma (RCC) is a prevalent and increasingly diagnosed malignancy associated with high mortality and recurrence rates. Traditional diagnostic and therapeutic approaches have limitations due to the disease's molecular heterogeneity. This review aims to explore how the integration of omics sciences-genomics, transcriptomics, proteomics, and metabolomics-can enhance t...

Applications of artificial intelligence in abdominal imaging.

The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challenges in disease detection, classification, and personalized care. AI applications, particularly those leveraging deep learning and radiomics, have demonstrated remarkable accuracy in detecting a wide range of abdominal conditions, including but not lim...

Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage...

Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability.

: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, and its prognosis is closely linked to the International Society...

AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights.

Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital ...

Integrative multi-omics reveal NSUN2 facilitates glycolysis and histone lactylation-driven immune evasion in renal carcinoma.

Clear cell renal carcinoma (ccRCC) is the most prevalent and aggressive subtype of kidney cancer. Targeting ccRCC metabolism is a promising therapeuti...

Predictive machine learning model for 30-day hospital readmissions in a tertiary healthcare setting.

MOTIVATION: Hospital readmissions represent a major challenge for healthcare systems due to their impact on patient outcomes and associated costs. As ...

MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images.

Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enh...

Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.

RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunctio...

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) pl...

Cardiovascular, Kidney, Liver, and Metabolic Interactions in Heart Failure: Breaking Down Silos.

Over the past few decades, the rising burden of metabolic disease, including type 2 diabetes, prediabetes, obesity, and metabolic dysfunction-associat...

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework t...

Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin.

BACKGROUND: Acute kidney injury (AKI) is a significant concern among hospitalized patients receiving aminoglycosides. Identifying the risk factors ass...

Unlocking Enhanced Ionic Transport: A Machine Learning-Driven AIMD Study on Doping, Defects, and Strain in Antiperovskite Solid-State Electrolytes.

Ab initio molecular dynamics (AIMD) simulations have been employed to investigate doped antiperovskite solid-state electrolyte (AP SSE) structures, sp...

Deciphering Coulombic Efficiency of Lithium Metal Anodes by Screening Electrolyte Properties.

Coulombic efficiency (CE) is a quantifiable indicator for the reversibility of lithium metal anodes in high-energy-density batteries. However, the qua...

Metabolic reprogramming and immune microenvironment profiling in clear cell renal cell carcinoma: implications for prognosis, targeted therapy, and drug resistance.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of kidney cancer, distinguished by intricate interactions between metabolic reprogr...

A Machine Learning Method for Predicting Acute Kidney Injury in Patients with Intracranial Hemorrhage.

Intracranial hemorrhage (ICH) is a critical and urgent condition in clinical practice. Recent research has highlighted acute kidney injury (AKI) that ...

Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis.

BACKGROUND: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. Here, we i...

Rebuilding Earth's first skeletal animals: the original morphology of (Ediacaran, Brazil).

The evolutionary onset of animal biomineralization in the late Ediacaran ( 555-538 Ma) is marked by the global appearance of enigmatic tubular fossils...

Exploring Hidden Dangers: Predicting Mycotoxin-like Toxicity and Mapping Toxicological Networks in Hepatocellular Carcinoma.

Mycotoxins are potent triggers of hepatocellular carcinoma (HCC) due to their intricate interplay with cellular macromolecules and signaling pathways....