Leukemia

Exploratory Evaluation of Dual Digital Platforms for Acute Brain Conditions in Medically Underserved Rural Areas : A Chuncheon, Gangwon-do Experience.

OBJECTIVE: To describe an exploratory initial experience with two complementary digital platforms consisting of an AI-based telemedicine system and an SNS-based transfer coordination network, for acute brain conditions in rural areas with limited or no on-site neurosurgical coverage. METHODS: We retrospectively reviewed patients with acute brain conditions who first presented to rural hospitals an...

Machine learning-driven conditional survival prediction model for atypical teratoid/rhabdoid tumor.

PURPOSE: Atypical teratoid/rhabdoid tumor (AT/RT) is a rare and highly malignant pediatric brain tumor with dismal prognosis. We aimed to characterize dynamic survival patterns using conditional survival (CS) and annual hazard rate (AHR) analyses, identify key prognostic factors through machine learning (ML)-based feature selection, and develop an interpretable CS-nomogram for individualized progn...

Updating CMV protocols in lung transplant patients: a single-center case study modeling use of generative AI for antimicrobial stewardship protocol development and economic impact analysis.

OBJECTIVE: Antimicrobial Stewardship Programs (ASPs) need healthcare economic analyses to support and inform ASP strategies. This work aimed to determ...

Impact of middle meningeal artery embolization on neurological complications and discharge outcomes in chronic subdural hematoma: A machine learning-based comparative study.

INTRODUCTION: Chronic subdural hematoma (cSDH) predominantly affects older adults, often those with prior head trauma, anticoagulation therapy, or chr...

Deep Learning Predicts Mutations and Outcomes in Gastrointestinal Stromal Tumors from Whole-Slide Images.

Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase (KIT) and platelet-deri...

Integrative single-cell and spatial transcriptomics analysis reveals a baicalein-responsive 10-gene signature for non-small cell lung cancer.

BACKGROUND: Non‑small cell lung cancer (NSCLC) remains a leading cause of cancer‑related mortality worldwide. Baicalein, a natural flavonoid, has show...

Development and validation of pathomics signature for predicting prognosis of advanced high-grade serous ovarian carcinoma patients after platinum-based chemotherapy.

In patients with advanced high-grade serous ovarian carcinoma (HGSOC), the standard treatment typically involves platinum-based chemotherapy after deb...

Association of Staphylococcus aureus bloodstream infection with 7-day incident delirium in critically Ill adults: a propensity-weighted competing risk analysis.

BACKGROUND: Delirium is a frequent manifestation of acute brain dysfunction in critically ill patients with bloodstream infections (BSI). While the as...

Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.

PURPOSE: Accurate prediction of recurrence risk is crucial for personalizing therapy in human epidermal growth factor receptor 2-positive (HER2-positi...

Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.

OBJECTIVE: We compared three magnetic resonance imaging (MRI) sequences-native zero echo time (ZTE), deep learning (DL)-chemical shift correction (CSC...

Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.

BACKGROUND: Chronic kidney disease (CKD) is a global health burden characterized by heterogeneous progression trajectories. Without timely and appropr...

Structural damage assessment in the spine of patients with axial spondyloarthritis - results from an international OMERACT multi-reader exercise using MRI-based synthetic CT.

OBJECTIVE: To develop and perform preliminary cross-sectional validation of a magnetic resonance imaging (MRI)-based outcome measure for assessing spi...

TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record trajectories.

Longitudinal electronic health record (EHR) trajectories are highly heterogeneous, sparse, and irregular, making unsupervised temporal pattern discove...

Understanding resistance in glioblastoma: insights into personalized and targeted therapeutic strategies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despite combined treatments, including surgical removal...

3D chromatin architecture-related genes orchestrate LUAD evolution and therapy resistance: insights from integrative machine learning and spatial single-cell mapping.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent and lethal malignancy. The three-dimensional (3D) chromatin architecture significantly influence...

Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.

OBJECTIVES: This study evaluated the accuracy, time efficiency, and workflow consistency of artificial intelligence (AI)-assisted versus human expert ...

Agreement between three state-of-the-art deep learning bone age estimation models and chronological age in a large contemporary pediatric cohort.

BACKGROUND: Radiographic bone age estimation is routinely performed in children to evaluate short stature, early or late puberty, and endocrine disord...

How to quickly determine whether patients with chronic cough need corticosteroid treatment--construction of a predictive model for corticosteroid-responsive cough in chronic cough.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-r...

An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.

Acute Lymphoblastic Leukemia (ALL) is one of the most aggressive hematological malignancies, and its early diagnosis remains challenging due to non-sp...

PPAOT predictor: ARKA-RASTR strategy-driven rodent acute oral toxicity prediction for pyrazole and pyrrolidine scaffolds-based chemicals with intelligent mechanistic interpretation.

Pyrazole and pyrrolidine represent two classes of high-frequency nitrogen-containing "privileged scaffolds" in drug discovery and industrial chemicals...