, a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial resistance and strain diversity. This comprehensive review critically analyzes the limitations of con...
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.
BACKGROUND: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcrip...
AIMS: The aim of this study was to conduct a bibliometric analysis of the relevant literature on Duchenne muscular dystrophy (DMD) to ascertain its current status, identify key areas of research and demonstrate the evolution of the field.
INTRODUCTION: The identification of peptides eluted from HLA complexes by mass spectrometry (MS) can provide critical data for deep learning models of antigen presentation prediction and promote neoantigen vaccine design. A major challenge remains in...
Cancer immunotherapy has emerged as a novel clinical therapeutic option for a variety of solid tumors over the past decades. The application of immunotherapy in primary and metastatic brain tumors continues to grow despite limitations due to the phys...
BACKGROUND: Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.
Fluid overload is associated with increased morbidity and mortality after pediatric cardiac surgery. Management of fluid overload can be difficult and conventional tools may increase the risk of acute kidney injury. This study aimed to study the effe...
PurposeWe aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH) cases on a real-world heterogeneous clinical dataset. Furthermore, we delved deeper into evaluatin...
BACKGROUND: Manually creating multiple-choice questions (MCQ) is inefficient. Automatic item generation (AIG) offers a scalable solution, with two main approaches: template-based and non-template-based (AI-driven). Template-based AIG ensures accuracy...
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