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Epstein-Barr Virus Infections

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Plasma Epstein-Barr Virus DNA load for diagnostic and prognostic assessment in intestinal Epstein-Barr Virus infection.

Frontiers in cellular and infection microbiology
BACKGROUND: The prospective application of plasma Epstein-Barr virus (EBV) DNA load as a noninvasive measure of intestinal EBV infection remains unexplored. This study aims to identify ideal threshold levels for plasma EBV DNA loads in the diagnosis ...

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Nature communications
Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce ...

Deep Learning Fuzzy Inference: An Interpretable Model for Detecting Indirect Immunofluorescence Patterns Associated with Nasopharyngeal Cancer.

The American journal of pathology
The detection of serum Epstein-Barr virus antibodies by immunofluorescence assay (IFA) is considered the gold standard screening test for nasopharyngeal cancer (NPC) in high-risk populations. Given the high survival rate after early detection in asym...

Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm.

JAMA network open
IMPORTANCE: Epstein-Barr virus (EBV)-associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular test, EBV-encoded small RNA in situ hybridization. EBV-GC has 2 histologic characteristics, lymphoid...

Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology.

Scientific reports
The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal E...

Deep Learning-Based Stratification of Gastric Cancer Patients from Hematoxylin and Eosin-Stained Whole Slide Images by Predicting Molecular Features for Immunotherapy Response.

The American journal of pathology
Determining the molecular characteristics of cancer patients is crucial for optimal immunotherapy decisions. The aim of this study was to screen immunotherapy beneficiaries by predicting key molecular features from hematoxylin and eosin-stained image...

Etiological stratification and prognostic assessment of haemophagocytic lymphohistiocytosis by machine learning on onco-mNGS data and clinical data.

Frontiers in immunology
INTRODUCTION: Hemophagocytic lymphohistiocytosis (HLH) is a rare, complicated and life threatening hyperinflammatory syndrome that maybe triggered by various infectious agents, malignancies and rheumatologic disorders. Early diagnosis and identificat...

Can Machine Learning Models Based on Computed Tomography Radiomics and Clinical Characteristics Provide Diagnostic Value for Epstein-Barr Virus-Associated Gastric Cancer?

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to explore whether machine learning model based on computed tomography (CT) radiomics and clinical characteristics can differentiate Epstein-Barr virus-associated gastric cancer (EBVaGC) from non-EBVaGC.

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning.

Rheumatology (Oxford, England)
OBJECTIVES: EBV is a widespread virus implicated in various diseases, including SLE. However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients w...