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Related Concept Videos

Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
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Updated: May 24, 2026

Isolation and Quantification of Epstein-Barr Virus from the P3HR1 Cell Line
09:14

Isolation and Quantification of Epstein-Barr Virus from the P3HR1 Cell Line

Published on: September 28, 2022

Leveraging Clinical Data Warehouses to Detect Potential False-Negatives EBV Patients Automatically.

Morgane Pierre-Jean1, Pauline Comacle2, Denis Delamarre1

  • 1Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000 Rennes, France.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Epstein-Barr virus (EBV) detection can be challenging. A new algorithm identifies patients with negative EBV serology but positive PCR results, improving diagnostic accuracy and patient care.

Keywords:
Automatic alertsEpstein-Barr virus serologycaredata warehouse

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Rapid, Safe, and Simple Manual Bedside Nucleic Acid Extraction for the Detection of Virus in Whole Blood Samples
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Published on: June 30, 2018

Area of Science:

  • Infectious Diseases
  • Medical Diagnostics
  • Bioinformatics

Background:

  • Epstein-Barr virus (EBV) is highly prevalent, infecting over 90% of adults.
  • Accurate and timely EBV detection is crucial for managing severe infections and preventing complications.
  • Recent implementation of a new EBV serology kit at Rennes University Hospital revealed discrepancies with PCR results.

Purpose of the Study:

  • To develop an algorithm for identifying patients with potential false-negative EBV serology results.
  • To improve the accuracy of EBV infection diagnosis by cross-referencing serology with clinical data.
  • To enable proactive patient management by alerting clinicians to potential EBV cases.

Main Methods:

  • Utilized a clinical data warehouse to analyze patient records.
  • Developed a keyword-based algorithm to search clinical notes for EBV infection symptoms.
  • Identified patients with negative EBV serology but potential positive PCR results based on clinical data.
  • Implemented an automated email alert system for virologists.

Main Results:

  • Successfully identified potential false-negative EBV serology cases.
  • The algorithm effectively flagged patients requiring further PCR testing.
  • Automated alerts enable prompt intervention by clinical teams.

Conclusions:

  • The developed algorithm enhances the detection of Epstein-Barr virus infections.
  • Integrating clinical data analysis with serology improves diagnostic yield.
  • Automated alerts facilitate timely and improved patient care for EBV infections.