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Updated: Jun 2, 2026
In vivo Imaging Method to Distinguish Acute and Chronic Inflammation
Published on: August 16, 2013
Differentiation between viral and bacterial acute infections using chemiluminescent signatures of circulating
Daria Prilutsky1, Evgeni Shneider, Alex Shefer
1Department of Virology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Abstract:
Oftentimes the etiological diagnostic differentiation between viral and bacterial infections is problematic, while clinical management decisions need to be made promptly upon admission. Thus, alternative rapid and sensitive diagnostic approaches need to be developed. Polymorphonuclear leukocytes (PMNs) or phagocytes act as major players in the defense response of the host during an episode of infection, and thereby undergo functional changes that differ according to the infections. PMNs functional activity can be characterized by quantification and localization of respiratory burst production and assessed by chemiluminescent (CL) byproduct reaction. We have assessed the functional states of PMNs of patients with acute infections in a luminol-amplified whole blood system using the component CL approach. In this study, blood was drawn from 69 patients with fever (>38 °C), and diagnosed as mainly viral or bacterial infections in origin. Data mining algorithms (C4.5, Support Vector Machines (SVM) and Naïve Bayes) were used to induce classification models to distinguish between clinical groups. The model with the best predictive accuracy was induced using C4.5 algorithm, resulting in 94.7% accuracy on the training set and 88.9% accuracy on the testing set. The method demonstrated a high predictive diagnostic value and may assist the clinician one day in the distinction between viral and bacterial infections and the choice of proper medication.
Insights
Distinguishing viral from bacterial infections is challenging. This study uses polymorphonuclear leukocyte (PMN) function analysis and machine learning to accurately differentiate infection types, aiding prompt clinical decisions.
Area of Science:
- Infectious Diseases
- Immunology
- Computational Biology
Background:
- Differentiating viral from bacterial infections is clinically challenging, impacting timely treatment.
- Polymorphonuclear leukocytes (PMNs) exhibit distinct functional changes during infections.
- Rapid and sensitive diagnostic methods are crucial for prompt clinical management.
Purpose of the Study:
- To assess PMN functional states in patients with acute infections.
- To develop a diagnostic model for distinguishing viral from bacterial infections.
- To evaluate the utility of chemiluminescence (CL) and data mining for infection diagnostics.
Main Methods:
- Collected blood samples from 69 patients with fever (>38 °C).
- Assessed PMN functional activity using a luminol-amplified whole blood chemiluminescence (CL) system.
- Applied data mining algorithms (C4.5, SVM, Naïve Bayes) to classify infection types.
Main Results:
- The C4.5 algorithm achieved 94.7% accuracy on the training set and 88.9% on the testing set.
- The CL-based method demonstrated high predictive diagnostic value.
- Functional PMN analysis effectively distinguished between viral and bacterial infections.
Conclusions:
- PMN functional analysis via CL assays combined with data mining offers a promising approach for differentiating viral and bacterial infections.
- This method can potentially assist clinicians in selecting appropriate antimicrobial therapies.
- Further validation may lead to a rapid, sensitive diagnostic tool for acute infections.
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