Startseite Overview of dengue diagnostic limitations and potential strategies for improvement
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Overview of dengue diagnostic limitations and potential strategies for improvement

  • Aruna Devi Selvaraj , Anand Ramaian Santhaseela ORCID logo und Elavarasan Tamilmani ORCID logo EMAIL logo
Veröffentlicht/Copyright: 29. Januar 2025
Diagnosis
Aus der Zeitschrift Diagnosis Band 12 Heft 3

Abstract

Introduction

Dengue is a viral infection caused by any one of the four related dengue virus (DENV) serotypes, 1–4. DENV is a single-stranded RNA virus belonging to the genus Flavivirus. Dengue can cause a range of symptoms, from mild to severe life-threatening illness. Currently, treatment for DENV is limited to supportive care, with better outcomes achieved through early diagnosis. The WHO has suggested that dengue mortality can be reduced to nearly zero by implementing appropriate clinical management strategies, such as early laboratory diagnosis. This calls for diagnostic approaches that combine high sensitivity and specificity, while also being suitable for point-of-care testing (POCT) in remote locations with minimal staff training and low testing costs.

Content

In this paper, we outline the limitations of existing confirmatory dengue diagnostic methods, such as ELISA and RT-PCR, which are time-consuming, expensive, and require skilled personnel. We also highlight alternative strategies to overcome these challenges. Additionally, the paper emphasizes the growing clinical demand for diagnosing severe dengue to reduce the risk of death, which must be addressed by next-generation dengue diagnostic approaches.

Summary

We propose the adoption of alternative strategies, such as fluorescence immunoassay (FIA) and chemiluminescence immunoassay (CLIA), which have the potential to overcome the limitations of existing dengue diagnostic methods.

Outlook

Improvements in dengue diagnosis, with a specific focus on identifying severe dengue in POCT setting, can help achieve the goal of zero deaths from dengue.


Corresponding author: Elavarasan Tamilmani, Chief Technology Officer, Matrix Labs Private Limited, Chennai 600077, Tamil Nadu, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: E.T. and A.D.S carried out the conception, design and drafting of the paper. A.R.S carried out revising the manuscript with inputs from E.T. and A.D.S. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-10-23
Accepted: 2024-12-26
Published Online: 2025-01-29

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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