Blohmke, C.J., Muller, J., Gibani, M.M. et al. (15 more authors) (2019) Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases. EMBO Molecular Medicine, 11 (10). e10431. ISSN 1757-4676
Abstract
Misdiagnosis of enteric fever is a major global health problem, resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture‐confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 95%). Applying this signature to a culture‐negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data‐driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR‐based diagnostics for use in endemic settings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. Published under the terms of the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | biomarker; enteric fever; machine learning; transcriptomics |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Sep 2019 09:39 |
Last Modified: | 10 Dec 2021 12:26 |
Status: | Published |
Publisher: | EMBO Press |
Refereed: | Yes |
Identification Number: | 10.15252/emmm.201910431 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150382 |