Blohmke, C., Muller, J. orcid.org/0000-0002-1046-2968, Gibani, M. et al. (15 more authors) (Submitted: 2018) Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases. BioRxiv. (Submitted)
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 accurately distinguish culture-confirmed enteric fever cases from other febrile illnesses (AUROC>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 diagnostic for use in endemic settings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Author(s). For reuse permissions, please contact the Author(s). |
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: | 24 Oct 2018 15:35 |
Last Modified: | 24 Oct 2018 15:35 |
Published Version: | https://doi.org/10.1101/327429 |
Status: | Submitted |
Identification Number: | 10.1101/327429 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135962 |