Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases.
EMBO Mol Med
; 11(10): e10431, 2019 10.
Article
en En
| MEDLINE
| ID: mdl-31468702
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Fiebre Tifoidea
/
Reacción en Cadena de la Polimerasa
/
Perfilación de la Expresión Génica
/
Técnicas de Diagnóstico Molecular
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
País como asunto:
Asia
Idioma:
En
Año:
2019
Tipo del documento:
Article