Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence.
MALDI-TOF MS analysis
NP samples
SARS-CoV-2
machine learning
viral transport media
Journal
Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047
Informations de publication
Date de publication:
2021
2021
Historique:
received:
30
01
2021
accepted:
11
03
2021
entrez:
19
4
2021
pubmed:
20
4
2021
medline:
20
4
2021
Statut:
epublish
Résumé
The high infectivity of SARS-CoV-2 makes it essential to develop a rapid and accurate diagnostic test so that carriers can be isolated at an early stage. Viral RNA in nasopharyngeal samples by RT-PCR is currently considered the reference method although it is not recognized as a strong gold standard due to certain drawbacks. Here we develop a methodology combining the analysis of from human nasopharyngeal (NP) samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with the use of machine learning (ML). A total of 236 NP samples collected in two different viral transport media were analyzed with minimal sample preparation and the subsequent mass spectra data was used to build different ML models with two different techniques. The best model showed high performance in terms of accuracy, sensitivity and specificity, in all cases reaching values higher than 90%. Our results suggest that the analysis of NP samples by MALDI-TOF MS and ML is a simple, safe, fast and economic diagnostic test for COVID-19.
Identifiants
pubmed: 33869258
doi: 10.3389/fmed.2021.661358
pmc: PMC8047105
doi:
Types de publication
Journal Article
Langues
eng
Pagination
661358Informations de copyright
Copyright © 2021 Deulofeu, García-Cuesta, Peña-Méndez, Conde, Jiménez-Romero, Verdú, Serrando, Salvadó and Boadas-Vaello.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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