Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence

  1. García-Cuesta, Esteban
  2. Salvadó, Victoria
  3. Conde, José Elías 1
  4. Peña-Méndez, Eladia María 1
  5. Verdú, Enrique
  6. Boadas-Vaello, Pere
  7. Serrando, María Teresa
  8. Jiménez-Romero, Orlando
  9. Deulofeu, Meritxell
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
Frontiers in Medicine

ISSN: 2296-858X

Año de publicación: 2021

Volumen: 8

Tipo: Artículo

DOI: 10.3389/FMED.2021.661358 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Frontiers in Medicine

Resumen

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.

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