MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

Journal: Malaria journal
PMID: 40251568

Abstract

BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microscopic expertise is waning in non-endemic regions. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) is nowadays the standard method in routine microbiology laboratories for bacteria and fungi identification in high-income countries, but is rarely used for parasite detection. This study aims to employ MALDI-TOF MS for identifying malaria by distinguishing P. falciparum-positive from P. falciparum-negative sera.

Authors

  • Fateneba Kone
    Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany.
  • Lucie Conrad
    Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany.
  • Jean T Coulibaly
    Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire.
  • Kigbafori D Silué
    UFR Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire.
  • Sören L Becker
    Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany.
  • Brama Koné
    University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire.
  • Issa Sy
    Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany. issa.sy@uks.eu.

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