Fiche publication
Date publication
décembre 2023
Journal
Medical mycology
Auteurs
Membres identifiés du Cancéropôle Est :
Pr VILLENA Isabelle
Tous les auteurs :
Mohammad N, Antoine H, Lefebvre A, Menvielle L, Toubas D, Ranque S, Villena I, Tannier X, Normand AC, Piarroux R
Lien Pubmed
Résumé
Aspergillosis of the new-born remains a rare but severe disease. We report four cases of primary cutaneous A. flavus infections in premature new-borns linked to incubators contamination by putative clonal strains. Our objective was to evaluate the ability of MALDI-TOF coupled to Convolutional Neural Network (CNN) for clone recognition in a context where only a very small number of strains are available for machine learning. Clinical and environmental A. flavus isolates (n = 64) were studied, 15 were epidemiologically related to the four cases. All strains were typed using microsatellite length polymorphism. We found a common genotype for 9/15 related strains. The isolates of this common genotype were selected to obtain a training dataset (6 clonal isolates/25 non-clonal) and a test dataset (3 clonal isolates/31 non-clonal), and spectra were analysed with a simple CNN model. On the test dataset using CNN model, all 31 non clonal isolates were correctly classified, 2/3 clonal isolates were unambiguously correctly classified whereas the third strain was undetermined (i.e the CNN model was unable to discriminate between GT8 and non-GT8). Clonal strains of A. flavus have persisted in the neonatal intensive care unit for several years. Indeed, two strains of A. flavus isolated from incubators in September 2007, are identical to the strain responsible for the second case that occurred 3 years later. MALDI-TOF is a promising tool for detecting clonal isolates of A. flavus using CNN even with a limited training set for limited cost and handling time.
Mots clés
Aspergillus flavus , Convolutional Neural Network, MALDI-TOF, primary cutaneous aspergillosis, strain typing
Référence
Med Mycol. 2023 12 23;:
