Improving the Detection of Epidemic Clones in
Candida parapsilosis
MALDI TOF
artificial intelligence
deep learning
epidemiology
neural network
outbreak
Journal
Microorganisms
ISSN: 2076-2607
Titre abrégé: Microorganisms
Pays: Switzerland
ID NLM: 101625893
Informations de publication
Date de publication:
20 Apr 2023
20 Apr 2023
Historique:
received:
14
03
2023
revised:
11
04
2023
accepted:
17
04
2023
medline:
28
4
2023
pubmed:
28
4
2023
entrez:
28
4
2023
Statut:
epublish
Résumé
Identifying fungal clones propagated during outbreaks in hospital settings is a problem that increasingly confronts biologists. Current tools based on DNA sequencing or microsatellite analysis require specific manipulations that are difficult to implement in the context of routine diagnosis. Using deep learning to classify the mass spectra obtained during the routine identification of fungi by MALDI-TOF mass spectrometry could be of interest to differentiate isolates belonging to epidemic clones from others. As part of the management of a nosocomial outbreak due to
Identifiants
pubmed: 37110493
pii: microorganisms11041071
doi: 10.3390/microorganisms11041071
pmc: PMC10146746
pii:
doi:
Types de publication
Journal Article
Langues
eng
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