Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining Fe
Staphylococcus aureus
Fe3O4 MNPs
MALDI-MS
MRSA
Machine learning
Neural network
Journal
Mikrochimica acta
ISSN: 1436-5073
Titre abrégé: Mikrochim Acta
Pays: Austria
ID NLM: 7808782
Informations de publication
Date de publication:
18 Apr 2024
18 Apr 2024
Historique:
received:
06
02
2024
accepted:
30
03
2024
medline:
19
4
2024
pubmed:
18
4
2024
entrez:
18
4
2024
Statut:
epublish
Résumé
Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe
Identifiants
pubmed: 38635063
doi: 10.1007/s00604-024-06342-z
pii: 10.1007/s00604-024-06342-z
doi:
Substances chimiques
Methicillin
Q91FH1328A
Magnetite Nanoparticles
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
273Subventions
Organisme : National Science and Technology Council
ID : 111-2113-M-A49-019-MY3
Organisme : National Science and Technology Council
ID : 111-2628-M-A49-008-MY4
Informations de copyright
© 2024. The Author(s).
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