Using novel micropore technology combined with artificial intelligence to differentiate Staphylococcus aureus and Staphylococcus epidermidis.
Sci Rep
; 14(1): 6994, 2024 03 24.
Article
en En
| MEDLINE
| ID: mdl-38523156
ABSTRACT
Methods for identifying bacterial pathogens are broadly categorised into conventional culture-based microbiology, nucleic acid-based tests, and mass spectrometry. The conventional method requires several days to isolate and identify bacteria. Nucleic acid-based tests and mass spectrometry are relatively rapid and reliable, but they require trained technicians. Moreover, mass spectrometry requires expensive equipment. The development of a novel, inexpensive, and simple technique for identifying bacterial pathogens is needed. Through combining micropore technology and assembly machine learning, we developed a novel classifier whose receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.94, which rapidly differentiated between Staphylococcus aureus and Staphylococcus epidermidis in this proof-of-concept study. Morphologically similar bacteria belonging to an identical genus can be distinguished using our method, which requires no specific training, and may facilitate the diagnosis and treatment of patients with bacterial infections in remote areas and in developing countries.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Infecciones Estafilocócicas
/
Ácidos Nucleicos
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
País de afiliación:
Japón