Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification (2024)
- Authors:
- USP affiliated authors: INADA, NATALIA MAYUMI - IFSC ; BLANCO, KATE CRISTINA - IFSC ; BAGNATO, VANDERLEI SALVADOR - IFSC ; PATIÑO, CLAUDIA PATRICIA BARRERA - IFSC ; SOARES, JENNIFER MACHADO - IFSC
- Unidade: IFSC
- DOI: 10.1117/12.3001639
- Subjects: ÓPTICA; APRENDIZADO COMPUTACIONAL; ANTIBIÓTICOS
- Keywords: Staphylococcus aureus; FTIR; Antibiotic resistant
- Language: Inglês
- Imprenta:
- Publisher place: Bellingham
- Date published: 2024
- Source:
- Título: Proceedings of SPIE
- ISSN: 0277-786X
- Volume/Número/Paginação/Ano: v. 12822, p. 1282202-1- 1282202-6, Mar. 2024
- Conference titles: Photonics West
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
PATIÑO, Claudia Patricia Barrera et al. Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification. Proceedings of SPIE. Bellingham: Instituto de Física de São Carlos, Universidade de São Paulo. Disponível em: https://doi.org/10.1117/12.3001639. Acesso em: 29 dez. 2025. , 2024 -
APA
Patiño, C. P. B., Soares, J. M., Blanco, K. C., Inada, N. M., & Bagnato, V. S. (2024). Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification. Proceedings of SPIE. Bellingham: Instituto de Física de São Carlos, Universidade de São Paulo. doi:10.1117/12.3001639 -
NLM
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification [Internet]. Proceedings of SPIE. 2024 ; 12822 1282202-1- 1282202-6.[citado 2025 dez. 29 ] Available from: https://doi.org/10.1117/12.3001639 -
Vancouver
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification [Internet]. Proceedings of SPIE. 2024 ; 12822 1282202-1- 1282202-6.[citado 2025 dez. 29 ] Available from: https://doi.org/10.1117/12.3001639 - Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms
- Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning
- Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm
- FTIR-derived feature insights for predicting time-dependent antibiotic resistance progression
- Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms
- Time evolution of bacterial resistance observed with principal component analysis
- Study of the action of curcuminoids in the photodynamic inactivation of bacteria resistant to antibiotics
- Advances in the clinical application of photodynamic action for pharyngotonsillitis treatment (Conference Presentation)
- Evolution of surviving Streptoccocus pyogenes from pharyngotonsillitis patients submit to multiple cycles of antimicrobial photodynamic therapy
- Prevention of rheumatic fever by continuous photodynamic therapeutic
Informações sobre o DOI: 10.1117/12.3001639 (Fonte: oaDOI API)
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