Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm (2023)
- Authors:
- USP affiliated authors: BLANCO, KATE CRISTINA - IFSC ; INADA, NATALIA MAYUMI - IFSC ; BAGNATO, VANDERLEI SALVADOR - IFSC ; PATIÑO, CLAUDIA PATRICIA BARRERA - IFSC ; SOARES, JENNIFER MACHADO - IFSC
- Unidade: IFSC
- DOI: 10.3390/antibiotics12101502
- Subjects: RESISTÊNCIA MICROBIANA ÀS DROGAS; ANTIBIÓTICOS; APRENDIZADO COMPUTACIONAL
- Keywords: Staphylococcus aureus; FTIR spectroscopy; Antibiotic-resistant bacteria; Amoxicillin induced; Gentamicin induced; Erythromycin induced; Machine learning algorithms
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Antibiotics
- ISSN: 2079-6382
- Volume/Número/Paginação/Ano: v. 12, n. 10, p. 1502-1-1502-18 + supplementary materials, Oct. 2023
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
PATIÑO, Claudia Patricia Barrera et al. Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm. Antibiotics, v. 12, n. 10, p. 1502-1-1502-18 + supplementary materials, 2023Tradução . . Disponível em: https://doi.org/10.3390/antibiotics12101502. Acesso em: 02 abr. 2026. -
APA
Patiño, C. P. B., Soares, J. M., Blanco, K. C., Inada, N. M., & Bagnato, V. S. (2023). Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm. Antibiotics, 12( 10), 1502-1-1502-18 + supplementary materials. doi:10.3390/antibiotics12101502 -
NLM
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm [Internet]. Antibiotics. 2023 ; 12( 10): 1502-1-1502-18 + supplementary materials.[citado 2026 abr. 02 ] Available from: https://doi.org/10.3390/antibiotics12101502 -
Vancouver
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm [Internet]. Antibiotics. 2023 ; 12( 10): 1502-1-1502-18 + supplementary materials.[citado 2026 abr. 02 ] Available from: https://doi.org/10.3390/antibiotics12101502 - Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms
- Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification
- Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning
- Time evolution of bacterial resistance observed with principal component analysis
- Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms
- FTIR-derived feature insights for predicting time-dependent antibiotic resistance progression
- Study of the action of curcuminoids in the photodynamic inactivation of bacteria resistant to antibiotics
- Evolution of surviving Streptoccocus pyogenes from pharyngotonsillitis patients submit to multiple cycles of antimicrobial photodynamic therapy
- Prevention of rheumatic fever by continuous photodynamic therapeutic
- Advances in the clinical application of photodynamic action for pharyngotonsillitis treatment (Conference Presentation)
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