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
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
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: 29 dez. 2025. -
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 2025 dez. 29 ] 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 2025 dez. 29 ] Available from: https://doi.org/10.3390/antibiotics12101502 - Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification
- 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
- 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: 10.3390/antibiotics12101502 (Fonte: oaDOI API)
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