Machine Learning in FTIR Spectrum for the Identification of Antibiotic Resistance: A Demonstration with Different Species of Microorganisms

Autor(es)

Patino, Claudia Patricia Barrera ; Soares, Jennifer Machado ; Blanco, Kate Cristina ; Bagnato, Vanderlei Salvador

• Tipo de documento: Artigo Científico
• Fonte: ANTIBIOTICS-BASEL; v. 13, n. 9, p. 21-pg., 2024-09-01.

Resumo

Recent studies introduced the importance of using machine learning algorithms in research focused on the identification of antibiotic resistance. In this study, we highlight the importance of building solid machine learning foundations to differentiate antimicrobial resistance among microorganisms. Using advanced machine learning algorithms, we established a methodology capable of analyzing the FTIR structural profile of the samples of Streptococcus pyogenes and Streptococcus mutans (Gram-positive), as well as Escherichia coli and Klebsiella pneumoniae (Gram-negative), demonstrating cross-sectional applicability in this focus on different microorganisms. The analysis focuses on specific biomolecules-Carbohydrates, Fatty Acids, and Proteins-in FTIR spectra, providing a multidimensional database that transcends microbial variability. The results highlight the ability of the method to consistently identify resistance patterns, regardless of the Gram classification of the bacteria and the species involved, reinforcing the premise that the structural characteristics identified are universal among the microorganisms tested. By validating this approach in four distinct species, our study proves the versatility and precision of the methodology used, in addition to bringing support to the development of an innovative protocol for the rapid and safe identification of antimicrobial resistance. This advance is crucial for optimizing treatment strategies and avoiding the spread of resistance. This emphasizes the relevance of specialized machine learning bases in effectively differentiating between resistance profiles in Gram-negative and Gram-positive bacteria to be implemented in the identification of antibiotic resistance. The obtained result has a high potential to be applied to clinical procedures. (AU)

• Processo FAPESP: 13/07276-1 - CEPOF - Centro de Pesquisa em Óptica e Fotônica
• Beneficiário: Vanderlei Salvador Bagnato
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 24/00100-0 - Combate da resistência antimicrobiana em bactérias multirresistentes por ação fotodinâmica
• Beneficiário: Jennifer Machado Soares
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 23/17384-8 - Identificação da Resistência de Bactérias a Antibióticos por Meio de Absorção de Grupos Bioquímicos Específicos com Implementação de Algoritmos de Aprendizado de Máquina.
• Beneficiário: Claudia Patricia Barrera Patiño
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 14/50857-8 - INCT 2014 - de Óptica Básica e Aplicada às Ciências da Vida
• Beneficiário: Vanderlei Salvador Bagnato
• Modalidade de apoio: Auxílio à Pesquisa - Temático