Multidimensional calibration spaces in Staphylococcus Aureus detection using chitosan-based genosensors and electronic tongue

Autor(es)

Coatrini-Soares, Andrey ; Soares, Juliana Coatrini ; Popolin-Neto, Mario ; de Mello, Suelen Scarpa ; Sanches, Edgar Ap. ; Paulovich, Fernando V. ; Oliveira Jr, Osvaldo N. ; Mattoso, Luiz Henrique Capparelli

• Tipo de documento: Artigo Científico
• Fonte: International Journal of Biological Macromolecules; v. 271, p. 10-pg., 2024-05-29.

Resumo

Mastitis diagnosis can be made by detecting Staphylococcus aureus (S. aureus), which requires high sensitivity and selectivity. Here, we report on microfluidic genosensors and electronic tongues to detect S. aureus DNA using impedance spectroscopy with data analysis employing visual analytics and machine learning techniques. The genosensors were made with layer-by-layer films containing either 10 bilayers of chitosan/chondroitin sulfate or 8 bilayers of chitosan/sericin functionalized with an active layer of cpDNA S. aureus. The specific interactions leading to hybridization in these genosensors allowed for a low limit of detection of 5.90 x 10-19 mol/L. The electronic tongue had four sensing units made with 6-bilayer chitosan/chondroitin sulfate films, 10-bilayer chitosan/chondroitin sulfate, 8-bilayer chitosan/sericin, and 8-bilayer chitosan/gold nanoparticles modified with sericin. Despite the absence of specific interactions, various concentrations of DNA S. aureus could be distinguished when the impedance data were plotted using a dimensionality reduction technique. Selectivity of S. aureus DNA was confirmed using multidimensional calibration spaces, based on machine learning, with accuracy up to 89 % for the genosensors and 66 % for the electronic tongue. Hence, with these computational methods one may opt for the more expensive genosensors or the simpler and cheaper electronic tongue, depending on the sensitivity level required to diagnose mastitis. (AU)

• Processo FAPESP: 18/22214-6 - Rumo à convergência de tecnologias: de sensores e biossensores à visualização de informação e aprendizado de máquina para análise de dados em diagnóstico clínico
• Beneficiário: Osvaldo Novais de Oliveira Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 20/09835-1 - IARA - Inteligência Artificial Recriando Ambientes
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia
• Processo FAPESP: 18/18953-8 - Filmes nanoestruturados aplicados em biossensores microfluídicos para detecção de mastite bacteriana
• Beneficiário: Andrey Coatrini Soares
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado