Low-cost bacterial nanocellulose-based interdigitated biosensor to detect the p53 cancer biomarker

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dc.contributor.authorBondancia, Thalita J.
dc.contributor.authorSoares, Andrey Coatrini
dc.contributor.authorPopolin-Neto, Mário
dc.contributor.authorGomes, Nathalia O.
dc.contributor.authorRaymundo-Pereira, Paulo A.
dc.contributor.authorBarud, Hernane S.
dc.contributor.authorMachado, Sergio A.S.
dc.contributor.authorRibeiro, Sidney J.L. [UNESP]
dc.contributor.authorMelendez, Matias E.
dc.contributor.authorCarvalho, André L.
dc.contributor.authorReis, Rui M.
dc.contributor.authorPaulovich, Fernando V.
dc.contributor.authorOliveira, Osvaldo N.
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.contributor.institutionFederal Institute of São Paulo (IFSP)
dc.contributor.institutionUniversity of Araraquara (UNIARA)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionMolecular Oncology Research Center
dc.contributor.institutionDalhousie University (DAL)
dc.contributor.institutionUniversity of Minho
dc.contributor.institutionNational Cancer Institute (INCA)
dc.date.accessioned2023-03-02T00:28:09Z
dc.date.available2023-03-02T00:28:09Z
dc.date.issued2022-03-01
dc.description.abstractLow-cost sensors to detect cancer biomarkers with high sensitivity and selectivity are essential for early diagnosis. Herein, an immunosensor was developed to detect the cancer biomarker p53 antigen in MCF7 lysates using electrical impedance spectroscopy. Interdigitated electrodes were screen printed on bacterial nanocellulose substrates, then coated with a matrix of layer-by-layer films of chitosan and chondroitin sulfate onto which a layer of anti-p53 antibodies was adsorbed. The immunosensing performance was optimized with a 3-bilayer matrix, with detection of p53 in MCF7 cell lysates at concentrations between 0.01 and 1000 Ucell. mL−1, and detection limit of 0.16 Ucell mL−1. The effective buildup of the immunosensor on bacterial nanocellulose was confirmed with polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS) and surface energy analysis. In spite of the high sensitivity, full selectivity with distinction of the p53-containing cell lysates and possible interferents required treating the data with a supervised machine learning approach based on decision trees. This allowed the creation of a multidimensional calibration space with 11 dimensions (frequencies used to generate decision tree rules), with which the classification of the p53-containing samples can be explained.en
dc.description.affiliationSão Carlos Institute of Physics University of São Paulo (USP), São Paulo
dc.description.affiliationNanotechnology National Laboratory for Agriculture (LNNA) Embrapa Instrumentação, SP
dc.description.affiliationFederal Institute of São Paulo (IFSP)
dc.description.affiliationInstitute of Mathematics and Computer Sciences (ICMC) University of São Paulo (USP)
dc.description.affiliationSão Carlos Institute of Chemistry University of São Paulo (USP), São Paulo
dc.description.affiliationBiopolymers and Biomaterials Laboratory (BIOPOLMAT) University of Araraquara (UNIARA), São Paulo
dc.description.affiliationInstitute of Chemistry São Paulo State University (UNESP), São Paulo
dc.description.affiliationBarretos Cancer Hospital Molecular Oncology Research Center, São Paulo
dc.description.affiliationFaculty of Computer Science (FCS) Dalhousie University (DAL)
dc.description.affiliationLife and Health Sciences Research Institute (ICVS) School of Medicine University of Minho
dc.description.affiliationMolecular Carcinogenesis Program Research Center National Cancer Institute (INCA)
dc.description.affiliationUnespInstitute of Chemistry São Paulo State University (UNESP), São Paulo
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipInstituto Nacional de Ciência e Tecnologia em Eletrônica Orgânica
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdCNPq: 160290/2019-8
dc.description.sponsorshipIdCNPq: 164569/2020-0
dc.description.sponsorshipIdFAPESP: 2016/01919-6
dc.description.sponsorshipIdFAPESP: 2018/18953-8
dc.description.sponsorshipIdFAPESP: 2018/22214-6
dc.description.sponsorshipIdFAPESP: 2019/01777-5
dc.description.sponsorshipIdFAPESP: 2020/09587-8
dc.description.sponsorshipIdCNPq: 311757/2019-7
dc.description.sponsorshipIdCNPq: 423952/2018-8
dc.identifierhttp://dx.doi.org/10.1016/j.msec.2022.112676
dc.identifier.citationBiomaterials Advances, v. 134.
dc.identifier.doi10.1016/j.msec.2022.112676
dc.identifier.issn2772-9508
dc.identifier.scopus2-s2.0-85129396209
dc.identifier.urihttp://hdl.handle.net/11449/241795
dc.language.isoeng
dc.relation.ispartofBiomaterials Advances
dc.sourceScopus
dc.subjectBacterial nanocellulose
dc.subjectImmunosensors
dc.subjectInformation visualization
dc.subjectMachine learning
dc.subjectMultidimensional calibration space
dc.subjectp53
dc.titleLow-cost bacterial nanocellulose-based interdigitated biosensor to detect the p53 cancer biomarkeren
dc.typeArtigo

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