Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3

Autor(es):Mostrar menos -

Rodrigues, Valquiria C. ^{[1, 2]} ; Soares, Juliana C. ^[2] ; Soares, Andrey C. ^[3] ; Braz, Daniel C. ^[2] ; Melendez, Matias Eliseo ^{[4, 5]} ; Ribas, Lucas C. ^[6] ; Scabini, Leonardo F. S. ^[2] ; Bruno, Odemir M. ^[2] ; Carvalho, Andre Lopes ; Reis, Rui Manuel ^{[7, 8]} ; Sanfelice, Rafaela C. ^[9] ; Oliveira, Jr., Osvaldo N. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Sch Engn, Dept Mat Engn, BR-13563120 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Sao Carlos Inst Phys, BR-13566590 Sao Carlos - Brazil

^[3] Embrapa Instrumentacao, Nanotechnol Natl Lab Agr LNNA, BR-13560970 Sao Carlos - Brazil

^[4] Barretos Canc Hosp, Mol Oncol Res Ctr, BR-14784400 Barretos - Brazil

^[5] Little Prince Coll, Pele Little Prince Res Inst, Little Prince Complex Curitiba, BR-80250060 Curitiba, PR - Brazil

^[6] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos - Brazil

^[7] ICVS 3Bs PT Govt Associate Lab, Braga - Portugal

^[8] Univ Minho, Sch Med, Life & Hlth Sci Res Inst ICVS, Braga - Portugal

^[9] Univ Fed Triangulo Mineiro, Dept Chem Engn, Uberaba, MG - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Talanta; v. 222, JAN 15 2021.
• Citações Web of Science: 0

Resumo

The development of simple detection methods aimed at widespread screening and testing is crucial for many infections and diseases, including prostate cancer where early diagnosis increases the chances of cure considerably. In this paper, we report on genosensor s with different detection principles for a prostate cancer specific DNA sequence (PCA3). The genosensors were made with carbon printed electrodes or quartz coated with layerby-layer (LbL) films containing gold nanoparticles and chondroitin sulfate and a layer of a complementary DNA sequence (PCA3 probe). The highest sensitivity was reached with electrochemical impedance spectroscopy with the detection limit of 83 pM in solutions of PCA3, while the limits of detection were 2000 pM and 900 pM for cyclic voltammetry and UV-vis spectroscopy, respectively. That detection could be performed with an optical method is encouraging, as one may envisage extending it to colorimetric tests. Since the morpholog y of sensing units is known to be affected in detection experiments, we applied machine learning algorithms to classify scanning electron microscopy images of the genosensors and managed to distinguish those exposed to PCA3containing solutions from control measurements with an accuracy of 99.9%. The performance in distinguishing each individual PCA3 concentration in a multiclass task was lower, with an accuracy of 88.3%, which means that further developments in image analysis are required for this innovative approach. (AU)

• Processo FAPESP: 19/07811-0 - Redes neurais artificiais e redes complexas: um estudo integrativo de propriedades topológicas e reconhecimento de padrões
• Beneficiário: Leonardo Felipe dos Santos Scabini
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• 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: 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
• Processo FAPESP: 16/23763-8 - Modelagem e análise de redes complexas para visão computacional
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Bolsas no Brasil - Doutorado