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

Autor: Lucas Correia Ribas, Daniel Cesar Braz, Odemir Martinez Bruno, Leonardo F. S. Scabini, Osvaldo N. Oliveira, Rafaela C. Sanfelice, Valquiria da Cruz Rodrigues, Juliana C. Soares, Matias Eliseo Melendez, André Lopes Carvalho, Andrey Soares, Rui Manuel Reis
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Talanta
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
ISSN: 1873-3573
0039-9140
Popis: 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 genosensors with different detection principles for a prostate cancer specific DNA sequence (PCA3). The genosensors were made with carbon printed electrodes or quartz coated with layer-by-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 morphology 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 PCA3-containing 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.
Graphical abstract Image 1
Highlights • Low-cost biosensors fabricated with gold nanoparticles and chondroitin sulfate used for detecting PCA3 biomarker. • PCA3 detection from machine learning with accuracy of 99.9%. • The highest sensitivity was reached with electrochemical impedance spectroscopy with the detection limit of 83 pM.
Databáze: OpenAIRE
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