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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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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]
Total Authors: 12
[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
Total Affiliations: 9
Document type: Journal article
Source: Talanta; v. 222, JAN 15 2021.
Web of Science Citations: 0

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)

FAPESP's process: 19/07811-0 - Artificial neural networks and complex networks: an integrative study of topological properties and pattern recognition
Grantee:Leonardo Felipe dos Santos Scabini
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
Grantee:Osvaldo Novais de Oliveira Junior
Support type: Research Projects - Thematic Grants
FAPESP's process: 18/18953-8 - Nanostructured films applied in microfluidic biosensors to mastitis detection
Grantee:Andrey Coatrini Soares
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
Grantee:Lucas Correia Ribas
Support type: Scholarships in Brazil - Doctorate