About: 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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About: Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3
Creator	Rodrigues, V Braz, Daniel Bruno, Odemir Melendez, Matias Oliveira, Osvaldo Ribas, Lucas Rodrigues, Valquiria Sanfelice, Rafaela Scabini, Leonardo Soares, Andrey Soares, Juliana Soares, J Braz, D Bruno, O Carvalho, A Lopes Carvalho, Andre Manuel, Rui Melendez, M Oliveira, O Reis, R Reis, Vieira Ribas, L Sanfelice, R Scabini, L Soares, A
source	Elsevier; PMC
abstract	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.
has issue date	2020-08-07(xsd:dateTime)
bibo:doi	10.1016/j.talanta.2020.121444
has license	no-cc
sha1sum (hex)	bd7b2125255327f392ac8cdb67e20a196e21fe1d
schema:url	https://doi.org/10.1016/j.talanta.2020.121444
resource representing a document's title	Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3
has PubMed Central identifier	PMC7413169
schema:publication	Talanta
resource representing a document's body	covid:bd7b2125255327f392ac8cdb67e20a196e21fe1d#body_text
is schema:about of	named entity 'diagnosis' named entity 'Talanta' named entity 'DETECTION' named entity 'doi' named entity 'optical detection' named entity 'gene expression' named entity 'quartz' named entity 'Hitachi' named entity 'electrochemical impedance spectroscopy' named entity 'gene' named entity 'prostate cancer' named entity 'tumor' named entity 'LbL' named entity 'infrared' named entity 'PCA3' named entity 'prostate enlargement' named entity 'prostate infections' named entity 'Clone' named entity 'impedance spectroscopy' named entity 'elemental analysis' named entity 'supervised machine learning' named entity 'differential display' named entity 'prostate cancer' named entity 't-SNE' named entity 'overfitting' named entity 'gene' named entity 'data analysis' named entity 'rectal examination' named entity 'chemical groups' named entity 'surface-enhanced Raman scattering' named entity 'limits of detection' named entity 'electrochemical' named entity 'neglected diseases' named entity 'nucleic acids' named entity 'prostate' named entity 'LbL' named entity 'biomarkers' named entity 'human visual perception' named entity 'colorimetric' named entity 'Polarization' named entity 'PCA3' named entity 'PCA3' named entity 'impedance' named entity 'limit of detection' named entity 'vacuum chamber' named entity 'PCA3' named entity '1-NN' named entity 'PSA test' named entity 'impedance' named entity 'PCA3' named entity 'chondroitin sulphate' named entity 'chitosan' named entity 'developing countries' named entity 'UV-vis spectroscopy' named entity 'spectra' named entity 'Cyclic voltammetry' named entity 'spectroscopy' named entity 'UV light' named entity 'complementary DNA'

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