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Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2
[Image: see text] Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839157/ https://www.ncbi.nlm.nih.gov/pubmed/33553890 http://dx.doi.org/10.1021/acsomega.0c04929 |
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author | Samacoits, Aubin Nimsamer, Pattaraporn Mayuramart, Oraphan Chantaravisoot, Naphat Sitthi-amorn, Pitchaya Nakhakes, Chajchawan Luangkamchorn, Lumrung Tongcham, Phongsakhon Zahm, Ugo Suphanpayak, Suchada Padungwattanachoke, Natta Leelarthaphin, Nutcha Huayhongthong, Hathaichanok Pisitkun, Trairak Payungporn, Sunchai Hannanta-anan, Pimkhuan |
author_facet | Samacoits, Aubin Nimsamer, Pattaraporn Mayuramart, Oraphan Chantaravisoot, Naphat Sitthi-amorn, Pitchaya Nakhakes, Chajchawan Luangkamchorn, Lumrung Tongcham, Phongsakhon Zahm, Ugo Suphanpayak, Suchada Padungwattanachoke, Natta Leelarthaphin, Nutcha Huayhongthong, Hathaichanok Pisitkun, Trairak Payungporn, Sunchai Hannanta-anan, Pimkhuan |
author_sort | Samacoits, Aubin |
collection | PubMed |
description | [Image: see text] Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/μL on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts. |
format | Online Article Text |
id | pubmed-7839157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-78391572021-01-27 Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2 Samacoits, Aubin Nimsamer, Pattaraporn Mayuramart, Oraphan Chantaravisoot, Naphat Sitthi-amorn, Pitchaya Nakhakes, Chajchawan Luangkamchorn, Lumrung Tongcham, Phongsakhon Zahm, Ugo Suphanpayak, Suchada Padungwattanachoke, Natta Leelarthaphin, Nutcha Huayhongthong, Hathaichanok Pisitkun, Trairak Payungporn, Sunchai Hannanta-anan, Pimkhuan ACS Omega [Image: see text] Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/μL on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts. American Chemical Society 2021-01-20 /pmc/articles/PMC7839157/ /pubmed/33553890 http://dx.doi.org/10.1021/acsomega.0c04929 Text en © 2021 The Authors. Published by American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. |
spellingShingle | Samacoits, Aubin Nimsamer, Pattaraporn Mayuramart, Oraphan Chantaravisoot, Naphat Sitthi-amorn, Pitchaya Nakhakes, Chajchawan Luangkamchorn, Lumrung Tongcham, Phongsakhon Zahm, Ugo Suphanpayak, Suchada Padungwattanachoke, Natta Leelarthaphin, Nutcha Huayhongthong, Hathaichanok Pisitkun, Trairak Payungporn, Sunchai Hannanta-anan, Pimkhuan Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2 |
title | Machine Learning-Driven and Smartphone-Based Fluorescence
Detection for CRISPR Diagnostic of SARS-CoV-2 |
title_full | Machine Learning-Driven and Smartphone-Based Fluorescence
Detection for CRISPR Diagnostic of SARS-CoV-2 |
title_fullStr | Machine Learning-Driven and Smartphone-Based Fluorescence
Detection for CRISPR Diagnostic of SARS-CoV-2 |
title_full_unstemmed | Machine Learning-Driven and Smartphone-Based Fluorescence
Detection for CRISPR Diagnostic of SARS-CoV-2 |
title_short | Machine Learning-Driven and Smartphone-Based Fluorescence
Detection for CRISPR Diagnostic of SARS-CoV-2 |
title_sort | machine learning-driven and smartphone-based fluorescence
detection for crispr diagnostic of sars-cov-2 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839157/ https://www.ncbi.nlm.nih.gov/pubmed/33553890 http://dx.doi.org/10.1021/acsomega.0c04929 |
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