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Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically val...

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Autores principales: Rohaim, Mohammed A., Clayton, Emily, Sahin, Irem, Vilela, Julianne, Khalifa, Manar E., Al-Natour, Mohammad Q., Bayoumi, Mahmoud, Poirier, Aurore C., Branavan, Manoharanehru, Tharmakulasingam, Mukunthan, Chaudhry, Nouman S., Sodi, Ravinder, Brown, Amy, Burkhart, Peter, Hacking, Wendy, Botham, Judy, Boyce, Joe, Wilkinson, Hayley, Williams, Craig, Whittingham-Dowd, Jayde, Shaw, Elisabeth, Hodges, Matt, Butler, Lisa, Bates, Michelle D., La Ragione, Roberto, Balachandran, Wamadeva, Fernando, Anil, Munir, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552048/
https://www.ncbi.nlm.nih.gov/pubmed/32883050
http://dx.doi.org/10.3390/v12090972
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author Rohaim, Mohammed A.
Clayton, Emily
Sahin, Irem
Vilela, Julianne
Khalifa, Manar E.
Al-Natour, Mohammad Q.
Bayoumi, Mahmoud
Poirier, Aurore C.
Branavan, Manoharanehru
Tharmakulasingam, Mukunthan
Chaudhry, Nouman S.
Sodi, Ravinder
Brown, Amy
Burkhart, Peter
Hacking, Wendy
Botham, Judy
Boyce, Joe
Wilkinson, Hayley
Williams, Craig
Whittingham-Dowd, Jayde
Shaw, Elisabeth
Hodges, Matt
Butler, Lisa
Bates, Michelle D.
La Ragione, Roberto
Balachandran, Wamadeva
Fernando, Anil
Munir, Muhammad
author_facet Rohaim, Mohammed A.
Clayton, Emily
Sahin, Irem
Vilela, Julianne
Khalifa, Manar E.
Al-Natour, Mohammad Q.
Bayoumi, Mahmoud
Poirier, Aurore C.
Branavan, Manoharanehru
Tharmakulasingam, Mukunthan
Chaudhry, Nouman S.
Sodi, Ravinder
Brown, Amy
Burkhart, Peter
Hacking, Wendy
Botham, Judy
Boyce, Joe
Wilkinson, Hayley
Williams, Craig
Whittingham-Dowd, Jayde
Shaw, Elisabeth
Hodges, Matt
Butler, Lisa
Bates, Michelle D.
La Ragione, Roberto
Balachandran, Wamadeva
Fernando, Anil
Munir, Muhammad
author_sort Rohaim, Mohammed A.
collection PubMed
description Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
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spelling pubmed-75520482020-10-14 Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2 Rohaim, Mohammed A. Clayton, Emily Sahin, Irem Vilela, Julianne Khalifa, Manar E. Al-Natour, Mohammad Q. Bayoumi, Mahmoud Poirier, Aurore C. Branavan, Manoharanehru Tharmakulasingam, Mukunthan Chaudhry, Nouman S. Sodi, Ravinder Brown, Amy Burkhart, Peter Hacking, Wendy Botham, Judy Boyce, Joe Wilkinson, Hayley Williams, Craig Whittingham-Dowd, Jayde Shaw, Elisabeth Hodges, Matt Butler, Lisa Bates, Michelle D. La Ragione, Roberto Balachandran, Wamadeva Fernando, Anil Munir, Muhammad Viruses Article Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories. MDPI 2020-09-01 /pmc/articles/PMC7552048/ /pubmed/32883050 http://dx.doi.org/10.3390/v12090972 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rohaim, Mohammed A.
Clayton, Emily
Sahin, Irem
Vilela, Julianne
Khalifa, Manar E.
Al-Natour, Mohammad Q.
Bayoumi, Mahmoud
Poirier, Aurore C.
Branavan, Manoharanehru
Tharmakulasingam, Mukunthan
Chaudhry, Nouman S.
Sodi, Ravinder
Brown, Amy
Burkhart, Peter
Hacking, Wendy
Botham, Judy
Boyce, Joe
Wilkinson, Hayley
Williams, Craig
Whittingham-Dowd, Jayde
Shaw, Elisabeth
Hodges, Matt
Butler, Lisa
Bates, Michelle D.
La Ragione, Roberto
Balachandran, Wamadeva
Fernando, Anil
Munir, Muhammad
Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
title Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
title_full Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
title_fullStr Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
title_full_unstemmed Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
title_short Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
title_sort artificial intelligence-assisted loop mediated isothermal amplification (ai-lamp) for rapid detection of sars-cov-2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552048/
https://www.ncbi.nlm.nih.gov/pubmed/32883050
http://dx.doi.org/10.3390/v12090972
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