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High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader

Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an autom...

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Autores principales: Feng, Steve, Tseng, Derek, Di Carlo, Dino, Garner, Omai B., Ozcan, Aydogan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156953/
https://www.ncbi.nlm.nih.gov/pubmed/27976700
http://dx.doi.org/10.1038/srep39203
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author Feng, Steve
Tseng, Derek
Di Carlo, Dino
Garner, Omai B.
Ozcan, Aydogan
author_facet Feng, Steve
Tseng, Derek
Di Carlo, Dino
Garner, Omai B.
Ozcan, Aydogan
author_sort Feng, Steve
collection PubMed
description Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well-turbidity, with results returned to the smartphone in ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors. This mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance.
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spelling pubmed-51569532016-12-20 High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader Feng, Steve Tseng, Derek Di Carlo, Dino Garner, Omai B. Ozcan, Aydogan Sci Rep Article Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well-turbidity, with results returned to the smartphone in ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors. This mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance. Nature Publishing Group 2016-12-15 /pmc/articles/PMC5156953/ /pubmed/27976700 http://dx.doi.org/10.1038/srep39203 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Feng, Steve
Tseng, Derek
Di Carlo, Dino
Garner, Omai B.
Ozcan, Aydogan
High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
title High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
title_full High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
title_fullStr High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
title_full_unstemmed High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
title_short High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
title_sort high-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156953/
https://www.ncbi.nlm.nih.gov/pubmed/27976700
http://dx.doi.org/10.1038/srep39203
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