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Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning
Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural netw...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042729/ https://www.ncbi.nlm.nih.gov/pubmed/35494737 http://dx.doi.org/10.1039/d1ra05778b |
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author | Wang, Shu Dong, Hao Shen, Wanzhu Yang, Yong Li, Zhigang Liu, Yong Wang, Chongwen Gu, Bing Zhang, Long |
author_facet | Wang, Shu Dong, Hao Shen, Wanzhu Yang, Yong Li, Zhigang Liu, Yong Wang, Chongwen Gu, Bing Zhang, Long |
author_sort | Wang, Shu |
collection | PubMed |
description | Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of Staphylococcus aureus (S. aureus), comprising 30 strains of MSSA and 30 strains of MRSA were used to build the CNN classification model. The developed method exhibited 100% identification accuracy for MSSA and MRSA, and is thus a promising tool for the rapid detection of drug-sensitive and drug-resistant bacterial strains. |
format | Online Article Text |
id | pubmed-9042729 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-90427292022-04-28 Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning Wang, Shu Dong, Hao Shen, Wanzhu Yang, Yong Li, Zhigang Liu, Yong Wang, Chongwen Gu, Bing Zhang, Long RSC Adv Chemistry Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of Staphylococcus aureus (S. aureus), comprising 30 strains of MSSA and 30 strains of MRSA were used to build the CNN classification model. The developed method exhibited 100% identification accuracy for MSSA and MRSA, and is thus a promising tool for the rapid detection of drug-sensitive and drug-resistant bacterial strains. The Royal Society of Chemistry 2021-10-25 /pmc/articles/PMC9042729/ /pubmed/35494737 http://dx.doi.org/10.1039/d1ra05778b Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Wang, Shu Dong, Hao Shen, Wanzhu Yang, Yong Li, Zhigang Liu, Yong Wang, Chongwen Gu, Bing Zhang, Long Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning |
title | Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning |
title_full | Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning |
title_fullStr | Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning |
title_full_unstemmed | Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning |
title_short | Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning |
title_sort | rapid sers identification of methicillin-susceptible and methicillin-resistant staphylococcus aureus via aptamer recognition and deep learning |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042729/ https://www.ncbi.nlm.nih.gov/pubmed/35494737 http://dx.doi.org/10.1039/d1ra05778b |
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