A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network

In this study, a fluorescent biosensor was developed for the sensitive detection of <i>Salmonella</i> typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on the bacterial cells....

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Autores principales:	Qiwei Hu, Siyuan Wang, Hong Duan, Yuanjie Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fluorescent biosensor low-gradient magnetic field deep learning faster region-based convolutional neural networks <i>Salmonella</i> detection Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/a614f38c01674dd895e6aa7f93f31924
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spelling	oai:doaj.org-article:a614f38c01674dd895e6aa7f93f319242021-11-25T16:55:32ZA Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network10.3390/bios111104472079-6374https://doaj.org/article/a614f38c01674dd895e6aa7f93f319242021-11-01T00:00:00Zhttps://www.mdpi.com/2079-6374/11/11/447https://doaj.org/toc/2079-6374In this study, a fluorescent biosensor was developed for the sensitive detection of <i>Salmonella</i> typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on the bacterial cells. First, magnetic nanobeads (MNBs) coated with capture antibodies were used to separate target bacteria from the sample background, resulting in the formation of magnetic bacteria. Then, fluorescein isothiocyanate fluorescent microspheres (FITC-FMs) modified with detection antibodies were used to label the magnetic bacteria, resulting in the formation of fluorescent bacteria. After the fluorescent bacteria were attracted against the bottom of an ELISA well using a low-gradient magnetic field, resulting in the conversion from a three-dimensional (spatial) distribution of the fluorescent bacteria to a two-dimensional (planar) distribution, the images of the fluorescent bacteria were finally collected using a high-resolution fluorescence microscope and processed using the faster R-CNN algorithm to calculate the number of the fluorescent spots for the determination of target bacteria. Under the optimal conditions, this biosensor was able to quantitatively detect <i>Salmonella</i> typhimurium from 6.9 × 10<sup>1</sup> to 1.1 × 10<sup>3</sup> CFU/mL within 2.5 h with the lower detection limit of 55 CFU/mL. The fluorescent biosensor has the potential to simultaneously detect multiple types of foodborne bacteria using MNBs coated with their capture antibodies and different fluorescent microspheres modified with their detection antibodies.Qiwei HuSiyuan WangHong DuanYuanjie LiuMDPI AGarticlefluorescent biosensorlow-gradient magnetic fielddeep learningfaster region-based convolutional neural networks<i>Salmonella</i> detectionBiotechnologyTP248.13-248.65ENBiosensors, Vol 11, Iss 447, p 447 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fluorescent biosensor low-gradient magnetic field deep learning faster region-based convolutional neural networks <i>Salmonella</i> detection Biotechnology TP248.13-248.65
spellingShingle	fluorescent biosensor low-gradient magnetic field deep learning faster region-based convolutional neural networks <i>Salmonella</i> detection Biotechnology TP248.13-248.65 Qiwei Hu Siyuan Wang Hong Duan Yuanjie Liu A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network
description	In this study, a fluorescent biosensor was developed for the sensitive detection of <i>Salmonella</i> typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on the bacterial cells. First, magnetic nanobeads (MNBs) coated with capture antibodies were used to separate target bacteria from the sample background, resulting in the formation of magnetic bacteria. Then, fluorescein isothiocyanate fluorescent microspheres (FITC-FMs) modified with detection antibodies were used to label the magnetic bacteria, resulting in the formation of fluorescent bacteria. After the fluorescent bacteria were attracted against the bottom of an ELISA well using a low-gradient magnetic field, resulting in the conversion from a three-dimensional (spatial) distribution of the fluorescent bacteria to a two-dimensional (planar) distribution, the images of the fluorescent bacteria were finally collected using a high-resolution fluorescence microscope and processed using the faster R-CNN algorithm to calculate the number of the fluorescent spots for the determination of target bacteria. Under the optimal conditions, this biosensor was able to quantitatively detect <i>Salmonella</i> typhimurium from 6.9 × 10<sup>1</sup> to 1.1 × 10<sup>3</sup> CFU/mL within 2.5 h with the lower detection limit of 55 CFU/mL. The fluorescent biosensor has the potential to simultaneously detect multiple types of foodborne bacteria using MNBs coated with their capture antibodies and different fluorescent microspheres modified with their detection antibodies.
format	article
author	Qiwei Hu Siyuan Wang Hong Duan Yuanjie Liu
author_facet	Qiwei Hu Siyuan Wang Hong Duan Yuanjie Liu
author_sort	Qiwei Hu
title	A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network
title_short	A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network
title_full	A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network
title_fullStr	A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network
title_full_unstemmed	A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network
title_sort	fluorescent biosensor for sensitive detection of <i>salmonella</i> typhimurium using low-gradient magnetic field and deep learning via faster region-based convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a614f38c01674dd895e6aa7f93f31924
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A Fluorescent Biosensor for Sensitive Detection of <i>Salmonella</i> Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network

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