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

Qiwei Hu,Hong Duan,Siyuan Wang,Yuanjie Liu

doi:10.3390/bios11110447

Abstract

In this study, a fluorescent biosensor was developed for the sensitive detection of Salmonella 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 Salmonella typhimurium from 6.9 × 101 to 1.1 × 103 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.

Highlights

In recent years, food safety has attracted more and more attention because it poses a threat to human health and causes huge economic losses
To demonstrate the feasibility of the faster region-based convolutional neural networks (R-CNN) model for the identification of fluorescent spots, the images for different concentrations of the FITC fluorescent microspheres (FMs) ranging from 50 to 0.1 μg/mL were collected under the excitation of an ultraviolet lamp
We developed a method for the recognition of fluorescent spots based on deep learning via the faster R-CNN algorithm

Summary

Introduction

Food safety has attracted more and more attention because it poses a threat to human health and causes huge economic losses. According to the report of World Health Organization (WHO), it was estimated that about 600 million people became sick and 420 thousand people died due to the consumption of contaminated food. Low-income and middle-income countries lose about 110 billion dollars due to unsafe food each year [1]. As a major foodborne pathogenic bacteria, Salmonella has been found in various foods and is responsible for millions of infections annually, such as fever, headache, nausea, vomiting, abdominal pain and diarrhea [2,3,4].

Methods

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Conclusion