• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于低梯度磁场和深度学习的快速区域卷积神经网络荧光生物传感器用于灵敏检测鼠伤寒沙门氏菌

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

机构信息

Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China.

Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.

出版信息

Biosensors (Basel). 2021 Nov 11;11(11):447. doi: 10.3390/bios11110447.

DOI:10.3390/bios11110447
PMID:34821663
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8615454/
Abstract

In this study, a fluorescent biosensor was developed for the sensitive detection of 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 typhimurium from 6.9 × 10 to 1.1 × 10 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.

摘要

在这项研究中,开发了一种荧光生物传感器,用于使用低梯度磁场和基于更快区域卷积神经网络 (R-CNN) 的深度学习来灵敏检测鼠伤寒沙门氏菌,以识别细菌细胞上的荧光斑点。首先,用捕获抗体涂覆的磁性纳米珠 (MNB) 用于从样品背景中分离目标细菌,从而形成磁性细菌。然后,用检测抗体修饰的异硫氰酸荧光素荧光微球 (FITC-FM) 用于标记磁性细菌,从而形成荧光细菌。在使用低梯度磁场将荧光细菌吸引到底部的 ELISA 孔后,荧光细菌从三维(空间)分布转变为二维(平面)分布,最后使用高分辨率荧光显微镜收集荧光细菌的图像,并使用更快的 R-CNN 算法进行处理,以计算荧光斑点的数量,用于检测目标细菌。在最佳条件下,该生物传感器能够在 2.5 小时内定量检测浓度范围为 6.9×10 至 1.1×10 CFU/mL 的鼠伤寒沙门氏菌,检测限为 55 CFU/mL。荧光生物传感器有望通过用其捕获抗体涂覆的磁性纳米珠和用其检测抗体修饰的不同荧光微球同时检测多种食源性病原体细菌。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/8207818eb318/biosensors-11-00447-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/c67de9f054d6/biosensors-11-00447-sch001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/27f7a78942a1/biosensors-11-00447-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/0e55ea5017f1/biosensors-11-00447-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/155c068d0308/biosensors-11-00447-g003a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/8207818eb318/biosensors-11-00447-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/c67de9f054d6/biosensors-11-00447-sch001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/27f7a78942a1/biosensors-11-00447-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/0e55ea5017f1/biosensors-11-00447-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/155c068d0308/biosensors-11-00447-g003a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e017/8615454/8207818eb318/biosensors-11-00447-g004a.jpg

相似文献

1
A Fluorescent Biosensor for Sensitive Detection of Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network.基于低梯度磁场和深度学习的快速区域卷积神经网络荧光生物传感器用于灵敏检测鼠伤寒沙门氏菌
Biosensors (Basel). 2021 Nov 11;11(11):447. doi: 10.3390/bios11110447.
2
A microfluidic biosensor for online and sensitive detection of Salmonella typhimurium using fluorescence labeling and smartphone video processing.一种使用荧光标记和智能手机视频处理进行在线和灵敏检测鼠伤寒沙门氏菌的微流控生物传感器。
Biosens Bioelectron. 2019 Sep 1;140:111333. doi: 10.1016/j.bios.2019.111333. Epub 2019 May 22.
3
An ultrasensitive biosensor for fast detection of Salmonella using 3D magnetic grid separation and urease catalysis.一种基于 3D 磁性网格分离和脲酶催化的快速检测沙门氏菌的超灵敏生物传感器。
Biosens Bioelectron. 2020 Jun 1;157:112160. doi: 10.1016/j.bios.2020.112160. Epub 2020 Mar 20.
4
A pipette-adapted biosensor for Salmonella detection.一种用于沙门氏菌检测的适配移液器的生物传感器。
Biosens Bioelectron. 2022 Dec 15;218:114765. doi: 10.1016/j.bios.2022.114765. Epub 2022 Oct 4.
5
Rapid and sensitive detection of Salmonella Typhimurium using nickel nanowire bridge for electrochemical impedance amplification.基于镍纳米线桥的电化学阻抗扩增快速灵敏检测鼠伤寒沙门氏菌。
Talanta. 2020 May 1;211:120715. doi: 10.1016/j.talanta.2020.120715. Epub 2020 Jan 7.
6
A Rapid and Sensitive Biosensor Based on Viscoelastic Inertial Microfluidics.基于黏弹性惯性微流控的快速灵敏生物传感器。
Sensors (Basel). 2020 May 11;20(9):2738. doi: 10.3390/s20092738.
7
Simultaneous detection of Escherichia coli O157:H7 and Salmonella Typhimurium using quantum dots as fluorescence labels.使用量子点作为荧光标记物同时检测大肠杆菌O157:H7和鼠伤寒沙门氏菌。
Analyst. 2006 Mar;131(3):394-401. doi: 10.1039/b510888h. Epub 2005 Dec 7.
8
An impedance biosensor based on magnetic nanobead net and MnO nanoflowers for rapid and sensitive detection of foodborne bacteria.一种基于磁性纳米珠网和MnO纳米花的阻抗生物传感器,用于快速灵敏地检测食源细菌。
Biosens Bioelectron. 2021 Feb 1;173:112800. doi: 10.1016/j.bios.2020.112800. Epub 2020 Nov 6.
9
Rapid and sensitive detection of Salmonella Typhimurium on eggshells by using wireless biosensors.利用无线生物传感器快速灵敏地检测鸡蛋壳上的鼠伤寒沙门氏菌。
J Food Prot. 2012 Apr;75(4):631-6. doi: 10.4315/0362-028X.JFP-11-339.
10
A colorimetric immunosensor for determination of foodborne bacteria using rotating immunomagnetic separation, gold nanorod indication, and click chemistry amplification.基于旋转免疫磁分离、金纳米棒示踪和点击化学放大的用于食源性致病菌检测的比色免疫传感器
Mikrochim Acta. 2020 Mar 3;187(4):197. doi: 10.1007/s00604-020-4169-z.

引用本文的文献

1
The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.人工智能在推动生物传感器技术发展中的作用:过去、现在和未来展望。
Adv Mater. 2025 Aug;37(34):e2504796. doi: 10.1002/adma.202504796. Epub 2025 Jun 16.
2
The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants.集成生物传感器与人工智能的多参数多模态技术在食品污染物快速检测中的应用
Foods. 2024 Jun 19;13(12):1936. doi: 10.3390/foods13121936.
3
Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection.

本文引用的文献

1
Biosensors Coupled with Signal Amplification Technology for the Detection of Pathogenic Bacteria: A Review.生物传感器与信号放大技术在病原菌检测中的应用:综述。
Biosensors (Basel). 2021 Jun 9;11(6):190. doi: 10.3390/bios11060190.
2
Cell-based fluorescent microsphere incorporated with carbon dots as a sensitive immunosensor for the rapid detection of Escherichia coli O157 in milk.基于细胞的荧光微球结合碳点作为灵敏免疫传感器用于快速检测牛奶中的大肠杆菌O157 。
Biosens Bioelectron. 2021 May 1;179:113057. doi: 10.1016/j.bios.2021.113057. Epub 2021 Feb 2.
3
Colorimetric method for Salmonella spp. detection based on peroxidase-like activity of Cu(II)-rGO nanoparticles and PCR.
机器学习辅助生物传感技术:一种用于提升食品安全检测智能化水平的新兴强大工具。
Curr Res Food Sci. 2024 Jan 12;8:100679. doi: 10.1016/j.crfs.2024.100679. eCollection 2024.
4
Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0.智能生物传感器有望为食品安全4.0提供更智能的解决方案。
Foods. 2024 Jan 11;13(2):235. doi: 10.3390/foods13020235.
5
Development of a Dual Mode UCNPs-MB Biosensor in Combination with PCR for Sensitive Detection of .开发一种双模上转换纳米粒子-磁珠生物传感器与 PCR 结合,用于灵敏检测.
Biosensors (Basel). 2023 Apr 13;13(4):475. doi: 10.3390/bios13040475.
6
A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning.基于多光子效应和机器学习的生物传感器框架。
Biosensors (Basel). 2022 Sep 1;12(9):710. doi: 10.3390/bios12090710.
基于 Cu(II)-rGO 纳米粒子过氧化物酶样活性和 PCR 的沙门氏菌检测比色法。
Anal Biochem. 2021 Feb 15;615:114068. doi: 10.1016/j.ab.2020.114068. Epub 2020 Dec 17.
4
Comparison of double antigen sandwich and indirect enzyme-linked immunosorbent assay for the diagnosis of hepatitis C virus antibodies.双抗原夹心酶联免疫吸附试验与间接酶联免疫吸附试验诊断丙型肝炎病毒抗体的比较。
J Clin Lab Anal. 2020 Nov;34(11):e23481. doi: 10.1002/jcla.23481. Epub 2020 Aug 15.
5
Application of Biosensors for Detection of Pathogenic Food Bacteria: A Review.生物传感器在食源性病原体检测中的应用:综述。
Biosensors (Basel). 2020 May 30;10(6):58. doi: 10.3390/bios10060058.
6
Machine Learning for Precision Breast Cancer Diagnosis and Prediction of the Nanoparticle Cellular Internalization.用于精准乳腺癌诊断和纳米颗粒细胞内化预测的机器学习
ACS Sens. 2020 Jun 26;5(6):1689-1698. doi: 10.1021/acssensors.0c00329. Epub 2020 Jun 17.
7
A web-based automated machine learning platform to analyze liquid biopsy data.一个基于网络的用于分析液体活检数据的自动化机器学习平台。
Lab Chip. 2020 Jun 21;20(12):2166-2174. doi: 10.1039/d0lc00096e. Epub 2020 May 18.
8
Pooling of Laying Hen Environmental Swabs and Efficacy of Salmonella Detection.蛋鸡环境拭子的混合及沙门氏菌检测的功效
J Food Prot. 2020 Jun 1;83(6):943-950. doi: 10.4315/JFP-19-467.
9
Optical biosensors: an exhaustive and comprehensive review.光学生物传感器:详尽全面的综述。
Analyst. 2020 Mar 2;145(5):1605-1628. doi: 10.1039/c9an01998g.
10
White blood cells detection and classification based on regional convolutional neural networks.基于区域卷积神经网络的白细胞检测与分类。
Med Hypotheses. 2020 Feb;135:109472. doi: 10.1016/j.mehy.2019.109472. Epub 2019 Nov 4.