化学
大肠杆菌
信号(编程语言)
生物物理学
生物化学
基因
计算机科学
生物
程序设计语言
作者
Yang Zhang,Feng Hu,Kaiyi Zheng,Haoran Li,Tianxi Yang,Chuping Zhao,Roujia Zhang,Xiaodong Zhai,Junjun Zhang,Ruiyun Zhou,Xiaowei Huang,Zhihua Li,Jiyong Shi,Zhiming Guo,Shipeng Gao,Xiaobo Zou
标识
DOI:10.1021/acs.analchem.5c04495
摘要
Pathogenic Escherichia coli (E. coli), particularly E. coli O157:H7, is a major foodborne pathogen with significant clinical relevance, necessitating accurate and rapid subtype identification. However, the high genetic variability and biological similarity among E. coli strains pose challenges for conventional signal-strain detection methods, often resulting in false-positive outcomes. In this study, we developed a novel biosensing strategy based on plasmonic nanostructures functionalized with heterogeneous recognition elements that target two distinct epitopes of E. coli O157:H7. The sensor incorporates biological silent Raman tags for ratiometric signal output and magnetic enrichment to improve selectivity and minimize interference from nontarget bacteria. This design ensures excellent reproducibility and operational stability. The biosensor demonstrated an impressive limit of detection (LOD) of 1.2 CFU/mL, outperforming most existing methods. Furthermore, a cutoff value of 0.32 for the signal ratio yielded a positive predictive value of 98% and a negative predictive value of 94%, demonstrating a clear signal boundary and high accuracy for various types of signals. These results highlight the potential of our plasmonic biosensor as a rapid, ultrasensitive, and reliable point-of-care diagnostic tool for pathogen detection in complex food matrices.
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