A Dual Fluorescence Turn‐On Sensor Array Formed by Poly(para‐aryleneethynylene) and Aggregation‐Induced Emission Fluorophores for Sensitive Multiplexed Bacterial Recognition

Author:

Yu Yang1,Ni Weiwei1,Hu Qin2,Li Huihai1,Zhang Yi1,Gao Xu1,Zhou Lingjia1,Zhang Shuming1,Ma Shuoyang1,Zhang Yanliang3,Huang Hui1ORCID,Li Fei1,Han Jinsong1ORCID

Affiliation:

1. State Key Laboratory of Natural Medicines, National R&D Center for Chinese Herbal Medicine Processing Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University Nanjing 211109 China

2. Department of Laboratory Medicine The Second Affiliated Hospital of Chongqing Medical University Chongqing 400010 China

3. Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine Nanjing Research Center for Infectious Diseases of Integrated Traditional Chinese and Western Medicine Nanjing 210006 China

Abstract

AbstractBacterial infections have emerged as the leading causes of mortality and morbidity worldwide. Herein, we developed a dual‐channel fluorescence “turn‐on” sensor array, comprising six electrostatic complexes formed from one negatively charged poly(para‐aryleneethynylene) (PPE) and six positively charged aggregation‐induced emission (AIE) fluorophores. The 6‐element array enabled the simultaneous identification of 20 bacteria (OD600=0.005) within 30s (99.0 % accuracy), demonstrating significant advantages over the array constituted by the 7 separate elements that constitute the complexes. Meanwhile, the array realized different mixing ratios and quantitative detection of prevalent bacteria associated with urinary tract infection (UTI). It also excelled in distinguishing six simulated bacteria samples in artificial urine. Remarkably, the limit of detection for E. coli and E. faecalis was notably low, at 0.000295 and 0.000329 (OD600), respectively. Finally, optimized by diverse machine learning algorithms, the designed array achieved 96.7 % accuracy in differentiating UTI clinical samples from healthy individuals using a random forest model, demonstrating the great potential for medical diagnostic applications.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Wiley

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