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

作者全名："Yu, Yang; Ni, Weiwei; Hu, Qin; Li, Huihai; Zhang, Yi; Gao, Xu; Zhou, Lingjia; Zhang, Shuming; Ma, Shuoyang; Zhang, Yanliang; Huang, Hui; Li, Fei; Han, Jinsong"

作者地址："[Yu, Yang; Ni, Weiwei; Li, Huihai; Zhang, Yi; Gao, Xu; Zhou, Lingjia; Zhang, Shuming; Ma, Shuoyang; Huang, Hui; Li, Fei; Han, Jinsong] China Pharmaceut Univ, Coll Engn, Natl R&D Ctr Chinese Herbal Med Proc, Dept Food Qual & Safety,State Key Lab Nat Med, Nanjing 210009, Peoples R China; [Hu, Qin] Chongqing Med Univ, Affiliated Hosp 2, Dept Lab Med, Chongqing 400010, Peoples R China; [Zhang, Yanliang] Nanjing Univ Chinese Med, Nanjing Hosp Chinese Med, Nanjing Res Ctr Infect Dis Integrated Tradit Chine, Nanjing 210022, Peoples R China"

通信作者："Huang, H; Li, F; Han, JS (通讯作者)，China Pharmaceut Univ, Coll Engn, Natl R&D Ctr Chinese Herbal Med Proc, Dept Food Qual & Safety,State Key Lab Nat Med, Nanjing 210009, Peoples R China."

来源：ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

ESI学科分类：CHEMISTRY

WOS号：WOS:001184914300001

JCR分区：Q1

影响因子：16.1

年份：2024

卷号：63

期号：16

开始页：　

结束页：　

文献类型：Article

关键词：Sensor Array; Bacteria Identification; Poly(para-aryleneethynylene); Aggregation-Induced Emission; Machine Learning

摘要："Bacterial 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."

基金机构："National Natural Science Foundation of China; Natural Science Foundation of Jiangsu Province [BK20200578, CPUQNJC22_04]; [82072017]; [32272415]; [52003298]"

基金资助正文："This project was supported by the National Natural Science Foundation of China (82072017, 32272415 and 52003298), Natural Science Foundation of Jiangsu Province (BK20200578) and Funding of Double First-Rate Discipline Innovation Team (CPUQNJC22_04). The authors thank Dr. Hui-Min Xu from The Public Laboratory Platform at China Pharmaceutical University for assistance with NMR techniques."