{"title":"基于 \"一体化 \"半导体聚合物点传感器和机器学习的多重细菌识别技术","authors":"","doi":"10.1016/j.talanta.2024.126917","DOIUrl":null,"url":null,"abstract":"<div><div>The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified sensor array utilizing “All-in-One” Pdots for efficient discrimination of diverse bacterial samples. The “All-in-One” Pdots sensor (AOPS) were synthesized using three components that exhibit fluorescence resonance energy transfer (FRET) effect, facilitating the efficient integration of multiple discrimination channels to generate specific fluorescence response patterns through a single detection under single-wavelength excitation. Additionally, machine learning techniques were employed to visually represent the fluorescence response patterns of AOPS upon exposure to bacterial metabolites derived from diverse bacterial species. The as-prepared sensor platform demonstrated excellent performance in analyzing eight common bacteria, drug-resistant strains, mixed bacterial samples, bacterial biofilms and real samples, presenting significant potential in the identification of complex samples for bacterial analysis.</div></div>","PeriodicalId":435,"journal":{"name":"Talanta","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiplexed bacterial recognition based on “All-in-One” semiconducting polymer dots sensor and machine learning\",\"authors\":\"\",\"doi\":\"10.1016/j.talanta.2024.126917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified sensor array utilizing “All-in-One” Pdots for efficient discrimination of diverse bacterial samples. The “All-in-One” Pdots sensor (AOPS) were synthesized using three components that exhibit fluorescence resonance energy transfer (FRET) effect, facilitating the efficient integration of multiple discrimination channels to generate specific fluorescence response patterns through a single detection under single-wavelength excitation. Additionally, machine learning techniques were employed to visually represent the fluorescence response patterns of AOPS upon exposure to bacterial metabolites derived from diverse bacterial species. The as-prepared sensor platform demonstrated excellent performance in analyzing eight common bacteria, drug-resistant strains, mixed bacterial samples, bacterial biofilms and real samples, presenting significant potential in the identification of complex samples for bacterial analysis.</div></div>\",\"PeriodicalId\":435,\"journal\":{\"name\":\"Talanta\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Talanta\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0039914024012967\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Talanta","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0039914024012967","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Multiplexed bacterial recognition based on “All-in-One” semiconducting polymer dots sensor and machine learning
The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified sensor array utilizing “All-in-One” Pdots for efficient discrimination of diverse bacterial samples. The “All-in-One” Pdots sensor (AOPS) were synthesized using three components that exhibit fluorescence resonance energy transfer (FRET) effect, facilitating the efficient integration of multiple discrimination channels to generate specific fluorescence response patterns through a single detection under single-wavelength excitation. Additionally, machine learning techniques were employed to visually represent the fluorescence response patterns of AOPS upon exposure to bacterial metabolites derived from diverse bacterial species. The as-prepared sensor platform demonstrated excellent performance in analyzing eight common bacteria, drug-resistant strains, mixed bacterial samples, bacterial biofilms and real samples, presenting significant potential in the identification of complex samples for bacterial analysis.
期刊介绍:
Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome.
Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.