{"title":"Automatically Signal-Enhanced Lateral Flow Immunoassay for Ultrasensitive Salivary Cortisol Detection","authors":"Seonjong Kim, Min-Gon Kim","doi":"10.1021/acs.analchem.4c04700","DOIUrl":null,"url":null,"abstract":"Competitive lateral flow immunoassay (LFI) is a commonly used platform for detecting cortisol and other small molecules. However, its low sensitivity is a critical limitation for application in point-of-care tests. Thus, various signal enhancement methods have been developed, requiring complex operational steps and specialized detectors. In this study, we propose an automatic signal-enhanced LFI (asLFI), providing 100 times improved colorimetric sensitivity by applying 10,000-fold fewer AuNP conjugates compared to conventional competitive LFI, involving a single operational step. The incorporation of fewer AuNP conjugates improves the sensitivity of competitive LFI but does not produce a visible signal spontaneously. asLFI facilitates automatic signal enhancement without requiring additional operational steps, affording visible colorimetric signals. asLFI demonstrated high sensitivity in detecting cortisol (limit of detection = 3.8 pg mL<sup>–1</sup>) across a detection range of 0.01–10 ng mL<sup>–1</sup> with linearity correlation (<i>R</i><sup>2</sup> = 0.9159). Furthermore, a strong correlation with ELISA was observed, validating the performance of the asLFI sensor with 18 human saliva samples (<i>R</i><sup>2</sup> = 0.9763). This novel asLFI sensor offers simple operation, affording ultrasensitive point-of-care tests for the circadian monitoring of cortisol.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"35 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.analchem.4c04700","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Competitive lateral flow immunoassay (LFI) is a commonly used platform for detecting cortisol and other small molecules. However, its low sensitivity is a critical limitation for application in point-of-care tests. Thus, various signal enhancement methods have been developed, requiring complex operational steps and specialized detectors. In this study, we propose an automatic signal-enhanced LFI (asLFI), providing 100 times improved colorimetric sensitivity by applying 10,000-fold fewer AuNP conjugates compared to conventional competitive LFI, involving a single operational step. The incorporation of fewer AuNP conjugates improves the sensitivity of competitive LFI but does not produce a visible signal spontaneously. asLFI facilitates automatic signal enhancement without requiring additional operational steps, affording visible colorimetric signals. asLFI demonstrated high sensitivity in detecting cortisol (limit of detection = 3.8 pg mL–1) across a detection range of 0.01–10 ng mL–1 with linearity correlation (R2 = 0.9159). Furthermore, a strong correlation with ELISA was observed, validating the performance of the asLFI sensor with 18 human saliva samples (R2 = 0.9763). This novel asLFI sensor offers simple operation, affording ultrasensitive point-of-care tests for the circadian monitoring of cortisol.
期刊介绍:
Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.