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Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus

Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of si...

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Autores principales: Yang, Guang, Li, Yaxi, Tang, Chenling, Lin, Feng, Wu, Tianfu, Bao, Jiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9447405/
https://www.ncbi.nlm.nih.gov/pubmed/36072130
http://dx.doi.org/10.3390/chemosensors10080330
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author Yang, Guang
Li, Yaxi
Tang, Chenling
Lin, Feng
Wu, Tianfu
Bao, Jiming
author_facet Yang, Guang
Li, Yaxi
Tang, Chenling
Lin, Feng
Wu, Tianfu
Bao, Jiming
author_sort Yang, Guang
collection PubMed
description Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of signals. In the present study, we developed a smartphone-based application to analyze signals from protein microarrays to quantify disease biomarkers. The application adopted Android Studio open platform for its wide access to smartphones, and Python was used to design a graphical user interface with fast data processing. The application provides multiple user functions such as “Read”, “Analyze”, “Calculate” and “Report”. For rapid and accurate results, we used ImageJ, Otsu thresholding, and local thresholding to quantify the fluorescent intensity of spots on the microarray. To verify the efficacy of the application, three antigens each with over 110 fluorescent spots were tested. Particularly, a positive correlation of over 0.97 was achieved when using this analytical tool compared to a standard test for detecting a potential biomarker in lupus nephritis. Collectively, this smartphone application tool shows promise for cheap, efficient, and portable on-site detection in point-of-care diagnostics.
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spelling pubmed-94474052022-09-06 Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus Yang, Guang Li, Yaxi Tang, Chenling Lin, Feng Wu, Tianfu Bao, Jiming Chemosensors (Basel) Article Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of signals. In the present study, we developed a smartphone-based application to analyze signals from protein microarrays to quantify disease biomarkers. The application adopted Android Studio open platform for its wide access to smartphones, and Python was used to design a graphical user interface with fast data processing. The application provides multiple user functions such as “Read”, “Analyze”, “Calculate” and “Report”. For rapid and accurate results, we used ImageJ, Otsu thresholding, and local thresholding to quantify the fluorescent intensity of spots on the microarray. To verify the efficacy of the application, three antigens each with over 110 fluorescent spots were tested. Particularly, a positive correlation of over 0.97 was achieved when using this analytical tool compared to a standard test for detecting a potential biomarker in lupus nephritis. Collectively, this smartphone application tool shows promise for cheap, efficient, and portable on-site detection in point-of-care diagnostics. 2022-08 2022-08-13 /pmc/articles/PMC9447405/ /pubmed/36072130 http://dx.doi.org/10.3390/chemosensors10080330 Text en https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Guang
Li, Yaxi
Tang, Chenling
Lin, Feng
Wu, Tianfu
Bao, Jiming
Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
title Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
title_full Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
title_fullStr Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
title_full_unstemmed Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
title_short Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
title_sort smartphone-based quantitative analysis of protein array signals for biomarker detection in lupus
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9447405/
https://www.ncbi.nlm.nih.gov/pubmed/36072130
http://dx.doi.org/10.3390/chemosensors10080330
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