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Liu G, Wang J, Wang J, Cui X, Wang K, Chen M, Yang Z, Gao A, Shen Y, Zhang Q, Gao G, Cui D. Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers. Talanta 2024; 273:125868. [PMID: 38458085 DOI: 10.1016/j.talanta.2024.125868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 03/10/2024]
Abstract
Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this study, we innovatively developed zwitterionic silica-coated MNPs (MNP@Si-Zwit/COOH) with outstanding antifouling capabilities and effectively utilised them for the simultaneous identification of the nucleocapsid protein (N protein) of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and influenza A/B. The carboxyl-functionalised MNPs with 10% zwitterionic ligands (MNP@Si-Zwit 10/COOH) exhibited a wide linear dynamic detection range and the most pronounced signal-to-noise ratio when used as probes in the ICT. The relative limit of detection (LOD) values were achieved in 12 min by using a magnetic assay reader (MAR), with values of 0.0062 ng/mL for SARS-CoV-2 and 0.0051 and 0.0147 ng/mL, respectively, for the N protein of influenza A and influenza B. By integrating computer vision and deep learning to enhance the image processing of immunoassay results for multiplex detection, a classification accuracy in the range of 0.9672-0.9936 was achieved for evaluating the three proteins at concentrations of 0, 0.1, 1, and 10 ng/mL. The proposed MNP-based ICT for the multiplex diagnosis of biomarkers holds substantial promise for applications in both medical institutions and self-administered diagnostic settings.
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Affiliation(s)
- Guan Liu
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Junhao Wang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Jiulin Wang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Xinyuan Cui
- Radiology Department of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai, 200025, PR China
| | - Kan Wang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Mingrui Chen
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Ziyang Yang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Ang Gao
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China
| | - Yulan Shen
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, PR China.
| | - Qian Zhang
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
| | - Guo Gao
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
| | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China; National Engineering Research Center for Nanotechnology, Shanghai, 200241, PR China; Henan Medical School, Henan University, Henan, 475004, PR China.
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Wu KH, Huang WC, Wang JC, Wang SH. Paper-based colorimetric sensor using Photoshop and a smartphone app for the quantitative detection of carbofuran. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:1043-1049. [PMID: 38268410 DOI: 10.1039/d3ay02211k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
We developed a smartphone-assisted microchemistry analyzer for the quantitative detection of carbofuran using a paper-based colorimetric sensor, Photoshop software, and a smartphone app. The changes in color of the carbofuran enzymatic reaction in the paper-based sensor were captured and analyzed using a smartphone-controlled analyzer with an LED light source and a smartphone camera. The high accuracy of this method was demonstrated for the determination of carbofuran with a linear response in the range 0.05-1.0 ppm and limits of detection (LOD) of 0.02 and 0.018 ppm using Photoshop and smartphone app colorimetric analysis, respectively. These two methods not only show the high sensitivity and highly quantitative relationships between the concentrations of commercial carbofuran and characteristic color values of the blue channel in smartphone images but were also applied to infusions of tea. Moreover, the smartphone app is able to GPS tag the location of the test and transmit the results to a website that displays quantitative results from carbofuran samples on a map.
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Affiliation(s)
- Kuo-Hui Wu
- Department of Chemistry and Materials Engineering, Chung Cheng Institute of Technology, National Defense University, Tahsi, Taoyuan, 33551, Taiwan.
| | - Wen-Chien Huang
- Department of Chemistry and Materials Engineering, Chung Cheng Institute of Technology, National Defense University, Tahsi, Taoyuan, 33551, Taiwan.
| | - Je-Chuang Wang
- Department of Chemistry and Materials Engineering, Chung Cheng Institute of Technology, National Defense University, Tahsi, Taoyuan, 33551, Taiwan.
| | - Shih-Hsien Wang
- Department of Chemistry and Materials Engineering, Chung Cheng Institute of Technology, National Defense University, Tahsi, Taoyuan, 33551, Taiwan.
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