1. Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers.
- Author
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Liu, Guan, Wang, Junhao, Wang, Jiulin, Cui, Xinyuan, Wang, Kan, Chen, Mingrui, Yang, Ziyang, Gao, Ang, Shen, Yulan, Zhang, Qian, Gao, Guo, and Cui, Daxiang
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DEEP learning , *DETECTION limit , *TRANSCRANIAL magnetic stimulation , *BIOMARKERS , *COMPUTER vision , *GOLD nanoparticles - 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. [Display omitted] • Zwitterionic magnetic immunochromatographic assay systems were prepared. • Multiplex and diagnosis of biomarkers were performed under a magnetic reader. • Integration of computer vision and Deep-Learning were employed for imaging. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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