Machine Learning Approach to Enhance the Performance of MNP-Labeled Lateral Flow Immunoassay
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ARTICLE
https://doi.org/10.1007/s40820-019-0239-3
Cite as Nano-Micro Lett. (2019) 11:7
Machine Learning Approach to Enhance the Performance of MNP‑Labeled Lateral Flow Immunoassay
Received: 21 November 2018 Accepted: 29 December 2018 © The Author(s) 2019
Wenqiang Yan1, Kan Wang1 *, Hao Xu2, Xuyang Huo3 *, Qinghui Jin4,5, Daxiang Cui1 * Wenqiang Yan and Kan Wang have contributed equally to this work. * Kan Wang, [email protected]; Xuyang Huo, [email protected]; Daxiang Cui, [email protected] Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Key Laboratory of Thin Film and Microfabrication (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China 2 School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China 3 Department of Biomedical Engineering, JiLin Medical University, JiLin 132013, People’s Republic of China 4 State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, People’s Republic of China 5 Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, People’s Republic of China 1
HIGHLIGHTS • An ultrasensitive multiplex biosensor was designed to quantify magnetic nanoparticles on immunochromatography test strips. • A machine learning model was constructed and used to classify both weakly positive and negative samples, significantly enhancing specificity and sensitivity. • A waveform reconstruction method was developed to appropriately restore the distorted waveform for weak magnetic signals.
ABSTRACT The use of magnetic nanoparticle (MNP)-labeled immu-
nochromatography test strips (ICTSs) is very important for point-ofcare testing (POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin (HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was
1–1000 mIU mL−1 and the ideal detection limit was 0.014 mIU mL−1, which was well below the clinical threshold. Quantitative detection Vol.:(0123456789)
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results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications su
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