Abstract

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.

Alternate abstract:

The use of magnetic nanoparticle (MNP)-labeled immunochromatography test strips (ICTSs) is very important for point-of-care 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 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 such as environmental monitoring, food analysis, and national security.

Details

Title
Machine Learning Approach to Enhance the Performance of MNP-Labeled Lateral Flow Immunoassay
Author
Yan, Wenqiang 1 ; Wang, Kan 1 ; Xu, Hao 2 ; Huo, Xuyang 3 ; Jin, Qinghui 4 ; Cui, Daxiang 1 

1  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, People’s Republic of China 
2  School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China 
3  Department of Biomedical Engineering, JiLin Medical University, JiLin, People’s Republic of China 
4  State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, People’s Republic of China; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, People’s Republic of China 
Pages
1-15
Publication year
2019
Publication date
Dec 2019
Publisher
Springer Nature B.V.
ISSN
23116706
e-ISSN
21505551
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1007/s40820-019-0239-3
ProQuest document ID
2322328662
Copyright
Nano-Micro Letters is a copyright of Springer, (2019). All Rights Reserved., © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.