Evidence map›Paper›PMID 34137967›Full record

ArticleNano-micro letters2019

Machine Learning Approach to Enhance the Performance of MNP-Labeled Lateral Flow Immunoassay.

Wenqiang Yan, Kan Wang, Hao Xu, Xuyang Huo, Qinghui Jin, Daxiang Cui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano-micro letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Transformative impact of point-of-care testing in critical care.World journal of critical care medicine · 2025
    Article
  6. Review
  7. Artificial Intelligence in Point-of-Care Testing.Annals of laboratory medicine · 2023
    Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. A Novel NiFeSensors (Basel, Switzerland) · 2020
    Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Wenqiang Yan *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.
Kan Wang *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. wk_xa@163.com.
Hao XuSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
Xuyang HuoDepartment of Biomedical Engineering, JiLin Medical University, JiLin, 132013, People's Republic of China. huoxuyang@jlmu.cn.
Qinghui JinState Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, People's Republic of China.
Daxiang CuiDepartment 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. dxcui@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

Immunochromatography test stripsMachine learningMagnetic nanoparticlesPoint-of-care testingSupport vector machine

Identifiers

PMID34137967
PMCPMC7770769

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.