Evidence map›Paper›PMID 35414865›Full record

ArticleChemical science2022

Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers.

Ying Zhou, Shuofeng Yuan, Kelvin Kai-Wang To, Xiaohan Xu, Hongyan Li, Jian-Piao Cai, Cuiting Luo, Ivan Fan-Ngai Hung, Kwok-Hung Chan, Kwok-Yung Yuen and 3 more

Abstract read
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In one paragraph

Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

13 authors.

Ying ZhouDepartment of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.
Shuofeng YuanState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.ORCID https://orcid.org/0000-0001-7996-1119
Kelvin Kai-Wang ToState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.ORCID https://orcid.org/0000-0002-1921-5824
Xiaohan XuDepartment of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.
Hongyan LiDepartment of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.
Jian-Piao CaiState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
Cuiting LuoState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
Ivan Fan-Ngai HungDepartment of Clinical Microbiology, and Infection Control, The University of Hong Kong-Shenzhen Hospital Shenzhen Guangdong Province China.ORCID https://orcid.org/0000-0002-1556-2538
Kwok-Hung ChanState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
Kwok-Yung YuenState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.ORCID https://orcid.org/0000-0002-2083-1552
Yu-Feng LiCAS-HKU Joint Laboratory of Metallomics on Health and Environment, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Beijing Metallomics Facility, National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences Beijing China.ORCID https://orcid.org/0000-0002-5013-5849
Jasper Fuk-Woo ChanState Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.ORCID https://orcid.org/0000-0001-6336-6657
Hongzhe SunDepartment of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.ORCID https://orcid.org/0000-0001-6697-6899

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing COVID-19 pandemic caused by SARS-CoV-2 highlights the urgent need to develop sensitive methods for diagnosis and prognosis. To achieve this, multidimensional detection of SARS-CoV-2 related parameters including virus loads, immune response, and inflammation factors is crucial. Herein, by using metal-tagged antibodies as reporting probes, we developed a multiplex metal-detection based assay (MMDA) method as a general multiplex assay strategy for biofluids. This strategy provides extremely high multiplexing capability (theoretically over 100) compared with other reported biofluid assay methods. As a proof-of-concept, MMDA was used for serologic profiling of anti-SARS-CoV-2 antibodies. The MMDA exhibits significantly higher sensitivity and specificity than ELISA for the detection of anti-SARS-CoV-2 antibodies. By integrating the high dimensional data exploration/visualization tool (tSNE) and machine learning algorithms with in-depth analysis of multiplex data, we classified COVID-19 patients into different subgroups based on their distinct antibody landscape. We unbiasedly identified anti-SARS-CoV-2-nucleocapsid IgG and IgA as the most potently induced types of antibodies for COVID-19 diagnosis, and anti-SARS-CoV-2-spike IgA as a biomarker for disease severity stratification. MMDA represents a more accurate method for the diagnosis and disease severity stratification of the ongoing COVID-19 pandemic, as well as for biomarker discovery of other diseases.

Identifiers

PMID35414865
PMCPMC8926254

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.