Evidence map›Paper›PMID 33714475›Full record

ArticleTalanta2021

Anti-SARS-CoV-2 IgG and IgM detection with a GMR based LFIA system.

Qiaoge Bayin, Lei Huang, Chunhui Ren, Yusheng Fu, Xing Ma, Jinhong Guo

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Talanta, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
5.5field-weighted citation impact, top 3% of its field
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

27 citing papers in PubMed, 82 citations in OpenAlex.

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  7. Recent progress on rapid diagnosis of COVID-19 by point-of-care testing platforms.Chinese chemical letters = Zhongguo hua xue kuai bao · 2023
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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 at 2 institutions in 1 country.

Qiaoge BayinSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
Lei HuangSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
Chunhui RenSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
Yusheng FuSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
Xing MaState Key Lab of Advanced Welding and Joining, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China; Ministry of Education Key Lab of Micro-systems and Micro-structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, PR China.
Jinhong GuoSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China. Electronic address: guojinhong@uestc.edu.cn.
University of Electronic Science and Technology of China · CNHarbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since December 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused millions of deaths and seriously threatened the safety of human life; indeed, this situation is worsening and many people are infected with the new coronavirus every day. Therefore, it is very important to understand patients' degree of infection and infection history through antibody testing. Such information is useful also for the government and hospitals to formulate reasonable prevention policies and treatment plans. In this paper, we develop a lateral flow immunoassay (LFIA) method based on superparamagnetic nanoparticles (SMNPs) and a giant magnetoresistance (GMR) sensing system for the simultaneously quantitative detection of anti-SARS-CoV-2 immunoglobulin M (IgM) and G (IgG). A simple and time-effective co-precipitation method was utilized to prepare the SMNPs, which have good dispersibility and magnetic property, with an average diameter of 68 nm. The Internet of Medical Things-supported GMR could transmit medical data to a smartphone through the Bluetooth protocol, making patient information available for medical staff. The proposed GMR system, based on SMNP-supported LFIA, has an outstanding advantage in cost-effectiveness and time-efficiency, and is easy to operate. We believe that the suggested GMR based LFIA system will be very useful for medical staff to analyze and to preserve as a record of infection in COVID-19 patients.

Indexed as

AnimalsAntibodies, ImmobilizedAntibodies, ViralCattleCell PhoneHumansImmunoassayImmunoglobulin GImmunoglobulin MInternet of ThingsLimit of DetectionMagnetic Iron Oxide NanoparticlesMagnetic PhenomenaSARS-CoV-2Antibodies, ImmobilizedAntibodies, ViralImmunoglobulin GImmunoglobulin MAntibody testingGiant magnetoresistance-based sensing systemLateral flow immunoassaySARS-CoV-2Superparamagnetic nanoparticle

Identifiers

PMID33714475
PMCPMC7874965
OpenAlexW3128415990

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.