Evidence map›Paper›PMID 33796489›Full record

ArticleFrontiers in cellular and infection microbiology2021

Predictive Analysis of the Neutralization Activity in Convalescent Plasmas From COVID-19 Recovered Patients in Zhejiang Province, China, January-March, 2020.

Yajie Yuan, Liang Yu, Zi Jin, Yongjun Wang, Meng Gao, Haojie Ding, Xunhui Zhuo, Xiao Zhu, Fei Gao, Xiaojun Zheng and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2021. 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
0.2field-weighted citation impact, top 48% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
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

15 authors at 6 institutions in 1 country.

Yajie YuanInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Liang YuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Zi JinInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Yongjun WangBlood Center of Zhejiang Province, Hangzhou, China.
Meng GaoInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Haojie DingInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Xunhui ZhuoInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Xiao ZhuGuangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China.
Fei GaoDepartment of research and development, Hangzhou AllTest Biotech Co., Ltd, Hangzhou, China.
Xiaojun ZhengDepartment of research and development, Hangzhou AllTest Biotech Co., Ltd, Hangzhou, China.
Guoqing YingCollege of Pharmacy, Zhejiang University of Technology, Hangzhou, China.
Xiaowei XuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Qingming KongInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Shaohong LuInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Hangjun LvInstitute of Parasitic Diseases, Hangzhou Medical College, Hangzhou, China.
Hangzhou Medical College · CNHangzhou DAC Biotech (China) · CNState Key Laboratory of Diagnosis and Treatment of Infectious DiseasesBlood Center of Zhejiang Province · CNGuangdong Medical College · CNZhejiang University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Convalescent plasma (CP) transfusion is considered to be the priority therapeutic option for COVID-19 inpatients when no specific drugs are available for emerging infections. An alternative, simple, and sensitive method is urgently needed for clinical use to detect neutralization activity of the CP to avoid the use of inconvenient micro-neutralization assay. Method: This study aims to explore optimal index in predicting the COVID-19 CP neutralization activity (neutralizing antibody titers, NAb titers) in an indirect ELISA format. Fifty-seven COVID-19-recovered patients plasma samples were subjected to anti-SARS-CoV-2 RBD, S1, and N protein IgG antibody by indirect ELISA. Results: ELISA-RBD exhibited high specificity (96.2%) and ELISA-N had high sensitivity (100%); while ELISA-S1 had low sensitivity (86.0%) and specificity (73.1%). Furthermore, ELISA-RBD IgG titers and pseudovirus-based NAb titers correlated significantly, with R Conclusion: ELISA-RBD could be a substitute for the neutralization assay in resource-limited situations to screen potential plasma donors for further plasma infusion therapy.

Indexed as

AnimalsAntibodies, NeutralizingAntibodies, ViralAntiviral AgentsBlood DonorsChinaChlorocebus aethiopsCohort StudiesCOVID-19COVID-19 SerotherapyEnzyme-Linked Immunosorbent AssayHEK293 CellsHumansImmunization, PassiveImmunoglobulin GPlasmaAntibodies, NeutralizingAntibodies, ViralAntiviral AgentsImmunoglobulin Gconvalescent plasmaIgG antibodyindirect ELISAneutralizing antibodySARS-CoV-2

Identifiers

PMID33796489
PMCPMC8008148
OpenAlexW3138694072

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.