Evidence map›Paper›PMID 32793931›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Neutralizing antibody against SARS-CoV-2 spike in COVID-19 patients, health care workers and convalescent plasma donors: a cohort study using a rapid and sensitive high-throughput neutralization assay.

Cong Zeng, John P Evans, Rebecca Pearson, Panke Qu, Yi-Min Zheng, Richard T Robinson, Luanne Hall-Stoodley, Jacob Yount, Sonal Pannu, Rama K Mallampalli and 4 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 10 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Cong Zeng
John P Evans
Rebecca Pearson
Panke Qu
Yi-Min Zheng
Richard T Robinson
Luanne Hall-Stoodley
Jacob Yount
Sonal Pannu
Rama K Mallampalli
Linda Saif
Eugene Oltz
Gerard Lozanski
The Ohio State University · US

Funding

Regulation of Cardiolin Byosynthesis in Epithelial InjuryP01HL114453 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MALLAMPALLI, RAMA K · 2014 to 2023
$21.3M
F box-Induced Acute Lung Injury and ParkinR01HL096376 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MALLAMPALLI, RAMA K · 2010 to 2025
$5.1M
NHLBI NIH HHS P01 HL114453NHLBI NIH HHS R01 HL096376
6 · The paper itself

Abstract

Rapid and specific antibody testing is crucial for improved understanding, control, and treatment of COVID-19 pathogenesis. Herein, we describe and apply a rapid, sensitive, and accurate virus neutralization assay for SARS-CoV-2 antibodies. The new assay is based on an HIV-1 lentiviral vector that contains a secreted intron Gaussia luciferase or secreted Nano-luciferase reporter cassette, pseudotyped with the SARS-CoV-2 spike (S) glycoprotein, and is validated with a plaque reduction assay using an authentic, infectious SARS-CoV-2 strain. The new assay was used to evaluate SARS-CoV-2 antibodies in serum from individuals with a broad range of COVID-19 symptoms, including intensive care unit (ICU) patients, health care workers (HCWs), and convalescent plasma donors. The highest neutralizing antibody titers were observed among ICU patients, followed by general hospitalized patients, HCWs and convalescent plasma donors. Our study highlights a wide phenotypic variation in human antibody responses against SARS-CoV-2, and demonstrates the efficacy of a novel lentivirus pseudotype assay for high-throughput serological surveys of neutralizing antibody titers in large cohorts.

Identifiers

PMID32793931
PMCPMC7418758
OpenAlexW3047727017

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.