Evidence map›Paper›PMID 35298515›Full record

ArticlePloS one2022

SerumCovid database: Description and preliminary analysis of serological COVID-19 diagnosis in healthcare workers.

Isis Didier Lins, Leonardo Streck Raupp, Caio Bezerra Souto Maior, Felipe Cavalcanti de Barros Felipe, Márcio José das Chagas Moura, João Mateus Marques de Santana, Alexsandro Dos Santos, Marcelo Victor de Arruda Freitas, Ramon Nascimento Silva, Ewerton Henrique da Conceição and 4 more

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

14 authors.

Isis Didier LinsCEERMA-Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil.
Leonardo Streck RauppCEERMA-Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil.
Caio Bezerra Souto MaiorCEERMA-Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil.ORCID 0000-0002-5141-8701
Felipe Cavalcanti de Barros FelipeCEERMA-Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil.
Márcio José das Chagas MouraCEERMA-Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil.
João Mateus Marques de SantanaCEERMA-Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Universidade Federal de Pernambuco, Recife, Brazil.
Alexsandro Dos SantosVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
Marcelo Victor de Arruda FreitasVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
Ramon Nascimento SilvaVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
Ewerton Henrique da ConceiçãoVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
José Cândido FerrazVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
Alice AraújoVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
Mariana FernandesVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.
Ana Lisa GomesVitória Academic Center, Universidade Federal de Pernambuco, Vitória de Santo Antão, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Serological databases represent an important source of information to perceive COVID-19 impact on health professionals involved in combating the disease. This paper describes SerumCovid, a COVID-19 serological database focused on the diagnosis of health professionals, providing a preliminary analysis to contribute to the understanding of the antibody response to the SARS-CoV-2. The study population comprises 321 samples from 236 healthcare and frontline workers fighting COVID-19 in Vitória de Santo Antão, Brazil. Samples were collected from at least six days of symptoms to more than 100 days. The used immunoenzymatic assays were Euroimmun Anti-SARS-CoV-2 ELISA IgG and IgA. The most common gender in SerumCovid is female, while the most common age group is between 30 and 39 years old. However, no statistical differences were observed in either genders or age categories. The most reported symptoms were fatigue, headaches, and myalgia. Still, some subjects presented positive results for IgA after 130 days. Based on a temporal analysis, we have not identified general patterns as subjects presented high and low values of IgA and IgG with different evolution trends. Unexpectedly, for subjects with both serological tests, the outcome of IgA and IgG tests were the same (either positive or negative) for more than 80% of the samples. Therefore, SerumCovid helps better understand how COVID-19 affected healthcare and frontline workers, which increases knowledge about the infection and enables direct prevention actions.

Indexed as

COVID-19 Serological TestingAdolescentAdultAntibodies, ViralBrazilCOVID-19Databases as TopicEnzyme-Linked Immunosorbent AssayFemaleHealth PersonnelHumansImmunoglobulin AImmunoglobulin GMaleMiddle AgedSARS-CoV-2Antibodies, ViralImmunoglobulin AImmunoglobulin G

Identifiers

PMID35298515
PMCPMC8929608

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.