Evidence map›Paper›PMID 33492524›Full record

ReviewArchives of virology2021

Serological assays and host antibody detection in coronavirus-related disease diagnosis.

Sayeh Dowlatshahi, Ehsan Shabani, Mohammad J Abdekhodaie

Open access · bronzeAbstract readReview
In one paragraph

Review in Archives of virology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.2field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 21 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Sayeh DowlatshahiDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.
Ehsan ShabaniDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.
Mohammad J AbdekhodaieDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran. abdmj@sharif.edu.
Sharif University of Technology · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronaviruses (CoV) are a family of viral pathogens that infect both birds and mammals, including humans. Seven human coronaviruses (HCoV) have been recognized so far. HCoV-229E, -OC43, -NL63, and -HKU1 account for one-third of common colds with mild symptoms. The other three members are severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome (MERS)-CoV, and SARS-CoV-2. These viruses are responsible for SARS, MERS, and CoV disease 2019 (COVID-19), respectively. A variety of diagnostic techniques, including chest X-rays, computer tomography (CT) scans, analysis of viral nucleic acids, proteins, or whole virions, and host antibody detection using serological assays have been developed for the detection of these viruses. In this review, we discuss conventional serological tests, such as enzyme-linked immunosorbent assay (ELISA), western blot (WB), immunofluorescence assay (IFA), lateral flow immunoassay (LFIA), and chemiluminescence immunoassay (CLIA), as well as biosensor-based assays that have been developed for diagnosing HCoV-associated diseases since 2003, with an in-depth focus on COVID-19.

Indexed as

Antibodies, ViralBiosensing TechniquesBlotting, WesternCOVID-19COVID-19 Serological TestingEnzyme-Linked Immunosorbent AssayFluorescent Antibody TechniqueHumansLuminescent MeasurementsMiddle East Respiratory Syndrome CoronavirusSARS-CoV-2Severe Acute Respiratory SyndromeSevere acute respiratory syndrome-related coronavirusAntibodies, Viral

Identifiers

PMID33492524
PMCPMC7830048
OpenAlexW3124813749

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.