Evidence map›Paper›PMID 32928412›Full record

ReviewTalanta2020

Diagnostic techniques for COVID-19 and new developments.

Elham Sheikhzadeh, Shimaa Eissa, Aziah Ismail, Mohammed Zourob

Abstract readReview
In one paragraph

Review in Talanta, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 1 pooled it
–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

71 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
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  10. Article
  11. CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification.Machine learning and knowledge extraction · 2023
    Article
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11 more citing papers are in PubMed but not listed here.

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

4 authors.

Elham SheikhzadehInstitute for Research in Molecular Medicine (INFORMM), Health Campus, Universiti Sains Malaysia16150 Kubang Kerian, Kelantan, Malaysia.
Shimaa EissaDepartment of Chemistry, Alfaisal University, Al Zahrawi Street, Al Maather, Al Takhassusi Road, Riyadh, 11533, Saudi Arabia.
Aziah IsmailInstitute for Research in Molecular Medicine (INFORMM), Health Campus, Universiti Sains Malaysia16150 Kubang Kerian, Kelantan, Malaysia.
Mohammed ZourobDepartment of Chemistry, Alfaisal University, Al Zahrawi Street, Al Maather, Al Takhassusi Road, Riyadh, 11533, Saudi Arabia; King Faisal Specialist Hospital and Research Center, Zahrawi Street, Al Maather, Riyadh, 12713, Saudi Arabia. Electronic address: mzourob@alfaisal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 pandemic is a serious global health issue today due to the rapid human to human transmission of SARS-CoV-2, a new type of coronavirus that causes fatal pneumonia. SARS -CoV-2 has a faster rate of transmission than other coronaviruses such as SARS and MERS and until now there are no approved specific drugs or vaccines for treatment. Thus, early diagnosis is crucial to prevent the extensive spread of the disease. The reverse transcription-polymerase chain reaction (RT-PCR) is the most routinely used method until now to detect SARS-CoV-2 infections. However, several other faster and accurate assays are being developed for the diagnosis of COVID-19 aiming to control the spread of infection through the identification of patients and immediate isolation. In this review, we will discuss the various detection methods of the SARS-CoV-2 virus including the recent developments in immunological assays, amplification techniques as well as biosensors.

Indexed as

Biosensing TechniquesClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19COVID-19 TestingEarly DiagnosisHumansImmunoassayPandemicsPneumonia, ViralPolymerase Chain ReactionBiosensorsCOVID-19.Immunological assays.Molecular methods.SARS-CoV-2.

Identifiers

PMID32928412
PMCPMC7358765

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.