Evidence map›Paper›PMID 33487783›Full record

ReviewTrends in analytical chemistry : TRAC2021

COVID-19: A challenge for electrochemical biosensors.

Jiri Kudr, Petr Michalek, Lada Ilieva, Vojtech Adam, Ondrej Zitka

Abstract readReview
In one paragraph

Review in Trends in analytical chemistry : TRAC, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Article
  2. Review
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  4. Exosomal transmission of viruses, a two-edged biological sword.Cell communication and signaling : CCS · 2023
    Review
  5. Review
  6. Article
  7. Article
  8. Article
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  11. Article
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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

5 authors.

Jiri KudrDepartment of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic.
Petr MichalekDepartment of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic.
Lada IlievaDepartment of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic.
Vojtech AdamDepartment of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic.
Ondrej ZitkaDepartment of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease (COVID-19) caused by SARS-CoV-2 has spread since the end of 2019 and has resulted in a pandemic with unprecedented socioeconomic consequences. This situation has created enormous demand for the improvement of current diagnostic methods and the development of new diagnostic methods for fast, low-cost and user-friendly confirmation of SARS-CoV-2 infection. This critical review focuses on viral electrochemical biosensors that are promising for the development of rapid medical COVID-19 diagnostic tools. The molecular biological properties of SARS-CoV-2 as well as currently known biochemical attributes of infection necessary for biosensor development are outlined. The advantages and drawbacks of conventional diagnostic methods, such as quantitative reverse-transcription polymerase chain reaction (qRT-PCR), are critically discussed. Electrochemical biosensors focusing on viral nucleic acid and whole viral particle detection are highlighted and discussed in detail. Finally, future perspectives on viral electrochemical biosensor development are briefly mentioned.

Indexed as

AntibodyCRISPR/Cas9DiagnosticsElectrochemistryElectrode

Identifiers

PMID33487783
PMCPMC7813498

What OpenQuestion holds

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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.