Evidence map›Paper›PMID 32818132›Full record

Review3 Biotech2020

Biosensors: frontiers in rapid detection of COVID-19.

Rachel Samson, Govinda R Navale, Mahesh S Dharne

Open access · bronzeAbstract readReview
In one paragraph

Review in 3 Biotech, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed
4.0field-weighted citation impact, top 4% 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

85 citing papers in PubMed, 188 citations in OpenAlex.

  1. Article
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  6. ACS pharmacology & translational science · 2025
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25 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

3 authors at 1 institution in 1 country.

Rachel Samson *Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Govinda R Navale *Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Mahesh S DharneAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.ORCID 0000-0002-3965-7320
National Chemical Laboratory · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid community-spread of novel human coronavirus 2019 (nCOVID19 or SARS-Cov2) and morbidity statistics has put forth an unprecedented urge for rapid diagnostics for quick and sensitive detection followed by contact tracing and containment strategies, especially when no vaccine or therapeutics are known. Currently, quantitative real-time polymerase chain reaction (qRT-PCR) is being used widely to detect COVID-19 from various types of biological specimens, which is time-consuming, labor-intensive and may not be rapidly deployable in remote or resource-limited settings. This might lead to hindrance in acquiring realistic data of infectivity and community spread of SARS-CoV-2 in the population. This review summarizes the existing status of current diagnostic methods, their possible limitations, and the advantages of biosensor-based diagnostics over the conventional ones for the detection of SARS-Cov-2. Novel biosensors used to detect RNA-viruses include CRISPR-Cas9 based paper strip, nucleic-acid based, aptamer-based, antigen-Au/Ag nanoparticles-based electrochemical biosensor, optical biosensor, and Surface Plasmon Resonance. These could be effective tools for rapid, authentic, portable, and more promising diagnosis in the current pandemic that has affected the world economies and humanity. Present challenges and future perspectives of developing robust biosensors devices for rapid, scalable, and sensitive detection and management of COVID-19 are presented in light of the test-test-test theme of the World Health Organization (WHO).

Indexed as

BiosensorsCOVID-19Rapid detectionSARS-CoV-2

Identifiers

PMID32818132
PMCPMC7417775
OpenAlexW3047756296

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.