Evidence map›Paper›PMID 34766062›Full record

ReviewSensors international2021

A comprehensive review on current COVID-19 detection methods: From lab care to point of care diagnosis.

Bishal Singh, Brateen Datta, Amlan Ashish, Gorachand Dutta

Abstract readReview
In one paragraph

Review in Sensors international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  8. Batteryless wireless magnetostrictive FeSensors and actuators. A, Physical · 2023
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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

4 authors.

Bishal SinghSchool of Medical Science and Technology (SMST), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
Brateen DattaSchool of Medical Science and Technology (SMST), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
Amlan AshishSchool of Medical Science and Technology (SMST), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
Gorachand DuttaSchool of Medical Science and Technology (SMST), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Without a doubt, the current global pandemic affects all walks of our life. It affected almost every age group all over the world with a disease named COVID-19, declared as a global pandemic by WHO in early 2020. Due to the high transmission and moderate mortality rate of this virus, it is also regarded as the panic-zone virus. This potentially deadly virus has pointed up the significance of COVID-19 research. Due to the rapid transmission of COVID-19, early detection is very crucial. Presently, there are different conventional techniques are available for coronavirus detection like CT-scan, PCR, Sequencing, CRISPR, ELISA, LFA, LAMP. The urgent need for rapid, accurate, and cost-effective detection and the requirement to cut off shortcomings of traditional detection methods, make scientists realize to advance new technologies. Biosensors are one of the reliable platforms for accurate, early diagnosis. In this article, we have pointed recent diagnosis approaches for COVID-19. The review includes basic virology of SARS-CoV-2 mainly clinical and pathological features. We have also briefly discussed different types of biosensors, their working principles, and current advancement for COVID-19 detection and prevention.

Indexed as

BiomarkerBiosensorsCOVID-19DiagnosisImmunoassayPoint of careSARS-CoV 2

Identifiers

PMID34766062
PMCPMC8302821

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.