Evidence map›Paper›PMID 36790948›Full record

ArticleIEEE sensors journal2021

Portable Sensing Devices for Detection of COVID-19: A Review.

Deniz Sadighbayan, Ebrahim Ghafar-Zadeh

Abstract read
In one paragraph

Article in IEEE sensors journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

2 authors.

Deniz SadighbayanBiologically Inspired Sensors and Actuators Laboratory (BioSA), Faculty of ScienceDepartment of BiologyYork University Toronto ON M3J 1P3 Canada.ORCID https://orcid.org/0000-0003-3477-7689
Ebrahim Ghafar-ZadehBiologically Inspired Sensors and Actuators Laboratory (BioSA), Lassonde School of Engineering, Department of Electrical Engineering and Computer Science, Faculty of ScienceDepartment of BiologyYork University Toronto ON M3J 1P3 Canada.ORCID https://orcid.org/0000-0002-7090-2304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus pandemic is the most challenging incident that people have faced in recent years. Despite the time-consuming and expensive conventional methods, point-of-care diagnostics have a crucial role in deterrence, timely detection, and intensive care of the disease's progress. Hence, this detrimental health emergency persuaded researchers to accelerate the development of highly-scalable diagnostic devices to control the propagation of the virus even in the least developed countries. The strategies exploited for detecting COVID-19 stem from the already designed systems for studying other maladies, particularly viral infections. The present report reviews not only the novel advances in portable diagnostic devices for recognizing COVID-19, but also the previously existing biosensors for detecting other viruses. It discusses their adaptability for identifying surface proteins, whole viruses, viral genomes, host antibodies, and other biomarkers in biological samples. The prominence of different types of biosensors such as electrochemical, optical, and electrical for detecting low viral loads have been underlined. Thus, it is anticipated that this review will assist scientists who have embarked on a competition to come up with more efficient and marketable in-situ test kits for identifying the infection even in its incubation time without sample pretreatment. Finally, a conclusion is provided to highlight the importance of such an approach for monitoring people to combat the spread of such contagious diseases.

Indexed as

COVID-19point-of-care diagnosticsportable biosensorsSARS-CoV-2

Identifiers

PMID36790948
PMCPMC8769007

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.