Evidence map›Paper›PMID 36934198›Full record

ArticleScientific reports2023

The design of a point of care FET biosensor to detect and screen COVID-19.

Nisreen Alnaji, Asma Wasfi, Falah Awwad

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

3 authors.

Nisreen AlnajiDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates.
Asma WasfiDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates.
Falah AwwadDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates. f_awwad@uaeu.ac.ae.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Graphene field effect transistor (FET) biosensors have attracted huge attention in the point-of-care and accurate detection. With the recent spread of the new emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the need for rapid, and accurate detection & screening tools is arising. Employing these easy-to-handle sensors can offer cheap, rapid, and accurate detection. Herein, we propose the design of a reduced graphene oxide (rGO) FET biosensor for the detection of SARS-CoV-2. The main objective of this work is to detect the SARS-CoV-2 spike protein antigen on spot selectively and rapidly. The sensor consists of rGO channel, a pair of golden electrodes, and a gate underneath the channel. The channel is functionalized with COVID-19 spike protein antibodies to achieve selectivity, and with metal nanoparticles (MNPs) such as copper and silver to enhance the bio-sensing performance. The designed sensor successfully detects the SARS-CoV-2 spike protein and shows singular electrical behavior for detection. The semi-empirical modeling approach combined with none-equilibrium Green's function were used to study the electronic transport properties of the rGO-FET biosensor before and after the addition of the target molecules. The sensor's selectivity is also tested against other viruses. This study provides a promising guide for future practical fabrication.

Indexed as

Biosensing TechniquesCOVID-19GraphiteHumansPoint-of-Care SystemsSARS-CoV-2Spike Glycoprotein, Coronavirusgraphene oxideGraphiteSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2

Identifiers

PMID36934198
PMCPMC10024292

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Registered trials

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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.