Evidence map›Paper›PMID 34571481›Full record

ArticleBiosensors & bioelectronics2022

Aptamer-based electrochemical biosensor for rapid detection of SARS-CoV-2: Nanoscale electrode-aptamer-SARS-CoV-2 imaging by photo-induced force microscopy.

Juan Carlos Abrego-Martinez, Maziar Jafari, Siham Chergui, Catalin Pavel, Diping Che, Mohamed Siaj

Abstract read
In one paragraph

Article in Biosensors & bioelectronics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.

0numbers the graph read from it
0cells of the map it votes in
72citing 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

72 citing papers in PubMed.

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12 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

6 authors.

Juan Carlos Abrego-MartinezDepartment of Chemistry and Biochemistry, Université Du Québec à Montréal, Montréal, QC, H3C 3P8, Canada.
Maziar JafariDepartment of Chemistry and Biochemistry, Université Du Québec à Montréal, Montréal, QC, H3C 3P8, Canada.
Siham CherguiDepartment of Chemistry and Biochemistry, Université Du Québec à Montréal, Montréal, QC, H3C 3P8, Canada.
Catalin PavelAzure Biosystems Canada, Montréal, QC, H4P 2N5, Canada.
Diping CheAzure Biosystems Canada, Montréal, QC, H4P 2N5, Canada.
Mohamed SiajDepartment of Chemistry and Biochemistry, Université Du Québec à Montréal, Montréal, QC, H3C 3P8, Canada. Electronic address: siaj.mohamed@uqam.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid, mass diagnosis of the coronavirus disease 2019 (COVID-19) is critical to stop the ongoing infection spread. The two standard screening methods to confirm the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are polymerase chain reaction (PCR), through the RNA of the virus, and serology by detecting antibodies produced as a response to the viral infection. However, given the detection complexity, cost and relatively long analysis times of these techniques, novel technologies are urgently needed. Here, we report an aptamer-based biosensor developed on a screen-printed carbon electrode platform for rapid, sensitive, and user-friendly detection of SARS-CoV-2. The aptasensor relies on an aptamer targeting the receptor-binding domain (RBD) in the spike protein (S-protein) of the SARS-CoV-2. The aptamer immobilization on gold nanoparticles, and the presence of S-protein in the aptamer-target complex, investigated for the first time by photo-induced force microscopy mapping between 770 and 1910 cm

Indexed as

Biosensing TechniquesCOVID-19Metal NanoparticlesElectrodesGoldHumansMicroscopy, Atomic ForceSARS-CoV-2GoldAptasensorDetectionPhoto-induced force microscopySARS-CoV-2S-protein

Identifiers

PMID34571481
PMCPMC8405239

What OpenQuestion holds

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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.