Evidence map›Paper›PMID 36010342›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Impedimetric Detection Based on Label-Free Immunoassay Developed for Targeting Spike S1 Protein of SARS-CoV-2.

Arzum Erdem, Huseyin Senturk, Esma Yildiz, Meltem Maral

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 11 citations in OpenAlex.

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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 at 1 institution in 1 country.

Arzum ErdemAnalytical Chemistry Department, Faculty of Pharmacy, Ege University, Bornova, Izmir 35100, Turkey.ORCID 0000-0002-4375-8386
Huseyin SenturkAnalytical Chemistry Department, Faculty of Pharmacy, Ege University, Bornova, Izmir 35100, Turkey.
Esma YildizAnalytical Chemistry Department, Faculty of Pharmacy, Ege University, Bornova, Izmir 35100, Turkey.
Meltem MaralAnalytical Chemistry Department, Faculty of Pharmacy, Ege University, Bornova, Izmir 35100, Turkey.
Ege University · TR

Funding

Scientific and Technological Research Council of Turkey TÜBİTAK Project No. 120S419
6 · The paper itself

Abstract

After the COVID-19 pandemic started all over the world, great importance was placed on the development of sensitive and selective bioanalytical assays for the rapid detection of the highly pathogenic SARS-CoV-2 virus causing COVID-19 disease. In this present work, an impedimetric immunosensor was developed and applied for rapid, reliable, sensitive and selective detection of the SARS-CoV-2 S1 protein. To detect the SARS-CoV-2 virus, targeting of the spike S1 protein was achieved herein by using S1 protein-specific capture antibody (Cab-S1) immobilized screen-printed electrode (SPE) in combination with the electrochemical impedance spectroscopy (EIS) technique. With the impedimetric immunosensor, the detection limit for S1 protein in buffer medium was found to be 0.23 ng/mL (equal to 23.92 amol in 8 µL sample) in the linear concentration range of S1 protein from 0.5 to 10 ng/mL. In the artificial saliva medium, it was found to be 0.09 ng/mL (equals to 9.36 amol in 8 µL sample) in the linear concentration range of S1 protein between 0.1 and 1 ng/mL. The selectivity of the impedimetric immunosensor toward S1 protein was tested against influenza hemagglutinin antigen (HA) in the buffer medium as well as in artificial saliva.

Indexed as

COVID-19electrochemical immunosensorselectrochemical impedance spectroscopySARS-CoV-2 S1 protein

Identifiers

PMID36010342
PMCPMC9407092
OpenAlexW4292261163

What OpenQuestion holds

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LicenceCC BY
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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.