Evidence map›Paper›PMID 34381287›Full record

ArticleMaterials letters2021

Clinical utility of novel biosensing platform: Diagnosis of coronavirus SARS-CoV-2 at point of care.

Alaa A A Aljabali, Kaushik Pal, Angel Serrano-Aroca, Kazuo Takayama, Kamal Dua, Murtaza M Tambuwala

Abstract read
In one paragraph

Article in Materials letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Alaa A A AljabaliDepartment of Pharmaceutics and Pharmaceutical Technology, Yarmouk University-Faculty of Pharmacy, Irbid 566, Jordan.
Kaushik PalFederal University of Rio de Janeiro, Cidade Universitária, Laboratório de Biopolímeros e Sensores/LaBioS Centro de Tecnologia - Cidade Universitária, Rio de Janeiro, RJ 21941-901, Brazil.
Angel Serrano-ArocaBiomaterials and Bioengineering Lab, Translational Research Centre San Alberto Magno, Catholic University of Valencia San Vicente M'artir, c/Guillem de Castro 94, 46001 Valencia, Spain.
Kazuo TakayamaCenter for iPS Cell Research and Application, Kyoto University, Kyoto 606-8507, Japan.
Kamal DuaDiscipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Murtaza M TambuwalaSchool of Pharmacy and Pharmaceutical Science, Ulster University, Coleraine BT52 1SA, Northern Ireland, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection is the first step in the fight against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, an efficient, rapid, selective, specific, and inexpensive SARS-CoV-2 diagnostic method is the need of the hour. The reverse transcription-polymerase chain reaction (RT-PCR) technology is massively utilized to detect infection with SARS-CoV-2. However, scientists continue to strive to create enhanced technology while continually developing nanomaterial-enabled biosensing methods that can provide new methodologies, potentially fulfilling the present demand for rapid and early identification of coronavirus disease 2019 (COVID-19) patients. Our review presents a summary of the recent diagnosis of SARS-CoV-2 of COVID-19 pandemic and nanomaterial-available biosensing methods. Although limited research on nanomaterials-based nanosensors has been published, allowing for biosensing approaches for diagnosing SARS-CoV-2, this study highlights nanomaterials that provide an enhanced biosensing strategy and potential processes that lead to COVID-19 diagnosis.

Indexed as

BiosensorsCOVID-19 clinical diagnosticsNanomaterialsPolymerase chain reactionSARS-CoV-2

Identifiers

PMID34381287
PMCPMC8343387

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.