Evidence map›Paper›PMID 33495747›Full record

ReviewEmergent materials2021

Smart materials-integrated sensor technologies for COVID-19 diagnosis.

Özgecan Erdem, Esma Derin, Kutay Sagdic, Eylul Gulsen Yilmaz, Fatih Inci

Abstract readReview
In one paragraph

Review in Emergent materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  6. Review
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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

5 authors.

Özgecan Erdem *UNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
Esma Derin *UNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
Kutay SagdicUNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
Eylul Gulsen YilmazUNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
Fatih InciUNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After the first case has appeared in China, the COVID-19 pandemic continues to pose an omnipresent threat to global health, affecting more than 70 million patients and leading to around 1.6 million deaths. To implement rapid and effective clinical management, early diagnosis is the mainstay. Today, real-time reverse transcriptase (RT)-PCR test is the major diagnostic practice as a gold standard method for accurate diagnosis of this disease. On the other side, serological assays are easy to be implemented for the disease screening. Considering the limitations of today's tests including lengthy assay time, cost, the need for skilled personnel, and specialized infrastructure, both strategies, however, have impediments to be applied to the resource-scarce settings. Therefore, there is an urgent need to democratize all these practices to be applicable across the globe, specifically to the locations comprising of very limited infrastructure. In this regard, sensor systems have been utilized in clinical diagnostics largely, holding great potential to have pivotal roles as an alternative or complementary options to these current tests, providing crucial fashions such as being suitable for point-of-care settings, cost-effective, and having short turnover time. In particular, the integration of smart materials into sensor technologies leverages their analytical performances, including sensitivity, linear dynamic range, and specificity. Herein, we comprehensively review major smart materials such as nanomaterials, photosensitive materials, electrically sensitive materials, their integration with sensor platforms, and applications as wearable tools within the scope of the COVID-19 diagnosis.

Indexed as

COVID-19DiagnosticsPoint-of-careSensorsSmart materials

Identifiers

PMID33495747
PMCPMC7817967

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.