Evidence map›Paper›PMID 34205401›Full record

ReviewDiagnostics (Basel, Switzerland)2021

Detection of COVID-19 Virus on Surfaces Using Photonics: Challenges and Perspectives.

Bakr Ahmed Taha, Yousif Al Mashhadany, Nur Nadia Bachok, Ahmad Ashrif A Bakar, Mohd Hadri Hafiz Mokhtar, Mohd Saiful Dzulkefly Bin Zan, Norhana Arsad

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Review
  17. Review
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

7 authors.

Bakr Ahmed TahaUKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.ORCID 0000-0002-8922-3993
Yousif Al MashhadanyDepartment of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq.ORCID 0000-0003-3943-8395
Nur Nadia BachokUKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.
Ahmad Ashrif A BakarUKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.ORCID 0000-0002-9060-0346
Mohd Hadri Hafiz MokhtarUKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.
Mohd Saiful Dzulkefly Bin ZanUKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.ORCID 0000-0002-1440-5434
Norhana ArsadUKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.ORCID 0000-0003-4543-8383

Funding

Pusat Penyelidikan Automotif, Universiti Kebangsaan Malaysia [ grant numbers GUP-2019-010]
6 · The paper itself

Abstract

The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers' effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment.

Indexed as

coronavirusCOVID-19laser diagnosis techniqueslight–matter interactionoptical biosensorphotonics

Identifiers

PMID34205401
PMCPMC8234865

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.