Evidence map›Paper›PMID 38727849›Full record

ReviewMikrochimica acta2024

Optical biosensors for diagnosis of COVID-19: nanomaterial-enabled particle strategies for post pandemic era.

Yusuf Samil Tekin, Seyda Mihriban Kul, Osman Sagdic, Nadnudda Rodthongkum, Brian Geiss, Tugba Ozer

Abstract readReview
In one paragraph

Review in Mikrochimica acta, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
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.

Yusuf Samil TekinDepartment of Biomedical Engineering, Graduate Education Institute, Malatya Turgut Ozal University, 44210, Battalgazi, Malatya, Turkey.
Seyda Mihriban KulDepartment of Food Engineering, Faculty of Chemical-Metallurgical Engineering, Yildiz Technical University, 34220, Istanbul, Turkey.
Osman SagdicDepartment of Food Engineering, Faculty of Chemical-Metallurgical Engineering, Yildiz Technical University, 34220, Istanbul, Turkey.
Nadnudda RodthongkumMetallurgy and Materials Science Research Institute, Chulalongkorn University, Soi Chula 12, Phayathai Road, Bangkok, 10330, Patumwan, Thailand.
Brian GeissDepartment of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, 80523-1019, USA. brian.geiss@colostate.edu.
Tugba OzerDepartment of Bioengineering, Faculty of Chemical-Metallurgical Engineering, Yildiz Technical University, 34220, Istanbul, Turkey. tozer@yildiz.edu.tr.ORCID http://orcid.org/0000-0002-5928-9940

Funding

A SARS-CoV-2 NFC ePAD BiosensorR01EB031510 · NIBIB · COLORADO STATE UNIVERSITY · PI DANDY, DAVID S, GEISS, BRIAN · 2021 to 2022
$1.0M
NIBIB NIH HHS R01 EB031510
6 · The paper itself

Abstract

The COVID-19 pandemic underlines the need for effective strategies for controlling virus spread and ensuring sensitive detection of SARS-CoV-2. This review presents the potential of nanomaterial-enabled optical biosensors for rapid and low-cost detection of SARS-CoV-2 biomarkers, demonstrating a comprehensive analysis including colorimetric, fluorescence, surface-enhanced Raman scattering, and surface plasmon resonance detection methods. Nanomaterials including metal-based nanomaterials, metal-organic frame-based nanoparticles, nanorods, nanoporous materials, nanoshell materials, and magnetic nanoparticles employed in the production of optical biosensors are presented in detail. This review also discusses the detection principles, fabrication methods, nanomaterial synthesis, and their applications for the detection of SARS-CoV-2 in four categories: antibody-based, antigen-based, nucleic acid-based, and aptamer-based biosensors. This critical review includes reports published in the literature between the years 2021 and 2024. In addition, the review offers critical insights into optical nanobiosensors for the diagnosis of COVID-19. The integration of artificial intelligence and machine learning technologies with optical nanomaterial-enabled biosensors is proposed to improve the efficiency of optical diagnostic systems for future pandemic scenarios.

Indexed as

Biosensing TechniquesCOVID-19NanostructuresSARS-CoV-2ColorimetryHumansSpectrum Analysis, RamanBiomarkersCOVID-19Machine learningOptical detectionSARS-CoV-2Virus

Identifiers

PMID38727849
PMCPMC11087243

What Socratic 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 Socratic graph.