Evidence map›Paper›PMID 35047829›Full record

ArticleSensors and actuators reports2021

Current status, advances, challenges and perspectives on biosensors for COVID-19 diagnosis in resource-limited settings.

Yu Shen, Touhid Bin Anwar, Ashok Mulchandani

Abstract read
In one paragraph

Article in Sensors and actuators reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
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  11. Development of gold nanoparticle-based biosensors for COVID-19 diagnosis.Beni-Suef University journal of basic and applied sciences · 2022
    Review
  12. Review
  13. Review
  14. Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view.Microchemical journal : devoted to the application of microtechniques in all branches of science · 2021
    Article
  15. 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

3 authors.

Yu ShenChemical and Environmental Engineering Department, University of California Riverside, Riverside, CA, 92521 USA.
Touhid Bin AnwarChemical and Environmental Engineering Department, University of California Riverside, Riverside, CA, 92521 USA.
Ashok MulchandaniChemical and Environmental Engineering Department, University of California Riverside, Riverside, CA, 92521 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the COVID-19 pandemic has profoundly impacted human life, prompt diagnostic tests are becoming an essential part of the social activities. However, the expensive and time-consuming laboratory-based traditional methods do not suffice the enormous needs for massive number of tests, especially in resource-limited settings. Therefore, more affordable, rapid, sensitive and specific field-practical diagnostic devices play an important role in the fight against the disease. In this review, we present the current status and advances in the biosensing technologies for diagnosing COVID-19, ranging from commercial achievements to research developments. Starting from a brief introduction to the disease biomarkers, this review summarizes the working principles of the biosensing technologies, followed by a review of the commercial products and research advances in academia. We recapitulate the literatures with a wide scope of bio/marker detections, embracing nucleic acids, viral proteins, human immune responses, and other potential bio/markers. Further, the challenges and perspectives for their employment in future point-of-care applications are discussed, with an extended appraisal on the practical strategies to enlarge the testing capability without high cost. This critical review provides a comprehensive insight into the diagnostic tools for COVID-19 and will encourage the industry and academia in the field of diagnostic biosensing for future evolvement to large-scale point-of-care screening of COVID-19.

Indexed as

ASSURED criteriaBiosensorsCOVID-19DiagnosticsHealthcarePoint-of-care

Identifiers

PMID35047829
PMCPMC7831652

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.