Evidence map›Paper›PMID 35075405›Full record

ReviewMRS communications2022

Rapid testing for coronavirus disease 2019 (COVID-19).

Alexander Biby, Xiaochuan Wang, Xinliang Liu, Olivia Roberson, Allya Henry, Xiaohu Xia

Abstract readReview
In one paragraph

Review in MRS communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
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  3. Article
  4. Review
  5. Predicting the wicking rate of nitrocellulose membranes from recipe data: a case study using ANN at a membrane manufacturing in South Korea.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2024
    Article
  6. Review
  7. Article
  8. Article
  9. Review
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  12. Article
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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

6 authors.

Alexander BibyDepartment of Chemistry, University of Central Florida, Orlando, FL 32816 USA.
Xiaochuan WangSchool of Social Work, University of Central Florida, Orlando, FL 32816 USA.
Xinliang LiuSchool of Global Health Management & Informatics, University of Central Florida, Orlando, FL 32816 USA.
Olivia RobersonDepartment of Chemistry, University of Central Florida, Orlando, FL 32816 USA.
Allya HenrySchool of Social Work, University of Central Florida, Orlando, FL 32816 USA.
Xiaohu XiaDepartment of Chemistry, University of Central Florida, Orlando, FL 32816 USA.ORCID 0000-0002-6372-7712

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid testing, generally refers to the paper-based diagnostic platform known as "lateral flow assay" (LFA), has emerged as a critical asset to the containment of coronavirus disease 2019 (COVID-19) around the world. LFA technology stands out amongst peer platforms due to its cost-effective design, user-friendly interface, and low sample-to-readout times. This article aims to introduce its design, use, and practicality for the purpose of diagnosing SARS-CoV-2 infection. A connection is made from the normal COVID-19 immune response to the design and efficacy of rapid testing. Interference in test results is a challenge shared by most diagnostic platforms and can be rooted in various underlying issues. The current knowledge and situation about interference in rapid COVID-19 tests due to variant strains as well as vaccination are discussed. The cost and societal impact are reviewed as they play important roles in determining how to properly implement public testing practices. Perspectives on improving the performance, especially detection sensitivity, of LFA for COVID-19 are provided. Graphical abstract:

Indexed as

BiomedicalCOVID-19NanostructureOptical propertiesVirus

Identifiers

PMID35075405
PMCPMC8769796

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.