Evidence map›Paper›PMID 35859097›Full record

ArticleJournal of medical virology2022

Rapid and universal detection of SARS-CoV-2 and influenza A virus using a reusable dual-channel optic fiber immunosensor.

Yi Yang, Rongtao Zhao, Yule Wang, Dan Song, Bo Jiang, Xudong Guo, Wanying Liu, Feng Long, Hongbin Song, Rongzhang Hao

Abstract read
In one paragraph

Article in Journal of medical virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

10 authors.

Yi YangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Rongtao ZhaoChinese PLA Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-9845-2405
Yule WangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Dan SongSchool of Environment and Natural Resources, Renmin University of China, Beijing, China.
Bo JiangDepartment of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, China.
Xudong GuoChinese PLA Center for Disease Control and Prevention, Beijing, China.
Wanying LiuChinese PLA Center for Disease Control and Prevention, Beijing, China.
Feng LongSchool of Environment and Natural Resources, Renmin University of China, Beijing, China.
Hongbin SongChinese PLA Center for Disease Control and Prevention, Beijing, China.
Rongzhang HaoChinese PLA Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-1527-427X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Establishment of rapid on-site detection technology capable of concurrently detecting SARS-Cov-2 and influenza A virus is urgent to effectively control the epidemic from these two types of important viruses. Accordingly, we developed a reusable dual-channel optical fiber immunosensor (DOFIS), which utilized the evanescent wave-sensing properties and tandem detection mode of the mobile phase, effectively accelerating the detection process such that it can be completed within 10 min. It could detect the nucleoprotein of multiple influenza A viruses (H1N1, H3N2, and H7N9), as well as the spike proteins of the SARS-CoV-2 Omicron and Delta variants, and could respond to 20 TCID

Indexed as

Biosensing TechniquesCOVID-19Influenza A Virus, H1N1 SubtypeInfluenza A Virus, H7N9 SubtypeInfluenza, HumanHumansImmunoassayInfluenza A Virus, H3N2 SubtypeSARS-CoV-2dual-channel optic fiber immunosensorinfluenza A virusmobile phase detectionSARS-CoV-2

Identifiers

PMID35859097
PMCPMC9349508

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.