Evidence map›Paper›PMID 35903482›Full record

ArticleFrontiers in microbiology2022

A Visual Assay of a Loop-Mediated Isothermal Amplification Based Vertical Immunoassay for SARS-CoV-2 RNA Detection.

Mengtao Yu, Pei Huang, Yuanguo Li, Yumeng Song, Xingqi Liu, Na Feng, Hongli Jin, Yujie Bai, Haili Zhang, Yuanyuan Li and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 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
0.2field-weighted citation impact, top 54% of its field
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, 3 citations in OpenAlex.

  1. 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

13 authors at 2 institutions in 1 country.

Mengtao YuState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Pei HuangState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Yuanguo LiChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China.
Yumeng SongState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Xingqi LiuState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Na FengChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China.
Hongli JinState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Yujie BaiState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Haili ZhangState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Yuanyuan LiState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Xianzhu XiaChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China.
Yuwei GaoChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China.
Hualei WangState Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Jilin University · CNChinese Academy of Agricultural Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SARS-CoV-2 is a novel coronavirus that has caused a global pandemic. To date, 504,907,616 people have been infected and developed coronavirus disease 2019 (COVID-19). A rapid and simple diagnostic method is needed to control this pandemic. In this study, a visual nucleic acid detection method combining reverse transcription loop-mediated isothermal amplification and a vertical flow visualization strip (RT-LAMP-VF) was successfully established and could detect 20 copies/μl of SARS-CoV-2 RNA transcript within 50 min at 61°C. This assay had no cross-reactivity with a variety of coronaviruses, including human coronavirus OC43, 229E, HKU1, NL63, severe acute respiratory syndrome-related coronavirus (SARSr-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and bat coronavirus HKU4, exhibiting very high levels of diagnostic sensitivity and specificity. Most strikingly, this method can be used for detecting multiple SARS-CoV-2 variants, including the Wuhan-Hu-1 strain, Delta, and Omicron variants. Compared with the RT-qPCR method recommended by the World Health Organization (WHO), RT-LAMP-VF does not require special equipment and is easy to perform. As a result, it is more suitable for rapid screening of suspected SARS-CoV-2 samples in the field and local laboratories.

Indexed as

detectionreverse transcription loop-mediated isothermal amplificationSARS-CoV-2variantsvisualization

Identifiers

PMID35903482
PMCPMC9325585
OpenAlexW4285088583

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.