Evidence map›Paper›PMID 37256455›Full record

ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2023

Real-time recombinase-aided amplification assay for rapid amplification of the IS1081 gene of Mycobacterium tuberculosis.

Yuanyuan Liu, Weicong Ren, Zhongtan Xue, Yuedong Miao, Wei Wang, Xuxia Zhang, Cong Yao, Yuanyuan Shang, Shanshan Li, Fengling Mi and 1 more

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Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

2 citing papers in PubMed, 5 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

11 authors at 1 institution in 1 country.

Yuanyuan LiuDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Weicong RenDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Zhongtan XueDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Yuedong MiaoDepartment of Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Wei WangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Xuxia ZhangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Cong YaoDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Yuanyuan ShangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Shanshan LiDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Fengling MiDepartment of Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China. mfl667@163.com.
Yu PangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China. pangyupound@163.com.ORCID http://orcid.org/0000-0001-6803-9807
Capital Medical University · CN

Funding

Beijing Hospitals Authority Ascent Plan DFL20191601Capital Health Research and Development of Special Fund 2020-1-1041Digestive Medical Coordinated Development Center of Beijing Hospitals Authority ZYLX202122
6 · The paper itself

Abstract

Mycobacterium tuberculosis (MTB), the etiological agent of tuberculosis (TB), is the leading cause of death due to a single infectious agent worldwide. Rapid and accurate diagnosis of MTB is critical for controlling TB especially in resource-limited countries, since any diagnosis delay increases the chances of transmission. Here, a real-time recombinase-aided amplification (RAA) assay targeting conserved positions in IS1081 gene of MTB, is successfully established to detect MTB. The intact workflow was completed within 30 min at 42 °C with no cross-reactivity observed for non-tuberculous mycobacteria and other clinical bacteria, and the detection limit for recombinant plasmid of MTB IS1081 was 163 copies/reaction at 95% probability, which was approximately 1.5-fold increase in analytical sensitivity for the detection of MTB, compared to conventional quantitative real-time PCR (qPCR; 244 copies/reaction). Furthermore, the result of clinical performance evaluation revealed an increased sensitivity of RAA assay relative to qPCR was majorly noted in the specimens with low bacteria loads. Our results demonstrate that the developed real-time RAA assay is a convenient, sensitive, and low-cost diagnostic tool for the rapid detection of MTB.

Indexed as

Mycobacterium tuberculosisTuberculosisHumansNucleic Acid Amplification TechniquesRecombinasesSensitivity and SpecificityRecombinasesIsothermal amplificationMycobacterium tuberculosis (MTB)Rapid diagnosisRecombinase-aided amplification (RAA)

Identifiers

PMID37256455
OpenAlexW4378781310

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Registered trials

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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.