Evidence map›Paper›PMID 38938885›Full record

ArticleFrontiers in cellular and infection microbiology2024

Rapid detection of Mycobacterium tuberculosis based on cyp141 via real-time fluorescence loop-mediated isothermal amplification (cyp141-RealAmp).

Yinyin Zhu, Zi Feng, Yinfang Xu, Sha Luo, Ruixian Zhang, Xudong Shi, Xuping Wu, Hongying Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yinyin Zhu *Department of Microbial Testing, Nanjing Center for Disease Control and Prevention Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China.
Zi Feng *Department of Microbial Testing, Nanjing Center for Disease Control and Prevention Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China.
Yinfang Xu *Department of Infectious Diseases, the Affiliated Zhongda Hospital of Southeast University, Nanjing, Jiangsu, China.
Sha LuoThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Ruixian ZhangThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Xudong ShiThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Xuping WuThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Hongying ZhangDepartment of Microbial Testing, Nanjing Center for Disease Control and Prevention Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rapid detection of Mycobacterium tuberculosis (MTB) is essential for controlling tuberculosis. Results: By targeting MTB cyp141, this technique could detect as low as 10 copies/reaction within 30 min, and it was successfully rejected by other mycobacteria and other bacterial species tested. Of the 169 patients, there was no statistical difference between the detection rate of cyp141-RealAmp (92.90%, 95% CI: 89.03-96.07) and that of Xpert MTB/RIF (94.67%, 95% CI: 91.28-98.06) ( Conclusion: The cyp141-RealAmp assay was shown to be effective, responsive, and accurate in this study. This method offers a prospective strategy for the speedy and precise detection of MTB.

Indexed as

Molecular Diagnostic TechniquesMycobacterium tuberculosisNucleic Acid Amplification TechniquesSensitivity and SpecificitySputumTuberculosis, PulmonaryAdultDNA PrimersFemaleFluorescenceHumansMaleMiddle AgedTuberculosisDNA Primerscytochrome P450LAMPMycobacterium bovisMycobacterium tuberculosisMycobacterium tuberculosis complextuberculosisXpert MTB/RIF ; cyp141-RealAmp

Identifiers

PMID38938885
PMCPMC11208306

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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.