Evidence map›Paper›PMID 39222096›Full record

ArticleApplied microbiology and biotechnology2024

Rapid lateral flow test for Mycobacterium tuberculosis complex and non-tuberculous mycobacteria differentiation.

Ponrut Phunpae, Weeraya Thongkum, Wutthichai Panyasit, Witida Laopajon, Nuchjira Takheaw, Supansa Pata, Umpa Yasamut, Watchara Kasinrerk, Chatchai Tayapiwatana

Abstract read
In one paragraph

Article in Applied microbiology and biotechnology, 2024. 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Ponrut Phunpae *Division of Clinical Microbiology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.
Weeraya Thongkum *Division of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.
Wutthichai PanyasitOffice of Disease Prevention and Control Region 1, Chiang Mai, 50100, Thailand.
Witida LaopajonDivision of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.
Nuchjira TakheawDivision of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.
Supansa PataDivision of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.
Umpa YasamutDivision of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.
Watchara KasinrerkDivision of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand. watchara.k@cmu.ac.th.
Chatchai TayapiwatanaDivision of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand. asimi002@hotmail.com.ORCID http://orcid.org/0000-0002-4714-7945

Funding

Chiang Mai University IMO66-01-001Chiang Mai University TGCMU2566P004Office of National Higher Education Science Research and Innovation Policy Council C10F630145
6 · The paper itself

Abstract

The diagnosis of mycobacterial infections, including both the Mycobacterium tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), poses a significant global medical challenge. This study proposes a novel approach using immunochromatographic (IC) strip tests for the simultaneous detection of MTBC and NTM. Traditional methods for identifying mycobacteria, such as culture techniques, are hindered by delays in distinguishing between MTBC and NTM, which can affect patient care and disease control. Molecular methods, while sensitive, are resource-intensive and unable to differentiate between live and dead bacteria. In this research, we developed unique monoclonal antibodies (mAbs) against Ag85B, a mycobacterial secretory protein, and successfully implemented IC strip tests named 8B and 9B. These strips demonstrated high concordance rates with conventional methods for detecting MTBC, with positivity rates of 93.9% and 85.9%, respectively. For NTM detection, the IC strip tests achieved a 63.2% detection rate compared to culture methods, considering variations in growth rates among different NTM species. Furthermore, this study highlights a significant finding regarding the potential of MPT64 and Ag85B proteins as markers for MTBC detection. In conclusion, our breakthrough method enables rapid and accurate detection of both MTBC and NTM bacteria within the BACTEC MGIT system. This approach represents a valuable tool in clinical settings for distinguishing between MTBC and NTM infections, thereby enhancing the management and control of mycobacterial diseases. KEY POINTS: • Panel of mAbs for differentiating MTB versus NTM • IC strips for diagnosing MTBC and NTM after the BACTEC MGIT • Combined detection of MTP64 and Ag85B enhances diagnostic accuracy.

Indexed as

Antibodies, MonoclonalAntigens, BacterialBacterial ProteinsMycobacterium tuberculosisNontuberculous MycobacteriaTuberculosisAcyltransferasesAntibodies, BacterialChromatography, AffinityHumansMycobacterium Infections, NontuberculousSensitivity and SpecificityAcyltransferasesAntibodies, BacterialAntibodies, Monoclonalantigen 85B, Mycobacterium tuberculosisAntigens, BacterialBacterial ProteinsAg85 proteinImmunochromatographic strip testsMycobacterium tuberculosis complexNon-tuberculous mycobacteria

Identifiers

PMID39222096
PMCPMC11369055

What OpenQuestion holds

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LicenceCC BY
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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.