Evidence map›Paper›PMID 39006288›Full record

ArticleAmerican journal of translational research2024

Enhanced detection of rifampicin and isoniazid resistance in mycobacterium tuberculosis using AuNP-qPCR: a rapid and accurate method.

Mouhai He, Lingli Hu

Abstract read
In one paragraph

Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

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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

1 citing paper in PubMed.

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4 · The record

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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

2 authors.

Mouhai HeCollege of Medical Technology and Nursing, Hunan Institute of Traffic Engineering Hengyang 421009, Hunan, China.
Lingli HuDepartment of Ultrasound, Hengyang Central Hospital Hengyang 421001, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the resistance of Mycobacterium tuberculosis to Rifampicin (RIF) and Isoniazid (INH) using enhanced qPCR methodologies.

methodsThis study compared the detection of drug-resistant mutations in the rpoB and katG genes using AuNP-qPCR and No-AuNP-qPCR. Calibration curves were constructed to correlate the amount of template with the Ct values for resistant strains.

resultsThe AuNP-qPCR method demonstrated high efficacy in detecting RIF resistance with an area under the curve (AUC) of 0.951, sensitivity of 97.92%, specificity of 87.5%, and overall accuracy of 95.31%. Similarly, INH resistance detection by AuNP-qPCR showed an AUC of 0.981, sensitivity of 98.08%, specificity of 94.44%, and accuracy of 97.14%. Comparatively, No-AuNP-qPCR yielded lower performance metrics for RIF resistance (AUC: 0.867, sensitivity: 91.67%, specificity: 75%, accuracy: 87.5%) and INH resistance (AUC: 0.882, sensitivity: 88.46%, specificity: 83.33%, accuracy: 87.14%).

conclusionsAuNP-qPCR exhibits over traditional qPCR methods, making it a promising tool for rapid and precise detection of drug resistance in Mycobacterium tuberculosis. This method's robust performance underscores its potential to improve diagnostic protocols and contribute to more effective management of tuberculosis treatment.

Indexed as

drug resistancegold nanoparticlesMycobacterium tuberculosisPCRrifampicin and isoniazid

Identifiers

PMID39006288
PMCPMC11236652

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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.