Evidence map›Paper›PMID 26206684›Full record

ArticleAnnals of laboratory medicine2015

Detection of first-line anti-tuberculosis drug resistance mutations by allele-specific primer extension on a microsphere-based platform.

Seung Heon Lee, Hee Baeg Choi, Sung Yul Yu, Uck Jin Chang, Chang Ki Kim, Hee Jin Kim

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Article in Annals of laboratory medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Molecular Diagnosis of Tuberculosis.Chonnam medical journal · 2018
    Review
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Seung Heon LeeKorean Institute of Tuberculosis, Cheongju, Korea. seung6992@hanmail.net.
Hee Baeg ChoiGenes Laboratories, Seongnam, Korea.
Sung Yul YuDepartment of Pathology, Semyung University, Jecheon, Korea.
Uck Jin ChangGenes Laboratories, Seongnam, Korea.
Chang Ki KimKorean Institute of Tuberculosis, Cheongju, Korea.
Hee Jin KimKorean Institute of Tuberculosis, Cheongju, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResistance of Mycobacterium tuberculosis to anti-tuberculosis (TB) drugs is almost exclusively due to spontaneous chromosomal mutations in target genes. Rapid detection of drug resistance to both first- and second-line anti-TB drugs has become a key component of TB control programs. Technologies that allow rapid, cost-effective, and high-throughput detection of specific nucleic acid sequences are needed. This study was to develop a high-throughput assay based on allele-specific primer extension (ASPE) and MagPlex-TAG microspheres to detect anti-TB drug resistance mutations.

methodsDNA samples from 357 M. tuberculosis clinical isolates and H37Rv were amplified by multiplex PCR using four primer sets, followed by multiplex ASPE using 23 TAG-ASPE primers. The products were sorted on the TAG-ASPE array and detected by using the Luminex xMAP system. Genotypes were also determined by sequencing.

resultsGenetic drug susceptibility typing by the TAG-ASPE method was 100% concordant with those obtained by sequencing. Compared with phenotypic drug susceptibility testing (DST) as a reference method, the sensitivity and specificity of the TAG-ASPE method were 83% (95% confidence interval [CI], 79-88%) and 97% (95% CI, 90-100%) for isoniazid. For rifampin testing, the sensitivity and specificity were 90% (95% CI, 86-93%) and 100% (95% CI, 99-100%). Also, the sensitivity and specificity were 58% (95% CI, 51-65%) and 86% (95% CI, 79-93%) for ethambutol.

conclusionsThis study demonstrated the TAG-ASPE method is suitable for highly reproducible, cost-effective, and high-throughput clinical genotyping applications.

Indexed as

MicrospheresAllelesAntitubercular AgentsBase SequenceDNA, BacterialDNA PrimersDrug Resistance, Multiple, BacterialGenotypeHumansMicrobial Sensitivity TestsMultiplex Polymerase Chain ReactionMycobacterium tuberculosisSensitivity and SpecificitySequence Analysis, DNATuberculosis, Multidrug-ResistantAntitubercular AgentsDNA, BacterialDNA PrimersAllele-specific primer extensionDrug-resistanceGenotypingMycobacteirum tuberculosis

Identifiers

PMID26206684
PMCPMC4510500

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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.