Evidence map›Paper›PMID 25759689›Full record

ArticleFrontiers in microbiology2015

Drug-resistant tuberculosis can be predicted by Mycobacterial interspersed repetitive unit locus.

Wen Yu-Feng, Jiang Chao, Cheng Xian-Feng

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Genotyping ofInternational journal of bacteriology · 2016
    Article
  4. 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

3 authors at 2 institutions in 1 country.

Wen Yu-FengSchool of Public Health, Wannan Medical College Wuhu, China.
Jiang ChaoSchool of Public Health, Wannan Medical College Wuhu, China.
Cheng Xian-FengInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Nanjing, China.
Wannan Medical College · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is unknown whether MIRU-VNTR (Mycobacterial Interspersed Repetitive Unit-Variable Number of Tandem Repeat) is associated with drug resistance of Mycobacterium tuberculosis. The purpose of this study was to explore the ability of 24 MIRU loci to predict the drug resistance of Isoniazid (INH), Rifampicin (RFP), Streptomycin (SM), Ethambutol (EMB) and Pyrazinamide (PZA). We collected the drug resistance and MIRU loci information of 109 strains of M. tuberculosis from an open database. The results of multivariate logistic regression showed that the VNTR polymorphism of MTUB04 was related to INH resistance [odds ratio (OR) = 2.82, P = 0.00], RFP resistance (OR = 1.91, P = 0.02), SM resistance (OR = 1.98, P = 0.01) and EMB resistance (OR = 1.95, P = 0.03). MIRU40 was associated with INH resistance (OR = 2.22, P = 0.00). MTUB21 was connected with INH resistance (OR = 1.63, P = 0.02) and SM resistance (OR = 1.69, P = 0.01). MIRU26 was correlated with SM resistance (OR = 1.52, P = 0.04). MIRU39 was associated with EMB resistance (OR = 4.07, P = 0.02). The prediction power of MIRU loci were 0.84, 0.70, 0.85, and 0.74 respectively for INH (predicted by MTUB04, MIRU20, and MTUB21), RFP (predicted by MTUB04), SM (predicted by MTUB21 and MIRU26) and EMB (MTUB04 and MIRU39) through ROC analysis. Our results showed that MIRU loci were related to anti-tuberculosis drug and could predict the drug resistance of tuberculosis.

Indexed as

drug resistanceMIRUMycobacterial interspersed repetitive unittuberculosisvariable number of tandem repeats

Identifiers

PMID25759689
PMCPMC4338821
OpenAlexW1998140365

What OpenQuestion holds

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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.