Evidence map›Paper›PMID 22479472›Full record

ArticlePloS one2012

Genetic diversity of Mycobacterium tuberculosis isolates from Tibetans in Tibet, China.

Haiyan Dong, Li Shi, Xiuqin Zhao, Ba Sang, Bing Lv, Zhiguang Liu, Kanglin Wan

Abstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. First Insight into the Molecular Epidemiology ofBioMed research international · 2017
    Trial
  2. Trial
  3. Trial
  4. Article
  5. Biodiversity ofMicroorganisms · 2022
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Genotypic Diversity ofBioMed research international · 2017
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

7 authors.

Haiyan DongState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, People's Republic of China.
Li Shi
Xiuqin Zhao
Ba Sang
Bing Lv
Zhiguang Liu
Kanglin Wan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis (TB) is a serious health problem in Tibet where Tibetans are the major ethnic group. Although genotyping of Mycobacterium tuberculosis (M. tuberculosis) isolates is a valuable tool for TB control, our knowledge of population structure of M. tuberculosis circulating in Tibet is limited. METHODOLOGY/PRINCIPAL

findingsIn our study, a total of 576 M. tuberculosis isolates from Tibetans in Tibet, China, were analyzed via spoligotyping and 24-locus MIRU-VNTR. The Beijing genotype was the most prevalent family (90.63%, n = 522). Shared-type (ST) 1 was the most dominant genotype (88.89%, n = 512). We found that there was no association between the Beijing genotype and sex, age and treatment status. In this sample collection, 7 of the 24 MIRU-VNTR loci were highly or moderately discriminative according to their Hunter-Gaston discriminatory index. An informative set of 12 loci had similar discriminatory power with 24 loci set. CONCLUSIONS/SIGNIFICANCE: The population structure of M. tuberculosis isolates in Tibetans is homogeneous and dominated by Beijing genotype. The analysis of 24-locus MIRU-VNTR data might be useful to select appropriate VNTR loci for the genotyping of M. tuberculosis.

Indexed as

Genetic VariationAdolescentAdultAgedAged, 80 and overChildDNA, BacterialFemaleGenotypeHumansMaleMiddle AgedMinisatellite RepeatsMultilocus Sequence TypingMycobacterium tuberculosisPhylogenyDNA, Bacterial

Identifiers

PMID22479472
PMCPMC3316506

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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.