Evidence map›Paper›PMID 22701501›Full record

ArticleClinical & developmental immunology2012

Identification of RD5-encoded Mycobacterium tuberculosis proteins as B-cell antigens used for serodiagnosis of tuberculosis.

Miao-Miao Zhang, Jun-Wei Zhao, Zhan-Qiang Sun, Jun Liu, Xiao-Kui Guo, Wen-Di Liu, Shu-Lin Zhang

Open access · goldAbstract read
In one paragraph

Article in Clinical & developmental immunology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
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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 at 3 institutions in 1 country.

Miao-Miao ZhangDepartment of Microbiology and Parasitology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Jun-Wei Zhao
Zhan-Qiang Sun
Jun Liu
Xiao-Kui Guo
Wen-Di Liu
Shu-Lin Zhang
Shanghai Jiao Tong University · CNHenan University of Traditional Chinese Medicine · CNWuxi No.2 People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Comparative genomic studies have identified several Mycobacterium tuberculosis-specific genomic regions of difference (RDs) which are absent in the vaccine strains of Mycobacterium bovis BCG and which may be useful in the specific diagnosis of tuberculosis (TB). In this study, all encoded proteins from DNA segment RD5 of Mycobacterium tuberculosis, that is, Rv3117-Rv3121, were recombined and evaluated by enzyme-linked immunosorbent assays for antibody reactivity with sera from HIV-negative pulmonary TB patients (n = 60) and healthy controls (n = 32). The results identified two immunodominant antigens, that is, Rv3117 and Rv3120, both of which revealed a statistically significant antigenic distinction between healthy controls and TB patients (P < 0.05). In comparison with the well-known early-secreted antigen target 6 kDa (ESAT-6) (sensitivity 21.7%, specificity 90.6%), the higher detection sensitivity and higher specificity were achieved (Rv3117: sensitivity 25%, specificity 96.9%; Rv3120: sensitivity 31.7%, specificity 96.9%). Thus, the results highlight the immunosensitive and immunospecific nature of Rv3117 and Rv3120 and indicate promise for their use in the serodiagnosis of TB.

Indexed as

Antibodies, BacterialAntigens, BacterialB-LymphocytesEnzyme-Linked Immunosorbent AssayHumansMycobacterium tuberculosisRecombinant ProteinsSensitivity and SpecificityTuberculosisAntibodies, BacterialAntigens, BacterialRecombinant Proteins

Identifiers

PMID22701501
PMCPMC3373134
OpenAlexW2033049864

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.