ArticleHeliyon2019
Tuberculosis diagnosis: algorithm that May discriminate latent from active tuberculosis.
Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Association of HLA class I allele and tuberculosis susceptibility: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Immunological testing and machine learning in detecting latent tuberculosis among high-risk groups (nature review).Frontiers in medicine · 2025Review
- Monocyte-related gene biomarkers for latent and active tuberculosis.Bioengineered · 2021Article
- Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy.Frontiers in microbiology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis of tuberculosis still faces a lot of challenges and is one of the priorities in the field of tuberculosis management. Deciphering the complex tuberculosis pathogenicity network could provide biomarkers for diagnosis. We discussed the distribution of HLA-B17, -DQB and -DRB together with QuantiFERON test results in tuberculosis infection. A case control study was done during which a total of 337 subjects were enrolled comprising 227 active tuberculosis (ATB), 46 latent tuberculosis infection (LTBI) and 64 healthy controls (HC). Sequence-specific primer polymerase chain reaction and immune epitope database were used to genotype samples and determine the epitope binding ability of the over-represented alleles respectively. QuantiFERON test was done according to manufacturer's instructions. The peptides HLA-B*5801 and HLA-DRB1*12 and the peptides HLA-B*5802 and HLA-DQB1*03 were found to be associated with latent tuberculosis while the haplotypes DRB1*10-DQB1*02 and DRB1*13-DQB1*06 were found to be associated with active tuberculosis (All p-values≤0.05). The association of HLA-B*5801 and HLA-B*5802 with latent tuberculosis was linked to their ability to bind or not mycobacterial antigens. DRB1*10-DQB1*02 haplotype was found to be over-represented in LTBI compared to ATB (p-value = 0.0015) while DRB1*13-DQB1*06 was found to be under-represented in LTBI compared to ATB (p-value = 0.0335). The DRB1*10-DQB1*02 haplotype was only found in the LTBI when compared with the ATB group. The present study suggests the following algorithm to discriminate LTBI from ATB: QuantiFERON+ and DRB1*10-DQB1*02 haplotype + may indicate LTBI; QuantiFERON+ and DRB1*10-DQB1*02 haplotype - may indicate ATB.
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Registered trials
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.