IFN-γ promoter polymorphisms do not affect QuantiFERON<SUP>®</SUP> TB Gold In-Tube test results in a Canadian population
Bibliographic record
Abstract
BACKGROUND: Several studies have shown polymorphisms within the interferon-gamma (IFN-γ) promoter influence cytokine expression. The interferon-gamma release assay (IGRA) relies on the ability to produce IFN-γ in response to tuberculosis (TB) specific antigens. This study determined the relationship between the IFN-γ +874 A/T promoter polymorphism and the performance of the QuantiFERON®-TB Gold In-Tube (QFT-GIT) test in an ethnically diverse Canadian population. METHODS: A total of 190 participants were categorised into three groups based on history of and exposure to TB: active TB (n = 55), TB exposed (n = 55) and presumably TB unexposed controls (n = 80). All participants underwent QFT-GIT testing, and DNA was extracted from whole blood and probed for polymorphism at position +874 (T/A) of intron 1 of IFN-γ. Statistical relationships between the QFT-GIT results, polymorphisms and demographic data were evaluated. RESULTS: IFN-γ +874 genotype frequencies among the entire study population (n = 190) were A/A (45.8%), T/A (39.5%), and T/T (14.7%). Among the three study groups, there was no correlation between QFT-GIT results and the IFN-γ +874 A/T genotype, and no correlation of genotype with IFN-γ production in response to either Mycobacterium tuberculosis antigens or mitogenic stimulation. CONCLUSION: Our results indicate that the IFN-γ +874 promoter polymorphism does not influence QFT-GIT performance in this study population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".