Association of IL-1beta and IL-1 receptor antagonist haplotypes with rate of decline in lung function in smokers
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence that the cytokine network is central to the immunopathology of inflammatory airway diseases. The interleukin 1 (IL-1) receptor antagonist (IL-1RN) is a naturally occurring anti-inflammatory agent that binds to the IL-1 receptor but does not possess agonist activity. Each of the genes of the IL-1 locus on chromosome 2q14 is polymorphic. The IL1RN gene contains an 86 bp tandem repeat and allele 2 of this polymorphism has been associated with various inflammatory diseases. The IL-1beta (IL1B) gene contains a promoter polymorphism (C-511T) that has been associated with inflammatory diseases and is in linkage disequilibrium with the IL1RN polymorphism. METHODS: We investigated whether polymorphisms in the IL1B and IL1RN genes were associated with rate of decline of lung function. Genotypes were determined in 284 smokers with a rapid decline in lung function and 306 smokers with no decline in lung function. RESULTS: None of the genotypes was associated with the rate of decline of lung function. However, the distribution of IL1B/IL1RN haplotypes was different between smokers with a rapid decline in lung function and those with no decline in lung function (p=0.0005). CONCLUSION: These results suggest that IL1B/IL1RN haplotypes play a role in the rate of decline in lung function in smokers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".