Genetic variants in<i>TNF</i><b>α</b>,<i>TGFB1, PTGS1</i>and<i>PTGS2</i>genes are associated with diisocyanate-induced asthma
Bibliographic record
Abstract
Diisocyanates are the most common cause of occupational asthma, but risk factors are not well defined. A case-control study was conducted to investigate whether genetic variants in inflammatory response genes (TNFα, IL1α, IL1β, IL1RN, IL10, TGFB1, ADAM33, ALOX-5, PTGS1, PTGS2 and NAG-1/GDF15) are associated with increased susceptibility to diisocyanate asthma (DA). These genes were selected based on their role in asthmatic inflammatory processes and previously reported associations with asthma phenotypes. The main study population consisted of 237 Caucasian French Canadians from among a larger sample of 280 diisocyanate-exposed workers in two groups: workers with specific inhalation challenge (SIC) confirmed DA (DA(+), n = 95) and asymptomatic exposed workers (AW, n = 142). Genotyping was performed on genomic DNA, using a 5' nuclease PCR assay. After adjusting for potentially confounding variables of age, smoking status and duration of exposure, the PTGS1 rs5788 and TGFB1 rs1800469 single nucleotide polymorphisms (SNP) showed a protective effect under a dominant model (OR = 0.38; 95% CI = 0.17, 0.89 and OR = 0.38; 95% CI = 0.18, 0.74, respectively) while the TNFα rs1800629 SNP was associated with an increased risk of DA (OR = 2.08; 95% CI = 1.03, 4.17). Additionally, the PTGS2 rs20417 variant showed an association with increased risk of DA in a recessive genetic model (OR = 6.40; 95% CI = 1.06, 38.75). These results suggest that genetic variations in TNFα, TGFB1, PTGS1 and PTGS2 genes contribute to DA susceptibility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".