TSLP and TSLP receptors variants are associated with smoking
Bibliographic record
Abstract
BACKGROUND: To search for new prevention markers for early detection of the diseases caused by tobacco, we aimed to investigate the polymorphisms in TSLP and TSLPRs associated with cigarette smoking in the Saudi population. MATERIALS AND METHODS: Samples were collected from 177 smokers and 126 healthy controls. Three TSLP SNPs [rs3806933, rs2289276, and rs10043985], three TSLPR SNPs [rs36133495, rs36177645, and rs36139698], and two IL7R SNPs rs1053496 and rs12516866 were analyzed by genotyping. RESULTS: Two TSLP SNPs (rs10043985 and rs3806933) and one TSLPR SNP (rs36139698) showed significant correlations with smoking behavior, but not IL7R rs12516866 and rs1053496. rs10043985 showed a clear association with long-term smoking regardless of daily cigarette consumption. rs2289276 was associated with short-term smoking but not with daily cigarette consumption. rs3806933 was highly associated with different smoker subgroups. Rs36139698 was highly associated with long-term smokers who consumed ≥20 cigarettes/day, and the "T" allele was associated only with individuals who smoked ≤20 cigarettes/day. Rs36139698 corresponds to a P195L substitution and produces a TSLPR mutant with a predicted ΔΔG increase of 2.15 kcal/mol and has a more stable structure than the wild-type variant. CONCLUSIONS: Investigating TSLP and TSLPR polymorphisms is crucial for elucidating the mechanisms underlying tobacco-induced diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".