Effect of smoking on the genetic makeup of toll-like receptors 2 and 6
Bibliographic record
Abstract
Background: Cigarette smoking is a major risk factor for lung cancer, asthma, and oral cancer, and is central to the altered innate immune responsiveness to infection. Many hypotheses have provided evidence that cigarette smoking induces more genetic changes in genes involved in the development of many cigarette-related diseases. This alteration may be from single-nucleotide polymorphisms (SNPs) in innate immunity genes, especially the toll-like receptors (TLRs). Objective: In this study, the genotype frequencies of TLR2 and TLR6 in smoking and nonsmoking population were examined. Methods: Saliva samples were collected from 177 smokers and 126 nonsmokers. The SNPs used were rs3804100 (1350 T/C, Ser450Ser) and rs3804099 (597 T/C, Asn199Asn) for TLR2 and rs3796508 (979 G/A, Val327Met) and rs5743810 (745 T/C, Ser249Pro) for TLR6. Results: Results showed that TLR2 rs3804100 has a significant effect in short-term smokers (OR =2.63; P =0.04), and this effect is not observed in long-term smokers (>5 years of smoking). Therefore, this early mutation may be repaired by the DNA repair system. For TLR2 rs3804099, the variation in genotype frequencies between the smokers and control patients was due to a late mutation, and its protective role appears only in long-term smokers (OR =0.40, P =0.018). In TLR6 rs5743810, the TT genotype is significantly higher in smokers than in nonsmokers (OR =6.90). The effect of this SNP is observed in long-term smokers, regardless of the smoking regime per day. Conclusion: TLR2 (rs3804100 and rs3804099) and TLR6 (rs5743810) can be used as a potential index in the diagnosis and prevention of more diseases caused by smoking. Keywords: polymorphism, toll-like receptor, genotyping, smoking, TLR2, TLR6
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".