Immunomodulatory effects of the tobacco-specific carcinogen, NNK, on alveolar macrophages
Bibliographic record
Abstract
Lung cancer is strongly associated with cigarette smoking. More than 20 lung carcinogens have been identified in cigarette smoke and one of the most abundant is 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). We hypothesized that NNK modulates alveolar macrophage (AM) mediator production, thus contributing to carcinogenesis. An AM cell line, NR8383, was treated with [3H]NNK and lipopolysaccharide (LPS), and NNK metabolites released in supernatants were analysed by high-performance liquid chromatography (HPLC). NNK was metabolized by carbonyl reduction to 4-(methylnitrosamino)-1-(3-pyridyl)-1-butan-1-ol (NNAL) or activated by alpha-carbon hydroxylation. AMs were also treated with NNK (100-1000 micro M), with and without LPS, for different periods of time (6-72 h), and mediators released in supernatants were quantified by enzyme-linked immunosorbent assay (ELISA) or the Griess reaction. NNK inhibited (in a concentration-dependent manner) AM production of tumour necrosis factor (TNF), macrophage inflammatory protein-1alpha (MIP-1alpha), interleukin (IL)-12 and nitric oxide (NO), whereas IL-10 production was increased. Cyclooxygenase inhibitors - NS-398 and indomethacin - and anti-prostaglandin E2 (anti-PGE2) antibody abrogated the NNK-inhibitory effect on MIP-1alpha production by AM. NNK stimulated the release of PGE2, and exogenous PGE2 inhibited AM MIP-1alpha production, suggesting that the NNK immunomodulatory effect may be mediated by PGE2 production. Thus, in addition to its carcinogenic effects, NNK may contribute to the lung immunosuppression observed in tobacco 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".