Impairment of human NK cell cytotoxic activity and cytokine release by cigarette smoke
Bibliographic record
Abstract
NK cells play essential roles in innate host defense against microbial infections and tumor surveillance. Although evidence suggests that smoking has adverse effects on the immune system, little is known about whether smoking compromises NK cell effector functions. In this study, we show that cigarette smoke-conditioned medium (SCM) dose-dependently inhibits in vitro IFN-gamma production by polyinosinic:polycytidylic acid (poly I:C)-activated PBMC and NK cells isolated from nonsmoking individuals. Similarly, SCM attenuated poly I:C-induced TNF-alpha production by PBMC and NK cells. The inhibitory effect of cigarette smoke on TNF-alpha production was reversible. PBMC and NK cells isolated from smokers displayed significant reduction of IFN-gamma and TNF-alpha secretions compared with nonsmokers in response to poly I:C activation. We further observed that SCM attenuated NK cell cytotoxic activity, which was associated with decreased up-regulation of perforin expression. Attenuated cytotoxic activity was also observed in PBMCs isolated from smokers. Finally, anti-IL-12 mAb-blocking data revealed that an attenuation of IFN-gamma production by PBMC was indirect, likely via attenuation of IL-12 production, and the effect on NK cells was IL-12-independent. Our data indicate that cigarette smoke compromises function of human NK cells. This may contribute to a higher incidence of viral infections and cancer among 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".