Cigarette Smoke Decreases Pulmonary Dendritic Cells and Impacts Antiviral Immune Responsiveness
Bibliographic record
Abstract
We investigated the impact of cigarette smoke exposure on respiratory immune defense mechanisms. Mice were exposed to two cigarettes daily, 5 d/wk, for 2-4 mo. Tobacco smoke decreased the number of dendritic cells (DCs) in the lung tissue. Furthermore, smoke exposure dramatically reduced the percentage of B7.1-expressing DCs. Because DCs are believed to be indispensable to the initiation of adaptive immune responses, we investigated the impact of cigarette smoke on immune responsiveness toward adenovirus. Mice were exposed to two cigarettes for 2-4 mo and inoculated with 2 x 10(8) pfu of a replication-deficient adenovirus on three occasions, 2 wk apart, during the last month of tobacco smoke exposure. Smoke exposure specifically prevented the expansion and maximal activation of CD4 T cells and reduced the number of both activated CD4 and CD8 T cells. Consequently, smoke exposure shifted the activated CD4:CD8 T cell ratio from 3 to 1.5 when compared with sham exposure. Significant decreases were also observed in serum adenovirus-specific pan IgG, IgG1, and IgG2a immunoglobulin levels, which was associated with diminished viral neutralization capacity. We demonstrate that chronic tobacco smoke exposure impairs the immune response against adenovirus. This may, in part, explain the increased prevalence of viral infections in chronic obstructive pulmonary disease.
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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".