Exposure to Cigarette Smoke Inhibits the Pulmonary T-Cell Response to Influenza Virus and<i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
Smoking is associated with increased susceptibility to tuberculosis and influenza. However, little information is available on the mechanisms underlying this increased susceptibility. Mice were left unexposed or were exposed to cigarette smoke and then infected with Mycobacterium tuberculosis by aerosol or influenza A by intranasal infection. Some mice were given a DNA vaccine encoding an immunogenic M. tuberculosis protein. Gamma interferon (IFN-γ) production by T cells from the lungs and spleens was measured. Cigarette smoke exposure inhibited the lung T-cell production of IFN-γ during stimulation in vitro with anti-CD3, after vaccination with a construct expressing an immunogenic mycobacterial protein, and during infection with M. tuberculosis and influenza A virus in vivo. Reduced IFN-γ production was mediated through the decreased phosphorylation of transcription factors that positively regulate IFN-γ expression. Cigarette smoke exposure increased the bacterial burden in mice infected with M. tuberculosis and increased weight loss and mortality in mice infected with influenza virus. This study provides the first demonstration that cigarette smoke exposure directly inhibits the pulmonary T-cell response to M. tuberculosis and influenza virus in a physiologically relevant animal model, increasing susceptibility to both pathogens.
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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".