Synthetic Serine Elastase Inhibitor Reduces Cigarette Smoke–induced Emphysema in Guinea Pigs
Bibliographic record
Abstract
To test whether a serine elastase inhibitor could prevent or reduce emphysema, we exposed guinea pigs to cigarette smoke acutely, or daily for 6 months, and treated some animals with the neutrophil elastase inhibitor ZD0892. Acute smoke exposure increased lavage neutrophils and increased desmosine and hydroxyproline, measures of elastin and collagen breakdown; all these measures were reduced by ZD0892. Long-term smoke exposure produced emphysema and increases in lavage neutrophils, desmosine, hydroxyproline, and plasma tumor necrosis factor alpha (TNF-alpha). ZD0892 treatment returned lavage neutrophils, desmosine, and hydroxyproline levels to control values, and decreased airspace enlargement by 45% and TNF-alpha by 30%. Animals exposed to smoke for 4 months and then to smoke plus ZD0892 for 2 months were not protected against emphysema. Mice exposed to smoke showed increases in gene expression of neutrophil chemoattractant macrophage inflammatory protein-2, macrophage chemoattractant protein-1, and TNF-alpha at 2 hours along with increased plasma TNF-alpha; ZD0892 prevented the increases in macrophage inflammatory protein-2 and macrophage chemoattractant protein-1 expression and reduced plasma TNF-alpha levels to baseline. These data demonstrate that a serine elastase inhibitor ameliorates the inflammatory and destructive effects of cigarette smoke, and that these effects are mediated in part by neutrophils and by smoke-driven TNF-alpha production.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".