A neutrophil elastase inhibitor reduces cigarette smoke-induced remodelling of lung vessels
Bibliographic record
Abstract
Cigarette smoking produces pulmonary hypertension (PHT) through unknown mechanisms. In animal models acute smoke exposure induces cell proliferation in the small arteries adjacent to the alveolar ducts, and chronic exposure results in muscularisation of these vessels, with changes related to the development of PHT. Studies indicate that serine-elastase inhibitors can prevent experimental monocrotaline-induced PHT. This study examined whether they could also prevent cigarette smoke-induced pulmonary vascular disease. Guinea-pigs were exposed to cigarette smoke or air for 6 months. Some animals also received ZD0892, an orally active, synthetic, selective, serine-elastase inhibitor. The percentage of muscularised, small, pulmonary arteries was determined by morphometric analysis of histological sections and vascular cell proliferation by proliferating cell nuclear antigen staining. Vascular cell proliferation was markedly increased in the smoke-exposed animals and the percentage of completely muscularised small vessels was increased four-fold. Cell proliferation indices correlated with muscularisation indices. In the animals treated with ZD0892, the number of completely muscularised vessels was reduced by 50% and cell proliferation was reduced by 61%. These data suggest that smoke-induced cell proliferation leads to pulmonary arterial muscularisation. Serine-elastase inhibitors appear to be able to reduce cell proliferation and vascular remodelling.
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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 | 0.001 |
| 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".