α-1-Antitrypsin Ameliorates Cigarette Smoke–induced Emphysema in the Mouse
Bibliographic record
Abstract
Serine elastase inhibitors have been proposed as a treatment for cigarette smoke-induced emphysema, but little is known about whether such agents actually are effective. We recently reported that a synthetic serine elastase inhibitor, ZD0892, provided some protection against emphysema in a guinea pig model. For these experiments, we used transgenic mice that expressed extremely low levels of human alpha-1-antitrypsin (A1AT) but were tolerant of exogenous human A1AT. Mice were exposed to daily cigarette smoke for up to 6 months; some animals received 20 mg of human A1AT (Prolastin) every 48 hours. Treatment with A1AT produced an approximate twofold increase in serum A1AT levels and elastase inhibitory capacity and abolished smoke-induced elevations in lavage neutrophils and matrix breakdown products (desmosine and hydroxyproline) measured from 2 to 30 days of smoke exposure. A1AT oxidized to remove antiproteolytic activity did not increase serum elastase inhibitory capacity but did prevent neutrophil influx. Treatment with A1AT for 6 months provided 63% protection against increased airspace size (emphysema) and abolished smoke-mediated increases in plasma tumor necrosis factor-alpha. We conclude that A1AT therapy ameliorates smoke-induced inflammation and matrix breakdown, possibly via an antiinflammatory mechanism related to tumor necrosis factor-alpha suppression, and provides partial protection against emphysema.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".