α1-Antitrypsin Suppresses TNF-α and MMP-12 Production by Cigarette Smoke–Stimulated Macrophages
Bibliographic record
Abstract
We have previously observed that mice exposed to cigarette smoke and treated with exogenous alpha(1)-antitrypsin (A1AT) were protected against the development of emphysema and against smoke-induced increases in serum TNF-alpha. To investigate possible mechanisms behind this latter observation, we cultured alveolar macrophages lavaged from C57 mice. Smoke-conditioned medium caused alveolar macrophages to increase secretion of macrophage metalloelastase (MMP-12) and TNF-alpha, and this effect was suppressed in a dose-response fashion by addition of A1AT. Macrophages from animals exposed to smoke in vivo and then lavaged also failed to increase MMP-12 and TNF-alpha secretion when the animals were pretreated with A1AT. Because proteinase activated receptor-1 (PAR-1) is known to control MMP-12 release, macrophages were treated with the G protein-coupled receptor inhibitor, pertussis toxin; this suppressed both TNF-alpha and MMP-12 release, while a PAR-1 agonist (TRAP) increased TNF-alpha and MMP-12 release. Smoke-conditioned medium caused increased release of the prothrombin activator, tissue factor, from macrophages. Hirudin, a thrombin inhibitor, and aprotinin, an inhibitor of plasmin, reduced smoke-mediated TNF-alpha and MMP-12 release, and A1AT inhibited both plasmin and thrombin activity in a cell-free functional assay. These findings extend our previous suggestion that TNF-alpha production by alveolar macrophages is related to MMP-12 secretion. They also suggest that A1AT can inhibit thrombin and plasmin in blood constituents that leak into the lung after smoke exposure, thereby preventing PAR-1 activation and MMP-12/TNF-alpha release, and decreasing smoke-mediated inflammatory cell influx.
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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 | 0.000 | 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".