Acute Cigarette Smoke–Induced Connective Tissue Breakdown Is Mediated by Neutrophils and Prevented by α 1-Antitrypsin
Bibliographic record
Abstract
Recent studies have suggested that macrophage-derived metalloproteases are the critical mediators of cigarette smoke-induced emphysema, in contrast to earlier hypotheses that this process was mediated by neutrophil elastase. To determine whether smoke can acutely induce connective tissue breakdown in the lung and to examine the mediators of this process, we exposed C57-BL/6 mice to whole cigarette smoke and used high-performance liquid chromatography to examine lavage fluid levels of desmosine (DES), a marker of elastin breakdown, and hydroxyproline (HP), a marker of collagen breakdown. Smoke produced a dose-response increase in lavage neutrophils, DES, and HP, but not lavage macrophages (MACs). This effect was evident by 6 h after exposure to two cigarettes. Pretreatment with an antibody against polymorphonuclear leukocytes (PMNs) reduced lavage PMNs to undetectable levels after smoke exposure, did not affect MAC numbers, and prevented increases in lavage DES and HP. Intraperitoneal injection of a commercial human alpha1-antitrypsin (alpha1AT) 24 h before smoke exposure increased serum alpha1AT levels approximately 3-fold and completely abolished smoke-induced connective tissue breakdown as well as the increase in lavage PMNs, again without affecting MAC numbers. We conclude that in this model cigarette smoke can acutely induce connective tissue breakdown and that this effect is mediated by neutrophil-derived serine proteases, most likely neutrophil elastase. Exogenous alpha1AT is protective and appears to inhibit both matrix degradation and PMN influx, suggesting that alpha1AT has anti-inflammatory as well as antiproteolytic effects in this system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".