Expression of Matrix Metalloproteinase-1 in Alveolar Macrophages, Type II Pneumocytes, and Airways in Smokers: Relationship to Lung Function and Emphysema
Bibliographic record
Abstract
BACKGROUND: An imbalance between proteolytic enzymes and their inhibitors is thought to be involved in the pathogenesis of chronic obstructive pulmonary disease. Matrix metalloproteinase-1, also known as interstitial collagenase, has been implicated as a potentially important proteinase in the genesis of chronic obstructive pulmonary disease and, more specifically, emphysema. METHODS: We performed quantitative immunohistochemical assessment of matrix metalloproteinase-1 expression in the resected lung of 20 smokers/ex-smokers who had varying severity of airflow obstruction and emphysema and compared this with the lungs of 5 nonsmokers. Emphysema was measured using a morphometric measure of the lungs' surface area/volume ratio and with qualitative and quantitative computed tomography (CT) measures of emphysema. RESULTS: There were significantly more matrix metalloproteinase-1-expressing alveolar macrophages and type II pneumocytes as well as a greater percentage of small airways that stained positively for matrix metalloproteinase-1 in the lungs of smokers than in those of nonsmokers (p < 0.0001, p < 0.0001, and p = 0.0003, respectively). The extent of staining of type II pneumocytes and airways for matrix metalloproteinase-1 was significantly related to the extent of smoking (p = 0.012 and p = 0.013, respectively). In addition, the extent of matrix metalloproteinase-1 staining of alveolar macrophages was related to the lung surface area/volume ratio and to qualitative estimates of emphysema on CT. CONCLUSION: These findings suggest that cigarette smoking increases expression of matrix metalloproteinase-1 in alveolar macrophages as well as in alveolar and small airway epithelial cells. Smokers who develop emphysema have increased alveolar macrophage expression of matrix metalloproteinase-1.
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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 | 0.001 |
| 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 | 0.000 |
| 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".