Pathogenesis of COPD. Part I. The role of protease-antiprotease imbalance in emphysema.
Bibliographic record
Abstract
This review covers protease-antiprotease imbalance in the development of emphysema in smokers. This imbalance is likely to play a major pathogenic role in the development of emphysema in subjects with severe alpha1-antitrypsin deficiency who smoke because of a deficient antiprotease protection against neutrophil elastase release in the lung. Neutrophil elastase is a potent elastolytic enzyme, and its instillation in the lungs of animals results in emphysema. Smoking attracts neutrophils to the lungs and there is an additional accumulation of neutrophils, because the abnormal antitrypsin polymerizes in the lungs and acts as a chemo-attractant to neutrophils. In subjects who do not have antitrypsin deficiency, the case for elastolytic injury by neutrophils causing emphysema is less definite, because of the lack of a severe deficiency of active alpha1-ntitrypsin leading to unopposed elastolysis by neutrophil elastase. It is likely that alveolar macrophages play a pathogenic role in emphysema; they express potent elastolytic enzymes, cathepsins and matrix metalloproteases (MMPs), which are induced by smoking. The numbers of macrophages are increased in the region of the respiratory bronchiole, where centrilobular emphysema develops in smokers. Macrophage cathepsins are inhibited by an antiprotease cystatin C, while the MMPs are inhibited by the tissue inhibitors of metalloproteases (TIMPs). Some pro-inflammatory mediators induce release of MMPs from macrophages without inducing increase in TIMPs, leading to possible protease-antiprotease imbalance. Studies of proteases in alveolar macrophages obtained by bronchoalveolar lavage and studies on lung tissue indicate increased protease expression in subjects with chronic obstructive pulmonary disease (COPD) compared to subjects without COPD.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".