Bibliographic record
Abstract
PURPOSE OF REVIEW: The protease-antiprotease theory of emphysema is widely accepted, but exactly which cells/proteases play a role continues to be a controversial subject. RECENT FINDINGS: Reports in humans show increased metalloproteinase activity in emphysema, but the exact role of metalloproteinases remains unclear. In laboratory animals, neutrophils turn out to be crucial, and neutrophil influx correlates well with measurements of matrix destruction. Neutrophil influx is linked to metalloproteinase activity, and in mice MMP12-induced release of tumor necrosis factor-alpha drives neutrophil infiltration. Serine elastase inhibitors, knockout of neutrophil elastase, and interference with tumor necrosis factor-alpha signaling all provide significant protection against smoke-induced emphysema, but metalloproteinase inhibitors may yield greater protection. In genetically modified mice, emphysema can be produced by overexpression of mediators such as tumor necrosis factor-alpha, interferon-gamma, or interleukin-13, and these models show evidence of metalloproteinase, cysteine protease, and serine protease attack with complicated relationships among these mediators. Collagen breakdown also appears to be important in the genesis of emphysema. Pro-apoptotic agents produce emphysema as well, possibly via an elastolytic pathway. SUMMARY: The idea that a single protease or a single type of inflammatory cell is responsible for human emphysema is unlikely to be true; rather, there are complex interactions among proteases, and between proteases and other mediators. The problem at this time is attempting to sort out the numerous candidate effector agents and to determine which of the animal models are relevant to human disease, since there may be considerable discrepancies in the types of proteases and their roles between laboratory animals and humans. There is now good evidence from animal models that antiproteolytic therapy can be of benefit in ameliorating cigarette smoke-induced 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".