Role of matrix metalloprotease-2 and MMP-9 in experimental lung fibrosis in mice
Bibliographic record
Abstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a diffuse parenchymal lung disease characterized by exuberant deposition of extracellular matrix (ECM) proteins in the lung interstitium, which contributes to substantial morbidity and mortality in IPF patients. Matrix metalloproteinases (MMPs) are a large family of zinc-dependent endopeptidases, many of which have been implicated in the regulation of ECM degradation in lung fibrosis. However, the roles of MMP-2 and -9 (also termed gelatinases A and B) have not yet been explored in lung fibrosis in detail. METHODS: AdTGF-β1 was applied via orotracheal routes to the lungs of WT, MMP-2 KO, MMP-9 KO and MMP-2/-9 dKO mice on day 0 to induce lung fibrosis. Using hydroxyproline assay, FlexiVent based lung function measurement, histopathology, western blot and ELISA techniques, we analyzed MMP-2 and MMP-9 levels in BAL fluid and lung, collagen contents in lung and lung function in mice on day 14 and 21 post-treatment. RESULT: IPF lung homogenates exhibited significantly increased levels of MMP-2 and MMP-9, relative to disease controls. Enzymatically active MMP-2 and MMP-9 was increased in lungs of mice exposed to adenoviral TGF-β1, suggesting a role for these metalloproteinases in lung fibrogenesis. However, we found that neither MMP-2 or MMP-9 nor combined MMP-2/-9 deletion had any effect on experimental lung fibrosis in mice. CONCLUSION: Together, our data strongly suggest that both gelatinases MMP-2 and MMP-9 play only a subordinate role in experimental lung fibrosis in mice.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".