Collaborative interactions between neutrophil elastase and metalloproteinases in extracellular matrix degradation in three-dimensional collagen gels
Bibliographic record
Abstract
BACKGROUND: Extended culture of monocytes and fibroblasts in three-dimensional collagen gels leads to degradation of the gels (see linked study in this issue, "Fibroblasts and monocytes contract and degrade three-dimensional collagen gels in extended co-culture"). The current study, therefore, was designed to evaluate production of matrix-degrading metalloproteinases by these cells in co-culture and to determine if neutrophil elastase could collaborate in the activation of these enzymes. Since co-cultures produce prostaglandin E2 (PGE2), the role of PGE2 in this process was also evaluated. METHODS: Blood monocytes from healthy donors and human fetal lung fibroblasts were cast into type I collagen gels and maintained in floating cultures for three weeks. Matrix metalloproteinases (MMPs) were assessed by gelatin zymography (MMPs 2 and 9) and immunoblotting (MMPs 1 and 3). The role of PGE2 was explored by direct quantification, and by the addition of exogenous indomethacin and/or PGE2. RESULTS: Gelatin zymography and immunoblots revealed that MMPs 1, 2, 3 and 9 were induced by co-cultures of fibroblasts and monocytes. Neutrophil elastase added to the medium resulted in marked conversion of latent MMPs to lower molecular weight forms consistent with active MMPs, and was associated with augmentation of both contraction and degradation (P < 0.01). PGE2 appeared to decrease both MMP production and activation. CONCLUSION: The current study demonstrates that interactions between monocytes and fibroblasts can mediate tissue remodeling.
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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 | 0.000 |
| 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 | 0.001 | 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".