Improved bioengineered cartilage tissue formation following cyclic compression is dependent on upregulation of MT1‐MMP
Bibliographic record
Abstract
The generation of bioengineered cartilage tissue suitable for transplantation is a potential therapy to treat damaged cartilage. We have shown previously that the physical and biomechanical properties of bioengineered cartilage can be improved by the application of 30 min of cyclic compression by a mechanism involving sequential upregulation of gene and protein levels of membrane type-1 matrix metalloproteinase (MT1-MMP) and MMP-13. In the current study, we demonstrated that MT1-MMP is critical to this response, as blocking the upregulation of MT1-MMP prevented the improvement in tissue formation. MT1-MMP seems to act by inducing tissue remodeling as evidenced by the presence of aggrecan degradation products by Western blot analysis and increased release of matrix molecules into the media. Release of these molecules was diminished when MT1-MMP upregulation was prevented. This matrix degradation was likely due to MT1-MMP, as under conditions where MMP-13 expression is maintained (stimulation in the presence of MT1-MMP siRNA) the release of these matrix molecules into the media was still prevented. It also appears that MT1-MMP does not regulate MMP-13 gene expression, as MT1-MMP-siRNA pretreatment had no effect on MMP-13 expression following mechanical stimulation. Further analysis of the anabolic genes and proteins involved in mechanically stimulated cartilage will lead to better understanding of the mechanism(s) underlying tissue formation yielding improved bioengineered cartilage.
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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 | 0.000 |
| 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 | 0.000 | 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".