MMP levels in the response to degradable implants in the presence of a hydroxamate‐based matrix metalloproteinase sequestering biomaterial <i>in vivo</i>
Bibliographic record
Abstract
The inflammatory response to an implanted tissue engineered construct alters the remodeling that occurs and this can diminish the intended therapeutic effect. It was hypothesized that the use of a hydroxamate-based matrix metalloproteinase (MMP) sequestering biomaterial (MI) in the form of approximately 200 microm microspheres would lower the amount and activity of MMP in vivo in response to a subcutaneous, degradable implant (gelatin or Integra disc). MMP degrade extracellular matrix, facilitating inflammatory cell migration and local remodeling of the implant environment. Gelatin or Integra discs were implanted subcutaneously in the backs of CD1 mice together with 30 mg of MI microspheres or with 30 mg of similarly sized control poly(methyl methacrylate) (PMMA) microspheres in a paired study. To sample the implant space, weakly adsorbed protein or attached cells were recovered from explanted discs by soaking the discs in PBS overnight at 4 degrees C. Unexpectedly, MMP-2, -8, -9, and TIMP-1 levels were surprisingly similar in this recovered fluid for the two treatments. Also, there were significantly more (and at day 4 an order of magnitude more) leukocytes recovered from the gelatin discs coimplanted with the MI microspheres than with the PMMA control. It is suggested that the MI microspheres disturbed the natural MMP control pathway leading to high-leukocyte numbers, especially at early times. These results highlight the challenge associated with controlling the fate of tissue engineered constructs in vivo.
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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 | 0.001 |
| 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".