<i>In vivo</i> degradation behavior of enzyme-degradable poly(trimethylene carbonate)-based biohybrid networks of varying water content
Bibliographic record
Abstract
Abstract Polymeric biohybrid networks have significant potential as supportive materials for soft connective tissue regeneration. Their success in this regard is determined by their initial mechanical properties, which are dependent on their water content, as well as the rate at which these properties change with time due to cell mediated degradation. In this study the in vivo degradation and tissue response following implantation of matrix metalloproteinase (MMP)-degradable poly(trimethylene carbonate) (PTMC)-based biohybrid networks were assessed in a Wistar rat model. The networks examined varied in equilibrium water content from circa 20% to 70% w/w. The networks degraded through MMP secretion by inflammatory cells at the tissue-material interface, generating a mass loss profile consistent with surface erosion but modulus and sol content changes consistent with a bulk erosion process. This degradation profile was explained in terms of a population gradient in MMP concentration from the surface to the bulk of the networks due to diffusion restrictions. A histological analysis of the tissue surrounding the implants confirmed a moderate tissue response comparable to that observed towards a Vicryl TM suture, suggesting that these new materials can be considered biocompatible.
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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".