High modulus, enzyme-degradable poly(trimethylene carbonate)–peptide biohybrid networks formed from triblock prepolymers
Bibliographic record
Abstract
Biohybrid networks have the potential to have stiffnesses equivalent to that of native soft connective tissues as well as cell-mediated degradation behavior. Most strategies to generate such materials to date have utilized crosslinking of two separate and orthogonally functionalized polymers. Herein we describe a triblock prepolymer consisting of a central enzyme degradable peptide block flanked by two synthetic, hydrolysis resistant poly(trimethylene carbonate) blocks (PTMC) or poly(ethylene glycol)-PTMC blocks terminated in methacrylate groups. To form these prepolymers heterobifunctional PTMC and PEG-PTMC were prepared, possessing a vinyl sulfone terminus and a methacrylate terminus. These polymers were conjugated to a di-cysteine containing peptide through a Michael-type addition to form cross-linkable prepolymers. These prepolymers were then photo-cured to form enzyme degradable networks. The compressive moduli of the resulting water swollen networks was within the range of many soft connective tissues and was inversely proportional to the water solubility of the prepolymers. The prepolymer water solubility in turn could be tuned by adjusting PTMC molecular weight or by the addition of a PEG block. In vitro degradation only occurred in the presence of matrix metalloproteinases, and was fastest for networks prepared with prepolymers of higher water solubility.
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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".