Development of Biodegradable Polyurethane Scaffolds Using Amino Acid and Dipeptide-Based Chain Extenders for Soft Tissue Engineering
Bibliographic record
Abstract
The inherent flexibility of polyurethane (PU) chemistry allows the incorporation of specific chemical moieties into the backbone structure conferring a unique biological function to these synthetic polymers. We describe here the synthesis and characterization of a PU containing a Gly-Leu linkage, the cleavage site of several matrix metalloproteinases. A Gly-Leu dipeptide was introduced into the chain extender of the polyurethane through the reaction with 1,4-cyclohexane dimethanol. PUs synthesized with the Gly-Leu-based chain extender had a high weight-average molecular weight (M(w) > 125 x 10(3)) and were phase segregated, semi-crystalline polymers with a low soft-segment glass-transition temperature (T(g) < -50 degrees C). Uniaxial tensile testing of PU films indicated that the polymer could withstand high ultimate tensile strengths (approx. 13 MPa) and were flexible with breaking strains of approx. 900%. The Gly-Leu PU had a significantly higher initial modulus, yield stress and ultimate stress compared to a PU previously developed in our laboratory containing a phenylalanine-based chain extender (Phe PU). The Gly-Leu-based chain extender allowed for better hard segment packing and hydrogen bonding leading to enhanced mechanical properties. Electrospinning was used to form scaffolds with randomly organized fibers and an average fiber diameter of approx. 3.6 mum for both the Gly-Leu and Phe PUs. Mouse embryonic fibroblasts were successfully cultured on the PU scaffolds out to 28 days. Further investigations into cell-mediated polymer degradation will help to identify the suitability of this new biomaterial as scaffolds for soft tissue applications.
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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) | 0.000 | 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".