Characterization of biodegradable polyurethane microfibers for tissue engineering
Bibliographic record
Abstract
A polyurethane designed to be biodegradable via hydrolysis and enzyme-mediated chain cleavage, has been investigated for its use as a temporary scaffold in tissue-engineering applications. The phase-segregated nature of the polyurethane imparts elastomeric properties that are attractive for soft tissue engineering. This polyurethane has been electrospun in order to create scaffolds that incorporate several biomimetic features including small fiber diameter, large void volume, and an interconnected porous network. Material properties were evaluated via gel-permeation chromatography, differential scanning calorimetry and Raman spectroscopy before and after processing. Analysis by gel-permeation chromatography showed that the molecular weights were similar, indicating that the bulk of the polymer chains were not degraded during processing. Thermal analysis revealed that the glass transition temperature did not shift and Raman spectra of the bulk polyurethane film compared to the electrospun mat were identical, confirming that the conformation of the polymer was unaffected by the shear and electric field used in the electrospinning process. In addition, field emission scanning electron microscopy revealed that the morphology of the electrospun mats had a broad fiber diameter distribution, and mechanical analysis showed that the mats had an ultimate tensile stress of 1.33 MPa and ultimate tensile strain of 78.6%. The degradation profile was investigated in the presence of chymotrypsin. These results were compared to a previous study of thin films of this polyurethane, and it was found that the increase of surface area aided the surface-mediated erosion of the material. It is believed that an electrospun matrix of this biodegradable polyurethane shows promise for use in soft tissue engineering and regenerative medicine 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".