Optimization of a vinyl-lysine-urethane scaffold for use in oral mucosa regeneration
Bibliographic record
Abstract
This work focused on using a biodegradable vinyl-lysine-urethane (VLU) scaffold and rendering it more biocompatible by replacing methacrylate components, with a cross-linker (hydroxyethylmethacrylate---lysine diisocyanate---hydroxyethylmethacrylate). The cross-linker was observed to influence the wet-state mechanical properties of the scaffolds, in a linear manner (R2 = 0.84). Also an optimized VLU scaffold, formulated to contain the least amount of catalyst (dibutyl-tin-dilaurate 0.01 mL/mmol Polycarbonate diol), was used to generate an in vitro model of oral mucosa. The in vitro biodegradation (using human salivary derived esterase), degradation product cytotoxicity, and ability of the scaffold to support in-growth of epithelial and fibroblast cell lines was assessed. The scaffold degraded at a rate of 9.2wt%/month, biodegradation products were non-toxic towards SCC-15 cells, and both cell types adhered and proliferated into the scaffold. This study is the first use of such a synthetic, biodegradable, mechanically strong scaffold to help create a full-thickness oral mucosa model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".