Poly(methacrylic acid‐<i>co</i>‐methyl methacrylate) beads promote vascularization and wound repair in diabetic mice
Bibliographic record
Abstract
Topical application of beads made from poly(methacrylic acid-co-methyl methacrylate) (45 mol % methacrylic acid, MAA) increased the number of blood vessels and improved 1.5 x 1.5 cm full thickness wound closure in a diabetic mouse (db/db) model. Three groups were compared: MAA beads, control poly(methyl methacrylate) beads (PMMA), and no bead blanks. MAA bead treatment significantly increased percent wound closure at all timepoints (7, 14, and 21 days) with MAA bead-treated wounds almost closed at day 21 (91 +/- 5.4% MAA vs. 79 +/- 3.2% PMMA or 76 +/- 4.8% no beads; p < 0.05). This was consistent with the expected significant increase in vascularity in the MAA group at days 7 and 14. For example at day 14, MAA bead-treated wounds had a vascular density of 22.7 +/- 2.6 vessels/hpf compared with 17.0 +/- 2.0 vessels/hpf in the PMMA bead group (p < 0.05). Epithelial gap and migration measurements suggested that the increased vascularity leads to enhanced epithelial cell migration as a principal means of wound closure. Although studies are underway to elucidate the mechanism of this angiogenic response, the results presented here support the notion that such materials, perhaps in other forms, may be useful in wound care or in other situations where vascularity is to be enhanced without the use of exogenous growth factors.
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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".