065 Improving Integra™“Take” with Negatively Charged Methacrlylic Acid Beads
Bibliographic record
Abstract
Introduction : Integra™ has gained acceptance as a dermal replacement for large burns with limited donor sites. Integra™ vascularization takes between 14 and 21 days. Accelerating the vascularization of Integra™ could improve “take” rates and potentially decrease the obligatory waiting period between application and autografting. Hypothesis : Application of negatively charged methacrylic acid (MAA) beads will enhance vascularization of Integra™ Methods Male Wistar rats (n = 11) had two 2 × 3 cm contra lateral dorsal full thickness wounds excised and grafted using Integra™. The MAA beads were applied under the Integra™ topically on the wound bed. Three experimental treatments were compared: Group 1‐ high‐dose MAA beads, Group 2‐ low‐dose MAA beads, Group 3‐ Integra only. Laser Doppler Imaging (LDI) to assess perfusion, as well as digital photography was done on days 7,10 and 14. Tissue samples for H + E and Factor VIII histological staining were collected on day 14. Results : The average “take” was 99%(p < 0.05) for high‐dose MAA beads, 98%(p < 0.05) for low‐dose MAA beads and 82% in the Integra™ only group at day 7. At day 7 (p < 0.01) and day 10 (p < 0.05), the low‐dose MAA group had significantly greater perfusion than the Integra™ only group. There were no statistically significant differences at day 14. Microvessel density (MVD) counts revealed a >40% increase in the number of vessels in both the low‐dose MAA (p < 0.05) and high‐dose MAA (p < 0.05) groups when compared to the Integra™ only group. There was no difference in LDI perfusion or MVD counts between low and high‐dose MAA groups. Conclusion : Factor VIII staining revealed enhanced angiogenesis in Integra™ treated with low and high‐dose negatively charged MAA beads. Low‐dose negatively charged MAA beads improved and accelerated the vascularization of Integra™ in this rodent 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".