An in vivo comparison of wound healing characteristics of two commercial acellular dermal matrices
Bibliographic record
Abstract
OBJECTIVES: Many acellular dermal matrices (ADMs) are available for use in periodontal surgical procedures. However, few studies exist evaluating their in vivo healing properties. The objectives of this study were to compare the wound healing and remodeling of two ADMs used for gingival augmentation procedures in the rat model. MATERIALS AND METHODS: This was a nonrandomized controlled split-mouth study. Envelope flaps were surgically created in the maxillary quadrants of 24 Sprague Dawley rats. Each received either (a) AlloDerm Regenerative Tissue Matrix, or (b) OrACELL. Gingival tissue from one mandibular quadrant served as the untreated control. Six male and six female rats were treated for 7 or 21 days. Biopsies were processed for histologic analysis (H&E, Picro-sirius red, Verhoeff's solution) or RNA analysis (RT-PCR) to analyze the expression of type I collagen (Col1a1), fibronectin (Fn-1) and VEGF-A (Vegf-A). RESULTS: There was a greater density of fibroblasts in OrACELL compared to AlloDerm at both timepoints. There was a greater density of elastin present in AlloDerm compared to OrACELL at 7 days but no differences at 21 days. There were no differences between test groups in the percentage of birefringent collagen or in the expression of Vegf-A or Fn-1. At 7 days, there were significantly more fibroblasts for males in the OrACELL group compared to females. At 21 days, there was a significantly greater expression of Col1a1 for males in the OrACELL group compared to females. CONCLUSIONS: Early wound healing and remodeling of OrACELL appeared to occur more rapidly than AlloDerm and was accelerated in male rats. Whether these results have clinical implications for soft tissue grafting procedures in humans remains to be determined.
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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 | 0.001 | 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".