Effects of Topical Elk Velvet Antler on Cutaneous Wound Healing in Streptozotocin-Induced Diabetic Rats
Bibliographic record
Abstract
OBJECTIVE: Wound repair is a finely orchestrated process involving cellular, molecular, physiologic, and biochemical interactions that restore the integrity of damaged tissue. Cyclic replacement of deer antlers requires rapid regenerative growth, in many ways analogous to that encountered during tissue repair. Molecular mechanisms regulating these processes are not fully understood, but it is increasingly apparent that growth factors are important mediators. Previous studies have shown that elk velvet antler (EVA) contains various growth factors and that a water-soluble extract stimulates dermal fibroblast growth in vitro. DESIGN: The efficacy of EVA water-soluble extract on wound healing in streptozotocin-induced diabetic rats was EVAluated using a full-thickness cutaneous wound model. Animals were randomly selected to receive topical application of either control or EVA gel. Daily photographs of the wounds served to measure the rate of wound closure. Wound-edge biopsies obtained on postoperative days 2 and 10 allowed histologic evaluation and measurement of transforming growth factor-beta 1 (TGF-beta (1)) concentrations by enzyme-linked immunoabsorbent assay. RESULTS: Wounds treated with the EVA topical gel were significantly smaller by postoperative day 6. TGF- beta (1) protein expression was not different in EVA-treated wounds compared to control wounds. CONCLUSIONS: This study indicates that topical treatment with an EVA water-soluble extract accelerates repair of cutaneous wounds in diabetic rats. Further studies are warranted to reveal the mechanisms involved in EVA enhancement of wound closure and to determine if this compound is an economical pharmacologic agent in the treatment of normal and compromised wounds.
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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".