Wound‐healing activity of the skin of the common grape (<i>Vitis Vinifera</i>) variant, cabernet sauvignon
Bibliographic record
Abstract
The common Grape L. (Vitaceae) is regarded as an important medicinal plant. European healers have suggested the use of grapevine sap, juice, and whole grape in the treatment of pain, allergic reactions, inflammation, and to promote wound healing. We evaluated grape-skin powder for its wound-healing activity using an excision wound model in rats. Animals were randomly divided into three groups of six (n = 6) each. The test group animals were treated topically with the grape-skin powder (100 mg/kg/day). The controls and standard group animals were treated with petroleum jelly and mupirocin ointment respectively. Healing was assessed by the rate of wound contraction, period of epithelialization, and hydroxyproline content. On day 13, treatment of the wounds with grape-skin powder enhanced significantly the rate of wound contraction (100 %). Treated animals showed significant decrease in the epithelialization period (p < 0.000) and increase in the hydroxyproline content (p < 0.05) when compared to control and the standard. Histological analysis was also consistent with the proposal that grape-skin powder exhibits significant wound-healing potential. Increased rate of wound contraction, hydroxyproline content, and decrease in epithelialization time in the treated animals support the use of grape-skin powder in the management of wound healing.
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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".