Wound‐healing Properties of the Oils of <i>Vitis vinifera</i> and <i>Vaccinium macrocarpon</i>
Bibliographic record
Abstract
Vitis vinifera (grape) and Vaccinium macrocarpon (cranberry) are well known medicinal plants; most of the pharmacologically active phytochemicals have been isolated from the skin, fruit juice, fermented extract and alcohol fractions of the plants above. Here, the pharmacological properties of the phytochemical constituents present in oils of cranberry and grape were investigated. The oil of grape and cranberry has been evaluated for their wound healing activity by using an excision wound model in rats. The animals were divided into four groups of six each (n = 6). The experimental group 1 and 2 animals were treated topically with the grape and cranberry oil (100 mg/kg body weight), respectively. The controls were treated with petroleum jelly. The standard group of animals were treated with mupirocin ointment (100 mg/kg body weight). The healing was assessed by the rate of wound contraction and hydroxyproline content. On day 13, animals treated with cranberry oil exhibited a (88.1%) reduction in the wound area compared with grape-oil treated (84.6%), controls (74.1%) and standard group animals (78.4%) (p < 0.001). The hydroxyproline content of the granulation tissue was significantly higher in the animals treated with cranberry and the grape-oil (p < 0.000). Comparative investigation of the curative properties of the oils of V. vinifera and V. macrocarpon revealed a significant result which suggests their wound-healing potential.
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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".