Influence of the Oil Phase on the Wound Healing Activity of Sea Cucumber Extract-Based Cream Formulations
Bibliographic record
Abstract
Sea cucumbers are attractive marine natural sources as they are enriched with functional biomaterials that can contribute in accelerating wound healing. The present study was carried out to prepare cream formulations comprising extract of sea cucumber with different type of oil phase, namely F1 (olive oil), F2 (tea tree oil) and F3 (lemongrass oil) to assess the influence of the oil on the physicochemical properties and the wound healing efficacy of the creams. The formulated creams showed satisfactory physicochemical characteristics such as homogeneity, spreadability, rheology, pH, and showed no evidence of phase separation even when the creams were kept at extreme conditions. The ex vivorelease profile of sea cucumber extract from the formulated creams was determined by using a Franz diffusion cells. F3 demonstrated a constant and yield the highest release percentage of sea cucumber extract, followed by F2 and F1. Topical application of the formulated creams on the excision wound in rats showed a significant wound healing efficacy compared to the control group. Among the creams formulation, F1 demonstrated a significantly higher rate of wound closure compared to F2, F3, and positive control. The wound healing efficacy of the formulated creams were not dependent on the ability of the oils in promoting skin permeation for the release of sea cucumber extract. This study depicted that lemongrass oil acted as a good skin permeation enhancer for the release of sea cucumber extract while olive oil worked in a more synergistic manner with sea cucumber extract in promoting 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".