Influence of the Oil Phase on the Wound Healing Activity of Sea Cucumber Extract-Based Cream Formulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it