Antioxidant properties of topical Caulerpa sp. extract on UVB-induced photoaging in mice
Bibliographic record
Abstract
Caulerpa sp., a genus of seaweed native to the Indo-Pacific region, has been known for its antioxidant properties and health benefits when consumed as food. Previous studies have reported Caulerpa sp.’s potential as a strong antioxidant, but its effects on the skin in a topical preparation, especially its role in ultraviolet (UV) protection, have not been studied extensively. Our study investigated the protective effects of 0.2% and 0.4% Caulerpa sp. extract gels on photoaging in the UVB-irradiated skin of Wistar mice. The subjects were divided into naive control, vehicle control, and 3 treatment groups (0.2% Caulerpa sp. extract gel, 0.4% Caulerpa sp. extract gel, and 0.02% astaxanthin gel as a standard antioxidant). The groups, except the naive control group, received a total of 840 mJ/cm2 of UVB irradiation in four weeks. Protective effects of the extract were measured through the evaluation of collagen expression, MMP-1 expression and levels, and 8-OhDG expression. Mice who received topical application of Caulerpa sp. extract gel had higher collagen expression, better-preserved collagen structure, lower levels of MMP-1, and less MMP-1 and 8-OHdG expressions compared to the vehicle control group. There was no difference between different concentrations of the extract. Our findings demonstrated that topical application of Caulerpa sp. extract gel significantly protected UVB-irradiated mice skin from photoaging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".