“Bleu VS Yellow”: Marennine and extracellular polysaccharides for potential application in cosmetic and pharmaceutical applications
Bibliographic record
Abstract
Haslea ostrearia , a widely distributed marine pennate diatom, produces the unique blue pigment marennine and various extracellular polysaccharides (EPS), exhibiting promising bioactive properties. Previous studies have demonstrated the antibacterial, antioxidant, and antiproliferative effects of purified marennine extracts, while EPS fractions remain primarily understudied despite their potential applications in dermatology and cosmetics. Based on these prior findings, we hypothesized that marennine and EPS could influence skin-related biological processes, particularly gene expression related to hydration and aging, as well as lipid metabolism. To test this hypothesis, we investigated the effects of marennine and EPS at different concentrations (1, 10 and 100 µg mL⁻¹) on dermal fibroblast skin cells. Gene expression analysis targeted key markers associated with hydration and anti-aging, while lipidomic profiling assessed potential alterations in skin cell lipid composition. Our results demonstrated a significant upregulation of genes linked to skin hydration and elasticity, supporting the bioactive potential of these compounds. However, lipidomic analyses revealed no significant changes in the structural lipid composition of the skin cells across all tested concentrations. These findings highlight the potential of marennine and EPS as bioactive ingredients for cosmetic formulations aimed at improving skin hydration and anti-aging properties. Furthermore, their bioactivity suggests possible pharmaceutical applications, particularly in dermatological treatments requiring natural bioactive compounds with antioxidative and protective properties.
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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".