Anti-Inflammation Activities of Mycosporine-Like Amino Acids (MAAs) in Response to UV Radiation Suggest Potential Anti-Skin Aging Activity
Bibliographic record
Abstract
Certain photosynthetic marine organisms have evolved mechanisms to counteract UV-radiation by synthesizing UV-absorbing compounds, such as mycosporine-like amino acids (MAAs). In this study, MAAs were separated from the extracts of marine green alga Chlamydomonas hedleyi using HPLC and were identified as porphyra-334, shinorine, and mycosporine-glycine (mycosporine-Gly), based on their retention times and maximum absorption wavelengths. Furthermore, their structures were confirmed by triple quadrupole MS/MS. Their roles as UV-absorbing compounds were investigated in the human fibroblast cell line HaCaT by analyzing the expression levels of genes associated with antioxidant activity, inflammation, and skin aging in response to UV irradiation. The mycosporine-Gly extract, but not the other MAAs, had strong antioxidant activity in the 2,2-diphenyl-1-picryhydrazyl (DPPH) assay. Furthermore, treatment with mycosporine-Gly resulted in a significant decrease in COX-2 mRNA levels, which are typically increased in response to inflammation in the skin, in a concentration-dependent manner. Additionally, in the presence of MAAs, the UV-suppressed genes, procollagen C proteinase enhancer (PCOLCE) and elastin, which are related to skin aging, had increased expression levels equal to those in UV-mock treated cells. Interestingly, the increased expression of involucrin after UV exposure was suppressed by treatment with the MAAs mycosporine-Gly and shinorine, but not porphyra-334. This is the first report investigating the biological activities of microalgae-derived MAAs in human cells.
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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) | 0.000 | 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 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".