Structure‐Photoprotective Capacity Relationship of Phenolic Hydroxyl, Methoxy, and Ethenyl Linker Moieties of Phenolic Acids
Bibliographic record
Abstract
As constituents of many plants, polyphenols can neutralize free radicals. By inactivating free radicals, skin homeostasis can be restored, which may prevent further damage and premature skin aging. To prevent the damage caused by UV exposure, photoprotection is becoming increasingly important. Plants known to be rich in polyphenols have been investigated for their use as sunscreens. The synergistic effect, which is due to the specific composition of plant extracts, is a disadvantage for applications such as large-scale sunscreens. The great diversity of polyphenols present in such plants can be an obstacle to the identification of the most effective polyphenol families. Investigating polyphenol families by family could provide more information than plant extracts with various polyphenols and a multitude of other molecules. In this study, we focused on the phenolic acids subclass. Eleven phenolic acids have been investigated for their potential use as sunscreen. According to its Boots Star rating or to its critical wavelength, p-coumaric acid shows good potential for use as a sunscreen especially for exposure to UVB radiation. The absence of cytotoxicity of coumaric acid on normal human dermal fibroblasts (NHDF), even at high concentrations, further validates the potential use of this acid in sunscreen formulations.
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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) | 0.002 | 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".