In vitro studies to assess the potential of Quercetin as a topical sunscreen; photooxidative properties, photostability and inhibition of UV\nradiation-mediated skin damage
Bibliographic record
Abstract
Protection from the negative effects of solar radiation can be achieved by wearing protective clothing, avoiding exposure to sunlight or by the application of topical sunscreens. In this thesis, a number of studies were designed to determine if quercetin is suitable for use as a topical sunscreen. The first objective was to determine if quercetin could protect against UV-induced lipid oxidation. Quercetin is twice as effective at preventing UVB-induced oxidation as preventing UVA-induced oxidation. The difference between UVA- and UVB- induced oxidation is believed to be due to the presence of an excited state form of quercetin in the UVA system. The second objective was to determine the UV photostability of quercetin in solution. Three photoproducts of quercetin form regardless of whether UVA or UVB radiation is used. These photoproducts are 2,4,6-trihydroxybenzaldehyde, quercetin depside and hydroxytyrosol. The slow rate of formation, less than 20% loss of starting material over 11 hours, and non-toxic nature of the photoproducts indicate that photostability of quercetin is not an obstacle to its use as a sunscreen. The third objective was to determine the ability of quercetin to inhibit photosensitization by ketoprofen. Quercetin was shown to be effective in preventing decomposition of ketoprofen until it was consumed in the formation of the three quercetin photoproducts. This ability of quercetin to prevent ketoprofen photosensitization indicates a beneficial effect for the use of quercetin as a topical sunscreen. The fourth objective was to determine if quercetin can prevent UV-induced damage in a biological system. Quercetin was found to significantly reduce secretion of matrix metalloprotease 1 (MMP-1) upon UVA or UVB exposure, but had no effect on secretion of tumor necrosis factor α (TNF-α) in HaCaT cells. Topical application of quercetin to UVA or UVB exposed EpiDerm skin mimics significantly reduced both MMP-1 and TNF-α secretion. These results indicate that quercetin is effective in decreasing or eliminating several harmful effects of UVA and UVB radiation in the skin without major loss of starting material and without formation of toxic photoproducts. As such, quercetin appears to be a good candidate for inclusion into topical 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".