Do the polyphenolic compounds from natural products can protect the skin from ultraviolet rays?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Excessive exposure to the sun is a major cause that augment the skin cancer, erythema, edema, abnormal pigmentation and finally suppress the immune system. Due to stratospheric ozone depletion and climate change UV levels are increasing, there is an urgent need to protect human skin from the harmful effects of UV. Compounds with photo- protection activity are very useful in reducing the effect of ultraviolet rays. For this reason, today’s sunscreens that contain one or more different types of chemical filters are used to protect the skin from ultraviolet rays. In the market, several synthetic, UV filter molecules are available, but they have limited use because these active molecules may create adverse effects on human skin such as, cancer, estrogenic activity, or photosensitivity reactions, contact dermatitis, mutations. Therefore, the development of formulations containing plant extracts and algae that may be potentially safer is being extensively studied. For this purpose, we can refer to polyphenol compounds such as flavonoids, which are a branch of natural substances that act as catalysts in the optical phase of photosynthesis, and act as anti-stress agents in plants by removing oxygen free radicals. Natural flavonoids have light and direct protection potential due to their ability to absorb ultraviolet rays and due to their antioxidant ability, as well as anti-inflammatory and immune modulating agents. In this article, the sun photoprotection properties of compounds with polyphenolic structure such as flavonoids derived from plant extracts or algae, as well as new methods in optimizing skin protection products against UV radiation have been investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it