DNA repair enzymes in sunscreens and their impact on photoageing—A systematic review
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
BACKGROUND: DNA damage is one of the main factors responsible for photoageing and is predominantly attributed to ultraviolet irradiation (UV-R). Photoprotection by conventional sunscreens is exclusively prophylactic, and of no value, once DNA damage has occurred. As a result, the demand for DNA repair mechanisms inhibiting, reversing or delaying the pathologic events in UV-exposed skin has sparked research on anti-photoageing and strategies to improve the effect of conventional sunscreens. This review provides an overview of recent developments in DNA repair enzymes used in sunscreens and their impact on photoageing. METHODS: A systematic review of the literature, up to March 2019, was conducted using the electronic databases, PubMed and Web of Science. Quality assessment was carried out using the Newcastle-Ottawa scale (NOS) to ensure inclusion of adequate quality studies only (NOS > 5). RESULTS: Out of the 352 publications, 52 were considered relevant to the key question and included in the present review. Two major enzymes were found to play a major role in DNA damage repair in sunscreens: photolyase and T4 endonuclease V. These enzymes are capable of identifying and removing UV-R-induced dimeric photoproducts. Clinical studies revealed that sunscreens with liposome-encapsulated types of photolyase and/or T4 endonuclease V can enhance these repair mechanisms. CONCLUSION: There is a lack of randomized controlled trials demonstrating the efficacy of DNA repair enzymes on photoageing, or a superiority of sunscreens with DNA repair enzymes compared to conventional sunscreens. Further studies are mandatory to further reveal pathogenic factors of photoageing and possible therapeutic strategies against it.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 it