The efficacy of a broad-spectrum sunscreen to protect engineered human skin from tissue and DNA damage induced by solar ultraviolet exposure.
Bibliographic record
Abstract
Sunscreens are known to protect against sunlight-induced erythema and sunburn, but their efficiency at protecting against skin cancer is still a matter of debate. Specifically, the capacity of sunscreens to prevent or reduce tissue and DNA damage has not been thoroughly investigated. The present study was undertaken to assess the ability of a chemical broad-spectrum sunscreen to protect human skin against tissue and DNA damage after solar UV radiation. Engineered human skin was generated and either treated or not with a broad-spectrum SPF 30 sunscreen and exposed to increasing doses of simulated sunlight (SSL). Immediately after irradiation, histological, immunohistochemical, and molecular quantitative analyses were performed. The unprotected irradiated engineered human skin showed significant epidermal disorganization accompanied by a complete absence of laminin deposition. The sunscreen prevented SSL-induced epidermal damage at low doses and allowed laminin deposition at almost all SSL doses tested. The frequencies of cyclobutane pyrimidine dimers, pyrimidine (6-4) pyrimidone photoproducts, and photooxidative lesions measured by alkaline gel electrophoresis and radioimmunoassay were significantly reduced by the sunscreen. Thus, tissue and DNA damage may provide excellent quantitative end points for assessing the photoprotective efficacy of sunscreens.
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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) | 0.001 | 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 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".