Evaluation of percutaneous permeation of repellent DEET and sunscreen oxybenzone from emulsion-based formulations in artificial membrane and human skin
Bibliographic record
Abstract
Insect repellent DEET and sunscreen ingredient oxybenzone play an essential role in minimizing vector-borne diseases and skin cancers. The purpose of this study was to investigate the effects of emulsion type, addition of thickening agent and droplet size in three emulsion-based lotions on percutaneous permeation of DEET and oxybenzone using in vitro diffusion experiments, in order to minimize overall systemic permeation of the substances. Formulation C (water-in-oil emulsion) significantly increased overall permeation of DEET through human skin (56%) compared to Formulation A (oil-in-water emulsion). Formulation B (oil-in-water emulsion with thickening agent xanthan gum) significantly decreased the size of oil droplet containing DEET (16%), but no effect on oil droplets containing oxybenzone. Adding xanthan gum also increased overall permeation of DEET and oxybenzone (21% and 150%) when compared to Formulation A; presence of both ingredients in Formulation B further increased their permeation (36% and 23%) in comparison to its single counterparts. Overall permeation of oxybenzone through LDPE was significantly higher by 26%-628% than that through human skin; overall permeation of DEET through human skin was significantly higher by 64%-338% than that through LDPE.
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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".