In Vitro Dermal Absorption of Di(2-ethylhexyl) adipate (DEHA) in a Roll-On Deodorant Using Human Skin
Bibliographic record
Abstract
In vitro dermal absorption experiments were conducted using a roll-on deodorant that contains 1.56% di(2-ethylhexyl) adipate (DEHA), a plasticizer widely used in consumer products. Human skin specimens were fitted in Bronaugh flow-through Teflon diffusion cells. The diffusion cells were maintained at 32 °C to reflect the skin temperature. Two amounts (low dose: 5 mg of the product; high dose: 100 mg) were applied, in triplicate, each on four different human skins. DEHA was determined in the receiver solution at 6-h intervals, using headspace solid-phase microextraction gas chromatography-mass spectrometry (GC-MS). After 24 h, the experiment was terminated and masses of DEHA in the skin depot, skin wash, and upper and lower chambers of the diffusion cell were determined. A significant portion of applied DEHA, 28% in the low amount application and 34% in the high one, was found in the skin depot. In comparison, only 0.04% and 0.002% of applied DEHA were found in the receiver solutions for the low and high doses, respectively. Under our experimental conditions, an apparent steady-state flux of low DEHA mass penetrating from skin into the receiver solution was observed with a penetration rate of 2.2 ng/cm(2)/h for both the low and high doses. The average mass recovery was 81% for the low dose application and 56% for the high dose.
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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) | 0.001 | 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) | 0.001 | 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".