Effect of Cold Storage on<i>In Vitro</i>Human Skin Absorption of Six<sup>14</sup>C-Radiolabeled Environmental Contaminants: Benzo[a]Pyrene, Ethylene Glycol, Methyl Parathion, Naphthalene, Nonyl Phenol, and Toluene
Bibliographic record
Abstract
Dermal absorption of human breast skin obtained fresh from a local hospital was tested before and after freezer storage at -19 degrees C for 30 or 60 d. Dermatomed skin (0.4-0.5 mm) was tested in vitro using the Bronaugh flow-through diffusion cells perfused at 1.5 ml/h with receiver solution (Hanks HEPES buffered basal saline containing 4% bovine serum albumin [BSA]). Six 14C-radiolabeled chemicals ranging in lipophilicity were tested, including benzo[a]pyrene (BaP), ethylene glycol (EG), methyl parathion (MP), naphthalene (Nap), nonyl phenol (NP), and toluene (Tol). There was significantly lower percent dermal absorption into the receiver solution for two of the six chemicals (BaP and Tol) with the skin depot excluded. However, with percent dermal absorption defined as that including the skin depot, with the exception of the BaP data for skin frozen 30 d, there was no significant difference between percent dermal absorption data for fresh unfrozen controls and those stored frozen for all 6 test chemicals for both 30 and 60 d freezer storage times. These results suggested with skin depot included that freezer storage may have potential for preserving human skin for in vitro absorption tests of environmental contaminants; however, optimal freezer storage conditions such as temperature and storage duration and their effects on skin viability and dermal metabolism need to be determined.
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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) | 0.003 | 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".