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Record W2093141929 · doi:10.1080/15287394.2011.589104

A Novel “By Difference” Method for Assessing Dermal Absorption of Polycyclic Aromatic Hydrocarbons from Soil at Federal Contaminated Sites

2011· article· en· W2093141929 on OpenAlexaffabout
Richard P. Moody, Andrey V. Tytchino, Anna Yip, Sanya Petrovic

Bibliographic record

VenueJournal of Toxicology and Environmental Health · 2011
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsHealth Canada
Fundersnot available
KeywordsTenaxContaminationAbsorption (acoustics)ChemistryEnvironmental chemistryChromatographyAcetoneSoil contaminationSoil waterGas chromatographyEnvironmental scienceOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

A highly precautionary cost-effective method for estimating dermal absorption using data from 24-h skin soap washes from in vitro dermal absorption tests in Bronaugh flow-through diffusion cells with human skin is reported. Skin was dosed with 16 U.S. Environmental Protection (EPA) priority polycyclic aromatic hydrocarbons (PAH) applied in mixture each at 2 μg/ml (ppm) in acetone without soil. Concurrent tests were conducted with an unspiked aqueous suspension of PAH-contaminated soil obtained from a Canadian federal contaminated site. Percentage dermal absorption was estimated "by difference" from the applied dose and that detected by high-performance liquid chromatography (HPLC) in 24-h skin soap washes. The dermal absorption for 11 PAH ranged from 71 to 88.3% without and with soil, respectively. Lower absorption was found for 5 PAH in soil, in the range of 26.4 to 60.8%. Data could not be corrected for evaporative loss due to inconsistent data from Tenax adsorbent. Corroboratory gas chromatography/mass spectroscopy (GC/MS) tests are needed. Previously published in vitro data from the authors' laboratory supported use of the "by difference" method.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.400
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2011
Admission routes2
Has abstractyes

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