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Record W2031850108 · doi:10.1080/15287390802706322

Contaminated Soils (II):<i>In Vitro</i>Dermal Absorption of Nickel (Ni-63) and Mercury (Hg-203) in Human Skin

2009· article· en· W2031850108 on OpenAlexaff
Richard P. Moody, Julie Joncas, Mark Richardson, Sanya Petrovic, I. Chu

Bibliographic record

VenueJournal of Toxicology and Environmental Health · 2009
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsHealth Canada
Fundersnot available
KeywordsMercury (programming language)BioavailabilityChemistryDepotEnvironmental chemistryChlorideBovine serum albuminAbsorption (acoustics)NickelCadmiumAtomic absorption spectroscopyNuclear chemistryChromatographyPharmacologyMaterials scienceBiology

Abstract

fetched live from OpenAlex

Dermal absorption of heavy metal soil contaminants was tested in vitro with chloride salts of radioactive nickel (Ni-63) and mercury (Hg-203). Aqueous soil suspensions, spiked with either Ni-63 or Hg-203, were applied to fresh viable human breast skin tissue in Bronaugh diffusion cells perfused with Hanks HEPES buffered (pH 7.4) receptor containing 4% bovine serum albumin (BSA). Receptor fractions were collected every 6 h for 24 h when skin was soap washed. Tests were conducted concurrently in triplicate with and without soil for each skin specimen. Mean percent dermal absorption including the skin depot for Ni-63 was 1 and 22.8% with and without soil, respectively, while for Hg-203, values of 46.6 and 78.3% were obtained. Excluding the skin depot and considering only absorption in receptor, there was 0.5 and 1.8% absorption of Ni-63 with and without soil, respectively, and 1.5 and 1.4% for Hg-203. The potential bioavailability of the skin depot is discussed in relation to dermal exposure to these metals in contaminated soil.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.268
Teacher spread0.258 · 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 designObservational
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

Citations18
Published2009
Admission routes1
Has abstractyes

Explore more

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