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Record W2168360225 · doi:10.1002/em.20540

Induction of <i>lacZ</i> mutations in Muta™Mouse primary hepatocytes

2009· article· en· W2168360225 on OpenAlexafffund
Guosheng Chen, John D. Gingerich, Lynda M. Soper, George R. Douglas, Paul A. White

Bibliographic record

VenueEnvironmental and Molecular Mutagenesis · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsHealth Canada
FundersNatural Sciences and Engineering Research Council of CanadaHealth Canada
KeywordsMutagenMutantMolecular biologyHepatocyteMutation frequencyBiologyIn vitro2-AcetylaminofluoreneBiochemistryChemistryCarcinogenGeneMicrosome

Abstract

fetched live from OpenAlex

We have developed an in vitro mutation assay using primary hepatocytes from the transgenic MutaMouse. Primary hepatocytes were isolated using a two-step perfusion method with purification by Percoll, cultured, and treated with benzo[a]pyrene (BaP), 2-amino-1-methyl-6-phenyl- imidazo[4,5-b]pyridine (PhIP), 3-nitrobenzoanthrone (3-NBA), and cigarette smoke condensate (CSC). The mean lacZ mutant frequency (MF) for the solvent control was approximately twofold greater than the spontaneous MF observed in liver tissue. A concentration-dependent increase in MF (up to 3.7-fold above control) was observed following exposure to BaP. Fourfold and twofold increases in mutant frequency were observed for 3-NBA and PhIP exposures, respectively, without the addition of any exogenous metabolic activation. A slight but statistically significant increase in lacZ MF was observed for CSC, but only at the lowest concentration. This is the first report demonstrating that mutations can be detected in cultured primary hepatocytes from MutaMouse. The preliminary results presented suggest that the MutaMouse primary hepatocyte mutagenicity assay can be used as a cost-effective tool for screening of environmental mutagens and therapeutic products.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.580

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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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

Citations16
Published2009
Admission routes2
Has abstractyes

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