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Record W2167868741 · doi:10.1002/em.20629

Mutation spectrum in FE1‐MUTA<sup>TM</sup>Mouse lung epithelial cells exposed to nanoparticulate carbon black

2010· article· en· W2167868741 on OpenAlexaff
Nicklas Raun Jacobsen, Paul A. White, John D. Gingerich, Peter Möller, Anne Thoustrup Saber, George R. Douglas, Ulla Vogel, Håkan Wallin

Bibliographic record

VenueEnvironmental and Molecular Mutagenesis · 2010
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsHealth Canada
Fundersnot available
KeywordsCarbon blackMutationCancer researchGeneticsBiologyChemistryCell biologyGene

Abstract

fetched live from OpenAlex

It has been shown previously that carbon black (CB), Printex 90 exposure induces cII and lacZ mutants in the FE1-Muta(TM) Mouse lung epithelial cell line and causes oxidatively damaged DNA and the production of reactive oxygen species (ROS). The purpose of this study was to determine the mutation spectrum in the cII gene of Printex 90 exposed cells. Cells exposed to CB have a substantially different mutation spectrum in the cII gene compared with vehicle exposed controls. The mutation spectra differ both in the positions (P < 0.0001) and types of the mutations (P < 0.0001). Exposure to Printex 90 increased the number of single base deletions by 2.3-fold and larger deletions by 1.9-fold. Most single base deletions were within two repetitive sequences in cII, but the large deletions were not. The mechanism behind the large deletions is not yet known. The largest increases in base substitutions were observed in G:C→T:A, G:C→C:G, and A:T→T:A transversion mutations; this is in keeping with a genetic finger print of ROS and is further substantiated by the observations that Printex 90 generates ROS and oxidatively damaged DNA.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.197
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations73
Published2010
Admission routes1
Has abstractyes

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