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Record W2135533561 · doi:10.1093/carcin/23.1.213

Increased frequency of DNA deletions in pink-eyed unstable mice carrying a mutation in the Werner syndrome gene homologue

2002· article· en· W2135533561 on OpenAlexafffund
Michel Lebel

Bibliographic record

VenueCarcinogenesis · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsHôtel-Dieu de QuébecUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsBiologyMolecular biologyMutantWerner syndromeGeneGenome instabilityPremature agingMutation frequencyLocus (genetics)GeneticsHelicaseDNADNA damage

Abstract

fetched live from OpenAlex

Werner syndrome (WS) is a rare autosomal recessive disorder characterized by genomic instability and the premature onset of a number of age-related diseases, including cancers. Accumulating evidence indicates that the WS gene product is involved in resolving aberrant DNA structures that may arise during the process of DNA replication and/or transcription. To estimate the frequency of DNA deletions directly in the skin of mouse embryos, mice with a deletion of part of the murine WRN helicase domain were created. These mutant mice were then crossed to the pink-eyed unstable animals, which have a 70 kb internal duplication at the pink-eyed dilution (p) gene. This report indicates that the frequency of deletion of the duplicated sequence at the p locus is elevated in mice with a mutation in the WRN allele when compared with wild-type mice. In addition, the inhibitor of topoisomerase I camptothecin also increases the frequency of deletion at the p locus. This frequency is even more elevated in WRN mutant mice treated with camptothecin. In contrast, while the inhibition of poly(ADP-ribose) polymerase (PARP) activity by 3-aminobenzamide increases the frequency of DNA deletion, mutant WRN mice are not significantly more sensitive to the inhibition of PARP activity than wild-type animals.

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 score0.075
Threshold uncertainty score0.556

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 head0.015
GPT teacher head0.219
Teacher spread0.203 · 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

Citations39
Published2002
Admission routes2
Has abstractyes

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