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Pathological Aggression in “Fierce” Mice Corrected by Human Nuclear Receptor 2E1

2005· article· en· W2010450338 on OpenAlexafffund
Brett S. Abrahams, Melvin C. H. Kwok, Eric Trinh, Saeed Budaghzadeh, Sazzad M. Hossain, Elizabeth M. Simpson

Bibliographic record

VenueJournal of Neuroscience · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of British ColumbiaChildren's & Women's Health Centre of British Columbia
FundersBritish Columbia Knowledge Development FundCanadian Institutes of Health ResearchScottish Rite Charitable Foundation of Canada
KeywordsGene knockinTransgeneGenetically modified mouseBiologyGeneAggressionLocus (genetics)GeneticsPathologicalMutantclone (Java method)NeurosciencePsychologyPathologyMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

"Fierce" mice, homozygous for the deletion of nuclear receptor 2E1 (NR2E1), show abnormal brain-eye development and pathological aggression. To evaluate functional equivalency between mouse and human NR2E1, we generated mice transgenic for a genomic clone spanning the human NR2E1 locus and bred these animals to fierce mice deleted for the corresponding mouse gene. In fierce mutants carrying human NR2E1, structural brain defects were eliminated and eye abnormalities ameliorated. Excitingly, behavior in these "rescue" mice was indistinguishable from controls. Because no artificial promoter was used to drive transgene expression, promoter and regulatory elements within the human NR2E1 clone are functional in mouse. Normal behavior in rescue animals suggests that mechanisms underlying the behavioral abnormalities in fierce mice may also be conserved in humans. Our data support the hypothesis that variation at NR2E1 may contribute to human behavioral disorders. Use of this rescue paradigm with other genes will permit the direct evaluation of human genes hypothesized to play a causal role in psychiatric disease but for which evidence is lacking or equivocal.

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 score0.084
Threshold uncertainty score0.294

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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations55
Published2005
Admission routes2
Has abstractyes

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