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Record W2623233193 · doi:10.1111/jne.12493

Non‐neural androgen receptors affect sexual differentiation of brain and behaviour

2017· review· en· W2623233193 on OpenAlexafffund
D. Ashley Monks, Ashlyn Swift‐Gallant

Bibliographic record

VenueJournal of Neuroendocrinology · 2017
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAndrogen receptorAndrogenSexual differentiationBiologyPhenotypeEndocrinologyInternal medicineTestosterone (patch)GeneGeneticsHormoneProstate cancerMedicineCancer

Abstract

fetched live from OpenAlex

Although gonadal testosterone is the principal endocrine factor that promotes masculine traits in mammals, the development of a male phenotype requires local production of both androgenic and oestrogenic signals within target tissues. Much of our knowledge concerning androgenic components of testosterone signalling in sexual differentiation comes from studies of androgen receptor (Ar) loss of function mutants. Here, we review these studies of loss of Ar function and of AR overexpression either globally or selectively in the nervous system of mice. Global and neural mutations affect socio-sexual behaviour and the neuroanatomy of these mice in a sexually differentiated manner. Some masculine traits are affected by both global and neural mutation, indicative of neural mediation, whereas other masculine traits are affected only by global mutation, indicative of an obligatory non-neural androgen target. These results support a model in which multiple sites of androgen action coordinate to produce masculine phenotypes. Furthermore, AR overexpression does not always have a phenotype opposite to that of loss of Ar function mutants, indicative of a nonlinear relationship between androgen dose and masculine phenotype in some cases. Potential mechanisms of Ar gene function in non-neural targets in producing masculine phenotypes are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.102
GPT teacher head0.391
Teacher spread0.289 · 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 designOther design
Domainnot available
GenreReview

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

Citations13
Published2017
Admission routes2
Has abstractyes

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