Nonneural Androgen Receptors Affect Sexual Differentiation of Brain and Behavior
Bibliographic record
Abstract
Testosterone, acting via estrogenic and androgenic pathways, is the major endocrine mechanism promoting sexual differentiation of the mammalian nervous system and behavior, but we have an incomplete knowledge of which cells and tissues mediate these effects. To distinguish between neural and nonneural actions of androgens in sexual differentiation of brain and behavior, we generated a loxP-based transgenic mouse, which overexpresses androgen receptors (ARs) when activated by Cre. We used this transgene to overexpress AR globally in all tissues using a cytomegalovirus (CMV)-Cre driver (CMV-AR), and we used a Nestin-Cre driver to overexpress AR only in neural tissue (Nes-AR). We then examined whether neural or global AR overexpression can affect socio-sexual behaviors using a resident-intruder paradigm. We found that both neural and global AR overexpression resulted in decreased aggressive behaviors and increased thrusting during mounting of intruders, consistent with a neural site of action. Global, but not neural, AR overexpression in males led to an increase in same-sex anogenital investigation. Together, these results suggest novel roles for nonneural AR in sexual differentiation of mice, and indicate that excess AR can lead to a paradoxical reduction of male-typical behavior.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".