Diminished pheromone‐induced sexual behavior in neurokinin‐1 receptor deficient (TACR1<sup>−/−</sup>) mice
Bibliographic record
Abstract
Studies in mice with targeted deletions of tachykinin genes suggest that tachykinins and their receptors influence emotional behaviors such as aggression, depression and anxiety. Here, we investigated whether TAC1- and TAC4-encoded peptides (substance P and hemokinin-1, respectively) and the neurokinin-1 receptor (NK-1R) are involved in the modulation of sexual behaviors. Male mice deficient for the NK-1R (TACR1 (-/-)) exhibited decreased exploration of female urine in contrast to C57BL/6 control mice and mice deficient for NK-1R ligands such as TAC1 (-/-), TAC4 (-/-) and the newly generated TAC1 (-/-) /TAC4 (-/-) mice. In comparison to C57BL/6 mice, mounting frequency and duration were decreased in male TACR1 (-/-) mice, while mounting latency was increased. Decreased preference for sexual pheromones was also seen in female TACR1 (-/-) mice. Furthermore, administration of the NK-1R-antagonist L-703,606 decreased investigation of female urine by male C57BL/6 mice, suggesting an involvement of NK-1R in urine sniffing behavior. Our results provide evidence for the NK-1R in facilitating sexual approach behavior, as male TACR1 (-/-) mice exhibited blunted approach behavior toward females following the initial interaction compared with C57BL/6 mice. NK-1R signaling may therefore play an important role in pheromone-induced sexual behavior.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".