Natural variations in maternal behaviour in the rat : neural mechanisms and environmental regulation
Bibliographic record
Abstract
Oxytocin-estrogen interactions are critical to the neuroendocrine and behavioural changes that occur in the maternal rat. The question that remains is whether these interactions mediate the expression of individual differences in maternal care and the generational transmission of this behaviour. Maternal licking/grooming (LG) is a normally distributed, stable behaviour in the lactating rat that is transmitted to female offspring. Levels of oxytocin receptor (OTR) binding in the medial preoptic area (MPOA), a region critical for maternal behaviour, are elevated in females exhibiting high vs . low levels of LG. Central administration of a selective OTR antagonist reduces LG behaviour in High LG females to levels comparable to those observed in Low LG females, supporting the functional role of these receptors in LG. The female offspring of High and Low LG mothers differ in estrogen sensitivity in the MPOA. Ovariectomized, estrogen-treated female offspring of High LG mothers exhibit increased OTR binding and Fos immunoreactivity whereas the offspring of Low LG mothers show no estrogen-mediated increases. Estrogen up-regulation of OTR levels is known to involve estrogen receptor alpha. Levels of estrogen receptor alpha in the MPOA are elevated in High LG compared to Low LG mothers and their female offspring. Mesolimbic dopamine activity is a potential downstream target of estrogen-oxytocin interactions, and we have shown that nucleus accumbens dopamine activity and levels of D1 and D3 dopamine receptors are associated with high levels of LG behaviour.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".