The effects of parity and maternal behavior on gene expression in the medial preoptic area and the medial amygdala in postpartum and virgin female rats: A microarray study.
Bibliographic record
Abstract
To determine the pattern of gene expression in brains associated with mothering during the postpartum period, in the present study we assessed gene expression through microarrays in four groups of female rats: two groups of new mothers that were experiencing the hormonal and neurochemical changes associated with pregnancy and parturition, and two groups of virgin females that were not. Within each of these parity groups we assessed one group of animals that was exposed to and responded to pups and engaged in maternal behavior, and one group left without any exposure to pups and therefore had no maternal experience. We explored the pattern of expression of genes related to the hormones, neurotransmitters, and modulatory neuropeptides associated with maternal behavior within the medial preoptic area (MPOA) and the medial amygdala (MeA) in the rat. Within the MPOA there were significant main effects of pup exposure for the dopamine-related genes (DRD4 and dopamine transporter, DAT), the glucocorticoid-related gene (CYPX1B1a), the opioid receptor μ-1 gene (OPRM1) and the gamma-aminobutyric acid (GABA) receptor gene (GABAbRid). OPRM1 and the serotonin-related gene that regulates biosynthesis of serotonin (5HTR2A) showed a main effect of parity. For both sets of analyses, higher gene expression was associated with pup exposure and parity. Genes expressed in the MeA tended to reside in the glucocorticoid family. The microarrays were able to identify, on a transcriptional level, a list of candidate genes involved in maternal behavior and the factors that surround it.
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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) | 0.000 | 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) | 0.001 | 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".