Epigenetic Programming by Maternal Behavior and Pharmacological Intervention<i>Nature Versus Nurture: Let's Call The Whole Thing Off</i>
Bibliographic record
Abstract
The nature of maternal care that an infant receives can effect the child's emotional and cognitive development, which is endured into adulthood. Similarly, maternal behavior in rodents is associated with long-term programming of individual differences in behavioral and hypothalamic-pituitary-adrenal responses to stress in the offspring. One critical question is how is such 'environmental programming' established and sustained in the offspring? This review discusses a novel mechanism to explain how maternal licking/grooming behavior in the rat can alter the hippocampal glucocorticoid receptor (GR) expression in the offspring, which concomitantly alters the HPA axis and the stress responsiveness of these animals. Both in vivo and in vitro studies show that maternal behavior increases GR expression in the offspring via increased hippocampal serotonergic tone accompanied by increased histone acetylase transferase activity, histone acetylation and DNA demethylation mediated by the transcription factor NGFI-A. In summary, this research demonstrates that an epigenetic state of a gene can be established through early-in-life experience, and is potentially reversible in adulthood. Accordingly, epigenetic modifications of specific genomic regions in response to variations in environmental conditions might serve as a major source of variation in biological and behavioral phenotypes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".