Maternal touch and feed as critical regulators of behavioral and stress responses in the offspring
Bibliographic record
Abstract
For half a century, Seymour Levine's pioneering work on the interactions between mother and infant have helped us understand the critical early factors that shape physiology and behavior in the adult offspring. The work from my laboratory described in this review was based on many experiments by Levine and coworkers demonstrating that the quantity and quality of maternal milk and of maternal-infant contact influence different aspects of the hypothalamus-pituitary-adrenal (HPA) activity in the neonate. We have extended this work by showing that maternal high-fat feeding during the prenatal and lactational period blunts stress responsiveness in neonatal pups, in part mediated by increased circulating leptin levels in the offspring. The blunting of stress responses during this specific neonatal period might be beneficial to prevent the negative effects of exaggerated glucocorticoid secretion on the developing brain. In line with Levine's previous work, we found that maternal licking of the pups reduced stress responsiveness and inflammation in pups subjected to modest repeated pain during the first weeks of life and that it also blunted adult sensitivity to thermal pain. These studies have important implications for human infants as mechanisms aimed at reducing stress responsiveness can be considered protective to the developing brain from exaggerated and untimely neuroendocrine and sympathetic stimulation. Non-invasive interventions targeted at maternal nutrition and maternal care are relatively easy to implement and might have a significant effect on the health outcome of the offspring, particularly in a vulnerable population of term and pre-term babies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".