Environmental enrichment as an intervention for adverse health outcomes of prenatal stress
Bibliographic record
Abstract
Prenatal stress (PS) has complex neurological, behavioural and physiological consequences for the developing offspring. The phenotype linked to PS usually lasts into adulthood and may even propagate to subsequent generations. The often uncontrollable exposure to maternal stress and the lasting consequences emphasize the urgent need for treatment strategies that effectively reverse stress programming. Exposure to complex beneficial experiences, such as environmental enrichment (EE), is one of the most powerful therapies to promote neuroplasticity and behavioural performance at any time in life. A small number of studies have previously used EE to postnatally treat consequences of PS in the attempt to reverse deficits that were primarily induced in utero . This review discusses the available data on postnatal EE exposure in prenatally stressed individuals. The goal is to determine if EE is a suitable treatment option that reverses adverse consequences of stress programming and enhances stress resiliency. Moreover, this review discusses data with respect to relevant hypotheses including the cumulative stress and the mismatch hypotheses. The articles included in this review emphasize that EE reverses most behavioural, physiological and neural deficits associated with PS. Differing responses may be dependent on the timing and variability of stress and EE, exercise, and potentially vulnerable and resilient phenotypes of PS. Results from this study suggest that enrichment may provide an effective therapy for clinical populations suffering from the effects of PS or early life trauma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".