Awareness of maternal stress, consequences for the offspring and the need for early interventions to increase stress resilience
Bibliographic record
Abstract
Experimental and clinical studies suggest that prenatal experiences may influence health trajectories up to adulthood and high age. According to the hypothesis of developmental origins of health and disease exposure of pregnant women to stress, nutritional challenges, infection, violence, or war may "program" risks for diseases in later life. Stress and anxieties can exist or be provoked in parents after fertility treatment, after information or diagnosis of fetal abnormalities and demand simultaneous caring concepts to support the parents. In vulnerable groups, it is therefore important to increase the stress resilience to avoid harmful consequences for the growing child. "Enriched environment" defines a key paradigm to decipher how interactions between genes and environment change the structure and function of the brain. The regulation of the fetal hippocampal neurogenesis and morphology during pregnancy is one example of this complex interaction. Animal experiments have demonstrated that an enriched environment can revert consequences of stress in the offspring during critical periods of brain plasticity. Epigenetic markers of stress or wellbeing during pregnancy might even be diagnosed by fragments of placental DNA in the maternal circulation that show characteristic methylation patterns. The development of fetal senses further illustrates how external stimulation may impact individual preferences. Here, we therefore not only discuss how maternal stress influences cognitive development and resilience, but also design possibilities of non-invasive interventions for both mothers and children summarized and evaluated in the light of their potential to improve the health of future generations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".