Bibliographic record
Abstract
Major depressive disorder of the mother affects 6 to 17% of pregnancies worldwide and can lead to negative outcomes, such as preterm delivery and later mental health problems of the child. It has been proposed that developmental programming has long-lasting effects in the offspring that might be mediated by epigenetic mechanisms, such as DNA methylation. Altered stress regulation or impaired immunological function of the mother can potentially affect DNA methylation processes of the fetus, changing gene expression levels in utero. These underlying biological processes can be tested in animal models, where pharmacological experiments using epigenetic drugs can prove causality. Recent human studies show that DNA methylation changes of hypothesis-driven candidate gene regions, such as the promoter of the glucocorticoid receptor and the serotonin transporter, were associated with maternal depression in peripheral tissue samples of newborns' cord blood, infants' saliva, or adults' peripheral blood. In addition, epigenome-wide association studies using blood cells show modest but significant changes in a subset of genes involved in immune functions. These DNA methylation changes were found mainly in enhancers, which point to regulatory effects in gene expression. Limited number of studies using brain tissue showed a significant overlap of differentially methylated genes in the different studies. In conclusion, prenatal maternal depression can induce covalent modifications in the offspring's DNA, which are detectable at birth in leukocytes and could be potentially present in other tissues, consistent with the hypothesis that system-wide epigenetic changes are involved in life-long responses to the psychosocial environment in utero. Birth Defects Research 109:888-897, 2017. © 2017 Wiley Periodicals, Inc.
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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) | 0.000 | 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) | 0.002 | 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".