Fetal programming of neuropsychiatric disorders
Bibliographic record
Abstract
Starting from the Developmental Origins of Health and Disease (DOHaD) hypotheses proposed by David Barker, namely fetal programming, in the past years, there is a growing evidence of the major role played by epigenetic factors during the intrauterine life and the perinatal period. Furthermore, it has been assessed that these factors can affect the health status in infancy and even in adulthood. In this review, we focus our attention on the fetal programming of the brain, analyzing the most recent literature concerning the epigenetic factors that can influence the development of neuropsychiatric disorders such as bipolar disorders, major depressive disorders, and schizophrenia. The perinatal epigenetic factors have been divided in two main groups: maternal factors and fetal factors. The maternal factors include diet, smoking, alcoholism, hypertension, malnutrition, trace elements, stress, diabetes, substance abuse, and exposure to environmental toxicants, while the fetal factors include hypoxia/asphyxia, placental insufficiency, prematurity, low birth weight, drugs administered to the mother or to the baby, and all factors causing intrauterine growth restriction. A better comprehension of the possible mechanisms underlying the pathogenesis of these diseases may help researchers and clinicians develop new diagnostic tools and treatments to offer these patients a tailored medical treatment strategy to improve their quality of life. Birth Defects Research (Part C) 108:207-223, 2016. © 2016 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".