Prenatal exposure to common environmental factors affects brain lipids and increases risk of developing autism spectrum disorders
Bibliographic record
Abstract
The prevalence of autism spectrum disorders (ASDs) has been on the rise over recent years. The presence of diverse subsets of candidate genes in each individual with an ASD and the vast variability of phenotypical differences suggest that the interference of an exogenous environmental component may greatly contribute to the development of ASDs. The lipid mediator prostaglandin E2 (PGE2 ) is released from phospholipids of cell membranes, and is important in brain development and function; PGE2 is involved in differentiation, synaptic plasticity and calcium regulation. The previous review already described extrinsic factors, including deficient dietary supplementation, and exposure to oxidative stress, infections and inflammation that can disrupt signaling of the PGE2 pathway and contribute to ASDs. In this review, the structure and establishment of two key protective barriers for the brain during early development are described: the blood-brain barrier; and the placental barrier. Then, the first comprehensive summary of other environmental factors, such as exposure to chemicals in air pollution, pesticides and consumer products, which can also disturb PGE2 signaling and increase the risk for developing ASDs is provided. Also, how these exogenous agents are capable of crossing the protective barriers of the brain during critical developmental periods when barrier components are still being formed is described. This review underlines the importance of avoiding or limiting exposure to these factors during vulnerable periods in development.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".