Neuroinflammation in Autism: Plausible Role of Maternal Inflammation, Dietary Omega 3, and Microbiota
Bibliographic record
Abstract
Several genetic causes of autism spectrum disorder (ASD) have been identified. However, more recent work has highlighted that certain environmental exposures early in life may also account for some cases of autism. Environmental insults during pregnancy, such as infection or malnutrition, seem to dramatically impact brain development. Maternal viral or bacterial infections have been characterized as disruptors of brain shaping, even if their underlying mechanisms are not yet fully understood. Poor nutritional diversity, as well as nutrient deficiency, is strongly associated with neurodevelopmental disorders in children. For instance, imbalanced levels of essential fatty acids, and especially polyunsaturated fatty acids (PUFAs), are observed in patients with ASD and other neurodevelopmental disorders (e.g., attention deficit hyperactivity disorder (ADHD) and schizophrenia). Interestingly, PUFAs, and specifically n-3 PUFAs, are powerful immunomodulators that exert anti-inflammatory properties. These prenatal dietary and immunologic factors not only impact the fetal brain, but also affect the microbiota. Recent work suggests that the microbiota could be the missing link between environmental insults in prenatal life and future neurodevelopmental disorders. As both nutrition and inflammation can massively affect the microbiota, we discuss here how understanding the crosstalk between these three actors could provide a promising framework to better elucidate ASD etiology.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".