Brain Disorders and Chemical Pollutants: A Gap Junction Link?
Bibliographic record
Abstract
The incidence of brain pathologies has increased during last decades. Better diagnosis (autism spectrum disorders) and longer life expectancy (Parkinson's disease, Alzheimer's disease) partly explain this increase, while emerging data suggest pollutant exposures as a possible but still underestimated cause of major brain disorders. Taking into account that the brain parenchyma is rich in gap junctions and that most pollutants inhibit their function; brain disorders might be the consequence of gap-junctional alterations due to long-term exposures to pollutants. In this article, this hypothesis is addressed through three complementary aspects: (1) the gap-junctional organization and connexin expression in brain parenchyma and their function; (2) the effect of major pollutants (pesticides, bisphenol A, phthalates, heavy metals, airborne particles, etc.) on gap-junctional and connexin functions; (3) a description of the major brain disorders categorized as neurodevelopmental (autism spectrum disorders, attention deficit hyperactivity disorders, epilepsy), neurobehavioral (migraines, major depressive disorders), neurodegenerative (Parkinson's and Alzheimer's diseases) and cancers (glioma), in which both connexin dysfunction and pollutant involvement have been described. Based on these different aspects, the possible involvement of pollutant-inhibited gap junctions in brain disorders is discussed for prenatal and postnatal exposures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".