Neuroinflammation and schizophrenia – is there a link?
Bibliographic record
Abstract
Introduction: a historical prelude to modern understandingHippocrates, often regarded as the founding figure in empirical medicine, was the first to acknowledge the impact of environmental factors on general and mental health in his work "In airs, waters, and places."However, it took over two millennia for substantial epidemiological studies to explore the relationship between mental disorders, such as schizophrenia, and environmental factors like birth seasonality.Although Tramer's groundbreaking 1929 study identified a pattern of winter-spring births in 3100 patients with psychosis, a consistent association with schizophrenia was established only by the end of 20th century (1). Maternal immune activation (MIA) and increased relative risk of schizophrenia in offspringExtensive epidemiological studies in the last two decades have consistently shown that exposure to prenatal MIA significantly increases the odds of developing schizophrenia later in life (2, 3).Khandaker et al.'s (2013) systematic review highlighted a two-to fivefold heightened risk (3), while a more recent metanalysis by Zhou et al. (2021), encompassing 23 observational studies, identified a more modest yet steady increase in psychosis risk among children born to mothers who experienced infections during pregnancy (OR = 1.25, 95% confidence interval (CI): 1.1-1.41;p = 0.001) (4).It is hypothesized that inflammatory molecules, such as cytokines and chemokines (e.g., TNF-a, IL-1b, IL-6), triggered by prenatal pathogenic exposure, may penetrate the placenta, impair fetal brain development, and cause lasting disturbances in neurodevelopmental trajectories (5, 6).Support for this theory comes from various Frontiers in Psychiatry frontiersin.
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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 | 0.001 | 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 | 0.001 |
| 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 | 0.000 |
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".