Neuroinflammation and Oxidative Stress in Psychosis and Psychosis Risk
Bibliographic record
Abstract
Although our understanding of psychotic disorders has advanced substantially in the past few decades, very little has changed in the standard of care for these illnesses since the development of atypical anti-psychotics in the 1990s. Here, we integrate new insights into the pathophysiology with the increasing interest in early detection and prevention. First, we explore the role of N-methyl-d-aspartate receptors in a subpopulation of cortical parvalbumin-containing interneurons (PVIs). Postmortem and preclinical data has implicated these neurons in the positive and negative symptoms, as well as the cognitive dysfunction present in schizophrenia. These neurons also appear to be sensitive to inflammation and oxidative stress during the perinatal and peripubertal periods, which may be mediated in large part by aberrant synaptic pruning. After exploring some of the molecular mechanisms through which neuroinflammation and oxidative stress are thought to exert their effects, we highlight the progress that has been made in identifying psychosis prior to onset through the identification of individuals at clinical high risk for psychosis (CHR). By combining our understanding of psychosis pathogenesis with the increasing characterization of endophenotypes that precede frank psychosis, it may be possible to identify patients before they present with psychosis and intervene to reduce the burden of the disease to both patients and families.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".