The Underlying Neurobiological Mechanisms of Psychosis: Focus on Neurotransmission Dysregulation, Neuroinflammation, Oxidative Stress, and Mitochondrial Dysfunction
Bibliographic record
Abstract
Psychosis, defined as a set of symptoms that results in a distorted sense of reality, is observed in several psychiatric disorders in addition to schizophrenia. This paper reviews the literature relevant to the underlying neurobiology of psychosis. The dopamine hypothesis has been a major influence in the study of the neurochemistry of psychosis and in development of antipsychotic drugs. However, it became clear early on that other factors must be involved in the dysfunction involved in psychosis. In the current review, it is reported how several of these factors, namely dysregulation of neurotransmitters [dopamine, serotonin, glutamate, and γ-aminobutyric acid (GABA)], neuroinflammation, glia (microglia, astrocytes, and oligodendrocytes), the hypothalamic-pituitary-adrenal axis, the gut microbiome, oxidative stress, and mitochondrial dysfunction contribute to psychosis and interact with one another. Research on psychosis has increased knowledge of the complexity of psychotic disorders. Potential new pharmacotherapies, including combinations of drugs (with pre- and probiotics in some cases) affecting several of the factors mentioned above, have been suggested. Similarly, several putative biomarkers, particularly those related to the immune system, have been proposed. Future research on both pharmacotherapy and biomarkers will require better-designed studies conducted on an all stages of psychotic disorders and must consider confounders such as sex differences and comorbidity.
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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 | 0.001 |
| 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) | 0.001 | 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".