Psychotic Disorders and exosomes: An overview of current evidence and future directions
Bibliographic record
Abstract
Due to its reliance on heterogeneous symptomatology, the accurate diagnosis of psychotic disorders remains a challenging task in clinical practice. Precise and early diagnosis of psychotic disorders facilitates early intervention, which has been shown to have substantial benefits for long-term outcomes. Still, the lack of specific biomarkers is an important limitation in early diagnosis and intervention. Exosomes, which act as messengers between cells, including brain cells, contain a vast array of molecules that hold promise for unveiling disorder-specific abnormalities. In this review, we discuss recent evidence highlighting the potential of circulating exosomes and brain-derived exosomes as valuable tools for the identification of accessible, non-invasive, and blood-based biomarkers of psychotic symptomatology and risk. We discuss current limitations in biomarker discovery studies focusing on exosomes. To enhance diagnosis specificity and treatment response, we also provide guidance for future investigations that need to target biomarkers of risk and relapse, as well as consider duration of untreated psychosis, biological sex, and other factors in the multifactorial biosignature of psychosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".