Psychedelics in Psychiatry: Neuroplastic, Immunomodulatory, and Neurotransmitter Mechanisms
Bibliographic record
Abstract
and interleukin 1, 6, and 10 production and release; and 3) modulation of serotonergic, dopaminergic, glutamatergic, GABAergic, and norepinephrinergic receptors, transporters, and turnover systems. We discuss arising concerns and ways to assess potential neurobiological changes, dependence, and immunosuppression. Although larger cohorts are required to corroborate preliminary findings, the results obtained so far are promising and represent a critical opportunity for improvement of pharmacotherapies in psychiatry, an area that has seen limited therapeutic advancement in the last 20 years. Studies are underway that are trying to decouple the psychedelic effects from the therapeutic effects of these compounds. SIGNIFICANCE STATEMENT: Psychedelic compounds are emerging as potential novel therapeutics in psychiatry. However, understanding of molecular mechanisms mediating improvement remains limited. This paper reviews the available evidence concerning the effects of psychedelic compounds on pathways that modulate neuroplasticity, immunity, and neurotransmitter systems. This work aims to be a reference for psychiatrists who may soon be faced with the possibility of prescribing psychedelic compounds as medications, helping them assess which compound(s) and regimen could be most useful for decreasing specific psychiatric symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".