Psilocin, the Psychoactive Metabolite of Psilocybin, Modulates Select Neuroimmune Functions of Microglial Cells in a 5-HT2 Receptor-Dependent Manner
Bibliographic record
Abstract
Neuroinflammation that is caused by microglia, the main immune cells of the brain, contributes to neurodegenerative diseases. Psychedelics, including psilocybin and lysergic acid diethylamide (LSD), possess certain anti-inflammatory properties and, therefore, should be considered as drug candidates for treating neuroinflammatory pathologies. When ingested, psilocybin is rapidly dephosphorylated to yield psilocin, which crosses the blood–brain barrier and exerts psychotropic activity by interacting with the 5-hydroxytryptamine 2A receptors (5-HT2ARs) on neurons. Since microglia express all three 5-HT2R isoforms, we hypothesized that, by interacting with these receptors, psilocin beneficially modulates select neuroimmune functions of microglia. We used microglia-like cell lines to demonstrate that psilocin, at non-toxic concentrations, did not affect the secretion of tumor necrosis factor (TNF) by immune-stimulated microglial cells, but significantly inhibited their phagocytic activity, the release of reactive oxygen species (ROS), and nitric oxide (NO) production. The inhibitory activity of psilocin on the latter two functions was similar to that of two selective 5-HT2R agonists, namely, 25I-NBOH and Ro60-0175. The role of this subfamily of receptors was further demonstrated by the application of 5-HT2R antagonists cyproheptadine and risperidone. Psilocin should be considered a novel drug candidate that might be effective in treating neuroimmune disorders, such as neurodegenerative diseases, where reactive microglia are significant contributors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".