Psilocybin and Eugenol Reduce Inflammation in Human 3D EpiIntestinal Tissue
Bibliographic record
Abstract
Inflammation plays a pivotal role in the development and progression of inflammatory bowel disease (IBD), by contributing to tissue damage and exacerbating the immune response. The investigation of serotonin receptor 2A (5-HT2A) ligands and transient receptor potential (TRP) channel ligands is of significant interest due to their potential to modulate key inflammatory pathways, mitigate the pathological effects of inflammation, and offer new avenues for therapeutic interventions in IBD. This study investigates the anti-inflammatory effects of 5-HT2A ligands, including psilocybin, 4-AcO-DMT, and ketanserin, in combination with TRP channel ligands, including capsaicin, curcumin, and eugenol, on the inflammatory response induced by tumor necrosis factor (TNF)-α and interferon (IFN)-γ in human 3D EpiIntestinal tissue. Enzyme-linked immunosorbent assay was used to assess the expression of pro-inflammatory markers TNF-α, IFN-γ, IL-6, IL-8, MCP-1, and GM-CSF. Our results show that psilocybin, 4-AcO-DMT, and eugenol significantly reduce TNF-α and IFN-γ levels, while capsaicin and curcumin decrease these markers to a lesser extent. Psilocybin effectively lowers IL-6 and IL-8 levels, but curcumin, capsaicin, and 4-AcO-DMT have limited effects on these markers. In addition, psilocybin can significantly decrease MCP-1 and GM-CSF levels. While ketanserin lowers IL-6 and GM-CSF levels, there are no effects seen on TNF-α, IFN-γ, IL-8, or MCP-1. Although synergistic effects between 5-HT2A and TRP channel ligands are minimal in this study, the results provide further evidence of the anti-inflammatory effects of psilocybin and eugenol. Further research is needed to understand the mechanisms of action and the feasibility of using these compounds as anti-inflammatory therapies for conditions like IBD.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".