LPS-Induced Liver Inflammation Is Inhibited by Psilocybin and Eugenol in Mice
Bibliographic record
Abstract
Background/Objectives: Liver inflammatory diseases are a major global health burden and are often exacerbated by inflammation driven by lipopolysaccharides (LPS) through toll-like receptor 4 signaling. This study evaluates the anti-inflammatory effects of psilocybin and eugenol in an LPS-induced liver inflammation model in C57BL/6J mice. Methods: Mice were treated with psilocybin (0.88 mg/kg) and/or eugenol (17.59 mg/kg) either before (pre-treatment) or after (post-treatment) LPS injection. Results: Psilocybin and eugenol, individually and in combination, significantly reduced the LPS-induced mRNA levels of pro-inflammatory cytokines, with post-treatment administration exhibiting stronger effects than pre-treatment. Psilocybin alone displayed the most pronounced anti-inflammatory response, especially for IL-1β, IL-6, and MCP-1, while its combination with eugenol in 1:50 ratio demonstrated similar results, with strongly reduced COX-2 and TNF-α. Histological analysis revealed improved nuclear circularity and reduced inflammatory infiltration in the treatment groups. Eugenol alone showed potential adverse effects, including increased MCP-1 and GM-CSF, but this was mitigated by the co-administration of psilocybin. Conclusions: These findings highlight psilocybin and its combination with eugenol as promising therapies for hepatic inflammation, suggesting their application in treating acute and chronic liver diseases. Future research should explore their long-term effects, the mechanisms underlying their anti-inflammatory actions, and their therapeutic efficacy in humans.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".