Therapeutic Potential of Fungally Derived Psilocybin Extract in Morphine-Dependent Mice: A Research Protocol
Bibliographic record
Abstract
Introduction: Psilocybin is a naturally occurring tryptamine derivative psychedelic compound potently produced by fungi members of the genus Psilocybe. Previous literature has highlighted psilocybin as a serotonin 2A receptor agonist with striking effects on neural plasticity and cognition. Recent studies explore the usage of psilocybin in addressing addictive behaviours and substance abuse. Small psilocybin doses have shown promising anti-addictive and withdrawal-minimizing properties in alcohol-dependent mice. This study will further investigate this emerging field through a novel lens. We propose a novel study to elucidate psylobicin’s effect on opioid addiction in mouse models. Methods: Morphine-dependent mice will be administered either saline vehicle or differing doses of psilocybin during a morphine-available period to monitor consumption. Though mechanistically ambiguous, psilocybin’s hypothesized entry into serotonin-dependent stress-conciliating pathways supports the hypothesis that mean morphine consumption will decrease in a dose-dependent manner to similar levels to popular opioid receptor agonist therapeutics, such as methadone. Furthermore, we will examine psilocybin's impact on withdrawal behaviour. After morphine deprival on dependent mice, sustained doses of psilocybin, methadone, and positive and negative controls will be administered. Results: By analyzing stress-indicative behaviours in mice, the efficacy of psilocybin as a withdrawal assistance agent can be elucidated. Results from the morphine-dependent mice are expected to consume less morphine than controls, and minimize withdrawal symptoms at a similar level to popular therapeutic options like methadone. Discussion: If successful, psilocybin’s anti-addictive potential will help provide a cheaper, more accessible therapeutic option in addressing the growing opioid crisis. Furthermore, controlled psilocybin dosages have been shown to have lesser dependence potential compared to modern opioid addiction therapeutics. Conclusion: This study’s novel approach will provide meaningful support in exploring the growing field of mycology and its potential to address other substance use disorders. Future research may explore outside the limitations of the mouse model, like the oral administration of the compounds rather than intraperitoneal injection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".