A scoping review of the effects of mushroom and fungus extracts in rodent models of depression and tests of antidepressant activity
Bibliographic record
Abstract
One of the most important developments in psychopharmacology in the past decade has been the emergence of novel treatments for mood disorders, such as psilocybin for treatment-resistant depression. Psilocybin is most commonly found in different species of mushroom; however, the literature on mushroom and fungus extracts with potential antidepressant activity extends well beyond just psilocybin-containing mushrooms, and includes both psychedelic and non-psychedelic species. In the current review, we systematically review the preclinical literature on mushroom and fungus extracts, and their effects of animal models of depression and tests of antidepressant activity. The PICO structure, PRISMA checklist and the Cochrane Handbook for systematic reviews of intervention were used to guide the search strategy. A scoping search was conducted in electronic databases PubMed, CINAHL, Embase and Web of Science. The literature search identified 50 relevant and suitable published studies. These included 19 different species of mushrooms, as well as seven different species of other fungi. Nearly all studies reported antidepressant-like effects of treatment with extracts. Treatments were most commonly delivered orally, in both acute and chronically administered studies to predominantly male rodents. Multiple animal models of depression were used, the most common being unpredictable chronic mild stress, while the tail suspension test and forced swim test were most frequently used as standalone antidepressant screens. Details on each experiment with mushroom and fungus species are discussed in detail, while an evaluation is provided of the strengths and weaknesses of these studies.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".