Insights into therapeutic potential and practical applications of natural toxins from poisonous mushrooms
Bibliographic record
Abstract
IntroductionMushrooms, belonging to the phyla Ascomycota and Basidiomycota, comprise approximately 14,000 known species, among which a small fraction are toxic. While toxic mushrooms are primarily associated with adverse health effects, recent research highlights their potential as sources of bioactive compounds with promising therapeutic applications.MethodsA systematic review was conducted using four major electronic databases: Web of Science, Google Scholar, PubMed, and ScienceDirect. The literature search, completed on July 1, 2024, utilized keywords including "Poisonous mushrooms," "Mushroom toxins," "Mycotoxins," "Beta-glucans," "Psilocybin," and "Therapeutic applications." Articles were selected based on specific inclusion criteria, focusing on studies investigating the biochemical, toxicological, and pharmacological properties of toxic mushroom compounds. Studies unrelated to mushrooms, non-peer-reviewed sources, or those with outdated or incomplete data were excluded.ResultsThis review examines key toxic mushroom compounds such as amanitins, phallotoxins, ibotenic acid, muscimol, orellanine, and gyromitrin, emphasizing their biosynthesis, structural features, and health effects. Despite their toxicity, compounds like beta-glucans, polysaccharides, lectins, and psilocybin exhibit immune-modulating, anticancer, and neuroprotective properties. These bioactive compounds have shown promise in targeting cancer stem cells and enhancing neurotransmitter activity, positioning them as potential therapeutic agents.DiscussionUnderstanding the therapeutic potential of toxic mushroom-derived bioactive compounds bridges toxicology and pharmacology, offering novel avenues for drug discovery. Comparative analysis with existing treatments highlights their unique advantages in modern medicine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".