Amanita muscaria extract potentiates production of proinflammatory cytokines by dsRNA-activated human microglia
Bibliographic record
Abstract
Recent interest in mushrooms and their components as potential therapies for mental health, along with recent government and health authority approvals, has necessitated a more comprehensive understanding of their effects on the cellular microenvironment of the brain. Amanita muscaria has been ingested as a treatment for a variety of ailments for centuries, most notably those affecting the central nervous system and conditions associated with neuroinflammation. However, the effects of these extracts on neuroinflammatory cells, such as microglia, are unknown. The effect of commercially-sourced A. muscaria extract (AME-1) on human microglial cell line (HMC3) expression of surface receptors such as CD86, CXCR4, CD45, CD125 and TLR4 was determined by flow cytometry. AME-1 upregulated expression of all of these receptors. The effect of AME-1 on HMC3 production of IL-8 and IL-6 was determined and compared to tumor necrosis factor (TNF), polyinosinic-polycytidylic acid [poly(I:C)], substance P and lipopolysaccharide (LPS), all known activators of HMC-3 and primary microglia. HMC3 produced both IL-8 and IL-6 when activated with LPS, TNF and poly(I:C) but not when they were activated with substance P. Although AME-1 at higher concentrations increased IL-8 production of HMC3 on its own, AME-1 notably potentiated HMC3 production of IL-8 in response to poly(I:C). AME-1 altered expression of toll-like receptor 3 (TLR3) mRNA but not surface protein by HMC3. AME-1 also did not significantly alter expression of retinoic acid-inducible gene I (RIG-I) or melanoma differentiation-associated protein 5 (MDA5), both cytosolic sensors of dsRNA. Metabolomics analysis showed that AME-1 contained several metabolites, including the autophagy inducer, trehalose. Like AME-1, trehalose also potentiated HMC3 poly(I:C) mediated production of IL-8. This study suggests that A. muscaria extracts can modify HMC3 inflammatory responses, possibly due to their trehalose content.
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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) | 0.000 | 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) | 0.002 | 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".