The protective effect of muscimol against systemic inflammatory response in endotoxemic mice is independent of GABAergic and cholinergic receptors
Bibliographic record
Abstract
Systemic inflammatory response syndrome plays an important role in the development of sepsis. GABAergic and cholinergic pathways activation are considered important for inflammatory response regulation. Tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-12, IL-10, as well as inducible nitric oxide synthase (iNOS)-derived nitric oxide (NO) are important inflammatory mediators involved in the pathogenesis of sepsis. Muscimol, an active compound from the mushroom Amanita muscaria (L.) Lam., is a potent GABAA agonist, inhibits inflammatory response via activating GABAA receptor and vagus nerve. However, the effect of muscimol on lipopolysaccharide (LPS)-induced systemic inflammatory response is still unclear. Therefore, we studied the effects of muscimol on systemic inflammatory response and survival rate in endotoxemic mice. Mice endotoxemia was induced by LPS. Muscimol was given to mice or RAW264.7 cells 30 min before LPS (10 mg/kg, i.p., or 10 ng/mL, respectively). Mice received GABAergic and cholinergic receptor antagonists 30 min before muscimol and LPS. Muscimol decreased TNF-α, IL-1β, IL-12, iNOS-derived NO, and increased IL-10 levels and survival rate after LPS treatment. Muscimol significantly decreased nuclear factor kappa B (NF-κB) activity, increased IκB expression, and decreased pIKK expression in LPS-treated RAW264.7 cells. GABAergic and cholinergic antagonists failed to reverse muscimol’s protection in LPS-treated mice. In conclusion, muscimol protected against systemic inflammatory response in endotoxemic mice may be partially independent of GABAergic and cholinergic receptors.
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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 | 0.000 |
| 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 | 0.001 | 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".