Exploring the Immunomodulatory Potential of Natures Immuno: A Study on Mushroom Formulations in Alleviating LPS-Induced Inflammation.
Bibliographic record
Abstract
Abstract Introduction: Inflammatory diseases, such as pulmonary inflammation, lung cancer, and asthma, pose a significant global economic impact, affecting health and quality of life. Proinflammatory cytokines released by the immune system can be activated by Lipopolysaccharides (LPS), an endotoxin mimicking infection by Gram-negative bacteria. Literature suggests an increased risk of inflammatory diseases in females compared to males. Natures Immuno, a formulation extracted from five commonly used mushrooms (Turkey Tail, Cordyceps, Reishi, Shiitake, and Agaricus), aims to address this issue. However, the combined influence of these mushrooms on the immune system is poorly understood. To explore this, we conducted a series of studies evaluating the effect of the treating with this formulation on LPS-induced inflammation. Methods: C57Bl/6 male and female mice (n = 7 per group) were challenged with or without LPS. After the challenge, they were divided into eight groups. Four groups received 1 drop of Natures Immuno twice daily for 5 days, while four groups acted as a control. Inflammatory differences were compared in blood, bronchoalveolar lavage fluid, and lung tissue samples at the end of the treatment periods. Results: Lung inflammatory markers in Natures Immuno-treated mice were significantly lower than in LPS-treated mice. Conclusions: These findings suggest that treatment with these five mushrooms may assist in supporting the immune system against LPS-induced inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".