The Potential of Cordyceps sinensis as a Novel Treatment for Reducing Muscle Pain in Fibromyalgia
Bibliographic record
Abstract
Fibromyalgia is a widespread and debilitating condition characterized by chronic musculoskeletal pain and fatigue. Recent studies have shown increased lactate levels in the muscle tissue of fibromyalgia patients, which is also a symptom characteristic of lactic acidosis, a condition known to cause musculoskeletal pain. This similarity suggests that lactate buildup may contribute to the pain and fatigue experienced by fibromyalgia patients and highlights lactate accumulation as a potential target for pain management. Cordyceps sinensis (C. sinensis) is a fungus known to inhibit blood lactic acid production, enhance mitochondrial function, and improve muscular endurance; however, its application has not been investigated in the context of fibromyalgia. This study aims to investigate the potential therapeutic impact of C. sinensis in alleviating symptoms of muscle fatigue in fibromyalgia by reducing lactate levels and improving mitochondrial function. The therapeutic potential of C. sinensis will be assessed in Sprague-Dawley rats that are experimentally induced with fibromyalgia-like symptoms. For a duration of one week, rats will receive either no treatment (control group) or treatment in the form of C. sinensis mycelia water extract (experimental group), and measurements of blood lactate and pyruvate levels, mitochondrial function, and pain will be conducted throughout the study. We expect C. sinensis therapy to significantly reduce blood lactate levels and enhance mitochondrial function in the experimental group compared to the control group, ultimately reducing muscle pain. If the results of this study support our hypothesis, they indicate the potential efficacy of C. sinensis in alleviating fibromyalgia symptoms and treating fibromyalgia, which would catalyze further research into fungi-based interventions.
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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) | 0.000 | 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) | 0.001 | 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".