Statins alleviate experimental nerve injury-induced neuropathic pain
Bibliographic record
Abstract
The statins are a well-established class of drugs that lower plasma cholesterol levels by inhibiting HMG-CoA (3-hydroxy-3-methyl-glutaryl-coenzyme A) reductase. They are widely used for the treatment of hypercholesterolemia and for the prevention of coronary heart disease. Recent studies suggest that statins have anti-inflammatory effects beyond their lipid-lowering properties. We sought to investigate whether statins could affect neuropathic pain by mediating nerve injury-associated inflammatory responses. The effects of hydrophilic rosuvastatin and lipophilic simvastatin were examined in the mouse partial sciatic nerve ligation model. Systemic daily administration of either statin from days 0 to 14 completely prevented the development of mechanical allodynia and thermal hyperalgesia. When administered from days 8 to 14 after injury, both statins dose-dependently reduced established hypersensitivity. After treatment, the effects of the statins were washed out within 2 to 7 days, depending on dose. Effects of both statins in alleviating mechanical allodynia were further confirmed in a different injury-associated neuropathic pain model, mental nerve chronic constriction, in rats. Both statins were able to abolish interleukin-1β expression in sciatic nerve triggered by nerve ligation. Additionally, quantitative analysis with Iba-1 and glial fibrillary acid protein immunoreactivity demonstrated that rosuvastatin and simvastatin significantly reduced the spinal microglial and astrocyte activation produced by sciatic nerve injury. The increase of interleukin-1β mRNA in the ipsilateral side of spinal cords was also reduced by the treatment of either statin. We identified a potential new application of statins in the treatment of neuropathic pain. The pain-alleviating effects of statins are likely attributable to their immunomodulatory effects.
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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 | 0.005 | 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 | 0.000 |
| 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 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".