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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".