Spine-Adjusting Instrument (Impulse®) Attenuates Nociception and Modulates Oxidative Stress Markers in the Spinal Cord and Sciatic Nerve of a Rat Model of Neuropathic Pain
Bibliographic record
Abstract
OBJECTIVE: Oxidative stress plays an important role in neuropathic pain (NP). Spinal manipulative therapy (SMT) can exert beneficial effects on pain outcomes in humans and in animal models. SMT can also modulate oxidative stress markers in both humans and animals. We aimed to determine the effect of Impulse®-assisted SMT (ISMT) on nociception and oxidative stress biomarkers in the spinal cords and sciatic nerves of rats with NP. METHODS: NP was induced by chronic constriction injury (CCI) of the sciatic nerve. Animals were randomly assigned to naive, sham (rats with sciatic nerve exposure but without ligatures), or CCI, with and without ISMT. ISMT was applied onto the skin area corresponding to the spinous process of L4-L5, three times per week for 2 weeks. Mechanical threshold, latency to paw withdrawal in response to thermal stimulus, and oxidative stress biomarkers in the spinal cord and sciatic nerve were the main outcomes evaluated. RESULTS: ISMT significantly increased mechanical threshold and withdrawal latency after CCI. In the spinal cord, ISMT prevented the increase of pro-oxidative superoxide anion generation and hydrogen peroxide levels. Lipid hydroperoxide levels both in the spinal cord and in the sciatic nerve were attenuated by ISMT. Total antioxidant capacity increased in the spinal cords and sciatic nerves of CCI rats with and without ISMT. CCI and ISMT did not significantly change the total thiol content of the spinal cord. CONCLUSIONS: Our findings suggest that reduced oxidative stress in the spinal cord and/or nerve may be an important mechanism underlying a therapeutic effect of SMT to manage NP nonpharmacologically.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".