Evidence that pregabalin reduces neuropathic pain by inhibiting the spinal release of glutamate
Bibliographic record
Abstract
Pregabalin is an anti-convulsant that successfully treats many neuropathic pain syndromes, although the mechanism of its anti-hyperalgesic action remains elusive. This study aims to help delineate pregabalin's anti-hyperalgesic mechanisms. We assessed the effectiveness of pregabalin at decreasing mechanical and cold hypersensitivity induced in a rat model of neuropathic pain. Thus, we compared the effectiveness of pre- or post-treatment with systemic or intrathecal (i.t.) pregabalin at reducing the development and maintenance of the neuropathic pain symptoms. Pregabalin successfully decreased mechanical and cold hypersensitivity, as a pre-treatment, but was less effective at suppressing cold hypersensitivity when administered as a post-treatment. Furthermore, both i.t. and systemic administration of pregabalin were effective in reducing the behavioral hypersensitivity, with the exception of systemic post-treatment on cold hypersensitivity. We also examined pregabalin's effects at inhibiting hind paw formalin-induced nociception in naïve rats and formalin-induced release of excitatory amino acids in the spinal cord dorsal horn (SCDH) both in naïve rats and in rats with neuropathic pain. Pregabalin dose-dependently reduced nociceptive scores in the formalin test. We also present the first evidence that pregabalin reduces the formalin-induced release of glutamate in SCDH. Furthermore, i.t. pregabalin reduces the enhanced noxious stimulus-induced spinal release of glutamate seen in neuropathic rats. These data suggest that pregabalin reduces neuropathic pain symptoms by inhibiting the release of glutamate in the SCDH.
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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) | 0.000 | 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) | 0.005 | 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".