Attenuation of hyperalgesia in a rat model of neuropathic pain after intrathecal pre- or post-treatment with a neurokinin-1 antagonist
Bibliographic record
Abstract
Although many studies have demonstrated a role for substance P in pain, there have been conflicting reports implicating the involvement of substance P in neuropathic pain models. In this study, the non-peptide neurokinin-1 (NK-1) receptor antagonist, L-732,138 was chronically administered by intrathecal (i.t.) injection to rats with mono-neuropathy produced by sciatic nerve constriction. Rats exhibited tactile allodynia and cold hyperalgesia over a 16-day testing period. L-732,138 (5-200 nmol) administered i.t. prior to and for 3 consecutive days post-surgery attenuated the mechanical allodynia and cold hyperalgesia on days 4 and 8 post-surgery. The effects of i.t. L-732,138 were also determined in rats with established nerve injury-induced neuropathy. The NK-1 receptor antagonist was injected for 4 consecutive days starting on day 8 post-sciatic nerve injury. Administration of L-732,138 (5-200 nmol) i.t. produced both anti-allodynic and anti-hyperalgesic effects on day 12, but the effect was not permanent, as nociceptive thresholds were similar to controls by day 16. These results demonstrate that substance P is involved both in the induction and the maintenance of neuropathic pain and provides justification for the development and administration of substance P antagonists for the management of clinical neuropathic pain.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".