Knockdown of spinal metabotropic glutamate receptor 1 (mGluR<sub>1</sub>) alleviates pain and restores opioid efficacy after nerve injury in rats
Bibliographic record
Abstract
1. Nerve injury often produces long-lasting spontaneous pain, hyperalgesia and allodynia that are refractory to treatment, being only partially relieved by clinical analgesics, and often insensitive to morphine. With the aim of assessing its therapeutic potential, we examined the effect of antisense oligonucleotide knockdown of spinal metabotropic glutamate receptor 1 (mGluR(1)) in neuropathic rats. 2. We chronically infused rats intrathecally with either vehicle, or 50 microg day(-1) antisense or missense oligonucleotides beginning either 3 days prior to or 5 days after nerve injury. Cold, heat and mechanical sensitivity was assessed prior to any treatment and again every few days after nerve injury. 3. Here we show that knockdown of mGluR(1) significantly reduces cold hyperalgesia, heat hyperalgesia and mechanical allodynia in the ipsilateral (injured) hindpaw of neuropathic rats. 4. Moreover, we show that morphine analgesia is reduced in neuropathic rats, but not in sham-operated rats, and that knockdown of mGluR(1) restores the analgesic efficacy of morphine. 5. We also show that neuropathic rats are more sensitive to the excitatory effects of intrathecally injected N-methyl-D-aspartate (NMDA), and have elevated protein kinase C (PKC) activity in the spinal cord dorsal horn, two effects that are reversed by knockdown of mGluR(1). 6. These results suggest that activity at mGluR(1) contributes to neuropathic pain through interactions with spinal NMDA receptors and PKC, and that knockdown of mGluR(1) may be a useful therapy for neuropathic pain in humans, both to alleviate pain directly, and as an adjunct to opioid analgesic treatment.
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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 | 0.000 |
| 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) | 0.002 | 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".