Spinal and Opioids Inhibit Both Thermal and Mechanical Pain in Rats
Bibliographic record
Abstract
The expression and contribution of μ (MOPR) and δ opioid receptors (DOPR) in polymodal nociceptors have been recently challenged. Indeed, MOPR and DOPR were shown to be expressed in distinct subpopulation of nociceptors where they inhibit pain induced by noxious heat and mechanical stimuli, respectively. In the present study, we used electrophysiological measurements to assess the effect of spinal MOPR and DOPR activation on heat-induced and mechanically induced diffuse noxious inhibitory controls (DNICs). We recorded from wide dynamic range neurons in the spinal trigeminal nucleus of anesthetized rats. Trains of 105 electrical shocks were delivered to the excitatory cutaneous receptive field. DNICs were triggered either by immersion of the hindpaw in 49°C water or application of 300 g of mechanical pressure. To study the involvement of peptidergic primary afferents in the activation of DNIC by noxious heat and mechanical stimulations, substance P release was measured in the spinal cord by visualizing neurokinin type 1 receptor internalization. We found that the activation of spinal MOPR and DOPR similarly attenuates the DNIC and neurokinin type 1 receptor internalization induced either by heat or mechanical stimuli. Our results therefore reveal that the activation of spinal MOPR and DOPR relieves both heat-induced and mechanically induced pain with similar potency and suggest that these receptors are expressed on polymodal, substance P-expressing neurons.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".