Distribution of delta and mu opioid receptor mRNA in rodent dorsal root ganglia neurons
Bibliographic record
Abstract
Primary afferents are responsible for transmitting signals produced by noxious stimuli from the periphery to the spinal cord. Mu and delta opioid receptors (MOP and DOP) have analgesic properties and are highly expressed in dorsal root ganglia (DRG) neurons. In humans, spinal DOP is almost exclusively located on central terminals of DRG neurons, whereas in rodents, it is expressed both on presynaptic terminals and spinal neurons. In this study, we aimed to assess the distribution of MOP and DOP in the DRGs of mice and rats. Using in situ hybridization and immunofluorescence, we visualized MOP and DOP mRNA together with various neuronal markers. In rats and mice, we show that both receptors are expressed, albeit to different extents, in all types of neurons, namely, large and medium myelinated neurons (NF200-positive), small nonpeptidergic (IB4- or P2X3R-positive) and peptidergic C fibres (Tac1-positive). Overall, DOP mRNA was found to be mainly expressed in large and medium myelinated neurons, whereas MOP mRNA was mainly found in C fibres. The distribution of MOP and DOP, however, slightly differs between rats and mice, with a higher proportion of small nonpeptidergic C fibres expressing DOP mRNA in mice than in rats. We further found that neither morphine nor inflammation affected the distribution of the receptor mRNA. Because of their location, our results confirm that MOP and DOP have the potential to alleviate similar types of pain and that this effect could slightly differ between species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".