Co‐expression of μ and δ opioid receptors by mouse colonic nociceptors
Bibliographic record
Abstract
Background and Purpose To better understand opioid signalling in visceral nociceptors, we examined the expression and selective activation of μ and δ opioid receptors by dorsal root ganglia (DRG) neurons innervating the mouse colon. Experimental Approach DRG neurons projecting to the colon were identified by retrograde tracing. δ receptor‐GFP reporter mice, in situ hybridization, single‐cell RT‐PCR and μ receptor‐specific antibodies were used to characterize expression of μ and δ receptors. Voltage‐gated Ca2+ currents and neuronal excitability were recorded in small diameter nociceptive neurons (capacitance <30 pF) by patch clamp and ex vivo single‐unit afferent recordings were obtained from the colon. Key Results In situ hybridization of oprm1 expression in Fast Blue‐labelled DRG neurons was observed in 61% of neurons. μ and δ receptors were expressed by 36–46% of colon DRG neurons, and co‐expressed by ~25% of neurons. μ and δ receptor agonists inhibited Ca2+ currents in DRG, effects blocked by opioid antagonists. One or both agonists inhibited action potential firing by colonic afferent endings. Incubation of neurons with supernatants from inflamed colon segments inhibited Ca2+ currents and neuronal excitability. Antagonists of μ, but not δ receptors, inhibited the effects of these supernatant on Ca2+ currents, whereas both antagonists inhibited their actions on neuronal excitability. Conclusions and Implications A significant number of small diameter colonic nociceptors co‐express μ and δ receptors and are inhibited by agonists and endogenous opioids in inflamed tissues. Thus, opioids that act at μ or δ receptors, or their heterodimers may be effective in treating visceral pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".