Some of the effects of the selective sigma ligand (+)pentazocine are mediated via a naloxone-sensitive receptor
Bibliographic record
Abstract
Recently, in an attempt to isolate the nonopioid sigma receptor, Su and colleagues purified a protein from rat liver and brain which appeared to resemble the original sigma opioid receptor as proposed by Martin in 1976, and for which the nonopiate sigma-1 ligand (+)pentazocine presents a high affinity. Previous in vivo electrophysiological studies from our laboratory have demonstrated that several selective sigma-1 ligands potentiate the neuronal response to NMDA. The goal of the present series of experiments was to assess the effects of some selective sigma-1 ligands on the potentiation of the NMDA response and to determine if this potentiation was mediated by the naloxone-sensitive sigma receptor. Extracellular unitary recordings from pyramidal neurons of the CA3 region of the rat dorsal hippocampus were obtained. The sigma-1 ligands BD 737, L 687-384, and JO-1784 (igmesine), administered intravenously at low doses, potentiated the NMDA response but the opiate antagonist naloxone failed to reverse this potentiation. However, the potentiation of the NMDA response induced by the sigma-1 ligand (+)pentazocine was suppressed by naloxone but not by the mu antagonist cyprodime hydrobomide, the kappa antagonist DIPPA nor by the delta antagonist naltrindole. (+/-) Cyclazocine, which presents a high affinity for the above-mentioned sigma-opiate receptor acted as an antagonist by suppressing the potentiation of the NMDA response induced by both JO-1784 and (+)pentazocine. These results suggest that the effects induced by some sigma-1 ligands may, in fact, be sensitive to naloxone while others may not. The original classification of sigma receptors as opiates might have been partly accurate.
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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 | 0.000 | 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 | 0.000 |
| 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".