Comparison between δ‐opioid receptor functional response and autoradiographic labeling in rat brain and spinal cord
Bibliographic record
Abstract
The distribution of delta-opioid receptors (DORs) in the rat central nervous system has been previously characterized by radioligand binding and immunohistochemistry. However, the functional neuroanatomy of DORs has not been mapped in any detail; this is potentially important, because these receptors appear to be primarily cytosolic. Opioid receptors can couple to G(i/o) G proteins, a process that is detected by agonist-stimulated [35S]guanylyl-5'-O-(gamma-thio)-triphosphate ([35S]GTPgammaS) binding. The purpose of this study was therefore to determine the distribution of functional DORs, as assessed by [35S]GTPgammaS autoradiographic labeling in response to the DOR agonist deltorphin II. For comparison, adjacent sections were labeled with [125I]deltorphin II or the DOR antagonist [125I]AR-M100613. In all three assays, mu-opioid receptors were blocked pharmacologically. The distributions of [125I]deltorphin II and [125I]AR-M100613 were highly correlated but not identical. Deltorphin II increased [35S]GTPgammaS binding in a concentration-dependent and naltrindole-sensitive manner. The regional [35S]GTPgammaS response to deltorphin II was only moderately predicted by agonist or antagonist radioligand binding (r = 0.67 and 0.50, respectively). [35S]GTPgammaS responses to deltorphin II were strongest in the extended striatum (caudate putamen, nucleus accumbens, olfactory tubercle) and cerebral cortex. In contrast, some areas reported to mediate DOR analgesia (brainstem, spinal cord) possessed a much lower [35S]GTPgammaS response. These findings demonstrate the existence of a partial mismatch between DOR radioligand binding and [35S]GTPgammaS response. This divergence possibly reflects regional heterogeneity in G-protein receptor coupling, or in the subcellular localization of DOR.
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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) | 0.000 | 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) | 0.003 | 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".