Differential barcoding of opioid receptors trafficking
Bibliographic record
Abstract
Over the past several years, studies have highlighted the δ-opioid receptor (DOPr) as a promising therapeutic target for chronic pain management. While exhibiting milder undesired effects than most currently prescribed opioids, its specific agonists elicit effective analgesic responses in numerous animal models of chronic pain, including inflammatory, neuropathic, diabetic, and cancer-related pain. However, as compared with the extensively studied μ-opioid receptor, the molecular mechanisms governing its trafficking remain elusive. Recent advances have denoted several significant particularities in the regulation of DOPr intracellular routing, setting it apart from the other members of the opioid receptor family. Although they share high homology, each opioid receptor subtype displays specific amino acid patterns potentially involved in the regulation of its trafficking. These precise motifs or "barcodes" are selectively recognized by regulatory proteins and therefore dictate several aspects of the itinerary of a receptor, including its anterograde transport, internalization, recycling, and degradation. With a specific focus on the regulation of DOPr trafficking, this review will discuss previously reported, as well as potential novel trafficking barcodes within the opioid and nociceptin/orphanin FQ opioid peptide receptors, and their impact in determining distinct interactomes and physiological responses.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".