Orexin Receptor Multimerization versus Functional Interactions: Neuropharmacological Implications for Opioid and Cannabinoid Signalling and Pharmacogenetics
Bibliographic record
Abstract
Orexins/hypocretins are neuropeptides formed by proteolytic cleavage of a precursor peptide, which are produced by neurons found in the lateral hypothalamus. The G protein-coupled receptors (GPCRs) for these ligands, the OX1 and OX2 orexin receptors, are more widely expressed throughout the central nervous system. The orexin/hypocretin system has been implicated in many pathways, and its dysregulation is under investigation in a number of diseases. Disorders in which orexinergic mechanisms are being investigated include narcolepsy, idiopathic sleep disorders, cluster headache and migraine. Human narcolepsy has been associated with orexin deficiency; however, it has only rarely been attributed to mutations in the gene encoding the precursor peptide. While gene variations within the canine OX2 gene hcrtr2 have been directly linked with narcolepsy, the majority of human orexin receptor variants are weakly associated with diseases (the idiopathic sleep disorders, cluster headache and polydipsia-hyponatremia in schizophrenia) or are of potential pharmacogenetic significance. Evidence for functional and/or heterodimerization between wild-type variant orexin receptors and opioid and cannabinoid receptors is discussed in the context of its relevance to depression and epilepsy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".