Multiple Receptor Subtypes for the CGRP Super-Family
Bibliographic record
Abstract
Molecular evidence for the existence of multiple receptors for CGRP has been rather difficult to obtain. Over 10 years after suggesting the existence of at least two classes (CGRP1 and CGRP2) of CGRP receptors on the basis of pharmacological data[1], molecular data on the CGRP2 receptor subtype are still lacking as well as potent and selective antagonists. The situation is somewhat different for the functional CGRP1 subtype which is likely composed of diverse subunits CRLR, RAMP1 and possibly RCP[2]. Moreover, BIBN 4096BS was recently reported as the first nonpeptide highly potent CGRP1 receptor antagonist[3]. However, in situ hybridization and receptor autoradiographic data have clearly shown the existence of major mismatches (e.g., cerebellum) between the discrete localization of CRLR, RAMP1, and specific CGRP binding sites supporting the existence of CGRP receptor subtypes. Functional studies have also provided evidence in that regard (for a recent review: [4]). Accordingly, additional studies aiming at cloning additional CGRP receptors are certainly warranted. Similarly, recent evidence from various laboratories including ours suggests the existence of more than one class (CRLR and RAMP2) of adrenomedullin receptors at least in the rat brain. In contrast, most evidence suggests the existence of a single class of amylin receptors. In brief, it appears that multiple receptors or receptor complexes do exist for CGRP and related peptides but their composition is apparently unique among the GPCR super-family and additional data are needed to fully establish the molecular organization of each subtype. Supported by CIHR of Canada.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 0.002 |
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".