In Vitro and In Vivo Pharmacological Characterization of Galanin and Galnon
Bibliographic record
Abstract
The neuropeptide galanin and its receptors (GAL‐R1, GAL‐R2, and GAL‐R3) are densely localized in areas of the brain thought to regulate stress, fear and anxiety. Several studies suggest a role for galanin in mediating anxiety‐like behaviors in rodent models. Here we report that both galanin (0.1 μg, icv) and galnon (0.3, 1.0 and 3.0 mg/kg, ip) show dose‐dependent anxiolytic‐like activity in the mouse elevated zero maze and four‐plate assays. Further, M35 (10 μg, icv), a non‐selective galanin receptor antagonist, was able to block galnon’s (0.3 mg/kg, ip) effects in the four‐plate model. The in vitro pharmacological basis for these behavioral effects was explored using radioligand binding and a functional calcium mobilization assay. While human galanin was able to displace 125 I‐galanin in GAL‐R1 and human Bowes melanoma cell membranes (Ki = 0.2 nM and 0.17 nM, respectively), galnon only showed affinity for galanin receptors expressed in human Bowes melanoma cells (5.5 μM). Consistent with this finding, galnon only showed functional agonism in Bowes melanoma cells (EC50 = 109 nM). This finding suggests that galnon acts as an agonist at galanin receptors in Bowes melanoma cells but not GALR1 receptors. Taken together, these studies provide further evidence for galaninergic input in anxiety‐related behaviors and suggest a functional role for galnon via GAL‐R2 or GAL‐R3 receptors.
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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 | 0.000 | 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 | 0.001 |
| 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".