Nausea-Induced 5-HT Release in the Interoceptive Insular Cortex and Regulation by Monoacylglycerol Lipase (MAGL) Inhibition and Cannabidiol
Bibliographic record
Abstract
Abstract Using the rat conditioned gaping model of nausea, the interoceptive insular cortex (IIC) has been identified as a critical site for the regulation of lithium chloride (LiCl)-induced nausea. Indirect evidence supports a model where serotonin (5-HT) acts on postsynaptic 5-HT3receptors and its release is suppressed by elevating 2-arachidonylglycerol (2-AG) by monoacylglycerol lipase (MAGL) inhibition to suppress nausea. Here, we directly test the hypothesis that systemic LiCl elevates 5-HT in the IIC, and this is prevented by pretreatments that reduce 5-HT release. Using male Sprague Dawley rats, LiCl (but not saline), elevated 5-HT selectively in the IIC, for 20 min after LiCl administration (127.2 mg/kg, i.p.). Systemic pretreatment with the MAGL inhibitor, MJN110, prevented the LiCl-induced elevation of 5-HT in the IIC. Systemic cannabidiol (CBD), which reduces LiCl-induced nausea by acting at 5-HT1Asomatodendritic autoreceptors, also prevented LiCl-induced elevation of 5-HT in the IIC. Since 5-HT3receptor agonists delivered to the IIC produce nausea, we tested and confirmed the hypothesis that the intra-IIC administration of 5-HT3receptor antagonist, ondansetron, but not MJN110, would prevent LiCl-induced conditioned gaping reactions produced by intra-IIC administration of the 5-HT3receptor agonist,m-chlorophenylbiguanide (mCPBG). Finally, we demonstrate that exposure to a LiCl-paired flavor (but not a saline-paired flavor) produces elevated 5-HT release in the IIC, while rats display conditioned gaping reactions. These results confirm that LiCl-induced nausea is triggered by elevated 5-HT release in the IIC and is attenuated by treatments that reduce 5-HT availability in this region.
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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) | 0.000 | 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) | 0.001 | 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".