Regional alterations in the endocannabinoid system in an animal model of depression: effects of concurrent antidepressant treatment
Bibliographic record
Abstract
It has been suggested that disturbances in endocannabinoid signaling contribute to the development of depressive illness; however, at present there is insufficient evidence to allow for a full understanding of this role. To further this understanding, we performed an analysis of the endocannabinoid system in an animal model of depression. Male rats exposed to chronic, unpredictable stress (CUS) for 21 days exhibited a reduction in sexual motivation, consistent with the hypothesis that CUS in rats induces depression-like symptoms. We determined the effects of CUS, with or without concurrent treatment with the antidepressant imipramine (10 mg/kg), on CP55940 binding to the cannabinoid CB(1) receptor; whole tissue endocannabinoid content; and fatty acid amide hydrolase (FAAH) activity in the prefrontal cortex, hippocampus, hypothalamus, amygdala, midbrain and ventral striatum. Exposure to CUS resulted in a significant increase in CB(1) receptor binding site density in the prefrontal cortex and a decrease in CB(1) receptor binding site density in the hippocampus, hypothalamus and ventral striatum. Except in the hippocampus, these CUS-induced alterations in CB(1) receptor binding site density were attenuated by concurrent antidepressant treatment. CUS alone produced a significant reduction in N-arachidonylethanolamine (anandamide) content in every brain region examined, which was not reversed by antidepressant treatment. These data suggest that the endocannabinoid system in cortical and subcortical structures is differentially altered in an animal model of depression and that the effects of CUS on CB(1) receptor binding site density are attenuated by antidepressant treatment while those on endocannabinoid content are not.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".