Serum Endocannabinoid Content is Altered in Females with Depressive Disorders: A Preliminary Report
Bibliographic record
Abstract
BACKGROUND: Preclinical research has suggested that the endocannabinoid system may be involved in the etiology and/or treatment of depression; however, there are no published studies examining circulating endocannabinoid content in patients with clinical depression. METHODS: This study examined the endocannabinoids (anandamide; AEA) and 2-arachidonylglycerol (2-AG) in serum from ambulatory, medication-free female patients diagnosed with minor or major depression, and in controls matched for demographic characteristics. RESULTS: Serum 2-AG content was significantly decreased in patients diagnosed with major depression, and this decrease was correlated significantly and negatively with duration of the depressive episode, such that 2-AG content was progressively lower the longer the depressive episode. While AEA was not associated with major depression PER SE, a strong negative correlation was found between serum AEA content and Hamilton ratings for cognitive and somatic anxiety, suggesting that AEA content may relate to the anxiety dimension of affective disorders. In subjects with minor depression, serum AEA was significantly elevated, with 2-AG content demonstrating a similar, but statistically insignificant trend. DISCUSSION: These are the first clinical data to indicate that the endocannabinoid system may be disturbed in affective disease, and suggest that future research is required to determine the relevance of these changes with respect to disease manifestation and pharmacotherapy.
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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 | 0.000 |
| 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".