Therapeutic-like activity of cannabidiolic acid methyl ester (HU-580) in the MK-801 mouse model of schizophrenia: role for cannabinoid CB1 and serotonin-1A receptors
Bibliographic record
Abstract
Schizophrenia is an incurable psychotic illness. Those diagnosed have limited pharmacological treatment options, many of which do not provide long term relief and come with unpleasant side effects. The endocannabinoid and serotonergic systems are important neuromodulators in psychotic illness. We hypothesize that cannabidiolic acid methyl ester (HU-580) that exerts action on both these systems could have therapeutic potential by antagonizing cannabinoid receptor-1 (CB1R) and agonizing 5-hydroxytryptamine receptor-1A (5-HT1AR). We employed behavioural and brain protein analyses in male and female mice exposed to MK-801, which precipitated schizophrenia-related reactivity across a number of behavioural dimensions. C57BL/6 mice were subjected to a battery of behavioral tests, and we found that subchronic treatment of MK-801 (once daily for seven days) induced positive-like, negative-like, and cognitive-related behavioral deficits; primarily in females. Sub-chronic treatment of MK-801 (once daily for 17 days) induced positive- and negative-like behavioral deficits in females. Low-dose (0.01ug/kg) but not high-dose (0.05 ug/kg) treatment rescued female mice from schizophrenia-related behavioral deficits. Altogether, these data suggest that HU-580 may have dose-dependent antipsychotic-like potential that rely on mechanisms that recruit CB1R and 5-HT1AR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".