Therapeutic‐like activity of cannabidiolic acid methyl ester in the MK‐801 mouse model of schizophrenia: Role for cannabinoid CB1 and serotonin‐1A receptors
Bibliographic record
Abstract
Schizophrenia is a psychotic disorder with an increasing prevalence and incidence over the last two decades. The condition presents with a diverse array of positive, negative, and cognitive impairments. Conventional treatments often yield unsatisfactory outcomes, especially with negative symptoms. We investigated the role of prefrontocortical (PFC) N-methyl-D-aspartate receptors (NMDARs) in the pathophysiology and development of schizophrenia. We explored the potential therapeutic effects of cannabidiolic acid (CBDA) methyl ester (HU-580), an analogue of CBDA known to act as an agonist of the serotonin-1A receptor (5-HT1AR) and an antagonist of cannabinoid type 1 receptor (CB1R). C57BL/6 mice were intraperitoneally administered the NMDAR antagonist, dizocilpine (MK-801, .3 mg/kg) once daily for 17 days. After 7 days, they were concurrently given HU-580 (.01 or .05 μg/kg) for 10 days. Behavioural deficits were assessed at two time points. We conducted enzyme-linked immunosorbent assays to measure the concentration of PFC 5-HT1AR and CB1R. We found that MK-801 effectively induced schizophrenia-related behaviours including hyperactivity, social withdrawal, increased forced swim immobility, and cognitive deficits. We discovered that low-dose HU-580 (.01 μg/kg), but not the high dose (.05 μg/kg), attenuated hyperactivity, forced swim immobility and cognitive deficits, particularly in female mice. Our results revealed that MK-801 downregulated both CB1R and 5-HT1AR, an effect that was blocked by both low- and high-dose HU-580. This study sheds light on the potential antipsychotic properties of HU-580, particularly in the context of NMDAR-induced dysfunction. Our findings could contribute significantly to our understanding of schizophrenia pathophysiology and offer a promising avenue for exploring the therapeutic potential of HU-580 and related compounds in alleviating symptoms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".