Sex-Specific Cannabidiol- and Iloperidone-Induced Neuronal Activity Changes in an In Vitro MAM Model System of Schizophrenia
Bibliographic record
Abstract
Cortical circuit dysfunction is thought to be an underlying mechanism of schizophrenia (SZ) pathophysiology with normalization of aberrant circuit activity proposed as a biomarker for antipsychotic efficacy. Cannabidiol (CBD) shows potential as an adjunctive antipsychotic therapy; however, potential sex effects in these drug interactions remain unknown. In the present study, we sought to elucidate sex effects of CBD coadministration with the atypical antipsychotic iloperidone (ILO) on the activity of primary cortical neuron cultures derived from the rat methylazoxymethanol acetate (MAM) model used for the study of SZ. Spontaneous network activity measurements were obtained using a multielectrode array at baseline and following administration of CBD or ILO alone, or combined. At baseline, MAM male neurons displayed increased bursting activity whereas MAM female neurons exhibited no difference in bursting activity compared to sex-matched controls. CBD administered alone showed a rapid but transient increase in neuronal activity in the MAM networks, an effect more pronounced in females. Furthermore, ILO had an additive effect on CBD-induced elevations in activity in the MAM male neurons. In the MAM female neurons, CBD or ILO administration resulted in time-dependent elevations in neuronal activity, but the short-term CBD-induced increases in activity were lost when CBD and ILO were combined. Our findings indicate that CBD induces rapid increases in cortical neuronal activity, with sex-specific drug interactions upon ILO coadministration. This suggests that sex should be a consideration when implementing adjunct therapy for treatment of SZ.
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) | 0.000 | 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".