Dissociable changes in spike and wave discharges following exposure to injected cannabinoids and smoked cannabis in Genetic Absence Epilepsy Rats from Strasbourg
Bibliographic record
Abstract
Abstract There is significant interest in the use of cannabinoids for the treatment of many epilepsies including absence epilepsy (AE). Genetic Absence Epilepsy Rats from Strasbourg (GAERS) model many aspects of AE including the presence of spike‐and‐wave discharges (SWDs) on electroencephalogram (EEG) and behavioral comorbidities, such as elevated anxiety. However, the effects of cannabis plant‐based phytocannabinoids have not been tested in GAERS. Therefore, we investigated how SWDs in GAERS are altered by the two most common phytocannabinoids, Δ 9 ‐tetrahydrocannabinol (THC) and cannabidiol (CBD), and exposure to smoke from two different chemovars of cannabis. Animals were implanted with bipolar electrodes in the somatosensory cortex and EEGs were recorded for 2 hr. Injected THC (1–10 mg/kg, i.p.) dose‐dependently increased SWDs to over 200% of baseline. In contrast, CBD (30–100 mg/kg, i.p.) produced a ~50% reduction in SWDs. Exposure to smoke from a commercially available chemovar of high‐THC cannabis (Mohawk, Aphria Inc.) increased SWDs whereas a low‐THC/high‐CBD chemovar of cannabis (Treasure Island, Aphria Inc.) did not significantly affect SWDs in GAERS. Pre‐treatment with a CB1R antagonist (SR141716A) did not prevent the high‐THC cannabis smoke from increasing SWDs, suggesting that the THC‐mediated increase may not be CB1R‐dependent. Plasma concentrations of THC and CBD were similar to previously reported values following injection and smoke exposure. Compared to injected CBD, it appears Treasure Island did not increase plasma levels sufficiently to observe an anti‐epileptic effect. Together these experiments provide initial evidence that acute phytocannabinoid administration exerts the biphasic modulation of SWDs and may differentially impact patients with AE.
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 | 0.000 | 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".