Neural and behavioral correlates of edible cannabis-induced poisoning: characterizing a novel preclinical model
Bibliographic record
Abstract
ABSTRACT Accidental exposure to Δ 9 -tetrahydrocannabinol (THC)-containing edible cannabis, leading to cannabis poisoning, is common in children and pets; however, the neural mechanisms underlying these poisonings remain unknown. Therefore, we examined the effects of acute edible cannabis-induced poisoning on neural activity and behavior. Adult Sprague-Dawley rats (6 males, 7 females) were implanted with electrodes in the prefrontal cortex (PFC), dorsal hippocampus (dHipp), cingulate cortex (Cg), and nucleus accumbens (NAc). Cannabis poisoning was then induced by exposure to a mixture of Nutella (6 g/kg) and THC-containing cannabis oil (20 mg/kg). Subsequently, cannabis tetrad and neural oscillations were examined 2, 4, 8, and 24 h after THC exposure. In another cohort (16 males, 15 females), we examined the effects of cannabis poisoning on learning and prepulse inhibition, and the serum and brain THC and 11-hydroxy-THC concentrations. Cannabis poisoning resulted in sex differences in brain and serum THC and 11-hydroxy-THC levels over a 24-h period. It also caused gamma power suppression in the Cg, dHipp, and NAc in a sex- and time-dependent manner. Cannabis poisoning also resulted in hypolocomotion, hypothermia, and anti-nociception in a time-dependent manner and impairments in learning and prepulse inhibition. Our results suggest that the impairments in learning and information processing may be due to the decreased gamma power in the dHipp and PFC. Additionally, most of the changes in neural activity and behavior appear 2 hours after ingestion, suggesting that interventions at or before this time might be effective in reversing or reducing the effects of cannabis poisoning.
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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) | 0.000 | 0.000 |
| 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".