Assessing the efficacy of Zebrafish seizure models for testing cannabinoids
Bibliographic record
Abstract
Approximately 1% of the world's population is purported to be affected by epilepsy. Of which 30% have multi-drug resistant epilepsy, which often leads to the requirement for strong anti-seizure medications or cocktails thereof. In general this leads to an ever increasing side effect profile that is often debilitating in and of itself. It has been purported that cannabinoids, in particular cannabidiol (CBD), can mitigate, to some degree, epileptic seizures. Unfortunately, the evidence in support of this is largely anecdotal in nature. In the current study we have made use of a previously developed zebrafish model of induced neuro-hyperactivity following exposure to pentylenetetrazole (PTZ) along with a transgenic zebrafish model of idiopathic generalized epilepsy to test the effect of CBD, tetrahydorcannabidnol (THC) and cannabinol (CBN). We have found that both CBD and CBN appear to be able to reduce the neurohyperactivity in the PTZ model along with the seizure like activity in the transgenic model. THC on the other hand appears to have little to no effect beyond simple sedation. It also appears that when applied together CBD and THC may act synergistically to increase the effect of CBD. This study would then support the use of cannabinoids for the treatment of epileptic seizures.
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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 | 0.001 | 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 | 0.001 | 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) | 0.003 | 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".