Analysis of the Uptake, Metabolism, and Behavioral Effects of Cannabinoids on Zebrafish Larvae
Bibliographic record
Abstract
The Cannabis sativa plant contains numerous phytocannabinoids and terpenes with known or potential biological activity. For decades, plant breeders have been breeding the Cannabis plant to control for a desired ratio of the major cannabinoids. A high-throughput in vivo model to understand the relationship between the chemical composition of different strains and their therapeutic potential then becomes of value. Measuring changes in the behavioral patterns of zebrafish larvae is an established model with which to test the biological activity of neuroactive compounds. However, there is currently little information regarding the uptake kinetics and metabolism of compounds by larvae. In this study, we chose to compare the uptake kinetics and metabolism of Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) alone or in combination with their effects on larval behavior. We have shown that both compounds have distinct behavioral patterns and concentration response profiles. Additionally, the uptake kinetics observed for each compound appears to correlate with the change in behavior observed in the behavioral assays. When combinations of THC and CBD were tested there were shifts in both the behavioral activity and the uptake kinetics of each compound compared with when they were tested alone. Finally, the THC/CBD-derived metabolites detected in the larvae are similar to those found in mammalian systems. This study thus provides a model for further testing of additional cannabinoids and potentially plant extracts.
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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) | 0.000 | 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".