Development of a Preclinical Inhalation Model to Test Vaporized Cannabis Distillates
Bibliographic record
Abstract
Despite their growing popularity, cannabis vape products remain understudied. Cannabis vape cartridges are used with battery-powered devices that aerosolize cannabis flower extracts containing high concentrations of cannabinoids such as THC. These types of products are commonly known as cannabis distillates. The potency of these products presents challenges in establishing effective dosing for preclinical studies. Currently, there are no established, standardized preclinical models for testing the safety and efficacy of these products in ways analogous to human use patterns. Thus, the in vivo cannabis distillate exposure regime required to achieve physiologically relevant doses in comparison to what is achieved in humans remains undetermined. To address this gap, a standardized preclinical murine model for inhalation of vaporized cannabis distillates has been developed using a computer-controlled delivery system. This protocol details procedures to administer cannabis vape distillates using a regimented puff topography to mice by a nose-only exposure tower. Methods to monitor mouse behavioral outcomes post-exposure and the utilization of a semi-quantitative ELISA to confirm THC delivery into the systemic circulation are also provided. This protocol will allow for the investigation of the pulmonary and systemic responses to cannabis vape distillate products by researchers interested in exploring the impact of cannabis vaping using real-world delivery protocols, thereby providing an opportunity for rigorous safety and therapeutic evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".