Developing a Method to Determine Salivary THC Concentration
Bibliographic record
Abstract
Introduction: Cannabis impairs cognitive and psychomotor performance, which can negatively affect driving skills. The main psychoactive ingredient in cannabis is ∆9-tetrahydrocannabinol (THC). Due to the recent legalization of cannabis in Canada, there is an urgent need for a roadside test to identify THC impaired drivers. The legal limit while driving is calculated based on blood THC concentration, but saliva samples are the most convenient to collect roadside. Thus, the objectives of this study are to (1) determine the relationship between salivary and serum THC concentration and (2) develop a suitable roadside method to determine salivary THC concentration. Methods: THC doses between 0.2 mg/Kg to 100 mg/Kg will be orally administered to 36 mice (18 female, 18 male) in a repeated-measures design. Saliva and blood samples will be collected in 15 min intervals from 0 to 6 hours after administration. Gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS) will be used to determine THC concentration in the saliva and blood samples. The relationship between salivary and serum THC concentration will be modelled. In addition, a series of azo dyes will be applied to the saliva samples to determine salivary THC concentrations in a simple and rapid manner. The samples will be dissolved in NaOH, and various dyes will be added. An acid-base reaction will create a phenolate anion from the phenolic group of the ∆9-THC molecule, which will then attack the diazo group of the azo dye to produce a coloured end-product. Expected Results: We expect a positive linear relationship between the logged salivary and blood serum THC concentrations. We predict that each azo dye will produce a single colour within a specific and different range of THC concentration, so a distinct set of colours produced from many dyes can be associated with a narrow range of salivary THC concentration. Discussion: The colours produced from the azo dye reactions can be associated with salivary THC concentrations, which can then be correlated to serum THC concentrations. We use a mouse model in this study to have a more controlled investigation of the relationship between salivary and serum THC concentration, but future investigations should apply the results to humans. Conclusion: This study aims to determine salivary THC concentration in a suitable roadside method and correlate the results to serum THC concentration. The implications of this study are to be able to detect THC impaired drivers in a simple and rapid manner.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".