An ultra-high-pressure liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for the detection of cannabinoids in whole blood using solid phase extraction
Bibliographic record
Abstract
A novel high-throughput method using automated solid-phase extraction (SPE) coupled with ultra-high-pressure liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) was developed in order to quantify Δ9-tetrahydrocannabinol (THC) and its major metabolites and screen for several synthetic cannabinoids. Cannabinoids and synthetic cannabinoids were extracted from ovine whole blood using Phenomenex Strata-X Drug B SPE columns. Multiple reaction monitoring (MRM) mode was used to monitor at least two transitions of the analyte and its deuterated internal standard. Calibration was fitted quadratically (R2 > 0.995) over a range from 1 to 20 ng/mL for THC, 11-hydroxy-THC, cannabinol and cannabidiol, and 10 to 200 ng/mL for 11-nor-Δ9-carboxy-tetrahydrocannabinol and its glucuronide. Inter-assay accuracy and precision were evaluated over n = 3 analyses spanning six days. With the exception of cannabidiol, the accuracies ranged from 0.52% to 8.63% and the coefficient of variation (%CV) was found to range from 1.91% to 7.69%. Intra-assay accuracy and the precision of these analytes (n = 16) was found to range from 0.16% to 11.42% and from 1.10% to 8.40%, respectively. Cannabidiol intra- and inter-assay accuracy and precision were more variable than for other analytes. The procedure minimized specimen handling, extraction and reduced runtime as compared with an existing gas chromatography–mass spectrometry method and permitted the qualitative identification of several synthetic cannabinoid species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".