Simultaneous screening and quantitation of stimulant drugs in dried blood spot (DBS) samples using ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS)
Bibliographic record
Abstract
Dried blood spots (DBS) have seen recent development as an alternative to whole blood samples for drug testing. The ease of use of DBS makes the technique suitable and attractive for drug testing applications. Quantitation of drug concentrations in the ng/mL range can be achieved from DBS samples using 10 to 20 µL of blood. The establishment of drug per se limits for drivers requires timely methods of sampling blood for accurate drug concentration measurements. Method validation for the analysis of forensically relevant stimulant drugs and their metabolites in DBS samples by ultra-performance liquid chromatography-quadrupole time-of-flight high-resolution mass spectrometry (UPLC-QTOF-MS) is presented. DBS blood samples were prepared by spiking 20 µL of blood-containing analytes ranging from 10 to 1000 ng/mL onto Whatman® 903 Protein Saver cards. Analytes were extracted from whole DBS samples punched from protein saver cards by sonication in water followed by PRiME® MCX μElution solid phase extraction. An LOQ of 10 ng/mL was achieved for all analytes. Precision (0.09% to 14.67%), bias (–17.15% to 13.91%) and matrix effects (–18.7% to 23.4%) were suitable for validation in 10/14 analytes. DBS drug stability varied by analyte. DBS sampling may assist in overcoming forensic challenges associated with blood sampling, and accurate drug quantitation even at low concentrations is possible. The results of this work suggest that DBS microsampling is suitable for drug-impaired driving cases in jurisdictions where per se limits for drugs exist, particularly for zero-tolerance drug limits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".