Rapid analysis of fentanyls and other novel psychoactive substances in substance use disorder patient urine using paper spray mass spectrometry
Bibliographic record
Abstract
RATIONALE: Drug overdose deaths due to fentanyls and other novel psychoactive substances (NPS) are on the rise. The higher potencies of fentanyl analogs compared with morphine require new technologies to identify and quantitate NPS. METHODS: Paper spray tandem mass spectrometry (MS/MS) and high-resolution mass spectrometry were used to identify and measure fentanyl analogs as well as common drugs of abuse in urine samples from substance use disorder clinics. Ten-microliter urine samples were deposited directly on paper spray cartridges previously loaded with internal standards, dried, and analyzed with no other sample treatment. Quantitative results were obtained using MS/MS. Individual drugs were identified using high-resolution accurate mass spectrometry, and confirmed by data-dependent MS/MS. RESULTS: of 0.99 or better for eight representative fentanyl analogs. Cartridges preloaded with internal standards demonstrated satisfactory quantitative results compared with LC/MS. Direct identification and confirmation of fentanyl analogs and other common drugs of abuse in urine using high-resolution accurate mass and MS/MS fragmentation were demonstrated at low picogram levels. CONCLUSIONS: Paper spray mass spectrometry can reliably identify and quantitate fentanyl analogs and other drugs of abuse in urine. Using paper spray cartridges as collection devices reduces exposure and transportation risks associated with biological fluids. Cartridges preloaded with labeled internal standards can be effective for targeted screening of fentanyl analogs and other drugs of abuse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".