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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".