Rapid analysis of metabolites and drugs of abuse from urine samples by desorption electrospray ionization-mass spectrometry
Bibliographic record
Abstract
Urine samples obtained from drug abusers were screened for drugs of abuse and their metabolites using DESI-MS and the results obtained were compared to results obtained from GC-MS experiments. The detected analyte classes included amphetamines, opiates, cannabinoids and benzodiazepines. The compounds detected were codeine, morphine, oxymorphone, 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol, Delta(9)-tetrahydrocannabinol, alprazolam, temazepam, oxazepam, N-desmethyldiazepam (nordiazepam) and hydroxytemazepam. Identities of all the analytes were confirmed by tandem mass spectrometry, matching MS/MS spectra with authentic standard compounds. The concentrations of the analytes in the samples were obtained from semi-quantitative GC-MS studies and were in the range of 270-22,000 ng mL(-1). The analytes could be detected by DESI even after a hundred-fold dilution indicating that the sensitivity of DESI was more than adequate for this study. Selectivity in the DESI-MS measurements for different kinds of analytes could be increased further by optimizing the spray solvent composition: the use of an entirely aqueous solvent enhanced the signal of polar analytes, such as the benzodiazepines, whereas the use of a spray solvent with a high organic content increased the signal of less polar analytes, such as codeine and morphine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".