High‐throughput quantification of drugs of abuse in biofluids via 96‐solid‐phase microextraction–transmission mode and direct analysis in real time mass spectrometry
Bibliographic record
Abstract
Rationale The workload of clinical laboratories has been steadily increasing over the last few years. High‐throughput (HT) sample processing allows scientists to spend more time undertaking matters of critical thinking rather than laborious sample processing. Herein we introduce a HT 96‐solid‐phase microextraction (SPME) transmission mode (TM) system coupled to direct analysis in real time (DART) mass spectrometry (MS). Methods Model compounds (opioids) were extracted from urine and plasma samples using a 96‐SPME‐TM device. A standard voltage and pressure (SVP) DART source was used for all experiments. Examination of SPME‐TM performance was done using high‐resolution mass spectrometry (HRMS) in full scan mode (100–500 m/z ), whereas quantitation of opioids was performed using triple quadrupole MS in multiple reaction monitoring mode and by using a matrix‐matched internal standard correction method. Results Thirteen points (0.5 to 200 ng mL −1 ) were used to establish a calibration curve. Low limits of quantitation (LOQ) were obtained (0.5 to 25 ng mL −1 ) for matrices used. Acceptable accuracy (71.4–129.4%) and repeatability (1.1–24%) were obtained for validation levels tested (0.5, 30 and 90 ng mL −1 ). In less than 1.5 hours, 96 samples were extracted, desorbed and processed using the 96‐SPME‐TM system coupled to DART‐MS. Conclusions A rapid HT method for detection of opioids in urine and plasma samples was developed. This study demonstrated that ambient ionization mass spectrometry coupled to robust sample preparation methods such as SPME‐TM can rapidly and efficiently screen/quantify target analytes in a HT context.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".