Rapid Analysis of Fentanyl and Fentanyl Analogues from Whole Blood Using SPME Coupled to the Microfluidic Open Interface
Bibliographic record
Abstract
Fentanyl and its analogues are potent opioids that pose a significant threat to society. Over the last several years, considerable focus has been on the concerning trend of increasing fentanyl usage among drug users. Fentanyl analogues are mainly synthesized to evade analytical detection or increase their potency; thus, very low concentrations are sufficient to achieve a therapeutic effect. In an effort to help combat the synthetic opioid epidemic, developing targeted mass spectrometric methods for quantifying fentanyl and its analogues at ultralow concentrations is incredibly important. Most methods used to analyze fentanyl and its analogues from whole blood require manual sample preparation protocols (solid-phase extraction or liquid–liquid extraction), followed by chromatographic separation and mass spectrometric detection. The main disadvantages of these methods are the tedious sample preparation workflows, resulting in lengthy analysis times. To mitigate these issues, we present a targeted method capable of analyzing 96 samples containing fentanyl, several fentanyl analogues, and a common fentanyl (analogue) precursor simultaneously in 2.4 min per sample. This is possible by using a high-throughput solid phase microextraction workflow on the Concept96 autosampler followed by manual coupling of solid-phase microextraction fibers to the microfluidic open interface for tandem mass spectrometry analysis. Our quantitative method is capable of extremely sensitive analysis, with limits of quantification ranging from 0.002 to 0.031 ng mL –1 and linearity ranging from 0.010 to 25.0 ng mL –1 . The method shows very good reproducibility (1–18%), accuracy (81–100%) of calibration and validation points, and good interday reproducibility (6–15%).
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".