High-Throughput and Rapid Screening of Drugs of Abuse in Saliva by Multi-Segment Injection Using Solid-Phase Microextraction-Automated Microfluidic Open Interface-Mass Spectrometry
Bibliographic record
Abstract
There is great demand for analytical methods capable of providing high-throughput and rapid screening, especially for anti-doping and clinical point-of-care applications. In this work, automated microfluidic open interface-mass spectrometry (MOI-MS) was used for coupling with high-throughput, automated solid-phase microextraction (SPME) to achieve this objective. The design of the MOI-MS interface provides a continuous and stable electrospray fluid flow to the MS without introducing any bubble, a feature that we exploit to introduce the concept of multi-segment injection for the determination of multiple samples in a single MS run. By eliminating the need to start a new MS run between sample assays, the developed approach provides significantly simplified protocols controlled by programmed software and increased reproducibility. Furthermore, the biocompatible SPME device, which utilizes coating consisting of hydrophilic–lipophilic balanced particles embedded in a polyacrylonitrile (PAN) binder, can be directly used for biological sample analysis, as the PAN acts as both a binder and a matrix-compatible barrier, thus enabling the enrichment of small molecules while eliminating interferences associated with the presence of interfering macromolecules. The above design was employed to develop a fast, quantitative method capable of analyzing drugs of abuse in saliva samples in as little as 75 s per sample. The findings indicate that the developed method provides good analytical performance, with limits of detection ranging between 0.05 and 5 ng/mL for analysis of 16 drugs of abuse, good calibration linear correlation coefficients ( R 2 ≥ 0.9957), accuracy between 81 and 120%, and excellent precision (RSD% < 13%). Finally, a proof-of-concept experiment was performed to demonstrate the method’s suitability for real-time analysis in anti-doping applications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".