Multiclass, Multiresidue Drug Analysis, Including Aminoglycosides, in Animal Tissue Using Liquid Chromatography Coupled to Tandem Mass Spectrometry<sup>†</sup>
Bibliographic record
Abstract
A multiresidue, multiclass semiquantitative screening analysis of 39 drug residues covering 8 drug classes, including aminoglycosides in veal muscle, based on a single multiresidue extraction routine and using high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS), is presented. Sample preparation involves extraction of a 5 g diced tissue sample with 10 mL of acetonitrile/ water (86:14), incubated at 60 degrees C for 1 h, and then cooled for 10 min in ice. Formic acid is added to the suspension, then mixed, and centrifuged. The supernatant is retained, and the pellet is extracted with 10 mL of water for aminoglycosides and again centrifuged. Approximately 9.5 mL of each of the supernatants from both extracts is combined and diluted with water to 25 mL. The final solution is then defatted with 20 mL of hexane prior to analysis. Liquid chromatography for the aminoglycosides is carried out with ZIC-HILIC and for the remainder of the compounds with an Atlantis dC18 minicolumn. LC-ESI-MS/MS in positive and negative ionization modes (three injections total) is carried out, and two ion transitions per analyte are monitored. The method provides semiquantitative analysis to identify incurred positive drug classes in a rapid and cost-effective manner. Of particular interest is the detection of numerous compounds in the low nanograms per gram concentration range, which are not typically detected using receptor-based screening methods. All identified drugs were confirmed using internationally recognized regulatory methods, with no apparent false positives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".