A High-Throughput Analytical Method for Determination of Aminoglycosides in Veal Tissues by Liquid Chromatography/Tandem Mass Spectrometry with Automated Cleanup
Bibliographic record
Abstract
A liquid chromatographic/tandem mass spectrometric (LC/MS/MS) method was developed for determining dihydrostreptomycin, gentamicin C1, and neomycin in veal kidney, liver, and muscle. The extraction prior to injection on the automated cleanup/analysis system is very simple, permitting preparation of 24 veal samples for analysis in half a day of work. The extracts are purified online on a reversed-phase column, with the help of an ion-pairing agent, and the analytes are separated on a Nucleosil C18 column prior to analyses by electrospray MS/MS. The cleanup is sufficient to minimize ion suppression/enhancement phenomena and permits quantification of the analytes extracted from veal tissues. Four secondary ions were measured for every analyte, which gives unambiguous identification of the compounds under analysis. Calibration curves were linear for all analytes between 50 and 5000 ppb, and recoveries in kidney were 76, 57, and 51%, respectively, for dihydrostreptomycin, gentamicin C1, and neomycin. Estimated limits of detection for kidney were, respectively, 0.1, 0.1, and 0.4 ppb. When compared to an LC method with fluorescence detection, the method gave equivalent results for kidneys incurred with neomycin. This rugged method has been applied to the analysis of more than 1000 veal samples over a 1-year period.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".