Investigation of morantel metabolism and its application in veterinary drug residue screening
Bibliographic record
Abstract
BACKGROUND: Food Safety authorities routinely test food of animal origin to verify that veterinary drug residues (VDRs) are within maximum residue limits. For morantel, a gas chromatography - mass spectrometry (GC-MS) confirmatory method is effective, but it involves an extensive sample preparation that is not suitable for multiple analytes. Furthermore, the resulting marker residue is not specific to morantel. Alternatively, using major morantel metabolites as specific VDRs in liquid chromatography - mass spectrometry (LC-MS) screening methods would increase productivity, especially when implemented in multi-residue screening methods. RESULTS: ) experiments were used to determine the structures of metabolites. Multiple phase I and phase II metabolites of morantel were identified, including hydroxylated and cysteine-conjugated metabolites. Five major metabolites, identified from in vitro metabolism studies using porcine or bovine liver S9 fractions, were thoroughly investigated. The presence of these metabolites was confirmed using pseudo-incurred bovine liver tissue. Metabolic reaction sites were proposed for these metabolites, including a hydroxylation site for one metabolite, that contradicts previous findings. SIGNIFICANCE: These five major metabolites reported are specific to morantel and can be readily implemented in a multi-residue screening method. This workflow is adaptable for other veterinary drugs and the major advantages of using this approach include time efficiency in sample preparation and the ability to incorporate the VDRs into multiple-residue screening methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".