Ultratrace Analysis of Nine Macrolides, Including Tulathromycin A (Draxxin), in Edible Animal Tissues with Minicolumn Liquid Chromatography Tandem Mass Spectrometry
Bibliographic record
Abstract
The analysis of nine macrolides is presented, including tulathromycin A (Draxxin), in beef, poultry, and pork muscle with a simple multiresidue extraction and analysis method using high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry. The sample preparation method involves extraction with acetonitrile and defatting with hexane followed by dilution of the extracts for analysis. Separation of the nine macrolides was performed using an Atlantis dC 18, 3 mum, 3.9 mm x 20 mm minicolumn (guard column). Detection was carried out with two multiple reaction monitoring experiments per macrolide. The method detection limits (MDLs) were based on three times standard deviation of eight repeat spikes at 3.0 ng/g of a mix of the nine macrolides in the various tissues. The MDLs and retention times for the macrolides were as follows: lincomycin, 0.19 ng/g (t R = 5.00 min); tulathromycin, 0.46 ng/g (t R = 5.63 min); spiramycin, 0.21 ng/g (t R = 6.06 min); pirlimycin, 0.10 ng/g (t R = 6.04 min); clindamycin, 0.16 ng/g (t R = 6.20 min); tilmicosin, 0.29 ng/g (t R = 6.38 min); erythromycin, 0.19 ng/g (t R = 6.62 min); tylosin, 0.10 ng/g (t R = 6.72 min); and josamycin, 0.09 ng/g (t R = 6.98 min). Precision at 25 ng/g (n = 4) ranged from 2.3 to 9.4% for the compounds from beef muscle. Of interest is the detection of incurred residues of tulathromycin A in edible calf tissue at 0.10-7 mug/g, which is presented here for the first time.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".