Development of an electrospray ionization mass spectrometric method for the quantification of theophylline in horse serum
Bibliographic record
Abstract
A rapid and selective method has been developed for the determination of theophylline in horse serum by LC-ESI/MS/MS. The analytical method includes a protein precipitation extraction for sample preparation, liquid chromatography separation technique and ionspray tandem mass spectrometry. The drug was extracted from serum using a protein precipitation with acetonitrile and the supernatants were analyzed using an LC-ESI/MS/MS instrument. The chromatography was performed using a 50 x 2.1 mm C(8) analytical column and an isocratic mobile phase composes of 60:40 acetonitrile-0.5% formic acid in water with a flow rate fixed at 350 microL/min. A linear (weighted 1/concentration) relationship was used to perform the calibration over an analytical range of 0.1-20 ppm. The intra-batch precision and accuracy at LLOQ, medium and high concentration were 11.7, 6.9 and 5.4% and 95.8, 107.8 and 95.8%, respectively, and the inter-batch precision and accuracy at LLOQ, medium and high concentration were 10.4, 7.9 and 7.3% and 97.3, 105.2 and 95.9%, respectively. This LC-ESI/MS/MS method for the determination of theophylline in horse serum has been proved to within generally accepted criteria used for bioanalytical assay and was used successfully during clinical investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".