A sensitive and specific precursor ion scanning approach in liquid chromatography/electrospray ionization tandem mass spectrometry to detect methylprednisolone acetate and its metabolites in rat urine
Bibliographic record
Abstract
A new, simple, sensitive and specific liquid chromatography/electrospray ionization tandem mass spectrometric (LC/ESI-MS/MS) method in precursor ion scanning (PIS) mode has been developed for the rapid detection of methylprednisolone acetate (MPA) and its metabolites in rat urine. A suitable product ion specific for methylprednisolone (MP) and MPA was selected after a fragmentation study on 20 (cortico)steroids at different collision energies (5-40 eV). Urine samples were simply treated with acetonitrile then dried in a SpeedVac system. The method was validated and compared with other PIS methods for detecting corticosteroids in human urine. It was more sensitive, with limit of detection (LOD) and lower limit of quantitation (LLOQ), respectively, of 5 and 10 ng mL(-1). The method was applied for the analysis of rat urine collected before and after (24, 48, 72 h) intra-articular (IA) injection of a marketed formulation of MPA (Depo-Medrol(R)). MS/MS acquisitions were taken at different collision energies for the precursor ions of interest, detected in PIS mode, to verify the MP-related structure. Six different metabolites were detected in rat urine, and their chemical structures were assigned with a computational study.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".