Simultaneous quantitation and identification of intact Nandrolone phase II oxo‐metabolites based on derivatization and inject LC–MS/(HRMS) methodology
Bibliographic record
Abstract
Abstract Α sensitive and selective derivatization and inject method for the quantification of intact nandrolone phase II oxo‐metabolites was developed and validated using liquid chromatography ‐ (tandem high resolution) mass spectrometry (LC–MS/(HRMS)). For the derivatization, Girard's reagent T (GRT) was used directly in natural urine samples and the analysis of the metabolites of interest was performed by direct injection into LC–MS/(HRMS) system operating in positive ionization mode. Derivatization enabled the efficient detection of nandrolone oxo‐metabolites, while at the same time producing intense product ions under collision‐induced dissociation (CID) conditions that are related to metabolites of the steroid backbone and not to the conjugated moieties. Glucuronide and sulfate metabolites of nandrolone were chromatographically resolved and quantified in the same run in the range of 1–100 ng mL −1 , while at the same time structure identification could be performed for each metabolite. Full validation of the method was performed according to the World Anti‐Doping Agency (WADA) International Standard for Laboratories (ISL). Nandrolone oxo‐metabolites were quantified in two sets of urine samples, the first set consisted of real urine samples previously detected as negative and the second set consisted of urine samples collected from two excretion studies after nandrolone decanoate administration. The results for 19‐norandrosterone glucuronide (19‐NAG) and 19‐noretiocholanolone glucuronide (19‐NEG) were compared with those obtained by traditional gas chromatography ‐ (tandem) mass spectrometry (GC–MS/[MS]) method.
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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 | 0.001 | 0.005 |
| 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".