Simultaneous detection of stable isotope‐labeled and unlabeled <scp>l</scp>‐tryptophan and of its main metabolites, <scp>l</scp>‐kynurenine, serotonin and quinolinic acid, by gas chromatography/negative ion chemical ionization mass spectrometry
Bibliographic record
Abstract
A method for the detection of unlabeled and (15)N2 -labeled L-tryptophan (L-Trp), L-kynurenine (L-Kyn), serotonin (5-HT) and quinolinic acid (QA) in human and rat plasma by GC/MS is described. Labeled and unlabeled versions of these four products were analyzed as their acyl substitution derivatives using pentafluoropropionic anhydride and 2,2,3,3,3-pentafluoro-1-propanol. Products were then separated by GC and analyzed by selected ion monitoring using negative ion chemical ionization mass spectrometry. L-[(13)C11, (15)N2]-Trp, methyl-serotonin and 3,5-pyridinedicarboxylic acid were used as internal standards for this method. The coefficients of variation for inter-assay repeatability were found to be approximately 5.2% for L-Trp and (15)N2-Trp, 17.1% for L-Kyn, 16.9% for 5-HT and 5.8% for QA (n = 2). We used this method to determine isotope enrichments in plasma L-Trp over the course of a continuous, intravenous infusion of L-[(15) N2 ]Trp in pregnant rat in the fasting state. Plasma (15)N2-Trp enrichment reached a plateau at 120 min. The free Trp appearance rate (Ra) into plasma was 49.5 ± 3.35 µmol/kg/h. The GC/MS method was applied to determine the enrichment of (15)N-labeled L-Trp, L-Kyn, 5-HT and QA concurrently with the concentration of non-labeled L-Trp, L-Kyn, 5-HT and QA in plasma. This method may help improve our understanding on L-Trp metabolism in vivo in animals and humans and potentially reveal the relative contribution of the four pathways of L-Trp metabolism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".