Simultaneous determination of trimethylamine and trimethylamine <i>N</i>‐oxide in mouse plasma samples by hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry
Bibliographic record
Abstract
A method was developed that applies hydrophilic interaction liquid chromatography with tandem mass spectrometry in the multiple reaction monitoring mode to separate and accurately quantify trimethylamine and trimethylamine N-oxide in a single chromatographic run. This was achieved by converting trimethylamine to ethyl betaine, which is less volatile and hence results in greatly improved quantitation. Ethyl betaine also gives a similar response to trimethylamine N-oxide using positive-ion electrospray ionization mass spectrometry. It is readily separated from trimethylamine N-oxide by hydrophilic liquid chromatography in a 5 min run and with improved peak shape compared to underivatized trimethylamine. Validation of the method yielded a limit of detection (S/N ≥ 3) of 0.5 ng/mL for trimethylamine and 0.25 ng/mL for trimethylamine N-oxide. Method accuracies of 91.4-105.3% with precisions of 0.4-5.5% were obtained for standard mixtures over the range of 2.5-500 ng/mL. Recoveries measured for the extraction of trimethylamine and trimethylamine N-oxide spikes into mouse plasma were both >90%. The method, which simultaneously measures trimethylamine and trimethylamine N-oxide, was successfully applied to mouse plasma samples and could be adapted for use with other biological fluids.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".