Quantitative assay of plasma homocysteine thiolactone by gas chromatography/mass spectrometry
Bibliographic record
Abstract
Enzymatic cyclization of homocysteine forms a reactive thiolactone that may play an important role in its cardiovascular toxicity, but reliable quantitation of the free thiolactone metabolite in physiological fluids has not been reported. We have therefore used a highly selective gas chromatography/mass spectrometry (GC/MS) technique combined with the sensitivity of negative chemical ionization (NCI) to develop a quantitative method for the detection of homocysteine thiolactone (HcyTL) in plasma. To improve accuracy the deuterated isomer d(4)-HcyTL was synthesized and added to plasma as internal standard. The plasma was then treated with silica solid-phase extraction and derivatized with heptafluorobutyric anhydride. The derivative was analyzed by GC/MS in NCI mode with methane as the reagent gas and quantified by analyzing for the HcyTL ion [M(-)[bond]HF] and its d(4)-HcyTL counterpart in single-ion monitoring mode. The calibration curve showed a dynamic linear range up to 40 nmol/L. Within-day precision (n = 20, nominal concentration 5.2 nmol/L) was 0.96% and between-day precision was 3.9%, with a detection limit of 1.7 nmol/L and quantification limit of 5.2 nmol/L. Two human plasma samples had HcyTL concentrations of 18 and 25 nmol/L. This facile method for quantitation of homocysteine thiolactone opens the way for more detailed clinical studies of its potential role in homocysteine-induced arteriosclerosis and vaso-occlusive disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".