Analysis of tricarboxylic acid cycle intermediates in dried blood spots by ultraperformance liquid chromatography-tandem mass spectrometry
Bibliographic record
Abstract
Background: We developed a novel method for measuring the concentrations of tricarboxylic acid (TCA) cycle intermediates in dried blood spots (DBS) using liquid chromatography-tandem mass spectrometry (LC-MS/ MS). Analytes were derivatized before analysis using 4-[2-(N,N-dimethylamino) ethylaminosulfonyl]-7-(2-aminoethylamino)-2,1,3-benzoxa-diazole (DAABD-AE), a reagent that imparts powerful chromatographic and mass spectrometric properties onto carboxyl group-containing analytes. Methodology: Extraction and derivatization of TCA cycle intermediates were achieved in a single step by incubating a 3.2 mm circle of the DBS samples with DAABD-AE for 1 hour at 65 C. From the resultant mixture, 1.0 µl was injected into the LC-MS/MS. Results: Total analytical run time to separate target analytes from other interfering components in the sample was 8 minutes. The peaks corresponding to malic, fumaric, citric, succinic, and 2-ketoglutaric acid appeared at 3.55, 3.62, 3.64, 3.67, and 3.68 minutes, respectively. The method was adequately reproducible with a coefficient of variation for intraday (n = 15) and inter-day (n = 13) studies of 5.2%-18.4%. Reference intervals in DBS from controls (n = 125) were as follow (µmol/l): citric (36.6-126.4), 2-ketoglutaric (9.1-42.1), succinic (1.2-2.4), fumaric (2.4-9.0), and malic acid (15.9-39.3). Compared to controls, the levels of citric, succinic, and malic acids were statistically different in patients (n = 7) with a p-value of< 0.05. No statistically significant difference was detected in concentrations of 2-ketoglutaric and fumaric acids. Conclusion: We describe a simple, quick, and sensitive method to measure TCA cycle intermediates in DBS samples. That TCA cycle plays a central role in cellular metabolism; this method should be useful in studying these metabolites in health and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".