Novel HILIC-ESI-MS method for urinary profiling of MSUD and methylmalonic aciduria biomarkers
Bibliographic record
Abstract
Methyl malonic acid and branched-chain keto acids are important biomarkers for the diagnosis of cobalamin deficiencies and maple syrup urine disease. We report the development and validation of a HILIC-ESI-MS2 method for the quantification of these organic acids from neonatal urine. The samples were 100 times diluted and analyzed on a ZIC-HILIC column with 25-mM formic acid in water: 25-mM formic acid in acetonitrile (45:55) at a flow rate of 0.8 mL/min with a runtime of only 6 minutes. The method demonstrated a lower limit of detection of 10 ng/mL, Limit of Quantification (LOQ) of 50 ng/mL, linearity of r2 ≥ 0.990 and recoveries of 87-105% for all analytes. The intraday and interday precision CV's were <10% and 12%, respectively. Extensive stability studies demonstrated the analytes to be stable in stock and in matrix with a percent change within ±15%. The Bland-Altman analysis of the developed method with the gold standard GCMS method demonstrated a bias of 0.44, 0.11, 0.009 and -0.19 for methyl malonic acid, 3-methyl-2-oxovaleric acid, 2-hydroxy-3methylbutyric acid and 4-methyl-2-oxovaleric acid, respectively, proving the methods are comparable. The newly developed method involves no derivatization and has a simple sample preparation and a low runtime, enabling it to be easily automated with a high sample throughput in a cost-effective manner.
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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 | 0.000 | 0.001 |
| 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".