LC–MS‐Based Simultaneous Determination of Biomarkers in Dried Urine Spots for the Detection of Cofactor‐Dependent Metabolic Disorders in Neonates
Bibliographic record
Abstract
ABSTRACT Deficiency of cofactors for various enzymes can lead to inborn errors of metabolism. These conditions frequently occur as seizures, which lead to permanent brain damage. Newborn screening for biomarkers associated with these disorders can help in early detection and treatment. Our objective was to establish a liquid chromatography mass spectrometry technique for quantifying biomarkers in dried urine spots to detect specific vitamin‐responsive inborn errors metabolism. Biomarkers were extracted from dried urine spots using a methanol:0.1% v/v formic acid solution (75:25) containing an internal standard mixture. Separation was achieved using a Luna PFP column (150 mm × 4.6 mm, 3 µm) under gradient elution conditions. The LC–MS technique was validated as per ICH M10 guidelines. Urine samples from healthy newborns in Udupi district, South India, were analyzed to establish reference values for these biomarkers. The method demonstrated excellent linearity ( R 2 > 0.99) with low limits of quantification: 0.1 µg/mL for leucine, isoleucine, valine, proline, hydroxyproline, methylmalonic acid, and 3‐hydroxyisovaleric acid; 0.01 µg/mL for pipecolic acid and α‐aminoadipic semialdehyde; and 0.03 µg/mL for piperideine‐6‐carboxylate. Interconvertibility between urine and dried urine spot assays was observed from the results of the regression and Bland–Altman analyses. Reference intervals for these biomarkers in the Udupi neonatal population were established using the validated dried urine spot method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".