A novel method for quantitation of acylglycines in human dried blood spots by UPLC-tandem mass spectrometry
Bibliographic record
Abstract
BACKGROUND: Several acylcarnitines used as primary markers on dried blood filter papers (DBS) for newborn screening lack specificity and contribute to a higher false positive rate. The analysis of urine acylglycines is useful in the diagnosis of inborn errors of metabolism (IEM) including medium chain acyl-CoA dehydrogenase deficiency (MCADD), isovaleric acidemia, and beta-ketothiolase deficiency (BKTD). Currently, no method for analyzing acylglycines from DBS has been published. METHODS: Acylglycines were extracted from two 3.2 mm DBS punches and butylated using Butanol-HCl. Ultra Performance Liquid Chromatography (UPLC-MS/MS) with run time of 10 min permits resolution and quantitation of 15 acylglycines; including several isobaric. Method development was completed. Reference intervals (n = 573) were established for four birth weight groups. Furthermore, samples from patients with a confirmed IEM (n = 11), and false positive screens (n = 78) were analyzed to validate the interpretation obtained from the newly established reference intervals. RESULTS: Calibration curves were linear from 0.005 to 25.0 μM. Ion suppression was evaluated as minimal (2 to 10%). Samples from known patients were used to validate the reference intervals. For C5OH-related disorders, tiglylglycine (TG), TG/acetylglycine (AG) ratio, 3methylcrotonylglycine (3MCG) and 3MCG/AG ratio increased specificity. Propionylglycine (PG) and PG/acetylglycine ratio were two discriminatory markers in the investigation of C3-related disorders. Hexanoylglycine (HG), octanoylglycine (OG), suberylglycine (SG), and the ratios HG/AG, OC/AG and SG/AG were excellent markers of MCADD deficiency. CONCLUSION: This method shows potential application as a second tier screen in order to reduce the false positive rate for a number of IEM targeted by newborn screening.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".