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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".