Long-Term Stability of Amino Acids and Acylcarnitines in Dried Blood Spots
Bibliographic record
Abstract
BACKGROUND: Dried blood filter cards, collected for newborn screening, are often stored for long periods of time. They may be suitable for the retrospective diagnosis of inborn errors of metabolism, but no data are currently available on the long-term stability of amino acids and acylcarnitine species. METHODS: We analyzed amino acids and acylcarnitines by tandem mass spectrometry in 660 anonymous, randomly selected filter cards from 1989 through 2004. We assessed long-term stability of metabolites by linear regression and estimated annual decrease of concentration for each metabolite. RESULTS: Concentrations of free carnitine increased by 7.6% per year during the first 5 years of storage and decreased by 1.4% per year thereafter. Alanine, arginine, leucine, methionine, and phenylalanine decreased by 6.5%, 3.3%, 3.1%, 7.3%, and 5.7% per year, respectively. Acetylcarnitine, propionylcarnitine, citrulline, glycine, and ornithine decreased by 18.5%, 27.4%, 8.1%, 14.7%, and 16.3% per year during the first 5 years, respectively; thereafter the decline was more gradual. Tyrosine decreased by 1.7% per year during the first 5 years and 7.9% per year thereafter. We could not analyze medium- and long-chain acylcarnitine species because of low physiological concentrations. CONCLUSIONS: Estimation of the annual decrease of metabolites may allow for the retrospective diagnosis of inborn errors of metabolism in filter cards that have been stored for long periods of time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".