Tandem mass spectrometric determination of purine metabolites and adenosine deaminase activity for newborn screening of ADA–SCID
Bibliographic record
Abstract
Background: Screening newborns for severe combined immunodeficiency (SCID) aims for early identification and treatment of the affected newborns. Adenosine deaminase (ADA) deficiency, a defect in the purine metabolic pathway, is a major cause of SCID and is characterized by the accumulation of adenosine (Ado) and deoxyadenosine (dAdo) in dried blood spots (DBSs). If left untreated, infants with this disorder are at risk of life-threatening infections. Analysis of T-cell receptor excision circles (TRECs) in DBS samples is the gold-standard screening method. However, TREC analysis is insufficient to determine SCID etiology, and a fraction of ADA–SCID may not be detected. Methods: We used the original DBS screening sample to measure Ado, dAdo, and ADA activity. Erythro-9-(2-hydroxy-3-nonyl) adenine was used as an ADA inhibitor to imitate ADA deficiency, making it possible to create quality control material with pathological enzyme activity and metabolite levels. Quantification was achieved by tandem mass spectrometric analysis with a run time of 2.5 min. Results: The 95th percentile reference intervals (n = 588) of Ado and dAdo were 0.9–3.0 and 0.1–0.4 µmol/L, respectively. The 95th percentile reference interval (n = 200) of ADA activity using13C10,15N5Ado and15N5dAdo as substrates were 0.8–1.6 and 0.4–0.7 pmol/DBS, respectively. In confirmed ADA patients (n = 4), Ado and dAdo were significantly elevated, whereas ADA activity was almost absent. Conclusion: These novel methods are applied, in our lab, to samples with low TRECs, with no false negative or false positives encountered to date. The potential of using these methods as a primary screening approach for ADA–SCID is in the process of validation. Statement of novelty: New mass spectrometric methods to simultaneously measure adenosine, deoxyadenosine, guanosine, and deoxguanosine, as well as ADA activity in neonatal DBS samples have been developed. This methodology highlights the metabolic nature of ADA–SCID and complements TREC analysis by providing additional biochemical information.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".