Advancing Newborn Screening in Washington State: A Novel Multiplexed LC-MS/MS Proteomic Assay for Wilson Disease and Inborn Errors of Immunity
Bibliographic record
Abstract
For many genetic disorders, there are no specific metabolic biomarkers nor analytical methods suitable for newborn population screening, even where highly effective preemptive treatments are available. The direct measurement of signature peptides as a surrogate marker for the protein in dried blood spots (DBSs) has been shown to successfully identify patients with Wilson Disease (WD) and three life-threatening inborn errors of immunity, X-linked agammaglobulinemia (XLA), Wiskott-Aldrich syndrome (WAS), and adenosine deaminase deficiency (ADAD). A novel proteomic-based multiplex assay to detect these four conditions from DBS using high-throughput LC-MS/MS was developed and validated. The clinical validation results showed that the assay can accurately identify patients of targeted disorders from controls. Additionally, 30,024 newborn DBS samples from the Washington State Department of Health Newborn Screening Laboratory have been screened from 2022 to 2024. One true presumptive positive case of WD was found along with three false positive cases. Five false positives for WAS were detected, but all of them were premature and/or low-birth-weight babies and four of them had insufficient DNA for confirmation. The pilot study demonstrates the feasibility and effectiveness of utilizing this multiplexed proteomic assay for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".