Newborn Screening of Inborn Errors of Metabolism by Capillary Electrophoresis−Electrospray Ionization-Mass Spectrometry: A Second-Tier Method with Improved Specificity and Sensitivity
Bibliographic record
Abstract
The advent of electrospray-ionization mass spectrometry (ESI-MS) has given rise to expanded newborn screening programs for the early detection of inborn errors of metabolism (IEM). Despite the benefit of high-throughput screening for disease prognosis, conventional ESI-MS methods are limited by inadequate specificity, complicated sample handling, and low positive predictive outcome that can contribute to a high rate of false-positives. Herein, we report a robust strategy for neonatal screening based on capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS) that offers a convenient platform for the direct analysis of amino acids, acylcarnitines, and their stereoisomers from dried blood spot (DBS) extracts without chemical derivatization. On-line sample preconcentration with desalting by CE-ESI-MS allowed for improved concentration sensitivity when detecting poorly responsive metabolites in complex biological samples without ionization suppression or isomeric/isobaric interferences. Method validation demonstrated that accurate yet precise quantification can be achieved for 20 different amino acid and acylcarnitine biomarkers associated with IEMs when using a single non-deuterated internal standard. CE-ESI-MS represents a promising second-tier method in newborn screening programs that is compatible with ESI-MS/MS technology in cases when improved specificity and sensitivity is warranted for diagnosis confirmation and subsequent monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".