UPLC-MS/MS High-Risk Screening for Sphingolipidoses Using Dried Urine Spots
Bibliographic record
Abstract
Background: Early detection of sphingolipidoses is crucial to prevent irreversible complications and improve patient outcomes. The use of urine samples dried on filter paper (DUS) is a non-invasive strategy that simplifies the collection, storage, and shipping of samples compared to using liquid urine specimens. Objectives: (1) Develop and validate a multiplex ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) methodology using DUS to quantify twenty-one lysosphingolipids normalized to creatinine for eight different sphingolipidoses. (2) Establish normal reference values to evaluate the clinical utility of the methodology. Methods: Samples were eluted from a 5 cm filter paper disk (~1 mL of urine) and extracted on Oasis MCX solid-phase extraction cartridges prior to injection in the UPLC-MS/MS system. Results: Urinary lysosphingolipids were stable on DUS at −80 °C and −30 °C for 117 days, at 21.5 °C and 4 °C for at least 26 days, and at 35 °C for 3 days. Globotriaosylsphingosine, glucosylsphingosine, and their analogs were elevated in patients with Fabry disease and Gaucher disease, respectively, compared to controls (p-value < 0.0001). The analysis of related analog profiles suggests a better overall reliability in detecting patients early, especially for Fabry patients. Conclusions: This approach is feasible and might be useful for the early detection, monitoring, and follow-up of patients with sphingolipidoses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".