A sensitive and specific LC-MS/MS method for rapid diagnosis of Niemann-Pick C1 disease from human plasma
Bibliographic record
Abstract
Niemann-Pick type C1 (NPC1) disease is a rare, progressively fatal neurodegenerative disease for which there are no FDA-approved therapies. A major barrier to developing new therapies for this disorder has been the lack of a sensitive and noninvasive diagnostic test. Recently, we demonstrated that two cholesterol oxidation products, specifically cholestane-3β,5α,6β-triol (3β,5α,6β-triol) and 7-ketocholesterol (7-KC), were markedly increased in the plasma of human NPC1 subjects, suggesting a role for these oxysterols in diagnosis of NPC1 disease and evaluation of therapeutics in clinical trials. In the present study, we describe the development of a sensitive and specific LC-MS/MS method for quantifying 3β,5α,6β-triol and 7-KC human plasma after derivatization with N,N-dimethylglycine. We show that dimethylglycine derivatization successfully enhanced the ionization and fragmentation of 3β,5α,6β-triol and 7-KC for mass spectrometric detection of the oxysterol species in human plasma. The oxysterol dimethylglycinates were resolved with high sensitivity and selectivity, and enabled accurate quantification of 3β,5α,6β-triol and 7-KC concentrations in human plasma. The LC-MS/MS assay was able to discriminate with high sensitivity and specificity between control and NPC1 subjects, and offers for the first time a noninvasive, rapid, and highly sensitive method for diagnosis of NPC1 disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".