Analysis of Glycosaminoglycans in Cerebrospinal Fluid from Patients with Mucopolysaccharidoses by Isotope-Dilution Ultra-Performance Liquid Chromatography–Tandem Mass Spectrometry
Bibliographic record
Abstract
BACKGROUND: New therapies for the treatment of mucopolysaccharidoses that target the brain, including intrathecal enzyme replacement, are being explored. Quantitative analysis of the glycosaminoglycans (GAGs) that accumulate in these disorders is required to assess the disease burden and monitor the effect of therapy in affected patients. Because current methods lack the required limit of quantification and specificity to analyze GAGs in small volumes of cerebrospinal fluid (CSF), we developed a method based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). METHODS: Samples of CSF (25 μL) were evaporated to dryness and subjected to methanolysis. The GAGs were degraded to uronic acid-N-acetylhexosamine dimers and mixed with internal standards derived from deuteriomethanolysis of GAG standards. Specific dimers derived from heparan, dermatan and chondroitin sulfates (HS, DS and CS) were separated by UPLC and analyzed by electrospray ionization MS/MS using selected reaction monitoring for each targeted GAG product and its corresponding internal standard. RESULTS: CSF from control pediatric subjects (n = 22) contained <0.38 mg/L HS, 0.26 mg/L DS, and 2.8 mg/L CS, whereas CSF from patients with Hurler syndrome (n = 7) contained concentrations of DS and HS that were at least 6-fold greater than the upper control limits. These concentrations were reduced by 17.5% to 82.5% after allogeneic transplantation and treatment with intrathecal and intravenous enzyme replacement therapy. CONCLUSIONS: The method described here has potential value in monitoring patients with mucopolysaccharidoses receiving treatment targeted to the brain.
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 | 0.000 | 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) | 0.000 | 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".