ArticleMolecular and cellular biochemistry2019
Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.
Article in Molecular and cellular biochemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review.Journal of medical Internet research · 2026Article
- Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis.BioData mining · 2026Article
- Advances in mucopolysaccharidosis research: the impact of mass spectrometry-based approaches.Clinical proteomics · 2025Review
- Application of tandem mass spectrometry in the screening and diagnosis of mucopolysaccharidoses.Orphanet journal of rare diseases · 2024Review
- Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.Turkish journal of medical sciences · 2024Article
- Epidemiology of Mucopolysaccharidoses Update.Diagnostics (Basel, Switzerland) · 2021Review
- Whole genome sequencing for mutation discovery in a single case of lysosomal storage disease (MPS type 1) in the dog.Scientific reports · 2020Article
- Diagnosis support systems for rare diseases: a scoping review.Orphanet journal of rare diseases · 2020Article
- Enzyme replacement therapy in mice lacking arylsulfatase B targets bone-remodeling cells, but not chondrocytes.Human molecular genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used for the qualitative and quantitative analysis of GAGs. Specific enzyme assays and targeted gene sequencing were performed to confirm the diagnosis. Machine learning tools were used to develop CART model based on GAG profile. Qualitative and quantitative CART models showed 96.3% and 98.3% accuracy, respectively, in the differential diagnosis of MPS. The thresholds of different GAGs diagnostic of specific MPS types were established. In 60 MPS positive cases, 46 different mutations were identified in six specific genes. Among 31 different mutations identified in IDUA, nine were nonsense mutations and two were gross deletions while the remaining were missense mutations. In IDS gene, four missense, two frameshift, and one deletion were identified. In NAGLU gene, c.1693C > T and c.1914_1914insT were the most common mutations. Two ARSB, one case each of SGSH and GALNS mutations were observed. LC-MS/MS-based GAG pattern showed higher accuracy in the differential diagnosis of MPS. The mutation spectrum of MPS, specifically in IDUA and IDS genes, is highly heterogeneous among the cases studied.
Indexed as
Identifiers
30903511What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.