ArticleOrphanet journal of rare diseases2021
Article in Orphanet journal of rare diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Untargeted metabolomics reveals distinct metabolic profiles in MUT-type methylmalonic acidemia.Molecular and cellular pediatrics · 2026Article
- Nusinersen corrects L-arginine deficiency in the cerebrospinal fluid of patients with severe spinal muscular atrophy.Neurobiology of disease · 2025Article
- Identification of Biochemical Determinants for Diagnosis and Prediction of Severity in 5q Spinal Muscular Atrophy UsingInternational journal of molecular sciences · 2024Article
- Comparison of clean catch and bag urine using LC-MS/MS proteomics in infants.Pediatric nephrology (Berlin, Germany) · 2024Article
- Biomarkers in 5q-associated spinal muscular atrophy-a narrative review.Journal of neurology · 2023Review
- Molecular Biomarkers for the Diagnosis, Prognosis, and Pharmacodynamics of Spinal Muscular Atrophy.Journal of clinical medicine · 2023Review
- MiR34 contributes to spinal muscular atrophy and AAV9-mediated delivery of MiR34a ameliorates the motor deficits in SMA mice.Molecular therapy. Nucleic acids · 2023Article
- Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer.International journal of molecular sciences · 2022Review
- The Role of Sphingomyelin and Ceramide in Motor Neuron Diseases.Journal of personalized medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of
resultsUrine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool.
conclusionsThis study provides preliminary evidence for the use of
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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.