Publications by authors named "Ramon Bonte"

Uveal melanomas (UM) are detected earlier. Consequently, tumors are smaller, allowing for novel eye-preserving treatments. This reduces tumor tissue available for genomic profiling.

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Article Synopsis
  • Untargeted metabolomics (UM) is being used to screen for inborn errors of metabolism (IEM), and this study analyzed various outlier detection methods for identifying patient profiles.
  • Different outlier detection methods showed varying levels of effectiveness in detecting IEM, with some methods performing consistently well across datasets, while others excelled in more balanced sample conditions.
  • The study highlights the importance of using PCA transformations to enhance method performance and notes that while some methods succeeded in detecting 90% of IEM patients without false positives, further refinements are needed for reliable clinical application.
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The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing genetic variant(s) in patients suspected of having an inborn error of metabolism (IEM). The measured metabolite levels could provide additional phenotypical evidence to elucidate the degree of pathogenicity for variants found in genes associated with metabolic processes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is deficient or not.

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Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.

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Routine diagnostic screening of inborn errors of metabolism (IEM) is currently performed by different targeted analyses of known biomarkers. This approach is time-consuming, targets a limited number of biomarkers and will not identify new biomarkers. Untargeted metabolomics generates a global metabolic phenotype and has the potential to overcome these issues.

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Background: ECHS1 encodes a mitochondrial enzyme involved in the degradation of essential amino acids and fatty acids. Recently, ECHS1 mutations were shown to cause a new severe metabolic disorder presenting as Leigh or Leigh-like syndromes. The objective of this study was to describe a family with 2 siblings affected by different dystonic disorders as a resulting phenotype of ECHS1 mutations.

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We present the first two reported unrelated patients with an isolated sedoheptulokinase (SHPK) deficiency. The first patient presented with neonatal cholestasis, hypoglycemia, and anemia, while the second patient presented with congenital arthrogryposis multiplex, multiple contractures, and dysmorphisms. Both patients had elevated excretion of erythritol and sedoheptulose, and each had a homozygous nonsense mutation in SHPK.

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