Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Contains fulltext : 238438.pdf (Publisher’s version ) (Open Access

Molecular mechanisms of cilium dysfunction

Contains fulltext : 92742.pdf (publisher's version ) (Open Access)Radboud Universiteit Nijmegen, 27 maart 2012Promotores : Cremers, F.P.M., Roepman, R.268 p

Iatrogenic anemia/Twenty-five million liters of blood into the sewer : comment

With great interest, we have read the articles by Stefanini [1] and Levi [2] on the waste of patient blood that is left over after laboratory testing. Especially in vulnerable patient populations that are subjected to frequent laboratory testing, iatrogenic anaemia can become a significant problem. A patient population that definitely meets these criteria is the population that is cared for at the Intensive Care Unit (ICU). Based on data from our laboratory information system and data from observation of the practice of blood collection on the ICU (see below), we could calculate that during the average ICU stay of 3.5 days, 140 ml of blood is lost for laboratory purposes

Protocolled Redefinition of the Therapeutic Range for Unfractionated Heparin: Lost in Translation?

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Practical Value of Anti-Xa Activity in the Evaluation of Extracorporeal Circuit Anticoagulation during Haemodialysis: Results of a Cross-Sectional Single-Centre Study

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Functional disruption of pyrimidine nucleoside transporter CNT1 results in a novel inborn error of metabolism with high excretion of uridine and cytidine

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Variable Selection in Untargeted Metabolomics and the Danger of Sparsity

The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables