IntroductionThe aim of this study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers.MethodsWe compared the metabolic profile of patients with RA with that of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers.ResultsRA patients were diagnosed with a sensitivity of 93% and a specificity of 70% in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90% and a specificity of 94%. Glyceric acid, D-ribofuranose and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased compared with healthy controls.ConclusionsMetabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was without regard to the presence of antibodies against cyclic citrullinated peptides.
A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.
A linearized form of Zoeppritz equations combined with the convolution model is widely used in inversion of amplitude variation with offset (AVO) seismic data. This is shown to introduce a "modeling error," compared with using the full Zoeppritz equations, whose magnitude depends on the degree of subsurface heterogeneity. Then, we evaluate a methodology for quantifying this modeling error through a probability distribution. First, a sample of the unknown probability density describing the modeling error is generated. Then, we determine how this sample can be described by a correlated Gaussian probability distribution. Finally, we develop how such modeling errors affect the linearized AVO inversion results. If not accounted for (which is most often the case), the modeling errors can introduce significant artifacts in the inversion results, if the signal-to-noise ratio is less than 2, as is the case for most AVO data obtained today. However, if accounted for, such artifacts can be avoided. The methodology can easily be adapted and applied to most linear AVO inversion methods, by allowing the use of the inferred modeling error as a correlated Gaussian noise model.
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