The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

Celiac disease (CD) is an immune reaction as a consequence of ingestion of gluten. Diagnosis of CD is not easily using the clinical tests. Then, the discovery of appropriate methods for CD diagnosis is necessary. This study was concentrated to seek the metabolic biomarkers causes of CD compare to healthy subjects.In the present study, we classify CD and healthy subjects using classification and regression tree (CART). To find metabolites in serum which are helpful for the diagnosis of CD, the metabolic profiling was employed using the proton nuclear magnetic resonance spectroscopy ( 1 HNMR). Based on CART results, it was concluded that just using one descriptor, CD and control groups could be classified separately. The 89 % of data in the test set was predicted correctly by the obtained classification model. Our study indicates that quantitative metabolite analysis of serum can be employed to distinguish healthy from CD subjects.

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This paper intends to report and describe a variety of activities such as modeling, simulation, design, implementation, test and analysis in order to design and develop EMUBAT, a battery emulator for space applications. The first step of EMUBAT development is battery modeling which is performed by using a typical equivalent circuit. The required parameters for battery modeling are extracted by means of electrical characteristics curves which are presented in battery datasheets or obtained from battery testing. Then, the orbit parameter modeling is investigated for LEO and GEO satellites. Battery thermal conditions due to its in-orbit operations are modeled in the next step. Finally, the hardware configuration and the software structure of EMUBAT are described. Besides, a battery test setup is presented, which is established to facilitate the process of obtaining the required data and parameters, for EMUBAT modeling and simulation. This test setup also makes it possible to have an easier and more precise verification of the emulator outputs.