Clinical Characteristics and Metabolomics Investigation for Patients with Dengue Virus 2 Infection

OBJECTIVES Dengue is an endemic viral disease affecting tropical and subtropical regions around the world. The aim of this study was to retrospectively study the clinical characteristics of dengue fever (DF) and evaluate the metabolomic changes. METHODS The medical records of 50 patients presented with dengue virus 2 (DENV-2) RNA positive were reviewed. Serum was collected from 20 patients diagnosed with DF and 20 healthy volunteers. The changes in serum metabolites were explored by UPLC-Q-TOF MS spectrometer. RESULTS Fever, nausea and vomiting, asthenia, skin hyperemia were the most common clinical symptoms. The results of the laboratory examinations showed that leukopenia (76%, 38/50), thrombocytopenia (92%, 46/50), increase of aspartate aminotransferase (96%, 48/50), increase of free fatty acids (FFA) (68%, 34/50), increase of creatine kinase (CK) (44%, 22/50) were more common. Based on orthogonal projection to latent structures-discriminant analysis (OPLS-DA), 2 up-regulated and 15 down-regulated metabolites were identified, contributing to DF progress to some extent. Among them, LysoPC (18:2(9Z,12Z)) were highly positive correlated with PE (21:0/20:5(5Z,8Z,11Z,14Z,17Z)) and 13'-Hydroxy-alpha-tocopherol. The identified biomarkers were mainly involved in glycerophospholipids metabolism pathway. CONCLUSIONS The metabolic abnormalities of glycerophospholipids involved in the occurrence of DF caused by DENV-2.

Variables Selection in the Ultraviolet, Visible and Near Infrared Range for Calibration of a Mixture of Vegetable Oils by Absorbance Spectra

The aim of the work was a multivariate calibration of the concentration of unrefined sunflower oil, considered as adulteration, in a mixture with flaxseed oil. The relevance of the study is due to the need to develop a simple and effective method for detecting the falsification of flaxseed oil which is superior in the content of essential polyunsaturated fatty acids to olive oil. A few works only are devoted to identifying adulteration of flaxseed oil, unlike olive oil.Multivariate calibration carried out using a model based on the principal component analysis, cluster analysis and projection to latent structures of absorbance spectra in UV, visible and near IR ranges. Calibration uses three methods for spectral variables selection: the successive projections algorithm, the method of searching combination moving window, and method for ranking variables by correlation coefficient.The application of the successive projections algorithm, ranking variables by correlation coefficient and searching combination moving window makes it possible to reduce the value of the root mean square error of prediction from 0.63 % for wideband projection to latent structures to 0.46 %, 0.50 %, and 0.03 %, respectively.The developed method of multivariate calibration by projection to latent structures of absorbance spectra in UV, visible and near IR ranges using the spectral variables selection by searching combination moving window is a simple and effective method of detecting adulteration of flaxseed oil.

Калибровка температуры по нормированным спектрам АП-конверсионной флуоресценции германатных стекол и стеклокерамик, активированных ионами эрбия и иттербия

To select erbium and ytterbium doped germanate glasses and glass ceramics, which are most suitable as sensitive elements of fluorescent temperature sensors, a multivariate model of temperature calibration has been developed based on principal component analysis, cluster analysis and interval projection to latent structures of up-conversion green fluorescence spectra. The calibration model used 95 spectral variables for the GeO2-Na2O-Yb2O3-MgO-La2O3-Er2O3 glass-ceramic is characterized by the best quality parameters: the root-mean-square error is 0.37 K, the residual prediction deviation for the test subset is greater than 102, and the relative error does not exceed 0.20%.

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

GC-MS Based Metabolite Profiling to Monitor Ripening-Specific Metabolites in Pineapple (Ananas comosus)

Pineapple is one of the most cultivated tropical, non-climacteric fruits in the world due to its high market value and production volume. Since non-climacteric fruits do not ripen after harvest, the ripening stage at the time of harvest is an important factor that determines sensory quality and shelf life. The objective of this research was to investigate metabolite changes in the pineapple ripening process by metabolite profiling approach. Pineapple (Queen variety) samples from Indonesia were subjected to GC-MS analysis. A total of 56, 47, and 54 metabolites were annotated from the crown, flesh, and peel parts, respectively. From the principal component analysis (PCA) plot, separation of samples based on ripening stages from C0–C2 (early ripening stages) and C3–C4 (late ripening stages) was observed for flesh and peel parts, whereas no clear separation was seen for the crown part. Furthermore, orthogonal projection to latent structures (OPLS) analysis suggested metabolites that were associated with the ripening stages in flesh and peel parts of pineapple. This study indicated potentially important metabolites that are correlated to the ripening of pineapple that would provide a basis for further study on pineapple ripening process.