Association between skin lymphangiogenesis parameters and arterial hypertension status in patients: An observational study.

BACKGROUND: Recent studies have indicated that the skin lymphatic system and interstitium may play a role in the pathophysiology of arterial hypertension (AH). OBJECTIVES: We aimed to determine whether the set of pathway parameters described previously in rodents would allow for the distinction between hypertensive and normotensive patients. MATERIAL AND METHODS: Molecular and histopathological parameters from the skin and blood of patients with AH (AH group, n = 53), resistant AH (RAH group, n = 32) and control (C group, n = 45) were used, and a statistical multivariate bootstrap methodology combining partial least squares-discriminant analysis (PLS-DA) and selectivity ratio (SR) were applied. RESULTS: The C vs RAH model presented the best prediction performance (AUC test = 0.90) and had a sensitivity and specificity of 73.68% and 83.33%, respectively. However, the parameters selected for the C vs AH group model were the most important for the pathway described in the rodent model, i.e., greater density of the skin lymphatic vessels (D2-40 expression) and greater number of macrophages (CD68 expression), higher expression of the messenger ribonucleic acid (mRNA) of nuclear factor of activated T cells 5 (NFAT5), vascular endothelial growth factor C (VEGFC) and podoplanin (PDPN) in the skin, greater concentration of hyaluronic acid (HA) in the skin, and lower serum concentration of VEGF-C. CONCLUSIONS: Our study suggests that the NFAT5/VEGF-C/lymphangiogenesis pathway, previously described in rodent studies, may also be present in human HA. Further experiments are needed to confirm our findings.

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra.

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

Sodium accumulation in the skin is associated with higher density of skin lymphatic vessels in patients with arterial hypertension.

PURPOSE: Recent studies, conducted mainly on the rodent model, have demonstrated that regulatory pathway in the skin provided by glycosaminoglycans, nuclear factor of activated T cells 5 (NFAT5), vascular endothelial growth factor C (VEGF-C) and process of lymphangiogenesis may play an important role in extrarenal regulation of sodium (Na+) balance, body water volume, and blood pressure. We aimed to investigate the concentrations and relations among the main factors of this pathway in human skin to confirm that this regulatory axis also exists in humans. PATIENTS AND METHODS: Skin specimens from patients diagnosed with arterial hypertension and from control group were histologically and molecularly examined. RESULTS: The primary hypertensive and control groups did not differ in Na+ â€‹concentrations in the skin. However, the patients with hypertension and higher skin Na+ concentration had significantly greater density of skin lymphatic vessels. Higher skin Na+concentration was associated with higher skin water content. In turn, skin water content correlated with factors associated with lymphangiogenesis, i.e. NFAT5, VEGF-C, and podoplanin (PDPN) mRNA expression in the skin. The strong mutual pairwise correlations of the expressions of NFAT5, VEGF-C, vascular endothelial growth factor D (VEGF-D) and PDPN mRNA were noted in the skin in all of the studied groups. CONCLUSIONS: Our study confirms that skin interstitium and the lymphatic system may be important players in the pathophysiology of arterial hypertension in humans. Based on the results of our study and existing literature in this field, we propose the hypothetical model which might explain the phenomenon of salt-sensitivity.

Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals.

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal's baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed.

Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation.

Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the 1HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.

Fusion of the 1H NMR data of serum, urine and exhaled breath condensate in order to discriminate chronic obstructive pulmonary disease and obstructive sleep apnea syndrome.

Chronic obstructive pulmonary disease, COPD, affects the condition of the entire human organism and causes multiple comorbidities. Pathological lung changes lead to quantitative changes in the composition of the metabolites in different body fluids. The obstructive sleep apnea syndrome, OSAS, occurs in conjunction with chronic obstructive pulmonary disease in about 10-20 % of individuals who have COPD. Both conditions share the same comorbidities and this makes differentiating them difficult. The aim of this study was to investigate whether it is possible to diagnose a patient with either COPD or the OSA syndrome using a set of selected metabolites and to determine whether the metabolites that are present in one type of biofluid (serum, exhaled breath condensate or urine) or whether a combination of metabolites that are present in two biofluids or whether a set of metabolites that are present in all three biofluids are necessary to correctly diagnose a patient. A quantitative analysis of the metabolites in all three biofluid samples was performed using 1H NMR spectroscopy. A multivariate bootstrap approach that combines partial least squares regression with the variable importance in projection score (VIP-score) and selectivity ratio (SR) was adopted in order to construct discriminant diagnostic models for the groups of individuals with COPD and OSAS. A comparison study of all of the discriminant models that were constructed and validated showed that the discriminant partial least squares model using only ten urine metabolites (selected with the SR approach) has a specificity of 100 % and a sensitivity of 86.67 %. This model (AUCtest = 0.95) presented the best prediction performance. The main conclusion of this study is that urine metabolites, among the others, present the highest probability for correctly identifying patents with COPD and the lowest probability for an incorrect identification of the OSA syndrome as developed COPD. Another important conclusion is that the changes in the metabolite levels of exhaled breath condensates do not appear to be specific enough to differentiate between patients with COPD and OSAS.

Prediction of the hydrophilic antioxidant capacity of tomato pastes from the IR and fluorescence excitation-emission spectra of extracts and intact samples.

The performance of the recently proposed excitation-emission fluorescence method was compared to the method using infrared measurements for the evaluation of the antioxidant properties of intact samples and extracts that had been obtained from tomato pastes. The oxygen radical absorbance capacity assay (ORAC) was applied in order to estimate the antioxidant capacity of the extracts, while the Folin-Ciocalteu reagent was adopted for the evaluation of the total phenolic content. The optimal extraction conditions for tomato pastes (three minutes of sonication under 80°C) were determined using the central composite design. Chemometric models such as the partial least squares regression and its N-way variant were further constructed in order to predict the antioxidant capacity or total phenolic content of the samples using either the IR or fluorescence spectra. The prediction errors that were obtained for the total antioxidant content were evaluated as the Trolox equivalents from the ORAC assay and were found to be equal to 2.011 (14.21%) for the fluorescence and 2.426 (17.15%) for the IR spectra, respectively. The prediction errors of the total phenolic content expressed as gallic acid equivalents were 0.067 (10.78%) for the fluorescence and 0.033 (5.36%) for the IR spectra, which were used as independent variables in the regression models.

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints.

In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel "laundering", is an illegal action that causes a substantial loss in a government's budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets.

The effect of conjugated linoleic acids (CLA) supplementation on the activity of enzymes participating in the formation of arachidonic acid in liver microsomes of rats--probable mechanism of CLA anticancer activity.

The aim of the present research was to examine the effect of conjugated linoleic acids (CLA) supplementation on the activity of enzymes that take part in the synthesis of arachidonic acid (AA) and also to investigate the relation between their activity and the neoplastic process. The enzyme activities were established indirectly, because their measure was the amount of AA formed in vitro, being developed from linoleic acid as determined in liver microsomes of Spraque-Dawley rats. In addition, the indices of Δ6-desaturase (D6D) and Δ5-desaturase (D5D) were determined. To this aim, the method of high per-formance liquid chromatography with UV/VIS detection was used. Between the examined groups, statistically significant differences were observed in the activities of enzymes as well as D6D. The carcinogenic agent applied (DMBA) was found to significantly increase the activity of the examined enzymes. Negative correlation was found between the activities of desaturases and CLA supplementation, whereas the activity of those enzymes was a little higher in the group of rats with chemically induced cancer process. The neoplastic process has a stimulating effect on the activity of D6D. The decrease of D6D activity, resulting from the presence of CLA in the animals' diet, may confirm the anticancer properties of these isomers.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

Wine authenticity verification as a forensic problem: an application of likelihood ratio test to label verification.

The aim of the study was to investigate the applicability of the likelihood ratio (LR) approach for verifying the authenticity of 178 samples of 3 Italian wine brands: Barolo, Barbera, and Grignolino described by 27 parameters describing their chemical compositions. Since the problem of products authenticity may be of forensic interest, the likelihood ratio approach, expressing the role of the forensic expert, was proposed for determining the true origin of wines. It allows us to analyse the evidence in the context of two hypotheses, that the object belongs to one or another wine brand. Various LR models were the subject of the research and their accuracy was evaluated by the Empirical cross entropy (ECE) approach. The rates of correct classifications for the proposed models were higher than 90% and their performance evaluated by ECE was satisfactory.

Optimization of extraction based on the thin-layer chromatographic fingerprints of common thyme.

The main goal of this study was to establish the optimum accelerated solvent extraction (ASE) conditions for the isolation of the total phenolics contained in common thyme (Thymus vulgaris L.) based on the results of TLC. Additionally, two different extraction methods, i.e., Soxhlet extraction and ASE, were compared in terms of their relative performance. Binary methanol-water mixtures in different volume proportions were used as the extraction solvents. Thirteen experiments utilizing different methanol concentrations and different extraction temperatures were designed using response surface methodology. Additionally, the temperature dependence of rosmarinic and caffeic acid extraction yields was analyzed by applying HPLC. Finally, the optimum working conditions established within the framework of the assumed central composite experimental design were determined to be 27% methanol at 130°C. It was found that the extraction carried out by ASE provided higher total phenolic yields than that done in the Soxhlet apparatus. The final aim of this study was a comparison of the six different Thymus species in terms of their total phenolic content and their TLC fingerprints. It was found that the phenolic content of four species, T. pulegioides, T. kosteleckyanus, T. citriodorus, and T. marschallianus, differs from the phenolic content of T. vulgaris and T. serpyllum.

A study of the antioxidant properties of beers using electron paramagnetic resonance.

The antioxidant properties of various kinds of beers were investigated using electron paramagnetic resonance (EPR) spectroscopy. This was possible by measuring the changes in the intensity of the EPR spectrum that resulted from the interaction of the stable radical DPPH (1,1-diphenyl-2-picrylhydrazyl) with the antioxidants found in a beer sample. The antioxidant capacity was then presented in Trolox Equivalents, e.g. µM trolox in a beer sample of 100ml. The influence of the type, colour, the content of the extract and alcohol on the antioxidant activities of commercial beer samples was investigated using two-way hierarchical clustering and analysis of variance. The results showed that all of the beers investigated exhibit antioxidant properties. By performing an analysis of variance, it was found that the value of the antioxidant capacity significantly (0.05 level of significance) depends on the content of the extract and the colour of the beer. It seems that additives also influence the antioxidant properties to some extent, but neither the alcohol content nor the kind of fermentation affects the antioxidant properties of beer.

A comparison of positive matrix factorization and the weighted multivariate curve resolution method. Application to environmental data.

In recent years, positive matrix factorization, PMF, has gained popularity in environmental sciences and it has been recommended by the U.S. Environmental Protection Agency as a general modeling tool in air quality control. Among the attractive features contributing to its popularity is that measurement uncertainty information can be incorporated into the PMF model, which allows the handling of missing measurements and data below the reporting limits. In addition, the solutions obtained from PMF obey constraints such as the non-negativity of the source compositions and source contributions of samples that make their interpretation physically meaningful. A less popular multivariate curve resolution method based on a weighted alternating least-squares algorithm, MCR-WALS, also incorporates the measurement error information and non-negativity constraints, which makes this method a potential tool when obtaining composition and contribution profiles of environmental data. Both methods use the same loss function, but they differ in the way the profiles are obtained. The goal of this study was to compare the performance of PMF with the performance of MCR-WALS for data sets simulated with different correlation and error structures. The results showed that the profiles extracted by both methods are virtually the same for data with different error structures.

Discrimination of biofilm samples using pattern recognition techniques.

Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination-DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms.

How to construct a multiple regression model for data with missing elements and outlying objects.

The aim of this study is to show the usefulness of robust multiple regression techniques implemented in the expectation maximization framework in order to model successfully data containing missing elements and outlying objects. In particular, results from a comparative study of partial least squares and partial robust M-regression models implemented in the expectation maximization algorithm are presented. The performances of the proposed approaches are illustrated on simulated data with and without outliers, containing different percentages of missing elements and on a real data set. The obtained results suggest that the proposed methodology can be used for constructing satisfactory regression models in terms of their trimmed root mean squared errors.