Multivariate Statistical Analysis as a Supplementary Tool for Interpretation of Variations in Salivary Cortisol Level in Women with Major Depressive Disorder

Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer’s and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients’ age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods

Interactions Between Paracetamol and Hypromellose in the Solid State

Hydroxypropyl methylcellulose (hypromellose) is a widely known excipient commonly used in the preparation of drug formulations. It can interact with some active pharmaceutical ingredients (APIs), thereby contributing to a reduction in crystallinity, serve as a solvent for API or form stable dispersion with no tendency to aggregation. The aim of the present study was to investigate the effect of hypromellose on the solubility, miscibility and amorphization of paracetamol in mixture with this polymer. Homogenized mixtures of paracetamol with hypromellose were studied using differential scanning calorimetry (DSC), hot-stage microscopy (HSM), Fourier transform infrared (FT-IR) and Raman methods to obtain a deeper insight into the interactions between ingredients in solid state including phase diagram construction for crystalline API and amorphous polymer. A DSC study revealed potential interaction between ingredients resulting in reduced paracetamol crystallinity. This was proved using heating-cooling-heating test to confirm paracetamol amorphization. FT-IR and Raman investigations excluded chemical reaction and hydrogen bonding between ingredients. The phase diagram developed facilitates predictions on the solubility of API in polymer, on the mutual miscibility of ingredients and on the temperature of mixture glass transition

Trace elements in medicinal plants traditionally used in the treatment of diabetes—do they have a role in the claimed therapeutic effect?

Medicinal plants are often used in the treatment of diabetes mellitus, although knowledge about their mode of action and the substances responsible for their antidiabetic potential is limited. It is well known that some trace elements play a role in glucose metabolism and insulin action. Thus, a particular trace elements profile could be associated with the antidiabetic properties observed for some medicinal plants. Methods: Infusions (n = 102) prepared from commercial herbal products (n = 34) containing medicinal plants indicated for the treatment of diabetes (n = 16 different plant species) and infusions (n = 60) prepared from commercial herbal products (n = 20) containing medicinal plants without such an indication (n = 7 different plant species) were analyzed by ICP-MS for their trace elements content. In both groups, results varied significantly between different medicinal plants and also between different origins (brands) of the same medicinal plant. Significant differences (p < 0.05) between the two groups were found for nine elements, including four trace elements related to glucose metabolism (Mn, B, V, and Se), but with lower median contents in the group of medicinal plants for diabetes. Except for some particular species (e.g., Myrtilli folium) in which the trace element Mn may play a role in its antidiabetic effect, globally, a direct association between the claimed antidiabetic properties and a specific trace element profile of the studied medicinal plants was not evident.info:eu-repo/semantics/publishedVersio

Consumer activism in times of economic crisis and recovery : a cross-country analysis of the role of social capital in boycotting products

Purpose: This paper’s objective is twofold: 1) to show how the outbreak of an economic crisis and then an economic recovery affect consumer activism; and 2) to examine how social capital moderates the effects of economic crisis and economic recovery. Design/Methodology/Approach: Drawing on the economic and sociological literature, this study develops a set of hypotheses that explain the role of social capital in consumer activism under different economic conditions. In order to test research predictions, the study uses a reliable data source that is European Social Survey. Findings: The research findings clearly demonstrate that social capital at the country level boosts consumer activism during an economic recovery. Intriguingly, the study shows that social capital seems to have a neutral effect on boycotting products during an economic crisis. Practical Implications: This study suggests that consumers are likely to become more sensitive to unethical behaviour by companies in a situation of economic recovery. Thus, firms should be particularly careful about ethically questionable situations in that time. Originality/Value: The added value arises from showing the role of social capital in consumer activism, in different economic conditions.peer-reviewe

Graphene growth on Ge(100)/Si(100) substrates by CVD method

The successful integration of graphene into microelectronic devices is strongly dependent on the availability of direct deposition processes, which can provide uniform, large area and high quality graphene on nonmetallic substrates. As of today the dominant technology is based on Si and obtaining graphene with Si is treated as the most advantageous solution. However, the formation of carbide during the growth process makes manufacturing graphene on Si wafers extremely challenging. To overcome these difficulties and reach the set goals, we proposed growth of high quality graphene layers by the CVD method on Ge(100)/Si(100) wafers. In addition, a stochastic model was applied in order to describe the graphene growth process on the Ge(100)/Si(100) substrate and to determine the direction of further processes. As a result, high quality graphene was grown, which was proved by Raman spectroscopy results, showing uniform monolayer films with FWHM of the 2D band of 32 cm−1

DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial