Amphetamines in wastewater of the city Poznan (Poland)--estimation of drug abuse.

The aim of the study was to determine the profile of amphetamines consumed by a community in Poland. Amphetamine, methamphetamine and MDMA (ecstasy) were detected in wastewater samples collected from the main Wastewater Treatment Plant in the city of Poznan (about 687 000 people) using liquid chromatography/tandem mass spectrometry (LC-MS-MS). Back-calculations used in the sewage epidemiology approach were applied to estimate the level of consumption of the drugs being analyzed. These types of studies were carried out for the first time in Poland for a considerable period--from June 2009 to December 2010. The analysis of variance (ANOVA) confirmed significant monthly differences in amphetamine consumption. The concentration of amphetamine, methamphetamine and MDMA in wastewater samples and the levels of their consumption were lower than reported in other European countries, but unexpectedly, the ratio of consumed methamphetamine to MDMA and the consumption level of methamphetamine were relatively high. This study shows that sewage epidemiology is a promising tool, especially when combined with classical methods, to estimate illicit drugs use in a particular population. Therefore, efforts should be made to monitor the profiles and consumption levels of drugs and to extend the scope of the research to other illicit substances, especially cannabinoids and cocaine.

ANN and Bayesian classification models for virtual screening of endocrine-disrupting chemicals.

The identification of endocrine-disrupting chemicals (EDCs) is one of the important goals of environmental chemical hazard screening. The adverse health effects of EDCs in humans have been demonstrated to involve the developmental, reproductive, neurological, cardiovascular, metabolic, and immune systems. The present study reports QSAR classification studies on a large database comprising 8,212 compounds collected from the Estrogenic Activity Database and National Center for Biotechnology Information Database. In this study, four classification models (Bayesian Categorization Model with molecular fingerprints or molecular descriptors as an input and Neural Classification Models with and without Bayesian regularization) were used. Evaluation of these binomial classification methods indicated that the Bayesian method (Bayesian QSAR) works as an excellent method for prediction with fingerprints used as input. In the case of the multilayer perceptron with molecular descriptors as inputs, changing the training mode by introducing a Bayesian regularization algorithm significantly improved ANNs' predictive power. Our goal was to test two popular classification methods suitable for processing large data sets. Such datasets were required to ensure the prediction performances and applicability of the models as a virtual screening tool for an extensive database.

Trends of Amphetamine Type Stimulants DTR Mass Load in Poznan Based on Wastewater Analysis.

BACKGROUND: The aim of this study was to determine the monthly DTR mass load of amphetamine-type compounds in Poland as well as an investigation of cyclical behaviour by using time series analysis and especially trends analysis. METHODS: Amphetamine, methamphetamine and MDMA (ecstasy) were detected in wastewater samples collected from the main Wastewater Treatment Plant in the city of Poznan using liquid chromatography / tandem mass spectrometry (LC-MS-MS). Back-calculations used in the sewage epidemiology approach were applied to estimate the DTR mass load level of the drugs analyzed. Trends analysis was performed by fitting the data to a simple linear regression and then by using smoothing by means of a moving average (Mat lab 2013a). Trend analysis displays a steady tendency of increase or decrease throughout time series. When we plot the observation against time, we may notice that a straight line can describe the increase or decrease in the series as time goes on. Simple linear regression and method of last squares to estimate parameters of a straight-line model were used. Additionally, a lagged plot (autocorrelation plot) was used to investigate an appearance of correlation between amphetamines throughout time. RESULTS: Trends analysis showed the slight increase in consumption of amphetamine and decreasing trend in case of ecstasy and methamphetamine within the investigated period. There is also visible, strong correlation between ecstasy and methamphetamine consumption which cannot be stated in case of amphetamine. CONCLUSION: Trends analysis is a very useful tool to analyse the increasing or decreasing tendency in consumption of illicit drugs based on the DTR mass load data.

New complexes of La(III), Ce(III), Pr(III), Nd(III), Sm(III), Eu(III) and Gd(III) ions with morin.

New solid complex compounds of La(III), Ce(III), Pr(III), Nd(III), Sm(III), Eu(III) and Gd(III) ions with morin were synthesized. The molecular formula of the complexes is Ln(C(15)H(9)O(7))(3).nH(2)O, where Ln is the cation of lanthanide and n=6 for La(III), Sm(III), Gd(III) or n=8 for Ce(III), Pr(III), Nd(III) and Eu(III). Thermogravimetric studies and the values of dehydration enthalpy indicate that water occurring in the compounds is not present in the inner coordination sphere of the complex. The structure of the complexes was determined on the basis of UV-visible, IR, MS, (1)H NMR and (13)C NMR analyses. It was found that in binding the lanthanide ions the following groups of morin take part: 3OH and 4CO in the case of complexes of La, Pr, Nd, Sm and Eu, or 5OH and 4CO in the case of complexes of Ce and Gd. The complexes are five- and six-membered chelate compounds.

The use of artificial neural networks for the selection of the most appropriate thermal parameters and for the classification of a set of phenylcarbamic acid derivates.

The objective of this work was to apply artificial neural networks (ANNs) to the classification group of 43 derivatives of phenylcarbamic acid. To find the appropriate clusters Kohonen topological maps were employed. As input data, thermal parameters obtained during DSC and TG analysis were used. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of various input variables. Additionally, sensitivity analysis was carried out to eliminate less important thermal variables. As a result, one classification model was obtained, which can assign our compounds to an appropriate class. Because the classes contain groups of molecules structurally related, it is possible to predict the structure of the compounds (for example the position of the substitution alkoxy group in the phenyl ring) on the basis of obtained parameters.