Based on the strong antimicrobial activity observed, this new tricyclic flavonoid has a good potential for the design of new antimicrobial agents.

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The emergence of pathogenic multidrug-resistant bacteria demands new approaches in finding effective antibacterial agents. Synthetic flavonoids could be a reliable solution due to their important antimicrobial activity. We report here the potent in vitro antibacterial activity of ClCl-flav—a novel synthetic tricyclic flavonoid. The antimicrobial effects were tested using the minimum inhibitory concentration (MIC), time kill and biofilm formation assays. Fluorescence microscopy and scanning electron microscopy were employed to study the mechanism of action. MTT test was used to assess the cytotoxicity of ClCl-flav. Our results showed that Gram positive bacteria were more sensitive (MIC = 0.24 μg/mL) to ClCl-flav compared to the Gram negative ones (MIC = 3.9 μg/mL). We found that our compound showed significantly enhanced antibacterial activities, 32 to 72-fold more active than other synthetic flavonoids. ClCl-flav showed bactericidal activity at concentrations ranging from 0.48 to 15.62 μg/mL. At twice the MIC, all Escherichia coli and Klebsiella pneumoniae cells were killed within 1 h. Also ClCl-flav presented good anti-biofilm activity. The mechanism of action is related to the impairment of the cell membrane integrity. No or very low cytotoxicity was evidenced at effective concentrations against Vero cells. Based on the strong antibacterial activity and cytotoxicity assessment, ClCl-flav has a good potential for the design of new antimicrobial agents.

The emergence of drug-resistant microbes left us with a great need for new antimicrobial agents. Flavonoids, with their wide range of biological activities, are good candidates in this respect. Although naturally occurring flavonoids are the most studied ones, semi-synthetic or synthetic flavonoids have proven to have great potential, inhibiting and even killing microbes at concentrations below 1 lg ml À1 . The substitution pattern of these flavonoids often includes hydroxy groups, halogens or other heteroatomic rings, such as pyridine, piperidine or 1,3-dithiolium cations. However, the great variety in substituents makes it difficult to draw any definitive conclusion regarding their structureactivity relationship.

The conversion of n-heptanes into aromatic hydrocarbons benzene, toluene and xylenes (BTX), by the chromatographic pulse method in the temperature range of 673 - 823K was performed over the HZSM-5 and Ag-HZSM-5 zeolites modified by ion exchange with AgNO3 aqueous solutions. The catalysts, HZSM-5 (SiO2/Al2O3 = 33.9), and Ag-HZSM-5 (Ag1-HZSM-5 wt. % Ag1.02, Ag2-HZSM-5 wt. % Ag 1.62; and Ag3-HZSM-5 wt. % Ag 2.05 having different acid strength distribution exhibit a conversion and a yield of aromatics depending on temperature and metal content. The yield of aromatic hydrocarbons BTX appreciably increased by incorporating silver cations Ag+ into HZSM-5.

In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen), or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry) and GC-MS (gas chromatography-mass spectrometry) spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA). The scores of the forensic compounds on the main principal components (PCs) were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network) with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%), as well as a good selectivity (a rate of true negatives TN = 92.77%). A comparative analysis of the validation results of all expert systems is presented, and the input variables with the highest discrimination power are discussed.

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