Quantum chemical study of a derivative of 3-substituted dithiocarbamic flavanone.

The aim of this work is to characterize a quite novel 3-dithiocarbamic flavonoid by vibrational spectroscopy in conjunction with Density Functional Theory (DFT) calculations. Quantum mechanics calculations of energies, geometries and vibrational wavenumbers in the ground state were carried out by using hybrid functional B3LYP with 6-311G(d,p) as basis set. The results indicate a remarkable agreement between the calculated molecular geometries, as well as vibrational frequencies, and the corresponding experimental data. In addition, a complete assignment of all the absorption bands present in the vibrational spectrum has been performed. In order to assess its chemical potential, quantum molecular descriptors characterizing the interactions between the 3-dithiocarbamic flavonoid and its biological receptors have been computed. The frontier molecular orbitals and the HOMO-LUMO energy gap have been used in order to explain the way in which the new molecule can interact with other species and to characterize its molecular chemical stability/reactivity. The molecular electrostatic potential (MEP) map, computed in order to identify the sites of the studied flavonoid that are most likely to interact with electrophilic and nucleophilic species, is discussed.

Global Clustering Quality Coefficient Assessing the Efficiency of PCA Class Assignment.

An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA) is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their dispersion. We are proposing a set of indicators inspired from analytical geometry that may be used for an objective quantitative assessment of the data clustering quality as well as a global clustering quality coefficient (GCQC) that is a measure of the overall predictive power of the PCA models. The use of these indicators for evaluating the efficiency of the PCA class assignment is illustrated by a comparative study performed for the identification of the preprocessing function that is generating the most efficient PCA system screening for amphetamines based on their GC-FTIR spectra. The GCQC ranking of the tested feature weights is explained based on estimated density distributions and validated by using quadratic discriminant analysis (QDA).

Principal component analysis coupled with artificial neural networks--a combined technique classifying small molecular structures using a concatenated spectral database.

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.

[Evaluation of the anti-inflammatory activity and phytochemical screening of Annona senegalensis leaves]. / Évaluation de l'activité anti-inflammatoire et screening phytochimique des feuilles de Annona senegalensis.

OBJECTIVE: This study aims to highlight the anti-inflammatory activity of ethanol extract of Annona senegalensis and do its phytochemical screening. METHODS: Rats were divided into three groups. The first group received only saline injection and instillation by intraperitoneal injection on days D0 and D7. This phase was the sensitization of that group. Then, on days D21, D22 and D23, the rats of the same group (Group 1) were injected with saline under anesthesia. The second group (Group 2) was composed of rats had not undergone treatment with the extract of Annona senegalensis. The rats in this batch have been sensitized by intraperitoneal injection (50 µL) of a solution of albumin (50 mg/rat) dissolved in aluminum hydroxide on days 0 and 7. Then during the challenge phase, saline containing 0.9% sodium chloride were injected intraperitoneally on days D21, D22 and D23. The sacrifice took place at D24 or 24 hours after the last challenge to ovalbumin. Similarly, rats of the third group (Group 3) have been sensitized by ovalbumin combined with aluminum hydroxide on days D0 and day D7. Then during the stage of provocation, rats in this batch received at days D21, D22 and D23, conjugated injection of albumin and ethanol extract of Annona senegalensis (injection of 0.4 mL of 7.10(-2) mg/kg body weight). The aqueous extract of Annona senegalensis has been previously prepared in saline. Twenty four hours after the last injection corresponding to D23, the rats were sacrificed under anesthesia. Secondary metabolites have been characterized by physico-chemical properties. RESULTS: Rats in the control (Group 1) gave an average of 24 ± 0.02 mast cells, 7 ± 0.1 macrophages, 9 ± 0.05 eosinophils. In the control group was not revealed the presence of neutrophils. Following the steps of provocation and awareness albumin (Group 2), we observed a significant increase in the number of inflammatory cells compared to control group (p < 0.001). Indeed, mast cells and macrophages have suffered increased respectively to 164 ± 0.01 and 253 ± 0.04. While eosinophils have increased from 9 ± 0.05 to 81 ± 0.01. There were 31 ± 0.02 neutrophils in Group 2. Group 3 treated with Annona senegalensis (7.10(-2) mg/kg) induced a significant decrease in the number of inflammatory cells compared to control group (p < 0.001). Indeed, mast cells decreased from 164 ± 0.01 to 89 ± 0.03. Similarly, the number of macrophages decreased from 253 ± 0.04 to 175 ± 0.06 and neutrophils decreased from 31 ± 0.02 to 10 ± 0.05. Finally, the eosinophils have suffered a decrease (from 81 ± 0.01 to 61 ± 0.08). However, after treatment with the extract, the values of different cell types have always been significantly higher (p < 0.001) compared to those in the control group (except neutrophils). This result indicates that the extract of Annona senegalensis did not completely inhibit the inflammatory effect induced by albumin. The major classes of secondary metabolites, terpenoids, coumarins, flavonoids and tannins were detected in the leaves of the plant. However, they are low in alkaloids and substances quinone. CONCLUSION: The extract induced a significant decrease in the number of inflammatory cells. This effect is likely due to higher concentrations of tannins and phenolic compounds in the extract of plant. Thus this study provides a scientific validation of the use of this plant against asthma and cough in the Ivorian pharmacopoeia. However, its mechanism of action remains to be elucidated.