Evaluation of antileishmanial, cytotoxic and antioxidant activities of essential oils extracted from plants issued from the leishmaniasis-endemic region of Sned (Tunisia)

In this study, we tested 10 essential oils (EOs) extracted from 10 plants issued from Sned region (Tunisia) to evaluate both their leishmanicidal effects against Leishmania major and L. infantum, and their cytotoxicity against murine macrophage cell line RAW 264.7 (ATCC, TIB-71). The antioxidant activity was also monitored by the DDPH method, while the chemical composition of active EO was assessed by GC-MS analysis. The results showed that the EOs obtained from Thymus hirtus sp. algeriensis (rich on monoterpenoids, especially linalool at 17.62% and camphor at 13.82%) is significantly active against both L. major and L. infantum, whereas Ruta chalepensis EO (rich on 2-undecanone at 84.28%) is only active against L. infantum. Both oil extracts showed low cytotoxicity towards murine macrophages. The characteristic ratios (IC?? Raw264.7 cells/IC?? L. infantum and IC?? Raw264.7 cells/IC?? L. major) were, respectively, 2.7 and 1.57 for T. hirtus sp. algeriensis, and 1.34 and 0.19 for R. chalepensis. However, when measuring the antioxidant effects (DDPH method), the two latter EOs presented a moderate 2,2-diphenyl-2-picrylhydrazyl hydrate scavenging effects compared to EOs from Eucaliptus globulus, Pinus halepensis, Pituranthos tortuosus, Rosmarinus officinalis, Tetraclinis articulata or to BHT.     

Mathematical Analysis of Reaction–Diffusion Equations Modeling the Michaelis–Menten Kinetics in a Micro-Disk Biosensor

In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis–Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction–diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg–Marquardt training (LMT) algorithm. NNs–LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as “pdex4”. Furthermore, the data set is validated by the processing of the NNs–LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.     

Properties of the TDAE Molecule Within Density-Functional Theory

Density-functional-based methodologies have been used to calculate the geometric, electronic, and vibrational properties of the TDAE molecule. The molecule has a low first-ionization energy but, in contrast to alkali atoms, releases the electron without appreciably changing the effective molecular size. In addition to ionization energies and geometries we present the vibrational spectrum of the molecule and determine the infrared (IR) and Raman intensities as well. A discussion of some of the energetics that impact the ionicity and subsequent magnetism of TDAE–C₆₀ is presented. Further, we identify one Raman active mode, at 1682 cm^–1, which is particularly interesting since it may be used to probe the TDAE charge state experimentally.