Unsupervised Pattern Recognition Chemometrics for Distinguishing Different Egyptian Olive Varieties Using a New Integrated Densitometric Reversed-Phase High-Performance Thin-Layer Chromatography—Image Analysis Technique

JPC – Journal of Planar Chromatography – Modern TLC - The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were...     

Biochemical Properties of Tyrosinase from Aspergillus terreus and Penicillium copticola; Undecanoic Acid from Aspergillus flavus,an Endophyte of Moringa oleifera,Is a Novel Potent Tyrosinase Inhibitor

Tyrosinase is a copper-containing monooxygenase catalyzing the O-hydroxylation of tyrosine to 3,4-dihydroxyphenylalanine then to dopaquinone that is profoundly involved in melanin synthesis in eukaryotes. Overactivation of tyrosinase is correlated with hyperpigmentation that is metabolically correlated with severe pathological disorders, so, inhibition of this enzyme is the most effective approach in controlling the overproduction of melanin and its hazardous effects. Thus, searching for a powerful, selective inhibitor of human tyrosinase to limit the hyper-synthesis of melanin is a challenge. Unlike the difficulty of overexpression of human tyrosinase, using fungal tyrosinase as a model enzyme to the human one to evaluate the mechanistics of enzyme inhibition in response to various compounds is the most feasible strategy. Thus, the purification of highly catalytic-efficient fungal tyrosinase, exploring a novel inhibitor, and evaluating the mechanistics of enzyme inhibition are the main objectives of this work. Aspergillus terreus and Penicillium copticola were reported as the most potential tyrosinase producers. The biochemical properties suggest that this enzyme displays a higher structural and catalytic proximity to human tyrosinase. Upon nutritional bioprocessing by Plackett–Burman design, the yield of tyrosinase was increased by about 7.5-folds, compared to the control. The purified tyrosinase was strongly inhibited by kojic acid and A. flavus DCM extracts with IC₅₀ values of 15.1 and 12.6 µg/mL, respectively. From the spectroscopic analysis, the main anti-tyrosinase compounds of A. flavus extract was resolved, and verified as undecanoic acid. Further studies are ongoing to unravel the in vivo effect and cytotoxicity of this compound in fungi and human, that could be a novel drug to various diseases associated with hyperpigmentation by melanin.     

Validated ecofriendly chromatographic method for quantitative determination of anti‐migraine quaternary mixture

A novel ecofriendly, cost and time saving high‐performance thin‐layer chromatographic method was developed and validated for simultaneous determination of metoclopramide, ergotamine, caffeine, and paracetamol in bulk and pharmaceutical formulation. The separation was carried out on silica gel plates, using ethyl acetate:ethanol:ammonia (9:1:0.1, v/v/v) as a developing system. Ultraviolet detection was carried out at 272 nm. The resulting retention times were 0.15, 0.36, 0.49, and 0.74 min for metoclopramide, ergotamine, caffeine, and paracetamol, respectively. The greenness profile assessment was achieved to the proposed method to evaluate its greenness characters to the environment with acceptable results. Validation parameters were checked according to International Conference of Harmonization guidelines to achieve the international requirements for quality control analysis of the proposed drugs.     

On modifying properties of polymeric melts by nanoscopic particles

We study geometric and energetic factors that partake in modifying properties of polymeric melts via inserting well‐dispersed nanoscopic particles (NP). Model systems are cis‐1,4‐polybutadiene melts including a single atomic clusters of size varied in the range 10–150 atoms (3–7 Å in radius; 0.1–1.5% v/v). We modify the interactions between the chains and the particle by tuning attractive van der Waals interactions. Using molecular dynamics, we study equilibrium fluctuations and dynamical properties at the interface. The NPs move in the polymer matrix in two different regimes corresponding to trapped and free diffusion, depending on the NP size. Furthermore, degree of crowding around the NP by the polymer chains is quantified. Effect of NP size and interaction strength both on volume and volumetric fluctuations is manifested in mechanical properties, quantified here by bulk modulus, K. Tuning NP size and nonbonded interactions results in ～15% enhancement in K by addition of a maximum of 1.5% v/v NP. © 2012 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2012     

MASS SPECTROMETRIC STUDIES ON SPIROCYCLIC DERIVATIVES OF CYCLODIPHOSPH(V)AZANES

Abstract

The formation and subsequent fragmentation of spirocyclodiphosph(V)azanes (II-III) has been studied by mass spectrometry using the link-field scan technique and structural inferencies are drawn from these data. ³¹P nmr measurements for compound (III) are also described.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.