Polyphenolics with Strong Antioxidant Activity from Acacia nilotica Ameliorate Some Biochemical Signs of Arsenic-Induced Neurotoxicity and Oxidative Stress in Mice

Neurotoxicity is a serious health problem of patients chronically exposed to arsenic. There is no specific treatment of this problem. Oxidative stress has been implicated in the pathological process of neurotoxicity. Polyphenolics have proven antioxidant activity, thereby offering protection against oxidative stress. In this study, we have isolated the polyphenolics from Acacia nilotica and investigated its effect against arsenic-induced neurotoxicity and oxidative stress in mice. Acacia nilotica polyphenolics prepared from column chromatography of the crude methanol extract using diaion resin contained a phenolic content of 452.185 ± 7.879 mg gallic acid equivalent/gm of sample and flavonoid content of 200.075 ± 0.755 mg catechin equivalent/gm of sample. The polyphenolics exhibited potent antioxidant activity with respect to free radical scavenging ability, total antioxidant activity and inhibition of lipid peroxidation. Administration of arsenic in mice showed a reduction of acetylcholinesterase activity in the brain which was counteracted by Acacia nilotica polyphenolics. Similarly, elevation of lipid peroxidation and depletion of glutathione in the brain of mice was effectively restored to normal level by Acacia nilotica polyphenolics. Gallic acid methyl ester, catechin and catechin-7-gallate were identified in the polyphenolics as the major active compounds. These results suggest that Acacia nilotica polyphenolics due to its strong antioxidant potential might be effective in the management of arsenic induced neurotoxicity.     

Efficient deep blue organic light emitting diodes based on [2,7,7,13,13-pentamethyl-9-(10-phenylanthracen-9-yl)-7,13-dihydrobenzo[5,6]-s-indaceno[1,2-g]quinoline] derivatives

Abstract

In this study, as the ongoing effort to develop efficient blue OLEDs, two deep blue emitters based on indenoquinoline-substituted anthracene derivatives have been synthesized and characterized. Multilayer organic light emitting diodes were fabricated with the following sequence: indium-tin-oxide (ITO)/4,4’,4’’-tris[2-naphthyl(phenyl)amino (2-TNATA)/4,4’-bis(N-(1-naphthyl)-N-phenylamino) biphenyl (NPB)/Blue emitting materials/Bathophenanthroline (Bphen)/lithium quinolate (Liq)/Al. All the devices showed efficient blue emissions. Particularly, a device using ‘2,7,7,13,13-pentamethyl-9-(10-phenylanthracen-9-yl)?7,13-dihydrobenzo[5,6]-s-indaceno[1,2-g] quinoline’ as an emitter showed a maximum external quantum efficiency (EQE) of 4.92% with the Commission Internationale De L’Énclairage (CIE) coordinates of (0.15, 0.10) at 8.0?V.     

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.     

Entropy Could Quantify Brain Activation Induced by Mechanical Impedance-Restrained Active Arm Motion: A Functional NIRS Study

Brain activation has been used to understand brain-level events associated with cognitive tasks or physical tasks. As a quantitative measure for brain activation, we propose entropy in place of signal amplitude and beta value, which are widely used, but sometimes criticized for their limitations and shortcomings as such measures. To investigate the relevance of our proposition, we provided 22 subjects with physical stimuli through elbow extension-flexion motions by using our exoskeleton robot, measured brain activation in terms of entropy, signal amplitude, and beta value; and compared entropy with the other two. The results show that entropy is superior, in that its change appeared in limited, well established, motor areas, while signal amplitude and beta value changes appeared in a widespread fashion, contradicting the modularity theory. Entropy can predict increase in brain activation with task duration, while the other two cannot. When stimuli shifted from the rest state to the task state, entropy exhibited a similar increase as the other two did. Although entropy showed only a part of the phenomenon induced by task strength, it showed superiority by showing a decrease in brain activation that the other two did not show. Moreover, entropy was capable of identifying the physiologically important location.     

Synthesis and size control of monodisperse copper nanoparticles by polyol method

We describe herein the synthesis of metallic copper nanoparticles in the presence of poly(vinylpyrrolidone), employed as a protecting agent, via a polyol method in ambient atmosphere. The obtained copper particles were confirmed by XRD to be crystalline copper with a face-centered cubic (fcc) structure. We observed monodisperse spherical copper nanoparticles with a diameter range 45+/-8 nm. The particle size and its distribution are controlled by varying the synthesis parameters such as the reducing agent concentration, reaction temperature, and precursor injection rate. The precursor injection rate plays an important role in controlling the size of the copper nanoparticles. On the basis of XPS and HRTEM results, we demonstrate that the surface of the copper is surrounded by amorphous CuO and that poly(vinylpyrrolidone) is chemisorbed on the copper surface.     

Role of the interface in multicomponent materials

Studies of the relationship between interfacial structure and mechanical properties in multicomponent materials are reviewed in this article. The following categories are considered for role of the interface in multicomponent systems: Interpenetrating polymer network(IPN), catalytic effect of silane coupling agent, morphological differences of filler surface, particle-particle interaction and particle size of the filler. The interfacial role in terms of the reinforcement mechanism of the composite and the behavior in the melt state is also discussed in the multicomponent system.     

Nanoporous polyimide film from poly(ethylene glycol-co-imide) using a one-step heat calcination process

Polyamic acid (PAA) made from 3,3, 4,4-biphenyltetracarboxylic dianhydride and 4,4-diaminodiphenyl ether was synthesized and used as the backbone of nanoporous polyimide. Thermally labile polyethylene glycol (PEG) at 10, 20 or 30 wt% was introduced to the end group of PAA. Self-assembled PAA-b-PEG micelle-like nanoparticles could be formed as a result of amphiphilic characteristics of hydrophobic PAA and hydrophilic PEG. Thermal imidization and degradation of spin-coated amphiphilic PAA-b-PEG film were performed sequentially through one-step heat treatment to obtain polyimide film with nanopores. Pore sizes decreased with increasing amount of PEG block and their dielectric constants decreased from 2.71 ± 0.13 to 2.38 ± 0.11.     

Enhanced luminance of white organic light-emitting diode with yellow nanophosphors-embedded blue polymer emitting layer

White organic light-emitting device was achieved through an incorporation of yellow YAG nanophosphors into blue polyfluorene emitting layer: electrode/YAG@polyfluorene/hole-transport/injection layers/ITO glass. The brightness of the proposed device (230 cd/m² at 30 V) was enhanced by a factor of about two in comparison with that of phosphor-free reference device. It is attributed to the increased local electric field caused by bumps of nanophophors on the emitting layer. With increase of voltage, the blue-green emission decreased whereas the yellow emission increased. It is due to the effective energy transfer from the blue-green to the yellow bands.