Vitamin E and its anticancer effects

Abstract

Vitamin E is a lipid soluble vitamin comprising of eight natural isoforms, namely, α, β, δ, γ isoforms of tocopherol and α, β, δ, γ isoforms of tocotrienol. Many studies have been performed to elucidate its role in cancer. Until last decade, major focus was on alpha tocopherol and its anticancer effects. However, major clinical trials using alpha-tocopherol like SELECT trial and ATBC trial did not yield meaningful results. Hence there was a shift of focus to gamma-tocopherol, delta-tocopherol and tocotrienol. Unlike alpha-tocopherol, gamma-tocopherol and delta-tocopherol can scavenge reactive nitrogen species in addition to reactive oxygen species. Antiangiogenic effect, inhibition of HMG CoA reductase enzyme and inhibition of NF-κB pathway make the anti-cancer effects of tocotrienols unique compared to other vitamin E isoforms. Preclinical research on non-alpha tocopherol isoforms of vitamin E showed promising data on their anticancer effects. In this review, we deal with the current understanding on the potential mechanisms involved in the anticancer effects of vitamin E and the controversies in this field over last three decades. We also highlight the need to conduct further research on the anticancer effects of non-alpha-tocopherol isoforms in larger population and clinical setting.     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.     

Pectic Galactan Polysaccharide-Based Gene Delivery System for Targeting Neuroinflammation

Treating neuroinflammation-related injuries and disorders through manipulation of neuroinflammation functions is being heralded as a new therapeutic strategy. In this study, a novel pectic galactan (PG) polysaccharide based gene therapy approach is developed for targeting reactive gliosis in neuroinflammation. Galectin-3 (Gal-3) is a cell protein with a high affinity to β-galactoside sugars and is highly expressed in reactive gliosis. Since PG carries galactans, it can target reactive gliosis via specific carbohydrate interaction between galactan and Gal-3 on the cell membrane, and therefore can be utilized as a carrier for delivering genes to these cells. The carrier is synthesized by modifying quaternary ammonium groups on the PG. The resulting quaternized PG (QPG) is found to form complexes with plasmid DNA with a mean diameter of 100 nm and have the characteristics required for targeted gene therapy. The complexes efficiently condense large amounts of plasmid per particle and successfully bind to Gal-3. The in vivo study shows that the complexes are biocompatible and safe for administration and can selectively transfect reactive glial cells of an induced cortical lesion. The results confirm that this PG-based delivery system is a promising platform for targeting Gal-3 overexpressing neuroinflammation cells for treating neuroinflammation-related injuries and neurodegenerative diseases.     

A simple model for the semicontinuous heterophase polymerization: Ethyl methacrylate as a case example

A simple but comprehensive model considering homogeneous and micellar nucleation, coagulation, entry of radicals to particles and to micelles and radicals' exit from particles, is presented. The model is validated, in a starved semicontinuous heterophase polymerization of ethyl methacrylate, at three monomer addition rates. The model accurately describes the overall and instantaneous conversion, the average particle density and diameter, and the number and weight average molar masses evolutions over time. It is found that even though the average number of radicals is much smaller than 0.5, the system is not 0-1. An empirical function was used to describe the gel effect. The homogeneous nucleation was the prevailing mechanism for particle formation and large exit rates of radicals were observed. POLYM. ENG. SCI., 60: 223–232, 2019. © 2019 Society of Plastics Engineers     

Transport and solvent sensing characteristics of styrene butadiene rubber nanocomposites containing imidazolium ionic liquid modified carbon nanotubes

Obtaining strong interfacial interaction between filler and polymer matrix is very crucial for the fabrication of polymer nanocomposites with superior performance. Present study is aimed to fabricate high performance styrene butadiene rubber (SBR) nanocomposites with imidazolium type ionic liquid modified multiwalled carbon nanotube (MWCNT). Ionic liquid facilitates the dispersion of MWCNT in rubber matrix and it is obvious from transmission electron microscopy images. Diffusion of toluene through SBR nanocomposite membranes has been investigated as a function of surface modified MWCNT (f-MWCNT) content to analyze the chain dynamics and filler-polymer interactions. O₂ gas barrier effect of nanocomposites with special reference to the filler loading is explored. The substantial improvement in the barrier effect in presence of filler interpreted on the grounds of a theoretical model describing permeability of heterogeneous systems. Finally solvent sensing characteristics of prepared nanocomposites are also analyzed and it is observed that prepared nanocomposites can be used as a flexible solvent sensor.     

Global Performance Indices for Dynamic Crystals as Organic Thermal Actuators

Crystal adaptronics is an emergent materials science discipline at the intersection of solid-state chemistry and mechanical engineering that explores the dynamic nature of mechanically reconfigurable, motile, and explosive crystals. Adaptive molecular crystals bring to materials science a qualitatively new set of properties that associate long-range structural order with softness and mechanical compliance. However, the full potential of this class of materials remains underexplored and they have not been considered as materials of choice in an engineer's toolbox. A set of general performance metrics that can be used for quantification of the performance of these prospective dynamic materials as micro- and macroactuators is presented. The indices are calculated on two selected representatives of thermosalient solids—materials that undergo rapid martensitic transitions accompanied with macroscopic locomotion. Benchmarking of their performance against extensive set of data for the existing actuator classes and visualization using materials property charts uncover the hidden potential and advantages of dynamic crystals, while they also reveal their drawbacks for actual application as actuators. Altogether the results indicate that, if the challenges with fabrication and implementation in devices are overcome, adaptive molecular crystals can have far-reaching implications for emerging fields such as smart microelectronics and soft microrobotics.