Prevention of breast cancer by dietary polyphenols—role of cancer stem cells

Abstract

Breast cancer is a common malignancy with poor prognosis. Cancer cells are heterogeneous and cancer stem cells (CSCs) are primarily responsible for tumor relapse, treatment-resistance and metastasis, so for breast cancer stem cells (BCSCs). Diets are known to be associated with carcinogenesis. Food-derived polyphenols are able to attenuate the formation and virulence of BCSCs, implying that these compounds and their analogs might be promising agents for preventing breast cancer. In the present review, we summarized the origin and surface markers of BCSCs and possible mechanisms responsible for the inhibitory effects of polyphenols on BCSCs. The suppressive effects of common dietary polyphenols against BCSCs, such as curcumin, epigallocatechin gallate (EGCG) and related polyphenolic compounds were further discussed.     

Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

Multiply Charged Conjugated Polyelectrolytes as a Multifunctional Interlayer for Efficient and Scalable Perovskite Solar Cells

A series of anionic conjugated polyelectrolytes (CPEs) is synthesized based on poly(fluorene-co-phenylene) by varying the side-chain ionic density from two to six per repeat units (MPS2-TMA, MPS4-TMA, and MPS6-TMA). The effect of MPS2, 4, 6-TMA as interlayers on top of a hole-extraction layer of poly(bis(4-phenyl)-2,4,6-trimethylphenylamine (PTAA) is investigated in inverted perovskite solar cells (PeSCs). Owing to the improved wettability of perovskites on hydrophobic PTAA with the CPEs, the PeSCs with CPE interlayers demonstrate a significantly enhanced device performance, with negligible device-to-device dependence relative to the reference PeSC without CPEs. By increasing the ionic density in the MPS-TMA interlayers, the wetting, interfacial defect passivation, and crystal growth of the perovskites are significantly improved without increasing the series resistance of the PeSCs. In particular, the open-circuit voltage increases from 1.06 V for the PeSC with MPS2-TMA to 1.11 V for the PeSC with MPS6-TMA. The trap densities of the PeSCs with MPS2,4,6-TMA are further analyzed using frequency-dependent capacitance measurements. Finally, a large-area (1 cm²) PeSC is successfully fabricated with MPS6-TMA, showing a power conversion efficiency of 18.38% with negligible hysteresis and a stable power output under light soaking for 60 s.     

Recent Biophysical Issues About the Preparation of Solute-Filled Lipid Vesicles

Here we report some recent biophysical issues on the preparation of solute-filled lipid vesicles and their relevance to the construction of “synthetic cells.” First, we introduce the “semi-synthetic minimal cells” as the liposome-based cell-like systems, which contain a minimal number of biomolecules required to display simple and complex biological functions. Next, we focus on recent aspects related to the construction of synthetic cells. Emphasis is given to the interplay between the methods of synthetic cell preparation and the physics of solute encapsulation. We briefly introduce the notion of structural and compositional “diversity” in synthetic cell populations.     

Deposition,opto-electronic and structural characterization of polymorphous silicon thin films to be applied in a solar cell structure

In this study we analyze the optoelectronic properties and structural characterization of hydrogenated polymorphous silicon thin films as a function of the deposition parameters. The films were grown by plasma enhanced chemical vapor deposition (PECVD) using a gas mixture of argon (Ar), hydrogen (H₂) and dichlorosilane (SiH₂Cl₂). High-resolution transmission electron microscopy images and Raman measurements confirmed the existence of very different internal structures (crystalline fractions from 12% to 54%) depending on the growth parameters. Variations of as much as one order of magnitude were observed in both the photoconductivity and effective absorption coefficient between the samples deposited with different dichlorosilane/hydrogen flow rate ratios. The optical and transport properties of these films depend strongly on their structural characteristics, in particular the average size and densities of silicon nanocrystals embedded in the amorphous silicon matrix. From these results we propose an intrinsic polymorphous silicon bandgap grading thin film to be applied in a p–i–n junction solar cell structure. The different parts of the solar cell structure were proposed based on the experimental optoelectronic properties of the pm-Si:H thin films studied in this work.     

An analytical model for optimizing the performance of graphene based silicon Schottky barrier solar cells

In this paper, a model taking into account the effects of carrier loss mechanisms has been developed. The model simulates the photovoltaic properties of the graphene/n-type silicon Schottky barrier solar cells (G/n-Si_SBSC), and it can reproduce the experimentally determined parameters of the G/n-Si_SBSC. To overcome the low efficiencies of G/n-Si_SBSC, their performances have been optimized by modifying the work function of graphene and Si properties, accounted for variation of its thickness and doping level. The obtained results show that the work function of graphene has the major impact on the device performance. Also, the temperature dependence of the G/n-Si_SBSC performance is investigated.     

Mutagenic Effects of Iron Oxide Nanoparticles on Biological Cells

In recent years, there has been an increased interest in the design and use of iron oxide materials with nanoscale dimensions for magnetic, catalytic, biomedical, and electronic applications. The increased manufacture and use of iron oxide nanoparticles (IONPs) in consumer products as well as industrial processes is expected to lead to the unintentional release of IONPs into the environment. The impact of IONPs on the environment and on biological species is not well understood but remains a concern due to the increased chemical reactivity of nanoparticles relative to their bulk counterparts. This review article describes the impact of IONPs on cellular genetic components. The mutagenic impact of IONPs may damage an organism’s ability to develop or reproduce. To date, there has been experimental evidence of IONPs having mutagenic interactions on human cell lines including lymphoblastoids, fibroblasts, microvascular endothelial cells, bone marrow cells, lung epithelial cells, alveolar type II like epithelial cells, bronchial fibroblasts, skin epithelial cells, hepatocytes, cerebral endothelial cells, fibrosarcoma cells, breast carcinoma cells, lung carcinoma cells, and cervix carcinoma cells. Other cell lines including the Chinese hamster ovary cells, mouse fibroblast cells, murine fibroblast cells, Mytilus galloprovincialis sperm cells, mice lung cells, murine alveolar macrophages, mice hepatic and renal tissue cells, and vero cells have also shown mutagenic effects upon exposure to IONPs. We further show the influence of IONPs on microorganisms in the presence and absence of dissolved organic carbon. The results shed light on the transformations IONPs undergo in the environment and the nature of the potential mutagenic impact on biological cells.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.