Pharmacological Properties of 4′, 5, 7-Trihydroxyflavone (Apigenin) and Its Impact on Cell Signaling Pathways

Plant bioactive compounds, particularly apigenin, have therapeutic potential and functional activities that aid in the prevention of infectious diseases in many mammalian bodies and promote tumor growth inhibition. Apigenin is a flavonoid with low toxicities and numerous bioactive properties due to which it has been considered as a traditional medicine for decades. Apigenin shows synergistic effects in combined treatment with sorafenib in the HepG2 human cell line (HCC) in less time and statistically reduces the viability of tumor cells, migration, gene expression and apoptosis. The combination of anti-cancerous drugs with apigenin has shown health promoting potential against various cancers. It can prevent cell mobility, maintain the cell cycle and stimulate the immune system. Apigenin also suppresses mTOR activity and raises the UVB-induced phagocytosis and reduces the cancerous cell proliferation and growth. It also has a high safety threshold, and active (anti-cancer) doses can be gained by consuming a vegetable and apigenin rich diet. Apigenin also boosted autophagosome formation, decreased cell proliferation and activated autophagy by preventing the activity of the PI3K pathway, specifically in HepG2 cells. This paper provides an updated overview of apigenin’s beneficial anti-inflammatory, antibacterial, antiviral, and anticancer effects, making it a step in the right direction for therapeutics. This study also critically analyzed the effect of apigenin on cancer cell signaling pathways including the PI3K/AKT/MTOR, JAK/STAT, NF-κB and ERK/MAPK pathways.     

Novel Design of RNA Aptamers as Cancer Inhibitors and Diagnosis Targeting the Tyrosine Kinase Domain of the NT-3 Growth Factor Receptor Using a Computational Sequence-Based Approach

Aptamers, the nucleic acid analogs of antibodies, bind to their target molecules with remarkable specificity and sensitivity, making them promising diagnostic and therapeutic tools. The systematic evolution of ligands by exponential enrichment (SELEX) is time-consuming and expensive. However, regardless of those issues, it is the most used in vitro method for selecting aptamers. Therefore, recent studies have used computational approaches to reduce the time and cost associated with the synthesis and selection of aptamers. In an effort to present the potential of computational techniques in aptamer selection, a simple sequence-based method was used to design a 69-nucleotide long aptamer (mod_09) with a relatively stable structure (with a minimum free energy of −32.2 kcal/mol) and investigate its binding properties to the tyrosine kinase domain of the NT-3 growth factor receptor, for the first time, by employing computational modeling and docking tools.     

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.     

Role of the n+1 amino acid residue on the deamidation of asparagine in pentapeptides

Deamidation plays an important role in biochemical phenomena such as aging. The role of the n + 1 residue on the deamidation of asparagine (asparagine being the nth residue) in three pentapeptide chains (GGNGG, GGNMG and GGNIG) has been analysed with hybrid computational tools. Potentials of mean force at 300 K were calculated from the MD/replica exchange simulations using weighted histogram analysis (WHAM) in explicit water. The snapshots were clustered taking into account the requirements of the plausible deamidation mechanisms, as such the tautomerisation of the asparagine side chain as initial step has been confirmed, based on the proximity of water to the deamidation site. The ultimate goal being to gain an insight on the peptide backbone N-H acidity, quantum mechanical calculations have been carried out. For this purpose, the distribution of Φ/Ψ, Φ₂/Ψ and end-to-end distances deduced from the WHAM diagrams have been considered and a total of 110 structures have been sampled. These neutral pentapeptides as well as their corresponding anions have been optimised (B3LYP/6-31++G(d,p)) in implicit water in order to gain an insight on the peptide backbone N-H acidity. In this study, we have shown that the open conformations of the neutrals and the anions, which display a β sheet like structure are well populated and their pK_as rank in the same order as the deamidating half-lives, that is the peptides that deaminate fastest can more readily access conformations that are more acidic.     

Novel optical polarizer design based on metasurface nano aperture for biological sensing in mid-infrared regime

Since Metasurfaces are playing important roles in optical devices such as optical polarizers and detectors, in this article, we have proposed a novel shape of nano aperture antenna for mid-infrared applications such as bio-sensing and other potential optical applications based on plasmonic characteristic of the gold layer over the SiN substrate. The transmittance tensor is obtained for vertical and horizontal linear polarization and base on boundary condition of the metasurface, the circular polarizations are extracted which are confirmed by the electric field distributions. We have shown by the parametric studies that the phase difference is altered by the gap and slot width and so with the dimension modification, we are able to achieve circular polarizer in the optical range. The biological materials with a thickness of 80 nm have then been placed over the metasurface layer and the figures of merits have been obtained. We have revealed that when the circular polarization is osculated to the metasurface at a special frequency the linear polarization is obtained in the other side of the metasurface. The nano aperture has been modeled and the finite difference time domain calculations are performed in CST Microwave Studio as a commercial full wave simulation software.     

Black-body radiation of noncommutative gauge fields

The black-body radiation is considered in a theory with noncommutative electromagnetic fields; that is noncommutativity is introduced in field space, rather than in real space. A direct implication of the result on cosmic microwave background map is argued.     

Design and development of a fiber-optic immunosensor utilizing near-infrared fluorophores

The design and application of a fluorescent fiber-optic immunosensor (FFOI) are reported. The FFOI is utilized for the detection of antibody/antigen binding within the near-infrared (NIR) spectral region. The technique is developed through the combined use of fiber-optic, semiconductor laser-excitation, fluorescence detection, NIR dye, and immunochemical techniques. The antibody is immobilized on the FFOI and utilized as a recognition component for trace amounts of specific antigen. The FFOI is constructed to utilize an antibody sandwich technique. The assay involves the immobilization of the capture antibody on the sensing tip of the FFOI followed by the exposure of the immobilized sensing tip to the antigen. The antigen-coated FFOI is then introduced to a second antibody previously labeled with the NIR dye. Typical measurements are performed in about 15 min. A semiconductor laser provides the excitation (780 nm) of the immune complex. The resulting emission is detected by a silicon photodiode detector (820 nm). The intensity of the resulting fluorescence is directly proportional to the concentration of the antigen. The sensitivity of the analysis reaches 10 ng/ml and the response time is 10–15 min.     

A new correlation based on artificial neural networks for predicting the natural gas compressibility factor

In this study a new correlation of natural gas compressibility factor based on theory of Mohammadikhah-Mohebbi-Abolghasemi??s equation of state (MMA EOS) is developed using an artificial neural network. In MMA EOS, the compressibility factor as a function of M-factor (BP/RT) is expressed. An artificial neural network (ANN) is designed in which the M-factor, reduced temperature, and reduced pressure are selected as input variables, whereas the natural gas compressibility factor is selected as output. Then, a new correlation based on the weights of ANN is obtained. Results of this correlation are compared with some other equations and experimental data. Proposed correlation for 597 data points has an average absolute deviation (AAD%) of 0.6% and a correlation coefficient (R ² value) of 0.9999.     

Atomic force microscopy investigation of surface roughness generated between SiO2 micro-pits in CHF3/Ar plasma

The present paper investigates the surface roughness generated by reactive ion etching (RIE) on the location between silicon dioxide (SiO₂) micro-pits structures. The micro-pit pattern on polymethyl methacrylate (PMMA) mask was created by an electron beam lithography tool. By using PMMA as a polymer resist mask layer for pattern transfer in RIE process, the carbon (C) content in etching process is increased, which leads to decrease of F/C ratio and causes domination of polymerization reactions. This leads to high surface roughness via self-organized nanostructure features generated on SiO₂ surface which was analyzed using atomic force microscopy (AFM) technique. The etching chemistry of CHF₃ plasma on PMMA masking layer and SiO₂ is analyzed to explain the polymerization. The surface root-mean-square (RMS) roughness below 1 nm was achieved by decreasing the RF power to 150 W and process pressure lower than 10 mTorr.