Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.     

Influence of Chemical Reaction on Mass Transport in Yield Stress Exhibiting Flow Regime

Theoretical Foundations of Chemical Engineering - In this study, the simulations for first-order chemical reactions (constructive and destructive) in the flow of the Casson fluid with...     

Investigation of improved dielectric and thermal properties of ternary nanocomposite PMMA/MXene/ZnO fabricated by in-situ bulk polymerization

Electric power system applications demand for high-temperature dielectric materials. The improved performance of polymer nanocomposites requires improvement in their thermal conductivity & stability, dielectric stability and processing technique. However, they often lose their dielectric properties with a rise in temperature. Here, we offer a solution by incorporating electrically conducting material (MXene) and semiconducting inorganic nanoparticles (ZnO NPs) into an insulating PMMA polymer matrix to maintain high dielectric constant, both at the room and high temperature. Therefore, to achieve desirable thermal and dielectric properties is the main objective of the present study based on the homogeneous distribution of the nanofillers by in-situ bulk polymerization assisted by strong sonication in the corresponding polymer. The introduction of MXene and ZnO NPs into the PMMA not only acquires a substantial increment in the dielectric constant, to attain a value 437, with minimum energy loss of 0.36 at 25 Hz, but also improves the thermal conductivity of PMMA up to 14 times by causing the reduction of thermal resistance, which is actually responsible for the poor thermal conductivity of amorphous pure PMMA polymer. More importantly, hybrid PMMA/4:2 wt% MXene:ZnO nanocomposite leads to an excellent thermal stability. Moreover, further characterization of the synthesized nanocomposites by FTIR, SEM and XRD leads to the evaluation of strong interaction of ternary components with PMMA matrix.     

Assessment on the mechanical,structural, and thermal attributes of green graphene-based water soluble polymer electrolyte composites

In the current study, graphene oxide (GO) was prepared using green chemistry with modified Hummer's method without incorporating sodium nitrate (NaNO₃). Solvent casting was employed to fabricate GO-doped poly(ethylene oxide) (PEO), that is, PEO/GO composites with various proportion of Na₂SO₄ and were then subjected to characterization via advanced spectroscopic techniques for different physicochemical aspects to estimate their potential applications as marketable products. XRD analysis explored that fabricated composites are more crystalline than neat PEO. PEO/GO/Na₂SO₄ composite films offered maximum crystallinity. SEM displayed the same trend. TG/DTA thermogram exposed better thermal stability than pristine polymer. FTIR studies confirmed complexation among hybrid's components. Elongation-at-break and Young's modulus displayed an enhancing behavior with an incremental loading of salt and filler. In terms of mechanical performance, composite of PEO with 0.37 wt % GO and 0.08 g salt was found to be an ideal composition during the course of study. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48376.     

Studies on batch adsorptive removal of cadmium and nickel from synthetic waste water using silty clay originated from Balochistan–Pakistan

The potentials of silty clay(SC), acquired from Chaman, Balochistan, were investigated as adsorbent for Ni(Ⅱ)and Cd(Ⅱ) removal from contaminated media. The influence of different operating factors like dose, pH, temperature, and time of contact was explored, and optimum values were noted under batch adsorption method. Isothermal study was conducted with varying concentrations of solutions on optimized conditions and different adsorption models i.e., Langmuir, Freundlich, Temkin and Dubinin–Radushkevich(D–R) isotherm, which were employed to interpret the process. The isothermal data of both Ni(Ⅱ) and Cd(Ⅱ) were well fitted to Langmuir isotherm suggesting the formation of monolayer of metal ions on silty clay. The values of adsorption capacity noted for Ni(Ⅱ) and Cd(Ⅱ) were 3.603 mg·g~(-1) and 5.480 mg·g~(-)1, respectively. Kinetic studies affirmed that pseudo second order(PSO) kinetics was being obeyed by the removal of Ni(Ⅱ) and Cd(Ⅱ). Thermodynamic variables like free energy change(ΔG°), enthalpy change(ΔH°) and entropy change(ΔS°) were calculated. The negative value of ΔG° and the positive values of ΔH° and ΔS° unfolded that the removal process of both metal ions of by SC was spontaneous, endothermic and feasible.     

Synthesis of Sulfur‐Based Biocompatible Nonionic Surfactants and Their Nano‐Vesicle Drug Delivery

Nonionic surfactants are capable of forming nano‐range vesicles upon self‐assembling in an aqueous medium. These vesicles are highly stable, low in toxicity, and cost‐effective. Owing to their ability to solubilize both hydrophilic and hydrophobic substances, they are of great interest for drug solubilization and delivery. This study describes the synthesis and characterization of two new nonionic surfactants and their screening for biocompatibility and drug loading potentials in nano‐scale niosomal vesicles. They were characterized through mass spectroscopy, ¹HNMR, and FT‐IR. Their critical micelle concentration (CMC) was investigated using UV–vis spectrophotometry. The biocompatibility study was carried out through blood hemolysis and in vitro cytotoxicity assays. The surfactants have very low CMC values, are highly hemo‐compatible, and were nontoxic when tested against a cell culture. They were able to form nano‐range niosomal vesicles with large variation in their size. Both new surfactants were able to encapsulate increased amounts of the drug, in this case clarithromycin. The chemical nature of the drug remained intact in the niosomal vesicles. The results suggest that these nonionic surfactants could be promising drug delivery vehicles.     

Evaluating the lucrative role of probiotics in the aquaculture using microscopic and biochemical techniques

The present study focused to elucidate the promising role of probiotics in the fish culture, by employing microscopic and biochemical parameters to rationalize the possible outcomes. After preliminary feeding trial histopathological analysis revealed clear and visible changes in the intestinal and liver cells of CTRL (control group) compared to probiotics supplemented group, the bacterial inoculations resulted in shorten and swelled villi, while liver kupffer cells increased many folds in the CTRL. Moreover, there was clear hypertrophy and lamellar fusion in the gills tissues examined under microscope. Furthermore, intriguing results were obtained, such as a substantial increase in the weight gain, specific growth rate (%BW/day), FCE%, survival%, and improved secretions of amylase, cellulase, and protease activities. Likewise, high crude protein, fats content, while low moisture and total Ash was noticed in EXP I (Probiotics supplemented) compared to CTRL. The microscopic and biochemical analysis disclosed the positive role of probiotics on the fish health, thus we suggest its supplementations in the fish diet.     

Netted radar: Network communications design and optimisation

Networks of phased array radars are generally able to provide better counter stealth target detection and classification. Each radar sensor (or node) generates information which requires transmission to a central authority that is able to evaluate the information. This requires a communications network to be established to allow transmission of information to and from any node. Each radar node is limited by range and degree and relies on the formation of a multi-hop network to facilitate these transmissions.This paper presents a model whereby the radar beam itself is used in the formation of a multi-hop network. The phased array’s multi-functional nature allows rapid switching between communications and radar function. A model of how the communication system could operate is presented, and an evolutionary optimisation algorithm based upon the concept of Pareto optimality is used for the topological design of the network. Finally, a simulation environment is presented to show the simulated performance of the communication model and designed networks.     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.