Nanomaterials for Antiangiogenic Therapies for Cancer: A Promising Tool for Personalized Medicine

Angiogenesis is one of the hallmarks of cancer. Several studies have shown that vascular endothelium growth factor (VEGF) plays a leading role in angiogenesis progression. Antiangiogenic medication has gained substantial recognition and is commonly administered in many forms of human cancer, leading to a rising interest in cancer therapy. However, this treatment method can lead to a deteriorating outcome of resistance, invasion, distant metastasis, and overall survival relative to its cytotoxicity. Furthermore, there are significant obstacles in tracking the efficacy of antiangiogenic treatments by incorporating positive biomarkers into clinical settings. These shortcomings underline the essential need to identify additional angiogenic inhibitors that target numerous angiogenic factors or to develop a new method for drug delivery of current inhibitors. The great benefits of nanoparticles are their potential, based on their specific properties, to be effective mechanisms that concentrate on the biological system and control various important functions. Among various therapeutic approaches, nanotechnology has emerged as a new strategy for treating different cancer types. This article attempts to demonstrate the huge potential for targeted nanoparticles and their molecular imaging applications. Notably, several nanoparticles have been developed and engineered to demonstrate antiangiogenic features. This nanomedicine could effectively treat a number of cancers using antiangiogenic therapies as an alternative approach. We also discuss the latest antiangiogenic and nanotherapeutic strategies and highlight tumor vessels and their microenvironments.     

Correction to: Two New Co(II) Complexes of Picolinate: Synthesis,Crystal Structure,Spectral Characterization, α-Glucosidase İnhibition and TD/DFT Study

Journal of Inorganic and Organometallic Polymers and Materials -     

Protein Kinase C as a Therapeutic Target in Non-Small Cell Lung Cancer

Driver-directed therapeutics have revolutionized cancer treatment, presenting similar or better efficacy compared to traditional chemotherapy and substantially improving quality of life. Despite significant advances, targeted therapy is greatly limited by resistance acquisition, which emerges in nearly all patients receiving treatment. As a result, identifying the molecular modulators of resistance is of great interest. Recent work has implicated protein kinase C (PKC) isozymes as mediators of drug resistance in non-small cell lung cancer (NSCLC). Importantly, previous findings on PKC have implicated this family of enzymes in both tumor-promotive and tumor-suppressive biology in various tissues. Here, we review the biological role of PKC isozymes in NSCLC through extensive analysis of cell-line-based studies to better understand the rationale for PKC inhibition. PKC isoforms α, ε, η, ι, ζ upregulation has been reported in lung cancer, and overexpression correlates with worse prognosis in NSCLC patients. Most importantly, PKC isozymes have been established as mediators of resistance to tyrosine kinase inhibitors in NSCLC. Unfortunately, however, PKC-directed therapeutics have yielded unsatisfactory results, likely due to a lack of specific evaluation for PKC. To achieve satisfactory results in clinical trials, predictive biomarkers of PKC activity must be established and screened for prior to patient enrollment. Furthermore, tandem inhibition of PKC and molecular drivers may be a potential therapeutic strategy to prevent the emergence of resistance in NSCLC.     

In Situ Liquid-Cell TEM Observation of Multiphase Classical and Nonclassical Nucleation of Calcium Oxalate

Calcium oxalate (CaOx) is the major phase in kidney stones and the primary calcium storage medium in plants. CaOx can form crystals with different lattice types, water contents, and crystal structures. However, the conditions and mechanisms leading to nucleation of particular CaOx crystals are unclear. Here, liquid-cell transmission electron microscopy and atomistic molecular dynamics simulations are used to study in situ CaOx nucleation at different conditions. The observations reveal that rhombohedral CaOx monohydrate (COM) can nucleate via a classical pathway, while square COM can nucleate via a non-classical multiphase pathway. Citrate, a kidney stone inhibitor, increases the solubility of calcium by forming calcium-citrate complexes and blocks oxalate ions from approaching calcium. The presence of multiple hydrated ionic species draws additional water molecules into nucleating CaOx dihydrate crystals. These findings reveal that by controlling the nucleation pathways one can determine the macroscale crystal structure, hydration state, and morphology of CaOx.     

Thermo-bioconvection effect on hybrid nanofluid flow over a stretching/shrinking melting wedge

The research focused on nanomaterial solutions and their flow characteristics in relation to their usage. The application of such composites in biological rheological models, in particular, has received a lot of interest. The use of nanofluids in cooling tiny electronic devices like microchips and associated devices cannot be emphasized. Our goal is to explore the influence of a binary chemical reaction and Arrhenius activation energy on a hybrid nanofluid over a melting wedge in a spongy media. It is anticipated that the water-based nanoparticle contains gyrotactic microbes. By using appropriate similarity variables, the resultant dimensional nonlinear boundary-layer model is reduced and turned into a dimensionless form. A Chebyshev spectral collocation approach is useful in solving the highly nonlinear model. In terms of physical importance, the effects of important factors on developing profiles are displayed graphically and explained. Computational outcomes are obtained via MATHEMATICA. The plot of residual error is also shown to demonstrate the method's rapid convergence. According to the study's findings, by increasing the melting parameter, the rate of heat transportation at the wall decreases greatly on the average of 12.81%, but the Sherwood number becomes effective for the chemical reaction rate with a rate of about 24.81%.     

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current Dynamometer based on experimentally obtained system operational data. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), Radial Basis Network (RBNN), Neuro Fuzzy Network (ANFIS) coupled MPC have quite satisfying performances. The presented results indicate that, amongst them, GRNN appears to provide the best performance.