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Natural products have been used for centuries to treat various human ailments. In recent decades, multi-drug combinations that utilize natural products to synergistically enhance the therapeutic effects of cancer drugs have been identified and have shown success in improving treatment outcomes. While drug synergy research is a burgeoning field, there are disagreements on the definitions and mathematical parameters that prevent the standardization and proper usage of the terms synergy, antagonism, and additivity. This contributes to the relatively small amount of data on the antagonistic effects of natural products on cancer drugs that can diminish their therapeutic efficacy and prevent cancer regression. The ability of natural products to potentially degrade or reverse the molecular activity of cancer therapeutics represents an important but highly under-emphasized area of research that is often overlooked in both pre-clinical and clinical studies. This review aims to evaluate the body of work surrounding the antagonistic interactions between natural products and cancer therapeutics and highlight applications for high-throughput screening (HTS) and deep learning techniques for the identification of natural products that antagonize cancer drug efficacy.RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data’s computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies. Here, we present a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus. We evaluated the DEG detection functionality while using qRT-PCR data serving as a reference and observed a very high correlation of the logarithmized gene expression fold changes.The acknowledgement that uncontrolled and excessive use of fossil resources has become a prime concern with regard to environmental deterioration, has shifted the orientation of economies towards the implementation of sustainable routes of production, through the valorization of biomass. Green chemistry plays a key role in this regard, defining the framework of processes that encompass eco-friendly methodologies, which aim at the development of highly efficient production of numerous bioderived chemicals, with minimum environmental aggravation. One of the major concerns of the chemical industry in establishing sustainable routes of production, is the replacement of fossil-derived, volatile solvents, with bio-based benign ones, with low vapor pressure, recyclability, low or no toxicity, availability and low cost. Glycerol is a natural substance, inexpensive and non-toxic, and it is a principal by-product of biodiesel industry resulting from the transesterification process. The ever-growing market of biodiesel has created a significant surplus of glycerol production, resulting in a concomitant drop of its price. Thus, glycerol has become a highly available, low-cost liquid, and over the past decade its use as an alternative solvent has been gaining unprecedented attention. This review summarizes the utilization of glycerol and glycerol-based deep eutectic mixtures as emerging solvents with outstanding prospect in bioactive polyphenol extraction.Myostatin inhibition therapy has held much promise for the treatment of muscle wasting disorders. This is particularly true for the fatal myopathy, Duchenne Muscular Dystrophy (DMD). Following on from promising pre-clinical data in dystrophin-deficient mice and dogs, several clinical trials were initiated in DMD patients using different modality myostatin inhibition therapies. check details All failed to show modification of disease course as dictated by the primary and secondary outcome measures selected the myostatin inhibition story, thus far, is a failed clinical story. These trials have recently been extensively reviewed and reasons why pre-clinical data collected in animal models have failed to translate into clinical benefit to patients have been purported. However, the biological mechanisms underlying translational failure need to be examined to ensure future myostatin inhibitor development endeavors do not meet with the same fate. Here, we explore the biology which could explain the failed translation of myostatin inhibitors in the treatment of DMD.Mechanotransduction is a physiological process in which external mechanical stimulations are perceived, interpreted, and translated by cells into biochemical signals. Mechanical stimulations exerted by extracellular matrix stiffness and cell-cell contacts are continuously applied to living cells, thus representing a key pivotal trigger for cell homeostasis, survival, and function, as well as an essential factor for proper organ development and metabolism. Indeed, a deregulation of the mechanotransduction process consequent to gene mutations or altered functions of proteins involved in perceiving cellular and extracellular mechanics can lead to a broad range of diseases, from muscular dystrophies and cardiomyopathies to cancer development and metastatization. Here, we recapitulate the involvement of focal adhesion kinase (FAK) in the cellular conditions deriving from altered mechanotransduction processes.Due to deep learning’s accurate cognition of the street environment, the convolutional neural network has achieved dramatic development in the application of street scenes. Considering the needs of autonomous driving and assisted driving, in a general way, computer vision technology is used to find obstacles to avoid collisions, which has made semantic segmentation a research priority in recent years. However, semantic segmentation has been constantly facing new challenges for quite a long time. Complex network depth information, large datasets, real-time requirements, etc., are typical problems that need to be solved urgently in the realization of autonomous driving technology. In order to address these problems, we propose an improved lightweight real-time semantic segmentation network, which is based on an efficient image cascading network (ICNet) architecture, using multi-scale branches and a cascaded feature fusion unit to extract rich multi-level features. In this paper, a spatial information network is designed to transmit more prior knowledge of spatial location and edge information.