COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.
Background
Autophagy is a catabolic process for degradation of intracellular components. Damaged proteins and organelles are engulfed in double-membrane vesicles ultimately fused with lysosomes. These vesicles, known as phagophores, develop to form autophagosomes. Encapsulated components are degraded after autophagosomes and lysosomes are fused. Autophagy clears denatured proteins and damaged organelles to produce macromolecules further reused by cells. This process is vital to cell homeostasis under both physiologic and pathologic conditions.
Main body
While the role of autophagy in cancer is quite controversial, the majority of studies introduce it as an anti-tumorigenesis mechanism. There are evidences confirming this role of autophagy in cancer. Mutations and monoallelic deletions have been demonstrated in autophagy-related genes correlating with cancer promotion. Another pathway through which autophagy suppresses tumorigenesis is cell cycle. On the other hand, under hypoxia and starvation condition, tumors use angiogenesis to provide nutrients. Also, autophagy flux is highlighted in vessel cell biology and vasoactive substances secretion from endothelial cells. The matrix proteoglycans such as Decorin and Perlecan could also interfere with angiogenesis and autophagy signaling pathway in endothelial cells (ECs). It seems that the connection between autophagy and angiogenesis in the tumor microenvironment is very important in determining the fate of cancer cells.
Conclusion
Matrix glycoproteins can regulate autophagy and angiogenesis linkage in tumor microenvironment. Also, finding details of how autophagy and angiogenesis correlate in cancer will help adopt more effective therapeutic approaches.
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