The prognosis of pancreatic cancer remains dismal, with little advance in chemotherapy because of its high frequency of chemoresistance. Metformin is widely used to treat type II diabetes, and was shown recently to inhibit pancreatic cancer stem cell proliferation. In the present study, we investigated the role of metformin in chemoresistance of pancreatic cancer cells to gemcitabine, and its possible cellular and molecular mechanisms. Metformin increases sensitivity of pancreatic cancer cells to gemcitabine. The mechanism involves, at least in part, the inhibition of CD133+ cells proliferation and suppression of P70S6K signaling activation via inhibition of ERK phosphorylation. Studies of primary tumor samples revealed a relationship between P70S6K signaling activation and the malignancy of pancreatic cancer. Analysis of clinical data revealed a trend of the benefit of metformin for pancreatic cancer patients with diabetes. The results suggested that metformin has a potential clinical use in overcoming chemoresistance of pancreatic cancer.
Knowledge Graphs (KGs) are composed of structured information in the form of entities and relations. And the process of extracting entities and relations from data is called Knowledge Extraction. Knowledge extraction is a fundamental task in the field of Natural Language Processing (NLP) and a key part of knowledge graph construction. In this paper, we provide comprehensive research on knowledge extraction in knowledge graph construction. We first introduce the technical architecture of the KGs and the classification of knowledge extraction. Then, we systematically categorize existing works based on the development of knowledge extraction. Finally, we review current open-source tools for knowledge extraction and summarize their advantages and disadvantages.
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