Albumin Nano-Encapsulation of Piceatannol Enhances Its Anticancer Potential in Colon Cancer Via Downregulation of Nuclear p65 and HIF-1 alpha

Piceatannol (PIC) is known to have anticancer activity, which has been attributed to its ability to block the proliferation of cancer cells via suppression of the NF-kB signaling pathway. However, its effect on hypoxia-inducible factor (HIF) is not well known in cancer. In this study, PIC was loaded into bovine serum albumin (BSA) by desolvation method as PIC-BSA nanoparticles (NPs). These PIC-BSA nanoparticles were assessed for in vitro cytotoxicity, migration, invasion, and colony formation studies and levels of p65 and HIF-1α. Our results indicate that PIC-BSA NPs were more effective in downregulating the expression of nuclear p65 and HIF-1α in colon cancer cells as compared to free PIC. We also observed a significant reduction in inflammation induced by chemical colitis in mice by PIC-BSA NPs. Furthermore, a significant reduction in tumor size and number of colon tumors was also observed in the murine model of colitis-associated colorectal cancer, when treated with PIC-BSA NPs as compared to free PIC. The overall results indicate that PIC, when formulated as PIC-BSA NPs, enhances its therpautice potential. Our work could prompt further research in using natural anticancer agents as nanoparticels with possiable human clinical trails. This could lead to the development of a new line of safe and effective therapeutics for cancer patients

Correction: Aljabali, A.A.A.; et al. Albumin Nano-Encapsulation of Piceatannol Enhances Its Anticancer Potential in Colon Cancer via down Regulation of Nuclear p65 and HIF-1α. Cancers 2020, 12, 113

The authors wish to make the following corrections to this paper [...]

Toward an Intelligent Crawling Scheduler for Archiving News Websites Using Reinforcement Learning

Web crawling is one of the fundamental activities for many kinds of web technology organizations and companies such as Internet Archive and Google. While companies like Google often focus on content delivery for users, web archiving organizations such as the Internet Archive pay more attention to the accurate preservation of the web. Crawling accuracy and efficiency are major concerns in this task. An ideal crawling module should be able to keep up with the changes in the target web site with minimal crawling frequency to maximize the routine crawling efficiency. In this project, we investigate using information from web archives' history to help the crawling process within the scope of news websites. We aim to build a smart crawling module that can predict web content change accurately both on the web page and web site structure level through modern machine learning algorithms and deep learning architectures. At the end of the project: We have collected and processed raw web archive collections from Archive.org and through our frequent crawling jobs. We have developed methods to extract identical copies of web page content and web site structure from the web archive data. We have implemented baseline models for predicting web page content change and web site structure change, web page content change with supervised machine learning algorithms; We have implemented two different reinforcement learning models for generating a web page crawling plan: a continuous prediction model and a sparse prediction model. Our results show that the reinforcement learning modal has the potential to work as an intelligent web crawling scheduler.NSF IIS-1619028Items: Archive_team_final_report.pdf: PDF version of the final report Archive_team_final_presentation.pdf: PDF version of the final presentation Archive_team_final_presentation.pptx: PPTX version of the final presentation Report_ 6604-WebArchive_Overleaf_zip.zip: a zip of Overleaf latex files for the final repor