Echinomycin, a DNA bis-intercalator peptide antibiotic, was complexed with γCD and loaded into PEGylated liposomes. The liposomes encapsulating echinomycin showed potent anti-proliferative and anti-invasive effect against U-87 MG glioblastoma cells.

No abstract

Combinatorial therapeutic strategies using siRNA and small molecules to eradicate tumors are emerging. Targeting multiple signaling pathways decreases the chances of cancer cells switching and adapting new signaling processes that may occur when using a single therapeutic modality. Aberrant functioning of Notch-1, Wnt/β-catenin, and STAT3 proteins and their crosstalk signaling pathways have been found to be involved in tumor survival, drug resistance, and relapse. In the current study, we describe a therapeutic potential of single and combinations of siRNA designed for silencing Notch-1, Wnt/β-catenin, and STAT3 in MCF7_DoxS (wild type) and MCF7_DoxR (doxorubicin resistant) breast cancer cells. The MCF7_DoxR cells were developed through treatment with a gradual increase in doxorubicin concentration, the expression of targeted genes was investigated, and the expression profiling of CD44/CD24 of the MCF7_DoxS and MCF7_DoxR cells were detected by flow cytometry. Both MCF7_DoxS and MCF7_DoxR breast cancer cells were treated with single and combinations of siRNA to investigate synergism and were analyzed for their effect on cell proliferation with and without doxorubicin treatment. The finding of this study showed the overexpression of targeted genes and the enrichment of the CD44−/CD24+ phenotype in MCF7_DoxR cells when compared to MCF7_DoxS cells. In both cell lines, the gene silencing efficacy showed a synergistic effect when combining STAT3/Notch-1 and STAT3/Notch-1/β-catenin siRNA. Interestingly, the chemosensitivity of MCF7_DoxS and MCF7_DoxR cells to doxorubicin was increased when combined with siRNA treatment. Our study shows the possibility of using single and combinations of siRNA to enhance the chemosensitivity of cancer cells to conventional antitumor chemotherapy.

No abstract

Online services depend primarily on customer feedback and communications. When this kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source of consumer data related to services and products. Well developed methods like sentiment analysis can offer insightful analyses and aid service providers in predicting outcomes based on their reviews-which, in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is more challenging on Arabic online social networks, due to the complexity of the Arabic language and its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze customers' opinions of an e-Payment service through an Arabic social network. The proposed approach yields excellent results compared to other approaches. Feature analysis is also conducted on consumer reviews to identify influencing keywords.