Systemic chemotherapeutics remain the standard of care for most malignancies even though they frequently suffer from narrow therapeutic index, poor serum solubility, and off-target effects. In this study, we have encapsulated etoposide, a topoisomerase inhibitor effective against a wide range of cancers, in surface-modified liposomes decorated with anti-GD2 antibodies. We characterized the properties of the liposomes using a variety of methods including dynamic light scattering, electron microscopy, and Fourier transformed infrared spectroscopy. We examined whether these immunoliposomes were able to target cell lines expressing varying levels of surface GD2 and affect cellular proliferation. Anti-GD2 liposomes were generally targeted in a manner that correlated with GD2 expression and inhibited proliferation in cell lines to which they were efficiently targeted. The mechanism by which the immunoliposomes entered targeted cells appeared to be via clathrin-dependent uptake as demonstrated using flow cytometry and confocal microscopy. These studies suggest that anti-GD2-targeted, etoposide-loaded liposomes represent a potential strategy for more effective delivery of anti-cancer drugs that could be used for GD2 positive tumors.

In this paper, we introduce a framework for classifying images according to high-level sentiment. We subdivide the task into three primary problems: emotion classification on faces, human pose estimation, and 3D estimation and clustering of groups of people. We introduce novel algorithms for matching body parts to a common individual and clustering people in images based on physical location and orientation. Our results outperform several baseline approaches.