Fluorescein isothiocyanate (FITC)-encapsulated SiO(2) core-shell particles with a nanoscale ZnO finishing layer have been synthesized for the first time as multifunctional "smart" nanostructures. Detailed characterization studies confirmed the formation of an outer ZnO layer on the SiO(2)-FITC core. These approximately 200 nm sized particles showed promise toward cell imaging and cellular uptake studies using the bacterium Escherichia coli and Jurkat cancer cells, respectively. The FITC encapsulated ZnO particles demonstrated excellent selectivity in preferentially killing Jurkat cancer cells with minimal toxicity to normal primary immune cells (18% and 75% viability remaining, respectively, after exposure to 60 microg/ml) and inhibited the growth of both gram-positive and gram-negative bacteria at concentrations > or =250-500 microg/ml (for Staphylococcus aureus and Escherichia coli, respectively). These results indicate that the novel FITC encapsulated multifunctional particles with nanoscale ZnO surface layer can be used as smart nanostructures for particle tracking, cell imaging, antibacterial treatments and cancer therapy.

This work reports a new method to improve our recent demonstration of zinc oxide (ZnO) nanoparticles (NPs) selectively killing certain human cancer cells, achieved by incorporating Fe ions into the NPs. Thoroughly characterized cationic ZnO NPs (∼6 nm) doped with Fe ions (Zn(1-x )Fe (x) O, x = 0-0.15) were used in this work, applied at a concentration of 24 μg/ml. Cytotoxicity studies using flow cytometry on Jurkat leukemic cancer cells show cell viability drops from about 43% for undoped ZnO NPs to 15% for ZnO NPs doped with 7.5% Fe. However, the trend reverses and cell viability increases with higher Fe concentrations. The non-immortalized human T cells are markedly more resistant to Fe-doped ZnO NPs than cancerous T cells, confirming that Fe-doped samples still maintain selective toxicity to cancer cells. Pure iron oxide samples displayed no appreciable toxicity. Reactive oxygen species generated with NP introduction to cells increased with increasing Fe up to 7.5% and decreased for >7.5% doping.

No abstract

Graph representation learning methods generate numerical vector representations for the nodes in a network, thereby enabling their use in standard machine learning models. These methods aim to preserve relational information, such that nodes that are similar in the graph are found close to one another in the representation space. Similarity can be based largely on one of two notions: connectivity or structural role. In tasks where node structural role is important, connectivity based methods show poor performance. Recent work has begun to focus on scalability of learning methods to massive graphs of millions to billions of nodes and edges. Many unsupervised node representation learning algorithms are incapable of scaling to large graphs, and are unable to generate node representations for unseen nodes. In this work, we propose Inferential SIR-GN, a model which is pre-trained on random graphs, then computes node representations rapidly, including for very large networks. We demonstrate that the model is able to capture node's structural role information, and show excellent performance at node and graph classification tasks, on unseen networks. Additionally, we observe the scalability of Inferential SIR-GN is comparable to the fastest current approaches for massive graphs.

No abstract