Encapsulating anticancer drugs in nanoparticles has proven to be an effective mechanism to alter the pharmacokinetic and pharmacodynamic profiles of the drugs, leading to clinically useful cancer therapeutics like Doxil and DaunoXome. Underdeveloped tumor vasculature and lymphatics allow these first-generation nanoparticles to passively accumulate within the tumor, but work to create the next-generation nanoparticles that actively participate in the tumor targeting process is underway. Lipid nanoparticles functionalized with targeting peptides are among the most often studied. The goal of this article is to review the recently published literature of targeted nanoparticles to highlight successful designs that improved in vivo tumor therapy, and to discuss the current challenges of designing these nanoparticles for effective in vivo performance.

A fiber optic bead-based sensor array platform has been employed to discriminate between six different odors and air carrier gas. Six different bead sensor types, with over 250 replicates of each, were monitored before, during, and after odor exposure to produce time-dependent fluorescence response patterns that were unique for each sensor-analyte combination. A total of 2,683 sensors were analyzed with respect to changes in their fluorescence, and signals from identical sensor beads were averaged to improve signal-to-noise ratios. Analyte classification rates of 100% were achieved for three complex (coffee bean) odors and three pure (simple) odors (toluene, acetone, 1,3-dinitrotoluene) measured at their highest relative concentrations. When lower odor concentrations were employed, the system exhibited better than 85% classification rates for analyte discrimination. Sensor response repeatability to these odor stimuli has also been quantified statistically, which is vital in defining the detection limit of the overall system. These results demonstrate, for the first time, the utility of our bead array technology for discriminating between different odor types at various dilution levels.

Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robots

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