Imaging intracellular and systemic in vivo gold nanoparticles to enhance radiotherapy

Nanoparticles offer alternative options in cancer therapy both as drug delivery carriers and as direct therapeutic agents for cancer cell inactivation. More recently, gold nanoparticles (AuNPs) have emerged as promising radiosensitizers achieving significantly elevated radiation dose enhancement factors when irradiated with both kilo-electron-volt and mega-electronvolt X-rays. Use of AuNPs in radiobiology is now being intensely driven by the desire to achieve precise energy deposition in tumours. As a consequence, there is a growing demand for efficient and simple techniques for detection, imaging and characterization of AuNPs in both biological and tumour samples. Spatially accurate imaging on the nanoscale poses a serious challenge requiring high- or super-resolution imaging techniques. In this mini review, we discuss the challenges in using AuNPs as radiosensitizers as well as various current and novel imaging techniques designed to validate the uptake, distribution and localization in mammalian cells. In our own work, we have used multiphoton excited plasmon resonance imaging to map the AuNP intracellular distribution. The benefits and limitations of this approach will also be discussed in some detail. In some cases, the same "excitation" mechanism as is used in an imaging modality can be harnessed tomake it also a part of therapymodality (e.g. phototherapy)-such examples are discussed in passing as extensions to the imaging modality concerned.

Interpretable semantic vectors from a joint model of brain-and text-based meaning

Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advantage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representation of semantics. Evaluations show that the model 1) matches a behavioral measure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technologies and across subjects. We believe that the model is thus a more faithful representation of mental vocabularies.