Topical Application of Activity-based Probes for Visualization of Brain Tumor Tissue

Several investigators have shown the utility of systemically delivered optical imaging probes to image tumors in small animal models of cancer. Here we demonstrate an innovative method for imaging tumors and tumor margins during surgery. Specifically, we show that optical imaging probes topically applied to tumors and surrounding normal tissue rapidly differentiate between tissues. In contrast to systemic delivery of optical imaging probes which label tumors uniformly over time, topical probe application results in rapid and robust probe activation that is detectable as early as 5 minutes following application. Importantly, labeling is primarily associated with peri-tumor spaces. This methodology provides a means for rapid visualization of tumor and potentially infiltrating tumor cells and has potential applications for directed surgical excision of tumor tissues. Furthermore, this technology could find use in surgical resections for any tumors having differential regulation of cysteine cathepsin activity

Properties of glutamate receptors in rat spinal cord motoneurons

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Markov chain Monte Carlo simulation in dynamic generalized linear mixed models

Dynamic generalized linear mixed models are proposed as a regression tool for nonnormal longitudinal data. This framework is an interesting combination of dynamic models, by other name state space models, and mixed models, also known as random effect models. The main feature is, that both time- and unit-specific parameters are allowed, which is especially attractive if a considerable number of units is observed over a longer period. Statistical inference is done by means of Markov chain Monte Carlo techniques in a full Bayesian setting. The algorithm is based on iterative updating using full conditionals. Due to the hierarchical structure of the model and the extensive use of Metropolis-Hastings steps for updating this algorithm mainly evaluates (log-)likelihoods in multivariate normal distributed proposals. It is derivative-free and covers a wide range of different models, including dynamic and mixed models, the latter with slight modifications. The methodology is illustrated through an analysis of artificial binary data and multicategorical business test data. (orig.)Available from TIB Hannover: RR 6137(8) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman