分子バイオマーカーと診断のジャーナル

A targeted nanoconjugate is being developed for non-invasive detection of gene expression in cells expressing the JC virus oncoprotein, T-antigen, which has been associated with medulloblastoma and other cancers. JC virus T-antigen localizes predominantly to the nucleus via a classical monopartite nuclear localization signal (NLS). An antibody fragment which recognizes JC virus T-antigen was attached to cross-linked dextran coated iron oxide nanoparticles. Radiolabeled conjugates were added to mouse medulloblastoma cells expressing the target T-antigen to test their ability to bind to tumor cells and be internalized by the cells. All conjugates containing targeting antibody bound to cells and were internalized, with increasing levels over time. There was no difference in cell binding or internalization among conjugates containing 2, 4, 6 or 8 antibody fragments per nanoparticle. Conjugates with only nonspecific antibody on nanoparticles, or unconjugated nonspecific antibody, had significantly lower total binding and internalization than conjugates with targeting antibody. Unconjugated targeting antibody had equivalent or lower cell uptake compared with targeted nanoparticle conjugates. Specificity of uptake was demonstrated by >80% reduction of nanoconjugate uptake in the presence of 100 fold excess of unconjugated antibody. The presence of a membrane translocation peptide (Tat) on the nanoparticles in addition to targeting antibody did not improve nanoconjugate internalization over the internalization caused by the antibody alone. This antibody nanoconjugate demonstrates feasibility of targeting a nuclear protein and suggests that a minimum number of antibody fragments per nanoparticle are sufficient for achieving binding specificity and efficient uptake into living cells.

This paper presents the result of extensive experiments on the blood serum samples of 20 leishmaniasis patients and 44 healthy individuals using nuclear magnetic resonance (NMR) spectroscopy and the Chenomx software. The concentrations of selenium in all 64 serum samples were also measured using an atomic absorption device. The healthy and patient groups were completely differentiated using the partial least square method (PLS). In addition, important variables that could highly infl uence the group of patients were detected by the PLS loading plot. This paper also presents the results corresponding to linear and non-linear modeling of selenium concentration in serum. Stepwise multiple linear regression (MLR) was used to select the fi ve most important descriptors. In the multiple linear regression modeling approach the results obtained for R2 training, test and validation sets were 0.98, 0.97 and 0.94 respectively. Employing the same descriptors in the MLR modeling approach, a non-linear artifi cial neural network (ANN) with a 5-3- 1 structure was constructed; the results obtained from this model showed no signifi cant improvement compared with those of MLR. There was good agreement between the experimental values of selenium concentration and the values from the two models.