Aging of the subventricular zone neural stem cell niche: evidence for quiescence-associated changes between early and mid-adulthood.

Stem cells can exist in either active or quiescent states. In the aging hippocampus, adult neural stem cells (aNSCs) shift into a quiescent state, contributing to age-related reductions in hippocampal neurogenesis. Here, we focused on the subventricular zone (SVZ) stem cell niche of the adult forebrain, asking to what extent quiescence-associated changes in aNSCs are initiated between early and middle-age. Immunohistochemical and label retention experiments revealed that the overall output of the SVZ stem cell system was already highly decreased in middle-aged mice (12-months-old) compared with young adult mice (2-month-old), as measured by reduced marker expression for multiple neural precursor sub-populations and diminished addition of SVZ-derived neuroblasts to the olfactory bulbs (OBs). These changes were associated with significant cytological aberrations within the SVZ niche, including an overall atrophy of the SVZ and accumulation of large lipid droplets within ependymal cells, which are key support cells of the SVZ niche. Importantly, the reduced output of the middle-aged SVZ stem cell system correlated with quiescence-associated changes in middle-aged aNSCs. Specifically, while tissue culture experiments showed that young adult and middle-aged forebrains possessed equal numbers of neurosphere-forming aNSCs, the middle-aged neurospheres exhibited differences in their in vitro properties, and middle-aged aNSCs in vivo divided less frequently. These findings demonstrate that aNSCs begin undergoing quiescence-associated changes between early and mid-adulthood in the mouse SVZ, and serve as a useful framework for further studies aimed at defining the early events involved in aging-associated quiescence of aNSCs.

A comparison of logistic regression to decision tree induction in the diagnosis of carpal tunnel syndrome.

This paper aims to compare and contrast two types of model (logistic regression and decision tree induction) for the diagnosis of carpal tunnel syndrome using four ordered classification categories. Initially, we present the classification performance results based on more than two covariates (multivariate case). Our results suggest that there is no significant difference between the two methods. Further to this investigation, we present a detailed comparison of the structure of bivariate versions of the models. The first surprising result of this analysis is that the classification accuracy of the bivariate models is slightly higher than that of the multivariate ones. In addition, the bivariate models lend themselves to graphical analysis, where the corresponding decision regions can easily be represented in the two-dimensional covariate space. This analysis reveals important structural differences between the two models.