Citations

Background: Different clinical syndromes can arise from Alzheimer’s disease (AD) neuropathology, including dementia of the Alzheimer’s type (DAT), logopenic primary progressive aphasia (lvPPA), and posterior cortical atrophy (PCA). Objective: To assess similarities and differences in patterns of white matter tract degeneration across these syndromic variants of AD. Methods: Sixty-four subjects (22 DAT, 24 lvPPA, and 18 PCA) that had diffusion tensor imaging and showed amyloid-β deposition on PET were assessed in this case-control study. A whole-brain voxel-based analysis was performed to assess differences in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity across groups. Results: All three groups showed overlapping diffusion abnormalities in a network of tracts, including fornix, corpus callosum, posterior thalamic radiations, superior longitudinal fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus. Subtle regional differences were also observed across groups, with DAT particularly associated with degeneration of fornix and cingulum, lvPPA with left inferior fronto-occipital fasciculus and uncinate fasciculus, and PCA with posterior thalamic radiations, superior longitudinal fasciculus, posterior cingulate, and splenium of the corpus callosum. Conclusion: These findings show that while each AD phenotype is associated with degeneration of a specific structural network of white matter tracts, striking spatial overlap exists among the three network patterns that may be related to AD pathology.

Abstract Purpose: Recent studies suggest that SIRT1-activating compounds (STAC) are a promising class of anticancer drugs, although their mechanism of action remains elusive. The main goal of this study is to determine the role of STACs as a potential therapy for pancreatic cancer. In addition, we also explored the mechanism by which these compounds affect pancreatic cancer. Experimental design: Using in vitro (cell culture experiments) and in vivo (xenograft experiments) approaches, we studied the role of SIRT1 agonists (STAC) in human pancreatic cancer cell viability and growth. Results: We show that SIRT1 is highly expressed in pancreatic cancer cells and that the STACs SRT1720, SRT1460, and SRT3025 inhibited cell growth and survival of pancreatic cancer cells. STACs enhanced the sensitivity of pancreatic cells to gemcitabine and paclitaxel, indicating that these drugs could be used in combination with other chemotherapy drugs. We also show that STACs were very effective in inhibiting tumor xenograft growth. In mechanistic studies, we observed that STACs activated a SIRT1 lysosomal–dependent cell death. Furthermore, the effect of STACs on cell viability was also dependent on the expression of the endogenous SIRT1 inhibitor DBC1. Conclusions: Taken together, our results reveal an essential role for SIRT1 and lysosomes in the death pathway regulated by STACs in pancreatic cancer cells. Clin Cancer Res; 22(10); 2496–507. ©2015 AACR.

© 2016 Heart Rhythm Society, the American College of Cardiology Foundation, and the Society for Cardiovascular Angiography and Interventions

AbstractKernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant‐ and microbiome‐based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.