University of Rochester

About: University of Rochester is a education organization based out in Rochester, New York, United States. It is known for research contribution in the topics: Population & Laser. The organization has 63915 authors who have published 112762 publications receiving 5484122 citations. The organization is also known as: Rochester University.

apoE isoform-specific disruption of amyloid β peptide clearance from mouse brain

Abstract: Neurotoxic amyloid beta peptide (Abeta) accumulates in the brains of individuals with Alzheimer disease (AD). The APOE4 allele is a major risk factor for sporadic AD and has been associated with increased brain parenchymal and vascular amyloid burden. How apoE isoforms influence Abeta accumulation in the brain has, however, remained unclear. Here, we have shown that apoE disrupts Abeta clearance across the mouse blood-brain barrier (BBB) in an isoform-specific manner (specifically, apoE4 had a greater disruptive effect than either apoE3 or apoE2). Abeta binding to apoE4 redirected the rapid clearance of free Abeta40/42 from the LDL receptor-related protein 1 (LRP1) to the VLDL receptor (VLDLR), which internalized apoE4 and Abeta-apoE4 complexes at the BBB more slowly than LRP1. In contrast, apoE2 and apoE3 as well as Abeta-apoE2 and Abeta-apoE3 complexes were cleared at the BBB via both VLDLR and LRP1 at a substantially faster rate than Abeta-apoE4 complexes. Astrocyte-secreted lipo-apoE2, lipo-apoE3, and lipo-apoE4 as well as their complexes with Abeta were cleared at the BBB by mechanisms similar to those of their respective lipid-poor isoforms but at 2- to 3-fold slower rates. Thus, apoE isoforms differentially regulate Abeta clearance from the brain, and this might contribute to the effects of APOE genotype on the disease process in both individuals with AD and animal models of AD.

Skin cancer in organ transplant recipients: Epidemiology, pathogenesis, and management.

Abstract: In the United States more than 100,000 people are living with solid organ transplants. The intense immunosuppressive regimens necessary for prolonged survival of allografts significantly increase the rates of both internal and cutaneous malignancies in recipients of solid organ transplants. Skin cancer is the most common cancer in patients after transplantation. Because of the early onset and high tumor burden in transplant recipients, dermatologists have significant challenges in managing the treatment of these patients. This article describes the epidemiology and clinical presentation of skin cancer during posttransplantation immunosuppression, discusses pathogenic cofactors, and reviews the optimal management for mild and severe skin cancer in transplant recipients.

A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

Abstract: Summary: This paper studies the problem of building multiclass classifiers for tissue classification based on gene expression. The recent development of microarray technologies has enabled biologists to quantify gene expression of tens of thousands of genes in a single experiment. Biologists have begun collecting gene expression for a large number of samples. One of the urgent issues in the use of microarray data is to develop methods for characterizing samples based on their gene expression. The most basic step in the research direction is binary sample classification, which has been studied extensively over the past few years. This paper investigates the next step---multiclass classification of samples based on gene expression. The characteristics of expression data (e.g. large number of genes with small sample size) makes the classification problem more challenging. The process of building multiclass classifiers is divided into two components: (i) selection of the features (i.e. genes) to be used for training and testing and (ii) selection of the classification method. This paper compares various feature selection methods as well as various state-of-the-art classification methods on various multiclass gene expression datasets. Our study indicates that multiclass classification problem is much more difficult than the binary one for the gene expression datasets. The difficulty lies in the fact that the data are of high dimensionality and that the sample size is small. The classification accuracy appears to degrade very rapidly as the number of classes increases. In particular, the accuracy was very low regardless of the choices of the methods for large-class datasets (e.g. NCI60 and GCM). While increasing the number of samples is a plausible solution to the problem of accuracy degradation, it is important to develop algorithms that are able to analyze effectively multiple-class expression data for these special datasets.