University of Pittsburgh

About: University of Pittsburgh is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 87042 authors who have published 201012 publications receiving 9656783 citations. The organization is also known as: Pitt & Western University of Pennsylvania.

Production of α1,3-Galactosyltransferase-Deficient Pigs

Abstract: The enzyme alpha1,3-galactosyltransferase (alpha1,3GT or GGTA1) synthesizes alpha1,3-galactose (alpha1,3Gal) epitopes (Galalpha1,3Galbeta1,4GlcNAc-R), which are the major xenoantigens causing hyperacute rejection in pig-to-human xenotransplantation. Complete removal of alpha1,3Gal from pig organs is the critical step toward the success of xenotransplantation. We reported earlier the targeted disruption of one allele of the alpha1,3GT gene in cloned pigs. A selection procedure based on a bacterial toxin was used to select for cells in which the second allele of the gene was knocked out. Sequencing analysis demonstrated that knockout of the second allele of the alpha1,3GT gene was caused by a T-to-G single point mutation at the second base of exon 9, which resulted in inactivation of the alpha1,3GT protein. Four healthy alpha1,3GT double-knockout female piglets were produced by three consecutive rounds of cloning. The piglets carrying a point mutation in the alpha1,3GT gene hold significant value, as they would allow production of alpha1,3Gal-deficient pigs free of antibiotic-resistance genes and thus have the potential to make a safer product for human use.

Asthma endotypes: A new approach to classification of disease entities within the asthma syndrome

Abstract: It is increasingly clear that asthma is a complex disease made up of number of disease variants with different underlying pathophysiologies. Limited knowledge of the mechanisms of these disease subgroups is possibly the greatest obstacle in understanding the causes of asthma and improving treatment and can explain the failure to identify consistent genetic and environmental correlations to asthma. Here we describe a hypothesis whereby the asthma syndrome is divided into distinct disease entities with specific mechanisms, which we have called "asthma endotypes." An "endotype" is proposed to be a subtype of a condition defined by a distinct pathophysiological mechanism. Criteria for defining asthma endotypes on the basis of their phenotypes and putative pathophysiology are suggested. Using these criteria, we identify several proposed asthma endotypes and propose how these new definitions can be used in clinical study design and drug development to target existing and novel therapies to patients most likely to benefit. This PRACTALL (PRACtical ALLergy) consensus report was produced by experts from the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology.

Eighth annual INTERMACS report: Special focus on framing the impact of adverse events.

Abstract: Background The Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database now includes >20,000 patients from >180 hospitals. Methods The eighth annual report of INTERMACS updates the first decade of patient enrollment. Results In the current era, >95% of implants are continuous flow devices. Overall survival continues to remain >80% at 1 year and 70% at 2 years. Review of major adverse events shows minimal advantage for patients with ambulatory heart failure pre-implant. Stroke, major infection, and continued inotrope requirement during the first 3 months have a major effect on subsequent survival. Conclusions Greater application of durable devices to patients with ambulatory heart failure will mandate more effective neutralization or prevention of major adverse events.

Learning Structured Sparsity in Deep Neural Networks

Abstract: High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs. SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently accelerate the DNN’s evaluation. Experimental results show that SSL achieves on average 5.1X and 3.1X speedups of convolutional layer computation of AlexNet against CPU and GPU, respectively, with off-the-shelf libraries. These speedups are about twice speedups of non-structured sparsity; (3) regularize the DNN structure to improve classification accuracy. The results show that for CIFAR-10, regularization on layer depth reduces a 20-layer Deep Residual Network (ResNet) to 18 layers while improves the accuracy from 91.25% to 92.60%, which is still higher than that of original ResNet with 32 layers. For AlexNet, SSL reduces the error by ~1%.