Vanderbilt University

About: Vanderbilt University is a education organization based out in Nashville, Tennessee, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 45066 authors who have published 106528 publications receiving 5435039 citations. The organization is also known as: Vandy.

Effect of Introduction of the Pneumococcal Conjugate Vaccine on Drug-Resistant Streptococcus pneumoniae

Abstract: BACKGROUND Five of seven serotypes in the pneumococcal conjugate vaccine, introduced for infants in the United States in 2000, are responsible for most penicillin-resistant infections. We examined the effect of this vaccine on invasive disease caused by resistant strains. METHODS We used laboratory-based data from Active Bacterial Core surveillance to measure disease caused by antibiotic-nonsusceptible pneumococci from 1996 through 2004. Cases of invasive disease, defined as disease caused by pneumococci isolated from a normally sterile site, were identified in eight surveillance areas. Isolates underwent serotyping and susceptibility testing. RESULTS Rates of invasive disease caused by penicillin-nonsusceptible strains and strains not susceptible to multiple antibiotics peaked in 1999 and decreased by 2004, from 6.3 to 2.7 cases per 100,000 (a decline of 57 percent; 95 percent confidence interval, 55 to 58 percent) and from 4.1 to 1.7 cases per 100,000 (a decline of 59 percent; 95 percent confidence interval, 58 to 60 percent), respectively. Among children under two years of age, disease caused by penicillin-nonsusceptible strains decreased from 70.3 to 13.1 cases per 100,000 (a decline of 81 percent; 95 percent confidence interval, 80 to 82 percent). Among persons 65 years of age or older, disease caused by penicillin-nonsusceptible strains decreased from 16.4 to 8.4 cases per 100,000 (a decline of 49 percent). Rates of resistant disease caused by vaccine serotypes fell 87 percent. An increase was seen in disease caused by serotype 19A, a serotype not included in the vaccine (from 2.0 to 8.3 per 100,000 among children under two years of age). CONCLUSIONS The rate of antibiotic-resistant invasive pneumococcal infections decreased in young children and older persons after the introduction of the conjugate vaccine. There was an increase in infections caused by serotypes not included in the vaccine.

Alport's Syndrome, Goodpasture's Syndrome, and Type IV Collagen

Abstract: Defects in type IV collagen, a collagenous protein involved in the formation of basement membranes, have been implicated in hereditary Alport's syndrome and acquired Goodpasture's syndrome. Mutations in genes corresponding to the building blocks of type IV collagen cause Alport's syndrome, whereas autoantibodies against structures that are usually hidden in the recesses of collagen IV cause Goodpasture's syndrome.

Strong association of de novo copy number mutations with sporadic schizophrenia.

Abstract: Schizophrenia is an etiologically heterogeneous psychiatric disease, which exists in familial and nonfamilial (sporadic) forms. Here, we examine the possibility that rare de novo copy number (CN) mutations with relatively high penetrance contribute to the genetic component of schizophrenia. We carried out a whole-genome scan and implemented a number of steps for finding and confirming CN mutations. Confirmed de novo mutations were significantly associated with schizophrenia (P = 0.00078) and were collectively approximately 8 times more frequent in sporadic (but not familial) cases with schizophrenia than in unaffected controls. In comparison, rare inherited CN mutations were only modestly enriched in sporadic cases. Our results suggest that rare de novo germline mutations contribute to schizophrenia vulnerability in sporadic cases and that rare genetic lesions at many different loci can account, at least in part, for the genetic heterogeneity of this disease.

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Abstract: Motivation: Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types. Results: Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets. Availability: The software system GEMS is available for download from http://www.gems-system.org for non-commercial use. Contact: alexander.statnikov@vanderbilt.edu