<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shimizu, Chisato</style></author><author><style face="normal" font="default" size="100%">Jain, Sonia</style></author><author><style face="normal" font="default" size="100%">Davila, Sonia</style></author><author><style face="normal" font="default" size="100%">Hibberd, Martin L</style></author><author><style face="normal" font="default" size="100%">Lin, Kevin O</style></author><author><style face="normal" font="default" size="100%">Molkara, Delaram</style></author><author><style face="normal" font="default" size="100%">Frazer, Jeffrey R</style></author><author><style face="normal" font="default" size="100%">Sun, Shelly</style></author><author><style face="normal" font="default" size="100%">Baker, Annette L</style></author><author><style face="normal" font="default" size="100%">Newburger, Jane W</style></author><author><style face="normal" font="default" size="100%">Rowley, Anne H</style></author><author><style face="normal" font="default" size="100%">Shulman, Stanford T</style></author><author><style face="normal" font="default" size="100%">Davila, Sonia</style></author><author><style face="normal" font="default" size="100%">Burgner, David</style></author><author><style face="normal" font="default" size="100%">Breunis, Willemijn B</style></author><author><style face="normal" font="default" size="100%">Kuijpers, Taco W</style></author><author><style face="normal" font="default" size="100%">Wright, Victoria J</style></author><author><style face="normal" font="default" size="100%">Levin, Michael</style></author><author><style face="normal" font="default" size="100%">Eleftherohorinou, Hariklia</style></author><author><style face="normal" font="default" size="100%">Coin, Lachlan</style></author><author><style face="normal" font="default" size="100%">Popper, Stephen J</style></author><author><style face="normal" font="default" size="100%">Relman, David A</style></author><author><style face="normal" font="default" size="100%">Fury, Wen</style></author><author><style face="normal" font="default" size="100%">Lin, Calvin</style></author><author><style face="normal" font="default" size="100%">Mellis, Scott</style></author><author><style face="normal" font="default" size="100%">Tremoulet, Adriana H</style></author><author><style face="normal" font="default" size="100%">Burns, Jane C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transforming growth factor-beta signaling pathway in patients with Kawasaki disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Circ Cardiovasc Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Circ Cardiovasc Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aorta</style></keyword><keyword><style  face="normal" font="default" size="100%">Australia</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronary Vessels</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Great Britain</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunoglobulins, Intravenous</style></keyword><keyword><style  face="normal" font="default" size="100%">Linkage Disequilibrium</style></keyword><keyword><style  face="normal" font="default" size="100%">Mucocutaneous Lymph Node Syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein-Serine-Threonine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptors, Transforming Growth Factor beta</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Smad3 Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Transforming Growth Factor beta</style></keyword><keyword><style  face="normal" font="default" size="100%">Transforming Growth Factor beta2</style></keyword><keyword><style  face="normal" font="default" size="100%">United States</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">16-25</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Transforming growth factor (TGF)-β is a multifunctional peptide that is important in T-cell activation and cardiovascular remodeling, both of which are important features of Kawasaki disease (KD). We postulated that variation in TGF-β signaling might be important in KD susceptibility and disease outcome.

METHODS AND RESULTS: We investigated genetic variation in 15 genes belonging to the TGF-β pathway in a total of 771 KD subjects of mainly European descent from the United States, the United Kingdom, Australia, and the Netherlands. We analyzed transcript abundance patterns using microarray and reverse transcriptase-polymerase chain reaction for these same genes, and measured TGF-β2 protein levels in plasma. Genetic variants in TGFB2, TGFBR2, and SMAD3 and their haplotypes were consistently and reproducibly associated with KD susceptibility, coronary artery aneurysm formation, aortic root dilatation, and intravenous immunoglobulin treatment response in different cohorts. A SMAD3 haplotype associated with KD susceptibility replicated in 2 independent cohorts and an intronic single nucleotide polymorphism in a separate haplotype block was also strongly associated (A/G, rs4776338) (P=0.000022; odds ratio, 1.50; 95% confidence interval, 1.25 to 1.81). Pathway analysis using all 15 genes further confirmed the importance of the TGF-β pathway in KD pathogenesis. Whole-blood transcript abundance for these genes and TGF-β2 plasma protein levels changed dynamically over the course of the illness.

CONCLUSIONS: These studies suggest that genetic variation in the TGF-β pathway influences KD susceptibility, disease outcome, and response to therapy, and that aortic root and coronary artery Z scores can be used for phenotype/genotype analyses. Analysis of transcript abundance and protein levels further support the importance of this pathway in KD pathogenesis.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boxwala, Aziz A</style></author><author><style face="normal" font="default" size="100%">Kim, Jihoon</style></author><author><style face="normal" font="default" size="100%">Grillo, Janice M</style></author><author><style face="normal" font="default" size="100%">Ohno-Machado, Lucila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using statistical and machine learning to help institutions detect suspicious access to electronic health records.</style></title><secondary-title><style face="normal" font="default" size="100%">J Am Med Inform Assoc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Am Med Inform Assoc</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Security</style></keyword><keyword><style  face="normal" font="default" size="100%">Electronic Health Records</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Logistic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Management Audit</style></keyword><keyword><style  face="normal" font="default" size="100%">Pilot Projects</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Software Validation</style></keyword><keyword><style  face="normal" font="default" size="100%">United States</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jul-Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">498-505</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE: To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs.

METHODS: From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures.

RESULTS: The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM.

LIMITATIONS: The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction.

CONCLUSION: The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lacson, Ronilda</style></author><author><style face="normal" font="default" size="100%">Pitzer, Erik</style></author><author><style face="normal" font="default" size="100%">Kim, Jihoon</style></author><author><style face="normal" font="default" size="100%">Galante, Pedro</style></author><author><style face="normal" font="default" size="100%">Hinske, Christian</style></author><author><style face="normal" font="default" size="100%">Ohno-Machado, Lucila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DSGeo: software tools for cross-platform analysis of gene expression data in GEO.</style></title><secondary-title><style face="normal" font="default" size="100%">J Biomed Inform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Biomed Inform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Communication Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Information Storage and Retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">Medical Informatics Applications</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">709-15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Gene Expression Omnibus (GEO) is the largest resource of public gene expression data. While GEO enables data browsing, query and retrieval, additional tools can help realize its potential for aggregating and comparing data across multiple studies and platforms. This paper describes DSGeo-a collection of valuable tools that were developed for annotating, aggregating, integrating, and analyzing data deposited in GEO. The core set of tools include a Relational Database, a Data Loader, a Data Browser, and an Expression Combiner and Analyzer. The application enables querying for specific sample characteristics and identifying studies containing samples that match the query. The Expression Combiner application enables normalization and aggregation of data from these samples and returns these data to the user after filtering, according to the user's preferences. The Expression Analyzer allows simple statistical comparisons between groups of data. This seamless integration makes annotated cross-platform data directly available for analysis.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record></records></xml>