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Item does not contain fulltext, OBJECTIVES: This study examined short-term cardiac catheterization rates and medication changes after cardiac imaging. BACKGROUND: Noninvasive cardiac imaging is widely used in coronary artery disease, but its effects on subsequent patient management are unclear. METHODS: We assessed the 90-day post-test rates of catheterization and medication changes in a prospective registry of 1,703 patients without a documented history of coronary artery disease and an intermediate to high likelihood of coronary artery disease undergoing cardiac single-photon emission computed tomography, positron emission tomography, or 64-slice coronary computed tomography angiography. RESULTS: Baseline medication use was relatively infrequent. At 90 days, 9.6% of patients underwent catheterization. The rates of catheterization and medication changes increased in proportion to test abnormality findings. Among patients with the most severe test result findings, 38% to 61% were not referred to catheterization, 20% to 30% were not receiving aspirin, 35% to 44% were not receiving a beta-blocker, and 20% to 25% were not receiving a lipid-lowering agent at 90 days after the index test. Risk-adjusted analyses revealed that compared with stress single-photon emission computed tomography or positron emission tomography, changes in aspirin and lipid-lowering agent use was greater after computed tomography angiography, as was the 90-day catheterization referral rate in the setting of normal/nonobstructive and mildly abnormal test results. CONCLUSIONS: Overall, noninvasive testing had only a modest impact on clinical management of patients referred for clinical testing. Although post-imaging use of cardiac catheterization and medical therapy increased in proportion to the degree of abnormality findings, the frequency of catheterization and medication change suggests possible undertreatment of higher risk patients. Patients were more likely to undergo cardiac catheterization after computed tomography angio

Advances in proteomics and protein expression techniques have lead to the elucidation of large amounts of protein data. Various data mining algorithms and mathematical models provide methods for analyzing this data; however, there are two issues that nee

Advances in proteomics and protein expression techniques have lead to the elucidation of large amounts of protein data. Various data mining algorithms and mathematical models provide methods for analyzing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange formats so they can be exchanged across the World Wide Web, and also read into data mining software in a consistent format and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. In this paper we summarize the structure of Protein Ontology we developed earlier, its current applications to various protein families, and its future development.

Biomedical Ontologies are huge. It is not possible for any one person to manage and engineer a complete ontology. They would need the help of Research Assistants and other people to develop and maintain the ontology. In the process of developing and maintaining the ontology theResearch Assistants may enter incorrect data, resulting in low quality of the ontology. In this paper we will propose a conceptual framework to solve these ontology management and ontology development issues. There can be N assistants entering data into the ontology. All the data entered initially is stored in an intermediate ontology. The administrator of the ontology has a set of rules, which makes a checklist that checks and validates the data in intermediate ontology for correctness according to the ontology schema. We use the Case Study of Protein Ontology for this proposed approach to develop interfaces for assistants and administrators. The proposed approach can easily be extended to other biomedical ontologies just by tweaking the administrator rule set according to the ontology.