The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research.

BACKGROUND: Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. RESULTS: The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. CONCLUSIONS: The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

An opt-out influenza vaccination policy improves immunization rates in primary care.

PURPOSE/OBJECTIVE: During the 2007-08 influenza season 36% of outpatients seen at our urban family medicine center received an influenza immunization. We explored the expected increase in vaccinations from an opt-out policy using standing orders in a lower-income population. METHODS: A comparison of vaccination rates during the periods 10/1/2007 to 3/31/2008 (P1) versus 10/1/2008 to 3/31/2009 (P2) with adjustments for cohort non-independence. RESULTS: The overall P2 vaccination rate increased to 49% [p<.000001]. P2 rates were significantly higher for those with diabetes, both genders, African American and European American patients from 3 to 64 years old, and in all insurance groups. The vaccination rates for patients with Medicaid insurance (37% and 54%) were higher than the rates for patients with commercial insurance (31% and 43%). CONCLUSIONS: The opt-out policy is associated with a moderate (1.4 fold) increase in the vaccination rate. Primary care resource constraints may limit further improvement.

Bioinformatics of the urinary proteome.

Proteomics-based biomarker discovery studies usually entail the isolation of peptide fragments from candidate biomarkers of interest. Detection of such peptides from biological or clinical samples and identification of the corresponding full-length protein and the gene encoding that protein provide the means to gather a wealth of information. This information, termed annotation because it is attached to the gene or protein sequence under study, describes relationships to human disease, cytogenetic map position, protein domains, protein-protein and small molecule interactions, tissues or cell types in which the gene is expressed, as well as several other aspects of gene and protein function. Bioinformatics tools are employed and genome databases are mined to retrieve this information. Coupled with extensive gene and protein annotation, detected peptides are better placed in a biological context with respect to the health status of the subject. Examples of the status include cancers (bladder, kidney), metabolic disorders (diabetes and kidney function), and the nutritional state of the subject.