Citations

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease. This paper is part of the special issue of OMICS on data standards.

Abstract Background: Glycerol kinase deficiency (GKD) is an X-linked recessive disorder that presents in both isolated and complex forms. The contiguous deletion that leads to GKD also commonly affects NR0B1 (DAX1), the gene associated with adrenal hypoplasia congenita, and DMD, the Duchenne muscular dystrophy gene. Molecular testing to delineate this deletion is expensive and has only limited availability. Methods: We designed a multiplex PCR assay for the detection and mapping of a contiguous deletion potentially affecting the IL1RAPL1, NR0B1, GK, and DMD genes in a 29-month-old male patient with GKD. Results: Multiplex PCR detected a contiguous deletion that involved the IL1RAPL1, NR0B1, GK, and DMD genes. Although the patient had a creatine kinase concentration within the reference interval, further mapping with PCR revealed that exon 74 was the last intact exon at the 3′ end of the DMD gene. Conclusions: Multiplex PCR is an effective and inexpensive way to detect and map the contiguous deletion in cases of complex GKD. The extension of a deletion to include DMD exon 75 in a patient with a creatine kinase concentration within the reference interval suggests that this region of the gene may not be essential for protein function.

Purpose The purpose of this study was to determine whether an algorithm that recommended individualized changes in therapy would help providers to change therapy appropriately and improve glycemic control in their patients. Methods The algorithm recommended specific doses of oral agents and insulin based on a patient's medications and glucose or A1C levels at the time of the visit. The prospective observational study analyzed the effect of the algorithm on treatment decisions and A1C levels in patients with type 2 diabetes. Results The study included 1250 patients seen in pairs of initial and follow-up visits during a 7-month baseline and/or a subsequent 7-month algorithm period. The patients had a mean age of 62 years, body mass index of 33 kg/m2, duration of diabetes of 10 years, were 94% African American and 71% female, and had average initial A1C level of 7.7%. When the algorithm was available, providers were 45% more likely to intensify therapy when indicated (P = .005) and increased therapy by a 20% greater amount (P < .001). A1C level at follow-up was 90% more likely to be <7% in the algorithm group, even after adjusting for differences in age, sex, body mass index, race, duration of diabetes and therapy, glucose, and A1C level at the initial visit (P < .001). Conclusions Use of an algorithm that recommends patient-specific changes in diabetes medications improves both provider behavior and patient A1C levels and should allow quantitative evaluation of provider actions for that provider's patients.