A UML profile for the OBO relation ontology

Background: Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain. Results: We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO). Conclusions: The use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology.

Liver transplant outcome: a comparison between high and low MELD score recipients

OBJECTIVE: To compare low and high MELD scores and investigate whether existing renal dysfunction has an effect on transplant outcome. METHODS: Data was prospectively collected among 237 liver transplants (216 patients) between March 2003 and March 2009. Patients with cirrhotic disease submitted to transplantation were divided into three groups: MELD > 30, MELD < 30, and hepatocellular carcinoma. Renal failure was defined as a ± 25% decline in estimated glomerular filtration rate as observed 1 week after the transplant. Median MELD scores were 35, 21, and 13 for groups MELD > 30, MELD < 30, and hepatocellular carcinoma, respectively. RESULTS: Recipients with MELD > 30 had more days in Intensive Care Unit, longer hospital stay, and received more blood product transfusions. Moreover, their renal function improved after liver transplant. All other groups presented with impairment of renal function. Mortality was similar in all groups, but renal function was the most important variable associated with morbidity and length of hospital stay. CONCLUSION: High MELD score recipients had an improvement in the glomerular filtration rate after 1 week of liver transplantation.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Antismooth Muscle and Antiactin Antibodies Are Indirect Markers of Histological and Biochemical Activity of Autoimmune Hepatitis

Reactivity and titers of autoantibodies vary during the course of autoimmune hepatitis (AIH), and some autoantibodies have been associated with disease activity and adverse outcomes after treatment. The aim of this study was to assess the autoantibody behavior in AIH and its significance as predictors of biochemical and histological remission. A total of 117 patients with AIH (mean age 18.6 [4-69] years) were evaluated and tested for auto- antibodies at disease onset and successively (mean 3.2 [2-6] times) after a mean follow-up evaluation of 70 [20-185] months. Antismooth muscle (ASMA), antiliver kidney micro- some type 1 (anti-LKM1), antiliver cytosol type 1 (anti-LC1), antimitochondrial, antinu- clear (ANA), and antiactin antibodies (AAA) were determined at disease onset and 379 other times during the follow-up evaluation through indirect immunofluorescence in rodent tissues, HEp-2 cells, and human fibroblasts. Anti-SLA/LP were assessed 45 times in the follow-up evaluation of 19 patients using enzyme-linked immunosorbent assay (ELISA). Upon admission, AIH types 1 and 2 were observed in 95 and 17 patients, respectively. Five subjects had AIH with anti-SLA/LP as the sole markers. Patients initially negative for AAA did not develop these antibodies thereafter. ANA were detected de novo in six and three subjects with AIH types 1 and 2, respectively. After treatment, only ASMA ( > 1:80) and AAA ( > 1:40) were significantly associated with biochemical (76.9% and 79.8%) and histological features (100% and 100%) of disease activity ( P < 0.001). Conclusion: With the exception of ANA, the autoantibody profile does not markedly vary in the course of AIH. The persistence of high titers of ASMA and/or AAA in patients with AIH is associated with disease activity.