Gliopathy of Demyelinating and Non-Demyelinating Strains of Mouse Hepatitis Virus.

Demyelination in the central nervous system induced by neurovirulent strains of Mouse Hepatitis Virus (MHV) is mediated by the viral spike glycoprotein, but it is not clear whether the mechanism of this disease pathology involves direct viral infection of oligodendrocytes. Detailed studies of glial cell tropism of MHV are presented, demonstrating that direct MHV infection of oligodendrocytes differs between demyelinating (RSA59) and non-demyelinating (RSMHV2) viral strains both in vitro and in vivo. Our results indicate that direct injury of mature oligodendrocytes is an important mechanism of virus-induced demyelination. In vivo, RSA59 infection was identified in spinal cord gray and white matter, but infected oligodendrocytes were restricted to white matter. In contrast, RSMHV2 infection was restricted to gray matter neurons and was not localized to oligodendrocytes. In vitro, RSA59 can infect both oligodendrocyte precursors and differentiated oligodendrocytes, whereas RSMHV2 can infect oligodendrocyte precursors but not differentiated oligodendrocytes. Viral spreading through axonal means to white matter and release of the demyelinating strain MHV at the nerve end is critical for oligodendrocytes infection and subsequent demyelination. Understanding the mechanisms by which known viruses effect demyelination in this animal model has important therapeutic implications in the treatment of human demyelinating disease.

Drug knowledge expressed as computable semantic triples.

The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,000 deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT™ codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of "Drug" HasIndication "SNOMED CT™". The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.