Neuropsychiatric Lupus with Antibody-Mediated Striatal Encephalitis

Systemic lupus erythematosus is a chronic autoimmune disease characterized by the production of autoantibodies resulting in tissue injury across multiple organs; up to 50% of patients develop neurologic involvement, collectively referred to as neuropsychiatric systemic lupus erythematosus. The cases in this clinical report will highlight a subtype of neuropsychiatric systemic lupus erythematosus demonstrating imaging findings of striatal inflammation responsive to plasmapheresis similar to those in the subset of N-methyl-D-aspartate receptor autoimmune encephalitis that involves the striatum. Although the cause for this striking imaging appearance is not definitely known, literature will be presented supporting the hypothesis that it is due to peripheral anti-double-stranded DNA antibodies entering the central nervous system to cross-react with N-methyl-D-aspartate receptor antigens.

Autoimmune Encephalitis: Pathophysiology and Imaging Review of an Overlooked Diagnosis

Autoimmune encephalitis is a relatively new category of immune-mediated disease involving the central nervous system that demonstrates a widely variable spectrum of clinical presentations, ranging from the relatively mild or insidious onset of cognitive impairment to more complex forms of encephalopathy with refractory seizure. Due to its diverse clinical features, which can mimic a variety of other pathologic processes, autoimmune encephalitis presents a diagnostic challenge to clinicians. Imaging findings in patients with these disorders can also be quite variable, but recognizing characteristic findings within limbic structures suggestive of autoimmune encephalitis can be a key step in alerting clinicians to the potential diagnosis and ensuring a prompt and appropriate clinical work-up. In this article, we review antibody-mediated encephalitis and its various subtypes with a specific emphasis on the role of neuroimaging in the diagnostic work-up.

Analysis of large screening data sets via adaptively grown phylogenetic-like trees

As the use of high-throughput screening systems becomes more routine in the drug discovery process, there is an increasing need for fast and reliable analysis of the massive amounts of the resulting data. At the forefront of the methods used is data reduction, often assisted by cluster analysis. Activity thresholds reduce the data set under investigation to manageable sizes while clustering enables the detection of natural groups in that reduced subset, thereby revealing families of compounds that exhibit increased activity toward a specific biological target. The above process, designed to handle primarily data sets of sizes much smaller than the ones currently produced by high-throughput screening systems, has become one of the main bottlenecks of the modern drug discovery process. In addition to being fragmented and heavily dependent on human experts, it also ignores all screening information related to compounds with activity less than the threshold chosen and thus, in the best case, can only hope to discover a subset of the knowledge available in the screening data sets. To address the deficiencies of the current screening data analysis process the authors have developed a new method that analyzes thoroughly large screening data sets. In this report we describe in detail this new approach and present its main differences with the methods currently in use. Further, we analyze a well-known, publicly available data set using the proposed method. Our experimental results show that the proposed method can improve significantly both the ease of extraction and amount of knowledge discovered from screening data sets.