While group II agonists are effective in several animal models of schizophrenia, they are reported to lack efficacy in moderating the effects of phencyclidine (PCP) on prepulse inhibition of acoustic startle in animal models of sensory processing deficits found in this disorder.

The objective of this study was to re-examine the efficacy of a group II metabotropic glutamate agonist and NAAG peptidase inhibitors in prepulse inhibition models of schizophrenia across two strains of mice.

The method used was an assay to determine the efficacy

of these drugs in moderating the reduction in prepulse inhibition of acoustic startle in mice treated with PCP and www.selleckchem.com/products/bv-6.html d-amphetamine.

The group II agonist LY354740 (5 and 10 mg/kg) moderated the effects of PCP on prepulse inhibition of acoustic startle in DBA/2 but not C57BL/6 mice. In contrast, two NAAG peptidase inhibitors, ZJ43 (150 mg/kg) and 2-PMPA (50, 100, and 150 mg/kg), did not significantly affect the PCP-induced reduction in prepulse inhibition in either strain.

These data demonstrate that the efficacy of group II agonists in this model of sensory motor processing is strain-specific in mice. The difference between the effects of the group II agonist and the peptidase

inhibitors in the DBA/2 mice may relate to the difference in efficacy of NAAG and the agonist at mGluR2.”
“The complex Morin Hydrate interactions between biomolecules and the consequences of these interactions BAY 11-7082 cost are known as biomolecular events. Such events particularly in proteins play a key role in several aspects of proteomics. The major source of extraction of biomolecular events is the biomedical literature. Event trigger word detection is generally the first step in computationally mining the biomedical literature for biomolecular events. In this work, we study how to efficiently map the dependency graph of a candidate sentence

into semantic/syntactic features, and use these semantic/syntactic features to detect bio-event triggers from the biomedical literature. The key factor in our method was the use of the hash operation to iteratively compute the dependency graph and utilize the properties of the hash operation to map the dependency graph into neighborhood hash features. The experimental results showed that neighborhood hash features can effectively represent the semantic/syntactic information in the sentence dependency graph. Furthermore, neighborhood hash features and basic features are complementary in the detection of biomolecular triggers. This approach, based on neighborhood hash features, achieved state-of-the-art performance on BioNLP datasets with respect to comparable evaluations. (C) 2012 Elsevier Ltd. All rights reserved.