Gene/Protein Disease Symptom Drug Enzyme Compound
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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The synthesis and structure-activity relationships of a number of 1,3-bis(aryloxy)propanes, which are in vivo antagonists of LTD4 in the guinea pig, are described. One of these compounds, 4 (Wy-44,329), was not only approximately equipotent with the standard 1 (FPL 55712) in the LTC4 (ID50 = 0.17 and 0.23 mg/kg iv, respectively) and LTD4 (ID50 = 0.11 and 0.15 mg/kg iv, respectively) challenge models but also possessed greater potency in the ovalbumin challenge model (ID50 = 0.47 mg/kg and 4.1 mg/kg iv, respectively) and a longer duration of action. This compound was a competitive LTD4 antagonist on guinea pig ileum (pA2 = 9.4) and possessed mediator release (rat PCA, ID50 = 0.26 mg/kg iv) and 5-lipoxygenase (IC50 = 32 microM vs. 5-HETE) inhibitory activities.
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PMID:Novel 1,3-bis(aryloxy)propanes as leukotriene D4 antagonists. 380 66

Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities-such as EEG, fMRI, LFP, and spike trains-offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modeling of LFP and spike train data, and present a novel Bayesian method for neural decoding to infer behavioral and experimental conditions. This model performs supervised dual-dimensionality reduction: it learns low-dimensional representations of two different sources of information that not only explain variation in the input data itself, but also predict extra-neuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential PCA and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference, and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone, and combined LFP and spike train data. We compare two methods for modeling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights.
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PMID:A Bayesian supervised dual-dimensionality reduction model for simultaneous decoding of LFP and spike train signals. 2852 31