Liver X Receptor Agonist Modifies the DNA Methylation Profile of Synapse and Neurogenesis-Related Genes in the Triple Transgenic Mouse Model of Alzheimer's Disease.

The liver X receptor agonist, GW3965, improves cognition in Alzheimer's disease (AD) mouse models. Here, we determined if short-term GW3965 treatment induces changes in the DNA methylation state of the hippocampus, which are associated with cognitive improvement. Twenty-four-month-old triple-transgenic AD (3xTg-AD) mice were treated with GW3965 (50 mg/kg/day for 6 days). DNA methylation state was examined by modified bisulfite conversion and hybridization on Illumina Infinium Methylation BeadChip 450 k arrays. The Morris water maze was used for behavioral analysis. Our results show in addition to improvement in cognition methylation changes in 39 of 13,715 interrogated probes in treated 3xTg-AD mice compared with untreated 3xTg-AD mice. These changes in methylation probes include 29 gene loci. Importantly, changes in methylation status were mainly from synapse-related genes (SYP, SYN1, and DLG3) and neurogenesis-associated genes (HMGB3 and RBBP7). Thus, our results indicate that liver X receptors (LXR) agonist treatment induces rapid changes in DNA methylation, particularly in loci associated with genes involved in neurogenesis and synaptic function. Our results suggest a new potential mechanism to explain the beneficial effect of GW3965.

Computerized classification of normal and abnormal lung sounds by multivariate linear autoregressive model.

This work proposes multichannel acquisition of lung sounds by a microphone array, feature extraction by a multivariate AR (MAR) model, dimensionality reduction of the feature vectors (FV) by SVD and PCA and, their classification by a supervised neural network. A microphone array of 25 sensors was attached on the thoracic surface of the subjects, who were breathing at 1.5 L/sec. The supervised neural network used the backpropagation learning algorithm based on the Levenberg-Marquardt rule. Figures of merit for the classification task by the neural net include the percentage of correct classification during training, testing and validation phases as well as sensitivity, specificity and performance. MAR in combination with PCA provided the best average percentage of correct classification with acoustic information not seen by the neural network during the training phase (87.68%). The results state the advantages of a microphone array for the classification of normal and abnormal acoustic pulmonary information in diffuse interstitial pneumonia and for this goal, the authors assume that not only the crackles and their number indicates the severity of the disease, but the basal respiratory signal could be also affected.