Search

The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents’ spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model. [ABSTRACT FROM AUTHOR]

Browning of white adipose tissue (WAT) has been highlighted as a new possible therapeutic target for obesity, diabetes and lipid metabolic disorders, because WAT browning could increase energy expenditure and reduce adiposity. The new clusters of adipocytes that emerge with WAT browning have been named ‘beige’ or ‘brite’ adipocytes. Recent reports have indicated that the renin-angiotensin system (RAS) plays a role in various aspects of adipose tissue physiology and dysfunction. The biological effects of angiotensin II, a major component of RAS, are mediated by two receptor subtypes, angiotensin II type 1 receptor (AT1R) and type 2 receptor (AT2R). However, the functional roles of angiotensin II receptor subtypes in WAT browning have not been defined. Therefore, we examined whether deletion of angiotensin II receptor subtypes (AT1aR and AT2R) may affect white-to-beige fat conversion in vivo. AT1a receptor knockout (AT1aKO) mice exhibited increased appearance of multilocular lipid droplets and upregulation of thermogenic gene expression in inguinal white adipose tissue (iWAT) compared to wild-type (WT) mice. AT2 receptor-deleted mice did not show miniaturization of lipid droplets or alteration of thermogenic gene expression levels in iWAT. An in vitro experiment using adipose tissue-derived stem cells showed that deletion of the AT1a receptor resulted in suppression of adipocyte differentiation, with reduction in expression of thermogenic genes. These results indicate that deletion of the AT1a receptor might have some effects on the process of browning of WAT and that blockade of the AT1 receptor could be a therapeutic target for the treatment of metabolic disorders. [ABSTRACT FROM AUTHOR]