The central administration of vitisin a, extracted from Vitis vinifera, improves cognitive function and related signaling pathways in a scopolamine-induced dementia model.

Neurodegenerative disorders, such as Alzheimer's disease (AD), are characterized by cognitive function loss and progressive memory impairment. Vitis vinifera, which is consumed in the form of fruits and wines in various countries, contains several dietary stilbenoids that have beneficial effects on neuronal disorders related to cognitive impairment. However, few studies have investigated the hypothalamic effects of vitisin A, a resveratrol tetramer derived from V. vinifera stembark, on cognitive functions and related signaling pathways. In this study, we conducted in vitro, ex vivo, and in vivo experiments with multiple biochemical and molecular analyses to investigate its pharmaceutical effects on cognitive functions. Treatment with vitisin A increased cell viability and cell survival under H2O2-exposed conditions in a neuronal SH-SY5 cell line. Ex vivo experiments showed that vitisin A treatment restored the scopolamine-induced disruption of long-term potentiation (LTP) in the hippocampal CA3-CA1 synapse, indicating the restoration of synaptic mechanisms of learning and memory. Consistently, central administration of vitisin A ameliorated scopolamine-induced disruptions of cognitive and memory functions in C57BL/6 mice, as evidenced by Y-maze and passive avoidance tests. Further studies showed that vitisin A upregulates BDNF-CREB signaling in the hippocampus. Together, our findings suggest that vitisin A exhibits neuroprotective effects, at least partially, by upregulating BDNF-CREB signaling and LTP.

Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.

Patent valuation is required to revitalize patent transactions, but calculating a reasonable value that consumers and suppliers could satisfy is difficult. When machine learning is used, a quantitative evaluation based on a large volume of data is possible, and evaluation can be conducted quickly and inexpensively, contributing to the activation of patent transactions. However, due to patent characteristics, securing the necessary training data is challenging because most patents are traded privately to prevent technical information leaks. In this study, the derived marketable value of a patent through event study is used for patent value evaluation, matching it with the semantic information from the patent calculated using latent Dirichlet allocation (LDA)-based topic modeling. In addition, an ensemble learning methodology that combines the predicted values of multiple predictive models was used to determine the prediction stability. Base learners with high predictive power for each fold were different, but the ensemble model that was trained on the base learners' predicted values exceeded the predictive power of the individual models. The Wilcoxon rank-sum test indicated that the superiority of the accuracy of the ensemble model was statistically significant at the 95% significance level.