4 research outputs found

    Effects of a Dehydroevodiamine-Derivative on Synaptic Destabilization and Memory Impairment in the 5xFAD, Alzheimer's Disease Mouse Model

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    Carboxy-dehydroevodiamine·HCl (cx-DHED) is a derivative of DHED, which improves memory impairment. Carboxyl modification increases solubility in water, indicating that its bioavailability is higher than that of DHED. Cx-DHED is expected to have better therapeutic effects on Alzheimer's disease (AD) than DHED. In this study, we investigated the therapeutic effects of cx-DHED and the underlying mechanism in 5xFAD mice, transgenic (Tg) mouse model of AD model mice. In several behavioral tests, such as Y-maze, passive avoidance, and Morris water maze test, memory deficits improved significantly in cx-DHED-treated transgenic (Tg) mice compared with vehicle-treated Tg mice. We also found that AD-related pathologies, including amyloid plaque deposition and tau phosphorylation, were reduced after the treatment of Tg mice with cx-DHED. We determined the levels of synaptic proteins, such as GluN1, GluN2A, GluN2B, PSD-95 and Rabphilin3A, and Rab3A in the brains of mice of each group and found that GluN2A and PSD-95 were significantly increased in the brains of cx-DHED-treated Tg mice when compared with the brains of Tg-vehicle mice. These results suggest that cx-DHED has therapeutic effects on 5xFAD, AD model mice through the improvement of synaptic stabilization

    SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis

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    International audienceWe report the construction of a Korean evaluation-annotated corpus, hereafter called 'Evaluation Annotated Dataset (EVAD)', and its use in Aspect-Based Sentiment Analysis (ABSA) extended in order to cover e-commerce reviews containing sentiment and non-sentiment linguistic patterns. The annotation process uses Semi-Automatic Symbolic Propagation (SSP). We built extensive linguistic resources formalized as a Finite-State Transducer (FST) to annotate corpora with detailed ABSA components in the fashion e-commerce domain. The ABSA approach is extended, in order to analyze user opinions more accurately and extract more detailed features of targets, by including aspect values in addition to topics and aspects, and by classifying aspectvalue pairs depending whether values are unary, binary, or multiple. For evaluation, the KoBERT and KcBERT models are trained on the annotated dataset, showing robust performances of F1 0.88 and F1 0.90, respectively, on recognition of aspect-value pairs
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