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Alzheimer's disease is the most common form of dementia leading to the irreversible loss of neurons, and Tau hyperphosphorylation has an important role in the pathology of Alzheimer's disease. Ginkgolide A is one of the active components of Ginkgo biloba extracts which has been proven to have neuroprotective effects, but the effect of ginkgolide A on Tau hyperphosphorylation has not yet been reported. In this study, the effects of ginkgolide A on cell viability, Tau hyperphosphorylation, and the PI3K-Akt signaling pathway in N2a cell lines were explored, and methods such as the MTT assay, ELISA, and Western blots techniques were used. The results showed that ginkgolide A could increase cell viability and suppress the phosphorylation level of Tau in cell lysates, meanwhile, GSK3β was inhibited with phosphorylation at Ser9. Moreover, treatment of the cells with ginkgolide A promoted phosphorylation of PI3K and Akt, suggesting that the activation of the PI3K-Akt signaling pathway may be the mechanism for ginkgolide A to prevent the intracellular accumulation of p-Tau induced by okadaic acid and to protect the cells from Tau hyperphosphorylation-related toxicity.

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The purpose is to realize the intelligent reform of piano online teaching and the intelligent optimization of wireless networks. Empirical research is realized with quantitative research and algorithm simulation as the starting point. First, regression fitting algorithm and Relief F weight algorithm are adopted to extract the effectiveness of each characteristic variable. Next, under the guidance of metric learning theory, K-Nearest Neighbors (KNN) in Projected Feature Space (P-KNN) algorithm is proposed to complete the hierarchical recognition of piano teaching influence features. Metric Learning With Support Vector Machine (ML-SVM) classification algorithm is employed to identify the feature performance affecting piano teaching. Finally, the performance of P-KNN algorithm and ML-SVM algorithm is compared with KNN algorithm and Information-Theoretic-Metric-Learning (ITML) algorithm. It is concluded that the recognition accuracies of P-KNN and ML-SVM are 82.78% and 83.97%, respectively. Based on the quantitative research on the characteristics affecting piano teaching, artificial intelligence and wireless network optimization are combined to explore the implementation path of intelligent technology in piano teaching reform, reflect the use value of modern science and technology in piano teaching, and innovate the process of music online education reform of piano teaching.

Ebola virus (EBOV) is highly lethal due to virally encoded immune antagonists, and the combination of EBOV VP24 with karyopherin alpha (KPNA) will trigger anti-interferon (IFN) signaling. The crystal structure of VP24-KPNA5 has been proposed in recent studies, but the precise binding mechanisms are still unclear. In order to explore the VP24-KPNA5 protein binding micro-mechanisms, Molecular Dynamic (MD) simulations and Molecular Mechanics Generalized Born Surface Area (MM-GB/SA) energy calculation are performed. The obtained results show that EBOV VP24 binding to KPNA5 will rigidify their binding-face, and both proteins will be compacted during binding. According to the analyses of binding free energies of WT and the eight mutant systems, MUT3 makes the most effective contributions to the interaction; additionally MUT4, R398A and the double mutant have the second most effective influence. Hydrogen bond analysis demonstrates that inhibitors which can interfere with the formation of hydrogen bonds D480-T138, E483-R137 and D205-R396 will prevent the anti-IFN effect. Meanwhile, by combining the decomposition of binding free energies (DC) with computational alanine scanning (CAS) results, it is shown that VP24 residues R137 and T138 will be potential targets for EBOV VP24 inhibitors, and KPNA5 residues R396, R398, R480, Y477 and F484 will be potential targets to prevent KPNA5 binding to VP24, which will ultimately block anti-IFN signaling. Our investigations provide theoretical data to understand the binding modes of VP24-KPNA5. The precise binding mechanisms of the complex may shed light on the development of potential novel inhibitors against EBOV infection.