Glioblastoma is the most common type of astrocytoma in the brain. Due to its high invasiveness and chemoresistance, patients with advanced stage of glioblastoma have a poor prognosis. SNAI1, an important regulator of epithelial-mesenchymal transition, has been associated with metastasis in various carcinoma cells. However, its roles in glioblastoma cells have been poorly characterized. To examine roles of SNAI1 in glioblastoma cells, we knockdowned SNAI1 expression using siRNA. SNAI1 siRNA increased the expression level of E-cadherin and decreased that of vimentin. In the water-soluble tetrazolium salt (WST-1) assay, SNAI1 siRNA inhibited the proliferation of U87-MG and GBM05 glioblastoma cells. Moreover, in the Boyden chamber assay and Matrigel invasion assay, SNAI1 siRNA inhibited serum-induced migration and invasion of glioblastoma cells. These results suggested that SNAI1 is involved in the proliferation and migration of glioblastoma cells.

WTAP is overexpressed in cholangiocarcinoma and regulates motility of cholangiocarcinoma cells.

In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840-0.989 and NRMSE% = 10.693-15.894%; normal group: r = 0.847-0.988 and NRMSE% = 10.920-19.216%; fast group: r = 0.823-0.953 and NRMSE% = 12.009-20.182%; healthy group: r = 0.836-0.976 and NRMSE% = 12.920-18.088%; and AIS group: r = 0.917-0.993 and NRMSE% = 7.914-15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.

No abstract

No abstract