Allicin, an extract of garlic, has antitumor effects in multiple tumor types. However, the efficacy of allicin for treating glioblastoma has not yet been examined. This study examined the antitumor effect of allicin on human cytomegalovirus (HCMV)-infected glioblastoma multiforme (GBM) and its role in cytokine signaling. Materials and Methods: HCMV-infected glioblastoma was modeled by transfection of U87MG glioblastoma cells with HMCV proteins. MTT assay was used to assess the effect of allicin on the proliferation of glioma cells. Western blot analysis was used to detect the effect of allicin on the expression of intermediate-early gene 2 (IE2) and p53. Reverse transcription-quantitative polymerase chain reaction was used to assess and the levels of interleukin (IL)-6 and interferon (IFN)-β. Single cell gel electrophoresis was used to analyze changes in radiotherapy-induced DNA damage. Results: Transfection of the IE2 protein led to decreased p53 expression and increased glioblastoma cell proliferation. Allicin inhibited this proliferation in a dose-and time-dependent manner. An inhibitory effect on cytokine release was observed in GBM cells treated with allicin. After treatment with allicin, p53 levels increased significantly, whereas expression of the inflammatory factors such as IL-6 and IFN-β decreased. U87MG cells treated with allicin and 10 Gy irradiation had increased intracellular DNA damage compared to either treatment alone. Conclusion: Allicin inhibited proliferation of glioblastoma cells in vitro. Allicin also inhibited cytokine release, upregulated p53 activity, and increased the sensitivity of glioblastoma to radiotherapy. These results suggest that allicin is effective against HCMVinfected glioblastomas.

The initialization of Convolutional Neural Networks (CNNs) is about providing reasonable initial values for the convolution kernels and the fully connected layers. In this paper, we proposed a convolution kernel initialization method based on the two-dimensional principal component analysis (2DPCA), in which a parametric equalization normalization method is used to adjust the scale between each neuron weight. After that the weight initial value can be adaptively adjusted according to different data samples. This method enables each neuron to fully back-propagate errors and accelerate network model training. Finally, a network model was built and experiments were performed using Tanh and ReLU activation functions. The experimental results verify the effectiveness of the proposed method through the distribution of histograms and the curve comparison diagrams of model training.

Brain tumors are common solid pediatric malignancies and the main reason for cancer-related death in the pediatric setting. Recently, evidence has revealed that noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), play a critical role in brain tumor development and progression. Therefore, in this review article, we describe the functions and molecular mechanisms of ncRNAs in multiple types of cancer, including medulloblastoma, pilocytic astrocytoma, ependymoma, atypical teratoid/rhabdoid tumor, glioblastoma, diffuse intrinsic pontine glioma, and craniopharyngioma. We also mention the limitations of using ncRNAs as therapeutic targets because of the nonspecificity of ncRNA targets and the delivery methods of ncRNAs. Due to the critical role of ncRNAs in brain oncogenesis, targeting aberrantly expressed ncRNAs might be an effective strategy to improve the outcomes of pediatric patients with brain tumors.