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    Suppression of PI3K/Akt/mTOR pathway in chrysoeriol-induced apoptosis of rat C6 glioma cells
    (2022-01-01)
    Wongkularb, Suppanut
    ;
    Limboonreung, Tanapol
    ;
    Tuchinda, Patoomratana
    ;
    Chongthammakun, Sukumal
    Chrysoeriol, a dietary methoxyflavonoid which is found in tropical medicinal plants, has been shown to have antioxidant, anti-inflammatory, and antineoplastic properties. The present study aimed to investigate the effects of chrysoeriol and its related mechanisms in rat C6 glioma cells. Cell viability in rat C6 glioma cells were measured by MTT assay. The protein expression levels of cleaved caspase-3, caspase-3, pro-apoptotic (Bax), anti-apoptotic protein (Bcl-2), and Annexin V were detected by Western blot analysis and immunocytochemical staining. Results showed that chrysoeriol significantly decreased cell viability and induced apoptosis in rat C6 glioma cells. Chrysoeriol significantly increased the levels of Bax/Bcl-2 ratio and cleaved caspase-3/caspase-3 ratio. Moreover, treatment with chrysoeriol significantly reduced the phosphorylation of PI3K, Akt, and mTOR expression in ratios. These results suggest that chrysoeriol promote apoptosis in rat C6 glioma cells via suppression of the PI3K/Akt/mTOR signaling pathway, thereby demonstrating the potential antineoplastic effects of chrysoeriol on glioma cells.
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    An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences
    (2020-01-01)
    Coupet, Matthieu
    ;
    Urruty, Thierry
    ;
    Leelanupab, Teerapong
    ;
    Naudin, Mathieu
    ;
    Bourdon, Pascal
    Gliomas are the most common central nervous system tumors. They represent 1.3% of cancers and are the 15th most common cancer for men and women. For the diagnosis of such pathology, doctors commonly use Magnetic Resonance Imaging (MRI) with different sequences. In this work, we propose a global framework using convolutional neural networks to create an intelligent assistant system for neurologists to diagnose the brain gliomas. Within this framework, we study the performance of different neural networks on four MRI modalities. This work allows us to highlight the most specific MRI sequences so that the presence of gliomas in brain tissue can be classified. We also visually analyze extracted features from the different modalities and networks with an aim to improve the interpretability and analysis of the performance obtained. We apply our study on the MRI sequences that are obtained from BraTS datasets.