Abstract.Endostatin has previously been demonstrated to efficiently inhibit the angiogenesis and growth of endothelial cells. However, the role of endostatin in the tumor microenvironment remains to be elucidated. To investigate the antitumor effect of endostatin in lung cancer, the present study was designed to explore the alterations of microvessel density in Lewis lung cancer models and the expression of vascular endothelial growth factor (VEGF), interleukin (IL)-6, IL-17, interferon (IFN)-γ and hypoxia inducible factor (HIF)-1α, following endostatin therapy. It was demonstrated that the growth and angiogenesis of tumors were markedly suppressed by treatment with endostatin, compared with control group. The microvessel density in mice treated with endostatin was significantly inhibited in a dose-dependent manner. The expression levels of VEGF, IL-6 and IL-17 in tumors were decreased, however IFN-γ and HIF-1α expression levels were increased, following treatment with endostatin. In addition, the proportion of myeloid derived suppressor cells and tumor associated macrophages (TAMs; M2 type) were significantly decreased, whereas those of mature dendritic cells and TAMs (M1 type) were increased, and cluster of differentiation (CD)8 + T cells were recruited to infiltrate the tumors following treatment with endostatin. In addition, the expression levels of IL-6, IL-10, tumor growth factor-β and IL-17 in tumor tissue were potently decreased with endostatin therapy. These results indicated that endostatin efficiently inhibited tumor angiogenesis and reversed the immunosuppressive microenvironment associated with the presence of tumors.
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a [Formula: see text]-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level [Formula: see text]-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.
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