No abstract

Cisplatin is a well-studied and widely used chemotherapeutic agent and is effective in the treatment of the advanced human non-small cell lung cancer (NSCLC). Curcumin is a yellow pigment derived from the rhizome of Curcuma longa and has been proved to have antioxidant and antitumor properties. XRCC1 is an important scaffold protein involved in base excision repair and plays an important role in the development of lung cancer. In this study, we characterize the role of curcumin in the cytotoxicity, p38 MAPK activation, and XRCC1 expression affected by cisplatin in NSCLC cells. We show that curcumin enhanced the cytotoxicity induced by cisplatin in two NSCLC cells, A549 and H1703. Treatment with cisplatin alone increased XRCC1 mRNA and protein expression through p38 MAPK activation. Moreover, SB2023580 (p38 inhibitor) decreased the XRCC1 mRNA and protein stability upon cisplatin treatment. Knockdown of XRCC1 in NSCLC cells by transfection of XRCC1 siRNA or inactivation of p38 MAPK resulted in enhancing the cytotoxicity and cell growth inhibition induced by cisplatin. Curcumin inhibited the expression of XRCC1 in cisplatin-exposed NSCLC cells. Furthermore, transfection with constitutive active MKK6 or HA-p38 MAPK vectors rescued the XRCC1 protein level and also the cell survival suppressed by cisplatin and curcumin combination in A549 and H1703 cells. These findings suggested that the downregulation of XRCC1 expression by curcumin can enhance the chemosensitivity of cisplatin in NSCLC cells.

A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as the role of front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks are developed to take complementary advantage of parallel computing capability of convolutional neural networks and temporal sensitivity of recurrent neural networks. Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio range above -10 dB, and achieves significantly improved classification accuracy from 80% to 92.1% at high signal to noise ratio range, while drastically reduces the average training and prediction time by approximately 74% and 67%, respectively. Response patterns learned by the proposed architecture are visualized to better understand the physics of the model. Furthermore, a comparative study is performed to investigate the impacts of various sequential convolutional recurrent neural network structure settings on classification performance. A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.INDEX TERMS automatic modulation classification, convolutional neural networks, cognitive radio, deep learning, recurrent neural networks, spectrum monitoring.

No abstract

Extranodal natural killer/T-cell lymphoma (ENKTL) is a distinct subtype of non-Hodgkin's lymphoma and is rare in the colon. Synchronous adenocarcinoma and ENKTL of the colon has not been reported in the literature. In the present study, we report a 63-year-old male who suffered from intermittent bloody stools for 2 mo. He did not have fever, body weight loss or night sweat. Endoscopic and imaging studies revealed a 4.5-cm ulcerative mass in the ascending colon and a 3.0-cm polypoid, easy bleeding mass in the sigmoid colon, respectively. Thought to have double carcinoma of the colon, he received simultaneous right hemicolectomy and sigmoidectomy. The pathological diagnosis was a synchronous ENKTL (ascending colon) and adenocarcinoma (sigmoid colon). The literature on synchronous adenocarcinoma and malignant lymphoma of the colon was also reviewed.