Background/Aims: Neural precursor cell-expressed developmentally down-regulated gene 4 (NEDD4) plays an important role in tumor cell growth, yet its role in hepatocellular carcinoma (HCC) remains unclear. This study is to establish NEDD4 as a prognostic biomarker by which the survival of HCC patients can be predicted and to reveal the role of NEDD4 in hepatocellular carcinoma cell growth. Methods: The expression of NEDD4 in 219 HCC specimens was assessed by immunohistochemistry. Postoperative overall survival and time to recurrence were evaluated by univariate and multivariate analyses. The roles of NEDD4 in hepatocellular carcinoma cell proliferation and invasion were determined. Results: The patients with low NEDD4 expression tumors had an average cumulative survival of 64.9 ± 6.5 months during follow-up while the patients with high NEDD4 expression tumors had an average cumulative survival of 20.3 ± 15.8 months. NEDD4 silencing inhibited Huh7 cell proliferation and altered cell cytoskeletal assembly, and NEDD4 depletion furthermore seemed to suppress cell migration and invasion. A possible molecular mechanism for the observed effects might be that NEDD4 silence led to an increase in PTEN (phosphatase and tensin homologue) expression, which in turn resulted in the inactivation of STAT3, AKT, and ERK1/2. Conclusion: Our findings indicate that NEDD4 may participate in the HCC progression and may therefore be a potential target for HCC therapy.
Optical signal-to-noise ratio (OSNR) monitoring is one of the core tasks of advanced optical performance monitoring (OPM) technology, which plays an essential role in future intelligent optical communication networks. In contrast to many regression-based methods, we convert the continuous OSNR monitoring into a classification problem by restricting the outputs of the neural network-based classifier to discrete OSNR intervals. We also use a low-bandwidth coherent receiver for obtaining the time domain samples and a long short-term memory (LSTM) neural network as the chromatic dispersion-resistant classifier. The proposed scheme is cost efficient and compatible with our previously proposed multi-purpose OPM platform. Both simulation and experimental verification show that the proposed OSNR monitoring technique achieves high classification accuracy and robustness with low computational complexity.
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