Background Long noncoding RNA NEAT1 has been implicated in glioma progression. However, the effect of NEAT1 on glycolysis of glioma cell and the potential mechanism remain unclear. Methods In vitro experiments, including CCK-8, colony formation, ECAR, and lactate detection assays were performed to evaluate the effect of NEAT1 on proliferation and glycolysis of glioma cell. RNA pulldown and RIP assays were performed to identify the interaction between NEAT1 and PGK1. Truncated mutation of NEAT1 and PGK1 was used to confirm the specific interactive domains between NEAT1 and PGK1. Animal studies were performed to analyze the effect of NEAT1/PGK1 on glioma progression. Results NEAT1 knockdown significantly suppressed the proliferation and glycolysis of glioma cells. NEAT1 could specifically interact with PGK1, which promotes PGK1 stability. Hairpin A of NEAT1 is essential for interaction with M1 domain of PGK1. Depletion of NEAT1 markedly inhibited tumor growth in mice, while PGK1 could reverse this effect. Higher expression of NEAT1 was associated with poor overall survival of GBM patients. Conclusions NEAT1 over expression promotes glioma progression through stabilizing PGK1. NEAT1/PGK1 axis is a candidate therapeutic target for glioma treatment.

In recent years, AVS-M audio standard targeting at wireless network and mobile multimedia applications has been developed by China Audio and Video Coding Standard Workgroup. AVS-M demonstrates a similar framework with AMR-WB+. This paper analyses the whole framework and the core algorithms of AVS-M with an emphasis on the implementation of the real-time encoder and decoder on DSP platform. A comparison between the performances of AVS-M and AMR-WB+ is also given.

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on a convolutional neural network (CNN) algorithm in glioma diagnosis and tumor segmentation. 66 patients with gliomas who were diagnosed and treated in the hospital were selected as the research objects. The patients were rolled into the high-grade glioma group (HGG, 46 cases) and the low-grade glioma group (LGG, 20 cases) according to the World Health Organization glioma grading standard. All patients received a conventional plain scan and a DCE-MRI. Parameters such as volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp) were calculated, and the parameters of patients of each grade were analyzed. The efficacy of each parameter in diagnosing glioma was analyzed through a receiver operating characteristic curve. All images were segmented by the CNN algorithm. The CNN algorithm showed good performance in DCE-MRI image segmentation. The mean, standard deviation, kurtosis, and skewness of Ktrans and Ve, the standard deviation and skewness of Kep, and the mean and standard deviation of Vp were statistically considerable in differentiating HGG and LGG P < 0.05 . ROC analysis showed that the standard deviation of Ktrans (0.885) had the highest diagnostic accuracy in distinguishing HGG and LGG. The values of Ktrans, Ve, and Vp were positively correlated with Ki-67 (r = 0.346, P = 0.014; r = 0.335, P = 0.017; r = 0.323, P = 0.022). In summary, the CNN-based DCE-MRI technology had high application value in glioma diagnosis and tumor segmentation.

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