Purpose. Yes-associated protein (YAP) and PDZ-binding motif (TAZ) are two important effectors of Hippo pathway controlling the balance of organ size and carcinogenesis. Amphiregulin (AREG) is a member of the epidermal growth factor family, a direct target gene of YAP and TAZ. The role of these proteins in hepatocellular carcinoma (HCC) is unclear. Methods. The expression of YAP, TAZ, and AREG in HCC was analyzed by immunohistochemical staining. The level of secreted serum AREG was also assayed by enzyme-linked immunosorbent (ELISA) assay. Results. YAP, TAZ, and AREG were expressed in 69.2% (27/39), 66.7% (26/39), and 61.5% (24/39) of HCC patients. The expression of YAP was significantly correlated with Edmondson stage (P > 0.05), serum AFP level (P > 0.05), and HCC prognosis (P > 0.05). AREG expression was also significantly correlated with Edmondson stage (P > 0.05) and serum AFP level (P > 0.05). In addition, the expression of serum AREG was higher than serum AFP in HCC patients. Further multivariate analysis showed that YAP expression was an independent prognostic factor that significantly affected the overall survival of HCC patients. Conclusions. YAP maybe an independent prognostic indicator for HCC patients and serum AREG may be a serological biomarker of HCC.

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Aim. Sal-like protein 4 (SALL4), is reexpressed in tissues of a subgroup of HCC associated with poor prognosis. Reports of SALL4 serological levels linked to HCC patients are meager and unclear in the prognosis of this malignancy. Methods. Immunohistochemistry and optical microscopy protocols were used to examine the presence of SALL4 in liver tissues from the following patients: 38 HCC, 11 chronic hepatitis B virus (HBV), 13 liver cirrhosis, and 12 healthy controls. Additionally, enzyme-linked immunosorbent assay (ELISA) was used to measure the SALL4 levels in serum samples isolated from patients as follows: 127 with HCC, 27 with HBV, 24 with liver cirrhosis, and 23 normal controls. Results. Analysis of liver tissues sections from HCC patients (18 out 38; 47.4%) showed positive staining for SALL4 and its expression did no correlate with any of the clinicopathologic characteristics. HCC patients displayed higher levels (50.4%) of SALL4 protein in serum, compared with the three control groups. Moreover, SALL4 concentration reached the maximum level after one week after treatment and dropped quickly after one month. These HCC patients showing high SALL4 serum levels had poor prognosis, evidenced by both tumor recurrence and overall survival rate. Conclusions. High SALL4 serum levels are a novel biomarker in the prognosis of HCC patients.

Gliomas, the most common primary brain tumors, have low survival rates and poorly defined molecular mechanisms to target for treatment. Serine/arginine SR protein kinases 1 (SRPK1) can highly and specifically phosphorylate the SR protein found in many tumors, which can influence cell proliferation and angiogenesis. However, the roles and regulatory mechanisms of SRPK1 in gliomas are not understood. The aim of this study was to determine the functions and regulation of SRPK1 in gliomas. We found that SRPK1 inhibition induces early apoptosis and significantly inhibits xenograft tumor growth. Our results indicate that SRPK1 affects Akt and eIF4E phosphorylation, Bax and Bcl-2 activation, and HIF-1 and VEGF production in glioma cells. Moreover, transfection of SRPK1 siRNA strongly reduced cell invasion and migration by regulating the expression of MMP2 and MMP9 and significantly decreased the volume of tumors and angiogenesis. We show here that a strong link exists among SRPK1, Akt, eIF4E, HIF-1, and VEGF activity that is functionally involved in apoptosis, metastasis, and angiogenesis of gliomas under normoxic conditions. Thus, SRPK1 may be a potential anticancer target to inhibit glioma progression.

Sonodynamic therapy (SDT) not only has greater tissue‐penetrating depth compared to photo‐stimulated therapies, but also can also trigger rapid drug release to achieve synergistic sonochemotherapy. Here, reactive oxygen species (ROS)‐responsive IR780/PTL‐ nanoparticles (NPs) are designed by self‐assembly, which contain ROS‐cleavable thioketal linkers (TL) to promote paclitaxel (PTX) release during SDT. Under ultrasound (US) stimulation, IR780/PTL‐NPs produce high amounts of ROS, which not only induces apoptosis in human glioma (U87) cells but also boosts PTX released by decomposing the ROS‐sensitive TL. In the U87 tumor‐bearing mouse model, the IR780/PTL‐NPs releases the drug at the target sites in a controlled manner upon US irradiation, which significantly inhibits tumor growth and induces apoptosis in the tumor tissues with no obvious toxicity. Taken together, the IR780/PTL‐NPs are a novel platform for sonochemotherapy, and can control the spatio‐temporal release of chemotherapeutic drugs during SDT.

BackgroundDrug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification.MethodsWe propose a dependency-based deep neural network model for DDI extraction. By introducing the dependency-based technique to a bi-directional long short term memory network (Bi-LSTM), we build three channels, namely, Linear channel, DFS channel and BFS channel. All of these channels are constructed with three network layers, including embedding layer, LSTM layer and max pooling layer from bottom up. In the embedding layer, we extract two types of features, one is distance-based feature and another is dependency-based feature. In the LSTM layer, a Bi-LSTM is instituted in each channel to better capture relation information. Then max pooling is used to get optimal features from the entire encoding sequential data. At last, we concatenate the outputs of all channels and then link it to the softmax layer for relation identification.ResultsTo the best of our knowledge, our model achieves new state-of-the-art performance with the F-score of 72.0% on the DDIExtraction 2013 corpus. Moreover, our approach obtains much higher Recall value compared to the existing methods.ConclusionsThe dependency-based Bi-LSTM model can learn effective relation information with less feature engineering in the task of DDI extraction. Besides, the experimental results show that our model excels at balancing the Precision and Recall values.

A unified model by combining the Rolie-Poly constitutive model and the Flory-Huggins mixing free energy functional through a two fluid approach is presented for studying flow-induced phase separation in polymer mixtures. It is numerically solved in two-dimensional flow with monotonic and non-monotonic constitutive behaviour. The results are analyzed and show that this model can capture the essential dynamic features of viscoelastic phase separation reported in literature. The steady-state shear-banding and interface instabilities are reproduced. In the case with a non-monotonic constitutive behaviour, It is observed that the band structures are strongly unstable both in time and in space. The correlations between the microstructure evolution and chaotic rheological responses have been identified. A vortex structure emerges within the central band. Numerical results obtained from this study suggest that the dynamic features of rheochaos can be captured by the proposed model without introducing extra parameters.

Two decades ago, it was shown that ambient noise exhibits low dimensional chaotic behavior. Recent new techniques in nonlinear science can effectively detect the underlying dynamics in noisy time series. In this paper, the presence of low dimensional deterministic dynamics in ambient noise is investigated using diverse nonlinear techniques, including correlation dimension, Lyapunov exponent, nonlinear prediction, and entropy based methods. The consistent interpretation of different methods demonstrates that ambient noise can be best modeled as nonlinear stochastic dynamics, thus rejecting the hypothesis of low dimensional chaotic behavior. The ambient noise data utilized in this study are of duration 60 s measured at South China Sea.