Protective Effects of Chinese Traditional Medicine Buyang Huanwu Decoction on Myocardial Injury

Many clinical studies have reported that Buyang Huanwu Decoction (BYHWD) has a protective effect on ischemic heart disease (IHD). In the present study, the protective effect of BYHWD on myocardial ischemia was investigated. Different doses of BYHWD and Compound Danshen Dropping Pills (CDDP) were lavaged to rats, respectively, isoproterenol (ISO) was intraperitoneally injected in to all animals to induce myocardial ischemia except the control group. Electrocardiogram (ECG) of each animal was recorded; activities of lactate dehydrogenase (LDH), creatine kinase (CK) and aspartate aminotransferase (AST) in serum were detected. As the results of ECG showed, pre-treatment with BYHWD inhibited ischemic myocardial injury, and the activities of LDH, CK and AST were lower than those in the myocardial ischemia model group, which suggests that BYHWD rescues the myocardium from ischemia status. To research the potential mechanism, the level of nitric oxide (NO), nitric oxide syntheses (NOS) and inducible nitric oxide syntheses (iNOS), the expression of iNOS and ligand of cluster of differentiation 40 (CD40L) were detected. The results revealed that BYHWD significantly decreased the level of NO, NOS and iNOS in serum. Moreover, BYHWD decreased the expression of iNOS and CD40L in myocardial tissues. These results indicate that the protective effect of BYHWD on myocardial ischemia and mechanism are associated with inhibition of iNOS and CD40L expression

All-fiber normal-dispersion single-polarization passively mode-locked laser based on a 45°-tilted fiber grating

An all-fiber normal-dispersion Yb-doped fiber laser with 45- tilted fiber grating (TFG) isto the best of our knowledgeexperimentally demonstrated for the first time. Stable linearly-chirped pulses with the duration of 4 ps and the bandwidth of 9 nm can be directly generated from the laser cavity. By employing the 45 TFG with the polarization-dependent loss of 33 dBoutput pulses with high polarization extinction ratio of 26 dB are implemented in the experiment. Our result shows that the 45 TFG can work effectively as a polarizerwhich could be exploited to singlepolarization all-fiber lasers

Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis

The signal-to-noise ratio (SNR) of seismic data is the key to seismic data processing, and it also directly affects interpretation of seismic data results. The conventional denoising method, independent variable analysis, uses adjacent traces for processing. However, this method has problems, such as the destruction of effective signals. The widespread use of velocity and acceleration geophones in seismic exploration makes it possible to obtain different types of signals from the same geological target, which is fundamental to the joint denoising of these two types of signals. In this study, we propose a joint denoising method using seismic velocity and acceleration signals. This method selects the same trace of velocity and acceleration signal for Independent Component Analysis (ICA) to obtain the independent initial effective signal and separation noise. Subsequently, the obtained effective signal and noise are used as the prior information for a Kalman filter, and the final joint denoising results are obtained. This method combines the advantages of low-frequency seismic velocity signals and high-frequency and high-resolution acceleration signals. Simultaneously, this method overcomes the problem of inconsistent stratigraphic reflection caused by the large spacing between adjacent traces, and improves the SNR of the seismic data. In a model data test and in field data from a work area in the Shengli Oilfield, the method increases the dominate frequency of the signal from 20 to 40 Hz. The time resolution was increased from 8.5 to 6.8 ms. The test results showed that the joint denoising method based on seismic velocity and acceleration signals can better improve the dominate frequency and time resolution of actual seismic data

Molecular Dynamics Simulations to Investigate the Binding Mode of the Natural Product Liphagal with Phosphoinositide 3-Kinase α

Phosphatidylinositol 3-kinase α (PI3Kα) is an attractive target for anticancer drug design. Liphagal, isolated from the marine sponge Aka coralliphaga, possesses the special “liphagane” meroterpenoid carbon skeleton and has been demonstrated as a PI3Kα inhibitor. Molecular docking and molecular dynamics simulations were performed to explore the dynamic behaviors of PI3Kα binding with liphagal, and free energy calculations and energy decomposition analysis were carried out by use of molecular mechanics/Poisson-Boltzmann (generalized Born) surface area (MM/PB(GB)SA) methods. The results reveal that the heteroatom rich aromatic D-ring of liphagal extends towards the polar region of the binding site, and the D-ring 15-hydroxyl and 16-hydroxyl form three hydrogen bonds with Asp810 and Tyr836. The cyclohexyl A-ring projects up into the upper pocket of the lipophilic region, and the hydrophobic/van der Waals interactions with the residues Met772, Trp780, Ile800, Ile848, Val850, Met922, Phe930, Ile932 could be the key interactions for the affinity of liphagal to PI3Kα. Thus, a new strategy for the rational design of more potent analogs of liphagal against PI3Kα is provided. Our proposed PI3Kα/liphagal binding mode would be beneficial for the discovery of new active analogs of liphagal against PI3Kα

Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD