Emodin inhibits proliferation and invasion, and induces apoptosis in human esophageal cancer cell line ECA109

Purpose: To determine the anticancer effects of emodin in human esophageal carcinoma cell line ECA109.Methods: Cell viability was determined by MTT assay, while cell invasion and apoptosis were measured by Transwell assay and flow cytometry, respectively. Expression levels of MMP-2, Bax, Bcl-2 and caspase-3 proteins were determined by Western blot.Results: Flow cytometry data showed that the proportion of apoptotic cells was increased by emodin treatment. Apoptotic rates produced by 10, 20 and 50 μM emodin were 13.9 ± 3.8, 25.6 ± 6.2 and 39.8 ± 7.7 %, respectively. Transwell assay data revealed concentration-dependent suppression of the invasive rate of ECA109 cells by emodin (10, 20 and 50 μM) was 30.0 ± 4.5, 56.0 ± 6.8 and 69.0 ± 8.1 %, respectively. Furthermore, emodin treatment inhibited expressions of MMP-2 and Bcl-2 proteins, but induced the expression of Bax and caspase-3, when compared with control groups.Conclusion: These results suggest that emodin inhibits cell proliferation and cell invasion, but induces cell apoptosis in human esophageal cancer cell line ECA109. Thus, emodin is a potential candidate for development of an effective chemotherapeutic agent against esophageal cancer.Keywords: Emodin, Esophageal Cancer, Apoptosis, Cell invasion, Bax, Caspase-

MiR-148a-3p suppresses the progression of gastric cancer cells through targeting ATP6AP2

Purpose: Gastric cancer (GC) is one of the most frequent tumors with high mortality rate, worldwide. A proper understanding of the mechanism  underlying its progression is required for its diagnosis and development of novel treatment option. MicroRNAs are associated with the development and advancement of different types of cancer, including GC. The current research was aimed at investigating the molecular and biological function of miR-148a-3p in GC development.Methods: A human normal gastric epithelial cell line, GES-1 (control) as well as four GC cell lines (NUGC-4, SNU-520, STKM-2 and MKN-74) were employed for the study. MiR-148a-3p and ATP6AP2 expression levels in GC cell lines were examined by RT-qPCR technique. Transfection procedure was used to upregulate miR-148a-3p expression in the MKN-45 cell line. MTT assay was utilized to evaluate cell viability in GC cell lines. The molecular interaction between miR-148a-3p and ATP6AP2 was predicted using bioinformatics system and the prediction was then validated by luciferase reporter assay.Results: Expression levels of miR-148-3p was low, whilst that of ATP6AP2 was high in GC cell lines. MiR-148a-3p overexpression resulted in the reduction of cell viability in GC cell lines. More so, it was confirmed that miR-148-3p, as a post-transcriptional regulator inhibited ATP6AP2 expression by having a negative association with it in GC cells. More so, ATP6AP2 was found to be a direct target of miR-148a-3p.Conclusion: Our results revealed that miR-148a-3p plays a crucial function in GC development through targeting ATP6AP2. This finding could be explored in the discovery of new therapeutic approaches for GC treatment. Keywords: ATP6AP2, Cell viability, Gastric cancer, miR-148a-3p, Progressio

Funneled potential and flux landscapes dictate the stabilities of both the states and the flow: Fission yeast cell cycle.

Using fission yeast cell cycle as an example, we uncovered that the non-equilibrium network dynamics and global properties are determined by two essential features: the potential landscape and the flux landscape. These two landscapes can be quantified through the decomposition of the dynamics into the detailed balance preserving part and detailed balance breaking non-equilibrium part. While the funneled potential landscape is often crucial for the stability of the single attractor networks, we have uncovered that the funneled flux landscape is crucial for the emergence and maintenance of the stable limit cycle oscillation flow. This provides a new interpretation of the origin for the limit cycle oscillations: There are many cycles and loops existed flowing through the state space and forming the flux landscapes, each cycle with a probability flux going through the loop. The limit cycle emerges when a loop stands out and carries significantly more probability flux than other loops. We explore how robustness ratio (RR) as the gap or steepness versus averaged variations or roughness of the landscape, quantifying the degrees of the funneling of the underlying potential and flux landscapes. We state that these two landscapes complement each other with one crucial for stabilities of states on the cycle and the other crucial for the stability of the flow along the cycle. The flux is directly related to the speed of the cell cycle. This allows us to identify the key factors and structure elements of the networks in determining the stability, speed and robustness of the fission yeast cell cycle oscillations. We see that the non-equilibriumness characterized by the degree of detailed balance breaking from the energy pump quantified by the flux is the cause of the energy dissipation for initiating and sustaining the replications essential for the origin and evolution of life. Regulating the cell cycle speed is crucial for designing the prevention and curing strategy of cancer

Design of hydrofoil for the resistance improvement of planing boat based on CFD technology

The purpose of this study was to design a hydrofoil which would improve boat performance through enhanced resistance reduction. Commercial CFD code STARCCM+ was used to solve the Unsteady Reynolds Averaged Navier Stokes Equations for the flow around the boat. Uncertainity study is conducted in order to obtain an effective and reliable numerical calculation method. The method was then validated by direct comparison of the numerical data at different speeds with the test data of USV01 planing boats. Accordingly, twelve hydrofoil design cases were considered, and their resistance reduction performance at 8 m/s was predicted and compared with each other through the numerical calculation method. Effects of hydrofoil parameters such as longitudinal installation position, span, attack angle, installation height on the resistance reduction performance were investigated. One of 12 cases was chosen to investigate the resistance reduction effect of hydrofoil at different speeds. The results show that the hydrofoil, with proper installation position and design parameters, has a significant resistance reduction effect. At 8 m/s, the hydrofoil designed in this paper can reduce boat resistance by up to 30.74%.To analyze the principle of hydrofoil, the flow field around hull and hydrofoil was numerically simulated and studied

Influence on the probability flux by changing the cycling activation strength <i>γ</i> which represents the jumping probability from the G1/G0 state to the activated G1 state (START phase), while fixing <i>μ</i> = 0.8, <i>c</i> = 0.001.

<p>(a) Steady-state probability flux versus <i>γ</i>. (b) Robustness Ratio (RR) of flux spectrum versus <i>γ</i>. (c) Entropy production rate (<i>dS</i>/<i>dt</i>) versus RR of flux. (d) Robustness Ratio (RR) of flux spectrum versus steady-state probability of “native” cycle (<i>P</i>_{<i>Circle</i>}).</p

Fission yeast cell cycle temporal evolution steps.

<p>Fission yeast cell cycle temporal evolution steps.</p

Relationship between the most prominent frequency, entropy production and flux by changing <i>γ</i> = (1%, 30%), while fixing <i>μ</i> = 5, <i>c</i> = 0.001.

<p>(a)-(c) Variation of most prominent frequency, flux and entropy production rate when changing the activation strength <i>γ</i>. (d)-(f) Positive correlation between the most prominent frequency, entropy production and flux when changing <i>γ</i>.</p

Influence on the system robustness from the variation of the perturbation parameter <i>c</i>, by fixing <i>μ</i> = 5, <i>γ</i> = 60%.

<p>(a) Steady-state probability of “native” cycle (<i>P</i>_{<i>Circle</i>}) versus <i>c</i>. (b) Robustness Ratio (RR) versus <i>c</i>. (c) Entropy production rate (<i>dS</i>/<i>dt</i>) versus <i>c</i>. (d) Entropy production rate (<i>dS</i>/<i>dt</i>) versus Robustness Ratio.</p