Potentiation of Anticancer Drugs: Effects of Pentoxifylline on Neoplastic Cells

The drug efflux activity of P-glycoprotein (P-gp, a product of the mdr1 gene, ABCB1 member of ABC transporter family) represents a mechanism by which tumor cells escape death induced by chemotherapeutics. In this study, we investigated the mechanisms involved in the effects of pentoxifylline (PTX) on P-gp-mediated multidrug resistance (MDR) in mouse leukemia L1210/VCR cells. Parental sensitive mouse leukemia cells L1210, and multidrug-resistant cells, L1210/VCR, which are characterized by the overexpression of P-gp, were used as experimental models. The cells were exposed to 100 μmol/L PTX in the presence or absence of 1.2 μmol/L vincristine (VCR). Western blot analysis indicated a downregulation of P-gp protein expression when multidrug-resistant L1210/VCR cells were exposed to PTX. The effects of PTX on the sensitization of L1210/VCR cells to VCR correlate with the stimulation of apoptosis detected by Annexin V/propidium iodide apoptosis necrosis kit and proteolytic activation of both caspase-3 and caspase-9 monitored by Western blot analysis. Higher release of matrix metalloproteinases (MMPs), especially MMP-2, which could be attenuated by PTX, was found in L1210/VCR than in L1210 cells by gelatin zymography in electrophoretic gel. Exposure of resistant cells to PTX increased the content of phosphorylated Akt kinase. In contrast, the presence of VCR eliminated the effects of PTX on Akt kinase phosphorylation. Taken together, we conclude that PTX induces the sensitization of multidrug-resistant cells to VCR via downregulation of P-gp, stimulation of apoptosis and reduction of MMPs released from drug-resistant L1210/VCR cells. These facts bring new insights into the mechanisms of PTX action on cancer cells

Acetate-induced apoptosis in colorectal carcinoma cells involves lysosomal membrane permeabilization and cathepsin D release

Colorectal carcinoma (CRC) is one of the most common causes of cancer-related mortality. Short-chain fatty acids secreted by dietary propionibacteria from the intestine, such as acetate, induce apoptosis in CRC cells and may therefore be relevant in CRC prevention and therapy. We previously reported that acetic acid-induced apoptosis in Saccharomyces cerevisiae cells involves partial vacuole permeabilization and release of Pep4p, the yeast cathepsin D (CatD), which has a protective role in this process. In cancer cells, lysosomes have emerged as key players in apoptosis through selective lysosomal membrane permeabilization (LMP) and release of cathepsins. However, the role of CatD in CRC survival is controversial and has not been assessed in response to acetate. We aimed to ascertain whether LMP and CatD are involved in acetate-induced apoptosis in CRC cells. We showed that acetate per se inhibits proliferation and induces apoptosis. More importantly, we uncovered that acetate triggers LMP and CatD release to the cytosol. Pepstatin A (a CatD inhibitor) but not E64d (a cathepsin B and L inhibitor) increased acetateinduced apoptosis of CRC cells, suggesting that CatD has a protective role in this process. Our data indicate that acetate induces LMP and subsequent release of CatD in CRC cells undergoing apoptosis, and suggest exploiting novel strategies using acetate as a prevention/therapeutic agent in CRC, through simultaneous treatment with CatD inhibitors.This work was supported by the Fundação para a Ciência e Tecnologia (FCT) research project PTDC/BIA-BCM/69448/2006 and FCT PhD grants for SA (SFRH/BD/64695/2009) and CO (SFRH/BD/77449/2011). This work was also supported by FEDER through POFC—COMPETE, and by national funds from FCT through the project PEst-C/BIA/UI4050/2011

Changes in the nanoparticle aggregation rate due to the additional effect of electrostatic and magnetic forces on mass transport coefficients

The need may arise to be able to simulate the migration of groundwater nanoparticles through the ground. Transportation velocities of nanoparticles are different from that of water and depend on many processes that occur during migration. Unstable nanoparticles, such as zero-valent iron nanoparticles, are especially slowed down by aggregation between them. The aggregation occurs when attracting forces outweigh repulsive forces between the particles. In the case of iron nanoparticles that are used for remediation, magnetic forces between particles contribute to attractive forces and nanoparticles aggregate rapidly. This paper describes the addition of attractive magnetic forces and repulsive electrostatic forces between particles (by 'particle', we mean both single nanoparticles and created aggregates) into a basic model of aggregation which is commonly used. This model is created on the basis of the flow of particles in the proximity of observed particles that gives the rate of aggregation of the observed particle. By using a limit distance that has been described in our previous work, the flow of particles around one particle is observed in larger spacing between the particles. Attractive magnetic forces between particles draw the particles into closer proximity and result in aggregation. This model fits more closely with rapid aggregation which occurs between magnetic nanoparticles.Ministry of Education of the Czech Republic of the Technical University in Liberec [7822]; Ministry of Education of the Czech Republic [FR-TI1/456]; Ministry of Industry and Trad

Investigation of power losses of two-stage two-phase converter with two-phase motor

The paper deals with determination of losses of two-stage power electronic system with two-phase variable orthogonal output. The simulation is focused on the investigation of losses in the converter during one period in steady-state operation. Modeling and simulation of two matrix converters with R-L load is shown in the paper. The simulation results confirm a very good time-waveform of the phase current and the system seems to be suitable for low-cost application in automotive/aerospace industries and in application with high frequency voltage sources

Forecasting Conditional Correlation for Exchange Rates using Multivariate GARCH models with Historical Value-at-Risk application

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the  used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model

Forecasting Conditional Correlation for Exchange Rates using Multivariate GARCH models with Historical Value-at-Risk application

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the  used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model

Forecasting Conditional Correlation for Exchange Rates using Multivariate GARCH models with Historical Value-at-Risk application

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the  used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model