Artesunate Enhances the Cytotoxicity of 5-Aminolevulinic Acid-Based Sonodynamic Therapy against Mouse Mammary Tumor Cells In Vitro

Sonodynamic therapy (SDT) kills tumor cells through the synergistic effects of ultrasound (US) and a sonosensitizer agent. 5-Aminolevulinic acid (5-ALA) has been used as a sonodynamic sensitizer for cancer treatment. However, studies have shown that 5-ALA-based SDT has limited efficacy against malignant tumors. In this study, we examined whether artesunate (ART) could enhance the cytotoxicity of 5-ALA-based SDT against mouse mammary tumor (EMT-6) cells in vitro. In the ART, ART + US, ART + 5-ALA, and ART + 5-ALA + US groups, the cell survival rate correlated with ART concentration, and decreased with increasing concentrations of ART. Morphologically, many apoptotic and necrotic cells were observed in the ART + 5-ALA + US group. The percentage of reactive oxygen species-positive cells in the ART + 5-ALA + US group was also significantly higher than that in the 5-ALA group (p = 0.0228), and the cell death induced by ART + 5-ALA + US could be inhibited by the antioxidant N-acetylcysteine. These results show that ART offers great potential in enhancing the efficacy of 5-ALA-based SDT for the treatment of cancer. However, these results are only based on in vitro studies, and further in vivo studies are required

Leakage Current Properties of Cation-Substituted BiFeO3Ceramics

Cation-doped BiFeO3 ceramics were fabricated by sintering coprecipitated and calcined powders at 700–900 C to study the effect of cation doping on the leakage current properties of the sintered samples. Among the dopants examined in this study, Ti4þ, Sn4þ, or Zr4þ doping was found to effectively reduce the leakage current of the samples. In particular, a marked decrease in the leakage current density was achieved at 10% Ti4þ doping, which also resulted in the structure change from rhombohedral to cubic. The codoping of Ti4þ/Zn2þ ions or Ti4þ/Ni2þ ions brought about a substantial reduction in the leakage current density of the bulk samples by about four or five orders of magnitude at a small doping amount of 2%. This can be explained by the combined effect of Ti4þ doping, which basically contributes to the decreased number of oxygen vacancies in the sample, and Zn2þ or Ni2þ doping, which might assist the homogeneous substitution of Ti4þ ions for the Fe3þ sites

A Stochastic Parser Based on a Structural Word Prediction Model

n this paper, we present a stochastic language model using dependency. This model considers a sentence as a word sequence and predicts each word fi'om left to right. The history at each step of pre- diction is a sequence of partial parse tj'ees covering the preceding words. First our model predicts the partial parse trees which have a dependency relation with the next word among them 'and then predicts the next word from only the trees which have a dependency relation with the next word. Our model is a generarive stochastic model, thus this can be used not only as a parser but also as a language model of a speech recognizer. In our experiment, we prepared about 1,000 syntactically annotated Japanese sentences extracted fi'om a financial newspaper and estime;ted the parameters of our model. We built a parser based on our model and tested it on approximately 100 sentences of the same newspaper. The accm'acy of the dependency relation was 89.9%, the highest. accuracy level obtained by Japanese stocha.stic parsers