This paper developed small-sized PEGylated gold nanoprisms (GNPs) to induce photothermal therapy at low laser power density. Upon irradiation of laser at low power density (2 W cm -2 ), these PEGylated GNPs showed ultrahigh photothermal conversion efficacy (η ≈ 70%) and produced enough heat to kill human breast cancer cells. Figure 3. Representative DLS distribution of PEGylated (a) and CTAC-capped GNPs (b) in water. The dash lines represent the normalized DLS distribution. The spikes represent the raw intensity of DLS distribution. DLS distribution of PEGylated (c) and CTACcapped GNPs (d) reported as the correlation function.Since the localized surface plasmon resonance is highly sensitive to the shape of nanoparticles, the extinction spectrum of GNPs has been simulated by using the finitedifference time-domain (FDTD) method. As shown in Figure 4, when the edge length and thickness of GNPs were set as 55 and 20 nm, the simulation data was consistent with the experimental result in which the extinction peaked at 679 nm. Figure 4. (a) Perspective view of established GNPs model. (b) The scattering, absorption and extinction spectra of GNPs simulated by FDTD solutions. photothermal properties of PEGylated GNPsThe photothermal conversion efficacy of PEGylated GNPs was investigated as a function of concentration under NIR laser at 2 W cm -2 . Upon irradiation for 10 min, we observed that the maximum equilibrium temperature of the solution gradually increased with the concentration. The solution at 40 µg/mL showed a 39.3 ∘ C increase in temperature after irradiation, while no significant change was observed in ultrapure water (Figure 5a).These results demonstrated that PEGylated GNPs generated heat upon NIR laser irradiation with excellent photothermal conversion efficacy. Furthermore, PEGylated GNPs exhibited remarkable photothermal stability under NIR laser irradiation for five cycles (Figure 5b).
Abstract. Endostar, a modified recombinant human endostatin, inhibits the growth of a variety of tumors by suppressing neovascularization. Vascular endothelial growth factor (VEGF) has an important role in malignant ascites formation. In order to determine whether Endostar can suppress the formation of ascites and prolong survival times, mouse models of malignant ascites were established using S180 and H22 tumor cells. The experimental mice were randomly divided into four groups: The three treatment groups received different doses of Endostar (4, 8 and 16 mg/kg), and the control group received 0.9% w/v NaCl. The volume of ascites, and the tumor cell, red blood cell (RBC), VEGF protein and mRNA content of the ascites was measured alongside the peritoneal permeability and the mouse survival time. In vitro analysis of cultured Endostar-treated S180 and H22 cells was also performed in order to examine cellular proliferation and the level of VEGF secreted protein and mRNA. The results revealed that Endostar suppressed the ascites volume, decreased the level of tumor cells, RBCs and VEGF in the ascites fluid, and lowered the permeability of the peritoneum. The tumor cells collected from the ascites in the Endostar-treated mice demonstrated a decrease in the expression of VEGF mRNA. The survival rates of the 8 and 16 mg/ kg Endostar-treated mice were longer than those of the controls. The in vitro experiments revealed a significant inhibition of VEGF protein secretion and VEGF mRNA by Endostar, but no effect on cellular proliferation. In conclusion, Endostar lowers ascites production by downregulating VEGF expression, and may therefore be effective for the treatment of malignant ascites. IntroductionMalignant ascites is caused by the accumulation of fluid in the peritoneal cavity following the intraperitoneal (i.p.) spread of tumor cells. Ascites can occur in a variety of cancers, including ovarian (37%), pancreaticobiliary (21%), gastric (18%), esophageal (4%), colorectal (4%) and breast (3%) cancer (1-3). Ascites may result in a poor quality of life due to symptoms of abdominal distention, pain, nausea, vomiting, anorexia, dyspnea, limb edema, insomnia and fatigue (1-3).Chemotherapy is a common first-line treatment for patients with ascites. However, limited evidence exists concerning its efficacy in patients with recurrent ascites. The i.p. administration of radioisotopes, or chemotherapy and peritoneovenous shunting procedures, has also been used (3,4). However, data regarding these approaches is also lacking. These methods are able to relieve the symptoms of recurrent ascites, but do not prolong survival (4). Repeated paracentesis is associated with regular hospital admissions and can lead to a number of problems, including pain, protein loss, hypovolemia, infection, peritonitis and bowel perforation (3,4). The preclinical and clinical evidence concerning anti-angiogenic agents, such as vascular endothelial growth factor (VEGF) antagonists and matrix metalloproteinase inhibitors, suggest that these agents may have...
Most of the existing clustering algorithms are often based on Euclidean distance measure. However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures. Thus, it is necessary to combine multiple distance measures into clustering. However, the weights for different distance measures are hard to set. Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique. Recently a new clustering algorithm called ‘multiobjective evolutionary clustering based on combining multiple distance measures’ (MOECDM) was proposed to integrate Euclidean and Path distance measures together for partitioning the dataset with different structures. However, it is time-consuming due to the large-sized genes. This paper proposes a fast multiobjective fuzzy clustering algorithm for partitioning the dataset with different structures. In this algorithm, a real encoding scheme is adopted to represent the individual. Two fuzzy clustering objective functions are designed based on Euclidean and Path distance measures, respectively, to evaluate the goodness of each individual. An improved evolutionary operator is also introduced accordingly to increase the convergence speed and the diversity of the population. In the final generation, a set of nondominated solutions can be obtained. The best solution and the best distance measure are selected by using a semisupervised method. Afterwards, an updated algorithm is also designed to detect the optimal cluster number automatically. The proposed algorithms are applied to many datasets with different structures, and the results of eight artificial and six real-life datasets are shown in experiments. Experimental results have shown that the proposed algorithms can not only successfully partition the dataset with different structures, but also reduce the computational cost.
In many remote sensing applications, users usually prefer a multispectral image with both high spectral and high spatial information. This high quality image could be obtained by pan-sharpening techniques which fuse a high resolution panchromatic (PAN) image and a low resolution multispectral (MS) image. In this paper, we propose a new technique to do so based on the adaptive intensity-hue-saturation (IHS) transformation model and evolutionary optimization. The basic idea is to reconstruct the target image through a parameterized adaptive IHS transformation. An optimization objective is thus introduced by considering the relations between the fused image and the original PAN and MS images. The control parameters are optimized by an evolutionary algorithm. Experimental results show that our new approach is practical and performs much better than some state-of-the-art techniques according to the performance metrics.
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