Transcatheter Arterial Embolization Combined with Anti-vascular Agent Combretastatin A4 Phosphate Inhibits Growth and Vascularization of Liver Tumor in an Animal Model.

OBJECTIVE: This study aimed to investigate the effect of combretastatin A4 phosphate (CA4P) on proliferation, migration, and capillary tube formation of human umbilical vein endothelial cells (HUVECs) and the efficacy of transcatheter arterial embolization combined with CA4P in the treatment of rabbit VX2 liver tumor. METHODS: The effects of different concentrations of CA4P on proliferation, migration and capillary tube formation of HUVECs were investigated by cell proliferation assay, wound healing assay and capillary tube formation assay, respectively. Thirty-two rabbits implanted with liver VX2 tumors were randomly divided into 4 groups. After catheterization of the left hepatic artery, the infusion was performed using normal saline (group A), CA4P aqueous solution (group B), lipiodol and polyvinyl alcohol particles (group C), and CA4P lipiodol emulsion and polyvinyl alcohol particles (group D), respectively. Half of the animals in each group were euthanized for immunohistochemical analysis to evaluate microvessel density (MVD) at 3 days post-treatment. The other half were examined by MRI and histology to evaluate tumor growth and necrosis at 7 days post-treatment. RESULTS: CA4P could inhibit the proliferation, migration, and tube formation of HUVECs in cell experiments. After interventional treatment, the level of MVD in group D was lower than that in group C (P<0.01). The tumor volume in group C or D was lower than that in group A or B (P<0.01). The tumor necrosis rate was higher in group D than in the other groups. CONCLUSION: The study suggests that CA4P could inhibit the proliferation, migration, and capillary tube formation of HUVECs, and transcatheter arterial embolization combined with CA4P could inhibit the growth of VX2 tumor and obviously induce tumor necrosis.

Coumestrol ameliorates doxorubicin-induced cardiotoxicity via activating AMPKα.

Doxorubicin (DOX) acts as the cornerstone in multiple tumour chemotherapy regimens, however, its clinical application is often impeded due to the induction of a severe cardiotoxicity that eventually provokes left ventricular dysfunction and congestive heart failure. Coumestrol (CMT) is a common dietary phytoestrogen with pleiotropic pharmacological effects. The present study aims to investigate the role and mechanism of CMT on DOX-induced cardiotoxicity. Mice were intragastrically administrated with CMT (5 mg/kg/day) for consecutive 2 weeks and then received a single intraperitoneal injection of DOX (15 mg/kg) to mimic the clinical toxic effects after 8-day additional feeding. To verify the role of 5' AMP-activated protein kinase alpha (AMPKα), AMPKα2 global knockout mice were used. H9C2 cells were cultured to further validate the beneficial role of CMT in vitro. CMT administration notably ameliorated oxidative damage, cell apoptosis and cardiac dysfunction in DOX-treated mice. Besides, we observed that DOX-induced reactive oxygen species overproduction and cardiomyocyte apoptosis were also reduced by CMT incubation in H9C2 cells. Mechanistically, CMT activated AMPKα and Ampkα deficiency abolished the beneficial effects of CMT in vivo and in vitro. Finally, we proved that protein kinase A (PKA) was required for CMT-mediated AMPKα activation and cardioprotective effects. CMT activated PKA/AMPKα pathway to alleviate DOX-induced oxidative damage, cell apoptosis and cardiac dysfunction. Our findings provide a promising therapeutic agent for cancer patients receiving anthracycline chemotherapy.

[Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

[Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM model. The results indicate that combining the semi-transmission hyperspectral imaging technology with CARS-SPA and AFSA-SVM can accurately detect hollow heart of potato, and also provide technical support for rapid non-destructive detecting of hollow heart of potato.