Apoptosis of hepatocarcinoma cells Hepg2 induced by Huaier extract through regulation of HBx and CEACAM1 gene expression..

Huaier can effectively inhibit the growth of tumor cells by enhancing the immune system. However, the mechanism of its function is still not clear. The current study aimed to explore the possible mechanism of Huaier in inhibiting human hepatocarcinoma cells by observing its effect on proliferation and invasion in hepatocarcinoma cells, HepG2 and HepG2-X, which stably express the HBx gene, and by comparing the levels of mRNA transcription and protein expression of HBx and CEACAM1 in HepG2 cells and HepG2-X cells when treated with different concentrations of Huaier. HepG2 cells and HepG2-X cells were treated with 0, 1.5, 3.0, and 6.0 g/L-1 Huaier extract in vitro. MTT assay was used to measure the inhibition of cell proliferation. The transwell cell model coated with Matrigel glue was used to detect the invasion of HepG2 and HepG2-X cells in vitro. Flowcytometry was used to observe changes in cell cycle. Real-time PCR and Western blot were used to detect HBx and CEREAM1 mRNA transcription and protein expression separately. Huaier extract can inhibit HepG2 and HepG2-X cell proliferation in a time- and dose-dependent manner. The A value of HepG2-X cells in each group was higher than that of HepG2 cells. Compared with the control group, the invasion ability of HepG2 and HepG2-X cells decreased significantly after treatment with Huaier extract, in a dose-dependent manner. The cell cycle of HepG2 and HepG2-X was arrested at S phase. The distribution of G0/G1 phase decreased gradually with the increase of the concentration of Huaier extract, and the proportion of G0/G1 phase distribution declined. After treating with Huaier extract, mRNA transcription and protein expression of HBx in HepG2 and HepG2-X declined, while those of CEACAM1 increased, reflecting a dose-dependent manner (P less than 0.05). Therefore, we concluded that the inhibitory effect of Huaier extract on hepatocarcinoma cell proliferation might function through down regulation of HBx gene expression and upregulation of CEACAM1 gene expression.

Task decomposition and module combination based on class relations: a modular neural network for pattern classification.

In this paper, we propose a new method for decomposing pattern classification problems based on the class relations among training data. By using this method, we can divide a K-class classification problem into a series of ((2)(K)) two-class problems. These two-class problems are to discriminate class Ci from class Cj for i=1, ..., K and j = i+1, while the existence of the training data belonging to the other K-2 classes is ignored. If the two-class problem of discriminating class Ci from class Cj is still hard to be learned, we can further break down it into a set of two-class subproblems as small as we expect. Since each of the two-class problems can be treated as a completely separate classification problem with the proposed learning framework, all of the two-class problems can be learned in parallel. We also propose two module combination principles which give practical guidelines in integrating individual trained network modules. After learning of each of the two-class problems with a network module, we can easily integrate all of the trained modules into a min-max modular (M3) network according to the module combination principles and obtain a solution to the original problem. Consequently, a large-scale and complex K-class classification problem can be solved effortlessly and efficiently by learning a series of smaller and simpler two-class problems in parallel.

Inverting feedforward neural networks using linear and nonlinear programming.

The problem of inverting trained feedforward neural networks is to find the inputs which yield a given output. In general, this problem is an ill-posed problem because the mapping from the output space to the input space is a one-to-many mapping. In this paper, we present a method for dealing with the inverse problem by using mathematical programming techniques. The principal idea behind the method is to formulate the inverse problem as a nonlinear programming (NLP) problem, a separable programming (SP) problem, or a linear programming (LP) problem according to the architectures of networks to be inverted or the types of network inversions to be computed. An important advantage of the method over the existing iterative inversion algorithm is that various designated network inversions of multilayer perceptrons (MLP's) and radial basis function (RBF) neural networks can be obtained by solving the corresponding SP problems, which can be solved by a modified simplex method, a well-developed and efficient method for solving LP problems. We present several examples to demonstrate the proposed method and the applications of network inversions to examining and improving the generalization performance of trained networks. The results show the effectiveness of the proposed method.

Observation of cavity quantum-electrodynamic effects in a Nd:glass microsphere.

In a Nd:glass microspherical cavity the enhancement and inhibition of spontaneous-emission processes that are due to cavity QED effects have been observed. The rates of the enhanced spontaneous emission are location dependent and reach a maximum value of more than 10(3) times the free-space value. The large enhancement strongly modifies the decay processes of Nd ions in glass, and the radiative properties of Nd:glass have been changed. As a result a new spectrum including new lasing wavelengths in the Nd:glass sphere has been observed.

Failure to detect hemagglutination-inhibiting antibodies with intact avian influenza virions.

Avian influenza viruses replicate in a variety of mammals and birds, yet hemagglutination inhibition tests show that postinfection sera from these animals (e.g., ferrets and ducks) have insignificant levels of antibodies (Hinshaw et al., Infect. Immun. 34:354-361, 1981). This suggested that avian influenza viruses, in contrast to mammalian viruses, may not induce a significant humoral response. Studies reported here indicate that avian influenza viruses do induce high levels of antibodies in ferrets, ducks, and mice and produce long-lived memory for cytotoxic T-cells in mice. The failure to detect hemagglutination-inhibiting antibodies to avian viruses was explained by the finding that antibodies to avian influenza viruses were not detectable in hemagglutination inhibition tests with intact virus yet were readily demonstrable when hemagglutinin subunits were used. In addition, these sera contained high levels of neutralizing antibodies to the avian virus. These findings suggest that the hemagglutinins of avian and mammalian influenza viruses may differ in their accessibility to antibodies or the biological consequence of antibody attachment or both. The practical consequence of these studies is that hemagglutination inhibition tests with intact avian viruses fail to detect antibody and do not correlate with virus neutralization. The avian virus used in these studies, A/Mallard/NY/6870/78 (H2N2), replicated and caused mortality in BALB/c mice, emphasizing that the host range and virulence of avian viruses extends to mammals. The above findings suggest that avian viruses could infect mammals in nature, yet seroepidemiological studies with conventional hemagglutination inhibition tests could give misleading results.