ABSTRACT
BACKGROUND:Liver fibrosis is the early stage of terminal liver diseases. Effective treatment for liver fibrosis can prevent the occurrence of terminal liver diseases. Bone marrow mesenchymal stem cel transplantation is a promising method to treat liver fibrosis. OBJECTIVE:To study the therapeutic effect of bone marrow mesenchymal stem cels on liver fibrosis in rats. METHODS: Eighteen Sprague-Dawely rats were randomized into three groups: control, model and cel transplantation groups. Animal models of carbon tetrachloride-induced liver fibrosis were made in the latter two groups. After modeling, 1 mL bone marrow mesenchymal stem cels (5×105) or the same volume of normal saline was injectedviathe tail vein into the rats in the cel transplantation and model groups, respectively. Rats in the control group were given no treatment. Degree of liver fibrosis, liver function, histological changes of the liver were detected and observed in the three groups at 4 weeks after treatment. RESULTS AND CONCLUSION:In the control group, the liver tissues had normal structure with no fibrosis; in the model group, proliferation of fibrous tissues in the portal area of the liver, inflammatory cel infiltration, vacuolar degeneration and irregular arrangement of liver cels, and tissue structure damage were observed; in the transplantation group, liver tissue damage was severer than the control group but milder than the model group. Levels of serum hyaluronidase, type IV colagen and procolagen III were significantly lower in the cel transplantation group than the model group (P< 0.05). These findings indicate that bone marrow mesenchymal stem cel transplantation can aleviate liver fibrosis and improve liver function in rats with carbon tetrachloride-induced liver fibrosis.
ABSTRACT
Due to the minimum free energy model, it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings. Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy. An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper. The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually. The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.
Subject(s)
Algorithms , Cluster Analysis , Nucleic Acid Conformation , RNA , ChemistryABSTRACT
Cancer gene expression data have the characteristics of high dimensionalities and small samples so it is necessary to perform dimensionality reduction of the data. Traditional linear dimensionality reduction approaches can not find the nonlinear relationship between the data points. In addition, they have bad dimensionality reduction results. Therefore a multiple weights locally linear embedding (LLE) algorithm with improved distance is introduced to perform dimensionality reduction in this study. We adopted an improved distance to calculate the neighbor of each data point in this algorithm, and then we introduced multiple sets of linearly independent local weight vectors for each neighbor, and obtained the embedding results in the low-dimensional space of the high-dimensional data by minimizing the reconstruction error. Experimental result showed that the multiple weights LLE algorithm with improved distance had good dimensionality reduction functions of the cancer gene expression data.
Subject(s)
Humans , Algorithms , Gene Expression Regulation, Neoplastic , Genes, Neoplasm , Neoplasms , GeneticsABSTRACT
MicroRNA (miRNA) is a family of endogenous single-stranded RNA about 22 nucleotides in length. Through targeting 3' UTR of message RNA (mRNA), they play important roles in post-transcriptional regulatory functions. For further research of miRNA function, the identification of more miRNA positive targets is needed urgently. Aiming at the high-dimensional small sample data sets in miRNA target prediction, an algorithm of eliminating redundant features is proposed based on v-SVM in this paper, and classification and features selection are also fused. The algorithm of eliminating redundant features optimizes the combination of features, and then constructs the best features combination which can represent miRNA and targets interaction model. The prior parameter v (0 < u < or = 1) controls the compression proportion of data set and selects more distinguishing support vectors. Finally, the classifier model of miRNA target prediction is built. The unbiased assessment of the classifier is achieved with a completely independent test dataset. Experiment results indicated that in both classification recognition and generalization performance of miRNA targets predicition, this model was superior to the present machine learning algorithms such as miTarget, NBmiRTar and TargetMiner, etc.
Subject(s)
MicroRNAs , Models, Theoretical , Support Vector MachineABSTRACT
Objective To evaluate the clinical significance of serum IL-8 and TNF-α in patients with ulcerative colitis. Methods Enzyme-linked immunosorbent assay(ELISA) was used to detect the level of serum IL-8 and TNF-α in47 patiens wih active phase UC(severe stage 14 cases,medium stage 18 cases,mild stage 15 cases) ,21 patients with catabasis UC and 20 healthy individuals. Results The levels of IL-8 and TNF-α in patients with active phage UC were significantly higher than those of patients with catabasis UC and healthy individuals(P ＜ 0.05). The levels of IL-8 and TNF-α in patients with catabasis UC were higher than healthy individuals(P ＜0.05). The levels of IL-8 and TNF-α in severe UC were higher than those of patients with medium UC and mild UC (P ＜ 0.05). The levels of IL-8 and TNF-α in medium UC were higher than those of patients with mild UC (P ＜ 0.05). Conclusion The IL-8 and TNF-α may play an important role in the pathogenesis of UC. The combined levelsof serum IL-8 and TNF-α were potential markers in the evaluation of the activity and seriousness of UC.