Interaction of Interleukin 7 Receptor (IL7R) and IL6 Gene Polymorphisms with Smoking Associated with Susceptibility to Asthma in Chinese Han Adults.

BACKGROUND: the aim of this study was to investigate the relationship between the risk of asthma and multiple single nucleotide polymorphisms (SNPs) in interleukin 7 receptor (IL7R) and IL6 genes, as well as the gene- environment interactions. METHODS: This is a hospital- based case- control study. A total of 430 patients with asthma were continuously recruited. Four SNPs within IL7R and IL6 gene were genotyped by PCR based restriction fragment length polymorphism. The Hardy- Weinberg balance of all participants was tested by SNPstats. The best interaction combination of four SNPs in IL7R and IL6 genes and smoking was screened by generalized multifactor dimensionality reduction (GMDR). Logistic regression was used to test the association between four SNPs and asthma, and stratified analysis for rs1800795 gene-smoking interaction, synergy index (SI) was calculated. RESULTS: The rs1494558-G and rs1800795-C were associated with an increased risk of asthma, adjusted ORs (95% CI) was 1.81 (1.29-2.42) and 1.75 (1.20-2.28), respectively. GMDR indicated that the test accuracy for two-locus model involving rs1800795 and smoking was 0.5721, and the p = .011, the results providing evidence for rs1800795 gene-smoking interaction. The asthma risk was higher in smokers with GC or CC genotype than the sum of risks in subjects with smoking or GC or CC genotype alone, compared to the never smokers with GG genotype, the OR (95%CI) was 4.97 (3.01-7.24), and the synergy index (SI) was 1.68 (1.08-2.60). CONCLUSIONS: The rs1494558-G and rs1800795-C alleles, gene- environment interaction between rs1800795 and smoking were all associated with increased asthma risk.

Influence of TPU/EVA Phase Morphology Evolution on Supercritical Carbon Dioxide Extrusion Foaming.

Ethylene-vinyl acetate copolymer (EVA) was added at different contents to the thermoplastic polyurethane (TPU) matrix to form a non-compatible blending system, and foaming materials with high pore density were prepared using the supercritical carbon dioxide extrusion method. The influence of the phase morphology and crystal morphology of the TPU/EVA blend on its foaming behavior was studied. The results show that EVA changed the phase morphology and crystal morphology of the blends, leading to the improved melt viscosity and crystallinity of the blend system. At the same time, interfacial nucleation increases the density of cells and decreases the cell thickness and size, which is beneficial for improving the foaming properties of the blends. For the EVA content of 10% (mass fraction), the cell size is small (105.29 µm) and the cell density is the highest (3.74 × 106 cells/cm3). Based on the TPU/EVA phase morphology and crystal morphology, it is found that the sea-island structure of the blend has better foaming properties than the bicontinuous structure.

Identification of prognostic biomarkers related to the tumor microenvironment in thyroid carcinoma.

Thyroid Carcinoma (THCA) is the most common endocrine tumor that is mainly treated using surgery and radiotherapy. In addition, immunotherapy is a recently developed treatment option that has played an essential role in the management of several types of tumors. However, few reports exist on the use of immunotherapy to treat THCA. The study downloaded the miRNA, mRNA and lncRNA data for THCA patients from the TCGA database ( https://portal.gdc.cancer.gov/ ). Thereafter, the tumor samples were divided into cold and hot tumors, based on the immune score of the tumor microenvironment. Moreover, the differentially expressed lncRNAs and miRNAs were obtained. Finally, the study jointly constructed a ceRNA network through differential analysis of the mRNA data for cold and hot tumors. The study first assessed the level of immune infiltration in the THCA tumor microenvironment then divided the samples into cold and hot tumors, based on the immune score. Additionally, a total of 568 up-regulated and 412 down-regulated DEGs were screened by analyzing the differences between hot and cold tumors. Thereafter, the study examined the differentially expressed genes for lncRNA and miRNA. The results revealed 629 differentially expressed genes related to lncRNA and 114 associated with miRNA. Finally, a ceRNA network of the differentially expressed genes was constructed. The results showed a five-miRNA hubnet, i.e., hsa-mir-204, hsa-mir-128, hsa-mir-214, hsa-mir-150 and hsa-mir-338. The present study identified the immune-related mRNA, lncRNA and miRNA in THCA then constructed a ceRNA network. These results are therefore important as they provide more insights on the immune mechanisms in THCA. The findings also provides additional information for possible THCA immunotherapy.

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.