Flow diagram of the study selection process.

<p>Flow diagram of the study selection process.</p

Begg's funnel plots for IL-10 −1082 A/G polymorphism and IS risk.

<p>((A) Allele model (A vs. G); (B) Homozygote model (AA vs. GG); (C) Heterozygote model(AG vs. GG); (D) Dominant model (AA+AG vs. GG); (E) Recessive model (AA vs. AG+GG)).</p

Relationship between Interleukin-10 −1082A/G Polymorphism and Risk of Ischemic Stroke: A Meta-Analysis

<div><p>Objective</p><p>To analyze the association between −1082A/G polymorphism in interleukin-10 (IL-10) gene and ischemic stroke (IS) risk by meta-analysis.</p><p>Methods</p><p>We carried out a systematic electronic search in PubMed, BIOSIS Previews, Science Direct, Chinese National Knowledge Infrastructure, Chinese Biomedical Database, Weipu database and WANGFANG Database. Pooled odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated to assess the strength of the association.</p><p>Results</p><p>7 studies were included. There was no significant association between IL-10 −1082A/G polymorphism and IS risk under all genetic models in overall estimates (A vs. G: OR = 1.23,95%CI = 0.85–1.79;AA vs. GG: OR = 1.01,95%CI = 0.47–2.19; AG vs. GG: OR = 0.76, 95%CI = 0.38–1.55; AA+AG vs. GG: OR = 0.89,95%CI = 0.46–1.73; AA vs. AG+GG: OR = 1.39, 95%CI = 0.91–2.13). Similarly, no associations were found in subgroup analysis based on ethnicity and source of controls. However, removing the study deviating from Hardy–Weinberg equilibrium (HWE) produced statistically significant associations for overall estimates under recessive model(AA VS. AG+GG OR 1.58, 95% CI 1.04–2.42) and among Asians in all genetic models (A VS.G OR 1.64, 95% CI 1.07–2.53; AA vs. GG OR1.91, 95% CI 1.31–2.80; AG vs. GG OR1.44, 95% CI 1.09–1.91; AA+AG vs. GG OR 1.54, 95% CI 1.18–2.01;AA VS. AG+GG OR 1.79, 95% CI 1.07–3.00). Even after Bonferroni correction, the associations were observed still significantly in Asians under the two models (AA vs. GG OR1.91, 95% CI 1.31–2.80, <i>P</i> = 0.0008; AA+AG vs. GG OR 1.54, 95% CI 1.18–2.01, <i>P</i> = 0.001).</p><p>Conclusion</p><p>This meta-analysis indicates that IL10 −1082 A/G polymorphism is associated with IS susceptibility in Asians and the −1082 A allele may increase risk of IS in Asians. Considering the sample size is small and between-study heterogeneity is remarkable, more studies with subtle design are warranted in future.</p></div

Publication bias tests for association between IL-10-1082A/G polymorphism and IS.

<p>Publication bias tests for association between IL-10-1082A/G polymorphism and IS.</p

Forest plots for association between IL-10 −1082 A/G polymorphism and IS risk in different genetic models.

<p>((A) Allele model(A vs. G); (B) Homozygote model (AA vs. GG); (C) Heterozygote model (AG vs. GG); (D) Dominant model (AA+AG vs. GG); (E) Recessive model (AA vs. AG+GG)).</p

Forest plots for association between IL-10 −1082 A/G polymorphism and IS risk based on ethnicity for studies in Hardy-Weinberg equilibrium.

<p>((A) Allele model (A vs. G); (B) Homozygote model (AA vs. GG); (C) Heterozygote model (AG vs. GG); (D) Dominant model (AA+AG vs. GG); (E) Recessive model (AA vs. AG+GG)).</p

Characteristics of studies included in the meta-analysis.

<p>Characteristics of studies included in the meta-analysis.</p

Summary risk estimates for association between IL-10-1082A/G polymorphism and IS.

<p>NA, data not available;</p><p>M, Statistical model.</p><p>R, random-effects model; F, fixed-effects model.</p><p>P_Z, P value for Z test; P_H, P value for heterogeneity.</p

Sensitivity analysis: Study deviated from HWE were excluded in Asians under all models and for overall studies in recessive model.

<p>M, Statistical model.</p><p>R, random-effects model; F, fixed-effects model.</p><p>P_Z, P value for Z test; P_H, P value for heterogeneity.</p><p>SA-A: Sensitivity analysis (Study deviated from HWE were exclude).in Asians.</p><p>SA-O: Sensitivity analysis (Study deviated from HWE were exclude).in overall studies.</p><p>^*:the association is sill significant after Bonferronic correction for multiple testing.</p

Air Cushion Convection Inhibiting Icing of Self-Cleaning Surfaces

Anti-icing surfaces/interfaces are of considerable importance in various engineering fields under natural freezing environment. Although superhydrophobic self-cleaning surfaces show good anti-icing potentials, promotion of these surfaces in engineering applications seems to enter a “bottleneck” stage. One of the key issues is the intrinsic relationship between superhydrophobicity and icephobicity is unclear, and the dynamic action mechanism of “air cushion” (a key internal factor for superhydrophobicity) on icing suppression was largely ignored. Here we report that icing inhibition (i.e., icing-delay) of self-cleaning surfaces is mainly ascribed to air cushion and its convection. We experimentally found air cushion on the porous self-cleaning coating under vacuum environments and on the water/ice-coating interface at low temperatures. The icing-delay performances of porous self-cleaning surfaces compared with bare substrate, up to 10–40 min under 0 to ∼−4 °C environments close to freezing rain, have been accurately real-time recorded by a novel synergy method including high-speed photography and strain sensing voltage. Based on the experimental results, we innovatively propose a physical model of “air cushion convection inhibiting icing”, which envisages both the static action of trapped air pocket without air flow and dynamic action of air cushion convection. Gibbs free energy of water droplets increased with the entropy of air derived from heat and mass transfer between warmer air underneath water droplets and colder surrounding air, resulting in remarkable ice nucleation delay. Only when air cushion convection disappears can ice nucleation be triggered on suitable Gibbs free energy conditions. The fundamental understanding of air cushion on anti-icing is an important step toward designing optimal anti-icing surfaces for practical engineering application