Modeling of Toxicity-Relevant Electrophilic Reactivity for Guanine with Epoxides: Estimating the Hard and Soft Acids and Bases (HSAB) Parameter as a Predictor

According to the electrophilic theory in toxicology, many chemical carcinogens in the environment and/or their active metabolites are electrophiles that exert their effects by forming covalent bonds with nucleophilic DNA centers. The theory of hard and soft acids and bases (HSAB), which states that a toxic electrophile reacts preferentially with a biological macromolecule that has a similar hardness or softness, clarifies the underlying chemistry involved in this critical event. Epoxides are hard electrophiles that are produced endogenously by the enzymatic oxidation of parent chemicals (e.g., alkenes and PAHs). Epoxide ring opening proceeds through a S_N2-type mechanism with hard nucleophile DNA sites as the major facilitators of toxic effects. Thus, the quantitative prediction of chemical reactivity would enable a predictive assessment of the molecular potential to exert electrophile-mediated toxicity. In this study, we calculated the activation energies for reactions between epoxides and the guanine N7 site for a diverse set of epoxides, including aliphatic epoxides, substituted styrene oxides, and PAH epoxides, using a state-of-the-art density functional theory (DFT) method. It is worth noting that these activation energies for diverse epoxides can be further predicted by quantum chemically calculated nucleophilic indices from HSAB theory, which is a less computationally demanding method than the exacting procedure for locating the transition state. More importantly, the good qualitative/quantitative correlations between the chemical reactivity of epoxides and their bioactivity suggest that the developed model based on HSAB theory may aid in the predictive hazard evaluation of epoxides, enabling the early identification of mutagenicity/carcinogenicity-relevant S_N2 reactivity

Meta-analysis of RCTs comparing the effect of mind-body therapies with control interventions on inflammatory markers: CRP (panel a), IL-6 (panel b), and TNF-α (panel c). Legends:

<p>CRP = c-reactive protein; IL-6 = interleukin 6; Med = meditation; QG = Qi Gong; TC = Tai Chi; TNF-α = Tumor necrosis factor; Tx = treatment; RA = rheumatoid arthritis; SMD = standardized mean difference. Risk of bias: L = low; M = medium; H = high (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100903#pone.0100903.s002" target="_blank">Table S2</a> for details). Zautra 2008 reported only subgroup results (grp 1 = RA patients with depression; grp 2 = RA patients without depression) and data from each subgroup were entered in the meta-analysis separately. P-values adjacent to I-squared results are p-values for heterogeneity testing (P<0.05 indicates significant heterogeneity), and p-values adjacent to meta-analysis pooled results (diamonds) are p-values for the pooled effect sizes.</p

Summary of Evidence Reviewed Categorized by Inflammatory and Antiviral Outcomes.

<p>Summary of Evidence Reviewed Categorized by Inflammatory and Antiviral Outcomes.</p

Study Eligibility Criteria.

<p>Study Eligibility Criteria.</p

Technical details on the interpretations for effect size (ES).

<p>Technical details on the interpretations for effect size (ES).</p

Literature Search and Study Selection. Legends:

<p>CD4 = cluster of differentiation 4 protein; CRP = c-reactive protein; IL-6 = interleukin-6; INF-γ = Interferon-gamma; NK count = natural killer cell count; TNF-α = Tumor necrosis factor. ^aThe studies were conducted in 9 countries (United States, China, India, Australia, Spain, Germany, Iran, Norway, and the United Kingdom). ^bThe sum of study number exceeds the total number of studies included due to some studies reported multiple outcomes across categories.</p

Subgroup meta-analysis of RCTs comparing the effect of mind-body therapies with control interventions on CRP and IL-6 by clinical populations (panels a and b), by MBT types (panels c and d), and by control types (panels e and f). Legends:

<p>SMD = standardized mean difference. MBT = mind-body therapies. P-values adjacent to I-squared results are p-values for heterogeneity testing (P<0.05 indicates significant heterogeneity and “I-squared = n/a” indicates that there was only 1 study in the subgroup so heterogeneity was not applicable), and p-values adjacent to meta-analysis pooled results (diamonds) are p-values for the pooled effect sizes.</p

Model for Surface Diffusion of Adsorbed Gas in Nanopores of Shale Gas Reservoirs

Surface diffusion plays a key role in gas mass transfer due to the majority of adsorbed gas within abundant nanopores of organic matter in shale gas reservoirs. Surface diffusion simulation is very complex as a result of high reservoir pressure, surface heterogeneity, and nonisothermal desorption in shale gas reservoirs. In this paper, a new model of surface diffusion for adsorbed gas in shale gas reservoirs is established, which is based on a Hwang model derived under a low pressure condition and considers the effect of adsorbed gas coverage under high pressure. Additionally, this new model considers the effects of surface heterogeneity, isosteric sorption heat, and nonisothermal gas desorption. Results show that (1) the surface diffusion coefficient increases with pressure and temperature, while it decreases with activation energy and gas molecular weight; (2) contributions of viscous flow, Knudsen diffusion, and surface diffusion to the total gas mass transfer are varying during the development of shale gas reservoirs, which are mainly controlled by nanopore-scale and pressure; (3) in micropores (pore radius of <2 nm), the contribution of surface diffusion to the gas mass transfer is dominant, up to 92.95%; in macropores (pore radius of >50 nm), the contribution is less than 4.39%, which is negligible; in mesopores (2 nm < pore radius < 50 nm), the contribution is between micropores and macropores

Meta-analysis of RCTs comparing the effect of mind-body therapies with control interventions on enumerative markers: CD4 count (panel a) and NK count (panel b). Legends:

<p>CD4 = cluster of differentiation 4 protein; Med = meditation; NK count = natural killer cell count; Tx = treatment; SMD = standardized mean.</p

Rbm46 regulates mouse embryonic stem cell differentiation by targeting <i>β-Catenin</i> mRNA for degradation

<div><p>Embryonic stem cells (ESCs) are pluripotent cells and have the capability for differentiation into any of the three embryonic germ layers. The Wnt/<i>β-Catenin</i> pathway has been shown to play an essential role in ESC differentiation regulation. Activation of <i>β-Catenin</i> by post-translational modification has been extensively studied. However, mechanism(s) of post-transcriptional regulation of <i>β-Catenin</i> are not well defined. In this study, we report an RNA recognition motif-containing protein (RNA binding motif protein 46, RBM46) which regulates the degradation of <i>β-Catenin</i> mRNA. Our results show that Rbm46 is distributed primarily in the cytoplasm of mouse ESCs (mESCs) and is elevated during the process of ESC differentiation. In addition, overexpression of Rbm46 results in differentiation of mESCs into trophectoderm, while knock-down of <i>Rbm46</i> leads to mESC differentiation into endoderm. β-Catenin, a key effector in the Wnt pathway which has been reported to play a significant role in the regulation of ESC differentiation, is post-transcriptionally regulated by Rbm46. Our study reveals Rbm46 plays a novel role in the regulation of ESC differentiation.</p></div