α-Glucosidase inhibitory and cytotoxic botryorhodines from mangrove endophytic fungus <i>Trichoderma</i> sp. 307

<p>One new depsidone, botryorhodine H (<b>1</b>), together with three known analogues, botryorhodines C, D and G (<b>2</b>–<b>4</b>), were obtained from the mangrove endophytic fungus <i>Trichoderma</i> sp. 307 by co-culturing with <i>Acinetobacter johnsonii</i> B2. Structures were determined by 1D and 2D NMR analyses and high-resolution mass spectrum. Compounds <b>1</b>–<b>3</b> showed α-glucosidase inhibitory activity with IC₅₀ ranging from 8.1 to 11.2 μM, and compound <b>1</b> exhibited potent cytotoxicity against rat prolactinoma MMQ and rat pituitary adenoma GH3 cell lines (IC₅₀ = 3.09 and 3.64 μM).</p

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid–liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid–liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells

New lasiodiplodins from mangrove endophytic fungus <i>Lasiodiplodia</i> sp. 318^#

<p>Two new lasiodiplodins (<b>1</b>–<b>2</b>) together with three known analogues, were isolated from a mangrove endophytic fungus, <i>Lasiodiplodia</i> sp. 318^#. Their structures were established by spectroscopic techniques (1D- and 2D-NMR, HR-ESI-MS, etc.), and electronic circular dichroism. Cytotoxic activities of compounds <b>1</b>–<b>5</b> were evaluated <i>in vitro</i>. Compound <b>4</b> was the most potent, with IC₅₀ values of 5.29 μM against MMQ, 13.05 μM against GH3. Preliminary structural-activity analysis indicated that the functional group (resorcinol-3-OH) contributed greatly to the binding of Lasiodiplodins to the cytotoxic activities.</p

No association between <i>IFNL3 (IL28B)</i> genotype and response to peginterferon alfa-2a in HBeAg-positive or -negative chronic hepatitis B

<div><p>Background & aims</p><p>It has yet to be firmly established whether host <i>IFNL3</i> (<i>IL28B</i>) genotype influences interferon responsiveness in patients with chronic hepatitis B. We investigated associations between single-nucleotide polymorphisms (SNPs) in the <i>IFNL3</i> region and response to peginterferon alfa-2a in 701 patients enrolled in three large, randomized, international studies.</p><p>Methods</p><p>Responses were defined as hepatitis B surface antigen (HBsAg) loss and/or hepatitis B e antigen (HBeAg) seroconversion plus hepatitis B virus (HBV) DNA <2000 IU/ml in HBeAg-positive patients, and HBsAg loss and/or HBV DNA <2000 IU/ml in HBeAg-negative patients (24 weeks after end of treatment). Associations between treatment response and the number of copies of the poor-response allele at three SNPs (rs8099917, rs12980275, rs12979860) were explored with logistic regression models in Asian and white patients.</p><p>Results</p><p>The HBeAg-positive and -negative populations comprised 465 (92% Asian, 50% HBV genotype C) and 236 (79% Asian, 41% HBV genotype C) patients, respectively, and had respective response rates of 26% and 47%. The <i>IFNL3</i> genotype was strongly associated with ethnicity. There was no association between <i>IFNL3</i> genotype and treatment response in HBeAg-positive or -negative patients. Independent predictors of treatment response were: sex, HBV DNA level and alanine aminotransferase level in HBeAg-positive Asian patients; age in HBeAg-negative Asian patients; and HBV DNA in HBeAg-negative white patients.</p><p>Conclusions</p><p>This is the largest analysis to date of associations between <i>IFNL3</i> genotype and peginterferon response in patients with chronic hepatitis B. The data suggest that <i>IFNL3</i> polymorphism is not a major determinant of the response to peginterferon alfa-2a in either HBeAg-positive or HBeAg-negative patients.</p></div

Rates of HBeAg seroconversion in HBeAg-positive patients according to genotype at rs12980275, rs12979860, and rs8099917.

<p>Some patients did not have data available for all genotypes, and percentages are calculated on the basis of the number of patients with data.</p

Associations between baseline covariates and treatment outcome in univariate logistic regression analyses.

<p>Associations are shown for HBeAg-positive Asian (A) and white (B) patients, and in HBeAg-negative Asian (C) and white (D) patients. ALT, alanine aminotransferase; HBeAg, hepatitis B e antigen; HBV, hepatitis B virus. After adjusting for significant baseline covariates, there were no statistically significant associations between <i>IFNL3</i> genotypes and response in the final logistic regression models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199198#pone.0199198.g003" target="_blank">Fig 3</a>).</p

Response rates according to host <i>IFNL3</i> genotype.

<p>Response rates according to genotype at rs12980275, rs12979860, and rs8099917 in HBeAg-positive Asian (A) and white (B) patients, and in HBeAg-negative Asian (C) and white (D) patients. Some patients did not have data available for all genotypes, and response rates are calculated on the basis of the number of patients with data.</p

Demographic and clinical characteristics of patients Included in the analyses.

<p>Demographic and clinical characteristics of patients Included in the analyses.</p

Associations between baseline covariates and treatment outcome in multivariate logistic regression analyses.

<p>Associations are shown for HBeAg-positive (A) and HBeAg-negative (B) patients. ALT, alanine aminotransferase; HBeAg, hepatitis B e antigen; HBV, hepatitis B virus.</p