171 research outputs found

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    <p>Antibody responses and protection of offspring when mothers were immunized via the IN route and their offspring via the IN route<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157041#t004fn001" target="_blank"><sup>a</sup></a>.</p

    Sweet syndrome induced by FLT3 inhibitors: case report and literature review

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    Acute febrile neutrophilic dermatosis, also commonly referred to as Sweet syndrome, is often associated with tumors, infections, immune disorders and medications. FLT3 inhibitor-induced Sweet syndrome is a rare complication. We report a patient with relapsed and refractory acute monocytic leukemia harboring high-frequency FLT3-ITD and DNMT3a mutations. The FLT3 inhibitor gilteritinib was administered for reinduction therapy after failure of chemotherapy with a combination of venetoclax, decitabine, aclarubicin, cytarabine and granulocyte colony-stimulating factor. The leukemia patient achieved remission after 1 month of treatment. However, Sweet syndrome induced by gilteritinib, which was confirmed by skin biopsy, developed during induction therapy. Similar cases of Sweet syndrome following FLT3 inhibitor therapy for acute myeloid leukemia were reviewed. Attention should be given to this rare complication when FLT3 inhibitors are used for acute myeloid leukemia therapy, and appropriate treatments need to be administered in a timely manner.</p

    PBDEs Emission from Waste Printed Wiring Boards during Thermal Process

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    Polybrominated diphenyl ethers (PBDEs) contained in waste printed wiring board (PWB) matrix and surface dust can be emitted into the air during thermal process, which is widely used to detach the electronic components from the base boards of waste PWB. In this study, PBDEs concentrations in air and dust samples were detected in a PWB-heating workshop, and then heating experiments of PBDEs-containing materials in a quartz tube furnace were performed to investigate the PBDEs emission mechanism. The results showed that the mean concentrations of Σ<sub>8</sub>PBDEs in PM<sub>10</sub> and TSP were 479 and 1670 ng/m<sup>3</sup>, respectively. Compared with surface dust collected from waste PWB (15600 ng/g), PBDEs concentrations in dust from the workshop floor (31 100 ng/g), heating machine inside (84 700 ng/g), and the cyclone extractor (317 000 ng/g), were condensed after thermal process. Heating experiments showed that the emission rates of PBDEs from PBDEs-containing dust were obviously higher than those from PWB fragments in the first 1-h time. The cumulative amounts of PBDEs emitted from dust increased rapidly at first, and then leveled off to become asymptotic to the maximum amounts. At the temperature of 300 °C, the PBDEs emission from dust mainly occurred within the first 5 min, and the average emission rates for BDE-28, -47, and -99 among the first 5 min were 1230, 4480, and 1950 ng/(g·min), respectively. During the initial 1-h period, the trends of PBDEs emission from PWB fragments had a linear increase, and the emission rates of penta-BDE (BDE-47, -99, -100) at different temperatures were at a range of 9.75–11.5 ng/(g·min). All the results showed that PBDEs emission from PWB waste happened during thermal process, and management strategies were provided to reduce the occupational exposure level of PBDEs for workers

    Study of Hydrophobic Clustering in Partially Sulfonated Polystyrene Solutions with a Systematic Coarse-Grained Model

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    Systematically coarse-grained (CG) force field models are state point dependent and therefore are usually not transferable to different chemistries and thermodynamic conditions. Fragment-based approaches for deriving CG polymer force fields increase the transferability of the models, but several challenges remain, in particular to describe conformational transitions of polymers driven by changes in the solvent environment or chemical composition of the chain. Herein, we describe a coarse-graining approach for deriving implicit-solvent CG polymer models, which can be used to study hydrophobic collapse transitions in aqueous solutions. On the basis of an earlier reported CG model for sodium poly­(styrenesulfonate) (PSSNa) [Macromolecules 2012, 45, 2551−2561], we describe an approach to rescale nonpolar pair potentials of mean force based on a solvent-accessible-surface-area argument, thus allowing to reduce the fraction of charged monomers in the chain and to account for partial chain collapse. Based on a comparison with extensive detailed-atomistic, explicit-solvent, simulations, it is demonstrated that the rescaling function for nonpolar interactions is chain length dependent. Extrapolation of the rescaling factor to long chains provides a new way to perform CG simulations of polymer solutions with moderately hydrophobic polyelectrolyte chains. Upon application to a 50%-charged PSSNa chain we observe pearl-necklace conformations in a cascade evolving manner with increasing chain length, in agreement with theoretical predictions for polyelectrolytes in poor solvent

    Molecular Dynamics Simulation of Salt Diffusion in Polyelectrolyte Assemblies

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    The diffusion of salt ions and charged probe molecules in polyelectrolyte (PE) assemblies is often assumed to follow a theoretical hopping model, in which the diffusing ion hops between charged sites of chains based on electroneutrality. However, experimental verification of diffusing pathway at such microscales is difficult, and the corresponding molecular mechanisms remain elusive. In this study, we perform all-atom molecular dynamics simulations of salt diffusion in the PE assembly of poly­(sodium-4-styrenesulfonate) (PSS) and poly­(diallyldimethylammonium chloride) (PDAC). Besides the ion hopping mode, the diffusing trajectories are found to present common features of a jump process, that is, subjecting to PE relaxation, water pockets in the structure open and close; thus, the ion can move from one pocket to another. Anomalous subdiffusion of ions and water is observed because of the trapping scenarios in these water pockets. The jump events are much rarer compared with ion hopping but significantly increases salt diffusion with increasing temperature. Our result strongly indicates that salt diffusion in hydrated PDAC/PSS is a combined process of ion hopping and jump motion. This provides a new molecular explanation for the coupling of salt motion with chain motion and the nonlinear increase of salt diffusion at glass-transition temperature

    Role of Salt and Water in the Plasticization of PDAC/PSS Polyelectrolyte Assemblies

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    In this work, we investigate the effect of salt and water on plasticization and thermal properties of hydrated poly­(diallyldimethylammonium chloride) (PDAC) and poly­(sodium 4-styrenesulfonate) (PSS) assemblies via molecular dynamics simulations and modulated differential scanning calorimetry (MDSC). Commonly, both water and salt are considered to be plasticizers of hydrated polyelectrolyte assemblies. However, the simulation results presented here show that while water has a plasticizing effect, salt can also have an opposite effect on the PE assemblies. On one hand, the presence of salt ions provides additional free volume for chain motion and weakens PDAC–PSS ion pairing due to electrostatic screening, which contributes toward plasticization of the complex. On the other hand, salt ions bind water in their hydration shells, which decreases water mobility and reduces the plasticization by hydration. Our MDSC results connect the findings to macroscopic PE plasticization and the glass-transition-like thermal transition <i>T</i><sub>tr</sub> under controlled PE hydration and salt content. This work identifies and characterizes the dual nature of salt both as plasticizer and hardener of PE assemblies and maps the interconnection of the influence of salt with the degree of hydration in the system. Our findings provide insight into the existing literature data, bear fundamental significance in understanding of hydrated polyelectrolyte assemblies, and suggest a direct means to tailor the mechanical characteristics of PE assemblies via interplay of water and salt

    Silver(I) complexes with halo-substituted cyanoanilines: synthesis, characterization and antibacterial activity

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    <p>Four Ag(I) complexes, [Ag(L<sub>1</sub>)<sub>2</sub>](NO<sub>3</sub>) (<b>1</b>), [Ag(L<sub>2</sub>)(NO<sub>3</sub>)] (<b>2</b>), [Ag(L<sub>3</sub>)<sub>3</sub>](NO<sub>3</sub>) (<b>3</b>), and [Ag(L<sub>4</sub>)<sub>2</sub>](NO<sub>3</sub>) (<b>4</b>), with ligands derived from halo-containing cyanoanilines (L<sub>1</sub> = 4-amino-3fluorobenzonitrile, L<sub>2</sub> = 4-amino-3-chlorobenzonitrile, L<sub>3</sub> = 4-amino-3-bromobenzonitrile, L<sub>4</sub> = 4-amino-2-bromobenzonitrile) were synthesized and characterized by C, H, and N elemental analysis, IR and <sup>1</sup>H NMR spectroscopy and single crystal X-ray diffraction. Complexes <b>1</b>–<b>4</b> crystallized in the triclinic space group C2/c, P2(1)/n, P-1 and C2/c, respectively. In <b>1</b> and <b>4</b>, Ag<sup>+</sup> is four-coordinate with L<sub>1</sub> or L<sub>4</sub> to form 1-D <sub>∞</sub>{[Ag(L<sub>1</sub>/L<sub>4</sub>)<sub>2</sub>]<sup>+</sup>} polymeric cations. In <b>2</b>, Ag<sup>+</sup> is three-coordinate by two L<sub>2</sub> ligands and one NO<sub>3</sub><sup>−</sup> ligand to form a 1-D <sub>∞</sub>{[Ag(L<sub>2</sub>)(NO<sub>3</sub>)]} zigzag chain. In <b>3</b>, Ag<sup>+</sup> is four-coordinate by L<sub>3</sub> to form a dinuclear [Ag(L<sub>3</sub>)<sub>3</sub>]<sup>+</sup> cation. The NO<sub>3</sub><sup>−</sup> is a 4-connector bridging group in <b>1</b> and <b>3</b> and a 5-connector bridging group in <b>2</b> and <b>4</b>. The intermolecular hydrogen bonds and Ag⋯O weak interactions play important roles in forming 3-D networks of <b>1</b>–<b>4</b>. The antibacterial activities for <b>1</b>–<b>4</b> were evaluated against <i>Bacillus</i> <i>subtilis</i>, Staphylococcus <i>aureus</i> and <i>E</i>scherichia <i>coli</i> with MTT method. The antibacterial results indicated that <b>2</b> showed the best inhibitory activity against the test bacterial strains, and was as potent as chloramphenicol.</p

    Maquetas Taller vertical 2014-2: Granjas urbanas

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    <p>Normal mice (without <i>H</i>. <i>pylori</i> infection) were assigned as a control group (CG group) and mice models infected with <i>H</i>. <i>pylori</i> were divided into model group (MG group, only treated with saline solution), triple combination therapy group (TG group, the daily medicine intake is 0.5 ug clarithromycin, 0.02 ug omeprazole, and 1 ug amoxicillin), low/middle/high concentrations of flavonoid glycoside group (LF/MF/HF group, treated with one daily dose of flavonoid glycoside at 32/64/128 ug), low/middle/high concentrations of flavonoid glycoside and common concentration of amoxicillin group (LFA/MFA/HFA group, treated with one daily dose of Flavonoid glycoside at 32/64/128 ug and amoxicillin at 1 ug). Analysis of G cells and somatostatin gray value in each group (n = 10). *<i>P</i> < 0.05 vs model group.</p

    The mRNA levels of Gastrin in the gastric mucosa of <i>H</i>. <i>pylori</i> infected mice in different groups (n = 6).

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    <p>*<i>P</i> < 0.05 vs model group</p><p>The mRNA levels of Gastrin in the gastric mucosa of <i>H</i>. <i>pylori</i> infected mice in different groups (n = 6).</p

    Hematoxylin and Eosin staining of gastric tissues (HE×200).

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    <p>Normal mice (without <i>H</i>. <i>pylori</i> infection) were assigned as a control group (CG group) and mice models infected with <i>H</i>. <i>pylori</i> were divided into model group (MG group, only treated with saline solution), triple combination therapy group (TG group, the daily medicine intake is 0.5 ug clarithromycin, 0.02 ug omeprazole, and 1 ug amoxicillin), low/middle/high concentrations of flavonoid glycoside group (LF/MF/HF group, treated with one daily dose of Flavonoid glycoside at 32/64/128 ug), low/middle/high concentrations of flavonoid glycoside and common concentration of amoxicillin group (LFA/MFA/HFA group, treated with one daily dose of flavonoid glycoside at 32/64/128 ug and amoxicillin at 1 ug). Pathological score of mice gastric antrum in each group (±SD, n = 10). *<i>P</i> < 0.05 vs model group.</p
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