Main baseline epidemiological and virological characteristics of the patients with and without cirrhosis.

<p>Main baseline epidemiological and virological characteristics of the patients with and without cirrhosis.</p

Results of univariate and multivariate analyses of serological parameters by clinical outcomes in 105 patients with chronic hepatitis.

<p>Multivariate models included HDV-RNA, HBV-DNA, HBsAg, age, sex, alcohol consumption, HBeAg and IFN.</p><p>*Four patients with missing values.</p

ROC analysis of HDV RNA levels.

<p>With a view to identifying levels of HDV viremia correlated to a higher propensity of disease progression, the ROC analysis identified 5.78 logHDV RNA (i.e. approximately 600,000 copies/mL) as the best cut-off value for predicting the development of cirrhosis (AUC = 0.73) in patients with chronic hepatitis.</p

Comparison of the main epidemiological and virological characteristics of the 193 patients (current cohort) and 299 patients (general cohort [8]) at study entry.

<p>nd = not done. In the general cohort <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092062#pone.0092062-Romeo1" target="_blank">[8]</a> HBsAg, HDV RNA and HBV DNA were not quantified.</p><p>na = not applicable. In the original cohort <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092062#pone.0092062-Romeo1" target="_blank">[8]</a>, mean age and median follow-up were calculated from the first evidence of chronic liver disease. In the current cohort, mean age and follow-up were calculated at entry into the current study, corresponding to first access to our unit as well as time of collection of the first serum sample available for testing.</p><p>*The proportion of cirrhosis was slightly higher in the current cohort because we are a referral centre for both HBV infection and advanced liver diseases. Therefore, we often received from other Italian institutions patients with HBV related cirrhosis before the diagnosis of HDV coinfection was made.</p

Results of univariate and multivariate analyses according to clinical outcomes in the 193 patients.

<p>Multivariate models included HDV-RNA, HBV-DNA, HBsAg, age, sex, alcohol consumption, HBeAg and IFN.</p><p>*Seven patients with missing values.</p

Nanoscale Transformations of Alumina-Supported AuCu Ordered Phase Nanocrystals and Their Activity in CO Oxidation

In this work we applied colloidal preparation methods to synthesize AuCu nanocrystals (NCs) in the ordered tetragonal phase with an atomic composition close to 50:50. We deposited the NCs on a support (Al₂O₃), studied their transformations upon different redox treatments, and evaluated their catalytic activity in the CO oxidation reaction. The combined analyses by energy dispersive X-ray spectroscopy (EDX)-scanning transmission electron microscopy (STEM), selected area electron diffraction (SAED), and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) highlighted a phase segregation between gold and copper upon the high-temperature (350 °C) oxidizing treatment. While gold remained localized in the NCs, copper was finely dispersed on the support, likely in the form of oxide clusters. AuCu alloyed NCs, this time in the form of solid solution, face-centered cubic phase, were then restored upon a reducing treatment at the same temperature, and their catalytic activity was significantly enhanced in comparison to that of the oxidized system. The composition of the NCs and consequently the CO oxidation reaction rate were also affected by the CO/O₂ reacting atmosphere: regardless of the pretreatment, the same catalytic activity was approached over time on stream at temperatures as low as 100 °C. Consistently, the same situation was observed on the catalyst surface as probed by EDX-STEM, SAED, and DRIFTS. All of these transformations were found to be fully reversible

The Crucial Role of the Support in the Transformations of Bimetallic Nanoparticles and Catalytic Performance

The combination of two or more metals, forming alloys, core–shells, or other complex heterometallic nanostructures, has substantially spanned the available options to finely tune electronic and structural properties, opening a myriad of opportunities that has yet to be fully explored in different fields. In catalysis, the rational exploitation and design of bimetallic and trimetallic catalysts has just started. Several major aspects such as stability, phase segregation, and alloy–dealloy mechanisms have yet to be deeply understood and correlated with intrinsic factors such as nanoparticle size, composition, and structure and with extrinsic factors, or external agents, such as temperature, reaction gases, and support. Here, by combining model catalysts based on AuCu nanoparticles supported on silica or alumina with in situ characterization techniques under redox pretreatments and CO oxidation reaction, we demonstrate the crucial role of the support with regard to determining the stable active phase of bimetallic supported catalysts. This strategy, associated with theoretical studies, could lead to the rational design of unique active sites

Nanosized, Hollow, and Mn-Doped CeO₂/SiO₂ Catalysts via Galvanic Replacement: Preparation, Characterization, and Application as Highly Active Catalysts

We prepared 20–30 nm hollow CeO₂ nanoparticles with 6–9-nm-thick porous shells by performing an easy, cost-effective, and water-based galvanic replacement on SiO₂-supported Mn₃O₄ nanoparticles with Ce³⁺. The low-density, defected structure doped with residual Mn and the easily reducible surface makes the catalysts highly reactive for both CO oxidation and soot combustion reactions

Demographic and clinical characteristics of HCV patients concomitantly undergoing TE and liver biopsy assessments of liver stiffness.

<p>Demographic and clinical characteristics of HCV patients concomitantly undergoing TE and liver biopsy assessments of liver stiffness.</p

Receiver operating characteristic (ROC) curves, areas under the curve (AUROC), and cut-off values in the different scenarios for significant fibrosis (Panel A) and severe fibrosis (Panel B).

<p>Receiver operating characteristic (ROC) curves, areas under the curve (AUROC), and cut-off values in the different scenarios for significant fibrosis (Panel A) and severe fibrosis (Panel B).</p