Enzymatic resolution of rac-1,1-dimethyl-1-sila-cyclohexan-2-ol by ester hydrolysis or transesterification using a crude lipase preparation of Candida cylindracea

No abstract availabl

Претензионная работа по топливу для предприятий энергетики

Background and aims: CREB (cAMP response element binding protein) transcription factors are key regulators of homeostatic functions in the liver, and CRE binding is increased in hepatic inflammation. During chronic hepatitis B virus (HBV) infection, mutations or deletions in the pre-S region are frequently observed. These mutations can affect the pre-S2/S promoter controlling HBV envelope protein expression (hepatitis B surface antigen (HBsAg)) and have been associated with worsened clinical outcome. We aimed to test if CREB activation impacts on HBsAg expression. Methods: The effect of the CREB inducer protein kinase A (PKA) was tested by coexpression with HBV wild-type vector in vitro. Luciferase reporter gene constructs were cloned to identify novel regulatory regions for the HBV pre-S2/S promoter. Electrophoretic mobility shift assay (EMSA) gelshift and supershift experiments were conducted to confirm DNA transcription factor binding. Results: Coexpression of HBV and PKA resulted in HBV-S mRNA induction and enhanced small envelope protein expression. We identified a CREB binding motif in the transcribed part of the pre-S2 region, contributing to basal S promoter activity via binding of activating transcription factor 2 (ATF2). A second CREB motif closely linked to the S-ATG showed a similar binding pattern involving ATF2 and CREB1, without appearing essential for basal promoter activity. Moreover, a sequence in the pre-S2 region is responsible for further transcriptional induction via CREB activators such as PKA and forskolin. EMSA experiments indicate that CREB1 and ATF4 are involved in complex formation conferring PKA dependent promoter activation. Conclusions: Our data suggest a novel mechanism by which HBV may utilise CREB/PKA signal transduction pathways of hepatocytes to enhance its HBsAg expression during homeostasis and hepatic inflammation

The significance of macrophage polarization subtypes for animal models of tissue fibrosis and human fibrotic diseases.

The systemic and organ-specific human fibrotic disorders collectively represent one of the most serious health problems world-wide causing a large proportion of the total world population mortality. The molecular pathways involved in their pathogenesis are complex and despite intensive investigations have not been fully elucidated. Whereas chronic inflammatory cell infiltration is universally present in fibrotic lesions, the central role of monocytes and macrophages as regulators of inflammation and fibrosis has only recently become apparent. However, the precise mechanisms involved in the contribution of monocytes/macrophages to the initiation, establishment, or progression of the fibrotic process remain largely unknown. Several monocyte and macrophage subpopulations have been identified, with certain phenotypes promoting inflammation whereas others display profibrotic effects. Given the unmet need for effective treatments for fibroproliferative diseases and the crucial regulatory role of monocyte/macrophage subpopulations in fibrogenesis, the development of therapeutic strategies that target specific monocyte/macrophage subpopulations has become increasingly attractive. We will provide here an overview of the current understanding of the role of monocyte/macrophage phenotype subpopulations in animal models of tissue fibrosis and in various systemic and organ-specific human fibrotic diseases. Furthermore, we will discuss recent approaches to the design of effective anti-fibrotic therapeutic interventions by targeting the phenotypic differences identified between the various monocyte and macrophage subpopulations

Оценка воздействия дамбы в д. Босоногово Бердюжского района на окружающую среду Тюменской области

В статье рассмотрена положительная и отрицательная оценка воздействия дамбы в д. Босоногово Бердюжского района на окружающую среду Тюменской области.The article considers a positive and negative assessment of the impact of a dam in the village of Bosonogovo, Berdyuga district, on the environment of the Tyumen region

CD28 between tolerance and autoimmunity: The side effects of animal models [version 1; referees: 2 approved]

Regulation of immune responses is critical for ensuring pathogen clearance and for preventing reaction against self-antigens. Failure or breakdown of immunological tolerance results in autoimmunity. CD28 is an important co-stimulatory receptor expressed on T cells that, upon specific ligand binding, delivers signals essential for full T-cell activation and for the development and homeostasis of suppressive regulatory T cells. Many in vivo mouse models have been used for understanding the role of CD28 in the maintenance of immune homeostasis, thus leading to the development of CD28 signaling modulators that have been approved for the treatment of some autoimmune diseases. Despite all of this progress, a deeper understanding of the differences between the mouse and human receptor is required to allow a safe translation of pre-clinical studies in efficient therapies. In this review, we discuss the role of CD28 in tolerance and autoimmunity and the clinical efficacy of drugs that block or enhance CD28 signaling, by highlighting the success and failure of pre-clinical studies, when translated to humans

A randomized, placebo-controlled trial of cenicriviroc for treatment of nonalcoholic steatohepatitis with fibrosis

The aim of this study was to evaluate cenicriviroc (CVC), a dual antagonist of C-C chemokine receptor types 2 and 5, for treatment of nonalcoholic steatohepatitis (NASH) with liver fibrosis. A randomized, double-blind, multinational phase 2b study enrolled subjects with NASH, a nonalcoholic fatty liver disease activity score [NAS] ≥4, and liver fibrosis (stages 1–3, NASH Clinical Research Network) at 81 clinical sites. Subjects (N = 289) were randomly assigned CVC 150 mg or placebo. Primary outcome was ≥2-point improvement in NAS and no worsening of fibrosis at year 1. Key secondary outcomes were: resolution of steatohepatitis and no worsening of fibrosis; improvement in fibrosis by ≥1 stage and no worsening of steatohepatitis. Biomarkers of inflammation and adverse events were assessed. Full study recruitment was achieved. The primary end point of NAS improvement in the intent-to-treat population and resolution of steatohepatitis was achieved in a similar proportion of subjects on CVC (N = 145) and placebo (N = 144) (16% vs 19%, P = 0.52 and 8% vs 6%, P = 0.49, respectively). However, the fibrosis end point was met in significantly more subjects on CVC than placebo (20% vs 10%; P = 0.02). Treatment benefits were greater in those with higher disease activity and fibrosis stage at baseline. Biomarkers of systemic inflammation were reduced with CVC. Safety and tolerability of CVC were comparable to placebo. Conclusions: After 1 year of CVC treatment, twice as many subjects achieved improvement in fibrosis and no worsening of steatohepatitis compared with placebo. Given the urgent need to develop antifibrotic therapies in NASH, these findings warrant phase 3 evaluation

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness