A potential benefit from infectious disease specialist and stationary ward in rational antibiotic therapy of complicated skin and skin structure infections

Background: Management practices of complicated skin and skin structure infections (cSSSI) were compared between two areas with similar healthcare structure and low prevalence of antimicrobial resistance.Methods: The high affinity to public health-care in the Nordic countries enabled population-based approach used in this retrospective study. The study population (n=460) consisted of all adult residents from Helsinki (Finland) and Gothenburg (Sweden) treated in hospital due to cSSSI during 2008-2011.Results: The majority of patients in Helsinki (57%) visited more than one ward during their hospital stay while in Gothenburg the majority of patients (85%) were treated in one ward only. Background and disease characteristics were largely similar in both cities but patients in Helsinki were younger [mean(SD) 59(18) versus 63(19) years, p=.0117], and greater proportions had diabetes (50% versus 32%, pPeer reviewe

Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

Improving the accuracy of toxicity prediction models for liver injuries is a key element in evaluating the safety of drugs and chemicals. Mechanism-based information derived from expression (transcriptomic) data, in combination with machine-learning methods, promises to improve the accuracy and robustness of current toxicity prediction models. Deep neural networks (DNNs) have the advantage of automatically assembling the relevant features from a large number of input features. This makes them especially suitable for modeling transcriptomic data, which typically contain thousands of features. Here, we gaged gene- and pathway-level feature selection schemes using single- and multi-task DNN approaches in predicting chemically induced liver injuries (biliary hyperplasia, fibrosis, and necrosis) from whole-genome DNA microarray data. The single-task DNN models showed high predictive accuracy and endpoint specificity, with Matthews correlation coefficients for the three endpoints on 10-fold cross validation ranging from 0.56 to 0.89, with an average of 0.74 in the best feature sets. The DNN models outperformed Random Forest models in cross validation and showed better performance than Support Vector Machine models when tested in the external validation datasets. In the cross validation studies, the effect of the feature selection scheme was negligible among the studied feature sets. Further evaluation of the models on their ability to predict the injury phenotype per se for non-chemically induced injuries revealed the robust performance of the DNN models across these additional external testing datasets. Thus, the DNN models learned features specific to the injury phenotype contained in the gene expression data

Clark County Parks and Recreation

As our primary assignment in PUA 791 (Topics in Administration), the following report has been prepared. It includes an examination of the current policies governing the programs offered by the Clark County Parks and Recreation Department as well as a comparison (benchmark) of two similar metropolitan areas. The following analysis was completed during the months of February thru July, 2006. It consisted of a series of interviews with Recreation Department managers from various agencies as well as information gathered from Management of Park and Recreation Text, 2005 and CAPRA (Commission for Accreditation of Park and Recreation Agencies)

Identification of novel small molecules that elevate Klotho expression

The absence of Klotho (KL) from mice causes the development of disorders associated with human aging and decreased longevity, whereas increased expression prolongs lifespan. With age, KL protein levels decrease, and keeping levels consistent may promote healthier aging and be disease-modifying. Using the KL promoter to drive expression of luciferase, we conducted a high-throughput screen to identify compounds that activate KL transcription. Hits were identified as compounds that elevated luciferase expression at least 30%. Following validation for dose-dependent activation and lack of cytotoxicity, hit compounds were evaluated further in vitro by incubation with opossum kidney and Z310 rat choroid plexus cells, which express KL endogenously. All compounds elevated KL protein compared with control. To determine whether increased protein resulted in an in vitro functional change, we assayed FGF23 (fibroblast growth factor 23) signalling. Compounds G–I augmented ERK (extracellular-signal-regulated kinase) phosphorylation in FGFR (fibroblast growth factor receptor)-transfected cells, whereas co-transfection with KL siRNA (small interfering RNA) blocked the effect. These compounds will be useful tools to allow insight into the mechanisms of KL regulation. Further optimization will provide pharmacological tools for in vivo studies of KL

Are metal-free pristine carbon nanotubes electrocatalytically active?

Metal-free (i.e., residual metallic impurities-blocked) carbon nanotubes (CNTs) do show electrocatalytic activity for H2 evolution, O2 evolution and O2 reduction reactions (HER, OER & ORR) in alkaline solutions, but their activities strongly depend on the number of walls or inner tubes with a maximum for CNTs with 2–3 walls

Identification of the Toxicity Pathways Associated With Thioacetamide-Induced Injuries in Rat Liver and Kidney

Ingestion or exposure to chemicals poses a serious health risk. Early detection of cellular changes induced by such events is vital to identify appropriate countermeasures to prevent organ damage. We hypothesize that chemically induced organ injuries are uniquely associated with a set (module) of genes exhibiting significant changes in expression. We have previously identified gene modules specifically associated with organ injuries by analyzing gene expression levels in liver and kidney tissue from rats exposed to diverse chemical insults. Here, we assess and validate our injury-associated gene modules by analyzing gene expression data in liver, kidney, and heart tissues obtained from Sprague-Dawley rats exposed to thioacetamide, a known liver toxicant that promotes fibrosis. The rats were injected intraperitoneally with a low (25 mg/kg) or high (100 mg/kg) dose of thioacetamide for 8 or 24 h, and definite organ injury was diagnosed by histopathology. Injury-associated gene modules indicated organ injury specificity, with the liver being most affected by thioacetamide. The most activated liver gene modules were those associated with inflammatory cell infiltration and fibrosis. Previous studies on thioacetamide toxicity and our histological analyses supported these results, signifying the potential of gene expression data to identify organ injuries

In Silico Resources to Assist in the Development and Evaluation of Physiologically-Based Kinetic Models

Since their inception in pharmaceutical applications, physiologically-based kinetic (PBK) models are increasingly being used across a range of sectors, such as safety assessment of cosmetics, food additives, consumer goods, pesticides and other chemicals. Such models can be used to construct organ-level concentration-time profiles of xenobiotics. These models are essential in determining the overall internal exposure to a chemical and hence its ability to elicit a biological response. There are a multitude of in silico resources available to assist in the construction and evaluation of PBK models. An overview of these resources is presented herein, encompassing all attributes required for PBK modelling. These include predictive tools and databases for physico-chemical properties and absorption, distribution, metabolism and elimination (ADME) related properties. Data sources for existing PBK models, bespoke PBK software and generic software that can assist in model development are also identified. On-going efforts to harmonise approaches to PBK model construction, evaluation and reporting that would help increase the uptake and acceptance of these models are also discussed

Computational chemistry for graphene-based energy applications: progress and challenges

YesResearch in graphene-based energy materials is a rapidly growing area. Many graphene-based energy applications involve interfacial processes. To enable advances in the design of these energy materials, such that their operation, economy, efficiency and durability is at least comparable with fossil-fuel based alternatives, connections between the molecular-scale structure and function of these interfaces are needed. While it is experimentally challenging to resolve this interfacial structure, molecular simulation and computational chemistry can help bridge these gaps. In this Review, we summarise recent progress in the application of computational chemistry to graphene-based materials for fuel cells, batteries, photovoltaics and supercapacitors. We also outline both the bright prospects and emerging challenges these techniques face for application to graphene-based energy materials in future.vesk