Staphylococcal Toxic Shock Syndrome 2000–2006: Epidemiology, Clinical Features, and Molecular Characteristics

Circulating strains of Staphylococcus aureus (SA) have changed in the last 30 years including the emergence of community-associated methicillin-resistant SA (MRSA). A report suggested staphylococcal toxic shock syndrome (TSS) was increasing over 2000-2003. The last population-based assessment of TSS was 1986.Population-based active surveillance for TSS meeting the CDC definition using ICD-9 codes was conducted in the Minneapolis-St. Paul area (population 2,642,056) from 2000-2006. Medical records of potential cases were reviewed for case criteria, antimicrobial susceptibility, risk factors, and outcome. Superantigen PCR testing and PFGE were performed on available isolates from probable and confirmed cases.Of 7,491 hospitalizations that received one of the ICD-9 study codes, 61 TSS cases (33 menstrual, 28 non-menstrual) were identified. The average annual incidence per 100,000 of all, menstrual, and non-menstrual TSS was 0.52 (95% CI, 0.32-0.77), 0.69 (0.39-1.16), and 0.32 (0.12-0.67), respectively. Women 13-24 years had the highest incidence at 1.41 (0.63-2.61). No increase in incidence was observed from 2000-2006. MRSA was isolated in 1 menstrual and 3 non-menstrual cases (7% of TSS cases); 1 isolate was USA400. The superantigen gene tst-1 was identified in 20 (80%) of isolates and was more common in menstrual compared to non-menstrual isolates (89% vs. 50%, p = 0.07). Superantigen genes sea, seb and sec were found more frequently among non-menstrual compared to menstrual isolates [100% vs 25% (p = 0.4), 60% vs 0% (p<0.01), and 25% vs 13% (p = 0.5), respectively].TSS incidence remained stable across our surveillance period of 2000-2006 and compared to past population-based estimates in the 1980s. MRSA accounted for a small percentage of TSS cases. tst-1 continues to be the superantigen associated with the majority of menstrual cases. The CDC case definition identifies the most severe cases and has been consistently used but likely results in a substantial underestimation of the total TSS disease burden

Panton-Valentine Leukocidin Is Not the Primary Determinant of Outcome for Staphylococcus aureus Skin Infections: Evaluation from the CANVAS Studies

The impact of Panton-Valentine leukocidin (PVL) on the severity of complicated skin and skin structure infections (cSSSI) caused by Staphylococcus aureus is controversial. We evaluated potential associations between clinical outcome and PVL presence in both methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) isolates from patients enrolled in two large, multinational phase three clinical trials assessing ceftaroline fosamil for the treatment of cSSSI (the CANVAS 1 and 2 programs). Isolates from all microbiologically evaluable patients with monomicrobial MRSA or MSSA infections (n = 473) were genotyped by PCR for pvl and underwent pulsed-field gel electrophoresis (PFGE). Genes encoding pvl were present in 266/473 (56.2%) isolates. Infections caused by pvl-positive S. aureus were associated with younger patient age, North American acquisition, and presence of major abscesses (P<0.001 for each). Cure rates of patients infected with pvl-positive and pvl-negative S. aureus were similar overall (93.6% versus 92.8%; P = 0.72), and within MRSA-infected (94.5% vs. 93.1%; P = 0.67) and MSSA-infected patients (92.2% vs. 92.7%; P = 1.00). This finding persisted after adjustment for multiple patient characteristics. Outcomes were also similar when USA300 PVL+ and non-USA300 PVL+ infections were compared. The results of this contemporary, international study suggest that pvl presence was not the primary determinant of outcome in patients with cSSSI due to either MRSA or MSSA

Surveillance of Transmitted Antiretroviral Drug Resistance among HIV-1 Infected Women Attending Antenatal Clinics in Chitungwiza, Zimbabwe

The rapid scale-up of highly active antiretroviral therapy (HAART) and use of single dose Nevirapine (SD NVP) for prevention of mother-to-child transmission (pMTCT) have raised fears about the emergence of resistance to the first line antiretroviral drug regimens. A cross-sectional study was conducted to determine the prevalence of primary drug resistance (PDR) in a cohort of young (<25 yrs) HAART-naïve HIV pregnant women attending antenatal clinics in Chitungwiza, Zimbabwe. Whole blood was collected in EDTA for CD4 counts, viral load, serological estimation of duration of infection using the BED Calypte assay and genotyping for drug resistance. Four hundred and seventy-one women, mean age 21 years; SD: 2.1 were enrolled into the study between 2006 and 2007. Their median CD4 count was 371cells/µL; IQR: 255–511 cells/µL. Two hundred and thirty-six samples were genotyped for drug resistance. Based on the BED assay, 27% were recently infected (RI) whilst 73% had long-term infection (LTI). Median CD4 count was higher (p<0.05) in RI than in women with LTI. Only 2 women had drug resistance mutations; protease I85V and reverse transcriptase Y181C. Prevalence of PDR in Chitungwiza, 4 years after commencement of the national ART program remained below WHO threshold limit (5%). Frequency of recent infection BED testing is consistent with high HIV acquisition during pregnancy. With the scale-up of long-term ART programs, maintenance of proper prescribing practices, continuous monitoring of patients and reinforcement of adherence may prevent the acquisition and transmission of PDR

Integration of rule-based models and compartmental models of neurons

Synaptic plasticity depends on the interaction between electrical activity in neurons and the synaptic proteome, the collection of over 1000 proteins in the post-synaptic density (PSD) of synapses. To construct models of synaptic plasticity with realistic numbers of proteins, we aim to combine rule-based models of molecular interactions in the synaptic proteome with compartmental models of the electrical activity of neurons. Rule-based models allow interactions between the combinatorially large number of protein complexes in the postsynaptic proteome to be expressed straightforwardly. Simulations of rule-based models are stochastic and thus can deal with the small copy numbers of proteins and complexes in the PSD. Compartmental models of neurons are expressed as systems of coupled ordinary differential equations and solved deterministically. We present an algorithm which incorporates stochastic rule-based models into deterministic compartmental models and demonstrate an implementation ("KappaNEURON") of this hybrid system using the SpatialKappa and NEURON simulators.Comment: Presented to the Third International Workshop on Hybrid Systems Biology Vienna, Austria, July 23-24, 2014 at the International Conference on Computer-Aided Verification 201

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community

Protecting the unseen majority: Land cover and environmental factors linked with soil bacterial communities and functions in New Zealand

The biodiversity in soil ecosystems is simultaneously incredibly rich and poorly described. In countries such as New Zealand, where high endemism in plant species emerged following extended geographical isolation, it is likely similar evolutionary pressures extended to soil microbial communities (our biodiversity ‘dark matter’). However, we have little understanding of the extent of microbial life in New Zealand soils, let alone estimates of endemism, rates of species loss or gain, or implications for systems where plants and their microbiomes have co-evolved. In this study, we tested for the impacts of land-cover type (native forest, planted forest with exotic conifers, and pastoral agriculture) on soil bacterial communities and their functional potential, using environmental microarrays (PhyloChip and GeoChip, respectively). This evaluation was conducted across four environmentally different locations (Hokitika, Banks Peninsula, Craigieburn, and Eyrewell). The environment from which samples were collected was the largest and most significant factor associated with variation in bacterial community assemblage and function. As such, novel pockets of bacterial biodiversity, with discrete ecosystem function, may be present in New Zealand. There was some evidence to suggest that change in land cover affected soil bacterial species, but not their functions. Secondary testing found this effect was restricted to differences between native forest and agricultural land use. Bacterial communities and functions between native and planted forests were similar. Analysis of soil environmental properties among samples found that land cover effects were underpinned by changes in soil pH that typically accompanies application of lime in agricultural systems, but is uncommon in planted forests. When compared with other studies conducted in New Zealand, we conclude that: (1) different locations can harbour distinct communities of soil microbial diversity, and (2) land-use intensification, not land cover change per se, shifts microbial biodiversity through alteration of primary habitat conditions, particularly soil pH