Safety and physiological effects of two different doses of elosulfase alfa in patients with morquio a syndrome: A randomized, double-blind, pilot study.

The primary treatment outcomes of a phase 2, randomized, double-blind, pilot study evaluating safety, physiological, and pharmacological effects of elosulfase alfa in patients with Morquio A syndrome are herewith presented. Patients aged ≥7 years and able to walk ≥200 m in the 6-min walk test (6MWT) were randomized to elosulfase alfa 2.0 or 4.0 mg/kg/week for 27 weeks. The primary objective was to evaluate the safety of both doses. Secondary objectives were to evaluate effects on endurance (6MWT and 3-min stair climb test [3MSCT]), exercise capacity (cardio-pulmonary exercise test [CPET]), respiratory function, muscle strength, cardiac function, pain, and urine keratan sulfate (uKS) levels, and to determine pharmacokinetic parameters. Twenty-five patients were enrolled (15 randomized to 2.0 mg/kg/week and 10 to 4.0 mg/kg/week). No new or unexpected safety signals were observed. After 24 weeks, there were no improvements versus baseline in the 6MWT, yet numerical improvements were seen in the 3MSCT with 4.0 mg/kg/week. uKS and pharmacokinetic data suggested no linear relationship over the 2.0-4.0 mg/kg dose range. Overall, an abnormal exercise capacity (evaluated in 10 and 5 patients in the 2.0 and 4.0 mg/kg/week groups, respectively), impaired muscle strength, and considerable pain were observed at baseline, and there were trends towards improvements in all domains after treatment. In conclusion, preliminary data of this small study in a Morquio A population with relatively good endurance confirmed the acceptable safety profile of elosulfase alfa and showed a trend of increased exercise capacity and muscle strength and decreased pain

Evaluation of the novel substrate RUGtm for the detection of Escherichia coli in water from temperate (Zurich, Switzerland) and tropical (Bushenyi, Uganda) field sites

Direct testing of water quality to promote drinking water safety contributes to the sustainable development goals, which call for universal access to safely-managed drinking water services by 2030. Enzyme–substrate tests offer a potentially simple and reliable approach for the detection and quantification of fecal indicator bacteria, including Escherichia coli (E. coli). The novel aquatest (AT) based on resorufin-β-D-glucuronide methyl ester (RUG™) (AT-RUG) is an enzyme–substrate test that overcomes several drawbacks of other established tests. In this study, AT-RUG was used to detect and quantify E. coli in water from temperate (Zurich, Switzerland) and tropical (Bushenyi, Uganda) regions. Quantitative results of AT-RUG were compared with IDEXX Colilert-18® (C-18), m-TEC and m-ColiBlue24®. In temperate waters, AT-RUG was found to be as sensitive as m-TEC (97.0%) and C-18 (98.5%) and showed strong agreement with the reference methods. The false-positive rate for E. coli detection in temperate waters using AT-RUG was 6%. AT-RUG performed well at incubation temperatures of 37 °C and 45 °C, but not at 24 °C. In tropical waters, AT-RUG sensitivity was 94.1% compared to m-ColiBlue24®. AT-RUG detected significantly more E. coli than m-ColiBlue24®, suggesting it is a more conservative estimate. At both field sites, AT-RUG was able to effectively indicate categorical concentrations of E. coli in water samples indicating the level of risks of fecal contamination of water supplies. This study indicates that AT-RUG is a reliable and accurate medium for the detection and quantification of E. coli in temperate and tropical waters

Effects of X-ray dose on rhizosphere studies using X-ray computed tomography

X-ray Computed Tomography (CT) is a non-destructive imaging technique originally designed for diagnostic medicine, which was adopted for rhizosphere and soil science applications in the early 1980s. X-ray CT enables researchers to simultaneously visualise and quantify the heterogeneous soil matrix of mineral grains, organic matter, air-filled pores and water-filled pores. Additionally, X-ray CT allows visualisation of plant roots in situ without the need for traditional invasive methods such as root washing. However, one routinely unreported aspect of X-ray CT is the potential effect of X-ray dose on the soil-borne microorganisms and plants in rhizosphere investigations. Here we aimed to i) highlight the need for more consistent reporting of X-ray CT parameters for dose to sample, ii) to provide an overview of previously reported impacts of X-rays on soil microorganisms and plant roots and iii) present new data investigating the response of plant roots and microbial communities to X-ray exposure. Fewer than 5% of the 126 publications included in the literature review contained sufficient information to calculate dose and only 2.4% of the publications explicitly state an estimate of dose received by each sample. We conducted a study involving rice roots growing in soil, observing no significant difference between the numbers of root tips, root volume and total root length in scanned versus unscanned samples. In parallel, a soil microbe experiment scanning samples over a total of 24 weeks observed no significant difference between the scanned and unscanned microbial biomass values. We conclude from the literature review and our own experiments that X-ray CT does not impact plant growth or soil microbial populations when employing a low level of dose (<30 Gy). However, the call for higher throughput X-ray CT means that doses that biological samples receive are likely to increase and thus should be closely monitored

Estimating food production in an urban landscape

There is increasing interest in urban food production for reasons of food security, environmental sustainability, social and health benefits. In developed nations urban food growing is largely informal and localised, in gardens, allotments and public spaces, but we know little about the magnitude of this production. Here we couple own-grown crop yield data with garden and allotment areal surveys and urban fruit tree occurrence to provide one of the first estimates for current and potential food production in a UK urban setting. Current production is estimated to be sufficient to supply the urban population with fruit and vegetables for about 30 days per year, while the most optimistic model results suggest that existing land cultivated for food could supply over half of the annual demand. Our findings provide a baseline for current production whilst highlighting the potential for change under the scaling up of cultivation on existing land

Telavancin versus Vancomycin for Hospital-Acquired Pneumonia due to Gram-positive Pathogens

The results from two methodologically identical double-blind studies indicate that telavancin is noninferior to vancomycin based on clinical response in the treatment of hospital-acquired pneumonia due to Gram-positive pathogens

Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data

<p>Abstract</p> <p>Background</p> <p>Researchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups differ from one another. Thus, <it>post hoc </it>tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template correlations work extremely well, especially when the researcher has <it>a priori </it>information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased by the researcher's assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally, different patterns may have different probabilities of occurring by chance, importantly influencing researchers' conclusions about a pattern and its constituent genes.</p> <p>Results</p> <p>We developed a four step, <it>post hoc </it>pattern matching (PPM) algorithm to automate single channel gene expression pattern identification/significance. First, 1-Way Analysis of Variance (ANOVA), coupled with <it>post hoc </it>'all pairwise' comparisons are calculated for all genes. Second, for each ANOVA-significant gene, all pairwise contrast results are encoded to create unique pattern ID numbers. The # genes found in each pattern in the data is identified as that pattern's 'actual' frequency. Third, using Monte Carlo simulations, those patterns' frequencies are estimated in random data ('random' gene pattern frequency). Fourth, a Z-score for overrepresentation of the pattern is calculated ('actual' against 'random' gene pattern frequencies). We wrote a Visual Basic program (StatiGen) that automates PPM procedure, constructs an Excel workbook with standardized graphs of overrepresented patterns, and lists of the genes comprising each pattern. The visual basic code, installation files for StatiGen, and sample data are available as supplementary material.</p> <p>Conclusion</p> <p>The PPM procedure is designed to augment current microarray analysis procedures by allowing researchers to incorporate all of the information from post hoc tests to establish unique, overarching gene expression patterns in which there is no overlap in gene membership. In our hands, PPM works well for studies using from three to six treatment groups in which the researcher is interested in treatment-related patterns of gene expression. Hardware/software limitations and extreme number of theoretical expression patterns limit utility for larger numbers of treatment groups. Applied to a published microarray experiment, the StatiGen program successfully flagged patterns that had been manually assigned in prior work, and further identified other gene expression patterns that may be of interest. Thus, over a moderate range of treatment groups, PPM appears to work well. It allows researchers to assign statistical probabilities to patterns of gene expression that fit <it>a priori </it>expectations/hypotheses, it preserves the data's ability to show the researcher interesting, yet unanticipated gene expression patterns, and assigns the majority of ANOVA-significant genes to non-overlapping patterns.</p