46 research outputs found

    Lyopreserved amniotic membrane is cellularly and clinically similar to cryopreserved construct for treating foot ulcers

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    We compared cellular viability between cryopreserved and lyopreserved amniotic membranes and clinical outcomes of the lyopreserved construct in a prospective cohort study of 40 patients with neuropathic foot ulcers. Patients received weekly application of lyopreserved membrane for 12 weeks with standard weekly debridement and offloading. We evaluated the proportion of foot ulcers that closed, time to closure, closure trajectories, and infection during therapy. We used chi-square tests for dichotomous variables and independent t-tests for continuous variables with an alpha of α =.10. Cellular viability was equivalent between cryo- and lyopreserved amniotic tissues. Clinically, 48% of subjects' wounds closed in an average of 40.0 days. Those that did not close were older (63 vs 59 years, P =.011) and larger ulcers at baseline (7.8 vs 1.6 cm2, P =.012). Significantly more patients who achieved closure reached a 50% wound area reduction in 4 weeks compared with non-closed wounds (73.7% vs 47.6%, P =.093). There was no difference in the slope of the wound closure trajectories between closed and non-closed wounds (0.124 and 0.159, P =.85), indicating the rate of closure was similar. The rate of closure was 0.60 mm/day (SD = 0.47) for wounds that closed and 0.50 mm/day (SD = 0.58) for wounds that did not close (P =.89)

    What is Microbial Dormancy?

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    Life can be stressful. One way to deal with stress is to simply wait it out. Microbes do this by entering a state of reduced activity and increased resistance commonly called ‘dormancy’. But what is dormancy? Different scientific disciplines emphasize distinct traits and phenotypic ranges in defining dormancy for their microbial species and system-specific questions of interest. Here, we propose a unified definition of microbial dormancy, using a broad framework to place earlier discipline-specific definitions in a new context. We then discuss how this new definition and framework may improve our ability to investigate dormancy using multi-omics tools. Finally, we leverage our framework to discuss the diversity of genomic mechanisms for dormancy in an extreme environment that challenges easy definitions – the permafrost

    A Meta-Analysis of Effects of Bt Crops on Honey Bees (Hymenoptera: Apidae)

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    L.) are the most important pollinators of many agricultural crops worldwide and are a key test species used in the tiered safety assessment of genetically engineered insect-resistant crops. There is concern that widespread planting of these transgenic crops could harm honey bee populations.We conducted a meta-analysis of 25 studies that independently assessed potential effects of Bt Cry proteins on honey bee survival (or mortality). Our results show that Bt Cry proteins used in genetically modified crops commercialized for control of lepidopteran and coleopteran pests do not negatively affect the survival of either honey bee larvae or adults in laboratory settings.Although the additional stresses that honey bees face in the field could, in principle, modify their susceptibility to Cry proteins or lead to indirect effects, our findings support safety assessments that have not detected any direct negative effects of Bt crops for this vital insect pollinator

    Jet Formation at the Spill Site and Resulting Droplet Size Distributions

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    The size distribution of oil droplets and gas bubbles forming at the exit geometry of a deep-sea blowout is one of the key parameters to understand its propagation and fate in the ocean, whether with regard to rising time to the surface, drift by ocean currents, dissolution or biodegradation. While a large 8 mm droplet might rise to the sea surface within minutes or hours, microdroplets \u3c 100 ÎŒm may take weeks or months to surface, if at all. On the other hand, a microdroplet or bubble dissolutes faster due to its larger surface to volume ratio and is also more available for biodegrading bacteria. To be able to properly model these effects, it is necessary to understand the drop formation processes near the discharge point and to predict the evolving droplet size distribution (DSD) for the specific conditions. In this chapter, the general breakup mechanisms and flow regimes of an oil-in-water jet are discussed in Sect. 4.1. Section 4.2 focuses on the different approaches to determine the DSD in the laboratory and field settings and critically reviews the existing datasets. State-of-the-art models for the prediction of the DSD of a subsea oil discharge are presented alongside a new approach based on the turbulent kinetic energy (TKE) in Sect. 4.3, while Sect. 4.4 takes a closer look at the specific effects of the deep sea on the DSD. Based on this, Sect. 4.5 discusses the advantages and limitations of subsea dispersant injection. Section 4.6 provides a summary of the chapter and gives an outlook to unresolved questions
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