A sensitive genetic-based detection capability for Didymosphenia geminata

It is now well recognized that the increase in global transportation over the last two decades has brought with it an increased potential for the introduction of unwanted microorganisms (aquatic or terrestrial) that may have drastic effects on human and ecosystem health and agriculture. We have developed and validated a unique genetic fingerprinting tool for D. geminata. In concert, we developed field collection and preservation techniques specific for D. geminata along with genetic-based procedures that can now reliably detect D. geminate from a complex environmental community with a high degree of sensitivity. Recent work (Phase 2) has shown that the described methods will provide detection levels from <1 – 10,000 cells ml-1. We contend that the genetic based detection approaches used in this study offer great promise to meet the increasing demands to monitor the global threat from invasive micro-organisms

Characterization of Pfiesteria Ichthyocidal Activity

Letter to the Editor regarding article: Drgon, T., et al. 2005. Characterization of ichthyocidal activity of Pfiesteria piscicida: Dependence on the dinospore cell density. Appl. Environ. Microbiol. 71:519–52

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness

Diversity of Diatom Communities in Delaware Tidal Wetland and Their Relationship to Water Quality

Diatoms are strongly influenced by water quality and serve as indicators of water quality degradation in freshwater systems. Here, sediment and water samples were collected from four sites in Blackbird Creek, DE, a salt marsh characterized as mostly freshwater to low saline brackish (&lt;8ppt). Recent changes in land use resulted in increased agricultural activity, suggesting the need to develop water quality indicators in this region. To test the hypothesis that diatom community composition changes seasonally with variations in water quality parameters, sediment and water samples were collected in 2009 and 2010 for analysis. Water temperature, salinity, pH, and dissolved oxygen were measured as well as water and sediment dissolved nutrient concentrations (nitrate, ammonia, and total and reactive phosphorous). DNA was extracted from sediments and changes in diatom community composition were evaluated by amplification of 18S rRNA gene using diatom-specific primers, followed by Terminal Restriction Fragment Length Polymorphism (TRFLP) analysis. Shannon (H') index for TRFLP profiles ranged from 2.5 to 3.0 and Simpson (Ds) index was 0.9 which infers moderate levels of diatom species richness and high diversity in these study sites. Although there were no water quality parameters that were significantly correlated with diatom community composition as determined by TRFLP patterns, temperature was the most highly correlated (r = 0.203). Dissolved oxygen, salinity, and pH of water also had moderate but insignificant impacts on the diatom community. Further analysis of cloned 18S rRNA sequences revealed the presence of diatom taxa that tolerate wide salinity ranges, and included Navicula, Cyclotella, Thalassiosira and Skeletonema. Entomoneis sp. were also present in the spring and fall seasons. Overall, results in this study demonstrate significant differences in water qualities among the study years but little change in diatom community composition between study sites and seasons, but may serve as a baseline for future studies

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness

Rapid microbial dynamics in response to an induced wetting event in Antarctic Dry Valley Soils

The cold deserts of the McMurdo Dry Valleys (MDV), Antarctica, host a high level of microbial diversity. Microbial composition and biomass in arid vs. ephemerally wetted regions are distinctly different, with wetted communities representing hot spots of microbial activity that are important zones for biogeochemical cycling. While climatic change is likely to cause wetting in areas not historically subject to wetting events, the responses of microorganisms inhabiting arid soils to water addition is unknown. The purpose of this study was to observe how an associated, yet non-wetted microbial community responds to an extended addition of water. Water from a stream was diverted to an adjacent area of arid soil with changes in microbial composition and activities monitored via molecular and biochemical methods over 7 weeks. The frequency of genetic signatures related to both prokaryotic and eukaryotic organisms adapted to MDV aquatic conditions increased during the limited 7 week period, indicating that the soil community was transitioning into a typical “high-productivity” MDV community. This work is consistent with current predictions that MDV microbial communities in arid regions are highly sensitive to climate change, and further supports the notion that changes in community structure and associated biogeochemical cycling may occur much more rapidly than predicted

BRCA1/2 mutation testing in breast cancer patients: a prospective study of the long-term psychological impact of approach during adjuvant radiotherapy

This study assessed psychological distress during the first year after diagnosis in breast cancer patients approached for genetic counseling at the start of adjuvant radiotherapy and identified those vulnerable to long-term high distress. Of the approached patients some chose to receive a DNA test result (n = 58), some were approached but did not fulfill criteria for referral (n = 118) and some declined counseling and/or testing (n = 44). The comparative group consisted of patients not eligible for genetic counseling (n = 182) and was therefore not approached. Patients actively approached for genetic counseling showed no more long-term distress than patients not eligible for such counseling. There were no differences between the subgroups of approached patients. Predictors for long-term high distress or an increase in distress over time were pre-existing high distress and a low quality of life, having children, and having no family members with breast cancer. It is concluded that breast cancer patients can be systematically screened and approached for genetic counseling during adjuvant radiotherapy without imposing extra psychological burden. Patients vulnerable to long-term high distress already displayed high distress shortly after diagnosis with no influence of their medical treatment on their level of distress at long-term

The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing

International audienceCurrent sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707