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The rapid spread of coronavirus disease 2019 (COVID-19) required swift preparation to protect healthcare personnel (HCP) and patients, especially considering shortages of personal protective equipment (PPE). Due to the lack of a pre-existing biocontainment unit, we needed to develop a novel approach to placing patients in isolation cohorts while working with the pre-existing physical space.
To prevent disease transmission to non–COVID-19 patients and HCP caring for COVID-19 patients, to optimize PPE usage, and to provide a comfortable and safe working environment.
An interdisciplinary workgroup developed a combination of approaches to convert existing spaces into COVID-19 containment units with high-risk zones (HRZs). We developed standard workflow and visual management in conjunction with updated staff training and workflows. The infection prevention team created PPE standard practices for ease of use, conservation, and staff safety.
The interventions resulted in 1 possible case of patient-to-HCP transmission and zero cases of patient-to-patient transmission. PPE usage decreased with the HRZ model while maintaining a safe environment of care. Staff on the COVID-19 units were extremely satisfied with PPE availability (76.7%) and efforts to protect them from COVID-19 (72.7%). Moreover, 54.8% of HCP working in the COVID-19 unit agreed that PPE monitors played an essential role in staff safety.
The HRZ model of containment unit is an effective method to prevent the spread of COVID-19 with several benefits. It is easily implemented and scaled to accommodate census changes. Our experience suggests that other institutions do not need to modify existing physical structures to create similarly protective spaces.
A method for three-dimensional reconstruction of objects from defocused images collected at multiple illumination directions in high-resolution transmission electron microscopy is presented. The method effectively corrects for the Ewald sphere curvature by taking into account the in-particle propagation of the electron beam. Numerical simulations demonstrate that the proposed method is capable of accurately reconstructing biological molecules or nanoparticles from high-resolution defocused images under conditions achievable in single-particle electron cryo-microscopy or electron tomography with realistic radiation doses, non-trivial aberrations, multiple scattering, and other experimentally relevant factors. The physics of the method is based on the well-known Diffraction Tomography formalism, but with the phase-retrieval step modified to include a conjugation of the phase (i.e., multiplication of the phase by a negative constant). At each illumination direction, numerically backpropagating the beam with the conjugated phase produces maximum contrast at the location of individual atoms in the molecule or nanoparticle. The resultant algorithm, Conjugated Holographic Reconstruction, can potentially be incorporated into established software tools for single-particle analysis, such as, for example, RELION or FREALIGN, in place of the conventional contrast transfer function correction procedure, in order to account for the Ewald sphere curvature and improve the spatial resolution of the three-dimensional reconstruction.
This study aimed to explore the association between hyperglycemia in pregnancy (type 2 diabetes (T2D) and gestational diabetes mellitus (GDM)) and child developmental risk in Europid and Aboriginal women.
PANDORA is a longitudinal birth cohort recruited from a hyperglycemia in pregnancy register, and from normoglycemic women in antenatal clinics. The Wave 1 substudy included 308 children who completed developmental and behavioral screening between age 18 and 60 months. Developmental risk was assessed using the Ages and Stages Questionnaire (ASQ) or equivalent modified ASQ for use with Aboriginal children. Emotional and behavioral risk was assessed using the Strengths and Difficulties Questionnaire. Multivariable logistic regression was used to assess the association between developmental scores and explanatory variables, including maternal T2D in pregnancy or GDM.
After adjustment for ethnicity, maternal and child variables, and socioeconomic measures, maternal hyperglycemia was associated with increased developmental “concern” (defined as score ≥1 SD below mean) in the fine motor (T2D odds ratio (OR) 5.30, 95% CI 1.77–15.80; GDM OR 3.96, 95% CI 1.55–10.11) and problem-solving (T2D OR 2.71, 95% CI 1.05–6.98; GDM OR 2.54, 95% CI 1.17–5.54) domains, as well as increased “risk” (score ≥2 SD below mean) in at least one domain (T2D OR 5.33, 95% CI 1.85–15.39; GDM OR 4.86, 95% CI 1.95–12.10). Higher maternal education was associated with reduced concern in the problem-solving domain (OR 0.27, 95% CI 0.11–0.69) after adjustment for maternal hyperglycemia.
Maternal hyperglycemia is associated with increased developmental concern and may be a potential target for intervention so as to optimize developmental trajectories.
Early in the COVID-19 pandemic, the World Health Organization stressed the importance of daily clinical assessments of infected patients, yet current approaches frequently consider cross-sectional timepoints, cumulative summary measures, or time-to-event analyses. Statistical methods are available that make use of the rich information content of longitudinal assessments. We demonstrate the use of a multistate transition model to assess the dynamic nature of COVID-19-associated critical illness using daily evaluations of COVID-19 patients from 9 academic hospitals. We describe the accessibility and utility of methods that consider the clinical trajectory of critically ill COVID-19 patients.
The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.
Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.
The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.
The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.
A chloroacetamide herbicide by application timing factorial experiment was conducted in 2017 and 2018 in Mississippi to investigate chloroacetamide use in a dicamba-based Palmer amaranth management program in cotton production. Herbicides used were S-metolachlor or acetochlor, and application timings were preemergence, preemergence followed by (fb) early postemergence, preemergence fb late postemergence, early postemergence alone, late postemergence alone, and early postemergence fb late postemergence. Dicamba was included in all preemergence applications, and dicamba plus glyphosate was included with all postemergence applications. Differences in cotton and weed response due to chloroacetamide type were minimal, and cotton injury at 14 d after late postemergence application was less than 10% for all application timings. Late-season weed control was reduced up to 30% and 53% if chloroacetamide application occurred preemergence or late postemergence only, respectively. Late-season weed densities were minimized if multiple applications were used instead of a single application. Cotton height was reduced by up to 23% if a single application was made late postemergence relative to other application timings. Chloroacetamide application at any timing except preemergence alone minimized late-season weed biomass. Yield was maximized by any treatment involving multiple applications or early postemergence alone, whereas applications preemergence or late postemergence alone resulted in up to 56% and 27% yield losses, respectively. While no yield loss was reported by delaying the first of sequential applications until early postemergence, forgoing a preemergence application is not advisable given the multiple factors that may delay timely postemergence applications such as inclement weather.
We present the data and initial results from the first pilot survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The survey covers
of an area covered by the Dark Energy Survey, reaching a depth of 25–30
rms at a spatial resolution of
11–18 arcsec, resulting in a catalogue of
220 000 sources, of which
180 000 are single-component sources. Here we present the catalogue of single-component sources, together with (where available) optical and infrared cross-identifications, classifications, and redshifts. This survey explores a new region of parameter space compared to previous surveys. Specifically, the EMU Pilot Survey has a high density of sources, and also a high sensitivity to low surface brightness emission. These properties result in the detection of types of sources that were rarely seen in or absent from previous surveys. We present some of these new results here.
We present a detailed overview of the cosmological surveys that we aim to carry out with Phase 1 of the Square Kilometre Array (SKA1) and the science that they will enable. We highlight three main surveys: a medium-deep continuum weak lensing and low-redshift spectroscopic HI galaxy survey over 5 000 deg2; a wide and deep continuum galaxy and HI intensity mapping (IM) survey over 20 000 deg2 from
$z = 0.35$
to 3; and a deep, high-redshift HI IM survey over 100 deg2 from
$z = 3$
to 6. Taken together, these surveys will achieve an array of important scientific goals: measuring the equation of state of dark energy out to
$z \sim 3$
with percent-level precision measurements of the cosmic expansion rate; constraining possible deviations from General Relativity on cosmological scales by measuring the growth rate of structure through multiple independent methods; mapping the structure of the Universe on the largest accessible scales, thus constraining fundamental properties such as isotropy, homogeneity, and non-Gaussianity; and measuring the HI density and bias out to
$z = 6$
. These surveys will also provide highly complementary clustering and weak lensing measurements that have independent systematic uncertainties to those of optical and near-infrared (NIR) surveys like Euclid, LSST, and WFIRST leading to a multitude of synergies that can improve constraints significantly beyond what optical or radio surveys can achieve on their own. This document, the 2018 Red Book, provides reference technical specifications, cosmological parameter forecasts, and an overview of relevant systematic effects for the three key surveys and will be regularly updated by the Cosmology Science Working Group in the run up to start of operations and the Key Science Programme of SKA1.
Nutritional factors and infectious agents may contribute to paediatric growth deficits in low- and middle-income countries; however, the contribution of enteric pathogens is only beginning to be understood. We analysed the stool from children <5 years old from an open cohort, cluster-randomised controlled trial of a point-of-collection water chlorinator in urban Bangladesh. We compared the presence/absence of 15 enteric pathogens detected via multiplex, molecular methods in the stool with concurrent Z-scores/Z-score cut-offs (−2 standard deviations (s.d.)) for height-for-age (HAZ/stunting), weight-for-age (WAZ/underweight) and weight-for-height (WHZ/wasting), adjusted for sociodemographic and trial-related factors, and measured caregiver-reported diarrhoea. Enteric pathogen prevalence in the stool was high (88% had ≥1 enteric pathogen, most commonly Giardia spp. (40%), Salmonella enterica (33%), enterotoxigenic E. coli (28%) and Shigella spp. (27%)) while reported 7-day diarrhoea prevalence was 6%, suggesting high subclinical infection rates. Many children were stunted (26%) or underweight (24%). Adjusted models suggested Giardia spp. detection was associated with lower HAZ (−0.22 s.d., 95% CI −0.44 to 0.00; prevalence ratio for stunting: 1.39, 95% CI 0.94–2.06) and potentially lower WAZ. No pathogens were associated with reported diarrhoea in adjusted models. Giardia spp. carriage may be associated with growth faltering, but not diarrhoea, in this and similar low-income settings. Stool-based enteric pathogen detection provides a direct indication of previous exposure that may be useful as a broader endpoint of trials of environmental interventions.
Research on psychotic illness is loosening emphasis on diagnostic stringency in favour of including a more dimensionally based conceptualization of psychopathology and pathobiology. However, to clarify these notions requires investigation of the full scope of psychotic diagnoses.
The Cavan–Monaghan First Episode Psychosis Study ascertained cases of first episode psychosis across all 12 DSM-IV psychotic diagnoses via all routes to care: public, private or forensic; home-based, outpatient or inpatient. There was no arbitrary upper age cut-off and minimal impact of factors associated with variations in social milieu, ethnicity or urbanicity. Cases were evaluated epidemiologically and assessed for psychopathology, neuropsychology, neurology, antecedent factors, insight and quality of life.
Among 432 cases, the annual incidence of any DSM-IV psychotic diagnosis was 34.1/100 000 of population and encompassed functional psychotic diagnoses, substance-induced psychopathology and psychopathology due to general medical conditions, through to psychotic illness that defied contemporary diagnostic algorithms. These 12 DSM-IV diagnostic categories, including psychotic disorder not otherwise specified, showed clinical profiles that were consistently more similar than distinct.
There are considerable similarities and overlaps across a broad range of diagnostic categories in the absence of robust discontinuities between them. Thus, psychotic illness may be of such continuity that it cannot be fully captured by operational diagnostic algorithms that, at least in part, assume discontinuities. This may reflect the impact of diverse factors each of which acts on one or more overlapping components of a common, dysfunctional neuronal network implicated in the pathobiology of psychotic illness.
Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom.
We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians.
Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window.
Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice.