Cross-sectional analysis to explore the awareness, attitudes and actions of UK adults at high risk of severe illness from COVID-19

OBJECTIVES: This study explored the impact of COVID-19 on people identified as at high risk of severe illness by UK government, and in particular, the impact of lockdown on access to healthcare, medications and use of technological platforms. DESIGN: Online survey methodology. SETTING: UK. PARTICIPANTS: 1038 UK adults were recruited who were either identified by UK government as at high risk of severe illness from COVID-19 or self-identified as at high risk with acute or other chronic health conditions not included in the UK government list. Participants were recruited through social media advertisements, health charities and patient organisations. MAIN OUTCOME MEASURES: The awareness, attitudes and actions survey which explores the impact of COVID-19, on including access to healthcare, use of technology for health condition management, mental health, depression, well-being and lifestyle behaviours. RESULTS: Nearly half of the sample (44.5%) reported that their mental health had worsened during the COVID-19 lockdown. Management of health conditions changed including access to medications (28.5%) and delayed surgery (11.9%), with nearly half of the sample using telephone care (45.5%). Artificial Intelligence identified that participants in the negative cluster had higher neuroticism, insecurity and negative sentiment. Participants in this cluster reported more negative impacts on lifestyle behaviours, higher depression and lower well-being, alongside lower satisfaction with platforms to deliver healthcare. CONCLUSIONS: This study provides novel evidence of the impact of COVID-19 on people identified as at high risk of severe illness. These findings should be considered by policy-makers and healthcare professionals to avoid unintended consequences of continued restrictions and future pandemic responses

VersaCount: customizable manual tally software for cell counting

<p>Abstract</p> <p>Background</p> <p>The manual counting of cells by microscopy is a commonly used technique across biological disciplines. Traditionally, hand tally counters have been used to track event counts. Although this method is adequate, there are a number of inefficiencies which arise when managing large numbers of samples or large sample sizes.</p> <p>Results</p> <p>We describe software that mimics a traditional multi-register tally counter. Full customizability allows operation on any computer with minimal hardware requirements. The efficiency of counting large numbers of samples and/or large sample sizes is improved through the use of a "multi-count" register that allows single keystrokes to correspond to multiple events. Automatically updated multi-parameter values are implemented as user-specified equations, reducing errors and time required for manual calculations. The user interface was optimized for use with a touch screen and numeric keypad, eliminating the need for a full keyboard and mouse.</p> <p>Conclusions</p> <p>Our software provides an inexpensive, flexible, and productivity-enhancing alternative to manual hand tally counters.</p

Observation of Strong Resonant Behavior in the Inverse Photoelectron Spectroscopy of Ce Oxide

X-ray emission spectroscopy and resonant inverse photoelectron spectroscopy (RIPES) have been used to investigate the photon emission associated with the Ce 3d5/2 and Ce 3d3/2 thresholds. Strong resonant behavior has been observed in the RIPES of a Ce oxide near the 5/2 and 3/2 edges

Understanding Healthcare Students’ Experiences of Racial Bias: A Narrative Review of the Role of Implicit Bias and Potential Interventions in Educational Settings

Implicit racial bias is a persistent and pervasive challenge within healthcare education and training settings. A recent systematic review reported that 84% of included studies (31 out of 37) showed evidence of slight to strong pro-white or light skin tone bias amongst healthcare students and professionals. However, there remains a need to improve understanding about its impact on healthcare students and how they can be better supported. This narrative review provides an overview of current evidence regarding the role of implicit racial bias within healthcare education, considering trends, factors that contribute to bias, and possible interventions. Current evidence suggests that biases held by students remain consistent and may increase during healthcare education. Sources that contribute to the formation and maintenance of implicit racial bias include peers, educators, the curriculum, and placements within healthcare settings. Experiences of implicit racial bias can lead to psychosomatic symptoms, high attrition rates, and reduced diversity within the healthcare workforce. Interventions to address implicit racial bias include an organizational commitment to reducing bias in hiring, retention, and promotion processes, and by addressing misrepresentation of race in the curriculum. We conclude that future research should identify, discuss, and critically reflect on how implicit racial biases are enacted and sustained through the hidden curriculum and can have detrimental consequences for racial and ethnic minority healthcare students

A Cross-Sectional Survey on Knowledge and Perceptions of Health Risks Associated with Arsenic and Mercury Contamination from Artisanal Gold mining in Tanzania.

An estimated 0.5 to 1.5 million informal miners, of whom 30-50% are women, rely on artisanal mining for their livelihood in Tanzania. Mercury, used in the processing gold ore, and arsenic, which is a constituent of some ores, are common occupational exposures that frequently result in widespread environmental contamination. Frequently, the mining activities are conducted haphazardly without regard for environmental, occupational, or community exposure. The primary objective of this study was to assess community risk knowledge and perception of potential mercury and arsenic toxicity and/or exposure from artisanal gold mining in Rwamagasa in northwestern Tanzania. A cross-sectional survey of respondents in five sub-villages in the Rwamagasa Village located in Geita District in northwestern Tanzania near Lake Victoria was conducted. This area has a history of artisanal gold mining and many of the population continue to work as miners. Using a clustered random selection approach for recruitment, a total of 160 individuals over 18 years of age completed a structured interview. The interviews revealed wide variations in knowledge and risk perceptions concerning mercury and arsenic exposure, with 40.6% (n=65) and 89.4% (n=143) not aware of the health effects of mercury and arsenic exposure respectively. Males were significantly more knowledgeable (n=59, 36.9%) than females (n=36, 22.5%) with regard to mercury (x²=3.99, p<0.05). An individual's occupation category was associated with level of knowledge (x²=22.82, p=<0.001). Individuals involved in mining (n=63, 73.2%) were more knowledgeable about the negative health effects of mercury than individuals in other occupations. Of the few individuals (n=17, 10.6%) who knew about arsenic toxicity, the majority (n=10, 58.8%) were miners. The knowledge of individuals living in Rwamagasa, Tanzania, an area with a history of artisanal gold mining, varied widely with regard to the health hazards of mercury and arsenic. In these communities there was limited awareness of the threats to health associated with exposure to mercury and arsenic. This lack of knowledge, combined with minimal environmental monitoring and controlled waste management practices, highlights the need for health education, surveillance, and policy changes

Towards strange metallic holography

We initiate a holographic model building approach to `strange metallic' phenomenology. Our model couples a neutral Lifshitz-invariant quantum critical theory, dual to a bulk gravitational background, to a finite density of gapped probe charge carriers, dually described by D-branes. In the physical regime of temperature much lower than the charge density and gap, we exhibit anomalous scalings of the temperature and frequency dependent conductivity. Choosing the dynamical critical exponent $z$ appropriately we can match the non-Fermi liquid scalings, such as linear resistivity, observed in strange metal regimes. As part of our investigation we outline three distinct string theory realizations of Lifshitz geometries: from F theory, from polarised branes, and from a gravitating charged Fermi gas. We also identify general features of renormalisation group flow in Lifshitz theories, such as the appearance of relevant charge-charge interactions when $z \geq 2$. We outline a program to extend this model building approach to other anomalous observables of interest such as the Hall conductivity.Comment: 71 pages, 8 figure

Matrix Models for the Black Hole Information Paradox

We study various matrix models with a charge-charge interaction as toy models of the gauge dual of the AdS black hole. These models show a continuous spectrum and power-law decay of correlators at late time and infinite N, implying information loss in this limit. At finite N, the spectrum is discrete and correlators have recurrences, so there is no information loss. We study these models by a variety of techniques, such as Feynman graph expansion, loop equations, and sum over Young tableaux, and we obtain explicitly the leading 1/N^2 corrections for the spectrum and correlators. These techniques are suggestive of possible dual bulk descriptions. At fixed order in 1/N^2 the spectrum remains continuous and no recurrence occurs, so information loss persists. However, the interchange of the long-time and large-N limits is subtle and requires further study.Comment: 35 pages, 11 eps figures; v.2 minor typos fixe

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. / Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. / Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from 66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from <1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. / Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P &lt; 0.0001), lower modularity (P &lt; 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions