345 research outputs found

    Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors

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    The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data

    Associations of neighbourhood environmental attributes and socio-economic status with health-related quality of life in urban mid-aged and older adults : Mediation by physical activity and sedentary behaviour

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    This study examined the associations of objectively assessed physical features of the neighbourhood environment with physical and mental aspects of health-related quality of life (HRQoL) as measured by the SF-36, and the roles of physical activity and sedentary behaviour in these associations. We used data from a national sample of Australian mid-aged and older adults living in urban areas (N = 4141). Environmental attributes were computed for 1-km-radius areas surrounding participants' residential addresses. Neighbourhood socio-economic status (SES) and average annual concentrations of PM2.5 were the only attributes related to HRQoL in the expected direction in the total- and direct-effect regression models. All other environmental attributes were related to HRQoL via physical activity behaviours and leisure-time sitting. The associations of most environmental features with HRQoL mediated by physical activity and sedentary behaviours were inconsistent, positive through some pathways and negative through others. This study suggests that neighbourhood SES may in part benefit HRQoL by helping promote an active lifestyle. Neighbourhood attributes defining walkability may benefit HRQoL by providing opportunities for walking and resistance training and, through these, by helping reduce leisure-time sitting. However, the same attributes also may limit opportunities for household activities and gardening and negatively impact on HRQoL through these pathways

    The neighbourhood environment and profiles of the metabolic syndrome

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    Background There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components. Methods We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles. Results LCA yielded three latent classes, one including only participants without MetS (“Lower probability of MetS components” profile). The other two classes/profiles, consisting of participants with and without MetS, were “Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure” and “Higher probability of MetS components”. Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS. Conclusions This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components

    Assessment of interatomic potentials for atomistic analysis of static and dynamic properties of screw dislocations in W

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    Screw dislocations in bcc metals display non-planar cores at zero temperature which result in high lattice friction and thermally activated strain rate behavior. In bcc W, electronic structure molecular statics calculations reveal a compact, non-degenerate core with an associated Peierls stress between 1.7 and 2.8 GPa. However, a full picture of the dynamic behavior of dislocations can only be gained by using more efficient atomistic simulations based on semiempirical interatomic potentials. In this paper we assess the suitability of five different potentials in terms of static properties relevant to screw dislocations in pure W. As well, we perform molecular dynamics simulations of stress-assisted glide using all five potentials to study the dynamic behavior of screw dislocations under shear stress. Dislocations are seen to display thermally-activated motion in most of the applied stress range, with a gradual transition to a viscous damping regime at high stresses. We find that one potential predicts a core transformation from compact to dissociated at finite temperature that affects the energetics of kink-pair production and impacts the mechanism of motion. We conclude that a modified embedded-atom potential achieves the best compromise in terms of static and dynamic screw dislocation properties, although at an expense of about ten-fold compared to central potentials

    Face Masks and Cough Etiquette Reduce the Cough Aerosol Concentration of Pseudomonas aeruginosa in People with Cystic Fibrosis

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    People with cystic fibrosis (CF) generate Pseudomonas aeruginosa in droplet nuclei during coughing. The use of surgical masks has been recommended in healthcare settings to minimize pathogen transmission between patients with CF.To determine if face masks and cough etiquette reduce viable P. aeruginosa aerosolized during coughing.Twenty-five adults with CF and chronic P. aeruginosa infection were recruited. Participants performed six talking and coughing maneuvers, with or without face masks (surgical and N95) and hand covering the mouth when coughing (cough etiquette) in an aerosol-sampling device. An Andersen Cascade Impactor was used to sample the aerosol at 2 meters from each participant. Quantitative sputum and aerosol bacterial cultures were performed, and participants rated the mask comfort levels during the cough maneuvers.During uncovered coughing (reference maneuver), 19 of 25 (76%) participants produced aerosols containing P. aeruginosa, with a positive correlation found between sputum P. aeruginosa concentration (measured as cfu/ml) and aerosol P. aeruginosa colony-forming units. There was a reduction in aerosol P. aeruginosa load during coughing with a surgical mask, coughing with an N95 mask, and cough etiquette compared with uncovered coughing (P

    Indoor hospital air and the impact of ventilation on bioaerosols: a systematic review

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    Hospital-acquired infections (HAI) continue to persist in hospitals, despite the use of increasingly strict infection control precautions. Opportunistic airborne transmission of potentially pathogenic bioaerosols may be one possible reason for this persistence. Therefore, we aimed to systematically review the concentrations and compositions of indoor bioaerosols in different areas within hospitals and the effects of different ventilation systems. Electronic databases (Medline and Web of Science) were searched to identify articles of interest. The search was restricted to articles published from 2000 to 2017 in English. Aggregate data was used to examine the differences in mean colony forming units per cubic metre (CFU/m3) between different hospital areas and ventilation types. A total of 36 journal articles met the eligibility criteria. The mean total bioaerosol concentrations in the different areas of the hospitals were highest in the inpatient facilities (77 CFU/m3, 95% confidence interval (CI), 55-108) compared with the restricted (4 CFU/m3, 95% CI, 10-15) and public areas (14 CFU/m3, 95% CI, 10-19). Hospital areas with natural ventilation had the highest total bioaerosol concentrations (201 CFU/m3, 95% CI, 135-300) compared with areas using conventional mechanical ventilation systems (20 CFU/m3, 95% CI, 16-24). Hospital areas using sophisticated mechanical ventilation systems (such as increased air changes per hour, directional flow and filtration systems) had the lowest total bioaerosol concentrations (9 CFU/m3, 95% CI, 7-13). Operating sophisticated mechanical ventilation systems in hospitals contributes to improved indoor air quality within hospitals, which assists in reducing the risk of airborne transmission of HAI

    Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

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    SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

    Avoidable mortality attributable to anthropogenic fine particulate matter (Pm2.5) in Australia

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    Ambient fine particulate matter 2.5) air pollution increases premature mortalityglobally. Some PM2.5 is natural, but anthropogenic PM2.5 is comparatively avoidable. We determinedthe impact of long-term exposures to the anthropogenic PM component on mortality in Australia.PM2.5-attributable deaths were calculated for all Australian Statistical Area 2 (SA2; n = 2310) regions.All-cause death rates from Australian mortality and population databases were combined withannual anthropogenic PM2.5 exposures for the years 2006–2016. Relative risk estimates were derivedfrom the literature. Population-weighted average PM2.5 concentrations were estimated in eachSA2 using a satellite and land use regression model for Australia. PM2.5-attributable mortality wascalculated using a health-impact assessment methodology with life tables and all-cause death rates.The changes in life expectancy (LE) from birth, years of life lost (YLL), and economic cost of lostlife years were calculated using the 2019 value of a statistical life. Nationally, long-term populationweighted average total and anthropogenic PM2.5 concentrations were 6.5 µg/m3(min 1.2–max 14.2)and 3.2 µg/m3(min 0–max 9.5), respectively. Annually, anthropogenic PM2.5-pollution is associatedwith 2616 (95% confidence intervals 1712, 3455) deaths, corresponding to a 0.2-year (95% CI 0.14, 0.28)reduction in LE for children aged 0–4 years, 38,962 (95%CI 25,391, 51,669) YLL and an average annualeconomic burden of 6.2billion(956.2 billion (95%CI 4.0 billion, $8.1 billion). We conclude that the anthropogenicPM2.5-related costs of mortality in Australia are higher than community standards should allow,and reductions in emissions are recommended to achieve avoidable mortality

    The Infection of Chicken Tracheal Epithelial Cells with a H6N1 Avian Influenza Virus

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    Sialic acids (SAs) linked to galactose (Gal) in α2,3- and α2,6-configurations are the receptors for avian and human influenza viruses, respectively. We demonstrate that chicken tracheal ciliated cells express α2,3-linked SA, while goblet cells mainly express α2,6-linked SA. In addition, the plant lectin MAL-II, but not MAA/MAL-I, is bound to the surface of goblet cells, suggesting that SA2,3-linked oligosaccharides with Galβ1–3GalNAc subterminal residues are specifically present on the goblet cells. Moreover, both α2,3- and α2,6-linked SAs are detected on single tracheal basal cells. At a low multiplicity of infection (MOI) avian influenza virus H6N1 is exclusively detected in the ciliated cells, suggesting that the ciliated cell is the major target cell of the H6N1 virus. At a MOI of 1, ciliated, goblet and basal cells are all permissive to the AIV infection. This result clearly elucidates the receptor distribution for the avian influenza virus among chicken tracheal epithelial cells and illustrates a primary cell model for evaluating the cell tropisms of respiratory viruses in poultry
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