1,601 research outputs found

    Assessment on Practicing Correct Body Posture and Determinant Analyses in a Large Population of a Metropolitan Area

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    An incorrect posture can generate stress of the spine and can be the cause of musculoskeletal disorders. Considering the extensive use of the computer, which worsens posture disorders, among workers, is important to analyze the phenomenon in order to reduce his impact on industry. The aim of this study is to assess determinants regarding posture in a large population of a metropolitan area. A total of 1177 questionnaires was analyzed. The majority of sample showed good knowledge and attitude regarding correct posture; most of the sample, 70.4% was aware of the definition of posture and 68.7% feel that not enough attention is paid at posture at workplace. Despite the good predisposition, only 2.8% of the sample consult a specialist for posture. The multiple linear regression analysis shows that those who have higher knowledge and best attitudes will consequently have good behaviors in maintaining a correct posture. Furthermore, age and education resulted main drivers of correct posture in any model considered. The results enlighten the necessity of conducting further studies to analyze attitudes of the general population and suggest improving educational and training programs to the enrichment of knowledge and to correct posture behaviors

    Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults.

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    The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score". The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ‚Č• 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ŌČ = 0.95). Factor structure was consistent across age and sex. COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health

    Au@MNPs-based electrochemical immunosensor for vitamin D3 serum samples analysis

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    We report a new sensitive label-free electrochemical immunosensor to detect Vitamin D3 (25-OHD3) in untreated serum samples. To this aim, a graphite screen printed electrode (SPE) was modified using cysteamine (CYM) functionalized core-shell magnetic nanoparticles (Au@MNPs) then, the 25-OHD3 antibody (AbD) was immobilized via glutaraldehyde crosslinking. The several steps involved in the immunosensor development and 25-OHD3 analysis were monitored by using differential pulse voltammetry (DPV). The developed immunosensor showed a LOD of 2.4 ng mL‚ąí1 and a linear range between 7.4 and 70 ng mL‚ąí1. The effectiveness of the immunosensor in human serum analysis was assessed by comparing the results obtained with the chemiluminescence-immunoassay (CLIA) reference method. The high sensitivity and excellent agreement with the reference method suggest its potential use as a POCT to monitor hypovitaminosis 25-OHD levels

    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5‚Äď7 vast areas of the tropics remain understudied.8‚Äď11 In the American tropics, Amazonia stands out as the world‚Äôs most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13‚Äď15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon‚Äôs biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region‚Äôs vulnerability to environmental change. 15%‚Äď18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

    Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults

    No full text
    Background: The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology ‚ÄúP-score‚ÄĚ. Methods: The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1‚Äď4 items (‚ÄúCOH-FIT items‚ÄĚ) were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ‚Č• 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. Results: From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ŌČ = 0.95). Factor structure was consistent across age and sex. Conclusions: COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health

    Effect of polydispersity and bubble clustering on the steady shear viscosity of dilute bubble suspensions in Newtonian media

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    This work examines the steady shear viscosity of dilute polydisperse bubble suspensions generated in a mixture of mineral oil and span 80. We proved theoretically that, in polydisperse bubble suspensions, the shear-thinning behavior spans a capillary number (Ca) range between 0.01 and 100, instead of occurring at Ca~1, which is the case for monodisperse suspensions. However, for the effect of polydispersity to become apparent, the bubble size distribution should be bimodal, with very small and very large bubbles having similar volume fractions. In any other case, we can consider the polydisperse suspension as monodisperse, with a volume-weighted average diameter (d43). To confirm the theoretical results, we carried out steady shear rheological tests. Our measurements revealed an unexpected double power-law decay of the relative viscosity. To investigate this behavior further, we visualized the produced bubble suspensions under shear. The visualization experiments revealed that bubbles started forming clusters and threads at average capillary number around 0.01, where we observed the first decay of viscosity. CFD simulations confirmed that under the presence of bubble clusters and threads the fluid streamlines distort less, thus resulting in a decrease of the suspension viscosity. Consequently, we can attribute the first decay of the relative viscosity to the formation of bubble clusters and threads, proving that the novel shear-thinning behavior we observed is due to a combination of bubble clustering and deformation

    An Observational Study to Develop a Predictive Model for Bacterial Pneumonia Diagnosis in Severe COVID-19 Patients‚ÄĒC19-PNEUMOSCORE

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    In COVID-19 patients, antibiotics overuse is still an issue. A predictive scoring model for the diagnosis of bacterial pneumonia at intensive care unit (ICU) admission would be a useful stewardship tool. We performed a multicenter observational study including 331 COVID-19 patients requiring invasive mechanical ventilation at ICU admission; 179 patients with bacterial pneumonia; and 152 displaying negative lower-respiratory samplings. A multivariable logistic regression model was built to identify predictors of pulmonary co-infections, and a composite risk score was developed using & beta;-coefficients. We identified seven variables as predictors of bacterial pneumonia: vaccination status (OR 7.01; 95% CI, 1.73-28.39); chronic kidney disease (OR 3.16; 95% CI, 1.15-8.71); pre-ICU hospital length of stay & GE; 5 days (OR 1.94; 95% CI, 1.11-3.4); neutrophils & GE; 9.41 x 10(9)/L (OR 1.96; 95% CI, 1.16-3.30); procalcitonin & GE; 0.2 ng/mL (OR 5.09; 95% CI, 2.93-8.84); C-reactive protein & GE; 107.6 mg/L (OR 1.99; 95% CI, 1.15-3.46); and Brixia chest X-ray score & GE; 9 (OR 2.03; 95% CI, 1.19-3.45). A predictive score (C19-PNEUMOSCORE), ranging from 0 to 9, was obtained by assigning one point to each variable, except from procalcitonin and vaccine status, which gained two points each. At a cut-off of & GE;3, the model exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 84.9%, 55.9%, 69.4%, 75.9%, and 71.6%, respectively. C19-PNEUMOSCORE may be an easy-to-use bedside composite tool for the early identification of severe COVID-19 patients with pulmonary bacterial co-infection at ICU admission. Its implementation may help clinicians to optimize antibiotics administration in this setting

    Giants of the Amazon:How does environmental variation drive the diversity patterns of large trees?

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    Evaluation of Sampling with Partial Replacement and Double Sampling in a Managed Forest in the Brazilian Amazon

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    Abstract We compared sampling techniques in a managed native forest in Paragominas, Par√°, Brazil. Our goal in this study was to evaluate the feasibility of using Double Sampling (DS) and Sampling with Partial Replacement (SPR), when compared to Continuous Forest Inventory (CFI), to estimate the wood stock for trees with DBH ‚Č• 20 cm in a managed forest. In our results, DS had the best volume prediction, generating a sampling error of 5.20% (11.48 m¬≥ ha-1) on the second occasion 3.86% (8.78 m¬≥ ha-1). The average volume increment, estimated for the forest in the monitored period (2014-2016) was 6.88 m¬≥ ha-1, with a relative sampling error of 63.09%. Therefore, as an alternative and of low cost, we suggest using DS in successive forest inventories in monitoring areas of forest resources in the Brazilian Amazon

    Pervasive gaps in Amazonian ecological research

    Get PDF
    Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%‚Äď18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost
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