Factors associated with stress, anxiety, and depression during social distancing in Brazil

OBJECTIVE: To estimate the prevalence of clinical signs and symptoms of severe/extreme stress, anxiety, and depression, as well as their associated factors, among Brazilians during social distancing. METHODS: This is a cross-sectional study conducted in April/May 2020 with 3,200 Brazilians over 18 years old. Respondents’ sociodemographic and clinical data were collected using an online questionnaire, which also included the 21-item Depression, Anxiety and Stress Scale (DASS-21) to assess emotional symptoms. Unadjusted and adjusted prevalence ratios and their respective 95% confidence intervals were estimated using Poisson regression models with robust variance. RESULTS: Our results show the prevalence of severe/extreme stress was 21.5%, anxiety 19.4%, and depression 21.5%. In the final model, sociodemographic, clinical, and Covid-19-related factors were associated with severe/extreme stress, anxiety, and depression in Brazilians during social distancing due to the Covid-19 pandemic. We found the main factors associated with severe/extreme depression to be young women, brown, single, not religious, sedentary, presenting reduced leisure activities, history of anxiety and depression, increased medication use, and Covid-19 symptoms. CONCLUSION: This study may help develop and systematically plan measures aimed to prevent, early identify, and properly manage clinical signs and symptoms of stress, anxiety, and depression during the Covid-19 pandemic. DESCRIPTORS: Mental Disorders, epidemiology. Stress, Psychological. Social Isolation. Coronavirus Infections. Health Surveys

Effect of High vs Low Doses of Chloroquine Diphosphate as Adjunctive Therapy for Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection: A Randomized Clinical Trial.

There is no specific antiviral therapy recommended for coronavirus disease 2019 (COVID-19). In vitro studies indicate that the antiviral effect of chloroquine diphosphate (CQ) requires a high concentration of the drug.To evaluate the safety and efficacy of 2 CQ dosages in patients with severe COVID-19. This parallel, double-masked, randomized, phase IIb clinical trial with 81 adult patients who were hospitalized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was conducted from March 23 to April 5, 2020, at a tertiary care facility in Manaus, Brazilian Amazon. - Patients were allocated to receive high-dosage CQ (ie, 600 mg CQ twice daily for 10 days) or low-dosage CQ (ie, 450 mg twice daily on day 1 and once daily for 4 days). - Label: Main Outcomes and Measures Primary outcome was reduction in lethality by at least 50% in the high-dosage group compared with the low-dosage group. Data presented here refer primarily to safety and lethality outcomes during treatment on day 13. Secondary end points included participant clinical status, laboratory examinations, and electrocardiogram results. Outcomes will be presented to day 28. Viral respiratory secretion RNA detection was performed on days 0 and 4. Out of a predefined sample size of 440 patients, 81 were enrolled (41 [50.6%] to high-dosage group and 40 [49.4%] to low-dosage group). Enrolled patients had a mean (SD) age of 51.1 (13.9) years, and most (60 [75.3%]) were men. Older age (mean [SD] age, 54.7 [13.7] years vs 47.4 [13.3] years) and more heart disease (5 of 28 [17.9%] vs 0) were seen in the high-dose group. Viral RNA was detected in 31 of 40 (77.5%) and 31 of 41 (75.6%) patients in the low-dosage and high-dosage groups, respectively. Lethality until day 13 was 39.0% in the high-dosage group (16 of 41) and 15.0% in the low-dosage group (6 of 40). The high-dosage group presented more instance of QTc interval greater than 500 milliseconds (7 of 37 [18.9%]) compared with the low-dosage group (4 of 36 [11.1%]). Respiratory secretion at day 4 was negative in only 6 of 27 patients (22.2%). - Label: Conclusions and Relevance The preliminary findings of this study suggest that the higher CQ dosage should not be recommended for critically ill patients with COVID-19 because of its potential safety hazards, especially when taken concurrently with azithromycin and oseltamivir. These findings cannot be extrapolated to patients with nonsevere COVID-19

Tendência Temporal no Implante Percutâneo de Bioprótese Aórtica: Análise de 10 Anos do Registro TAVIDOR

Resumo Fundamento O implante percutâneo de bioprótese valvar aórtica (TAVI) consolidou-se como opção terapêutica da estenose aórtica de grau importante. Dados sobre as características evolutivas dos procedimentos e dos resultados obtidos com a técnica ao longo da última década, em escala nacional, são desconhecidos. Objetivos Analisar a tendência temporal referente ao perfil demográfico, características dos procedimentos e desfechos hospitalares de pacientes submetidos a TAVI na Rede D’Or São Luiz. Métodos Registro observacional envolvendo 29 instituições nacionais. Comparou-se características dos procedimentos realizados de 2012 a 2017 (Grupo 1) e de 2018 a 2023 (Grupo 2). Foram considerados significantes os resultados com valor de p 8%. Foi mais frequente o emprego de anestesia geral, monitorização ecocardiográfica transesofágica e via de acesso por dissecção. Maior taxa de sucesso do procedimento (95,4% versus 89,5%; p = 0,018) foi aferida em implantes efetivados a partir de 2018, assim como menor mortalidade (3,9% versus 11,6%; p = 0,004) e necessidade de marcapasso definitivo (8,5% versus 17,9%; p = 0,008). Conclusões A análise temporal de 10 anos do Registro TAVIDOR demonstra uma queda na complexidade clínica dos pacientes. Além disso, o avanço para técnicas de implante minimalistas, somadas à evolução tecnológica dos dispositivos, podem ter contribuído para desfechos favoráveis dentre aqueles cujo implante ocorreu no último quinquênio

Pervasive gaps in Amazonian ecological research

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,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s 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.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s 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’s vulnerability to environmental change. 15%–18% 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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 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,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 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%–18% 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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 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,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 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%–18% 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