Analysis of the Factors That Influence the Clinical Outcome of Severe Acute Respiratory Syndrome Caused by SARS-CoV-2 in Pregnant Women

Introduction: The new coronavirus (SARS-CoV-2) pandemic has shown to cause even more severe problems among pregnant women, increasing the incidence of complications before and after childbirth, especially cardiorespiratory problems, such as the Severe Acute Respiratory Syndrome (SARS). Objectives: To describe the clinical outcome of SARS caused by SARS-CoV-2 in Brazilian pregnant women and to compare the rates of morbidity and mortality from other causes in this group, stratified by the following variables: gestational age and age group. Methodology: Observational, analytical study based on documents whose data were collected from the 2020 Epidemiological Report No. 40 in the database of the Brazilian Department of Health, from which morbidity and mortality data were extracted to calculate the lethality rate and compare rates using a binomial test with a significance level of 0.05. Results: Of the total number of pregnant women hospitalized for SARS, 4,467 (46.6%) were confirmed for COVID-19 and, of these, 233 died, corresponding to a lethality rate of 5.2%. Morbidity was higher in the third trimester of pregnancy, but the disease was more severe in the second trimester (7%), being worse in women aged 40 years and older (40–49; 8.7% and 50–59; 15.3%). A significant difference was observed in the rate of cases between the COVID-19 SARS group and the group with other causes in all gestational strata and age groups. As for deaths, a significant difference was found in the rates between the first and third trimesters, and in pregnant women aged 10 to 19 years. Conclusion: Considering the variables under analysis, evidence shows that pregnant women at an advanced age and in the second trimester of pregnancy contribute to the lethal outcome of the disease. Other variables associated with the presence of comorbidities and quality of care for pregnant women should be considered in the model in future studies

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