Comitê de Ética em Pesquisas. Necessidade obrigatória. Obrigatoriedade necessária

Univ Pernambuco, Fac Ciencias Med, Disciplina Cirurgia Cardiotorac, Recife, PE, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilUniversidade Federal de São Paulo, EPM, São Paulo, BrazilWeb of Scienc

ROOBY study: A critical view

Univ Pernambuco, Fac Ciencias Med, FCM UPE, Div Cirurgia Cardiovasc, Recife, PE, BrazilPronto Socorro Cardiol Pernambuco PROCAPE, Recife, PE, BrazilUniv Miami, CT Surg, Jackson Mem Hosp, Coral Gables, FL 33124 USAUniv Fed Sao Paulo, UNIFESP, Div Cirurgia Cardiovasc, Sao Paulo, BrazilUniv Fed Sao Paulo, UNIFESP, Div Cirurgia Cardiovasc, Sao Paulo, BrazilWeb of Scienc

Surgical aortic valve replacement and patient-prosthesis mismatch a meta-analysis of 108 182 patients

OBJECTIVES: This study sought to evaluate the impact of patient–prosthesis mismatch (PPM) on the risk of perioperative, early-, mid- and long-term mortality rates after surgical aortic valve replacement. METHODS: Databases were searched for studies published until March 2018. The main outcomes of interest were perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality. RESULTS: The search yielded 3761 studies for inclusion. Of these, 70 articles were analysed, and their data were extracted. The total num- ber of patients included was 108 182 who underwent surgical aortic valve replacement. The incidence of PPM after surgical aortic valve re- placement was 53.7% (58 116 with PPM and 50 066 without PPM). Perioperative mortality [odds ratio (OR) 1.491, 95% confidence interval (CI) 1.302–1.707; P < 0.001], 1-year mortality (OR 1.465, 95% CI 1.277–1.681; P < 0.001), 5-year mortality (OR 1.358, 95% CI 1.218–1.515; P < 0.001) and 10-year mortality (OR 1.534, 95% CI 1.290–1.825; P < 0.001) were increased in patients with PPM. Both severe PPM and moderate PPM were associated with increased risk of perioperative mortality, 1-year mortality, 5-year mortality and 10-year mortality when analysed together and separately, although we observed a higher risk in the group with severe PPM. CONCLUSIONS: Moderate/severe PPM increases perioperative, early-, mid- and long-term mortality rates proportionally to its severity. The findings of this study support the implementation of surgical strategies to prevent PPM in order to decrease mortality rates

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