Mortality reduction with use of oral beta-blockers in patients with acute coronary syndrome

OBJECTIVES: Recent studies have revealed a relationship between beta-blocker use and worse prognosis in acute coronary syndrome, mainly due to a higher incidence of cardiogenic shock. However, the relevance of this relationship in the reperfusion era is unknown. The aim of this study was to analyze the outcomes of patients with acute coronary syndrome that started oral beta-blockers within the first 24 hours of hospital admission (group I) compared to patients who did not use oral beta-blockers in this timeframe (group II). METHODS: This was an observational, retrospective and multicentric study with 2,553 patients (2,212 in group I and 341 in group II). Data regarding demographic characteristics, coronary treatment and medication use in the hospital were obtained. The primary endpoint was in-hospital all-cause mortality. The groups were compared by ANOVA and the chi-square test. Multivariate analysis was conducted by logistic regression and results were considered significant when

Idade, Insuficiência Renal e Transfusão são Preditores de Risco de Longa Permanência Hospitalar após Cirurgia de Revascularização do Miocárdio

Resumo Fundamento: A identificação de fatores de riscos na cirurgia cardiovascular auxilia na previsibilidade resultando na otimização de desfechos e redução de custos. Objetivo: Identificação dos preditores de risco pré e intraoperatórios para internação prolongada após cirurgia de revascularização do miocárdio (CRM) no Estado de São Paulo. Métodos: Análise transversal no banco de dados REPLICCAR II, registro prospectivo, consecutivo, multicêntrico que incluiu cirurgias de revascularização miocárdica realizadas entre agosto de 2017 e julho de 2019. O desfecho principal foi o tempo de internação prolongado, definida como período de pós-operatório superior a 14 (quatorze) dias. Para a identificação dos preditores foram realizadas análises de regressão logística uni- e multivariada. Os valores de p menores de 0,05 foram considerados significativos. Resultados: A mediana de idade foi de 63 (57-70) anos e 26,55% eram do sexo feminino. Dos 3703 pacientes analisados, 228 (6,16%) apresentaram longa permanência hospitalar (LPH) após a CRM e a mediana da internação foi de 17 (16-20) dias. Foram preditores da LPH após a CRM: idade >60 anos (OR 2,05; IC95% 1,43 - 2,87; p 60 anos, insuficiência renal e a transfusão de hemácias no intraoperatório foram preditores independentes de LPH após a CRM. A identificação destas variáveis pode ajudar no planejamento estratégico multiprofissional visando melhoria de resultados e otimização de recursos no estado de São Paulo

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

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ