ORBITA Trial: Redefining the Role of Intervention in the Treatment of Stable Coronary Disease?

Pontificia Univ Catolica Rio Grande do Sul PUCRS, Sao Lucas Hosp, Porto Alegre, RS, BrazilUniv Sao Paulo EPM UNIFESP, Escola Paulista Med, Disciplina Cirurgia Cardiovasc & Cardiol, Sao Paulo, SP, BrazilUNIFESP, Escola Paulista Med, Disciplina Cirurgia Cardiovasc & Cardiol, Sao Paulo, SP, BrazilWeb of Scienc

Prevalence of peripheral arterial disease in patients with heart failure with preserved ejection fraction

OBJECTIVES: To describe the prevalence of the reduced ankle-brachial index (ABI) in patients with heart failure (HF) with preserved ejection fraction (HFpEF) attended at a HF clinic in the metropolitan region of Porto Alegre, and to compar the patients to those with reduced ejection fraction (HFrEF). METHODS: A descriptive observational study, included patients referred to the heart failure clinic in HU-Ulbra with HFpEF or HFrEF and diastolic dysfunction, and measurements of ABIs using vascular Doppler equipment were performed in both groups. RESULTS: The sample consisted of 106 patients with HF, 53.9% of the patients had HFpEF, and 19.4% had a diagnosis of peripheral arterial disease (PAD) (ABI less than 0.9). PAD was identified in 24.1% of the patients with HFpEF, while15.8% of patients in the HFrEF group were diagnosed with PAD. CONCLUSION: Our results did not identify a significantly different prevalence of altered and compatible PAD values in patients with HFpEF. However, we showed a prevalence of 19.4%, a high value if we consider similar populations

Guidelines for surgery of aortic diseases from Brazilian Society of Cardiovascular Surgery

Sociedade Brasileira de Cirurgia CardiovascularUniversidade Federal do Rio Grande do SulPUCRS Hospital São LucasFamerp Pós-GraduaçãoUnicampUNIFESPSBCCVUFRGS Faculdade de Medicina-Cirurgia CardiovascularEPM, UNIFESPUNIFESPEPMSciEL

Proposed Risk Score in Patients with Aortic Stenosis Submitted to Valve Replacement Surgery

ABSTRACT Introduction: Due to Brazilian population aging, prevalence of aortic stenosis, and limited number of scores in literature, it is essential to develop risk scores adapted to our reality and created in the specific context of this disease. Methods: This is an observational historical cohort study with analysis of 802 aortic stenosis patients who underwent valve replacement at Hospital São Lucas, Pontifícia Universidade Católica do Rio Grande do Sul, from 1996 to 2018. With the aid of logistic regression, a weighted risk score was constructed based on the magnitude of the coeficients β of the logistic equation. Two performance statistics were obtained: area under the receiver operating characteristic curve and the chi-square (χ2) of Hosmer-Lemeshow goodness-of-fit with Pearson’s correlation coeficient between the observed events and predicted as a model calibration estimate. Results: The risk predictors that composed the score were valve replacement surgery combined with coronary artery bypass grafting, prior renal failure, New York Heart Association class III/IV heart failure, age > 70 years, and ejection fraction < 50%. The receiver operating characteristic curve area was 0.77 (95% confidence interval: 0.72-0.82); regarding the model calibration estimated between observed/predicted mortality, Hosmer-Lemeshow test χ2 = 3,70 (P=0.594) and Pearson’s coeficient r = 0.98 (P<0.001). Conclusion: We propose the creation of a simple score, adapted to the Brazilian reality, with good performance and which can be validated in other institutions

Guidelines for surgery of aortic diseases from the Brazilian Society of Cardiovascular Surgery: updated in 2009

PUCRS Hospital São LucasFamerpUnicampUNIFESPUFRGS HCPA Faculdade de MedicinaUNIOESTE HUOPFAG Curso de MedicinaUNIFESP-EPMUNIFESP, EPMSciEL

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset

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