Difficulties Reported by Hiv-Infected Patients Using Antiretroviral Therapy in Brazil

OBJECTIVE: To describe the degree of difficulty that HIV-infected patients have with therapy treatment. INTRODUCTION: Patients’ perceptions about their treatment are a determinant factor for improved adherence and a better quality of life. METHODS: Two cross-sectional analyses were conducted in public AIDS referral centers in Brazil among patients initiating treatment. Patients interviewed at baseline, after one month, and after seven months following the beginning of treatment were asked to classify and justify the degree of difficulty with treatment. Logistic regression was used for analysis. RESULTS: Among 406 patients initiating treatment, 350 (86.2%) and 209 (51.5%) returned for their first and third visits, respectively. Treatment perceptions ranged from medium to very difficult for 51.4% and 37.3% on the first and third visits, respectively. The main difficulties reported were adverse reactions to the medication and scheduling. A separate logistic regression indicated that the HIV-seropositive status disclosure, symptoms of anxiety, absence of psychotherapy, higher CD4+ cell count (> 200/mm³) and high (> 4) adverse reaction count reported were independently associated with the degree of difficulty in the first visit, while CDC clinical category A, pill burden (> 7 pills), use of other medications, high (> 4) adverse reaction count reported and low understanding of medical orientation showed independent association for the third visit. CONCLUSIONS: A significant level of difficulty was observed with treatment. Our analyses suggest the need for early assessment of difficulties with treatment, highlighting the importance of modifiable factors that may contribute to better adherence to the treatment protocol

Associação entre o Perfil Hemodinâmico da Insuficiência Cardíaca à Admissão Hospitalar e Mortalidade - Programa Boas Práticas Clínicas em Cardiologia

Resumo Fundamento: A insuficiência cardíaca (IC) é responsável por alta carga de internações hospitalares. A sua forma de apresentação está relacionada ao prognóstico da doença. Objetivos: Descrever a associação entre o perfil hemodinâmico de admissão hospitalar na IC aguda, baseado em congestão (úmido ou seco) e perfusão (frio ou quente), e desfechos de mortalidade, tempo de internação e chance de reinternação. Métodos: Estudo de coorte, envolvendo pacientes do projeto "Boas Práticas Clínicas em Cardiologia", internados por IC aguda em hospitais públicos brasileiros, entre março de 2016 a dezembro de 2019, com seguimento de seis meses. Foram realizadas análises das características populacionais e do perfil hemodinâmico de admissão, além de análises de sobrevivência pelos modelos de Cox para associação entre o perfil de admissão e mortalidade, e regressão logística para chance de reinternação, considerando nível de significância estatística de 5%. Resultados: Foram avaliados 1978 pacientes, com idade média foi 60,2 (±14,8) anos e fração de ejeção média do ventrículo esquerdo de 39,8% (±17,3%). Houve altas taxas de mortalidade no seguimento de seis meses (22%), com associação entre os perfis hemodinâmicos frios e a mortalidade hospitalar (HR=1,72; IC95% 1,27-2,31; p < 0,001) e em 6 meses (HR= 1,61, IC 95% 1,29-2,02). A taxa de reinternação em 6 meses foi de 22%, sendo maior para os pacientes admitidos em perfis úmidos (OR 2,30; IC95% 1,45-3,65; p < 0,001). Conclusões: A IC aguda no Brasil apresenta altas taxas de mortalidade e reinternações e os perfis hemodinâmicos de admissão hospitalar são bons marcadores prognósticos dessa evolução

Cardiovascular Statistics - Brazil 2021.

This is the 2021 edition of the Cardiovascular Statistics – Brazil , a multi-institutional effort to periodically provide updated information on the epidemiology of heart diseases and stroke in Brazil. The report incorporates official statistics provided by the Brazilian Ministry of Health and other government agencies, by the GBD project led by the IHME of the University of Washington, as well as data generated by other sources and scientific studies, such as cohorts and registries, on CVDs and their risk factors. The document is directed to researchers, clinicians, patients, healthcare policy makers, media professionals, the public, and others who seek comprehensive national data available on heart disease and stroke

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

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data