Depression and quality of life in Brazilian and Portuguese older people communities

We aimed to compare the association of depression with aspects of quality of life (QoL) among older people users of primary health care (PHC) living in Brazil and Portugal. We carried out an observational, cross-sectional and comparative study with a quantitative approach in the PHC scope in Brazil and Portugal, where we obtained a nonrandom sample of 150 participants aged 65 years or older (100 Brazilians and 50 Portuguese). We used the socioeconomic and health data questionnaire, the Medical Outcomes Short-Form Health Survey QoL (SF-36) questionnaire and the Beck Inventory. Among the socioeconomic profiles, most were females aged between 65 and 80 years in both countries. There was a significant difference between groups in the income variable, with 100.0% of Portuguese people earning up to 1 minimum wage (P value 50.0) within the categorical variables of “absent” and “mild” depression. The Emotional role functioning, Physical role functioning, Physical functioning, Mental health, Total score domains and the Mental health and Physical health summary measures stood out with this behavior in Brazil and in Portugal, where these latter 2 presented moderate to strong correlation values (ρ > 0.400) in Portugal. Greater associations of depression on QoL were revealed in Portugal than in Brazil. Among their most expressive associations, the Physical role functioning (odds ratio [OR] = 4.776; 95.0% confidence interval [CI]: 2.41–9.43), Physical functioning (OR = 3.037; 95.0% CI: 3.037), Vitality (OR = 6.000; 95.0% CI: 1.56–23.07) and Total score (OR = 3.727; 95.0% CI: 2.24–6.17) domains and the Mental health summary measure (OR = 3.870; 95.0% CI: 2.13–7.02) stood out. Aspects related to the emotional, physical, functional and mental health components stood out. The association and correlation with depression were more expressive in Portugal compared to Brazil. However, similar results were obtained in Brazil but with less relevance

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

Updated cardiovascular prevention guideline of the Brazilian Society of Cardiology: 2019

Sem informação113478788

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

PROSPECÇÃO TECNOLÓGICA PARA BIOCOMPOSITO DE HIDROXIAPATITA COM POLIURETANOS E PROPRIEDADES OSTEOINDUTORAS

Os biopolimeros são macromoléculas que basicamente não agridem o meio ambiente no processo de degradação, e que apresentam características que os tornam viáveis no processo de reconstituição de tecido, liberação de fármacos, produção de embalagens para alimentos dentre outras. Os poliuretanos (PU) são polímeros que dentre outros mecanimos de obtenção, podem ser sintetizados utilizando óleos vegetais e gorduras animais como materiais de partida, e que ao longo dos anos têm sido descritos como promissor na produção de próteses para diversas finalidades. Ao combinar polímeros com bioceramicas, biovidros ou similares as características combinam-se a fim de obter biomateriais que apresentem capacidade de ser atribuídos a sistemas biológicos, de tal forma que aumente a velocidade de reconstituição e sustentação principalmente do tecido ósseo. Com isso, o objetivo deste trabalho foi avaliar por meio de competências tecnológicas e artigos relacionados a materiais constituidos basicamente de hidroxiapatita com poliuretano e caracteristicas osteoindutoras no intervalo de anos entre 2000 à 2014. Os resultados descrevem que, o país que mais se destaca em quantidade de patentes depositadas relacionando todas as palavras chave é a China, seguida pela República da Coréia, e que a classificação internacional que mais teve frequência nas competências foi a que relaciona necessidades humanas (A). Palavras Chave: Hidroxiapatita; Poliuretano; Osteoindutor. </p