An interrelationship between neuroinflammation and progression of Alzheimer's disease (AD) / A inter-relação entre a neuroinflamação e a progressão da Doença de Alzheimer (DA)

Alzheimer's disease (AD) is among the major neurodegenerative diseases in the world, characterized by progressive decline in cognitive abilities, behavioral abnormalities and functional loss in daily activities. Several studies indicate the effectiveness of using biomarkers as a diagnostic source of the disease, as well as suggesting the analysis of inflammatory cytokines as accompanying factors. There are several inflammatory cytokines that are well described in the pathogenesis of AD, such as Interleukin-6 (IL-6), IL-1ß, tumor necrosis factor (TNF-?), oxidative stress, among others. Some chemokines, although presenting a protective and modulating character of neuroinflammation, also appear as proinflammatory proteins, depending on the course of the disease, such as CX3C. This review considers that neuroinflammation has a degenerative character in the Central Nervous System (CNS), and understands that it needs more evidence regarding these data, especially in the pharmacotherapy question

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

Habitar a metrópole: os apartamentos quitinetes de Adolf Franz Heep

The restructuring of the housing market and the emergence of a new housing typology in Sao Paulo from the mid-1940s, the kitchenette apartment, coincided with changes in the parameters that guided disciplinary discourse and architectural practice in Brazil. Analyze the moment the new typology was formulated, their initial motivations and subsequent developments, allows not only to recover the trajectory of the German architect Adolf Franz Heep (1902-1978) as investigate the dialogue between European architectural avant-garde, the North-American experiences, the local architectural production and the local demands

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

A História da Alimentação: balizas historiográficas

Os M. pretenderam traçar um quadro da História da Alimentação, não como um novo ramo epistemológico da disciplina, mas como um campo em desenvolvimento de práticas e atividades especializadas, incluindo pesquisa, formação, publicações, associações, encontros acadêmicos, etc. Um breve relato das condições em que tal campo se assentou faz-se preceder de um panorama dos estudos de alimentação e temas correia tos, em geral, segundo cinco abardagens Ia biológica, a econômica, a social, a cultural e a filosófica!, assim como da identificação das contribuições mais relevantes da Antropologia, Arqueologia, Sociologia e Geografia. A fim de comentar a multiforme e volumosa bibliografia histórica, foi ela organizada segundo critérios morfológicos. A seguir, alguns tópicos importantes mereceram tratamento à parte: a fome, o alimento e o domínio religioso, as descobertas européias e a difusão mundial de alimentos, gosto e gastronomia. O artigo se encerra com um rápido balanço crítico da historiografia brasileira sobre o tema

Epidemic of sylvatic yellow fever in the Bacia do Rio Doce region Minas Gerais, Brazil: December 2002 to March 2003

Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.lMinistério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Belém, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasilia, DF, Brasil