Perfil epidemiológico dos pacientes com tumores de glândulas salivares em Araçatuba e região

Introdução: Os tumores de glândulas salivares pertencem a um grupo de neoplasias raras. Considerando a escassa literatura acerca do assunto, são imprescindíveis estudos que demonstrem os aspectos epidemiológicos desses tumores. Objetivo: Caracterizar o perfil epidemiológico dos pacientes com tumores de glândulas salivares de Araçatuba e região. Material e Métodos: O estudo utilizou o banco de dados de um laboratório de patologia privado, no período de janeiro de 2016 a junho de 2020, sendo incluídos todos os laudos com diagnóstico de tumor de glândula salivar provenientes de hospitais públicos e privados da cidade de Araçatuba e região. Foram coletados dados sociodemográficos (idade e sexo) e clínicos (tamanho, tipo de nódulo e localização do tumor) a partir dos formulários médicos de requisição, e os dados anatomopatológicos, por meio dos laudos. Testes de qui-quadrado e G-independência foram realizados para a correlação das frequências das variáveis categóricas e a análise de variância (ANOVA) para verificar diferença nos tamanhos dos nódulos. Resultados: Dos 131 casos analisados, constatou-se que havia 23 neoplasias malignas (17,6%), 28 lesões não neoplásicas (21,3%) e 80 neoplasias benignas (61,1%). A maioria desses tumores afetou pacientes na sexta década de vida, com média de 52 anos, ocorrendo majoritariamente no sexo feminino (81 casos). A glândula parótida (99 casos) foi a mais acometida, sendo a média do tamanho dos nódulos de 2,8 cm. A neoplasia benigna mais encontrada foi o adenoma pleomórfico, com 47 casos (35,9%), enquanto o carcinoma mucoepidermoide configurou a neoplasia maligna mais comum, com 5 casos (3,8%). Conclusão: Houve maior prevalência de tumores benignos de glândulas salivares em pacientes adultos do sexo feminino, sendo a glândula parótida o sítio mais acometido. O adenoma pleomórfico representou o tipo histológico mais frequente, seguido do tumor de Warthin. O carcinoma mucoepidermoide foi a neoplasia maligna mais prevalente

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

The Amazon Tall Tower Observatory (ATTO): Overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols

The Amazon Basin plays key roles in the carbon and water cycles, climate change, atmospheric chemistry, and biodiversity. It has already been changed significantly by human activities, and more pervasive change is expected to occur in the coming decades. It is therefore essential to establish long-term measurement sites that provide a baseline record of present-day climatic, biogeochemical, and atmospheric conditions and that will be operated over coming decades to monitor change in the Amazon region, as human perturbations increase in the future. The Amazon Tall Tower Observatory (ATTO) has been set up in a pristine rain forest region in the central Amazon Basin, about 150 km northeast of the city of Manaus. Two 80 m towers have been operated at the site since 2012, and a 325 m tower is nearing completion in mid-2015. An ecological survey including a biodiversity assessment has been conducted in the forest region surrounding the site. Measurements of micrometeorological and atmospheric chemical variables were initiated in 2012, and their range has continued to broaden over the last few years. The meteorological and micrometeorological measurements include temperature and wind profiles, precipitation, water and energy fluxes, turbulence components, soil temperature profiles and soil heat fluxes, radiation fluxes, and visibility. A tree has been instrumented to measure stem profiles of temperature, light intensity, and water content in cryptogamic covers. The trace gas measurements comprise continuous monitoring of carbon dioxide, carbon monoxide, methane, and ozone at five to eight different heights, complemented by a variety of additional species measured during intensive campaigns (e.g., VOC, NO, NO2, and OH reactivity). Aerosol optical, microphysical, and chemical measurements are being made above the canopy as well as in the canopy space. They include aerosol light scattering and absorption, fluorescence, number and volume size distributions, chemical composition, cloud condensation nuclei (CCN) concentrations, and hygroscopicity. In this paper, we discuss the scientific context of the ATTO observatory and present an overview of results from ecological, meteorological, and chemical pilot studies at the ATTO site. © Author(s) 2015

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

Antioxidant, antihyperglycemic, and antidyslipidemic effects of Brazilian-native fruit extracts in an animal model of insulin resistance

Objective: Insulin resistance (IR) plays an important role in the development of many diseases, such as diabetes mellitus. Therefore, the aim of the present study was to evaluate the effects of the extracts from fruits native to Brazil on metabolic parameters and hepatic oxidative markers in an animal model of insulin resistance induced by dexamethasone (DEX). Methods: Wistar rats received water or extracts of Eugenia uniflora or Psidium cattleianum, once a day for 21 days. For the last 5 days, the rats received an intraperitoneal injection of saline or DEX. Results: DEX caused a reduction in body weight gain and relative pancreatic weight, as well as glucose intolerance, and an increase in serum glucose and triacylglycerol levels. The extracts were found to prevent hyperglycemia and hypertriglyceridemia. DEX caused an increase in the levels of thiobarbituric acid-reactive substances and reactive oxygen species production in the liver of rats, and both extracts prevented these changes. In addition, hepatic glutathione peroxidase activity was reduced by DEX. However, total thiol content and activities of catalase, superoxide dismutase, and delta-aminolevulinate dehydratase were not altered in any of the tested groups. Conclusion: Fruit extracts of E. uniflora and P. cattleianum exhibited considerable antihyperglycemic, antidyslipidemic, and antioxidant effects, and may be useful in the therapeutic management of alterations due to IR