O PERFIL EPIDEMIOLÓGICO DA MENINGITE NO ESTADO DE GOIÁS ENTRE 2010 E 2020

Meningitis is a disease resulting from inflammation of the meninges of an infectious nature, generally caused by bacteria and viruses. It is known that the disease is a serious public health problem and is part of the National List of Compulsory Notification of Diseases, Conditions and Public Health Events. Given the high mortality and morbidity of meningitis, it is extremely important to know the epidemiological profile of the pathology in question for the development of health measures to combat the disease. The objective of the present work was to evaluate and describe the epidemiological profile of meningitis in the state of Goiás from 2010 to 2020, highlighting which etiological agents were most prevalent and how the distribution occurred between sex, age group and serogroups, also evaluating the evolution of the disease. The research was carried out through a qualitative and retrospective study using data collected in the Notifiable Diseases Information System (SINAN), analyzed and listed using descriptive statistics. As a result, it was seen that, in the state of Goiás, the disease occurs more predominantly in males, aged between 20 and 39 years. Furthermore, meningitis of viral etiology was the most involved in meningitis cases, but was not responsible for the highest death rate. Meningitis of undefined bacterial etiology was responsible for the highest percentage of deaths. In areas with a higher population concentration, meningitis occurred in greater numbers. It was concluded, from the study, that meningitis was established during the decade analyzed, and still is a public health problem, despite the reduction over the years, which deserves special attention. In this sense, the need for vaccination coverage and other more efficient prophylaxis methods is highlighted.A meningite é uma doença decorrente da inflamação das meninges de caráter infectocontagioso, geralmente causada por bactérias e vírus. Sabe-se que a doença se configura como um grave problema de saúde pública e faz parte da Lista Nacional de Notificação Compulsória de Doenças, Agravos e Eventos de Saúde Pública. Tendo em vista a alta mortalidade e morbidade da meningite, é de suma importância conhecer o perfil epidemiológico da patologia em questão para o desenvolvimento de medidas de saúde que combatam a doença. O objetivo do presente trabalho foi avaliar e descrever o perfil epidemiológico da meningite no estado de Goiás no período de 2010 a 2020, ressaltando quais agentes etiológicos foram mais prevalentes e como se deu a distribuição entre sexo, faixa etária e sorogrupos, avaliando, também, a evolução da doença. A pesquisa foi feita por meio de um estudo qualitativo e retrospectivo por meio dados coletados no Sistema de Informações de Agravos de Notificação (SINAN), analisados e elencados utilizando a estatística descritiva. Como resultado, foi visto que, no estado de Goiás, a doença se dá de forma mais predominante no sexo masculino, na faixa etária entre 20 e 39 anos. Ademais, a meningite de etiologia viral foi a mais envolvida nos casos de meningite, mas não foi a responsável pela maior taxa de óbitos. Meningites de etiologia bacteriana não definida foram responsáveis pela maior porcentagem de óbitos. Em áreas com maior concentração populacional a meningite ocorreu em maior número. Foi concluído, a partir do estudo, que as meningites se configuraram durante a década analisada, e ainda se configuram, como um problema de saúde pública, apesar da redução ao longo dos anos, que merece atenção especial. Nesse sentido, ressalta-se a necessidade de uma cobertura vacinal e demais métodos de profilaxia mais eficientes

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

Converging multidimensional sensor and machine learning toward high-throughput and biorecognition element-free multidetermination of extracellular vesicle biomarkers

Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs (CV) themselves and carried proteins (CP). However, simple, high-throughput, and accurate determination of these targets remains a key challenge. Herein, we address the simultaneous monitoring of CV and CP from a single impedance spectrum without using recognizing elements by combining a multidimensional sensor and machine learning models. This multidetermination is essential for diagnostic accuracy because of the heterogeneous composition of EVs and their molecular cargoes both within the tumor itself and among patients. Pencil HB cores acting as electric double-layer capacitors were integrated into a scalable microfluidic device, whereas supervised models provided accurate predictions, even from a small number of training samples. User-friendly measurements were performed with sample-to-answer data processing on a smartphone. This new platform further showed the highest throughput when compared with the techniques described in the literature to quantify EVs biomarkers. Our results shed light on a method with the ability to determine multiple EVs biomarkers in a simple and fast way, providing a promising platform to translate biofluid-based diagnostics into clinical workflows5718641871FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2018/24214-3; 17/18139- 6; 17/02317-2; 2015/00301-6; 2014/50867-