Escassez de neurologistas na Amazônia brasileira.

Brazil is an emerging country with increasing economic development in the last 20 years and with the seventh richest economy in the world. However, Brazil's ranking on the United Nations Human Development Index (79th position, based on 2014 data) is paradoxically low compared to its economic status. The continental dimensions of Brazil widen the inequalities between its regions, and the Amazon is the least developed area in the country. In this article, we discuss some of the reasons for the shortage of neurologists in the Brazilian Amazon. In addition, we discuss possible new strategies to reduce these inequalities in public health in the near future.O Brasil é um país emergente com crescente desenvolvimento econômico nos últimos 20 anos e com a sétima economia mais rica do mundo. No entanto, o ranking do Brasil no Índice de Desenvolvimento Humano das Nações Unidas (79ª posição, com base em dados de 2014) é paradoxalmente baixo em comparação com seu status econômico. As dimensões continentais do Brasil ampliam as desigualdades entre suas regiões, e a Amazônia é a área menos desenvolvida do país. Neste artigo, discutimos algumas das razões para a escassez de neurologistas na Amazônia brasileira. Além disso, discutimos possíveis novas estratégias para diminuir essas desigualdades em saúde pública em um futuro próximo

Modelo de predição diagnóstica para discinesias induzidas por levodopa na doença de Parkinson

Background: There are currently no methods to predict the development of levodopa-induced dyskinesia (LID), a frequent complication of Parkinson's disease (PD) treatment. Clinical predictors and single nucleotide polymorphisms (SNP) have been associated to LID in PD. Objective: To investigate the association of clinical and genetic variables with LID and to develop a diagnostic prediction model for LID in PD. Methods: We studied 430 PD patients using levodopa. The presence of LID was defined as an MDS-UPDRS Part IV score ≥1 on item 4.1. We tested the association between specific clinical variables and seven SNPs and the development of LID, using logistic regression models. Results: Regarding clinical variables, age of PD onset, disease duration, initial motor symptom and use of dopaminergic agonists were associated to LID. Only CC genotype of ADORA2A rs2298383 SNP was associated to LID after adjustment. We developed two diagnostic prediction models with reasonable accuracy, but we suggest that the clinical prediction model be used. This prediction model has an area under the curve of 0.817 (95% confidence interval [95%CI] 0.77‒0.85) and no significant lack of fit (Hosmer-Lemeshow goodness-of-fit test p=0.61). Conclusion: Predicted probability of LID can be estimated with reasonable accuracy using a diagnostic clinical prediction model which combines age of PD onset, disease duration, initial motor symptom and use of dopaminergic agonists.Introdução: No momento, não há métodos para se predizer o desenvolvimento de discinesias induzidas por levodopa (DIL), uma frequente complicação do tratamento da doença de Parkinson (DP). Preditores clínicos e polimorfismos de nucleotídeo único (SNP) têm sido associados às DIL na DP. Objetivo: Investigar a associação entre variáveis clínicas e genéticas com as DIL e desenvolver um modelo de predição diagnóstica de DIL na DP. Métodos: Foram avaliados 430 pacientes com DP em uso de levodopa. A presença de DIL foi definida como escore ≥1 no item 4.1 da MDS-UPDRS Parte IV. Nós testamos a associação entre variáveis clínicas específicas e sete SNPs com o desenvolvimento de DIL, usando modelos de regressão logística. Resultados: Em relação às variáveis clínicas, idade de início da doença, duração da doença, sintomas motores iniciais e uso de agonistas dopaminérgicos estiveram associados às DIL. Apenas o genótipo CC do SNP rs2298383 no gene ADORA2A esteve associado com DIL após o ajuste. Nós desenvolvemos dois modelos preditivos diagnósticos com acurácia razoável, mas sugerimos o uso do modelo preditivo clínico. Esse modelo de predição tem uma área sob a curva de 0,817 (intervalo de confiança de 95% [IC95%] 0,77‒0,85) e sem perda significativa de ajuste (teste de qualidade de ajuste de Hosmer-Lemeshow p=0,61). Conclusão: A probabilidade prevista de DIL pode ser estimada, com acurácia razoável, por meio do uso de um modelo preditivo diagnóstico clínico, que combina a idade de início da doença, duração da doença, sintomas motores iniciais e uso de agonistas dopaminérgicos

Genética da doença de Parkinson no Brasil : revisão sistemática de formas monogênicas

Background: Increasing numbers of mutations causing monogenic forms of Parkinson's disease (PD) have been described, mostly among patients in Europe and North America. Since genetic architecture varies between different populations, studying the specific genetic profile of Brazilian patients is essential for improving genetic counseling and for selecting patients for clinical trials. Objective: We conducted a systematic review to identify genetic studies on Brazilian patients and to set a background for future studies on monogenic forms of PD in Brazil. Methods: We searched MEDLINE, EMBASE and Web of Science from inception to December 2019 using terms for "Parkinson's disease", "genetics" and "Brazil". Two independent reviewers extracted the data. For the genes LRRK2 and PRKN, the estimated prevalence was calculated for each study, and a meta-analysis was performed. Results: A total of 32 studies were included, comprising 94 Brazilian patients with PD with a causative mutation, identified from among 2,872 screened patients (3.2%). PRKN mutations were causative of PD in 48 patients out of 576 (8.3%). LRRK2 mutations were identified in 40 out of 1,556 patients (2.5%), and p.G2019S was the most common mutation (2.2%). Conclusions: PRKN is the most common autosomal recessive cause of PD, and LRRK2 is the most common autosomal dominant form. We observed that there was a lack of robust epidemiological studies on PD genetics in Brazil and, especially, that the diversity of Brazil’s population had not been considered.Introdução: Um número crescente de mutações causando formas monogênicas de doença de Parkinson (DP) tem sido descrito, principalmente entre pacientes da Europa e da América do Norte. Como a arquitetura genética varia entre diferentes populações, entender os perfis genéticos específicos de pacientes brasileiros é essencial para um melhor aconselhamento genético e para a seleção de participantes para ensaios clínicos. Objetivo: Revisão sistemática para identificar estudos genéticos brasileiros na área e definir o cenário para estudos futuros das formas monogênicas de DP no Brasil. Métodos: Nós pesquisamos as bases de dados MEDLINE, EMBASE e Web of Science desde a criação até dezembro de 2019, usando termos para “Parkinson’s disease”, “genetics” e “Brazil”. A extração de dados foi feita por dois revisores independentes. Para os genes LRRK2 e PRKN, calculamos a prevalência estimada para cada estudo e realizamos uma meta-análise. Resultados: Um total de 32 estudos foram incluídos e 94 pacientes brasileiros com DP com mutações causativas foram identificados em 2872 pacientes avaliados (3.2%). As mutações no PRKN causaram DP em 48 de 576 pacientes (8.3%). As mutações no LRRK2 foram identificadas em 40 de 1566 pacientes (2.5%), sendo a mutação mais comum a p.G2019S (2.2%). Conclusões: As mutações na PRKN são a causa mais comum de DP autossômica recessiva, e as mutações no LRRK2 a causa mais comum de DP autossômica dominante. Nós observamos uma falta de estudos epidemiológicos robustos em genética de DP, especialmente por não levar em conta a diversidade de nossa população

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

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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

Harbinger of storm: influence of Oliver Sacks on levodopa therapy in early 1970s

ABSTRACT Most known by his literary ability, the words of the neurologist Oliver Sacks (1933-2015) also had an impact on scientific community about the role of levodopa on parkinsonisms. Different from the most authors and based on his experience described on the book “Awakenings”, he had a pessimistic opinion about levodopa, which was related on many articles written by himself and colleagues in early 1970s. We reviewed the scientific contribution of Oliver Sacks associated to levodopa therapy on parkinsonisms, and how he advised caution with its complications before the majority of physicians