Perfil clínico e epidemiológico dos pacientes com doença renal crônica submetidos a hemodiálise em São João Del Rei –MG/ Clinical and epidemiological profile of patients with chronic kidney disease submitted to hemodialysis in São João Del Rei -MG

 A doença renal crônica (DRC) é considerada um problema de saúde pública, nesse sentido ações de prevenção, controle dos fatores de risco e etiologias subjacentes são importantes. O objetivo do presente estudo foi analisar a frequência das etiologias para predição da progressão da RDC em pacientes com hemodiálise em São João del Rei-MG. Realizou-se um estudo envolvendo143 pacientes de ambos os sexos analisando à hemodiálise. Os dados coletados foram: sexo, idade, início do tratamento, etnia, tabagismo, uso do álcool, atividade física, índice de massa corporal (IMC) e etiologia. Foi realizada uma análise das distribuições de frequências e percentagem. Utilizou-se os testes ? 2 e Exato de Fisherpara verificar diferenças entre proporções (? <0,05). Do total de pacientes, 39,16% eram mulheres e 60,84% homens, 60,84% brancos e 39,16% não brancos. A idade média foi de 56,26 ± 14,62 anos. Quanto ao IMC, 6,99% apresentavam peso baixo, 40,56% sobrepeso / obesidade e 52,45% peso adequado. Reconhecida às etiologias, a hipertensão arterial sistêmica foi a mais frequente (40,56%), seguida pelo diabetes mellitus (33,57%), glomerulonefrite (15,38%), outras (6,99%) e rins policísticos (3 , 50%). A comparação com o censo da Sociedade Brasileira de Nefrologia 2016 mostrou diferença somente para etiologia indefinida (p <0,05).A distribuição das etiologias por sexo / etnia para cada faixa etária apontada 45-65 anos como a mais representativa. Delineou-se um perfil detalhado dos pacientes em tratamento, traçando um paralelo entre as características demográficas e clínicas. Estes resultados podem auxiliar nas decisões para melhorar a assistência à pacientes com um RDC.

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

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

Health-related quality of life outcomes in head and neck cancer : results from a prospective, real-world data study with Brazilian patients treated with intensity modulated radiation therapy, conformal and conventional radiation techniques

Purpose: To compare global health-related quality of life (HRQoL) and overall survival (OS) in patients with head and neck cancer treated with intensity modulated radiation therapy (IMRT), conformal radiation therapy (3DCRT) or conventional ra- diation therapy (2DRT). Methods and Materials: In this real-world, multi-institutional and prospective study, HRQoL outcomes were assessed using the European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30) and European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Head and Neck 43 (H&N43) questionnaires. Item response theory was used to generate a global HRQoL score, based on the 71 questions from both forms. The effect of treatment modality on HRQoL was studied using multivariate regression analyses. Survival was estimated using the Kaplan-Meyer method, and groups were compared by the log-rank test. Results: Five hundred and seventy patients from 13 institutions were included. Median follow-up was 12.2 months. Concern- ing the radiation technique, 29.5% of the patients were treated with 2DRT, 43.7% received 3DCRT, and 26.8% were treated with IMRT. A higher proportion of patients receiving 2DRT had a treatment interruption of more than 5 days (69% vs 50.2% for 3DCRT and 42.5% for IMRT). IMRT had a statistically significant positive effect on HRQoL compared with 3DCRT (bZ 2.627, standard error Z 0.804, P Z .001) and 2DRT had a statistically significant negative effect compared with 3DCRT (bZ 5.075, standard error Z 0.926, P < .001). Patients receiving 2DRT presented a worse OS (P Z .01). There were no differences in OS when IMRT was compared with 3DCRT. Conclusions: IMRT provided better HRQoL than 3DCRT, which provided better HRQoL than 2DRT. Patients receiving 2DRT presented a worse OS, which might be related to more frequent treatment interruptions. Ó 2020 Elsevier Inc. All rights reserved