Hand occupational injuries: cases in a rehabilitation centre

The purpose of this study was to characterize work-related cases of hand injury admitted to the Hand Therapy sector of Maria Amélia Lins Hospital in Belo Horizonte, MG. Medical charts of 711 patients having attended the sector between January, 2004 and December, 2005 were reviewed; 238 were found to be of patients with work-related injuries, of which 87% were male, mean age 34 years old (sd=10.64). In 45% of the sample the right side of the body was injured and most cases showed impairment at the non-dominant side (52%). Workers in maintenance/overhaul (35%), and in trade and services (33%) had greater accident indices; machinery was the major causal agent (57%). Tendon (29%) and bone (23%) were the most frequently injured structures, fingers (73%) and hands (18%) being specially affected. Most patients (80%) took between 2 to 60 days post-accident to start rehabilitation and treatment median duration was 55 days. Associations between patients' occupation and causal agent, and between occupation and injured structure were significant (pO objetivo do estudo foi caracterizar os casos de lesões na mão relacionadas ao trabalho atendidos no Setor de Terapia da Mão do Hospital Maria Amélia Lins, em Belo Horizonte, MG. Foram analisados 711 protocolos de avaliação dos pacientes atendidos de janeiro 2004 a dezembro 2005, dos quais 238 corresponderam a acidentes do trabalho, com 87% de homens e média de idade 34 anos. Em 45% dos casos, a lesão foi no lado direito, sendo o não-dominante mais acometido (52%). Manutenção e/ou reparação (35%) e serviços e/ou comércio (33%) foram as categorias ocupacionais com maior índice de acidentes e as máquinas o principal agente causador (57%). Tendão (29%) e osso (23%) foram as estruturas mais lesadas, sendo atingidos principalmente os dedos (73%) e as mãos (18%). A grande maioria dos pacientes (80%) levaram de 2 a 60 dias após o acidente para iniciar a reabilitação e a mediana do tempo de tratamento foi 55 dias. As associações da ocupação do paciente com o agente causador e com a estrutura lesada foram significativas (

Emergências hiperglicêmicas e seus impactos na sala de emergência: uma revisão de literatura / Hyperglycemic emergencies and their impacts in the emergency room: a literature review

Introdução: Hiperglicemia é uma causa muito comum nas emergências médicas, sendo uma alteração de descompensação do metabolismo. Os estados hiperglicêmicos agudos compreendem a cetoacidose diabética e o coma hiperosmolar hiperglicêmico não cetótico. Objetivo: Analisar as duas principais condições hiperglicêmicas, que representam um desafio para o clínico e o médico generalista em salas de emergências. Métodos: Trata-se de uma revisão integrativa da literatura que incluiu estudos, com dados de pacientes em situação de emergência hiperglicêmica, publicados entre 2010 e 2020, disponíveis na íntegra em inglês, em espanhol ou em português, na base de dados LILACS, SciELO, PubMed, com os termos: “Hiperglicemia”, “Emergência”, “Departamento/Sala de emergência”, “Crise hiperglicêmica” e “Impactos”. Atenderam aos critérios de inclusão 19 artigos, os quais, após a leitura na íntegra, tiveram as informações sintetizadas, agrupadas por semelhanças ao tema e analisadas de forma descritiva. Resultados e discussão:  Crises hiperglicêmicas podem ocorrer em pacientes portadores de diabetes mellitus tipo 1 ou tipo 2, assim, requer manejo rápido e tratamento da causa base, visando evitar a mortalidade.

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

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

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Whole-body vibration and musculoskeletal diseases in professional truck drivers

Background: Most occupational diseases do not fit the paradigm of medical interpretation of the health-disease process based on linear causality, in which it would be possible to find a single cause for each type of disease. Objectives: to conduct a systematic review in order to investigate the association between wholebody vibration (WBV) and musculoskeletal disorders (MSD) in professional truck drivers (PTD). Methods: The scientific databases of PubMed, Cochrane, Lilacs and Scielo were used to collect articles published from 2000 until the present time. Two independent reviewers adopted inclusion and quality criteria to evaluate the selected articles. Results: From adopted inclusion and quality criteria, nine articles were chosen to identify the association between MSD and WBV in PTD. The results showed that MSD seems to be closely associated to exposure to WBV in these workers, mainly due to high prevalence and symptoms of low back pain. Two cohort studies showed exposure to WBV as risk for MSD. Only one, with case-control design, did not show WBV as a significant factor. Conclusions: In this study the importance of exposure analysis of WBV in the occurrence of MSD in PTD was elucidated. This study showed the importance of WBV exposure analysis on the occurrence of MSD in PTD. There is adequate information to provide rationale for the reduction of WBV exposure to the lowest possible level, to ensure the health of these workers. Studies with a greater power of investigation, of a prospective, design, should be encouraged, supplanting those only of association

Whole-body vibration and musculoskeletal diseases in professional truck drivers

Abstract Background: Most occupational diseases do not fit the paradigm of medical interpretation of the health-disease process based on linear causality, in which it would be possible to find a single cause for each type of disease. Objectives: to conduct a systematic review in order to investigate the association between whole-body vibration (WBV) and musculoskeletal disorders (MSD) in professional truck drivers (PTD). Methods: The scientific databases of PubMed, Cochrane, Lilacs and Scielo were used to collect articles published from 2000 until the present time. Two independent reviewers adopted inclusion and quality criteria to evaluate the selected articles. Results: From adopted inclusion and quality criteria, nine articles were chosen to identify the association between MSD and WBV in PTD. The results showed that MSD seems to be closely associated to exposure to WBV in these workers, mainly due to high prevalence and symptoms of low back pain. Two cohort studies showed exposure to WBV as risk for MSD. Only one, with case-control design, did not show WBV as a significant factor. Conclusions: In this study the importance of exposure analysis of WBV in the occurrence of MSD in PTD was elucidated. This study showed the importance of WBV exposure analysis on the occurrence of MSD in PTD. There is adequate information to provide rationale for the reduction of WBV exposure to the lowest possible level, to ensure the health of these workers. Studies with a greater power of investigation, of a prospective, design, should be encouraged, supplanting those only of association