Infecção por Mycobacterium tuberculosis após artroplastia total de joelho: relato de caso: Mycobacterium tuberculosis infection after total knee arthroplasty: case report

A alta prevalência de tuberculose associada ao aumento na acessibilidade às próteses ortopédicas devem levantar suspeita da possibilidade de infecção por Mycobacterium tuberculosis pós artroplastia. O diagnóstico dessa patologia é um desafio para os cirurgiões devido: sinais e sintomas inespecíficos, exames laboratoriais sem alterações significativas, tempo de incubação para cultura de Mycobacterium (6-12 semanas) e baixa suspeita diagnóstica devido a sua raridade. O caso descrito é de um paciente sem história prévia de tuberculose, com infecção pós artroplastia total de joelho (ATJ) que foi submetido a procedimentos cirúrgicos: desbridamento e troca da prótese em duas etapas, além do tratamento medicamentoso para tuberculose após o diagnóstico da doença. Tem-se por objetivo desse trabalho, além do relato de um fato incomum, chamar a atenção dos cirurgiões para tal possibilidade visto que não há um guideline para diagnóstico e tratamento de tuberculose pós ATJ

Avaliação de bloqueio do canal adutor na analgesia pós-operatória e resultados funcionais precoces na reconstrução artroscópica do ligamento cruzado anterior (LCA)

Objetivo: Este estudo teve como objetivo avaliar os efeitos do bloqueio do canal adutor na analgesia pós-operatória e nos resultados funcionais precoces em pacientes submetidos à reconstrução artroscópica do ligamento cruzado anterior (LCA). Métodos: Foi utilizado um desenho de estudo prospectivo com um grupo de pacientes submetidos à reconstrução artroscópica do LCA com bloqueio do canal adutor e outro grupo controle sem o bloqueio. Foram avaliados os níveis de dor pós-operatória, a necessidade de analgésicos adicionais, a função do joelho e a amplitude de movimento. Resultados: Os pacientes submetidos ao bloqueio do canal adutor apresentaram níveis significativamente mais baixos de dor pós-operatória (média de 2,3 ± 0,5 na escala de dor) em comparação com o grupo controle (média de 4,1 ± 0,7 na escala de dor). Além disso, o grupo com bloqueio adutor teve uma menor necessidade de analgésicos adicionais nas primeiras 24 horas após a cirurgia. Quanto aos resultados funcionais, não houve diferença significativa na função do joelho ou na amplitude de movimento entre os grupos. Conclusões: O bloqueio do canal adutor demonstrou ser eficaz na redução da dor pós-operatória em pacientes submetidos à reconstrução artroscópica do LCA, reduzindo a necessidade de analgésicos adicionais nas primeiras horas após a cirurgia. No entanto, não houve impacto significativo nos resultados funcionais precoces do joelho. Este estudo fornece evidências de nível moderado sobre a utilidade do bloqueio adutor na analgesia pós-operatória em cirurgias de LCA

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