25 research outputs found
Intensive Care Unit Acquired Weakness (ICU-AW): a brief and practical review
Intensive care unit-acquired weakness (ICU-AW) is an increasingly complication of survivors of critical illness. It should be suspected in the presence of a patient with a flaccid tetraparesis or tetraplegia with hyporeflexia or absent deep tendon reflexes and difficult to weaning from mechanical ventilation in the absence of different diagnoses. Important risk factors are age, sepsis, illness duration and severity, some drugs (neuromuscular blockers, steroids). Electrophysiological studies have shown an axonal damage of involved peripheral nerves (critical illness polyneuropathy). However, muscle can also be primitively affected (critical illness myopathy) leading to ICUAW with inconstant myopathic damage patterns in electromyographic studies. Mixed forms can are present (critical illness polyneuromyopathy. Although the pathophysiology remains obscure, the hypothesis of an acquired channelopathy is substantial.Electroneuromyography is crucial for diagnosis. Muscular and nerve biopsy are necessary for diagnosis confirmation. Aggressive treatment of baseline disease, prevention, through avoiding or minimizing precipitating factors, strict glycemic control, and early rehabilitation combining mobilization with physiotherapy and muscle electrical muscle stimulation, are the keys to improving recovery of the affected individuals. This narrative review highlights the current literature regarding the etiology and diagnosis of ICU-AW
Composição sérica dos ácidos graxos em pacientes com diabete melito tipo 2 microalbuminúria
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Escarabeíneos (Coleoptera: Scarabaeidae: Scarabaeinae) de uma área de campo nativo no bioma Pampa, Rio Grande do Sul, Brasil
Scarabaeinae (Coleoptera, Scarabaeidae) de um bosque de eucalipto introduzido em uma região originalmente campestre
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
The myasthenic patient in crisis: an update of the management in Neurointensive Care Unit
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
Intensive Care Unit Acquired Weakness (ICU-AW): a brief and practical review
<p>Intensive care unit-acquired weakness (ICU-AW) is an increasingly complication of survivors of critical illness. It should be suspected in the presence of a patient with a flaccid tetraparesis or tetraplegia with hyporeflexia or absent deep tendon reflexes and difficult to weaning from mechanical ventilation in the absence of different diagnoses. Important risk factors are age, sepsis, illness duration and severity, some drugs (neuromuscular blockers, steroids). Electrophysiological studies have shown an axonal damage of involved peripheral nerves (critical illness polyneuropathy). However, muscle can also be primitively affected (critical illness myopathy) leading to ICUAW with inconstant myopathic damage patterns in electromyographic studies. Mixed forms can are present (critical illness polyneuromyopathy. Although the pathophysiology remains obscure, the hypothesis of an acquired channelopathy is substantial.Electroneuromyography is crucial for diagnosis. Muscular and nerve biopsy are necessary for diagnosis confirmation. Aggressive treatment of baseline disease, prevention, through avoiding or minimizing precipitating factors, strict glycemic control, and early rehabilitation combining mobilization with physiotherapy and muscle electrical muscle stimulation, are the keys to improving recovery of the affected individuals. This narrative review highlights the current literature regarding the etiology and diagnosis of ICU-AW.</p><p><a href="http://dx.doi.org/10.7175/rhc.v6i1.1037">http://dx.doi.org/10.7175/rhc.v6i1.1037</a></p