Medical complications in traumatic spinal cord injury: pulmonary, cardiovascular, genitourinary and gastrointestinal

O trauma raquimedular é uma das principais causas de morte entre a população jovem. Além disso, várias complicações são devidas a déficits neurológicos secundários ao trauma raquimedular. A principal complicação é a pulmonar, com alto risco de óbito. A causa mais importante de óbito entre os pacientes com trauma raquimedular é a pneumonia. Entretanto, outras complicações podem estar presentes. Os autores revisaram as complicações clínicas em pacientes com trauma raquimedular._________________________________________________________________________________________ ABSTRACT: The traumatic spinal injury is one of the most important causes of death in young people. Additionally, many complications are due to neurological deficits secondary to spinal cord injury. The major complication is pulmonary with high risk of death. The most important cause of death among patients with spinal injury is pneumonia. However, others complications can be present. The authors reviewed the clinical complications in patients with spinal injury

Sobrecarga de ferro transfusional em portadores de anemia falciforme: comparação entre ressonância magnética e ferritina sérica

OBJETIVO: Identificar variáveis preditoras de sobrecarga de ferro em portadores de anemia falciforme e correlacionar indicadores bioquímicos e imaginológicos. MATERIAiS E MÉTODOS: Foi realizado estudo transversal envolvendo 32 portadores de anemia falciforme, que foram submetidos a dosagem sérica de ferro, ferritina e a ressonância magnética do fígado. Foram realizadas cinco sequências gradiente-eco e uma spin-eco. A intensidade de sinal foi obtida em cada sequência pelas médias das regiões de interesse no fígado e musculatura paravertebral para obter a razão da intensidade de sinal (RIS) fígado/músculo. A partir da RIS foi obtida a concentração hepática estimada de ferro (CHEF) pela fórmula: e[5,808 - (0,877 × T2*) - (1,518 × PI)], onde T2* é a RIS na sequência com TE de 13 ms e PI é a RIS da sequência com ponderação intermediária. Os pacientes foram agrupados segundo o regime de transfusão de hemácias (regulares mensais versus esporádicas). RESULTADOS: Os grupos transfusionais foram comparados pelas variáveis clínico-laboratoriais, sendo significativas as diferenças entre RIS, CHEF e ferritina sérica: o grupo que recebeu transfusões regulares apresentou sobrecarga de ferro hepático mais intensa. CONCLUSÃO: A ressonância magnética foi ferramenta eficiente para avaliação de sobrecarga hepática de ferro em portadores de anemia falciforme

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others