Clinical usefulness of tomographic standards for covid-19 pneumonia diagnosis : experience from a Brazilian reference center

Background: COVID-19 is a new disease and the most common complication is pneumonia.The Radiological Society of North America (RSNA) proposed an expert consensus for imagingclassification for COVID-19 pneumonia.Objective: To evaluate sensitivity, specificity, accuracy, and reproducibility of chest CT stan-dards in the beginning of the Brazilian COVID-19 outbreak.Methods: Cross-sectional study performed from March 1st to April 14th, 2020. Patients withsuspected COVID-19 pneumonia submitted to RT-PCR test and chest computed tomography(CT) were included. Tw o thoracic radiologists blinded for RT-PCR and clinical and laboratoryresults classified every patient scan according to the RSNA expert consensus. A third thoracicradiologist also evaluated in case of discordance, and consensus was reached among thethree radiologists. A typical appearance was considered a positive chest CT for COVID-19pneumonia. Sensitivity, specificity, positive and negative predictive values were calculated.Cohen’s kappa coefficient was used to evaluate intra- and inter-rater agreements.Results: A total of 159 patients were included (mean age 57.9 ± 18.0 years; 88 [55.3%] males):86 (54.1%) COVID-19 and 73 (45.9%) non-COVID-19 patients. Eighty (50.3%) patients had apositive CT for COVID-19 pneumonia. Sensitivity and specificity of typical appearance were88.3% (95%CI, 79.9–93.5) and 94.5% (95%CI, 86.7–97.8), respectively. Intra- and inter-rateragreement were assessed (Cohen’s kappa = 0.924, P = 0.06; Cohen’s kappa=0.772, P = 0.05,respectively)

Mortality among patients with tuberculosis requiring intensive care: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>To describe the characteristics of patients with tuberculosis (TB) requiring intensive care and to identify the factors that predicts in-hospital mortality in a city of a developing country with intermediate-to-high TB endemicity.</p> <p>Methods</p> <p>We conducted a retrospective, cohort study, between November 2005 and November 2007. The patients with TB requiring intensive care were included. Predictors of mortality were assessed. The primary outcome was the in-hospital mortality.</p> <p>Results</p> <p>During the study period, 67 patients with TB required intensive care. Of them, 62 (92.5%) had acute respiratory failure and required mechanical ventilation. Forty-four (65.7%) patients died. Coinfection with human immunodeficiency virus was present in 46 (68.7%) patients. Early intensive care unit admission and ventilator-associated pneumonia were independently associated with the in-hospital mortality.</p> <p>Conclusions</p> <p>In this study we found a high mortality rate in TB patients requiring intensive care, especially in those with an early ICU admission.</p

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