Imaging findings in COVID-19 pneumonia

The coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), emerged in Wuhan city and was declared a pandemic in March 2020. Although the virus is not restricted to the lung parenchyma, the use of chest imaging in COVID-19 can be especially useful for patients with moderate to severe symptoms or comorbidities. This article aimed to demonstrate the chest imaging findings of COVID-19 on different modalities: chest radiography, computed tomography, and ultrasonography. In addition, it intended to review recommendations on imaging assessment of COVID-19 and to discuss the use of a structured chest computed tomography report. Chest radiography, despite being a low-cost and easily available method, has low sensitivity for screening patients. It can be useful in monitoring hospitalized patients, especially for the evaluation of complications such as pneumothorax and pleural effusion. Chest computed tomography, despite being highly sensitive, has a low specificity, and hence cannot replace the reference diagnostic test (reverse transcription polymerase chain reaction). To facilitate the confection and reduce the variability of radiological reports, some standardizations with structured reports have been proposed. Among the available classifications, it is possible to divide the radiological findings into typical, indeterminate, atypical, and negative findings. The structured report can also contain an estimate of the extent of lung involvement (e.g., more or less than 50% of the lung parenchyma). Pulmonary ultrasonography can also be an auxiliary method, especially for monitoring hospitalized patients in intensive care units, where transfer to a tomography scanner is difficult

Lung Lesion Burden found on Chest CT as a Prognostic Marker in Hospitalized Patients with High Clinical Suspicion of COVID-19 Pneumonia: a Brazilian experience

OBJECTIVE: To investigate the relationship between lung lesion burden (LLB) found on chest computed tomography (CT) and 30-day mortality in hospitalized patients with high clinical suspicion of coronavirus disease 2019 (COVID-19), accounting for tomographic dynamic changes. METHODS: Patients hospitalized with high clinical suspicion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a dedicated and reference hospital for COVID-19, having undergone at least one RT-PCR test, regardless of the result, and with one CT compatible with COVID-19, were retrospectively studied. Clinical and laboratory data upon admission were assessed, and LLB found on CT was semi-quantitatively evaluated through visual analysis. The primary outcome was 30-day mortality after admission. Secondary outcomes, including the intensive care unit (ICU) admission, mechanical ventilation used, and length of stay (LOS), were assessed. RESULTS: A total of 457 patients with a mean age of 57±15 years were included. Among these, 58% presented with positive RT-PCR result for COVID-19. The median time from symptom onset to RT-PCR was 8 days [interquartile range 6-11 days]. An initial LLB of ≥50% using CT was found in 201 patients (44%), which was associated with an increased crude at 30-day mortality (31% vs. 15% in patients with LLB of <50%, p<0.001). An LLB of ≥50% was also associated with an increase in the ICU admission, the need for mechanical ventilation, and a prolonged LOS after adjusting for baseline covariates and accounting for the CT findings as a time-varying covariate; hence, patients with an LLB of ≥50% remained at a higher risk at 30-day mortality (adjusted hazard ratio 2.17, 95% confidence interval 1.47-3.18, p<0.001). CONCLUSION: Even after accounting for dynamic CT changes in patients with both clinical and imaging findings consistent with COVID-19, an LLB of ≥50% might be associated with a higher risk of mortality

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

Rocky Mountain spotted fever: brain imaging findings

One thing many law students \u27 and practicing lawyers \u27 often struggle with is critically thinking about the entirety of the case they are working on Shallow analysis of the full range of possible legal issues presented by a situation can lead an advocate to be unprepared when new issues arise unexpectedly in a case My article proposes OODA \u27 Observe Orient Decide Act \u27 as a useful way to approach litigation planning and strategy formationbrbrOODA was conceived by military strategist Col John Boyd as a way to outthink and outmaneuver an enemy But it turns out that OODA is a good strategy for solving any kind of problem including legal ones The common law evolves through an endless series of OODA processes This article describes OODA then shows through a hypothetical legal problem how a lawyer can use OODA to clearly analyze the problem in all of its nuancesbrbrThis article adds not only to legal writing pedagogy but clinical teaching as well It demonstrates a useful tool that could be used in case rounds to help students anticipate opposing arguments or unforeseen legal issue