Background Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has been associated with a significant risk of thrombotic events in critically ill patients. Aim To summarize the findings of a multinational observational cohort of patients with SARS-CoV-2 and cerebrovascular disease. Methods Retrospective observational cohort of consecutive adults evaluated in the emergency department and/or admitted with coronavirus disease 2019 (COVID-19) across 31 hospitals in four countries (1 February 2020–16 June 2020). The primary outcome was the incidence rate of cerebrovascular events, inclusive of acute ischemic stroke, intracranial hemorrhages (ICH), and cortical vein and/or sinus thrombosis (CVST). Results Of the 14,483 patients with laboratory-confirmed SARS-CoV-2, 172 were diagnosed with an acute cerebrovascular event (1.13% of cohort; 1130/100,000 patients, 95%CI 970–1320/100,000), 68/171 (40.5%) were female and 96/172 (55.8%) were between the ages 60 and 79 years. Of these, 156 had acute ischemic stroke (1.08%; 1080/100,000 95%CI 920–1260/100,000), 28 ICH (0.19%; 190/100,000 95%CI 130–280/100,000), and 3 with CVST (0.02%; 20/100,000, 95%CI 4–60/100,000). The in-hospital mortality rate for SARS-CoV-2-associated stroke was 38.1% and for ICH 58.3%. After adjusting for clustering by site and age, baseline stroke severity, and all predictors of in-hospital mortality found in univariate regression (p < 0.1: male sex, tobacco use, arrival by emergency medical services, lower platelet and lymphocyte counts, and intracranial occlusion), cryptogenic stroke mechanism (aOR 5.01, 95%CI 1.63–15.44, p < 0.01), older age (aOR 1.78, 95%CI 1.07–2.94, p = 0.03), and lower lymphocyte count on admission (aOR 0.58, 95%CI 0.34–0.98, p = 0.04) were the only independent predictors of mortality among patients with stroke and COVID-19. Conclusions COVID-19 is associated with a small but significant risk of clinically relevant cerebrovascular events, particularly ischemic stroke. The mortality rate is high for COVID-19-associated cerebrovascular complications; therefore, aggressive monitoring and early intervention should be pursued to mitigate poor outcomes.

Additive manufacturing (AM), widely known as 3D printing, is a direct digital manufacturing process, where a component can be produced layer by layer from 3D digital data with no or minimal use of machining, molding, or casting. AM has developed rapidly in the last 10 years and has demonstrated significant potential in cost reduction of performance-critical components. This can be realized through improved design freedom, reduced material waste, and reduced post processing steps. Modeling AM processes not only provides important insight in competing physical phenomena that lead to final material properties and product quality but also provides the means to exploit the design space towards functional products and materials. The length-and timescales required to model AM processes and to predict the final workpiece characteristics are very challenging. Models must span length scales resolving powder particle diameters, the build chamber dimensions, and several hundreds or thousands of meters of heat source trajectories. Depending on the scan speed, the heat source interaction time with feedstock can be as short as a few microseconds, whereas the build time can span several hours or days depending on the size of the workpiece and the AM process used. Models also have to deal with multiple physical aspects such as heat transfer and phase changes as well as the evolution of the material properties and residual stresses throughout the build time. The modeling task is therefore a multi-scale, multi-physics endeavor calling for a complex interaction of multiple algorithms. This paper discusses models required to span the scope of AM processes with a particular focus towards predicting as-built material characteristics and residual stresses of the final build. Verification and validation examples are presented, the over-spanning goal is to provide an overview of currently available modeling tools and how they can contribute to maturing additive manufacturing.

Powder Bed Additive Manufacturing offers unique advantages in terms of manufacturing cost, lot size and product complexity compared to traditional processes such as casting, where a minimum lot size is mandatory to achieve economic competitiveness. Many studies -both experimental and numerical -are dedicated to the analysis of how process parameters such as heat source power, scan speed and scan strategy affect the final material properties. Apart from the general urge to increase the build rate using thicker powder layers, the coating process and how the powder is distributed on the processing table has receive27d very little attention to date. This paper focuses on the first step of every powder bed build process: Coating the process table. A numerical study is performed to investigate how powder is transferred from the source to the processing table. A solid coating blade is modelled to spread commercial Ti-6Al-4V powder. The resulting powder layer is analyzed statistically to determine the packing density and its variation across the processing table. The results are compared with literature reports using so called "rain" models. A parameter study is performed to identify the influence of process table displacement and wiper velocity on the powder distribution. The achieved packing density and how that affects subsequent heat source interaction with the powder bed is also investigated numerically.

Thyroglossal duct cysts are usually located in the midline of the neck. The coexistence of carcinomas in thyroglossal duct cysts is extremely rare, with most being papillary carcinomas. Usually, the diagnosis is only made postoperatively after excision of the cyst. Although the Sistrunk procedure is often regarded as adequate, controversies exist concerning the need for thyroidectomy depending on histopathological findings. We report the case of a 31-year-old man diagnosed with papillary carcinoma within a thyroglossal duct cyst, who underwent total thyroidectomy as has been recommended for differentiated papillary cancer.

Background: Stroke is the second most common cause of death worldwide and a frequent cause of adult disability in developed countries. No single outcome measure can describe or predict all dimensions of recovery and disability after acute stroke. Several scales have proven reliability and validity in stroke trials. Objectives: The aim of the work was to evaluate the FOUR score predictability for outcome of patients with acute ischemic stroke in comparison with the NIHSS and the GCS. Methods: 127 adult patients with acute ischemic stroke were enrolled. NIHSS, GCS, and FOUR score were collected at 24 and 72 h. Patients were prospectively followed up for the following outcomes; In-hospital or 30 days mortality and Modified Rankin Scale (mRS) at 3 months. The areas under receiver operating characteristic curve (AUC) were compared between the three scores. Results: Twenty-five (19.7%) patients died, and seventy-two (56.7%) had unfavourable outcome. The NIHSS, the GCS, and the FOUR score were not different in predicting in-hospital mortality (AUC: 0.783, 0.779, 0.975,. The NIHSS, the GCS, and the FOUR score done at 24-h were not different in predicting unfavourable outcome (AUC: 0.893, 0.868, and 0.865, respectively). However, the NIHSS done at 72-h showed significantly higher AUC than the GCS score (0.958 versus 0.931, p = 0.041), and higher than the Four score (0.958 versus 0.909, p = 0.011).

Background and purpose Coronavirus disease 2019 (COVID-19) is associated with a small but clinically significant risk of stroke, the cause of which is frequently cryptogenic. In a large multinational cohort of consecutive COVID-19 patients with stroke, we evaluated clinical predictors of cryptogenic stroke, short-term functional outcomes and in-hospital mortality among patients according to stroke etiology. Methods We explored clinical characteristics and short-term outcomes of consecutively evaluated patients 18 years of age or older with acute ischemic stroke (AIS) and laboratory-confirmed COVID-19 from 31 hospitals in 4 countries (3/1/20–6/16/20). Results Of the 14.483 laboratory-confirmed patients with COVID-19, 156 (1.1%) were diagnosed with AIS. Sixty-one (39.4%) were female, 84 (67.2%) white, and 88 (61.5%) were between 60 and 79 years of age. The most frequently reported etiology of AIS was cryptogenic (55/129, 42.6%), which was associated with significantly higher white blood cell count, c-reactive protein, and D-dimer levels than non-cryptogenic AIS patients (p</=0.05 for all comparisons). In a multivariable backward stepwise regression model estimating the odds of in-hospital mortality, cryptogenic stroke mechanism was associated with a fivefold greater odds in-hospital mortality than strokes due to any other mechanism (adjusted OR 5.16, 95%CI 1.41–18.87, p = 0.01). In that model, older age (aOR 2.05 per decade, 95%CI 1.35–3.11, p < 0.01) and higher baseline NIHSS (aOR 1.12, 95%CI 1.02–1.21, p = 0.01) were also independently predictive of mortality. Conclusions Our findings suggest that cryptogenic stroke among COVID-19 patients carries a significant risk of early mortality.

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Abstract:In powder bed based Additive Manufacturing (AM) processes like Selective Laser Melting (SLM) or Electron Beam Melting (EBM), the spatial distribution of the individual powder particles is typically unknown. Nevertheless, the distribution of particles in the heat affected zone defines the thermophysical properties of the region being processed by the heat source and therefore plays a crucial role in heat transfer processes. In this work, the spatial distribution of individual particles and their influence on the AM process is numerically investigated. Two powder bed configurations are compared: One powder bed is generated using the discrete element method (DEM) to model the coating process; the second powder bed is arranged in the BCC structure. The melting and solidification of both configurations are modelled. The predicted melt pool dimensions are compared with experimentally determined values. The results indicate that modelling the coating process is necessary to ensure accurate modelling of the heat source powder bed interaction as well as an accurate prediction of the melt pool characteristics. Keywords