ABSTRACT
Background: Cerebral amyloid angiopathy related inflammation (CAA-RI) is a neuroinflammatory disease that is associated with perivascular amyloid- deposition. Case presentation: A middle-aged woman with a remote history of autoimmune disorders presented with unilateral migraine headaches, dizziness, unsteadiness, and fogginess 36 hours after administration of mRNA vaccine against SARS-CoV-2. Initially, unilateral leptomeningeal enhancement on MRI on the same side of headaches raised suspicion for leptomeningeal involvement of her known cutaneous T-cell lymphoma in remission. After two relatively unremarkable CSF analyses, she underwent a brain biopsy which showed amyloid deposits in vessels instead of lymphomatous infiltration. She was diagnosed with CAA-RI, and the headache and cognitive symptoms responded well to high-dose corticosteroids with a slow taper. Discussion/conclusion: We review the clinical literature of CAA-RI and its potential association with amyloid-related imaging abnormalities (ARIA) after administration of immunotherapy against amyloid.Copyright © 2022
ABSTRACT
In this study, we conducted a computational fluid dynamics analysis to estimate the trajectory of the virus-laden droplets. As numerical models, two human body models with airways were prepared. These models are represented by unstructured grids. Having calculated the unsteady airflow in the room, we simulated the trajectory of droplets emitted by the human speaking. In addition, inhaling the droplets into the lung of the conversation partner was simulated. The number of the droplets adhered to the respiratory lining of the partner was counted separately on the nasal cavity, oral cavity, trachea, bronchi, and bronchial inlet surface. The diameters of the droplets were also investigated in the same manner. It was noticeable that more than 80% of the droplets inhaled by the conversation partner adhered to the bronchial inlet surface. Also, the conversation partner did not inhale droplets larger than 35 μm in diameter. It was found that when the distance between two people was 0.75 m, more droplets adhered to the partner’s torso. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this study, the flow field around face masks was visualized and evaluated using computational fluid dynamics. The protective efficiency of face masks suppressing droplet infection owing to differences in the shape, medium, and doubling usage is predicted. Under the ongoing COVID-19 pandemic condition, many studies have been conducted to highlight that airborne transmission is the possible transmission route. However, the virus infection prevention effect of face masks has not been sufficiently discussed and, thus, remains as a controversial issue. Therefore, we aimed to provide a beneficial index for the society. The topology-free immersed boundary method, which is advantageous for complex shapes, was used to model the flow in the constriction area, including the contact surface between the face and mask. The jet formed from the oral cavity flow out through the surface of the mask and leaks from the gap between the face and mask. A Darcy-type model of porous media was used to model the flow resistance of masks. A random variable stochastic model was used to measure particle transmittance. We evaluated the differences in the amount of leakage and deposition of the droplets during exhalation and inhalation, depending on the differences in the conditions between the surgical and cloth masks owing to coughing and breathing. The obtained results could be useful for epidemiological measures by numerically showing the particle suppression effect of the face mask. This includes both exhalation and inhalation. © 2022 Author(s).
ABSTRACT
Transmission of infectious respiratory diseases through airborne dispersion of viruses poses a great risk to public health. In several major diseases, one of the main modes of transmission is through respiratory droplets. Virus laden respiratory droplets and aerosols can be generated during coughing, sneezing and speaking. These droplets and aerosols can remain suspended in air and be transported by airflow posing a risk of infection in individuals who might come in contact with them. With this background, in this work, we present a numerical framework for simulation of dispersion of respiratory sputum droplets using implicit large-eddy simulations. A combination of discrete Lagrangian droplet model and fully compressible Navier-Stokes flow solver is employed in this study. The method is applied to analyze cases such as droplet dispersion during speech and cough under different environmental settings. Furthermore, the performance of the numerical framework is evaluated through strong and weak scaling analysis. © 2021 ACM.