ABSTRACT
Objective: NA Background: We aimed to further characterize the neurologic manifestations observed in patients hospitalized with Coronavirus disease 2019 (COVID-19), particularly ischemic stroke in a diverse population. Design/Methods: We retrospectively reviewed data records of 50 patients with COVID-19 (48% African American and 24% Latino) who were evaluated by the neurology services in 2020. Patients were categorized into 2 groups based on timing of developing neurological manifestations: the "Neuro first" group had neurological manifestations upon initial assessment, and the "COVID first" group developed neurological symptoms greater than 24 hours after hospitalization. The demographics, comorbidities, disease severity and neurological symptoms of both groups were analyzed. Statistical analysis was performed to compare the two groups. We further analyzed acute ischemic stroke patients by comparing with historic patients with AIS without COVID-19 admitted in the same time frame in 2019 and 2020. Results: Most common neurological manifestations observed were encephalopathy (n = 30), cerebrovascular disease (n = 20), cognitive impairment (n = 13), seizures (n = 13), hypoxic brain injury (n = 7), dysgeusia (n = 5), and extraocular movement abnormalities (n = 5). The "COVID- 19 first" group had more severe/critical disease course (83.3% vs 53.8%, p 0.025). Out of 13 patients with AIS and COVID-19, Latinos and African Americans compromised the majority of our cohort (76.8%). Most strokes were cortical (84.6%) and more than 50% had no identifiable source. COVID-19 was associated with discharge to mRS>2 (p 0.046, OR 3.82, CI 1.02-14.3). Conclusions: Neurologic manifestations of COVID-19 are highly variable and can occur prior to the diagnosis of or as a complication of the viral infection. The COVID-19 patients who developed neurologic symptoms later in hospitalization had more severe disease courses. We noted a high percentage of African American and Latino individuals in both groups. Concurrent AIS and COVID-19 was associated with worse outcomes.
ABSTRACT
Wireless communication systems play an essential role in everyday life situations and enable a wide range of location-based services to their users. The imminent adoption of 5G networks worldwide and the future establishment of next-generation wireless networks will allow various applications, such as autonomous vehicles, connected robotics, and most recently, crowd monitoring for fighting infectious diseases, such as COVID-19. In this context, radio localization techniques have become an essential tool to provide solid performance for mobile positioning systems, through increased accuracy or less computational time. With this in mind, we propose a trilateration-based approach using machine learning (ML) and sequential least-square programming (SLSQP) optimization to estimate the outdoor position of mobile terminals in cellular networks. The ML technique employed is the k-nearest neighbors (k-NN). The optimization methods analyzed are Nelder–Mead (NM), genetic algorithms (GA), and SLSQP. Different environments (noise-free and noisy) and network scenarios (different numbers of base stations) are considered to evaluate the approaches. Numerical results indicate that the k-NN/SLSQP technique has similar accuracy compared to the k-NN/GA with eight generations. Both perform better than k-NN/NM in all scenarios and environments. When comparing computational times, our proposal is considerably more time-efficient. Aside from that, SLSQP computational time is less affected by network scenarios with more base stations in comparison with GA. That feature is significant considering the ultra-dense base station deployment forecasted for the next-generation cellular networks. © 2021 Elsevier B.V.