PurposeWhile there is strong evidence supporting the importance of telemedicine in stroke, its role in other areas of neurology is not as clear. The goal of this review is to provide an overview of evidence-based data on the role of teleneurology in the care of patients with neurologic disorders other than stroke.Recent findingsStudies across multiple specialties report noninferiority of evaluations by telemedicine compared with traditional, in-person evaluations in terms of patient and caregiver satisfaction. Evidence reports benefits in expediting care, increasing access, reducing cost, and improving diagnostic accuracy and health outcomes. However, many studies are limited, and gaps in knowledge remain.SummaryTelemedicine use is expanding across the vast array of neurologic disorders. More studies are needed to validate and support its use.

Introduction The etiology of acute exacerbations of myasthenia gravis (MG) is not well understood and further characterization can lead to improved preventative measures. This study aims to characterize factors contributing to MG exacerbations. Methods A total of 127 MG patient charts were reviewed retrospectively (2011‐2016) to obtain demographics, immunizations, pharmaceutical records, contributing factors of each MG exacerbation, emergency department (ED) visits, hospitalizations, and duration. Results There were 212 exacerbations requiring 106 ED visits and 141 hospitalizations (average admission 6.5 days). Highest contributors were infections (30%) and medications that may worsen MG (19%), with 24% unattributed. Infection related exacerbations were associated with 44.3% of ED visits and 39.7% of hospitalizations. Patients prescribed beta‐blockers were associated with more exacerbations (P < .01). Patients prescribed medications that may worsen MG had a higher exacerbation frequency shortly after administration. Discussion Infections and cautioned medications are frequently factors in acute MG exacerbations needing urgent medical attention and warrant caution.

Each new generation of GPUs vastly increases the resources available to GPGPU programs. GPU programming models (like CUDA) were designed to scale to use these resources. However, we find that CUDA programs actually do not scale to utilize all available resources, with over 30% of resources going unused on average for programs of the Parboil2 suite that we used in our work. Current GPUs therefore allow concurrent execution of kernels to improve utilization. In this work, we study concurrent execution of GPU kernels using multiprogram workloads on current NVIDIA Fermi GPUs. On two-program workloads from the Parboil2 benchmark suite we find concurrent execution is often no better than serialized execution. We identify that the lack of control over resource allocation to kernels is a major serialization bottleneck. We propose transformations that convert CUDA kernels into elastic kernels which permit fine-grained control over their resource usage. We then propose several elastic-kernel aware concurrency policies that offer significantly better performance and concurrency compared to the current CUDA policy. We evaluate our proposals on real hardware using multiprogrammed workloads constructed from benchmarks in the Parboil 2 suite. On average, our proposals increase system throughput (STP) by 1.21x and improve the average normalized turnaround time (ANTT) by 3.73x for two-program workloads when compared to the current CUDA concurrency implementation.

Neuroinflammation leads to neurodegeneration, cognitive defects, and neurodegenerative disorders. Neurotrauma/traumatic brain injury (TBI) can cause activation of glial cells, neurons, and neuroimmune cells in the brain to release neuroinflammatory mediators. Neurotrauma leads to immediate primary brain damage (direct damage), neuroinflammatory responses, neuroinflammation, and late secondary brain damage (indirect) through neuroinflammatory mechanism. Secondary brain damage leads to chronic inflammation and the onset and progression of neurodegenerative diseases. Currently, there are no effective and specific therapeutic options to treat these brain damages or neurodegenerative diseases. Flavone luteolin is an important natural polyphenol present in several plants that show anti-inflammatory, antioxidant, anticancer, cytoprotective, and macrophage polarization effects. In this short review article, we have reviewed the neuroprotective effects of luteolin in neurotrauma and neurodegenerative disorders and pathways involved in this mechanism. We have collected data for this study from publications in the PubMed using the keywords luteolin and mast cells, neuroinflammation, neurodegenerative diseases, and TBI. Recent reports suggest that luteolin suppresses systemic and neuroinflammatory responses in Coronavirus disease 2019 . Studies have shown that luteolin exhibits neuroprotective effects through various mechanisms, including suppressing immune cell activation, such as mast cells, and inflammatory mediators released from these cells. In addition, luteolin can suppress neuroinflammatory response, activation of microglia and astrocytes,

Abstract-The StreamIt programming model has been proposed to exploit parallelism in streaming applications on general purpose multicore architectures. This model allows programmers to specify the structure of a program as a set of filters that act upon data, and a set of communication channels between them. The StreamIt graphs describe task, data and pipeline parallelism which can be exploited on modern Graphics Processing Units (GPUs), which support abundant parallelism in hardware.In this paper, we describe the challenges in mapping StreamIt to GPUs and propose an efficient technique to software pipeline the execution of stream programs on GPUs. We formulate this problem -both scheduling and assignment of filters to processors -as an efficient Integer Linear Program (ILP), which is then solved using ILP solvers. We also describe a novel buffer layout technique for GPUs which facilitates exploiting the high memory bandwidth available in GPUs. The proposed scheduling exploits both the scalar units in GPU, to exploit data parallelism, and multiprocessors, to exploit task and pipeline parallelism. Further it takes into consideration the synchronization and bandwidth limitations of GPUs, yielding speedups between 1.87X and 36.83X over a single threaded CPU.