Frontiers in neurology
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Frontiers in neurology · Jan 2020
Hemodynamic and Morphological Differences Between Unruptured Carotid-Posterior Communicating Artery Bifurcation Aneurysms and Infundibular Dilations of the Posterior Communicating Artery.
Objective: Posterior communicating artery bifurcation aneurysms (PcomA-BAs) and infundibular dilations (PcomA-IDs) are found at the junction between the internal carotid artery (ICA) and the posterior communicating artery (PcomA). Several studies found that PcomA-IDs potentially progress to aneurysms and can even rupture. In our clinical practice, digital subtraction angiography (DSA) helps differentiate PcomA-IDs from unruptured PcomA-BAs. ⋯ Binary logistic regression analysis showed that small size and DPcomA as well as APcomA were all independent significant factors characterizing the status of PcomA-IDs and the ROC analysis for independent risk factors indicated the cutoff values of size, APcomA, and DPcomA were 3.45 mm, 66.27°, and 1.24 mm, respectively. Conclusions: Size, DpcomA, and ApcomA could independently characterize the status of PcomA-IDs. These might help us better differentiate them from real aneurysms and guide its management.
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Frontiers in neurology · Jan 2020
Triage of Acute Ischemic Stroke in Confirmed COVID-19: Large Vessel Occlusion Associated With Coronavirus Infection.
The outbreak of COVID-19 has posed a significant challenge to global healthcare. Acute stroke care requires rapid bedside attendance, imaging, and intervention. ⋯ We present our experience with an in-hospital stroke code called on a COVID-19-positive patient with a left middle cerebral artery syndrome and the challenges faced for timely examination, imaging, and decision to intervene. The outlook for the ongoing COVID-19 pandemic necessitates the development of protocols to sustain timely and effective acute stroke care while mitigating healthcare-associated transmission.
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Frontiers in neurology · Jan 2020
ReviewNeurological Manifestations of COVID-19 (SARS-CoV-2): A Review.
Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been associated with many neurological symptoms but there is a little evidence-based published material on the neurological manifestations of COVID-19. The purpose of this article is to review the spectrum of the various neurological manifestations and underlying associated pathophysiology in COVID-19 patients. Method: We conducted a review of the various case reports and retrospective clinical studies published on the neurological manifestations, associated literature, and related pathophysiology of COVID-19 using PUBMED and subsequent proceedings. ⋯ There is a need to diagnose these manifestations at the earliest to limit long term sequelae. Much research is needed to explore the role of SARS-CoV-2 in causing these neurological manifestations by isolating it either from cerebrospinal fluid or brain tissues of the deceased on autopsy. We also recommend exploring the risk factors that lead to the development of these neurological manifestations.
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SARS-CoV-2 is a highly pathogenic coronavirus that has caused an ongoing worldwide pandemic. Emerging in Wuhan, China in December 2019, the virus has spread rapidly around the world. Corona virus disease 2019 (COVID-19), which is caused by SARS-CoV-2, has resulted in significant morbidity and mortality. ⋯ This includes headache, anosmia, meningoencephalitis, acute ischemic stroke, and several presumably post/para-infectious syndromes and altered mental status not explained by respiratory etiologies. Interestingly, previous studies in animal models emphasized the neurotropism of coronaviruses; thus, these CNS manifestations of COVID-19 are not surprising. This minireview scans the literature regarding the involvement of the CNS in coronavirus infections in general, and in regard to the recent SARS-CoV-2, specifically.
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Frontiers in neurology · Jan 2020
ReviewMachine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?
The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. ⋯ Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.