Objective Evaluating the degree of extracranial stenosis is important in predicting the risk of cerebrovascular events and to assess if the patient can benefit from any intervention. Non-invasive methods, like Doppler Ultrasonography (DUS) are preferred to invasive methods such as Digital Subtraction Angiography (DSA). Methods In this retrospective study, the level of agreement between DUS and DSA regarding the degree of stenosis of Internal Carotid Arteries (ICAs) and Vertebral Arteries (VAs) was assessed. The degree of ICA stenosis was classified into 5 groups. DSA was assumed as the gold standard. VA stenosis was classified into two groups of more or less than 50% stenosis. Results A total of 428 ICAs were assessed. Based on DSA results, DUS could estimate the degree of arterial stenosis in groups of 0–15% stenosis and 100% stenosis most accurately, and the least accuracy was in groups of 50–69% and 70–99% stenosis. The overall agreement between DUS and DSA in the classified ICA stenosis was moderate (Weighted Kappa = 0.565, P < 0.001). Also, the agreement of DUS and DSA when classifying ICA stenosis into two groups of above and below 50%, was moderate (Kappa = 0.583, P < 0.001). DUS was most sensitive and specific in the group of 100% stenosis (Sensitivity: 0.75 Specificity: 0.99) as well as the group of 1–15% stenosis (Sensitivity: 0.80 Specificity: 0.76). Also, DUS was least sensitive in group of 50–69% stenosis (Sensitivity: 0.11 Specificity: 0.94). Regarding VAS, 108 arteries were assessed and the agreement between DUS and DSA was fair (Kappa = 0.248, CI95 = −0.013 - 0.509, P < 0.01). Conclusions DUS can be used as the first-line screening tool for detecting extra cranial arteries stenosis. The practicality of the DUS as a screening tool for extracranial VAs stenosis appears to be limited.
The primary treatment for Parkinson’s disease (PD) is supplementation of levodopa (L-dopa). With disease progression, people may experience motor and non-motor fluctuations, whereby the PD symptoms return before the next dose of medication. Paradoxically, in order to prevent wearing-off, one must take the next dose while still feeling well, as the upcoming off episodes can be unpredictable. Waiting until feeling wearing-off and then taking the next dose of medication is a sub-optimal strategy, as the medication can take up to an hour to be absorbed. Ultimately, early detection of wearing-off before people are consciously aware would be ideal. Towards this goal, we examined whether or not a wearable sensor recording autonomic nervous system (ANS) activity could be used to predict wearing-off in people on L-dopa. We had PD subjects on L-dopa record a diary of their on/off status over 24 hours while wearing a wearable sensor (E4 wristband®) that recorded ANS dynamics, including electrodermal activity (EDA), heart rate (HR), blood volume pulse (BVP), and skin temperature (TEMP). A joint empirical mode decomposition (EMD) / regression analysis was used to predict wearing-off (WO) time. When we used individually specific models assessed with cross-validation, we obtained > 90% correlation between the original OFF state logged by the patients and the reconstructed signal. However, a pooled model using the same combination of ASR measures across subjects was not statistically significant. This proof-of-principle study suggests that ANS dynamics can be used to assess the on/off phenomenon in people with PD taking L-dopa, but must be individually calibrated. More work is required to determine if individual wearing-off detection can take place before people become consciously aware of it.
Since the Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many studies have shown that besides common COVID-19 symptoms, patients may develop various neuropsychiatric conditions including anxiety, mood disorders, psychosis, neurodegenerative diseases (e.g., dementia), insomnia, and even substance abuse disorders. COVID-19 can also worsen the patients underlying neuropsychiatric and neurodevelopmental conditions during or after the system phase of disease. In this review, we discuss the impact of SARS-CoV-2 infection on development or status of neuropsychiatric conditions during or following COVID-19.
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