BACKGROUND AND PURPOSE:Recently some series have been published about the use of Onyx for the treatment of DAFVs with satisfactory results. Our aim was to describe the treatment of different types of intracranial DAVFs with transcatheter injection of Onyx through an arterial approach.

Background: Early diagnosis of stroke optimizes reperfusion therapies, but behavioral measures have incomplete accuracy. EEG has high sensitivity for immediately detecting brain ischemia. This pilot study aimed to evaluate feasibility and utility of EEG for identifying patients with a large acute ischemic stroke during Emergency Department evaluation, as these data might be useful in the pre-hospital setting. Methods: A 3-minute resting EEG was recorded using a dense-array (256-lead) system in patients with suspected acute stroke arriving at the Emergency Department of a US Comprehensive Stroke Center. Results: An EEG was recorded in 24 subjects, 14 with acute cerebral ischemia (including 5 with large acute ischemic stroke) and 10 without acute cerebral ischemia. Median time from stroke onset to EEG was 6.6 hours; and from Emergency Department arrival to EEG, 1.9 hours. Delta band power (p=0.004) and the alpha/delta frequency band ratio (p=0.0006) each significantly distinguished patients with large acute ischemic stroke (n=5) from all other patients with suspected stroke (n=19), with the best diagnostic utility coming from contralesional hemisphere signals. Larger infarct volume correlated with higher EEG power in the alpha/delta frequency band ratio within both the ipsilesional (r=−0.64, p=0.013) and the contralesional (r=−0.78, p=0.001) hemispheres. Conclusions: Within hours of stroke onset, EEG measures (1) identify patients with large acute ischemic stroke and (2) correlate with infarct volume. These results suggest that EEG measures of brain function may be useful to improve diagnosis of large acute ischemic stroke in the Emergency Department, findings that might be useful to pre-hospital applications.

Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset. CCS CONCEPTS• Information systems → Social networking sites; • Security and privacy → Social network security and privacy; KEYWORDSCivil unrest, Social bots, Content polluters, Missing links, Twitter * Currently works at Tyto.ai † D2D CRC Stream Lead This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

C oronavirus disease 2019 (COVID-19) has a 3.1% fatality rate and is spread primarily through droplets and contact. Anesthetists working in hospitals may perform procedures and provide care involving the respiratory system [eg, cardiopulmonary resuscitation (CPR), airway management, intubation, chest compression, and face mask ventilation], which increases their risk of viral infection and death. To help mitigate these adverse outcomes, the European Resuscitation Council has prioritized staff safety and recommends that personal protective equipment (PPE) is worn by rescuers performing CPR. The minimum PPE recommendation includes a respirator mask (FFP2 or N95 respirator mask if FFP3 is not available).While the N95 mask has met safety guidelines in the past, there are downsides to the N95 mask design: it is only 95% effective against aerosol particle penetration compared with the 99% of FFP3 masks, it does not have a face-tight seal, and the shape and movement may compromise its efficacy (a study found 61% of those wearing N95 masks failed at least one third of chest compression sessions, and 18% experienced mask failures). Value and cup-type N95 mask efficacy was especially low.Powered air-purifying respirators, while not being perfectly protective and though they are more cumbersome and time consuming to put on, are shown to be more protective than N95 masks while providing CPR.Health care workers should consider the risk and benefits of available PPE when approaching situations involving CPR for optimal safety.

Cerebral venous and sinus thrombosis is an uncommon but potentially lethal event. Although thrombosis accounts for only 1% of all strokes, if it is left untreated patients suffer from continuing headaches, vague neurological complaints, and may even progress to coma and death. New endovascular techniques and technology allow the possibility of more aggressive thrombolysis and thrombectomy in the setting of acute thrombosis. The authors present a case of recanalization of an extensive cerebral thrombosis using a new endovascular retrieval device.