Background Concerns regarding potential neurological complications of COVID-19 are being increasingly reported, primarily in small series. Larger studies have been limited by both geography and specialty. Comprehensive characterisation of clinical syndromes is crucial to allow rational selection and evaluation of potential therapies. The aim of this study was to investigate the breadth of complications of COVID-19 across the UK that affected the brain. Methods During the exponential phase of the pandemic, we developed an online network of secure rapid-response case report notification portals across the spectrum of major UK neuroscience bodies, comprising the Association of British Neurologists (ABN), the British Association of Stroke Physicians (BASP), and the Royal College of Psychiatrists (RCPsych), and representing neurology, stroke, psychiatry, and intensive care. Broad clinical syndromes associated with COVID-19 were classified as a cerebrovascular event (defined as an acute ischaemic, haemorrhagic, or thrombotic vascular event involving the brain parenchyma or subarachnoid space), altered mental status (defined as an acute alteration in personality, behaviour, cognition, or consciousness), peripheral neurology (defined as involving nerve roots, peripheral nerves, neuromuscular junction, or muscle), or other (with free text boxes for those not meeting these syndromic presentations). Physicians were encouraged to report cases prospectively and we permitted recent cases to be notified retrospectively when assigned a confirmed date of admission or initial clinical assessment, allowing identification of cases that occurred before notification portals were available. Data collected were compared with the geographical, demographic, and temporal presentation of overall cases of COVID-19 as reported by UK Government public health bodies.

This study explores the factors that affect students' adoption of Twitter as an information source. It relies on a modified technology acceptance model (TAM). Data were gathered using a survey of 400 social sciences students from Kuwait University. Structural equation modeling was employed to examine the proposed relationships of six factors-perceived ease of use, perceived usefulness, perceived enjoyment, social influence, behavioral intention, and actual use-on Twitter usage as an information source. The findings show that perceived enjoyment and social influence are stronger predictors of behavioral intention than perceived usefulness. Conversely, perceived ease of use was not significant antecedents of behavioral intention. The study also found that perceived ease of use influences only by perceived enjoyment. As expected, behavioral intention was an important antecedent of actual use. This study was limited to students of the College of Social Sciences at Kuwait University. Additional studies on the use of social media as an information source are recommended. This study is beneficial for higher educational institutions and academic libraries eager to understand the factors that motivate student adoption of Twitter as an information source for educational purposes.

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Machine Learning (ML) relates to the use of computer-derived algorithms and systems to enhance knowledge in order to facilitate decision making. In surgery, ML has the potential to shape clinical decision making and the management of postoperative complications in three ways: (a) by using the predicted probability of postoperative complications or survival to determine and guide optimal treatment; (b) by identifying anomalous data and patterns representing high-risk physiological states during the perioperative period and taking measures to minimise the impact of the existing risks; (c) to facilitate post-hoc identification of physiological trends, phenotypic patient characteristics, morphological characteristics of diseases, and human factors that may help alert surgeons to relevant risk factors in future patients. The accuracy, validity and integrity of data that are input into ML predictive models are central to its future success. ML could reduce errors by drawing attention to known risks of complications through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care through unsupervised learning. The success of ML in enhancing patient care will be determined by the human potential to incorporate data science techniques into daily clinical practice.

Self-Driving Cars: a qualitative study into the opportunities, challenges and perceived acceptability for people with epilepsyDriving restrictions faced by people with epilepsy (PWE) represent a crucial and modifiable factor that predicts their social participation and employability. In the case of refractory epilepsy, a person may lose the ability to drive forever -a handicap associated with significant detrimental effects, including reduced employability and a poorer household income (1). Encouragingly, progress in self-driving car technology provides renewed hope for PWE restricted from driving. A self-driving car is any car in which steering or acceleration/deceleration are controlled by the car. Differing degrees of automation exist, but relevant to PWE are those which require no human input to drive (fully autonomous vehicles). These are vehicles with no steering wheel-and in which, the driver, for all intents and purposes is a passenger.The UK government aim to have self-driving cars on the road by 2021 (2). Promisingly, Department of Transport guidelines stipulate: "It would seem reasonable to allow ownership or use of a fully automated vehicle without the need to hold a driving license" (3). This could mean that all PWE will be able to 'drive' in the imminent future and although this innovation is exciting, it is important to consider the views of PWE.