Licorice consumption as a cause of posterior reversible encephalopathy syndrome: a case report

INTRODUCTION: A 49-year-old woman was admitted to our hospital because of thunderclap headache and blurred vision. At the time of presentation, her blood pressure was 219/100 mmHg, her arterial pH was 7.64 and her potassium level was 2.7 mM/l. METHODS: The combination of sequential computed tomography (CT) and the triad of hypertension, hypokalemia and metabolic alkalosis in this patient suggested the diagnosis. Supplementary anamnesis and long-term follow-up confirmed it. RESULTS: Brain computed tomography imaging showed minor bleeding in the left Sylvian fissure and bilateral occipital edema, suggestive of posterior reversible encephalopathy syndrome (PRES). Repeated brain CT after 10 days showed a complete resolution of radiological signs. The patient informed us that she had quit smoking 2 weeks ago and had started consuming large amounts of licorice instead of smoking. After she abandoned licorice consumption, her blood pressure normalized. Her latest blood pressure reading was 106/60 mmHg without the use of any antihypertensive drugs. CONCLUSIONS: To the best of our knowledge, this is the first case report describing licorice consumption as a cause of PRES. Glycyrrhizic acid, a component of licorice, inhibits 11β-hydroxysteroid dehydrogenase and subsequently causes mineralocorticoid excess. Mineralocorticoid excess in turn causes high blood pressure and ultimately gives rise to malignant hypertension. Physicians should remember that licorice use is a very easy-to-treat cause of hypertension, hypertensive encephalopathy and PRES

Is locally advanced head and neck cancer ‘increasing’ in the Netherlands? The paradox of absolute numbers, standardized incidence rates and proportional share

Background: Several reports have indicated that locally advanced head and neck cancer (LAHNC) has increased in the past decade. However, incidence trends cannot be easily compared because slightly different definitions of LAHNC were used. Aim: To investigate if the incidence of locally advanced disease (LAD) in the oral cavity, oropharynx, hypopharynx and larynx is indeed increasing over time, considering the growing and ageing population in the Netherlands. Patients and methods: Data were obtained from the Netherlands Cancer Registry (NCR). Primary head and neck squamous cell carcinomas of the oral cavity, oropharynx, hypopharynx and larynx diagnosed between 1989 and 2017 were included. Yearly numbers and European standardized incidence rates (ESRs) were reported by extent of disease. The annual percentage change (APC) over time in ESRs was calculated to assess trends. Results: Absolute numbers and ESRs of LAD increased over time for oral cavity and most prominently for oropharynx carcinomas (before 1996, APC: 0.4, 95%CI: 0.1; 0.8 and APC: 5.7 (95%CI: 3.1; 8.4) after 1996: 1.5 (1.0; 2.0) respectively. For hypopharynx cancer the absolute number and ESR of LAD increased until 1997 and declined with 0.8% per year since 1997. Absolute numbers of patients with larynx cancer stayed stable over time, while ESR decreased (APC: −0.8 (95%CI: −1.1; −0.6)). Conclusion: The perception of an increasing trend in LAD in the Netherlands can be attributed to the increasing incidence of oral cavity and oropharynx carcinomas. For LAD of the hypopharynx a decreasing trend was observed. In larynx cancer, the proportional share of LAD of the larynx increased, even though incidence rates declined.</p

How does our motor system determine its learning rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability

A neural surveyor to map touch on the body

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization

Driving the Future: The Relation between Driving and Prospective Memory in Adults with an Autism Spectrum Disorder

Difficulties with autonomy impact several quality-of-life outcomes in people with autism spectrum disorder (ASD). Driving is an important step towards gaining autonomy by allowing the development and maintenance of work- and social-related contacts. Nonetheless, people with ASD depend highly on friends and family for their transportation needs. Due to the complexity of the driving task, specific ASD characteristics might interfere negatively with driving. The driving task consists of several subtasks, running in parallel. This requires the ability to switch in a smooth manner (e.g., shifting, steering, changing lanes, and keeping traffic rules into account). An additional difficulty concerns sudden changes in the traffic environment (e.g., traffic density, weather conditions). Therefore, driving is a complex goal-directed task that places high demands on perceptual, cognitive, and motor processes. The little research that exists suggests that people with ASD experience difficulties more specifically in complex driving situations, requiring multi-tasking and inducing increased cognitive load. Applied to autonomy, in order to maintain work and social contacts, it is not only necessary to handle the vehicle, but also to navigate through rural, urban, and highway traffic environments while concurrently remembering appointments and obeying a schedule. People with ASD however experience difficulties with coordinating and sequencing activities, and with planning ahead. Following this, prospective memory (PM) might interfere negatively with driving. PM is the ability to remember to carry out intended actions in the future while being engaged in other ongoing activities. Two subtypes of PM are event-based PM (EBPM) and time-based PM (TBPM). The former refers to the execution of intentions at certain events (i.e., prospective cues), the latter refers to the execution of intentions at certain times. This driving simulator study aims to investigate PM (i.e., EBPM and TBPM) as an underlying mechanism of driving in adults with ASD. To this end, a pc-based ‘virtual reality (VR) city task’ was translated to a driving simulator environment. The influence of several cognitive abilities (e.g., working memory, planning), from which the importance is indicated in previous literature, is also investigated. Data collection is ongoing and will be finished in December. The analyses are planned in January