Introduction It is now accepted that Parkinson's disease (PD) is not simply due to dopaminergic dysfunction, and there is interest in developing non-dopaminergic approaches to disease management. Adenosine A 2A receptor antagonists represent a new way forward in the symptomatic treatment of PD. Areas covered In this narrative review, we summarize the literature supporting the utility of adenosine A 2A antagonists in PD with a specific focus on istradefylline, the most studied and only adenosine A 2A antagonist currently in clinical use. Expert opinion: At this time, the use of istradefylline in the treatment of PD is limited to the management of motor fluctuations as supported by the results of randomized clinical trials and evaluation by Japanese and USA regulatory authorities. The relatively complicated clinical development of istradefylline was based on classically designed studies conducted in PD patients with motor fluctuations on an optimized regimen of levodopa plus adjunctive dopaminergic medications. In animal models, there is consensus that a more robust effect of istradefylline in improving motor function is produced when combined with low or threshold doses of levodopa rather than with high doses that produce maximal dopaminergic improvement. Exploration of istradefylline as a 'levodopa sparing' strategy in earlier PD would seem warranted.

Levodopa is the most effective medication for the treatment of the motor symptoms of Parkinson's disease. However, over time, the clinical response to levodopa becomes complicated by a reduction in the duration and reliability of motor improvement (motor fluctuations) and the emergence of involuntary movements (levodopa-induced dyskinesia). Strategies that have been attempted in an effort to delay the development of these motor complications include levodopa sparing and continuous dopaminergic therapy. Once motor complications occur, a wide array of medical treatments is available to maximize motor function through the day while limiting dyskinesia. Here, we review the clinical features, epidemiology, and risk factors for the development of motor complications, as well as strategies for their prevention and medical management.

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The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score–a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores–with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials.

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A 38-year-old construction worker with no medical history presented with back pain, urinary retention, and flaccid lower extremity paralysis. Three weeks prior to presentation, he fell from a ladder with no immediate injury. Two weeks after the fall, he presented to another hospital for back pain and urinary retention. MRI of the lumbar, cervical, and thoracic spine without contrast were reportedly normal, and his back pain improved with an oral methylprednisolone dose pack (figure). The urinary retention remained, and he was discharged with an indwelling catheter. Within a week of the initial urinary symptoms,

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