BackgroundAs coronavirus disease 2019 (COVID-19) vaccination campaign underway, little is known about the vaccination coverage and the underlying barriers of the vaccination campaign in patients with Parkinson's disease (PD).ObjectiveTo investigate the vaccination status and reasons for COVID-19 vaccine acceptance and hesitancy among PD patients.MethodsIn concordance with the CHERRIES guideline, a web-based, single-center survey was promoted to patients with PD via an online platform from April 2022 and May 2022. Logistic regression models were used to identify factors related to COVID-19 vaccine hesitancy.ResultsA total of 187 PD cases participated in this online survey (response rate of 23%). COVID-19 vaccination rate was 54.0%. Most participants had a fear of COVID-19 (77.5%) and trusted the efficacy (82.9%) and safety (66.8%) of COVID-19 vaccine. Trust in government (70.3%) and concerns about the impact of vaccine on their disease (67.4%) were the most common reasons for COVID-19 vaccine acceptance and hesitancy, respectively. COVID-19 vaccine hesitancy was independently associated with the history of flu vaccination (OR: 0.09, p < 0.05), trust in vaccine efficacy (OR: 0.15, p < 0.01), male gender (OR: 0.47, p < 0.05), disease duration of PD (OR: 1.08, p < 0.05), and geographic factor (living in Shanghai or not) (OR: 2.87, p < 0.01).ConclusionsThe COVID-19 vaccination rate remained low in PD patients, however, most individuals understood benefits of vaccination. COVID-19 vaccine hesitancy was affected by multiple factors such as geographic factor, history of flu vaccination, disease duration and trust in efficacy of vaccine. These findings could help government and public health authorities to overcome the barrier to COVID-19 vaccination and improve vaccine roll-out in PD patients.

Leg design is crucial to the performance of a legged vehicle. To achieve good walking efficiency, a leg should be able to generate a horizontal straight line by a single actuator in the walking phase. In the return phase, additional actuator(s) is used to lift and place the foot. In this paper, a study of a cam-controlled, single-actuator-driven leg is presented. It does not require additional actuator for the return motion. In addition, the legs are driven by continuous, constant speed rotary input and they move in a constant speed. Therefore, it is conceivable that the legged vehicle can be driven by a traditional rotary engine. The cam curve is designed to have connectivity of third derivative (jerk). The driving torque is analyzed. The gait and stability related to such leg mechanism are discussed. This leg mechanism has the potential to be the leg of a practical and affordable walking machine.

In this paper, we propose a new method for the extraction of blood vessels in retinal images. This approach starts with a Hessian-based multiscale filtering method to enhance blood vessels in gray retinal images. Subsequently, a new radial symmetry transformation, which is based on line kernels, is proposed to improve the detection of vessel structures and restrain the response of nonvessel structures. Finally, an iterated segmentation algorithm is used to extract retinal vessels. The proposed approach has been tested on the two publicly available databases, DRIVE and STARE. The experimental results show the feasibility of the proposed method.

Establishing accurate anatomical correspondences plays a critical role in multi-modal medical image registration and region detection. Although many features based registration methods have been proposed to detect these correspondences, they are mostly based on the point descriptor which leads to high memory cost and could not represent local region information. In this paper, we propose a new region descriptor which depicts the features in each region, instead of in each point, as a vector. First, feature attributes of each point are extracted by a Gabor filter bank combined with a gradient filter. Then, the region descriptor is defined as the covariance of feature attributes of each point inside the region, based on which a cost function is constructed for multi-modal image registration. Finally, our proposed region descriptor is applied to both multi-modal region detection and similarity metric measurement in multi-modal image registration. Experiments demonstrate the feasibility and effectiveness of our proposed region descriptor.