COVID-19 vaccine acceptance and hesitancy in patients with Parkinson's disease.

Background: As 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). Objective: To investigate the vaccination status and reasons for COVID-19 vaccine acceptance and hesitancy among PD patients. Methods: In 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. Results: A 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). Conclusions: The 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.

Protein nanoparticle-induced osmotic pressure gradients modify pulmonary edema through hyperpermeability in acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS), caused by noncardiogenic pulmonary edema (PE), contributes significantly to Coronavirus 2019 (COVID-19)-associated morbidity and mortality. We explored the effect of transmembrane osmotic pressure (OP) gradients in PE using a fluorescence resonance energy transfer-based Intermediate filament (IF) tension optical probe. Angiotensin-II- and bradykinin-induced increases in intracellular protein nanoparticle (PN)-OP were associated with inflammasome production and cytoskeletal depolymerization. Intracellular protein nanoparticle production also resulted in cytomembrane hyperpolarization and L-VGCC-induced calcium signals, which differed from diacylglycerol-induced calcium increment via TRPC6 activation. Both pathways involve voltage-dependent cation influx and OP upregulation via SUR1-TRPM4 channels. Meanwhile, intra/extracellular PN-induced OP gradients across membranes upregulated pulmonary endothelial and alveolar barrier permeability. Attenuation of intracellular PN, calcium signals, and cation influx by drug combinations effectively relieved intracellular OP and pulmonary endothelial nonselective permeability, and improved epithelial fluid absorption and PE. Thus, PN-OP is pivotal in pulmonary edema in ARDS and COVID-19, and transmembrane OP recovery could be used to treat pulmonary edema and develop new drug targets in pulmonary injury.

Recognition of Freezing of Gait in Parkinson's Disease Based on Machine Vision.

Background: Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment. Objective: A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital. Methods: In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model. Results: We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%. Conclusion: A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.

Multi-Scale Sparse Graph Convolutional Network For the Assessment of Parkinsonian Gait

Automated assessment of patients with Parkinson's disease (PD) is urgently required in clinical practice to improve the diagnostic efficiency and objectivity and to remotely monitor the motor disorder symptoms and general health of these patients, especially in view of the travel restrictions due to the recent coronavirus epidemic. Gait motor disorder is one of the critical manifestations of PD, and automated assessment of gait is vital to realize automated assessment of PD patients. To this end, we propose a novel two-stream spatial-temporal attention graph convolutional network (2s-ST-AGCN) for video assessment of PD gait motor disorder. Specifically, the skeleton sequence of human body is extracted from videos to construct spatial-temporal graphs of joints and bones, and a two-stream spatial-temporal graph convolutional network is then built to simultaneously model the static spatial information and dynamic temporal variations. The multi-scale spatial-temporal attention-aware mechanism is also designed to effectively extract the discriminative spatial-temporal features. The deep supervision strategy is then embedded to minimize classification errors, thereby guiding the weight update process of the hidden layer to promote significant discriminative features. Besides, two model-driven terms are integrated into this deep learning framework to strengthen multi-scale similarity in the deep supervision and realize sparsification of discriminative features. Extensive experiments on the clinical video dataset show that the proposed model exhibits good performance with an accuracy of 65.66% and an acceptable accuracy of 98.90%, which is much better than that of the existing sensor- and vision-based methods for Parkinsonian gait assessment. Thus, the proposed method is potentially useful for assessing PD gait motor disorder in clinical practice.

Utility of Deep Brain Stimulation Telemedicine for Patients With Movement Disorders During the COVID-19 Outbreak in China.

OBJECTIVE: To explore the utility of deep brain stimulation (DBS) telemedicine in the management of patients with movement disorders from January 2019 to March 2020, covering the main period of the COVID-19 outbreak in China. MATERIALS AND METHODS: We obtained data from 40 hospitals around China that employed DBS tele-programming for their outpatients with Parkinson's disease or dystonia from January 2019 to March 2020. Data were obtained on the number and nature of patients' DBS health care service requests, reasons for their requests, the number of DBS telemedicine sessions subsequently completed, safety issues, and the patients' satisfaction with the DBS tele-programing parameter adjustments made. RESULTS: There were 909 DBS tele-programming health service requests (from 196 patients) completed during the study period. The results showed: 1) the number of DBS telemedicine sessions requested and the number of patients examined increased during the COVID-19 outbreak in February and March 2020 when compared with the monthly numbers in 2019; 2) the most common reason for the patients' health service requests was poor symptom control; 3) the most common DBS tele-programming adjustment made was voltage change; 4) overall, most (89%) DBS tele-programming adjustment sessions were experienced by the patients as satisfactory; and 5) significant adverse events and unexpected treatment interruptions caused by connection failure or other hardware- or software-related problems did not occur. CONCLUSIONS: DBS telemedicine could have a unique role to play in maintaining the delivery of DBS treatment and medical care to outpatients with movement disorders during the COVID-19 pandemic.

Deep Brain Stimulation for Parkinson's Disease During the COVID-19 Pandemic: Patient Perspective.

BACKGROUND: Public health guidelines have recommended that elective medical procedures, including deep brain stimulation (DBS) surgery for Parkinson's disease (PD), should not be scheduled during the coronavirus (COVID-19) pandemic to prevent further virus spread and overload on health care systems. However, delaying DBS surgery for PD may not be in the best interest of individual patients and is not called for in regions where virus spread is under control and inpatient facilities are not overloaded. METHODS: We administered a newly developed phone questionnaire to 20 consecutive patients with PD who received DBS surgery in Ruijin Hospital in Shanghai during the COVID-19 pandemic. The questionnaire was designed to gather the patients' experiences and perceptions on the impact of COVID-19 on their everyday activities and access to medical care. RESULTS: Most of the patients felt confident about the preventive measures taken by the government and hospitals, and they have changed their daily living activities accordingly. Moreover, a large majority of patients felt confident obtaining access to regular and COVID-19-related health care services if needed. Routine clinical referral, sense of security in the hospital during the outbreak, and poor control of PD symptoms were the three main reasons given by patients for seeking DBS surgery during the COVID-19 pandemic. CONCLUSION: The COVID-19 pandemic has considerably impacted medical care and patients' lives but elective procedures, such as DBS surgery for PD, do not need to be rescheduled when the health care system is not overloaded and adequate public health regulations are in place.

Deep brain stimulation telemedicine programming during the COVID-19 pandemic: treatment of patients with psychiatric disorders.

OBJECTIVE: The ongoing coronavirus disease 2019 (COVID-19) pandemic has considerably affected the delivery of postoperative care to patients who have undergone deep brain stimulation (DBS) surgery. DBS teleprogramming technology was developed and deployed in China before the COVID-19 outbreak. In this report, the authors share their experiences with telemedical DBS treatment of patients with psychiatric disorders during the COVID-19 outbreak. METHODS: Four patients (2 with obsessive-compulsive disorder, 1 with major depressive disorder, and 1 with anorexia nervosa) underwent DBS surgery at Ruijin Hospital and received continuous postoperative DBS telemedicine case management from January 2020 to July 2020. DBS teleprogramming, individualized psychological support, and medical consultations were provided via the authors' DBS telemedicine platform, which also incorporated a synchronous real-time video communication system. RESULTS: Forty-five DBS telemedicine sessions were conducted; there was no unexpected loss of network connection during the sessions. Of these, 28 sessions involved DBS teleprogramming. Adjustments were made to the stimulation voltage, frequency, pulse width, and contact site in 21, 12, 9, and 9 sessions, respectively. Psychological support and troubleshooting were provided during the remaining telemedicine sessions. Modest to substantial clinical improvements after DBS surgery were observed in some but not all patients, whereas stimulation-related side effects were reported by 2 patients and included reversible sleep and mood problems, headache, and a sensation of heat. CONCLUSIONS: DBS telemedicine seems to offer a feasible, safe, and efficient strategy for maintaining the delivery of medical care to psychiatric patients during the COVID-19 outbreak. The authors propose that implementation of a comprehensive DBS telemedicine system, which combines DBS teleprogramming with psychological counseling, medical consultations, and medication prescriptions and delivery, could be an efficient and effective approach to manage the mental health and quality of life of patients with psychiatric disorders during future local or global public health crises.

Sparse Adaptive Graph Convolutional Network for Leg Agility Assessment in Parkinson's Disease.

Motor disorder is a typical symptom of Parkinson's disease (PD). Neurologists assess the severity of PD motor symptoms using the clinical rating scale, i.e., MDS-UPDRS. However, this assessment method is time-consuming and easily affected by the perception difference of assessors. In the recent outbreak of coronavirus disease 2019, telemedicine for PD has become extremely urgent for clinical practice. To solve these problems, we developed an automated and objective assessment method of the leg agility task in the MDS-UPDRS using videos and a graph neural network. In this study, a sparse adaptive graph convolutional network (SA-GCN) was proposed to achieve fine-grained quantitative assessment of skeleton sequences extracted from videos. Specifically, the sparse adaptive graph convolutional unit with a prior knowledge constraint was proposed to perform adaptive spatial modeling of physical and logical dependency for skeleton sequences, thus achieving the sparse modeling of the discriminative spatial relationships. Subsequently, a temporal context module was introduced to construct the remote context dependency in the temporal dimension, hence determining the global changes of the task. A multi-domain attention learning module was also developed to integrate the static spatial features and dynamic temporal features, and then to emphasize the salient feature selection in the channel domain, thereby capturing the multi-domain fine-grained information. Finally, the evaluation results using a dataset with 148 patients and 870 samples confirmed the effectiveness and reliability of our scheme, and the method outperformed other related state-of-the-art methods. Our contactless method provides a new potential tool for automated PD assessment and telemedicine.