- Efficacy and Safety of Taltirelin Hydrate in Patients With Ataxia Due to Spinocerebellar Degeneration
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Jin Whan Cho, Jee-Young Lee, Han-Joon Kim, Joong-Seok Kim, Kun-Woo Park, Seong-Min Choi, Chul Hyoung Lyoo, Seong-Beom Koh
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J Mov Disord. 2025;18(1):35-44. Published online October 21, 2024
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DOI: https://doi.org/10.14802/jmd.24127
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Abstract
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- Objective
We conducted this study to assess the efficacy and safety of taltirelin hydrate (TH) in patients with ataxia due to spinocerebellar degeneration (SCD).
Methods Patients were randomly assigned to either the taltirelin group (5 mg orally, twice daily) or the control group. The primary endpoint was the change in the Korean version of the Scale for the Assessment and Rating of Ataxia (K-SARA) score at 24 weeks. The secondary endpoints included changes in the K-SARA score at 4 and 12 weeks as well as the Clinical Global Impression Scale, the five-level version of the EuroQol five-dimensional questionnaire, the Tinetti balance test, and gait analysis at 4, 12, and 24 weeks.
Results A total of 149 patients (hereditary:nonhereditary=86:63) were enrolled. There were significant differences in the change in the K-SARA score at 24 weeks from baseline between the taltirelin group and the control group (-0.51±2.79 versus 0.36±2.62, respectively; p=0.0321). For the K-SARA items, the taltirelin group had significantly lower “Stance” and “Speech disturbance” subscores than the control group (-0.04±0.89 versus 0.23±0.79 and -0.07±0.74 versus 0.18±0.67; p=0.0270 and 0.0130, respectively). However, there were no significant differences in changes in other secondary efficacy outcome measures at 24 weeks from baseline between the two treatment arms (p>0.05).
Conclusion Clinicians might consider the use of TH in the treatment of patients with ataxia due to SCD.
- Gait Analysis in Patients With Parkinson’s Disease: Relationship to Clinical Features and Freezing
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Seong-Beom Koh, Kun-Woo Park, Dae-Hie Lee, Se Ju Kim, Joon-Shik Yoon
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J Mov Disord. 2008;1(2):59-64.
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DOI: https://doi.org/10.14802/jmd.08011
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18,681
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Abstract
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Background:
The purpose of our study was to investigate gait dynamics and kinematics in patients with Parkinson’s disease (PD) and to correlate these features with the predominant clinical features and with the presence of the freezing of gait (FOG). We measured the temporospatial and kinematic parameters of gait in 30 patients with PD (M:F=12:18, age=68.43±7.54) using a computerized video motion analysis system.
Methods:
We divided the subjects into subgroups: (1) tremor-dominant (TD) group and postural instability and gait disturbance (PIGD) group and (2) FOG group and non-FOG group. We compared the gait parameters between the subgroups.
Results:
The walking velocity and stride length were reduced significantly in the PIGD group compared to the TD group. The PIGD group showed a significantly reduced range of motion in the pelvic and lower extremity joints by kinematics. Stride time variability was significantly increased and the pelvic oblique range was significantly reduced in the freezing gait disorder group.
Conclusion:
Our findings suggest that there are differences in the perturbation of the basal ganglia-cortical circuits based on major clinical features. The reduction of the pelvic oblique range of motion may be a compensatory mechanism for postural instability and contributes to stride time variability in patients with FOG.
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Citations
Citations to this article as recorded by 
- A machine learning model for prediction of sarcopenia in patients with Parkinson’s Disease
Minkyeong Kim, Doeon Kim, Heeyoung Kang, Seongjin Park, Shinjune Kim, Jun-Il Yoo, Kyung-Wan Baek PLOS ONE.2024; 19(1): e0296282. CrossRef - Machine learning approach for predicting state transitions via shank acceleration data during freezing of gait in Parkinson’s disease
Ashima Khosla, Neelesh Kumar, Preeti Khera Biomedical Signal Processing and Control.2024; 92: 106053. CrossRef - Effects of freezing of gait on vertical ground reaction force in Parkinson's disease
Mohammad Etoom, Ibrahem Hanafi, Alhadi M. Jahan, Auwal Abdullahi, Omar M. Elabd Human Movement Science.2024; 98: 103301. CrossRef - The gait parameters in patients with Parkinson’s Disease under STN-DBS therapy and associated clinical features
Halil Onder, Ege Dinc, Kubra Yucesan, Selcuk Comoglu Neurological Research.2023; 45(8): 779. CrossRef - Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson’s Disease Management Optimization
Robert Radu Ileșan, Claudia-Georgiana Cordoș, Laura-Ioana Mihăilă, Radu Fleșar, Ana-Sorina Popescu, Lăcrămioara Perju-Dumbravă, Paul Faragó Biosensors.2022; 12(4): 189. CrossRef - Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: A Novel Deep One-Class Classifier
Nader Naghavi, Eric Wade IEEE Journal of Biomedical and Health Informatics.2022; 26(4): 1726. CrossRef - Development of Neuro-Degenerative Diseases’ Gait Classification Algorithm Using Convolutional Neural Network and Wavelet Coherence Spectrogram of Gait Synchronization
Febryan Setiawan, An-Bang Liu, Che-Wei Lin IEEE Access.2022; 10: 38137. CrossRef - Functional gait assessment in early and advanced Parkinson’s disease
Hany Mohamed Eldeeb, Heba Samir Abdelraheem The Egyptian Journal of Neurology, Psychiatry and Neurosurgery.2021;[Epub] CrossRef - Statistical methods for analysis of Parkinson’s disease gait pattern and classification
Anup Nandy Multimedia Tools and Applications.2019; 78(14): 19697. CrossRef - Prediction of Freezing of Gait in Parkinson’s Disease Using Statistical Inference and Lower–Limb Acceleration Data
Nader Naghavi, Eric Wade IEEE Transactions on Neural Systems and Rehabilitation Engineering.2019; 27(5): 947. CrossRef - Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem
Nader Naghavi, Aaron Miller, Eric Wade Sensors.2019; 19(18): 3898. CrossRef - Computer-Vision Based Diagnosis of Parkinson’s Disease via Gait: A Survey
Navleen Kour, Sunanda, Sakshi Arora IEEE Access.2019; 7: 156620. CrossRef - A comparison of soft computing models for Parkinson’s disease diagnosis using voice and gait features
Rekh Ram Janghel, Anupam Shukla, Chandra Prakash Rathore, Kshitiz Verma, Swati Rathore Network Modeling Analysis in Health Informatics and Bioinformatics.2017;[Epub] CrossRef
- Cruciform Pontine MRI Hyperintensities (“Hot Cross Bun” Sign) in Non-Multiple System Atrophy Patients
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Seong-Beom Koh, Kun-Woo Park, Dae-Hie Lee
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J Mov Disord. 2008;1(2):107-108.
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DOI: https://doi.org/10.14802/jmd.08022
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10,832
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- The “Hot Cross Bun Sign” in Spinocerebellar Ataxia Types 2 and 7–Case Reports and Review of Literature
Ansuya Kasavelu Naidoo, Cait‐Lynn Deanne Wells, Yashvir Rugbeer, Neil Naidoo Movement Disorders Clinical Practice.2022; 9(8): 1105. CrossRef
- The Characteristics of Cognitive Impairment in Parkinson’s Disease and Recognition of Cognitive Symptom by Questionnaire
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Hee Young Shin, Won Yong Lee, Kun-Woo Park
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J Mov Disord. 2008;1(1):38-46.
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DOI: https://doi.org/10.14802/jmd.08007
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Abstract
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Objective:
Parkinson’s disease (PD) is characterized by motor and non-motor symptoms including cognitive, autonomic, sleep, and sensory disturbances. Cognitive impairment may occur in up to 80% of PD patients, and dementia in approximately 30%. The purpose of this study is to evaluate the frequency of cognitive impairment and the characteristics of cognitive deficits and to know the possibility of early detection of cognitive deficits in outpatient clinics with the questionnaire for patients and caregivers.
Methods:
A total of 129 consecutive patients with idiopathic Parkinson’s disease were visited movement clinic from March 2006 to August 2006. Eighty-five patients performed cognitive test and questionnaires. All patients had motor symptoms with Hoehn and Yahr stage 0.5 to 3 (mean: 1.98±0.617), and evaluated with cognition by K-MMSE (Korean version of Mini-mental status examination), 7-MS (7-minutes screen test), and demographic features.
Results:
The frequency of cognitive impairment in PD patients was 44.7% (38/85), among them thirty (78.9%) patients complained memory disturbance. The characteristics of cognitive test were retrieval defect in memory, visuospatial dysfunction and categorical word fluency. With questionnaire, the complaint of memory decline and difficulties in activity of daily living (ADL) w ere important points of cognitive deficit in PD patients. However questionnaire did not showed significant correlation between complain of memory decline and cognitive deficit, only regular check with cognitive function test revealed the patient’s early cognitive impairment.
Conclusions:
The cognitive impairment was frequent in PD patients. The characteristics of cognitive testing w ere retrieval defect in memory function and frontal executive dysfunction.
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