Neuropathogenesis-on-chips for neurodegenerative diseases.

Developing diagnostics and treatments for neurodegenerative diseases (NDs) is challenging due to multifactorial pathogenesis that progresses gradually. Advanced in vitro systems that recapitulate patient-like pathophysiology are emerging as alternatives to conventional animal-based models. In this review, we explore the interconnected pathogenic features of different types of ND, discuss the general strategy to modelling NDs using a microfluidic chip, and introduce the organoid-on-a-chip as the next advanced relevant model. Lastly, we overview how these models are being applied in academic and industrial drug development. The integration of microfluidic chips, stem cells, and biotechnological devices promises to provide valuable insights for biomedical research and developing diagnostic and therapeutic solutions for NDs.

Clinical and Genetic Characteristics Associated With Survival Outcome in Late-Onset Huntington's Disease in South Korea.

BACKGROUND AND PURPOSE: The onset of Huntington's disease (HD) usually occurs before the age of 50 years, and the median survival time from onset is 15 years. We investigated survival in patients with late-onset HD (LoHD) (age at onset ≥60 years) and the associations of the number of mutant CAG repeats and age at onset (AAO) with survival in patients with HD. METHODS: Patients with genetically confirmed HD at six referral centers in South Korea between 2000 and 2020 were analyzed retrospectively. Baseline demographic, clinical, and genetic characteristics and the survival status as at December 2020 were collected. RESULTS: Eighty-seven patients were included, comprising 26 with LoHD (AAO=68.77±5.91 years, mean±standard deviation; 40.54±1.53 mutant CAG repeats) and 61 with common-onset HD (CoHD) (AAO=44.12±8.61 years, 44.72±4.27 mutant CAG repeats). The ages at death were 77.78±7.46 and 53.72±10.86 years in patients with LoHD and CoHD, respectively (p<0.001). The estimated survival time was 15.21±2.49 years for all HD patients, and 10.74±1.95 and 16.15±2.82 years in patients with LoHD and CoHD, respectively. More mutant CAG repeats and higher AAO were associated with shorter survival (hazard ratio [HR]=1.05, 95% confidence interval [CI]=1.01-1.09, p=0.019; and HR=1.17, 95% CI=1.03-1.31, p=0.013; respectively) for all HD patients. The LoHD group showed no significant factors associated with survival after disease onset, whereas the number of mutant CAG repeats had a significant effect (HR=1.12, 95% CI=1.01-1.23, p=0.034) in the CoHD group. CONCLUSIONS: Survival after disease onset was shorter in patients with LoHD than in those with CoHD. More mutant CAG repeats and higher AAO were associated with shorter survival in patients with HD.

Detection of freezing of gait in Parkinson's disease from foot-pressure sensing insoles using a temporal convolutional neural network.

Backgrounds: Freezing of gait (FoG) is a common and debilitating symptom of Parkinson's disease (PD) that can lead to falls and reduced quality of life. Wearable sensors have been used to detect FoG, but current methods have limitations in accuracy and practicality. In this paper, we aimed to develop a deep learning model using pressure sensor data from wearable insoles to accurately detect FoG in PD patients. Methods: We recruited 14 PD patients and collected data from multiple trials of a standardized walking test using the Pedar insole system. We proposed temporal convolutional neural network (TCNN) and applied rigorous data filtering and selective participant inclusion criteria to ensure the integrity of the dataset. We mapped the sensor data to a structured matrix and normalized it for input into our TCNN. We used a train-test split to evaluate the performance of the model. Results: We found that TCNN model achieved the highest accuracy, precision, sensitivity, specificity, and F1 score for FoG detection compared to other models. The TCNN model also showed good performance in detecting FoG episodes, even in various types of sensor noise situations. Conclusions: We demonstrated the potential of using wearable pressure sensors and machine learning models for FoG detection in PD patients. The TCNN model showed promising results and could be used in future studies to develop a real-time FoG detection system to improve PD patients' safety and quality of life. Additionally, our noise impact analysis identifies critical sensor locations, suggesting potential for reducing sensor numbers.

Establishment of a registry of clinical data and bioresources for rare nervous system diseases.

Rare diseases are predominantly genetic or inherited, and patients with these conditions frequently exhibit neurological symptoms. Diagnosing and treating many rare diseases is a complex challenge, and their low prevalence complicates the performance of research, which in turn hinders the advancement of therapeutic options. One strategy to address this issue is the creation of national or international registries for rare diseases, which can help researchers monitor and investigate their natural progression. In the Republic of Korea, we established a registry across 5 centers that focuses on 3 rare diseases, all of which are characterized by gait disturbances resulting from motor system dysfunction. The registry will collect clinical information and human bioresources from patients with amyotrophic lateral sclerosis, spinocerebellar ataxia, and hereditary spastic paraplegia. These resources will be stored at ICreaT and the National Biobank of Korea. Once the registry is complete, the data will be made publicly available for further research. Through this registry, our research team is dedicated to identifying genetic variants that are specific to Korean patients, uncovering biomarkers that show a strong correlation with clinical symptoms, and leveraging this information for early diagnosis and the development of treatments.

Evaluating the Validity and Reliability of the Korean Version of the Scales for Outcomes in Parkinson's Disease-Cognition.

OBJECTIVE: The Scales for Outcomes in Parkinson's Disease-Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson's disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). METHODS: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test-retest method with an intraclass correlation coefficient (ICC) and Cronbach's alpha coefficient were used to assess reliability. Spearman's rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. RESULTS: The Cronbach's alpha coefficient was 0.797, and the ICC was 0.887. Spearman's rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). CONCLUSION: Our. RESULTS: demonstrate that the K-SCOPA-Cog has good reliability and validity.