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Akademický článek

Advances in cell transplantation therapy and gene therapy in Parkinson's disease

Autor: XU Bao⁃lei, CHAN Piu

Publikováno v: Chinese Journal of Contemporary Neurology and Neurosurgery, Vol 22, Iss 4, Pp 243-252 (2022)

The role of cell transplantation therapy and gene therapy in the treatment of Parkinson's disease (PD) has attracted more and more attention. It has developed from laboratory research tools to clinical products for patients. The purpose of cell trans

Externí odkaz: https://doaj.org/article/37a53ddcce5a45a5ad1770eaf87f2ff1

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Akademický článek

Importance of digital health in patient management of Parkinson's disease

Autor: CHAN Piu, ZHANG Qi⁃hao

Publikováno v: Chinese Journal of Contemporary Neurology and Neurosurgery, Vol 22, Iss 3, Pp 127-130 (2022)

With the continuous iteration of technological innovation, the medical and health industry ushered in a new stage of digital health development. This article expounds the connotation and application of digital health, and discusses the development, a

Externí odkaz: https://doaj.org/article/e6c4b32c0b074c918cb187a0677ee1c6

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Akademický článek

Brain pathological analysis of Parkinson's disease combined with cerebral small vessel disease: one case report and literature review

Autor: CAO Ling⁃xiao, DONG Wei, LI Yuan, LI Shao⁃xiang, CHAN Piu, HUANG Yue

Publikováno v: Chinese Journal of Contemporary Neurology and Neurosurgery, Vol 21, Iss 04, Pp 255-261 (2021)

Objective To report on the pathological characteristics of brain tissue in a case of Parkinson's disease (PD) with cerebral small vessel disease (CSVD). Methods and Results The male patient, 69 years old, was suddenly died. His brain tissue was donat

Externí odkaz: https://doaj.org/article/53670ddf508e49999d5e6816474229b6

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Report

BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning

Autor: Yang, Yanwu, Ye, Chenfei, Su, Guinan, Zhang, Ziyao, Chang, Zhikai, Chen, Hairui, Chan, Piu, Yu, Yue, Ma, Ting

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical

Externí odkaz: http://arxiv.org/abs/2403.01433