Showing papers by "Bing-Wen Soong published in 2008"
TL;DR: It is suggested that leukocyte mtDNA content correlates with the length of GAG repeat and may serve as an index of the severity of poly-Q diseases.
47 citations
TL;DR: The ICA algorithm was able to extract periodic discharges and epileptiform discharges from raw EEG of patients with CJD at as early as 3 to 5 weeks after disease onset, enabling the early diagnosis of CJD.
Abstract: Sporadic Creutzfeldt-Jakob disease (sCJD) is the most common human prion disease. EEG is the method of choice to support the diagnosis of a human prion disease. Periodic sharp wave complexes (PSWCs) on the EEG usually indicate a progressive stage of CJD. However, PSWCs only become obvious at around 8 to 12 weeks after the onset of clinical symptoms, and in a few cases, even later. Independent component analysis (ICA) is a new technique to separate statistically independent components from a mixture of data. This study recruited seven patients who fit the criteria of CJD between 2002 and 2005 and 10 patients with Alzheimer's disease (AD) as control subjects. Using an ICA algorithm, we were able to split typical PSWCs into several independent temporal components in conjunction with spatial maps. The PSWCs were not observed in the initial EEG studies of patients with either AD or CJD. However, the ICA algorithm was able to extract periodic discharges and epileptiform discharges from raw EEG of patients with CJD at as early as 3 to 5 weeks after disease onset. Such discharges otherwise could hardly be discerned by visual inspection. In conclusion, ICA may increase the sensitivity of EEG and facilitate the early diagnosis of CJD.
30 citations
TL;DR: The novel P0G123S mutation is associated with typical findings of late-onset demyelinating polyneuropathy in the electrophysiologic and pathologic studies, putatively resulting from aberrant intracellular trafficking of the mutant P0 protein, which compromises the adhesiveness of the cells.
Abstract: Objectives: To characterize the clinical and cellular phenotypes of a novel MPZ mutation identified in a Chinese family with Charcot–Marie–Tooth (CMT) disease type 1B. Methods: The family was evaluated clinically, electrophysiologically, pathologically, and genetically. The wild-type and mutant P 0 fused with fluorescent proteins were expressed in vitro to monitor their intracellular trafficking. Adhesion assay was also performed to evaluate the adhesiveness of cells. Results: The novel MPZ mutation, c.367G>A, is associated with a late-onset demyelinating CMT phenotype with autosomal dominant inheritance. The median motor nerve conduction velocities of patients in this family ranged from 15.7 to 19.6 m/second. The neuropathologic studies from a sural nerve biopsy revealed a severe loss of myelinated fibers, and some onion bulb formation with clusters of regenerative fibers. Fluorescence analysis demonstrated that the mutant protein was retained ectopically in the endoplasmic reticulum and Golgi apparatus. Adhesion assay demonstrated a defective adhesiveness of cells expressing the mutant P 0 G123S protein. Conclusion: The novel P 0 G123S mutation is associated with typical findings of late-onset demyelinating polyneuropathy in the electrophysiologic and pathologic studies, putatively resulting from aberrant intracellular trafficking of the mutant P 0 protein, which compromises the adhesiveness of the cells.
14 citations
01 Jan 2008
TL;DR: A hierarchical clustering (HC) technique was first applied on the down-sampled data to initialize the model parameters for each tissue cluster followed by automatic segmentation using the expectation maximization (EM) algorithm, which demonstrated that HC-EM is effective in multi-tissue classification on DWI raw data.
Abstract: Tissue segmentation based on diffusion-weighted images (DWI) provides complementary information of tissue contrast to the structural MRI for facilitating the tissue segmentation. In the previous literatures, DWI-based brain tissue segmentation was carried out using the parametric images, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC). However, the information of directions of neural fibers was very limited in the parametric images. To fully utilize the directional information, we propose a novel method to perform tissue segmentation directly on the DWI raw image data. Specifically, a hierarchical clustering (HC) technique was first applied on the down-sampled data to initialize the model parameters for each tissue cluster followed by automatic segmentation using the expectation maximization (EM) algorithm. The whole brain DWI raw data of five normal subjects were analyzed. The results demonstrated that HC-EM is effective in multi-tissue classification on DWI raw data.
10 citations