Evaluating Korean Personal Assistance Services Classification System

Objective To evaluate the utility of using the Personal Assistance Services classification system (PAS-CS) that examines individuals with disabilities for services and government funding. To this end, this study also tests for significant differences in PAS-CS scores across disability grades and disability types. Methods A retrospective analysis was conducted using the 2014 National Survey on People with Disabilities (NSPD) data set. We selected patients with three types of disabilities (physical disabilities, brain lesions, and visual impairments). We compared the average PAS-CS scores of patients with different disability types and grades using general linear models with multiple comparisons. Results A total of 4,810 patients were included in the analysis. Patients with brain lesions had the highest average PAS-CS scores in activities of daily living (ADL) and instrumental activities of daily living (IADL) domains. Patients with visual impairments had the highest average scores in ‘Disease-specific disability’ and ‘Social-environment’ domains. For patients with physical disabilities and visual impairments, no PAS-CS domains were significantly different between patients with disability grade III and those with disability grade IV (p>0.05). Conclusion The PAS-CS scores of disability grades were not equivalent among individuals with different disability types. The Korean Ministry of Health and Welfare currently only considers certain disability grades for PAS preeligibility, as a result disregarding the characteristics of different disability types. Thus, the current PAS-CS requires modifications

Deep Learning-Based Ultrasonic Testing to Evaluate the Porosity of Additively Manufactured Parts with Rough Surfaces

Ultrasonic testing (UT) has been actively studied to evaluate the porosity of additively manufactured parts. Currently, ultrasonic measurements of as-deposited parts with a rough surface remain problematic because the surface lowers the signal-to-noise ratio (SNR) of ultrasonic signals, which degrades the UT performance. In this study, various deep learning (DL) techniques that can effectively extract the features of defects, even from signals with a low SNR, were applied to UT, and their performance in terms of the porosity evaluation of additively manufactured parts with rough surfaces was investigated. Experimentally, the effects of the processing conditions of additive manufacturing on the resulting porosity were first analyzed using both optical and scanning acoustic microscopy. Second, convolutional neural network (CNN), deep neural network, and multi-layer perceptron models were trained using time-domain ultrasonic signals obtained from additively manufactured specimens with various levels of porosity and surface roughness. The experimental results showed that all the models could evaluate porosity accurately, even that of the as-deposited specimens. In particular, the CNN delivered the best performance at 94.5%. However, conventional UT could not be applied because of the low SNR. The generalization performance when using newly manufactured as-deposited specimens was high at 90%

Clinical and biological implications of CD133-positive and CD133-negative cells in glioblastomas

A number of recent reports have demonstrated that only CD133-positive cancer cells of glioblastoma multiforme (GBM) have tumor-initiating potential. These findings raise an attractive hypothesis that GBMs can be cured by eradicating CD133-positive cancer stem cells (CSCs), which are a small portion of GBM cells. However, as GBMs are known to possess various genetic alterations, GBMs might harbor heterogeneous CSCs with different genetic alterations. Here, we compared the clinical characteristics of two GBM patient groups divided according to CD133-positive cell ratios. The CD133-low GBMs showed more invasive growth and gene expression profiles characteristic of mesenchymal or proliferative subtypes, whereas the CD133-high GBMs showed features of cortical and well-demarcated tumors and gene expressions typical of proneuronal subtype. Both CD133-positive and CD133-negative cells purified from four out of six GBM patients produced typical GBM tumor masses in NOD-SCID brains, whereas brain mass from CD133-negative cells showed more proliferative and angiogenic features compared to that from CD133-positive cells. Our results suggest, in contrast to previous reports that only CD133-positive cells of GBMs can initiate tumor formation in vivo CD133-negative cells also possess tumor-initiating potential, which is indicative of complexity in the identification of cancer cells for therapeutic targeting