• Clinical and Experimental Pediatrics
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• v.58 no.4
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• pp.117-122
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• 2015

Global developmental delay (GDD) is a relatively common early-onset chronic neurological condition, which may have prenatal, perinatal, postnatal, or undetermined causes. Family history, physical and neurological examinations, and detailed history of environmental risk factors might suggest a specific disease. However, diagnostic laboratory tests, brain imaging, and other evidence-based evaluations are necessary in most cases to elucidate the causes. Diagnosis of GDD has recently improved because of remarkable advances in genetic technology, but this is an exhaustive and expensive evaluation that may not lead to therapeutic benefits in the majority of GDD patients. Inborn metabolic errors are one of the main targets for the treatment of GDD, although only a small proportion of GDD patients have this type of error. Nevertheless, diagnosis is often challenging because the phenotypes of many genetic or metabolic diseases often overlap, and their clinical spectra are much broader than currently known. Appropriate and cost-effective strategies including up-to-date information for the early identification of the "treatable" causes of GDD are needed for the development of well-timed therapeutic applications with the potential to improve neurodevelopmental outcomes.

• Journal of mucopolysaccharidosis and rare diseases
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• v.4 no.1
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• pp.7-10
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• 2018

Rare diseases are life threatening or chronically debilitating diseases with a low prevalence (less than 2,000 people in a population), which includes lysosomal storage diseases. These diseases are often seen as unimportant especially in developing countries, such as Indonesia, due to small number of patients. National Rare Disease Center in Indonesia was pioneered almost 20 years ago and officially established in 2017 by the Indonesian Minister of Health. Lysosomal storage disease become the most commonly found inborn errors of metabolism (IEM) in Indonesia due to easily accessible diagnostic facilities. Currently there are 7 patients receiving ERT in this mixed-donation scheme, one patient with Gaucher disease and 6 patients with MPS type II. Few challenges for ERT in Indonesia include importation through special access scheme, preparation of ERT infusion in intensive care settting, and cost of treatment. Even with limited resources, healthcare professionals in Indonesia have been giving the best care possible for rare disease patients, especially to provide diagnostic facilities through collaboration and treatment options for treatable rare diseases. Improvements in care for rare disease patients are still needed.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.49-56
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• 2020

This study aims to examine the effects of CF about learners' English writing through FL on English improvement and the factors that influence class satisfaction. For achieving this purpose, response to CF and feelings about CF were selected as predictive variables. It is intended to investigate how these variables predict learners' satisfaction. A total of 94 university students were placed into two groups: 48 experimental group, who received CF on their writing through FL, and 46 control group given traditional instruction. All the participants took pre/post tests including writing tasks, and the experimental group completed a questionnaire after the instructional treatment. The findings indicated that FL affected English improvement and both response to CF and feelings about CF predicted class satisfaction. Based on the findings, this study sheds light on the implications of how to manage the FL class efficiently.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.69-83
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• 2006

This paper describes an image disparity estimation method using a segmented-region based stereo matching. Segmented-region based disparity estimation yields a disparity map as the unit of segmented region. However, there is a problem that it estimates disparity imprecisely. The reason is that because it not only have matching errors but also apply an identical way to disparity estimation, which is not considered each type of matched regions. To solve this problem, we proposes a disparity estimation method which is considered the type of matched regions. That is, the proposed method classifies whole matched regions into similar-matched region, dissimilar-matched region, false-matched region and miss-matched region by analyzing the type of matched regions. We then performs proper disparity estimation for each type of matched regions. This method minimizes the error in estimating disparity which is caused by inaccurate matching and also improves the accuracy of disparity of the well-matched regions. For the purpose of performance evaluations, we perform tests on a variety of scenes for synthetic, indoor and outdoor images. As a result of tests, we can obtain a dense disparity map which has the improved accuracy. The remarkable result is that the accuracy of disparity is also improved considerably for complex outdoor images which are barely treatable in the previous methods.