http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
스마트폰 카메라를 이용한 눈꺼풀처짐 환자의 눈꺼풀 깜박임 특성 분석
주태성(Taesung Joo),주진호(Jin-Ho Joo),박인기(In-Ki Park),신재호(Jae-Ho Shin) 대한안과학회 2021 대한안과학회지 Vol.62 No.7
목적: 눈꺼풀처짐 환자군과 정상 대조군 사이의 눈꺼풀 깜박임 특성을 스마트폰을 통해 비교하고자 하였다. 대상과 방법: 눈꺼풀각막반사간거리가 2.5 mm 이하인 노년 널힘줄눈꺼풀처짐 환자군 20명과 정상 대조군 10명을 대상으로 눈꺼풀처짐 유무로 2개 군으로, 환자군을 70세 기준으로 2개 군으로 나누어 대조군 포함 3개 군으로 나누었다. 스마트폰으로 촬영한 사진과 깜박임 영상을 통해 각 군에서 눈꺼풀틈새, 눈꺼풀올림근 기능, 눈꺼풀각막반사간거리, 눈깜박임 사이 간격, 눈깜박임 시간, 분당 눈깜박임 횟수, 눈깜박임 속도를 비교하였다. 결과: 정상 대조군과 비교해 눈꺼풀처짐 환자군에서 눈꺼풀틈새, 눈꺼풀올림근 기능, 눈꺼풀각막반사간거리, 눈깜박임 속도는 작았다. 70세 기준으로 나눈 2개의 환자군과 대조군을 포함한 총 3개 군을 비교했을 때 환자군에서 상기 값들은 나이에 의한 차이는 없었고 대조군에 비해 작았다. 눈꺼풀틈새, 눈꺼풀올림근 기능, 눈꺼풀각막반사간거리가 눈깜박임 속도와 상관관계를 보였으며 눈깜박임 속도의 receiver operating characteristic (ROC) curve에서 area under the receiver operating characteristic (AUROC) curve값이 0.969로 높았고 판별기준점은 32.36 mm/s이었다. 결론: 스마트폰을 통해서도 눈꺼풀 깜박임 운동에 대해 분석할 수 있었으며, 노년 널힘줄눈꺼풀처짐 환자군에서 눈꺼풀틈새, 눈꺼풀올림근 기능, 눈꺼풀각막반사간거리, 눈깜박임 속도가 정상 대조군에 비해 작으며 나이의 영향을 받지 않는 점을 확인할 수 있었다. 또한 눈꺼풀처짐 정도와 눈깜박임 속도 사이의 상관관계와 눈깜박임 속도의 ROC curve를 통해 눈깜박임 속도가 눈꺼풀처짐 진단에 가치가 있을 것이라고 본다. Purpose: To compare eyelid blink characteristics between patients with ptosis and healthy controls using a smartphone camera. Methods: The ptosis group consisted of 20 senile aponeurotic ptosis patients with margin reflex distance1 ≤2.5 mm and the control group consisted of 10 healthy subjects without ptosis. The ptosis group was further divided into two groups based on an age cutoff of 70 years. Palpebral fissure height, levator function, margin reflex distance1, inter-blink interval, blink duration, blink rate, and blink velocity were measured and compared between the three groups based on photographs of the eyelids and videos of blinking taken with a smartphone camera. Results: The palpebral fissure height, levator function, margin reflex distance1, and blink velocity were lower in the ptosis groups than in the control group but these values did not differ between the two ptosis groups. The palpebral fissure height, levator function, and margin reflex distance1 were correlated with blink velocity. In the receiver operating characteristic (ROC) curve of blink velocity, the area under the receiver operating characteristic (AUROC) curve value was as high as 0.969 and the cut-off value was 32.36 mm/s. Conclusions: It is possible to analyze eyelid blink characteristics using a smartphone camera and the results confirmed that palpebral fissure height, levator function, margin reflex distance1, and blink velocity were lower in the senile aponeurotic ptosis group than in the healthy control group and were unaffected by age. Additionally, blink velocity is valuable for diagnosis of ptosis due to the correlation between the degree of ptosis, blink velocity, and the ROC curve of blink velocity.
척추관협착증 환자에서 수중보행 훈련이 운동기능 및 보행에 미치는 효과
고태성(Ko TaeSung),오승준(Oh Seung Jun),고주연(Ko Joo Yeon) 대한치료과학회 2012 대한치료과학회지 Vol.4 No.1
Objective: Investigate the effects of Underwater Gait training on the balance in a spinal stenosis patient. Method: A Patient who had suffered from the spinal stenosis performed gait training with a Underwater Gait training. We tested Underwater Gait training program on subject 30 min per course, 5 days a week for 8 weeks. Before and after the experiments, we measured balance and motor functional ability and got the result of this following. After Underwater Gait training, difference of COP moving area and Kpa of both feet increased. Results: The results of this study showed that gait training with an Underwater treadmill, after training, had meaningful difference of COP moving area and Kpa of both feet. Conclusion: This study showed that gait training with a Underwater treadmill increased balance and motor functional ability that resulted in enhancement of motor performance.
Rank-based clustering analysis for the time-course microarray data.
Yi, Sung-Gon,Joo, Yoon-Jeong,Park, Taesung World Scientific Publishing Company 2009 Journal of bioinformatics and computational biolog Vol.7 No.1
<P>Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course microarray experiments in which gene expression is monitored over time, we are interested in clustering genes that show similar temporal profiles and identifying genes that show a pre-specified candidate profile. Unfortunately, many traditional clustering methods used for analyzing microarray data do not effectively detect temporal profiles for the time-course microarray data. We propose a rank-based clustering analysis for the time-course microarray data. Our clustering method consists of two steps: the first step discretizes the expression data into groups and then transform them into the rank data, the second step performs the rank-based clustering analysis. Our testing procedure uses the bootstrap samples to select the genes that show similar patterns for the candidate profiles. Simulation study is performed to evaluate the performance of the proposed rank-based method. The results are illustrated with the breast cancer data and the Arabidopsis cold stress data.</P>