• Title/Summary/Keyword: 카파값

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Concordance of Seropositivity between Helicobacter pylori and Hepatitis A Virus IgG in Children of Gwangju and Chonnam Area (광주.전남 지역에 거주하는 소아에서 Helicobacter pylori와 A형 간염 바이러스의 혈청 IgG 항체 양성률 비교)

  • Kim, Seon-Young;Kim, Young-Ok;Chung, Hae-Yul;Kim, Byung-Ju;Ma, Jae-Sook
    • Pediatric Gastroenterology, Hepatology & Nutrition
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    • v.4 no.2
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    • pp.191-198
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    • 2001
  • Purpose: This study was undertaken to investigate the seroepidemiologic pattern of Helicobacter pylori (H. pylori) and hepatitis A virus (HAV) infections in children. Methods: A total of 315 serum samples were obtained from healthy children, living in Gwangju and Chonnam area. All serum samples were assayed for H. pylori IgG level using enzyme immunoassay techniques. HAV IgG level in serum were tested by a competitive radio-immunoassay in 215 subjects. The age-specific seroprevalence of H. pylori and HAV was separately analysed. The concordance of seropositivity and seronegativity between H. pylori and HAV infection was examined by the kappa statistic analysis. Results: Seropositivity was found in 17.5% (55/315) and 30.2% (65/215) of the subjects for H. pylori and HAV, respectively. Cross-tabulation of these data showed that 21 subjects (9.8%) were seropositive and 135 (62.8%) were seronegative for both H. pylori and HAV, 15 (7.0%) were seropositive for only H. pylori and 44 (20.5%) for only HAV. The seroprevalence of H. pylori and HAV increased significantly with age. There was a slight agreement between H. pylori and HAV seropositivity (${\kappa}$=0.26). Conclusion: This study shows a slight similarity in the concordance of seropositivity and seronegativity between H. pylori and HAV infection and provides evidence that H. pylori and HAV may share a common mode of transmission.

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Automatic Detection of Stage 1 Sleep (자동 분석을 이용한 1단계 수면탐지)

  • 신홍범;한종희;정도언;박광석
    • Journal of Biomedical Engineering Research
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    • v.25 no.1
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    • pp.11-19
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    • 2004
  • Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50% or the relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen's Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70%. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3% in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.