10 research outputs found

    Point-of-care testing for the detection of SARS-CoV-2: a systematic review and meta-analysis

    Get PDF
    Objective: To evaluate the diagnostic accuracy of the Food and Drug Administration Emergency Use Authorization (FDA-EUA) authorized point-of-care tests (POCTs) for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Materials and methods: A systematic literature search was conducted using the PubMed, Embase, and Web of Science databases for articles published till August 10, 2020. We included studies providing information regarding diagnostic test accuracy of FDA-EUA POCTs for SARS-CoV-2 detection. The methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The review protocol is registered in the International Prospective Register of Systematic Reviews (protocol number CRD42020202248). Results: We included 26 studies describing a total of 3242 samples. The summary sensitivity and specificity were 0.94 [95% confidence interval (CI): 0.88-0.97] and 1.00 (95% CI: 0.99-1.00), respectively. The area under the summary receiver operating characteristic curve was 1.00 (95% CI: 0.99-1.00). A pooled analysis based on the index test revealed a summary sensitivity and specificity of Cepheid Xpert Xpress SARS-CoV-2 [0.99 (95% CI: 0.97-1.00) and 0.99 (95% CI: 0.94-1.00, respectively)] and ID NOW COVID-19 [0.78 (95% CI: 0.74-0.82) and 1.00 (95% CI: 0.98-1.00), respectively]. Conclusions: FDA-EUA POCTs, especially molecular assays, have high sensitivity, specificity, and overall diagnostic accuracy for detecting SARS-CoV-2. If approved, FDA-EUA POCTs can provide a rapid and practical way to identify infected individuals early on and help to limit the strain on the healthcare system. However, more high-quality clinical data are required to support our results.ope

    Cannabidiol for Treating Lennox-Gastaut Syndrome and Dravet Syndrome in Korea

    Get PDF
    Background: For the first time in Korea, we aimed to study the efficacy and safety of cannabidiol (CBD), which is emerging as a new alternative in treating epileptic encephalopathies. Methods: This study was conducted retrospectively with patients between the ages of 2-18 years diagnosed with Lennox-Gastaut syndrome (LGS) or Dravet syndrome (DS) were enrolled from March to October 2019, who visited outpatient unit at 3 and 6 months to evaluate medication efficacy and safety based on caregiver reporting. Additional evaluations, such as electroencephalogram and blood tests, were conducted at each period also. CBD was administered orally at a starting dose of 5 mg/kg/day, and was maintained at 10 mg/kg/day. Results: We analyzed 34 patients in the LGS group and 10 patients in the DS group between the ages of 1.2-15.8 years. In the 3-month evaluation, the overall reduction of seizure frequency in the LGS group was 52.9% (>50% reduction in 32.3% of the cases), and 29.4% in the 6-month evaluation (more than 50% reduction in 20.6%). In DS group, the reduction of seizure frequency by more than 50% was 30% and 20% in the 3-month and 6-month evaluation, respectively. Good outcomes were defined as the reduction of seizure frequency by more than 50% and similar results were observed in both LGS and DS groups. Adverse events were reported in 36.3% of total patients of which most common adverse events were gastrointestinal problems. However, no life-threatening adverse event was reported in both LGS and DS during the observation period. Conclusion: In this first Korean study, CBD was safe and tolerable for use and could be expected to potentially reduce the seizure frequency in pediatric patients with LGS or DS.ope

    Using the lactate-to-albumin ratio to predict mortality in patients with sepsis or septic shock: a systematic review and meta-analysis

    Get PDF
    Objective: This study aimed to investigate whether the lactate-to-albumin ratio (LAR) can predict mortality in patients with sepsis or septic shock. Patients and methods: A systematic search of the PubMed, EMBASE, Web of Science, and Google Scholar databases was conducted on December 16, 2021, for relevant articles that provided the predictive performance of LAR for mortality in patients with sepsis or septic shock. Results: Eight studies encompassing a total of 4,723 patients were included in this paper. The pooled sensitivity, specificity, and diagnostic odds ratio of the LAR for predicting mortality were 0.71 (95% confidence interval [CI]: 0.54-0.84), 0.68 (95% CI: 0.58-0.76) and 5.23 (95% CI: 2.62-10.45), respectively. The area under the summary receiver operating characteristic curve was 0.74 (95% CI: 0.70-0.78). Conclusions: The current evidence suggests that LAR is moderately predictive of mortality among patients with sepsis or septic shock and may be beneficial to identify high-risk patients.ope

    Effects of Cannabidiol on Adaptive Behavior and Quality of Life in Pediatric Patients With Treatment-Resistant Epilepsy

    Get PDF
    Background and purpose: Data regarding the effects of cannabidiol (CBD) on the quality of life (QOL) are currently inadequate. We assessed the QOL of pediatric patients with epilepsy who were treated with CBD. Methods: This prospective, open-label study included pediatric and adolescent patients (aged 2-18 years) with Dravet syndrome or Lennox-Gastaut syndrome. Oral CBD was administered at 10 mg/kg/day. The Korean version of the Quality Of Life in Childhood Epilepsy (QOLCE) questionnaire was administered when CBD treatment began and again after 6 months. Adaptive behavior was measured using the Korean versions of the Child Behavior Checklist (K-CBCL) and the second edition of the Vineland Adaptive Behavior Scales (Vineland-II). Results: This study included 41 patients (11 with Dravet syndrome and 30 with Lennox-Gastaut syndrome), of which 25 were male. The median age was 4.1 years. After 6 months, 26.8% (11/41) of patients experienced a โ‰ฅ50% reduction in the number of seizures. The total score for the QOLCE questionnaire did not change from baseline to after 6 months of CBD treatment (85.71ยฑ39.65 vs. 83.12ยฑ48.01, respectively; p=0.630). The score in the motor skills domain of Vineland-II reduced from 48.67ยฑ13.43 at baseline to 45.18ยฑ14.08 after 6 months of treatment (p=0.005). No other Vineland-II scores and no K-CBCL scores had changed after 6 months of CBD treatment. Conclusions: CBD is an efficacious antiseizure drug used to treat Dravet syndrome and Lennox-Gastaut syndrome. However, it did not improve the patient QOL in our study, possibly because all of our patients had profound intellectual disabilities.ope

    Prognostic Factors for Absence Epilepsy in Childhood

    Get PDF
    Purpose: Childhood absence epilepsy (CAE) is a common form of idiopathic generalized epilepsy with onset middle childhood and has typically a good prognosis, but remission rates vary. We aimed to analyze unfavorable prognostic factors in children initially diagnosed with CAE. Methods: We retrospectively reviewed 48 patients under 13 years of age who were diagnosed with CAE at the Severance Childrenโ€™s Hospital, Seoul, Korea. We analyzed clinical information in cluding comorbidity through neuropsychological test. Results: Thirteen of the 48 patients (27%) showed an unfavorable prognosis, with clinical sei zures or seizure waves on electroencephalogram persistent even after 12 months of anticonvul sant therapy. The mean age at absence seizure onset was 6.51ยฑ2.36 years. The most commonly used antiepileptic drug (AED) was ethosuximide, and the median duration of initial AEDs was 25.63ยฑ24.41 months. The presence of comorbidity and clinical absence seizures after 6 months of AEDs correlated with an unfavorable prognosis. Motor seizures were the most unfavorable prognostic factor during follow-up. Conclusion: This study shows that clinical absence seizures after 6 months of AED, comorbidity, and motor seizure are the most important predictive factors of an unfavorable prognosis for ab sence epilepsy in childhood. This study suggests that when these factors are observed, early in tervention needs to be considered.ope

    Development of Risk Analysis and Coal Production Forecasting Models Applicable to Longwall Mines

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2017. 2. ์ „์„์›.๋กฑ์›” ๊ด‘์‚ฐ์€ ์ง€ํ•˜ ๊ณต๊ฐ„์—์„œ ์ž‘์—…์ด ์ด๋ฃจ์–ด์ง„๋‹ค๋Š” ์ ๊ณผ ๋Œ€๊ทœ๋ชจ ์ž๋™ํ™” ๊ธฐ๊ณ„์žฅ๋น„๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค๋Š” ์ ์—์„œ ์ฑ„ํƒ„ ์ž‘์—…์„ ์ง„ํ–‰ํ•˜๋Š” ๋™์•ˆ ์ˆ˜๋งŽ์€ ๋ฆฌ์Šคํฌ์— ๋…ธ์ถœ๋˜๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋กฑ์›” ๊ด‘์‚ฐ์—์„œ์˜ ๋ฆฌ์Šคํฌ ๋ถ„์„์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์ •์„ฑ์  ๋ฆฌ์Šคํฌ ๋ถ„์„์ด ์ฃผ๋ฅผ ์ด๋ฃจ๊ณ  ์žˆ์œผ๋ฉฐ, ์ตœ๊ทผ ๋‹ค์–‘ํ•œ ๋ถ„์„ ๋ฐฉ๋ฒ•๋“ค์˜ ๊ฐœ๋ฐœโ‹…๋ณด๊ธ‰์œผ๋กœ ๋ฆฌ์Šคํฌ ๋ฐœ์ƒ ๊ฐ€๋Šฅ์„ฑ์ด๋‚˜ ํ”ผํ•ด์ •๋„๋ฅผ ์ˆ˜์น˜์ ์œผ๋กœ ์ œ์‹œํ•˜๋Š” ์ •๋Ÿ‰์  ๋ฆฌ์Šคํฌ ๋ถ„์„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ์ถ”์„ธ์ด๋‹ค. ํ•˜์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„ ํŠน์ • ์‚ฌ๊ฑด์— ๋Œ€ํ•œ ๋ฆฌ์Šคํฌ ๋ถ„์„์— ๊ตญํ•œ๋˜์–ด ์žˆ์œผ๋ฉฐ, ํ”„๋กœ์ ํŠธ ์ „๋ฐ˜์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ด๊ณ  ์ •๋Ÿ‰์ ์ธ ๋ฆฌ์Šคํฌ ๋ถ„์„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง๊นŒ์ง€ ๋ฏธํกํ•œ ์‹ค์ •์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์šฐ์„ , ๋กฑ์›” ๊ด‘์‚ฐ์—์„œ ๋ฐœ์ƒ ๊ฐ€๋Šฅํ•œ ๋ฆฌ์Šคํฌ๋ฅผ ์ฒœ๋ฐ˜ ๋ถ•๋ฝ, ๋ฐ˜ํŒฝ, ๋กฑ์›” ๋ง‰์žฅ ๋ถ•๊ดด, ์ฑ„ํƒ„ ์žฅ๋น„์˜ ํ์ƒ‰ ๋˜๋Š” ๊ณ ์žฅ, ์šด๋ฐ˜ ์žฅ๋น„์˜ ํ์ƒ‰ ๋˜๋Š” ๊ณ ์žฅ, ๋กฑ์›” ํŒจ๋„ ์žฌ๋ฐฐ์น˜ ์ž‘์—… ์ง€์—ฐ, ์ง€๋ฐ˜ ์นจํ•˜, ์ž์—ฐ๋ฐœํ™” ๋“ฑ ์ด 8๊ฐ€์ง€๋กœ ๋ถ„๋ฅ˜ํ•˜๊ณ , ๋ฆฌ์Šคํฌ๋Š” ์œ„ํ—˜์š”์†Œ๊ฐ€ ๋ฐœ์ƒํ•  ํ™•๋ฅ ๊ณผ ๊ทธ๋กœ ์ธํ•œ ํ”ผํ•ด์ •๋„(์˜ํ–ฅ)์˜ ๊ณฑ์ด๋ผ๋Š” ๋ฆฌ์Šคํฌ ์ •์˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฒฐํ•จ์ˆ˜๋ถ„์„์„ ํ†ตํ•ด ๋ฆฌ์Šคํฌ์˜ ๋ฐœ์ƒ ํ™•๋ฅ ์„ ๋ถ„์„ํ•˜๊ณ , ๊ณ„์ธต๋ถ„์„์  ์˜์‚ฌ๊ฒฐ์ •๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋ฆฌ์Šคํฌ์˜ ์˜ํ–ฅ๋„๋ฅผ ๋ถ„์„ํ•œ ๋’ค, ์ด๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ๋ฆฌ์Šคํฌ ๋ ˆ๋ฒจ์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋Ÿ‰์  ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ชจ๋ธ์€ ํ˜ธ์ฃผ์˜ ์Šคํ”„๋ง๋ฒ ์ผ ๊ด‘์‚ฐ๊ณผ ์•™๊ตฌ์Šคํ”Œ๋ ˆ์ด์Šค ๊ด‘์‚ฐ์— ์ ์šฉํ•˜์—ฌ ๋ชจ๋ธ์˜ ์ ํ•ฉ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ฒ€์ฆ์€ ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ชจ๋ธ์— ์˜ํ•ด ์‚ฐ์ •๋œ ๋ฆฌ์Šคํฌ ๋ ˆ๋ฒจ๊ณผ ์ƒ๊ธฐ ๋‘๊ฐœ ๊ด‘์‚ฐ์—์„œ ์ตœ๊ทผ 5๋…„๊ฐ„ ๋ฐœ์ƒํ•œ ๋ฆฌ์Šคํฌ๋“ค๋กœ ์ธํ•œ ๋‹ค์šดํƒ€์ž„๊ณผ์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์Šคํ”„๋ง๋ฒ ์ผ ๊ด‘์‚ฐ๊ณผ ์•™๊ตฌ์Šคํ”Œ๋ ˆ์ด์Šค ๊ด‘์‚ฐ์˜ ๋ฆฌ์Šคํฌ ๋ ˆ๋ฒจ๊ณผ ๋‹ค์šดํƒ€์ž„ ์‚ฌ์ด์˜ ๊ฒฐ์ •๊ณ„์ˆ˜์™€ ํ‰๊ท  ์ œ๊ณฑ๊ทผ ์˜ค์ฐจ๊ฐ€ ๊ฐ๊ฐ 0.955, 2.664์™€ 0.965, 1.795๋กœ ๋‚˜ํƒ€๋‚˜ ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ชจ๋ธ์˜ ์ ํ•ฉ์„ฑ์ด ๋งค์šฐ ์šฐ์ˆ˜ํ•œ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์ผ๋ฐ˜์ ์œผ๋กœ ๋กฑ์›” ๊ด‘์‚ฐ์—์„œ ์ƒ์‚ฐ๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•  ๋•Œ ํŒจ๋„๋ณ„ ์ƒ์‚ฐ๋Ÿ‰์€ ํŒจ๋„์˜ ๊ธธ์ด, ํญ, ๋†’์ด๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์‚ฐ์ •ํ•˜๊ฒŒ ๋˜๋Š”๋ฐ, ์‹ค์ œ ์ƒ์‚ฐ์„ ํ•˜๋‹ค ๋ณด๋ฉด ์ง€์งˆ ์กฐ๊ฑด์˜ ๋ณ€ํ™”๋กœ ์ธํ•ด ์˜ˆ์ƒํ–ˆ๋˜ ์ƒ์‚ฐ๋Ÿ‰๊ณผ ์‹ค์ œ ์ƒ์‚ฐ๋Ÿ‰์— ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๊ฒŒ ๋œ๋‹ค. ํ•˜์ง€๋งŒ, ์ƒ์‚ฐ๊ณ„ํš ์ˆ˜๋ฆฝ ์‹œ ์ด๋Ÿฌํ•œ ์ƒ์‚ฐ๋Ÿ‰์˜ ์ฐจ์ด๋ฅผ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋Ÿ‰์ ์ธ ๊ธฐ์ค€์ด ์—†์–ด ์„ค๊ณ„ ๋‹น์‹œ ๊ณตํ•™์ž์˜ ์ฃผ๊ด€์  ํŒ๋‹จ์— ์˜ํ•ด ์ƒ์‚ฐ ๊ฐ€๋Šฅ ๋ฌผ๋Ÿ‰์„ ์‚ฐ์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ์•”๋ฐ˜๊ณตํ•™ ์‹œ์Šคํ…œ๊ณผ ๋‹ค์ค‘ ์„ ํ˜• ํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ์ง€์งˆ ์กฐ๊ฑด์„ ๊ณ ๋ คํ•œ ํŒจ๋„๋ณ„ ์ƒ์‚ฐ๋Ÿ‰ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์•”๋ฐ˜๊ณตํ•™ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ์—๋Š” ํƒ„์ธต ๋‘๊ป˜๋ณ€ํ™”, ํƒ„์ธต ๋‘๊ป˜, ํƒ„์ธต ๊ฒฝ์‚ฌ, ์ฒœ๋ฐ˜์ƒํƒœ, ๋ฐ”๋‹ฅ์ƒํƒœ, ํƒ„์ธต ๊นŠ์ด ๋“ฑ 6๊ฐœ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋‹ค์ค‘์„ ํ˜• ํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ์—๋Š” ํ”ผ์–ด์Šจ ์ƒ๊ด€๊ณ„์ˆ˜์™€ ๋‹ค์ค‘ ๊ณต์„ ์„ฑ ๋ถ„์„์„ ํ†ตํ•ด ์ƒ๊ด€์„ฑ ๋‚ฎ์€ ํƒ„์ธต ๋‘๊ป˜์™€ ๋†’์€ ๋ถ„์‚ฐํŒฝ์ฐฝ์ง€์ˆ˜๋ฅผ ๋ณด์ด๋Š” ํƒ„์ธต ๋‘๊ป˜๋ณ€ํ™”๋ฅผ ์ œ์™ธํ•œ 4๊ฐœ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์˜ˆ์ธก ๋ชจ๋ธ์˜ ๊ฐœ๋ฐœ ๋ฐ ๊ฒ€์ฆ์—๋Š” ํ˜ธ์ฃผ์˜ ์Šคํ”„๋ง๋ฒ ์ผ ๊ด‘์‚ฐ๊ณผ ์•™๊ตฌ์Šคํ”Œ๋ ˆ์ด์Šค ๊ด‘์‚ฐ์—์„œ ํ™•๋ณดํ•œ 31๊ฐœ์˜ ํŒจ๋„ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, 24๊ฐœ์˜ ํŒจ๋„ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๊ณ , ์˜ˆ์ธก ๋ชจ๋ธ์— ์‚ฌ์šฉ๋˜์ง€ ์•Š์€ 7๊ฐœ์˜ ํŒจ๋„ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์•”๋ฐ˜๊ณตํ•™ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ์€ ๊ฒฐ์ •๊ณ„์ˆ˜ 0.777, ํ‰๊ท  ์ œ๊ณฑ๊ทผ ์˜ค์ฐจ 0.905์˜ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ์œผ๋ฉฐ, ๋‹ค์ค‘ ์„ ํ˜• ํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ์€ ๊ฒฐ์ •๊ณ„์ˆ˜ 0.759, ํ‰๊ท  ์ œ๊ณฑ๊ทผ ์˜ค์ฐจ 1.149์˜ ๊ฒฐ๊ณผ๋ฅผ ์–ป์–ด ์ง€์งˆ์กฐ๊ฑด์„ ๊ณ ๋ คํ•œ ์˜ˆ์ธก ๋ชจ๋ธ์„ ํ†ตํ•ด์„œ ์‹ค์ œ ์ƒ์‚ฐ๋Ÿ‰์— ๊ฐ€๊นŒ์šด ์ƒ์‚ฐ๋Ÿ‰์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค.Longwall mines have been exposed many risks due to working in the underground and using the large-scale automation machinery equipments. Generally, risk analysis in longwall mines is mainly performed by qualitative risk analysis and recently, as various analytical methods have been developed and introduced, researches on quantitative risk analysis, which present numerically likelihood of occurrence or consequence, is increasing. However, most of them are limited for specific events and research on comprehensive and quantitative risk analysis for the overall project is insufficient. In this study, to overcome this limitation, firstly, the risks that can occur in longwall mines are classified into 8 itemsroof fall, floor heave, collapse of longwall face, jammed or broken of coal extraction equipments, jammed or broken of coal transportation equipments, delay of longwall relocation, subsidence and spontaneous combustion. And then a quantitative risk analysis model has been developed using fault tree analysis (FTA) and analytic hierarchy process (AHP) based on the definition of Risk= likelihoodร—consequence. In this model, FTA is used to calculate the risk probability and AHP is used to evaluate the risk impact. The developed risk analysis model is validated by applying it to Springvale and Angus Place coal mines in Australia. The validations are done by correlation analysis between the risk levels calculated by the risk analysis model and the downtime occurrences caused by the risks in the two coal mines over the past 5 years. In conclusion, the coefficient of determination (R2) and root mean square error (RMSE) of Springvale (R2=0.955, RMSE=2.664) and Angus Place (R2=0.965, RMSE=1.795) coal mines have been obtained. These indicate that the risk levels evaluated by risk analysis model closely coincide with the downtime occurrences. Also, generally, when planning the production in the longwall mine, the production of panel is calculated considering the length, width and height of panel. But there is a difference between the expected and the actual production due to the geological conditions. However there is no quantitative criterion that can reflect the difference in production when making the production plan, and therefore it is calculated by the subjective judgement of the engineers. In this study, in order to minimize the errors caused by personal judgement, the production forecasting models considering geological conditions have been developed using rock engineering systems (RES) and multiple linear regression analysis. The RES based model uses 6 parameters (variation in seam thickness, seam thickness, dip of seam, roof quality, floor quality and depth of seam). And in the multiple linear regression based model, 4 parameters except variation in seam thickness (high variation index factor) and seam thickness (low correlation) are used through Pearson correlation coefficient and multi-collinearity analysis. For the development and validation of the production forecasting models, 31 panel data sets are used from the Springvale and Angus Place coal mine. The production forecasting models are developed using 24 panel data sets and validated the suitability of the forecasting models by using 7 panel data sets not used in the forecasting models. In conclusion, the coefficient of determination (R2) and root mean square error (RMSE) for the RES based model (R2=0.777, RMSE=0.905) and the multiple linear regression based model (R2=0.759, RMSE=1.149) have been obtained. These indicate that the production can be forecasted close to actual production through the forecasting models considering geological conditions.1. ์„œ๋ก  1 2. ๋ฆฌ์Šคํฌ ๊ด€๋ฆฌ ๊ฐœ์š” 6 2.1 ๋ฆฌ์Šคํฌ ์ •์˜ ๋ฐ ๊ด€๋ จ ๊ฐœ๋… 6 2.1.1 ๋ฆฌ์Šคํฌ ์ •์˜ 6 2.1.2 ๋ฆฌ์Šคํฌ ๊ด€๋ จ ๊ฐœ๋… 9 2.2 ๋ฆฌ์Šคํฌ ๊ด€๋ฆฌ 10 2.2.1 ๋ฆฌ์Šคํฌ ๊ด€๋ฆฌ ๊ณ„ํš ์ˆ˜๋ฆฝ 13 2.2.2 ๋ฆฌ์Šคํฌ ์‹๋ณ„ 13 2.2.3 ์ •์„ฑ์  ๋ฆฌ์Šคํฌ ๋ถ„์„ ์ˆ˜ํ–‰ 14 2.2.4 ์ •๋Ÿ‰์  ๋ฆฌ์Šคํฌ ๋ถ„์„ ์ˆ˜ํ–‰ 14 2.2.5 ๋ฆฌ์Šคํฌ ๋Œ€์‘ ๊ณ„ํš ์ˆ˜๋ฆฝ 15 2.2.6 ๋ฆฌ์Šคํฌ ๋ชจ๋‹ˆํ„ฐ๋ง ๋ฐ ํ†ต์ œ 15 2.3 ์ž์›๊ฐœ๋ฐœ ํ”„๋กœ์ ํŠธ์—์„œ์˜ ๋ฆฌ์Šคํฌ ๋ถ„์„ ์‚ฌ๋ก€ 17 2.3.1 ์ •์„ฑ์  ๋ฆฌ์Šคํฌ ๋ถ„์„ ์‚ฌ๋ก€ 17 2.3.2 ์ •๋Ÿ‰์  ๋ฆฌ์Šคํฌ ๋ถ„์„ ์‚ฌ๋ก€ 21 2.4 ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ฐฉ๋ฒ• ์„ ์ • 29 2.4.1 ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ฐฉ๋ฒ• ๋น„๊ต 29 2.3.2 ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ฐฉ๋ฒ• ์„ ์ • 31 3. ๋กฑ์›” ๊ด‘์‚ฐ์—์„œ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•  ๋ฆฌ์Šคํฌ ์š”์ธ 32 3.1 ๋กฑ์›” ์ฑ„ํƒ„ ๊ฐœ์š” 32 3.1.1 ํŒจ๋„ ๊ตด์ง„ 34 3.1.2 ๋กฑ์›” ์ฑ„ํƒ„ 37 3.1.3 ์„ํƒ„ ์šด๋ฐ˜ 40 3.2 ์ง€์งˆ ์กฐ๊ฑด์— ๋”ฐ๋ฅธ ๋ฆฌ์Šคํฌ ์š”์ธ 41 3.2.1 ์•”๋ฐ˜์—ญํ•™์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๋ฆฌ์Šคํฌ ์š”์ธ 42 3.2.2 ์ง€์ธต ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๋ฆฌ์Šคํฌ ์š”์ธ 50 3.3 ์„ค๊ณ„ ์กฐ๊ฑด์— ๋”ฐ๋ฅธ ๋ฆฌ์Šคํฌ ์š”์ธ 54 3.3.1 ํŒจ๋„ ์„ค๊ณ„ 54 3.3.2 ์ฑ„์ค€๊ฐฑ๋„ ๊ฐœ์„ค ๋ฐฉ๋ฒ• 55 3.3.3 ํ•„๋ผ ์„ค๊ณ„ 57 3.3.4 ์ฑ„ํƒ„๊ธฐ ์„ค๊ณ„ 60 3.3.5 ์ฒœ๋ฐ˜์ง€์ง€๋Œ€ ์„ค๊ณ„ 63 3.3.6 AFC ์„ค๊ณ„ 65 3.4 ์ƒ์‚ฐ๊ด€๋ฆฌ ์กฐ๊ฑด์— ๋”ฐ๋ฅธ ๋ฆฌ์Šคํฌ ์š”์ธ 67 3.4.1 ๊ด‘์‚ฐ ์•ˆ์ „ ๊ด€๋ จ ๋ฆฌ์Šคํฌ ์š”์ธ 68 3.4.2 ์žฅ๋น„ ๊ด€๋ จ ๋ฆฌ์Šคํฌ ์š”์ธ 80 3.4.3 ๊ด‘์‚ฐ ์šด์˜ ๊ด€๋ จ ๋ฆฌ์Šคํฌ ์š”์ธ 81 4. ๋กฑ์›” ๊ด‘์‚ฐ์— ์ ์šฉ ๊ฐ€๋Šฅํ•œ ๋ฆฌ์Šคํฌ ๋ถ„์„ ๋ชจ๋ธ ๊ฐœ๋ฐœ 84 4.1 ๊ฒฐํ•จ์ˆ˜๋ถ„์„์„ ํ†ตํ•œ ๋ฆฌ์Šคํฌ ๋ฐœ์ƒ ํ™•๋ฅ  ๋ถ„์„ 84 4.1.1 ๊ฒฐํ•จ์ˆ˜๋ถ„์„ ๊ฐœ์š” 84 4.1.2 ์ง€๋ฐ˜ ๊ด€๋ จ ๋ฆฌ์Šคํฌ ์„ ์ • 89 4.1.3 ๋กฑ์›” ์žฅ๋น„ ๊ด€๋ จ ๋ฆฌ์Šคํฌ ์„ ์ • 97 4.1.4 ํ™˜๊ฒฝ ๊ด€๋ จ ๋ฆฌ์Šคํฌ ์„ ์ • 103 4.1.5 ๋ฆฌ์Šคํฌ ๋ฐœ์ƒ ํ™•๋ฅ  ๋ถ„์„ 110 4.2 ๊ณ„์ธต๋ถ„์„์  ์˜์‚ฌ๊ฒฐ์ •๋ฐฉ๋ฒ•์„ ํ†ตํ•œ ๋ฆฌ์Šคํฌ ์˜ํ–ฅ๋„ ๋ถ„์„ 113 4.2.1 ๊ณ„์ธต๋ถ„์„์  ์˜์‚ฌ๊ฒฐ์ •๋ฐฉ๋ฒ• ๊ฐœ์š” 113 4.2.2 ๊ณ„์ธต๋ถ„์„์  ์˜์‚ฌ๊ฒฐ์ •๋ฐฉ๋ฒ•์˜ ์ ˆ์ฐจ ๋ฐ ๋ฐฉ๋ฒ• 114 4.2.3 ๋ฆฌ์Šคํฌ ์˜ํ–ฅ๋„ ๋ถ„์„ 120 4.3 ๋ฆฌ์Šคํฌ ๋ถ„์„๋ชจ๋ธ์˜ ํ˜„์žฅ ์ ์šฉ 123 4.3.1 ํ˜ธ์ฃผ ์Šคํ”„๋ง๋ฒ ์ผ ์œ ์—ฐํƒ„ ๊ด‘์‚ฐ 123 4.3.2 ํ˜ธ์ฃผ ์•™๊ตฌ์Šคํ”Œ๋ ˆ์ด์Šค ์œ ์—ฐํƒ„ ๊ด‘์‚ฐ 141 4.3.3 ํ˜„์žฅ ์ ์šฉ ์‚ฌ๋ก€์— ๋Œ€ํ•œ ๋น„๊ต ๋ถ„์„ 157 5. ์ง€์งˆ ์กฐ๊ฑด์„ ๊ณ ๋ คํ•œ ์ƒ์‚ฐ๋Ÿ‰ ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ 159 5.1 ์•”๋ฐ˜๊ณตํ•™์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ 159 5.1.1 ์•”๋ฐ˜๊ณตํ•™ ์‹œ์Šคํ…œ ๊ฐœ์š” 159 5.1.2 ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ ์ ˆ์ฐจ ๋ฐ ๋ฐฉ๋ฒ• 161 5.1.3 ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ 171 5.2 ๋‹ค์ค‘ ์„ ํ˜• ํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ 174 5.2.1 ํšŒ๊ท€๋ถ„์„ ๊ฐœ์š” 174 5.2.2 ๋ณ€์ˆ˜๋“ค ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 177 5.2.3 ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ 183 5.3 ์˜ˆ์ธก ๋ชจ๋ธ์˜ ํ˜„์žฅ ์ ์šฉ 186 6. ๊ฒฐ ๋ก  191 ์ฐธ๊ณ ๋ฌธํ—Œ 196 Appendix A. Overview of longwall equipments 207 Appendix B. Questionnaire sheet 217 Appendix C. Results of questionnaire 250 Abstract 259Docto

    Potential role of stress-induced gluconeogenesis in disease aggravation and mortality in pyruvate dehydrogenase deficiency: A case-based hypothesis

    No full text
    Pyruvate dehydrogenase (PDH) deficiency is an inherited metabolic disorder caused by a defect in any subunit of the pyruvate dehydrogenase complex (PDHC), which has an essential role in glucose metabolism. The causes of disease progression in PDH deficiency are not fully understood yet. Based on repeated observations of a patient with PDH deficiency at our center, we hypothesized that stress-induced gluconeogenesis contributes to rapid exacerbation of the disease. This link has not been established previously.restrictio

    Paediatric Trauma Score as a non-imaging tool for predicting intracranial haemorrhage in patients with traumatic brain injury

    No full text
    To identify a useful non-imaging tool to screen paediatric patients with traumatic brain injury for intracranial haemorrhage (ICH). We retrospectively analysed patients aged < 15 years who visited the emergency department with head trauma between January 2015 and September 2020. We divided patients into two groups (ICH and non-ICH) and compared their demographic and clinical factors. Among 85 patients, 21 and 64 were in the ICH and non-ICH groups, respectively. Age (p = 0.002), Pediatric trauma score (PTS; p < 0.001), seizure (p = 0.042), and fracture (p < 0.001) differed significantly between the two groups. Factors differing significantly between the groups were as follows: age (odds ratio, 0.84, p = 0.004), seizure (4.83, p = 0.013), PTS (0.15, p < 0.001), and fracture (69.3, p < 0.001). Factors with meaningful cut-off values were age (cut-off [sensitivity, specificity], 6.5 [0.688, 0.714], p = 0.003) and PTS [10.5 (0.906, 0.81), p < 0.001]. Based on the previously known value for critical injury (โ‰ค 8 points) and the cut-off value of the PTS identified in this study (โ‰ค 10 points), we divided patients into low-risk, medium-risk, and high-risk groups; their probabilities of ICH (95% confidence intervals) were 0.16-12.74%, 35.86-89.14%, and 100%, respectively. PTS was the only factor that differed significantly between mild and severe ICH cases (p = 0.012). PTS is a useful screening tool with a high predictability for ICH and can help reduce radiation exposure when used to screen patient groups before performing imaging studies.ope

    Neutrophil-to-lymphocyte ratio for the diagnosis of pediatric acute appendicitis: a systematic review and meta-analysis

    Get PDF
    Objective: Acute appendicitis (AA) is one of the most common surgical emergencies and causes of acute abdominal pain in the pediatric population. However, it can be difficult to diagnose in children. We aimed to provide updated evidence on the diagnostic utility of the neutrophil-to-lymphocyte ratio (NLR) for AA, along with other conventional biomarkers, in pediatric patients. Materials and methods: We searched the PubMed, Embase, Cochrane Library, and Web of Science databases for eligible articles published up to May 16, 2021. Results: We included 19 studies comprising a total of 5,974 pediatric cases. The overall sensitivity and specificity of the NLR were 0.82 (95% confidence interval [CI]: 0.79-0.85) and 0.76 (95% CI: 0.69-0.81), respectively. The overall diagnostic odds ratio was 14.34 (95% CI: 9.05-22.73). The area under the summary receiver operating characteristic curve was 0.86 (95% CI: 0.83-0.89). The pooled sensitivity and specificity of other biomarkers were as follows: 0.79 (95% CI: 0.71-0.86) and 0.66 (95% CI: 0.54-0.77) for the white blood cell count, 0.73 (95% CI: 0.69-0.77) and 0.68 (95% CI: 0.55-0.79) for the C-reactive protein level, 0.75 (95% CI: 0.65-0.82) and 0.78 (95% CI: 0.72-0.83) for the absolute neutrophil count, and 0.83 (95% CI: 0.79-0.87) and 0.68 (95% CI: 0.53-0.80) for the neutrophil percentage, respectively. Conclusions: The NLR has moderate predictive power for AA and can be used as a simple, auxiliary tool for diagnosis. NLR can also help clinicians decide whether to perform imaging testing when the clinical symptoms or physical examination findings are vague.ope

    Age-adjusted quick Sequential Organ Failure Assessment score for predicting mortality and disease severity in children with infection: a systematic review and meta-analysis

    Get PDF
    We assessed the diagnostic accuracy of the age-adjusted quick Sequential Organ Failure Assessment score (qSOFA) for predicting mortality and disease severity in pediatric patients with suspected or confirmed infection. We conducted a systematic search of PubMed, EMBASE, the Cochrane Library, and Web of Science. Eleven studies with a total of 172,569 patients were included in the meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio of the age-adjusted qSOFA for predicting mortality and disease severity were 0.69 (95% confidence interval [CI] 0.53-0.81), 0.71 (95% CI 0.36-0.91), and 6.57 (95% CI 4.46-9.67), respectively. The area under the summary receiver-operating characteristic curve was 0.733. The pooled sensitivity and specificity for predicting mortality were 0.73 (95% CI 0.66-0.79) and 0.63 (95% CI 0.21-0.92), respectively. The pooled sensitivity and specificity for predicting disease severity were 0.73 (95% CI 0.21-0.97) and 0.72 (95% CI 0.11-0.98), respectively. The performance of the age-adjusted qSOFA for predicting mortality and disease severity was better in emergency department patients than in intensive care unit patients. The age-adjusted qSOFA has moderate predictive power and can help in rapidly identifying at-risk children, but its utility may be limited by its insufficient sensitivity.ope
    corecore