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실험적 급성 허혈성 신부전에서 Verapamil 이 혈중 Polyamine 동태에 미치는 영향
원동준(Dong Jun Won),권현민(Hyun Min Kwon),김용섭(Yong Seop Kim),구자룡(Ja Ryong Gu),권영주(Young Ju Kwon),조원용(Won Yong Cho),김형규(Hyung Kyu Kim) 대한내과학회 1991 대한내과학회지 Vol.40 No.6
N/A It has been proposd that calcium entry from an external medium increases intracellular free calcium to toxic levels during ischemic acute renal failure, and verapamil (ARF) has been suggested as the agentblocking calicium entry into renal cells and protecting renal function during ischemic injury. Polyamines, anorganic cations that play various roles in normal cellular proliferation and differentiation, accumulate in renal failare. Alsa it has been suggested tht the synthesis and metabolism of polyamine are influence by acute changes of the general condition, such as acute renal failure, and are mediated by a calcium influx into the cells. The study was designed to study the protective effects of systemic verapamil pretreatment on renal function and the influence on polyamine metabolism in experimental ischemic ARF in cats. For these purpose, the experimental animals were divided into 2 groups. While Group I (n=5) was an ischemic ARF model by renal artery clamping for 60 minutes, Group II (n=5) was ischemic ARF with systemic verapamil (5 ml/min/kg) pretreatment. The creatinine clearance and plasma and urinary polyamine were measured in each group before and after the renal artery clamp. The results were as follows: 1) Creatinine clearance before and after the renal artery clamp were 10.64±7.18 ml/min/kg and 2.09±1. 70 ml/min/kg in Group I, 4.47±3.38 ml min/kg and 0.60±0.79ml/min/kg in Group II, respectively, So creatinine clearance decreased more significantly in Group II campared with Group I. 2) Plasma polyamine increased after ischemia in Group I. In group I, plasma levels of putrescine, spermidine, and spermine before ischemia were 4.75±0.40 nmol/ml, 0.69±0.09 nmol/ml, and 0.83±0.63 nmol/ml, were elevated to 7.17±2.91 nmol/ml, 9.83±1.46 nmol/ ml, and 2.64±1.14nmol/ml after ischemia. But in Group II, the plasma level of polyamine was not changed, and especially, spermine decreased significantly from 0.83±0.27 before ischemia to 0.49±0.23 nmol/ml after ischenmine (p=0.033). 3) Urine polyamine excretion decreased after ischemia in Group I and Group II. In Group II, urinary excretion of spermidine and spermine before ischemia, 0.13±0.10 nmol/min and 0.17±0.13nmol/min, decreased after ischemia to 0.01±0.01nmol/min (p=0.019) and 0.032±0.26 nmol/min (p=0.0257). 4) In renal tissue, spermine content vas highest. In Group II, preischemic spermine were 397.20 nmol/g and increased to 646.66nmol/g after ischema, But there were no significant changes in the polyamine contents in Group II. From these data, it was suggested that systemic verapamil pretreatment exerts no protective effect on ischemic ARF. Plasma polyamines are elevated in ischemic ARF, and verapamil may protect these elevations.
원동준(Dong-Jun Won),김선겸(Sun-Kyum Kim),김영훈(Yeonghun Kim),송규원(Gyuwon Song) 한국정보과학회 2021 정보과학회논문지 Vol.48 No.7
최근 미세먼지의 다양한 예측 모델들을 통한 연구가 이루어지고 있지만 현재 PM10 농도 예측에 치중되어 있어 PM2.5 농도를 예측할 수 있는 모델 개발이 필요한 상황이다. 본 논문은 최근 약 2년간의 반월시화국가산업단지의 대기질, 기상, 교통 데이터를 수집하여 미세먼지(PM2.5)와 미세먼지(PM10), 이산화황(SO₂), 이산화질소(NO₂), 일산화탄소(CO), 오존(O₃), 온도, 습도, 풍향, 풍속, 강수량, 도로 구간별 차량속도 변수간의 상관관계 분석 및 회귀분석을 통해 변수의 유의성을 파악하고, 산업단지의 시간대별 PM2.5를 예측하는 데 활용하였다. 인공지능 기반의 Random Forest, XGBoost, LightGBM, Deep neural network과 Voting 모델을 통해 산업단지의 시간별 PM2.5 농도를 예측하고, RMSE를 기준으로 비교분석을 진행하였다. 예측 결과 RMSE는 각각 6.27, 6.41, 6.22, 6.64, 6.12로 각 모델 모두 에어코리아에서 예측하는 모델의 10.77에 비해 매우 높은 성능을 보여주었다. Recently, research on fine dust has been conducted through various prediction techniques. However, currently the research focused on PM10 concentration prediction, and thus it is necessary to develop a model capable of predicting PM2.5 concentration. In this paper, we have collected air quality, weather, and traffic of the Banwol Shihwa National Industrial Complex in the recent two years. The significance of the variable been identified through correlation analysis and regression analysis among PM2.5 and PM10, SO₂, NO₂, CO, O₃, temperature, humidity, wind direction, wind speed, precipitation, road section vehicle speed for each vehicle. Next, the data has been used to predict PM2.5 concentration based on time in the industrial complex. Through the artificial intelligence techniques, Random Forest, XGBoost, LightGBM, Deep neural network and Voting models, PM2.5 concentration industrial complexes been predicted on an hourly basis, and comparative analysis been conducted based on RMSE. As a result of prediction, RMSE was 6.27, 6.41, 6.22, 6.64, and 6.12, respectively, and each technique showed very high performance compared to 10.77 of the technique predicted by Air Korea.