• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.368-371
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• 1996

Background: Recently, measure of heart rate variability and the nonlinear "complexity" of heart rate dynamics have been used as indicators of cardiovascular health. Several investigators have demonstrated that heart rate variability decreased in aging, congestive heart failure and coronary heart disease. Because hypertensive patients showed alternation of cardiovascular homeostasis, we designed this study to evaluate the effect of anesthesia in hypertensive patients with approximate entropy (ApEn). Methods: With informed consent, eighteen normotensive patients and eighteen hypertensive patients were given no premedication. ECG data were collected from 10 minutes before induction to 15 minutes after induction. Collected ECG data were stored into computer binary files. We calculated ApEn from the collected ECG data. Results: Before induction, ApEn of hypertensive patients was significantly lower than normotensive patients(p<0.05). During induction and maintain of anesthesia, there was no difference of ApEn between two groups. During induction and maintain of anesthesia, in normotensive group, ApEn was significantly lower than that of preinduction(p<0.05). And ApEn during maintain of anesthesia was lower than that of induction(p<0.05). During maintain of anesthesia, in hypertensive group, ApEn was significantly lower than that of preinduction(p<0.05). Conclusions: Before induction, ApTn of hypertensive patients is significantly lower than normotensive patients. As anesthesia was deepened, ApEn of two groups were decreased. Because the baroreflex of hypertensive patients is already decreased, decreasing of ApEn of hypertensive patients during anesthesia is less than that of normotnesive patients.

• 환경영향평가
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• 제30권6호
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• pp.355-360
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• 2021

국내 물관리정책은 농도 중심의 물관리에서 부하량 관리 중심의 오염총량관리로 전환되면서 수질 및 수생태계에 변화를 가져왔다. 하지만 오염원의 변화와 유량 변동 등으로 총량제 시행 이후 수질 및 수생태계 건강성이 개선되었는지 판단하기 어려워 로그선형모델과 생물지표(저서성 대형무척추동물)를 활용하여 효과를 분석하였다. 그 결과 진위천수계의 BOD와 T-P농도는 각각 30%, 35% 저감되어 수질개선 효과를 보였으나, 저서생물지수(BMI) 결과 D등급에서 E등급으로 악화되었다. 진위천수계의 수생태계 건강성을 개선하기 위해서 생태하천을 가꾸는 노력이 필요하다.

• Journal of Ginseng Research
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• 제43권1호
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• pp.10-19
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• 2019

Background: Frequent overdose of paracetamol (APAP) has become the major cause of acute liver injury. The present study was designed to evaluate the potential protective effects of ginsenoside Rk1 on APAP-induced hepatotoxicity and investigate the underlying mechanisms for the first time. Methods: Mice were treated with Rk1 (10 mg/kg or 20 mg/kg) by oral gavage once per d for 7 d. On the 7th d, allmice treated with 250mg/kg APAP exhibited severeliverinjury after 24 h, and hepatotoxicitywas assessed. Results: Our results showed that pretreatment with Rk1 significantly decreased the levels of serum alanine aminotransferase, aspartate aminotransferase, tumor necrosis factor, and interleukin-$1{\beta}$ compared with the APAP group. Meanwhile, hepatic antioxidants, including superoxide dismutase and glutathione, were elevated compared with the APAP group. In contrast, a significant decrease in levels of the lipid peroxidation product malondialdehyde was observed in the ginsenoside Rk1-treated group compared with the APAP group. These effects were associated with a significant increase of cytochrome P450 E1 and 4-hydroxynonenal levels in liver tissues. Moreover, ginsenoside Rk1 supplementation suppressed activation of apoptotic pathways by increasing Bcl-2 and decreasing Bax protein expression levels, which was shown using western blotting analysis. Histopathological observation also revealed that ginsenoside Rk1 pretreatment significantly reversed APAP-induced necrosis and inflammatory infiltration in liver tissues. Biological indicators of nitrative stress, such as 3-nitrotyrosine, were also inhibited after pretreatment with Rk1 compared with the APAP group. Conclusion: The results clearly suggest that the underlying molecular mechanisms in the hepatoprotection of ginsenoside Rk1 in APAP-induced hepatotoxicity may be due to its antioxidation, antiapoptosis, anti-inflammation, and antinitrative effects.

• 한국산업보건학회지
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• 제29권2호
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• pp.195-207
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• 2019

Objectives: This study was performed to confirm whether plasma lead can be used as a chronic biomarker for the biological monitoring of exposure to lead. Methods: Lead concentrations in 66 plasma samples from retired lead workers (G.M. 60.25 years, Median 61.00 years) and 42 plasma samples from the general population (G.M. 53.76 years, Median 56.50 years) were measured using ICP/Mass. Tibia, whole blood, hemoglobin, hematocrit, and blood zinc protophorphyrin (ZPP) concentrations and urinary ${\delta}$-aminolevulinic acid (${\delta}-ALA$) were measured for correlation analysis with plasma lead. Results: The geometric mean concentration of lead in plasma was $0.23{\mu}g/L$ for the retired lead workers and $0.10{\mu}g/L$ for the general population sample. A simple correlation analysis of biomarkers showed that plasma lead concentration among the retired lead workers was highly correlated with lead concentration in the tibia and with blood lead concentration, and the plasma lead concentration among the general population correlated with ZPP concentration in the blood. The lead concentration in the tibia and the lead concentration in the whole blood increased with length of working period. As the period in the lead workplace increased, the ratio of lead in plasma to lead concentration in whole blood decreased. Conclusion: This study confirmed the possibility of a chronic biomarker of lead concentration in blood plasma as a biomarker. In the future, comparative studies with specific indicators will lead to more fruitful results.

• 한방비만학회지
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• 제18권2호
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• pp.83-95
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• 2018

Objectives: The aim of this study was optimized mixing ratio of mulberry leaf, mulberry fruit, and silkworm for amelioration of the metabolic syndrome by using response surface method (RSM). Methods: Antioxidant, antidiabetic and antihypertensive activities of fifteen mixed powder of mulberry leaf, mulberry fruit, and silkworm by RSM were measured as indicators of metabolic syndrome. Results: The optimal mixing ratio of mulberry leaves, mulberry fruits, and silkworm with the greatest antioxidant, antidiabetic and antihypertensive activities was as follows: 2.5890 of mulberry leaf (A), 0.1222 of mulberry fruit (B), 2.9999 of silkworm (C). At this time, it was predicted that the total polyphenol content was estimated to be 185.51 tannic acid equivalent mg/g, 1, 1-diphenyl-2-picrylhy drazyl radical scavenging activity 84.77%, 1-deoxynojirimycin content 415.66 mg/100 g, ${\alpha}-glucosidase$ inhibitory activity 64.31%, ${\gamma}-aminobutyric$ acid content 267.77 mg/100 g, potassium content 1,899.11 mg/100 g, and angiotensin-converting-enzyme inhibitory activity was estimated to be 73.78%. Conclusions: It was concluded that the significant effect of the mulberry leaf, mulberry fruit, and silkworm on the metabolic syndrome-related biological activity indices (antioxidant activity, antidiabetic activity and antihypertensive activity) was as follows: 2.5890 of mulberry leaf (A), 0.1222 of mulberry fruit (B), 2.9999 of silkworm (C).

• 한국물환경학회지
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• 제36권6호
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• pp.535-545
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• 2020

This study aims to conserve and monitor energy use in public sewage treatment plants by utilizing data from the SCADA system and by controlling the aeration rate required for maintaining effluent water quality. Power consumption in the sewage treatment process was predicted using the equipment's uptime, efficiency, and inherent power consumption. The predicted energy consumption was calibrated by measured data. Additionally, energy efficiency indicators were proposed based on statistical data for energy use, capacity, and effluent quality. In one case study, a sewage treatment plant operated via the SBR process used ~30% of energy consumed in maintaining the bioreactors and treated water tanks (included decanting pump and cleaning systems). Energy consumption analysis with the K-ECO Tool-kit was conducted for unit processing. The results showed that about 58.7% of total energy consumed was used in the preliminary and biological treatment rotating equipment such as the blower and pump. In addition, the energy consumption rate was higher to the order of 19.2% in the phosphorus removal process, 16.0% during sludge treatment, and 6.1% during disinfection and discharge. In terms of equipment energy usage, feeding and decanting pumps accounted for 40% of total energy consumed following 27% for blowers. By controlling the aeration rate based on the proposed feedback control system, the DO concentration was reduced by 56% compared pre-controls and the aeration amount decreased by 28%. The overall power consumption of the plant was reduced by 6% via aeration control.

• Journal of Dental Anesthesia and Pain Medicine
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• 제21권2호
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• pp.155-165
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• 2021

Background: This study aimed to assess the course of anxiety and pain during lower third molar (LTMo) surgery and explore the role of mobile and single-channel electroencephalography under clinical and surgical conditions. Methods: The State-Trait Anxiety Inventory (STAI), Corah's Dental Anxiety Scale (DAS), and Interval Scale of Anxiety Response (ISAR) were used. The patient self-rated anxiety (PSA), the pain felt during and after surgery, EEG, heart rate (HR), and blood pressure (BP) were assessed. Results: The Attention (ATT) and Meditation (MED) algorithms and indicators evaluated in this study showed several associations. ATT showed interactions and an association with STAI-S, pain during surgery, PSA level, HR, and surgical duration. MED showed an interaction and association with DAS, STAI-S, and pain due to anesthesia. Preclinical anxiety parameters may influence clinical perceptions and biological parameters during LTMo surgeries. High STAI-Trait and PSA scores were associated with postoperative pain, whereas high STAI-State scores were associated with more pain during anesthesia and surgery, as well as DAS, which was also associated with patient interference during surgery due to anxiety. Conclusions: The findings suggest that single-channel EEG is promising for evaluating brain responses associated with systemic reactions related to anxiety, surgical stress, and pain during oral surgery.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• 대한의용생체공학회:의공학회지
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• 제43권4호
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• pp.290-297
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• 2022

During surgery, Surgical instruments are often left behind due to accidents. Most of these are surgical gauze, so radioactive non-permeable gauze (X-ray gauze) is used for preventing of accidents which gauze is left in the body. This gauze is divided into wire and pad type. If it is confirmed that the gauze remains in the body, gauze must be detected by radiologist's reading by imaging using a mobile X-ray device. But most of operating rooms are not equipped with a mobile X-ray device, but equipped C-Arm equipment, which is of poorer quality than mobile X-ray equipment and furthermore it takes time to read them. In this study, Use C-Arm equipment to acquire gauze image for detection and Build dataset using artificial intelligence and select a detection model to Assist with the relatively low image quality and the reading of radiology specialists. mAP@50 and detection time are used as indicators for performance evaluation. The result is that two-class gauze detection dataset is more accurate and YOLOv5 model mAP@50 is 93.4% and detection time is 11.7 ms.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.