• Journal of Chest Surgery
• /
• v.39 no.12 s.269
• /
• pp.879-890
• /
• 2006

Background: The dysfunction of multiple organs is found to be caused by reactive oxygen species as a major modulator of microvascular injury after hemorrhagic shock. Hemorrhagic shock, one of many causes inducing acute lung injury, is associated with increase in alveolocapillary permeability and characterized by edema, neutrophil infiltration, and hemorrhage in the interstitial and alveolar space. Aggressive and rapid fluid resuscitation potentially might increased the risk of pulmonary dysfunction by the interstitial edema. Therefore, in order to improve the pulmonary dysfunction induced by hemorrhagic shock, the present study was attempted to investigate how to reduce the inflammatory responses and edema in lung. Material and Method: Male Sprague-Dawley rats, weight 300 to 350 gm were anesthetized with ketamine(7 mg/kg) intramuscular Hemorrhagic Shock(HS) was induced by withdrawal of 3 mL/100 g over 10 min. through right jugular vein. Mean arterial pressure was then maintained at $35{\sim}40$ mmHg by further blood withdrawal. At 60 min. after HS, the shed blood and Ringer's solution or 5% albumin was infused to restore mean carotid arterial pressure over 80 mmHg. Rats were divided into three groups according to rectal temperature level($37^{\circ}C$[normothermia] vs $33^{\circ}C$[mild hypothermia]) and resuscitation fluid(lactate Ringer's solution vs 5% albumin solution). Group I consisted of rats with the normothermia and lactate Ringer's solution infusion. Group II consisted of rats with the systemic hypothermia and lactate Ringer's solution infusion. Group III consisted of rats with the systemic hypothermia and 5% albumin solution infusion. Hemodynamic parameters(heart rate, mean carotid arterial pressure), metabolism, and pulmonary tissue damage were observed for 4 hours. Result: In all experimental groups including 6 rats in group I, totally 26 rats were alive in 3rd stage. However, bleeding volume of group I in first stage was $3.2{\pm}0.5$ mL/100 g less than those of group II($3.9{\pm}0.8$ mL/100 g) and group III($4.1{\pm}0.7$ mL/100 g). Fluid volume infused in 2nd stage was $28.6{\pm}6.0$ mL(group I), $20.6{\pm}4.0$ mL(group II) and $14.7{\pm}2.7$ mL(group III), retrospectively in which there was statistically a significance between all groups(p<0.05). Plasma potassium level was markedly elevated in comparison with other groups(II and III), whereas glucose level was obviously reduced in 2nd stage of group I. Level of interleukine-8 in group I was obviously higher than that of group II or III(p<0.05). They were $1.834{\pm}437$ pg/mL(group I), $1,006{\pm}532$ pg/mL(group II), and $764{\pm}302$ pg/mL(group III), retrospectively. In histologic score, the score of group III($1.6{\pm}0.6$) was significantly lower than that of group I($2.8{\pm}1.2$)(p<0.05). Conclusion: In pressure-controlled hemorrhagic shock model, it is suggested that hypothermia might inhibit the direct damage of ischemic tissue through reduction of basic metabolic rate in shock state compared to normothermia. It seems that hypothermia should be benefit to recovery pulmonary function by reducing replaced fluid volume, inhibiting anti-inflammatory agent(IL-8) and leukocyte infiltration in state of ischemia-reperfusion injury. However, if is considered that other changes in pulmonary damage and inflammatory responses might induce by not only kinds of fluid solutions but also hypothermia, and that the detailed evaluation should be study.

• Tuberculosis and Respiratory Diseases
• /
• v.44 no.2
• /
• pp.360-378
• /
• 1997

Background : Severe acute lung injury(ALI), also known as the adult respiratory distress syndrome(ARDS), is a heterogenous nature of dynamic and explosive clinical synrome that exacts a mortality of approximately 50%. Endotoxin(ETX) is an abundant component of the outer membrane of gram-negative bacteria capable of inducing severe lung injury in gram-negative sepsis and gram-negative bacterial pneumonia, which are among the most common predisposing causes of ARDS. The influx of PMNs into airway tissue is a pathological hallmark of LPS-induced lung injury. And there is a substantial evidence suggesting that cytokines are important mediators of lung injury in gram-negative sepsis. However, the kinetics of phagocytes and cytokines by an exact time sequence and their respective pathogenic importance remain to be elucidated. This study was performed to investigate the role of phagocytes and proinflammatory cytokines in ETX-induced ALI through a time course of changes in the concentration of protein, $TNF{\alpha}$ and IL-6, and counts of total and its differential cells in BALF. The consecutive histologic findings were also evaluated. Method : The experimental animals, healthy male Sprague-Dawley, weighted $200{\pm}50g$, were divided into control- and ALI- group. ALI was induced by an intravenous administration of ETX, 5mg/kg. Above mentioned all parameters were examined at 0(control), 3, 6, 24, 72 h after administration of ETX. $TNF{\alpha}$ and IL-6 cone. in BALF were measured by a bioassay. Results : The protein concentration and total leukocyte count(TC) in BALF was significantly increased at 3h compared to controls(p < 0.05). The protein conc. was significantly elavated during observation period, but TC was significantly decreased at 72h(p < 0.05 vs. 24h). There was a close relationship between TC and protein cone. in BALF(r = 0.65, p < 0.001). The PMN and monocyte count was well correlated with TC in BALF, and the correlation of PMN(r = 0.97, p < 0.001) appeared to be more meaningful than that of monocyte(r = 0.61, p < 0.001). There was also a significant correlation between protein cone. and PMN or monocyte count in BALF(PMN vs. monocyte : r = 0.55, p < 0.005 vs. r = 0.64, p < 0.001). The count of monocyte was significantly elavated during observation period though a meaningful reduction of PMN count in BALF at 72h, this observation suggested that monocyte may, at least, partipate in the process of lung injury steadly. In this study, there was no relationship between IL-6 and $TNF{\alpha}$ cone., and $TNF{\alpha}$ but not IL-6 was correlated with TC(r = 0.61, p < 0.05) and monocyte(r = 0.67, p < 0.05) in BALF only at 3, 6h after ETX introduced. In particular, the IL-6 cone. increased earlier and rapidly peaked than $TNF{\alpha}$ cone. in BALF. In histologic findings, the cell counts of lung slices were increased from 3 to 72h(p < 0.001 vs. NC). Alveolar wall-thickness was increased from 6 to 24h(p < 0.001 vs. NC). There was a significant correlation between the cell counts of lung slices and alveolar wall-thickness(r= 0.61, p < 0.001). This result suggested that the cellular infiltrations might be followed by the alterations of interstitium, and the edematous change of alveolar wall might be most rapidly recovered to its normal condition in the process of repair. Conclusion : We concluded that although the role of PMN is partly certain in ETX-induced ALI, it is somewhat inadequate to its known major impact on ALL Alveolar macrophage and/or non-immune cells such as pulmonary endothelial or epithelial cells, may be more importantly contributed to the initiation and perpetual progression of ETX-induced ALI. The IL-6 in ETX-induced ALI was independent to $TNF{\alpha}$, measured by a bioassay in BALF. The early rise in IL-6 in BALF implies multiple origins of the IL-6.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.