Purpose : The objective of this study was to determine whether the expressions of the two components of DNA-dependent protein kinase, Ku70 and DNA-PKcs, influence the response to radiotherapy (RT) and outcome of treatment of non-disseminated nasopharyngeal carcinoma (NPC) in patients who received definitive RT. Materials and Methods : Sixty-six patients with NPC who were treated with radiotherapy alone or with concurrent chemotherapy between June 1995 and December 2001 were divided into groups based on the levels of immunoreactivity for Ku70 and DNA-PKcs in pretreatment biopsy specimens. The over-expression of Ku70 or DNA-PKcs groups Included patients whose biopsy specimens showed at least 50% immunopositive tumor cells; patients in which less than 50% of the tumor cells in the biopsy tissues were immunopositive were placed in the low Ku70 and DNA-PKcs groups. The immunoreactivities for Ku70 and DNA-PKcs were retrospectively compared with the sensitivity of the tumor to radiation and the patterns of therapy failure. Univariate analyses were peformed to determine the prognostic factors that influenced locoregional control of NPC. Results : The five-year locoregional control rate was significantly higher in the low Ku70 group (Ku(-)) (85%) than in the high Ku70 group (Ku(+)) (42%) (p=0.0042). However, there were no differences in the metastases-free survival rates between the two groups (Ku70 (+), 82%; Ku70 (-), 78%; p=0.8672). Univariate analysis indicated that the over-expression of Ku70 surpassed other well-known predictive clinocopathologic parameters as an Independent prognostic factor for locoregionai control. Eighteen of 22 patients who had locoregional recurrences of the tumor displayed an over-expression of Ku70. No significant association was found between the level of DNA-PKcs expression and the clinical outcome. Conclusion : Our data suggest that the level of Ku70 expression can be used as a molecular marker to predict the response to RT and the locoregional control after RT and concurrent chemotherapy in patients with non-disseminated NPC.
A discounted cost model for preventive maintenance of armor units of rubble-mound breakwaters is mathematically derived by combining the deterioration model based on a discrete-time stochastic process of shock occurrence with the cost model of renewal process together. The discounted cost model of condition-based maintenance proposed in this paper can take into account the nonlinearity of cumulative damage process as well as the discounting effect of cost. By comparing the present results with the previous other results, the verification is carried out satisfactorily. In addition, it is known from the sensitivity analysis on variables related to the model that the more often preventive maintenance should be implemented, the more crucial the level of importance of system is. However, the tendency is shown in reverse as the interest rate is increased. Meanwhile, the present model has been applied to the armor units of rubble-mound breakwaters. The parameters of damage intensity function have been estimated through the time-dependent prediction of the expected cumulative damage level obtained from the sample path method. In particular, it is confirmed that the shock occurrences can be considered to be a discrete-time stochastic process by investigating the effects of uncertainty of the shock occurrences on the expected cumulative damage level with homogeneous Poisson process and doubly stochastic Poisson process that are the continuous-time stochastic processes. It can be also seen that the stochastic process of cumulative damage would depend directly on the design conditions, thus the preventive maintenance would be varied due to those. Finally, the optimal periods and scale for the preventive maintenance of armor units of rubble-mound breakwaters can be quantitatively determined with the failure limits, the levels of importance of structure, and the interest rates.
Synthetic Mn-tourmalines (tsilaisite) were obtained by hydrothermal synthesis under the condition of 2 Kbar,
Purpose: To evaluate the clinical stability and function after arthroscopic anterior cruciate ligament(ACL) reconstruction using fresh-frozen tibialis tendon allograft. Materials and Methods: Of the patients who underwent ACL reconstruction using tibialis tendon allograft from July 2002 to June 2003, thirty-one patients could be evaluated and the mean follow-up period was 19 months. Evaluations included were Lysholm knee score, 2000 International knee Documentation Committee (IKDC) subjective knee score, Lachman test, pivot shift test, KT-1000 arthrometer measurement and 2000 IKDC knee examination. Results: The mean Lysholm score was 88. Twenty-eight patients (90.3%) were good or exellent for the measured parameters. Twenty-seven patients(87.1%) was over 70 in IKDC subjective knee score. Thirty patients (96.8%) had 1+ firm end or negative Lachman test. 27 patients (87.1%) had a negative pivot shift. Thirty patients (96.8%) had less than 5mm difference of maximal manual difference by KT-1000 arthrometer. Twenty -nine patients (93.5%) were nearly normal or normal grade by 2000 IKDC knee examination. Complications were 1 case of failure and 1 case of infection. Conclusion: ACL reconstruction with the double-stranded fresh-frozen tibialis tendon allograft resulted in a reliable and predictable outcome after short-term follow-up.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
Extracorporeal life support (ECLS) system is a device for respiratory and/or heart failure treatment, and there have been many trials for development and clinical application in the world. Currently, a non-pulsatile blood pump is a standard for ECLS system. Although a pulsatile blood pump is advantageous in physiologic aspects, high pressure generated in the circuits and resultant blood cell trauma remain major concerns which make one reluctant to use a pulsatile blood pump in artificial lung circuits containing a membrane oxygenator. The study was designed to evaluate the hypothesis that placement of a pressure-relieving compliance chamber between a pulsatile pump and a membrane oxygenator might reduce the above mentioned side effects while providing physiologic pulsatile blood flow. The study was performed in a canine model of oleic acid induced acute lung injury (N=16). The animals were divided into three groups according to the type of pump used and the presence of the compliance chamber, In group 1, a non-pulsatile centrifugal pump was used as a control (n=6). In group 2 (n=4), a single-pulsatile pump was used. In group 3 (n=6), a single-pulsatile pump equipped with a compliance chamber was used. The experimental model was a partial bypass between the right atrium and the aorta at a pump flow of 1.8∼2 L/min for 2 hours. The observed parameters were focused on hemodynamic changes, intra-circuit pressure, laboratory studies for blood profile, and the effect on blood cell trauma. In hemodynamics, the pulsatile group II & III generated higher arterial pulse pressure (47
Background: Positive end, expiratory pressure (PEEP) has become one of the standard therapies for adult respiratory distress syndrome (ARDS). Total static compliance has been proposed as a guide to determine the size of PEEP ('best PEEP') which is of unproven clinical benefit and remains controversial. Besides increasing functional residual capacity and thus improving oxygenation, PEEP stimulate prostacyclin secretion and was proposed for the treatment of acute pulmonary embolism. But little is known about the effect of PEEP on hemodynamic and gas exchange disturbances in acute pulmonary embolism. Methods: To study the validity of total static compliance as a predictor of 'best PEEP' in ARDS and acute pulmonary embolism, experimental ARDS was induced in mongrel dog with oleic acid and acute pulmonary embolism with autologous blood clot. Then hemodynamic and gas exchange parameters were measured with serial increment of PEEP. Results:In ARDS group, total static compliance and oxygen transport were maximal at 5 cm
Purpose : When an x-ray beam of small field size is irradiated to target area containing an air cavity, such as larynx, the underdosing effect is observed in the region near the interfaces of air and soft tissue. With a larynx model, air cavity embedded in tissue-equivalent material, this study is intonded for examining Parameters, such as beam quality, field size, and cavity size, to affect the dose distribution near the air cavity. Materials and Methods : Three x-rar beams, 4-, 6- and 10-MV, were employed to Perform a measurement using a 2cm
In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.