• Journal of Chest Surgery
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• v.37 no.6
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• pp.524-529
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• 2004

Background: Since the Nuss procedure for the correction of pectus excavatum is in its early stage, there have been problems that need to be solved. We examined complications in a single-institute experience of the Nuss technique in order to develop possible solutions to prevent them. Material and Method: 335 consecutive patients, who underwent the modified Nuss procedure between August 1999 and October 2002, were studied retrospectively. Median age was 8 years (range 1 to 46). 264 patients (78.8%) were in pediatric group (age$\leq$15) and 71 patients (21.2%) were in adult group (age> 15). 193 patients (57.6%) had symmetric and 142 patients (42.4%) had asymmetric pectus configurations. Risk factors predicting postoperative complications were analyzed using multi-variate logistic regression. Result: Postoperative complication rates were 18.9% (61/335) in total patients. Frequent complications were pneumothorax 24 (7.5%), bar displacement 11 (3.4%), and wound seroma 10 (3.1%) in order. Early complications (within a month, 49 cases, 15.2%) were pneumothorax (n=23, 6.9%), wound seroma (n=12, 3.6%), and bar displacement (n=8, 2.4%). Late complications (after a month, 12 cases, 3.7%) were pericarditis and pericardial effusion (n=5, 1.5%), bar displacement (n=4, 1.2%), and hemothorax (n=3, 0.9%). Techniques were modified to prevent complications especially in bar shaping and fixation, which led to decrease complication rate in later experience (Operation Date 1: 15/51 (29.4%) vs Operation Date 2: 34/284 (12.0%), p=0.004). Grand Canyon type (eccentric long canal type) showed higher complication rate than other types (GC type: 12/30(40%) vs Others: 37/305 (12.1%), p<0.001). Major risk factors are severity of pectus (OR=2.88, p=0.038), Grand Canyon type (OR=2.82, p=0.044), and Op. Date 1 (OR=4.05, p=0.001). Conclusion: Major complications were related to severe eccentric type of pectus configuration (Grand Canyon type) and lack of surgeon's experience (Op. Date 1). Com-plication rate was reduced with accumulation of experience and advancement of surgical techniques. The Nuss procedure can be performed at a low risk of complications with our current technique.

• Applied Microscopy
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• v.30 no.4
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• pp.347-355
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• 2000

Zinc is one of the most abundant oligoelements in the living cell. It appears tightly bound to some metalloproteins and nucleic acids, loosely bound to some metallothioneins or even as free ion. Small amounts of zinc ions (in the nanomolar range) regulate a plentitude of enzymatic proteins, receptors and transcription factors, thus rolls need accurate homeostasis of zinc ions. Zinc is an essential catalytic or structural element of many proteins, and a signaling messenger that is released by neural activity at many central excitatory synapses. Growing evidences suggest that zinc may also be a key mediator and modulator of the neuronal death associated with transient global ischemia and sustained seizures, as well as perhaps other neurological disease stoles. Some neurons have developed mechanisms to accumulate zinc in specific membrane compartment ('vesicular zinc') which can be evidenced using histochemical techniques. Substances giving a bright colour or emitting fluorescence when in contact with divalent metal ions are currently used to detect them inside cells; their use leads to the so called 'direct' methods. The fixation and precipitation of metal ions as insoluble salt precipitates, their maintenance along the histological process and, finally, their demonstration after autometallographic development are essential steps for other methods, the so called 'indirect methods'. This study is a short report on the autometallograhical approaches for zinc detection in the central nervous system (CNS) by means of a modified selenium method.

• Journal of Korean Orthopaedic Sports Medicine
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• v.10 no.2
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• pp.61-68
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• 2011

Purpose: We evaluated the clinical outcome after arthroscopic repair in full thickness rotator cuff tears with and without delamination. Materials and Methods: From March 2006 to October 2008, we included 48 consecutive shoulders (31 males, 17 females; mean age 57.6 years; 45~68) who had arthroscopic double row repair for fullthickness tears of the rotator cuff. Mean rotator cuff tear size was 2.8 cm (range: 1.2~3.6) and the techniques of tendon-to-bone fixation varied according to the presence of delamination; separate row fixations of bursal and articular layer were used in delaminated tear. The mean follow-up was 26 months (range: 18~33) and functional and structural results were evaluated by American Shoulder and Elbow Surgeons (ASES), University of California at Los Angeles (UCLA) scale, isokinetic strength testing and magnetic resonance imaging (MRI) obtained mean 8 months (range:6~13) postoperatively. The patterns of delamination, age, sex, symptom duration, size of tear, satisfaction rate, retear rate ware compared and significance was set at p values < 0.05. Results: Postoperative functional shoulder score improved significantly in 44 shoulders (91.7%). Delamination was observed in 15 shoulders (31%) and it extended proximally and posteriorly in the majority of shoulders, and the articular layer was thicker (8/15, 53%) and more retracted (9/15, 60%) compared with the superficial bursal layer. Final follow up functional shoulder scores showed no differences between non-delaminated and delaminated tears and the presence of delamination had no correlations with sex, symptom duration, tear size and satisfaction rate, however, older age had more delaminated tears (p=0.041). Follow up MRI in 29 shoulders revealed that fourteen (48%) shoulders had complete healing; nine (31%), partial healing; six (21%), complete retear but the half of the retear group showed favorable clinical results. 79% (15/19) in non-delaminated tear and 80% (8/10) in delaminated tear were judged as healed tendon on MRI and double-layer double row repairs in delaminated tears resulted in nearly same rate of structural integrity of single-layer double row repairs (p=0.165). Conclusion: The incidence of delamination in our series was 31% and older age had more delaminated tears. Sex, symptom duration, preoperative size of the tear, functional results and satisfaction rate had no significant correlations with the presence of delamination. Nearly the same postoperative structural integrity was noted in both delaminated and non-delaminated tears.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.