OBJECTIVES: There are a lot of studies that analyze the interaction between the emotion of disgust and the functional brain images using fMRI and PET. But studies using sLORETA (standardized low resolution brain electromagnetic tomography) almost do not exist. The aim of this research is to explore the relationship of the emotion of disgust and the cortical activation using sLORETA analysis. METHODS: Forty five healthy young adults (27.1+/-2.6 years) participated in the study. While they were watching 4 neutral images and 4 disgusting images associated with mutilation selected from the international affective picture system (IAPS), participants' EEGs were taken for 30 seconds per one picture. Through these obtained EEG data, sLORETA analysis was performed to compare EEGs associated with neutral and negative images. RESULTS: During looking for visual disgusting stimulus, all participants reported unpleasantness, arousal and stress. In sLORETA analysis, the decrease of current density in theta wave was shown at left frontal superior gyrus (BA10) and middle gyrus (BA10, 11). This voxel cluster consists of a total of 11 voxels and the threshold of t value indicating statistically significant decreases in the current density (p<0.05) was -1.984. There were no differences between male and female in the degree of being disgusted by the stimuli. CONCLUSION: This finding may suggest that the activation of dorsolateral prefrontal cortex might be associated with regulating disgust emotion.
Arousal ; Brain ; Electroencephalography ; Female ; Humans ; Magnetic Resonance Imaging ; Magnets ; Male ; Prefrontal Cortex ; Young Adult

No abstract available.
Digoxigenin* ; DNA* ; Hepatitis B virus* ; Hepatitis B* ; Hepatitis*

PURPOSE: In this retrospective study, we attempted to evaluate the treatment outcome and the prognostic factors of thymoma treated with surgery, radiotherapy and chemotherapy. METHODS AND MATERIALS: Between 1979 and 1998, 55 patients with thymoma were treated at the Seoul National University Hospital. Of these, 11 patients underwent surgery only, 33 patients received postoperative radiotherapy and 11 patients received radiotherapy only. Twenty-three patients had gross total resection and 21 patients subtotal resection. For postoperative radiotherapy, the radiation dose consisted of 41.4-55.8 Gy. The average follow-up was 64 months, and ranged from 2 to 160 months. The sex ratio was 1:1 and the median age was 48 years (15-74 years). Overall survival and disease-free survival were determined via the Kaplan-Meier method, and the log-rank was employed to evaluate for differences in prognostic factor. RESULTS: The five- and 10-year survival rates were 87% and 65% respectively, and the median survival was 103 months. By univariate analysis, only stage ( p=0.0017) turned out to be significant prognostic factors of overall survival. Also, stage ( p=0.0007) was significantly predictive for overall survival in mutivariated analysis. CONCLUSION: This study showed the stage was found to be important prognostic factors, which influenced survival. Especially, as incomplete resection is related with poor results, complete resection is important to cure the invasive thymoma.
Disease-Free Survival ; Drug Therapy ; Follow-Up Studies ; Humans ; Radiotherapy ; Retrospective Studies ; Seoul ; Sex Ratio ; Survival Rate ; Thymoma* ; Treatment Outcome*

No abstract available.
Gangwon-do* ; Humans

Three-dimensional surgical accuracy between virtually planned and actual surgical movements of the maxilla in two-jaw orthognathic surgery.

Although procedures to reproduce the lost thumb through osteoplastic reconstruction and adjacent finger transfer operations appeared reasonably successful in providing for better prehension, nonetheless the methods lacked predictabiiity and too often the results were unacceptable esthetically. In recent years the development of microsurgery and surgical experiences has made it possible to free one stage transplantation of toe to replace missing thumb. Based on our past experiences with limb replantation since 1975, we accomplished the first toe to thumb transplantation done in Korea on October 28, 1978. Therafter we succeeded in one stage toe-to-thumb transplanatation in five cases. The shortest follow up period was thirteen months, and the longest, twenty-three months. One cases was excluded in this report due to short follow up period. Excellent results were achieved in all cases. There were no limping or pain while walking after removal of great toes or second toe. Great toe transplantation is more favorable donor area than second toe in toe-to-thumb transplantation. Free toe-to-thumb transplantation on making a thumb in missing thumb is the most excellent method of thumb reconstruction, but skillful technique and specialized microsurgical training is mandatory.
Extremities ; Fingers ; Follow-Up Studies ; Humans ; Korea ; Methods ; Microsurgery ; Replantation ; Thumb ; Tissue Donors ; Toes ; Transplantation ; Walking

Three-dimensional surgical accuracy between virtually planned and actual surgical movements of the maxilla in two-jaw orthognathic surgery.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.