No abstract available.
Antibodies, Monoclonal* ; Rickettsia typhi* ; Rickettsia*

No abstract available.
Antibodies, Monoclonal* ; Rickettsia typhi* ; Rickettsia*

OBJECTIVE: There are several lines of evidence suggested that a serotonergic dysfunction is involved in the susceptibility to suicide. Recently, the T102C polymorphism of the serotonin 2A receptor gene has been suggested to be associated with suicide, but the results of genetic study are still controversial. The purpose of this study was to test whether the T102C polymorphism of the serotonin 2A receptor gene indicates susceptibility to suicidal behavior. METHODS: The genotype and allele frequencies in the T102C polymorphism of the serotonin 2A receptor gene were studied by using TaqMan(R) assay to compare 80 Korean suicidal attempters with 125 Korean healthy controls. RESULTS: The genotype and allele frequencies did not differ between suicidal attempters and control subjects. And, there were no significant associations between the genotypes and violence or non-violence. We also did not find the significant association between the allelic frequencies and violence or non-violence. CONCLUSION: These findings suggest that the T102C polymorphism in serotonin 2A receptor gene is unlikely to play a role in the genetic susceptibility to suicidal behavior, violence or non-violence in Korean suicidal patients.
Gene Frequency ; Genetic Predisposition to Disease ; Genotype ; Humans ; Receptor, Serotonin, 5-HT2A* ; Serotonin* ; Suicide ; Violence

No abstract available in English.
Arthroscopy ; Knee Joint ; Knee

OBJECTIVE: This study aimed to analyze psychological characteristics of elderly patients. METHODS: The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) data of 110 elderly patients who visited the department of psychiatry was analyzed. We examined differences of MMPI-2 score according to sex and age. The elderly were classified into four clusters with similar characteristics. RESULTS: Depression, suicidal ideation, low motivation score was high in total sample. Depression, subjective depression, mental dullness, lassitude-malaise, psychasthenia, and fears score was higher among females than males. Through the cluster analysis, elderly were classified into four types of ‘high profile’, ‘1-2-7 profile’, ‘6-7-8-0 profile’, and ‘low profile’. CONCLUSION: The elderly patients who visited the department of psychiatry complain depression and helplessness. Females complained depression and anxiety more than males. Elderly were classified into four types of patients with high somatic complaints ‘high profile’, patients with high depression and helplessness ‘1-2-7 profile’, patients who were dissatisfied and could blame others ‘6-7-8-0 profile’, and patients who needed additional interview and projective test ‘low profile’.
Aged* ; Anxiety ; Cluster Analysis* ; Depression ; Female ; Humans ; Male ; Minnesota* ; Motivation ; Suicidal Ideation

OBJECTIVE: It has been known that the posterior pedicle screw fixation provides good mechanical stability in unstable burst fracture. But, posterior fixation without anterior column support may not be adequate to withstand the axial load and to keep the corrected kyphotic angle. We present results of one stage fixation by posterior approach in unstable burst fracture. METHODS: Nine patients with unstable burst fracture were treated with posterior fixation and intervertebral fusion using titanium mesh cages and pedicle screws. The canal decompression was achieved by laminectomy and partial pediculectomy through the posterior approach. In all cases, the short segment fixation and anterior column support with cage were performed on the one stage operation. RESULTS: Of nine patients, seven was satisfied with excellent clinical results except two cases of the Frankel's grade A. All patients had good stabilization of spinal column and enough decompression without any neurological complications. It was possible to maintain the corrected kyphotic angle with the circumferential stabilization(three column fixation). CONCLUSION: The anterior and posterior column fixation through the posterior approach provides good stability and decompression in the patients with unstable burst fracture.
Decompression* ; Humans ; Laminectomy ; Spine* ; Titanium

OBJECTIVES: This study investigated the effects of the grade of residents, sociodemographic variables, and clinical experience with suicidal patients on the knowledge and attitudes toward suicide among psychiatric residents in Korea. METHODS: A self-reporting survey including 30 questions was conducted together with an investigation of the sociodemographic background of the research subjects. The questionnaire was composed of general knowledge questions on suicide, suicide-related personal experience, attitudes toward suicide, and the treatment experience of suicide attempt patients. Chi-square test and binary logistic regression analysis were used to determine the differences and associations among the attitudes toward suicide, clinical experience of suicide, and sociodemographic characteristics. RESULTS: A significantly higher response rate was presented in the 4th grade on general knowledge of suicide and treatment experience with suicide attempt patients than in the 1st grade. Residents with previous history of psychiatric treatment suicide plans, or attempts presented a significantly higher level of permissive attitude toward suicide. Residents who had a previous history of suicide attempt among their own patients were more likely to think that they were more capable to prevent suicide of patients. CONCLUSION: Effective clinical practices are essential considering the fact that the highest suicide risk groups will inevitably be referred to psychiatric clinical services. The authors expect that the study results regarding suicide-related knowledge, attitudes, and the experience of psychiatric residents will contribute to the development of effective resident training programs for suicide-related clinical practice in Korea.
Cross-Sectional Studies* ; Education ; Humans ; Korea* ; Logistic Models ; Research Subjects ; Suicide

Objectives: Assessing the risks of youth suicide in educational and clinical settings is crucial.Therefore, this study developed a machine learning model to predict suicide attempts using the Korean Youth Risk Behavior Web-based Survey (KYRBWS). Methods: KYRBWS is conducted annually on Korean middle and high school students to assess their health-related behaviors. The KYRBWS data for 2021, which showed 1206 adolescents reporting suicide attempts out of 54848, was split into the training (n=43878) and test (n=10970) datasets. Thirty-nine features were selected from the KYRBWS questionnaire. The balanced accuracy of the model was employed as a metric to select the best model. Independent validations were conducted with the test dataset of 2021 KYRBWS (n=10970) and the external dataset of 2020 KYRBWS (n=54948). The clinical implication of the prediction by the selected model was measured for sensitivity, specificity, true prediction rate (TPR), and false prediction rate (FPR). Results: Balanced bag of histogram gradient boosting model has shown the best performance (balanced accuracy=0.803). This model shows 76.23% sensitivity, 83.08% specificity, 10.03% TPR, and 99.30% FPR for the test dataset as well as 77.25% sensitivity, 84.62% specificity, 9.31% TPR, and 99.45% FPR for the external dataset, respectively. Conclusion These results suggest that a specific machine learning model can predict suicide attempts among adolescents with high accuracy.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.