PURPOSE: The purpose of this study was to provide basic data to develop safety education programs in elementary school. METHOD: Analysis is made based on textbooks for 1st-6th-grade elementary school students published by Korea Ministry of Education & Human Resources Development. RESULTS: 1) Among the textbooks. only , , , and courses dealt with safety education. 2) The contents of safety education included traffic safety, water safety, indoor and outdoor safety, fire, emergency measures, misuse and abuse of drug, smoking, toy safety, sports safety, leasure safety and disasters. However, the contents were theoretical, fragmentary, local and limited. CONCLUSION: These results suggest that the consents of safety education must be strengthened, and more practical and more behavior-oriented in consideration of activities.
Disasters ; Education* ; Emergencies ; Fires ; Humans ; Korea ; Play and Playthings ; Smoke ; Smoking ; Sports ; Staff Development

The purpose of this study was to provide basic data for the development of medicinal herbs, which are useful for the treatment of chronic diseases and physical characteristics of the elderly. A total of 151 adult women aged 65 years or older currently living in Daejeon were enrolled. The contents of this study were parents' health status, recognition of the necessity for a dietary therapy, awareness of medicinal herbs, requirements for medicinal herbs, physician of medicinal herbs and obstacles to medicinal herbs. Based on these results, the differences in the perception of medicinal herbs according to the dietary needs group as well as differences in the perception of medicinal herbs according to medicinal herbicidal groups were analyzed. Of the 151 subjects, 90.7% were married, and 44.4% of them were aged between 40 and 49. Most of the patients were aged in their 70s (68.9%), and 79.5% of had chronic illnesses such as high blood pressure, hyperlipidemia, arteriosclerosis, and heart disease. It is recognized that dietary therapy is necessary for the treatment of diseases, and awareness of medicinal herbs is low but necessary. As for the requirements for medicinal herbs, the most favored was ‘porridge for the prevention and treatment of diseases’, and 72.2% of medicinal herbs were physicians. However, problems cited were availabitity of ingredients, cooking technique, price of ingredients, and others. In addition, both groups requiring dietary therapy and having high awareness of medicinal herbs felt that medicinal herbs would be helpful in treating diseases. Therefore, it is necessary to provide a simple medicinal herb recipe for each disease and to develop anti cooked medicinal herbs.
Adult ; Aged ; Arteriosclerosis ; Chronic Disease ; Cooking ; Diet* ; Female ; Heart Diseases ; Humans ; Hyperlipidemias ; Hypertension ; Plants, Medicinal

Purpose: The purpose of this research was to identify the risk perception of acute respiratory infections, safety climate and the use of personal protective equipment, and to verify the factors influencing use of personal protective equipment by nurses’ working in general hospitals. Methods: The survey was conducted with 133 nurses working in general hospitals with 200 beds to 499 beds. Data collection was done, from November 13 to December 12, 2017. Collected data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation coefficients, and stepwise multiple regression. Results Factors influencing the use of personal protective equipment related to acute respiratory infections in general hospital nurses were training (β=.36, p<.001), types of isolation facilities in the respective departments (negative pressure room) (β=.27, p<.001), patient instructions (β=.20, p=.027), and absence of job hindrances (β=.15, p=.042). The explanatory power of these results was 47.6%. Conclusion: The results of this study indicate that for general hospital nurses it is necessary to establish a physical safety climate such as a negative pressure room, and to encourage the use of personal protective equipment related to acute respiratory infections. Also, changes should be accompanied by training in the use of personal protective equipment, provision of patient care guidelines, compliance with standard precaution, and the creation of an organizational safety climate and support for the organization.

OBJECTIVES: Schizophrenia patients have deficits of prediction and learning related to dopaminergic dysfunction. It is hypothesized that there would be different characteristics in associative learning of reward approach and loss aversion between controls and patients. METHODS: Participants were 23 healthy participants and 20 out-patients fulfilling criteria for schizophrenia according DSM-IV-TR. Using a monetary incentive contingency reversal task, successful learning rates, numbers of trials and errors till learning, numbers of trials of maintaining learning, response times were measured. Characteristics of learning were compared between controls and patients. RESULTS: Physical anhedonia and PANSS negative symptom scores correlated with the number of trials while loss aversion was maintained. Overall correct response rates were decreased in patient group, particularly during reward approach learning. Patients required more trials and errors to learn reward approach than controls. There were no significant differences in learning performance and reaction times between groups during loss avoidance learning. CONCLUSION: These results support previous reports of deficits in reward-driven learning in schizophrenia. However, anhedonia and negative symptoms were associated with the preserved function of loss avoidance learning.
Anhedonia ; Avoidance Learning ; Humans ; Learning ; Motivation ; Outpatients ; Reaction Time ; Reinforcement (Psychology) ; Reward ; Schizophrenia

Although rheumatoid arthritis has been known to be a common autoimmune disease characterized by chronic inflammation mainly evident in diarthrodial joints, its pathogenesis remains to be clarified. In the present study, to investigate the pathogenic signaling system taken place in the rheumatoid joints, we assessed whether synovial fluid obtained from patients with rheumatoid arthritis contains inducers for proinflammatory cytokines such as interleukin (IL)-12 and IL-23. Peritoneal macrophages isolated from IL-12/IL-23 p40-YFP reporter mice were stimulated with synovial fluid, followed by flow cytometry to screen CD11b+ and YFP-expressing cells, reflective of IL-12/IL-23 p40-producing macrophages. The expression levels of Toll-like receptor (TLR)-2 and -4, which have a potential to mediate IL-12/IL- 23 p40 induction, were determined in synovial cells obtained from a patient with rheumatoid arthritis by RT-PCR analyses. One out of 10 synovial fluid from rheumatoid arthritis patients induced IL-12/IL-23 p40 expression, while all of 10 synovial fluid from osteoarthritis patients did not. Synoviocytes constitutively expressed Toll-like receptor (TLR)-2 and -4 which are candidate receptors for IL-12/IL-23 inducers. Upon LPS stimulation, the levels of TLR-2 and -4 were downregulated and upregulated, respectively. Taken together, these results suggest that some patients with rheumatoid arthritis elicit synovitis through TLR-2- and -4-mediated induction of proinflammatory cytokines IL-12 and IL-23.
Animals ; Arthritis, Rheumatoid ; Autoimmune Diseases ; Cytokines ; Flow Cytometry ; Fluorescence* ; Humans ; Inflammation ; Interleukin-12 ; Interleukin-23 ; Interleukins ; Joints ; Macrophages ; Macrophages, Peritoneal ; Mass Screening* ; Mice* ; Osteoarthritis ; Synovial Fluid* ; Synovitis ; Toll-Like Receptors

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

BACKGROUND: Early identification and appropriate management of mild chronic renal failure has been increasingly recognized as an important opportunity to delay the progression of renal disease. Many physicians rely on serum creatinine (Scr) as a screening test for renal impairment; however, Scr levels can remain within the normal range even when renal function is significantly impaired in certain group of patients. METHODS: The subjects were 20, 245 persons who were enrolled a regular health check up program, Jan. 1997 to Jun. 2001, at Ajou University Hospital, Korea. We identified GFR below 60 mL/min/1.73 m2 as abnormal and found the percentage of patients with Scr value from 1.1 mg/dL to 1.4 mg/dL and their sex and age distribution, using GFRs calculated by Cockroft-Gault Equation, MDRD Equation, and Kang's Equation. RESULTS: Average Scr was 1.07+-0.13 mg/dL in male and 0.82+-0.11 mg/dL in female. Among 34 University hospital in metro Seoul area, 11 hospital used 1.4 mg/dL as normal upper limit of Scr and 7 hospital used 1.3 mg/dL. Taking normal Scr upper limit as 1.3 or 1.4 mg/dL, the frequency of GFR below 60 mL/min/1.73 m2 was 3.9-50% in males and 75-100% in females, although some differences existed according to the equation used. CONCLUSION: Currently used normal Scr upper limit 1.3, 1.4 mL/dL was found to be much too high and we concluded a downward adjustment is needed and one-time test is not adequate to conclude renal impairment but several regular tests are required.
Age Distribution ; Creatinine* ; Female ; Glomerular Filtration Rate ; Humans ; Kidney Failure, Chronic ; Korea ; Male ; Mass Screening ; Reference Values* ; Seoul