No abstract available.
Cyclophosphamide* ; Doxorubicin* ; Drug Therapy, Combination* ; Etoposide* ; Small Cell Lung Carcinoma*

OBJECTIVES: This study aimed to measure the efficacy of different tooth-brushing methods for removing plaque in Korea. METHODS: This study was conducted with the approval of the Institutional Review Board (IRB) of the Seoul National University School of Dentistry (S-D20180021). Thirty participants aged between 19 and 30 years, who did not have periodontal disease, were enrolled in this observational study. Participants were given the same type of toothbrush and toothpaste and asked to brush their teeth as they usually would. During brushing, participants were recorded with a camcorder that was attached to a mirror. Participants were aware they were being recorded. After they had finished brushing their teeth, a dental plaque staining and oral plaque index (PI) examination was performed. The PI score was measured using the Turesky modified Quigley Hein Index. Brushing methods were classified as rolling, horizontal, vertical, circling, and oblique. Skipped surfaces were recorded separately. Following this, statistical analysis was performed using SPSS software. RESULTS: Most surfaces of the mouth were skipped. The most commonly used brushing method was the circling method, followed by the vertical, horizontal, rolling, and oblique methods. The most frequently used method on the vestibular surface was circling, with 52.92% of the oral surface skipped. The oblique brushing method had the lowest mean PI score with a mean±SD of 1.73±0.82. The mean PI score of the skipped surfaces was the highest with a mean±SD of 2.52±0.81. We also analyzed the linear mixed model considering the different lengths of time spent brushing. Both the brushing method used and the time spent brushing had a significant effect on the PI score, but no interactions between these were observed. In areas where a horizontal brushing method had been used, the PI score was significantly decreased. CONCLUSIONS: This study suggests that the horizontal brushing method is an efficient tooth-brushing method compared to the other methods. Additionally, tooth-brushing for more than 10 seconds on 3 to 4 teeth area was effective in removing dental biofilm.
Biofilms ; Dental Plaque Index ; Dental Plaque ; Dentistry ; Ethics Committees, Research ; Humans ; Korea ; Methods ; Mouth ; Observational Study ; Oral Hygiene ; Periodontal Diseases ; Seoul ; Tooth ; Toothpastes ; Video Recording

PURPOSE: The purpose of this study was to examine the association between the intake of semi-solid yogurt and periodontitis in Korean adults using a national database. METHODS: The data analyzed in this study are a subset of the sixth Korean National Health and Nutrition Examination Survey conducted in 2015 by the Korea Centers for Disease Control and Prevention. The sample size for this study was 4,727. We collected data on sociodemographic characteristics, oral health-related variables, oral and general health status, and intake of semi-solid yogurt. Semi-solid yogurt intake (YI) was calculated by multiplying the frequency of YI over the previous week by the average intake per serving. We assessed periodontal conditions using the Community Periodontal Index (CPI) and defined periodontitis as a CPI score ≥3. Multivariate logistic regression analyses were performed after adjusting for sociodemographic variables, and oral and general health behaviors and status. RESULTS: The mean weekly YI among those without periodontitis (1.03±0.06 cups) was significantly higher than among those with periodontitis (0.77±0.08 cups) (P<0.001). Individuals who consumed more than 2 cups of yogurt per day were 76% less likely to have periodontitis than those who consumed less than 1 cup of yogurt per week after adjusting for all covariates (odds ratio, 0.24; 95% confidence interval, 0.10–0.60). CONCLUSIONS: We found a significant association between increased intake of semi-solid yogurt and periodontal health. We therefore recommend daily consumption of semi-solid yogurt as a probiotic to improve periodontal health. Further longitudinal studies are required to elucidate plausible mechanisms through which probiotics impact periodontal disease, considering both periodontal pathogens and clinical periodontal parameters.
Adult ; Centers for Disease Control and Prevention (U.S.) ; Health Behavior ; Humans ; Korea ; Logistic Models ; Longitudinal Studies ; Nutrition Surveys ; Periodontal Diseases ; Periodontal Index ; Periodontitis ; Probiotics ; Sample Size ; Yogurt

OBJECTIVES: The primary objective of this study was to determine if the number of missing teeth could be predicted by oral disease pathogens, and the secondary objective was to assess whether deep learning is a better way of predicting the number of missing teeth than multivariable linear regression (MLR).METHODS: Data were collected through review of patient’s initial medical records. A total of 960 participants were cross-sectionally surveyed. MLR analysis was performed to assess the relationship between the number of missing teeth and the results of real-time PCR assay (done for quantification of 11 oral disease pathogens). A convolutional neural network (CNN) was used as the deep learning model and compared with MLR models. Each model was performed five times to generate an average accuracy rate and mean square error (MSE). The accuracy of predicting the number of missing teeth was evaluated and compared between the CNN and MLR methods.RESULTS: Model 1 had the demographic information necessary for the prediction of periodontal diseases in addition to the red and the orange complex bacteria that are highly predominant in oral diseases. The accuracy of the convolutional neural network in this model was 65.0%. However, applying Model 4, which added yellow complex bacteria to the total bacterial load, increased the expected extractions of dental caries to 70.2%.On the other hand, the accuracy of the MLR was about 50.0% in all models. The mean square error of the CNN was considerably smaller than that of the MLR, resulting in better predictability.CONCLUSIONS: Oral disease pathogens can be used as a predictor of missing teeth and deep learning can be a more accurate analysis method to predict the number of missing teeth as compared to MLR.
Bacteria ; Bacterial Load ; Citrus sinensis ; Dental Caries ; Hand ; Learning ; Linear Models ; Medical Records ; Methods ; Periodontal Diseases ; Periodontitis ; Pilot Projects ; Real-Time Polymerase Chain Reaction ; Tooth

No abstract available.
Doxorubicin* ; Drug Therapy* ; Etoposide* ; Stomach Neoplasms*

No abstract available.
Humans ; Idarubicin* ; Induction Chemotherapy* ; Leukemia, Myeloid, Acute*

BACKGROUND: In the measurement of bronchodilator reversibility, the forced expiratory volume in one second(FEV(1)) and the forced vital capacity(FVC) are commonly used parameters and recommended criteria for the reversibility requiring an increase of more than 200ml and 12% above the baseline, respectively. However, aged patients do not often meet the criteria of an increase in volume(>200ml) even though the medical history of that patient is adequate for asthma. This study investigated the role of the forced expiratory volume in six seconds(FEV(6)) in the bronchodilator reversibility test in elderly patients. METHODS: A total of 236 patients more than 65 years of age with a FEV(1)/FVC ratio<80% were enrolled in this study. The bronchodilator revesibility tests were examined. With the setting FEV(1) as the baseline, the patients were divided into three groups; Group I: FEV(1)> or = 80% of the predicted value, Group II: 60% Aged ; Asthma ; Forced Expiratory Volume ; Humans

This study focuses on the importance of non-face-to-face healthcare, based on the experience with the COVID-19 pandemic, and proposes a model for oral health management services utilizing personal data, known as “my data.” The model aims to develop a comprehensive system that collects and integrates individual health records for easy access. It includes assessing oral health through oral examination records and surveys, creating risk assessment algorithms tailored to various population groups, and standardizing medical terms and data. The goal is to accurately evaluate individual oral health conditions and provide personalized services. However, the success of the model requires considerations such as platform management, user experience enhancement, and medical data security. The research proposes possibilities for further investigation to enhance the oral health service model. This study introduces a novel approach to individual health management, establishing the foundation of effective health management even in non-face-to-face settings.