BACKGROUND: This study aimed to explore the associations of social-demographic, health-behavioral, and woman-specific factors with the non-screening status of the cervical cancer screening test in Korean adult women. METHODS: This study was a cross-sectional analysis of 9,698 Korean adult women aged 20 years or more who participated in the Korea National Health and Nutrition Examination Surveys V (2010–2012). Rao-Scott chi-square tests and survey logistic regression analyses were employed respectively to analyze the difference in the non-screening status of the cervical cancer screening test by each characteristic and to explore the factors related to the non-screening status. RESULTS: The rate of the non-screening status of the cervical cancer screening test was 53.5% over the previous 2 years. In the survey logistics regression analysis, age, marital status, educational levels, income levels, residential area, occupation, private health insurance, smoking, alcohol, obesity, menstrual status, pregnancy experience, and hormone replacement therapy were significantly associated with the non-screening status of the cervical cancer screening test. CONCLUSION On the basis of the results of this study, some social-demographic, health-behavioral, and woman-specific characteristics of Korean adult women seem to affect the non-screening status of the cervical cancer screening test. Therefore, appropriate health policies need to be designed, implemented, monitored, and evaluated to reduce the non-screening status of the cervical cancer screening test of them.

Purpose: We used spectral domain optical coherence tomography (SD-OCT) to assess the retinal and choroidal thicknesses of the rabbit, a commonly used animal model of ophthalmic disease. We report normative datasets. Methods: Semi-automated measurements were made on 15 normal right eyes of New Zealand white rabbits. Total retinal, inner retinal layer, outer retinal layer, choroidal, ganglion cell layer, ganglion cell complex, inner nuclear layer, and outer nuclear layer thicknesses were measured at fixed distances (0, 1, 2, 3, 4, and 5 mm) below the optic nerve head. Results: Total retinal layer (Pearson’s correlation coefficient [CC] = -0.778, p < 0.05), inner retinal layer (CC = -0.710, p < 0.05), outer retinal layer (CC = -0.495, p < 0.05), ganglion cell complex (CC = -0.292, p < 0.05), ganglion cell layer (CC = -0.284, p < 0.05), and outer nuclear layer thicknesses (CC = -0.760, p < 0.05) decreased with the distance from the optic nerve head. Inner nuclear layer thickness correlated negatively with the distance from the optic nerve head, but the correlation coefficient was low (CC = -0.263, p < 0.05). Choroidal thickness increased with the distance from the optic nerve head (CC = 0.511, p < 0.05). Conclusions Rabbit retinal thicknesses were measured and analyzed by the distance from the optic nerve head. The datasets will serve as standards when using rabbits.

Purpose: We used spectral domain optical coherence tomography (SD-OCT) to assess the retinal and choroidal thicknesses of the rabbit, a commonly used animal model of ophthalmic disease. We report normative datasets. Methods: Semi-automated measurements were made on 15 normal right eyes of New Zealand white rabbits. Total retinal, inner retinal layer, outer retinal layer, choroidal, ganglion cell layer, ganglion cell complex, inner nuclear layer, and outer nuclear layer thicknesses were measured at fixed distances (0, 1, 2, 3, 4, and 5 mm) below the optic nerve head. Results: Total retinal layer (Pearson’s correlation coefficient [CC] = -0.778, p < 0.05), inner retinal layer (CC = -0.710, p < 0.05), outer retinal layer (CC = -0.495, p < 0.05), ganglion cell complex (CC = -0.292, p < 0.05), ganglion cell layer (CC = -0.284, p < 0.05), and outer nuclear layer thicknesses (CC = -0.760, p < 0.05) decreased with the distance from the optic nerve head. Inner nuclear layer thickness correlated negatively with the distance from the optic nerve head, but the correlation coefficient was low (CC = -0.263, p < 0.05). Choroidal thickness increased with the distance from the optic nerve head (CC = 0.511, p < 0.05). Conclusions Rabbit retinal thicknesses were measured and analyzed by the distance from the optic nerve head. The datasets will serve as standards when using rabbits.

OBJECTIVES: To determine the impact of cigarette prices on the decision to initiate and quit smoking by taking into account the interdependence of smoking and other behavioral risk factors. METHODS: The study population consisted of 3,000 male Koreans aged > or =20. A survey by telephone interview was undertaken to collect information on cigarette price, smoking and other behavioral risk factors. A two-part model was used to examine separately the effect of price on the decision to be a smoker, and on the amount of cigarettes smoked. RESULTS: The overall price elasticity of cigarettes was estimated at -0.66, with a price elasticity of -0.02 for smoking participation and -0.64 for the amount of cigarettes consumed by smokers. The inclusion of other behavioral risk factors reduced the estimated price elasticity for smoking participation substantially, but had no effect on the conditional price elasticity for the quantity of cigarettes smoked. CONCLUSIONS: From the public health and financial perspectives, an increase in cigarette price would significantly reduce smoking prevalence as well as cigarette consumption by smokers in Korea.
Adult ; *Costs and Cost Analysis ; Health Behavior ; Humans ; Korea/epidemiology ; Male ; Middle Aged ; Risk Factors ; Smoking/*economics/*prevention & control ; Social Environment ; Socioeconomic Factors ; *Tobacco

OBJECTIVES: A positive association between air pollution and both the incidence and prevalence of diabetes mellitus (DM) has been reported in some epidemiologic and animal studies, but little research has evaluated the relationship between air pollution and diabetic coma. Diabetic coma is an acute complication of DM caused by diabetic ketoacidosis or hyperosmolar hyperglycemic state, which is characterized by extreme hyperglycemia accompanied by coma. We conducted a time-series study with a generalized additive model using a distributed-lag non-linear model to assess the association between ambient air pollution (particulate matter less than 10 μm in aerodynamic diameter, nitrogen dioxide [NO2], sulfur dioxide, carbon monoxide, and ozone) and emergency department (ED) visits for DM with coma in Seoul, Korea from 2005 to 2009. METHODS: The ED data and medical records from the 3 years previous to each diabetic coma event were obtained from the Health Insurance Review and Assessment Service to examine the relationship with air pollutants. RESULTS: Overall, the adjusted relative risks (RRs) for an interquartile range (IQR) increment of NO2 was statistically significant at lag 1 (RR, 1.125; 95% confidence interval [CI], 1.039 to 1.219) in a single-lag model and both lag 0-1 (RR, 1.120; 95% CI, 1.028 to 1.219) and lag 0-3 (RR, 1.092; 95% CI, 1.005 to 1.186) in a cumulative-lag model. In a subgroup analysis, significant positive RRs were found for females for per-IQR increments of NO2 at cumulative lag 0-3 (RR, 1.149; 95% CI, 1.022 to 1.291). CONCLUSIONS: The results of our study suggest that ambient air pollution, specifically NO2, is associated with ED visits for diabetic coma.
Air Pollutants ; Air Pollution* ; Animals ; Carbon Monoxide ; Coma ; Diabetes Mellitus ; Diabetic Coma* ; Diabetic Ketoacidosis ; Emergencies* ; Emergency Service, Hospital* ; Female ; Humans ; Hyperglycemia ; Hyperglycemic Hyperosmolar Nonketotic Coma ; Incidence ; Insurance, Health ; Korea* ; Medical Records ; Nitrogen Dioxide ; Nonlinear Dynamics ; Prevalence ; Seoul* ; Sulfur Dioxide

A pyramidal molar is which has completely fused roots with a solitary enlarged canal. The purpose of this retrospective study was to assess the prevalence and characteristics of pyramidal molars among adolescent.
A total of 1,612 patients’ panoramic radiographs were screened. A total of 12,896 first and second molars were evaluated. The relative incidence and the correlations regarding the location of pyramidal molar (maxillary versus mandibular) and gender were analyzed using the chi-square test.
The overall incidence of patients with pyramidal molars was 1.49%. 24 patients were found to have a pyramidal molar and it was more prevalent in women (18 women and 6 men). The prevalence of pyramidal molars from all first and second molars examined was 0.31%. 88 percent of pyramidal molars occurred in maxilla. All pyramidal molars were second molar.
Pyramidal molar has a relatively poor periodontal prognosis compared with common multi-rooted teeth and it is important to understand the structural characteristics of root canal during pulp treatment. Clinicians should be able to understand the anatomical properties of pyramidal molar and apply it to treatment and prognostic evaluation.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.