In Korea, the prevalence of depression is increasing in adolescents and the most common cause of death of adolescents has been reported as suicide. At a time of increasing predicament of mental health of adolescents, there are few studies on whether secondhand smoking is associated with mental health in adolescents. The objective of this study was to determine whether exposure to secondhand smoke is associated with mental health-related variables, such as depression, stress, and suicide, in Korean adolescents. Data from the eleventh Korea youth risk behavior web-based survey, a nationally representative survey of 62,708 participants (30,964 males and 31,744 females), were analyzed. For students of aged 12 to 18 years, extensive data including secondhand smoking, mental health, sociodemographic variables, and physical health were collected. Chi-square analysis, multiple logistic regression analysis and ordered logistic regression analysis were performed to estimate the association and dose-response relation between secondhand smoking and mental health. Compared with the non-exposed group, the odds ratios (OR) of depression, stress, suicidal ideation, suicidal planning and suicidal attempt in the secondhand smoking exposed group were 1.339, 1.192, 1.303, 1.437 and 1.505, respectively (all P < 0.001). When subjects were classified into two secondhand smoke exposure groups, with increasing secondhand smoking experience, higher was the OR for each mental health related variable, in a dose-response relation. Our findings suggest that secondhand smoking is associated with poor mental health such as depression, stress, and suicide, showing a dose-response relation in Korean adolescents.

A higher family adaptability resulted in a lower degree of anxiety and depression in terminally ill cancer patients. The higher the family cohesion, the lower the degree of depression in the patient. The presence of the family caregiver and the visiting time by family and friends did not affect the patient's anxiety and depression.

Sleep disorder and metabolic syndrome (MetS) are important health-related problems. Recently, sleep duration has decreased among Korean adults. In this study, we examined whether sleep quality is associated with MetS by analyzing 301 subjects, aged 20 years or over, without acute and severe illness who visited three primary care clinics. Sleep duration, sleep quality and the risk of sleep-related breathing disorder (SRBD) were assessed with a standardized sleeping-estimating instrument. MetS was defined according to the modified diagnostic criteria of the National Cholesterol Education Program Adult Treatment Panel-III using the Korean abdominal obesity definition. In the multiple logistic regression analysis, compared with the 7-hour sleep group, the adjusted odds ratios (ORs) of the ≤ 5-and ≥ 9-hour sleep groups for MetS were 4.89 and 5.98, respectively. Compared with the good-sleep quality and low-SRBD risk groups, the adjusted ORs of the poor-quality sleep and high-SRBD risk groups were 3.83 and 1.92, respectively (p < 0.05). In the ≤ 5-and ≥ 9-hour sleep groups, the prevalence of elevated triglyceride and high HOMA-IR was higher (p = 0.069). In the poor-quality sleep group, the prevalence of abdominal obesity, elevated triglyceride, low HDL cholesterol, high fasting insulin and high HOMA-IR were higher. In the high-SRBD risk group, the prevalence of abdominal obesity, obesity and elevated triglyceride were higher (p < 0.05). Overall, the ≤ 5-or ≥ 9-hour sleep duration, poor-quality sleep and high-SRBD risk are related with the high prevalence of MetS, perhaps through elevated insulin-resistance resulting from adiposity.

Objectives This study utilized the method of medical record review to determine characteristics of adverse events that occurred in the inpatient units of hospitals in Korea as well as the variations in adverse events between institutions. Design A two-stage retrospective medical record review was conducted. The first stage was a nurse review, where two nurses reviewed medical records of discharged patients to determine if screening criteria had been met. In the second stage, two physicians independently reviewed medical records of patients identified in the first stage, to determine whether an adverse event had occurred. Setting Inpatient units of six hospitals. Participants Medical records of 2,596 patients randomly selected were reviewed in the first stage review. Intervention(s) N/A. Main Outcome Measure(s) Adverse events. Results A total of 277 patients (10.7%) were confirmed to have had one or more adverse event(s), and a total of 336 adverse events were identified. Physician reviewers agreed about whether an adverse event had occurred for 141 patients (5.4%). The incidence rate of adverse events was at least 1.3% and a maximum of 19.4% for each hospital. Most preventability scores were less than four points (non-preventable), and there were large variations between reviewers and institutions. Conclusions Given the level of variation in the identified adverse events, further studies that include more medical institutions in their investigations are needed, and a third-party committee should be involved to address the reliability issues regarding the occurrence and characteristics of the adverse events.

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the β-Amyloid (Aβ) deposition. We designed a convolutional neural network (CNN) model that predicts the Aβ-positive and Aβ-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patientbased standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the Aβ-positive and Aβ-negative status.