BACKGROUND: Brief episode of coronary artery occlusion (i.e., ischemic preconditioning) makes the heart more resistant to injury from a subsequent ischemic insult. Although a great deal of effort has been made in studying ischemic preconditioning, the underlying mechanism of ischemic preconditioning and its effect on hypothermic insult has not been elucidated. This study was performed to see whether ischemic preconditioning protects against the depression of cardiac contractility induced by hypothermic cardioplegic arrest/reperfusion. And recently, adenosine was known to have some correlation with the mechanism of preconditioning. If so, does this effect remain after the blockade of adenosine receptor by 8-phenyl theophylline? METHOD: Twenty-four Sprague-Dawley rat weighed 250-350g were used and divided into three groups. Rat hearts were removed rapidly, and each isolated heart paced with a rate of 180/min was perused by modified Krebs-Hensleit buffer(KHB) solution on a Langendorff apparatus far an hour. After obtaining baseline data including left ventricular pressure(LVP), dp/dt, and coronary flow, cardiac arrest was induced by perfusion of 0degrees C crystalloid cardioplegic(St Thomas) solution. After that, all hearts were stored in the same St Thomas solution at salute temperature far 2 hours. In group I (control group), the hear was reperfused by KHB solution. In group II(preconditioning group), the heart was subjected to two 2-minute episode of global ischemia followed by 5 minute reperfusion with KHB solution(preconditioning) before cardiac arrest. In group III(phenyl theophylline group), the heart was subjected to preconditioning procedure and 8-phenyl theophylline at 10muM in concentration was added to KHB solution at time of reperfusion. Observing parameter was obtained in each group at 10, 20, 40 and 60 minutes after starting reperfusion and compared statistically by use of one way ANOVA test(STASTICA, release 4.5). P-value less than 0.05 was considered significant. RESULTS: Although depressed LVP, dp/dt, and Coronary flow were seen in all groups during the reperfusion period, the preconditioned group showed more effective recovery of LVP than that of the control group, especially at 10, 20 and 40 minutes(p<.05). We failed to demonstrate the difference between the phenyl theophylline group and the control group(p=NS). CONCLUSION: These results suggest that ischemic preconditioning has protective effect on recovery state of hypothermic cardioplegic arrest/reperfusion. Its protective effect was limited during early reperfusion stage and was blocked by adenosine blocker.
Adenosine ; Animals ; Coronary Vessels ; Depression ; Heart Arrest ; Heart* ; Ischemia ; Ischemic Preconditioning* ; Myocardium* ; Perfusion ; Rats ; Rats, Sprague-Dawley ; Receptors, Purinergic P1 ; Reperfusion ; Theophylline

BACKGROUND: Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.
Adenomyosis ; Biopsy ; Body Mass Index ; Conization ; Diabetes Mellitus ; Female ; Forests ; Humans ; Hypertension ; Korea ; Logistic Models ; Machine Learning ; Mass Screening ; Myoma ; Obstetric Labor, Premature ; Parity ; Parturition ; Placenta Previa ; Pregnancy ; Premature Birth ; Seoul

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were “depression” (title) and “random forest” (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1–100.0 for accuracy and 64.0–96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.

Background: and Purpose: This study uses an artificial-intelligence model (recurrent neural network) for evaluating the following hypothesis: social determinants of disease association in a middle-aged or old population are different across gender and age groups. Here, the disease association indicates an association among cerebrovascular disease, hearing loss and cognitive impairment. Methods: Data came from the Korean Longitudinal Study of Ageing (2014–2016), with 6,060 participants aged 53 years or more, that is, 2,556 men, 3,504 women, 3,640 aged 70 years or less (70−), 2,420 aged 71 years or more (71+). The disease association was divided into 8 categories: 1 category for having no disease, 3 categories for having 1, 3 categories for having 2, and 1 category for having 3. Variable importance, the effect of a variable on model performance, was used for finding important social determinants of the disease association in a particular gender/age group, and evaluating the hypothesis above. Results: Based on variable importance from the recurrent neural network, important social determinants of the disease association were different across gender and age groups:1) leisure activity for men; 2) parents alive, income and economic activity for women; 3) children alive, education and family activity for 70−; and 4) brothers/sisters cohabiting, religious activity and leisure activity for 70+. Conclusions The findings of this study support the hypothesis, suggesting the development of new guidelines reflecting different social determinants of the disease association across gender and age groups.

This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were “colonoscopy” (title) and “deep learning” (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0–95.0% for accuracy, 60.0–93.0% for sensitivity, 60.0–100.0% for specificity, 71.0–99.8% for the AUC, 70.1–93.3% for precision, 81.0–96.3% for F1, 57.2–89.5% for the IOU, 75.1–97.3% for Dice and 66–182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.

OBJECTIVES: To comprehensively examine the relationship between current sleep duration and past suicidal idea or attempt among Korean adolescents. METHODS: Data came from the 2009 Korea Youth Risk Behavior Web-based Survey with 75 066 participants (with the participation rate of 97.6%) in 800 middle and high schools. Binary logistic regression was conducted by gender and depressed mood to identify significant factors for suicidal ideation/attempt. The dependent variable was the log odds of suicidal ideation/attempt, while the independent/control variables were sleep duration and other demographic, socio-economic and health-related factors. RESULTS: A negative association between sleep duration and suicidal ideation or attempt was weaker for those with depressed mood than for those without such experience in Korea for Year 2009. The odds ratio of suicidal ideation/attempt regarding less than 4 hours of sleep compared to 6 to 7 hours of sleep, was smaller in a group with depressed mood than in a group without such experience, for example, 1.64 (95% confidence interval [CI], 1.29 to 2.08) vs. 2.06 (95% CI, 1.34 to 3.17) for men's suicidal ideation, 2.50 (95% CI, 1.69 to 3.69) vs. 3.89 (95% CI, 1.74 to 8.66) for men's suicidal attempt. A negative association between age (or self-rated health) and suicidal ideation/attempt was also weaker for those with depressed mood than for those without such experience in the nation for the year. CONCLUSIONS: There was a negative association between sleep duration and suicidal ideation/attempt in Korea for Year 2009 and this association was weaker for those with depressed mood than for those without such experience. Based on the findings of this study, adolescents' better mental health and longer, more comfortable sleep might help to prevent their suicidal ideation and attempt in Korea.
Adolescent ; Demography ; Female ; Humans ; Internet ; Logistic Models ; Male ; Odds Ratio ; Republic of Korea ; Sex Factors ; *Sleep ; Socioeconomic Factors ; *Suicidal Ideation ; Suicide, Attempted/statistics & numerical data ; Time Factors ; User-Computer Interface

PURPOSE: The purpose of this study was to evaluate the cost effectiveness of colorectal cancer screening interventions with their effects on health disparity being considered. MATERIALS AND METHODS: Markov cohort simulation was conducted with the cycle/duration of 1/40 year(s). Data came from the results of randomized trials and others. Participants were hypothetical cohorts aged 50 years as of year 2013 in 16 Korean provinces. The interventions until the age of 80 were annual organized fecal occult blood test (FOBT) (standard screening), annual FOBT with basic reminders for provinces with higher mortalities than the national average (targeted reminder) and annual FOBT with basic/enhanced reminders for all provinces (universal reminder 1 and 2). The comparison was non-screening, the outcome was quality-adjusted life years, and only medical costs for screening and treatment were considered from a societal perspective. The Atkinson incremental cost effectiveness ratio (Atkinson ICER), the incremental cost effectiveness ratio adjusted by the Atkinson Inequality Index, was used to evaluate the cost effectiveness of the four interventions with their impacts on regional health disparity being considered. RESULTS: Health disparity was smallest (or greatest) in non-screening (or the standard screening). The targeted reminder had smaller health disparity, and smaller Atkinson ICER with respect to standard screening, than did the universal reminder 1 and 2. CONCLUSION: The targeted reminder might be more cost effective than the universal reminders with their effects on health disparity being considered. This study helps to develop promotional effort for colorectal cancer screening with both the greatest cost effectiveness and the smallest health disparity.
Cohort Studies ; Colorectal Neoplasms* ; Cost-Benefit Analysis* ; Health Status Disparities ; Mass Screening* ; Mortality ; Occult Blood ; Quality-Adjusted Life Years ; Socioeconomic Factors

Traditionally, the Rhododendron species has been used in gastrointestinal disorder or hypertension. Grayanotoxin exists in honey, flowers, pollen, and the nectar of the Rhododendron species. We experienced 3 cases of Grayanotoxin intoxication. The symptoms of intoxication were nausea, vomiting, hypotension, bradycardia, diplopia, dizziness, and chest discomfort. Generally, the treatment for Grayanotoxin intoxication is fluid resuscitation and injection of atropine sulfate. The patients who were intoxicated with Grayanotoxin were discharged without complication after supportive care.
Atropine ; Bradycardia ; Diplopia ; Dizziness ; Flowers ; Honey ; Humans ; Hypertension ; Hypotension ; Nausea ; Plant Nectar ; Pollen ; Resuscitation ; Rhododendron ; Thorax ; Vomiting

Objective: This study employs machine learning and population-based data to examine major factors of antidepressant medication including nitrogen dioxides (NO2) seasonality. Methods: Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants with the age of 15–79 years, residence in the same districts of Seoul and no history of antidepressant medication during 2002–2012. The dependent variable was antidepressant-free months during 2013–2015 and the 103 independent variables for 2012 or 2015 were considered, e.g., particulate matter less than 2.5 micrometer in diameter (PM2.5), PM10, NO2, ozone (O3), sulphur dioxide (SO2) and carbon monoxide (CO) in each of 12 months in 2015. Results: It was found that the Cox hazard ratios of NO2 were statistically significant and registered values larger than 10 for every three months: March, June–July, October, and December. Based on random forest variable importance and Cox hazard ratios in brackets, indeed, the top 20 factors of antidepressant medication included age (0.0041 [1.69–2.25]), migraine and sleep disorder (0.0029 [1.82]), liver disease (0.0017 [1.33–1.34]), exercise (0.0014), thyroid disease (0.0013), cardiovascular disease (0.0013 [1.20]), asthma (0.0008 [1.19–1.20]), September NO2 (0.0008 [0.01]), alcohol consumption (0.0008 [1.31–1.32]), gender - woman (0.0007 [1.80–1.81]), July NO2 (0.0007 [14.93]), July PM10 (0.0007), the proportion of the married (0.0005), January PM2.5 (0.0004), September PM2.5 (0.0004), chronic obstructive pulmonary disease (0.0004), economic satisfaction (0.0004), January PM10 (0.0003), residents in welfare facilities per 1,000 (0.0003 [0.97]), and October NO2 (0.0003). Conclusion Antidepressant medication has strong associations with neighborhood conditions including NO2 seasonality and welfare support.