Risks of Dementia After Treatment with an Anticholinergic, Beta-3 Agonist, or Combination of Both for an Overactive Bladder: A Korean National Cohort Study.

BACKGROUND AND OBJECTIVE: An overactive bladder (OAB) is primarily managed with behavioural therapy and using anticholinergics and beta-3 agonists. Reports have shown that the use of anticholinergics by OAB patients was associated with an increased risk of new-onset dementia compared with those using beta-3 agonists. This study compares the risks of dementia among patients with an OAB starting on a beta-3 agonist alone, an anticholinergic alone, or a combination treatment. METHODS: Using data from the Korean National Health Insurance Service database, we studied a nationwide population cohort comprising patients newly diagnosed with an OAB who initiated their OAB medications between 2015 and 2020. The treatment types were categorised as anticholinergics (oxybutynin, solifenacin, tolterodine, trospium, fesoterodine, flavoxate, and propiverine) alone, a beta-3 agonist (mirabegron) alone, and combination therapy (an anticholinergic plus the beta-3 agonist). To evaluate the impact of cumulative drug exposure, we quantified the cumulative exposure to solifenacin and mirabegron as cumulative defined daily doses (cDDDs) using proportional hazards regression analyses, adjusted for factors known to be associated with dementia. KEY FINDINGS AND LIMITATIONS: Among the study's 3 452 705 patients, 671 974 were new users of a beta-3 agonist alone (19.5%), 1 943 414 new users of anticholinergics alone (56.3%), and 837 317 receiving combination therapy (24.3%). The most common anticholinergic used both alone and as part of a combination treatment was solifenacin (42.9% and 56.3%, respectively). There was an increased risk of dementia between the users of an anticholinergic alone (adjusted hazard ratio [aHR] = 1.213; 95% confidence interval [CI], 1.195-1.232) and those taking a combination treatment (aHR = 1.345; 95% CI, 1.323-1.366) compared with the users of beta-3 agonists alone after the adjustment of covariates. However, the incidence of dementia was also significantly higher, with an increase in the cumulative dose of mirabegron (aHR = 1.062 [1.021-1.106] for 28-120 cDDDs and aHR = 1.044 [1.004-1.084)] for patients who received >121 cDDDs compared with those who received <27 cDDDs). A marked increased risk of dementia was associated with the use of solifenacin, tolterodine, fesoterodine, and propiverine, both separately and in combination with mirabegron. CONCLUSIONS AND CLINICAL IMPLICATIONS: In this large Korean cohort, the use of anticholinergics with or without a beta-3 agonist increased the risk of new-onset dementia compared with the use of a beta-3 agonist alone. Given that the risk of dementia was most significantly elevated with combination treatments, care should be taken when considering combination treatment for OAB patients with risk factors for dementia. Furthermore, there could be a possible association between beta-3 agonists and dementia, although future studies are needed. PATIENT SUMMARY: This study investigated the risk of dementia induced by overactive bladder (OAB) treatment in a large Korean cohort. Two representative OAB treatment drugs, anticholinergics and beta-3 agonists, both increased the risk of new-onset dementia. Clinicians should be cautious in using OAB treatment drugs since no drugs could be concluded as safe.

Status and prediction of disapproval of the Korean workers' compensation insurance for diseases and injuries.

OBJECTIVE: Workers' diseases and injuries are often highly related to work. However, due to limited resources and unclear work relatedness, workers' compensation insurance cannot cover all diseases or injuries among workers. This study aimed to estimate the status and probability of disapproval from national workers' compensation insurance using basic information from Korean workers' compensation system. METHODS: The compensation insurance data for Korean workers consists of personal, occupational, and claims data. We describe the status of disapproval by workers' compensation insurance according to the type of disease or injury. A prediction model for disapproval by workers' compensation insurance was established by applying two machine-learning methods with a logistic regression model. RESULTS: Among 42 219 cases, there were significantly higher risks of disapproval by workers' compensation insurance for women, younger workers, technicians, and associate professionals. We established a disapproval model for workers' compensation insurance after the feature selection. The prediction model for workers' disease disapproval by the workers' compensation insurance showed a good performance, and the prediction model for workers' injury disapproval showed a moderate performance. CONCLUSION: This study is the first attempt to demonstrate the status and prediction of disapproval by workers' compensation insurance using basic information from the Korean workers' compensation data. These findings suggest that diseases or injuries have a low level of evidence of work relatedness or there is a lack of research on occupational health. It is also expected to contribute to the efficiency of the management of workers' diseases or injuries.

Effects of exposure to ambient air pollution on pulmonary function impairment in Korea: the 2007-2017 Korea National Health and Nutritional Examination Survey.

OBJECTIVES: This study aimed to investigate the association between pulmonary function and air pollution using 2007-2017 data from the Korea National Health and Nutrition Examination Survey, a nationwide cross-sectional representative survey. METHODS: A total of 27,378 participants that had sampling weights from a complex sample survey were included in this study. Using the data for forced expiratory volume in 1 second and forced vital capacity, the participants with pulmonary function impairment were classified according to the criteria of restrictive lung disease and chronic obstructive pulmonary disease (COPD). Exposure to ambient air pollution was estimated using the Community Multiscale Air Quality model. Multivariate linear and logistic regression analyses with complex samples were used to determine the associations between pulmonary function and air pollution after adjusting for covariates. RESULTS: In total, 13.2% of the participants aged >40 years had COPD, and 10.7% were classified as being in the restrictive lung disease group. According to the multivariate logistic regression model, the odds ratios for the fourth quartiles of particulate matter less than 10 µm in diameter (PM10), particulate matter less than 2.5 µm in diameter (PM2.5) and carbon monoxide (CO) with a 2-year lag period were 1.203 (95% confidence interval [CI], 1.036 to 1.396), 1.283 (95% CI, 1.101 to1.495), and 1.292 (95% CI, 1.110 to 1.504), respectively, using the restrictive lung disease group as an event after adjusting for covariates in the complex sample. CONCLUSIONS: Long-term exposure to PM10, PM2.5, and CO was significantly associated with pulmonary function, especially restrictive lung disease.

Forecasting imported COVID-19 cases in South Korea using mobile roaming data.

As the number of global coronavirus disease (COVID-19) cases increases, the number of imported cases is gradually rising. Furthermore, there is no reduction in domestic outbreaks. To assess the risks from imported COVID-19 cases in South Korea, we suggest using the daily risk score. Confirmed COVID-19 cases reported by John Hopkins University Center, roaming data collected from Korea Telecom, and the Oxford COVID-19 Government Response Tracker index were included in calculating the risk score. The risk score was highly correlated with imported COVID-19 cases after 12 days. To forecast daily imported COVID-19 cases after 12 days in South Korea, we developed prediction models using simple linear regression and autoregressive integrated moving average, including exogenous variables (ARIMAX). In the validation set, the root mean squared error of the linear regression model using the risk score was 6.2, which was lower than that of the autoregressive integrated moving average (ARIMA; 22.3) without the risk score as a reference. Correlation coefficient of ARIMAX using the risk score (0.925) was higher than that of ARIMA (0.899). A possible reason for this time lag of 12 days between imported cases and the risk score could be the delay that occurs before the effect of government policies such as closure of airports or lockdown of cities. Roaming data could help warn roaming users regarding their COVID-19 risk status and inform the national health agency of possible high-risk areas for domestic outbreaks.

Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina.

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season.

The Modified International Standard Classification of Occupations defined by the clustering of occupational characteristics in the Korean Working Conditions Survey.

The modified International Standard Classification of Occupations (ISCO) has been used empirically to report or investigate working conditions or worker status. We used principal component analysis and k-means clustering to analyze the working population based on 67 occupational characteristics among 23,060 workers from the fourth Korean Working Conditions Survey in 2014. The three-cluster approach classified workers into major groups 1-4 (managers, professionals, technicians, and clerical support workers), 5-6 (service, sales, agricultural, forestry, and fishery workers), and 7-9 (crafts, trades, machine operators, assemblers, and elementary occupations) based on the ISCO-08. The results of the current study suggest a well-defined clustered occupational classification that can be used to report or investigate workers.

Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region.

BACKGROUND: The effect of temperature and humidity on the incidence of influenza may differ by climate region. In addition, the effect of diurnal temperature range on influenza incidence is unclear, according to previous study findings. OBJECTIVES: The aim of this study was to analyze the effects of temperature, humidity, and diurnal temperature range on the incidence of influenza in Seoul, Republic of Korea, which is located in a temperate region. METHODS: We used Korean National Health insurance data to assess the weekly influenza incidence between 2010 and 2016, and used meteorological data from Seoul. To investigate the effect of temperature, relative humidity, and diurnal temperature range levels on influenza incidence, we used a distributed lag non-linear model. RESULTS: The risk of influenza incidence was significantly increased with low daily temperatures of 0-5°C and low (30%-40%) or high (70%) relative humidity. We found a positive significant association between diurnal temperature range and influenza incidence in this study. CONCLUSIONS: Influenza incidence increased with low temperature and low/high humidity in a temperate region. Influenza incidence also increased with high diurnal temperature range, after considering temperature and humidity.

Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries.

To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018-2019 seasonal influenza outbreak in the U.S., we collected the surveillance data of 164 countries using the FluNet database, search queries from Google Trends, and temperature from 2010 to 2018. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. We identified the time lag between two time-series which were weekly surveillances for ILI, total influenza (Total INF), influenza A (INF A), and influenza B (INF B) viruses between two countries using cross-correlation analysis. In order to forecast ILI, Total INF, INF A, and INF B of next season (after 26 weeks) in the U.S., we developed prediction models using linear regression, auto regressive integrated moving average, and an artificial neural network (ANN). As a result of cross-correlation analysis between the countries located in northern and southern hemisphere, the seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of ANN models for ILI for validation set in 2015-2019 was 0.758 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation between seasonal influenza patterns in the U.S., Australia, and Chile could be used to forecast the next seasonal influenza pattern, which can help to determine influenza vaccine strategy approximately six months ahead in the U.S.

Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea.

BACKGROUND: Death by suicide is a preventable public health concern worldwide. The aim of this study is to investigate the probability of suicide death using baseline characteristics and simple medical facility visit history data using Cox regression, support vector machines (SVMs), and deep neural networks (DNNs). METHOD: This study included 819,951 subjects in the National Health Insurance Service (NHIS)-Cohort Sample Database from 2004 to 2013. The dataset was divided randomly into two independent training and validation groups. To improve the performance of predicting suicide death, we applied SVM and DNN to the same training set as the Cox regression model. RESULTS: Among the study population, 2546 people died by intentional self-harm during the follow-up time. Sex, age, type of insurance, household income, disability, and medical records of eight ICD-10 codes (including mental and behavioural disorders) were selected by a Cox regression model with backward stepwise elimination. The area of under the curve (AUC) of Cox regression (0.688), SVM (0.687), and DNN (0.683) were approximately the same. The group with top .5% of predicted probability had hazard ratio of 26.21 compared to that with the lowest 10% of predicted probability. LIMITATIONS: This study is limited by the lack of information on suicidal ideation and attempts, other potential covariates such as information of medication and subcategory ICD-10 codes. Moreover, predictors from the prior 12-24 months of the date of death could be expected to show better performances than predictors from up to 10 years ago. CONCLUSIONS: We suggest a 10-year probability prediction model for suicide death using general characteristics and simple insurance data, which are annually conducted by the Korean government. Suicide death prevention might be enhanced by our prediction model.

Risk factors of suicide attempt among people with suicidal ideation in South Korea: a cross-sectional study.

BACKGROUND: Suicide is a serious public health concern worldwide, and the fourth leading cause of death in Korea. Few studies have focused on risk factors for suicide attempt among people with suicidal ideation. The aim of the present study was to investigate the risk factors and develop prediction models for suicide attempt among people with suicidal ideation in the Korean population. METHOD: This study included 1567 men and 3726 women aged 20 years and older who had suicidal ideation from the Korea National Health and Nutrition Examination Survey from 2007 to 2012. Among them, 106 men and 188 women attempted suicide. Multivariate logistic regression analysis with backward stepwise elimination was performed to find risk factors for suicide attempt. Sub-group analysis, dividing participants into under 50 and at least 50 years old was also performed. RESULTS: Among people with suicidal ideation, age, education, cancer, and depressive disorder were selected as risk factors for suicide attempt in men. Age, education, national basic livelihood security, daily activity limitation, depressive disorder, stress, smoking, and regular exercise were selected in women. Area under curves of our prediction models in men and women were 0.728 and 0.716, respectively. CONCLUSIONS: It is important to pay attention to populations with suicidal ideation and the risk factors mentioned above. Prediction models using the determined risk factors could be useful to detect high-risk groups early for suicide attempt among people with suicidal ideation. It is necessary to develop specific action plans for these high-risk groups to prevent suicide.

Increased fragility fracture risk in Korean women who snore: a 10-year population-based prospective cohort study.

BACKGROUND: Snoring is frequently associated with obstructive sleep apnea (OSA). Previous studies have shown that bone mineral density was significantly lower in patients with OSA than in controls; however, these studies did not focus on fractures. Fragility fractures can lead to long-term disabilities and a decrease in quality of life. The present study aimed to investigate the association between snoring and fragility fractures. METHODS: This study included 2969 men and 3220 women aged 40 years and older from the Ansung and Ansan cohort studies in Korea. During a 10-year follow-up period, 129 and 273 fracture cases were reported in men and women, respectively. RESULTS: Severe snoring (6-7 nights per week or sleep disturbance by snoring in the next room) was a statistically significant risk factor for fracture (p = 0.006, hazard ratio 1.68, 95% confidence interval 1.16-2.43) after adjusting for covariates related to fragility fracture in women. However, both snoring and severe snoring groups did not show significant associations with the fracture risk in men. CONCLUSIONS: Thus, information on the frequency of snoring in women may improve the accuracy of fragility fracture risk prediction, which can help in deciding whether intervention or treatment is necessary.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study.

BACKGROUND: Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. METHODS: The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. RESULTS: The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). CONCLUSIONS: The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

OBJECTIVES: Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes. MATERIALS AND METHODS: We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. RESULTS: The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. CONCLUSIONS: We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.

Patient exposure to extremely low-frequency magnetic fields during laparoscopic and robotic surgeries.

BACKGROUND: Laparoscopic and robotic surgeries require many electronic devices, and the hazard of extremely low-frequency magnetic fields (ELF-MFs) from these devices to humans remains uncertain. This study aimed to measure and compare patients' exposure levels to ELF-MFs in laparoscopic and robotic surgeries. METHODS: The intensity of ELF-MF exposure to patients was measured every 10 s during 30 laparoscopic surgeries and 30 robotic surgeries using portable ELF-MF measuring devices with logging capabilities. RESULTS: The mean ELF-MF exposures were 0.11 ± 0.07 µT for laparoscopic surgeries and 0.12 ± 0.10 µT for robotic surgeries. There were no significant differences between the laparoscopic and robotic surgeries. CONCLUSIONS: Patients' mean ELF-MF exposure levels in laparoscopic and robotic surgeries were lower than 0.2 µT, which is considered safe according to previous studies. However, because many medical devices have been implemented for multiple purposes in hospitals, the MF environment in hospitals regarding patient health should not be overlooked. Copyright © 2015 John Wiley & Sons, Ltd.

Exposure of Surgeons to Magnetic Fields during Laparoscopic and Robotic Gynecologic Surgeries.

STUDY OBJECTIVE: To measure and compare levels of extremely-low-frequency magnetic field (ELF-MF) exposure to surgeons during laparoscopic and robotic gynecologic surgeries. DESIGN: Prospective case-control study. DESIGN CLASSIFICATION: Canadian Task Force I. SETTING: Gynecologic surgeries at the Yonsei University Health System in Seoul, Korea from July to October in 2014. PATIENTS: Ten laparoscopic gynecologic surgeries and 10 robotic gynecologic surgeries. INTERVENTION: The intensity of ELF-MF exposure to surgeons was measured every 4 seconds during 10 laparoscopic gynecologic surgeries and 10 robotic gynecologic surgeries using portable ELF-MF measuring devices with logging capability. MEASUREMENT AND MAIN RESULTS: The mean ELF-MF exposures were .1 ± .1 mG for laparoscopic gynecologic surgeries and .3 ± .1 mG for robotic gynecologic surgeries. ELF-MF exposure levels to surgeons during robotic gynecologic surgery were significantly higher than those during laparoscopic gynecologic surgery (p < .001) after adjustment for duration of measurement. CONCLUSION: The present study demonstrated low levels of ELF-MF exposure to surgeons during robotic gynecologic surgery and conventional laparoscopic surgery, hoping to alleviate concerns regarding the hazards of MF exposure posed to surgeons and hospital staff.

Heart Rate Detection During Sleep Using a Flexible RF Resonator and Injection-Locked PLL Sensor.

Novel nonintrusive technologies for wrist pulse detection have been developed and proposed as systems for sleep monitoring using three types of radio frequency (RF) sensors. The three types of RF sensors for heart rate measurement on wrist are a flexible RF single resonator, array resonators, and an injection-locked PLL resonator sensor. To verify the performance of the new RF systems, we compared heart rates between presleep time and postsleep onset time. Heart rates of ten subjects were measured using the RF systems during sleep. All three RF devices detected heart rates at 0.2 to 1 mm distance from the skin of the wrist over clothes made of cotton fabric. The wrist pulse signals of a flexible RF single resonator were consistent with the signals obtained by a portable piezoelectric transducer as a reference. Then, we confirmed that the heart rate after sleep onset time significantly decreased compared to before sleep. In conclusion, the RF system can be utilized as a noncontact nonintrusive method for measuring heart rates during sleep.

Exposure of surgeons to extremely low-frequency magnetic fields during laparoscopic and robotic surgeries.

The development of new medical electronic devices and equipment has increased the use of electrical apparatuses in surgery. Many studies have reported the association of long-term exposure to extremely low-frequency magnetic fields (ELF-MFs) with diseases or cancer. Robotic surgery has emerged as an alternative tool to overcome the disadvantages of conventional laparoscopic surgery. However, there has been no report regarding how much ELF-MF surgeons are exposed to during laparoscopic and robotic surgeries. In this observational study, we aimed to measure and compare the ELF-MFs that surgeons are exposed to during laparoscopic and robotic surgery.The intensities of the ELF-MFs surgeons are exposed to were measured every 4 seconds for 20 cases of laparoscopic surgery and 20 cases of robotic surgery using portable ELF-MF measuring devices with logging capability.The mean ELF-MF exposures were 0.6 ±â€Š0.1 mG for laparoscopic surgeries and 0.3 ±â€Š0.0 mG for robotic surgeries (significantly lower with P < 0.001 by Mann-Whitney U test).Our results show that the ELF-MF exposure levels of surgeons in both robotic and conventional laparoscopic surgery were lower than 2 mG, which is the most stringent level considered safe in many studies. However, we should not overlook the effects of long-term ELF-MF exposure during many surgeries in the course of a surgeon's career.

Parathyroid hormone, calcium, and sodium bridging between osteoporosis and hypertension in postmenopausal Korean women.

The coexistence of osteoporosis and hypertension, which are considered distinct diseases, has been widely reported. In addition, daily intake of calcium and sodium, as well as parathyroid hormone levels (PTH), is known to be associated with osteoporosis and hypertension. This study aimed to determine the association of low calcium intake, high sodium intake, and PTH levels with osteoporosis and hypertension in postmenopausal Korean women. Data for postmenopausal Korean women aged 50 years or older were obtained from the Korea National Health and Nutrition Examination Survey 2008-2011. Osteoporosis was diagnosed using dual energy X-ray absorptiometry, while hypertension was diagnosed using blood pressure data. The odds ratios for osteoporosis and hypertension were calculated using logistic regression analysis for quartiles of the daily calcium intake, daily sodium intake, and PTH levels. Women with hypertension had a high coexistence of osteoporosis (43.6 vs. 36.5 %; P = 0.022), and vice versa (21.1 vs. 16.6 %; P = 0.022). PTH was significantly associated with osteoporosis and hypertension, and a high intake of calcium was strongly correlated with a low incidence of osteoporosis. This is the first study to report the characteristics of postmenopausal Korean women who have high dietary sodium intake and low dietary calcium intake, in association with the incidence of osteoporosis and hypertension. Osteoporosis and hypertension were strongly associated with each other, and PTH appears to be a key mediator of both diseases, suggesting a possible pathogenic link.

Prediction of ATLS hypovolemic shock class in rats using the perfusion index and lactate concentration.

It is necessary to quickly and accurately determine Advanced Trauma Life Support (ATLS) hemorrhagic shock class for triage in cases of acute hemorrhage caused by trauma. However, the ATLS classification has limitations, namely, with regard to primary vital signs. This study identified the optimal variables for appropriate triage of hemorrhage severity, including the peripheral perfusion index and serum lactate concentration in addition to the conventional primary vital signs. To predict the four ATLS classes, three popular machine learning algorithms with four feature selection methods for multicategory classification were applied to a rat model of acute hemorrhage. A total of 78 anesthetized rats were divided into four groups for ATLS classification based on blood loss (in percent). The support vector machine one-versus-one model with the Kruskal-Wallis feature selection method performed best, with 80.8% accuracy, relative classifier information of 0.629, and a kappa index of 0.732. The new hemorrhage-induced severity index (lactate concentration/perfusion index), diastolic blood pressure, mean arterial pressure, and the perfusion index were selected as the optimal variables for predicting the four ATLS classes by support vector machine one-versus-one with the Kruskal-Wallis method. These four variables were also selected for binary classification to predict ATLS classes I and II versus III and IV for blood transfusion requirement. The suggested ATLS classification system would be helpful to first responders by indicating the severity of patients, allowing physicians to prepare suitable resuscitation before hospital arrival, which could hasten treatment initiation.