Association Between Excessive Alcohol Consumption and Echocardiographic Parameters According to the Presence of Flushing Reaction in Korean Men: A Community-Based Study.

BACKGROUND: The objective of this study was to investigate the effect of excessive alcohol consumption on heart reflected by various echocardiographic parameters according to the presence or absence of flushing reaction that might reflect acetaldehyde metabolism. METHODS: A total of 854 Korean men without significant cardiovascular diseases who underwent echocardiography and participated in the Korean Healthy Twin Study were used as subjects of this study. These subjects were classified into 3 categories: nondrinker, moderate drinker (≤196 g/wk), and heavy drinker (>196 g/wk) within 2 strata of flushing reaction to alcohol drinking. Association between echocardiographic measurements and categories of the amount of alcohol consumption considering flushing reaction were evaluated using mixed linear regression model. RESULTS: The proportion of flushers among drinkers was 39.5% (278 of 703). In stratified analysis by flushing reaction, nonflushers showed significantly higher left ventricular mass index (ß: 4.605; 95% CI: 0.966, 8.243) and significantly lower ratio of peak early diastolic velocities (E peak) over peak late diastolic velocities of mitral inflow (ß: -0.103; 95% CI: -0.198, -0.008) in heavy drinkers compared to nondrinkers. Flushers showed significantly higher left atrial (LA) volume index (ß: 2.712; 95% CI: 0.456, 4.968) in heavy drinkers and significantly lower ratio of E peak over the peak early diastolic mitral annular velocities (ß: -0.493; 95% CI: -0.902, -0.085) in moderate drinkers compared to nondrinkers. However, the interaction according to flushing reaction was only statistically significant for the association between alcohol consumption and LA volume index (p for interaction = 0.004). CONCLUSIONS: Alcohol consumption is associated with changes in cardiac structure and function. Such association might be influenced by acetaldehyde metabolism.

Conditional Covariances for the Signal Lag Measurements in Fluoroscopic Imaging.

In fluoroscopic imaging, we can acquire X-ray image sequences using a flat-panel dynamic detector. However, lag signals from previous frames are added to the subsequently acquired images and produce lag artifacts. The lag signals also inflate the measured noise power spectrum (NPS) of a detector. In order to correct the measured NPS, the lag correction factor (LCF) is generally used. However, the nonuniform temporal gain (NTG), which is from inconsistent X-ray sources and readout circuits, can significantly distort the LCF measurements. In this paper, we propose a simple scheme to alleviate the NTG problem in order to accurately and efficiently measure the detector LCF. We first theoretically analyze the effects of NTG, especially on the correlation-based LCF measurement methods, where calculating the correlation coefficients are required. In order to remove the biases due to NTG, a notion of conditional covariance is considered for unbiased estimates of the correlation coefficients. Experiments using practical X-ray images acquired from a dynamic detector were conducted. The proposed approach could yield accurate LCF values similarly to the current approaches of the direct and U-L corrections with a low computational complexity. By calculating the correlation coefficients based on conditional covariance, we could obtain accurate LCF values even under the NTG environment. This approach does not require any preprocessing scheme of the direct or U-L correction and can provide further accurate LCF values than the method of IEC62220-1-3 does.

Measurement of the Lag Correction Factor in Low-Dose Fluoroscopic Imaging.

Lag signals occur at images sequentially acquired from a flat-panel (FP) dynamic detector in fluoroscopic imaging due to charge trapping in photodiodes and incomplete readouts. This lag signal produces various lag artifacts and prevents analyzing detector performances because the measured noise power spectrum (NPS) values are reduced. In order to design dynamic detectors, which produce low lag artifacts, accurately evaluating the detector lag through its quantitative measurement is required. A lag correction factor can be used to both examine the detector lag and correct measured NPS. To measure the lag correction factor, the standard of IEC62220-1-3 suggests a temporal power spectral density under a constant potential generator for the x-rays. However, this approach is sensitive to disturbing noise and thus becomes a problem in obtaining accurate estimates especially at low doses. The Granfors-Aufrichtig (GA) method is appropriate for noisy environments with a synchronized pulse x-ray source. However, for the x-ray source of a constant potential generator, gate-line scanning to read out charges produces a nonuniform lag signal within each image frame and thus the conventional GA method yields wrong estimates. In this paper, we first analyze the GA method and show that the method is an asymptotically unbiased estimate. Based on the GA method, we then propose three algorithms considering the scanning process and exposure leak, in which line estimates along the gate line are exploited. We extensively conducted experiments for FP dynamic detectors and compared the results with conventional algorithms.

Using deep learning for pixel-defect corrections in flat-panel radiography imaging.

Purpose: Flat-panel radiography detectors employ thin-film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects occur due to circuit shorts or opens in the TFT panel. The defects may degrade the image quality, as well as lower the production yield, and eventually raise the production cost. Hence, it is important to develop an appropriate defect correction algorithm for acquired images. Traditional correction algorithms are based on a complicated adaptive filtering technique, which exploits neighbor pixels, to faithfully preserve the edge components. Because of the complexity of the traditional sophisticated approaches, optimizing their correction performances is difficult. Approach: We considered various pixel-defect correction algorithms based on different deep learning models, such as the artificial neural network (ANN), convolutional neural network (CNN), concatenate CNN, and generative adversarial networks (GAN). We considered two cases of maximal defect sizes, 3 × 3 and 5 × 5 pixels , and conducted extensive learning experiments to find the best structures of the learning models using the mean square error (MSE) as the loss function. Results: To conduct experiments, practical chest x-ray images were acquired from a general radiography detector. The MSE values of the correction results from ANN, CNN, concatenate CNN, and GAN were 69.40, 75.13, 68.21, and 73.77, respectively, and were much smaller than that of the conventional template match correction method. Conclusions: A concatenate CNN showed the best defect-correction performance. However, ANN could achieve a similar correction performance with much smaller encoding complexity. Therefore, the single-layer ANN can efficiently conduct defect corrections in terms of both correction and complexity.

On the performance of the noise power spectrum from the gain-corrected radiography images.

Fixed pattern noise due to nonuniform amplifier gains and scintillator sensitivity should be alleviated in radiography imaging to acquire low-noise x-ray images from detectors. Here, the noise property of the detector is usually evaluated observing the noise power spectrum (NPS). A gain-correction scheme, in which uniformly illuminated images are averaged to design a gain map, can be applied to alleviate the fixed pattern noise problem. The normalized NPS (NNPS) of the gain-corrected image decreases as the number of images for the average increases and converges to an infimum, which can be achieved if the fixed pattern noise is completely removed. If we know the NNPS infimum of the detector, then we can determine the performance of the gain-corrected images compared with the achievable lower bound. We first construct an image-formation model considering the nonuniform gain and then consider two measurement methods based on subtraction and division to estimate the NNPS infimum of the detector. In order to obtain a high-precision NNPS infimum estimate, we consider a time-averaging method. For several flat-panel radiography detectors, we constructed the NNPS infimum measurements and compared them with NNPS values of the gain-corrected images. We observed that the NNPS values of the gain-corrected images approached the NNPS infimum as the number of images for the average increased.

Breast and Cervical Cancer Screening Behavior in Female Cancer Survivors: The Korea National Health and Nutrition Examination Survey, 2007-2012.

BACKGROUND: The aim of this study was to compare breast and cervical cancer screening rates between female cancer survivors and a population without cancer to identify factors related to cervical and breast cancer screening in cancer survivors. METHODS: We included 17,765 adults (738 cancer survivors and 17,027 individuals without cancer) in this study, all of whom who were 30 years of age or older and participated in the Fourth and Fifth Korean National Health and Nutritional Examination Surveys from 2007-2012. Multiple logistic regression analysis was performed to identify factors related to cervical and breast cancer screening uptake in female cancer survivors. RESULTS: The screening rate for breast cancer was 56.6%, which was higher than that in the non-cancer control group (P=0.001). The screening rate for cervical cancer was 51.4%, which was not different from that of the non-cancer control group. In terms of breast cancer screening, cancer survivors showed no significant difference in the rate of screening 5 years after their cancer diagnosis. However, cervical cancer survivors were less likely to have cervical cancer screening 10 years after their cancer diagnosis. There was no significant association between cancer screening and sociodemographic factors. CONCLUSION: Breast and cervical cancer screening rates in Korean female cancer survivors are low. Secondary primary cancer screening of female cancer survivors needs to be planned in a comprehensive manner, with the consideration of influences beyond sociodemographic factors.

The Association Between Shift Work and Health Behavior: Findings from the Korean National Health and Nutrition Examination Survey.

BACKGROUND: Shift workers are increasing worldwide, and various negative health effects of shift work have been reported. This study aimed to evaluate the relationship between shift work and health behavior. METHODS: This cross-sectional study included a total of 11,680 Korean adults (6,061 men and 5,619 women) aged ≥20 years old who participated in the Fifth Korean National Health and Nutrition Examination Survey, 2010-2012. Multiple logistic regression analysis was performed to evaluate the association between shift work and health behavior after adjusting for covariates. RESULTS: In men, shift work was associated with an increased risk of inadequate sleep (odds ratio [OR], 1.18; 95% confidence interval [CI], 1.00 to 1.40) compared to day work. In women, shift work was associated with an increased risk of smoking (OR, 1.73; 95% CI, 1.34 to 2.22) and inadequate sleep (OR, 1.24; 95% CI, 1.05 to 1.47) compared to day work. In an age-stratified subgroup analysis, female shift workers aged ≥50 years old demonstrated an increased risk of smoking (OR, 5.55; 95% CI, 3.60 to 8.55), alcohol consumption (OR, 2.22; 95% CI, 1.53 to 3.23), and inadequate sleep (OR, 1.50; 95% CI, 1.10 to 2.05) compared to female day workers. CONCLUSION: Shift work is associated with worse health behavior, and this is most evident in women aged ≥50 years. Targeted strategies to reduce the negative health effects of shift work should be implemented, with consideration of shift workers' demographic characteristics.

Molecular Dynamics Simulation Study on Energy Exchange Between Vibration Modes of a Square Graphene Nanoflake Oscillator.

Superlubricity in nanoscale graphene structures has been of interest for developing graphene-based nanoelectromechanical systems, as well as for the study of basic mechanical properties. Here, we investigated the translational and rotational motions of a square graphene nanoflake with retracting motions by performing classical molecular dynamics simulations. Our results show that the kinetic energy of the translational motion was exchanged into the kinetic energy of the rotational motion. Thus, square graphene nanoflake oscillators have very low quality factors in translational motions. We discuss that square graphene nanoflakes have great potential to be a core component in nanoelectromechanical systems by detecting their motions with ultrahigh sensitivity to facilitate the development of sensor, memory, and quantum computing.