Higher Physical Activity is Associated with Reduced Lower Urinary Tract Symptoms in Korean Men.

PURPOSE: Identifying and managing risk factors for lower urinary tract symptoms (LUTS) is crucial because it impacts the quality of life of elderly individuals. Lifestyle factors, including physical activity (PA), and their relationship with LUTS have not been well studied. This objective of this study was to investigate the association between PA and LUTS. MATERIALS AND METHODS: A total of 7,296 men were included in this cross-sectional study. PA was quantified in metabolic equivalent (MET)-hours per week, and LUTS severity was assessed using the international prostate symptom score. Logistic regression was used to analyze the association between PA and LUTS, including voiding and storage symptoms. RESULTS: The average age of the participants was 57.8 years, and the prevalence of LUTS was 41.3%. After adjusting for potential confounders, PA was inversely associated with the prevalence and severity of moderate-to-severe LUTS, showing a dose-response pattern (all p for trend <0.01). Compared to the minimal activity group, which engaged in <5 MET-hours per week of PA, the odds ratios for moderate to severe LUTS were 0.83 (95% confidence interval [CI]: 0.72-0.97) for men engaging in 15-30 MET-hours per week, 0.82 (95% CI: 0.71-0.95) for 30-60 MET-hours per week, and 0.72 (95% CI: 0.62-0.84) for ≥60 MET-hours per week. The possible protective effect of PA was still observed in the additional analysis for voiding and storage symptoms showing the same dose-response pattern (all p for trend <0.01). CONCLUSIONS: A higher PA level was associated with a lower prevalence and severity of total, voiding, and storage LUTS in a dose-dependent manner in Korean men.

Blood urea nitrogen to albumin ratio is associated with cerebral small vessel diseases.

Blood urea nitrogen (BUN) to albumin ratio (BAR) is a comprehensive parameter that reflects renal, inflammatory, nutritional, and endothelial functions. BAR has been shown to be associated with various cancers, pneumonia, sepsis, and pulmonary and cardiovascular diseases; however, few studies have been conducted on its association with cerebrovascular diseases. In this study, we evaluated the association between BAR and cerebral small vessel disease (cSVD) in health check-up participants. We assessed consecutive health check-up participants between January 2006 and December 2013. For the cSVD subtype, we quantitatively measured the volume of white matter hyperintensity (WMH) and qualitatively measured the presence of lacune and cerebral microbleeds (CMBs). The BAR was calculated by dividing BUN by albumin as follows: BAR = BUN (mg/dl)/albumin (g/dl). A total of 3012 participants were evaluated. In multivariable linear regression analysis, BAR showed a statistically significant association with WMH volume after adjusting for confounders [ß = 0.076, 95% confidence interval (CI): 0.027-0.125]. In multivariable logistic regression analyses, BAR was significantly associated with lacunes [adjusted odds ratio (aOR) = 1.20, 95% CI: 1.00-1.44] and CMBs (aOR = 1.28, 95% CI: 1.06-1.55). BAR was associated with all types of cSVD in the health check-up participants.

Monocyte to high-density lipoprotein cholesterol ratio is associated with cerebral small vessel diseases.

BACKGROUND: Inflammation is a major pathological mechanism underlying cerebrovascular disease. Recently, a new inflammatory marker based on the ratio between monocyte count and high-density lipoprotein (HDL) cholesterol has been proposed. In this study, we evaluated the relationship between monocyte-to-HDL cholesterol ratio (MHR) and cerebral small vessel disease (cSVD) lesions in health check-up participants. METHODS: This study was a retrospective cross-sectional study based on a registry that prospectively collected health check-up participants between 2006 and 2013. Three cSVD subtypes were measured on brain magnetic resonance imaging. White matter hyperintensity (WMH) volume, and lacunes and cerebral microbleeds (CMBs) were quantitatively and qualitatively measured, respectively. The MHR was calculated according to the following formula: MHR = monocyte counts (× 103/µL) / HDL cholesterol (mmol/L). RESULTS: In total, 3,144 participants were evaluated (mean age: 56 years, male sex: 53.9%). In multivariable analyzes adjusting for confounders, MHR was significantly associated with WMH volume [ß = 0.099, 95% confidence interval (CI) = 0.025 to 0.174], lacune [adjusted odds ratio (aOR) = 1.43, 95% CI = 1.07-1.91], and CMB (aOR = 1.51, 95% CI = 1.03-2.19). In addition, MHR showed a positive quantitative relationship with cSVD burden across all three subtypes: WMH (P < 0.001), lacunes (P < 0.001), and CMBs (P < 0.001). CONCLUSIONS: High MHR was closely associated with cSVD in health check-up participants. Because these associations appear across all cSVD subtypes, inflammation appears to be a major pathological mechanism in the development of various cSVDs.

Effect of anti-obesity agent HSG4112 on overweight and obese patients following 12 weeks of oral treatment: a study protocol for a randomised, double-blind, placebo-controlled, parallel-group, phase 2a clinical trial.

BACKGROUND: Glaceum Inc. has proposed HSG4112, a structural analogue of glabridin, as a novel anti-obesity compound. Animal studies and phase I human trials have shown that HSG4112 improves energy consumption, normalises weight, and is safe and drug-resistant. Based on these results, the company plans to conduct a phase 2a clinical trial to determine the safety and efficacy of HSG4112 in overweight and obese patients. METHODS: A 16-week randomised, double-blind, placebo-controlled, parallel-group trial will be conducted at five large hospitals in South Korea to assess the safety and efficacy of HSG4112 in overweight and obese patients. Participants who meet the inclusion/exclusion criteria will be assigned a subject number and randomly assigned to one of the four treatment groups (one group receiving a placebo) in a 1:1:1:1 ratio. The study's primary outcome will be to monitor the change in body weight (kg) from baseline to the end of treatment while monitoring safety and tolerability. DISCUSSION: This trial will evaluate the efficacy and safety of HSG4112 in overweight and obese adults. Upon proving the safety and effectiveness of the newly developed mechanism, it might significantly improve the perception of the product among medical personnel and obese patients. Furthermore, it may aid in managing chronic conditions that require long-term treatment. Trial registration: ClinicalTrials.gov, identifier [NCT05197556].

Association between long-term air pollution exposure and insulin resistance independent of abdominal adiposity in Korean adults.

Significant associations between air pollution (AP) and insulin resistance (IR) have been reported in limited populations or certain patient groups, but few studies have addressed this association in the general population, especially in Asians. Although abdominal fat is a major contributor to IR, previous studies have not fully controlled for its effect in the association between AP and IR. We investigated the association between exposure to AP and IR in Korean adults in the general population and whether this association is maintained even after controlling for the effects of abdominal fat, particularly visceral fat. This was a cross-sectional study. Data were obtained for Korean adults who participated in screening health checkups at Seoul National University Health Examination Center from 2006 to 2014. A total of 4251 men and women aged 22-84 years were included. IR was represented by the homeostasis model assessment of insulin resistance (HOMA-IR). Adiposity traits such as visceral adipose tissue (VAT) and subcutaneous adipose tissue areas were measured by computed tomography. We assessed the annual mean concentrations of air pollutants, including particulate matter with an aerodynamic diameter of 10 µm or less (PM10), nitrogen dioxide, sulfur dioxide, and carbon monoxide. HOMA-IR was significantly associated with increased annual mean exposure to PM10 in both men (ß = 0.15; 95% CI 0.09, 0.22) and women (ß = 0.16; 95% CI 0.09, 0.23), and these associations were maintained even after controlling for VAT area (both p < 0.05). The adjusted mean HOMA-IR increased gradually with the level of long-term PM10 exposure (low, intermediate, and high exposure) (all p for trend < 0.001) in the subgroup analysis. After adjusting for possible confounding factors, including VAT area, the annual mean exposure to PM10 was significantly associated with the presence of IR in both men (OR 1.18; 95% CI 1.03, 1.35) and women (OR 1.44; 95% CI 1.18, 1.76). Other air pollutants, such as NO2, SO2 and CO, did not show any significant associations with HOMA-IR or the presence of IR. Persistent exposure to PM10 is the main independent risk factor for IR and exhibits a dose-dependent association regardless of visceral fatness in both men and women.

The relationship between smoking cigarettes and metabolic syndrome: A cross-sectional study with non-single residents of Seoul under 40 years old.

INTRODUCTION: Young adults receive health screenings at lower rates than other age groups, and it may be difficult to detect diseases in the early stages for this group. We examined differences in health status relative to smoking in a young age group using the results of health screenings conducted in engaged and newly married couples in a cross-sectional database. METHODS: The participants in this study were 808 young adults who visited a municipal hospital health screening center from July 2017 to March 2019. They completed a self-administered questionnaire, and physical measurements and a blood test were taken. They were classified into non-cigarette smokers, past cigarette smokers, and current cigarette smokers according to smoking behavior. In this study, we compared metabolic syndrome, the main components of which include obesity, high blood pressure, high blood triglycerides, low levels of HDL cholesterol and insulin resistance, with smoking behavior. RESULTS: The mean age of the participants was 30.9±3.3 years (males 32.0±3.2, females 29.8±3.1), and 13.9% were current cigarette smokers (males 22.8%, females 5.1%). The proportion of men in their 30s was 76.6% for male group and 50.0% for female group, indicating that the male group had a relatively higher proportion of older and current smokers. Significant differences were found in age, sex, blood pressure, metabolic abnormalities, and drinking status according to smoking status. Cigarette smokers had a 2.4-fold greater risk of metabolic syndrome (95% confidence interval [CI], 1.43-3.96) than non-cigarette smokers; in particular, they had a 2.6-fold (95% CI, 1.44-4.55) greater risk of hypertriglyceridemia and a three-fold (95% CI, 1.45-6.35) greater risk of low HDL cholesterol. CONCLUSIONS: In comparison with non-single, young and generally healthy city dwellers, the risk of metabolic syndrome was significantly higher in smokers than in non-smokers, and in particular, it was confirmed that the risk of hypertriglyceridemia and low HDL cholesterolemia was higher. Smoking cessation is necessary, even for the young, because smoking may cause changes in blood lipids even if the smoking duration is short.

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study.

BACKGROUND: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. OBJECTIVE: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. METHODS: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. RESULTS: Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. CONCLUSIONS: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

EEG-Based Estimation on the Reduction of Negative Emotions for Illustrated Surgical Images.

Electroencephalogram (EEG) biosignals are widely used to measure human emotional reactions. The recent progress of deep learning-based classification models has improved the accuracy of emotion recognition in EEG signals. We apply a deep learning-based emotion recognition model from EEG biosignals to prove that illustrated surgical images reduce the negative emotional reactions that the photographic surgical images generate. The strong negative emotional reactions caused by surgical images, which show the internal structure of the human body (including blood, flesh, muscle, fatty tissue, and bone) act as an obstacle in explaining the images to patients or communicating with the images with non-professional people. We claim that the negative emotional reactions generated by illustrated surgical images are less severe than those caused by raw surgical images. To demonstrate the difference in emotional reaction, we produce several illustrated surgical images from photographs and measure the emotional reactions they engender using EEG biosignals; a deep learning-based emotion recognition model is applied to extract emotional reactions. Through this experiment, we show that the negative emotional reactions associated with photographic surgical images are much higher than those caused by illustrated versions of identical images. We further execute a self-assessed user survey to prove that the emotions recognized from EEG signals effectively represent user-annotated emotions.

Emotion Variation from Controlling Contrast of Visual Contents through EEG-Based Deep Emotion Recognition.

Visual contents such as movies and animation evoke various human emotions. We examine an argument that the emotion from the visual contents may vary according to the contrast control of the scenes contained in the contents. We sample three emotions including positive, neutral and negative to prove our argument. We also sample several scenes of these emotions from visual contents and control the contrast of the scenes. We manipulate the contrast of the scenes and measure the change of valence and arousal from human participants who watch the contents using a deep emotion recognition module based on electroencephalography (EEG) signals. As a result, we conclude that the enhancement of contrast induces the increase of valence, while the reduction of contrast induces the decrease. Meanwhile, the contrast control affects arousal on a very minute scale.

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach.

Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell's emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

A Multi-Column CNN Model for Emotion Recognition from EEG Signals.

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.

Adaptive reconstruction of pipe-shaped human organs from 3D ultrasonic volume.

In this paper, we introduce an adaptive scheme for reconstructing pipe-shaped human organs from the volume data acquired by 3D ultrasonic devices. No other methods but the contour-based scheme was used in the process of reconstructing the volume data into a 3D polygonal surface. In the first step, the algorithm extracts contours from the sampled slices of the volume data using the modified radial gradient method, in which the points are sampled on the boundary of the region of interest by radiating rays and connected through making use of the chain code algorithm. The contours are represented as the context-free grammar, and their parsing trees are traversed during the reconstruction. The generated polygonal surface is refined as the contours are being refined at the casting of the new rays between the existing rays to sample new points and to modify the contours according to these newly derived points. An adaptive scheme is achieved in casting the rays adaptively on the slices. The proposed algorithm is to be applied in reconstructing the pipe-shaped human organs, such as arteries or blood vessels, to a polygonal surface. In this paper, we present an innovative tiling algorithm that reconstructs pipe-shaped human organ from 3D ultrasonic datasets. A set of contours on slices through the ultrasonic datasets is extracted using a modified radial gradient method, and our algorithm tiles these to make a polygonal surface. The tiling is performed by traversing a set of parsing trees which represent the contours in a context-free grammar. This makes our algorithm more efficient than previous algorithms that reconstruct surfaces from a set of contours. The first step of the algorithm is to determine a contour on each slice of the 3D ultrasonic dataset. After removing unwanted artifacts from the slice by applying several noise-removing operators, the centroid pixel of region of interest on the slice is designated. A radial gradient method casts a set of rays from the centroid pixel to the boundary of the slice and computes the intersection points between the rays and the boundary cells of the object so as to determine the contours. The second step uses context-free grammar that represents the contours. Each edge of a contour can be classified into six categories according to its relation with the rays cast from the centroid pixel, and the contour can then be represented by a string in a context-free grammar whose terminal symbols are the six types of the edges. A polygonal surface between two contours is constructed by traversing the parsing trees of the contours and determining the corresponding edges. The third step is to refine the smooth surface constructed in the second step by casting more rays. Additional rays refine the contour by decomposing the edges on the contour and convert leaf node of the parsing tree to the root of a new sub-tree whose leaf nodes denote the newly created edges. Our algorithm was tested on a phantom object and an artery from the neck. Results show that the performance of the algorithm and the quality of the resulting surface are better than those of existing algorithms. We have implemented a navigation facility that allows users to investigate the pipe-shaped human organs interactively.