Enhanced Cholesterol-Lowering and Antioxidant Activities of Soymilk by Fermentation with Lactiplantibacillus plantarum KML06.

This study aimed to evaluate the cholesterol-lowering and antioxidant activities of soymilk fermented with probiotic Lactobacillaceae strains and to investigate the production of related bioactive compounds. Lactiplantibacillus plantarum KML06 (KML06) was selected for the fermentation of soymilk because it has the highest antioxidant, cholesterol-lowering, and ß-glucosidase activities among the 10 Lactobacillaceae strains isolated from kimchi. The genomic information of strain KML06 was analyzed. Moreover, soymilk fermented with KML06 was evaluated for growth kinetics, metabolism, and functional characteristics during the fermentation period. The number of viable cells, which was similar to the results of radical scavenging activities and cholesterol assimilation, as well as the amount of soy isoflavone aglycones, daidzein, and genistein, was the highest at 12 h of fermentation. These results indicate that soymilk fermented with KML06 can prevent oxidative stress and cholesterol-related problems through the production of soy isoflavone aglycones.

Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network.

The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training.

Long-term outcomes after endoscopic versus surgical resection of T1 colorectal carcinoma.

BACKGROUND: The long-term outcomes of patients with T1 colorectal cancer (CRC) who undergo endoscopic and/or surgical treatment are not well understood. Invasive CRC confined to the colonic submucosa (T1 CRC) is challenging in terms of clinical decision-making. We compared the long-term outcomes of T1 CRC by treatment method. METHODS: We examined 370 patients with pathological T1 CRC treated between 2000 and 2015 at Seoul St. Mary's Hospital. In total, 93 patients underwent endoscopic resection (ER) only, 82 underwent additional surgery after ER, and 175 underwent surgical resection only. Patients who did not meet the curative criteria were defined as "high-risk." High-risk patients were classified into three groups according to the treatment modalities: ER only (Group A: 35 patients), additional surgery after ER (Group B: 72 patients), and surgical resection only (Group C: 133 patients). The recurrence-free and overall survival (OS) rates, and factors associated with recurrence and mortality, were analyzed. Factors associated with lymph node metastasis (LNM) were subjected to multivariate analysis. RESULTS: Of the 370 patients, 7 experienced recurrence and 7 died. All recurrences occurred in the high-risk group and two deaths were in the low-risk group. In high-risk groups, there was no significant group difference in recurrence-free survival (P = 0.511) or OS (P =0.657). Poor histology (P =0.042) was associated with recurrence, and vascular invasion (P =0.044) with mortality. LNMs were observed in 30 of 277 patients who underwent surgery either initially or secondarily. Lymphatic invasion was significantly associated with the incidence of LNM (P < 0.001). CONCLUSIONS: ER prior to surgery did not affect the prognosis of high-risk T1 CRC patients, and did not worsen the clinical outcomes of patients who required additional surgery. Lymphatic invasion was the most important predictor of LNM.

Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations.

Realistic image generation is valuable in dental medicine, but still challenging for generative adversarial networks (GANs), which require large amounts of data to overcome the training instability. Thus, we generated lateral cephalogram X-ray images using a deep-learning-based progressive growing GAN (PGGAN). The quality of generated images was evaluated by three methods. First, signal-to-noise ratios of real/synthesized images, evaluated at the posterior arch region of the first cervical vertebra, showed no statistically significant difference (t-test, p = 0.211). Second, the results of an image Turing test, conducted by non-orthodontists and orthodontists for 100 randomly chosen images, indicated that they had difficulty in distinguishing whether the image was real or synthesized. Third, cephalometric tracing with 42 landmark points detection, performed on real and synthesized images by two expert orthodontists, showed consistency with mean difference of 2.08 ± 1.02 mm. Furthermore, convolutional neural network-based classification tasks were used to classify skeletal patterns using a real dataset with class imbalance and a dataset balanced with synthesized images. The classification accuracy for the latter case was increased by 1.5%/3.3% at internal/external test sets, respectively. Thus, the cephalometric images generated by PGGAN are sufficiently realistic and have potential to application in various fields of dental medicine.

Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach.

Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulation (ROSC). We developed and validated a machine learning-based system to predict good outcome in OHCA patients before ROSC. This prospective, multicenter, registry-based study analyzed non-traumatic OHCA data collected between October 2015 and June 2017. We used information available before ROSC as predictor variables, and the primary outcome was neurologically intact survival at discharge, defined as cerebral performance category 1 or 2. The developed models' robustness were evaluated and compared with various score metrics to confirm their performance. The model using a voting classifier had the best performance in predicting good neurological outcome (area under the curve = 0.926). We confirmed that the six top-weighted variables predicting neurological outcomes, such as several duration variables after the instant of OHCA and several electrocardiogram variables in the voting classifier model, showed significant differences between the two neurological outcome groups. These findings demonstrate the potential utility of a machine learning model to predict good neurological outcome of OHCA patients before ROSC.

Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test.

BACKGROUND: Generative adversarial network (GAN)-based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. OBJECTIVE: The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. METHODS: We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with <5 years of experience [Group I], 4 radiologists with 5-10 years of experience [Group II], and 2 radiologists with >10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image. RESULTS: The mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4% vs 1500/3000, 50.0%, respectively; P<.001). However, in terms of identifying synthetic images as fake, there was no significant difference in the specificity between the visual Turing test and random guessing (779/1500, 51.9% vs 750/1500, 50.0%, respectively; P=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0%; Group II, 726/1200, 60.5%; and Group III, 359/600, 59.8%; P=.36). Interreader agreements were poor (κ=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details. CONCLUSIONS: The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details.

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias.

OBJECTIVE: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). MATERIALS AND METHODS: The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1-5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). RESULTS: The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1-5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. CONCLUSION: The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

Association between long-term exposure to air pollutants and cardiopulmonary mortality rates in South Korea.

BACKGROUND: The association between long-term exposure to air pollutants, including nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and particulate matter 10 µm or less in diameter (PM10), and mortality by ischemic heart disease (IHD), cerebrovascular disease (CVD), pneumonia (PN), and chronic lower respiratory disease (CLRD) is unclear. We investigated whether living in an administrative district with heavy air pollution is associated with an increased risk of mortality by the diseases through an ecological study using South Korean administrative data over 19 years. METHODS: A total of 249 Si-Gun-Gus, unit of administrative districts in South Korea were studied. In each district, the daily concentrations of CO, SO2, NO2, O3, and PM10 were averaged over 19 years (2001-2018). Age-adjusted mortality rates by IHD, CVD, PN and CLRD for each district were averaged for the same study period. Multivariate beta-regression analysis was performed to estimate the associations between air pollutant concentrations and mortality rates, after adjusting for confounding factors including altitude, population density, higher education rate, smoking rate, obesity rate, and gross regional domestic product per capita. Associations were also estimated for two subgrouping schema: Capital and non-Capital areas (77:172 districts) and urban and rural areas (168:81 districts). RESULTS: For IHD, higher SO2 concentrations were significantly associated with a higher mortality rate, whereas other air pollutants had null associations. For CVD, SO2 and PM10 concentrations were significantly associated with a higher mortality rate. For PN, O3 concentrations had significant positive associations with a higher mortality rate, while SO2, NO2, and PM10 concentrations had significant negative associations. For CLRD, O3 concentrations were associated with an increased mortality rate, while CO, NO2, and PM10 concentrations had negative associations. In the subgroup analysis, positive associations between SO2 concentrations and IHD mortality were consistently observed in all subgroups, while other pollutant-disease pairs showed null, or mixed associations. CONCLUSION: Long-term exposure to high SO2 concentration was significantly and consistently associated with a high mortality rate nationwide and in Capital and non-Capital areas, and in urban and rural areas. Associations between other air pollutants and disease-related mortalities need to be investigated in further studies.

Correction to: Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest.

An amendment to this paper has been published and can be accessed via the original article.

Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest.

BACKGROUND: Emergency department overcrowding negatively impacts critically ill patients and could lead to the occurrence of cardiac arrest. However, the association between emergency department crowding and the occurrence of in-hospital cardiac arrest has not been thoroughly investigated. This study aimed to evaluate the correlation between emergency department occupancy rates and the incidence of in-hospital cardiac arrest. METHODS: A single-center, observational, registry-based cohort study was performed including all consecutive adult, non-traumatic in-hospital cardiac arrest patients between January 2014 and June 2017. We used emergency department occupancy rates as a crowding index at the time of presentation of cardiac arrest and at the time of maximum crowding, and the average crowding rate for the duration of emergency department stay for each patient. To calculate incidence rate, we divided the number of arrest cases for each emergency department occupancy period by accumulated time. The primary outcome is the association between the incidence of in-hospital cardiac arrest and emergency department occupancy rates. RESULTS: During the study period, 629 adult, non-traumatic cardiac arrest patients were enrolled in our registry. Among these, 187 patients experienced in-hospital cardiac arrest. Overall survival discharge rate was 24.6%, and 20.3% of patients showed favorable neurologic outcomes at discharge. Emergency department occupancy rates were positively correlated with in-hospital cardiac arrest occurrence. Moreover, maximum emergency department occupancy in the critical zone had the strongest positive correlation with in-hospital cardiac arrest occurrence (Spearman rank correlation ρ = 1.0, P < .01). Meanwhile, occupancy rates were not associated with the ED mortality. CONCLUSION: Maximum emergency department occupancy was strongly associated with in-hospital cardiac arrest occurrence. Adequate monitoring and managing the maximum occupancy rate would be important to reduce unexpected cardiac arrest.

The Set1 N-terminal domain and Swd2 interact with RNA polymerase II CTD to recruit COMPASS.

Methylation of histone H3 lysine 4 (H3K4) by Set1/COMPASS occurs co-transcriptionally, and is important for gene regulation. Set1/COMPASS associates with the RNA polymerase II C-terminal domain (CTD) to establish proper levels and distribution of H3K4 methylations. However, details of CTD association remain unclear. Here we report that the Set1 N-terminal region and the COMPASS subunit Swd2, which interact with each other, are both needed for efficient CTD binding in Saccharomyces cerevisiae. Moreover, a single point mutation in Swd2 that affects its interaction with Set1 also impairs COMPASS recruitment to chromatin and H3K4 methylation. A CTD interaction domain (CID) from the protein Nrd1 can partially substitute for the Set1 N-terminal region to restore CTD interactions and histone methylation. However, even when Set1/COMPASS is recruited via the Nrd1 CID, histone H2B ubiquitylation is still required for efficient H3K4 methylation, indicating that H2Bub acts after the initial recruitment of COMPASS to chromatin.

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy.

Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.

A Rare Fatal Bile Peritonitis after Malposition of Endoscopic Ultrasound-Guided 5-Fr Naso-Gallbladder Drainage.

Endoscopic ultrasound (EUS)-guided gallbladder (GB) drainage has recently emerged as a more feasible treatment than percutaneous transhepatic GB drainage for acute cholecystitis. In EUS-guided cholecystostomies in patients with distended GBs without pericholecystic inflammation or prominent wall thickening, a needle puncture with tract dilatation is often difficult. Guidewires may slip during the insertion of thin and flexible drainage catheters, which can also cause the body portion of the catheter to be unexpectedly situated and prolonged between the GB and intestines because the non-inflamed distended GB is fluctuant. Upon fluoroscopic examination during the procedure, the position of the abnormally coiled catheter may appear to be correct in patients with a distended stomach. We experienced such an adverse event with fatal bile peritonitis in a patient with GB distension suggestive of malignant bile duct stricture. Fatal bile peritonitis then occurred. Therefore, the endoscopist should confirm the indications for cholecystostomy and determine whether a distended GB is a secondary change or acute cholecystitis.

Fully automated 3D segmentation and separation of multiple cervical vertebrae in CT images using a 2D convolutional neural network.

BACKGROUND AND OBJECTIVE: We investigated a novel method using a 2D convolutional neural network (CNN) to identify superior and inferior vertebrae in a single slice of CT images, and a post-processing for 3D segmentation and separation of cervical vertebrae. METHODS: The cervical spines of patients (N == 17, 1684 slices) from Severance and Gangnam Severance Hospitals (S/GSH) and healthy controls (N == 24, 3490 slices) from Seoul National University Bundang Hospital (SNUBH) were scanned by using various volumetric CT protocols. To prepare gold standard masks of cervical spine in CT images, each spine was segmented by using conventional image-processing methods and manually corrected by an expert. The gold standard masks were preprocessed and labeled into superior and inferior cervical vertebrae separately in the axial slices. The 2D U-Net model was trained by using the disease dataset (S/GSH) and additional validation was performed by using the healthy control dataset (SNUBH), and then the training and validation were repeated by switching the two datasets. RESULTS: In case of the model was trained with the disease dataset (S/GSH) and validated with the healthy control (SNUBH), the mean and standard deviation (SD) of the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), mean surface distance (MSD), and Hausdorff surface distance (HSD) were 94.37%% ± 1.45%, 89.47%% ± 2.55%, 0.33 ± 0.12 mm and 20.89 ± 3.98 mm, and 88.67%% ± 5.82%, 80.83%% ± 8.09%, 1.05 ± 0.63 mm and 29.17 ± 19.74 mm, respectively. In case of the model was trained with the healthy control (SNUBH) and validated with the disease dataset (S/GSH), the mean and SD of DSC, JSC, MSD, and HSD were 96.23%% ± 1.55%, 92.95%% ± 2.58%, 0.39 ± 0.20 mm and 16.23 ± 6.72 mm, and 93.15%% ± 3.09%, 87.54%% ± 5.11%, 0.38 ± 0.17 mm and 20.85 ± 7.11 mm, respectively. CONCLUSIONS: The results demonstrated that our fully automated method achieved comparable accuracies with inter- and intra-observer variabilities of manual segmentation by human experts, which is time consuming.

[Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses]. / 딥러닝 기반 의료영상 분석을 위한 데이터 증강 기법.

Medical image analyses have been widely used to differentiate normal and abnormal cases, detect lesions, segment organs, etc. Recently, owing to many breakthroughs in artificial intelligence techniques, medical image analyses based on deep learning have been actively studied. However, sufficient medical data are difficult to obtain, and data imbalance between classes hinder the improvement of deep learning performance. To resolve these issues, various studies have been performed, and data augmentation has been found to be a solution. In this review, we introduce data augmentation techniques, including image processing, such as rotation, shift, and intensity variation methods, generative adversarial network-based method, and image property mixing methods. Subsequently, we examine various deep learning studies based on data augmentation techniques. Finally, we discuss the necessity and future directions of data augmentation.

Age and sex subgroups vulnerable to copycat suicide: evaluation of nationwide data in South Korea.

Media reports of a celebrity's suicide may be followed by copycat suicides, and the impact may vary in different age and sex subgroups. We proposed a quantitative framework to assess the vulnerability of age and sex subgroups to copycat suicide and used this method to investigate copycat suicides in relation to the suicides of 10 celebrities in South Korea from 1993 to 2013. By applying a detrending model to control for annual and seasonal fluctuations, we estimated the expected number of suicides within a copycat suicide period. The copycat effect was assessed in two ways: the magnitude of copycat suicide by dividing the observed by the expected number of suicides, and the mortality rate by subtracting the expected from the observed number of suicides. Females aged 20-29 years were the most vulnerable subgroup according to both the magnitude of the copycat effect (2.31-fold increase over baseline) and the mortality rate from copycat suicide (22.7-increase). Males aged 50-59 years were the second most vulnerable subgroup according to the copycat suicide mortality rate (20.5- increase). We hope that the proposed quantitative framework will be used to identify vulnerable subgroups to copycat effect, thereby helping devise strategies for prevention.

Risk Factors for Asymptomatic Colon Diverticulosis.

BACKGROUNDS/AIMS: The etiology of colon diverticulosis is related to a range of genetic, biological, and environmental factors, but the risk factors for asymptomatic diverticulosis of the colon are unclear. This study examined the risk factors for asymptomatic colon diverticulosis. METHODS: This retrospective study included examinees who underwent a colonoscopy for screening at the health check-up center of SAM Hospital between January 2016 and December 2016. The examinees with colon diverticulosis found by colonoscopy were compared with those without diverticulosis. The comparison factors were age, gender, alcohol consumption, smoking status, medical history, lipid profile, body mass index, visceral fat area, waist-hip ratio, and severity of a fatty liver. RESULTS: This study included 937 examinees and the overall prevalence of diverticulosis was 8.1% (76/937). Fatty liver was found in 69.7% (53/76) in cases of colon diverticulosis and 50.3% (433/861) in the control group (p=0.001). The average waist-hip ratio was 0.92±0.051 in colon diverticulosis and 0.90±0.052 in the control group (p=0.052). Multivariate analysis revealed the waist-hip ratio (OR=1.035, 95% CI 1.000-1.070, p=0.043), moderate fatty liver (OR=2.238, 95% CI 1.026-4.882, p=0.043), and severe fatty liver (OR=5.519, 95% CI 1.236-21.803, p=0.025) to be associated with an increased risk of asymptomatic colon diverticulosis. CONCLUSIONS: The waist-hip ratio, moderate fatty liver, and severe fatty liver are risk factors for asymptomatic colon diverticulosis. Central obesity, which can be estimated by the waist-hip ratio, and fatty liver might affect the pathogenesis of asymptomatic colon diverticulosis.

Deep Learning in Medical Imaging.

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learnable data, ANN has suffered from overfitting and vanishing gradient problems for training deep networks. The advancement of computing power with graphics processing units and the availability of large data acquisition, deep neural network outperforms human or other ML capabilities in computer vision and speech recognition tasks. These potentials are recently applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. In this review article, we will explain the history, development, and applications in medical imaging.