Leveraging HILIC/ERLIC Separations for Online Nanoscale LC-MS/MS Analysis of Phosphopeptide Isoforms from RNA Polymerase II C-terminal Domain.

The eukaryotic RNA polymerase II (Pol II) multi-protein complex transcribes mRNA and coordinates several steps of co-transcriptional mRNA processing and chromatin modification. The largest Pol II subunit, Rpb1, has a C-terminal domain (CTD) comprising dozens of repeated heptad sequences (Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7), each containing five phospho-accepting amino acids. The CTD heptads are dynamically phosphorylated, creating specific patterns correlated with steps of transcription initiation, elongation, and termination.

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

This study aimed to evaluate the cholesterol-lowering and antioxidant activities of soymilk fermented with probiotic strains and to investigate the production of related bioactive compounds. KML06 (KML06) was selected for the fermentation of soymilk because it has the highest antioxidant, cholesterol-lowering, and β-glucosidase activities among the 10 strains isolated from kimchi. The genomic information of strain KML06 was analyzed.

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.

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.

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.

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.

[Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses].

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.

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 (NO), carbon monoxide (CO), sulfur dioxide (SO), ozone (O), and particulate matter 10 μm or less in diameter (PM), 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.

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.

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.

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.

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.

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.

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.

A Perlin Noise-Based Augmentation Strategy for Deep Learning with Small Data Samples of HRCT Images.

Deep learning is now widely used as an efficient tool for medical image classification and segmentation. However, conventional machine learning techniques are still more accurate than deep learning when only a small dataset is available. In this study, we present a general data augmentation strategy using Perlin noise, applying it to pixel-by-pixel image classification and quantification of various kinds of image patterns of diffuse interstitial lung disease (DILD).

Concomitant or Sequential Therapy as the First-line Therapy for Eradication of Infection in Korea: A Systematic Review and Meta-analysis.

Background/aims: In Korea, increasing clarithromycin resistance has led to the need for an alternative first-line therapy for the eradication of () infection. Concomitant therapy (CT) and sequential therapy (ST) have been proposed as alternative regimens. The aim of this study was to compare the eradication rate from using CT and ST in Korea.