Publications by authors named "Jihye Yun"

Disrupting the interaction between matrix metalloproteinase-7 (MMP-7) and syndecan-2 (SDC-2) can yield anticancer effects in colon cancer cells. Here, a single-chain variable fragment (scFv) targeting the pro-domain of MMP-7 was generated as a potential candidate anticancer agent. Among the generated scFvs, those designated 1B7 and 1C3 showed the strongest abilities to inhibit the ability of MMP-7 pro-domain to directly interact with SDC-2 in vitro and decrease the cancer activities of human HT29 colon adenocarcinoma cells.

View Article and Find Full Text PDF

Objective: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (QCT) measures.

Methods: Patients with idiopathic pulmonary fibrosis (IPF) who underwent unenhanced chest CTs with vendor A and a pulmonary function test (PFT) were retrospectively evaluated. As deep-learning based ILD quantification software was mainly developed using vendor B CT, style-converted images from vendor A to B style were generated using RouteGAN.

View Article and Find Full Text PDF
Article Synopsis
  • Human induced pluripotent stem cells (hiPSCs) display clonal heterogeneity affecting their ability to differentiate into cardiomyocytes (CMs), necessitating a deeper understanding of these variations.
  • By analyzing multiple hiPSC clones from a single donor, researchers categorized them into productive (PC) and non-productive (NPC) groups based on their differentiation efficiency, uncovering distinct biological profiles.
  • Integrating RNA sequencing and chromatin accessibility data, the study identified biomarkers like TEK and SDR42E1 that are linked to CM differentiation potential, providing insights that could improve the selection of hiPSC clones for clinical use.
View Article and Find Full Text PDF

: This study was conducted to develop information and communication technology (ICT)-based oral functional rehabilitation exercise (OFRE) program content to effectively improve the oral function of the elderly people. : After selecting evidence-based effective OFRE items through systematic review, the final items were constructed through the validity evaluation of detailed items through an expert Delphi survey. The items were composed in a simple content form that can be performed directly and applied to ICT-based mobile applications.

View Article and Find Full Text PDF
Article Synopsis
  • A study was conducted to assess how well deep learning (DL) models could identify patients using paired chest radiographs (CXRs) and compare their accuracy to human radiologists.
  • The DL models were trained on a large dataset of over 240,000 CXRs and tested on various populations, while the performance of the models was compared against junior and senior radiology residents and expert radiologists.
  • The results showed that the SimChest DL model performed similarly to the radiologists in identifying patients, with an accuracy indicating that DL models can effectively screen for patient misidentification alongside human experts.
View Article and Find Full Text PDF

The bacterium Vibrio vulnificus causes fatal septicemia in humans. Previously, we reported that an extracellular metalloprotease, vEP-45, secreted by V. vulnificus, undergoes self-proteolysis to generate a 34 kDa protease (vEP-34) by losing its C-terminal domain to produce the C-ter100 peptide.

View Article and Find Full Text PDF
Article Synopsis
  • The study aimed to create a predictive model for post-treatment survival in hepatocellular carcinoma (HCC) patients by utilizing pre-treatment CT images alongside clinical data from 692 patients.
  • A 3D convolutional neural network (CNN) was employed to analyze CT features and incorporate patient-related factors and treatment options to estimate conditional survival probabilities over time.
  • The final model achieved high predictive performance metrics, demonstrating that combining imaging and clinical data significantly outperformed models that relied on only one type of data.
View Article and Find Full Text PDF
Article Synopsis
  • This study aimed to clarify the concept of oral function rehabilitation exercise (OFRE) using the International Classification of Functioning, Disability, and Health (ICF) framework, focusing specifically on older adults living in the community.
  • The researchers conducted a Delphi survey with experts to develop and confirm a conceptual model based on findings from previous studies, leading to a well-defined framework for OFRE interventions.
  • The final OFRE framework includes 21 factors affecting oral health and specifies intervention categories like warm-up and cool-down exercises, as well as exercises to improve masticatory and swallowing functions, which can help enhance the oral and overall health of older individuals.
View Article and Find Full Text PDF

Background: This study aimed to develop an instrument for assessing physical functioning among adults aged 50 years or older living in the community.

Methods: Based on a review of various national health surveys and cohort studies, a 144-item bank was constructed for assessing physical functioning. Focus group interviews were conducted among adults aged 50 years or older to investigate their level of understanding of 60 selected items, followed by a pretest of the items on a nationally representative sample (n=508).

View Article and Find Full Text PDF

The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients.

View Article and Find Full Text PDF

Previous work showed that matrix metalloproteinase-7 (MMP-7) regulates colon cancer activities through an interaction with syndecan-2 (SDC-2) and SDC-2-derived peptide that disrupts this interaction and exhibits anticancer activity in colon cancer. Here, to identify potential anticancer agents, a library of 1,379 Food and Drug Administration (FDA)-approved drugs that interact with the MMP-7 prodomain were virtually screened by protein-ligand docking score analysis using the GalaxyDock3 program. Among five candidates selected based on their structures and total energy values for interacting with the MMP-7 prodomain, the known mechanistic target of rapamycin kinase (mTOR) inhibitor, everolimus, showed the highest binding affinity and the strongest ability to disrupt the interaction of the MMP-7 prodomain with the SDC-2 extracellular domain in vitro.

View Article and Find Full Text PDF
Article Synopsis
  • Treatment decisions for hepatocellular carcinoma often differ from recommendations by established staging systems, highlighting the need for improved methods.
  • A machine learning-based clinical decision support system was developed using data from nine South Korean institutions, aiming to enhance initial treatment recommendations and post-treatment survival predictions.
  • The system demonstrated an accuracy of 67.27% for treatment recommendations, which improved to 87.27% when considering a second treatment option, and effectively predicts patient survival outcomes based on clinical data.
View Article and Find Full Text PDF

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data.

View Article and Find Full Text PDF

Objective: Cine magnetic resonance imaging (MRI) has emerged as a noninvasive method to quantitatively assess bowel motility. However, its accuracy in measuring various degrees of small bowel motility has not been extensively evaluated. We aimed to draw a quantitative small bowel motility score from cine MRI and evaluate its performance in a population with varying degrees of small bowel motility.

View Article and Find Full Text PDF

A major responsibility of radiologists in routine clinical practice is to read follow-up chest radiographs (CXRs) to identify changes in a patient's condition. Diagnosing meaningful changes in follow-up CXRs is challenging because radiologists must differentiate disease changes from natural or benign variations. Here, we suggest using a multi-task Siamese convolutional vision transformer (MuSiC-ViT) with an anatomy-matching module (AMM) to mimic the radiologist's cognitive process for differentiating baseline change from no-change.

View Article and Find Full Text PDF

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software.

Materials And Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions.

View Article and Find Full Text PDF

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA).

View Article and Find Full Text PDF
Article Synopsis
  • Low-dose chest CT screening is recommended for smokers but its ability to predict lung function is uncertain; researchers aimed to create a deep learning algorithm using CT images for this purpose.
  • The study involved 16,148 participants who underwent both low-dose CT and spirometry testing for lung function at a university hospital from 2015 to 2018, with data split into development and independent test sets.
  • The deep learning model showed high accuracy in predicting lung function metrics like FVC and FEV, achieving over 89% accuracy in identifying high-risk participants based on lung function thresholds.
View Article and Find Full Text PDF
Article Synopsis
  • Machine learning can work well, but it often struggles to make accurate predictions on new data, which is called out-of-sample generalizability.
  • To solve this problem, researchers are using a method called Federated ML that allows computers to share information about how well they're learning without actually sharing the data itself.
  • In a big study with 71 locations around the world, scientists created a model to help detect brain tumors more accurately, showing a significant improvement compared to older methods and hoping to help with rare illnesses and data sharing in healthcare.
View Article and Find Full Text PDF

Purpose: We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with radiologists' visual analysis.

Method: This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines.

View Article and Find Full Text PDF

Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace.

View Article and Find Full Text PDF

To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector.Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network.Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs.

View Article and Find Full Text PDF

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).

Materials And Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019.

View Article and Find Full Text PDF

Galanin is a neuropeptide expressed in the central and peripheral nervous systems, where it regulates various processes including neuroendocrine release, cognition, and nerve regeneration. Three G-protein coupled receptors (GPCRs) for galanin have been discovered, which is the focus of efforts to treat diseases including Alzheimer's disease, anxiety, and addiction. To understand the basis of the ligand preferences of the receptors and to assist structure-based drug design, we used cryo-electron microscopy (cryo-EM) to solve the molecular structure of GALR2 bound to galanin and a cognate heterotrimeric G-protein, providing a molecular view of the neuropeptide binding site.

View Article and Find Full Text PDF

A PHP Error was encountered

Severity: Warning

Message: fopen(/var/lib/php/sessions/ci_sessionctgn44s8q86fev1op7g7bqs7fedandej): Failed to open stream: No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 177

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)

Filename: Session/Session.php

Line Number: 137

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once