Publications by authors named "June Lim"

Obesity due to excessive fat accumulation, affects health and quality of life and increases the risk of diseases such as type 2 diabetes and cardiovascular conditions. Traditional causes, such as calorie excess and sedentary behavior, do not fully explain the obesity epidemic, leading to the hypothesis that endocrine-disrupting chemicals or obesogens contribute to obesity. The obesogenic mechanisms of representative obesogenic substances, such as bisphenols, have been discussed, mainly focusing on their interactions with estrogen receptors.

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Bisphenol A (BPA) is a representative obesogen that induces adipocyte differentiation and lipid accumulation by mimicking the action of hormones. is a plant belonging to the mustard family, and has antioxidant, anti-inflammatory, and anti-obesity effects. However, its efficacy against obesogen-induced obesity requires further investigation.

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Article Synopsis
  • This study focuses on improving patient outcomes in radiotherapy for malignant tumors by collecting comprehensive multi-modal data generated during treatment, rather than looking at single time-points.
  • The data collected from multiple institutions underwent strict integrity checks and was analyzed using AI models, leading to a substantial dataset that includes over 30,000 imaging scans and metadata from 5,019 patients.
  • Results showed that the AI models effectively validated most of the data quality, achieving a high classification accuracy, which suggests that this structured dataset can significantly enhance future research in radiotherapy efficiency.
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Background/aim: Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer.

Patients And Methods: In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant.

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EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability.

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  • Electrochemical production of hydrogen peroxide (HO) through a two-electron oxygen reduction reaction (2e ORR) is gaining interest for its sustainable and on-site benefits.
  • This study focuses on improving Ni-based catalysts, specifically designing atomically dispersed catalysts (Ni ADCs) to enhance HO production efficiency while addressing the low activity of traditional Ni catalysts.
  • Key findings indicate that using a coordinated precursor and controlled pyrolysis can create highly active Ni-N sites, which are crucial for achieving a record level of mass activity and selectivity in hydrogen peroxide synthesis.
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Purpose: This study proposed a three-dimensional (3D) multi-modal learning-based model for the automated prediction and classification of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT) images and clinical information.

Methods: We utilized clinical information and CT image data from 4239 patients with NSCLC across multiple institutions. Four deep learning algorithm-based multi-modal models were constructed and evaluated for lymph node classification.

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Sulforaphane (SFN) is an isothiocyanate commonly found in cruciferous vegetables. It is formed via the enzymatic hydrolysis of glucoraphanin by myrosinase. SFN exerts various biological effects, including anti-cancer, anti-oxidation, anti-obesity, and anti-inflammatory effects, and is widely used in functional foods and clinical medicine.

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This study focused on a novel strategy that combines deep learning and radiomics to predict epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT). A total of 1280 patients with NSCLC who underwent contrast-enhanced CT scans and EGFR mutation testing before treatment were selected for the final study. Regions of interest were segmented from the CT images to extract radiomics features and obtain tumor images.

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Three-dimensional (3D) microprinting is considered a next-generation manufacturing process for the production of microscale components; however, the narrow range of suitable materials, which include mainly polymers, is a critical issue that limits the application of this process to functional inorganic materials. Herein, we develop a generalised microscale 3D printing method for the production of purely inorganic nanocrystal-based porous materials. Our process is designed to solidify all-inorganic nanocrystals via immediate dispersibility control and surface linking-induced interconnection in the nonsolvent linker bath and thereby creates multibranched gel networks.

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The electrochemical production of HO via the two-electron oxygen-reduction reaction (2e ORR) has been actively studied using systems with atomically dispersed metal-nitrogen-carbon (M-N-C) structures. However, the development of well-defined M-N-C structures that restrict the migration and agglomeration of single-metal sites remains elusive. Herein, we demonstrate a Langmuir-Blodgett (LB) monolayer of cobalt phthalocyanine (CoPc) on monolayer graphene (LB CoPc/G) as a single-metal catalyst for the 2e ORR.

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The electrochemical production of HO the two-electron oxygen reduction reaction (2e ORR) has recently attracted attention as a promising alternative to the current anthraquinone process. Identification of active sites in O-doped carbon materials, which exhibit high activities and selectivities for the 2e ORR, is important for understanding the selective electrocatalytic process and achieving the rational design of active electrocatalysts. However, this is impeded by the heterogeneous distribution of various active sites on these catalysts.

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Objectives: The Montreal Cognitive Assessment (MoCA) is an increasingly used screening tool for cognitive impairment. The aim of this study was to examine how MoCA performed in identifying cognitive impairment (CI) domains in SLE patients compared with formal standardized neuropsychological testing (NPT). Factors related to SLE disease, immunologic and psychological state associated with CI were also explored.

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Background: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

Method: Korean Health Panel data from 2008 to 2015 were used for this study.

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Article Synopsis
  • Researchers created a deep learning model to detect tongue cancer using a dataset of 12,400 endoscopic images from five South Korean hospitals, focusing on patterns recognized by convolutional neural networks (CNN).
  • The best-performing model, DenseNet169, achieved a high mean area under the receiver operating characteristic curve (AUROC) of 0.895, indicating its effectiveness in distinguishing between cancerous and non-cancerous images.
  • When comparing sensitivities and specificities, the deep learning model showed competitive performance alongside general physicians and oncology specialists, suggesting it could be a useful tool in diagnosing tongue cancer.
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  • HO electrosynthesis is a clean chemical technology that relies on effective electrocatalysts for the two-electron oxygen reduction reaction (2e ORR).
  • The performance of oxygen-doped carbon catalysts for 2e ORR can be significantly affected by the types and amounts of cations in the electrolyte used.
  • An optimal electrolyte mixture (0.1 M KOH + 0.5 M KCl) was identified, achieving the highest reported mass activity for 2e ORR (250 ± 30 A g at 0.70 V) due to enhanced electron-transfer kinetics influenced by the cations.
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Rational control of the coordination environment of atomically dispersed catalysts is pivotal to achieve desirable catalytic reactivity. We report the reversible control of coordination structure in atomically dispersed electrocatalysts via ligand exchange reactions to reversibly modulate their reactivity for oxygen reduction reaction (ORR). The CO-ligated atomically dispersed Rh catalyst exhibited ca.

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Oxygen reduction reaction (ORR) plays a pivotal role in electrochemical energy conversion and commodity chemical production. Oxygen reduction involving a complete four-electron (4e-) transfer is important for the efficient operation of polymer electrolyte fuel cells, whereas the ORR with a partial 2e- transfer can serve as a versatile method for producing industrially important hydrogen peroxide (H2O2). For both the 4e- and 2e- pathway ORR, platinum-group metals (PGMs) have been materials of prevalent choice owing to their high intrinsic activity, but they are costly and scarce.

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Objectives: To determine if the depth of corticotomy done with the piezoelectric knife could play a role in the intensity of the regional acceleratory phenomenon (RAP).

Materials And Methods: Eighteen Sprague-Dawley rats were divided into two groups: untreated (3 rats) and treatment (15 rats). In the treatment group, a split-model design was used.

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Ordered mesoporous carbons (OMCs) have attracted considerable interest owing to their broad utility. OMCs reported to date comprise amorphous rod-like or tubular or graphitic rod-like frameworks, which exhibit tradeoffs between conductivity and surface area. Here we report ordered mesoporous carbons constructed with graphitic tubular frameworks (OMGCs) with tunable pore sizes and mesostructures via dual templating, using mesoporous silica and molybdenum carbide as exo- and endo-templates, respectively.

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A pilot study of biomarker-driven targeted therapy in patients with platinum-resistant recurrent ovarian cancer has been started in Korea. Archival tumor samples were tested for HRD and PD-L1 status. Treatment arms will be allocated according to the test results.

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Band-like transport behavior of H-doped transition metal dichalcogenide (TMD) channels in field effect transistors (FET) is studied by conducting low-temperature electrical measurements, where MoTe , WSe , and MoS are chosen for channels. Doped with H atoms through atomic layer deposition, those channels show strong n-type conduction and their mobility increases without losing on-state current as the measurement temperature decreases. In contrast, the mobility of unintentionally (naturally) doped TMD FETs always drops at low temperatures whether they are p- or n-type.

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Many undergoing in vitro fertilization-embryo transfer (IVF-ET) procedures treatments have been tried for older infertile patients, but still can not reverse the aging effect on oocyte, and infertility treatment is expensive, even for people in developed countries. The study aimed to compare outcomes following the application of luteal phase ovulation induction (LPOI) and ultra-short gonadotropin-releasing hormone agonist (GnRH-a) protocols in patients aged more than 40 years undergoing IVF-ET and to examine the effectiveness and feasibility of LPOI. A total of 266 IVF-ET cycles in 155 patients aged 40 years and over were retrospectively analyzed.

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Since transition metal dichalcogenide (TMD) semiconductors are found as two-dimensional van der Waals materials with a discrete energy bandgap, many TMD based field effect transistors (FETs) are reported as prototype devices. However, overall reports indicate that threshold voltage ( V) of those FETs are located far away from 0 V whether the channel is p- or n-type. This definitely causes high switching voltage and unintended OFF-state leakage current.

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A van der Waals (vdW) Schottky junction between two-dimensional (2D) transition metal dichalcogenides (TMDs) is introduced here for both vertical and in-plane current devices: Schottky diodes and metal semiconductor field-effect transistors (MESFETs). The Schottky barrier between conducting NbS and semiconducting n-MoS appeared to be as large as ∼0.5 eV due to their work-function difference.

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