김진성 교수

Phosphorylation of the histone variant H2AX forms γ-H2AX that marks DNA double-strand break (DSB). Here, we generated the sequencing-based maps of H2AX and γ-H2AX positioning in resting and proliferating cells before and after ionizing irradiation. Genome-wide locations of possible endogenous and exogenous DSBs were identified based on γ-H2AX distribution in dividing cancer cells without irradiation and that in resting cells upon irradiation, respectively. γ-H2AX-enriched regions of endogenous o

We have fabricated dual gate field effect transistors (FETs) with 12 nm-thin black phosphorus (BP) channel on glass substrate, where our BP FETs have a patterned-gate architecture with 30 nm-thick Al2O3 dielectrics on top and bottom of a BP channel. Top gate dielectric has simultaneously been used as device encapsulation layer, controlling the threshold voltage of FETs as well when FETs mainly operate under bottom gate bias. Bottom, top, and dual gate-controlling mobilities were estimated to be

BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians' workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contour

Institutional review board approval was obtained. Informed patient consent was not required. Study was compliant with HIPAA. Performance of an automated pulmonary nodule detection program was evaluated on multi-detector row CT images that were acquired once but reconstructed retrospectively at different section thicknesses and reconstruction intervals. From raw CT data in 10 patients with pulmonary nodules, three sets of CT images were reconstructed separately in each patient by selecting two se

Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both image and text-based clinical information. Inspired by the recent advancement of large language models (LLMs) that can facilitate the integration of the textural information and images, here we present an LLM-driven multimodal artificial intelligence (AI), namely LLMSeg, that utilizes the clinical information and is applic

Abstract Background Since intensity-modulated radiation therapy (IMRT) has become popular for the treatment of gynecologic cancers, the contouring process has become more critical. This study evaluated the feasibility of atlas-based auto-segmentation (ABAS) for contouring in patients with endometrial and cervical cancers. Methods A total of 75 sets of planning CT images from 75 patients were collected. Contours for the pelvic nodal clinical target volume (CTV), femur, and bladder were carefully

PURPOSE: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent of first and second generation MRI-based therapy delivery systems for MR-IGRT. The expanding use of these systems is driving interest in MRI-only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style