https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/492 Sun, 05 Apr 2026 15:15:19 GMT 2026-04-05T15:15:19Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/80158 Title: ATF6 expression governs megakaryocyte maturation through dual regulatory mechanisms Authors: Lee, Se-Ryeon; Lee, Yu-Seon; Kim, Jungsun; Jeong, Sang Hoon; Lee, Ju-Han; Sung, Hwa-Jung; Lee, Hong Abstract: BackgroundMegakaryocytes expand their genomes and secretory capacity to produce platelets, which burden the endoplasmic reticulum (ER) with a high proteostatic load. The role of activating transcription factor 6 (ATF6) in this setting has remained elusive.ObjectiveTo define the role of ATF6 in regulating ER proteostasis and megakaryocyte maturation in MEG-01 cells cultured without PMA, and to identify key mediators that relay ATF6-dependent transcriptional and secretory reprogramming.ResultsIncreasing ATF6 levels increased the unfolded protein response activity, ER-associated degradation, N-linked glycosylation, autophagy, AMPK and sirtuin signaling, and DNA repair linked to TP53, while reducing microRNA biogenesis, pre-mRNA processing, and senescence signatures. The knockdown exhibited a reciprocal pattern. Upstream analyses pointed to the restraint of proliferative drivers and support for genomic surveillance. Changes in ATF6 levels are relayed by SNURF and EGR1 to reshape megakaryocyte programs. SNURF and EGR1 function as positive and negative effectors, respectively. SNURF gain-of-function reinforced the unfolded protein response core, canonical megakaryocyte transcription factors, terminal maturation factors, and genes that preserved genome stability and shifted the secretome toward TIMP-1 and osteoprotegerin. EGR1 produced the opposite effects and reweighted interleukin-6, interleukin-1 beta, and interleukin-8. Class I MHC pathways changed in the same direction as the intracellular programs.ConclusionAltogether, these results define an ATF6-centered circuit that links endomitosis, proteostasis, genome integrity, and effector output, and provide a mechanistic rationale to explore whether targeted modulation can improve ex vivo platelet production and adjust thromboinflammatory signaling. Sun, 01 Mar 2026 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/80158 2026-03-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79419 Title: Automatic segmentation and labeling of T1, T7, and T12 thoracic vertebrae in neonatal chest radiographs: a deep learning approach using nnU-Net framework Authors: Jung, Sumin; Yun, Heerim; Cho, Hye Won; Kim, Jaeyoung; Yu, Donghoon; Son, Jinho; Choi, Byung Min Abstract: Introduction Identifying the thoracic vertebra visible on chest radiographs is a standard practice to assess proper position of a tube and catheter tips within their designated anatomical target regions in critically ill newborn infants. We introduce a fully automated deep learning system based on the nnU-Net architecture for segmenting and labeling T1, T7, and T12 in neonatal chest radiographs.Methods We retrospectively collect 14,660 neonatal chest radiographs from 10 university hospitals in Korea, including both infants with tubes or catheters and those without. All images were deidentified and annotated for T1, T7, and T12 vertebrae using rectangular bounding boxes, validated by pediatricians. We split the dataset into training (11,860), validation (1,400), and test (1,400) sets, maintaining an even distribution by gestational age and birth weight.Results The automatic segmentation algorithm demonstrated excellent agreement with human-annotated segmentation for the T1, T7 and T12 vertebrae [Dice similarity coefficient (DSC): 0.8327, 95% CI: 0.8237-0.8418; 0.8322, 95% CI: 0.8213-0.8432; 0.7998, 95% CI: 0.7864-0.8133, respectively]. To identify the approximate location of each vertebra, a relatively modest DSC threshold of 0.50 or 0.60 already yielded an accuracy above 90% for T1, T7, and T12.Conclusion Our deep learning-based automated algorithm built on the nnU-Net framework could accurately segment and label T1, T7, and T12 thoracic vertebrae in neonatal chest radiographs. This artificial intelligence-driven approach can map anatomical target regions based on thoracic vertebrae for assessing the positioning of a tube and catheter tips in a neonatal intensive care unit. Sun, 01 Feb 2026 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79419 2026-02-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79465 Title: Development of an automatic segmentation system for anterolateral thigh flap perforators in maxillofacial reconstruction Authors: Oh, Jisu; Ham, Sung Won; Heo, Jihye; Song, In Seok; Lee, Jee-Ho Abstract: The anterior thigh (ALT) flap is commonly used in reconstructive surgery, especially in maxillary reconstruction. Accurately identifying the perforator that supplies blood to the flap is critical for surgical success but is time-consuming and prone to variability since it is traditionally performed manually. Advances in artificial intelligence have shown convolutional neural networks (CNN) the potential to automate medical image segmentation. However, ALT flap perforator segmentation poses a unique challenge due to the small size of the perforator and its high anatomical variability. To address this challenge, we developed and validated a CNN-based automatic segmentation model for detecting ALT flap perforators on computed tomography angiography (CTA). Manual annotations of bilateral lateral femoral circumflex artery perforators were obtained from 80 patients using an image tracing program for comparison. The training for the development of an automatic segmentation system was then conducted based on these manual segmentation. The automatic segmentation system employed a two-stage cascaded approach: 2D detection with DeepLabv2 and 3D segmentation with ResNet152. Data augmentation techniques were applied to improve model generalization. Performance metrics included the dice similarity coefficient (DSC) and jaccard similarity coefficient (JSC). The automatic segmentation system achieved DSC and JSC values of 69.67 +/- 1.48 and 67.81 +/- 1.70, respectively. The distance differences between manual and automatic detection were 38.28 +/- 15.52 mm on the left side and 31.96 +/- 18.11 mm on the right side. The automatic segmentation system for ALT flap perforators demonstrates promising accuracy, highlighting its potential for clinical application. By reliably identifying perforator locations in CTA, the system can enhance the efficiency and precision of surgical planning, particularly for maxillofacial reconstruction. Sun, 01 Feb 2026 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79465 2026-02-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79483 Title: MT1B overexpression enhances malignancy of non-small cell lung cancer cells Authors: Park, Yoon Hee; LEE, Hong; Kim, Haewon; Park, Hayan; Park, Su A.; Choi, Jin Young; Park, Chaewon; Nam, Yoon Jeong; Lee, Hye Jin; Lee, Yu-Seon; Kim, Jea young; Lee, Byoungcheun; Kim, Hye-Jin; Lee, Ju Han; Jeong, Sang Hoon Abstract: Metallothioneins (MTs) are metal-binding proteins that are involved in heavy metal homeostasis and protection against oxidative stress. The MT1 family comprises several isoforms that are implicated in various diseases, including cancer. Although the dysregulated expression of MT1 isoforms has been observed in lung cancer, the specific role of MT isoform MT1B remains unclear. To investigate the role of MT1B in lung cancer progression, A549 lung cancer cells were transfected with an MT1B expression vector. In vitro assays were performed to assess cell viability, migration, invasion, and colony formation. Western blot analysis revealed increased expression of epithelial-mesenchymal transition (EMT) markers Snail, Vimentin, and N-cadherin, and decreased levels of E-cadherin, indicating EMT induction. In the xenograft model, the MT1B-transfected group formed tumors more rapidly and exhibited significantly increased tumor growth compared to the controls. In addition, RNA sequencing was performed to identify MT1B-dependent gene alterations, and Ingenuity Pathway Analysis (IPA) was applied to characterize the canonical pathways and predicted biological functions associated with these MT1B-specific genes. These findings suggest that cellular MT1B overexpression has the potential to promote lung cancer growth. [BMB Reports 2026; 59(2): 161-168] Sun, 01 Feb 2026 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79483 2026-02-01T00:00:00Z