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Tenovin-1 Induces Senescence and Decreases Wound-Healing Activity in Cultured Rat Primary Astrocytes
방민지,류온전,김도경,Darine Froy Mabunga,조규석,김유정,한설희,권경자,신찬영 한국응용약물학회 2019 Biomolecules & Therapeutics(구 응용약물학회지) Vol.27 No.3
Brain aging induces neuropsychological changes, such as decreased memory capacity, language ability, and attention; and is also associated with neurodegenerative diseases. However, most of the studies on brain aging are focused on neurons, while senescence in astrocytes has received less attention. Astrocytes constitute the majority of cell types in the brain and perform various functions in the brain such as supporting brain structures, regulating blood-brain barrier permeability, transmitter uptake and regulation, and immunity modulation. Recent studies have shown that SIRT1 and SIRT2 play certain roles in cellular senescence in peripheral systems. Both SIRT1 and SIRT2 inhibitors delay tumor growth in vivo without significant general toxicity. In this study, we investigated the role of tenovin-1, an inhibitor of SIRT1 and SIRT2, on rat primary astrocytes where we observed senescence and other functional changes. Cellular senescence usually is characterized by irreversible cell cycle arrest and induces senescence- associated β-galactosidase (SA-β-gal) activity. Tenovin-1-treated astrocytes showed increased SA-β-gal-positive cell number, senescence-associated secretory phenotypes, including IL-6 and IL-1β, and cell cycle-related proteins like phospho-histone H3 and CDK2. Along with the molecular changes, tenovin-1 impaired the wound-healing activity of cultured primary astrocytes. These data suggest that tenovin-1 can induce cellular senescence in astrocytes possibly by inhibiting SIRT1 and SIRT2, which may play particular roles in brain aging and neurodegenerative conditions.
직업적 외상 노출이 역치 하 외상 후 스트레스 증상을 보이는 소방공무원의 뇌 기능적 연결성에 미치는 영향: 휴지기 기능적 자기공명영상 연구
허율,방민지,이상혁,이강수 대한불안의학회 2022 대한불안의학회지 Vol.18 No.2
Objective : This study investigated brain functional connectivity in male firefighters who showed subclini- cal post-traumatic stress disorder (PTSD) symptoms. Methods : We compared the data of 17 firefighters who were not diagnosed with PTSD and 18 healthy con- trols who had no trauma exposure. The following instruments were applied to assess psychiatric symptoms: Korean version of the Post-traumatic stress disorder Checklist for DSM-5 (PCL-5-K), Beck Depression Invento- ry-II (BDI-II), Beck Anxiety Inventory (BAI). For all subjects, functional magnetic resonance imaging was per- formed, and functional connectivity was compared between the two groups (family-wise error-corrected p<0.05). Additionally, correlations between psychiatric symptoms and functional connectivity were explored. Results : The following connectivity was higher than that of healthy controls: 1) the central opercular cortex-superior temporal gyrus, 2) planum polare-parahippocampal gyrus, 3) angular gyrus-amygdala, and 4) temporal fusiform cortex-parahippocampal gyrus. The functional connectivity of 1) the lateral occipital cortex-inferior temporal gyrus, 2) superior parietal lobule-caudate, and 3) middle temporal gyrus-thalamus were lower in firefighters. In firefighters, the connectivity of the planum polare-parahippocampal gyrus showed a negative correlation with the severity of arousal symptoms (rho=-0.586, p=0.013). The connectivity of the mid- dle temporal gyrus-thalamus showed a positive correlation with the severity of intrusion (rho=0.552, p=0.022) and arousal symptoms (rho=0.619, p=0.008). The connectivity of the temporal fusiform cortex-parahippocam- pal gyrus was negatively correlated with intrusion (rho=-0.491, p=0.045) and arousal (rho=-0.579, p=0.015). Conclusion : Our results indicate that the brain functional connectivity is associated with occupational trauma exposure in firefighters without PTSD. Therefore, this study provides evidence that close monitoring and early intervention are important for firefighters with traumatic experience even at a subthreshold level.
하종현,방민지,이다복,서민기,류민상,김정식 대한전자공학회 2024 전자공학회논문지 Vol.61 No.4
Planar 형태의 NAND Flash memory의 Scaling down의 한계로 3D Vertical Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) NAND가 개발되었다. 3D Vertical SONOS NAND로 한정된 크기의 wafer에서 많은 transistor를 적층하여 planar type보다 더 많은 memory capacity를 확보할 수 있다. 하지만 vertical 구조로 변경되면서 공정 난이도 상승과 함께 공정에 소모되는 비용이 증가했다. 그래서 공정에 투입되는 비용을 줄이고 소자의 전기적 특성을 빠르고 정확하게 예측하는 기술의 필요성이 대두되었다. 본 논문에서는 TCAD simulation과 딥러닝을 통해 3D Vertical SONOS NAND의 polysilicon grain boundary distribution (Max-angle, Ycut, Xseed, Yseed, Aseed)과 geometrical variation (Width, Lcg)에 따른 전기적 특성 (Vtgm, Vti) 변동을 예측하고 분석했다. TCAD simulation 결과값을 바탕으로 학습한 딥러닝을 통해 전기적 특성을 예측했고 매우 높은 수치의 R2 score (Vtgm R2 score = 0.997, Vti R2 score = 0.999)로 TCAD simulation 결과값에 수렴한다는 것을 알 수 있다. 또한 SHapley Additive exPlanations (SHAP) value를 통해 input parameter의 중요도를 평가한 결과 Ycut과 Xseed parameter가 전기적 특성 변동에 가장 많은 영향을 준 것을 확인했다. 3D Vertical Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) NAND was developed as a solution to the scaling down of planar-type NAND Flash memory. With 3D Vertical SONOS NAND, it is possible to stack many transistors on a limited-size wafer to secure more memory capacity than the planar type. However, with the change to the vertical structure, the cost of the process has increased along with the increase in process difficulty. Therefore, there is a need for a technology that reduces the process's cost and predicts the device's electrical characteristics quickly and accurately. In this paper, we used TCAD simulation and deep learning to predict and analyze the variation of electrical characteristics (Vtgm and Vti) of 3D Vertical SONOS NAND due to polysilicon grain boundary distribution (Max-angle, Ycut, Xseed, Yseed, Aseed) and geometrical variation (Width, Lcg). The electrical characteristics were predicted using deep learning trained based on TCAD simulation results and converged to TCAD simulation results with very high R2 scores (Vtgm R2 score = 0.997, Vti R2 score = 0.999). We also evaluated the importance of the input parameters through the SHapley Additive exPlanations (SHAP) value. We found that the Ycut and Xseed had the most influence on the variation of electrical characteristics.
황혜진,오종수,방민지,원은수,이강수,최태규,이상혁,Hwang, Hye Jin,Oh, Jongsoo,Bang, Minji,Won, Eunsoo,Lee, Kang Soo,Choi, Tai Kiu,Lee, Sang-Hyuk 대한생물정신의학회 2019 생물정신의학 Vol.26 No.2
Objectives The objective of this study is to investigate differences in clinical characteristics between female panic disorder (PD) patients with abortion history (PD+A) and without abortion history (PD-A). Methods We examined data from 341 female patients diagnosed with PD. We divided the patients with PD into PD+A (82 patients) and PD-A (259 patients) to compare demographic and clinical characteristics. The following instruments were applied : stress coping strategies, NEO-neuroticism, the Anxiety Sensitivity Index-Revised (ASI-R), the Albany Panic and Phobia Questionnaire (APPQ), the Beck Depression Inventory, the Beck Anxiety Inventory (BAI) and the Sheehan Disability Scale. Results Compared to the PD-A, the PD+A group showed no significant difference in coping strategies. However, significantly higher scores in neuroticism, the ASI-R, the APPQ and the BAI were observed. In terms of health-related disability, the PD+A group did not show significant difference. Conclusions Our results suggest that the PD+A group may differ from the PD-A group in trait markers such as neuroticism and anxiety sensitivity, and abortion history may be associated with panic-related symptom severity. Our study suggests that further consideration is needed on such clinical characteristics in PD patients with abortion history.
심층신경망을 이용한 Total Ionizing Dose 및 Displacement Defect에 의한 Saddle Fin DRAM의 열화 특성 예측
류민상,하종현,이경엽,서민기,방민지,이다복,김정식 대한전자공학회 2023 전자공학회논문지 Vol.60 No.11
본 논문에서는 saddle fin dynamic random access memory (DRAM) 에 대한 total ionizing dose (TID) 와 displacement defect (DD) 영향을 Technology Computer-Aided Design (TCAD) simulation과 deep neural network (DNN) 를 사용해 조사하였다. Trap의 energy level, 농도, 위치 그리고 면적을 변수로 설정하였고, TCAD를 사용하여 saddle fin DRAM의 전류-전압 특성 dataset을 생성하였다. TCAD dataset을 전처리 과정을 거친 경우와 전처리를 하지 않은 경우로 나누어 DNN의 예측 정확도를 비교하였다. 그 결과 전처리 과정을 거쳐 훈련된 모델은 전처리 과정을 하지 않은 훈련 모델보다 mean square error (MSE) loss가 80 % 증가함과 동시에 R2 score가 37 % 증가하였다. 따라서 DNN을 활용한 정확한 예측을 위해서는 전처리 과정이 필수적이다. In this paper, the effects of total ionizing dose (TID) and displacement defect (DD) in saddle fin dynamic random access memory (DRAM) are investigated using technology computer-aided design (TCAD) simulation and deep neural network (DNN). TCAD is used for generating the current-voltage characteristic data of the saddle fin DRAM and the energy level, concentration, location, and area of the trap are utilized for variables. The TCAD dataset is divided into preprocessed and un-processed cases to compare the prediction accuracy of DNN. The result shows that the model trained with preprocessing has an 80 % increase in mean square error (MSE) loss and a 37 % increase in R2 score compared to the training model without preprocessing. Therefore, preprocessing of a dataset is necessary for high prediction accuracy using DNN.