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    SPH code development and validation for numerical simulation of liquid-liquid swirl coaxial injector

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2013. 8. ์—ฌ์žฌ์ต.์•ก์ฒด ๋กœ์ผ“ ์—”์ง„์—์„œ ์—ฐ๋ฃŒ์™€ ์‚ฐํ™”์ œ๋Š” ์ธ์ ํ„ฐ๋ฅผ ํ†ตํ•ด ์—ฐ์†Œ์‹ค๋กœ ๋ถ„์‚ฌ๋˜๋ฉฐ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์กฐ๊ฑด๋“ค๊ณผ ๋ณ€์ˆ˜๋“ค์— ์˜ํ•ด ๋‹ค์–‘ํ•œ ๋ถ„๋ฌดํŠน์„ฑ์„ ๊ฐ–๊ฒŒ ๋œ๋‹ค. ์•ก์ฒด ์ œํŠธ ์ƒํƒœ๋กœ ๋ถ„์‚ฌ๋œ ์—ฐ๋ฃŒ์™€ ์‚ฐํ™”์ œ๋Š” ์„œ๋กœ ๋ถ€๋”ชํžˆ๊ฑฐ๋‚˜ ์„ž์ด๋ฉด์„œ ์ž‘์€ ์•ก์ ๊ตฌ์กฐ๋กœ ๋ฏธ๋ฆฝํ™”๋˜๊ณ  ๊ธฐํ™”๋˜๋ฉด์„œ ์—ฐ์†Œ๊ณผ์ •์— ์ด๋ฅด๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ผ๋ จ์˜ ๊ณผ์ •์—์„œ ์•ก์ฒด์ œํŠธ์˜ ๋ฏธ๋ฆฝํ™” ํŠน์„ฑ์€ ์ธ์ ํ„ฐ์˜ ๋ถ„๋ฌดํŠน์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์ง€ํ‘œ์ด๋ฉฐ ์ธ์ ํ„ฐ์˜ ๋ถ„๋ฌดํŠน์„ฑ์€ ์—ฐ์†Œ๊ณผ์ •์˜ ํšจ์œจ๊ณผ ์•ˆ์ •์„ฑ์— ํฐ ์˜ํ–ฅ์„ ๋ผ์น˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ธ์ ํ„ฐ์˜ ๋ถ„๋ฌดํŠน์„ฑ์€ ๋งค์šฐ ๋‹ค์–‘ํ•œ ๋ฌผ๋ฆฌ์  ๋ณ€์ˆ˜์™€ ์‹คํ—˜์  ์กฐ๊ฑด๋“ค์— ์˜ํ•ด ์˜ํ–ฅ์„ ๋ฐ›๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์ œ ์‹คํ—˜์œผ๋กœ๋งŒ ๋ชจ๋“  ๋ถ„๋ฌดํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๊ธฐ ์—๋Š” ์–ด๋ ค์›€์ด ์กด์žฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ˆ˜์น˜์  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์ธ์ ํ„ฐ์˜ ๋ถ„๋ฌดํŠน์„ฑ ์—ฐ๊ตฌ๋Š” ์‹ค์ œ ์ธ์ ํ„ฐ ์‹คํ—˜์˜ ์ข‹์€ ์ฐธ๊ณ ์ž๋ฃŒ๋กœ์„œ, ๋˜ ๋” ๋‚˜์•„๊ฐ€ ์•ก์ฒด ๋กœ์ผ“ ์—”์ง„๊ฐœ๋ฐœ์— ์žˆ์–ด ํฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ์ง€๊ธˆ๊นŒ์ง€ ์ธ์ ํ„ฐ์˜ ์ˆ˜์น˜์  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๋Œ€๋ถ€๋ถ„ Eulerian ๊ธฐ๋ฒ•์˜ ๋ฐ”ํƒ•์œ„์—์„œ ์ด๋ฃจ์–ด์ ธ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•ก์ฒด์ œํŠธ์˜ ๋ฏธ๋ฆฝํ™”ํ˜„์ƒ๊ณผ ๋ณต์žกํ•œ ๊ณต๊ธฐ์™€์˜ ๊ฒฝ๊ณ„๋ฉด ๋ณ€ํ™”๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š”๋ฐ ์žˆ์–ด ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค์ด ๊ฐ–๋Š” ์„ ์ฒœ์ ์ธ ๋‹จ์ ์ด ์กด์žฌํ•˜๋ฉฐ ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„๊ต์  ์ƒˆ๋กœ์šด Smoothed Particle Hydrodynamics(SPH) ๋ผ๋Š” ํŒŒํ‹ฐํด ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ์ธ์ ํ„ฐ ์ข…๋ฅ˜ ์ค‘ ํ•˜๋‚˜์ธ ์•ก์ฒด-์•ก์ฒด ๋™์ถ•ํ˜• ์Šค์›” ์ธ์ ํ„ฐ์— ๋Œ€ํ•œ ์ˆ˜์น˜์  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ˆ˜์น˜์  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์œ„ํ•ด ๋จผ์ € ํ•ด์„์„ ์œ„ํ•œ SPH ์ฝ”๋“œ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€์œผ๋ฉฐ ๊ฐ ๊ฐœ๋ฐœ๋‹จ๊ณ„๋งˆ๋‹ค ๊ฒ€์ฆ๋ฌธ์ œ๋ฅผ ํ†ตํ•ด ์ฝ”๋“œ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์ฝ”๋“œ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฒซ ๋ฒˆ์งธ๋กœ ๋‹จ์ผ ์Šค์›” ์ธ์ ํ„ฐ์— ๋Œ€ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ ์‹ค์ œ์‹คํ—˜๊ณผ์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์—ฐ๋ฃŒ์™€ ์‚ฐํ™”์ œ๊ฐ€ ๋ชจ๋‘ ์‚ฌ์šฉ๋œ ์•ก์ฒด-์•ก์ฒด ๋™์ถ•ํ˜• ์Šค์›” ์ธ์ ํ„ฐ์— ๋Œ€ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์ด ์ˆ˜ํ–‰๋˜์—ˆ๊ณ  ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์‹ค์ œ์‹คํ—˜๊ณผ์˜ ๋น„๊ต๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค.In aircraft and rocket engines, fuel and oxidizer are injected as liquid jets and become atomized. The jet atomization is important since it strongly influences combustion efficiency and combustion instability. However, atomization of liquid jet is a physical phenomenon which is too complex to understand through the experiment alone. The state-of-the-art numerical methods can provide additional information about the complex jet atomization problem. Most jet spray and atomization simulations are done with Eulerian approach which has inherent disadvantage in representing jet breakups and droplets. A more phenomenologically natural method which is based on the full Lagrangian particles called SPH is used in this work. We develop the SPH code and perform validations that confirm the suitability of our SPH method for simulating liquid jet atomization problem. After that, we conduct the simulation about liquid-liquid swirl coaxial injector which is one of the famous liquid rocket injector. All results are compared with real experiment about the injector.์ดˆ ๋ก โ…ฐ ๋ชฉ ์ฐจ โ…ฒ ํ‘œ ๋ชฉ์ฐจ โ…ด ๊ทธ๋ฆผ ๋ชฉ์ฐจ โ…ด ์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์„ ํ–‰์—ฐ๊ตฌ์กฐ์‚ฌ 2 1.2 SPH 3 ์ œ 2 ์žฅ Numerical method 6 2.1 SPH ๊ณต์‹ 6 2.2 ์ง€๋ฐฐ๋ฐฉ์ •์‹ 8 2.3 Corrected SPH ์•Œ๊ณ ๋ฆฌ์ฆ˜ 10 2.4 ํ‘œ๋ฉด์žฅ๋ ฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 11 2.5 Kernel ํ•จ์ˆ˜ 12 2.6 Adaptive smoothing length ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 2.7 Time integration 14 ์ œ 3 ์žฅ ์ฝ”๋“œ๊ฒ€์ฆ๋ฌธ์ œ 15 3.1 ๋Œ๋ถ•๊ดด ๋ฌธ์ œ 15 3.2 Square fluid patch ๋ฌธ์ œ 18 3.3 Kelvin-Helmholtz ๋ถˆ์•ˆ์ •์„ฑ ๋ฌธ์ œ 20 3.4 Oscillating rod ๋ฌธ์ œ 22 ์ œ 4 ์žฅ ๋‹จ์ผ ์Šค์›” ์ธ์ ํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 26 4.1 ์Šค์›” ์ธ์ ํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์œ„ํ•œ ๊ฐ€์ • 26 4.2 ์•ก์ฒด ์ œํŠธ์˜ ๋ถ„์—ด ์›๋ฆฌ 28 4.2.1 Linear instability theory 28 4.2.2 Impact wave 29 4.3 Simulation set up 30 4.4 ์Šคํ”„๋ ˆ์ด ํ˜•์ƒ 33 4.5 ๋ถ„์—ด๊ธธ์ด 37 4.5.1 ๋ถ„์—ด์— ๋Œ€ํ•œ ํŒ๋‹จ๊ธฐ์ค€ 37 4.5.2 ๋ถ„์—ด๊ธธ์ด 38 ์ œ 5 ์žฅ ์•ก์ฒด-์•ก์ฒด ๋™์ถ•ํ˜• ์Šค์›” ์ธ์ ํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 41 5.1 Simulation set up 42 5.2 ์Šคํ”„๋ ˆ์ด ํ˜•์ƒ 45 5.3 ๋ถ„์—ด๊ธธ์ด 47 5.4 3D ๊ฒฐ๊ณผ 50 ์ œ 6 ์žฅ ๊ฒฐ๋ก  52 ์ฐธ๊ณ ๋ฌธํ—Œ 54 Abstract 57Maste

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ์—ฌ์žฌ์ต.The thermal behavior of energetic materials has various aspects such as slow decomposition reactions, fast explosion phenomenon, and very rapid detonation phenomenon. All these thermal behaviors of energetic material are basically based on thermal chemical reactions. Therefore, in order to numerically analyze the thermal behavior of high-energy materials, it is essential to construct an accurate chemical reaction rate equation experimentally. In this study, we have developed a chemical kinetic equation for unknown energetic materials by using the calorimetric method Differential Scanning Calorimetry (DSC). In addition, the research for explosion, detonation, and aging effects of energetic materials are numerically investigated using the established chemical reaction rate equation. Firstly, the kinetic analysis of a heavily aluminized cyclotrimethylene-trinitramine (RDX) using Differential Scanning Calorimetry (DSC) is conducted. The Friedman isoconversional method is applied to DSC experimental data and AKTS software is used for the analysis. The pre-exponential factor and activation energy are extracted as a function of product mass fraction. The extracted kinetic scheme does not assume multiple chemical steps to describe the complex response of energetic materials CHAPTER 1: INTRODUCTION. 1 CHAPTER 2: A Development of Thermal-Based Reactive Flow Model for Energetic Materials and Validation 6 2.1 Background and objective......... 6 2.2 DSC experiment for kinetics extraction.. 8 2.3 Reactive flow model validation 17 CHAPTER 3: Aging Effect Prediction of Energetic Materials.. 38 3.1 Background and objective......... 38 3.2 Experimental study for isoconversional kinetics calculation 41 3.3 Isoconversional kinetics calculation.. 43 3.4 Results and discussion for kinetics calculation 45 3.5 Aging-effect prediction... 56 CHAPTER 4: A Study on Multi-Scale Hot Spot Initiation of Detonation using Experiments and Simulation... 65 4.1 Background and objective. 65 4.2 Kinetics analysis of HMX-based explosive . 69 4.3 Micro-scale hot-spot simulation via Smoothed Particle Hydrodynamics. 78 4.4 Shock-to-Detonation Transition experiment and hydrocode simulation . 92 CHAPTER 5: CONCLUSION. 107 REFERENCES.. 110Docto

    ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด ์–‘์„ฑ ์œ ๋ฐฉ์•”์—์„œ NR4A1์˜ ERK ์‹ ํ˜ธ์ „๋‹ฌ ๊ฒฝ๋กœ ์–ต์ œ๋ฅผ ํ†ตํ•œ ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ ์กฐ์ ˆ

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    Breast cancer is one of the most prevalent carcinomas in worldwide. The estrogen receptor (ER)-positive breast cancer accounts for about 70% and is treated with endocrine therapy. Tamoxifen, commonly used treatment drug, is effective in this subtype. Nonetheless, approximately one-third of patients treated with tamoxifen gain acquired tamoxifen resistance, resulting in therapeutic challenges. Moreover, molecular mechanism targeting tamoxifen resistance still remains unclear. NR4A1 is known to play key roles in processes associated with carcinogenesis, apoptosis, DNA repair, proliferation, and inflammation. In recent studies, NR4A1 has been reported to act as an oncogene or a tumor suppressor in various cancer models including breast cancer. However, the role of NR4A1 in tamoxifen-resistant ER-positive breast cancer has not yet been defined. In this study, we demonstrate the clinical significance and functional role as well as molecular mechanistic effects of NR4A1 in tamoxifen-resistant ER-positive breast cancer. NR4A1 gene expression was downregulated in tamoxifen-resistant MCF7 (TamR) and T47D (T47D-TamR) compared to that in MCF7 and T47D cells. Kaplan-Meier plots were used to identify high expression of NR4A1 correlated with increased survival rates in patients with ER-positive breast cancer following tamoxifen treatment. Gain and loss of function experiments showed that NR4A1 restores sensitivity to tamoxifen by regulating cell proliferation, migration, invasion, and apoptotic abilities. In addition, NR4A1 localized to the cytoplasm enhanced the expression of apoptotic factors. Mechanistically, NR4A1 enhanced responsiveness to tamoxifen through suppressing ERK signaling in ER-positive breast cancer by in silico and in vitro analyses, suggesting that the NR4A1/ERK signaling axis modulates tamoxifen resistance. Taken together, our study reveals that NR4A1 could be a potential therapeutic strategy to overcome tamoxifen resistance in ER-positive breast cancer. ์œ ๋ฐฉ์•”์€ ์ „ ์„ธ๊ณ„์—์„œ ๊ฐ€์žฅ ํ”ํ•œ ์•” ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด ์–‘์„ฑ (ER-positive) ์œ ๋ฐฉ์•”์€ ์ „์ฒด ์œ ๋ฐฉ์•” ํ™˜์ž ์ค‘ ์•ฝ 70%๋ฅผ ์ฐจ์ง€ํ•˜๋ฉฐ, ๋‚ด๋ถ„๋น„ ์น˜๋ฃŒ (endocrine therapy)๋ฅผ ๋ฐ›๋Š”๋‹ค. ๋‚ด๋ถ„๋น„ ์น˜๋ฃŒ ์ค‘ ํ”ํžˆ ์‚ฌ์šฉ๋˜๋Š” ์น˜๋ฃŒ ์•ฝ๋ฌผ์ธ ํƒ€๋ชฉ์‹œํŽœ์€ ์ด๋Ÿฌํ•œ ํ™˜์ž๋“ค์— ํšจ๊ณผ์ ์ด๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ํƒ€๋ชฉ์‹œํŽœ ์น˜๋ฃŒ๋ฅผ ๋ฐ›์€ ํ™˜์ž์˜ ์•ฝ 3๋ถ„์˜ 1์€ ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ์ด ์ƒ๊ธฐ๋ฉฐ, ์ด๋Š” ๊ฒฐ๊ตญ ์น˜๋ฃŒ ํšจ๊ณผ๋ฅผ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ๋˜ํ•œ, ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ์— ๊ด€์—ฌํ•˜๋Š” ๋ถ„์ž์  ๊ธฐ์ „์€ ์—ฌ์ „ํžˆ ๋ถˆ๋ถ„๋ช…ํ•œ ์‹ค์ •์ด๋‹ค. NR4A1์€ ์•”ํ™” ๊ณผ์ •, ์„ธํฌ ์‚ฌ๋ฉธ, DNA ์ˆ˜์„ , ์„ธํฌ ์ฆ์‹, ์—ผ์ฆ๊ณผ ๊ฐ™์€ ๊ณผ์ •์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ตœ๊ทผ NR4A1์ด ์œ ๋ฐฉ์•”์„ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ ์•” ๋ชจ๋ธ์—์„œ ์ข…์–‘ ์œ ์ „์ž ํ˜น์€ ์ข…์–‘ ์–ต์ œ ์œ ์ „์ž๋กœ์˜ ๊ธฐ๋Šฅ์ด ๋ณด๊ณ ๋œ ๋ฐ” ์žˆ์œผ๋‚˜, ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ ์œ ๋ฐฉ์•”์—์„œ์˜ ์—ญํ• ์€ ์•Œ๋ ค์ง„ ๋ฐ”๊ฐ€ ์—†๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด ์–‘์„ฑ ์œ ๋ฐฉ์•”์—์„œ NR4A1์˜ ์ž„์ƒ์  ์ค‘์š”์„ฑ๊ณผ ๊ธฐ๋Šฅ์  ์—ญํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ถ„์ž์  ์กฐ์ ˆ ๊ธฐ์ „ ๋˜ํ•œ ํ™•์ธํ–ˆ๋‹ค. NR4A1์˜ ์œ ์ „์ž ๋ฐœํ˜„์ด ํƒ€๋ชฉ์‹œํŽœ ๋น„์ €ํ•ญ์„ฑ ์œ ๋ฐฉ์•” ์„ธํฌ์ฃผ (MCF7, T47D)์— ๋น„ํ•ด ์ €ํ•ญ์„ฑ ์„ธํฌ์ฃผ (TamR, T47D-TamR)์—์„œ ๊ฐ์†Œ๋˜์–ด ์žˆ์Œ์„ ํ™•์ธํ–ˆ์œผ๋ฉฐ, Kaplan-Meier ์ƒ์กด ๋ถ„์„์„ ํ†ตํ•ด ๋†’์€ NR4A1์˜ ๋ฐœํ˜„์ด ํƒ€๋ชฉ์‹œํŽœ ์น˜๋ฃŒ๋ฅผ ๋ฐ›์€ ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด ์–‘์„ฑ ์œ ๋ฐฉ์•” ํ™˜์ž์˜ ๋†’์€ ์ƒ์กด๋ฅ ๊ณผ ๊ด€๋ จ์ด ์žˆ์Œ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ธฐ๋Šฅ์ ์ธ ์—ฐ๊ตฌ๋กœ ๊ณผ๋ฐœํ˜„ ๋ฐ ๋ฐœํ˜„ ์ €ํ•ด ์‹คํ—˜์„ ํ†ตํ•ด, NR4A1์ด ์„ธํฌ ์ฆ์‹, ์ด๋™, ์นจ์ž… ๋ฐ ์„ธํฌ ์‚ฌ๋ฉธ ๋Šฅ๋ ฅ์„ ์กฐ์ ˆํ•จ์œผ๋กœ์จ ํƒ€๋ชฉ์‹œํŽœ์— ๋Œ€ํ•œ ๋ฏผ๊ฐ์„ฑ์„ ํšŒ๋ณต์‹œํ‚ด์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ ์„ธํฌ์งˆ์— ์œ„์น˜ํ•œ NR4A1์€ ์„ธํฌ ์‚ฌ๋ฉธ ์ธ์ž์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ด์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. In silico ๋ถ„์„ ๋ฐ in vitro ์‹คํ—˜์„ ์ด์šฉํ•œ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด, NR4A1์ด ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด ์–‘์„ฑ ์œ ๋ฐฉ์•”์—์„œ ERK ์‹ ํ˜ธ ์ „๋‹ฌ ๊ฒฝ๋กœ๋ฅผ ์–ต์ œํ•จ์œผ๋กœ์จ ํƒ€๋ชฉ์‹œํŽœ์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ด์„ ํ™•์ธํ–ˆ์œผ๋ฉฐ, ์ด ๊ฒฐ๊ณผ๋Š” NR4A1/ERK ์‹ ํ˜ธ ์ „๋‹ฌ ๊ฒฝ๋กœ๊ฐ€ ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ์„ ์กฐ์ ˆํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์˜ ๊ฒฐ๊ณผ๋“ค์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๋ฏธ๋ฃจ์–ด ๋ณผ ๋•Œ, NR4A1์ด ์—์ŠคํŠธ๋กœ๊ฒ ์ˆ˜์šฉ์ฒด ์–‘์„ฑ ์œ ๋ฐฉ์•”์—์„œ ํƒ€๋ชฉ์‹œํŽœ ์ €ํ•ญ์„ฑ์„ ๊ทน๋ณตํ•˜๋Š” ์ž ์žฌ์ ์ธ ์น˜๋ฃŒ์  ํƒ€๊ฒŸ์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.open์„
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