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    ํƒ€์•ก์„  ๋ฐœ์ƒ๊ณผ์ •์—์„œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์—ญํ• ๊ณผ ์กฐ์ง๊ณตํ•™์—์„œ์˜ ์ƒ์ฒด๋ชจ๋ฐฉ์  ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๋Œ€ํ•™์› ์น˜์˜๊ณผํ•™๊ณผ, 2021. 2. ๋ฐ•๊ฒฝํ‘œ.๊ตฌ๊ฐ•๊ฑด์กฐ์ฆ์€ ํƒ€์•ก์„ ์˜ ๊ธฐ๋Šฅ์ด์ƒ์œผ๋กœ ์ธํ•ด ํƒ€์•ก๋Ÿ‰์ด ๊ฐ์†Œํ•จ์œผ๋กœ์จ ๋ฐœ์ƒํ•œ๋‹ค. ์ •์ƒ์ ์ธ ํƒ€์•ก๋ถ„๋น„๋Š” ๊ตฌ๊ฐ•๊ฑด๊ฐ•์„ ์œ ์ง€ํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์š”์†Œ๋กœ์„œ ์ €์ž‘ ๋ฐ ์—ฐํ•˜๋ฅผ ๋•๋Š” ์œคํ™œ์ž‘์šฉ, ๊ตฌ๊ฐ• ๋‚ด pH ์œ ์ง€, ์น˜์•„์˜ ์žฌ๊ด‘ํ™”, ํ–ฅ๊ท , ๋ฐ ์กฐ์ง์žฌ์ƒ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ตฌ๊ฐ•๊ฑด์กฐ์ฆ์ด ๋ฐœ์ƒํ•˜๋ฉด ๊ตฌ๊ฐ• ๊ฑด๊ฐ•๊ณผ ์ „์‹  ๊ฑด๊ฐ•๊นŒ์ง€๋„ ์œ„ํ˜‘๋ฐ›๊ฒŒ ๋œ๋‹ค. ํƒ€์•ก์„ ์˜ ์†์ƒ์€ ํฌ๊ฒŒ ๋…ธํ™”, ์‡ผ๊ทธ๋ Œ ์ฆํ›„๊ตฐ๊ณผ ๊ฐ™์€ ์ž๊ฐ€๋ฉด์—ญ์งˆํ™˜, ๊ทธ๋ฆฌ๊ณ  ๋‘๊ฒฝ๋ถ€์•”์˜ ๋ฐฉ์‚ฌ์„ ์น˜๋ฃŒ์‹œ ๋ฐœ์ƒํ•˜๋Š” ๋ฐฉ์‚ฌ์„ ์œผ๋กœ ์ธํ•œ ์†์ƒ์œผ๋กœ ๋‚˜๋ˆ„์–ด ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฌ๋กœ์„œ๋Š” ์™ธ๋ถ€์—์„œ ๋ฐฐ์–‘ํ•œ ํƒ€์•ก์„  ์กฐ์ง์˜ ์ด์‹๊ณผ ํƒ€์•ก์„ ์˜ ์ž์—ฐ ์žฌ์ƒ์„ ๋„๋ชจํ•˜๋Š” ๋ฒ•์ด ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ํ•ด๊ฒฐ์ฑ…์œผ๋กœ ์ œ์‹œ๋˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋‹ค์–‘ํ•œ ์„ธํฌ์ง€์ง€์ฒด๋“ค์ด ํƒ€์•ก์„  ์กฐ์ง๊ณตํ•™์— ํ™œ์šฉ๋˜์—ˆ์œผ๋‚˜ ์ด ์ค‘ ์–ด๋–ค ์žฌ๋ฃŒ๋„ ํƒ€์•ก์„ ์˜ ๋ณต์žกํ•œ ๊ตฌ์กฐ์™€ ๋‹ค์–‘ํ•œ ์„ธํฌ๋“ค์˜ ์ •ํ™•ํ•œ ๋ฐฐ์น˜๋ฅผ ์žฌํ˜„ํ•ด ๋‚ด์ง€ ๋ชปํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํƒ€์•ก์„ ์˜ ๋ฐœ์ƒ๊ณผ์ •์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์„ธํฌ๋“ค์˜ ์กฐ์งํ™”์™€ ๊ตฌ์กฐํ™”๊ฐ€ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ผ์–ด๋‚˜๊ธฐ ๋•Œ๋ฌธ์— ํƒ€์•ก์„ ์˜ ์žฌ์ƒ๊ณผ ์กฐ์ง๊ณตํ•™์  ์ ‘๊ทผ์—๋Š” ํƒ€์•ก์„  ๋ฐœ์ƒ๊ณผ์ •์˜ ํŠน์ง• ์ค‘ ํ•˜๋‚˜์ธ โ€œ๋ถ„์ง€ํ˜•ํƒœํ˜•์„ฑ (Branching morphogenesis)โ€์— ๊ด€์—ฌํ•˜๋Š” ์ƒ์ฒด๋ฌผ์งˆ๋“ค๊ณผ ๊ทธ๋“ค์˜ ๋ฌผ๋ฆฌํ™”ํ•™์  ํŠน์„ฑ์„ ์ดํ•ดํ•˜๊ณ  ๊ณตํ•™์ ์œผ๋กœ ๋ชจ๋ฐฉํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ํŠนํžˆ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์€ ํƒ€ ์žฅ๊ธฐ๋“ค์˜ ๋ฐœ์ƒ๊ณผ์ •์—์„œ ๊ทธ ์—ญํ• ์ด ์ž˜ ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜ ํƒ€์•ก์„  ๋ฐœ์ƒ๊ณผ์ •์—์„œ์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์—ญํ• ์€ ์•„์ง ๋ฐํ˜€์ ธ ์žˆ์ง€ ์•Š์•˜๋‹ค. ์ œ1์žฅ ์—์„œ๋Š” ๋ฐฐ์•„ ํƒ€์•ก์„ ์˜ ์ฒด์™ธ ๋ฐฐ์–‘๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ํƒ€์•ก์„ ์˜ ๋ฐœ์ƒ๊ณผ์ •์—์„œ์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ๋ถ„ํฌ์™€ ์—ญํ• , ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ์กฐ์ง๊ณตํ•™์ ์œผ๋กœ ์‘์šฉํ•˜๋Š” ์—ฐ๊ตฌ๋‚ด์šฉ์„ ๋…ผํ•˜๊ณ  ์žˆ๋‹ค. ๋จผ์ €, ๋ฐœ์ƒํ•˜๋Š” ํƒ€์•ก์„ ์˜ ์ค‘๊ฐ„์—ฝ์—๋Š” ๋‹ค๋Ÿ‰์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด ์กด์žฌํ•จ์„ ํ™•์ธํ•˜์˜€๊ณ  ์ด์™€ ์ ‘ํ•˜๋Š” ์ƒํ”ผ์กฐ์ง์˜c-Kit+ ์ „๊ตฌ์„ธํฌ๋“ค์ด ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์ˆ˜์šฉ์ฒด์ธ CD44๋ฅผ ๋ฐœํ˜„ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์ƒ์‚ฐ์„ ์–ต์ œํ•˜๊ฑฐ๋‚˜ ๋ถ„ํ•ดํ•˜๋ฉด ํƒ€์•ก์„ ์˜ ๋ถ„์ง€ํ˜•ํƒœํ˜•์„ฑ์ด ์ค‘๋‹จ๋˜์—ˆ์œผ๋ฉฐ c-Kit+ ์ „๊ตฌ์„ธํฌ์˜ ์–‘ ๋˜ํ•œ ๊ธ‰๊ฒฉํ•˜๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๋˜ํ•œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ ์ƒ์‚ฐ ์ €ํ•ด์ œ๋‚˜ ๋ถ„ํ•ดํšจ์†Œ ์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด ์ค‘๋‹จ๋œ ๋ถ„์ง€ํ˜•ํƒœํ˜•์„ฑ์ด ๊ณ  ๋ถ„์ž๋Ÿ‰์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์„ ๋ฐฐ์–‘์•ก์— ์ฒจ๊ฐ€ํ•  ์‹œ ์ผ๋ถ€ ์žฌ๊ฐœ๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ € ๋ถ„์ž๋Ÿ‰์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์€ ํšŒ๋ณตํšจ๊ณผ๊ฐ€ ์—†์—ˆ๊ธฐ์— ํƒ€์•ก์„ ์˜ ๋ฐœ์ƒ๊ณผ์ •์—๋Š” ๊ณ  ๋ถ„์ž๋Ÿ‰์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด ์ค‘์š”ํ•จ์„ ์•Œ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ฐฐ์•„ ํƒ€์•ก์„ ์„ ์„ธํฌ ์ˆ˜์ค€์œผ๋กœ ๋ถ„ํ•ดํ•˜์—ฌ ์ด๋ฅผ ๋‹ค์‹œ ๋ฐฐ์–‘ํ™˜๊ฒฝ์— ์‹ฌ์–ด์ฃผ๋ฉด ๋ถ„ํ•ด๋œ ์„ธํฌ๋“ค์ด ๋‹ค์‹œ ๋ชจ์—ฌ ํƒ€์•ก์„  ๊ตฌ์กฐ๋ฅผ ์žฌ๊ตฌ์„ฑํ•˜๋Š”๋ฐ ์ด๋ ‡๊ฒŒ ์žฌ๊ตฌ์„ฑ๋˜์–ด ํ˜•์„ฑ๋œ ์„ธํฌ ๋ฉ์–ด๋ฆฌ๋ฅผ โ€œorgan germโ€ ์ด๋ผ ์ง€์นญํ•œ๋‹ค. Organ germ ์„ ์†์ƒ๋œ ํƒ€์•ก์„ ์— ์ด์‹ํ•˜์—ฌ ํƒ€์•ก์„  ๊ธฐ๋Šฅ์„ ์˜จ์ „ํžˆ ํšŒ๋ณต์‹œํ‚จ ์—ฐ๊ตฌ์‚ฌ๋ก€๋“ค์ด ๋‹ค์ˆ˜ ์ถœํŒ๋˜์—ˆ์ง€๋งŒ organ germ ์„ ์ฒด์™ธ์—์„œ ๋‹ค๋Ÿ‰ ๋ฐฐ์–‘ํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์•„์ง ๋ฏธ์ง€์ˆ˜์ด๋‹ค. ๋ฐฐ์•„ ํƒ€์•ก์„ ์˜ organ germ ํ˜•์„ฑ๊ณผ์ •์—์„œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์—ญํ• ์„ ์‹คํ—˜ํ•œ ๊ฒฐ๊ณผ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์ƒ์„ฑ์ด ์–ต์ œ๋˜๊ฑฐ๋‚˜ ๋ถ„ํ•ดํšจ์†Œ์— ์˜ํ•ด ๋ถ„ํ•ด๋˜๋ฉด organ germ ์˜ ํ˜•์„ฑ์ด ์–ต์ œ๋˜๊ณ  organ germ ๋‚ด์˜ c-Kit+ ์ „๊ตฌ์„ธํฌ์˜ ์–‘ ๋˜ํ•œ ์ค„์–ด๋“ฆ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ฐ˜๋Œ€๋กœ ๊ณ  ๋ถ„์ž๋Ÿ‰์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์„ ๋ฐฐ์–‘์•ก์— ์ฒจ๊ฐ€ํ•˜๋ฉด organ germ ์˜ ํ˜•์„ฑ์ด ์ด‰์ง„๋˜๊ณ  c-Kit+ ์ „๊ตฌ์„ธํฌ๋“ค์˜ ์–‘ ๋˜ํ•œ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ํ˜•ํƒœ, ์ฆ‰ ์ž์œ ๋กญ๊ฒŒ ์šฉ์•ก ๋‚ด์— ๋ถ€์œ ํ•˜๋Š” ํ˜•ํƒœ์™€ ์กฐ์ง ๋‚ด ์กด์žฌํ•˜๋Š” ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ๊ณผ ๊ฐ™์ด ํŠน์ • ํ‘œ๋ฉด์— ๊ณ ์ •๋œ ํ˜•ํƒœ์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด ๋ฐฐ์•„ํƒ€์•ก์„  organ germ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด์•˜๋‹ค. ํด๋ฆฌ๋„ํŒŒ๋ฏผ๊ณผ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์„ ๋ฐฐํ•ฉํ•œ ์šฉ์•ก ๋‚ด์— ๋ฐฐ์•„ํƒ€์•ก์„  ๋ฐฐ์–‘์— ์“ฐ์ด๋Š” ํด๋ฆฌ์นด๋ณด๋„ค์ดํŠธ ๋ง‰์„ ๋‹ด๊ถˆ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์„ ํ‘œ๋ฉด์— ๊ณ ์ •์‹œ์ผฐ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๊ฐ™์€ ๋ถ„์ž๋Ÿ‰์˜ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด๋ผ๋„ ์ˆ˜์šฉ์•ก ํ˜•ํƒœ ๋ณด๋‹ค ํ‘œ๋ฉด์— ๊ณ ์ •๋œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด ๋ฐฐ์•„ํƒ€์•ก์„  organ germ ํ˜•์„ฑ์„ ๋”์šฑ ์ด‰์ง„์‹œํ‚ค๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ์ œ2์žฅ์—์„œ๋Š” ์ฑ•ํ„ฐ 1์—์„œ ๋ฐœ๊ฒฌํ•œ ์‚ฌ์‹ค๋“ค์— ๋ฐ”ํƒ•ํ•˜์—ฌ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์„ ๋”์šฑ ํšจ๊ณผ์ ์œผ๋กœ ๋‹ค์–‘ํ•œ ํ‘œ๋ฉด์— ๊ณ ์ •์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก NiCHE (Nature-inspired Catechol-conjugated Hyaluronic acid Environment) ๋ผ๊ณ  ๋ช…๋ช…ํ•œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ ์ฝ”ํŒ… ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ํ™ํ•ฉ์ ‘์ฐฉ๋‹จ๋ฐฑ์งˆ์˜ ์ ‘์ฐฉ์„ฑ๋ถ„์ธ ์นดํ…Œ์ฝœ๊ธฐ๋ฅผ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์— ํ™”ํ•™์ ์œผ๋กœ ๊ฒฐํ•ฉ์‹œ์ผœ ์ ‘์ฐฉ์„ฑ์ด ์žˆ๋Š” ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ-์นดํ…Œ์ฝœ ์„ ๋งŒ๋“ค์—ˆ๋‹ค. ๋ฐฐ์•„ํƒ€์•ก์„ ์€ ์ฃผ๋ณ€ ํ™˜๊ฒฝ์˜ ๋ฌผ๋ฆฌ์ ์ธ ํŠน์„ฑ์— ๋ฏผ๊ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋”ฑ๋”ฑํ•œ ํ‘œ๋ฉด์—์„œ๋Š” ์ œ๋Œ€๋กœ ์ž๋ผ์ง€ ๋ชปํ•œ๋‹ค. ๋ถ€๋“œ๋Ÿฌ์šด ํ‘œ๋ฉด์ด๋‚˜ ํ•˜์ด๋“œ๋กœ๊ฒ”์—์„œ๋Š” ์ •์ƒ์ ์œผ๋กœ ๋ถ„์ง€ํ˜•ํƒœํ˜•์„ฑ์ด ์ง„ํ–‰๋˜์ง€๋งŒ ์ด๋Ÿฌํ•œ ์žฌ๋ฃŒ๋“ค์€ ๊ฐ•๋„๊ฐ€ ์•ฝํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ƒ์ฒด ๋‚ด์—์„œ ๋‚ด๊ตฌ์„ฑ์ด ๋–จ์–ด์ ธ ์ž„์ƒ์ ์ธ ์ ์šฉ์ด ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ๋‚˜๋Š” ๋‚ด๊ตฌ์„ฑ๊ณผ ์ƒ์ฒด์ ํ•ฉ์„ฑ ๋‘ ๊ฐ€์ง€ ๋ชจ๋‘๋ฅผ ๋งŒ์กฑํ•˜๋Š” ์žฌ๋ฃŒ๋ฅผ ์ฐพ๊ธฐ ๋ณด๋‹ค๋Š” ๊ธฐ์กด์— ์กด์žฌํ•˜๋Š” ๋‚ด๊ตฌ์„ฑ์ด ์ข‹์€ ์ƒ์ฒด์žฌ๋ฃŒ๋“ค์— ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ-์นดํ…Œ์ฝœ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‘œ๋ฉด์„ ์ฝ”ํŒ…ํ•ด ๋ณด์•˜๋‹ค. ํด๋ฆฌ์นด๋ณด๋„ค์ดํŠธ, ๊ณ ๊ฐ•๋„ ์•Œ์ง€๋„ค์ดํŠธ ํ•˜์ด๋“œ๋กœ๊ฒ”, ๊ทธ๋ฆฌ๊ณ  ํด๋ฆฌ์นดํ”„๋กœ๋ฝํ†ค์— ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์„ ์ถฉ๋ถ„ํžˆ ๋‘๊ป๊ฒŒ ๊ณ ์ •ํ•˜๋Š” ๋ฐ ์„ฑ๊ณตํ•˜์˜€๊ณ  ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด ์ฝ”ํŒ…๋œ ํ‘œ๋ฉด์—์„œ๋Š” ์žฌ๋ฃŒ์˜ ๋ฌผ์„ฑ๊ณผ ์ƒ๊ด€์—†์ด ๋ฐฐ์•„ํƒ€์•ก์„ ์˜ ๋ถ„์ง€ํ˜•ํƒœํ˜•์„ฑ์ด ์„ฑ๊ณต์ ์œผ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ•ํ™” ํšจ๊ณผ๋Š” ํ‘œ๋ฉด ์œ„์— ๊ณ ์ •๋œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ๋“ค์ด ๋ฐฐ์•„ํƒ€์•ก์„  ๋‚ด ํ˜ˆ๊ด€๋‚ดํ”ผ์„ธํฌ์˜ ์ฆ์‹์„ ๋•๊ธฐ ๋•Œ๋ฌธ์ด์—ˆ๋‹ค. ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์ด ์ฝ”ํŒ…๋œ ํ‘œ๋ฉด ์œ„์—์„œ CD44๋ฅผ ๋ฐœํ˜„ํ•˜๋Š” ํ˜ˆ๊ด€๋‚ดํ”ผ์„ธํฌ๋“ค์˜ ERK์˜ ์ธ์‚ฐํ™”๊ฐ€ ์ฆ๊ฐ€๋˜์—ˆ์œผ๋ฉฐ ํ˜ˆ๊ด€์‹ ์ƒ ๋˜ํ•œ ์ฆ๊ฐ€๋˜์—ˆ๋‹ค. VEGF ์–ต์ œ์ œ๋ฅผ ์ฒ˜๋ฆฌํ•˜์ž ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ ์ฝ”ํŒ…์— ์˜ํ•œ ๊ฐ•ํ™”ํšจ๊ณผ๊ฐ€ ์‚ฌ๋ผ์ง์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ์ด์ƒ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ํƒ€์•ก์„ ์˜ ๋ฐœ์ƒ๊ณผ์ •์—์„œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ์˜ ์—ญํ• ์ด ์ค‘์š”ํ•จ์„ ๋ฐํžˆ๊ณ  ์žˆ์œผ๋ฉฐ ์ด๋ฅผ ์กฐ์ง๊ณตํ•™์ ์œผ๋กœ ์‘์šฉํ•œ๋‹ค๋ฉด ํƒ€์•ก์„ ์— ์ตœ์ ํ™”๋œ ํžˆ์•Œ๋ฃจ๋ก ์‚ฐ ๊ธฐ๋ฐ˜์˜ ์กฐ์ง์žฌ์ƒ ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Dry mouth, or xerostomia, caused by salivary gland dysfunction significantly impacts oral/systemic health and quality of life. Recently, there has been a growing interest in replacing severely damaged salivary glands with artificial salivary gland functional units created in vitro by tissue engineering approaches. Although various materials have been used as scaffolds for salivary gland tissue engineering, none of them is effective enough to closely recapitulate the branched structural complexity and heterogeneous cell population of native salivary glands. Therefore, understanding and recapitulating the roles of biomacromolecules in salivary gland organogenesis is needed to solve these problems. Hyaluronic acid (HA) is a macromolecule abundant during salivary gland organogenesis, but its role remains unknown. In chapter I, the roles of HA during salivary gland organogenesis and artificial organ germ formation in solubilized and substrate-immobilized forms have been elucidated by using ex-vivo organotypic culture model of developing salivary glands. Dense HA layers encapsulating proliferative c-Kit+ progenitor cells expressing CD44, an HA receptor, were found. The blockage of HA synthesis, or degradation of HA, impaired eSMG growth by ablating the c-Kit+ progenitor cell population. It has been also found that high-molecular-weight (HMW) HA has a significant role in eSMG growth. Based on these findings, it is discovered that HA is also crucial for in vitro formation of salivary gland organ germs, one of the most promising candidates for salivary gland tissue regeneration. Supplementation of HMW HA in solution significantly enhanced salivary gland organ germ formation, and this effect was further increased when the HMW HA was immobilized on the substrate by polydopamine/HA co-immobilization. These results suggest that the current use of HA in salivary gland tissue engineering can be further optimized. In chapter II, based on the findings in chapter I, I designed hyaluronic acid-catechol (HACA) conjugates to establish a versatile hyaluronic acid coating platform named โ€œNiCHE (Nature-inspired catechol-conjugated hyaluronic acid environment)โ€ for boosting the salivary gland tissue engineering efficacy of previously reported biomaterials. By mimicking hyaluronic acid-rich niche in mesenchyme of embryonic submandibular glands (eSMGs) with NiCHE coating on substrates including polycarbonate membrane, stiff agarose hydrogel, and polycaprolactone scaffold, cell adhesion, vascular endothelial and progenitor cell proliferation, and branching of in vitro-cultured eSMGs were significantly enhanced. High mechanical stiffness of substrate is known to inhibit eSMG growth, but NiCHE coating significantly reduced such stiffness-induced negative effects, leading to successful differentiation of progenitor cells to functional acinar and myoepithelial cells. These enhancement effects of NiCHE coating were due to the increased proliferation of vascular endothelial cells via interaction between CD44 and surface-immobilized HAs. As such, NiCHE coating platform renders any kind of material highly effective for salivary gland tissue culture by mimicking in vivo embryonic mesenchymal HA. Based on the results, it is expected that NiCHE coating will expand the range of biomaterial candidates for salivary glands and other branching epithelial organs.Abstract 1 Contents 4 List of Figures 6 List of Tables 9 Abbreviations 10 General Introduction 11 1. Salivary gland dysfunction 11 2. Development of tissue-engineered artificial salivary glands 11 3. Branching morphogenesis of embryonic salivary gland 12 4. Limitations of salivary gland tissue engineering biomaterials 14 5. Hyaluronic acid in development of epithelial organs 14 Chapter I Developmental Roles of Hyaluronic Acid During Organogenesis of Salivary Glands and Its Application in Salivary Gland Organ Germ Formation 17 1. Introduction 18 2. Materials and Methods 22 3. Results 31 4. Discussion 65 Chapter II Development of NiCHE Platform: Nature-inspired Catechol-conjugated Hyaluronic Acid Environment Platform for Salivary Gland Tissue Engineering 72 1. Introduction 73 2. Materials and Methods 81 3. Results and Discussion 89 Conclusion 115 References 120 Abstract in Korean 128Docto

    Carboxymethyl ์ž‘์šฉ๊ธฐ๋ฅผ ๊ฐ€์ง€๋Š” ๋น„๋Œ€์นญ bis(sulfonyl)imide ์Œ์ด์˜จ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ƒˆ๋กœ์šด ์ด์˜จ์„ฑ ์•ก์ฒด ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2022.2. ๊น€์˜๊ทœ.Ionic liquids (ILs) are used in a wide range, and among them, they are spotlighted as next-generation electrolytes in lithium-ion batteries. Due to differentiated characteristics of ILs such as non-flammability, high ion conductivity, the possibility of various combinations, and stability in a wide voltage range, many new studies have been conducted to replace existing conventional electrolytes by using ILs. In this paper, pyridinium, imidazolium-based cation with large planarity, and asymmetric bis(sulfonyl)imide anion were selected to decrease viscosity and increase ionic conductivity, and six types of ILs were synthesized by using them. In the cation synthesis process, the reaction yield was higher than 95%, while it was 54% when the intermediate was formed in the anion synthesis process. And it was 90% or even more in all the remaining steps were obtained. The purity of the synthesized ILs was confirmed by 1H, 13C, 19F-NMR, and elemental analysis. Viscosity and ion conductivity were measured to investigate the performance of the synthesized ILs as an electrolyte. The viscosity of the pure synthesized ILs was high, and ion conductivity of 1-4 mS/cm was measured. And, to analyze the performance of the ILs as an electrolyte additive, a binary mixture mixed with a carbonate electrolyte (EC/DEC 1:2 mixture), which is widely used as an electrolyte, was prepared. As a result of measuring viscosity and ion conductivity by mixing ILs and carbonate electrolyte in various ratios, the viscosity became very low as the ratio of the ionic liquid decreased, and the ion conductivity tended to increase when the ratio of the ionic liquid decreased. Among the mixtures, the 20:80 binary mixture of C1A1-Ester and EMIMA1-Ester showed high ionic conductivity of 9.11 mS/cm and 9.03 mS/cm. The results of these studies have shown that the ILs synthesized in this paper have the potential to have a high effect when used as a battery electrolyte additive.ํ˜„์žฌ ์ด์˜จ์„ฑ ์•ก์ฒด๋Š” ๋„“์€ ๋ฒ”์œ„์—์„œ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๊ณ , ๊ทธ ์ค‘์—์„œ ๋ฆฌํŠฌ ์ด์˜จ ๋ฐฐํ„ฐ๋ฆฌ์˜ ์ฐจ์„ธ๋Œ€ ์ „ํ•ด์งˆ๋กœ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋‹ค. ์ด์˜จ์„ฑ ์•ก์ฒด์˜ ๋น„์ธํ™”์„ฑ, ๋†’์€ ์ด์˜จ์ „๋„๋„, ์—ฌ๋Ÿฌ ์กฐํ•ฉ์˜ ๊ฐ€๋Šฅ์„ฑ, ๋„“์€ ์ „์•• ๋ฒ”์œ„์—์„œ์˜ ์•ˆ์ •์„ฑ ๋“ฑ๊ณผ ๊ฐ™์€ ์ฐจ๋ณ„ํ™”๋œ ํŠน์„ฑ์œผ๋กœ ๊ธฐ์กด์˜ ์ „ํ•ด์งˆ์„ ๋Œ€์ฒดํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋งŽ์€ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ‰๋ฉด์„ฑ์ด ํฐ ํ”ผ๋ฆฌ๋””๋Š„ ๊ณ„์—ด๊ณผ ์ด๋ฏธ๋‹ค์กธ ๊ณ„์—ด ์–‘์ด์˜จ, ๋น„๋Œ€์นญ bis(sulfonyl)imide ๊ณ„์—ด์˜ ์Œ์ด์˜จ์„ ์„ ํƒํ•˜์—ฌ ์ ๋„๋ฅผ ๋‚ฎ์ถ”๊ณ  ์ด์˜จ์ „๋„๋„๋ฅผ ๋†’์ด๊ณ ์ž ํ•˜์˜€๊ณ , ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ 6๊ฐ€์ง€์˜ ์ด์˜จ์„ฑ ์•ก์ฒด๋ฅผ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ์–‘์ด์˜จ ํ•ฉ์„ฑ ๊ณผ์ •์—์„œ ๋ฐ˜์‘์˜ ์ˆ˜์œจ์ด 95% ์ด์ƒ์œผ๋กœ ๋†’์•˜์œผ๋ฉฐ, ์Œ์ด์˜จ ํ•ฉ์„ฑ ๊ณผ์ •์—์„œ๋Š” ์ค‘๊ฐ„์ฒด๋ฅผ ํ˜•์„ฑํ•  ๋•Œ์˜ 54%์˜ ์ˆ˜์œจ, ๋‚˜๋จธ์ง€ ์Šคํ…์—์„œ๋Š” ๋ชจ๋‘ 90% ์ด์ƒ์˜ ์ˆ˜์œจ์„ ์–ป์—ˆ๋‹ค. ํ•ฉ์„ฑํ•œ ์ด์˜จ์„ฑ ์•ก์ฒด๋Š” 1H, 13C, 19F-NMR ๋ฐ ์›์†Œ ๋ถ„์„ ๋ฐฉ๋ฒ•์œผ๋กœ ์ˆœ๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ํ•ฉ์„ฑํ•œ ์ด์˜จ์„ฑ ์•ก์ฒด์˜ ์ „ํ•ด์งˆ๋กœ์„œ์˜ ์„ฑ๋Šฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์ ๋„์™€ ์ด์˜จ์ „๋„๋„๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ํ•ฉ์„ฑํ•œ ์ด์˜จ์„ฑ ์•ก์ฒด์˜ ์ ๋„๋Š” 180 cP ์ด์ƒ์œผ๋กœ ๋†’์•˜๊ณ , 1-4 mS/cm์˜ ์ด์˜จ ์ „๋„๋„๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ, ์ด์˜จ์„ฑ ์•ก์ฒด๊ฐ€ ์ฒจ๊ฐ€์ œ๋กœ ์‚ฌ์šฉ๋˜์—ˆ์„ ๋•Œ์˜ ์„ฑ๋Šฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์ „ํ•ด์งˆ๋กœ ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ์นด๋ณด๋„ค์ดํŠธ ์ „ํ•ด์งˆ(EC/DEC 1:2 ํ˜ผํ•ฉ๋ฌผ)์— ํ•ฉ์„ฑํ•œ ์ด์˜จ์„ฑ ์•ก์ฒด๋ฅผ ์—ฌ๋Ÿฌ ๋น„์œจ์˜ ์ด์› ํ˜ผํ•ฉ๋ฌผ๋กœ ์ œ์กฐํ•˜์—ฌ ์ ๋„์™€ ์ด์˜จ์ „๋„๋„๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ์ด์˜จ์„ฑ ์•ก์ฒด์˜ ๋†๋„๊ฐ€ ๋ฌฝ์–ด์ง์— ๋”ฐ๋ผ, ์ ๋„ ๊ฐ’์ด 44 cP ์ดํ•˜๊นŒ์ง€ ๊ฐ์†Œํ•˜์—ฌ, ์œ ๋™์„ฑ์ด ๊ฐœ์„ ๋œ ๋ชจ์Šต์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ, ๋Œ€๋ถ€๋ถ„์˜ ๋น„์œจ์—์„œ ์ด์˜จ ์ „๋„๋„ ๊ฐ’์ด ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ํŠน์ • ๋†๋„์—์„œ๋Š” ์•ฝ 3๋ฐฐ๊ฐ€๋Ÿ‰ ์ฆ๊ฐ€ํ•œ ์ด์˜จ์ „๋„๋„ ๊ฐ’์„ ๋ณด์˜€๋‹ค. ํ•ฉ์„ฑํ•œ ํ˜ผํ•ฉ๋ฌผ ์ค‘ C1A1-Ester, EMIMA1-Ester์˜ 20:80 ํ˜ผํ•ฉ๋ฌผ์˜ ๊ฒฝ์šฐ ๊ฐ๊ฐ 9.11mS/cm, 9.03mS/cm์˜ ๋†’์€ ์ด์˜จ์ „๋„๋„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ ํ•ฉ์„ฑํ•œ ์ด์˜จ์„ฑ ์•ก์ฒด๊ฐ€ ๋ฐฐํ„ฐ๋ฆฌ ์ „ํ•ด์งˆ ์ฒจ๊ฐ€์ œ๋กœ ์ด์šฉ๋˜์—ˆ์„ ๋•Œ ๋†’์€ ํšจ๊ณผ๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ๋ ฅ์ด ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค.Table of Contents Abstract โ…ฐ List of Figures โ…ณ List of Tables โ…ด List of Abbreviations โ…ต 1. Introduction 1 1.1 Introduction of Ionic liquids 1 1.2 Application of Ionic liquids 4 1.2.1 Solvents in organic synthesis 4 1.2.2 Lubricants 8 1.2.3 Electrolyte or additive for electrochemical devices 9 2. Results and Discussion 12 2.1 Design of the target ILs 12 2.2 Synthesis of Cations 13 2.2.1 Synthesis of pyridinium-based cations 13 2.2.2 Synthesis of imidazole-based cation 15 2.2.3 Synthesis of the unsymmetrical bis(sulfonyl)-imide anion 16 2.2.4 Synthesis of the target ILs 18 2.3 Viscosity and ionic conductivity of synthesized ILs 20 2.4 Viscosity and ionic conductivity of a mixture of carbonate and ILs 24 3. Conclusion 29 References 31 Experimental Details 33 Appendices 45 Abstract in Korean 68์„

    ๊นŠ์€ ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ์ผ์ƒ ํ–‰๋™์— ๋Œ€ํ•œ ํ‰์ƒ ํ•™์Šต: ๋“€์–ผ ๋ฉ”๋ชจ๋ฆฌ ์•„ํ‚คํ…์ณ์™€ ์ ์ง„์  ๋ชจ๋ฉ˜ํŠธ ๋งค์นญ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2018. 8. ์žฅ๋ณ‘ํƒ.Learning from human behaviors in the real world is imperative for building human-aware intelligent systems. We attempt to train a personalized context recognizer continuously in a wearable device by rapidly adapting deep neural networks from sensor data streams of user behaviors. However, training deep neural networks from the data stream is challenging because learning new data through neural networks often results in loss of previously acquired information, referred to as catastrophic forgetting. This catastrophic forgetting problem has been studied for nearly three decades but has not been solved yet because the mechanism of deep learning has been not understood enough. We introduce two methods to deal with the catastrophic forgetting problem in deep neural networks. The first method is motivated by the concept of complementary learning systems (CLS) theory - contending that effective learning of the data stream in a lifetime requires complementary systems that comprise the neocortex and hippocampus in the human brain. We propose a dual memory architecture (DMA), which trains two learning structures: one gradually acquires structured knowledge representations, and the other rapidly learns the specifics of individual experiences. The ability of online learning is achieved by new techniques, such as weight transfer for the new deep module and hypernetworks for fast adaptation. The second method is incremental moment matching (IMM) algorithm. IMM incrementally matches the moment of the posterior distribution of neural networks, which is trained for the previous and the current task, respectively. To make the search space of posterior parameter smooth, the IMM procedure is complemented by various transfer learning techniques including weight transfer, L2-norm of the old and the new parameter, and a variant of dropout with the old parameter. To provide an insight into the success of two proposed lifelong learning methods, we introduce an insight by introducing two online learning methods of sum-product network, which is a kind of deep probabilistic graphical model. We discuss online learning approaches which are valid in probabilistic models and explain how these approaches can be extended to the lifelong learning algorithms of deep neural networks. We evaluate proposed DMA and IMM on two types of datasets: the various artificial benchmarks devised for evaluating the performance of lifelong learning and the lifelog dataset collected through the Google Glass for 46 days. The experimental results show that our methods outperform comparative models in various experimental settings and that our trials for overcoming catastrophic forgetting are valuable and promising.1 Introduction 1 1.1 Wearable Devices and Lifelog Dataset . . . . . . . . . . . . . . . 1 1.2 Lifelong Learning and Catastrophic Forgetting . . . . . . . . . . 2 1.3 Approach and Contribution . . . . . . . . . . . . . . . . . . . . . 3 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . 6 2 Related Works 8 2.1 Lifelong Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Application-driven Lifelong Learning . . . . . . . . . . . . . . . . 9 2.3 Classical Approach for Preventing Catastrophic Forgetting . . . . 9 2.4 Learning Parameter Distribution for for Preventing Catastrophic Forgetting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 Sequential Bayesian . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Approach to Simulating Parameter Distribution . . . . . 14 2.5 Learning Data Distribution for Preventing Catstrophic Forgetting 15 3 Preliminary Study: Online Learning of Sum-Product Networks 17 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Sum-Product Networks . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Representation of Sum-Product Networks . . . . . . . . . 19 3.2.2 Structure Learning of Sum-Product Networks . . . . . . . 22 3.3 Online Incremental Structure Learning of Sum-Product Networks 23 3.3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Non-Parametric Bayesian Sum-Product Networks . . . . . . . . . 29 3.4.1 Model 1: A Prior Distribution for SPN Trees . . . . . . . 29 3.4.2 Model 2: A Prior Distribution for a Class of dag-SPNs . . 34 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.5.1 History of Online Learning of Sum-Product Networks . . 38 3.5.2 Toward Lifelong Learning of Deep Neural Networks . . . 38 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4 Structure Learning for Lifelong Learning: Dual Memory Architecture 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 Complementary Learning Systems Theory . . . . . . . . . . . . . 44 4.3 Dual Memory Architectures . . . . . . . . . . . . . . . . . . . . . 46 4.4 Online Learning of Multiplicative-Gaussian Hypernetworks . . . 50 4.4.1 Multiplicative-Gaussian Hypernetworks . . . . . . . . . . 50 4.4.2 Evolutionary Structure Learning . . . . . . . . . . . . . . 52 4.4.3 Online Learning on Incremental Features . . . . . . . . . 53 4.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.5.1 Non-stationary Image Data Stream . . . . . . . . . . . . . 56 4.5.2 Lifelog Dataset . . . . . . . . . . . . . . . . . . . . . . . . 60 4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.6.1 Parameter-Decomposability in Deep Learning . . . . . . . 65 4.6.2 Online Bayesian Optimization . . . . . . . . . . . . . . . . 65 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5 Sequential Bayesian for Lifelong Learning: Incremental Moment Matching 68 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.2 Incremental Moment Matching . . . . . . . . . . . . . . . . . . . 69 5.2.1 Mean-based Incremental Moment Matching (mean-IMM) 70 5.2.2 Mode-based Incremental Moment Matching (mode-IMM) 71 5.3 Transfer Techniques for Incremental Moment Matching . . . . . . 74 5.3.1 Weight-Transfer . . . . . . . . . . . . . . . . . . . . . . . 74 5.3.2 L2-transfer . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.3.3 Drop-transfer . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.3.4 IMM Procedure . . . . . . . . . . . . . . . . . . . . . . . . 79 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.4.1 Disjoint MNIST Experiment . . . . . . . . . . . . . . . . 80 5.4.2 Shuffled MNIST Experiment . . . . . . . . . . . . . . . . 83 5.4.3 ImageNet to CUB Dataset . . . . . . . . . . . . . . . . . 85 5.4.4 Lifelog Dataset . . . . . . . . . . . . . . . . . . . . . . . . 88 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.5.1 A Shift of Optimal Hyperparameter via Space Smoothing 89 5.5.2 Bayesian Approach on lifelong learning. . . . . . . . . . . 90 5.5.3 Balancing the Information of an Old and a New Task. . . 90 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6 Concluding Remarks 92 6.1 Summary of Methods and Contributions . . . . . . . . . . . . . . 92 6.2 Suggestions for Future Research . . . . . . . . . . . . . . . . . . . 93 ์ดˆ๋ก 109Docto

    ๊ทธ ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ณผํ•™๊ต์œก๊ณผ(๋ฌผ๋ฆฌ์ „๊ณต), 2023. 2. ์กฐ์ •ํšจ.์šฐ๋ฆฌ ๊ต์œก์ด ๊ทธ๊ฐ„ ์ˆ˜๋งŽ์€ ๋น„ํŒ์„ ๋ฐ›์•„์˜จ ๊ฒƒ์€ ๋ถ„๋ช…ํ•œ ์‚ฌ์‹ค์ด๋‚˜, ์ด๋Ÿฌํ•œ ์‚ฌ์‹ค๋งŒ์œผ๋กœ ๊ต์œก๊ณ„์˜ ๊ต์œกํ•™์ž ๋ฐ ๊ต์œก์ž๋“ค์ด ๋…ธ๋ ฅํ•œ ๋ฐ”๊ฐ€ ์—†๋‹ค๊ณ  ํ”ํžˆ ์˜คํ•ดํ•˜๋Š” ์‹คํƒœ ๋˜ํ•œ ๋น„ํŒ ๋ฐ›์•„์•ผ ๋งˆ๋•…ํ•œ ๊ฒƒ์ด๋‹ค. ์šฐ๋ฆฌ์˜ ๊ต์œก๊ณ„๋Š” ๋ช…๋ฐฑํžˆ ์šฐ๋ฆฌ ๊ต์œก์˜ ๊ฐœ์„ ์„ ์œ„ํ•˜์—ฌ ๋งŽ์€ ๋…ธ๋ ฅ์„ ํ•ด์™”๋‹ค. ์œ ํ–‰์œผ๋กœ ํ”ํžˆ ํ‰๊ฐ€์ ˆํ•˜๋˜๋Š” ๊ทผ๋ž˜์˜ ํ‚ค์›Œ๋“œ ์ค‘์‹ฌ์˜ ๋…ธ๋ ฅ๋“ค๋„ ๋ถ„๋ช…, ๊ธ‰๋ณ€ํ•˜๋Š” ์ƒˆ๋กœ์šด ์‹œ๋Œ€๋ฅผ ์‚ด์•„๊ฐ€์•ผ ํ•  ์•„์ด๋“ค์—๊ฒŒ ๋„์›€์„ ์ฃผ๊ธฐ ์œ„ํ•œ ์ƒˆ ๊ต์œก์„ ์น˜์—ดํ•˜๊ฒŒ ๋ชจ์ƒ‰ํ•œ ๊ฒƒ์ด๋‹ค. ๊ทธ๋ ‡๊ธฐ์—, ๊ทธ ์ˆ˜๋งŽ์€ ๋…ธ๋ ฅ์—๋„, ์šฐ๋ฆฌ ๊ต์œก์ด ํ•™์ƒ๋“ค์—๊ฒŒ ๋‹ฟ์ง€ ์•Š๋Š”๋‹ค๋Š” ๋น„ํŒ์ด ์—ฌ์ „ํžˆ ์ œ๊ธฐ๋œ๋‹ค๋Š” ๊ฒƒ์€ ๋”์šฑ ๋ผˆ์•„ํ”ˆ ์ผ์ด๋‹ค. ์ด ์‚ฌํƒœ๋Š” ์ง„์ • ์šฐ๋ฆฌ๊ฐ€ ๊ทธ๊ฐ„ ์ƒˆ ๊ต์œก์˜ ๋ฐฉ์•ˆ์„ ์ ์ ˆํžˆ ๋ชจ์ƒ‰ํ•˜์ง€ ๋ชปํ•œ ๊ฒƒ์„ ์˜๋ฏธํ•˜๋Š”๊ฐ€? ๊ฒฐ๊ตญ ์šฐ๋ฆฌ๋Š” ๋˜๋‹ค๋ฅธ ์ƒˆ ๊ต์œก์„ ๋ชจ์ƒ‰ํ•ด์•ผ ํ•˜๋Š” ๊ฒƒ์ธ๊ฐ€? ์ด ์—ฐ๊ตฌ๋Š”, AI๋ฅผ ์ƒˆ๋กœ์šด ํ‚ค์›Œ๋“œ๋กœ ํ•˜๋Š” ๊ต์œก์˜ ๋ณ€ํ™”๊ฐ€ ๋ถˆ๊ฐ€ํ”ผํ•ด ๋ณด์ด๋Š” ์ง€๊ธˆ์˜ ํ๋ฆ„ ์†์—์„œ, ์‹œ๋Œ€์  ๋ฐฐ๊ฒฝ์— ๋งž๋Š” ์ƒˆ ๊ต์œก์ด ์–ด๋•Œ์•ผ ํ•˜๋Š” ์ง€๊ฐ€ ์•„๋‹ˆ๋ผ, ์ž์‹ ์—๊ฒŒ ์™€ ๋‹ฟ์ง€ ์•Š๋Š” ์ˆ˜์—…์„ ๋“ค์–ด์•ผ ํ•˜๋Š” ํ•™์ƒ์˜ ๊ณ ํ†ต๊ณผ ํ•™์ƒ์—๊ฒŒ ๋‹ฟ์ง€ ๋ชปํ•˜๋Š” ์ˆ˜์—…์„ ํ•ด์•ผ ํ•˜๋Š” ๊ต์‚ฌ์˜ ๊ณ ํ†ต์ด๋ผ๋Š” ์šฐ๋ฆฌ ๊ต์œก์˜ ๋ณธ์งˆ์ ์ธ ๋ฌธ์ œ๋ฅผ ๋ฐฐ๊ฒฝ์œผ๋กœ, ๋ฌผ๋ฆฌ ๊ต์œก์ด ์–ด๋•Œ์•ผ ํ•˜๋Š” ์ง€๋ฅผ ๊ณ ๋ฏผํ•œ ๊ฒฐ๊ณผ์ด๋‹ค. ๊ต์œก์€ ๋ถ„๋ช… ๊ต๊ณผ ๊ต์œก์ด๋ฉฐ, ๊ต์‚ฌ๋Š” ๋ช…๋ฐฑํžˆ ์ž์‹ ์˜ ๊ต๊ณผ์— ๋งค๋ฃŒ๋˜์–ด, ๊ทธ ๊ต๊ณผ๊ฐ€ ์šฐ๋ฆฌ ์•„์ด๋“ค์˜ ๋งˆ์Œ์—๋„ ๋‹ฟ๊ธฐ๋ฅผ ๊ฐ„์ ˆํžˆ ์†Œ๋งํ•˜๋Š” ์‚ฌ๋žŒ์ด๋‹ค. ๊ต์‚ฌ์—๊ฒŒ ์ง„์ •์œผ๋กœ ํ•„์š”ํ•œ ๊ฒƒ์€, ์ƒˆ ๊ต์œก์˜ ๋ฐฉ์นจ๊ณผ ํ…Œํฌ๋‹‰์ด ์•„๋‹ˆ๋ผ, ํ•™์ƒ์˜ ๋งˆ์Œ์— ๋‹ฟ์„ ์ˆ˜ ์žˆ๋Š”, ๊ต๊ณผ ๊ต์œก๋‹ค์šด ๊ต๊ณผ ๊ต์œก์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ ‘ํ•˜๊ณ  ๊ทธ ๊ต์œก์˜ ์‹ค์ฒœ์— ํ•„์š”ํ•œ ๊ทธ๊ฒƒ์˜ ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ์„ ํ™•์ธํ•˜๋Š” ์ผ์ด๋‹ค. ํ•™์ƒ์—๊ฒŒ ํ•„์š”ํ•œ ๊ฒƒ์€, ๊ตณ์ด ํ•™๊ต์— ๊ฐ€์ง€ ์•Š์•„๋„ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ์ฆ‰ํฅ์ ์ธ ์žฌ๋ฏธ ๋˜๋Š” ์„œ์ˆ ์ ์ธ ์ •๋ณด๊ฐ€ ์•„๋‹ˆ๋ผ, ๋ฐ˜๋“œ์‹œ ํ•™๊ต์— ๊ฐ€์•ผ๋งŒ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ๋Š”, ๊ต๊ณผ ๊ต์œก๋‹ค์šด ๊ต๊ณผ ๊ต์œก์ด๋‹ค. ์ด์™€ ๊ฐ™์€ ๋งฅ๋ฝ์—์„œ, ์šฐ๋ฆฌ์˜ ๋ฌผ๋ฆฌ ๊ต์œก๊ณ„ ๋˜ํ•œ ๋ฌผ๋ฆฌ ๊ต์œก๋‹ค์šด ๋ฌผ๋ฆฌ ๊ต์œก์„ ์œ„ํ•ด ์ˆ˜๋งŽ์€ ์—ฐ๊ตฌ์™€ ์‹œ๋„๋ฅผ ํ–‰ํ•ด ์™”๋‹ค. ๋ณธ ์—ฐ๊ตฌ์ž๋Š” ๊ทธ ์ค‘์—์„œ๋„ ํŠนํžˆ, ์œ„๋Œ€ํ•œ ๋ฌผ๋ฆฌํ•™์ž์ธ ์•„์ธ์Šˆํƒ€์ธ์˜ ์‹๊ฒฌ์„ ๋นŒ๋ ค ๋ฌผ๋ฆฌ ๊ต์œก๋‹ค์šด ๋ฌผ๋ฆฌ ๊ต์œก์„ ๋ชจ์ƒ‰ํ•˜๋Š” ์‹œ๋„์—์„œ ๋ถ„๋ช…ํ•œ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์•˜๋‹ค. ํ•˜์ง€๋งŒ, ๋™์‹œ์— ์—ฐ๊ตฌ์ž๋Š”, ๊ต์œก์˜ ํ˜„์žฅ์„ ์ง์ ‘ ๊ฒฝํ—˜ํ•˜๋ฉฐ ์–ป์€ ๊ตํ›ˆ์„ ๋ฐ”ํƒ•์œผ๋กœ, ๊ต์œก์˜ ์‹ค์ฒœ์—๋Š” ๊ต์‚ฌ๊ฐ€ ๊ทธ ๊ต์œก์— ๊ต์œก์  ํ™•์‹ ์„ ๊ฐ€์งˆ ๋งŒํ•œ ํƒ€๋‹นํ•œ ํ† ๋Œ€๊ฐ€ ๋ฐ˜๋“œ์‹œ ์š”๊ตฌ๋œ๋‹ค๊ณ  ๋ฏฟ๋Š”๋‹ค. ์ด๋•Œ ๊ต์œก์  ํ™•์‹ ์˜ ํ† ๋Œ€๊ฐ€ ๋˜๋Š” ๊ฒƒ์€ ๋ช…๋ฐฑํ•˜๊ฒŒ๋„ ๊ต์œก ์ด๋ก  ๋ฐ ๊ต์œก๊ด€์ด๋ฏ€๋กœ, ์•ž์„œ ์–ธ๊ธ‰ํ•œ ๋ฐ”์™€ ์ข…ํ•ฉํ•˜์ž๋ฉด, ์ •๋ง๋กœ ๋ณธ ์—ฐ๊ตฌ์ž์—๊ฒŒ ์ ˆ์‹คํ–ˆ๋˜ ๊ฒƒ์€, AI์‹œ๋Œ€์— ๊ฑธ๋งž๋Š” ์ƒˆ ๊ต์œก์˜ ๋ฐฉ์•ˆ์ด ์•„๋‹ˆ๋ผ, ์—ฐ๊ตฌ์ž ์ž์‹ ์—๊ฒŒ ๋ฌผ๋ฆฌ ๊ต์œก๋‹ค์šด ๋ฌผ๋ฆฌ ๊ต์œก์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋˜ ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ํ† ๋Œ€๊ฐ€ ๋˜๋Š” ๊ต์œก๊ด€์„ ํƒ์ƒ‰ํ•˜๋ฉฐ ๊ทธ ๊ต์œก์˜ ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ์„ ํ™•์ธํ•˜๋Š” ์ž‘์—…์ด์—ˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐ€์žฅ ๊ธฐ์ดˆ์ ์ธ ๊ณผ์ œ๋Š”, ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ์˜๋ฏธ๋ฅผ ๋ณด๋‹ค ๋ถ„๋ช…ํ•˜๊ฒŒ ํ•˜๋ฉด์„œ, ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ๋“ค์„ ๊ฒ€ํ† ํ•˜์—ฌ ๊ทธ ๊ต์œก์˜ ํ† ๋Œ€๊ฐ€ ๋˜๋Š” ๊ต์œก๊ด€์„ ํƒ์ƒ‰ํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์€ ๋ฌผ๋ฆฌํ•™์„ ์ง€์‹ ์ฒด๊ณ„๊ฐ€ ์•„๋‹Œ, ๊ทธ ์ง€์‹์˜ ๋ฐฐํ›„๊ฐ€ ๋˜๋Š” ์‹ค์ฒœ ํ™œ๋™์˜ ์ด์ฒด, ์ฆ‰ ์‹ค์ฒœ์ „ํ†ต์œผ๋กœ ๊ฐ„์ฃผํ•œ ์ฑ„ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์˜ ๋ฐฉ์‹์œผ๋กœ ๊ทธ ๋‚ด์šฉ์„ ํ’€์–ด๋‚˜๊ฐ€๋Š” ๋ฌผ๋ฆฌ ๊ต์œก์ž„์„ ํ™•์ธํ•˜์˜€๊ณ , ๊ทธ ๊ต์œก์ด ์‹ค์ฒœ์ „ํ†ต ๊ต์œก๊ด€๊ณผ ๋งž๋‹ฟ์•„ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์–ด์„œ, ๊ต์œก์—์„œ์˜ ์‹ค์ฒœ์ „ํ†ต์„ ์ƒ์„ธํžˆ ๋‹ค๋ฃจ์—ˆ๋˜ ํ™์€์ˆ™์˜ ์—ฐ๊ตฌ๋ฅผ ๊ฒ€ํ† ํ•˜์—ฌ, ์‹ค์ฒœ์ „ํ†ต ๊ต์œก๊ด€์ด ๋ช…์ œ์  ์ง€์‹ ์ „์ˆ˜์™€ ๊ทธ ์‘์šฉ์— ์ฃผ๋ ฅํ–ˆ๋˜ ์ง€์‹ ๊ต์œก์˜ ๋Œ€์•ˆ์„ ๋ชจ์ƒ‰ํ•˜๋Š” ์—ฌ๋Ÿฌ ๊ต์œกํ•™์  ๋…ผ์˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํƒ€๋‹นํ•˜๊ฒŒ ๋„์ถœ๋œ ๊ต์œก ์ด๋ก ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์— ๋”ํ•ด, ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ๋ฐฐ๊ฒฝ๊ณผ ์ฃผ์š” ํŠน์ง•์„ ์‚ดํ”ผ๋ฉฐ, ๊ทธ ๊ต์œก์ด ์‹ค์ฒœ์ „ํ†ต ๊ต์œก๊ด€์„ ์ „๋ฐ˜์ ์ธ ํ† ๋Œ€๋กœ ์‚ผ๋Š” ๊ต์œก์ž„์„ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ, ๊ทธ ๊ต์œก์˜ ์ „๋ฐ˜์  ์˜๋ฏธ๋ฅผ ๋“œ๋Ÿฌ๋‚ด๋ฉฐ ๊ทธ ํƒ€๋‹น์„ฑ์„ ํ”ผ๋ ฅํ•˜์˜€๋‹ค. ํ•œํŽธ, ์‹ค์ฒœ์ „ํ†ต ๊ต์œก๊ด€์— ๋Œ€ํ•œ ๊ฒ€ํ† ์˜ ๊ณผ์ •์—์„œ, ๊ทธ ๊ต์œก๊ด€์€ ํŠนํžˆ ๊ธฐ์กด์˜ ๊ต์œก์—์„œ ์ƒ๋Œ€์ ์œผ๋กœ ์ง€์‹์— ๋น„ํ•ด ๋ถ„๋ช… ๊ฒฝ์‹œ๋˜์–ด ์™”๋˜ ์ •์„œ์˜ ๊ต์œก์  ์ค‘์š”์„ฑ์„ ํŠนํžˆ ๊ฐ•์กฐํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ต์œก์—์„œ์˜ ์ •์„œ์— ๋Œ€ํ•œ ์‹ฌํ™” ๋…ผ์˜๊ฐ€ ํ–ฅํ›„ ๊ต์œก์— ์ค‘์š”ํ•œ ์‹œ์‚ฌ์ ์„ ๊ฐ€์งˆ ๊ฒƒ์œผ๋กœ ๋ณด์•„, ๊ต์œก์—์„œ์˜ ์ •์„œ์˜ ์„ฑ๊ฒฉ๊ณผ ๊ทธ๊ฒƒ์˜ ํš๋“์„ ์œ„ํ•œ ๊ฒฝํ—˜์„ ๊นŠ๊ฒŒ ๊ณ ๋ฏผํ•œ ๋“€์ด์— ์ฃผ๋ชฉํ•˜์—ฌ, ๊ต์œก์—์„œ์˜ ์ •์„œ์˜ ์˜๋ฏธ์™€ ์„ฑ๊ฒฉ์„ ์ง‘์ค‘์ ์œผ๋กœ ์‚ดํŽด๋ณด์•˜๋‹ค. ์ด๋•Œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋“€์ด์— ๋Œ€ํ•ด ์œ„ํ˜‘์ ์ธ ๋น„ํŒ์„ ๊ฐ€ํ•œ ์ดํ™์šฐ์˜ ๊ฒฌํ•ด์—๋„, ๋“€์ด๋ฅผ ์žฌ์กฐ๋ช…ํ•˜๋Š” ๊ฒƒ์ด ํƒ€๋‹นํ•˜๋‹ค๋Š” ๊ฒƒ์„ ํ”ผ๋ ฅํ•˜๋ฉฐ, ๋“€์ด์˜ ๊ต์œก๊ด€์˜ ํƒ€๋‹น์„ฑ์„ ํ™•๋ณดํ•˜์˜€๋‹ค. ์ดํ›„ ๋“€์ด์˜ ๊ต์œก๊ด€์˜ ์ดํ•ด์— ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ๋กœ ๊ฐ„์ฃผ๋˜๋Š” ๊ทธ์˜ ์˜ˆ์ˆ  ์ฒ ํ•™์„ ๊ฒ€ํ† ํ•˜๋ฉฐ, ๋ฌผ๋ฆฌ ๊ต์œก์—์„œ์˜ ์ •์„œ๊ฐ€ ์•„์ธ์Šˆํƒ€์ธ์ด ์ž์‹ ์˜ ์ €์„œ์—์„œ ๋ฌผ๋ฆฌํ•™์„ ์„ค๋ช…ํ•˜๋ฉฐ ํ”ผ๋ ฅํ–ˆ๋˜ ๋ฌผ๋ฆฌํ•™์—์„œ์˜ ์ •์„œ์™€ ๋™์ผํ•ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ, ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ์„ธ๋ถ€ ์š”์†Œ์ธ ์ •์„œ์˜ ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ์„ ๋“œ๋Ÿฌ๋‚ด์—ˆ๋‹ค. ์ด์— ๋”ํ•ด, ๋“€์ด์˜ ๊ต์œก๊ด€์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ทธ ๊ต์œก์˜ ๋˜๋‹ค๋ฅธ ํ•ต์‹ฌ์ ์ธ ์„ธ๋ถ€ ์š”์†Œ์ธ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์˜ ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ์„ ๋“œ๋Ÿฌ๋‚ด๋ฉฐ, ๋‘ ์š”์†Œ๋ฅผ ํ•ต์‹ฌ ๊ตฌ์„ฑ์š”์†Œ๋กœ ํ•˜๋Š” ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ๊ต์œก์  ํ† ๋Œ€์— ๋“€์ด์˜ ๊ต์œก๊ด€์ด ์‹ฌํ™” ํ™•์žฅ์  ์ธก๋ฉด ๋˜๋Š” ๊ตญ์ง€์  ์ธก๋ฉด์—์„œ ์„ธ๋ถ€์ ์œผ๋กœ ํฌํ•จ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค.Einsteinian Physics Education (EPE) is a new approach for physics education, inspired by Einstein's book, The Evolution of Physics. The book emphasizes the holistic understanding of how the human mind attempts to figure out connections between physics and nature, not individual theories and their applications. Although EPE stems from the great physicist, its educational meaning and validity as an effective physics education have not been fully explored. EPE has been investigated as the practices viewpoint of education that notices practice activities behind knowledge, not knowledge itself. In this study, I confirmed that the practices viewpoint can provide a pedagogical basis for EPE. Then, I found that it is important to further explore the meaning of emotion in EPE. Thus, I revisited Dewey's view on emotion in education. Dewey emphasized that aesthetic emotion is the most important part in his view of education. Indeed, Einstein is famous for emphasizing the beauty of internal harmony of nature captured by physics. Therefore, it is natural that the emotion in EPE must be the aesthetic emotion in Dewey's view of education. In conclusion, EPE has sufficient pedagogical foundations of the practices and emotion in education.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ ๊ณผ์ œ 7 โ…ก. ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก 9 1. ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก ๊ฐœ๊ด„ ๋ฐ ๋ฐ˜์„ฑ์  ๋…ผ์˜ 9 2. ๋ณธ์งˆ์ -์ด์ฒด์  ๊ด€์ ๊ณผ ์‹ค์ฒœ์ „ํ†ต 20 โ…ข. ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ์ „๋ฐ˜์  ๊ธฐ๋ฐ˜ 25 1. ๊ต์œก์˜ ์ž์œ ๊ต์œก์  ์„ฑ๊ฒฉ, ๊ต์œก์—์„œ์˜ ์ง€์‹ 25 2. ์ง€์‹ ๊ต์œก์˜ ๋ฌธ์ œ์™€ ๋Œ€์•ˆ 29 3. ์‹ค์ฒœ์ „ํ†ต์œผ๋กœ์˜ ์ž…๋ฌธ์œผ๋กœ์„œ์˜ ๊ต์œก์˜ ํƒ€๋‹น์„ฑ ๊ฒ€ํ†  38 4. ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ์ „๋ฐ˜์  ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ 54 โ…ฃ. ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ์„ธ๋ถ€์  ๊ธฐ๋ฐ˜ 60 1. ๊ต์œก์—์„œ์˜ ์ •์„œ ๋ฐ ๊ฒฝํ—˜, ๋“€์ด์˜ ๊ต์œก ์ด๋ก  60 2. ๋“€์ด์˜ ๊ฒฝํ—˜ ์ด๋ก ์— ๋Œ€ํ•œ ๋น„ํŒ๊ณผ ๊ต์œก์ธ์‹๋ก ์  ๊ด€์  69 3. ๋“€์ด์˜ ์˜ˆ์ˆ  ์ฒ ํ•™ 81 4. ์•„์ธ์Šˆํƒ€์ธ์‹ ๋ฌผ๋ฆฌ ๊ต์œก์˜ ์„ธ๋ถ€์  ์˜๋ฏธ์™€ ํƒ€๋‹น์„ฑ 96 โ…ค. ๊ฒฐ๋ก  ๋ฐ ๋…ผ์˜ 114 1. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  114 2. ๋…ผ์˜ ๋ฐ ์ œ์–ธ 117 ์ฐธ๊ณ ๋ฌธํ—Œ 120 Abstract 125์„

    ๋ฌด๊ฒฉ์ž ๊ธฐ๋ฒ• ๊ธฐ๋ฐ˜์˜ ๊ณต๋ ฅ-๊ตฌ์กฐ ์—ฐ๊ณ„ ํ•ด์„์„ ์œ„ํ•œ ๋ฌด๊ฒฉ์ž ๊ธฐ๋ฒ• ๊ตฌ์กฐ ํ•ด์„ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ๊น€๊ทœํ™.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฌด๊ฒฉ์ž ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ตฌ์กฐํ•ด์„ ํ”„๋กœ๊ทธ๋žจ์„ ๊ฐœ๋ฐœ ํ•˜์˜€๋‹ค. ๋ฌด๊ฒฉ์ž ๊ธฐ๋ฒ•์€ ๊ฒฉ์ž๋‚˜ ์š”์†Œ์˜ ์ •๋ณด ์—†์ด ์งˆ์ ์˜ ์—ฐ๊ฒฐ ์ •๋ณด๋งŒ์œผ๋กœ ๋„๋ฉ”์ธ์„ ํ•ด์„ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ์งˆ์ ์˜ ์—ฐ๊ฒฐ์ •๋ณด๋งŒ์„ ๊ฐ€์ง€๊ณ  ํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ณต์žกํ•œ ํ˜•์ƒ์— ๋Œ€ํ•ด์„œ ๊ฒฉ์ž๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ ๋ณด๋‹ค ๋น„๊ต์  ์ž์œ ๋กญ๊ณ  ์†์‰ฝ๊ฒŒ ์งˆ์ ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์›€์ง์ด๋Š” ๋ฌผ์ฒด๋ฅผ ํ•ด์„ ํ•  ๋•Œ ๊ฒฉ์ž๊ณ„ ์‹œ์Šคํ…œ์˜ ๊ฒฝ์šฐ ๊ฒฉ์ž๋ฅผ ์žฌ์ƒ์„ฑํ•˜๋Š” ๋งŽ์€ ์‹œ๊ฐ„์ด ์†Œ์š”๋˜์ง€๋งŒ, ์งˆ์  ์ •๋ณด๋งŒ์„ ์‚ฌ์šฉํ•˜๊ฒŒ ๋˜๋ฉด ๊ฒฉ์ž์˜ ์žฌ์„ฑ์„ฑ ์—†์ด ์›€์ง์ด๋Š” ๋ฌผ์ฒด์— ๋Œ€ํ•ด์„œ ํ•ด์„์„ ์ˆ˜ํ–‰ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ตฌ์กฐ ์ง€๋ฐฐ๋ฐฉ์ •์‹์œผ๋กœ๋Š” Cauchys Momentum Equation์„ ์‚ฌ์šฉ ํ•˜์˜€์œผ๋ฉฐ, constitutive equation ์œผ๋กœ Hooks Law๋ฅผ ์‚ฌ์šฉ ํ•˜์˜€๊ณ , strain-displacement ์‹์œผ๋กœ Green-Langrange tensor๋ฅผ ์‚ฌ์šฉ ํ•˜์˜€๋‹ค. ๊ฐ„๋‹จํ•œ ๊ตฌ์กฐ ๋ฌธ์ œ๋ฅผ ํ•ด์„ํ•˜๊ณ  ์ด๋ก  ์‹๊ณผ ๋น„๊ตํ•˜์—ฌ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ฐœ๋ฐœ๋œ ๋ฌด๊ฒฉ์ž ๊ตฌ์กฐ ํ•ด์„ ํ”„๋กœ๊ทธ๋žจ์€ ์—ฐ๊ตฌ์‹ค์˜ ์œ ๋™ํ•ด์„ ์ฝ”๋“œ์™€ ๊ฒฐํ•ฉํ•˜์—ฌ ๋ณต์žกํ•œ ํ˜•์ƒ, ๋Œ€๋ณ€ํ˜• ๋“ฑ์ด ์ผ์–ด๋‚˜๋Š” ๊ณต๋ ฅ-๊ตฌ์กฐ ์—ฐ๊ณ„๊ฐ€ ํ•„์š”ํ•œ ๋ฌธ์ œ๋“ค์„ ์ˆ˜์›”ํ•˜๊ฒŒ ํ•ด์„ ํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ ๋œ๋‹ค.1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ๊ณต๋ ฅ-๊ตฌ์กฐ ์—ฐ๊ณ„ ํ•ด์„ 2 2. ๋ฌด๊ฒฉ์ž ํ•ด์„ ๊ธฐ๋ฒ• 5 2.1 Element Free Galerkin Method 5 2.2 MLS Approximants 5 2.3 Weight Function 10 2.5 Discretiziation of the governing equation 14 2.6 Gauss-quadrature 18 3. ์ˆ˜์น˜ํ•ด์„ ๊ฒฐ๊ณผ 19 3.1 1D Bar Problem 19 3.2 2D Beam Problem 23 4. ๊ฒฐ ๋ก  ๋ฐ ํ–ฅํ›„ ๊ณ„ํš 27 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 28Maste

    Immediately transcripted genes in various hepatic ischemia models

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    PURPOSE: To elucidate the characteristic gene transcription profiles among various hepatic ischemia conditions, immediately transcribed genes and the degree of ischemic injury were compared among total ischemia (TI), intermittent clamping (IC), and ischemic preconditioning (IPC). METHODS: Sprague-Dawley rats were equally divided into control (C, sham-operated), TI (ischemia for 90 minutes), IC (ischemia for 15 minutes and reperfusion for 5 minutes, repeated six times), and IPC (ischemia for 15 minutes, reperfusion for 5 minutes, and ischemia again for 90 minutes) groups. A cDNA microarray analysis was performed using hepatic tissues obtained by partial hepatectomy after occluding hepatic inflow. RESULTS: THE CDNA MICROARRAY REVEALED THE FOLLOWING: interleukin (IL)-1ฮฒ expression was 2-fold greater in the TI group than in the C group. In the IC group, IL-1ฮฑ/ฮฒ expression increased by 2.5-fold, and Na+/K+ ATPase ฮฒ1 expression decreased by 2.4-fold. In the IPC group, interferon regulatory factor-1, osteoprotegerin, and retinoblastoma-1 expression increased by approximately 2-fold compared to that in the C group, but the expression of Na+/K+ ATPase ฮฒ1 decreased 3-fold. CONCLUSION: The current findings revealed characteristic gene expression profiles under various ischemic conditions. However, additional studies are needed to clarify the mechanism of protection against IPC.ope

    Fabrication of a nitric oxide gas sensor using microwires and basic research for its application

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    Purpose: Although nitric oxide (NO) is considered one of the initial signals that promote postsurgery liver regeneration, NOโ€™s exact role in the liver regeneration mechanism is still not clear. Therefore, developing a practical gas sensor and testing it in a clinical setting through basic research will lay the groundwork for advanced clinical research on the action mechanism of NO. Methods: A thin nano wire NO sensor was made by wrapping a pair of parlyene-coated gold wires around a needle. The NO blood concentration determined by measuring the potential difference across the oscilloscope using electrical conductivity. In order to measure changes in NO level before and after the surgery, the NO sensor was inserted in the hepatic portal vein and a 75 percent partial hepatectomy was performed. The NO blood concentration was measured regularly with both the NO kit and the sensor. Results: One significant challenge was separation of the wire upon insertion into the hepatic portal vein. Despite separation, the constant measurements of the wire-type sensor were similar to those measured by the NO measurement kit. Conclusion: The development of a needle-type sensor allowed for easier insertion. In the future, using difference in electric potential, as used in the NO sensor, may be a more effective method of measuring blood ion concentration.ope

    Retinoic Acid-induced Differentiation of Rat Mesenchymal Stem Cells into ฮฒ-Cell Lineage

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    Backgrounds: Type I diabetes mellitus (T1DM), an autoimmune disease, is associated with insulin deficiency due to the death of ฮฒ-cells. Bone marrow-derived mesenchymal stem cells (BM-MSCs) are capable of tissue repair and thus are a promising source of ฮฒ-cell surrogates. Methods: In this study, the therapeutic potential of BM-MSCs as ฮฒ-cell replacements was analyzed both in vitro and in vivo. First, we used retinoic acid (RA) to induce rat BM-MSCs to differentiate into cells of endodermal/pancreatic lineage. Then, differentiated rat BM-MSCs were syngeneically injected under the renal capsule of rats. Results: Analysis of gene expression revealed that rat BM-MSCs showed signs of early pancreatic development, and differentiated cells were qualitatively and quantitatively confirmed to produce insulin in vitro. In vivo study was performed for short-term (3 weeks) and long-term (8 weeks) period of time. Rats that were injected with differentiated MSCs exhibited a reduction in blood glucose levels throughout 8 weeks, and grafted cells survived in vivo for at least 3 weeks. Conclusions: These findings show that RA can induce differentiation of MSCs into the ฮฒ-cell lineage and demonstrate the potential of BM-MSCs to serve as therapeutic tools for T1DM.ope

    ๊ฒฝ์ • ๊ฒฝ์ฃผ์šฉ ๋ชจํ„ฐ๋ณดํŠธ ์ƒ์‚ฐ๊ณต์ • ๊ฐœ์„ ์„ ์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ ‘๊ทผ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ,2006.Maste
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