30 research outputs found

    ์ˆœํ™˜ ์ข…์–‘ ์œ ์ „์ž์˜ ์ •๋Ÿ‰ ๊ฒ€์ถœ์„ ์œ„ํ•œ ๋“ฑ์˜จ ๋””์ง€ํ„ธ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘ ํ”Œ๋žซํผ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€(๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ๊ธฐ๊ณ„์„ค๊ณ„์ „๊ณต), 2019. 2. ์ „๋ˆ„๋ฆฌ.Quantification of circulating tumor DNA facilitate detection of cancer without invasive detection and variables derived from characteristic of cancer patients against conventional cancer detection technique such as anti-body bio marker or tissue biopsy. For the detection and quantification of DNA biomarker, gene relate technologies are developed actively employing DNA amplification technique based on polymerase chain reaction (PCR). Especially, digital PCR specialized on quantify assay is commonly employed for research about the DNA samples which have extremely low concentration in entire liquid sample. But conventional digital PCR process generally require high cost equipment, well-trained technicians and time consuming caused by complex steps. So, the researches about microfluidic systems to substitute conventional digital PCR are on the progress in the contemporary society. In this study, a microfluidic platform that have possibility of quantification of ctDNA without high cost equipment and time consuming is through employing microfluidic systems and isothermal DNA amplification known as recombinase polymerase amplification (RPA). With isothermal DNA amplification technique, the limitations derived from conventional PCR, such as high temperature, are not remained. And also, designed microfluidic platform facilitate robust, fast and simple colony forming which is key-point of the digital PCR technics for the quantification of DNA samples. With this platform, liquid samples are easily isolated in 20,000 micro well structures with simple pressing force and generic DNAs extracted cancer cells are used to mimic actual ctDNA environment and extremely low concentration in patient blood samples. Extracted generic DNAs with mutation are detected with designed primers and probes successfully in microfluidic platform. This study provide the robust and easy-to-use microfluidic platform for quantification of ctDNA as point-of-care (POC) platform.ํ˜ˆ์•ก ๋‚ด ์ˆœํ™˜ ์ข…์–‘ ์œ ์ „์ž์˜ ์ •๋Ÿ‰ ๊ฒ€์ถœ์€ ๊ธฐ์กด์˜ ํ•ญ์ฒด ๊ธฐ๋ฐ˜ ๋ฐ”์ด์˜ค๋งˆ์ปค ๊ฒ€์‚ฌ๋‚˜ ์กฐ์ง ๊ฒ€์‚ฌ์— ๋น„ํ•˜์—ฌ ์•” ๊ฒ€์ถœ์„ ์นจ์Šต์„ฑ ๊ฒ€์‚ฌ ํ˜น์€ ๊ฐœ๊ฐœ์ธ์˜ ํ™˜์ž์— ๋”ฐ๋ผ ๋ณ€์ˆ˜๊ฐ€ ๊ฒฐ์ •๋˜๋Š” ์ผ ์—†์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ์œ ์ „์ž ๊ธฐ๋ฐ˜ ๋ฐ”์ด์˜ค๋งˆ์ปค์˜ ๊ฒ€์ถœ์„ ์œ„ํ•ด์„œ ๋งŽ์€ ์œ ์ „์ž ๊ด€๋ จ ๊ธฐ์ˆ ๋“ค์ด ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘(PCR) ๊ธฐ์ˆ ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ™œ๋ฐœํ•˜๊ฒŒ ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ํŠนํžˆ, ๋””์ง€ํ„ธ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘์€ ์ •๋Ÿ‰ ๋ถ„์„์— ํŠนํ™” ๋˜์–ด์žˆ๋Š” ๊ธฐ์ˆ ๋กœ, ์ „์ฒด ํ‘œ๋ณธ์—์„œ ๋†๋„๊ฐ€ ๋งค์šฐ ๋‚ฎ์€ ์œ ์ „์ž ํ‘œ๋ณธ์„ ๊ฒ€์ถœํ•  ๋•Œ ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ ๋””์ง€ํ„ธ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘ ๊ณผ์ •์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋ณต์žกํ•œ ๊ณผ์ •์œผ๋กœ ์ธํ•˜์—ฌ ๋†’์€ ๊ฐ€๊ฒฉ์˜ ์žฅ๋น„, ์ˆ™๋ จ๋œ ๊ธฐ์ˆ ์ž ๊ทธ๋ฆฌ๊ณ  ๋งŽ์€ ์‹œ๊ฐ„ ์†Œ๋ชจ๋ฅผ ๋™๋ฐ˜ํ•œ๋‹ค. ์ด๋กœ ์ธํ•ด, ๊ธฐ์กด์˜ ๋””์ง€ํ„ธ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘์„ ๋ฏธ์„ธ ์œ ์ฒด ์‹œ์Šคํ…œ์„ ํ†ตํ•˜์—ฌ ๋Œ€์ฒดํ•˜๋ ค๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ˜„๋Œ€์‚ฌํšŒ์—์„œ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฏธ์„ธ ์œ ์ฒด ์‹œ์Šคํ…œ๊ณผ ์žฌ์กฐํ•ฉ ํšจ์†Œ ์ค‘ํ•ฉํšจ์†Œ ์ฆํญ(RPA)๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๋“ฑ์˜จ ์œ ์ „์ž ์ฆํญ ๊ธฐ์ˆ ์„ ํ†ตํ•˜์—ฌ ํ˜ˆ์•ก ๋‚ด ์ˆœํ™˜ ์ข…์–‘ ์œ ์ „์ž์˜ ์ •๋Ÿ‰ ๊ฒ€์ถœ์„ ๊ณ ๊ฐ€์˜ ์žฅ๋น„์™€ ๋งŽ์€ ์‹œ๊ฐ„ ์†Œ๋ชจ ์—†์ด ๊ฐ€๋Šฅํ•œ ๋ฏธ์„ธ ์œ ์ฒด ํ”Œ๋žซํผ์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋“ฑ์˜จ ์œ ์ „์ž ์ฆํญ ๊ธฐ์ˆ ์„ ํ†ตํ•˜์—ฌ ๊ณ ์˜จ์—์„œ ์ง„ํ–‰๋˜๋Š” ๊ธฐ์กด์˜ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘์˜ ํ•œ๊ณ„์ ์„ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ๋˜ํ•œ ์„ค๊ณ„๋œ ํ”Œ๋žซํผ์„ ํ†ตํ•˜์—ฌ ๋””์ง€ํ„ธ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘์—์„œ ์ค‘์š”ํ•œ ํ‘œ๋ณธ์˜ ์ฝœ๋กœ๋‹ˆ ๊ตฌ์„ฑ์„ ์ •ํ™•ํ•˜๊ณ , ๋น ๋ฅด๋ฉฐ, ๊ฐ„๋‹จํ•˜๊ฒŒ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ํ”Œ๋žซํผ์—์„œ๋Š” ๊ฐ„๋‹จํ•œ ์••๋ ฅ์„ ๊ฐ€ํ•ด ์œ ์ฒด ํ‘œ๋ณธ์„ 20,000๊ฐœ์˜ ๋ฏธ์„ธ ์›ฐ ๊ตฌ์กฐ์— ๋ถ„๋ฆฌํ•ด๋‚ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋˜ํ•œ ์•”์„ธํฌ์—์„œ ์ถ”์ถœํ•œ ์œ ์ „์ž๋ฅผ ํ†ตํ•˜์—ฌ ์‹ค์ œ ํ™˜์ž์—์„œ์˜ ํ˜ˆ์•ก ๋‚ด ์ˆœํ™˜ ์ข…์–‘ ์œ ์ „์ž๋ฅผ ๋ชจ์‚ฌํ•˜๊ณ , ํ˜ˆ์•ก ํ‘œ๋ณธ์—์„œ ๋งค์šฐ ๋‚ฎ์€ ๋†๋„๋ฅผ ๊ฐ€์ง€๋Š” ์ˆœํ™˜ ์ข…์–‘ ์œ ์ „์ž์˜ ํ™˜๊ฒฝ์„ ์œ ์‚ฌํ•˜๊ฒŒ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์ถ”์ถœ๋œ ๋ณ€ํ˜• ์œ ์ „์ž๋Š” ์„ค๊ณ„๋œ ํ”„๋ผ์ด๋จธ, ํ”„๋กœ๋ธŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฏธ์„ธ ์œ ์ฒด ํ”Œ๋žซํผ์—์„œ ์„ฑ๊ณต์ ์œผ๋กœ ๊ฒ€์ถœ์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฅผ ํ†ตํ•˜์—ฌ ํ˜ˆ์•ก ๋‚ด ์ˆœํ™˜ ์ข…์–‘ ์œ ์ „์ž์˜ ์ •๋Ÿ‰ ๊ฒ€์ถœ์ด ํ™•์‹คํ•˜๋ฉฐ ์‚ฌ์šฉ์ด ์šฉ์ดํ•œ ํ”Œ๋žซํผ์„ ํ˜„์žฅํ˜„์‹œ๊ฒ€์‚ฌ(POC) ํ”Œ๋žซํผ์œผ๋กœ ์ œ์•ˆํ•œ๋‹ค.Table of Contents Abstract........................................................................โ…ฐ Table of Contents...........................................................โ…ณ List of figures................................................................โ…ต Chapter 1. Introduction.......................1 1.1 Study background...................................1 1.2 Purpose of Research...............................3 Chapter 2. Materials and Methods.........4 2.1 Device design and fabrication...........................4 2.2 Division of liquid sample in micro wells............6 2.3 Isothermal DNA amplification in platform.........7 2.4 Quantification of ctDNA..................................9 Chapter 3. Result.................................15 3.1 Detection of tumor DNA through digital RPA.....15 3.2 Real time detection of fluorescence signal.........16 3.3 Quantification with fluorescing micro wells.......17 Chapter 4. Discussion....................................................23 Chapter 5. Conclusion....................................................24 Bibliography Abstract (Korean)Maste

    The roles of reactive astrocytes in cognitive function via the morphological and molecular changes in Alzheimers disease animal model

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜๊ณผํ•™๊ณผ, 2020. 8. ๊น€ํ˜œ์„ .Numerous roles of astrocytes have been reported in the central nervous system. Astrocytes have the potential to exist in two states, the reactive and the resting states. Reactive astrocytes have morphological features such as the increase in thickness, number of processes and volume of cell body. Molecular changes also occur, such as an increase in the expression of glial fibrillary acidic protein (GFAP). However, the morphological and molecular dynamics during the memory formation in astrocytes remain largely unknown. Moreover, the pathophysiological roles of the reactive state of astrocytes are thought to be of importance in the pathogenesis of neurodegenerative diseases, including Alzheimers disease (AD). However, the detailed mechanisms underlying the transition of astrocytes from the resting state to the reactive state during neurodegenerative disease largely remain unclear. Here, I investigated the changes in astrocytes in the hippocampus of Fvb/n mice trained with contextual fear conditioning to memory induction, and the morphological and molecular dynamics were analyzed in astrocytes. One hour after fear conditioning, type II and type III astrocytes displayed a unique status, not reactive nor resting state, with an increased the number of processes and decreased GFAP expression. In addition, the protein level of excitatory amino acid transporter 2 (EAAT 2) was increased at 1 hour to 24 hours after fear conditioning while EAAT1 did not show any changes. Connexin 43 protein expression was found to be increased at 24 hours after fear conditioning test. After L-ฮฑ- aminoadipate treatment, an astrocyte-specific toxic molecule, mice showed the impairment of cognitive function. In addition, I investigated which pathways are involved in activating astrocytes from the resting state to the reactive state in an AD context such as JAK/STAT3, MAPK, NF-kB, and NFAT in primary cultured astrocytes treated with oligomeric amyloidฮฒ peptide (oAฮฒ) and in the hippocampus of 5XFAD mice. Treatment with oAฮฒinduced an increase in reactive astrocytes, as assessed by the protein expression of GFAP and this increase was caused by signal transducer and activator of transcription 3 (STAT3) phosphorylation in primary cultured astrocytes. The treatment with Stattic, an inhibitor of STAT3 phosphorylation, rescued the activation of astrocytes in primary cultured astrocytes. Additionally, the systemic administration of Stattic rescued the activation of astrocytes in the hippocampus of 6-month-old 5XFAD mice as well as impairments of cognitive function. Collectively, these results demonstrated that the status of astrocytes transit into a novel state, memory induction state, by hippocampus-based contextual memory process. These astrocytes in the memory induction state are thought to be not induced in 5XFAD mice brains, because astrocytes exist in the reactive state in the hippocampus of 5XFAD mice. Reactive astrocytes in the brains of 5XFAD mice were found to be induced via STAT3 phosphorylation and the impairments in learning and memory observed in the 5XFAD mice are rescued by the inhibition of STAT3 phosphorylation, suggesting that the inhibition of STAT3 phosphorylation in astrocytes may be a novel therapeutic target for cognitive impairment in AD.์ค‘์ถ”์‹ ๊ฒฝ๊ณ„์—์„œ ์„ฑ์ƒ๊ต์„ธํฌ์˜ ๋‹ค์–‘ํ•œ ์—ญํ• ๋“ค์ด ๋ณด๊ณ ๋˜์–ด ์™”๋‹ค. ์„ฑ์ƒ๊ต ์„ธํฌ๋Š” ์ž ์žฌ์ ์œผ๋กœ ํ™œ์„ฑํ™” ์ƒํƒœ์™€ ํœด์ง€๊ธฐ ์ƒํƒœ์˜ ๋‘ ๊ฐ€์ง€์˜ ์ƒํƒœ๋กœ ๋ณ€ ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ์ด ์žˆ๋‹ค. ํ™œ์„ฑํ™” ์„ฑ์ƒ๊ต์„ธํฌ๋Š” ๋Œ๊ธฐ์˜ ๋‘๊ป˜์™€ ๊ทธ ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ , ์„ธํฌ์ฒด ๋ถ€ํ”ผ์˜ ์ฆ๊ฐ€๊ฐ€ ํŠน์ง•์ ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ๋˜ํ•œ, ์‹ ๊ฒฝ๊ต ์„ฌ ์œ ์งˆ ์‚ฐ์„ฑ๋‹จ๋ฐฑ์งˆ (glial fibrillary acidic protein: GFAP)๊ณผ ๊ฐ™์€ ๋‹จ ๋ฐฑ์งˆ ๋ฐœํ˜„์˜ ์ฆ๊ฐ€๋‚˜ ์œ ์ „์ž ๋ฐœํ˜„์˜ ๋ณ€ํ™”๊ฐ€ ๋ถ„์ž์  ๋ณ€ํ™”๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ํ•˜ ์ง€๋งŒ, ์„ฑ์ƒ๊ต์„ธํฌ์—์„œ ๊ธฐ์–ต์„ ์œ ๋„ํ•˜๋Š” ๊ณผ์ • ์ค‘์— ์ด๋Ÿฌํ•œ ์ฆ‰๊ฐ์ ์ธ ํ˜•ํƒœ ํ•™์ , ๋ถ„์ž์  ๋ณ€ํ™”๋Š” ์•„์ง ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์ƒํƒœ์ด๋‹ค. ์•Œ์ธ ํ•˜์ด๋จธ๋ณ‘์„ ํฌ ํ•จํ•œ ํ‡ดํ–‰์„ฑ์‹ ๊ฒฝ๊ณ„์งˆํ™˜์˜ ๋ฐœ๋ณ‘ ๊ธฐ์ „์— ์žˆ์–ด ํ™œ์„ฑํ™” ์ƒํƒœ์˜ ์„ฑ์ƒ๊ต์„ธํฌ ์˜ ๋ณ‘๋ฆฌ์ƒ๋ฆฌํ•™์  ์—ญํ• ์€ ๋งค์šฐ ์ค‘์š”ํ•œ ์˜๋ฏธ๊ฐ€ ์žˆ์„ ๊ฒƒ์ด๋ผ ์ƒ๊ฐ๋œ๋‹ค. ๊ทธ ๋Ÿฌ๋‚˜ ํ‡ดํ–‰์„ฑ์‹ ๊ฒฝ๊ณ„์งˆํ™˜์ด ์ง„ํ–‰๋˜๋Š” ๋™์•ˆ ํœด์ง€๊ธฐ ์ƒํƒœ์˜ ์„ฑ์ƒ๊ต์„ธํฌ๊ฐ€ ํ™œ์„ฑํ™” ์ƒํƒœ๋กœ ์ „ํ™˜๋˜๋Š” ๊ตฌ์ฒด์ ์ธ ์ž‘์šฉ ์›๋ฆฌ์˜ ์ดํ•ด๋Š” ์—ฌ์ „ํžˆ ๋ช…๋ฐฑํ•˜๊ฒŒ ๋ฐํ˜€์ ธ ์žˆ์ง€ ์•Š๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” Fvb/n ๋งˆ์šฐ์Šค ๋‡Œ๋‚ด ํ•ด๋งˆ์— ์กด์žฌํ•˜๋Š” ์„ฑ์ƒ๊ต์„ธํฌ๋ฅผ ๊ณตํฌ ์ƒํ™ฉ ์กฐ๊ฑดํ™” ์‹คํ—˜ (contextual fear conditioning) ์„ ํ†ตํ•ด ๊ธฐ์–ต ์œ ๋„๋ฅผ ํ•˜์˜€๋‹ค. ์ดํ›„ ๊ธฐ์–ต ์œ ๋„ํ•œ ์„ฑ์ƒ๊ต์„ธํฌ์˜ ํ˜•ํƒœํ•™์  ๋ถ„์ž์  ๋ณ€ํ™”๋ฅผ ๋ถ„์„ ํ•˜์˜€๋‹ค. ๊ณตํฌ ์ƒํ™ฉ ์กฐ๊ฑดํ™” ์‹คํ—˜ 1์‹œ๊ฐ„ ํ›„, ํƒ€์ž… 2 ์™€ ํƒ€์ž… 3 ์„ฑ์ƒ๊ต์„ธํฌ๊ฐ€ ํ™œ์„ฑํ™”์™€ ํœด์ง€ ์ƒํƒœ์˜ ํŠน์ง•์ด ์•„๋‹Œ, ๋Œ๊ธฐ์˜ ์ˆ˜ ๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ , ์‹ ๊ฒฝ๊ต ์„ฌ์œ ์งˆ ์‚ฐ์„ฑ๋‹จ๋ฐฑ์งˆ์˜ ๋ฐœํ˜„์ด ๊ฐ์†Œ๋˜๋Š” ์ƒˆ๋กœ์šด ์ƒํƒœ ๋กœ ๊ด€์ธก๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ํฅ๋ถ„์„ฑ ์•„๋ฏธ๋…ธ์‚ฐ ์šด๋ฐ˜์ž 2 (excitatory amino acid transporter 2: EAAT2)์˜ ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์ด ๊ณตํฌ ์ƒํ™ฉ ์กฐ๊ฑดํ™” ํ›„ 1์‹œ๊ฐ„ ์—์„œ 24์‹œ๊ฐ„๊นŒ์ง€ ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์ด ์ฆ๊ฐ€ํ•˜์˜€์ง€๋งŒ, ํฅ๋ถ„์„ฑ ์•„๋ฏธ๋…ธ์‚ฐ ์šด๋ฐ˜์ž 1์€ ๋ณ€ํ™”๊ฐ€ ์—†๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ณตํฌ ์ƒํ™ฉ ์กฐ๊ฑดํ™” 24์‹œ๊ฐ„ ํ›„ ์ฝ”๋„ฅ์‹  43 (Connexin 43: Cx43) ๋‹จ๋ฐฑ์งˆ์˜ ๋ฐœํ˜„์˜ ์ฆ๊ฐ€๊ฐ€ ํ™•์ธ๋˜์—ˆ๋‹ค. ์„ฑ์ƒ๊ต์„ธํฌ ํŠน์ด์  ๋…์„ฑ ๋ฌผ์งˆ์ธ, ์—˜-์•ŒํŒŒ-์•„๋ฏธ๋…ธ์•„๋””ํŽ˜์ดํŠธ (L-ฮฑ- aminoadipate: LAA)๊ฐ€ ๋‡Œ๋‚ด ํ•ด๋งˆ์— ์ฒ˜๋ฆฌ๋œ ๋งˆ์šฐ์Šค์—์„œ ์ธ์ง€๋Šฅ๋ ฅ์˜ ์ €ํ•˜๊ฐ€ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ดˆ๋Œ€ ๋ฐฐ์–‘ ์„ฑ์ƒ๊ต์„ธํฌ์— ๋‹ค๋Ÿ‰์ฒด ์•„๋ฐ€๋กœ์ด๋“œ ํŽฉํ‹ฐ๋“œ (oAฮฒ)๋ฅผ ์ฒ˜์น˜ํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ 6๊ฐœ์›”๋ น ์•Œ์ธ ํ•˜์ด๋จธ๋ณ‘ ๋™๋ฌผ ๋ชจ๋ธ 5XFAD์˜ ํ•ด๋งˆ ๋ถ€์œ„์—์„œ ์–ด๋–ค ์‹ ํ˜ธ์ „๋‹ฌ ๊ฒฝ๋กœ๊ฐ€ ์—ฐ๊ด€๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์˜€๋‹ค. ์‹ ๊ฒฝ ๊ต ์„ฌ์œ ์งˆ ์‚ฐ์„ฑ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์˜ ์ฆ๊ฐ€๋ฅผ ํ†ตํ•ด์„œ ์ดˆ๋Œ€ ๋ฐฐ์–‘ ์„ฑ์ƒ๊ต์„ธํฌ์˜ ๋‹ค๋Ÿ‰์ฒด ์•„๋ฐ€๋กœ์ด๋“œ ํŽฉํ‹ฐ๋“œ ์ฒ˜์น˜๋Š” ํ™œ์„ฑํ™” ์„ฑ์ƒ๊ต์„ธํฌ์˜ ์ฆ๊ฐ€์˜ ์œ ๋„ํ•จ ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์ดˆ๋Œ€ ๋ฐฐ์–‘ ์„ฑ์ƒ๊ต์„ธํฌ์˜ ํ™œ์„ฑํ™”๋Š” ์‹ ํ˜ธ๋ณ€ํ™˜์ž- ์ „์‚ฌํ™œ์„ฑ์ž 3 (signal transducer and activator of transcription: STAT3)์˜ ์ธ์‚ฐํ™”๋ฅผ ํ†ตํ•ด ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ดˆ๋Œ€ ๋ฐฐ์–‘ ์„ฑ์ƒ๊ต์„ธํฌ ํ™œ์„ฑํ™”์™€ 6๊ฐœ์›”๋ น ์•Œ์ธ ํ•˜์ด๋จธ๋™๋ฌผ ๋ชจ๋ธ์˜ ์ธ์ง€๋Šฅ๋ ฅ ์ €ํ•ด๋ฅผ ์‹ ํ˜ธ๋ณ€ํ™˜์ž-์ „์‚ฌํ™œ์„ฑ ์ž 3 ์ธ์‚ฐํ™” ์–ต์ œ์ œ ์Šคํ…Œํ‹ฑ(Stattic)์˜ ์ „์‹  ์ ์šฉ์ด ์™„ํ™”์‹œํ‚ด์„ ํ™•์ธํ•˜ ์˜€๋‹ค. ์œ„ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•˜๋ฉด, ํ•ด๋งˆ ๊ธฐ๋ฐ˜ ๊ณตํฌ ์ƒํ™ฉ ์กฐ๊ฑดํ™” ์‹คํ—˜์— ์˜ํ•ด ๊ธฐ ์–ต ์œ ๋„๋œ ์„ฑ์ƒ๊ต์„ธํฌ๋Š” ์ƒˆ๋กœ์šด ํ˜•ํƒœ๋ฅผ ๋ณด์ด๋ฉฐ ์ด๋ฅผ, ๊ธฐ์–ต ์œ ๋„ ์ƒํƒœ๊ฐ€ ๋˜์—ˆ๋‹ค. ์•Œ์ธ ํ•˜์ด๋จธ๋ณ‘ ๋‡Œ ๋‚ด์—์„œ ํ™œ์„ฑํ™” ์ƒํƒœ์˜ ์„ฑ์ƒ๊ต์„ธํฌ๋กœ ์กด์žฌํ•จ์œผ ๋กœ ์ด๋Ÿฌํ•œ ๊ธฐ์–ต ์œ ๋„ ์ƒํƒœ ์„ฑ์ƒ๊ต์„ธํฌ๋Š” ์ •์ƒ์ ์ด์ง€ ์•Š์„ ๊ฒƒ์ด๋‹ค. ์•Œ์ธ  ํ•˜์ด๋จธ๋ณ‘ ๋‡Œ์˜ ํ™œ์„ฑํ™” ์„ฑ์ƒ๊ต์„ธํฌ๋Š” ์‹ ํ˜ธ๋ณ€ํ™˜์ž-์ „์‚ฌํ™œ์„ฑ์ž 3 ์ธ์‚ฐํ™”๋ฅผ ํ†ตํ•ด์„œ ๋‚˜ํƒ€๋‚˜๋ฉฐ, ํ•™์Šต๊ณผ ๊ธฐ์–ต๋ ฅ์˜ ์ €ํ•˜๊ฐ€ ์‹ ํ˜ธ๋ณ€ํ™˜์ž-์ „์‚ฌํ™œ์„ฑ์ž 3 ์ธ ์‚ฐํ™” ์–ต์ œ์ œ๋ฅผ ํ†ตํ•ด์„œ ์™„ํ™”๋˜์—ˆ๋‹ค. ์ด๋Š” ์‹ ํ˜ธ๋ณ€ํ™˜์ž-์ „์‚ฌํ™œ์„ฑ์ž 3 ์ธ์‚ฐ ํ™” ์–ต์ œ์ œ๋ฅผ ํ†ตํ•œ ์„ฑ์ƒ๊ต์„ธํฌ์˜ ํ™œ์„ฑํ™” ์กฐ์ ˆ์ด ์•Œ์ธ ํ•˜์ด๋จธ๋ณ‘์˜ ์ธ์ง€๋Šฅ ๋ ฅ์ €ํ•˜์˜ ์ƒˆ๋กœ์šด ์น˜๋ฃŒ ์ „๋žต์œผ๋กœ ์ œ์‹œ๋  ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์˜€๋‹ค.Introduction 1 Introduction summary 11 Chapter 1 12 Dynamics of astrocytes during memory induction 13 Material and Methods 15 Experimental scheme 21 Results 22 Discussion 45 Graphical summary 50 Chapter 2 51 Pathophysiological role of reactive astrocyte in Alzheimers disease model Introduction 52 Material and Methods 54 Experimental scheme 62 Results 63 Discussion 119 Graphical summary 127 Conclusion 128 References 130 Abstract in Korean 139Docto

    A Cooperative Game Approach

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021. 2. ์ด๋•์ฃผ.As machine learning thrives in both academia and industry at the moment, data plays a salient role in training and validating machines. Meanwhile, few works have been developed on the economic evaluation of the data in data exchange market. The contribution of our work is two-fold. First, we take advantage of semi-values from cooperative game theory to model revenue distribution problem. Second, we construct a model consisting of provider, firm, and market while considering the privacy and fairness of machine learning. We showed Banzhaf value could be a reliable alternative to Shapley value in calculating the contribution of each datum. Also, we formulate the firms revenue maximization problem and present numerical analysis in the case of binary classifier with classical data examples. By assuming the firm only uses high quality data, we analyze its behavior in four different scenarios varying the datas fairness and compensating cost for data providers privacy. It turned out that the Banzhaf value is more sensitive to the fairness of data than the Shapley value. We analyzed the maximum revenue proportion which the firm gives away to data providers, as well as the range of number of data the firm would acquire.๊ธฐ๊ณ„ํ•™์Šต์ด ํ˜„์žฌ ์ด๋ก ๊ณผ ์‹ค์ƒํ™œ ์ ์šฉ ๋ชจ๋‘์—์„œ ๋ฐœ์ „ํ•จ์— ๋”ฐ๋ผ ๋ฐ์ดํ„ฐ๋Š” ์ธ๊ณต์ง€๋Šฅ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๊ณ  ๊ฒ€์ฆํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋‹ค. ํ•œํŽธ, ๋ฐ์ดํ„ฐ ๊ตํ™˜ ์‹œ์žฅ์—์„œ ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์ œ์„ฑ ํ‰๊ฐ€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ดˆ๊ธฐ ๋‹จ๊ณ„์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๊ธฐ์—ฌ๋Š” ๋‘ ๊ฐ€์ง€ ๊ด€์ ์—์„œ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฒซ์งธ, ํ˜‘๋™ ๊ฒŒ์ž„ ์ด๋ก ์˜ ๊ฐœ๋…์ธ semi-value๋ฅผ ๋ชจ๋ธ ์ˆ˜์ต ๋ถ„๋ฐฐ ๋ฌธ์ œ์— ํ™œ์šฉํ•œ๋‹ค. ๋‘˜์งธ, ์ธ๊ณต์ง€๋Šฅ ๋ชจ๋ธ์˜ ๊ณต์ •์„ฑ๊ณผ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์„ฑ์„ ๊ณ ๋ คํ•œ ๋ฐ์ดํ„ฐ ์ œ๊ณต์ž, ๊ธฐ์—…, ์‹œ์žฅ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ Banzhaf ๊ฐ’์€ ๊ฐ ๋ฐ์ดํ„ฐ์˜ ๊ธฐ์—ฌ๋„๋ฅผ ๊ณ„์‚ฐํ•  ๋•Œ Shapley ๊ฐ’์˜ ๋Œ€์•ˆ์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ํšŒ์‚ฌ์˜ ์ˆ˜์ต ๊ทน๋Œ€ํ™” ๋ฌธ์ œ๋ฅผ ๋ชจ๋ธ๋งํ•˜์˜€๊ณ , ์ถ”๊ฐ€์ ์œผ๋กœ ๋ฐ์ดํ„ฐ ์˜ˆ์ œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ด์ง„ ๋ถ„๋ฅ˜ ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ ์ˆ˜์น˜ ๋ถ„์„์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด, Banzhaf ๊ฐ’์€ Shapley ๊ฐ’๋ณด๋‹ค ๋ฐ์ดํ„ฐ์˜ ๊ณต์ •์„ฑ์— ๋” ๋ฏผ๊ฐํ•˜๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ๊ธฐ์—…์ด ๊ณ ํ’ˆ์งˆ ๋ฐ์ดํ„ฐ๋งŒ์„ ์‚ฌ์šฉํ•œ๋‹ค๋Š” ๊ฐ€์ •ํ•˜์— ๋ฐ์ดํ„ฐ์˜ ๊ณต์ •์„ฑ๊ณผ ๋ฐ์ดํ„ฐ ์ œ๊ณต์ž์˜ ๊ฐœ์ธ์ •๋ณด์— ๋Œ€ํ•œ ๋ณด์ƒ๋น„์šฉ์„ ๋‹ฌ๋ฆฌํ•˜๋Š” ๋„ค ๊ฐ€์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ๊ธฐ์—…์˜ ํ–‰๋™์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ธฐ์—…์€ ๋ฐ์ดํ„ฐ๊ฐ€ ๊ณต์ •ํ• ์ˆ˜๋ก ๋ฐ์ดํ„ฐ ์ œ๊ณต์ž์—๊ฒŒ ๋” ํฐ ์ˆ˜์ต์„ ๋ณด์žฅํ•ด์ฃผ์—ˆ๊ณ , ๊ณ ์ •๋น„์šฉ์ด ์ž‘์•„์งˆ์ˆ˜๋ก ๊ฐ€๋ณ€๋น„์šฉ์„ ํ†ตํ•ด์„œ ๋ฐ์ดํ„ฐ ์ œ๊ณต์ž์—๊ฒŒ ์ˆ˜์ต์„ ๋‚˜๋ˆ ์ฃผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Problem Description 2 1.3 Organization of the Thesis 3 Chapter 2 Literature Review 4 2.1 Fair Machine Learning 4 2.2 Private Machine Learning 5 2.3 Data Valuation 6 2.3.1 Dataset Price Estimation 6 2.3.2 Equitable Price Estimation 7 Chapter 3 Data Market Model 8 3.1 Basic Assumptions and Model Settings 8 3.2 Firms Profit Maximizing Problem 10 3.3 Data Valuation 12 3.4 Binary Classification Setting 14 Chapter 4 Analysis 17 4.1 Semi-value Approximation 17 4.1.1 Convergence Analysis 17 4.1.2 Group Data Calculation 20 4.2 Binary Classification 22 4.2.1 Parameter Analysis 22 4.2.2 Scenario Analysis 24 4.2.2.1 Description 24 4.2.2.2 Synthetic Data 25 4.2.2.3 Shapley Value Based Valuation 26 4.2.2.4 Banzhaf Value Based Valuation 28 4.2.2.5 Comparative Analysis 30 4.3 Data Pricing 33 Chapter 5 Conclusion 35 Bibliography 38 ๊ตญ๋ฌธ์ดˆ๋ก 43Maste

    Intraoperative Transfusion is Independently Associated with a Worse Prognosis in Resected Pancreatic Cancer-a Retrospective Cohort Analysis

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    BACKGROUNDS: Investigate whether intraoperative transfusion is a negative prognostic factor for oncologic outcomes of resected pancreatic cancer. METHODS: From June 2004 to January 2014, the medical records of 305 patients were retrospectively reviewed, who underwent pancreatoduodenectomy, pylorus preserving pancreatoduodenectomy, total pancreatectomy, distal pancreatectomy for pancreatic cancer. Patients diagnosed with metastatic disease (n = 3) and locally advanced diseases (n = 15) were excluded during the analysis, and total of 287 patients were analyzed. RESULTS: The recurrence and disease-specific survival rates of the patients who received intraoperative transfusion showed poorer survival outcomes compared to those who did not (P = 0.031, P = 0.010). Through multivariate analysis, T status (HR (hazard ratio) = 2.04, [95% CI (confidence interval): 1.13-3.68], P = 0.018), N status (HR = 1.46 [95% CI: 1.00-2.12], P = 0.045), adjuvant chemotherapy (HR = 0.51, [95% CI: 0.35-0.75], P = 0.001), intraoperative transfusion (HR = 1.94 [95% CI: 1.23-3.07], P = 0.004) were independent prognostic factors of disease-specific survival after surgery. As well, adjuvant chemotherapy (HR = 0.67, [95% CI: 0.46-0.97], P = 0.035) was independently associated with tumor recurrence. Estimated blood loss was one of the most powerful factors associated with intraoperative transfusion (P < 0.001). CONCLUSIONS: Intraoperative transfusion can be considered as an independent prognostic factor of resected pancreatic cancer. As well, it can be avoided by following strict transfusion policy and using advanced surgical techniques to minimize bleeding during surgery.ope

    Preoperative prognostic nutritional index as an independent prognostic factor for resected ampulla of Vater cancer

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    INTRODUCTION: Prognostic nutritional index (PNI) reflects the nutritional and immunologic status of the patients. The clinical application of PNI is already well-known in various kinds of solid tumors. However, there is no study investigating the relationship between PNI and oncological outcome of the resected ampulla of Vater (AoV) cancer. MATERIALS AND METHODS: From January 2005 to December 2012, the medical records of patients who underwent pancreaticoduodenectomy for pathologically confirmed AoV cancer were retrospectively reviewed. Long-term oncological outcomes were compared according to the preoperative PNI value. RESULT: A total of 118 patients were enrolled in this study. The preoperative PNI was 46.13ยฑ6.63, while the mean disease-free survival was 43.88 months and the mean disease-specific survival was 55.3 months. In the multivariate Cox analysis, initial CA19-9 (p = 0.0399), lymphovascular invasion (p = 0.0031), AJCC 8th N-stage (p = 0.0018), and preoperative PNI (p = 0.0081) were identified as significant prognostic factors for resected AoV cancer. The disease-specific survival was better in the high preoperative PNI group (โ‰ค48.85: 40.77 months vs. >48.85: 68.05 months, p = 0.0015). A highly accurate nomogram was developed based on four clinical components to predict the 1, 3, and 5-year disease-specific survival probability (C-index 0.8169, 0.8426, and 0.8233, respectively). CONCLUSION: In resected AoV cancer, preoperative PNI can play a significant role as an independent prognostic factor for predicting disease-specific survival.ope

    Developing an in vivo porcine model of duct-to-mucosa pancreaticojejunostomy (Yonsei-PJDTM)

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    Laparoscopic pancreaticoduodenectomy (LPD) is technically feasible, but its safety is still controversial. Pancreas texture and the small size of the main pancreatic duct indicate laparoscopic pancreaticoduodenectomy (LPD) as a challenging procedure. Thus, LPD could be a risk factor for postoperative pancreatic fistula (POPF), longer hospital stay, and delayed adjuvant chemotherapy that affects long-term oncologic outcome. So, it is important to promote education on LPD especially techniques for pancreaticojejunostomy. A porcine model for duct-to-mucosa pancreaticojejunostomy (PJ) (Yonsei-PJDTM) was developed, and details of the model will be described in this report.ope

    Systemic inflammation response index correlates with survival and predicts oncological outcome of resected pancreatic cancer following neoadjuvant chemotherapy

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    Background: The Systemic Inflammation Response Index (SIRI) has been used to predict the prognosis of various cancers. This study examined SIRI as a prognostic factor in the neoadjuvant setting and determined whether it changing after chemotherapy is related to patient prognosis. Methods: Patients who underwent pancreatic surgery following neoadjuvant chemotherapy for pancreatic cancer were retrospectively analyzed. To establish the cut-off values, SIRIpre-neoadjuvant, SIRIpost-neoadjuvant, and SIRIquotient (SIRIpost-neoadjuvant/SIRIpre-neoadjuvant) were calculated and significant SIRI values were statistically determined to examine their effects on survival rate. Results: The study included 160 patients. Values of SIRIpost-neoadjuvant โ‰ฅ 0.8710 and SIRIquotient <0.9516 affected prognosis (hazard ratio [HR], 1.948; 95% confidence interval [CI], 1.210-3.135; โˆ—โˆ—P = 0.006; HR, 1.548; 95% CI, 1.041-2.302; โˆ—โˆ—P = 0.031). Disease-free survival differed significantly at values of SIRIpost-neoadjuvant < 0.8710 and SIRIpost-neoadjuvant โ‰ฅ 0.8710 (P = 0.0303). Overall survival differed significantly between SIRIquotient <0.9516 and SIRIquotient โ‰ฅ0.9516 (P = 0.0368). Conclusions: SIRI can predict the survival of patients with pancreatic ductal adenocarcinoma after resection and neoadjuvant chemotherapy. Preoperative SIRI value was correlated with disease-free survival, while changes in SIRI values were correlated with overall survival.ope

    Development of a metabolite calculator for diagnosis of pancreatic cancer

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    Background: Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy. Methods: A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQโ„ข p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses. Results: Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively. Conclusions: We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.ope

    Pancreaticoduodenectomy with combined hepatic artery and portal vein resection after laparoscopic division of pancreaticosplenic ligament due to FOLFIRINOX-induced hepatic toxicity related secondary hypersplenism

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    Pancreatic cancer is one of the dismal malignant disease in gastrointestinal tract. However, since the recent literature reporting median survival of FOLFIRINOX (leucovorin clcium, fluorouracil, irinotecan hydrochloride, oxaliplatin) chemotherapy was more than 12 months in metastatic pancreatic cancer was published, the positive attitude toward the treatment of the advanced pancreatic cancer is gradually expanded among the medical and surgical oncologists. Due to multiple combination of potent chemotherapeutic agents, potential adverse side effects should be concerned when considering FOLFIRINOX. Herein, we report a 55-year old male patient with locally advanced pancreatic cancer who successfully underwent curative resection following by laparoscopic division of pancreaticosplenic ligament due to long-term preoperative use of FOLFIRINOX related hepatic toxicity associated with secondary hypersplenism. The present case suggests the extended radical PD with combined major vascular resection following laparoscopic division of pancreaticosplenic ligament containing splenic artery and vein can improve the safety of curative resection and may expand the potential indication of pancreatic cancer in well-selected long-term use of preoperative FOLFIRINOX induced hepatic toxicity associated with secondary hypersplenism.ope

    Laparoscopic pancreaticoduodenectomy in pancreatic ductal adenocarcinoma

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    Laparoscopic pancreatoduodenectomy (LPD) in pancreatic cancer is primarily criticized for its technical and oncological safety. Although solid evidence has not yet been established, many institutions are performing LPD for pancreatic cancer patients, with continuous efforts to ensure oncologic safety. In this video, we demonstrated a case of standard LPD combined with vascular resection in pancreatic cancer.ope
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