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研究生:劉儀賢
研究生(外文):Yi-HsienLiu
論文名稱:細胞成長的生醫高分子微結構之製作
論文名稱(外文):Fabrication of Biopolymer Microstructures for Cell Growth
指導教授:陳顯禎
指導教授(外文):Shean-Jen Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工程科學系碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:54
中文關鍵詞:雙光子螢光影像雙光子聚合反應神經細胞通用繪圖處理器
外文關鍵詞:Two-photon fluorescence imagingtwo-photon polymerizationneuron cellGP2U
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本論文主要利用超快雷射非線性多光子激發技術來進行雙光子光聚合(polymerization)及光交聯(crosslinking),以加工出三維生醫材料微結構,並配合類神經細胞SK-N-SH及SH-SY5Y培養觀察其生長型態。微結構加工溶液包括孟加拉玫瑰素(rose Bengal,RB)作為光活化劑,以及水溶性三羥甲基丙烷三丙烯酸酯(water soluble ethoxylated trimethylolpropane triacrylate,WS-TMPTA)或牛血清蛋白(bovine serum albumin,BSA)當作反應單體。RB經雙光子激發後會產生高活性的單態氧,活化蛋白質使其形成鍵結並完成光交聯反應,或是與共同起始劑TEA (triethanolamine)反應,聚合TMPTA單體,完成光聚合反應。接著培養已經過電穿孔轉殖螢光蛋白的細胞於所製作的微結構上,擷取白光影像或是雙光子激發三維的影像。微結構製作及影像系統則有兩種不同的方式,單點掃描式以及時間與空間同調的廣視域式。單點掃描式製作的微結構較精細緻密,廣視域式則有時間上的優勢。
在影像快速處理方面,通用繪圖處理器(general-purpose computing on graphics processing units,GP2U或GPGPU)乃運用繪圖處理器(graphics processing units,GPU)來替代原本由中央處理器(central processing unit,CPU)進行的運算,繪圖處理器優異的平行處理能力可以大幅縮短許多運算的時間,以往必須由大型工作站級電腦所擔任的工作,便可由一般小型個人電腦執行。論文也利用NVIDIA公司所開發的CUDA (compute unified device architecture)技術,透過C語言編譯器來撰寫繪圖處理器運算的程式,對一些大資料量的影像處理進行優化。
In this thesis, nonlinear multiphoton excitation technique is utilized to fabricate three-dimensional (3D) biological microstructures via photo-crosslinking or photo-polymerization processing, and then the morphology of human neuroblastoma cells (SK-N-SH or SH-SY5Y) seeding on the microstructures is observed. The fabricated solution contains rose Bengal (RB), which was used as photo-activator, and bovine serum albumin (BSA) or water soluble ethoxylated trimethylolpropane triacrylate (WS-TMPTA) which was used as the reactive monomers. With two-photon excitation, RB can efficiently cause surrounding oxygen into singlet oxygen, and induce protein crosslinking reaction or TMPTA polymerization reaction with co-initiator TEA (triethanolamine). Moreover, fluorescence protein over-expressed SK-N-SH or SH-SY5Y cells were cultured in the fabricated microstructures, and then bright-field and two-photon 3D images were grabbed. Two types of nonlinear optical fabrication systems are adopted: one is “point-by-point scanning” and the other is “spatiotemporal focusing-based widefield.” Structures can be fabricated more precisely by the point scanning type, but that takes much more time. On the contrary, the widefield type can save the fabrication time.
In fast image processing, general-purpose computing on graphics processing units (GPGPU, GP2U), which typically handles computation only for computer graphics, to perform computation in applications that traditionally handled by central processing unit (CPU). Tasks which originally computed by workstations can be reduced to personal computers (PC). By excellent parallel computation ability in GPU, we can save lots of time. CUDA (compute unified device architecture), developed by NVIDIA corp., let programmer can easily access GPU resources via C/C++ compiler. In the thesis, we optimize imaging processing with this technology.
摘要 II
Abstract IV
致謝 VI
目錄 VIII
圖目錄 X
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 3
1-3 研究動機及目的 4
1-4 論文架構 5
第二章 生醫材料微結構及生物細胞之製備 7
2-1 多光子影像 8
2-2 雙光子吸收光致聚合機制 12
2-3 材料及試片製備 17
2-4 細胞培養簡介 20
第三章 系統與實驗架構 23
3-1 掃描式光學系統架構 23
3-2 時間與空間聚焦廣視域光學系統架構 28
3-3 三維模組建構及掃瞄 32
第四章 微結構上之細胞培養及運用繪圖處理器進行影像處理 35
4-1 細胞影像 35
4-2 微結構細胞培養 39
4-3 利用繪圖處理器進行影像處理 40
第五章 結論與未來建議 47
參考文獻 49
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