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系統識別號 U0026-2208201622434700
論文名稱(中文) 以光電動技術檢測糖尿病視網膜病變指標性蛋白Lipocalin 1
論文名稱(英文) Optoelectrokinetic Screening for the Biomarker Lipocalin 1 of Diabetic Retinopathy
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 104
學期 2
出版年 105
研究生(中文) 辜瑚肴
研究生(英文) Hu-Yao Ku
電子信箱 kukuyao96810@gmail.com
學號 P86034056
學位類別 碩士
語文別 英文
論文頁數 78頁
口試委員 指導教授-莊漢聲
口試委員-張憲彰
口試委員-許聖民
口試委員-陳國平
中文關鍵字 生醫檢測  糖尿病視網膜病變  指標性蛋白  微珠式三明治免疫分析  光電動技術  快速光電動圖紋法  濃縮  分選 
英文關鍵字 Biomedical diagnosis  Diabetic retinopathy  Biomarker  Bead-based sandwiched immunoassay  Optoelectrokinetic technology  Rapid electrokinetic patterning  Concentrate  Sorting 
學科別分類
中文摘要 大多疾病在潛伏期甚至是前期時病徵並非顯而易見,因此無法被輕易的檢測出來,當病發時候往往會導致治療的效率大幅下降。如糖尿病視網膜病變(Diabetic Retinopathy, DR)是糖尿病患者常見的併發症,其主要是因為視網膜上的血管長期處於高血糖的環境導致血管產生病變,是目前造成國人失明的主要原因。目前臨床上常使用螢光血管照影(Fluorescence Angiography, FA)來檢測該疾病,其僅提供血管結構的資訊,但是觀察血管的結構變化是必須靠醫師的臨床經驗去做判斷,並沒有一個定量的依據。當患者察覺有異狀發生時,大多已經步入後期的增生性糖尿病視網膜病變(Proliferative Diabetic Retinopathy, PDR),因此必須接受手術的治療才能回復部分視力,倘若能即早發現即早治療,則大部分的患者可以避免手術並且維持良好的視力狀況。因此本研究發展一套低濃度的檢測平臺,期望能夠在早期就能檢測定量到疾病,並落實非侵入式的檢測方式,所採用的非侵入式檢體為淚液,從文獻中可以知道淚脂質運載蛋白(Lipocalin 1, LCN1)在淚液中是糖尿病視網膜病變的指標性蛋白(Biomarker)。利用微珠式三明治免疫分析(Bead-based Sandwiched Immunoassay)使抗體抗原間產生專一性鍵結,加上快速光電動圖紋法(Rapid Electrokinetic Patterning, REP)這項光電動技術,讓帶有指標性蛋白的粒子濃縮聚集(Concentration)以提升螢光訊號。目前正常人的淚液中所含的LCN1的濃度約為3150 μg/mL,而糖尿病視網膜病變的指標性蛋白LCN1濃度會隨著疾病越趨嚴重而濃度越高,而本研究的偵測極限可到達15 pg/mL,對於糖尿病視網膜病變能夠達到早期偵測的目的。為了讓檢測平台對疾病的精準度有所提升,利用技術去分選(Sorting)不同大小的粒子,針對糖尿病視網膜病變淚液中不同的指標性蛋白進行交叉比對的檢測。這個檢測平台也可以應用到不同的疾病及檢體,舉凡唾液或尿液,根據不同疾病在不同樣本中所因應到的指標性蛋白去做免疫分析及訊號放大便可以檢測到該疾病。最後,期望此檢測平台可以輔助現今無法定量的眼科醫療現況,並落實居家照護,達到為糖尿病患者視力把關的目的。
英文摘要 Disease diagnosis in incubation period and early stages faces various challenges. As diseases elicit adverse effects upon diagnosis in late stages, therapeutic efficiency is reduced. For example, diabetic retinopathy (DR) is a common complication and major cause of vision loss among patients with diabetes mellitus. In clinical diagnosis, DR is examined through fluorescence angiography (FA). Although this method provide information on blood vessel conformation, the detection of changes in blood vessel conformation without a quantitative basis is dependent on a physician’s clinical experiences. In most cases, a patient’s condition has progressed to proliferative diabetic retinopathy (PDR) when abnormalities are detected. As a consequence, patients must undergo surgery to recover their eyesight. Therefore, signs and symptoms should be detected and treated in early stages to help prevent vision loss. In this research, a low-concentration non-invasive detection platform was developed to detect and quantify DR biomarkers in early stages. Tears were used as a biological sample because they contain DR biomarkers, such as lipocalin 1 (LCN1). A specific bond between an antigen and an antibody was formed through the bead-based sandwiched immunosensing. Rapid electrokinetic patterning (REP), an optoelectrokinetic technology, was employed to focus on immunosensed particles and enhance the fluorescence signal. Results revealed that the LCN1 concentration in the tears of normal control subjects was 3150 μg/mL. The LCN1 concentration in the tears of patients with DR increased with disease severity. The detection limit was 15 pg/mL, and this limit was sufficient to detect DR in early stages. REP was also applied to sort different particle sizes, cross-match different biomarkers in the tears of patients with DR, and enhance the accuracy of the detection platform. Our results suggested that the proposed detection platform could be used to diagnose different diseases and specimens and to detect DR by using biomarkers present in various specimens, such as saliva and urine, through immunosensing and signal amplification. The detection platform could also be utilized to address the quantification limitations in point-of-care ophthalmologic diagnosis. Further research should be conducted to protect the vision of diabetic mellitus patients.
論文目次 摘要 I
ABSTRACT II
CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER 1 INTRODUCTION 1
1.1 Motivation and Aims of the Research 1
1.2 Diabetic Retinopathy 2
1.2.1 Types of Diabetic Retinopathy 3
1.2.2 The State-of-the Art Diagnostics for Diabetic Retinopathy 4
1.3 Tear Film 4
1.3.1 Types of Tear Film 5
1.3.2 Tear Sampling 6
1.3.3 Storage of Tear Sample 9
1.4 Biological Marker 9
1.5 Immunoassay 10
1.5.1 Type of Immunoassay 11
1.5.2 Bead-based Sandwiched Immunoassay 12
1.6 Fluorescence Resonance Energy Transfer 13
1.7 Non-contact Particle Manipulation Mechanism 14
CHAPTER 2 MATERIALS AND METHODS 16
2.1 Procedure of Tear Sampling 16
2.2 Identification for Binding of Sandwiched Immunoassay 17
2.3 Sample Preparation for Bead-based Immunoassay 19
2.3.1 Materials Required 19
2.3.2 Equipments Required 20
2.3.3 Description for Bead-based FRET Fluorescence Immunosensing 20
2.3.4 Protocol for Bead-based FRET Fluorescence Immunosensing 22
2.3.5 Description for Bead-based QDs Signal Immunosensing 24
2.3.6 Protocol for Bead-based Dye/QDs Signal Immunosensing 25
2.4 Chip Configuration 28
2.5 Experimental Setup 29
2.6 Theoretical Concept of the Optoelectrokinetic Method 30
2.6.1 Concentration and Translation 32
2.6.2 Sorting 33
CHAPTER 3 RESULTS AND DISCUSSION 35
3.1 Verification for Bead-based Sandwiched Immunoassay 35
3.1.1 Verification for Capture Antibodis 35
3.1.2 Verification for Target Antigens 36
3.1.3 Verification for Specific Binding 38
3.2 Improvement for the Immunoassay 39
3.2.1 Wash Times 39
3.2.2 Blocking Reagents 41
3.3.3 Dosage of Probe Antibody 43
3.3.4 Multiple Batches for Probe Antibody 44
3.3 Simulation for the Bead-based Immunoassay Model 45
3.3.1 Intensity Variation among Different Particle Sizes 45
3.3.2 Intensity Variation among Different Numbers of Particles 46
3.3.3 Intensity Variation with REP Concentration 48
3.4 Bead-based FRET Fluorescence Immunosensing 52
3.4.1 Detection for FRET Fluorescent Signal 52
3.4.2 Signal Enhancement 54
3.4.3 Calibration Curve 55
3.5 Bead-based Dye Signal Immunosensing 56
3.5.1 Comparison between the 3 and 0.5 μm Particles 56
3.5.2 Bleaching Effect 57
3.6 Bead-based QDs Signal Immunosensing 58
3.7 Multiplexed Protein Based on Sorting 61
3.8 Biological Sample for Tears 63
CHAPTER 4 CONCLUSION 67
CHAPTER 5 PROSPECT 70
REFERENCES 71
APPENDIX 76


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