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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83662完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 胡文聰 | zh_TW |
| dc.contributor.advisor | Andrew M. Wo | en |
| dc.contributor.author | 張哲睿 | zh_TW |
| dc.contributor.author | Che-Jui Chang | en |
| dc.date.accessioned | 2023-03-19T21:13:25Z | - |
| dc.date.available | 2023-12-25 | - |
| dc.date.copyright | 2022-08-19 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | 1.Lakshmipriya, T., S.C.B. Gopinath, and T.-H. Tang, Biotin-Streptavidin Competition Mediates Sensitive Detection of Biomolecules in Enzyme Linked Immunosorbent Assay. PLOS ONE, 2016. 11(3): p. e0151153. 2.Lakshmipriya, T., et al., Signal enhancement in ELISA: Biotin-streptavidin technology against gold nanoparticles. Journal of Taibah University Medical Sciences, 2016. 11(5): p. 432-438. 3.Gorris Hans, H., M. Rissin David, and R. Walt David, Stochastic inhibitor release and binding from single-enzyme molecules. Proceedings of the National Academy of Sciences, 2007. 104(45): p. 17680-17685. 4.Rissin, D.M., H.H. Gorris, and D.R. Walt, Distinct and Long-Lived Activity States of Single Enzyme Molecules. Journal of the American Chemical Society, 2008. 130(15): p. 5349-5353. 5.Rissin, D.M. and D.R. Walt, Digital Readout of Target Binding with Attomole Detection Limits via Enzyme Amplification in Femtoliter Arrays. Journal of the American Chemical Society, 2006. 128(19): p. 6286-6287. 6.Rissin, D.M. and D.R. Walt, Digital Concentration Readout of Single Enzyme Molecules Using Femtoliter Arrays and Poisson Statistics. Nano Letters, 2006. 6(3): p. 520-523. 7.Rissin, D.M., et al., Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nature Biotechnology, 2010. 28(6): p. 595-599. 8.Guan, W., et al., Droplet Digital Enzyme-Linked Oligonucleotide Hybridization Assay for Absolute RNA Quantification. Scientific Reports, 2015. 5(1): p. 13795. 9.Akama, K., K. Shirai, and S. Suzuki, Highly sensitive multiplex protein detection by droplet-free digital ELISA: AKAMA et al. Electronics and Communications in Japan, 2018. 102. 10.Akama, K., K. Shirai, and S. Suzuki, Droplet-Free Digital Enzyme-Linked Immunosorbent Assay Based on a Tyramide Signal Amplification System. Analytical Chemistry, 2016. 88(14): p. 7123-7129. 11.Kordecki, A., H. Palus, and A. Bal, Practical vignetting correction method for digital camera with measurement of surface luminance distribution. Signal, Image and Video Processing, 2016. 10(8): p. 1417-1424. 12.Piccinini, F., et al., Multi-image based method to correct vignetting effect in light microscopy images. Journal of Microscopy, 2012. 248(1): p. 6-22. 13.Yuanjie, Z., et al. Single-image vignetting correction using radial gradient symmetry. in 2008 IEEE Conference on Computer Vision and Pattern Recognition. 2008. 14.Leong, F.J.W.M., M. Brady, and J.O.D. McGee, Correction of uneven illumination (vignetting) in digital microscopy images. Journal of Clinical Pathology, 2003. 56(8): p. 619. 15.Wonpil, Y., Practical anti-vignetting methods for digital cameras. IEEE Transactions on Consumer Electronics, 2004. 50(4): p. 975-983. 16.Kim, S.J. and M. Pollefeys, Robust Radiometric Calibration and Vignetting Correction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008. 30(4): p. 562-576. 17.Zheng, Y., et al., Single-Image Vignetting Correction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009. 31(12): p. 2243-2256. 18.Hart, P., How the Hough Transform Was Invented. Signal Processing Magazine, IEEE, 2009. 26: p. 18-22. 19.Duda, R.O. and P.E. Hart, Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM, 1972. 15(1): p. 11–15. 20.Paul, V.C.H., Man - Machine Collaboration in the Analysis of Bubble Chamber Photography for High - Energy Physics. Optical Engineering, 1964. 2(3): p. 79-82. 21.Bagui, O. and J. Zoueu, Red Blood Cells Counting by Circular Hough Transform Using Multispectral Images. Journal of Applied Sciences, 2014. 14: p. 3591-3594. 22.Okokpujie, K., et al., An improved iris segmentation technique using circular Hough transform, in IT convergence and security 2017. 2018, Springer. p. 203-211. 23.Ito, Y., et al. Detection of eyes by circular Hough transform and histogram of gradient. in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). 2012. 24.Cherabit, N., F.Z. Chelali, and A. Djeradi, Circular hough transform for iris localization. Science and Technology, 2012. 2(5): p. 114-121. 25.Khairosfaizal, W.M.K.W.M. and A.J. Nor'aini. Eyes detection in facial images using Circular Hough Transform. in 2009 5th International Colloquium on Signal Processing & Its Applications. 2009. 26.Illingworth, J. and J. Kittler, The Adaptive Hough Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987. PAMI-9(5): p. 690-698. 27.Denimal, E., et al., Automatic Biological Cell Counting Using a Modified Gradient Hough Transform. Microscopy and Microanalysis, 2017. 23(1): p. 11-21. 28.Smereka, M. and I. Dulęba, Circular Object Detection Using a Modified Hough Transform. Int. J. Appl. Math. Comput. Sci., 2008. 18(1): p. 85–91. 29.Meng, Y., et al., Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron, 2018. 106: p. 34-41. 30.Yadav, V.K., et al. Approach to Accurate Circle Detection: Multithreaded Implementation of Modified Circular Hough Transform. in Proceedings of International Conference on ICT for Sustainable Development. 2016. Singapore: Springer Singapore. 31.Chhabra, M. and A. Goyal. Accurate and Robust Iris Recognition Using Modified Classical Hough Transform. in Information and Communication Technology for Sustainable Development. 2018. Singapore: Springer Singapore. 32.Chiu, S.H. and J.J. Liaw, A proposed circle/circular arc detection method using the modified randomized hough transform. Journal of the Chinese Institute of Engineers, 2006. 29(3): p. 533-538. 33.Djekoune, A.O., K. Messaoudi, and M. Belhocine. A New Modified Hough Transform Method for Circle Detection. in IJCCI. 2013. 34.Sina, A.A.I., et al., Real time and label free profiling of clinically relevant exosomes. Scientific Reports, 2016. 6(1): p. 30460. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83662 | - |
| dc.description.abstract | 生物標記物在疾病管理中的應用在臨床上涵蓋了非常廣泛的範圍，包括疾病篩檢、診斷、臨床診斷指導建議、狀況監控等方面。隨著人們對於疾病在臨床階段前的篩檢或是特定濃度極低的生物標記物檢測的興趣提高，傳統的免疫分析方法需要有實質且重大的突破以達到需求。 本論文的目的是透過雙微米珠的三明治酵素結合免疫分析法實現ELISA 的結果。結果顯示在實驗設計方面提供了總體方法的初步驗證。這項工作為實驗中的每個單個步驟提供了一種定量分析的方法，並通過三明治ELISA給出了這種特殊方法的初步結果。需要未來的工作來進一步驗證其他參數。 | zh_TW |
| dc.description.abstract | The use of biomarkers for disease management spans a wide clinical spectrum, including screening, diagnosis, therapeutic guidance and monitoring. As interests grow in detecting disease in the preclinical stage or with specific biomarkers with extremely low concentration, substantial breakthrough from the traditional immunoassay approach is needed. The purpose of this thesis was to implement beads-based sandwich ELISA using micron-scaled particles observed by bright field and a fluorescence channel. Results provided preliminary validation in the overall approach in terms of experimental design. This work provides a method of quantitative analysis for each individual steps in experiments and gives preliminary results of this particular approach via sandwich ELISA. Future work is needed to further validate additional parameters. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T21:13:25Z (GMT). No. of bitstreams: 1 U0001-1508202214392000.pdf: 2432072 bytes, checksum: c402c9c5610f5c3f6edacb2e20d4401e (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員審定書 i 致謝 ii 摘要 iii Abstract iv Table of Contents v List of Figures vii List of Tables x Chapter 1. Introduction 1 Chapter 2. Material and Method 5 2.1 Beads Preparation 5 2.1.1 Antibodies Conjugation 5 2.1.2 Quenching 7 2.1.3 Washing Method 8 2.1.4 Procedure of Experiment 10 2.2 Objects Counting and Intensity Estimation 12 2.2.1 Efficiency Estimation of Antibodies and Sample Incubation 12 2.2.2 Vignetting Correction 14 2.2.3 Average Fluorescent Intensity Estimation 16 2.2.4 Circular Hough Transform 19 Chapter 3 Results and Discussion 25 3.1 Intensity Estimation 25 3.1.1 Correction of Vignetting Images 25 3.1.2 Comparing Intensity at Different Positions 30 3.2 Counting Algorithm 34 3.2.1 Optimization of Minimum Distance Exclusion 34 3.2.2 Validation and Limitation of Circular Hough Transform 36 3.3 Procedures Evaluation 37 3.3.1 Evaluation of Antibodies Conjugation 37 3.3.2 Evaluation of Quenching and Blocking 45 3.3.3 Serial Dilution of GFP231 Exosomes 49 3.3.4 GFP231 Exosomes spike in HAS solutions 53 3.4 Results affected by Washing Method 55 Chapter 4 Conclusions 57 Reference 58 Figure 2.1 Schematic of EDC/NHS Modification 6 Figure 2.2 Chemical Structure of EA 7 Figure 2.3 Schematic of Quenching Process 7 Figure 2.4 Traditional Washing through Magnetic Holder 9 Figure 2.5 New Washing Method through laminar flow 9 Figure 2.6 Procedure of Experiment 11 Figure 2.7 4-Connectivity 13 Figure 2.8 8-Connectivity 13 Figure 2.9 4-Connectivity Component 13 Figure 2.10 8-Connectivity Component 13 Figure 2.11 Schematic results of 4-connectivity labeling 13 Figure 2.12 Schematic results of 8-connectivity labeling 13 Figure 2.13 Esitmated high brightness beads 17 Figure 2.14 Esitmated medium brightness beads 18 Figure 2.15 Esitmated low brightness beads 18 Figure 2.16 Schematic of linear Hough Transform 19 Figure 2.17 Schematic of Circular Transform 19 Figure 2.18 Heatmap of Accumulate Array 24 Figure 2.19 Candidate Centers 24 Figure 2.20 Candidate Centers with Minimum Distance Exclusion 24 Figure 2.21 Candidate Centers with Under Area Value Check 24 Figure 3.1 Problems of vignetting. 26 Figure 3.2 3D mesh plot of vignetting problems shown in Figure 3.1. 26 Figure 3.3 Tested image—Before Correction. 27 Figure 3.4 3D Mesh Plot—Before correction. 27 Figure 3.5 Background image of the tested image. 28 Figure 3.6 Correction factor. 28 Figure 3.7 Test image—After Correction. 29 Figure 3.8 3D Mesh Plot—After correction. 29 Figure 3.9 Target beads at center. 31 Figure 3.10 Target beads at corner. 31 Figure 3.11 Comparing the same cluster of beads at different place. 32 Figure 3.13 Before and after correction of beads at corner of frame. 32 Figure 3.14 Comparing correction effect for beads at different position of frame by AFIE. 33 Figure 3.15 Incubation buffer for antibodies conjugation with Ps beads. 39 Figure 3.16 Three different kinds of Antibodies conjugated with Ps beads with two kinds of incubation buffer. 40 Figure 3.17 Three different kinds of Antibodies conjugated with Mg beads in two kinds of incubation buffer. 41 Figure 3.18 Mg beads and Ps beads conjugated with different concentration of antibodies. 42 Figure 3.19 Different concentration of Mg beads conjugation with same concentration of antibodies. 43 Figure 3.20 Time effect in EDC/NHS activation. 44 Figure 3.21 Testing blocking and quenching effect on EDC/NHS activated and no antibodies coated Mg beads. 46 Figure 3.22 Testing blocking and quenching effect on EDC/NHS activated and antibodies coated Mg beads. 47 Figure 3.23 Images for beads quenching with EA only. 47 Figure 3.24 Images for beads blocking with 2% BSA only. 48 Figure 3.25 Mg beads coated with 0.75ul of CD9 and CD81. 50 Figure 3.26 Mg beads coated with 0.375ul of CD9 and CD81. 51 Figure 3.27 Mg beads coated with 0.287ul of CD9 and CD81. 52 Figure 3.28 Different concentrations of GFP231 exosomes spiked in human serum simulated environment, and captured these exosomes through CD9,81 coated Mg beads and HER2 coated Mg beads. 54 Figure 3.29 Different washing method comparison. 56 Table 1 Time Consuming for Minimum Distance Exclusion 35 Table 2 Accuracy of counting algorithm 36 | - |
| dc.language.iso | en | - |
| dc.subject | 三明治酵素結合免疫分析 | zh_TW |
| dc.subject | 三明治酵素結合免疫分析 | zh_TW |
| dc.subject | 蛋白檢測 | zh_TW |
| dc.subject | 外泌體 | zh_TW |
| dc.subject | 蛋白檢測 | zh_TW |
| dc.subject | 外泌體 | zh_TW |
| dc.subject | sandwich ELISA | en |
| dc.subject | Exosomes | en |
| dc.subject | protein detection | en |
| dc.subject | sandwich ELISA | en |
| dc.subject | Exosomes | en |
| dc.subject | protein detection | en |
| dc.title | 培養液外泌體微米珠酵素結合免疫吸附三明治螢光分析方法之研究 | zh_TW |
| dc.title | Beads-based Sandwich ELISA for Fluorescent Detection of Exosomes from Cultured Medium | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 110-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李雨;許聿翔 | zh_TW |
| dc.contributor.oralexamcommittee | U Lei;Yu-Hsiang Hsu | en |
| dc.subject.keyword | 外泌體,蛋白檢測,三明治酵素結合免疫分析, | zh_TW |
| dc.subject.keyword | Exosomes,protein detection,sandwich ELISA, | en |
| dc.relation.page | 60 | - |
| dc.identifier.doi | 10.6342/NTU202202407 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2022-08-17 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 應用力學研究所 | - |
| 顯示於系所單位： | 應用力學研究所 | |
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