A型流感是每年引起人類社會嚴重關切的全球公共衛生威脅。目前為止，A型流感病毒已經被鑑定出具有18個血凝素（HA）亞型和11個神經氨酸酶（NA）亞型。病毒亞型的鑑定是相當重要的，由於病毒亞型的多樣性，很難預防新型流感的爆發，特別是對於跨物種病毒的傳播，因此A型流感的早期診斷和準確的病毒分型可以幫助臨床判斷適當的治療和有效的流感爆發管理，以及提供流感病毒的起源，感染性和抗病毒藥物的抗性等寶貴的訊息。然而，現行臨床實驗室使用的傳統分子檢測方法，具高耗時、高勞力、需要昂貴儀器以及訓練有素的操作員等缺點，導致難以在一般診所及偏遠地區使用，也可能因為人為操作的因素而導致實驗差異性。另外，醫院及診所最常使用的流感快速篩檢利用免疫測定的方式，能夠在30分鐘內得知檢測結果且使用方便，但因為其偵測敏感度低，也不能區分A型流感病毒的亞型，使其對於流感防治上的應用受到限制。因此，發展出一套具有高靈敏度、高特異性、快速、易用、能分辨病毒亞型和低成本的流感病毒檢測系統是至關重要的。文獻指出，Cohen M等人利用流感病毒能夠透過其HA與宿主細胞膜上表達的多醣分子進行專一性結合之現象，將多醣作為探針修飾於磁珠表面應用於抓取流感病毒。在此篇研究中，我們同樣利用對流感病毒具專一性及親和性的多醣修飾磁珠抓取流感病毒，再以一步式反轉錄聚合酶鏈式反應（one-step RT-PCR）的方式快速偵測流感病毒，另外，我們在單一晶片中利用陣列的方式來檢測病毒的HA及NA，比起一對一特定病毒的偵測方式，可以可以減少需要偵測的數目同時鑑定更多種不同亞型的病毒。本研究中採用的整合型微流體系統，能夠將病毒檢測過程全程自動化，並且最多同時能夠檢測12種不同A型流感病毒的亞型，檢測時間也縮短至不到2小時，比起傳統檢測方式需人工進行病毒RNA萃取以及反轉錄聚合酶鏈式反應(RT-PCR)的方法還要更快，並且減少人為造成的誤差。在此研究中，我們證實所使用的多醣體修飾磁珠，對於大部分的流感病毒抓取率高達50%以上，另外，此系統用來分辨不同病毒HA及NA的引子 (primer)，對於本研究中所使用的流感病毒之偵測極限為38至2973個病毒拷貝數，並且我們也成功利用此系統辨識出A型流感H1N1、A型流感H3N2、A型流感H5N1、A型流感H5N2、A型流感H7N9、B型流感維多利亞株、B型流感山形株。以上實驗結果證明，此整合型微流體系統有望成為一個強而有力的流感診斷工具。

Influenza A virus (InfA) is one of the global public health threats that causes serious concern in human society every year. Currently, InfA is further classified into many subtypes based on 18 different hemagglutinin (HA) subtypes and 11 different neuraminidase (NA) subtypes. Because of the diversity of viral subtypes, it is difficult to prevent a novel influenza outbreak, especially for cross-species virus transmission. Early diagnosis and accurate subtyping of InfA can help clinical judgment and guide treatment of viral infections and effective flu outbreak management. Moreover, identification of viral subtypes can also offer a lot of valuable information such as origin, infectivity and antiviral resistance of the influenza virus. However, accurate diagnostic methods such as nucleic acid-based approaches are relatively time-consuming and labor-intensive. Furthermore, it requires expensive apparatus and well-trained personnel. Alternatively, rapid influenza diagnostic tests using immunoassays exhibit relatively low sensitivity and specificity and cannot differentiate subtypes of the InfA even though they can be performed in less than 30 minutes. Therefore, there is a crucial need for development of a highly sensitive, highly specific, rapid, easy-to-use and fully automated device for detection and subtyping of influenza viruses. Recently, polysaccharides, which were widely known as targets of viral HA, have been reported as probes to capture influenza viruses. In this work, a new array-type microfluidic chip was therefore developed and integrated into an automatic control system for multiple typing and subtyping of influenza viruses from virus-containing samples by using polysaccharide-coated magnetic beads and one-step reverse-transcription polymerase chain reaction (RT-PCR) processes targeting specific HA and NA. It is the first time that multiple viral subtyping (up to 12 InfA subtypes) could be automated starting from virus pretreatment to optical detection on an integrated microfluidic system. The entire virus detection process was shortened to less than 2 hours which was faster than conventional on-bench RT-PCR method and chip-based microarray systems. Under optimal operating conditions, the limits of detection of the specific primers were experimentally found to be from 2973 to 38 copy numbers for different influenza viruses via on-chip RT-PCR. The results showed significant improvement (around two orders of magnitude) when compared to our previous studies. The subtyping results also demonstrated that this integrated microfluidic system could identify 5 subtypes of InfA and 2 strains of influenza B viruses. In summary, the array-type microfluidic chip system provides a rapid, sensitive, and fully automated approach for the rapid detection and multiple subtyping of InfA.

Abstract I
中文摘要 III
致謝 V
Table of contents VII
List of tables X
List of figures XII
Nomenclature and abbreviations XV
Chapter 1 Introduction 1
1.1. Influenza 1
1.2. The relationship between influenza viruses and polysaccharides 3
1.3. Diagnosis of influenza 6
1.4. Chip-based microarray systems and microfluidic systems applied on influenza diagnosis 10
1.5. Motivation and novelty 13
Chapter 2 Design and fabrication of the microfluidic system 15
2.1. Chip design and fabrication procedure 15
2.2. Custom-made control and detection system 19
2.3. Working principle and experimental procedure 21
2.3.1. Working principle 21
2.3.2. Experimental procedure 24
Chapter 3 Materials and methods 28
3.1. Sample preparation 28
3.1.1. Influenza virus strains and preparation 28
3.1.2. Pentasaccharides and magnetic beads 33
3.1.3. One-step RT-PCR 36
3.2. Characterization of chip performances 39
3.2.1. Measurement of pumping rate and pumping uniformity 39
3.2.2. Measurement of mixing index 41
3.3. Calculation of the thermolysis efficiency 43
3.4. Calculation of the capture rate 44
3.2. Experimental setup of capture abilities of pentasaccharides and magnetic beads 45
3.3. Experimental setup of optimization of mixing conditions 47
3.4. Experimental setup of sensitivity tests 48
3.5. Experimental setup of identification of subtypes of influenza virus samples 50
Chapter 4 Results and discussion 51
4.1. Performance of the integrated microfluidic system 51
4.1.1. Pumping rate and pumping uniformity of the consecutive micropumps 51
4.1.2. Mixing index of the open-type micromixer 55
4.2. Capture abilities of pentasaccharides and magnetic beads 58
4.2.1. Capture abilities of three kinds of pentasaccharides 58
4.2.2. Capture abilities of two kinds of pentasaccharide-coated magnetic beads 60
4.3. Optimization of mixing conditions of the open-type micromixer 62
4.3.1. Optimization of mixing pressure 62
4.3.2. Optimization of mixing frequency 64
4.3.3. Optimization of capture time 66
4.4. Thermolysis efficiency of the temperature control system 68
4.5. Specificity of the specific primers for identification of subtypes 70
4.6. Capture rates on the microfluidic system for influenza viruses 75
4.7. Sensitivity tests 77
4.8. Identification of subtypes of influenza virus samples 91
Chapter 5 Conclusions and future perspective 97
5.1. Conclusions 97
5.2. Future perspective 99
References 102
Publication list 113

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