Conventional methods for the detection of bacterial infection such as DNA or immunoassays are either expensive, time consuming, or not definitive; thus may not provide all the information sought by the medical professionals. In particular, it is difficult to obtain information about viability or drug effectiveness, which are crucial to formulate a treatment. Bacterial culture test is the “gold standard” because it is inexpensive and does not require extensive sample preparation, and most importantly, provides all the necessary information sought by healthcare professionals, such as bacterial presence, viability and drug effectiveness. These conventional culture methods, however, have a long turnaround time: anywhere between 1 day to 4 weeks. This thesis proposes to solve this problem by monitoring the growth of bacteria in thousands of nanowells simultaneously to identify its presence in the sample and its viability, faster. The segmentation of a sample with low bacterial concentration into thousands of nanoliter wells digitizes the samples and increases the effective concentration in those wells that contain bacteria. The user may then monitor the metabolism of the aerobic bacteria by using an oxygen sensitive fluorophore, ruthenium tris (2,2’-diprydl) dichloride hexahydrate (Ru(Bpy)3) by monitoring the dissolved oxygen concentration in the nanowells. Using E.Coli K12, it is demonstrated that the detection time of E.coli can be as fast as 35-60 minutes with sample concentrations varying from 104(62 minutes for detection), 106 (42 minutes) and 108 cells/mL (38 minutes). More importantly, throughout the thesis it is also demonstrated that reducing the well size can reduce the time of detection. Finally, this thesis will discuss how the drug effectiveness information can be obtained in this format by loading the wells with the drug and monitoring the metabolism of the bacteria. The method that is developed in this thesis is low cost, simple, requires minimal sample preparation and can potentially be used with a wide variety of samples in resource poor setting to detect bacterial infections such as Tuberculosis. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20420 |
| Date | 15 November 2016 |
| Creators | Ayyash, Sondos |
| Contributors | Selvaganapathy, Ponnambalam Ravi, Biomedical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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