Recent advances in micro technologies have equipped researches with novel tools for interacting with biological molecules and cells. This thesis focuses on the design, fabrication and application of microfluidic platforms for stimuli-responsive drug delivery and the electromechanical characterization of single cells.
Stimuli-responsive hydrogels are promising materials for controlled drug delivery due to their ability to respond to changes in local environmental conditions. In particular, nanohydrogel particles have been a topic of considerable interest due to their rapid response times compared to micro and macro-scale counterparts. Owing to their small size and thus low drug-loading capacity, these materials are unsuitable for prolonged drug delivery. To address this issue, stimuli-responsive implantable drug delivery micro devices by integrating microfabricated drug reservoirs with smart nano-hydrogel particles embedded composite membranes have been proposed.
In one proposed glucose-responsive micro device, crosslinked glucose oxidase enables the oxidation of glucose into gluconic acid, producing a microenvironment with lower pH values to modulate the pH-responsive nanoparticles. In vitro glucose-responsive drug release profiles were characterized and normoglycemic glucose levels in diabetic rats with device implantation were also recorded.
The biophysical properties of single cells have recently been demonstrated as an important indicator of disease diagnosis. Existing technologies are capable of characterizing single parameter either electrical or mechanical rapidly, but not both, which could only collect limited information for cell status evaluation. To address this issue, two microfluidic platforms capable of simultaneously characterizing both the electrical and mechanical properties of single cells based on electrodeformation and integrated impedance spectroscopy with micropipette aspiration have been proposed.
In one proposed microfluidic device, a negative pressure was used to suck cells continuously through the aspiration channel with impedance profiles measured. By interpreting impedance profiles, transit time and impedance amplitude ratio can be quantified as cellular mechanical and electrical property indicators. Neural network based cell classification was conducted, demonstrating that two biophysical parameters could provide a higher cell classification success rate than using electrical or mechanical parameter alone.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/42483 |
| Date | 15 November 2013 |
| Creators | Chen, Jian |
| Contributors | Sun, Yu |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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