(Keynote) New Microfluidic Platforms for Medical Screening and Diagnostics
Bibliographic record
Abstract
I. Introduction The field of microfluidics continues to show promise for applications in medical screening and diagnostics, and the study of human health and disease. Microfluidics has the potential to miniaturize instrumentation usually limited to the laboratory table top, offering the possibility of highly portable or wearable instruments for medical screening and diagnostics including the monitoring of biomarkers in real time. Microfluidics also offers unique devices and methods that enable scientists to study health and disease in new ways, from studying individual and monolayers of biological cells to mimicking organs on a chip. In order to fully develop these applications, new microfluidic platforms are required that incorporate the results of ongoing research in fields such as materials, biology, microfabrication, electronics, and computer engineering. This presentation discusses several platforms for the development of microfluidic systems for applications in biomedicine and biology. These platforms employ multiple recent advances in nanocomposite polymers, polymer microfabrication, printed circuit board technology, reconfigurable systems, and flexible electronics. These platforms are employed toward the development of new microfluidic instruments, including a reconfigurable microfluidic diagnostic unit, inexpensive and flexible biomedical screening devices, and biological cell research platforms. II. Example Platforms A. Reconfigurable Microfluidic Platform There is a growing need for instruments that perform fast, accurate, and inexpensive biological sample manipulation and processing with high-throughput. Microfluidics offers great possibilities for small, portable systems to perform fast manipulation and sorting of large numbers of samples, as well as multiplexed analysis of a single sample. Successful microfluidic-based devices exist, but their portability and throughput has been limited as they either require separate microfluidic channels for each test, which translates to a large array of permanently configured channels with a large footprint; require large off-chip support devices; or are based on other technologies that have relatively strict requirements for fluid sample manipulation and sample transfer between devices. We present on-going work to develop an instrument to address these problems of simultaneous high-throughput and portability. This instrument, a collaboration between microfluidics and computer engineering labs at Simon Fraser University, offers a new approach that is accomplished through the merging of electronically controlled functional magnetic materials for microfluidics with state-of-the-art field programmable technology. The instrument employs a compact, reconfigurable, microfluidics platform that is a combination of an array of small, individually-programmable, magnetic polymer valves [1] to address microfluidic channels that connect chambers containing sensors [2], heaters [3], mixers [4], etc., and a field programmable technology control that allows the highly portable instrument to configure itself “on-the-fly” for analysis of multiple samples using multiple tests. We present the reconfigurable microfluidics unit paradigm, and discuss technologies and techniques taken from the fields of composite materials and polymer microfabrication to develop the hardware for the reconfigurable microfluidic platform. B. Cost-Effective PCB-Based Platforms Printed Circuit Board (PCB) technology is a well understood and well utilized platform for electronics. We present a new PCB-based platform for microfluidic biomedical and biological testing. The standardized platform features integrated functional units, three-dimensional (3D) configurations, convenient device-instrumentation interconnections, and industry-compatible precision manufacturing using a custom three-metal fabrication process. PCB technology is demonstrated to not only form an integrated platform but is also utilized in the fabrication of functional elements for biosensing and bioprocessing devices and systems, such as reconfigurable microfluidic systems for biosample analysis. Example elements include a chemistry-based enzyme assay [2], and a molecule-based quantitative polymerase chain reaction (qPCR) screening device [3]. C. Flexible Microfluidic Platforms We present our most recent work in the development of microfluidic instruments of flexible substrates. We have previously presented work on flexible microfluidic platforms containing multiple layers of interconnected microfluidic channels on flexible substrates [5, 6], as well as conductive and magnetic functional polymers for flexible sensor [7] and actuator devices [1, 2, 8]. We present now the concept of integrating these technologies towards development of flexible microfluidic instrumentation for biological cell research [9] and wearable biomedical monitors. References M. Rahbar, et. al., J Micromech Microeng 26(5), 055012 (2016). H.-Y. Tseng, et. al., Measurement 73:158-161 (2015). H.-Y. Tseng, et. al. Sensors Actuators B, 204:459-466 (2014). M. Rahbar, et. al., J Micromech Microeng, 24(2):025003 (2014). D. Chung & B.L. Gray, J Micromech Microeng, 27(11), (2017). 6. Patel, et. al., J Micromech Microeng 23(6):065029 (2013). D. Chung, et. al., J Electrochemical Society 161(2):B3071-76 (2014). M. Rahbar & B.L. Gray, IEEE 17th International Conference on Nanotechnology (IEEE-NANO), Pittsburgh, (July 2017). B.L. Gray, et. al., IEEE 14th International Conference on Nanotechnology (IEEE-NANO), Toronto (August 2014), 986-990.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.118 | 0.043 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".