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Abstract Circulating Tumor Cells (CTCs), which migrate from original sites in a body to distant organs through blood, are a key factor in cancer detection. Emerging Label-free techniques owing to their inherent advantage to preserve characteristics of sorted cells and low consumption of samples can be promising to the prediction of cancer progression and metastasis research. Deterministic Lateral Displacement (DLD) is one of the label-free separation techniques employing a specific arrangement of micro-posts for continuous separation of suspended cells in a buffer based on the size of cells. Separation based solely on size is challenging since the size distributions of CTCs might overlap with those of normal blood cells. To address this problem, DLD can be combined with dielectrophoresis (DEP) technique which is the phenomenon of particle movement in a non-uniform electric field owing to the polarization effect. Although, DLD devices employ the laminar flow in low Reynolds number (Re) fluid flow due to predictability of such flow regimes, they should be improved to work in higher Re flow regime so as to attain high throughput devices. In this paper, a particle tracing simulation is developed to study the effects of different post shapes, shift fraction of micropost arrays, and dielectrophoresis forces on separation of CTCs from peripheral blood cells. Our numerical model and results provide a groundwork for design and fabrication of high-throughput DLD-DEP devices for improvement of CTC separation.
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Deterministic Lateral Displacement (DLD) Analysis Tool Utilizing Machine Learning towards High-Throughput Separation
Deterministic lateral displacement (DLD) is a microfluidic method for the continuous separation of particles based on their size. There is growing interest in using DLD for harvesting circulating tumor cells from blood for further assays due to its low cost and robustness. While DLD is a powerful tool and development of high-throughput DLD separation devices holds great promise in cancer diagnostics and therapeutics, much of the experimental data analysis in DLD research still relies on error-prone and time-consuming manual processes. There is a strong need to automate data analysis in microfluidic devices to reduce human errors and the manual processing time. In this work, a reliable particle detection method is developed as the basis for the DLD separation analysis. Python and its available packages are used for machine vision techniques, along with existing identification methods and machine learning models. Three machine learning techniques are implemented and compared in the determination of the DLD separation mode. The program provides a significant reduction in video analysis time in DLD separation, achieving an overall particle detection accuracy of 97.86% with an average computation time of 25.274 s.
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- Award ID(s):
- 1707056
- PAR ID:
- 10345250
- Date Published:
- Journal Name:
- Micromachines
- Volume:
- 13
- Issue:
- 5
- ISSN:
- 2072-666X
- Page Range / eLocation ID:
- 661
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/mi13050661
