Continuous Separation of Cancer Cells From Blood in a Microfluidic Channel Using Dielectrophoresis
Bibliographic record
Abstract
Currently, there is enormous interest in developing microfluidic lab-on-a chip devices for biological and clinical purposes. In this work, a method for continuous separation of cancer cells from diluted blood in a microfluidic device using dielectrophoresis is described. MDA-MB-231 breast cancer cells have been separated from normal blood cells with high level of accuracy that could enabled precise counting of the cancer cells in samples. The cancer cells were separated from the mixture of cells to a different daughter channel using two pairs of interdigitated comb-like electrode deposited in the microchannel. All experiments were performed with sucrose/dextrose conductivity adjusted medium. The AC signals used in the separation of cancer cells from the mixture are 20 V peak-to-peak with frequencies in range of 10–60 kHz. The separation is a result of balancing of magnitude of the dielectrophoretic force and hydrodynamic force on cells. The difference in response in response between cancer malignant cells and normal cells at a certain band of alternating current frequencies was used for rapid separation of cancer cells from blood. The significance of these experimental results are discussed, with detailed reporting on the preparation of cells and medium, flow condition and the fabrication process of the microfluidic separation microdevice. The present technique could potentially be applied to identify incident cancer at a stage and size that is not yet detectable by standard diagnostic techniques for detecting of cancer recurrences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".