Interdigitated comb‐like electrodes for continuous separation of malignant cells from blood using dielectrophoresis
Bibliographic record
Abstract
In this paper, a method for continuous flow separation of circulating malignant cells from blood in a microfluidic device using dielectrophoresis is discussed. Separation of MDA231 breast cancer cells after mixing with normal blood cells was achieved with a level of accuracy that enabled precise counting of the malignant cells, separation and eventually, sub-culturing. MDA231 cells were separated from the blood to a daughter channel using two pairs of interdigitated activated comb-like electrode structures. All experiments are performed with conductivity adjusted medium samples. The electrode pairs were positioned divergent and convergent with respect to the flow. The AC signals used in the separation are 20 V peak-to-peak with frequencies of 10-50 kHz. The separation is based on balance of magnitude of the dielectrophoretic force and hydrodynamic force. The difference in response between circulating 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 is discussed in this paper, with detailed reporting on the suspension medium, preparation of cells, flow condition and the fabrication process of the microfluidic chip. 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 (imaging and biochemical testing). Alternatively, it may also be used to detect 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".