An Integrated PDMS Microfluidic Device for Dielectrophoretic Separation of Malignant Cells
Bibliographic record
Abstract
Dielectrophoresis (DEP) has been successfully applied and demonstrated to provide novel and non-invasive means for characterizing, manipulating, trapping, separating and isolating microscopic sized particles, including biological cells. In this article, we report on the design, fabrication and performance of a novel, low cost, integrated Poly(dimethylsiloxane) (PDMS)/DEP microfluidic device capable of controlled manipulation of microscopic sized cells and particles that can be simultaneously utilized both for DEP spectral analysis and cell sorting. We have prototyped microfluidic channels, with DEP microelectrodes incorporated within PDMS channels. Previously, we have evaluated the operation and performance of a prototype device using various dielectric and biological particles, including yeast cells and polystyrene latex beads. In this paper, we report initial experimental observations on malignant cancerous cells. Non-viable cells, due to positive DEP, were attracted to the planar electrodes at frequencies between 200–600 kHz and were clearly repelled from the electrodes, due to negative DEP, at frequencies above 10 MHz.
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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) | 0.001 | 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".