Vortex chip incorporating an orthogonal turn for size-based isolation of circulating cells
Bibliographic record
Abstract
Size-based label-free separation of rare cells such as CTCs is attractive due to its wider applicability, simpler sample preparation, faster turnaround and better efficiency. Amongst such methods, vortex-trapping based techniques offer high throughput but operate at high flow velocities where the resulting hydrodynamic shear stress is likely to damage cells and compromise their viability for subsequent assays. We present here an orthogonal vortex chip which can carry out size-differentiated trapping at significantly lower (38% of previously reported) velocities. Composed of entry-exit channels that couple orthogonally to a trapping chamber, fluid flow in such configuration results in formation of a vortex which selectively traps larger particles above a critical velocity while smaller particles get ejected with the flow. We call this phenomenon the turn-effect. Critical velocities and optimal architectures for trapping of cells and particles of different sizes are characterized. We explain how shear-gradient lift, centrifugal and Dean flow drag forces contribute to the turn-effect by pushing particles into specific vortex orbits in a size- and velocity-dependent fashion. Selective trapping of human breast cancer cells mixed with whole blood at low concentration is demonstrated. The device shows promising results for gentle isolation of rare cells from blood.
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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".