Velocity valleys enable efficient capture and spatial sorting of nanoparticle-bound cancer cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of strategies for isolating rare cells from complex matrices like blood is important for a wide variety of applications including the analysis of bloodborne cancer cells, infectious pathogens, and prenatal testing. Due to their high colloidal stability and surface-to-volume ratio, antibody-coated magnetic nanoparticles are excellent labels for cellular surface markers. Unfortunately, capture of nanoparticle-bound cells at practical flow rates is challenging due to the small volume, and thus low magnetic susceptibility, of magnetic nanoparticles. We have developed a means to capture nanoparticle-labeled cells using microstructures which create pockets of locally low linear velocity, termed velocity valleys. Cells that enter a velocity valley slow down momentarily, allowing the magnetic force to overcome the reduced drag force and trap the cells. Here, we describe a model for this mechanism of cell capture and use this model to guide the rational design of a device that efficiently captures rare cells and sorts them according to surface expression in complex matrices with greater than 10,000-fold specificity. By analysing the magnetic and drag forces on a cell, we calculate a threshold linear velocity for capture and relate this to the capture efficiency. We find that the addition of X-shaped microstructures enhances capture efficiency 5-fold compared to circular posts. By tuning the linear velocity, we capture cells with a 100-fold range of surface marker expression with near 100% efficiency and sort these cells into spatially distinct zones. By tuning the flow channel geometry, we reduce non-specific cell adhesion by 5-fold.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it