Morphological Characteristics of CTCs and the Potential for Deformability‐Based Separation
Bibliographic record
Abstract
Circulating tumor cell (CTC) separation based on affinity capture of epithelial cell surface markers has demonstrated the potential to use CTC enumeration to predict disease outcomes in multiple cancers. This chapter describes the limitations of affinity-capture-based CTC separation methods and the known morphological differences between CTCs and leukocytes. It then reviews the mechanism for CTC separation based on biophysical parameters with a focus on filtration methods that enable CTC separation based on cellular deformability, as well as the problems imposed by clogging. The chapter further introduces two emerging mechanisms for deformability-based separation that could potentially eliminate clogging. Microfluidic filtration strategies aiming to enrich CTCs based on a combination of size and deformability are limited by clogging, which significantly reduces their selectivity and ability to extract the separated cells. The microfluidic ratchet and resettable cell trap mechanisms are examples of technologies designed to overcome this challenge.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".