Microfluidic concentration and separation of circulating tumor cell clusters from large blood volumes
Bibliographic record
Abstract
Circulating tumor cells (CTCs) are extremely rare in the blood, yet they account for metastasis. Notably, it was reported that CTC clusters (CTCCs) can be 50-100 times more metastatic than single CTCs, making them particularly salient as a liquid biopsy target. Yet they can split apart and are even rarer, complicating their recovery. Isolation by filtration risks loss when clusters squeeze through filter pores over time, and release of captured clusters can be difficult. Deterministic lateral displacement is continuous but requires channels not much larger than clusters, leading to clogging. Spiral inertial focusing requires large blood dilution factors (or lysis). Here, we report a microfluidic chip that continuously isolates untouched CTC clusters from large volumes of minimally (or undiluted) whole blood. An array of 100 μm-wide channels first concentrates clusters in the blood, and then a similar array transfers them into a small volume of buffer. The microscope-slide-sized PDMS device isolates individually-spiked CTC clusters from >30 mL per hour of whole blood with 80% efficiency into enumeration (fluorescence imaging), and on-chip yield approaches 100% (high speed video). Median blood cell removal (in base-10 logs) is 4.2 for leukocytes, 5.5 for red blood cells, and 4.9 for platelets, leaving less than 0.01% of leukocytes alongside CTC clusters in the product. We also demonstrate that cluster configurations are preserved. Gentle, high throughput concentration and separation of circulating tumor cell clusters from large blood volumes will enable cluster-specific diagnostics and speed the generation of patient-specific CTC cluster lines.
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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".