Scalable Multilayer Cell Collector to Capture Circulating Tumor Cells with an Unlimited Volume Capacity
Bibliographic record
Abstract
Circulating tumor cells (CTCs) have been suggested as the precursors of metastatic cancer. CTC-based characterization has thus been used to monitor tumor status before the onset of metastasis and has shown to be an independent factor. The low abundance of CTCs, however, makes it challenging to employ CTC as a clinical routine, thus making it impossible to address tumor heterogeneity. Here, we present a cell collection prototype for an efficient capture of CTCs from a large volume of body fluids such as blood. An antibody-PEG modified multilayer matrix column is engineered and connected to an apheresis-based circulation system. This setup allows us to capture CTCs repetitively from an unlimited sample volume through the circulation system, thereby increasing the capture count. Compared to conventional CTC capturing devices where the sample handling is generally limited to 1-10 mL, our collector is able to handle a wide range of fluidic sample (40-2000 mL) at a high flow rate (400 mL/min). By processing 90 min in circulation, we obtained an average capture efficiency of at least 75% for the colorectal cancer cell line HCT116 spiked in either 40-200 mL of buffer solution or 40 mL of a whole blood sample. This result highlights a possibility to construct personalized CTC libraries through high-throughput CTC collection for the study of tumor heterogeneity in precision medicine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".