Size and deformability based separation of circulating tumor cells from castrate resistant prostate cancer patients using resettable cell traps
Bibliographic record
Abstract
The enumeration and capture of circulating tumor cells (CTCs) are potentially of great clinical value as they offer a non-invasive means to access tumor materials to diagnose disease and monitor treatment efficacy. Conventional immunoenrichment of CTCs may fail to capture cells with low surface antigen expression. Micropore filtration presents a compelling label-free alternative that enriches CTCs using their biophysical rather than biochemical characteristics. However, this strategy is prone to clogging of the filter microstructure, which dramatically reduces the selectivity after processing large numbers of cells. Here, we use the resettable cell trap (RCT) mechanism to separate cells based on their size and deformability using an adjustable aperture that can be periodically cleared to prevent clogging. After separation, the output sample is stained and analyzed using multi-spectral analysis, which provides a more sensitive and unambiguous method to identify CTC biomarkers than traditional immunofluorescence. We tested the RCT device using blood samples obtained from 22 patients with metastatic castrate-resistant prostate cancer while comparing the results with the established CellSearch® system. The RCT mechanism was able to capture ≥5 CTCs in 18/22 (82%) patients with a mean count of 257 in 7.5 ml of whole blood, while the CellSearch system found ≥5 CTCs in 9/22 (41%) patients with a mean count of 25. The ~10× improvement in the CTC capture rate provides significantly more materials for subsequent analysis of these cells such as immunofluorescence, propagation by tissue culture, and genetic profiling.
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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 | 0.001 |
| 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".