Evaluation of Isolation Methods for Circulating Tumor Cells (CTCs)
Bibliographic record
Abstract
BACKGROUND: Detection of CTCs is a poor prognostic factor for many cancer types; however, their very low frequency represents an obstacle for their detection. The objective of the current study was to compare the performance of commonly used methods for CTCs isolation. METHODS: The evaluated methods using spiking experiments of MCF7, SKBR3 and MDA MB-231 breast cancer cell lines were (i) ficoll density gradient separation (DGS), (ii) red blood cell lysis (Erythrolysis) isolation, (iii) positive immunomagnetic selection (EpCAM Dynal beads), (iv) two different negative immunomagnetic separation systems (Dynal vs Miltenyi CD45 beads) as well as (v) the Cell Search platform and (vi) the ISET system. RESULTS: The recovery rates of Erythrolysis and DGS were 39% and 24%, respectively. Magnetic isolations are ranked from the worse to the best recovery rate as follows:, Myltenyi-anti-CD45 microbeads (24%); Dynal-anti-EpCAM beads (75%); Dynabeads-anti-CD45 (97%). CTCs isolation from blood samples using the CellSearch and ISET systems revealed that the recovery rate for Cell Search and ISET was 52% and 95%, respectively. CONCLUSIONS: Dynal-anti-CD45 beads have the best recovery rate compared to other magnetic methods. Furthermore the recovery rate of ISET was higher compared to Cell Search, especially for the more aggressive MDA-MB 231 cell line.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".