Comparison of sample preparation methods for rare cell isolation in microfluidic devices
Bibliographic record
Abstract
The analysis of circulating tumor cells (CTCs) is important for cancer diagnosis and prognosis. Microfluidics has been employed for CTC analysis due to its scaling advantages and high performance. However, pre-analytical methods for CTC sample preparation are often combined with microfluidic platforms because a large sample volume is required to detect extremely rare CTCs. Among pre-analytical methods, Ficoll-Paque™, OncoQuick™, and RosetteSep™ are commonly used to separate cells of interest. To compare their performance, we spiked L3.6pl pancreatic cancer cells into healthy blood samples and then employed each technique to prepare blood samples, followed by using a microfluidic platform to capture and detect L3.6pl cells. We found these three methods have similar performance, though the slight edge of RosetteSep™ over Ficoll-Paque™ is statistically significant. We also studied the effects of the tumor cell concentrations on the performance of the frequently used Ficoll-Paque™ method. Furthermore, we examined the repeatability and variability of each pre-analytical technique and the microfluidics-enabled detection. This study will provide researchers and clinicians with comparative data that can influence the choice of sample preparation method, help estimate CTC loss in each pre-analytical method, and correlate the results of clinical studies that employ different techniques.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".