Microfluidic‐Assisted CTC Isolation and In Situ Monitoring Using Smart Magnetic Microgels
Bibliographic record
Abstract
Capturing rare disease-associated biomarkers from body fluids can offer an early-stage diagnosis of different cancers. Circulating tumor cells (CTCs) are one of the major cancer biomarkers that provide insightful information about the cancer metastasis prognosis and disease progression. The most common clinical solutions for quantifying CTCs rely on the immunomagnetic separation of cells in whole blood. Microfluidic systems that perform magnetic particle separation have reported promising outcomes in this context, however, most of them suffer from limited efficiency due to the low magnetic force generated which is insufficient to trap cells in a defined position within microchannels. In this work, a novel method for making soft micromagnet patterns with optimized geometry and magnetic material is introduced. This technology is integrated into a bilayer microfluidic chip to localize an external magnetic field, consequently enhancing the capture efficiency (CE) of cancer cells labeled with the magnetic nano/hybrid microgels that are developed in the previous work. A combined numerical-experimental strategy is implemented to design the microfluidic device and optimize the capturing efficiency and to maximize the throughput. The proposed design enables high CE and purity of target cells and real-time time on-chip monitoring of their behavior. The strategy introduced in this paper offers a simple and low-cost yet robust opportunity for early-stage diagnosis and monitoring of cancer-associated biomarkers.
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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".