Development of a microfluidic platform for size-based hydrodynamic enrichment and PSMA-targeted immunomagnetic isolation of circulating tumour cells in prostate cancer
Bibliographic record
Abstract
Efforts to further improve the clinical management of prostate cancer (PCa) are hindered by delays in diagnosis of tumours and treatment deficiencies, as well as inaccurate prognoses that lead to unnecessary or inefficient treatments. The quantitative and qualitative analysis of circulating tumour cells (CTCs) may address these issues and could facilitate the selection of effective treatment courses and the discovery of new therapeutic targets. Therefore, there is much interest in isolation of elusive CTCs from blood. We introduce a microfluidic platform composed of a multiorifice flow fractionation (MOFF) filter cascaded to an integrated microfluidic magnetic (IMM) chip. The MOFF filter is primarily employed to enrich immunomagnetically labeled blood samples by size-based hydrodynamic removal of free magnetic beads that must originally be added to samples at disproportionately high concentrations to ensure the efficient immunomagnetic labeling of target cancer cells. The IMM chip is then utilized to capture prostate-specific membrane antigen-immunomagnetically labeled cancer cells from enriched samples. Our preclinical studies showed that the proposed method can selectively capture up to 75% of blood-borne PCa cells at clinically-relevant low concentrations (as low as 5 cells/ml), with the IMM chip showing up to 100% magnetic capture capability.
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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".