Development of a microfluidic platform for size-based enrichment and immunomagnetic isolation of circulating tumour cells
Bibliographic record
Abstract
Cancer is a leading cause of death worldwide.Efforts to improve the longevity and quality of life of cancer patients are hindered by delays in diagnosis of tumours and treatment deficiency, as well as inaccurate prognosis that leads to unnecessary or inefficient treatments.More accurate biomarkers may address these issues and could facilitate the selection of effective treatment courses and development of new therapeutic regimens.Circulating tumour cells (CTCs), which are cancer cells that are shed from tumours and enter the vasculature, hold such a promise.Therefore, there is much interest in the isolation of CTCs from the blood.However, this is not a trivial task given the extreme scarcity of CTCs in the circulation.In this thesis, the development of a microfluidic immunomagnetic approach for isolation of CTCs is presented.First, the design, microfabrication, and experimental evaluation of a novel integrated microfluidic magnetic chip for sensitive and selective isolation of immunomagnetically labelled cancer cells from blood samples is reported.In general, to ensure the efficient immunomagnetic labelling of target cancer cells in a blood sample, an excessive number of magnetic beads should be added to the sample.When an immunomagnetically labelled sample is processed through the chip, not only cancer cells but also free magnetic beads that are not bonded to any target cells would be captured.The accumulation of these beads could disrupt the capture and visual detection of target cells.This is an inherent drawback associated with immunomagnetic cell separation systems and has rarely been addressed in the past.Therefore, the design, microfabrication, and characterization of a microfluidic filter for continuous size-based removal of free magnetic beads from immunomagnetically labelled blood samples is presented next.Connected in tandem, the two chips developed in this work form a microfluidic platform for size-based enrichment and immunomagnetic isolation of CTCs.Preclinical studies showed that the proposed approach can capture up to 75% of bloodborne prostate cancer cells at clinically-relevant low concentrations (as low as 5 cells/mL) at an acceptable throughput (200 μL/min).The retrieval and successful propagation of captured prostate cancer cells is also investigated and discussed in this thesis.
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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 | 0.001 | 0.000 |
| 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".