Combination of Mechanical and Molecular Filtration for Enhanced Enrichment of Circulating Tumor Cells
Bibliographic record
Abstract
Circulating tumor cells (CTCs) have been linked to cancer progression but are difficult to isolate, as they are very rare and heterogeneous, covering a range of sizes and expressing different molecular receptors. Filtration has emerged as a simple and powerful method to enrich CTCs but only captures cells above a certain size regardless of molecular characteristics. Here, we introduce antibody-functionalized microfilters to isolate CTCs based on both size and surface receptor expression. We present a 3D printed filtration cartridge with microfabricated polymer filters with 8, 10, 12, 15, or 20 μm-diameter pores. Pristine filters were used to optimize sample dilution, rinsing protocol, flow rate, and pore size, leading to >80% for the recovery of spiked cancer cells with very low white blood cell contamination (<1000). Then, filters were functionalized with antibodies against either epithelial cell adhesion molecule (EpCAM) or epidermal growth factor receptor (EGFR) and the cartridges were used to enrich breast (MDA-MB-231, MCF-7) and renal (786-O, A-498) cancer cells expressing various levels of EpCAM and EGFR. Cancer cells were spiked into human blood, and when using filters with antibodies specific to a molecular receptor expressed on a cell, efficiency was increased to >96%. These results suggest that filtration can be optimized to target specific CTC characteristics such as size and receptor expression and that a diverse range of CTCs may be captured using particular combinations of pore size, filtration parameters, and antibody functionalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".