Real-time Detection of Breast Cancer Cells Using Peptide-functionalized Microcantilever Arrays
Bibliographic record
Abstract
Ligand-directed targeting and capturing of cancer cells is a new approach for detecting circulating tumor cells (CTCs). Ligands such as antibodies have been successfully used for capturing cancer cells and an antibody based system (CellSearch(®)) is currently used clinically to enumerate CTCs. Here we report the use of a peptide moiety in conjunction with a microcantilever array system to selectively detect CTCs resulting from cancer, specifically breast cancer. A sensing microcantilever, functionalized with a breast cancer specific peptide 18-4 (WxEAAYQrFL), showed significant deflection on cancer cell (MCF7 and MDA-MB-231) binding compared to when exposed to noncancerous (MCF10A and HUVEC) cells. The peptide-functionalized microcantilever allowed efficient capture and detection of cancer cells in MCF7 spiked human blood samples emulating CTCs in human blood. A detection limit of 50-100 cancer cells mL(-1) from blood samples was achieved with a capture yield of 80% from spiked whole blood samples. The results emphasize the potential of peptide 18-4 as a novel peptide for capturing and detecting cancer cells in conjunction with nanomechanical cantilever platform. The reported peptide-based cantilever platform represents a new analytical approach that can lead to an alternative to the various detection platforms and can be leveraged to further study CTCs.
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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 | 0.001 | 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".