Real-time Detection of Breast Cancer Cells Using Peptide-functionalized Microcantilever Arrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it