AFM-compatible microfluidic platform for affinity-based capture and nanomechanical characterization of circulating tumor cells
Bibliographic record
Abstract
Circulating tumor cells (CTCs) carried by the patient's bloodstream are known to lead to the metastatic spread of cancer. It is becoming increasingly clear that an understanding of the nanomechanical characteristics of CTCs, such as elasticity and adhesiveness, represents advancements in tracking and monitoring cancer progression and metastasis. In the present work, we describe a combined microfluidic-atomic force microscopy (AFM) platform that uses antibody-antigen capture to routinely isolate and nanomechanically characterize CTCs present in blood samples from prostate cancer patients. We introduce the reversible assembly of a microfluidic device and apply refined and robust chemistry to covalently bond antibodies onto its glass substrate with high density and the desired orientation. As a result, we show that the device can efficiently capture CTCs from patients with localized and metastatic prostate cancer through anti-EpCAM, anti-PSA, and anti-PSMA antibodies, and it is suitable for AFM measurements of captured intact CTCs. When nanomechanically characterized, CTCs originating from metastatic cancer demonstrate decreased elasticity and increased deformability compared to those originating from localized cancer. While the average adhesion of CTCs to the AFM tip surface remained the same in both the groups, there were fewer multiple adhesion events in metastatic CTCs than there were in their counterparts. The developed platform is simple, robust, and reliable and can be useful in the diagnosis and prognosis of prostate cancer as well as other forms of cancer.
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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 | 0.000 | 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".