Bibliographic record
Abstract
Microscale analysis has facilitated significant progress towards the development of approaches that enable the capture of rare circulating tumor cells (CTCs) from the blood of cancer patients. This is a critical capability for noninvasive tumor profiling. These advances have allowed the capture and enumeration of CTCs with unique sensitivity. However, it has become clear that simply counting tumor cells cannot provide the information that could help to make significant clinical decisions. CTCs are heterogeneous and they can change as they enter the bloodstream. Therefore, profiling of CTCs at single cell level is critical to unraveling the complex and dynamic properties of these potential cancer markers. In this paper we discuss new nanoparticle-enabled microscale technologies for CTC characterization, developed in our laboratory, which profiles CTCs based on their surface expression profile. Validation data presented here show that cancer cells with varying surface expression generate different binning profiles. We then applied the new technologies to reveal the dynamic phenotypes of CTCs in unprocessed blood from animal models. We will also discuss the application of these technologies in analyzing blood samples from cancer patients. While most technologies developed for analyzing CTCs are based on microscopic imaging we have developed and integrated new electrochemical sensors with our CTC capture strategies that enabled us to gather further molecular information on these rare cells.
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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 | 0.000 | 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".