Assessing Cancer Cell Migration and Metastatic Growth In Vivo in the Chick Embryo Using Fluorescence Intravital Imaging
Bibliographic record
Abstract
Cell migration and metastasis are key features of aggressive tumors. These processes can be difficult to study, as they often occur deep within the body of a cancer patient or an experimental animal. In vitro assays are able to model some aspects of these processes, and a number of assays have been developed to assess cancer cell motility, migration, and invasion. However, in vitro assays have inherent limitations that may miss important aspects of these processes as they occur in vivo. The chick embryo provides a powerful model for studying these processes in vivo, facilitated by the external and accessible nature of the chorioallantoic membrane (CAM), a well-vascularized tissue that surrounds the embryo. When coupled with multiple fluorescent approaches to labeling both cancer cells and the embryonic vasculature, along with image analysis tools, the chick CAM model offers cost-effective, rapid assays for studying cancer cell migration and metastasis in a physiologically-relevant, in vivo setting. Here, we present recent developments of detailed procedures for using shell-less chick embryos, coupled with fluorescent labeling of cancer cells and/or chick vasculature, to study cancer cell migration and metastasis in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".