Cancer Metastasis‐on‐a‐Chip for Modeling Metastatic Cascade and Drug Screening
Bibliographic record
Abstract
Microfluidic chips are valuable tools for studying intricate cellular and cell-microenvironment interactions. Traditional in vitro cancer models lack accuracy in mimicking the complexities of in vivo tumor microenvironment. However, cancer-metastasis-on-a-chip (CMoC) models combine the advantages of 3D cultures and microfluidic technology, serving as powerful platforms for exploring cancer mechanisms and facilitating drug screening. These chips are able to compartmentalize the metastatic cascade, deepening the understanding of its underlying mechanisms. This article provides an overview of current CMoC models, focusing on distinctive models that simulate invasion, intravasation, circulation, extravasation, and colonization, and their applications in drug screening. Furthermore, challenges faced by CMoC and microfluidic technologies are discussed, while exploring promising future directions in cancer research. The ongoing development and integration of these models into cancer studies are expected to drive transformative advancements in the field.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".