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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".