A review of microfluidic approaches for investigating cancer extravasation during metastasis
Bibliographic record
Abstract
Abstract Metastases, or migration of cancers, are common and severe cancer complications. Although the 5-year survival rates of primary tumors have greatly improved, those of metastasis remain below 30%, highlighting the importance of investigating specific mechanisms and therapeutic approaches for metastasis. Microfluidic devices have emerged as a powerful platform for drug target identification and drug response screening and allow incorporation of complex interactions in the metastatic microenvironment as well as manipulation of individual factors. In this work, we review microfluidic devices that have been developed to study cancer cell migration and extravasation in response to mechanical (section ‘Microfluidic investigation of mechanical factors in cancer cell migration’), biochemical (section ‘Microfluidic investigation of biochemical signals in cancer cell invasion’), and cellular (section ‘Microfluidic metastasis-on-a-chip models for investigation of cancer extravasation’) signals. We highlight the device characteristics, discuss the discoveries enabled by these devices, and offer perspectives on future directions for microfluidic investigations of cancer metastasis, with the ultimate aim of identifying the essential factors for a ‘metastasis-on-a-chip’ platform to pursue more efficacious treatment approaches for cancer metastasis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".