Recent advances in microfluidics-based cell migration research
Bibliographic record
Abstract
Cell migration is crucial for many biological processes, including normal development, immune response, and tissue homeostasis and many pathological processes such as cancer metastasis and wound healing. Microfluidics has revolutionized the research in cell migration since its inception as it reduces the cost of studies and allows precise manipulation of different parameters that affect cell migratory response. Over the past decade, the field has made great strides in many directions, such as techniques for better control of the cellular microenvironment, application-oriented physiological-like models, and machine-assisted cell image analysis methods. Here we review recent developments in the field of microfluidic cell migration through the following aspects: 1) the co-culture models for studying host-pathogen interactions at single-cell resolution; 2) the spatiotemporal manipulation of the chemical gradients guiding cell migration; 3) the organ-on-chip models to study cell transmigration; and 4) the deep learning image processing strategies for cell migration data analysis. We further discuss the challenges, possible improvement and future perspectives of using microfluidic techniques to study cell migration.
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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 | 0.003 | 0.003 |
| 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".