Microfluidic Devices for Studying the Effect of Netrin‐1 on Neutrophil and Breast Cancer Cell Migration
Bibliographic record
Abstract
Abstract Netrin‐1 is well‐known for its chemoattractive and chemorepulsive properties for axon guidance. Early studies report that netrin‐1 inhibits granulocyte migration. On the other hand, netrin‐1 can promote cancer cell migration and invasion. The underlying mechanisms are not well understood, which requires more in‐depth characterizations of netrin‐1 mediated immune and cancer cell migration. The present study, for the first time, employs microfluidic devices that are recently developed to quantitatively investigate the effects of netrin‐1 on the motility and chemotaxis of human blood neutrophils and human breast cancer cells under well‐controlled gradient conditions. The results show that netrin‐1 reduces chemokinetic motility of human neutrophils, which is accompanied with reduced cell polarization and spreading. In addition, netrin‐1 reduces neutrophil chemotaxis to N‐formyl‐Met‐Leu‐Phe on fibronectin substrate but interestingly not on collagen substrate. By contrast, netrin‐1 promotes the migration of human breast cancer cells. Furthermore, it is found that netrin‐1 reduces neutrophil chemotaxis to the supernatant of human breast cancer cell culture. Collectively, this microfluidic cell migration study provides quantitative characterizations of the effects of netrin‐1 on the motility and chemotaxis of neutrophils and breast cancer cells, and further suggests the potential role of netrin‐1 in regulating neutrophil recruitment to breast cancer microenvironments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".