Activin A as a Novel Chemokine Induces Migration of L929 Fibroblasts by ERK Signaling in Microfluidic Devices
Bibliographic record
Abstract
Activin A, a member of the transforming growth factor-beta (TGF-β) superfamily, contributes to tissue healing and fibrosis. As the innate tissue cells, fibroblasts also play an important role in wound healing and fibrosis. Herein, this study was aimed to investigate how activin A exhibited regulatory effects on adhesion and migration of fibroblasts. We found that activin A induced the migration of fibroblast cell line L929 cells in transwell chamber and microfluidic device. Activin A also promoted L929 cells adhesion, but did not affect L929 cells viability or proliferation. In addition, activin A induced α-SMA expression and TGF-β1 release, which were factors closely related to tissue fibrosis, but had no effect on IL-6 production, a pro-inflammatory cytokine. Furthermore, activin A elevated calcium levels in L929 cells and increased p-ERK protein levels. Activin A-induced migration of L929 cells was attenuated by ERK inhibitor FR180204. To conclude, these data indicated that activin A as a novel chemokine induced the chemotactic migration of L929 cells via ERK signaling and possessed the pro-fibrosis role. These findings provide a new insight into understanding of activin A in tissue fibrosis.
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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".