Activin A is a novel chemoattractant for migration of microglial BV2 cells
Bibliographic record
Abstract
BACKGROUND: Microglia are involved in many neurodegenerative diseases and repairment of traumatic injury to the CNS. Activin A is a neurotrophic and neuroprotective factor that can regulate the activities of macrophages/microglia. However, the effects of activin A on the migration of microglia are still unclear. In this study, the role of activin A in regulation of the microglia migration was investigated with the murine microglial BV2 cell. METHODS: The levels of cytokines were detected by enzyme-linked immunosorbent assay (ELISA). The protein expression was examined by Western blotting. The adhesion of BV2 cells was assayed by real-time cell analysis (RTCA). The migration of BV2 cells was determined by transwell chamber and microfluidics device. Smad3 was overexpressed or knocked down in BV2 cells by transfection of Smad3 or Smad3 shRNA-expressing plasmids. RESULTS: Activin A inhibited the release of nitric oxide (NO) and inflammatory cytokines of TNF-α and IL-6 and the expression of TNF-α and IL-6 mRNA by BV2 cells. In contrast, activin A promoted the production of TGF-β1. Activin A inhibited adhesion, promoted wound healing and migration which is related to the expression of N-cadherin and E-cadherin expression. Additionally, Smad3 overexpression in BV2 cells decreased the levels of TNF-α and IL-6, and promoted the wound healing, whereas Smad3 knockdown showed the opposite effects. CONCLUSIONS: These findings revealed that activin A regulated the biological behavior of BV2 cells via Smad3 signaling, suggesting that activin A may serve as a potential treatment target for neuroinflammation and glia scar formation in nervous system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".