MAP kinase mediates silica‐induced fibrotic nodule formation and collagen accumulation in fibroblasts
Bibliographic record
Abstract
It is well known that silica generates fibrosis around them in animals and human. However, the pathogenesis and mechanism of silica-induced fibrosis are still poorly understood. Here, we established a new strategy through which the effects of silica on fibrotic nodule formation, key extracellular matrix accumulation, and the mechanism involved were explored. To achieve this, human dermal fibroblasts were directly exposed to silica gel for various durations. Fibrotic nodule formation was evaluated by their microscopic appearance, type-1 procollagen, and fibronection expression in cell lysate and MMP-1 and-3 in conditioned media were analyzed by Western blotting. The results show an easily formation of nodule-like structures around silica gel in an in vitro-cultured system. The findings further revealed that silica gel stimulates collagen and fibronectin expression, while down-regulates matrix metalloproteinase-1 and -3 (MMP-1 and MMP-3) released in conditioned medium. To explore the mechanism involved, P38 and ERK1/2 Mitogen-Activated Protein Kinase (MAPK) signaling pathways were evaluated. Result showed that silica inhibits P38 and extracellular signal-regulated kinases (ERK1/2) MAP kinase phosphorylation. The addition of ERK1/2 inhibitor increases silica-stimulated type-1 collagen expression, reduces MMP-1 release and further enhances silica-induced nodule formation in dermal fibroblasts. These findings indicate that the inhibition of ERK1/2 MAPK signaling pathway may contribute to silica-caused fibrosis. In summary, our findings suggest that silica can directly cause fibrotic phenotype when fibroblasts contact with silica particles independent of any inflammation and other factors may exist in an in vivo condition.
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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".