MAP kinase mediates silica‐induced fibrotic nodule formation and collagen accumulation in fibroblasts
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it