Silibinin inhibits myofibroblast transdifferentiation in human tenon fibroblasts and reduces fibrosis in a rabbit trabeculectomy model
Bibliographic record
Abstract
PURPOSE: To investigate the effect of silibinin in myofibroblast transdifferentiation and in animal trabeculectomy models. METHODS: The effect of silibinin on the expression of α-smooth muscle actin (α-SMA) and vimentin in response to transforming growth factor-β1 (TGF-β1) was determined in human tenon fibroblasts (HTFs). Cell migration and collagen contraction arrays were used to demonstrate the functionality of silibinin-modulated HTFs. ELISA analysis was used to determine the effect of silibinin on the release of type 1 collagen and connective tissue growth factor (CTGF). The effect of silibinin on the activation of the TGF-β receptor-related pathway was evaluated by Western blotting. A rabbit model of trabeculectomy was established to assess the effect of silibinin in vivo. RESULTS: TGF-β1 elevated the expression of α-SMA and vimentin in HTFs; this elevation was inhibited by silibinin. TGF-β1 increased cell migration and collagen contraction of HTFs, which were also suppressed by silibinin. The production of both CTGF and type 1 collagen in TGF-β1-treated HTFs was inhibited by silibinin. The effects of silibinin on TGF-β1-stimulated HTFs were mediated via the down-regulation of TGF-β receptor-related SMAD signalling pathways. In the rabbit model of trabeculectomy, silibinin increased the period of decreasing intraocular pressure after trabeculectomy and reduced the production of collagen and α-SMA at the site of blebs in vivo. CONCLUSION: Silibinin inhibited the TGF-β receptor-related signalling pathway in TGF-β-treated HTFs and several of the downstream events associated with myofibroblast transdifferentiation. Silibinin also improved the outcome of trabeculectomies by reducing the fibrotic response in the bleb tissue in vivo.
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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 | 0.000 |
| 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".