Understanding the Role of Yes-Associated Protein (YAP) Signaling in the Transformation of Lens Epithelial Cells (EMT) and Fibrosis
Bibliographic record
Abstract
Fibrotic cataracts, posterior capsular opacification (PCO), and anterior subcapsular cataracts (ASC) are mainly attributed to the transforming growth factor-β (TGFβ)-induced epithelial-to-mesenchymal transition (EMT) of lens epithelial cells (LECs). Previous investigations from our laboratory have shown the novel role of non-canonical TGFβ signaling in the progression of EMT in LECs. In this study, we have identified YAP as a critical signaling molecule involved in lens fibrosis. The observed increase in nuclear YAP in capsules of human ASC patients points toward the involvement of YAP in lens fibrosis. In addition, the immunohistochemical (IHC) analyses on ocular sections from mice that overexpress TGFβ in the lens (TGFβtg) showed a co-expression of YAP and α-SMA in the fibrotic plaques when compared to wild-type littermate lenses, which do not. The incubation of rat lens explants with verteporfin, a YAP inhibitor, prevented a TGFβ-induced fiber-like phenotype, α-SMA, and fibronectin expression, as well as delocalization of E-cadherin and β-catenin. Finally, LECs co-incubated with TGFβ and YAP inhibitor did not exhibit an induction in matrix metalloproteinase 2 compared to those LECs treated with TGFβ alone. In conclusion, these data demonstrate that YAP is required for TGFβ-mediated lens EMT and 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".