Effect of anti-TGF-β <sub>2</sub> surface modification of polydimethylsiloxane on lens epithelial cell markers of posterior capsule opacification
Bibliographic record
Abstract
Posterior capsule opacification is the most common complication of cataract surgery. Lens epithelial cells remaining in the capsular bag following surgery can undergo epithelial-to-mesenchymal transition and migrate from the anterior to the posterior capsule, leading to fibrosis, capsular wrinkling, and ultimately vision loss. Transforming growth factor-beta 2 has been shown to play a major role in epithelial-to-mesenchymal transition. Covalent tethering of anti-transforming growth factor-beta 2 to the surface of the intraocular lens material may inhibit epithelial-to-mesenchymal transition and the subsequent events, thus leading to a reduction in posterior capsule opacification. In this work, the antibody was tethered to the surface of polydimethylsiloxane as a model lens material via a poly(ethylene) glycol spacer. Surface characterization using a variety of methods demonstrated successful modification. The surface density of the anti-transforming growth factor-beta 2 was approximately 0.5 µg/cm 2 . The presence of transforming growth factor-beta 2 in cell culture medium stimulated production of extracellular matrix components such as collagen, fibronectin, laminin, and the fibrotic marker α-smooth muscle actin, by HLE-B3 cells. These effects were decreased but not completely eradicated by the presence of the anti-transforming growth factor-beta 2 antibody on the polydimethylsiloxane surface. These results suggest that surface modification with appropriate antifibrotic molecules has the potential to modulate cellular changes following cataract surgery and lead to a reduction in posterior capsule opacification.
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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".