Collecting and deactivating TGF-β1 hydrogel for anti-scarring therapy in post-glaucoma filtration surgery
Why this work is in the frame
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Bibliographic record
Abstract
Scar formation can lead to glaucoma filtration surgery (GFS) failure, wherein transforming growth factor (TGF)-β is the core regulator. To reducing scar formation, this paper presents our study on the design of hydrogels to deactivate TGF-β1. We hypothesized that excess TGF-β1 can be removed from aqueous humor through the addition of oxidized hyaluronic acid (O-HA) hydrogels that are seeded with decorin (O-HA + D). Immunohistochemistry and enzyme-linked immunosorbent assay (ELISA) were performed to demonstrate the adsorption properties of O-HA + D hydrogel, thus reducing the TGF-β1 concentration in aqueous humor. In the light that collagen contraction in human Tenon's capsule fibroblasts (HTFs) and the angiogenesis of human umbilical vein endothelial cells (HUVECs) can be activated by TGF-β1 and β2, we performed the quantitative analysis of polymerase chain reaction to determine the effect of O-HA + D on the type I collagen, fibronectin, and angiogenesis. Our results illustrate that O-HA + D can inhibit the increase of α-SMA expression in HTF induced by TGF-β1 and that O-HA + D can inhibit the production of collagen I and fibronectin in HTF treated with TGF-β1. Furthermore, we performed in vivo studies by employing a rabbit model, where rabbits were treated with hydrogels post GFS. Our results demonstrate that, as compared with other groups, the rabbits treated with O-HA + D had the greatest reduction in inflammatory cells with reduced level of collagen in wounds. Taken together, the present study paves the way toward the treatment of post-glaucoma fibrosis following surgery.
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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 | 0.001 | 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