The effects of anti‐VEGF and kinin B<sub>1</sub> receptor blockade on retinal inflammation in laser‐induced choroidal neovascularization
Bibliographic record
Abstract
Background and Purpose Age‐related macular degeneration (AMD) is a complex neurodegenerative disease treated by anti‐VEGF intravitreal injections. As inflammation is potentially involved in retinal degeneration, the pro‐inflammatory kallikrein–kinin system is a possible alternative pharmacological target. Here, we investigated the effects of anti‐VEGF and anti‐B1 receptor treatments on the inflammatory mechanisms in a rat model of choroidal neovascularization (CNV). Experimental Approach Immediately after laser‐induced CNV, Long–Evans rats were treated by eye‐drop application of a B1 receptor antagonist (R‐954) or by intravitreal injection of B1 receptor siRNA or anti‐VEGF antibodies. Effects of treatments on gene expression of inflammatory mediators, CNV lesion regression and integrity of the blood‐retinal barrier was measured 10 days later in the retina. B1 receptor and VEGF‐R2 cellular localization was assessed. Key Results The three treatments significantly inhibited the CNV‐induced retinal changes. Anti‐VEGF and R‐954 decreased CNV‐induced up‐regulation of B1 and B2 receptors, TNF‐α, and ICAM‐1. Anti‐VEGF additionally reversed up‐regulation of VEGF‐A, VEGF‐R2, HIF‐1α, CCL2 and VCAM‐1, whereas R‐954 inhibited gene expression of IL‐1β and COX‐2. Enhanced retinal vascular permeability was abolished by anti‐VEGF and reduced by R‐954 and B1 receptor siRNA treatments. Leukocyte adhesion was impaired by anti‐VEGF and B1 receptor inhibition. B1 receptors were found on astrocytes and endothelial cells. Conclusion and Implications B1 receptor and VEGF pathways were both involved in retinal inflammation and damage in laser‐induced CNV. The non‐invasive, self‐administration of B1 receptor antagonists on the surface of the cornea by eye drops might be an important asset for the treatment of AMD.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".