Mast cells promote choroidal neovascularization in a model of age-related macular degeneration
Bibliographic record
Abstract
‘Wet’ age-related macular degeneration (AMD) is characterized by pathologic choroidal neovascularization (CNV) that destroys central vision. Abundant evidence points to inflammation and immune cell dysfunction in the progression of CNV in AMD. Mast cells are resident immune cells that control the inflammatory response. Mast cells accumulate and degranulate in the choroid of patients with AMD, suggesting they play a role in CNV. Activated mast cells secrete various biologically active mediators, including inflammatory cytokines and proteolytic enzymes such as tryptase. We investigated the role of mast cells in AMD using a model of CNV. Conditioned media from activated mast cells exerts proangiogenic effects on choroidal endothelial cells and choroidal explants. Laser-induced CNV in vivo was markedly attenuated in mice genetically depleted of mast cells (Kit W−sh/W−sh ) and in wild-type mice treated with mast cell stabilizer, ketotifen fumarate. Tryptase was found to elicit pronounced choroidal endothelial cell sprouting, migration and tubulogenesis; while tryptase inhibition diminished CNV. Transcriptomic analysis of laser-treated RPE/choroid complex revealed collagen catabolism and extracellular matrix (ECM) reorganization as significant events correlated in clusters of mast cell activation. Consistent with these analyses, compared to wildtype mice choroids of laser-treated mast cell-deficient mice displayed less ECM remodelling evaluated using collagen hybridizing peptide tissue binding. Findings herein provide strong support for mast cells as key players in the progression of pathologic choroidal angiogenesis and as potential therapeutic targets to prevent pathological neovascularization in ‘wet’ 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) | 0.000 | 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 | 0.001 | 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".