Red Light‐Triggered Anti‐Angiogenic and Photodynamic Combination Therapy of Age‐Related Macular Degeneration
Bibliographic record
Abstract
Choroidal neovascularization (CNV) is the key pathological event of wet age-related macular degeneration (wAMD) leading to irreversible vision loss. Currently, anti-angiogenic therapy with anti-vascular endothelial growth factor (VEGF) agents has become the standard treatment for wAMD, while it is still subject to several limitations, including the safety concerns of monthly intravitreal administration and insufficient efficacy for neovascular occlusion. Combined therapy with photodynamic therapy (PDT) and anti-angiogenic agents has emerged as a novel treatment paradigm. Herein, a novel and less-invasive approach is reported to achieve anti-angiogenic and photodynamic combination therapy of wAMD by intravenous administration of a photoactivatable nanosystem (Di-DAS-VER NPs). The nanosystem is self-assembled by reactive oxygen species (ROS)-sensitive dasatinib (DAS) prodrug and photosensitizer verteporfin (VER). After red-light irradiation to the diseased eyes, intraocular release of anti-angiogenic DAS is observed, together with selective neo-vessels occlusion by VER-generated ROS. Notably, Di-DAS-VER NPs demonstrates promising therapeutic efficacy against CNV with minimized systemic toxicity. The study enables an efficient intravenous wAMD therapy by integrating a photoactivation process with combinational therapeutics into one simple nanosystem.
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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".