Placental growth factor and its potential role in diabetic retinopathy and other ocular neovascular diseases
Bibliographic record
Abstract
The role of vascular endothelial growth factor (VEGF), including in retinal vascular diseases, has been well studied, and pharmacological blockade of VEGF is the gold standard of treatment for neovascular age-related macular degeneration, retinal vein occlusion and diabetic macular oedema. Placental growth factor (PGF, previously known as PlGF), a homologue of VEGF, is a multifunctional peptide associated with angiogenesis-dependent pathologies in the eye and non-ocular conditions. Animal studies using genetic modification and pharmacological treatment have demonstrated a mechanistic role for PGF in pathological angiogenesis. Inhibition decreases neovascularization and microvascular abnormalities across different models, including oxygen-induced retinopathy, laser-induced choroidal neovascularization and in diabetic mice exhibiting retinopathies. High levels of PGF have been found in the vitreous of patients with diabetic retinopathy. Despite these strong animal data, the exact role of PGF in pathological angiogenesis in retinal vascular diseases remains to be defined, and the benefits of PGF-specific inhibition in humans with retinal neovascular diseases and macular oedema remain controversial. Comparative effectiveness research studies in patients with diabetic retinal disease have shown that treatment that inhibits both VEGF and PGF may provide superior outcomes in certain patients compared with treatment that inhibits only VEGF. This review summarizes current knowledge of PGF, including its relationship to VEGF and its role in pathological angiogenesis in retinal diseases, and identifies some key unanswered questions about PGF that can serve as a pathway for future basic, translational and clinical research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".