Vascular Regeneration for Ischemic Retinopathies: Hope from Cell Therapies
Bibliographic record
Abstract
Retinal vascular diseases, such as diabetic retinopathy, retinopathy of prematurity, retinal vein occlusion, ocular ischemic syndrome and ischemic optic neuropathy, are leading causes of vision impairment and blindness. Whilst drug, laser or surgery-based treatments for the late stage complications of many of these diseases are available, interventions that target the early vasodegenerative stages are lacking. Progressive vasculopathy and ensuing ischemia is an underpinning pathology in many of these diseases, leading to hypoperfusion, hypoxia, and ultimately pathological neovascularization and/or edema in the retina and other ocular tissues, such as the optic nerve and iris. Therefore, repairing the retinal vasculature may prevent progression of ischemic retinopathies into late stage vascular complications. Various cell types have been explored for their vascular repair potential. Endothelial progenitor cells, mesenchymal stem cells and induced pluripotent stem cells are studied for their potential to integrate with the damaged retinal vasculature and limit ischemic injury. Clinical trials for some of these cell types have confirmed safety and feasibility in the treatment of ischemic diseases, including some retinopathies. Another promising avenue is mobilization of endogenous endothelial progenitors, whereby reparative cells are moved from their niche to circulating blood to target and home into ischemic tissues. Several aspects and properties of these cell types have yet to be elucidated. Nevertheless, we foresee that cell therapy, whether through delivery of exogenous or enhancement of endogenous reparative cells, will become a valuable and beneficial treatment for ischemic retinopathies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".