Bibliographic record
Abstract
Pathological retinal neovascularization is the hallmark of primary blinding diseases across all age groups, yet surprisingly little is known about the causative factors. These diseases include diabetic retinopathy and retinopathy of prematurity where progressive decay of retinal vasculature yields zones of neural ischemia. These avascular zones and the hypoxic neurons and glia that reside in them are the source of pro-angiogenic factors that mediate destructive pre-retinal angiogenesis. Central neurons such as retinal ganglion cells (RGCs), which are directly apposed to degenerating vasculature in ischemic retinopathies, require stable metabolic supply for proper function. However, we unexpectedly found that RGCs are resilient to hypoxia/ischemia and a generally compromised metabolic supply and instead of degenerating, trigger protective mechanisms of cellular senescence. Paradoxically, while potentially favoring neuronal survival, the senescent state of RGCs is incompatible with vascular repair as they adopt a senescence-associated secretory phenotype (SASP) that provokes release of a secretome of inflammatory cytokines that drives paracrine senescence and further exacerbates pathological angiogenesis. The mechanisms that lead to retinal cellular senescence and dormancy as well as the therapeutic potential of targeting these pathways will be discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.014 |
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".