Retinal Ischemia: Therapeutic Effects and Mechanisms of Paeoniflorin
Bibliographic record
Abstract
Retinal ischemia is a key factor in the progression of vision-threatening ocular diseases, including central retinal artery/vein occlusion, exudative age-related macular degeneration (eAMD), and proliferative diabetic retinopathy. This study investigates the effects of paeoniflorin along with its related neuroprotective molecular pathways in the treatment of retinal ischemia. Free radical or ischemic-like damage was induced by incubating retinal pigment epithelium (RPE) cells for 24 h with 1 mM hydrogen peroxide (H2O2) or by subjecting retinal neuronal cells to 8 h of oxygen–glucose deprivation (OGD). Both treatments caused significant cell loss. Treatment with paeoniflorin significantly increased cell viability at 0.5 mM in both cell types. In a Wistar rat model of retinal ischemia and reperfusion (I/R) elicited by sustained high intraocular pressure (HIOP), pre-treatment with 0.5 mM paeoniflorin mitigated the ischemia-induced decline in ERG b-wave amplitude, reduction in whole and inner retinal thickness, loss of fluorogold-labeled retinal ganglion cells, and formation of apoptotic cells. Meanwhile, paeoniflorin effectively downregulated pro-neovascular mediators β-catenin, hypoxia-inducible factor 1-alpha (HIF-1α), vascular endothelial growth factor (VEGF), and the pro-inflammatory/angiogenic biomarker angiopoietin-2 (Ang-2), producing effects similar to the Wnt/β-catenin inhibitor (dickkopf-related protein 1), anti-angiogenic pigment epithelium-derived factor (PEDF), and anti-VEGF Avastin (bevacizumab). These findings suggest that paeoniflorin may protect against retinal ischemia through its anti-inflammatory, anti-neovascular/angiogenic, antioxidative, and neuroprotective properties.
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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 | 0.001 |
| 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".