Novel Anti-Interleukin-1β Therapy Preserves Retinal Integrity: A Longitudinal Investigation Using OCT Imaging and Automated Retinal Segmentation in Small Rodents
Bibliographic record
Abstract
Retinopathy of prematurity (ROP) is the leading cause of blindness in neonates. Inflammation, in particular Interleukin-1β (IL-1β), is increased in early stages of the disorder, and contributes to inner and outer retinal vasoobliteration in the Oxygen-Induced Retinopathy (OIR) model of ROP. A small peptide antagonist of IL-1 receptor, composed of the amino acid sequence, rytvela, has been shown to exert beneficial anti-inflammatory effects without compromising immunovigilance-related NF-κB in reproductive tissues. We conducted a longitudinal study to determine the efficacy of ‘rytvela’ in preserving the integrity of the retina in OIR model, using optical coherence tomography (OCT) which provides high-resolution cross-sectional imaging of ocular structures in vivo. Sprague-Dawley rats subjected to OIR and treated or not with ‘rytvela’ were compared to IL-1 receptor antagonist (Kineret). OCT imaging and custom automated segmentation algorithm used to measure retinal thickness were obtained at P14 and P30; gold-standard immunohistochemistry (IHC) was used to confirm retinal anatomical changes. OCT revealed significant retinal thinning in untreated animals by P30, confirmed by IHC; these changes were coherently associated with increased apoptosis. Both rytvela and Kineret subsided apoptosis and preserved retinal thickness. As anticipated, Kineret diminished both SAPK/JNK and NF-κB axes, whereas rytvela selectively abated the former which resulted in preserved monocyte phagocytic function. Altogether, OCT imaging with automated segmentation is a reliable non-invasive approach to study longitudinally retinal pathology in small animal models of retinopathy.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".