Timed topical dexamethasone eye drops improve mitochondrial function to prevent severe retinopathy of prematurity
Bibliographic record
Abstract
Pathological neovascularization in retinopathy of prematurity (ROP) can cause visual impairment in preterm infants. Current ROP treatments which are not preventative and only address late neovascular ROP, are costly and can lead to severe complications. We showed that topical 0.1% dexamethasone eye drops administered prior to peak neovessel formation prevented neovascularization in five extremely preterm infants at high risk for ROP and suppressed neovascularization by 30% in mouse oxygen-induced retinopathy (OIR) modeling ROP. In contrast, in OIR, topical dexamethasone treatment before any neovessel formation had limited efficacy in preventing later neovascularization, while treatment after peak neovessel formation had a non-statistically significant trend to exacerbating disease. Optimally timed topical dexamethasone suppression of neovascularization in OIR was associated with increased retinal mitochondrial gene expression and decreased inflammatory marker expression, predominantly found in immune cells. Blocking mitochondrial ATP synthetase reversed the inhibitory effect of dexamethasone on neovascularization in OIR. This study provides new insights into topical steroid effects in retinal neovascularization and into mitochondrial function in phase II ROP, and suggests a simple clinical approach to prevent severe ROP.
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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 | 0.000 |
| 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".