La rétinopathie du prématuré
Bibliographic record
Abstract
High oxygen tension is a major factor in the genesis of retinopathy of prematurity (ROP). However, clinical and experimental evidence also suggest a significant role for high levels of carbon dioxide (CO(2)). Hypercapnia is a facilitator of nitration in vitro, and nitrative stress is known to have an important role in microvascular degeneration leading to ischemia in conditions such as ROP. We hereby present evidence that prolonged exposure to CO(2) impairs developmental retinal neovascularisation through a mechanism involving increased endothelial nitric oxide synthase and induction of a nitrative stress; effects of hypercapnia are independent of its hyperaemic effects. Moreover, in a model of oxygen-induced retinopathy, we demonstrate that an in vivo nitrative stress associated with retinal vasoobliteration results in nitration of cis-arachidonic acids into trans-arachidonic acids (TAAs). TAAs act in turn as mediators of nitrative stress by causing microvascular degeneration by inducing expression of the anti-angiogenic factor thrombospondin-1. These recent findings establish a previously unexplored means by which hypercapnia hinders efficient neovascularisation and provide new insight into the molecular mechanisms of nitrative stress on microvascular injury involving TAA, therefore opening new therapeutic avenues in the management of nitrative stress disorders such as in ischemic retinopathies (of prematurity and of diabetes) and encephalopathies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".