The cysteinyl leukotriene 2 receptor mediates retinal edema and pathological neovascularization in a murine model of oxygen‐induced retinopathy
Bibliographic record
Abstract
Leukotrienes have been implicated in the pathogenesis of degenerative diabetic retinopathy, with research focusing primarily on leukotriene B(4), with little attention devoted to the cysteinyl leukotrienes (cysLTs), which act through cysLT receptors (CysLT(1)R and CysLT(2)R). We demonstrate here the presence of CysLT(2)R in pericytes and endothelial cells of superficial retinal vasculature using an indirect assay by assessment of β-galactosidase expression in CysLT(2)R-knockout (KO) mice. Retinal damage was induced in KO and wild-type (WT) mice using an established oxygen-induced retinopathy (OIR) model. CysLT(2)R expression following OIR was intensely up-regulated compared to sham-treated controls. Staining with Griffonia simplicifolia lectin revealed enhanced tissue damage (as assessed by vasoobliteration/vasoproliferation) in KO mice compared to WT controls, yet the opposite was true with respect to retinal edema. However, vascular endothelial growth factor receptor 1 (VEGFR1) transcripts were increased by OIR similarly with respect to genotype. Intravitreal application of exogenous cysLTs elicited greater vasculature leakage (assessed ex vivo) in eyes from WT mice compared to KO mice. While mRNA encoding enzymes for various components of the leukotriene cascade were detected in sham- and OIR-treated retinas, only prostaglandins and hydroxyeicosatetraenoic acids, but not leukotrienes, were detected in A23187-treated retina preparations. Together, these results implicate the CysLT(2)R in the progression of ischemic retinopathy.
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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".