Lipoxins A4 and B4 inhibit glial cell activation via CXCR3 signaling in acute retinal neuroinflammation
Bibliographic record
Abstract
) have protective activities against neurodegenerative injury. Yet, lipoxin activities and downstream signaling in neuroinflammatory processes are not well understood. Here, we utilized a model of posterior uveitis induced by lipopolysaccharide endotoxin (LPS), which results in rapid retinal neuroinflammation primarily characterized by activation of resident macroglia (astrocytes and Müller glia), and microglia. Using this model, we observed that each lipoxin reduces acute inner retinal inflammation by affecting endogenous glial responses in a cascading sequence beginning with astrocytes and then microglia, depending on the timing of exposure; prophylactic or therapeutic. Subsequent analyses of retinal cytokines and chemokines revealed inhibition of both CXCL9 (MIG) and CXCL10 (IP10) by each lipoxin, compared to controls, following LPS injection. CXCL9 and CXCL10 are common ligands for the CXCR3 chemokine receptor, which is prominently expressed in inner retinal astrocytes and ganglion cells. We found that CXCR3 inhibition reduces LPS-induced neuroinflammation, while CXCR3 agonism alone induces astrocyte reactivity. Together, these data uncover a novel lipoxin-CXCR3 pathway to promote distinct anti-inflammatory and proresolution cascades in endogenous retinal glia.
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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) | 0.002 | 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".