Expression, distribution and function of kinin B<sub>1</sub>receptor in the rat diabetic retina
Bibliographic record
Abstract
Background and Purpose The kinin B1receptor contributes to vascular inflammation and blood‐retinal barrier breakdown in diabetic retinopathy (DR). We investigated the changes in expression, cellular localization and vascular inflammatory effect of B1receptors in retina of streptozotocin diabetic rats. Experimental Approach The distribution of B1receptors on retinal cell types was investigated by immunocytochemistry. Effects of B1receptor agonist, R‐838, and antagonist, R‐954, on retinal leukocyte adhesion, gene expression of kinin and VEGF systems, B1receptor immunoreactivity, microgliosis and capillary leakage were measured. Effect of B1receptor siRNA on gene expression was also assessed. Key Results mRNA levels of the kinin and VEGF systems were significantly enhanced at 2 weeks in streptozotocin (STZ)‐retina compared to control‐retina and were further increased at 6 weeks. B1receptor mRNA levels remained increased at 6 months. B1receptor immunolabelling was detected in vascular layers of the retina, on glial and ganglion cells. Intravitreal R‐838 amplified B1and B2receptor gene expression, B1receptor levels (immunodetection), leukostasis and vascular permeability at 2 weeks in STZ‐retina. Topical application (eye drops) of R‐954 reversed these increases in B1receptors, leukostasis and vascular permeability. Intravitreal B1receptor siRNA inhibited gene expression of kinin and VEGF systems in STZ‐retina. Microgliosis was unaffected by R‐838 or R‐954 in STZ‐retina. Conclusion and Implications Our results support the detrimental role of B1receptors on endothelial and glial cells in acute and advanced phases of DR. Topical application of the B1receptor antagonist R‐954 seems a feasible therapeutic approach for the treatment of DR.
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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 | 0.001 | 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".