Effect of <i>N</i>‐acetylcysteine on the early expression of inflammatory markers in the retina and plasma of diabetic rats
Bibliographic record
Abstract
PURPOSE: The aim of this study is to investigate markers of inflammation and oxidative stress in an early model of diabetic retinopathy, correlate retinal and plasma results and evaluate the influence of treatment by N-acetylcysteine (NAC), a free radical scavenger. METHODS: Four groups were studied: control (C), streptozotocin (STZ)-induced diabetic rats (D), STZ rats following 8 weeks of NAC (DT), and control rats following 8 weeks of NAC (CT). Plasma levels of free 15-F2t-isoprostane (15-F-2t-IsoP), superoxide dismutase (SOD) and tumour necrosis factor-alpha (TNF-alpha) were obtained. Primary antibodies against macrophages (ED-1), microglia (Ox-42), pericytes (NG-2), endothelial and perivascular cells (IB-4), haem oxygenase 1 (HO-1) and vascular endothelial growth factor (VEGF) were used. RESULTS: Expression of NG-2 was robust in C, CT, DT, and mild in D. The intensity of IB-4 was higher in D and DT compared with the C and CT. Ox-42 and ED-1 expression was higher in the D than in the DT, C or CT. Expression of VEGF and HO-1 was non-specific across the four groups. Plasma levels of 15-F-2t-IsoP and TNF-alpha were higher in the D as compared with the C, CT and DT. SOD levels were lower in the D when compared with the C, CT and D. CONCLUSIONS: Macrophage/microglia activation, pericyte loss and endothelial/perivascular cell changes occur early in the pathogenesis of DR. These changes are associated with an increase in plasma markers of oxidative stress and inflammation and are minimized by treatment with NAC. The results suggest that therapies that reduce free radicals will help minimize the early events in diabetic retinopathy in the STZ model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".