Chronic COX Inhibition Reduces Diabetes-Induced Hyperfiltration, Proteinuria, and Renal Pathological Markers in 36-Week B6-Ins2<sup>Akita</sup> Mice
Bibliographic record
Abstract
BACKGROUND/AIMS: The widespread use of non-steroidal anti-inflammatory drugs (NSAIDs) and the sizeable impact of diabetes on the development of end-stage renal disease substantiate the need to determine their effect on the evolution of diabetic nephropathy (DN). We hypothesized that chronic ibuprofen and NS-398 will differentially affect many aspects of DN in B6-Ins2(Akita) mice, including glomerular filtration rate (GFR), growth, and fibrosis. METHODS: B6-Ins2(Akita) and wild-type mice were separated into six groups. At 20 weeks of age, NSAIDs (1 mg/kg/day) were added to the drinking water daily. At 36 weeks indicators of renal function and DN were examined. RESULTS: Urinary PGE(2), PGEM, TXB(2), and 6-keto-PGF(1)alpha were increased in diabetics, and reduced by NSAIDs. Regional differences in cyclooxygenases were observed. Diabetics displayed hyperglycemia, albuminuria, and increased kidney/body weights, glomerular diameters, and FITC-inulin clearance. NSAIDs did not affect growth, but albuminuria and FITC-inulin clearance were reduced. Also, p27 and fibronectin were increased in diabetics and attenuated by ibuprofen. CONCLUSION: NSAIDs reduced diabetic change: GFR, albuminuria, p27, and fibronectin. The effects of ibuprofen are similar if not more beneficial than COX-2 inhibition by NS-398. This study has clinical relevance for diabetics prior to overt nephropathy. Future studies should reveal the effects of NSAIDs in a more severe disease environment.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".