Lowering Blood Pressure Blocks Mesangiolysis and Mesangial Nodules, but Not Tubulointerstitial Injury, in Diabetic eNOS Knockout Mice
Bibliographic record
Abstract
Recently, we and others reported that diabetic endothelial nitric oxide synthase knockout (eNOSKO) mice develop advanced glomerular lesions that include mesangiolysis and nodular lesions. Interestingly, insulin treatment lowered blood pressure and prevented renal lesions, raising the question as to whether these beneficial effects of insulin were due to its ability to lower either high glucose levels or high blood pressure. We, therefore, examined the effect of lowering blood pressure using hydralazine in this diabetic eNOSKO mouse model. Hydralazine treatment significantly blocked the development of mesangiolysis and microaneurysms, whereas tubulointerstitial injury was not prevented in these mice. Additionally, hydralazine did not reduce expression levels of either tubulointerstitial thrombospondin-1 or transforming growth factor-beta despite controlling blood pressure. On the other hand, the critical role of high glucose levels on the development of tubulointerstitial injury was suggested by the observation that serum glucose levels were correlated with tubulointerstitial injury, as well as with the expression levels of both transforming growth factor-beta and thrombospondin-1. Importantly, controlling blood glucose with insulin completely blocked tubulointerstitial injury in diabetic eNOSKO mice. These data suggest that glomerular injury is dependent on systemic blood pressure, whereas hyperglycemia may have a more important role in tubulointerstitial injury, possibly due to the stimulation of the thrombospondin-1-transforming growth factor-beta pathway in diabetic eNOSKO mice. This study could provide insights into the pathogenesis of advanced diabetic nephropathy in the presence of endothelial dysfunction.
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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) | 0.000 | 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 | 0.001 |
| 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".