Angiotensin-(1–7) prevents systemic hypertension, attenuates oxidative stress and tubulointerstitial fibrosis, and normalizes renal angiotensin-converting enzyme 2 and Mas receptor expression in diabetic mice
Bibliographic record
Abstract
We investigated the relationship between Ang-(1-7) [angiotensin-(1-7)] action, sHTN (systolic hypertension), oxidative stress, kidney injury, ACE2 (angiotensin-converting enzyme-2) and MasR [Ang-(1-7) receptor] expression in Type 1 diabetic Akita mice. Ang-(1-7) was administered daily [500 μg/kg of BW (body weight) per day, subcutaneously] to male Akita mice from 14 weeks of age with or without co-administration of an antagonist of the MasR, A779 (10 mg/kg of BW per day). The animals were killed at 20 weeks of age. Age-matched WT (wild-type) mice served as controls. Ang-(1-7) administration prevented sHTN and attenuated kidney injury (reduced urinary albumin/creatinine ratio, glomerular hyperfiltration, renal hypertrophy and fibrosis, and tubular apoptosis) without affecting blood glucose levels in Akita mice. Ang-(1-7) also attenuated renal oxidative stress and the expression of oxidative stress-inducible proteins (NADPH oxidase 4, nuclear factor erythroid 2-related factor 2, haem oxygenase 1), pro-hypertensive proteins (angiotensinogen, angiotensin-converting enzyme, sodium/hydrogen exchanger 3) and profibrotic proteins (transforming growth factor-β1 and collagen IV), and increased the expression of anti-hypertensive proteins (ACE2 and MasR) in Akita mouse kidneys. These effects were reversed by A779. Our data suggest that Ang-(1-7) plays a protective role in sHTN and RPTC (renal proximal tubular cell) injury in diabetes, at least in part, through decreasing renal oxidative stress-mediated signalling and normalizing ACE2 and MasR expression.
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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) | 0.001 | 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".