Angiotensin 1–7 mediates renoprotection against diabetic nephropathy by reducing oxidative stress, inflammation, and lipotoxicity
Bibliographic record
Abstract
The renin-angiotensin system, especially angiotensin II (ANG II), plays a key role in the development and progression of diabetic nephropathy. ANG 1-7 has counteracting effects on ANG II and is known to exert beneficial effects on diabetic nephropathy. We studied the mechanism of ANG 1-7-induced beneficial effects on diabetic nephropathy in db/db mice. We administered ANG 1-7 (0.5 mg·kg(-1)·day(-1)) or saline to 5-mo-old db/db mice for 28 days via implanted micro-osmotic pumps. ANG 1-7 treatment reduced kidney weight and ameliorated mesangial expansion and increased urinary albumin excretion, characteristic features of diabetic nephropathy, in db/db mice. ANG 1-7 decreased renal fibrosis in db/db mice, which correlated with dephosphorylation of the signal transducer and activator of transcription 3 (STAT3) pathway. ANG 1-7 treatment also suppressed the production of reactive oxygen species via attenuation of NADPH oxidase activity and reduced inflammation in perirenal adipose tissue. Furthermore, ANG 1-7 treatment decreased lipid accumulation in db/db kidneys, accompanied by increased expressions of renal adipose triglyceride lipase (ATGL). Alterations in ATGL expression correlated with increased SIRT1 expression and deacetylation of FOXO1. The upregulation of angiotensin-converting enzyme 2 levels in diabetic nephropathy was normalized by ANG 1-7. ANG 1-7 treatment exerts renoprotective effects on diabetic nephropathy, associated with reduction of oxidative stress, inflammation, fibrosis, and lipotoxicity. ANG 1-7 can represent a promising therapy for diabetic nephropathy.
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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 | 0.000 | 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".