Angiotensin-(1-7): A Novel Peptide to Treat Hypertension and Nephropathy in Diabetes?
Bibliographic record
Abstract
The renin-angiotensin system (RAS) plays a pivotal role in mammalian homeostasis physiology. The RAS can be delineated into a classical RAS (the pressor arm) including angiotensinogen (Agt), renin, angiotensin-converting enzyme (ACE), angiotensin II (Ang II) and angiotensin type 1 receptor (AT1R), and a counterbalancing novel RAS (the depressor arm) including Agt, renin, angiotensin-converting enzyme-2 (ACE-2), angiotensin-(1-7) (Ang 1-7) and Ang 1-7 receptor (or Mas receptor (MasR)). Hyperglycemia (diabetes) induces severe tissue oxidative stress, which stimulates the pressor arm of the renal RAS axis and leads to an increase in ACE/ACE-2 ratio, with excessive formation of Ang II. There is a growing body of evidence for beneficial effects of the depressor arm of RAS (ACE-2/Ang 1-7/MasR) axis in diabetes, hypertension and several other diseased conditions. Evidence from in vitro, in vivo and clinical studies reflects anti-oxidant, anti-fibrotic, and anti-inflammatory properties of Ang 1-7. Most of the currently available therapies only target suppression of the pressor arm of RAS with angiotensin receptor blockers (ARBs) and ACE inhibitors (ACEi). However, it is time to consider simultaneous activation of the depressor arm for more effective outcomes. This review summarizes the recent updates on the protective role of Ang 1-7 in hypertension and kidney injury in diabetes, as well as the possible underlying mechanism(s) of Ang 1-7 action, suggesting that the ACE-2/Ang 1-7/MasR axis can be developed as a therapeutic target for the treatment of diabetes-induced hypertension and renal damage.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".