Combination renin–angiotensin system blockade and angiotensin-converting enzyme 2 in experimental myocardial infarction: implications for future therapeutic directions
Bibliographic record
Abstract
The RAS (renin-angiotensin system) is activated after MI (myocardial infarction), and RAS blockade with ACEis [ACE (angiotensin-converting enzyme) inhibitors] or ARBs (angiotensin receptor blockers) slows but does not completely prevent progression to heart failure. Cardiac ACE is increased after MI and leads to the formation of the vasoconstrictor AngII (angiotensin II). The enzyme ACE2 is also activated after MI and degrades AngII to generate the vasodilator Ang-(1-7) [angiotensin-(1-7)]. Overexpression of ACE2 offers cardioprotective effects in experimental MI, but there is conflicting evidence as to whether the benefits of ACEis and ARBs are mediated through increasing ACE2 after MI. In the present study, we assessed the effect of an ACEi and ARB, alone and in combination, on cardiac ACE2 in a rat MI model. MI rats received vehicle, ACEi (ramipril; 1 mg/kg of body weight), ARB (valsartan; 10 mg/kg of body weight) or combination (ramipril at 1 mg/kg of body weight and valsartan at 10 mg/kg of body weight) orally for 28 days. Sham-operated rats were also studied and received vehicle alone. MI increased LV (left ventricular) mass (P<0.0001), impaired cardiac contractility (P<0.05) and activated cardiac ACE2 with increased gene (P<0.05) and protein expression (viable myocardium, P<0.05; border zone, P<0.001; infarct, P<0.05). Ramipril and valsartan improved remodelling (P<0.05), with no additional effect of dual therapy. Although ramipril inhibited ACE, and valsartan blocked the angiotensin receptor, neither treatment alone nor in combination augmented cardiac ACE2 expression. These results suggest that the cardioprotective effects of ramipril and valsartan are not mediated through up-regulation of cardiac ACE2. Strategies that do augment ACE2 after MI may be a useful addition to standard RAS blockade after MI.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".