Effects of Angiotensin-Converting Enzyme Inhibitor Versus Valsartan on Cellular Signaling Events in Heart Transplant
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) provide similar biologic effects in model systems and similar clinical impacts in humans. The changes in the cardiac angiotensin system signaling pathways in the human heart in response to ACE inhibitors versus ARBs have been incompletely studied. OBJECTIVE: To investigate the effects of ACE inhibitors versus valsartan on the angiotensin II signal transduction pathways in the transplanted human heart. METHODS: Twenty-seven stable cardiac transplant recipients were randomized to remain on ACE inhibitor therapy (n = 8) or to receive valsartan (n = 19). Two additional endomyocardial biopsy samples were obtained at baseline and after 9 months of therapy. The expression of cardiac angiotensin type I and II receptors and atrial natriuretic factor (ANF) was measured by quantitative polymerase chain reaction. The expression and phosphorylation levels of selected signal transduction pathways were analyzed by immunoblotting. RESULTS: The mean dose of valsartan was 114 +/- 41 mg/day. The use of valsartan resulted in a similar impact on blood pressure and biochemistry profile. There were no significant changes in the expression of angiotensin type I and II receptors and ANF with valsartan. Similarly, no significant changes in the expression and phosphorylation of Jun N-terminal kinase, extracellular signal-regulated kinase 1 and 2, and p38 mitogen-activated protein kinases or AKT, and mammalian target of rapamycin was observed in the valsartan-treated group. CONCLUSIONS: Valsartan use is associated with similar clinical and molecular cardiac effects as ACE inhibitor therapy in stable long-term cardiac transplant recipients.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".