Sacubitril suppresses experimental chronic heart allograft vasculopathy
Bibliographic record
Abstract
Chronic allograft vasculopathy limits graft and recipient survival after heart transplantation despite modern immune suppression. We examined the effect of sacubitril, an inhibitor of neprilysin neutral endopeptidase activity, on the progression of chronic allograft vasculopathy in HY-antigen-mismatched mouse heart transplantation. We found that sacubitril treatment of the recipient markedly blunted the progressive occlusion of the coronary arterial lumen versus the vehicle control. The proteome of the heart grafts was characterized, and notably identifies differential increased expression of several serine protease inhibitors, decreased transforming growth factor-beta superfamily pathway constituents, and matrix proteins among sacubitril-treated recipients. We observed reduced immune cell infiltration of the allograft, associated with suppression of graft vascular endothelial cell Cx3cl1 and Vcam1 expression among the sacubitril-treated recipients. Further, graft expression of proreparative apelin was increased, and endothelial cell-mesenchymal transdifferentiation was suppressed. In vitro, candidate signaling pathways via glucagon-like protein-1 and atrial natriuretic peptide receptor, but not apelin receptor, agonists phenocopied the effect of sacubitril in vivo. The results highlight direct and indirect proteinase inhibitory and favorable anti-inflammatory effects of sacubitril treatment that limit maladaptive repair of the graft vasculature.
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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".