<i>N</i>-acetyl-seryl-aspartyl-lysyl-proline treatment protects heart against excessive myocardial injury and heart failure in mice
Bibliographic record
Abstract
Myocardial infarction (MI) in mice results in cardiac rupture at 4–7 days after MI, whereas cardiac fibrosis and dysfunction occur later. N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) has anti-inflammatory, anti-fibrotic, and pro-angiogenic properties. We hypothesized that Ac-SDKP reduces cardiac rupture and adverse cardiac remodeling, and improves function by promoting angiogenesis and inhibiting detrimental reactive fibrosis and inflammation after MI. C57BL/6J mice were subjected to MI and treated with Ac-SDKP (1.6 mg/kg per day) for 1 or 5 weeks. We analyzed (1) intercellular adhesion molecule-1 (ICAM-1) expression; (2) inflammatory cell infiltration and angiogenesis; (3) gelatinolytic activity; (4) incidence of cardiac rupture; (5) p53, the endoplasmic reticulum stress marker CCAAT/enhancer binding protein homology protein (CHOP), and cardiomyocyte apoptosis; (6) sarcoplasmic reticulum Ca2+ ATPase (SERCA2) expression; (7) interstitial collagen fraction and capillary density; and (8) cardiac remodeling and function. Acutely, Ac-SDKP reduced cardiac rupture, decreased ICAM-1 expression and the number of infiltrating macrophages, decreased gelatinolytic activity, p53 expression, and myocyte apoptosis, but increased capillary density in the infarction border. Chronically, Ac-SDKP improved cardiac structures and function, reduced CHOP expression and interstitial collagen fraction, and preserved myocardium SERCA2 expression. Thus, Ac-SDKP decreased cardiac rupture, ameliorated adverse cardiac remodeling, and improved cardiac function after MI, likely through preserved SERCA2 expression and inhibition of endoplasmic reticulum stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".