The Metabolically Resistant Apelin-17 Analogue LIT01-196 Reduces Cardiac Dysfunction and Remodelling in Heart Failure After Myocardial Infarction
Bibliographic record
Abstract
Background To protect patients after myocardial infarction (MI) and preserve cardiac function, the development of new therapeutics remains an important issue. Apelin, a neuro-vasoactive peptide, increases aqueous diuresis and cardiac contractility while reducing vascular resistance. However, its in vivo half-life is very short. We therefore developed a metabolically resistant apelin-17 analog, LIT01-196 and investigated its effects on cardiac function and remodeling in a murine MI model. Methods The selectivity of LIT01-196 towards ApelinR was checked in vitro . Its in vivo half-life was assessed in male Swiss mice by radioimmunoassay. After permanent coronary artery ligation to induce MI, mice received subcutaneous administration of LIT01-196 (MI+LIT01-196, 9 mg/kg/day) or saline (MI+Vehicle) for 4 weeks. LV function was assessed using echocardiography and Millar catheter, vascular density by immunofluorescence and cardiac fibrosis by Sirius red staining. Real-time quantitative PCR measured mRNA expression of HF and fibrosis biomarkers and SERCA2. Results The in vivo half-life of LIT01-196, a specific and selective ApelinR agonist, was two and a half hours. MI+LIT01-196 mice showed significantly improved LV function, reduced HF biomarkers and enhanced cardiac contractility and SERCA2 expression compared with MI+Vehicle. LIT01-196 treatment almost doubled cardiac vascular density and maintained LV wall thickness post-MI. It also significantly reduced cardiac fibrosis and fibrosis biomarkers, without decreasing arterial blood pressure. Conclusions Chronic LIT01-196 treatment post-MI improves LV function without decreasing blood pressure, increases cardiac vascular density and reduces cardiac remodeling. This suggests that Apelin-R activation by LIT01-196, may constitute an original pharmacological approach for HF treatment 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 | 0.000 | 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".