Systemic delivery of biotherapeutic RNA to the myocardium transiently modulates cardiac contractility in vivo
Bibliographic record
Abstract
Lipid nanoparticles (LNP) represent a versatile platform for improving delivery of therapeutic nucleic acids. Yet, delivery to the myocardium remains a formidable challenge due to local barriers in the heart and systemic hindrances. In particular, plasma apolipoprotein E (apoE) directs LNP to the liver, limiting potential extrahepatic delivery. Here, we report a cardiotropic LNP (cLNP), which within 30 min post–intravenous injection accumulates in the heart of ApoE knockout ( Apoe −/− ) mice. The findings were confirmed for Apoe −/− rats and for wild-type mice after siRNA-mediated plasma apoE ablation. To test cardiac-specific functional effects as a proof of concept, we used cLNP loaded with siRNA to ATP2A2, encoding the sarcoplasmic-endoplasmic reticulum Ca 2+ ATPase 2a (SERCA2A). This cardiomyocyte-specific protein is a key regulator of contractility and relaxation. Intravenous administration of cLNP/siRNA-ATP2A2 in Apoe −/− mice led to near-complete ablation of SERCA2A in the myocardium and a potent modulation of contractility of the cardiomyocytes obtained from these mice. In summary, cardiotropic nanocarriers may allow the delivery and effect of RNA and other agents to the myocardium. Achieving this unmet medical need promises new types of treatment for heart diseases, which remains the leading cause of death worldwide.
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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) | 0.001 | 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".