Albumin Nanoparticle Formulation for Heart-Targeted Drug Delivery: In Vivo Assessment of Congestive Heart Failure
Bibliographic record
Abstract
Congestive heart failure is a fatal cardiovascular disease resulting in tissue necrosis and loss of cardiac contractile function. Inotropic drugs such as milrinone are commonly used to improve the myocardial contractility and heart function. However, milrinone is associated with severe side effects and lower circulation time. In this article, a novel protein nanoparticle formulation for heart-targeted delivery of milrinone has been designed and tested. The formulation was prepared using albumin protein conjugated with the targeting ligand, angiotensin II peptide to form nanoparticles following the ethanol desolvation method. The formulation was characterized for size, charge, and morphology and tested in a rat model of congestive heart failure to study pharmacokinetics, biodistribution, and efficacy. The overall cardiac output parameters were evaluated comparing the formulation with the control non-targeted drug, milrinone lactate. This formulation exhibited improved pharmacokinetics with a mean retention time of 123.7 min, half-life of 101.3 min, and clearance rate of 0.24 L/(kg*h). The targeted formulation also significantly improved ejection fraction and fractional shortening parameters thus improving cardiac function. This study demonstrates a new approach in delivering inotropic drugs such as milrinone for superior treatment of congestive heart failure.
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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".