Bioinspired Oligo-Urethane Nanoparticles for Delivering Exogenous C-Type Natriuretic Peptide: Synthetic Biomaterial Nanocarrier Complexes and Their Interactions with Cardiac Myofibroblasts
Bibliographic record
Abstract
In a healthy heart, cells naturally secrete C-type natriuretic peptide (CNP), a cytokine that protects against myofibroblast differentiation of cardiac fibroblasts and extracellular matrix deposition leading to fibrosis. CNP availability during myocardial remodeling is important to prevent cardiac fibrosis, but CNP is limited after an injury because of the loss of cardiomyocytes and the activation of cardiac fibroblasts to myofibroblasts. We hypothesized that the sustained release of exogenous CNP from oligo-urethane nanoparticles (NPs) would reduce differentiation of human cardiac fibroblasts toward a myofibrogenic phenotype. Our work used a modified form of a degradable polar hydrophobic ionic (D-PHI) oligo-urethane, which has shown the ability to self-assemble into NPs for the delivery of peptide and oligonucleotide biomolecules. The CNP-loaded NPs (NP CNP ) were characterized for a diameter of 129 ± 1.4 nm and a ζ potential of −46 ± 7.8 mV. Treatment of cardiac fibroblasts with NP CNP increased cyclic guanosine-monophosphate (cGMP) synthesis, confirming that exogenous CNP delivered via oligo-urethane NPs is bioactive and can induce downstream signaling that has been implicated in antagonizing transforming growth factor-β1 (TGF-β1)-induced myofibrogenic differentiation. It is also shown that treatment with NP CNP attenuated contraction of collagen gels by cardiac myofibroblasts stimulated with TGF-β1. Coating with heparin on the NP CNP (HEP-NP CNP ) exemplified an approach to extend the release of CNP from the NPs. Both HEP-NP CNP and NP CNP show minimal cell toxicity, studied up to 0.25 × 10 10 NPs/mL in culture media. These findings support further investigation of CNP delivery via NPs as a future therapy for suppressing cardiac fibrosis.
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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) | 0.000 | 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".