Design and development of Branched Poly(ß-aminoester) nanoparticles for Interleukin-10 gene delivery in a mouse model of atherosclerosis
Bibliographic record
Abstract
Atherosclerosis progression is a result of chronic and non-resolving inflammation, effective treatments for which still remain to be developed. We designed and developed branched poly(ß-amino ester) nanoparticles (NPs) containing plasmid DNA encoding IL-10, a potent anti-inflammatory cytokine to atherosclerosis. The NPs (NP-VHPK) are functionalized with a targeting peptide (VHPK) specific for VCAM-1, which is overexpressed by endothelial cells at sites of atherosclerotic plaque. The anionic coating affords NP-VHPK with significantly lower toxicity than uncoated NPs in both endothelial cells and red blood cells (RBCs). Following injection of NP-VHPK in ApoE−/− mice, Cy5-labelled IL-10 significantly accumulates in both whole aortas and aortic sinus sections containing plaque compared to injection with a non-targeted control. Furthermore, IL-10 gene delivery results in an attenuation of inflammation locally at the plaque site. NP-VHPK may thus have the potential to reduce the inflammatory component of atherosclerosis in a safe and effective manner. Atherosclerosis is a chronic inflammatory disease that results in the formation of lipid-laden plaques within vascular walls. Although treatments using drugs and antibodies are now beginning to address the inflammation in atherosclerosis, neither is sufficient for long-term therapy. In this paper, we introduce a strategy to deliver genes encoding the anti-inflammatory protein interleukin-10 (IL-10) in vivo. We showed that Branched Poly(ß-aminoester) carrying the IL-10 gene are able to localize specifically at the plaque via surface-functionalized targeting moieties against inflamed VCAM-1 and/or ICAM-1 and to facilitate gene transcription by ECs to increase the local concentration of the IL-10 within the plaque. To date, there is no report involving non-viral nanotechnology to provide gene-based therapies for atherosclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".