IL-10 Gene Transfection in Primary Endothelial Cells via Linear and Branched Poly(β-amino ester) Nanoparticles Attenuates Inflammation in Stimulated Macrophages
Bibliographic record
Abstract
Poly(β-amino esters) or PBAEs are highly efficient synthetic polymers optimized for gene delivery, a complicated process dependent on polymer properties such as hydrophobicity, charge, and degradability. The modular design of PBAEs has allowed for the identification of which polymer and nanoparticle properties significantly affect gene delivery efficiency in various cell types. However, these investigations need to be extended to more difficult-to-transfect cells such as primary endothelial cells, which hold enormous potential for atherosclerosis. Here a small library of 6 different PBAEs were screened for efficacy and safety in two types of primary endothelial cells (ECs). Nearly all polymers were more efficient than commercial transfection reagents ( p < 0.05), reaching 60% and 15% transfection efficiency in human and mouse primary ECs, respectively. The top performing PBAE was used to deliver a plasmid encoding the anti-inflammatory cytokine interleukin-10 (IL-10), which has the potential to reduce inflammation in atherosclerosis. Significant increases in IL-10 mRNA and protein were detectable in ECs 72 h after transfection with PBAE:IL-10 nanoparticles. Macrophages cultured in conditioned medium from IL10-transfected ECs showed activation of anti-inflammatory signaling pathways. In addition, these macrophages secreted significantly less (25%) tumor necrosis factor α (TNFα) when challenged with lipopolysaccharide (LPS). These results underline the capabilities of PBAEs to be expanded as a fine-tunable platform for anti-inflammatory gene delivery within the context of atherosclerosis.
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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".