Modulation of Macrophage Functional Polarity towards Anti-Inflammatory Phenotype with Plasmid DNA Delivery in CD44 Targeting Hyaluronic Acid Nanoparticles
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study was to modulate macrophage polarity from the pro-inflammatory M1 to anti-inflammatory M2 phenotype using plasmid DNA (pDNA) expressing interleukin-4 (IL4) or interleukin-10 (IL10)-encapsulated in hyaluronic acid-poly(ethyleneimine) (HA-PEI) nanoparticles (NPs). The HA-PEI/pDNA NPs with spherical shape, average size of 186 nm were efficiently internalized by J774A.1 macrophages. Transfection of HA-PEI/pDNA-IL4 and HA-PEI/pDNA-IL10 NPs increased IL4 and IL10 gene expression in J774 macrophages which could re-program the macrophages from M1 to M2 phenotype as evidenced by a significant increase in the Arg/iNOS level, and upregulation of CD206 and CD163 compared to untreated macrophages. Following intraperitoneal (IP) injection to C57BL/6 mice, HA-PEI NPs effectively targeted peritoneal macrophages over-expressing CD44 receptor. In an in vivo model of stimulated peritoneal macrophages, IP administration of HA-PEI/pDNA-IL4 and HA-PEI/pDNA-IL10 to C57BL/6 mice significantly increased the Arg/iNOS ratio and CD163 expression in the cells. Furthermore, HA-PEI/pDNA-IL10 NPs significantly increased peritoneal and serum IL10 levels which effectively suppressed LPS-induced inflammation by reducing level of TNF-α and IL-1β in peritoneal macrophages and in the peritoneal fluid. The results demonstrated that pDNA-IL10-encapsulate HA-PEI NPs skewed macrophage functional polarity from M1 toward an anti-inflammatory M2 phenotype which may be a promising platform for the treatment of inflammatory diseases.
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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 | 0.002 | 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 | 0.001 |
| 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 it