Development of Pro-resolving and Pro-efferocytic Nanoparticles for Atherosclerosis Therapy
Bibliographic record
Abstract
Atherosclerosis is a major contributor to cardiovascular diseases with a high global prevalence. It is characterized by the formation of lipid-laden plaques in the arteries, which eventually lead to plaque rupture and thrombosis. While the current lipid-lowering therapies are generally effective in lowering the risk of cardiovascular events, they do not address the underlying causes of disease. Defective resolution of inflammation and impaired efferocytosis are the main driving forces of atherosclerosis. Macrophages recognize cells for clearance by the expression of "eat me" and "do not eat me" signals, including the CD47-SIRPα axis. However, the "do not eat me" signal CD47 is overexpressed in atherosclerotic plaques, leading to compromised efferocytosis and secondary necrosis. In this context, prophagocytic antibodies have been explored to stimulate the clearance of apoptotic cells, but they are nonspecific and impact healthy tissues. In macrophages, downstream of signal regulatory protein α, lie protein tyrosine phosphatases, SHP 1/2, which can serve as effective targets for selectively phagocytosing apoptotic cells. While increasing the efferocytosis targets the end stages of lesion development, the underlying issue of inflammation still persists. Simultaneously increasing efferocytosis and reducing inflammation can be effective therapeutic strategies for managing atherosclerosis. For instance, IL-10 is a key anti-inflammatory mediator that enhances efferocytosis via phosphoSTAT3 (pSTAT3) activation. In this study, we developed a combination nanotherapy by encapsulating an SHP-1 inhibitor (NSC 87877) and IL-10 in a single nanoparticle platform [(S + IL)-NPs] to enhance efferocytosis and inflammation resolution. Our studies suggest that (S + IL)-NPs successfully encapsulated both agents, entered the macrophages, and delivered the agents into intracellular compartments. Additionally, (S + IL)-NPs decreased inflammation by suppressing pro-inflammatory markers and enhancing anti-inflammatory mediators. They also exhibited the potential for improved phagocytic activity via pSTAT3 activation. Our nanomedicine-mediated upregulation of the anti-inflammatory and efferocytic responses in macrophages shows promise for the treatment of atherosclerosis.
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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 | 0.001 | 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".