Targeting macrophages with multifunctional nanoparticles to detect and prevent atherosclerotic cardiovascular disease
Bibliographic record
Abstract
Despite the emergence of novel diagnostic, pharmacological, interventional, and prevention strategies, atherosclerotic cardiovascular disease remains a significant cause of morbidity and mortality. Nanoparticle (NP)-based platforms encompass diverse imaging, delivery, and pharmacological properties that provide novel opportunities for refining diagnostic and therapeutic interventions for atherosclerosis at the cellular and molecular levels. Macrophages play a critical role in atherosclerosis and therefore represent an important disease-related diagnostic and therapeutic target, especially given their inherent ability for passive and active NP uptake. In this review, we discuss an array of inorganic, carbon-based, and lipid-based NPs that provide magnetic, radiographic, and fluorescent imaging capabilities for a range of highly promising research and clinical applications in atherosclerosis. We discuss the design of NPs that target a range of macrophage-related functions such as lipoprotein oxidation, cholesterol efflux, vascular inflammation, and defective efferocytosis. We also provide examples of NP systems that were developed for other pathologies such as cancer and highlight their potential for repurposing in cardiovascular disease. Finally, we discuss the current state of play and the future of theranostic NPs. Whilst this is not without its challenges, the array of multifunctional capabilities that are possible in NP design ensures they will be part of the next frontier of exciting new therapies that simultaneously improve the accuracy of plaque diagnosis and more effectively reduce atherosclerosis with limited side effects.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".