The Multifaceted Uses and Therapeutic Advantages of Nanoparticles for Atherosclerosis Research
Bibliographic record
Abstract
Nanoparticles are uniquely suited for the study and development of potential therapies against atherosclerosis by virtue of their size, fine-tunable properties, and ability to incorporate therapies and/or imaging modalities. Furthermore, nanoparticles can be specifically targeted to the atherosclerotic plaque, evading off-target effects and/or associated cytotoxicity. There has been a wealth of knowledge available concerning the use of nanotechnologies in cardiovascular disease and atherosclerosis, in particular in animal models, but with a major focus on imaging agents. In fact, roughly 60% of articles from an initial search for this review included examples of imaging applications of nanoparticles. Thus, this review focuses on experimental therapy interventions applied to and observed in animal models. Particular emphasis is placed on how nanoparticle materials and properties allow researchers to learn a great deal about atherosclerosis. The objective of this review was to provide an update for nanoparticle use in imaging and drug delivery studies and to illustrate how nanoparticles can be used for sensing and modelling, for studying fundamental biological mechanisms, and for the delivery of biotherapeutics such as proteins, peptides, nucleic acids, and even cells all with the goal of attenuating atherosclerosis. Furthermore, the various atherosclerosis processes targeted mainly for imaging studies have been summarized in the hopes of inspiring new and exciting targeted therapeutic and/or imaging strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".