Cell Membrane Surface-Engineered Nanoparticles for Cardiovascular Diseases
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) continue to be the leading cause of global mortality, underscoring the need for effective diagnosis and treatment strategies. This chapter provides a short overview of CVDs and explores the potential of nanomedicine in revolutionizing their diagnosis and treatment. The challenges associated with traditional nanoparticle-based treatments, such as low efficacy and toxicity, have prompted a shift towards biomimetic nanotechnology. Specifically, cell membrane surface-engineered nanoparticles (CMSNs), which emulate cell membranes, offer the combined advantages of natural and synthetic nanomaterials. These benefits encompass immune evasion, precise targeting, pharmacological capabilities, and versatile functional mimicries. In this chapter, our primary focus lies in the application of CMSNs for the treatment of CVDs. We categorize all CMSNs employed in the context of CVDs and reported up to August 2023, based on the various types of cell membranes utilized in their preparation. These encompass membranes derived from macrophages, platelets, red blood cells, stem cells, and neutrophils. For each category, we provide a summary of their mechanisms of effectiveness, followed by a discussion of outcomes from previous studies. Furthermore, we delve into the advantages and challenges associated with the utilization of different types of CMSNs. Finally, we conclude with an outlook on the future of this field. Overall, the utilization of CMSN holds significant promise in enhancing the efficacy and precision of CVD interventions, paving the way for innovative and personalized approaches to cardiovascular healthcare.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".