Targeted Nanoparticle Binding to Hydroxyapatite in a High Serum Environment for Early Detection of Heart Disease
Bibliographic record
Abstract
environment on targeted binding of functionalized nanoparticles has been an active field of research over the past several years. Current research aims at better understanding the nature of the protein corona and how it may be possible for targeted binding to occur even in the presence of serum. Much of the current research focuses on nanoparticles targeted to particular cell receptors or features with the aim of cellular uptake. However, similar research has not been performed on nanoparticles that are targeted to non-protein disease features, such as hydroxyapatite (HA). HA is a crystalline calcium-phosphate mineral that is present in large quantities in bone, and in smaller quantities in diseased cardiovascular tissue in cases of atherosclerosis or various stenoses. Our work aims to gain a better understanding of the behavior of PEGylated, peptide-coated superparamagnetic iron oxide nanoparticles (SPIONs) in a biologically-relevant high-protein environment (50% serum). We first determined that specific binding to HA occurs at significantly higher rates than non-specific binding in the absence of serum protein. We then examined nanoparticle interactions with serum proteins, including determination of the relative quantities of protein in the hard vs. soft protein corona. Finally, we examined specific and non-specific binding of targeted SPIONs in 50% serum, and determined that targeted binding may still occur with significant (p < 0.05) selectivity. We hypothesize that this may be because the nature of the binding interactions between the peptides and the HA are, by definition, less specific than the protein-protein interactions required for nanoparticles to bind to specific cells or cell features. These results suggest that these targeted SPIONs may be further developed for use in early detection of heart diseases such as atherosclerosis and aortic stenosis.
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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 | 0.000 | 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 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".