Modification of the surface of superparamagnetic iron oxide nanoparticles to enable their safe application in humans
Bibliographic record
Abstract
Combined individually tailored methods for diagnosis and therapy (theragnostics) could be beneficial in destructive diseases, such as rheumatoid arthritis. Nanoparticles are promising candidates for theragnostics due to their excellent biocompatibility. Nanoparticle modifications, such as improved surface coating, are in development to meet various requirements, although safety concerns mean that modified nanoparticles require further review before their use in medical applications is permitted. We have previously demonstrated that iron oxide nanoparticles with amino-polyvinyl alcohol (a-PVA) adsorbed on their surfaces have the unwanted effect of increasing human immune cell cytokine secretion. We hypothesized that this immune response was caused by free-floating PVA. The aim of the present study was to prevent unwanted immune reactions by further surface modification of the a-PVA nanoparticles. After cross-linking of PVA to nanoparticles to produce PVA-grafted nanoparticles, and reduction of their zeta potential, the effects on cell viability and cytokine secretion were analyzed. PVA-grafted nanoparticles still stimulated elevated cytokine secretion from human immune cells; however, this was inhibited after reduction of the zeta potential. In conclusion, covalent cross-linking of PVA to nanoparticles and adjustment of the surface charge rendered them nontoxic to immune cells, nonimmunogenic, and potentially suitable for use as theragnostic agents.
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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) | 0.001 | 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".