Impact of magnetic nanoparticle surface coating on their long-term intracellular biodegradation in stem cells
Bibliographic record
Abstract
Magnetic nanoparticles (MNPs) internalized within stem cells have paved the way for remote magnetic cell manipulation and imaging in regenerative medicine. A full understanding of their interactions with stem cells and of their fate in the intracellular environment is then required, in particular with respect to their surface coatings. Here, we investigated the biological interactions of MNPs composed of an identical magnetic core but coated with different molecules: phosphonoacetic acid, polyethylene glycol phosphonic carboxylic acid, caffeic acid, citric acid, and polyacrylic acid. These coatings vary in the nature of the chelating function, the number of binding sites, and the presence or absence of a polymer. The nanoparticle magnetism was systematically used as an indicator of their internalization within human stem cells and of their structural long-term biodegradation in a 3D stem cell spheroid model. Overall, we evidence that the coating impacts the aggregation status of the nanoparticles and subsequently their uptake within stem cells, but it has little effect on their intracellular degradation. Only a high number of chelating functions (polyacrylic acid) had a significant protective effect. Interestingly, when the nanoparticles aggregated prior to cellular internalization, less degradation was also observed. Finally, for all coatings, a robust dose-dependent intracellular degradation rate was demonstrated, with higher doses of internalized nanoparticles leading to a lower degradation extent.
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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".