Cellular Uptake of Upconversion Nanoparticles Based on Surface Polymer Coatings and Protein Corona
Bibliographic record
Abstract
Upconversion nanoparticles (UCNPs) are materials that provide unique advantages for biomedical applications. There are constantly emerging customized UCNPs with varying compositions, coatings, and upconversion mechanisms. Cellular uptake is a key parameter for the biological application of UCNPs. Uptake experiments have yielded highly varying results, and correlating trends between cellular uptake with different types of UCNP coatings remains challenging. In this report, the impact of surface polymer coatings on the formation of protein coronas and subsequent cellular uptake of UCNPs by macrophages and cancer cells was investigated. Luminescence confocal microscopy and elemental analysis techniques were used to evaluate the different coatings for internalization within cells. Pathway inhibitors were used to unravel the specific internalization mechanisms of polymer-coated UCNPs. Coatings were chosen as the most promising for colloidal stability, conjugation chemistry, and biomedical applications. PIMA-PEG (poly(isobutylene- alt -maleic) anhydride with polyethylene glycol)-coated UCNPs were found to have low cytotoxicity, low uptake by macrophages (when compared with PEI, poly(ethylenimine)), and sufficient uptake by tumor cells for surface-loaded drug delivery applications. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) studies revealed that PIMA-coated NPs were preferentially internalized by the clathrin- and caveolar-independent pathways, with a preference for clathrin-mediated uptake at longer time points. PMAO-PEG (poly(maleic anhydride- alt -1-octadecene) with polyethylene glycol)-coated UCNPs were internalized by energy-dependent pathways, while PAA- (poly(acrylic acid)) and PEI-coated NPs were internalized by multifactorial mechanisms of internalization. The results indicate that copolymers of PIMA-PEG coatings on UCNPs were well suited for the next-generation of biomedical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".