Enhanced Cell Uptake of Superparamagnetic Iron Oxide Nanoparticles Functionalized with Dendritic Guanidines
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is a powerful tool for the diagnosis of disease and the study of biological processes such as cancer metastasis and inflammation. Superparamagnetic iron oxide (SPIO) nanoparticles have been shown to be effective contrast agents for labeling cells to provide high sensitivity in MRI, but this sensitivity depends on the ability to label cells with sufficient quantities of SPIO, which can be challenging for nonphagocytic cells such as cancer cells. To address this issue, a novel cell-penetrating polyester dendron with peripheral guanidines was developed and conjugated to the surface of SPIO. The functionalized nanoparticles were characterized by transmission electron microscopy, infrared spectroscopy, and dynamic light scattering, and it was found that the surface functionalization reaction proceeded to completion and did not have any adverse effects on the SPIO. In GL261 mouse glioma cells, the dendritic guanidine exhibited remarkably similar cell-penetrating capabilities to the HIV-Tat(47-57) peptide for the transport of fluorescein, and when conjugated to SPIO, it provided significantly enhanced uptake in comparison with nanoparticles having no dendron or dendrons with hydroxyl or amine peripheries. This uptake led to substantial decreases in the transverse relaxation time (T(2)) of labeled cells relative to control cells. While the nanoparticles functionalized with dendritic guanidines exhibited somewhat greater toxicity than those functionalized with dendrons having hydroxyl or amine peripheries, they were still relatively nontoxic at the low concentrations required for labeling.
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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".