RGD-functionalized ultrasmall iron oxide nanoparticles for targeted T<sub>1</sub>-weighted MR imaging of gliomas
Bibliographic record
Abstract
We report a convenient approach to prepare ultrasmall Fe3O4 nanoparticles (NPs) functionalized with an arginylglycylaspartic acid (RGD) peptide for in vitro and in vivo magnetic resonance (MR) imaging of gliomas. In our work, stable sodium citrate-stabilized Fe3O4 NPs were prepared by a solvothermal route. Then, the carboxylated Fe3O4 NPs stabilized with sodium citrate were conjugated with polyethylene glycol (PEG)-linked RGD. The formed ultrasmall RGD-functionalized nanoprobe (Fe3O4-PEG-RGD) was fully characterized using different techniques. We show that these Fe3O4-PEG-RGD particles with a size of 2.7 nm are water-dispersible, stable, cytocompatible and hemocompatible in a given concentration range, and display targeting specificity to glioma cells overexpressing αvβ3 integrin in vitro. With the relatively high r1 relaxivity (r1 = 1.4 mM(-1) s(-1)), the Fe3O4-PEG-RGD particles can be used as an efficient nanoprobe for targeted T1-weighted positive MR imaging of glioma cells in vitro and the xenografted tumor model in vivo via an active RGD-mediated targeting pathway. The developed RGD-functionalized Fe3O4 NPs may hold great promise to be used as a nanoprobe for targeted T1-weighted MR imaging of different αvβ3 integrin-overexpressing cancer cells or biological systems.
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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 | 0.001 | 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".