Rapid Synthesis of PEGylated Ultrasmall Gadolinium Oxide Nanoparticles for Cell Labeling and Tracking with MRI
Bibliographic record
Abstract
Ultrasmall paramagnetic Gd(2)O(3) nanoparticles have been developed as contrast agents for molecular and cellular preclinical MRI procedures. These small particles (mean diameter <5 nm) have the highest Gd density of all paramagnetic contrast agents. They generate strong positive contrast enhancement in T(1)-weighted MRI. Signal enhancement is modulated by the interactions of water molecules with Gd, and very small particles provide the optimal surface-to-volume ratios necessary to reach high relaxivities. Conventional Gd(2)O(3) nanocrystal synthesis techniques, and subsequent polyethylene glycol (PEG) grafting procedures are usually time-consuming and recovery losses are also limitative. The present study reports on a new, fast, and efficient one-pot Gd(2)O(3) synthesis technique that provides PEGylated nanoparticles of very small size (mean diameter = 1.3 nm). Readily coated with PEG, the particles are colloidally stable in aqueous media and provide high longitudial relaxivities and small r(2)/r(1) ratios (r(1) = 14.2 mM(-1) s(-1) at 60 MHz; r(2)/r(1) = 1.20), ideal for T(1)-weighted MRI. In this study, F98 brain cancer cells (glioblastoma multiforme) were labeled with the contrast agent and implanted in vivo (mice brains). The labeled cells appeared positively contrasted at least 48 h after implantation. Each one of the implanted animals developed a brain tumor. The performance of PEG-Gd(2)O(3) was also compared with that of commercially available iron oxide nanoparticles. This study demonstrated that ultrasmall PEG-Gd(2)O(3) nanoparticles provide strong positive contrast enhancement in T(1)-weighted imaging, and allow the visualization of labeled cells implanted in vivo.
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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".