Rapid Synthesis of PEGylated Ultrasmall Gadolinium Oxide Nanoparticles for Cell Labeling and Tracking with MRI
Why this work is in the frame
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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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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) | 0.000 | 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 it