Superparamagnetic Iron Oxide Nanoparticles Stabilized with Multidentate Block Copolymers for Optimal Vascular Contrast in<i>T</i><sub>1</sub>-Weighted Magnetic Resonance Imaging
Bibliographic record
Abstract
Ultrasmall superparamagnetic iron oxide nanoparticles (USPIOs) have been used as vascular contrast agents in magnetic resonance imaging (MRI), mainly for their capacity to generate negative contrast. To use USPIOs as positive contrast agents, it is necessary to achieve increased colloidal stability and signal-enhancement performance. Their molecular coatings must be carefully chosen, so that the vascular blood-pool contrast agents lead to long blood turnover times. However, to avoid long-term toxicological effects, they must also be cleared rapidly through the urinary or gastrointestinal pathways. In this context, highly stable USPIOs showing “positive” contrast in MRI and optimal clearance rates call for the development of robust biocompatible molecular coatings. In the present study, USPIOs were stabilized with a multidentate block copolymer (MDBC), using a one-pot polyol synthesis method in the presence of a MDBC. Two types of MDBCs having pendant COOH groups in the anchoring block were developed: a polymer with linear-poly(ethylene glycol) (PEG) blocks and a polymer containing brushed-PEG blocks. The synthesized superparamagnetic Fe 3 O 4 crystals were uniform (5–8 nm in diameter), showed ultrasmall hydrodynamic diameters in dynamic light scattering, and were stable in physiological liquids. MDBC-coated USPIOs were analyzed in relaxometry, and the formulations showing the strongest potential for T 1 -weighted vascular imaging ( r 2 / r 1: ∼4) were selected for in vivo MRI. Intravascular injections performed in the mouse model indicated long blood retention times and high signal enhancement in MRI for nanoparticles coated with linear-PEG block coatings. These results also indicate that MDBC/USPIOs could be used in vascular MRI applications, where the nanoparticles must transit the blood for several hours, followed by an efficient clearance in the next days following injection. The use of MDBCs as nanoparticle coatings could open new possibilities in the design of USPIOs for targeted molecular MRI.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".