Uniform and Length-Tunable, Paramagnetic Self-Assembled Nitroxide-Based Nanofibers for Magnetic Resonance Imaging
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is a widely used non-invasive medical imaging tool. Nanoparticle-based MRI contrast agents have received considerable attention due to their high loading capability for magnetic species, enhanced accumulation in lesions, and versatile surface functionalization. Anisotropic nanoparticles such as nanofibers can exhibit significant advantages over their well-explored spherical counterparts in terms of their pharmacokinetic and biodistribution profiles. Herein, we report the retrosynthetic design, synthesis, and characterization of uniform and length-tunable paramagnetic core-shell nanofibers for MRI through the use of the seeded-growth “living” crystallization-driven self-assembly (CDSA) approach. Triblock copolymer (TriBCP) precursors with a crystallizable polycarbonate core-forming segment, a nitroxide-bearing central region, and a hydrophilic poly(ethylene glycol) (PEG) terminal corona-forming segment were prepared via sequential living organocatalytic ring-opening polymerization (ROP). Low dispersity nanofibers of length ca. 80 nm relevant for biomedical applications were prepared for detailed studies by living CDSA and these possessed an average number of nitroxides per nanofiber of >8000. Subsequent evaluation of the water-proton relaxivities demonstrated that tuning the hydrophilicity of the central segment in the TriBCP allowed access to nanofibers with impressive performance compared to most existing polymer-based nitroxide-based contrast agents. As a result of their 1D morphology, the synthetic nanofibers therefore represent promising organic radical contrast agents (ORCAs) for MRI applications.
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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 | 0.000 | 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 | 0.001 | 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 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".