Preparation of polydopamine based redox-sensitive magnetic nanoparticles for doxorubicin delivery and MRI detection
Bibliographic record
Abstract
To improve the water-dispersity of superparamagnetic iron oxide nanoparticles (SPIONs), a novel polydopamine based redox-sensitive copolymer modified SPIONs were prepared for the biomedical application to deliver doxorubicin (DOX) and magnetic resonance imaging (MRI) detection. The major components of this nanoparticle include SPIONs and the redox-sensitive polydopamine (rPDA) crosslinked copolymer, where N,N-Bis(acryloyl) cystamine served as cross-linker, dopamine methacrylamide and a long-chain polyethylene glyco methyl ether methacrylate acted as comonomers. Here the rPDA@SPIONs were formed by the ligand exchange reaction of dopamine moiety with the oleic acid layer capped on the surface of SPIONs, and the inner area of the nanoparticles formed a reservoir for DOX, while the hydrophilic PEG moiety helped the nanoparticles well-dispersible in aqueous solution. The DOX-loaded rPDA@SPIONs demonstrated a high drug loading efficiency of 857 μg DOX per mg iron, and a strong T2 relaxivity of 123 mM<sup>-1</sup>·S<sup>-1</sup> for MRI. The drug release analysis of drug-loaded nanoparticles showed a sustained and high cumulative drug release in GSH up to 73% within 48 h, rather than the relatively low release rate of 37% in PBS (pH 7.4) without GSH. All the results showed that the designed magnetic nanoparticle may be a promising vehicle for anticancer drug delivery with stimuli-triggered drug release behavior, and also a foundation for building smart theranostic formulations for efficient detection through 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 | 0.001 | 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 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".