Progress and prospects of magnetic iron oxide nanoparticles in biomedical applications: A review
Bibliographic record
Abstract
Nanoscience has been considered as one of the most substantial research in modern science. The utilization of nanoparticle (NP) materials provides numerous advantages in biomedical applications due to their unique properties. Among various types of nanoparticles, the magnetic nanoparticles (MNPs) of iron oxide possess intrinsic features, which have been efficiently exploited for biomedical purposes including drug delivery, magnetic resonance imaging, Magnetic-activated cell sorting, nanobiosensors, hyperthermia, and tissue engineering and regenerative medicine. The size and shape of nanostructures are the main factors affecting the physicochemical features of superparamagnetic iron oxide nanoparticles, which play an important role in the improvement of MNP properties, and can be controlled by appropriate synthesis strategies. On the other hand, the proper modification and functionalization of the surface of iron oxide nanoparticles have significant effects on the improvement of physicochemical and mechanical features, biocompatibility, stability, and surface activity of MNPs. This review focuses on popular methods of fabrication, beneficial surface coatings with regard to the main required features for their biomedical use, as well as new applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".