Magnetic Nanoparticles for Imaging, Diagnosis, and Drug-Delivery Applications
Bibliographic record
Abstract
Magnetic Nanoparticles (MNPs) have gained interest within the research community due to their therapeutic potential in a variety of medical applications. MNPs are generally composed of a metallic core stabilized by the addition of an outer shell that can be further functionalized through the absorbance or conjugation of various targeting ligands. The magnetic properties of these nanoparticles can be utilized for imaging, localized drug delivery, and enhanced diagnostic detection. This chapter highlights the applications of MNPs to enhance magnetic resonance imaging (MRI) capabilities and improve the delivery of therapeutic agents to difficult-to-reach areas in the body. In addition, recent advances in the use of MNPs in stem cell therapy for both the tracking and monitoring of stem cell distribution in the body and improving engraftment and differentiation in stem cell therapy are discussed. Finally, examples of the incorporation of MNPs in diagnostic assays to improve rapid and realtime detection capabilities of many diseases, including cancer, cardiovascular diseases, and pathogen infections, are provided.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.025 | 0.017 |
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".